STATISTICS DIGEST - ASQasq.org/statistics/2015/02/asq-statistics-division... · 2015-08-04 · STATISTICS DIGEST The Newsletter of the ASQ Statistics Division Chair’s Message .....1

Continued on page 3

STATISTICS DIGESTThe Newsletter of the ASQ Statistics Division

Chairrsquos Message1

Past Chairrsquos Message 5

Editorrsquos Corner6

YOUDEN ADDRESSQuality and Statistics NowTHATrsquoS Entertainment 7

MINI-PAPERStatistical Engineering and TearingDown the Silos of QualityEngineering10

COLUMNSDesign of Experiments 13

Statistical Process Control16

Statistics for QualityImprovement18

Stats 101 22

Testing and Evaluation27

Standards InSide-Out29

FEATUREApproaching Statisticsas a Language31

Upcoming Conference Calendar 34

Statistics Division Committee Roster2015 35

IN THIS ISSUE

Vol 34 No 1 February 2015

Hello Statistics Divis ion It is unlikely that too many of you knowme so I will introduce myself first My name is Adam Pintar I havebeen a volunteer leader with the division since I first joined in 2010I was the treasurer for two and a half years and then I transitionedinto the chair-elect role (and now chair) You may wonder how Iserved half of a year It was due to the ASQ transition from a July toJune business year to following the calendar year With respect to

my education and professional life I will follow Joelrsquos pattern from last year and saythat I have a LinkedIn profile and an employee webpage so please look me up ineither place if you like (LinkedIn and NIST) I will just mention briefly that mygraduate work in statistics was done at Iowa State University and I currently work inthe Statistical Engineering Division at the National Institute of Standards andTechnology

I am honored to be the Chair of the Statistics Division And as the Chair I welcomeany member to contact me I know that sounds clicheacute However I sincerely meanit and I hope many of you take me up on the offer I am not as big of a football fanas Joel but if you would like to talk tennis I am your guy

It is an exciting year-to-come for the Statistics Division e biggest change is thedocument currently occupying your attention We have decided to switch from thetraditional newsletter to a publication with more statistics content is is the firstever Statistics Digest I hope that you enjoy it and I want to thank the editor MattBarsalou for all of his hard work e motivation for the switch was feedback in theform of survey responses regarding the previous version of the newsletter efeedback informed us that the mini-paper was the most popular item which impliedto us that statistics content was what our members valued most Who would havethought All of the traditional content omitted here (eg the treasurerrsquos report) willcontinue to be produced but it will be disseminated through other channelseg e-zines and the website

Other exciting news in the area of statistics content are our intentions to host aforeign language webinar and release in video form the narrated slide shows that wereonce available from the website As in previous years we plan to host 10 to 12webinars but unlike previous years we hope to host one webinar in Spanish isof course aligns well with the ASQ goal of becoming a global society but it alsoreflects the desire of the leadership committee to engage and provide benefits to alldivision members worldwide Some division members may recall the narrated slideshows on basic statistics that were available in the past for download from theStatistics Division website for a small fee Conditional on overcoming a few technicalhurdles which seems likely these will be available for viewing again soon and theywill be free of charge

Message from the Chairby Adam Pintar

Adam Pintar

2 ASQ Statistics DIVISION NEWSLETTER Vol 34 No 1 2015 asqorgstatistics

Submission Guidelines

Mini-PaperInteresting topics pertaining to the field ofstatistics should be understandable bynon-statisticians with some statisticalknowledge Length 1500-4000 words

Basic-ToolsApplication oriented and less technicalthan a Mini-Paper e focus should beon how to apply basic statistical conceptswith an emphasis on industry or servicese use of examples to illustrate points ishighly recommended Length 1000-3000 words

FeatureFocus should be on a statistical conceptcan either be of a practical nature or atopic that would be of interest topractitioners who apply statistics Length1000-3000 words

Case StudyFocus should be on an actual example ofthe application of a statistical method inindustry or services e names of thecompanies involved do not need to bementioned Length 1000-2500 words

General InformationAuthors should have a conceptualunderstanding of the topic and should bewilling to answer questions relating to thearticle through the newsletter Authors donot have to be members of the StatisticsDivision Submissions may be made atany time to newsletterasqstatdivorg

All articles will be reviewed eeditor reserves discretionary right indetermination of which articles arepublished Submissions should not beoverly controversial Confirmation ofreceipt will be provided within one weekof receipt of the email Authors willreceive feedback within two monthsAcceptance of articles does not imply anyagreement that a given article will bepublished

Visione ASQ Statistics Division promotes innovation and excellence in the application andevolution of statistics to improve quality and performance

Missione ASQ Statistics Division supports members in fulfilling their professional needs andaspirations in the application of statistics and development of techniques to improvequality and performance

Strategies1 Address core educational needs of members

bull Assess member needsbull Develop a ldquobase-level knowledge of statisticsrdquo curriculumbull Promote statistical engineeringbull Publish featured articles special publications and webinars

2 Build community and increase awareness by using diverse and effectivecommunications

bull Webinarsbull Newslettersbull Body of Knowledgebull Web sitebull Blog bull Social Media (LinkedIn and Twitter)bull Conference presentations (Fall Technical Conference WCQI etc)bull Short courses bull Mailings

3 Foster leadership opportunities throughout our membership and recognizeleaders

bull Advertise leadership opportunities positionsbull Invitations to participate in upcoming activitiesbull Student grants and scholarshipsbull Awards (eg Youden Nelson and Hunter)bull Recruit retain and advance members (eg Senior and Fellow status)

4 Establish and Leverage Alliancesbull ASQ Sections and other Divisionsbull Non-ASQ (eg ASA)bull CQE Certification bull Standards bull Outreach (professional and social)

Updated October 19 2013

Disclaimer

e technical content of material published in the ASQ Statistics Division Newsletter may not have beenrefereed to the same extent as the rigorous refereeing that is undergone for publication in Technometrics orJQT e objective of this newsletter is to be a forum for new ideas and to be open to differing points ofview e editor will strive to review all articles and to ask other statistics professionals to provide reviews ofall content of this newsletter We encourage readers with differing points of view to write to the editor and anopportunity to present their views via a letter to the editor e views expressed in material published in thisnewsletter represents the views of the author of the material and may or may not represent the officialviews of the Statistics Division of ASQ

Vision Mission and Strategies of the ASQ Statistics Division

asqorgstatistics ASQ Statistics DIVISION NEWSLETTER Vol 34 No 1 2015 3

Message from the ChairContinued from page 1

ere are many other activities planned for this year so keep your eyes open for e-zines and have a look at thehttpwwwasqorgstatistics website from time to time Since this is the first Statistics Digest I am going to spend the rest of themessage discussing a statistical topic I thought quite a lot about a topic I wanted it to be simple but still useful I finally decidedon simulation because I use it often and it can range from very simple to staggeringly complex depending upon the problem

I will not discuss computer simulation in general at is a subject for books My discussion will be about a simple example thatpopped up in my day-to-day work Since the example is related to my work and it is not perfectly clear what details that I mayor may not release about it I will disguise the details while still conveying the underlying structure Imagine manufacturingwidgets in many different factories (yes I know clicheacute again) Many physically distinct factories produce nominally identicalwidgets but in reality the widgets from different factories are not identical e goal of the analysis is to quantify (point estimateand uncertainty) the average value of a certain physical property of all widgets To do this factories are randomly sampled twoproduction days within each factory are randomly sampled (probably consecutive though) and two widgets within eachproduction day are randomly sampled e data table looks like this

In the interest of keeping things simple and short I will not provide the full details of the model I used for these data But it isimportant to note for this discussion that I wanted to assume that the four measurements from a single factory were independentobservations from a normal distribution with mean μ and standard deviation σ denoted N(μσ2) However while looking at theraw table of numbers I noticed that for a few factories both of the day 1 observations were less than the day 2 observations orvice versa I began to worry that days within a factory may be systematically different from one another I started to think abouthow likely this ordering would be given the assumption I desired to make Clearly P(A12 lt A21) and other similar quantitiesunder the N(μσ2) assumption are 05 However to complete the calculation we must also know quantities such asP(A12 lt A21 A12 lt A21) which are not as easy to infer e vertical bar notation means conditional probability

At this point I decided to do a quick simulation I will discuss other possible solutions shortly I initially did the simulation in Rbut while writing this note I checked that they could also be done with Excel e R code is posted at the end in Figure 1 Pleasesend me an email if you would like to know how I did the calculations in Excel I am also happy to hear critiques of my R codeas I am sure it could be improved e general (separate from computing environment) steps of the simulation are as follows

1 Generate 4n deviates from a N(01) distribution and arrange them into a rectangular array with n rows and 4 columnsIt is sufficient to use the N(01) distribution because if X follows a N(μσ2) distribution then Z = (X-μ)σ follows aN(01) distribution and the shift and scale preserves ordering

2 Considering each row to be a single trial count the number of trials for which the first two observations are both lessthan the second two call it C1

3 Count the numbers of trials for which the second two observations are both less than the first two call it C24 e approximate probability of the event of concern is (C1 + C2)n

e value turns out to be about 033 which eased my fear about considering the four observations from a single factory to beindependent and identical N(μσ2) deviates

Continued on page 4

4 ASQ Statistics DIVISION NEWSLETTER Vol 34 No 1 2015 asqstatdivorg


Now many of you may be thinking why not conduct a t-test I admit it that is a good idea I did not take that approach for threereasons Before I state them note that the subject matter expert with whom I was working screened the data for obviouslyproblematic observations before giving them to me So even in factories where the ordering issue occurred (both day oneobservations below both day two observations and vice versa) all the values within a factory still seemed close together e firstreason is that the t-test is a comparison of population means When my curiosity was initially raised the issue was the orderingso I latched onto that particular question and I simply did not think about comparing the means Second the ordering issueoccurred in multiple factories What if the t-test rejected the null hypothesis of equal means for one factory with the orderingissue but not another How would that be resolved Lastly the probability of the ordering issue occurring in any one factoryprovides a second sanity check for the N(μσ2) assumption Specifically since there were nƒ factories I expect the ordering issueto occur in 033nƒ factories so I can compare that value to the number of factories in which it actually occurred e data passedthat sanity check too

I am compelled now to mention that my approach does not conclusively prove that the N(μσ2) assumption is valid (the t-testcould not either) It only fails to provide conclusive evidence that the assumption is invalid at is an important distinction tokeep in mind

Some of you may also think that I should have leveraged the multivariate normal distribution to calculate the probability exactlyat is a good point too While writing this message I started wondering if the exact probability is 13 (I have only had thepatience to run the simulation long enough to get three significant figures 0333) Please email me if this is obviously true or ifyou actually do the calculation with the multivariate normal distribution

I am not trying to argue here that I solved my own problem in the best way possible I am only trying to impress that when youare faced with a question (a statistics question in particular) and the path forward is not clear simulation may work well Incomplicated sample size and the related power of a test calculations my experience has been that simulation can work very well(t-tests have formulas for such calculations) It has also been my experience that the process of constructing a simulation to do acomplicated calculation can add greatly to your understanding of the problem on which you are working

I look forward to serving as chair of the division this year and to hearing from you personally if you so choose

Figure 1 R Code

e mention of products commercial or otherwise does not imply endorsement by the authorrsquos institution nor are they necessarily the bestavailable for the purpose


