14. Statistical Techniques in Business & Economics (14)

14 ­ © 2004 by The McGraw­Hill Companies, Inc. All rights reserved. 14 ­ 2. When you have completed this chapter, you will be able to: 1. Understand the importance of an appropriate model . specification and multiple regression analysis. 2. Comprehend the nature and technique of . multiple regression models and the concept of. partial regression coefficients 3. Use the estimation techniques for multiple regression . models 4. Conduct an analysis of variance of an estimated modelCopyright © 2004 by The McGraw­Hill Companies, Inc. All rights reserved. 14 ­ 3. 5. Explain the goodness of fit of an estimated model 6. Draw inferences about the assumed (true) model . though a joint test of hypothesis (F test) on the . coefficients of all variables. 7. Draw inferences about the importance of the . independent variables through . tests of hypothesis (t­tests). 8. Identify the problems raised, and the remedies . thereof, by the presence of multicollinearity . in the data sets. 9. Identify the problems raised, and the remedies . thereof, by the presence of outliers/influential . observations in the data © 2004 by The McGraw­Hill Companies, Inc. All rights reserved. 14 ­ 4. 10. Identify the violation of model assumptions, including . linearity, homoscedasticity, autocorrelation, and . normality through simple diagnosic procedures. . 11. Use some simple remedial measures in the presence of . violations of the model assumptions 12. Write a research report on an investigation using. multiple regression analysis. 13. Comprehend the concept of partial correlations and . its importance in multiple regression © 2004 by The McGraw­Hill Companies, Inc. All rights reserved. 14 ­ 5. 14. Draw inferences about the importance of a subset of . the importance in multiple regression analysis. 15. Use qualitative variables, as well as their interactions . with other independent variables through a joint . test of hypothesis 16. Apply some advanced diagnostic checks and remedies . in multiple regression © 2004 by The McGraw­Hill Companies, Inc. All rights reserved. 14 ­ 6. Multiple Regression Analysis. For two independent variables, the general form of . the multiple regression equation is: y a b 1x 1 b2 x 2. x and x are the independent variables 1 2 a is the y­intercept