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;

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;

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;

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;

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;

f. explain the assumptions of a multiple regression model;

g. calculate and interpret the F-statistic, and describe how it is used in regression analysis;

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;

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

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

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

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

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

m. describe how model misspecification affects the results of a regression analysis and describe how to avoid common forms of misspecification;

n. describe models with qualitative dependent variables;

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