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h9- Two-Way ANOVA

# h9- Two-Way ANOVA - STAT 201 Handout 9 Two-Way ANOVA...

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STAT 201 — Handout 9 Two-Way ANOVA — Simpson’s Paradox revisited Recall the contingency analysis example: gender midterm performance Total Good Bad F M Total Difference among subjects with respect to extra factors, such as study habits, may mask any gender-midterm relationship. Therefore, we split the data into blocks according to the extra factor of study habits: Study habits = GOOD gender midterm performance Total Good Bad F M Total Study habits = BAD gender midterm performance Total Good Bad F M Total The split data help to bring out any gender-midterm relationship within each study habit block . We can do the same for any ANOVA study — Two-way ANOVA : We collect weight loss data on subjects who underwent diet programs. There are two subjects in each program-gender block. The numbers are weight loss in pounds (numbers in parentheses are mean weight loss within a program-gender combination): Program gender (main factor) (blocking factor) F M 1 2,4 (3) 1,2 (1.5) 2 5,3 (4) 7,6 (6.5) 3 1,1 (1) 3,5 (4) The main goal is to determine if the weight loss programs differ. However, since gender might interact with the different programs, we study the

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h9- Two-Way ANOVA - STAT 201 Handout 9 Two-Way ANOVA...

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