See attached files.
You are to use linear (single) regression only!
The setting: Imagine you are a real estate investor presented with a regression analysis of home sales in the neighborhood of one of your investment properties. Unfortunately, the report stops short of making the decision for you. Given the data as presented in those three worksheets, you need to use the StatTools regression module to determine:
? Which is the better predictor of selling price: appraised value, square footage, or number of bedrooms?
? How much value is added per $1,000 of appraised value?
? How much value is added per 100 square feet?
? How much value is added per bedroom?
? At what price should you offer your investment home, based only on an appraised value of $135,200?
The data for this problem comes from the problem below.
Explore the relationship between the selling prices (Y) and the appraised values (X) of the 150 homes in the
file P02_07.xlsx (attached) by estimating a simple linear regression model. Also, compute the standard error of estimate
se and the coefficient of determination R2for the estimated least squares line. Interpret these measures
and the least squares line for these data.
a. Is there evidence of a linear relationship between the selling price and appraised value? If so,
characterize the relationship (i.e., indicate whether the relationship is a positive or negative one, a strong
or weak one, etc.).
b. For which of the two remaining variables, the size of the home and the number of bedrooms in
the home, is the relationship with the home's selling price stronger? Justify your choice with
additional simple linear regression models.
1. Place all problems for this assignment in one file.
2. Use a different Worksheet for each Linear Regression (LR). Name those Worksheets LR Appraised Value, LR Square Footage, and LR Number of Bedrooms. Note: DO NOT Use Multiple Regression
The solution provides detailed step-by-step method of performing Linear Regression Analysis in EXCEL and Interpretation of Results.