Multiple Regression Was Used to Forecast Success in College (GPA)based

Multiple regression was used to forecast success in college (GPA)based upon SAT score, high school GPA, and hours spent on-line. Use the regression output shown and comment on the overall fit of the model, the usefulness of each independent variable, and the value to an admissions department of using the model to make admission decisions. What is the model's forecast for an applicant having a high school GPA of 2.5 and an SAT score of 1000 that spends 20 hours a week on-line? What other variables do you feel would make good indicators of college GPA? $\begin{array}{l}\text { Regression Statistics }\\\begin{array}{|l|c|c|c|c|c|}\hline \text { Multiple R } & 0.718 & & & \\\hline \text { R Square } & 0.516 & & & & \\\hline \text { Adjusted R Square } & 0.273 & & & & \\\hline \text { Standard Error } & 0.339 & & & & \\\hline \text { Observations } & 10 & & & & \\\hline & & & & & \\\hline \text { ANOVA } & & & & & \\\hline & \text {df} &\text {SS} &\text { M S }&\text { F} &\text {Significance F} \\\hline \text { Regression } & 3 & 0.735 & 0.245 & 2.128 & 0.198 \\\hline \text { Residual } & 6 & 0.690 & 0.115 & & \\\hline \text { Total } & 9 & 1.425 & & & \\\hline & & & & & \\\hline &\text { Coefficients } & \text { Standard Error } &\text { Stat } &\text { P-value } & \\\hline \text { Intercept } & 0.541 & 1.262 & 0.43 & 0.68 & \\\hline \text { HS GPA } & 0.423 & 0.307 & 1.38 & 0.22 & \\\hline \text { SAT } & 0.001 & 0.001 & 1.51 & 0.18 & \\\hline \text { On-Line } & 0.010 & 0.017 & 0.61 & 0.57 & \\\hline\end{array}\end{array}$