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# Use the “wage” data. Wage is the dependent variable and the independent variables are education and experience.

Determine the bivariate correlation between wage and education. What is the interpretation?

strong, negative, significant

moderate, positive, not significant

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moderate, negative, significant

strong, positive, not significant

moderate, positive, significant

strong, positive, significant

None of the above

Use the “wage” data. Wage is the dependent variable and the independent variables are education and experience. Determine the regression equation. What is the y-intercept?

.483

3.75

.070

-10.55

.453

2.31

.063

Use the “wage” data. Wage is the dependent variable and the independent variables are education and experience. How much money is each additional year of education worth?2.31

2.31 thousand bucks

0.06 thousand bucks

10.55 thousand bucks

.483 thousand bucks

0.0255 thousand bucks

0.845 thousand bucks

None of the above

Use the “wage” data. Wage is the dependent variable and the independent variables are education and experience. How much of the variance in the dependent variable is explained by these independent variables?

69.6%

48.4%

35.5%

6.89%

-10.55%

None of the abov3

Use the “wage” data. Wage is the dependent variable and the independent variables are education and experience. Imagine a person with zero education and zero experience. How much money would we expect them to earn according to this statistical model?

they would earn \$695

they would earn \$-10, 551, meaning they’d live on credit

they would earn \$10, 551

they would earn \$-2, 313

they would earn \$485

they would earn \$13, 393, the same as all high school dropouts

None of the above