Lecture 11 - ASSUMPTIONS of ANOVA equal variances(required for pooling normality(required for test distribution The null hypothesis in ANOVA is

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ASSUMPTIONS of ANOVA: equal variances (required for pooling) normality (required for test distribution) The null hypothesis in ANOVA is always: This implies that any combination of means are also equal 1 2 3 j μ = = = 1 2 3 ( ) / 2 + = The alternative hypothesis in ANOVA is always - The population means are different (at least one mean is different from another)
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**** Null hypothesis is tested by comparing two estimates of the population variance ( σ 2 ) : (MS B ) between-group estimate of ( σ 2 ) AFFECTED by whether the null is true (MS W ) within-group estimate of ( σ 2 ) UNAFFECTED by whether the null is true .
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-- when the null hypothesis is true (MS B ) F ratio = ------- = About 1 (MS w ) -- when the null hypothesis is not true (MS B ) F ratio = ------- = Much greater than 1 (MS w )
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S 2 _ X (n) estimates σ 2 just as well as any random samples would S 2 _ X (n) = MS B will be higher than the populations variance because the means are farther away from each other than would be expected by
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This note was uploaded on 09/28/2010 for the course PSYCH 100A 328-304-20 taught by Professor Shams during the Spring '10 term at UCLA.

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Lecture 11 - ASSUMPTIONS of ANOVA equal variances(required for pooling normality(required for test distribution The null hypothesis in ANOVA is

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