Provide brief, but complete, answers to the following questions. What are the as

Question

Provide brief, but complete, answers to the following questions. What are the assumptions of the regression errors in the classical linear regression model? What does the Gauss-Markov theorem state about the OLS estimators in the classical linear regression model? As you increase the significance level of any hypothesis test, what happens to the probability of rejecting the null hypothesis? Prove that that is, the sum of the product of residuals ei and the estimated Yi is always zero. Prove that that is, that the means of the actual Y values and the estimated Y values are the same.

Explanation / Answer

list the assumptions of the linear regression model. In decreasing order of importance, these assumptions are:

1. Validity. Most importantly, the data you are analyzing should map to the research question you are trying to answer. This sounds obvious but is often overlooked or ignored because it can be inconvenient. . . .

2. Additivity and linearity. The most important mathematical assumption of the regression model is that its deterministic component is a linear function of the separate predictors . . .

3. Independence of errors. . . .

4. Equal variance of errors. . . .

5. Normality of errors. . . .

Further assumptions are necessary if a regression coefficient is to be given a causal interpretation . . .

Normality and equal variance are typically minor concerns, unless you

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