Category: Concepts

Would it be useful to have a simple metric for measuring how well your manufacturing process is working? That is exactly what Overall Equipment Effectiveness (OEE) is all about. Let’s learn a little more about OEE.

Despite the type of data your process generates, there are typically three descriptors or characteristics you would want to know to properly describe your process. Let’s learn what those descriptive statistics are.

It is not practical to calculate the statistical parameters of your population by measuring everything. The Central Limit Theorem will help you use samples to get to your answer.

How can you tell whether the average of your two sample data sets are statistically different and not just due to random chance? The T test is what you would want to use to answer the question. 

The concept of a prediction band is usually associated with regression analysis. We will also contrast a prediction band with a confidence band.

Have you ever wondered why your doctor may keep you waiting in the waiting room or why you have to fast before doing a blood test? Let’s see if Lean Six Sigma can give you an answer.

You have two sets of data and want to test whether the means of the populations they came from are the same but the data is not normally distributed. What can you do? Let’s find out.

What kind of relationship do your X and Y variables have? Is it linear? Is it non-linear? Let’s learn more about how it can be determined graphically and statistically.

Does your data from yesterday represent the current state of your process better than that from a month ago? How can you place more emphasis on it yet still make use of older data? That’s what the Exponentially Weighted Moving Average does. 

A control chart is used to identify and alert you when your process has unexpectedly shifted. Traditional Shewhart charts are good for moderate to large shifts but not for small shifts. The CUSUM (Cumulative Sum) is better for identifying small shifts. Let’s see how.

Without having a defined way to measure the amount of something, the world would be chaotic. Thankfully, we have units and ways to quantify and measure them.

Errors are common when testing a statistical hypothesis. They can be difficult to avoid and by taking to preventive measures to protect yourself from one kind, you open yourself to the likelihood of another.

Limitations define life. Businesses face this reality every day in many different ways. That’s why leaders need to learn how to manage, overcome and capitalize on these constraints.

Being able to divide a sample into smaller groupings can be a vital tool for understanding what is happening in your processes.

In everything from everyday language to mathematical logic problems, we often need a way to express just how much or how often something occurs or exists, in order for a statement to be clear or make sense.

When sampling, it is important to avoid bias, a lack of precision, and a lack of reproducibility. Having an understanding of population and how they work can help you avoid this.

Diagrams can be extremely helpful as a visual aid to gain understanding of a project. This is a valuable type of diagram that you will likely come back to time and again for your team projects.

Errors are an issue that should be kept to a minimum, especially for those aspiring to a high Sigma level.

Concomitant variables are secondary variables that may affect a process under study but are not the significant parameters that the research is designed to understand or control.

You have compared your process data before and after an improvement. Is the difference real or just noise?  Statistical significance will help you answer that question.

Correlation allows you to determine whether sets of data have some sort of relationship. Goodman-Kruskal Gamma looks at pairs of data and determines a correlation. Let’s see how.

Variation and quality are not compatible with each other. The reality, however, is that anytime measurements are taken, there is going to be variation.

If you have ever taken a test and your results told you that you were in a certain percentile or quartile, you may already have a bit of an understanding of how quartiles and boxplots work. Q3 is an important part of fully understanding boxplots.

The concept of quality assurance began in the manufacturing industry and is now standard for most industries.