Past Chairrsquos Messageby Joel Smith

I started typing what felt like a really generic message however thatrsquos exactly what anyone who ever reads thesekinds of things would expect ldquoWow time flieshelliprdquo ldquoI really want to thankhelliprdquo ldquoItrsquos been a really great yearhelliprdquoetc But then I deleted it No one cares whether I think my year as chair passed quickly or not A newsletter ndashahem digest ndash is a really impersonal way to thank people whose work has meant so much and whether I feel itrsquosbeen a good year or not is irrelevant to a division members who can form their own opinions

Instead I want to talk a little bit about the state of statistics Yoursquore probably aware that roughly half of all peopleknow less than average about statistics which is a real shame Irsquod like to see everyone above average Schools arenrsquot teaching thefundamentals effectively and keep changing their curriculum as is evidenced by the constantly changing percentage of studentspassing the AP Statistics exam down a little one year and up another the next Itrsquos as though once something moves the percentagelower they fix it but once things improve they canrsquot seem to leave it alone and let us keep on climbing

Maybe educators should relate the content to something students understand like sports ndash tell a kid that Mike Trout has a 333batting average and he knows that Mike will hit the ball once every three times he has an at bat ndash unless Mike has a hot or coldstreak

Or maybe if we let kids gamble at a younger age theyrsquod understand statistics a little better Irsquove been to Las Vegas a few times andthe really good gamblers watch the slot machines like hawks As soon as someone gets up from a machine in frustration afterlosing several spins in a row a good gambler knows that machine is due to pay and has much better odds than the others Likewisethe good blackjack players know that when they are really down all they really need is to play enough hands for things to balanceout and make back their money This fact is so simple that I know hardly anyone whorsquos ever told me about losing money playingblackjack in a casino Most end up a little bit ahead but just canrsquot remember exactly how much

I guess itrsquos not feasible to get kids to Las Vegas or Atlantic City so maybe we should relate it to the lottery since almost every statehas one Of course everyone knows the odds of winning the lottery are really low but if you win the payout is so big it makesup for it There are a couple of tricks to making money in the lottery and the best players know them First you canrsquot win if youdonrsquot play so itrsquos important to always play And buying multiple tickets makes it much more likely yoursquoll win Buy only one andyoursquoll probably never win Finally donrsquot pick an obvious pattern like 1 2 3 4 5 and 6 ndash that never comes up instead go formore random-looking numbers like birthdays or ages Kids can understand that if you teach them

I just donrsquot see why students canrsquot be more prepared for statistics when they already understand the concepts before taking a classAsk a kid what the odds are of neither of two independent events occurring if each has probability 30 and he struggles to recallthe formula and solve the problem But tell him therersquos only a 30 chance of rain Saturday and again Sunday and he knows hecan safely plan a trip to the beach without worrying about bad weather ruining it Unless the weatherman was wrong about that30 of coursehellip

By nature of your being a members of the Statistics Division at this point yoursquore probably either thinking Irsquom a terrible statisticianor realizing all of this was written in jest There is a point though In the world and even in the organizations most of us workfor there are far more people who would not have realized something was wrong than who had your reaction Letrsquos all do whatwe can to turn that ratio the other way

Thanks for having me as your chair Yoursquoll certainly be in good hands in 2015 and I hope we have and continue to make somedifference in your professional life

Joel Smith


As this is my final issue as official Newsletter editor I would like to express my great appreciation for all those onthe Statistics Division Council who have worked with me over the last few years to put each issue together Theyhave provided the material tolerated my revisions and re-writes and reviewed 40-page drafts looking for waywardtypos and other errors I am now officially handing over the editor position to Matthew Barsalou who I knowhas spent countless hours developing the content for the new Statistics Digest

I am personally very pleased to see this new direction moving forward and am thrilled to welcome all our newregular contributors They will be focusing on key technical statistics topics of particular interest to our member communityMeanwhile information about Division affairs will be migrating to your monthly E-Zine which will be getting longer Lookthere for grant and scholarship info award nominations and conference announcements and Calls for Papers

We encourage you to provide feedback on the new Statistics Digest via our LinkedIn discussion group (group 2115190 if you arenot a member yet) We will be looking at your comments as we go forward into the future

I will continue to be behind the scenes here at the Statistics Division as the new Vice Chair of Member Development and stilltaking a proprietary interest in the new Newsletter

Best wishes to all

Welcome to the first issue of Statistics Digest It may be a bit odd to start with volume 34 no 1 however this isbecause Statistics Digest is a continuation of the Statistics Division Newsletter Statistics Digest will run less divisionnews and more technical content

We will be featuring regular columns with Design of Experiments covered by two of the leading researchers inthe field Dr Bradley Jones Principal Research Fellow at SASrsquos JMP Division and Arizona State Universityrsquos DrDouglas C Montgomery Statistical Process Control will be covered by the past-editor of Quality EngineeringDr Connie Borror who has authored over 75 journal articles and is a professor at Arizona State University

Statistics for quality improvement will be covered by Dr Gordon Clark who is the principal consultant at Clark Solutions Incand a professor at e Ohio State University Dr Jack B ReVelle of ReVelle Solutions LLC will be explaining basic statisticsconcepts in Stats 101 We also have Assistant Director of the Operational Evaluation Division and Test Science Task Leader atthe Institute for Defense Analysesrsquo Dr Laura J Freeman who will give us an overview of testing and evaluation using statisticsUniversity of Central Floridarsquos Dr Mark Johnson will discuss statistics in international standards in his column ldquoStandards InSide-Outrdquo

We will be continuing to run a Mini-Paper in every issue and will be adding at least one feature to each issue e content willrange from basic to cutting-edge so there will be something for everybody with an interest in statistics is issue includes theYouden Address by our own statistical process control columnist Connie Borror e Youden Address is named after Dr JackYouden and is awarded to those who exemplify Dr Youdenrsquos approach to experimentation and communicating statistical conceptsIf you were not fortunate enough to attend Geoff Viningrsquos 2014 Technical Communities Conference talk ldquoStatistical Engineeringand Tearing Down the Silos of Quality Engineeringrdquo you can read the resulting Mini-Paper in this issue

Mindy Hotchkiss will be handing Statistics Digest over to me and I would like to thank her for her years of dedication to theNewsletter e first ASQ Statistics Division Newsletter was published on 21 February 1980 and it is a privilege for me to be theeditor of a ldquonewrdquo publication with a 35 years of history behind it Please contact me at newsletterasqstatdivorg to discuss apossible Mini-Paper Basic-Tools Feature or Case Study if you would like to be a part of this continuing history

Editorrsquos Corner ndash Outgoing Editorby Mindy Hotchkiss

Editorrsquos Corner ndash Incoming Editorby Matt Barsalou

Mindy Hotchkiss

Matt Barsalou


YOuDEn ADDrEssQuality and statistics now THATrsquos Entertainmentby Connie M BorrorSchool of Mathematical and Natural Sciences Arizona State University

William ldquoJackrdquo Youden is one of my heroes I have never met him but his work was nothing short of brilliant In researchinghis life and work in preparation for this talk I learned about his contributions to the war effort during World War II I found itfascinating Also being a fan of classic films I wondered if Youden and others in his division during the war (which I will discussin more detail later) had ever appeared or been featured in short film clips during that period Newsreels were often played attheaters before and between movies to update filmgoers about the war effort selling war bonds or reminding people that ldquolooselips sink shipsrdquo ere were many of these films made during World War II so perhaps Youden could be seen somewhere in oneof those But unfortunately he was not So then I thought perhaps NIST or the Film Institute of the Academy of MotionPictures Arts and Sciences may have some clips of Youden (if not video then audio) that I could use for this talk Unfortunatelyagain no

But then thinking about Youden World War II short film clips and of course quality and statistics I turned my attention toclassic films and how the field of statistics has been portrayed or used in these films As a result my Youden talk is focused onquality and statistics in film ndash a fun look at how they are used directly or indirectly I limited my search on these topics to filmsmade prior to 1970 In this presentation only a few select films are discussed but they present a wide range of uses of statisticsstatistical thinking and quality (here motivated by a film discussing efficiency rather than just quality)

Statistics on ScreenAlthough the films featured here are pre-1970 I should at least mention how we have seen statistics used in popular culture infilm and television e most obvious example is the 2011 film Moneyball where statistical analysis is used in baseball Ontelevision we have seen statistics used or mentioned in one way or another in forensics (Numb3rs) politics (West Wing) andmedicine (House) to name a few On the comedy Friends a running joke was that no one knew what the character ChandlerBing did at his job It wasnrsquot until near the end of the showrsquos 10 year run that is was discovered (note his job was ldquostatisticalanalysis and data reconfigurationrdquo) Even the 1980rsquos TV series Miami Vice got in on the statistics act in an episode where acharacter proclaimed ldquoIrsquom a sergeant in statistical analysisrdquo

It just seems like there is a place for statistics (and quality) in all genres big screen and small

Make up Your Own Statistics Everyone Else DoesAs statisticians and engineers we often here the proclamation that statistics can be used to show or proof anything we wantSometimes it seems as though people just make numbers up while in conversation One good example of this in film is 1948rsquosAn Apartment for Peggy A less than desirable name for a movie I thought but it deals with two of my favorite subjects 1) life inthe United States following World War II and 2) universities It takes place right after World War II when veterans were returninghome which resulted in an incredibly large number of people enrolling in college thanks to the GI Bill In this film a veteranand his pregnant wife face the same dilemma many veterans faced during this time finding married housing on a universitycampus It was a real problem at the time with families living in makeshift housing such as travel trailers or university buildingsturned into sleeping quarters until more permanent housing could be obtained e husband in the film is becoming disillusionedwith the situation (going to school small stipend from the government a pregnant wife and no real place to live) He tells hiswife that a fellow veteran is working as a car salesman making $150 a week and could get him a job selling cars as well e wifeobjects and spouts off some statistics she just made up in her head which is what she does throughout the entire film She tellshim ldquoa recent survey said that 64 of all used car salesman wish they gone into some other fieldrdquo Of course she made that upand her husband calls her on it to which she replies ldquoof course I made it up someonersquos always making up statistics so it mightas well be me Yoursquod be surprised how many arguments I win with my statisticshellipno one ever bothers to checkrdquo So true Oneof the problems we encounter in society is that no one bothers to check

Continued on page 8


Sampling ndash What Could Go WrongSurvey results and the statistics they produce are often thrown around in film but rarely the theme for an entire film Oneexception is the film Magic Town from 1947 is film is all about conducting surveys and being able to identify public opinionon a myriad of topics Gallup polls are mentioned quite a bit in this film but the premise is that a pollster has decided that theremust be a little town out there that is a cross-section of America One that when polled on any topic the results would representthe opinion of the entire nation It would be ldquoUtopiardquo ndash surveying one small segment of the country instead of thousands ofpeople at great expense and get the same results As statisticians we would love that

Early in the film the pollster happens to find a small city whose survey results on a topic turn out to be identical to resultsobtained by the big polling companies but at a fraction of the cost (note the example given in the film shows that both surveysgive the exact same percentage down to the decimal No error in that) e pollster moves to this city to begin surveying thetownspeople on a myriad of topics ankfully he knew enough to make sure the citizens did not know he was polling them ndashno bias you know But over time the citizens find out and come to the conclusion that they as a town could capitalize on theirnew status us the beginning of the end ey begin selling their opinions In one scene the citizens discuss opinions as anldquoexportrdquo and we hear ldquowersquore tired of giving them away from now on wersquore selling themrdquo Of course it doesnrsquot end well for thenew public opinion capital of the US

