Leuven STATistics STATe of the Art Training Initiative › english › phd › PhD... · 14,15 November 2013 Regression and Analysis of Anna Ivanova Basic (English) 12 Variance, applications

Leuven STATistics STATe of the Art Training Initiative2013-2014

Course timetable 2013-2014

DATE TITLE PRESENTERS LEVEL AND MORE LANGUAGE ON PAGE

September 2013 30 September, Essential Tools for R Anna Ivanova Basic (English) 21, 3, 4 October

October 2013 8, 9, 10 October 2013 Fundamentele statistische Marlies Lacante Basis (Nederlands) 3methoden

22 October 2013 Fundamentele statistische Martine Beullens Basis (Nederlands) 5methoden, toegepast met SAS Eguide

22 October 2013 Fundamentele statistische Marlies Lacante Basis (Nederlands) 4methoden, toegepast met SPSS

22 October 2013 Fundamental Statistical Anna Ivanova Basic (English) 6Methods, applications with R

23-25 October 2013 Models for Longitudinal Geert Molenberghs, Advanced (English) 76-8 November 2013 and Incomplete data Geert Verbeke

November 2013 5,7 November 2013 Regressie- en variantieanalyse Anna Ivanova, Basis (Nederlands) 9Marlies Lacante

12 November 2013 Regressie- en variantieanalyse, An Carbonez Basis (Nederlands) 10toegepast met SPSS

14 November 2013 Regressie- en variantieanalyse, Martine Beullens Basis (Nederlands) 11toegepast met SAS Eguide

14,15 November 2013 Regression and Analysis of Anna Ivanova Basic (English) 12Variance, applications with R

18-19 November 2013 Advanced R programming topics Jan Wijffels Intermediate (English) 13

21, 22, 28, Optimization and Numerical Geert Molenberghs, Advanced (English) 1429 November 2013 Methods in Statistics Francis Tuerlinckx,

Katrijn van Deun, Tom Wilderjans

25, 26 November 2013 Uitbreiding bij Regressie- An Carbonez, Verdiepend (Nederlands) 16en variantieanalyse Marlies Lacante

December 2013 3 December 2013 Niet-parametrische statistiek Marlies Lacante Basis (Nederlands) 17

February 2014 10, 11, 13, Essential Tools for R Anna Ivanova Basic (English) 214 February 2014

March 2014 12, 19, 26 February 2014, Chemometrics Wouter Saeys Advanced (English) 1812, 19 March 2014

17, 18, 20 March 2014 Fundamental statistical methods Marlies Lacante Basic (English) 19

17, 18, 20 March 2014 Cluster analysis, principal Martine Beullens, Intermediate (English) 20component analysis and Anne-Marie De Meyer, exploratory factor analysis An Carbonezwith SAS, SPSS and R

24, 25 March 2014 Introduction to the analysis An Carbonez Basic (English) 21of contingency tables

25 March, 1, 22 April, Concepts of Multilevel, Geert Verbeke Advanced (English) 226, 20 May 2014 Longitudinal and Mixed models

31 March 2014, Introduction to correspondence Anne-Marie De Meyer Intermediate (English) 231 April 2014 analysis and multiple

correspondence analysis with SAS and SPSS

April 2014 28, 29 April 2014 Logistic Regression Models Anne-Marie De Meyer Intermediate (English) 24with SAS and SPSS

May 2014 5, 6 May 2014 Poisson regression with SAS Anne-Marie De Meyer Intermediate (English) 25and SPSS

7, 8, 9 May 2014 Inleiding tot enquêtering Marlies Lacante, Basis (Nederlands) 26Kristel Hoydonckx

1

Preface

I take pleasure and pride in welcoming you to the Leuven STATistics STATe of the Art Training Initiative, a scientific

and educational project of the Leuven Statistics Research Centre (LStat), offering a range of short courses.

Statistics in Leuven is varied and broad based. Statisticians are active throughout the university, in mathematics, computer

science, economy, psychology, education, bio-engineering, engineering, biology, chemistry, medicine, pharmacy, physical

education, psychology, social science, linguistics, etc. Many colleagues combine an excellent international scientific

reputation with highly effective teaching skills. At the same time, statistical consulting for internal and external clients

is a wholesome component of LStat’s mission.

It is therefore not surprising that the short course programme has been highly successful and in great demand. Celebrating

this success, we are shifting into higher gear and the time-honoured programme of short courses is gradually being

expanded with further highly relevant topics, many located at the heart of our faculty’s expertise. Due to increasing

demand, some courses are offered more than once per academic year.

A selected set of courses is offered in an open educational concept, in the sense that, for example, also contingents

of students of our highly successful MSc in Statistics partake in them. This ensures stimulating interaction.

Courses take place in one of the university’s campuses, dotted around the beautiful college town of Leuven.

Should your company or institute be looking for a tailor-made training initiative, perhaps on-site, then we will be

delighted to explore options and work towards an individualized proposal.

Professor Marlies Lacante

2013-2015 chair of Lstat

Leuven Statistics Research Centre

Celestijnenlaan 200 B, bus 5307

3001 HEVERLEE

+ 32 16 32 22 14

[email protected]

www.lstat.kuleuven.be

2

Essential tools for R

Course outlineThis course gives an introduction to the use of the statisticalsoftware language R. R is a language for data analysis andgraphics. This introduction course to R is aimed at beginners.The course covers data handling, graphics, mathematicalfunctions and some statistical techniques. R is for free andfor more information you can visit the site http://cran.r-project.org/

Target audienceEverybody who is interested in using the R programminglanguage. You will learn how to write and manage your R scripts.

Prerequisites There are no prerequisites.

PresenterAnna Ivanova is a research assistant at the Leuven Statistics Research Centre (LStat) of the KU Leuven. She obtained her Master degree in Statistics from the KU Leuven in 2004. She carries out statistical consultingand participates in statistical consulting projects.

Course MaterialsA .pdf file with the course material will be made available.

Dates 30 September, 1, 3, and 4 October 2013 from 9 hr to 12 hr or10, 11, 13 and 14 February 2014 from 9 hr to 12 hr

LanguageEnglish

PriceStaff and students KU Leuven and Association KU Leuven:go to: https://icts.kuleuven.be/cursus/

PhD students, non KU Leuven € 160Non profit/social sector € 250Private sector € 600

3

Fundamentele statistische methoden

BeschrijvingDeze basiscursus statistiek richt zich op het kiezen vangeschikte statistische methoden en het trekken van decorrecte conclusies uit de verkregen resultaten. Wiskundigegrondslagen van de gebruikte methoden komen in dezecursus slechts beknopt ter sprake. De nadruk ligt op toepassing in de praktijk. Men krijgt inzicht in het adequaatgebruik van basis-statistieken: centrummaten, spreidings-maten, tabellen, box-plots, enz. Daarnaast worden betrouwbaarheidsintervallen opgesteld en krijgt men de grondslagen van toetsen van hypothesen.

Inhoud van de cursus:• Beschrijvende grootheden: grafische en numerische

samenvatting van de data• Verdelingen: Binomiale, Poisson, Normale, T-verdeling• Steekproefverdeling van het gemiddelde• Betrouwbaarheidsintervallen• Hypothese testen omtrent een gemiddelde (één en

twee steekproeven)• Gepaarde t-test• Schatten en testen van proporties

DoelgroepIedereen die een opfrissing van fundamentele statistischetechnieken wenst.

VoorkennisEr wordt geen voorkennis ondersteld.

