- CFA Exams
- 2023 Level I
- Topic 1. Quantitative Methods
- Learning Module 2. Multiple Regression

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##### Learning Outcome Statements PDF Download

1. Multiple Linear Regressiona. 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;
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2. Testing the Significance of a Regression Coefficientc. 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;
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3. Confidence Intervals for Regression Coefficients in a Multiple Regressione. calculate and interpret 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 Modele. calculate and interpret 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 Modele. calculate and interpret 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 Modelf. explain the assumptions of a multiple regression model; | |

7. Testing Whether All Population Regression Coefficients are Equal to Zerog. calculate and interpret the F-statistic, and describe how it is used in regression analysis; | |

8. Is R^{2} Related to Statistical Significance?h. contrast and interpret the R^{2} and adjusted R^{2} in multiple regression;
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9. Using Dummy Variables in Regressionsj. formulate and interpret a multiple regression, including qualitative independent variables; | |

10. Heteroskedasticityk. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |

11. Serial Correlationk. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |

12. The Durbin-Watson Statistick. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference; | |

13. Multicollinearityl. describe multicollinearity, and explain its causes and effects in regression analysis; | |

14. Model Specification and Errors in Specificationm. 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 Variablesn. interpret an estimated logistic regression; | |

16. The Economic Meaning of the Results of Multiple Regression Analysiso. evaluate and interpret a multiple regression model and its results; |

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