Based on the charts below, Determine whether a statistically reliable oil consumption model can be estimated
Variables Entered/Removed^{a} 

Model 
Variables Entered 
Variables Removed 
Method 
1 
Number People, Home Index, Degree Days, Customer^{b} 
. 
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 
.889^{a} 
.790 
.766 
85.445 
a. Predictors: (Constant), Number People, Home Index, Degree Days, Customer 
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
962180.237 
4 
240545.059 
32.948 
.000^{b} 
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 
Coefficients^{a} 

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 
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.
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