LesgeverMarlies Lacante is sedert 1974 verbonden aan de onder-zoekseenheid Psychologie van de KU Leuven. Gedurendemeer dan 20 jaar was zij betrokken bij het statistiekonderwijsin de opleiding Psychologie. Momenteel doceert zij binnende academische Lerarenopleiding, binnen het Leuven Statistics Research Centre (Lstat) en binnen de MSc inStatistics. Ze is ook actief in het onderwijs onderzoek, met focus op survey onderzoek en met speciale aandachtvoor de onderzoeksmethodologie.

CursusmateriaalCursusmateriaal wordt als .pdf file ter beschikking gesteld.

Datum 8, 9, 10 oktober 2013 telkens van 9 u. tot 12 u.

TaalNederlands

PrijsPersoneel en studenten KU Leuven en Associatie KU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 120Non profit/sociale sector € 187,50Private sector € 450

4

BeschrijvingDit is een inleidende cursus tot het gebruik van SPSS. Aan de hand van cases wordt geïllustreerd hoe men metSPSS tot exploratie van gegevens komt. Hierbij wordt de nodige aandacht besteed aan het interpreteren van de verkregen output. Hypothesetesten voor onafhankelijkeen gepaarde groepen worden uitgevoerd en besproken.Er is tijd om zelf te werken met deze software.

DoelgroepIedereen die gegevens wenst te exploreren met SPSS.

Lesgever Marlies Lacante is sedert 1974 verbonden aan de onder -zoeks eenheid Psychologie van de KU Leuven. Gedurendemeer dan 20 jaar was zij betrokken bij het statistiekonderwijsin de opleiding Psychologie. Momenteel doceert zij binnende academische Lerarenopleiding, binnen het Leuven Statistics Research Centre (Lstat) en binnen de MSc inStatistics. Ze is ook actief in het onderwijs onderzoek, met focus op survey onderzoek en met speciale aandachtvoor de onderzoeksmethodologie.

VoorkennisDe technieken die aangeleerd werden bij FundamenteleStatistische Methoden.


Datum 22 oktober 2013, 9 u - 12 u en 13 u - 16 u.

Taal Nederlands

Prijs Personeel en studenten KU Leuven en AssociatieKU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 80Non profit/sociale sector € 125Private sector € 300

Fundamentele statistische methoden, toegepast met SPSS

5

BeschrijvingDit is een inleidende cursus tot het gebruik van SAS Enterprise Guide. Aan de hand van cases wordt geïllus-treerd hoe men met de SAS Eguide tot exploratie van gegevens komt. Hierbij wordt de nodige aandacht besteed aan het interpreteren van de verkregen output.Hypothese testen voor onafhankelijke en gepaarde groepenworden uitgevoerd en besproken. Er is tijd om zelf te werkenmet deze software.

DoelgroepIedereen die gegevens wenst te exploreren met SASEguide.

VoorkennisDe technieken die aangeleerd werden bij FundamenteleStatistische Methoden

Lesgever Martine Beullens is a research assistant at the LeuvenStatistics Research Centre (LStat) of the KU Leuven. She obtained her Master degree in Statistics from the KU Leuven in 2004. She carries out statistical consultingand participates in statistical consulting projects.


Datum 22 oktober 2013, 9 u - 12 u en 13 u - 16 u.

Taal Nederlands

Prijs Personeel en studenten KU Leuven en AssociatieKU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 80Non profit/sociale sector € 125Private sector € 300

Fundamentele statistische methoden,toegepast met SAS Eguide

6

Course outline By using cases, one explores data by using R. Attentionis paid to the interpretation of the output. Topics as exploring data, construction of confidence intervals andhypothesis testing is covered. This is a hands-on session.

Target audienceEverybody who wants to explore data by using R

Presenter Anna Ivanova is a research assistant at the Leuven Statistics Research Centre (LStat) of the KU Leuven. She obtained her Master degree in Statistics from the KU Leuven in 2004. She carries out statistical consultingand participates in statistical consulting projects.

Prerequisites Fundamental Statistical Methods (distributions, confidenceintervals, hypothesis testing) and Introduction to R.


Date 22 October 2013, 9 hr - 12 hr and 13 hr - 16 hr.

Language English

Price Staff and students KU Leuven and Association KU Leuven: go to: https://icts.kuleuven.be/cursus/PhD students, non KU Leuven € 80Non profit/social sector € 125Private sector € 300

Fundamental statistical methods,applications with R

7

Models for Longitudinal and Incomplete Data

Course outlineWe first present linear mixed models for continuoushierarchical data. The focus lies on the modeler’s perspectiveand on applications. Emphasis will be on model formulation,parameter estimation, and hypothesis testing, as well ason the distinction between the random-effects (hierarchical)model and the implied marginal model. Apart from classicalmodel building strategies, many of which have beenimplemented in standard statistical software, a number offlexible extensions and additional tools for model diagnosiswill be indicated. Second, models for non-Gaussian datawill be discussed, with a strong emphasis on generalizedestimating equations (GEE) and the generalized linearmixed model (GLMM). To usefully introduce this theme, a brief review of the classical generalized linear modelingframework will be presented. Similarities and differenceswith the continuous case will be discussed. The differencesbetween marginal models, such as GEE, and random-effects models, such as the GLMM, will be explained indetail. Third, it is oftentimes necessary to consider fullynon-linear models for longitudinal data. We will discusssuch situations, and place some emphasis on the non-linearmixed-effects model. Fourth, non-linear mixed models willbe discussed. Applications in the PK/PD world will bebrought to the front. Fifth, when analyzing hierarchical andlongitudinal data, one is often confronted with missingobservations, i.e., scheduled measurements have not beenmade, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken,missing data can cause seriously jeopardize results, and interpretation difficulties are bound to occur. Methodsto properly analyze incomplete data, under flexibleassumptions, are presented. Key concepts of sensitivityanalysis are introduced. All developments will be illustratedwith worked examples using the SAS System. However,the course is conceived such that it will be of benefit toboth SAS users and users of other platforms.

Target audienceThe targeted audience includes methodological andapplied statisticians and researchers in industry, publichealth organizations, contract research organizations, and academia. Important: The course will also serve forthe MSc in Statistics students.

Prerequisites Throughout the course, it will be assumed that theparticipants are familiar with basic statistical modelingconcepts, including linear models (regression and analysisof variance), as well as generalized linear models (logisticand Poisson regression) and basic knowledge of mixedand multilevel models. Moreover, pre-requisite knowledgeshould also include general estimation and testing theory(maximum likelihood, likelihood ratio). When registering forthis course, you have to mention the topics you havefollowed before and/or indicate where you becameacquainted with the requested material.

PresentersGeert Verbeke is Professor in Biostatistics at KU Leuvenand Universiteit Hasselt. He received the B.S. degree in mathematics (1989) from the KU Leuven, the M.S. inbiostatistics (1992) from Universiteit Hasselt, and earned a Ph.D. in biostatistics (1995) from the KU Leuven. Geert Verbeke has published extensively on longitudinaldata analyses. He has held visiting positions at theGerontology Research Center and the Johns HopkinsUniversity (Baltimore, MD). Geert Verbeke is Past Presidentof the Belgian Region of the International BiometricSociety, International Program Chair for the InternationalBiometric Conference in Montreal (2006), Board Memberof the American Statistical Association. He is past JointEditor of the Journal of the Royal Statistical Society, Series A (2005-2008) and currently editor of Biometrics(2010-2013). He is the director of the Leuven Center for Bio statistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute forBiostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium.

