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Unformatted text preview: 12. Comparing Groups: Analysis of Variance (ANOVA) Methods Response y Explanatory x vars Method Categorical Categorical Contingency tables (Ch. 8,15) (chisquared, etc., logistic regr.) Quantitative Quantitative Regression and correlation (Ch 9 bivariate, 11 multiple regr.) Quantitative Categorical ANOVA (Ch. 12) (Where does Ch. 7 on comparing 2 means or 2 proportions fit into this?) Ch. 12 compares the mean of y for the groups corresponding to the categories of the categorical explanatory vars (factors). Examples: y = mental impairment, xs = treatment type, gender, marital status y = income, xs = race, education (<HS, HS, college), type of job Comparing means across categories of one classification (1way ANOVA) Let g = number of groups Were interested in inference about the population means 1 , 2 , ... , g The analysis of variance (ANOVA) is an F test of H : 1 = 2 = = g H a : The means are not all identical The test analyzes whether the differences observed among the sample means could have reasonably occurred by chance, if H 0 were true (due to R. A. Fisher). Oneway analysis of variance Assumptions for the F significance test : The g population dists for the response variable are normal The population standard devs are equal for the g groups ( ) Randomization, such that samples from the g populations can be treated as independent random samples (separate methods used for dependent samples) Variability between and within groups (Picture of two possible cases for comparing means of 3 groups; which gives more evidence against H ?) The F test statistic is large (and P value is small) if variability between groups is large relative to variability within groups Both estimates unbiased when H 0 is true (then F tends to fluctuate around 1 according to F dist.) Betweengroups estimate tends to overestimate variance when H 0 false (then F is large, P value = righttail prob. small) 2 2 (betweengroups estimate of variance ) (withingroups estimate of variance ) F = Detailed formulas later, but basically Each estimate is a ratio of a sum of squares (SS) divided by a df value, giving a mean square (MS). The F test statistic is a ratio of the mean squares. P value = righttail probability from F distribution (almost always the case for F and chisquared tests). Software reports an ANOVA table that reports the SS values, df values, MS values, F test statistic, P value. (Looks like ANOVA table for regression.) Exercise 12.12: Does number of good friends depend on happiness? (GSS data) Very happy Pretty happy Not too happy Mean 10.4 7.4 8.3 Std. dev. 17.8 13.6 15.6 n 276 468 87 Do you think the population distributions are normal?...
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This note was uploaded on 07/12/2011 for the course STA 3030 taught by Professor Agresti during the Spring '11 term at University of Florida.
 Spring '11
 Agresti
 Correlation, Variance

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