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##### Learning Outcome Statements
 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; b. interpret estimated regression coefficients and their p-values; 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; d. interpret the results of hypothesis tests of regression coefficients; 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 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 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; 