Two-Sample Comparison ndash with n = 1Now I have to say it was difficult to find any film which discussed hypothesis testing or quality control atrsquos not too surprisingBut I did remember the film Cheaper by the Dozen (1950) e film is based on the lives of Frank and Lillian Gilbreth a husband-and-wife team of industrial engineers who are now celebrated for their groundbreaking work in the early part of the 20th centuryon efficiency (in particular time studies) e title of the film comes from the fact that they had 12 children ey would oftencarry out studies together and one particular scene in the film came to mind one day when I was preparing notes on two-sampletesting for a basic statistics class I wanted to emphasize the problem of anecdotal evidence and how to avoid it and in particularhow a sample size of n = 1 in each group was something to avoid A single observation for each group was not a good test escene in the film was very appropriate for this purpose In it Lillian Gilbreth has a stop watch and was timing Frank as hebuttoned his vest He first buttoned the vest from top to bottom with Lillian timing him He then buttoned the vest from bottomto top ndash again being timed So now we have a single time for top-to-bottom and a single time for bottom-to-top with thebottom-to-top taking less time To which Frank Gilbreth exclaims ldquobottom to top thatrsquos the answerrdquo A single run and theanswer is known Interestingly if you time it yourself (of course with todayrsquos technology) during this scene you will find differenttimes than Lillian did e movie is full of studies big and small At one time the children were to have their tonsils removedso Frank decided to have the surgeries conducted in his house while he filmed them e films were to be used to identify wherethe doctors might be wasting time or effort during an operation ere is also a scene depicting efficiency Frank Gilbrethenrolling his children in a new school demonstrates to the principal how to take a bath ldquoin less time than it takes to play a recordrdquo

Forensics and ldquoEarly Predictive Analyticsrdquoere is no lack of films involving forensics Even though the word ldquoforensicsrdquo is rarely used in older films it is there nonethelessEvery murder mystery revolves around some type of forensics and statistics and probability are there right behind it in thebackground You will often hear ldquothe probability of helliprdquo or ldquothe likelihood ofhelliprdquo is this or that number Estimates of insurancefrauds and probabilities of certain types of death are found in many films Even analytics can be found if we look hard enoughOf course the word ldquoanalyticsrdquo is never used but itrsquos there

My favorite example of ldquoearly predictive analyticsrdquo can be found in the 1928 silent movie e Lodger e film was one of onlya handful of silent films directed by the famous director Alfred Hitchcock But thatrsquos not why I like it In the film a serial killeris on the loose in London e police are obviously having a difficult time catching the killer even though he always strikes ona Tuesday evening in different locations around the city One detective finally decides to do a little crude cluster analysis notwhat it was called in the film of course and plotted each incident on a map of the city A triangular pattern began to emerge and

YOuDEn ADDrEssContinued from page 7

Continued on page 9



they predicted where he would strike next based on the pattern and location of the past murders Of course they were rightabout the location of where he would strike next and caught him It is interesting to watch this scene and the use of predictiveanalytics to identify the next strike given all the advances we have today in technology

ldquoYou(den) Ought to be in PicturesrdquoldquoYou Ought to be in Picturesrdquo is a famous song from the 1930s After researching Youden for this talk trying to find any film oraudio of him and being unsuccessful I decided he would make a great film subject Youden was assigned to the Bombing AccuracySubsection of the Eighth Air Force Operations Analysis Section during World War II Although I could not find film of Youdenhimself there are films available on the internet which document the work of the Eighth Air Force Division Youden developedand applied novel statistical methods (eg the Youden chart) to improve bombing accuracy both in Britain and then in the Pacificduring World War II He was later awarded the Medal of Freedom I refer the reader to Miser (1992) for more detail on Youdenrsquoscontributions during the war and the significant impact it had on the war effort I think his work and that of the Eighth AirForce during World War II is some of the most innovative and interesting It would make a great movie

ConclusionsIf you enjoy watching films or television programs where probability statistics or statistical analysis are mentioned as much as Ido you donrsquot have to look too hard to find them In this talk I have only highlighted a few films that at least briefly touch onstatistics ere are so many more that have appeared over the last three decades I was disappointed in not being able to findsome newsreel or audio clip of Youden If one is out there I hope we find it eventually ank you for this opportunity to talkabout three of my favorite topics film statistics and Dr William ldquoJackrdquo Youden

AcknowledgmentsI would also like to thank the following people for their help in preparing this presentation and paper

Will Guthrie NIST Sarah E Burke Arizona State University Cassie Blake Film Institute of AMPAS

References Briscoe Center for American History University of Texas at Austin Digital Collections httpwwwcahutexasedudbdmr MacArthur C (1991) Operations Analysis in the United States Army Eighth Air Force in World War II (History of Mathematics

Series Book 4) American Mathematical Society London Mathematical Society Miser H J (1992) ldquoCraft in Operations Researchrdquo Operations Research 40(4) 633-639 wwwsubzincom

About the AuthorDr Connie M Borror is a Professor in the School of Mathematical and Natural Sciences at Arizona State University West Sheearned her PhD in Industrial Engineering from Arizona State University in 1998 and joined the School of Mathematical andNatural Sciences in 2005 Her research interests include experimental design measurement systems analysis response surfacemethods and statistical process control and monitoring She has co-authored two books and over 75 journal articles in theseareas Dr Borror is a Fellow of the American Statistical Association and the American Society for Quality and is past-editor ofthe journal Quality Engineering


MInI-PAPErstatistical Engineering and Tearing Down the silos of Quality Engineeringby Geoff ViningVirginia Tech

Introductione nature of quality engineering is changing Quality engineersindustrial statisticians can no longer be simply data analystsOrganizations are finding it too easy to ship analysis overseas A manager or engineer in North America can send the data via theinternet to an analyst in India just before leaving work and can receive the full analysis the next morning Of course the analyst inIndia works for much less money than quality engineers in North America

Our survival as a profession depends on being able to add value e future requires us to be able to solve large unstructured complexproblems that are of crucial importance to the organization ese problems require multidisciplinary teams working together Qualityengineersindustrial statisticians must adapt to the new environment For many people this transition will not be easy e skill setrequired for success is quite different from what most quality engineersindustrial statisticians learned in the university

Role of Six SigmaSix Sigma programs are an important part of the foundation for much of this transformation Ultimately Six Sigma is a structuredstrategic approach for addressing important problems for organizations Six Sigma projects do not address truly large unstructuredcomplex problems however such problems typically need Six Sigma or something similar to address important sub-problems SixSigma provides insights into the skillset necessary for the quality engineerindustrial statistician in the new environment

e three basic components to the Six Sigma skillset are organizational psychology industrial statistics and project management Allthree components are vital

Too many industrial statisticians in the 1990s dismissed Six Sigma as just another repackaging of the tools we have used for manyyears Unfortunately very few industrial statisticians at that time had the necessary people and project management skills to drivethe success of Six Sigma Industrial statisticians were well-trained in the analytic tools from university However their universitycurricula provided no exposure to organizational psychology and project management

Organizational psychology especially change management is essential for success Getting people to work together as a well-oiledmachine is not trivial It requires solid training and practice in team building and team dynamics It takes quite a bit of skill to getteams through the ldquoforming storming norming and performingrdquo phases Organizational psychology puts a great deal of emphasison communication which is crucial for managing change

Industrial statistics provide a large arsenal of analytical tools ranging from the ldquoseven basic toolsrdquo to extremely sophisticated statisticalmethodologies e traditional areas of industrial statistics are statistical process control experimental design response surfacemethodology time series and modeling

Six Sigma places heavy emphasis on software for analysis which is both good and bad e software allows the engineermanager todo lower level analyses for which the standard Six Sigma training provides more than adequate preparation However the softwarealso allows the engineermanager to misapply more sophisticated tools in which they have little or no training

Proper project management is essential for generating a timely solution within budget especially for high impact complex projectsSuccess depends on proper time and resource management which requires the application of sound project management practicesGood organizational psychology and properly applied analytical tools are not enough to ensure timely success within the budgetaryconstraints

ere are several lessons learned from Six Sigma It is important to train subject matter experts in simple tools both involving ldquosoftrdquoand ldquohardrdquo skills Such training allows the subject matter expert to solve a large variety of problems and frees up the qualityengineerindustrial statistician for more technically challenging tasks It is vital for the subject matter experts to understand thelimitations of their training Subject matter experts need to know when to bring a statistical tool expert into the process An important

Continued on page 11


role for quality engineers is to be an integral part of problem solving teams as true colleagues Success requires true collaboration SixSigma emphasizes the need for top leadership involvement and commitment Such leadership ensures that the teams focus on largermeaningful to bottom-line results as measured by true dollar savings

Statistical EngineeringIt is clear that quality engineersindustrial statisticians must learn how to contribute the most value possible to their organizationsAs a result our future focus must be large unstructured complex problems whose solutions require collaboration among highprofile interdisciplinary teams ese problems cut across the organization and management views the solutions as vital for theentityrsquos health and well-being Success requires new tools and mindset

Roger Hoerl and Ron Snee propose a new field that they call statistical engineering ey are building upon the lessons learned fromSix Sigma Six Sigma provides a good strategic structure and a demonstrated structured problem solving approach What is missingis something more tactical that guides how we should deploy our tools We need to ask how we can generalize solution tactics tosolve future problems e goal of statistical engineering is the development of appropriate theory to apply known statistical principlesand tools in order to solve high impact problems for the benefit of humanity Ideally such theory minimizes ldquoone-offrdquo solutions eApril 2012 issue of Quality Engineering discusses statistical engineering in detail

e heart of statistical engineering is the scientific method which is an inductivedeductive problem solving process e basicsteps are

bull Understand the real problem at handbull Define the problembull Discover solutionsbull Abstract from the concretebull Develop a theorybull Test the theory using databull Modify the theory as necessary

For complex problems the scientific method requires true interdisciplinary collaboration

Every successful application of the scientific method requires data Essential elements are data collection data analysis and datainterpretation ese tasks go to the heart of what quality engineersindustrial statisticians do very well Quality EngineeringIndustrialStatistics are the handmaiden to the scientific method e analytical tools by themselves cannot solve any real problem Howeverthey can assist the subject matter expert to see better solutions more quickly e ability to collect to analyze and to interpret dataproperly plays a very important role in solving large unstructured complex problems Such problems require very good subjectmatter expertise combined with equally strong understanding of the analytical tools

SilosldquoSilosrdquo are a major impediment to success in finding good solutions to large unstructured complex problem What do we mean bysilos Silos often exist within disciplines For example engineers only speak with other engineers and statisticians speak only withother statisticians Silos also exist within industries For example automotive people speak only with automotive people and aerospacepeople speak only with aerospace people People who live only in their specific silo often ldquoreinvent the wheelrdquo because they are notaware that people in other disciplines or other industries have encountered similar problems and have developed good solutionsUnfortunately too many of us live only in our silo

Solutions to large unstructured complex problems require new approaches and new insights ese new approaches and insights inturn require cross-fertilization across disciplines and industries For example people now apply quality engineering toolsmethods tofinance risk management healthcare all of which are fields very different from manufacturing where these tools were developed

How do we tear down the silos First we must recognize the problem We must understand that interdisciplinary teams are thefuture Such teams are created for a specific problem and then move onto other tasks Each team memberrsquos subject matter expertiseis essential We must get better at applying solutions from other problems to address the problem at hand We must stop ldquomy onlytool is a hammer so every problem is a nailrdquo

MInI-PAPErContinued from page 10




Boundaries between disciplines must become more amorphous and less rigid It is important to stretch the overlap across disciplinesEach discipline and industry has its own language As a result we must learn other languages as we function more in aninterdisciplinary fashion In the process we learn new approaches to problems and we minimize re-inventing the wheel

