Learning Outcome Statements

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;

b. interpret estimated regression coefficients and their p-values;

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;

d. interpret the results of hypothesis tests of regression coefficients;

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;

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;

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;

6. Assumptions of the multiple linear regression model

f. explain the assumptions of a multiple regression model;

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;

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 regressions

j. formulate a multiple regression equation by using dummy variables to represent qualitative factors and interpret the coefficients and regression results;

10. Heteroskedasticity

k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;

11. Serial correlation

k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;

12. The Durbin-Watson statistic

k. explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;

13. Multicollinearity

l. describe multicollinearity and explain its causes and effects in regression analysis;

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;

15. Models with qualitative dependent variables

n. describe models with qualitative dependent variables;

16. The economic meaning of the results of multiple regression analysis

o. evaluate and interpret a multiple regression model and its results;