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School of Six Sigma Introduction to Statistics
Overview In this module we’re going to begin to explore statistics. By the end of this module you’ll have a better understanding of how statistics fits into the overall Six Sigma philosophy as well as what data and statistics are.
So far in this course we’ve covered how to do things like identifying projects, creating Primary Metric Graphs, constructing process maps, and using tools like the C&E Matrix and FMEA to begin
narrowing down a large list of inputs down to what the team believes to be the most critical, red Xs. At this point in the DMAIC process many problems can be solved.
Example I once spent a day at a company that paints the plastic covers for a major cell phone manufacturer. This company was struggling with very high defect rates. They were scrapping around 30% of everything they produced. By walking the process our team was able to create a process map in around 1 hour which
included identifying the inputs for each process step. We then used a C&E Matrix to narrow the list of inputs down to around 10. This took around 45 minutes.
We then started an FMEA and worked on that for around 4 hours. During the FMEA we quickly discovered some actions that could be implemented right away. As it turned out, these initial actions made an immediate impact and defect rates began to drop the very next day.
Obviously, things don’t always work out like this and I don’t mean to make it sound as though problems can always be solved so quickly. But we want you to realize that improvements can and should begin to happen early on in your Six Sigma projects. You don’t have to wait in order to make improvements.
Once we’ve narrowed our list of inputs down to a more manageable level, we’ll often need to use additional analysis and improvement techniques to characterize and then optimize the process in order to meet the overall goals of the project. To do this, we’ll begin to use some more advanced statistically based tools.
Using Statistics Just saying the word statistics is usually enough to make many people want to crawl up into a fetal position since they have flashbacks to boring professors delivering painful lectures during college. If this is you, please don’t worry. We plan to keep the use of statistics within this course extremely practical and useful.
We’ll do our very best to use different types of examples since we know our students work in many different industries. Our goal is to ensure every one of our students understands the tools and techniques we teach and, more importantly, remembers where to look when they need a refresher.
Definition of Terms As we begin this journey, let’s define some terms, namely data and statistics. Data, which is the plural form of the word datum, help us to better understand and characterize the behaviors of our processes, products, services, and customers. Statistics provide both individuals and companies with the methodology and ability to make sense of their data.
Two Types of Statistics There are generally two types of statistics that continuous improvement practitioners use. The first type is called Descriptive Statistics. As the name implies, descriptive statistics help us to describe our data. Put another way, they help tell us what’s going on. We do this by calculating things like the mean or median, which help us to describe a data sets measure of central tendency. We can also calculate the standard deviation or range, which help us to describe a
data sets measure of dispersion.
The second type of statistics we’ll be working with is called Inferential Statistics, which helps us to draw conclusions and make decisions through the use of tools and techniques such as hypothesis tests which we’ll explore in great detail later in the course.
Inferential statistics help us to make judgments about whether the results we’re seeing are simply due to chance or are in fact repeatable.
Conclusion
Throughout the rest of this course we’ll be exploring many different ways to best leverage the use of statistics. No single lean or Six Sigma project you work on will ever require the use of every statistical tool we cover. But, if you work in this field long enough, chances are very good you’ll eventually encounter situations where
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