Interpreting r  For the same two variables described in Exercise 1, if we find that r = 0, does that indicate that there is no association between those two variables?

The Simple Linear Regression model is   Y = b0 + b1*X1 + u   and the Multiple Linear Regression model with k variables is:   Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u   Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term,   Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 13 In the MLR model, what do we mean by Heteroskedasticity?   That the error term depends on the values of the explanatory variables That all the explanatory variables have different variance That the variance of the error term is a function of the explanatory variables That the variance of the error term is constant     QUESTION 14 Suppose that in the model Y=b0+b1*X1+u, we add a variable that is correlated with both Y and X1. What will happen…

Question 4   The sum of all the residuals:   A) Should be about 1 B) Should be about 0 C) Is equal to the product of the β’s D) Can be used to test for a linear relationship E) None of the above are correct

If the dependent variable Y correlates with the independent variable X, this suggest that X has a causal impact on Y. Select one: True False