tailieunhanh - Lecture Business statistics in practice (7/e): Chapter 15 - Bowerman, O'Connell, Murphree

Chapter 15 - Multiple regression and model building. After mastering the material in this chapter, you will be able to: Explain the multiple regression model and the related least squares point estimates, explain the assumptions behind multiple regression and calculate the standard error, calculate and interpret the multiple and adjusted multiple coefficients of determination,. | Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression and Model Building The Multiple Regression Model and the Least Squares Point Estimate Model Assumptions and the Standard Error R2 and Adjusted R2 (This section can be read anytime after reading Section ) The Overall F Test Testing the Significance of an Independent Variable Confidence and Prediction Intervals 15- Multiple Regression and Model Building Continued The Sales Territory Performance Case Using Dummy Variables to Model Qualitative Independent Variables Using Squared and Interaction Variances Model Building and the Effects of Multicollinearity Residual Analysis in Multiple Regression Logistic Regression 15- The Multiple Regression Model and the Least Squares Point Estimate Simple linear regression used one independent variable . | Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression and Model Building The Multiple Regression Model and the Least Squares Point Estimate Model Assumptions and the Standard Error R2 and Adjusted R2 (This section can be read anytime after reading Section ) The Overall F Test Testing the Significance of an Independent Variable Confidence and Prediction Intervals 15- Multiple Regression and Model Building Continued The Sales Territory Performance Case Using Dummy Variables to Model Qualitative Independent Variables Using Squared and Interaction Variances Model Building and the Effects of Multicollinearity Residual Analysis in Multiple Regression Logistic Regression 15- The Multiple Regression Model and the Least Squares Point Estimate Simple linear regression used one independent variable to explain the dependent variable Some relationships are too complex to be described using a single independent variable Multiple regression uses two or more independent variables to describe the dependent variable This allows multiple regression models to handle more complex situations There is no limit to the number of independent variables a model can use Multiple regression has only one dependent variable LO15-1: Explain the multiple regression model and the related least squares point estimates. 15- Model Assumptions and the Standard Error The model is y = β0 + β1x1 + β2x2 + + βkxk + Assumptions for multiple regression are stated about the model error terms, ’s LO15-2: Explain the assumptions behind multiple regression and calculate the standard error. 15- R2 and Adjusted R2 Total variation is given by the formula Σ(yi - ȳ)2 Explained variation is given by the formula Σ(ŷi - ȳ)2 Unexplained variation is given by the formula Σ(yi - ŷi)2 Total .