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, but this tool is often not well understood.
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Posts Tagged ‘hypothesis tests’
Making Sense of the Binary Logistic Regression Tool
Making 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 due instead to random chance.
Published
Making 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 to show that means are different.
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Making 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 basic level.
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Making 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 behind the tools’ calculations.
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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. It is the probability that the test will reject Ho when in fact it is false.
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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 percentages is available.
Published
Making Sense of the Binary Logistic Regression Tool
Making 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 due instead to random chance.
Published
Making 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 to show that means are different.
Published
Making 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 basic level.
Published
Making 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 behind the tools’ calculations.
Published
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. It is the probability that the test will reject Ho when in fact it is false.
Published
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 percentages is available.
Published
