Making Sense of the Two-Proportions Test
Use a two-proportions hypothesis test to determine whether a Six Sigma project actually improved the process. The test compares the percentages of two groups and only works when the raw data behind the...
View ArticleUsing 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. It is the probability that the test will reject...
View ArticleMaking Sense of Attribute Gage R&R Calculations
Using attribute gage R&R tools, analysts obtain the percentage of repeatability and the percentage of reproducibility. To better understand the percentages, analysts should understand the steps...
View ArticleMaking Sense of Linear Regression
Details of the use of linear regression are often considered difficult or confusing by those practitioners just beginning to delve into the Six Sigma toolkit. Making sense of the process starts at a...
View ArticleMaking Sense of ANOVA – Find Differences in Population Means
Analysis of Variance (ANOVA) is a statistical technique for determining the existence of differences among several population means. ANOVA is not used to show that variances are different; it is used...
View ArticleMaking Sense of the Two-Sample T-Test
The two-sample t-test is one of the most commonly used hypothesis tests in Six Sigma work. It is applied to compare whether the average difference between two groups is really significant or if it is...
View ArticleMaking Sense of the Binary Logistic Regression Tool
Sometimes Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How then can a regression equation be developed? The correct technique is something called logistic regression,...
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