Question

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

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

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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