Reading 1
Multiple Regression
4 modules 37 min
0/4
Exam Focus
Multiple linear regression models explain the variation in a dependent variable using more than one independent variables. You should know how to use an F test to test the effectiveness of nested models. Become familiar with the effects that heteroskedasticity, serial correlation, and multicollinearity have on regression results, and be able to identify common model misspecifications.
Finally, understand the role of influential observations in the estimated model, and the use of logistic regression models.
Modules
- Module 1.1Basics of Multiple Regression and Underlying AssumptionsDescribe the types of investment problems addressed by multiple linear regression and the regression process. · Formulate a multiple linear regression model, describe the relation between the dependent variable and several independent variables, and interpret estimated regression coefficients. · Explain the assumptions underlying a multiple linear regression model and interpret residual plots indicating potential violations of these assumptions.7 min · 3 LOS
- Module 1.2Evaluating Regression Model Fit and Interpreting Model ResultsEvaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit. · Formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests. · Calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable.8 min · 3 LOS
- Module 1.3Model SpecificationDescribe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification. · Explain the types of heteroskedasticity and how it affects statistical inference. · Explain serial correlation and how it affects statistical inference. · Explain multicollinearity and how it affects regression analysis.12 min · 4 LOS
- Module 1.4Extensions of Multiple RegressionDescribe influence analysis and methods of detecting influential data points. · Formulate and interpret a multiple regression model that includes qualitative independent variables. · Formulate and interpret a logistic regression model.10 min · 3 LOS