I.Decide whether the statement is true (T) or false (F). Each part is worth 2 points.

1. The sample covariance between the independent variables and the OLS residuals is zero. Consequently, the sample covariance between the OLS fitted values and the OLS errors is zero.

1. Omitting an important factor that is correlated with any of independent variables causes Assumption 4 (NoPerfectCollinearity) to fail and thus OLS to be biased.

1. Because an estimatoris a fixed number, obtained from a particular sample, an estimatorcannot be unbiased.

1. OLS no longer has the smallest variance among linear unbiased estimators in the presence of heteroskedasticity, which causes OLS to be biased.

1. A fitted value is defined as the estimated values of the dependent variable when the values of the independent variables for each observation are plugged into the OLS regression line. Generally, we exclude the intercept in obtaining the fitted values; with inclusion of the intercept, the answer could be very misleading.

1. The errors are computed from the data, whereas the residuals are never observable.

1. Multiple regression analysis allows us to explicitly control for many other factors which simultaneously affect the dependent variable.

1. R2never decreases when another explanatory variable is added to a regression. In other words, the sum of squared residuals (SSR) never increases when additional regressors are added to the model.

1. TheFstatistic is intended to detect whether asetof coefficients is different from zero, but it is never the best test for determining whether a single coefficient is different from zero.