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Two Sample Tests
Last week we were introduced to hypothesis testing using t and z-tests. This week we extend that set of tools to two sample tests. However, here we have a greater variety of tests. For the t-test alone there are three variants based on the independence of the groups and whether we can assume variances the same or variances between the groups as different. The variances issue is easy as we can always assume they are different as the test is robust to accept any variances but the dependent pairs is different. Why is it different and how might you use it?
This week we add a new PDF to our tool box – the F-statistic. A major use of this PDF is the comparison of variances but it is also the test statistic of Analysis of Variance (ANOVA). Even though ANOVA is comparing means of different groups, we use the F to test our hypotheses. So why do we do this?
Analysis of Variance (ANOVA) is one of the most widely used tests in statistics. There are many varieties but the biggest varieties are the one factor and two factor ANOVA tests. What are these tests and what are the differences between one factor and two factor ANOVA? Can you think of any examples where these tests might be used in your work place?
Ch. 10: Statistical Inferences About Two Populations
Explain dependent samples
Explain matched-pairs test
Explain related measures
Ch. 11: Analysis of Variance and Design of Experiments
Explain Completely randomized design
Explain Classification variable
Explain Tukey-Kramer procedure
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