ASQrsquos Technical Communities CouncilProfessional societies have a large role to play in breaking down silos ese societies need to create structures that foster

bull Communicationbull Cooperation bull Collaboration

We need to create more opportunities to interact across disciplines ASQrsquos Technical Communities Council (TCC) is one effortalong these lines

e TCC is an umbrella organization which ASQ that provides oversight and guidance to the societyrsquos technical communities whichare the groups whose primary purpose is to generate andor disseminate the quality body of knowledge (QBoK) and designated bythe ASQ Board of Directors as a technical community Current technical communities are the divisions the interest groups and thePublications Management Board e purpose of the TCC is to foster greater co-operation communication and collaboration acrossthe technical communities e TCC is less than two years old and is the successor to the Division Affairs Council (DAC) Properlymanaged the TCC represents realignment within ASQ to help people address large unstructured complex problems

One way to view the TCC is as ASQrsquos ldquostewardrdquo of the QBoK It ensures that member needs are met over the spectrum of ASQmembers ldquonewbierdquo beginner intermediate advanced and thought-leader e TCC must guide the technical communities as theygenerate timely new QBoK especially for emerging areas It must nurture new communities based upon these emerging areas

e TCC must break down the silos among the existing communities For example imagine collaboration among the AutomotiveStatistics and Reliability Divisions (all strong divisions) to develop an expanded QBoK specifically for the automotive industrybringing together true interdisciplinary teams of thought leaders and strong subject matter experts en imagine forming otherinterdisciplinary teams to modify that new QBoK to help address the needs of aerospace or other industries

Realizing such a vision requires new thinking and new structures Some of the resulting changes may cause some level of discomforteven pain However achieving such a vision truly drives ASQ closer to being the ldquoglobal voice of qualityrdquo

Concluding RemarksQuality engineeringindustrial statistics stand at a major crossroads ese disciplines must evolve to remain relevant Statisticalengineering provides an interesting pathway particularly since it requires working in truly interdisciplinary teams as real colleaguesFor many of us this transition is not easy however as a profession it is necessary helping us to make larger more meaningfulcontributions to our organizations and to society

About the Author Geoff Vining is a Professor of Statistics at Virginia Tech and an internationally recognized expert in qualityengineering and industrial statistics He is currently a member of the ASQ Board of Directors and Past Chair of the TCC He is aFellow of the ASQ Dr Vining served as Editor of the Journal of Quality Technology Editor-in-Chief of Quality Engineering Chair ofASQrsquos Publications Management Board and as Chair of the Statistics Division Dr Vining has received ASQrsquos Shewhart Medal andBrumbuagh Award as well as the Statistics Divisionrsquos William G Hunter and Lloyd Nelson Awards


COluMnDesign of Experimentsby Bradley Jones PhDJMP Division of SAS

Douglas C Montgomery PhDSchool of Computing Informatics and Decision Systems Engineering Arizona State University

Jones and Montgomery (2010) proposed a new graphic diagnostic toolfor evaluating designed experiments e suggested graph is a cell plotshowing the pairwise correlation between two potential model terms as acolored square If as in Figure 1 there are 45 terms of interest then thecell plot below shows 45x45 = 2025 colored cells


What does this plot tell meAbsolute values of the correlations are between zero (white) and one (black) e diagonal line of cells is black for any design butit is most desirable for all the off-diagonal cells to be white or at least on the white end of the color spectrum Every cell in Figure1 is either pure white or black Multiple cells in a row that are colored pure black indicate that the associated model terms areindistinguishable from one another In such a case we say that the two effects are confounded Looking at the first row of cellswe can see that the main effect of factor A is confounded with the DG two-factor interaction

e design shown in Table 1 below and graphed in Figure 1 is a regular fractional factorial design for nine factors in 16 runsMontgomery (2013) shows how to construct these designs We start with a full factorial design that has the desired number ofruns ndash in this case one having four factors and 16 runs en factor E is generated by the element-wise multiplication of thecolumns for factors C and D Similarly F = BD G = AD H = ABC and I = ABCD e result of this procedure is a design thatis orthogonal for the main effects but which confounds 12 of the 36 two-factor interactions with a main effect Suppose you ranthis experiment and observed large effects associated with factors A D and G You would like to claim with certainty that thesethree factors are all active However an equally explanatory model is the one with the terms D G and the DG interaction Sothe confounding in the design produces ambiguous analytical results e only data-driven way to resolve such ambiguity is todo further experimentation

Figure 1 Correlation plot for a textbook design

Bradley Jones Douglas C Montgomery

Evaluating Screening Designs



Design of ExperimentsContinued from page 13

Is there some way to avoid confounding main effects and two-factor interactionsFigure 2 shows our diagnostic plot for one of the 87 nonisomorphic orthogonal fractional factorial designs for nine factors in 16runs (see Table 2) Unlike Figure 1 the upper right section of Figure 2 has no black squares us no main effect is confoundedwith any two-factor interaction Moreover the largest absolute correlation of any main effect with any two-factor interaction isonly one-half On the negative side this design has more interactions correlated with every main effect even though the correlationsare not as large

Table 1 Minimum aberration 29-5 fractional factorial design

Table 2 Alternative orthogonal two-level fractional factorial design



Let us go back to the scenario we considered before Can we distinguish between the model having the main effects A D and Gand the alternative model having the main-effects D and G plus the DG two-factor interaction Absolutely is is true as longas the observed effects are sufficiently larger than their standard errors

What is the bottom lineComparing Figures 1 and 2 side by side provides a quick way of assessing the relative merits of the two designs We prefer designswhere the diagnostic plot contains fewer pure black squares For more information on these alternative non-regular fractionalfactorial designs for 6 to as many as 14 factors in 16 runs see Jones and Montgomery (2010) Shinde Montgomery and Jones(2014) and Jones Shinde and Montgomery (2015)

ReferencesJones B and Montgomery DC (2010) ldquoAlternatives to Resolution IV Screening Designs in 16 Runsrdquo International Journal of ExperimentalDesign and Process Optimisation Vol 1 No 4 pp 285-295

Jones B Shinde SM and Montgomery DC (2015) ldquoAlternatives to Resolution III Regular Fractional Factorial Designs for 9 ndash 14 Factorsin 16 Runsrdquo Applied Stochastic Models in Business and Industry in press

Montgomery DC (2013) Design and Analysis of Experiments 8th Edition Wiley Hoboken NJ

Shinde SM Montgomery DC and Jones B (2014) ldquoProjection Properties of No-Confounding Designs for Six Seven and Eight Factorsin 16 Runs International Journal of Experimental Design and Process Optimization Vol 4 No14 pp 1-26

Figure 2 Another orthogonal 16 run fractional factorial


Topics in Statistical Process Control

As part of the ASQ Statistics Division Newsletter (Statistics Digest) focus on more statistical andtechnical content topics related to statistical process control (SPC) will be one of the areas of emphasisin this new column e purpose will be to discuss new methods new applications of standardmethods as well as illustrations of both basic and advanced concepts involving SPC

Statistical process control is a field covering a wide range of methods and applications and involvesmany important tools useful for solving problems encountered in a process under study ereader may already be familiar with the term ldquothe magnificent sevenrdquo which references sevenmajor SPC tools Histogram (or stem and leaf plots) Pareto chart check sheet cause-and-effect

diagram scatter diagram defect concentration diagram and control chart

Over the years additional tools have been included in the SPC ldquotoolkitrdquo in order to include important information from forexample brainstorming activities among team members or groups involved in process improvement (ie affinity diagram) Toolsthat will allow for the inclusion of relationships among ideas (ie the interrelationship digraph) brought forth from team membersare also important

e control chart is the tool most commonly associated with SPC In fact the control chart and the acronym SPC are oftenused interchangeably even though SPC involves much more than just the use of control charts It is an important graphical toolto aid the user in determining if the variability present in the process of interest is natural (common cause) or influenced by somefactor(s) (assignable cause)

Process control has two central purposes monitoring and improvement Control charts are excellent tools for monitoring aprocess but they do not identify improvement opportunities or tell the user what the problem (opportunity) may be if an out-of-control signal occurs Determining the cause(s) of an out-of-control process can be accomplished using the remainingldquomagnificent sevenrdquo as well as many other tools such as designed experiments affinity diagrams and interrelationship digraphsfor example

Decisions and ConsiderationsBefore applying any tool in the SPC toolkit there are many decisions to be made and options the user should consider It is veryeasy to simply apply a control chart for example to data collected over time for some process ndash simply because the data is readilyavailable A sort of lsquoif we have it why not throw a control chart on itrsquo type of approach Many of us have done this (I know Ihave) without perhaps fully considering the process as a whole only to backtrack and reconfigure our approach later is is notalways a bad approach We can learn a great deal about a process this way However we can also minimize some of the inefficiencythat often accompanies this approach by carefully and thoughtfully studying a process with some key questions in mind (not anexhaustive list)

bull What is the purpose of this study

bull For what variable(s) do we (can we) collect data

bull What type of data is collected

COluMnstatistical Process Controlby Connie Borror PhDProfessor School of Mathematical and Natural Sciences Arizona State University


Connie Borror


bull How is the data collected

bull What is the quality characteristic of interest

o Is there more than one quality characteristic of interest

bull How often should samples be collected

bull How large of a sample is necessary for each time period

bull What is the maturity of the process andor monitoring method

o Has the process been analyzed to determine if only common cause variation exists Is the process unstable Has it beenmonitored before What phase are we working in Phase I or Phase II

bull If we implement a control chart for monitoring purposes what is the action plan if the process signals out of control

In future issues of this column we will discuss different aspects of statistical process control and provide some guidance foraddressing questions such as those listed above Examples and illustrations of SPC in a variety of fields such as traditionalmanufacturing healthcare service industries and forensics to name a few will be presented We will highlight SPC in bothunivariate and multivariate applications for new processes as well as established processes

Our goal is provide the reader with discussions of tools and techniques that can be immediately applicable in practice or provideideas for future research topics We will cover basic methods as well as more advanced or new methods in statistical processcontrol To that end we encourage the reader to contact us with questions to be addressed in future articles and suggestions forpossible topics in order to make the column one of greatest possible use and impact Suggestions and questions can be sent tocborrorasuedu

statistical Process ControlContinued from page 16

2015 Fall Technical Conference

e 59th annual Fall Technical Conference (FTC) will be held in Houston on October 8-9 2015 FTC has long been aforum for both statistics and quality and is co-sponsored by ASQ (Chemical amp Process Industries Division and the StatisticsDivision) and the American Statistical Association (Section on Physical and Engineering Sciences and Section on Qualityand Productivity)

If you are interested in presenting an applied or expository paper in any of the three parallel sessions (Statistics QualityControl or TutorialCase Studies) please contact one of the program committee members below

ASQ CPID Marc Banghart (marcasqrdorg) ASQ STAT Mindy Hotchkiss (MindyHotchkissrocketcom) ASA SPES Zhen Wang (ZhenWanglubrizolcom) ASA QampP Alix Ann Robertson (aarobersandiagov)

e deadline for abstract submissions is February 28 2015 Additional information about the abstract submission formatis available at the conference website (httpasqorgconferencesfall- technical)

Use of the Value Stream Map in a Lean Six Sigma Project Case Study Involving Designof Experiments

