Linear regression, multiple regression, time series

Please see attached file
First some questions about simple, linear, bivariate regression.
1 Of these correlation coefficient values, which represents the least strength of correlation?
A +0.4
B +0.1
C -0.2
D -0.5

2 The t test for significance of the correlation coefficient has how many degrees of freedom?
A n
B n-1
C n-2
D n-3

3 Which is NOT a good indicator of the goodness of fit of a regression equation?
A Correlation coefficient
B Coefficient of determination
C F statistic
D Equation slope

4 Which of these is NOT computed from the regression's standard error value?
A Confidence interval for slope
B Confidence interval for intercept
C Residuals
D Prediction interval for Y

5 Which is NOT an assumption made about the random errors in a regression model?
A The errors are normally distributed
B The errors have constant variance
C The errors are independent
D The errors are small

Now for multiple regression.

6 Suppose these models all have about the same R-squared value. Which is best?
A Bivariate regression with one independent variable
B Multivariate regression with two independent variables
C Multivariate regression with three independent variables
D Multivariate regression with four independent variables

7 The F test for significance of a multiple regression tests which null hypothesis?
A R2 = 0
B rho = 0
C beta 1 = beta 2 = beta 3 = ... = beta k = 0
D e1 = e 2 = e 3 = ... = e n = 0

8 Heteroscedasticity has to do with...
A Whether variance is constant
B Whether variance is autocorrelated
C Whether variance is normal
D Whether variance is known

And a couple of questions about time series.

9 Which is NOT a measure of fit for a time series trend model?
A MAD
B MUD
C MSD
D MAPE

10 Which is NOT a moving average trend analysis method?
A TMA
B CMA
C Exponential smoothing
D Deseasonalization