CONCEPTS, MODELS AND HANDS-ON APPLICATION WITH THE OPTION TO ANALYSE ONE’S OWN DATA

8

Geert Molenberghs is Professor of Biostatistics at theUniversiteit Hasselt and KU Leuven. He received the B.S.degree in mathematics (1988) and a Ph.D. in biostatistics(1993) from the Universiteit Antwerpen. Dr Molenberghspublished methodological work on surrogate markers inclinical trials, categorical data, longitudinal data analysis,and on the analysis of non-response in clinical andepidemiological studies. He served as Joint Editor forApplied Statistics (2001-2004), Co-editor for Biometrics(2007-2009) and as President of the InternationalBiometric Society (2004-2005). He currently is Co-editorfor Bio statistics (2010-2013). He was elected Fellow of the American Statistical Association and received the GuyMedal in Bronze from the Royal Statistical Society. He hasheld visiting positions at the Harvard School of PublicHealth (Boston, MA). He is founding director of the Centerfor Statistics at Hasselt University and currently the directorof the Interuniversity Institute for Biostatistics and statisticalBio informatics, I-BioStat, a joint initiative of the Hasselt and Leuven universities. Geert Molenberghs and GeertVerbeke are editors and authors of several books on theuse of linear mixed models for the analysis of longitudinaldata, and they have taught numerous short and longercourses on the topic in universities as well as industry, in Europe, North America, Latin America, and Australia.Both instructors received several Excellence in ContinuingEducation Awards of the American Statistical Association,for courses at Joint Statistical Meetings.


Background reading:• Verbeke, G. and Molenberghs, G. (2000) Linear Mixed

Models for Longitudinal Data. New York: Springer.• Molenberghs, G. and Kenward, M.G. (2007) Missing

Data in Clinical Studies. Chichester: John Wiley & Sons.• Molenberghs, G. and Verbeke, G. (2005) Models for

Repeated Discrete Data. New York: Springer.

DatesOctober 23, 2013: 9.00 hr - 12.00 hrOctober 24, 2013: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hrOctober 25, 2013: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hr

November 6, 2013: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hrNovember 7, 2013: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hrNovember 8, 2013: 9.00 hr - 12.00 hr

LanguageEnglish

PriceStaff and students KU Leuven and Association KU Leuven: go to: https://icts.kuleuven.be/cursus/PhD students, non KU Leuven € 400Non profit/social sector € 625Private sector € 1500

9

Regressie- en variantieanalyse

BeschrijvingRegressieanalyse is een krachtige techniek om eenrespons variabele te verklaren als functie van één of meerdereverklarende variabelen. Dit gebeurt via een lineair modelen kan gebruikt worden voor trendbeschrijving en/ofpredictie. Variantie analyse (ANOVA) is een statistischetechniek die gebruikt wordt bij het vergelijken van gemid delden in meerdere populaties.

Inhoud van de cursus:Dag 1: Regressieanalyse• Correlatie• Enkelvoudige lineaire regressie:

- Aanpassen van een lijn aan de data- Kleinste-kwadratenmethode: schatten van de

parameters, betrouwbaarheidsintervallen, significantie -toetsen, residu analyse

• Meervoudige lineaire regressie: - Kleinste-kwadratenmethode: schatten van de

parameters, betrouwbaarheidsintervallen, significantie -toetsen, residu analyse

- Selectiemethoden

Dag 2: Variantieanalyse• Eén-factor variantieanalyse

- Het vergelijken van gemiddelden - Anova model: schattingen van parameters, hypothesen

toetsen, Anova tabel, F-toets- Gemiddelden vergelijken: Contrasten, meervoudige

vergelijkingen• Twee- factor variantieanalyse

- Het twee-factor Anova model - Hoofdeffecten, interacties- Gemiddelden vergelijken

DoelgroepDeze cursus zal vooral belangrijk zijn voor personen diegegevens willen modelleren.

VoorkennisEr wordt verondersteld dat de cursisten kennis hebbenvan de fundamentele basismethoden van de statistiek.

LesgeversAnna Ivanova is een wetenschappelijke medewerker aanhet Leuven Statistics Research Centre (LStat) van de KU Leuven. Ze behaalde haar Master in Statistics diplomaaan de KU Leuven in 2004. Ze geeft statistische consultingen neemt deel aan statistische consulting projecten.

Marlies Lacante is sedert 1974 verbonden aan de onder -zoekseenheid Psychologie van de KU Leuven. Gedurendemeer dan 20 jaar was zij betrokken bij het statistiek -onderwijs in de opleiding Psychologie. Momenteel doceertzij binnen de academische Lerarenopleiding, binnen hetLeuven Statistics Research Centre (LStat) en binnen deMSc in Statistics. Ze is ook actief in het onderwijs -onderzoek, met focus op survey onderzoek en metspeciale aandacht voor de onderzoeksmethodologie.

CursusmateriaalCursusmateriaal wordt als .pdf file ter beschikking gesteld

Datum 5 en 7 november 2013 telkens van 9 u - 12 u en 13 u - 16 u.

PrijsPersoneel en studenten KU Leuven en Associatie KU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet-KU Leuven € 160Non profit/sociale sector € 250Private sector € 600

TaalNederlands

10

Regressie- en variantieanalyse,toegepast met SPSS

BeschrijvingDe technieken die aangeleerd werden bij Regressie- envariantieanalyse, worden hier toegepast met SPSS. Aan de hand van cases wordt geïllustreerd hoe men metSPSS tot het modelleren van gegevens komt. Hierbij wordtde nodige aandacht besteed aan het interpreteren van deverkregen output. Er is voldoende tijd om zelf te werkenmet deze software.

DoelgroepIedereen die gegevens wenst te modelleren via SPSS.

Lesgever An Carbonez is professor aan het Leuven Statistics Research Centre (Lstat) van de KU Leuven. Ze behaaldehaar doctoraat wiskunde aan de KU Leuven in 1992. Ze is coördinator van het Master of Statistics programmavan de KU Leuven. Ze is ook betrokken bij statistischeconsulting projecten en het geven van statistische oplei-dingen binnen bedrijven.

VoorkennisWe veronderstellen een basiskennis van SPSS. Cursistendienen eveneens vertrouwd te zijn met de methodiek aan-gebracht in Regressie- en variantieanalyse.


Datum 12 november 2013 van 9 u - 12 u en 13 u - 16 u.

TaalNederlands

PrijsPersoneel en studenten KU Leuven en AssociatieKU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 80Non profit/sociale sector € 125Private sector € 300

11

Regressie- en variantieanalyse,toegepast met SAS Eguide

BeschrijvingDe technieken die aangeleerd werden bij Regressie- envariantieanalyse, worden hier toegepast met SAS Eguide.Aan de hand van cases wordt geïllustreerd hoe men metde SAS Eguide tot het modelleren van gegevens komt.Hierbij wordt de nodige aandacht besteed aan het inter-preteren van de verkregen output. Er is voldoende tijd omzelf te werken met deze software.

DoelgroepIedereen die gegevens wenst te modelleren via SAS Eguide.

Lesgever Martine Beullens studeerde Wiskunde aan de universiteitvan Leuven. Sinds 1990 is zij als medewerker van de KU Leuven en nadien ook van de Federale Politie actiefmede-uitvoerder geweest van een aantal projecten in opdracht van de overheid aangaande de ontwikkeling ende statistische exploitatie van federale databanken bestaande uit gerechtelijke of politionele informatie. Momenteel is zij nog steeds werkzaam aan de KU Leuvenbinnen de Dienst Onderwijsvisie en -kwaliteit, afdeling Datamangement.

