1 .		The interpretation of the slope is exactly the same in a multiple linear regression model as compared to a simple linear regression model. [Hint]  True  False
2 .		The coefficient of multiple determination r2Y.12 measures the proportion of variation in Y that is explained by X1 and X2. [Hint]  True  False
3 .		Adding independent variables to the multiple regression model increases the value of the coefficient of determination. [Hint]  True  False
4 .		The adjusted r2 will increase when variables are added to the model which do not add explanatory power. [Hint]  True  False
5 .		The slopes in a multiple regression model are called net regression coefficients. [Hint]  True  False
6 .		You have just run a regression in which the value of the coefficient of multiple determination is 0.57. To determine if this indicates that the independent variables explain a significant portion of the variation in the dependent variable, you would perform an F-test. [Hint]  True  False
7 .		From the coefficient of multiple determination, we cannot detect the strength of the relationship between Y and any individual independent variable. [Hint]  True  False
8 .		A multiple regression is called "multiple" because it has several data points. [Hint]  True  False
9 .		A multiple regression is called "multiple" because it has several explanatory variables. [Hint]  True  False
10 .		In a multiple regression model, the value of the coefficient of multiple determination has to fall between 0 and 1. [Hint]  True  False
11 .		The partial F test criterion involves determining the contribution of the regression sum of squares made by each explanatory variable after all the other explanatory variables have been included in the model. [Hint]  True  False
12 .		The coefficients of partial determination measure the proportion of the variation in the dependent variable that is explained by each explanatory variable without controlling for the other explanatory variables. [Hint]  True  False
13 .		If there are m categories, there should only be m-1 dummy variables representing the categories in the regression model. [Hint]  True  False
14 .		An interaction variable is created by multiplying a dummy variable by the explanatory variable. [Hint]  True  False
15 .		A t test of the coefficient of the interaction variable will determine whether or not the interaction is statistically significant. [Hint]  True  False