The slope (b_1) in a simple linear regression model represent: 

a.) Predicted value of Y when X = 0. 

b.) The estimated change in Y per unit change in X. 

c.) Mean value of Y for a fixed value X. 

d.) Variation of the regression model. 

The coefficient of determination tells us: 

a.) That the coefficient of correlation is larger than one. 

b.) Whether Y intercept is equal to zero. 

c.) That the coefficient of correlation is less than minus one. 

d.) The proportion of total variation that is explained by the regression model. 

If the P-value is less than alpha in a two-tailed test 

a.) the null hypothesis should not be rejected. 

b.) the null hypothesis should be rejected. 

c.) the alternative hypothesis should be rejected. 

d.) a one-tailed test should be used. 

If the correlation coefficient (r) = 1.00, then 

a.) the coefficient of determination = 1.00. 

b.) the standard error of the estimate = 0.00. 

c.) all the (x, y) points form a perfectly straight line. 

d.) All of the above. 

The standard error of the estimate is a measure of 

a.) slope. 

b.) the variation around the regression line. 

c.) Y-intercept. 

d.) none of the above.