Question

In the simple linear regression model estimate Y = b₀ + b₁X

A. Y - estimated average predicted value, X – predictor, Y-intercept (b₁), slope (b₀)

B. Y - estimated average predicted value, X – predictor, Y-intercept (b₀), slope (b₁)

C. X - estimated average predicted value, Y – predictor, Y-intercept (b₁), slope (b₀)

D. X - estimated average predicted value, Y – predictor, Y-intercept (b₀), slope (b₁)

The slope (b₁) represents

A. the estimated average change in Y per unit change in X.

B. variation around the line of regression.

C. predicted value of Y when X = 0.

D. the predicted value of Y.

If the Durbin-Watson statistic has a value close to 0, which assumption is violated?

A. Homoscedasticity.

B. Independence of errors.

C. Normality of the errors.

D. None of the above.

The standard error of the estimate is a measure of

A. total variation of the Y variable.

B. the variation of the X variable.

C. explained variation.

D. the variation around the sample regression line

The coefficient of determination (r²) tells us

A. that the coefficient of correlation (r) is larger than 1.

B. the proportion of total variation that is explained.

C. whether r has any significance.

D. that we should not partition the total variation.

Answer 1

In the simple linear regression model estimate Y = b₀ + b₁X

B. Y - estimated average predicted value, X – predictor, Y-intercept (b₀), slope (b₁)

The slope (b₁) represents

A. the estimated average change in Y per unit change in X.

If the Durbin-Watson statistic has a value close to 0, which assumption is violated?

A. Homoscedasticity.

The standard error of the estimate is a measure of

D. the variation around the sample regression line

The coefficient of determination (r²) tells us

B. the proportion of total variation that is explained.