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Linear Regression Decision Model: Appraisers

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I need assistance doing linear regression decision model. It needs to be done in Excel. The data you need is in the excel file.

a.If the team of appraisers want to use a simple linear regression decision model (one X, one Y) based on either X1 or X2, which one of these independent variables do you recommend they use? Why?

b.Estimate the parameters, and therefore the prediction equation, for
Y = b0+ b1X1 + b2X2
For each additional areal foot , what is the change in heating cost?

c.Set up a binary variable (X3) for furnace age in combination with the model from part b. How much (%), if any, additional variability in heating cost, Y, does furnace age help explain?

d.What is the "best" model recommendation you can offer the home appraisal team using any combination of the independent variables from parts a through c above. Write the specific model.

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The solution provides step by step method for the calculation of regression analysis. Formula for the calculation and Interpretations of the results are also included in the attached Excel and Word documents.

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Linear Regression Decision Model - Appraisers
I need assistance doing linear regression decision model. It needs to be done in Excel. The data you need is in the excel file.
Answers
a. If the team of appraisers want to use a simple linear regression decision model (one X, one Y) based on either X1 or X2, which one of these independent variables do you recommend they use? Why?
The model adequacy of a regression model is measured using the R2 value.
X1, Outside Temp
In the case of independent variable X1, R2 = 0.6463. Thus 64.63% variability in the Heating Cost can be explained by the linear relationship between the Outside Temp and Heating Cost (as described by the regression equation). The standard error of estimate of the regression model is 64.689.
Details
Regression Statistics
Multiple R 0.803956307
R Square 0.646345743
Adjusted R Square 0.626698284
Standard Error 64.68938215
Observations 20
X2, Footage
In the case of independent variable X2, R2 = 0.8863. Thus 88.63% variability in the Heating Cost can be explained by the linear relationship between the Footage and Heating Cost (as described by the regression equation). The ...

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