- CFA Exams
- 2021 Level II
- Study Session 2. Quantitative Methods (1)
- Reading 5. Multiple Regression
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Learning Outcome Statements PDF Download
| 1. Multiple linear regression a. formulate a multiple regression equation to describe the relation between a dependent variable and several independent variables and determine the statistical significance of each independent variable; b. interpret estimated regression coefficients and their p-values; | |
| 2. Testing the significance of a regression coefficient c. formulate a null and an alternative hypothesis about the population value of a regression coefficient, calculate the value of the test statistic, and determine whether to reject the null hypothesis at a given level of significance; d. interpret the results of hypothesis tests of regression coefficients; | |
| 3. Confidence intervals for regression coefficients in a multiple regression e. calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables; | |
| 4. The standard error of estimate in multiple linear regression model e. calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables; | |
| 5. Predicting the dependent variable in a multiple regression model e. calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables; | |
| 6. Assumptions of the multiple linear regression model f. explain the assumptions of a multiple regression model; | |
| 7. Testing whether all population regression coefficients are equal to zero g. calculate and interpret the F-statistic, and describe how it is used in regression analysis; | |
| 8. Is R2 related to statistical significance? h. distinguish between and interpret the R2 and adjusted R2 in multiple regression; i. evaluate how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table; | |
| 9. Using dummy variables in regressions j. formulate a multiple regression equation by using dummy variables to represent qualitative factors and interpret the coefficients and regression results; | |
| 10. Heteroskedasticity k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |
| 11. Serial correlation k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |
| 12. The Durbin-Watson statistic k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |
| 13. Multicollinearity l. describe multicollinearity and explain its causes and effects in regression analysis; | |
| 14. Model specification and errors in specification m. describe how model misspecification affects the results of a regression analysis and describe how to avoid common forms of misspecification; | |
| 15. Models with qualitative dependent variables n. describe models with qualitative dependent variables; | |
| 16. The economic meaning of the results of multiple regression analysis o. evaluate and interpret a multiple regression model and its results; |
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