Question

SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.898764189 0.807777067 0.787891936 1.733802005 ANOVA Significance F MS 1.64527E-10 Regression Residual Total 366.3391391 122.113046 40.6221648 87.17601239 3.00606939 453.5151515 29 32 P-value Coefficients Standard Error tStat 1.983559616 12.6855096 2.3223E-13 0.773800662 -8.2381422 4.4022E-09 0.269085194 -0.2779617 0.78301395 0.609128063-4.49566030.00010276 Lower 95 Upper 95% Lower 95.0% Upper 95.0% 21.1056295929.219299421.105629629.2192994 7.957284.7920799 0.625136388 0.47554564 -0.6251364 0.47554564 3.9842396061.4926261 3.98423961.4926261 25.16246451 6.3746799 Effort in Job Search 0.07479537 2.73843284 Intercept GPA 7.957279953 -4.7920799 Internship

Part (c)            (2 points)

Interpret the estimated value of the coefficient on the “GPA” variable, i.e., explain what the number means in this regression.

• The coefficient for GPA is -6.3746. It means that for every unit it increases there is a decrease of -6.3746 units in time.

• Part (e)            (2 points)

  Is the estimate of the coefficient on the “GPA” variable statistically significant? Please answer “yes” or “no,” then explain how we can tell.

• The estimate is statistically significant; The P-value its 0.000000004 which is less than 0.05 therefore it is significant.

• Part (f)             (2 points)

  Is the estimate of the coefficient on the “GPA” variable of practical significance? Please answer “yes” or “no,” then explain how we can tell.

Answer 1

(F.) It is hard to say anything without knowing what are we predicting. I have no idea about variable y(dependent variable) .

I assume it is time to get a job.

Yes, GPA is practically significant. Highes Goa indicates that a candidate is technically equipped and hence will be placed in less time.