December 2020 CFA Level 2: Study Session 2. Quantitative Methods I - Reading 5. Multiple Regression

Reading 5. Multiple Regression

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Learning Outcome Statements

	Reading 5. Multiple Regression
	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;