PPT-One way-ANOVA

Analysis of Variance Lets say we conduct this experiment effects of alcohol on memory Basic Design Grouping variable IV manipulation with 2 or more levels Continuous dependentcriterion variable. Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. 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. The . Univariate. Approach. An ANOVA Factor Can Be. Independent Samples. Between Subjects. Correlated Samples. Within Subjects, Repeated Measures. Randomized Blocks, Split Plot. Matched Pairs if . k. 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 . 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. 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. 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 . Marshall University Genomics Core Facility. Two-Way ANOVA. In one-way ANOVA, we measured a continuous variable in three or more different categorical groups. We think of this as one dependent variable (the continuous “outcome” variable) and one independent variable (the group). 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.. 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. At its lowest level it is essentially an extension of the logic of . t. -tests to those situations where we wish to . compare the means of three or more samples concurrently.. ANOVA. One-way ANOVA. One IV and one DV.