PPT-Regression Model Building

Predicting Number of Crew Members of Cruise Ships Data Description n158 Cruise Ships Dependent Variable Crew Size 100s Potential Predictor Variables Age 2013 Year Built Tonnage 1000s of Tons. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model 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 . 1 . – . Simple Linear Model. Theory. Demand Theory: Q = f(Price). 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. 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 . 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. 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. Model . the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed . data.. Formally, the model for multiple linear regression, given . 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. 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