Introduction

Can we achieve the same benefits as eory of Constraints Lean Six Sigma (TLS) an integratedeory of Constraints (TOC) and Lean Six Sigma (LSS) improvement process by simply using valuestream maps in the define or measure phases of LSS e motivation for using the Value Stream Map(VSM) is to focus the Lean Six Sigma project on the constraint limiting overall system performanceExperimental results described by Pirasteh and Fox (2011) indicate TLS projects outperformed bothLean and Six Sigma projects since TLS focuses on areas that could result in larger overall system

benefits Both Pirasteh and Fox (2011) and Sproull (2009) highlight the VSM as a key tool in identifying the system constraintSproul defines the value stream as all those items one does to produce a product that creates value

Chen Li and Shady (2010) describe a case study where a VSM was used in specifying the focus of a LSS project e VSMidentified the system constraint and the LSS team used an experimental design to determine the levels of system factors to relievethe constraint e company in question is a small electrical manufacturing business in the Midwestern United States ecompany makes industrial switch gears and switchboards e company had not completed a LSS project but employees wantedto transform the company using LSS ey called their approach LeanSigma but we will view it a version of LSS ey formeda LSS team and started with the switchboard unit since it was the major manufacturing section and used the greatest amount ofpersonnel and equipment

Value Stream Map

e first step was to create a VSM of the current system e team made two walkthroughs with the manufacturing managere first one traced the product flow from the raw material receiving dock to the finished product shipping dock en the teamwalked from the shipping dock upstream to the raw material dock and collected detailed process information e resulting VSMappears in Figure 1 e fabrication operation produces 6 parts which are welded together in the welding operation and are thensent to the finishing operation e final two operations are assembly and wiring Figure 1 shows a generic VSM where theabbreviation CT is the average cycle time or the average time a part is in the operation e abbreviation PT is average processingtime and CO is the average changeover time Chen Li and Shady (2010) present the data values in Figure 1


COluMnstatistics for Quality Improvementby Gordon Clark PhDProfessor Emeritus of the Department of Integrated Systems at The Ohio State University and Principal Consultant at Clark-Solutions Inc

Gordon Clark


Observations

e VSM showed that the fabrication operation was the bottleneck operation since its cycle time of 1405 minutes is longestcycle time of the five operations Finishing is the operation with the next longest cycle time which is 128 minutes e productionprocess begins with fabrication so based on cycle time downstream operations would have to wait for fabrication to completeparts e fabrication operation consists of four processes ey are shearing plasma cutting de-burring and braking Two of

Figure 1 Value Stream Map


statistics for Quality ImprovementContinued from page 18

the 6 work pieces must be processed by the plasma cutter e plasma cutter has an average machine time of 42 minutes plus aloading time of 3 minutes and an unloading time of 2 minutes Clearly the plasma cutter was the bottleneck process in thefabrication operation with 94 minutes of its 1405 minute cycle time due to the plasma cutter (Chen Li and Shady 2010)

Kaizen event Reduce cycle time for the plasma cutting machine

e LSS team created a kaizen event to improve the fabrication operation It used the ldquo5 Whysrdquo approach to identify root causesfor poor performance For example the plasma cutter created defects that needed to be reworked and time was required forinspection due to the defects e defects were created because the plasma cutter was not operating at ideal parameter settings sothe team designed experiments to identify better parameter settings (Chen Li and Shady 2010)

Experimental Design

e experimental design and results are presented by Chen Li and Shady (2010) but Chen Li and Cox (2009) give a morein-depth presentation of the design and the analysis of results

Performance Measures

e plasma cutting machine produces holes on work pieces for installing hardware Holes having excessive beveled edges andpoor circularity cannot be used A beveled edge is one where the hole is not perpendicular to the face of the switchboard Figure2 shows the bevel magnitude which is the bevel performance measure Figure 3 shows the circularity measure which is thesmallest diameter deviation |Dtarget ndash Dsmallest| Figures 2 and 3 are similar to Figures 5 and 6 in Chen Li and Cox (2009)

Figure 2 Bevel Magnitude

Figure 3 Circularity Measure


Factors

e LSS team identified four controllable factors which were tip size feed rate voltage and amperage as well as twouncontrollable noise factors which where air pressure and pierce time e manufacturer was unable to control the uncontrollablefactors so they decided to run the experiments with three levels for the controllable factors and two levels for the uncontrollable(noise) factors



Taguchi Parameter Design

e LSS team chose a Taguchi parameter design because they believed it allowed for a reduction in the time and money to conductthe experiment compared to a factorial design at is the Taguchi design would require fewer experiments than a factorial designTaguchi parameter design is an example of a robust parameter design (Montgomery 2012) Taguchi developed his experimentaldesigns tobull Develop products that are robust to external variability sources bull Minimize variation about target values rather than conform to specifications limits

e complete Taguchi design consists of two arrays an inner array containing the controllable factors and outer array containingthe uncontrollable factors e inner array is an L9 orthogonal array and the outer array is an L4 orthogonal array Note that acomplete factorial design for the inner array would have 81 runs instead of 9 Each of the 9 runs in the inner array was testedacross the 4 runs in the outer array by the LSS team at gave a total sample size of 36 runs One can estimate interactionsamong controllable factors and uncontrollable factors but no information is provided to estimate interactions among controllablefactors is type of design is called a crossed array design (Montgomery 2012)

Parameter Design Performance Measures

Taguchi recommends analyzing both average values and variation Let yij be an experimental result where i specifies the innerarray row and j specifies the outer array row For each inner array row an average value of yij is calculated for the 4 values of jTaguchi recommends analyzing variation using a signal-to-noise ratio (SN) When small values of the performance measureare best the SN is

For both bevel magnitude and circularity measures the target value T is zero and smaller values of yij are preferred A value ofSN is computed for each inner array row and large values of the signal-to-noise ratio (SN) are preferred

Experimental ResultsLSS team decided to select the factor levels that had the highest number of preferred occurrences to resolve the conflicts amongthe results based on the performance measures e bevel magnitude results in Figure 4 and circularity results in Figure 5 werecreated based on data provided by Chen Li and Cox (2009)

Figure 4 Bevel magnitude results Continued on page 21



Figure 5 Circularity results

Project Effectiveness

e effectiveness of the preferred setting for the four factors was verified by 30 work pieces where each one of them met thequality requirements for subsequent assemblies Chen Li and Shady (2010) e cycle time of the plasma cutter was reducedfrom 47 minutes to 30 minutes and that reduced the fabrication operation cycle time to 1065 minutes so it was no longer thebottleneck operation In addition the output quality of the product was improved

Improved Approach and Controversy

is case study clearly shows that robust parameter design can improve system efficiency and effectiveness by improving the meanoutput and reducing variability Montgomery (2012) notes that Taguchi parameter design was used in the 1980s by largecorporations such as ATampT Bell Labs Ford Motor Company and Xerox However later studies showed that the experimentalprocedures and data analysis methods advocated by Taguchi could be significantly improved e next issue of this column willfeature an improved approach to robust parameter design

References

1 Chen J C Y Li and B D Shady (2010) From Value Stream Mapping Toward a LeanSigma Continuous Improvement Process AnIndustrial Case Study International Journal of Production Research 48(4) 1069-1086

2 Chen JC Y Li and R A Cox (2009) ldquoTaguchi-based Six Sigma Approach to Optimize Plasma Cutting Process An Industrial CaseStudyrdquo International Journal of Advanced Manufacturing Technology 41 760-769

3 Montgomery Douglas (1997) Design and Analysis of Experiments Fourth Edition John Wiley amp Sons New York 622-641

4 Montgomery Douglas (2012) Design and Analysis of Experiments Eighth Edition John Wiley amp Sons New York Chapter 12

5 Pirasteh R M and R E Fox (2011) Profitability with No Boundaries - Optimizing TOC Six Sigma and Lean Results Milwaukee WIASQ Quality Press

6 Sproull Bob (2009) e Ultimate Improvement Cycle Maximizing Profits through the Integration of Lean Six Sigma and the eory ofConstraints CRC Press Taylor amp Francis Group Boca Raton FL


Measures of Central Tendency and DispersionThe three most widely used measures of central tendency are the mean (also known as the arithmeticmean or average) the median (aka the center point) and the mode (the value that occurs mostfrequently) Each measure has its own symbol or notation as follows

COLUMNStats 101by Jack B ReVelle PhDConsulting Statistician at ReVelle Solutions LLC


Letrsquos use a common data pool to help better understand how to determine each measure of central tendency Our data pool hasfive values (n = 5) 7 3 5 1 and 9MeanSum all the values (data points) in the data pool and divide the sum by the number of values (the sample size ldquonrdquo)

MedianArrange the values in the data pool into an array of numbers from the largest value to the smallest or vice-versa The center pointis the median It has as many numbers above it as it does below This is true with an odd number of values in the data pool

If there is an even number of values in the data pool identify the two values in the middle of the array and calculate their averagevalue This average of the two centermost values is the median as below

Jack ReVelle

ModeArrange the values in the data pool into an array of numbers from the largest value to the smallest or vice-versa The value thatoccurs most frequently is the mode (unimodal or one mode) If two values occur most frequently the data pool is bimodal Ifthree or more values occur most frequently the data pool is multimodal

Reprinted with permission from Quality Press copy 2004 ASQ wwwasqorg No further distribution allowed without permission


stats 101Continued from page 22

Measures of DispersionThe six most widely used measures of dispersion are the range the average deviation the standard deviation the quartile thedecile and the percentile Data dispersion results from inconsistent unpredictable performance A predictable process producesresults that are consistent from one time period to the next An unpredictable process produces results that are widely dispersed

RangeIdentify the largest and the smallest values in a data pool The range is the absolute difference (without regard to the mathematicalsign) between these two values It is standard practice to subtract the smallest value from the largest value As a result the rangeis always either zero (if the two numbers are the same) or positive

Average DeviationThe average deviation (AD) is calculated using the formula


The notation ldquo| |rdquo indicates the absolute value of the difference without regard to the mathematical sign



The average deviation (D) which is also known as the mean deviation is the arithmetic mean of the absolute deviations of a setof data about the datarsquos average (mean) The D provides a single value representing the typical distance that data points are fromthe average It does not provide any idea about the spread scatter or density) of data around the average Consider thisexample

Suppose all we know about these two processes is the data the average and the average deviation D The mean in both cases isthe same but the D in Process 1 is much greater than the D in Process 2 Clearly Process 2 has better control than Process 1with respect to consistency of results

However we have no idea about the concentration of the data with respect to the average and so we need a better way to evaluatedata dispersion around this measure of central tendency This brings us to a discussion of the standard deviation (SD)

Standard DeviationThe sample standard deviation (SD) is calculated using this formula

In this example our data set has five values (n=5) they are in no particular order 7 3 5 1 and 9 We can see from the formulathat we will need to know their average x Thus


We can also see from the formula that we will need to know the value of the term (x1-x) for each value of x Thus



Next we divide Σ (x - x)2 by n - 1 In this example n is the size of our data pool ie n = 5 Thereforen - 1 = 5 - 1 = 4

Since we have finished our calculations under the square root sign we can complete the calculation of the sample standarddeviation (SD)


Average Deviation vs Standard DeviationUsing the SD as a measure of dispersion we can easily evaluate process consistency Although the D is much easier to calculatethe SD is far more useful than the D There are still some situations where the D is used instead of the SD in complex modelfitting Ds are less sensitive to extreme outliers (values far from the average or trend line) compared to SDs because they donrsquotsquare the distance before adding them to the values of other data points

