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# Making Sense of the Two-Proportions Test

Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection. A project was deployed to fix this problem. In April, the process produced 8,000 widgets and experienced a total of 63 rejects. Did the process really improve? Read..

# Making Sense of Linear Regression

Linear regression is one of the most commonly used hypothesis tests in Lean Six Sigma work. Linear regression offers the statistics for testing whether two or more sets of continuous data correlate with each other, i.e. whether one drives another one. Additionally, it shows the influence of discrete variables, too.

# Making Sense of ANOVA – Find Differences in Population Means

ANOVA (analysis of variances) is a statistical technique for determining the existence of differences among several population means. The technique requires the analysis of different forms of variances – hence the name. It is testing whether means are different. Read about an application example here.

# Making Sense of Binary Logistic Regression

In some situations, Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can a regression equation be developed in these cases? Black Belt training indicated that the correct technique is something called logistic regression or binary regression. But this tool is often not well understood.

# Making Sense of the Two-Sample T-Test

More often than not, Lean Six Sigma practitioners have to find out whether two groups that look quite the same are really the same. If averages of two groups are in question and the data is continuous and normally distributed, the two-sample t-test offers an answer to the question. Read how to run this test in SigmaXL.

# Making Sense of Attribute Gage R&R Calculations

Measurement error is unavoidable. There will always be some measurement variation due to the measurement system itself. Most problematic measurement system issues occour when measuring attribute data by relying on human judgment such as good/bad, pass/fail. Attribute Gage R&R helps to find and reduce these errors.

# Using the Power for Good Hypothesis Testing

Rejecting a null hypothesis when it is false is what every good hypothesis test should do. The “power of the test” is the measure of how good a test is.