PPT-ANOVA notes

NR 245 Austin Troy Based primarily on material accessed from Garson G David 2010 Univariate GLM ANOVA and ANCOVA Statnotes Topics in Multivariate Analysis httpfacultychassncsuedugarsonPA765statnotehtm. 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. 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?. 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?. Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. 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?. Hao. Chai. Dereck. . Shen. Skull dataset. 138 skulls from 10 regions. Thickness was measured at 219 locations on each skull. Other variables in dataset:. Age of person (at time of death). Sex of person. (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. Analysis of Variance. Let’s say we conduct this experiment: effects of alcohol on memory. Basic Design. Grouping variable . (IV, manipulation) with . 2 or more levels. Continuous dependent/criterion variable. 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 . Research Methods in Physical Activity. Research Methods in Physical Activity. Purpose and Protocol of the Statistical Test. The purpose of the statistical test is to evaluate the null hypothesis at a specific level of probability (e.g., p < .05). In other words, do the two levels of treatment differ significantly (p < .05) so that these differences are not attributable to a chance occurrence more than 5 times in 100?. 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. ANOVA is comparison of means. Each possible value of a factor or combination of factor is a treatment.. The ANOVA is a powerful and common statistical procedure in the social sciences. It can handle a variety of situations.. 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?.