VoorkennisWe veronderstellen een basiskennis van SAS Eguide. Cursisten dienen eveneens vertrouwd te zijn met de methodiek aangebracht in Regressie en variantie analyse.


Datum 14 november 2013 van 9 u - 12 u en 13 u - 16 u.

TaalNederlands

PrijsPersoneel en studenten KU Leuven en AssociatieKU Leuven: zie: https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 80Non profit/sociale sector € 125Private sector € 300

12

Regression and analysis of variance: applications with R

Course outline The linear models, provided by the course ‘Regressionand Analysis of Variance’, are applied on examples. In this course, the R package is used. By means of cases,we illustrate how to model your data in R and how to interpret the corresponding output. There is a hands-onsession to train you with the functionality of R.

Target audienceEverybody who wants to model data with R.

Presenter Anna Ivanova is a research assistant at the Leuven Statistics Research Centre (LStat) of the KU Leuven. She obtained her Master degree in Statistics from the KU Leuven in 2004. She carries out statistical consultingand participates in statistical consulting projects.

Prerequisites Everybody should be familiar with the techniques coveredin ‘Regression and Analysis of Variance’ and have a basicknowledge of working with R.


Date 14 November 2013 9 hr - 12 hr and 13 hr - 16 hr and 15 November from 9 hr - 12 hr.

Language English

PriceStaff and students KU Leuven and Association KU Leuven: go to: https://icts.kuleuven.be/cursus/PhD students, non KU Leuven € 120Non profit/social sector € 187,50Private sector € 450

13

Advanced R programming topics

Course outlineR is the lingua franca of statistical research and dataanalysis. But in order to get you up and running with R,and to get over the steep learning curve, you need toknow how to use it efficiently.

This course is a hands-on course covering the basic toolkityou need to have in order to use R efficiently for dataanalysis tasks.

It is an intermediate course aimed at users who have the knowledge from the course ‘Essential tools for R’ andwho want to go further to improve and speed up their dataanalysis tasks.

The following topics will be covered in detail• The apply family of functions and basic parallel

program ming for these, vectorisation, regular expres -sions, string manipulation functions and commonlyused functions from the base package. Useful otherpackages for data manipulation.

• Making a basic reproducible report using Sweave andknitr including tables, graphs and literate programming

• If you want to build your own R package to distributeyour work, you need to understand S3 and S4 methods,you need the basics of how generics work as well as Renvironments, what are namespaces and how are theyuseful. This will be covered to help you start up andbuild an R package.

• Basic tips on how to organise and develop R code andtest it.

Target audiencePeople who have had their initial use of R and want to goone step further.

This covers people using R for a few months already to several years. And more specifically users who want to extend their data manipulation techniques to speed uptheir day-to-day data analysis tasks.

Researchers from the university interested in makingreproducible research reports or users who want to use Ras a report generating tool.

R users interested in getting the fundamentals you needto know before you can create your own R package.

Business users who want to learn how to get the maximumout of R by speeding up their code, learn vectorisation,execute the basics of parallel programming and want tolearn how to build methods and code which isreproducible in production environments.

PrerequisitesInitial experience in R ranging from a few weeks to severalyears.

Course materialsA .pdf file with the course material will be made available.

PresenterJan Wijffels is the founder of www.bnosac.be - a consultancycompany specialised in statistical analysis and data mining.He holds a Master in Commercial Engineering, a MSc inStatistics and a Master in Artificial Intelligence and hasbeen using R for 8 years, developing and deploying R-basedsolutions for clients in the private sector. He has developedand co-developed the R packages ETLUtils and ffbase.

Date18 and 19 November 2013, 9 hr - 12 hr and 13 hr - 16 hr


LanguageEnglish

14

Optimization & Numerical Methods in

Course outlineNumerical problems are frequently encountered bystatisticians. Prominently, the estimation of the parametersof a statistical model requires the solution of anoptimization problem. In a few simple cases, closed-formsolutions exist but for many probability models the optimalparameter estimates have to be determined by means ofan iterative algorithm. The goal of this course is threefold.First, we want to offer the readers an overview of somefrequently used optimization algorithms in (applied)statistics. Second, we want to provide a framework forunderstanding the connections among severaloptimization algorithms as well as between optimizationand aspects of statistical inference. Third, although verycommon, optimization is not the only numerical problemand therefore some important related topics such asnumerical differentiation and integration will be covered.

Target audienceThe intended target audience includes PhD students andresearchers in a variety of fields, including biostatistics,psychometrics, educational measurement, public health,sociology. We aim at readers who apply and possiblydevelop statistical models and who wish to learn moreabout the basic concepts of numerical techniques, withan emphasis on optimization problems, and their use instatistics.

Prerequisites Participants should have a basic knowledge of theprinciples of statistical inference. This includes somefamiliarity with the concept of a likelihood function andlikelihood-based inference for linear, binomial, multinomial,and logistic regression models. Readers should also havea basic understanding of matrix algebra. A workingknowledge of the basic elements of univariate calculus isalso a prerequisite, including (the concepts of continuityof a function, derivative and integration).

PresentersFrancis Tuerlinckx is Professor of Psychology at the KU Leuven in Belgium. He received the Master degree inpsychology (1996) and a Ph.D. in psychology (2000) fromthe KU Leuven. He was a postdoc at the Department ofStatistics of Columbia University (New York). In general,Francis Tuerlinckx’ research deals with the mathematicalmodeling of various aspects of human behavior. Morespecifically, he works on item response theory, reactiontime modeling, and dynamical systems data analysis forhuman emotions.

Geert Molenberghs is Professor of Biostatistics at theUniversiteit Hasselt and KU Leuven in Belgium. He receivedthe B.S. degree in mathematics (1988) and a Ph.D. inbiostatistics (1993) from the Universiteit Antwerpen. Dr Molenberghs published methodological work on surrogatemarkers in clinical trials, categorical data, longitudinal dataanalysis, and on the analysis of non-response in clinicaland epidemiological studies. He served as Joint Editor forApplied Statistics (2001-2004), Co-editor for Biometrics(2007-2009) and as President of the InternationalBiometric Society (2004-2005). He currently is Co-editorfor Biostatistics (2010-2013). He was elected Fellow of theAmerican Statistical Association and received the GuyMedal in Bronze from the Royal Statistical Society. He hasheld visiting positions at the Harvard School of PublicHealth (Boston, MA). He is founding director of the Centerfor Statistics at Hasselt University and currently the directorof the Interuniversity Institute for Biostatistics and statisticalBioinformatics, I-BioStat, a joint initiative of the Hasselt andLeuven universities.

15

Statistics

Katrijn Van Deun obtained a Master in psychology, a Master’s degree in statistics and a PhD in psychology. Her main area of expertise is scaling, clustering andcomponent analysis techniques, which she applies in thefield of bioinformatics. She has various publications in bothmethodological and substantive journals in bioinformatics.Katrijn is secretary of the Dutch/Flemish Classificationsociety.

Tom Wilderjans is a post-doctoral researcher at the Fundfor Scientific Research (FWO-Flanders). He obtained aMaster’s degree (2005) and a PhD (2009) in MathematicalPsychology from the KU Leuven. Tom’s research dealswith multivariate data analysis (component analysis,clustering, and combinations thereof) and model selection.


Background reading:• Everitt, B.S. (1987). Introduction to Optimization

Methods and Their Application in Statistics. London:Chapman & Hall.