Since model fitting methods aim to reduce the total deviation from a trendline methods using SDs can end up creating a trendlinethat diverges away from the majority of points so as to be closer to an outlier Using the D reduces this distortion but at the costof complicating the calculation of the trendline The SD possesses mathematical properties and relationships which generallyspeaking makes it more useful in statistics However ldquousefulrdquo should never be confused with perfect

QuartilesA quartile is 25 of whatever is being evaluated There are four quartiles used to describe a data set The first (top) quartile andthe fourth (bottom) quartile represent the top 25 of all the values and the bottom 25 respectively The second and thirdquartiles are immediately above and below the median respectively

Furthermore we can see from the formula that we will need to know the sum of all these (x1 - x)2 terms Thus



DecilesA decile is 10 of whatever is being evaluated There are 10 deciles used to describe a data set

PercentilesA percentile is 1 of whatever is being evaluated There are 100 percentiles used to describe a data set Thirty percentiles are30 of whatever is being observed eg 30 of n = 20 units is (30 or 030) times (20) = (030) (20) = 6 units

Reprinted with permission from Quality Press copy2004 ASQwwwasqorgNo further distribution allowed without permission

Professional Networking on LinkedIn by Amy Ste Croix Social Media Manager

Looking for a place to connect with professionals who apply statistical thinking in their careers Check out and join theDivisionrsquos LinkedIn group where you can find webinar and conference announcements as well as a series of discussions onvarious statistics-related topics Participate in exiting divisions or start your own To get involved or find out moreinformation visit the Divisionrsquos LinkedIn page httpswwwlinkedincomgroupsASQ-Statistics-Division-2115190

ASQ Certification Exams Coming by Brian Sersion Certification Chair

Just a quick reminder to members about upcoming certification exam opportunities ndash most ASQ certification exams arebeing offered on June 2015 Since the application deadline is normally 3 weeks in advance of an exam be sure to beginplanning now

e Top 4 certifications held by our members are Certified Quality Engineer and Certified Quality Auditor (both offeredon June 6 2015) and Certified Six Sigma Black Belt and Certified Reliability Engineer (both offered March 7 2015)

For those of you who attend Fall Technical Conference the Division has begun offering the CQE SSBB and SSGB examsat FTC ere are also special administrations of many exams scheduled by ASQ Sections For more information checkout the ASQ Certification Home page httpasqorgcert


COluMnTesting and Evaluationby Laura Freeman PhDAssistant Director of the Operational Evaluation Division and Test Science Task Leader at the Institute for Defense Analyses


Defining the Science of TestingTesting and evaluation are essential aspects of the systems engineering process From system designto the consumerrsquos decisions to purchase the product testing and the resulting analyses can provideinsight and inform decisions Conducting tests and evaluating the results are an essential element ofdeveloping quality products that deliver the performance that are required by the end user

A scientific approach to testing provides a structured and defensible process that ensures the rightamount of testing is conducted to answer key questions A scientifically planned test requires theinput and expertise of all stakeholders including project management engineering and scientificexpertise and statistical expertise

As every system is different the engineering and scientific expertise will vary based on the system or process being tested Howeverthe need for statistical expertise is universal Why I like to think of statistics as the operationalization of the Scientific MethodStatistics provide us with the concrete methods and tools for generating new knowledge sought in testing Additionally thesestatistical methods are often independent of the application (although the appropriate methods are influenced by the application)e same statistical techniques that are used to compare a generic drug to a brand-name counterpart can be used to compare theaccuracy of a new guidance system in an air-to-ground missile to the original guidance system

So what is the (statistical) science of testing

In the test planning phase the field of Design of Experiments (DOE) or Experimental Design provides the science of testing DOEis a structured and purposeful methodology for test planning Unfortunately many have construed the term DOE or experimentaldesign to be synonymous with a narrow set of named test designs (eg factorial designs D-optimal designs) DOE is not just alisting of design types e essential elements of an experimental design process are (see Figure 1)

(1) Identify the questions to be answered also known as the goals or objectives of the test(2) Identify the quantitative metrics also known as response variables or dependent variables that will be measured to address

these key questions(3) Identify the factors that affect the response variables Also known as independent variables these factors frame the broad

categories of test conditions that affect the outcome of the test(4) Identify the levels for each factor e levels represent various subcategories between which analysts and engineers expect

test outcomes to vary significantly When performance is expected to vary linearly two levels are used Nonlinearperformance typically results in three or more levels

(5) Identify applicable test design techniques Examples include factorial designs response surface methodology andcombinatorial designs e applicable test design method depends on the question the metrics the types of factors(numeric or categorical) and available test resources

(6) Identify which combinations of factors and levels will be addressed in each test period If the test is to be a ldquoone-shotrdquotest with no follow-up planned then a more robust test may be required If testing can be sequential in nature thensmaller screening experiments aimed at determining the most important factors should precede more in-depthinvestigations

(7) Determine how much testing is enough by using relevant statistical measures (eg power prediction variance correlationin the factor space etc)

Laura Freeman


Testing and EvaluationContinued from page 27

Employing DOE in this holistic sense ensures efficient and adequate testing and also aids in determining how much testing issufficient As a result we can determine the optimal allocation of constrained test resources and provide an analytical trade-spacefor test planning

Statistical analysis methods provide the science of testing for the analysis of data ese empirical models allow for objectiveconclusions based on the observed data Parametric regression methods allow us to maximize information gained from test datawhile non-parametric methods can provide a robust assessment of the data free from model assumptions Bayesian methodsprovide avenues for integrating additional sources of information

It is important to clarify that the analysis model should reflect the observed data and not the planning process In designing testswe often assume a statistical model for the analysis however there is no limitation requiring the use of that model in the analysisOften qualities of the data observed (eg skewness lurking variables) lead us to employ different analysis methods than originallyplanned this is completely acceptable and can better inform test design on similar productsprocesses in the futureIn the Department of Defense where I spend my time working on testing and evaluation large scale efforts are underway toreplace current test strategies with a scientifically rigorous approach using design of experiments In some industries this mayseem well past due However these methods are far from common practice in testing as a whole In fact many system engineeringand operations research programs only provide brief introductions to experimental design and the benefits it brings to testingand evaluation

is column on testing and evaluation will discuss the statistical methods that provide the science of testing is inauguralcolumn is focused on DOE In future columns I look forward to sharing my thoughts on techniques methods and philosophieswe can all use to improve testing and evaluation whatever your field

Figure 1 e essential elements of an experimental design process



COluMnstandards Inside-Outby Mark Johnson PhDUniversity of Central FloridaStandards Representative for the Statistics Division

Reflections on Life in International Standards

Two events occurred in 1989 which had a major influence on my professional life in InternationalStandards One was very sad while the other historic is initial column on standards covers thesetwo events

On July 23 1989 Richard A Freund passed away Dick was a long time member of the StatisticsDivision whose legacy in the quality profession persists to this day As the Fall 1989 ASQC StatisticsDivision Newsletter reported his accomplishments and service to the Statistics Division were legion(including vice-president of publications and of membership services president 1972-73 chair 1973-

74 director-at-large editorial board of IQC and Technometrics) Not surprisingly his honors were many including Fellow of theASQC ASA ASTM and the Institute of Quality Assurance of Great Britain and he won the Shewhart Medal Lancaster Awardand the Brumbaugh Award (twice) Besides these well-known honors Dick was instrumental in the area of InternationalStandards Chairing the US Technical Advisory Group (TAG) to the International Organization of Standardizationrsquos (ISO)Technical Committee 176 on Quality Assurance and Chair of ASTMrsquos Committee E11 on Statistical Methods

I did not have the honor of meeting Dick in person but fortunately this column gives me the opportunity to pay my respects tohim and his legacy Because of his passing his travel funding for the ISO TC69 international meeting later in the year was madeavailable to me to participate in the Berlin meeting in November Dick had been the Chair of the US TAG to ISO TC69 aswell is funding re-allocation was made possible by the efforts of my distinguished colleague at Georgia Tech HarrisonWadsworth who was the ISO TC69SC1 Chair us knowing very little about International Standards I was now set to goto West Berlin for the Nov 12-18 1989 meeting as an official ANSI-sponsored US Delegate e travel planning took place inSeptember so I secured an airline ticket and made a reservation at a favorite hotel of the US delegation headed by Gus MundelAs preparation for the meeting Harry handed me a giant stack of documents (standards in various states of production fromcommittee drafts to nearly final published standards) that I would be responsible for representing the US position (although Iwas not very clear on what these positions were)

I was scheduled to take a red-eye flight on Saturday night November 11 Had I been more historically conscious I might havetraveled a bit earlier as the Berlin wall opened on November 9 1989 with a flood of East Germans re-uniting with family thatthey had not seen for almost thirty years Getting a flight given the circumstances however would have proven impossible Allflights were packed with the unified Berlin the place to be e mood in Berlin on arrival Sunday morning was of great excitementwith the East Germans wandering through town in a state of elation (rumor had it that each East German visitor received 50Deutsche marks upon passing into West Germany) e feeling in the streets was indeed incredible e selection of WestBerlin in November could not have been more fortuitous as typically these international meetings take place in the June-Julytime frame is would become a very difficult venue and timing to top for future ISO TC69 meetings

e ISO TC69 meeting itself was a great learning experience for me I was assigned to attend several different meetings scramblingto read the various documents that were under discussion Gus Mundel and Harry Wadsworth coached me on what to do withinthe breakout sessions I was charged with ldquodefendingrdquo to the extent possible some comments made by US experts who hadreviewed some of the documents One in particular involved a standard on normality tests and another was concerning tests onthe means e sessions on bulk sampling I found to be outside my expertise and interest level but as the subcommittee onmaterials sampling (SC3) later was absorbed into TC69 I managed not to do any harm Even the opening and closing plenarymeetings with all the delegates was very intriguing e roll call of delegates from the various countries gave me a sense of the

Mark Johnson


standards Inside-OutContinued from page 29

significance of standards internationally I met Dr Jon Stene of Denmark and Dr Yoshi Ojima of Japan at this meeting and welater spent many enjoyable hours crafting revisions to International terminology documents in the ISO 3534 series

Perhaps the most memorable event of the week was the afternoon I had free from meetings so I along with a fellow US delegateMicheacutele Boulanger went through Checkpoint Charlie into East Berlin and managed a quick visit to Pergamon Museum and theFernsehturm (today the Berlin TV tower or Alex tower) In contrast to West Berlin East Berlin was dingy and the architecturewas soot-covered and uninspiring

is memorable first meeting probably explains why I have had such an affinity and allegiance to international standards Withthe twenty-fifth anniversary of the fall of the Berlin wall (I have some rocks from the wall) I could not help but reminisce aboutmy start in International Standards e many long-time friends from other countries and the subsequent meetings in ParisTokyo Beijing London Copenhagen Stockholm Sun City Ottawa Kuala Lumpur Kansas City even and finally Vienna lastsummer have made the thousands of hours pouring over or writing standards well worth while I am most grateful to myinspirational predecessor Richard Freund whose unfortunate passing led to launching my quarter century of service to the standardscommunity and the utmost respect for his accomplishments and legacy

In future columns I will continue to extol the excitement and pleasure of working with standards while delving into some technicalaspects of standards I hope you will consider joining the US effort by contacting Standardsasqorg if this type of professionalactivity appeals to you

Ellis R Ott ScholarshipFor Applied Statistics and Quality Management

e Statistics Division of the American Society for Quality is pleased to announce the availability of $7500 scholarships tosupport students who are enrolled in or are accepted into enrollment in a masterrsquos degree or higher program with aconcentration in applied statistics andor quality management is includes the theory and application of statisticalinference statistical decision-making experimental design analysis and interpretation of data statistical process controlquality control quality assurance quality improvement quality management and related fields e emphasis is onapplications as opposed to theory Studies must take place at North American institutions

