A land developer wanted a model to estimate the selling price of beach lots. To
ID: 3434895 • Letter: A
Question
A land developer wanted a model to estimate the selling price of beach lots. To do so she recorded for each of 20 beach lots recently sold:
Y = Sale price of the beach lot in $10,000 units
X1 = Area of the lot (in hundreds of square feet)
X2 = elevation of the lot (in feet above sea level)
X3 = Slope of the lot toward the ocean (in degrees)
A statistical regression program generated the following output:
Multiple R: 0.8854
R Squared: 0.7838
Std. Error of Est.: 0.6075
Analysis of variance
Source
D of F
Sum of Squares
Mean Square
F-Ratio
Regression
3
21.409
7.136
19.345
Error
16
5.903
0.369
Individual Analysis of Variables
Variable
Coefficient
Std. Error
t-Value
Constant
-2.491
Area
0.099
0.058
1.713
Elevation
0.029
0.006
4.830
Slope
0.086
0.031
2.800
b) what percentage of the variation in selling price is explained by the three predictor variables?
Source
D of F
Sum of Squares
Mean Square
F-Ratio
Regression
3
21.409
7.136
19.345
Error
16
5.903
0.369
Explanation / Answer
b) what percentage of the variation in selling price is explained by the three predictor variables?
A statistical regression program generated the following output:
Multiple R: 0.8854
R Squared: 0.7838
Std. Error of Est.: 0.6075
R2 = 0.7838 = 78.38%
percentage of the variation in selling price is explained by the three predictor variables =78.38%
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