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. 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?. 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?. 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?. Statistical models in . R. --- Recap ---. Stefan Bentink . bentink@jimmy.harvard.edu. Linear Regression Models. residual error. regression coefficient. dependent variable. intercept. independent variable. @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. 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 - . Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. 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. Example Data Set. Y. X. 5. 20. 6. 23. 7. 27. 8. 33. 8. 31. 9. 35. 10. 43. 5. 19. 6. 25. 7. 29. 8. 31. Estimate two models. Model with y-intercept. Y = a b * X. Regression Statistics. Multiple R. 0.984. 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. Another dummy variable coding scheme. Contrasts. Multiple comparisons. Interactions. One. -factor . Analysis of variance. Categorical . Explanatory variable. Quantitative . Response variable. p. categories (groups).