Qualified applicants must have graduated in good academic standing in any field of undergraduate study Scholarship awardsare based on demonstrated ability academic achievement industrial and teaching experience involvement in student orprofessional organizations faculty recommendations and career objectives

Application instructions and forms should be downloaded from

httpasqorgstatisticsaboutawards-statisticshtml

Forms for the 2015-16 academic year will be accepted only between January 1 and April 1 2015

For more information contact

Dr Lynne B Hare55 Buckskin PathPlymouth MA 02360

Email lynnehareverizonnet

Governing Board Dr Susan Albin Dr Lynne Hare Prof J Stuart Hunter Mr Tom Murphy Mr Dean V NeubauerDr Robert Perry Dr Susan Schall and Dr Ronald Snee



FEATurEApproaching statistics as a languageby Eston MartzMinitab Inc

Not long ago I couldnrsquot abide statistics I did respect it but in the same way a gazelle respects a lion Since most of my experienceswith statistics indicated that close encounters resulted in pain I avoided contact whenever possible

So how is it that today I write about statistics for a leading statistical software company atrsquos simple all it took was completelyreinventing the way I thought about and approached statistics Why does my experience matter to you Because you know alltoo well the typical reactions we get when we tell people that we work with statistics and data analysis blank stares uncomfortablesilence horrible jokes or some variant of ldquoOh how nicehellipplease excuse me I need to go someplace where I donrsquot have to talkabout statisticsrdquo

People react this way because theyrsquore intimidated by statistics ey think itrsquos too hard to understand or that theyrsquove forgottenwhat they need to know or maybe that theyrsquore just not smart enough to get it atrsquos why I believe that my experience not justcoming to terms with but actually coming to love statistics may resonate with you Approaching statistics as a kind of conceptuallanguage rather than a peculiarly ambivalent branch of mathematics may offer a path to make data analysis more accessible tomore people or at least help us do a better job of communicating with our fellow humans who donrsquot love statistics

Stalked by StatisticsI never was mathematically inclined I could grasp mathematical concepts easily but working through a set of problems alwaysseemed like pointless drudgery And college courses convinced me statistics was just another branch of mathmdashone that didnrsquoteven have any correct answers Given the almost reflexive math aversion I had developed itrsquos hardly surprising I performed poorlyin my undergraduate statistics classes I came to dread and loathe the discipline As for the idea of using statistics in my careerInconceivable

Straight out of college I was hired as a feature writer for a science magazine A few years later I was editing the magazine myselfAnd I felt like a gazelle glimpsing a lionrsquos tail in the grass my environment delivered constant reminders that statistics existede science journals were full of them scientists cited statistics constantly and I needed to write about them in every article Idid

So when a statistics software company offered me a writing job it seemed like a great opportunitymdashbut also something of acosmic joke I was hired to be a writer not a statistician but I soon realized I needed to confront my dysfunctional relationshipwith statistics So I enrolled in a basic statistics course Now I felt like a gazelle trying to sneak quietly through the lionrsquos den Iwas terrified but determined to pass at least

When I received an A I couldnrsquot believe it What had changed Not the statistical formulas Perhaps I was somewhat moremotivated than I once was but that was offset by the need to balance my studies with working full time And my distaste formath remained intact

As I thought about it I realized I no longer saw statistics through a mathematical lens I had come to recognize statistics as a wayto describe understand and communicate about the world just like other languages ere are a million ways to describe a bladeof grass with words and a million ways to describe it with data too

Calculations and ConceptsOnce I began thinking of statistics as a language that enriches how we know and experience life the discipline immediatelybecame less threatening Fueled by the realization that I could approach statistics as a living language rather than a dry collectionof rote formulas and proofs I enrolled in more statistics courses and it seemed like no time at all before I had completed a masterrsquosdegree in applied statistics


Mathematics was a core element in my studies of course but simply solving equations wasnrsquot the point the meaning of thesolution was important and the numbers were just a tool to get there Even I with my math aversion am happy to use it as toolI had never been able to enjoy math classes but I loved studying statistics e difference was that in statistics doing the mathcorrectly is only the beginning

e real effort at least at the applied level is what comes next understanding interpreting and communicating the implicationsof the methods wersquove used including all their conditions caveats and shortcomings is ambiguity and uncertainty this lack ofobjective rightness is perhaps what makes statistics so daunting to so many As statisticians we know we never have the singlecorrect answer to the problems we study Our models are never truly adequate ere is always another factor to consider anotherway to evaluate and dissect the data another sample to take or another method that can be applied to the question at hand

Conceptually this is not so different from the study of literature where there is always another lens through which to refract thetext another frame of reference through which it can be interpreted As an undergraduate I thrived in such courses My moremathematically inclined friends were always uncomfortable in courses for which there were no right answers Today when I speakwith some of them in their roles as engineers they sometimes express frustration with the practical limitations of statistics toreveal the complete essence of a problem

Statisticians know the challenges involved in communicating results Many people have been taught that statistics is inaccessibleesoteric and intimidatingmdashand indeed many statistical concepts are undeniably difficult to grasp fully People go out of theirway to avoid statistics and many of those who make an effort still wonder if they understand or if they are victims of somestatistical sleight of hand

Maybe itrsquos incumbent on us to be better translators for this strange love wersquove chosen

Talking the Talk with ose Who Donrsquot Walk the Walk When we talk about an analysis with people who arenrsquot statistics-savvy we have two obligations First to make a concerted effortto convey clearly the results of our analysis and its strengths and limitations Most of us do this to some degree But second weshould take every opportunity we can to demystify and humanize statistics to help people appreciate not just the complexity butalso the art that goes into analysis To promote statistical literacy Here I think most of us can do better

ere is an impression among those not well versed in statistical methods that the discipline is something of a black box thatstatisticians know the magic buttons that transform a spreadsheet full of data into something meaningful A good statisticianknows the formulas and methods inside out and very smart ones expand the discipline with new techniques and applicationsBut an effective statistician is sensitive enough to the relationship between the language of statistics and the language his audiencespeaks to be able to bridge the gap between them

Statisticians who are trying to communicate about their work with the uninitiated are like ambassadors they need to be completelycognizant of local knowledge customs and beliefs and present their message in a way that will be understood by the recipients In other words unless theyrsquore speaking to a room full of other statisticians they need to stop talking like statisticians

Every academic disciplinemdashindeed every subculturemdashhas its lingo and jargon and for good reason Arcane language helpsidentify kindred spirits using the correct phrase proves you belong that you share a certain level of mutual knowledge In manysituations this is a real benefit For example it helps specialists communicate with each other clearly and efficiently becauseldquospeaking the same languagerdquo enables them to move directly to advanced or obscure points without needing to fill in for eachother the details that a layperson would require

I saw this in action when I worked with scientists in highly specialized agricultural niches the language entomologists used intheir conversations with their departmental peers was very different from the language they used even when speaking with scientistsfrom other departments such as agronomy and dairy nutrition In speaking with students and the public their language waseven more removed from the jargon-filled patois they employed ldquoin privaterdquo

Approaching statistics as a languageContinued from page 31



Using the most esoteric terms can help you communicate with a very select group of your peers but it can also keep everyoneelse from understanding or caring what yoursquore saying rough intuition or deliberate design the most effective communicatorsin any discipline understand this and adjust their messages accordingly

e language of statistics can seem particularly impenetrable and obtuse For example consider that the method we use to comparemeans is called ldquoAnalysis of Variancerdquo atrsquos hardly intuitive And references to skewness can be outright counterintuitive giventhat right-skewed data are clustered on the left side of a bar graph and left-skewed data clustered on the right

But my favorite example of statistical language that can seem like deliberate obfuscation to the uninitiated is the basic notionthat a statistical test has failed to reject the null hypothesis From a purely editorial viewpoint failing to reject the nullhypothesis induces shudders At minimum its clunky phrasing a needlessly circular equivalent to the word accept

Of course from a statistical perspective its also undeniably accurate Replacing failure to reject with accept would be wrongIn this case statisticians have a good reason to talk the way they do So wersquore left with a phrase thatrsquos precise and correct but alsoconfusinghellipand how often is it presented to lay audiences with no explanation Too often when it takes only seconds to referencethe idea of ldquoinnocent until proven guiltyrdquo When the evidence against the accused isnrsquot convincing that doesnrsquot make the accusedinnocent hence the existence of the ldquoNot guiltyrdquo verdict is is an easily understandable idea that immediately conveys why notaccepting the alternative hypothesis is not the same as accepting the null In other words ldquoNull until proven alternativerdquo

ldquoListen to What I Mean Not What I SayrdquoStatisticians face another difficulty with language and that is ironically the fact that the discipline includes so many commonwords Our problem is that the technical meanings of these words in statistics are not the same as their common connotationsso when we use them in a statistical context we may be saying things to non-statisticians that we didnrsquot intend

Consider just a few of the statistical terms that can mean one thing to statisticians and quite another to everyone else

bull Significant ndash Civilians interpret this as meaning you should pay attention to something Statisticians know that significantthings often have no importance at all

bull Normal ndash Statisticians who say data is ldquonormalrdquo should know that people may take this to mean it is ordinary orcommonplace not that it follows a certain distribution

bull Regression ndash In everyday parlance regression means shrinking or moving backwards and most people wonrsquot naturally relatethat idea to estimating an output variable based on its inputs

bull Average ndash Most people hear this not as a mathematical value but as a qualitative judgment meaning ldquoso-sordquo or fair bull Error ndash In a nonstatistical context itrsquos a mistake rather than a measure of an estimatersquos precision bull Bias ndash People hear this term as a reference to attitudinal prejudice not the accuracy of gage compared to a reference value bull Residual ndash For most residuals is a fancy word for leftovers not the difference between observed and fitted values bull Power ndash at a statistical test can be powerful without being influential seems like a contradiction unless you already know

it refers to the probability of finding a significant (there we go againhellip) effect when it truly exists bull Interaction ndash People tend to think of this word in the context of communication rather than effects of one factor being

dependent on another bull Confidence ndash Outside of its technical meaning in statistics this word carries an emotional charge that can create unintended

implications Irsquove seen acquaintances interpret statistical confidence as meaning the researchers really believe in their results

atrsquos just the tip of the iceberg Statistical terms like sample assumptions stability capability success failure risk representativeand uncertainty can all mean different things to the world outside our small statistical circle Making an effort to help the peoplewe communicate with appreciate the technical meanings of these terms as we use them would be an easy way to begin promotinghigher levels of statistical literacy

About the Author Eston Martz is a senior creative services specialist at Minitab Inc where he writes for and edits e MinitabBlog Before joining Minitab he was a feature writer for Penn State Agriculture magazine and executive editor in the College ofAgricultural Sciences at Penn State He earned a bachelorrsquos degree in English and a masterrsquos degree in applied statistics both fromPenn State



Upcoming Conference Calendar

Lean and Six Sigma Conference March 2-3 2015Phoenix AZhttpasqorgconferencessix-sigma

Whether yoursquore beginning your lean and Six Sigma journey or yoursquore a seasoned veteran this conference has something for youin more than 50 sessions and hands-on workshops Yoursquoll have the opportunity to network with others who have been there anddone that and learn from proven firsthand applications technical applications and best practices

