PPT-Topic 9: Multiple Regression

Intro to PS Research Methods Announcements Final on May 13 2 pm Homework in on Friday or before Final homework out Wednesday 21 probably Overview we often have theories involving . Symptoms of . collinearity. Collinearity. between independent variables . High r. 2. High . vif. of variables in model. Variables significant in simple regression, but not in multiple regression. Variables not significant in multiple regression, but multiple regression model (as whole) significant. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models . Part . 7 . – . Multiple Regression. Analysis. Model Assumptions. Objectives  Calculate regressions with one independent variable  Calculate regressions with multiple independent variables  Scatterplot of predicted and actual values  1. 2. 3. Outline. Jinmiao. Fu—Introduction and History . Ning. Ma—Establish and Fitting of the model. Ruoyu. Zhou—Multiple Regression Model in Matrix Notation. Dawei. . Xu. and Yuan Shang—Statistical Inference for Multiple Regression. Dummy variables as an independent variable. Dummy variable trap. Importance of the "reference group". Using dummy variables to test for equal means. Dummy variables for . Multiple categories. Ordinal variables. ;. some. do’s . and. . don’ts. Hans Burgerhof. Medical. . S. tatistics. and . Decision. Making. Department. of . Epidemiology. UMCG. . Help! Statistics! Lunchtime Lectures. When?. Where?. What?. Al M Best, PhD. Virginia Commonwealth University. Task Force on Design and Analysis . in Oral Health Research. Satellite Symposium, AADR. Boston, MA: March 10, 2015. Multivariable statistical modeling from 10,000 feet. Anderson, Sweeney, Williams, . Camm. , Cochran. © 2017 Cengage Learning. Slides by John . Loucks. St. Edwards University. Chapter . 15. Multiple . Regression. Multiple Regression Model. Least Squares Method. Copyright © Cengage Learning. All rights reserved. 13 Nonlinear and Multiple Regression Copyright © Cengage Learning. All rights reserved. 13.4 Multiple Regression Analysis Multiple Regression Analysis What. is . what. ? . Regression: One variable is considered dependent on the other(s). Correlation: No variables are considered dependent on the other(s). Multiple regression: More than one independent variable. S. ocial . S. ciences. Multiple Regression. Department . of Psychology. California State University Northridge. www.csun.edu. /. plunk. Multiple Regression. Multiple . regression predicts/explains . variance in a criterion (dependent) variable from the values of the predictor (independent) variables. . collinearity. Collinearity. between independent variables . High r. 2. High . vif. of variables in model. Variables significant in simple regression, but not in multiple regression. Variables not significant in multiple regression, but multiple regression model (as whole) significant. Materials for this lecture. Demo. Lecture . 2 . Multiple Regression.XLS. Read Chapter 15 Pages 8-9 . Read all of Chapter 16’s Section 13. Structural Variation. Variables you want to forecast are often dependent on other variables. http://ge.tt/3w2lhlr. Before installing Simetar do the following: . Download and install the latest Service Pack for Office 2003 or 2007 if using these systems. We do not run on Excel 2010 yet, install 2007.