Please try to type your solution for this question, so Ican read it without a problem. I truly appreciate you for typing inadvance.

The Question:

For some Mechanical Engineer students, data for 224 student’sGPAs in their first 4 semesters of college were compared usingseveral predictors, namely HSM, HSS, HSE, SATM, and SATV. Heresignificant level ? is 0.05.

Then, the researcher fit a regression model and got thefollowing results (intercept is included in the model):

ANOVA Table

Source	DF	Sum of Squares	Mean Squares	F ratio
Model(Reg)	(A)	(D)	(E)	(F)
Error(Residual)	(B)	105.65	0.4846433	Prob>F
Total	(C)	135.46		<0.001

            

Parameter Estimates Table

Term	Estimate	Std Error	t Ratio	Prob>|t|
Intercept	0.2187920884	0.40332538	0.54	0.5880
HSM	0.1464842907	0.03900487	(G)	0.0002
HSS	0.0274662163	0.03728366	0.74	0.4621
HSE	0.0770581216	0.03929423	1.96	0.0511
SATM	0.0009273573	0.00067983	1.36	0.1739
SATV	­.0004012914	0.00058484	-0.69	0.4933

1. What are the values for (A), (B), (C), (D), (E), (F) and(G)?

2. State the multiple regression model for this problem.

3. What % of the variation in GPA are we explaining by themultiple regression?

4. Specify the prediction equation (Include all theregressors)

5. Determine if this is a significant model, i.e. check ANOVAtable. (If significant, then p-value < ?.) Here significantlevel ? is 0.05.

6. Determine if each predictor variable is significant to themodel, give the reason, based on the JMP output (i.e. compare thetest statistics or p-value)