My answers:

1.) D. Linear regression is all about the residuals. IID, independent and identically distributed with normal distribution.

2.) C. You selection contrasts your answer to #1, constant mean homoscedastic.

3.) D. Yup, can figure them all out using degrees of freedom, etc.

4.) D. Yup, and why do they call it the hat matrix, it puts a hat on the y's, to distinguish they are estimates.

5.) D, Yup, easy enough formula to look up, though if you had more than 2 predictors I am not sure it would work.

6.) C, as well as Tolerance statistic

7.) D. And some people may also use the p-p plot

8.) E. Yup, the tube would represent constant errors, while in a funnel they either increase or decrease (not constant), double bow would be the model over then under then over then under predicts.

9.) B, would be my answer, so you would want to see how that particular variable fit data, C would distort this since it would be for all predictors at once, I am not 100% confident on B, but that is what I would put.

10.) this depends on the purpose of the model, but the general answer is that validation happens with new or hold out data. But given these are generic general questions, C could be a reasonable selection, as well as A or B if more details were provided.