Question

Based on the charts below, Determine  whether a statistically reliable oil consumption model can be estimated

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	Number People, Home Index, Degree Days, Customerb	.	Enter
a. Dependent Variable: Oil Usage
b. All requested variables entered.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.889a	.790	.766	85.445
a. Predictors: (Constant), Number People, Home Index, Degree Days, Customer

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	962180.237	4	240545.059	32.948	.000b
	Residual	255527.663	35	7300.790
	Total	1217707.900	39
a. Dependent Variable: Oil Usage
b. Predictors: (Constant), Number People, Home Index, Degree Days, Customer

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	-261.897	77.152		-3.395	.002
	Customer	1.242	1.231	.082	1.010	.320
	Degree Days	.282	.037	.609	7.625	.000
	Home Index	89.407	9.921	.722	9.012	.000
	Number People	6.850	10.675	.051	.642	.525
a. Dependent Variable: Oil Usage

Answer 1

Based on the result, we can see that F statistic value is 32.948 with p value = 0.000

so, we can say that the overall model is significant and statistically reliable oil consumption model can be estimated.

If we look at the slope coefficient for variables, then we can see that the p value corresponding to variable degree days and home index are 0.000. So, we can include these two variables in the final model for estimation.

Variable "number of people" and customer are not significant because the p values are big enough to be rejected. So, these two variables will not be included in the final model.

R squared value and r value are also large, so we can expect a good estimation model based on the given data set.