Please help with this question. I have attached a screenshot. There is no need to show evidence, just the answers only.

Write "T" or "F" if you agree or disagree with the following statements, respectively in the parentheses @ If the covariance between two random variables is zero, they are independent. If X and Y are independent E[Y | X ] : E[Y]. Conditional mean is generally a function of conditioning variables. Under the classical linear model condition, the error displays a conditional heteroskedastic variance. The ordinary least squares estimator is a biased estimator under the classical linear model assumption. Wald test statistic always follows a chi-squared distribution under the null hypothesis. Type-I error is an error committed when the null hypothesis is correct. If a p-value of a test statistic is greater than a, where a is the level of significance, then the null is rejected. The ordinary least squares estimator is a consistent estimator under the classical linear model condition. If the number of equations in the null hypothesis in 1, the Wald test statistic is identical to the F-test statistic