PPT-t -tests, ANOVA & Regression

Andrea Banino amp Punit Shah Samples vs Populations Descriptive vs Inferential William Sealy Gosset Student Distributions probabilities and Pvalues Assumptions of ttests. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model 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?. Assurance with Intelligence. Paul Gerrard. Gerrard. Consulting. 1 Old Forge Close. Maidenhead. Berkshire. SL6 2RD UK. e: paul@gerrardconsulting.com. w: http://gerrardconsulting.com. t: 01628 639173. 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?. (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. @UWE_KAR. Practice & Communication of Science. Notes for exam. Pen, pencil, eraser and ruler. Your own Cambridge Stats book. No writing (but highlights, tabs OK). We will check. UWE-approved calculator. 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: . One-Way, Two-Way, and . Multiway. .. 1. Group 3. 1 . – Intro & Hist. . - Na Chan. 2 . – Basics of ANOVA . - . Alla. . Tashlitsky. 3 - Data Collection - Bryan . Rong. 4 - Checking Assumptions in SAS - . 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.. 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.. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.