• Lange, K. (1999). Numerical Analysis for Statisticians.New York: Springer.

• Lange, K. (2004). Optimization. New York: Springer.

Dates21, 22, 28 and 29 November, 2013: 9 hr - 12.30 hr;13.30 hr - 17 hr

LanguageEnglish


16

Uitbreiding bij Regressie- en variantieanalyse

BeschrijvingDe resultaten van een lineaire regressieanalyse zijn sterkbeïnvloedbaar door speciale datapunten. Het detecterenvan uitschieters en invloedrijke waarnemingen wordt indeze cursus bestudeerd. Daarnaast wordt geïllustreerdhoe men via robuuste regressie dit probleem kanopvangen. De praktijk leert ook dat de resultaten van eenlineaire regressie ook sterk beïnvloed worden doorassociaties tussen verklarende variabelen. Dit probleemvan multi collineariteit wordt besproken en geïllustreerd.Verder is er een uitbreiding van variantieanalyse naarspecifieke deelhypothesen en covariantieanalyse. Er wordttelkens geïllustreerd hoe de analyses met SAS Eguide enSPSS kunnen uitgevoerd worden.

Inhoud van de cursus:Dag 1: Uitbreiding van regressie• Speciale datapunten: detectie van uitschieters en

invloedrijke waarnemingen• Inleiding tot robuuste regressie• Multicollineariteit

Dag 2: (halve dag)• Covariantie analyse

DoelgroepDeze cursus is bedoeld voor personen die regelmatiglineaire regressieanalyse wensen te gebruiken.

VoorkennisCursisten dienen vertrouwd te zijn met de methodiekaangebracht in ‘Regressie- en variantieanalyse’.

LesgeverMarlies Lacante is sedert 1974 verbonden aan deonderzoekseenheid Psychologie van de KU Leuven.Gedurende meer dan 20 jaar was zij betrokken bij hetstatistiekonderwijs in de opleiding Psychologie. Momenteeldoceert zij binnen de academische Leraren opleiding,binnen het Leuven Statistics Research Centre (LStat) enbinnen de MSc in Statistics. Ze is ook actief in hetonderwijsonderzoek, met focus op survey onderzoek enmet speciale aandacht voor de onderzoeks methodologie.

An Carbonez is professor aan het Leuven Statistics ResearchCentre (LStat) van de KU Leuven. Ze behaalde haar doctoraatwiskunde aan de KU Leuven in 1992. Ze is coördinatorvan het MSc in Statistics programma van de KU Leuven.Ze is ook betrokken bij statistische consulting projectenen het geven van statistische opleidingen binnenbedrijven.


Datum 25 november 2013 van 9 u - 12 u en 13 u - 16 u en 26 november 2013 van 9 u - 12 u.

PrijsPersoneel en studenten KU Leuven en Associatie KU Leuven: zie https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 120Non profit/sociale sector € 187,50Private sector € 450

TaalNederlands

17

Niet-parametrische statistiek

BeschrijvingDeze cursus behandelt een aantal statistische technieken- analoog aan parametrische statistiek (bv. t-test, variantie -analyse) - waarbij de klassieke onderstellingen uit de parametrische statistiek niet hoeven gemaakt te worden(distributievrije technieken), technieken gebaseerd op ‘ordeningen’ of ‘rankings’, alsook technieken specifiek geschikt voor nominale gegevens.

Inhoud van de cursus:• Chi- kwadraat goodness of fit testen• Testen mbt verschil tussen twee onafhankelijke

steekproeven• Testen mbt verschil tussen twee afhankelijke

steekproeven • Testen mbt verschil tussen meerdere onafhankelijke

steekproeven• Testen mbt verschil tussen meerdere afhankelijke

steekproeven• Kengetallen mbt de samenhang tussen variabelen

DoelgroepGebruikers van basis statistische technieken (t-test – variantie-analyse)

VoorkennisCursisten dienen vertrouwd te zijn met de methodiek aangebracht in ‘Fundamentele Statistische technieken’ envariantie analyse.

LesgeversMarlies Lacante is sedert 1974 verbonden aan de onder-zoekseenheid Psychologie van de KU Leuven. Gedurendemeer dan 20 jaar was zij betrokken bij het statistiekonderwijsin de opleiding Psychologie. Momenteel doceert zij binnende academische Lerarenopleiding, binnen het Leuven Statistics Research Centre (Lstat) en binnen de MSc inStatistics. Ze is ook actief in het onderwijsonderzoek, met focus op survey onderzoek en met speciale aandachtvoor de onderzoeksmethodologie.


Datum 3 december 2013 van 9 u tot 12 u.

PrijsPersoneel en studenten KU Leuven en Associatie KU Leuven: zie https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 40Non profit/sociale sector € 62,50Private sector € 150

TaalNederlands

18

Chemometrics

Course outlineThe goal of the course is to teach students how to performmultivariate sensor calibration. Students will become familiarwith the use of statistical concepts in chemometric appli -cations. Most attention will be given to the ideas underlyingthe different methods and the application of thesemethods to realistic examples. Theoretical considerationsand equations will be limited to what is needed to havesufficient insight to properly use the methods. Mostexamples will be related to spectroscopy and analyticalchemistry, but the scope is broader. By using a combinationof lectures, computer sessions and take home assignmentsthe students will really learn how to apply the chemometricmethods. The following aspects of chemometrics will behandled in this course:• Classical modelling concepts for quantitative calibration:

Classical Least Squares (CLS), Inverse Least Squares(ILS), Multivariate Linear Regression (MLR), PrincipleComponent Regression (PCR) and Partial LeastSquares (PLS).

• Necessary steps for the creation and successful deployment of calibrations; selection of calibrationstandards and assessment of the reliability of the models:(Test set validation vs. Cross-validation, model statistics).Special attention will be given to the methods for theselection of the number of principle components orlatent variables in the projection methods.

• Methods for data pre-processing with special attentionfor the phenomena of light scattering and instrumentdrift and the methods to deal with these phenomena:derivatives, standard normal variate (SNV), multiplicativesignal correction (MSC) and extended multiplicativesignal correction (EMSC).

• Variable selection in a chemometric context andsome commonly used methods for this.

• Qualitative analysis in a chemometric context: discrimination and classification

• New trends in chemometrics such as functional dataanalysis and augmented classical least squares (ACLS).

Target audienceThe intended target audience includes PhD students andresearchers in a variety of fields, including statistics,chemistry, biosciences and engineering. We aim at readerswho wish to learn more about multivariate calibration ofsensor systems and the use of statistical concepts inchemometric applications.

Prerequisites Knowledge of basic concepts of statistics and linearalgebra is required. Some notions of analytical chemistry,sensor technology and multivariate statistics are a plus.

PresenterWouter Saeys is Lecturer at Department of Biosystemsat the KU Leuven in Belgium. He received his Masterdegree in Bioscience Engineering (2002) and a PhD inBioscience Engineering (2006) from the KU Leuven. He wasa postdoctoral researcher at the School for ChemicalEngineering and Advanced Materials of the University ofNewcastle upon Tyne (UK) and at the Norwegian FoodResearch Institute – Matforsk (Ås, Norway). In generalWouter’s research deals with light transport modeling andoptical characterization of biological materials, multivariatedata analysis and chemometrics, process monitoring andcontrol. He is author of 35+ research articles (ISI).

