PPT-Introduction to ANOVA Questions

ANOVA makes assumptions about error for significance tests What are the assumptions What might happen why would it be a problem if the assumption of normality equality of error independence of error turned out to be false. Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. AMS 572 Group 5. Outline. Jia. Chen: Introduction of repeated measures ANOVA. Chewei. Lu: One-way repeated measures . Wei Xi: Two-factor repeated measures. Tomoaki. Sakamoto : Three-factor repeated measures. NR 245. Austin Troy. Based primarily on material accessed from Garson, G. David 2010. . Univariate GLM, ANOVA, and ANCOVA. . Statnotes. : Topics in Multivariate Analysis.. http://faculty.chass.ncsu.edu/garson/PA765/statnote.htm. Analysis of Variation. Math 243 Lecture. R. Pruim. The basic ANOVA situation. Two variables: 1 Categorical, 1 Quantitative. Main Question: Do the (means of) the quantitative variables depend on which group (given by categorical variable) the individual is in?. Cohen’s . d. and Omega Squared. Jason R. Finley. Mon April 1. st. , 02013. http://. www.jasonfinley.com. /tools. ω. 2. DEAL WITH IT. Effect Sizes to use. Comparison of means . (. t. test):. Cohen’s . Analysis of Variance. Jennifer Kensler. Laboratory for Interdisciplinary Statistical Analysis. Collaboration. . From our website request a meeting for personalized statistical advice. Great advice right now:. Methods for Dummies. Isobel Weinberg & Alexandra . Westley. Student’s t-test. Are these two data sets significantly different from one another? . William Sealy Gossett. Are these two distributions different?. ANOVA. . (GLM 5). Chapter . 14. Mixed ANOVA. Mixed. :. 1 or more Independent variable uses the same . participants (repeated measures). 1 or more Independent variable uses different . participants (between subjects). (a.k.a. Analysis of Variance). 1. Outline:. Testing for a difference. in means. Notation. Sums of squares. Mean. squares. The. F distribution. The. ANOVA table. Part II: multiple. comparisons. Worked example. ANOVA. Multiple Comparisons. Pairwise Comparisons and . Familywise. Error. . . fw. is the . alpha familywise. , the conditional probability of making one or more Type I errors in a family of . How does it work? . What is an F-ratio?. What is a grand mean?. What are the degrees of freedom for the F-ratio?. k-1, N-k. Post hoc tests. F-test in ANOVA is the so-called . omnibus test. . It tests the means globally. It says nothing about which particular means are different.. Data . Set. Popcorn Oil amt. Batch Yield. plain . little. large 8.2. gourmet little large 8.6. plain . . lots large 10.4. gourmet lots large 9.2. Understand the basic principles of ANOVA. Why it is done?. What it tells us?. Theory of one-way independent ANOVA. Following up an ANOVA. :. Planned contrasts/comparisons. Choosing contrasts. Coding contrasts. The . Multivariate. Approach. One-Way. Cross-Species-Fostering. House mice onto house mice, prairie deer mice, or domestic Norway rats.. After weaning, tested in apparatus with access to tunnels scented like clean pine shavings, house mouse, deer mouse, or rat..