- 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 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 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 modele. 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 modele. 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 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. distinguish between and interpret the R^{2} and adjusted R^{2} in multiple regression;
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9. Using dummy variables in regressionsj. formulate a multiple regression equation by using dummy variables to represent qualitative factors and interpret the coefficients and regression results; | |

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. describe models with qualitative dependent variables; | |

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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