PPT-Regression Model Building

LPGA Golf Performance 2008 Data Description Response logPrize WinningsRound Skewed data Potential Predictors Average Drive Distance Percentage of Drives Reaching Fairway Percentage of . Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistics and Data Analysis. Part . 6 – Regression Model-1. Conditional Mean . U.S. Gasoline Price. Monotonic but Non-Linear. The relationship between X and Y may be monotonic but not linear.. The linear model can be tweaked to take this into account by applying a monotonic transformation to Y, X, or both X and Y.. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Statistics and Data Analysis. Part . 10 . – . Qualitative Data. Modeling Qualitative Data. A Binary Outcome. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models . Part . 7 . – . Multiple Regression. Analysis. Model Assumptions. Alexander Swan & Rafey Alvi. Residuals Grouping. No regression analysis is complete without a display of the residuals to check that the linear model is reasonable.. Residuals often reveal subtleties that were not clear from a plot of the original data.. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 1 . – . Simple Linear Model. Theory. Demand Theory: Q = f(Price). Predicting Number of Crew Members of Cruise Ships. Data Description. n=158 . Cruise Ships. Dependent Variable – Crew Size (100s). Potential Predictor Variables. Age (2013 – Year Built). Tonnage (1000s of Tons). 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. Predicting Number of Crew Members of Cruise Ships. Data Description. n=158 Cruise Ships. Dependent Variable – Crew Size (100s). Potential Predictor Variables. Age (2013 – Year Built). Tonnage (1000s of Tons). 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. Definition. Dependent variable,. LHS variable,. explained. variable,. response. variable,…. Independent variable,. RHS variable,. explanatory variable,. Control variable,…. Error term,. disturbance,. Predicting Number of Crew Members of Cruise Ships. Data Description. n=158 . Cruise Ships. Dependent Variable – Crew Size (100s). Potential Predictor Variables. Age (2013 – Year Built). Tonnage (1000s of Tons).