Course MaterialsSlides from the lecturesPapers discussed in the lecturesSoftware manualDatasets for the take home assignmentsAdditional material (suggested)• A user-friendly guide to Multivariate Calibration and

Classification by Naes, Isaksson, Fearn and Davies,NIR Publications 2004

• Multivariate Calibration by Martens and Naes, 1989

DatesFebruary 12, 2014: 9.00 hr - 12.00 hr February 19, 2014: 9.00 hr - 12.00 hrFebruary 26, 2014: 9.00 hr - 12.00 hrMarch 12, 2014: 9.00 hr - 12.00 hr March 19, 2014: 9.00 hr - 12.00 hr

LanguageEnglish


19

Fundamental Statistical Methods

Course outlineThis basic course in statistics emphasizes on selecting theappropriate statistical method and drawing the rightconclusions from the obtained results. Mathematicaldetails will be kept to a minimum. The emphasis will be onunderstanding the concepts and on practical applications.The adequate use of basis statistical summaries (measuresof central tendency, measures of dispersion, box-plots,...)will be illustrated. The foundations of confidence intervalsand of testing hypotheses will be dealt with.

Course content:• Descriptive statistics: graphical and numerical

summaries of the data• Distributions: Binomial, Poisson, Exponential, Normal

and t-distribution• Distribution of the sample mean• Confidence intervals• Hypothesis tests for a population mean (one and

two samples)• Paired t-test• Estimating and testing for proportions

Target audienceAnyone who wishes to understand basic statisticaltechniques more thoroughly.

Prerequisites There are no prerequisites

PresentersAnna Ivanova is a research assistant at the LeuvenStatistics Research Centre (LStat) of the KU Leuven. She obtained her Master degree in Statistics from the KU Leuven in 2004. She carries out statistical consultingand participates in statistical consulting projects.

Marlies Lacante is professor at the Leuven StatisticsResearch Centre (LStat) and at the faculty of Psychologyand Educational sciences of the KU Leuven. She receiveda Master degree in psychology (1974) and a PhD inpsychology (1981) from the KU Leuven. For more than 20 years, she was involved in teaching statistics topsychology students. Currently, she teaches in the Masterof Science in Psychology, in the academic teacher trainingand at the Leuvens Statistics Research Centre (Lstat). She is active in the domain of educational research, with a focus on survey research and with special attentionto research methodology in this area.


Dates17, 18 and 20 March 2014 from 9.00 - 12.00 hr

LanguageEnglish


20

Course outlineMultivariate data consist of observations on two or morevariables for each individual or unit.

The variables will be generally correlated, and a variety oftechniques are available to analyse these data.

The objective of cluster analysis is to form groups ofobservations such that each group is as homogeneous as possible with respect to certain characteristics. The groups are as different as possible.

Principal component analysis is one of the popular toolsto summarize quantitative multivariate data. During thiscourse, PCA and exploratory factor analysis, will be intro -duced and the relation between them examined.

The emphasis of the course will be on the interpretationof the example data and on the results through the Biplot.Mathematical details are kept to a minimum.

For the exercises, participants can choose to use thestatistics package SAS (through Enterprise Guide), SPSSor R

Course content:

Cluster analysis• Hierarchical cluster analysis• Nonhierarchical cluster analysis

PCA and exploratory factor analysis• Linear combination of variables• Eigenvalues and eigenvectors • PCA scores and Factor scores• What is a loading or the factor pattern?• Screenplot• How many components or factors to retain? • Communalities• The biplot• The Varimax rotation

Target audienceData analysts and scientists involved in analysing multi -variate data.

Prerequisites A practical knowledge of basic statistics will be assumed,such as standard deviations and correlations.

PresentersAnne-Marie De Meyer is Professor at the KU Leuven inthe Faculty of Science, Department of Mathematics. She received her PhD in Mathematics (Applied Probability)in 1979 and is currently teaching statistics in the MSc in Statistics and in the Bachelor of Criminology. Since morethan 25 years she has been involved in the short courseprogram for a variety of courses in applied statistics andis also active in the statistical consulting of LStat.

An Carbonez is professor at the Leuven StatisticsResearch Centre (LStat) of the KU Leuven. She obtainedher PhD in Science at the KU Leuven in 1992. She iscoordinator of the MSc in Statistics, she is involved instatistical consulting projects and teaches statisticscourses in companies.

Martine Beullens graduated in Mathematics at KU Leuven.From 1990 onwards she has been working at KU Leuvenand the Federal Police on projects commissioned by theBelgian government on the development and statisticalexploitation of federal databanks containing judicial orpolice information. Currently she is working at KU Leuvenat the Teaching and Learning Vision and Quality departmentin the Data management section.


Dates17, 18 and 20 March 2014 from 9.00 - 12.00 hr

LanguageEnglish

PriceStaff and students KU Leuven: go tohttps://icts.kuleuven.be/cursus/Staff and students Association KU Leuven and PhDstudents, non-KU Leuven € 120Non profit/social sector € 187,50Private sector € 450

Cluster analysis, principal componentanalysis and exploratory factor analysis with SAS, SPSS and R.

21

Introduction to the analysis of contingency tables

Course outlineIn this course, chi-square tests and association measuresare used to identify if there is significant association incontingency tables and to determine how strong thisassociation is. By use of examples, it is illustrated thatexact tests are necessary in certain situations. There isenough time to practice with SAS Eguide, SPSS and R.

Course content:• Construction of a contingency table• Tests for independence: chi square tests• Association measures• Analysis of a 2x2 table: relative risk, odds ratio• Exact test

Target audienceEveryone who wants to analyse contingency tables.

Prerequisites Participants are familiar with basic statistical concepts(which are e.g. introduced in the course ‘FundamentalStatistical Methods’).

PresenterAn Carbonez is professor at the Leuven StatisticsResearch Centre (Lstat) of the KU Leuven. She obtainedher PhD in Science at the KU Leuven in 1992. She iscoordinator of the MSc in Statistics, she is involved instatistical consulting projects and teaches statisticscourses in companies.

Course MaterialsA .pdf file with the course material will be madeavailable.

Dates24 March 2014 9 u - 12 u en 13 u - 16 u25 March 2014 9 u - 12 u.

LanguageEnglish


22

Concepts of Multilevel, Longitudinal and Mixed models

Course outlineStarting from ANOVA models with random factor levels,the concepts of mixed models are introduced and thebasics about inference in random-effects models will beexplained. Afterwards, the mixed ANOVA model is extendedto general linear mixed models for continuous data. Finally,extensions to models for binary or count data will be brieflydiscussed. Omitting all theoretical details, sufficientbackground will be given such that practising statisticianscan apply mixed models in a variety of contexts, know howto use up-to-date software, and are able to correctlyinterpret generated outputs. Many applications, taken fromvarious disciplines, will be discussed.

Target audienceThe targeted audience includes methodological andapplied statisticians and researchers in industry, publichealth organizations, contract research organizations and academia. Important: this course will also serve theMSc in Statistics students.

Prerequisites The student knows the basics of statistical inference, andstatistical modeling (regression, Anova and general(ized)linear models).

PresenterGeert Verbeke is Professor in Biostatistics at KU Leuvenand Universiteit Hasselt. He received the B.S. degree inmathematics (1989) from the KU Leuven, the M.S. inbiostatistics (1992) from Universiteit Hasselt, and earneda Ph.D. in biostatistics (1995) from the KU Leuven. GeertVerbeke has published extensively on longitudinal dataanalyses. He has held visiting positions at the GerontologyResearch Center and the Johns Hopkins University(Baltimore, MD). Geert Verbeke is Past President of theBelgian Region of the International Biometric Society,International Program Chair for the International BiometricConference in Montreal (2006), Board Member of theAmerican Statistical Association. He is past Joint Editor ofthe Journal of the Royal Statistical Society, Series A (2005-2008) and currently editor of Biometrics (2010-2014).

