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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;
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