Now use multiple regression to explore the relationship between Final_exam (Y), assignment_grade (X1), and Tutorial_attend (X2). You may use Data Analysis Tool Pack for this. Carry out any relevant two-tailed hypothesis tests at the 5% significance level. Carefully interpret. If you had a magic wand and could request additional data for your analysis of this course, what extra variables would you request and why?

Final_exam	assignment_grade	Tutorial_attend
100	90	5
100	75	5
90	75	5
85	85	5
85	100	5
80	95	5
70	80	5
60	95	5
60	80	5
55	95	5
55	25	4
50	80	5
45	90	5
40	65	5
40	65	4
35	0	3
30	70	4
30	55	4
25	85	5
25	90	4
15	5	3
15	80	5
15	50	5
15	45	3
5	75	3
5	70	4
100	100	5
95	75	5
90	100	5
85	85	5
80	95	5
70	45	5
70	100	5
65	90	5
60	100	5
55	65	4
55	90	5
55	80	4
50	50	5
45	50	4
45	75	3
40	75	5
40	70	5
35	90	4
30	95	5
30	55	5
25	75	4
25	20	3
25	65	2
15	60	4
15	60	4
15	80	5
10	55	4
10	80	2
0	0	2

SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Statistics Regression Residual Total Intercept assignment_grade Tutorial_attend 0.612917385 0.375667721 0.351654941 22.82783542 df Coefficients 55 2 52 54 -35.7528379 0.218051562 15.33752081 MS 16305.00365 8152.501823 27097.72363 521.1100697 43402.72727 SS Standard Error t Stat P-value 15.54695813 -2.299667729 0.025513459 0.153416203 F 15.64449105 1.421307252 0.161197169 4.29606168 3.570135151 0.000778134 Significance F 4.79524E-06 Lower 95% Upper 95% Lower 95.0% -66.95009176 -4.555584041 -66.95009176 Upper 95.0% -4.555584041 -0.089800572 0.525903695 -0.089800572 0.525903695 6.716842368 23.95819925 6.716842368 23.95819925