World Conference on Quality and Improvement (WCQI)May 4-6 2015Nashville TNhttpasqorgwcqi

e 2015 WCQI theme is ldquoTransforming the World through Innovation Inspiration and Leadershiprdquo Focus areas are InnovationLeadership Risk and Change Practical Application of Quality Tools Techniques and Methodologies and the Future of Quality

Spring Research Conference (SRC)May 20-22 2015Cincinnati OHhttpwwwcventcomevents2015-spring-research-conferenceevent-summary-b7b5867fe2f6400bba4542da092df210aspx

is years conference theme is Bridging Statistics Research and Application to Foster Innovation e purpose of SRC is to promoteresearch in statistical methods that address problems in industry and technology and to stimulate interactions among statisticiansresearchers in the application areas and industrial practitioners

Quality and Productivity Research Conference (QPRC)June 10-12 2015Raleigh NChttpwwwqprc2015com

e goal of the conference is to stimulate interdisciplinary research among statisticians scientists and engineers in quality andproductivity industrial needs and the physical and engineering sciences Statistical issues and research approaches drawn fromcollaborative research will be highlighted

59th Annual Fall Technical Conference (FTC)October 8-9 2015Houston TXhttpasqorgconferencesfall-technical

e Fall Technical Conference has long been a forum for both statistics and quality and is co-sponsored by the American Societyfor Quality (Chemical and Process Industries Division and Statistics Division) and the American Statistical Association (Sectionon Physical and Engineering Sciences and Section on Quality and Productivity) e goal of this conference is to engage researchersand practitioners in a dialogue that leads to more effective use of statistics to improve quality and foster innovation

ENBIS-15September 6-10 2015Prague Czech Republichttpwwwenbisorgactivitieseventscurrent380_ENBIS_15_in_Pragueindex_ts=47199

e 15th Annual Conference of ENBIS will take place in the historic city of Prague Czech Republic e conference sessions arescheduled for September 7th to 9th with the pre-conference administrative meetings pre-conference courses and workshopstaking place on September 6th and post-conference administrative meetings post-conference courses and workshops taking placeon September 9th and 10th


Statistics Division Committee Roster 2015

CHAIR Adam Pintaradampintarnistgov 301-975-4554

CHAIR-ELECT Theresa utlauttheresa1utlautintelcom503-613-7763

TREASURER Timothy robinsontjrobinuwyoedu307-766-5108

SECRETARYGary Gehringgehringggmailcom306-787-8418

PAST CHAIR Joel smithjsmithminitabcom814-753-3224

Operations

OPERATIONS CHAIRJoel smithpastchairasqstatdivorg814-753-3224

MEMBERSHIP CHAIRlydia lilajmembershipchairasqstatdivorg703-209-0509

VOICE OF THE CUSTOMER CHAIRJoel smithpastchairasqstatdivorg814-753-3224

CERTIFICATION CHAIRBrian sersionbsersiongmailcom 513-363-0177

STANDARDS CHAIRMark Johnsonmejohnsomailucfedu407-823-2695

Member Development

MEMBER DEVELOPMENT CHAIRMindy Hotchkissmindyhotchkissrocketcom561-882-5331

OUTREACH CHAIR steve schuelka sjschuelkayahoocom219-689-3804

EXAMINING CHAIRDoug Hlavacekdouglashlavacekecolabcom651-795-5722

Content

CONTENT CHAIRMichael Jonercontentchairasqstatdivorg617-463-6618

NEWSLETTER EDITORMatthew Barsaloumatthewbarsalougmailcom+49-152-05421794

WEBINAR COORDINATORAmy ste Croixamystecroix5aolcom850-324-0904

SOCIAL MEDIA MANAGERAmy ste Croixamystecroix5aolcom850-324-0904

WEBSITE AND INTERNET LIAISONlandon Jensenlsjensenimflashcom801-767-3328

STATISTICS BLOG EDITORGordon Clarkgclark007columbusrrcom614-888-1746

Awards

AWARDS CHAIR scott Kowalskiawardschairasqstatdivorg407-718-9501

OTT SCHOLARSHIP CHAIRlynne Harelynnehareverizonnet774-413-5268

FTC STUDENTEARLY CAREERGRANTSTimothy robinsontjrobinuwyoedu307-766-5108

HUNTER AWARD CHAIRDoug Hlavacekexaminingasqstatdivorg651-795-5722

NELSON AWARD CHAIRFrank rossifrancisrossikraftfoodscom847-646-2000

BISGAARD AWARD CHAIRMurat Kulahcimukudtudk+45-45253382

YOUDEN AWARD CHAIRJoel smithpastchairasqstatdivorg814-753-3224

Conferences

ASQ TECHNICAL PROGRAMCOMMITTEE REPGordon Clarkgclark007columbusrrcom614-888-1746

FTC STEERING COMMITTEEPeter Parkerpeteraparkernasagov757-864-4709

FTC PROGRAM REPRESENTATIVEMindy Hotchkissmindyhotchkissrocketcom561-882-5331

FTC SHORT COURSE CHAIR Anne Driscollagryanvtedu540-231-0087

Auditing

AUDIT CHAIR steve schuelkaoutreachasqstatdivorg219-689-3805

By-Laws

PAST CHAIRJoel smithpastchairasqstatdivorg814-753-3224

Nominating

PAST CHAIR Joel smithpastchairasqstatdivorg814-753-3224

Planning

CHAIR Adam Pintarchairasqstatdivorg301-975-4554

APPOINTED

OFFICERS

e ASQ Statistics Division Newsletter ispublished three times a year by theStatistics Division of the AmericanSociety for Quality

All communications regarding thispublication EXCLUDINGCHANGE OF ADDRESS shouldbe addressed to

Matthew BarsalouEditor

email newsletterasqstatdivorg

Other communications relating to theASQ Statistics Division should beaddressed to

Adam PintarDivision Chair

email adampintarnistgovphone (301) 875-4554

Communications regarding change ofaddress should be sent to ASQ at

ASQPO Box 3005Milwaukee WI 53201-3005

is will change the address for allpublications you receive from ASQYou can also handle this by phone(414) 272-8575 or (800) 248-1946

Upcoming NewsletterDeadlines for Submissions

Issue Vol No Due DateJune 34 2 April 15

ASQ Statistics Division

VISIT THE STATISTICS DIVISION WEBSITEwwwasqorgstatistics

ASQ Periodicals with Applied Statistics content

Journal of Quality Technologyhttpwwwasqorgpubjqt

Quality Engineeringhttpwwwasqorgpubqe

Six Sigma Forumhttpwwwasqorgpubsixsigma

STATISTICS DIVISION RESOURCES

LinkedIn Statistics Division GrouphttpswwwlinkedincomgroupsASQ-Statistics-Division-2115190

Scan this to visit our LinkedIn group

Connect now by scanning this QR code with a smartphone (requires free QR app)

Check out our YouTube channel at

youtubecomasqstatsdivision

Knowledge of statistical methods is necessary but not sufficientknowledge of managerial methods is necessary but not sufficientknowledge of specific industry products and processes is necessarybut not sufficient Integration of these sources of knowledge is thekey

Frank M Gryna (2001) Quality Planning and Analysis From ProductDevelopment rough Use 4th ed


Submission Guidelines

Mini-PaperInteresting topics pertaining to the field ofstatistics should be understandable bynon-statisticians with some statisticalknowledge Length 1500-4000 words

Basic-ToolsApplication oriented and less technicalthan a Mini-Paper e focus should beon how to apply basic statistical conceptswith an emphasis on industry or servicese use of examples to illustrate points ishighly recommended Length 1000-3000 words

FeatureFocus should be on a statistical conceptcan either be of a practical nature or atopic that would be of interest topractitioners who apply statistics Length1000-3000 words

Case StudyFocus should be on an actual example ofthe application of a statistical method inindustry or services e names of thecompanies involved do not need to bementioned Length 1000-2500 words

General InformationAuthors should have a conceptualunderstanding of the topic and should bewilling to answer questions relating to thearticle through the newsletter Authors donot have to be members of the StatisticsDivision Submissions may be made atany time to newsletterasqstatdivorg

All articles will be reviewed eeditor reserves discretionary right indetermination of which articles arepublished Submissions should not beoverly controversial Confirmation ofreceipt will be provided within one weekof receipt of the email Authors willreceive feedback within two monthsAcceptance of articles does not imply anyagreement that a given article will bepublished

Visione ASQ Statistics Division promotes innovation and excellence in the application andevolution of statistics to improve quality and performance

Missione ASQ Statistics Division supports members in fulfilling their professional needs andaspirations in the application of statistics and development of techniques to improvequality and performance

Strategies1 Address core educational needs of members

bull Assess member needsbull Develop a ldquobase-level knowledge of statisticsrdquo curriculumbull Promote statistical engineeringbull Publish featured articles special publications and webinars

2 Build community and increase awareness by using diverse and effectivecommunications

bull Webinarsbull Newslettersbull Body of Knowledgebull Web sitebull Blog bull Social Media (LinkedIn and Twitter)bull Conference presentations (Fall Technical Conference WCQI etc)bull Short courses bull Mailings

3 Foster leadership opportunities throughout our membership and recognizeleaders

bull Advertise leadership opportunities positionsbull Invitations to participate in upcoming activitiesbull Student grants and scholarshipsbull Awards (eg Youden Nelson and Hunter)bull Recruit retain and advance members (eg Senior and Fellow status)

4 Establish and Leverage Alliancesbull ASQ Sections and other Divisionsbull Non-ASQ (eg ASA)bull CQE Certification bull Standards bull Outreach (professional and social)

Updated October 19 2013

Disclaimer

e technical content of material published in the ASQ Statistics Division Newsletter may not have beenrefereed to the same extent as the rigorous refereeing that is undergone for publication in Technometrics orJQT e objective of this newsletter is to be a forum for new ideas and to be open to differing points ofview e editor will strive to review all articles and to ask other statistics professionals to provide reviews ofall content of this newsletter We encourage readers with differing points of view to write to the editor and anopportunity to present their views via a letter to the editor e views expressed in material published in thisnewsletter represents the views of the author of the material and may or may not represent the officialviews of the Statistics Division of ASQ

Vision Mission and Strategies of the ASQ Statistics Division











Continued on page 4








Figure 1 R Code












Joel Smith






Best wishes to all








Mindy Hotchkiss

Matt Barsalou








Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS





































Continued on page 4








Figure 1 R Code












Joel Smith






Best wishes to all








Mindy Hotchkiss

Matt Barsalou








Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































Figure 1 R Code












Joel Smith






Best wishes to all








Mindy Hotchkiss

Matt Barsalou








Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS





































Joel Smith






Best wishes to all








Mindy Hotchkiss

Matt Barsalou








Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS
































Best wishes to all








Mindy Hotchkiss

Matt Barsalou








Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































Continued on page 8








Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































Continued on page 9
























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


















































































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS








































































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS



























































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS














































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS
























































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


















































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS









































Connie Borror



















Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS













































Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS




























Introduction




Value Stream Map




Gordon Clark


Observations








Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS
































Experimental Design







Factors



















References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS












































References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































References














Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































Jack ReVelle






















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS














































































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS







































































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS






























































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS





















































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS










































Laura Freeman


















Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS












































Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS





































Mark Johnson
















































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS










































































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


























































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS














































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS

































































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS
















































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS


































Operations






Member Development




Content







Awards








Conferences





Auditing


By-Laws


Nominating


Planning


APPOINTED

OFFICERS





















































Documents

STATISTICS DIGEST - ASQasq.org/statistics/2015/02/asq-statistics-division... · 2015-08-04 · STATISTICS DIGEST The Newsletter of the ASQ Statistics Division Chair’s Message .....1