Posted: July 18th, 2021

What happens to the regression f value if r2 increases holding

2.What happens to the regression F value if R2 increases holding everything else constant?
a. f value increases
b.f value decreases
c. f value remains the same
d. cannot be determined
4. What statistic is designed to detect multicollinearity in a regression model?
a. durbin-watson
b. coefficient t-value
c. standard error
d. variance inflation factor
5. What is meant when we refer to multiple regression coefficients as partial or net regression coefficients?
a. Any independent variable only explains a portion of the variation in the dependent variable
b. The coefficients have significance levels that achieve the confidence level desired.
c. The coefficients represent the average change in the dependent variable per unit change in the independent variable holding all other things constant
d. both a and b
e. none of the above
6. What statistic is used to determine if residuals are heteroscedastic?
a. durbin-watson
b. r-square
c. variance inflation factor
d. kb squared fit coefficient t-value
e.12th lag and 24th lbq
8. With time series data when error terms are significantly correlated (or autoregressive) and indicate a cycle pattern the problem is termed
a. heteroscedascity
b. positive serial correlation
c.multicollinearity.
d. negative serial correlation
e. homoscedasticity.
9. GE earnings in billions of dollars are estimated using U.S GNP (Gross National Product) in billions of dollars. The regression equation is Y = .065 + .02X.  Interpreting the equation
a.Y = $65 million if the value when X is equal to zero.
b.when X increases by one billion dollars the average change in Y is 20 million dollars
c.when X increases by one dollar the average change in Y is .02 billion dollars plus .065  billion dollars.
d. both a and b
10. Which statistic in a multiple regression model is the standard deviation of the difference between the actual value (Y) and the estimated value (Ŷ)?
a. standard error of coefficient
b.R spuared
c. sst
d. see
e. mean square regression

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