He is the director of the Leuven Center for Biostatisticsand statistical Bio informatics (L-BioStat), and vice-directorof the Interuniversity Institute for Biostatistics and statisticalBioinformatics (I-BioStat), a joint initiative of the Hasseltand Leuven universities in Belgium.

Course MaterialsA .pdf file with the copies of the transparencies used inthe course will be made available.

DatesMarch 25, 2014 13.00 hr - 16.00 hrApril 1, 2014 13.00 hr - 16.00 hrApril 22, 2014 13.00 hr - 16.00 hrMay 6, 2014 13.00 hr - 16.00 hrMay 20, 2014 13.00 hr - 16.00 hr

LanguageEnglish


23

Introduction to correspondence analysis and multiple correspon-dence analysis with SAS and SPSS

Course outlineCorrespondence analysis (CA), is an exploratory techniqueto simultaneously score the categories and the columncategories in a bivariate contingency table in a lower dimensional space The objective is also to clarify the relationship between the row and the column variable. It is well suited for large contingency tables. It can also be used for continuous variables as Age, which can begrouped in different age categories.

In three-way and high dimensional contingency tables, an introduction to multiple correspondence analysis (MCA) is presented, also called Principal components forcategorical data. MCA is a method to visualize the jointproperties of more than 2 categorical variables The individualobservations and the categories of the variables can bedisplayed in the same plot.

In this course, the focus will be on the data examples, the interpretation of the results and the Biplot. SAS and/orSPSS are used for the examples and exercises.

Course content:• Introduction and short historical overview• CA

- Revisit Pearson Chi-square statistic- Inertia and Eigenvalues- CA row and column coordinates- CA plots and association between row and

columns- Quality of the visual presentation- Illustration in SAS and in SPSS

• MCA - Super Indicator matrix - The Burt matrix- Joint presentation of categories- Illustration in SAS and in SPSS

Target audienceData analysts and scientists involved in analysing multivariatecategorical data.

Prerequisites A working knowledge of basic statistics (e.g. Pearson Chi-square Statistic) in contingency tables will be assumed.

PresenterAnne-Marie De Meyer is Professor at the KU Leuven inthe Faculty of Science, Department of Mathematics. She received her PhD in Mathematics (Applied Probability)in 1979 and is currently teaching statistics in the MSc inStatistics and in the Bachelor of Criminology. Since morethan 25 years she has been involved in the short courseprogram for a variety of courses in applied statistics andis also active in the statistical consulting of LStat.


Dates31 March and 1 April 2014 from 9.00 - 12.00 hr

LanguageEnglish

PriceStaff and students KU Leuven: go to https://icts.kuleuven.be/cursus/Staff and students Association KU Leuven and PhD students, non-KU Leuven € 80Non profit/social sector € 125Private sector € 300

24

Logistic Regression Models with SAS and SPSS

DescriptionThe focus is on the statistical model with a categoricaloutcome or response variable.

A categorical response variable can be a binary variable,an ordinal variable or a nominal variable and each typerequires a different model to describe its relationship withthe predictor variables.

We will define, interpret and illustrate the models for eachtype of outcome and place the models in the frameworkof the Generalized Linear Model

SAS (through SAS Enterprise Guide) and SPSS are usedin the applications.

Outline• Introduction• Binary Logistic Regression• Multinomial Logistic Regression for nominal outcome

variables• Proportional Odds Model - Ordinal Logistic Regression• Logistic regression in the framework of the Generalized

Linear Model

Target audienceData analysts in all disciplines

Prerequisites ‘Fundamental Statistical Methods’ and ‘Introduction to theanalysis of contingency tables’. Knowledge of the standard regression model

PresenterAnne-Marie De Meyer is Professor at the KU Leuven in the Faculty of Science, Department of Mathematics.She received her PhD in Mathematics (Applied Probability)in 1979 and is currently teaching statistics in the MSc inStatistics and in the Bachelor of Criminology. Since morethan 25 years she has been involved in the short courseprogram for a variety of courses in applied statistics andis also active in the statistical consulting of LStat.

Course MaterialsA .pdf file with the course material will be made available

Dates28 and 29 April 2014 from 9.00 - 12.00 hr

LanguageEnglish

PriceStaff and students KU Leuven: go tohttps://icts.kuleuven.be/cursus/Staff and students Association KU Leuven and PhDstudents, non-KU Leuven € 80Non profit/social sector € 125Private sector € 300

25

Poisson regression with SAS and SPSS

DescriptionThe Poisson distribution is a discrete distribution and isappropriate for modeling counts of observations. A Poissonregression model fits occurrences of an event or the rateof occurrence of an event as a function of some predictorvariables. For example: the number of occurrences or therate of a certain disease. The Poisson model is a specialcase of the Generalized Linear model. SAS (through SASEnterprise Guide) and SPSS are used in the applications.

Outline• Introduction to Poisson regression• The Poisson model in the framework of the Generalized

Linear model • Correction for overdispersion • The negative binomial model • Poisson regression models for rates

Target audienceData analysts in all disciplines

Prerequisites ‘Fundamental Statistical Methods’ and ‘Introduction to theanalysis of contingency tables’. Knowledge of the standard regression model

PresenterAnne-Marie De Meyer is Professor at the KU Leuven in the Faculty of Science, Department of Mathematics. She received her PhD in Mathematics (Applied Probability)in 1979 and is currently teaching statistics in the MSc inStatistics and in the Bachelor of Criminology. Since morethan 25 years she has been involved in the short courseprogram for a variety of courses in applied statistics andis also active in the statistical consulting of LStat.

Course MaterialsA .pdf file with the course material will be made available

Dates5 and 6 May 2014 from 9.00 - 12.00 hr

LanguageEnglish

PriceStaff and students KU Leuven: go to https://icts.kuleuven.be/cursus/Staff and students Association KU Leuven and PhD students, non-KU Leuven € 80Non profit/social sector € 125Private sector € 300

26

Inleiding tot enquêtering

BeschrijvingVia survey onderzoek wil men informatie verzamelen omtrentmensen, ideeën, opinies, houdingen, plannen, gezondheid,sociale - educatieve - of familiale achtergrond. Zulk soortonderzoek gebeurt vaak bij sociologische vraagstellingen,in psychologie, bij marktonderzoek, enz. … Informatie overdergelijke onderwerpen kan men moeilijk op ‘experimentelewijze’ verzamelen. Daarom moet men de personen inkwestie bevragen. Dit kan via een interview, een vragenlijst,een telefonische enquête, enz. ... Dit soort bevragingenkent een eigen methodologie en eigen onderzoeksregelsdie moeten gerespecteerd worden. In deze cursus wordtachtereenvolgens ingegaan op de verschillende stappenin dit onderzoeksproces.

Tevens zal een half dagdeel besteed worden aan deenquête service aan de KU Leuven, die gebaseerd is opde open-source software “Limesurvey”. Deze software laatgebruikers toe om snel zeer krachtige online enquêtes te ontwikkelen.

