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Long Island Real Estate Study

In: Business and Management

Submitted By gainz914
Words 3625
Pages 15
1. Mo and Nora are two real estate agents who wish to examine the relationship between the selling price (in K) of a home and its size (measured in sq ft). So they conducted a random survey of 90 houses in different locations. Some of the other variables they took into consideration are land size (acres), # of rooms, # of bathrooms, and whether or not a house has a pool.
2. a.
Regression Analysis: AppraisedValue versus House Size(sq ft)

The regression equation is
AppraisedValue = 49.10 + 0.2074 House Size(sq ft)

S = 121.230 R-Sq = 58.7% R-Sq(adj) = 58.3%

Analysis of Variance

Source DF SS MS F P
Regression 1 1841760 1841760 125.32 0.000
Error 88 1293302 14697
Total 89 3135062

Fitted Line: AppraisedValue versus House Size(sq ft)

2b.
The regression equation is
AppraisedValue = 49.10 + 0.2074 House Size(sq ft)

2c.
If the size of the house (sq ft) is 0, then the appraised value of the house is $ 49,100.
The appraised value increases by $ 207.40 per additional square foot of house size.
2d.
1841760/3135062 = 0.587
The coefficient of determination (r-sq) is 0.587, hence the linear relationship between X and Y is generally neutral because not all of the variation in Y is explained in X.
2e.
Sq root (1293302/n-2)= 121.23
The Standard error of the estimate Syx is 121.23 K which represents the average variation in the house prices around the fitted regression line. In other words, the average variation in the house prices is $121,230 around the fitted regression line.
2f.

Based on the fitted line plot, there is a strong positive linear relationship between the appraised value and the house size. Based on our analysis of the Residual Plots for appraised value, the fitted model appears to be appropriate and does not violate any regression assumptions. There is

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