Question

# SUMMARY OUTPUT Regression Statistics Multiple R 0.231960777 R Square 0.053805802 Adjusted R Square 0.034093423 Standard Error...

 SUMMARY OUTPUT Regression Statistics Multiple R 0.231960777 R Square 0.053805802 Adjusted R Square 0.034093423 Standard Error 5272.980333 Observations 50 ANOVA df SS MS F Significance F Regression 1 75893113.09 75893113.09 2.729543781 0.105035125 Residual 48 1334607437 27804321.59 Total 49 1410500550 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 6396.894057 3281.342486 1.949474669 0.057094351 -200.6871963 12994.47531 -2404.335972 15198.12409 HSRANK 64.68225855 39.15075519 1.6521331 0.105035125 -14.03561063 143.4001277 -40.32805468 169.6925718

a. According to your estimate, what is the predicted financial aid awarded to a

student with a high school GPA rank of 95%?

b. David’s GPA rank increased from 80% to 90%, by how much will his financial aid

change as a result?

c. The residual plot and the line of best fit plot for your regression.

d. Does student’s GPA rank in high school account for a large fraction of the

variations in the financial aid awarded?

e. Is your estimated coefficient significant?

Part A Predicted Financial Aid = 6396.894057+ 64.68225855*95 = 12541.70862

Part B Change in financial Aid = 90-80*(64.68225855) = 646.8225855

Part D 1% GPA rank changes Financial Aid only around 1% of its basic value which is not too much. Though if GPA is quite high, i.e. At its peak, it can result into a change of 10% of its base value. Which can be seen as important for some.

Part E Estimated coefficient has a p value of 0.105035125 which is more than 0.05. Therefore it is not significant at 95% confidence interval.

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