1. Multiple linear regression *a. 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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b. interpret estimated regression coefficients and their p-values;*
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2. Testing the significance of a regression coefficient *c. 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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d. interpret the results of hypothesis tests of regression coefficients;*
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3. Confidence intervals for regression coefficients in a multiple regression *e. 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;*
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4. The standard error of estimate in multiple linear regression model *e. 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;*
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5. Predicting the dependent variable in a multiple regression model *e. 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;*
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6. Assumptions of the multiple linear regression model *f. explain the assumptions of a multiple regression model;*
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7. Testing whether all population regression coefficients are equal to zero *g. calculate and interpret the F-statistic, and describe how it is used in regression analysis;*
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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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i. evaluate how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table;*
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9. Using dummy variables in regressions *j. formulate a multiple regression equation by using dummy variables to represent qualitative factors and interpret the coefficients and regression results;*
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10. Heteroskedasticity *k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;*
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11. Serial correlation *k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;*
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12. The Durbin-Watson statistic *k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;*
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13. Multicollinearity *l. describe multicollinearity and explain its causes and effects in regression analysis;*
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14. Model specification and errors in specification *m. describe how model misspecification affects the results of a regression analysis and describe how to avoid common forms of misspecification;*
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15. Models with qualitative dependent variables *n. describe models with qualitative dependent variables;*
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16. The economic meaning of the results of multiple regression analysis *o. evaluate and interpret a multiple regression model and its results;*
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