A real estate developer studying the business problem of estimating the consumption of heating oil (gallons per month) has decided to examine the effect of atmospheric temperature (Fahrenheit degree) and the amount of attic insulation (inches) on the oil consumption. Data are collected from a random sample of 15 houses for the moth of January. The regression results for a quadratic regression model, Y = Bo + B1X1 + B2X2 + B3X3 + e, is as follows, at the a= = 0.05 level of significance. Where X₁ is the temperature variable and X2 is the insulation amount variable. The translated squared nsulation amount predictor is X3 = X2. Source Regression Error Total df Sum of Squares Mean Squares F stat 229643.1645 6492.0649 3 ? 11 14 236135.2293 ? ? F crit 3.5874

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A real estate developer studying the business problem of estimating the
consumption of heating oil (gallons per month) has decided to examine the
effect of atmospheric temperature (Fahrenheit degree) and the amount of
attic insulation (inches) on the oil consumption. Data are collected from a
random sample of 15 houses for the moth of January. The regression results
for a quadratic regression model, Y = Bo + B1X1 + B₂X2 + B3X3 + €, is as
follows, at the a = 0.05 level of significance. Where X₁ is the temperature
variable and X2 is the insulation amount variable. The translated squared
insulation amount predictor is X3 = X2/1.
Source. df Sum of Squares Mean Squares F stat F crit
3.5874
3
?
Regression
Error
11
Total
14
Variable
Intercept
Temperature
Insulation
Insulation²
229643.1645
6492.0649
236135.2293
?
?
Coefficient St. Error Lower 95%
624.5864 42.4352
-5.3626
0.3171
-44.5868
14.9547
1.8667
1.1238
531.1872
?
?
?
Upper 95%
717.9856
?
?
?
R-Square= 0.9725, Adjusted R-Square= 0.9650, Standard Error of
Prediction = 24.2938, Number of Observations = 15
a) Conduct the F-Test for this regression model at the 5% level of sig-
nificance and decide if this model is a good fit for this data set and explain
why.
Transcribed Image Text:A real estate developer studying the business problem of estimating the consumption of heating oil (gallons per month) has decided to examine the effect of atmospheric temperature (Fahrenheit degree) and the amount of attic insulation (inches) on the oil consumption. Data are collected from a random sample of 15 houses for the moth of January. The regression results for a quadratic regression model, Y = Bo + B1X1 + B₂X2 + B3X3 + €, is as follows, at the a = 0.05 level of significance. Where X₁ is the temperature variable and X2 is the insulation amount variable. The translated squared insulation amount predictor is X3 = X2/1. Source. df Sum of Squares Mean Squares F stat F crit 3.5874 3 ? Regression Error 11 Total 14 Variable Intercept Temperature Insulation Insulation² 229643.1645 6492.0649 236135.2293 ? ? Coefficient St. Error Lower 95% 624.5864 42.4352 -5.3626 0.3171 -44.5868 14.9547 1.8667 1.1238 531.1872 ? ? ? Upper 95% 717.9856 ? ? ? R-Square= 0.9725, Adjusted R-Square= 0.9650, Standard Error of Prediction = 24.2938, Number of Observations = 15 a) Conduct the F-Test for this regression model at the 5% level of sig- nificance and decide if this model is a good fit for this data set and explain why.
b) Construct the 95% confidence interval estimator for the partial slope,
for all of the predictors here. Investigate and explain which ones of these
predictors are needed and which ones of these predictors are not needed.
c) Explain the predictive power (R-Square) of this regression equation
for the oil consumption prediction.
d) Explain the standard error of prediction for this regression equation
of the oil consumption prediction.
Transcribed Image Text:b) Construct the 95% confidence interval estimator for the partial slope, for all of the predictors here. Investigate and explain which ones of these predictors are needed and which ones of these predictors are not needed. c) Explain the predictive power (R-Square) of this regression equation for the oil consumption prediction. d) Explain the standard error of prediction for this regression equation of the oil consumption prediction.
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