Question

# Regression Statistics Multiple R 0.710723 R Square 0.505127 Adjusted R Square 0.450141 Standard Error 1.216847 Observations...

 Regression Statistics Multiple R 0.710723 R Square 0.505127 Adjusted R Square 0.450141 Standard Error 1.216847 Observations 21 ANOVA df SS MS F Significance F Regression 2 27.20518 13.60259 9.186487 0.00178 Residual 18 26.65291 1.480717 Total 20 53.8581 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 58.74307 12.66908 4.636728 0.000205 32.12632 85.35982 32.12632 85.35982 High School Grad -0.00133 0.000311 -4.28236 0.000448 -0.00198 -0.00068 -0.00198 -0.00068 Bachelor's -0.00016 5.46E-05 -3.00144 0.007661 -0.00028 -4.9E-05 -0.00028 -4.9E-05

In sentence form, describe the implications of your estimated beta coefficients (i.e. are they significant? what do they tell you about the relationship between that X and the Y?). Be sure to include beta-0, beta-1, and beta-2.

Sol:

For beta0,t=

 t=4.636728 p=0.000205

p<0.05

beta0 is signiifcant at 5% level of significance

For beta1

 t=-4.28236 p=0.000448

p<0.05

beta1 is significant at 5% level of significance

For beta2:

 t=-3.00144 p=0.007661

p<0.05,beta2 is significant at 5% level of significance

From Global F test

Ho:

beta1=beta2=0

Ha:

Atleast one of the beta is different from zero

F=9.186487

p=0.00178

p<0.05

Regression model is significant that is there is a linear relationship between y and independent variables.

Intrepretation of beta1:

beta1=

 -0.00133

for unit increase in High school grad, y decreases by 0.00133 on an average

beta2=

 -0.00016

for unit increase in Bachelor's, y decreases by 0.00016 on an average

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