PPT-Ch. 2: The Simple Regression Model

Definition Dependent variable LHS variable explained variable response variable Independent variable RHS variable explanatory variable Control variable Error term disturbance. The simple regression model formulas 4 Take aways 1 Introduction to linear regression Regression analysis is the art and science of fitting straight lines to patterns of data In a linear regression model the variable of interest the so called depend 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. Jennifer Kensler. Laboratory for Interdisciplinary Statistical Analysis. Collaboration. . From our website request a meeting for personalized statistical advice. Great advice right now:. Meet with LISA . 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 . Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Regression and Forecasting Models. Part 0 - Introduction. . Professor William Greene; . Economics . and IOMS Departments. NBA 2013/14 Player Heights and Weights. Data Description / Model. Heights (X) and Weights (Y) for 505 NBA Players in 2013/14 Season. . Other Variables included in the Dataset: Age, Position. Simple Linear Regression Model: Y = . MSc in Computing (Data Analytics). Lecture Outline. Simple Linear Regression. Multiple Regression. AVOVA vs Simple Linear Regression. AVOVA vs Simple Linear Regression. Scatter Plot. A scatter plot or . ;. 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?. David J Corliss, PhD. Wayne State University. Physics and Astronomy / Public Outreach. Model Selection Flowchart. NON-LINEAR. LINEAR MIXED. NON-PARAMETRIC. Decision: Continuous or Discrete Outcome. PROC LOGISTIC. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 9 . – . Model Building. Multiple Regression Models. Using Binary Variables . Logs and Elasticities. Jim Stock. Department of Economics, Harvard University. SDSCon. 2017, MIT. April 21, 2017. Thanks . to Robert . Kaufmann (BU), Felix Prentis (Oxford), and Giselle . Montamat. (Harvard). Climate science: the policy landscape. 1. Correlation indicates the magnitude and direction of the linear relationship between two variables. . Linear Regression: variable Y . (criterion) . is predicted by variable X . (predictor) . using a linear equation.. Instructor: Prof. Wei Zhu. 11/21/2013. AMS 572 Group Project. Motivation & Introduction – Lizhou Nie. A Probabilistic Model for Simple Linear Regression – Long Wang. Fitting the Simple Linear Regression Model – . . Lecture compiled by. Dr. . Parminder. . Kaur. Assistant Professor. Department of Commerce. For . B.Com. (. Prog. ) II . Sem. . Sec A. SIMPLE . LINEAR . REGRESSION. DEFINITION OF . REGRESSION .