(A). Identify the relationship between two variables based on the scatter diagram.

(B). Use the estimated regression equation to predict the value of y when x = 7.

(C). Identify the coefficient of determination R2 .

(D). Comment on the goodness of fit based on R2 .

A B C D E F G H J K L M N O P SUMMARY OUTPUT X Variable 1 Line Fit Plot Regression Statistics 40 Multiple R 0.94231834 30 R Square 0.88796386 Y - 20 Adjusted R S 0.8719587 Predicted Y 10 Standard Err 2.45387406 Linear (Y) Observations 0 9 2 6 8 -Linear (Predicted )) y = 3.8204x + 9.6699 X Variable 1 y = 3.8204x + 9.6699 ANOVA 2 = 0.888 2 = 1 df SS MS F Significance F Regression 1 334.071737 334.071737 55.4798393 0.0001434 Residual 7 42.1504854 6.02149792 Total 8 376.222222 Coefficients itandard Errol t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 9.66990291 1.79314332 5.39271056 0.0010164 5.42979273 13.9100131 5.42979273 13.9100131 X Variable 1 3.82038835 0.51290852 7.44847899 0.0001434 2.60755242 5.03322428 2.60755242 5.03322428 RESIDUAL OUTPUT Observation Predicted Y Residuals 1 13.4902913 -1.4902913 2 21.131068 2.86893204 3 17.3106796 -2.3106796 4 13.4902913 -1.4902913 5 21.131068 3.86893204 6 24.9514563 1.04854369 7 21.131068 0.86893204 8 28.7718447 -0.7718447 9 32.592233 -2.592233White Auto periodically has a special week-long sale. As part of the advertising campaigl White runs one or more television commercials during the weekend preceding the sale. Give are nine observations for two variables, x and y. Number of TV Ads (x) 3 2 3 4 3 5 6 Number of Cars Sold (y) 12 24 15 12 25 26 22 28 30