Inhoud van de cursus:• analyse van de onderzoeksvraag: wat wil men te

weten komen? • verzamelen van de gevraagde informatie • welke regels moet men in acht nemen bij het formuleren

van de vragen? (invloed van de vraagstelling op hetantwoord, betrouwbaarheid en validiteit)

• methoden van steekproeftrekkingen • verwerken van de gegevens • rapportering • hoe werkt de enquêteservice van de KU Leuven• algemene instellingen voor de enquête• beschikbare vraagtypes en hun mogelijkheden • werken met tokens• uitnodigen van de respondenten en opvolgen van de

responses• exporteren van de resultaten naar statistische pakketten

DoelgroepGebruikers van vragenlijstonderzoek

VoorkennisCursisten dienen vertrouwd te zijn met de methodiek aangebracht in ‘Fundamentele Statistische technieken’ ende cursus ‘Regressie- en variantie analyse’.

LesgeversMarlies Lacante is sedert 1974 verbonden aan de onder-zoekseenheid Psychologie van de KU Leuven. Gedurendemeer dan 20 jaar was zij betrokken bij het statistiekonderwijsin de opleiding Psychologie. Momenteel doceert zij binnende academische Lerarenopleiding, binnen het Leuven Statistics Research Centre (LStat) en binnen de MSc inStatistics. Ze is ook actief in het onderwijsonderzoek, met focus op survey onderzoek en met speciale aandachtvoor de onderzoeksmethodologie.

Kristel Hoydonckx is werkzaam aan de afdeling “Faciliteitenvoor onderzoek” van de KU Leuven en staat daar ondermeerin voor de enquêteservice.


Datum 7 en 8 mei 2014 telkens van 9 u - 12 u, 9 mei 2014 van 9 u - 12 u en 13 u - 16 u.

PrijsPersoneel en studenten KU Leuven en Associatie KU Leuven: zie https://icts.kuleuven.be/cursus/PhD studenten, niet KU Leuven € 160Non profit/sociale sector € 250Private sector € 600

TaalNederlands

Consulting was our historical embryo and remains a core business.

The statistical consulting service center acts as the main pivot to determine the ideal combination between the

customer and the most appropriate university entity.

We recommend that you contact us in an early stage of your project and write a short description of your problem

and send it to [email protected].

The LStat Statistical consulting Service covers

• Statistical support for researchers within the university. The LStat provides statistical help for university research

groups and for the central administration of the university. We help you with advice and we offer support with the

design of your study and with the statistical analysis of your data whether elementary or sophisticated. The first

hour of first-line consulting is provided free of charge.

• Statistical service and execution of projects for government and industry, in service or in partnership.

The LStat uses the administrative help of Leuven Research and Development (LRD) in the negotiation of the contracts

with industries and the private sector.

We have experience with major financial companies, inter national institutions, manufacturers, medical organisations,

marketing companies, FMCG as well as KMO’s.

Our solutions range from basic regression, multivariate techniques, analysis of variance, mixed models, data mining,

process control, to risk theory, categorical data analysis, longitudinal data analysis and tailor-made simulations and

calculations.

If you have any questions, do not hesitate to contact us at: [email protected] and take a look at our website:

http://lstat.kuleuven.be/consulting/

Statistical consulting Service

27

• REGISTRATION COSTS The indicated prices correspond to training for 1 person.

There are several fee categories: - Students and staff from KU Leuven and Association KU Leuven- PhD students from other universities € 80 / full day - non-profit sector, social sector €125 / full day - private sector € 300 / full day

Prices include all course material. If the course takes a whole day and the course takes place at the Arenberg campus in Heverlee a sandwich lunch is included as well.

Payments have to be settled before the start of the course.

• CONFIRMATIONYou will receive a confirmation upon receipt of your application form. This confirmation gives information on how to make the payment and on the course venue. Please contact us in case you do not receive a confirmation letter.

• DISCOUNT When you subscribe for several courses, you can get a discount of 10% if the total number of full training days equals or exceeds 5 days per person and a discount of 20% is attributed if you follow courses for at least 10 full days.

• CANCELLATION - If you are unable to attend a course for which you have registered, you can let a colleague replace you. - Full cancellation for a specific course always has to be done in writing. Administrative costs for cancellation

are set at € 20 when the cancellation is carried out more than 2 weeks before the course takes place. After that, the full course fee will be charged.

INFORMATION

For other questions on registration and extra information contact: tel. + 32 16 32 22 14 [email protected]

Practical Matters

28

Faculty of Science

Registration form Short courses in Statistics 2013-2014

Post or e mail this form to: LStat, Celestijnenlaan 200B, 3001 HEVERLEE, Belgium or [email protected] use the registration form at www.lstat.kuleuven.beStaff and students of (Association) KU Leuven should register online: https://icts.kuleuven.be/cursus/

Applicants details:

Mr. / Mrs. / Ms. Family name ____________________________________________ First name ______________________________

Company/Institute__________________________________________________

Street ________________________________________________________________________________ Number ______________________

P.O. Box ____________ Postcode______________ City ____________________________________ Country ______________________

E mail address ____________________________________________________

Fee category:

nn PhD students, non- KU Leuven Price per full day: € 80

nn Non profit/social sector Price per full day: € 125

nn Private sector Price per full day: € 300

Indicate the courses that you wish to attend:nn Essential Tools for R 30 September, 1, 3,4 October 2013

nn Fundamentele statistische methoden 8, 9, 10 October 2013

nn Fundamentele statistische methoden, toegepast met SAS Eguide 22 October 2013

nn Fundamentele statistische methoden, toegepast met SPSS 22 October 2013

nn Fundamental Statistical Methods, applications with R 22 October 2013

nn Models for Longitudinal and Incomplete data 23-25 October 2013, 6-8 November 2013

nn Regressie- en variantieanalyse 5, 7 November 2013

nn Regressie- en variantieanalyse, toegepast met SPSS 12 November 2013

nn Regressie- en variantieanalyse, toegepast met SAS Eguide 14 November 2013

nn Regression and Analysis of Variance, applications with R 14, 15 November 2013

nn Advanced R programming topics 18, 19 November 2013

nn Optimization and Numerical Methods in Statistics 21, 22, 28, 29 November 2013

nn Uitbreiding bij Regressie- en variantieanalyse 25, 26 November 2013

nn Niet-parametrische statistiek 3 December 2013

nn Essential Tools for R 10, 11, 13, 14 February 2014

nn Chemometrics 12, 19, 26 February, 12,19 March 2014

nn Fundamental statistical methods 17, 18, 20 March 2014

nn Cluster analysis, principal component analysis and exploratory

factor analysis with SAS, SPSS and R 17, 18, 20 March 2014

nn Introduction to the analysis of contingency tables. 24, 25 March 2014

nn Concepts of multilevel, longitudinal and mixed models 25 March, 1, 22 April, 6, 20 May 2014

nn Introduction to correspondence analysis and multiple correspondence

analysis with SAS and SPSS 31 March, 1 April 2014

nn Logistic Regression Models with SAS and SPSS 28, 29 April 2014

nn Poisson regression with SAS and SPSS 5, 6 May 2014

nn Inleiding tot enquêtering 7, 8, 9 May 2014

29

LEUVEN STATISTICSRESEARCH CENTRECelestijnenlaan 200 B

3001 HEVERLEE, Belgiëtel. + 32 16 32 22 14

[email protected]

v.u.

: P

rof.

M.

Laca

nte,

Leu

ven

Sta

tistic

s R

esea

rch

Cen

tre,

Cel

estij

nenl

aan

200

B,

3001

HE

VE

RLE

E,

Bel

gië

Documents

Leuven STATistics STATe of the Art Training Initiative › english › phd › PhD... · 14,15 November 2013 Regression and Analysis of Anna Ivanova Basic (English) 12 Variance, applications