PPT 17 Checking ANOVA Assumptions Fall 2010

PPT 17 Checking ANOVA Assumptions Fall 2010 - McGill...

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McGill University Advanced Business Statistics MGSC-272
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MGSC 272 Lognormal Distribution
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Checking ANOVA Assumptions Completely Randomized Design
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Detecting Nonnormal Populations 1. For each treatment construct a histogram, normal probability plot, or other graphical display that will detect skewness. 2. Conduct formal tests for normality, e.g, the Kolmogorov –Smirnov test, or the Anderson-Darling test for which the null hypothesis is that the probability distribution is normal. In practice, the normality assumption will often not be exactly satisfied, so that these test are of limited use in practice. 3. If the distribution departs greatly from normality, a normalizing transformation such as ln(y) or y may be necessary. c.f. Page 463 in Text
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Detecting Unequal Variances 1. For each treatment construct a box plot or dot plot for y and look for differences in variability. 1. For small sample sizes it is difficult to graphically detect differences in variability. In this situation we can test the
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This note was uploaded on 02/28/2012 for the course MANAGEMENT MGSC 272 taught by Professor Smith during the Spring '12 term at McGill.

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PPT 17 Checking ANOVA Assumptions Fall 2010 - McGill...

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