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 . 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.. SIT095. The Collection and Analysis of Quantitative Data II. Week 9. Luke Sloan. Introduction. Recap – Last Week. Workshop Feedback. Multinomial Logistic Regression in SPSS. Model Interpretation. In Class Exercise. SIT095. The Collection and Analysis of Quantitative Data II. Week 7. Luke Sloan. About Me. Name: Dr Luke Sloan. Office: 0.56 . Glamorgan. Email: . SloanLS@cardiff.ac.uk. To see me: . please email first. Eric Feigelson. Classical regression model. ``The expectation (mean) of the dependent (response) variable Y for a given value of the independent variable X (or vector of variables . X. ) is equal to a specified mathematical function . Stat-GB.3302.30, UB.0015.01. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction. . Professor William Greene; Economics and IOMS Departments. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 2 . – . Inference About the. Regression. The Linear Regression Model. . Logistic Regression III. Diagnostics and Model Selection. 2. Outline. • . Checking model assumptions. - outlying and influential points. - linearity. • . Checking model adequacy . . - Hosmer- Lemeshow test. 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 = . 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. 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. 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. 6-4.1 . Polynomial Models. 6-4 Other Aspects of Regression. 6-4.1 . Polynomial Models. 6-4 Other Aspects of Regression. 6-4.1 . Polynomial Models. Suppose that we wanted to test the contribution of the second-order terms to this model. In other words, what is the value of expanding the model to include the additional terms?. Definition. Dependent variable,. LHS variable,. explained. variable,. response. variable,…. Independent variable,. RHS variable,. explanatory variable,. Control variable,…. Error term,. disturbance,. 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