Question

Florida reappraises real estate every year, so the county appraiser's Web site lists the current "fair market value" of each piece of property. Property usually sells for somewhat more than the appraised market value. Here are the appraised market values and actual selling prices (in thousands of dollars) of condominium units sold in a beachfront building in a 93-month period:

Selling price 832 878 628 1067 924 792 645 854
Appraisal value 744.7 842.8 480.9 1005.2 787.9 758.3 548.9 634.3
Selling price 764 708 752 862 720 1099 1301 800
Appraisal value 641.7 514.8 552.9 747.4 475.5 756.3 1039.4 582.8
Here is part of the Minitab output for regressing selling price on appraised value:
Predictor Coef SE Coef T
Constant 227.0989 95.044 2.389
Appraisal 0.8991 0.133 6.76
S = 88.7959 R-Sq = 76.5% R-Sq (adj) = 74.9%
The equation of the least square regression line for predicting selling price from appraised value is (round your answer to at least four decimals):
price appraised value.

Answer 1

The equation of the least square regression line for predicting selling price from appraised value is:

`\text{Selling Price}=227.0989 + 0.8991\ \text{Appraised Value}`

Explanation:

The general form of the least square regression line is:

`y=a+bx`

Here,

y = dependent variable

x = independent variable

a = y-intercept

b = slope

The Minitab output for regressing selling price on appraised value is:

Predictor Coef SE Coef T

Constant 227.0989 95.044 2.389

Appraisal 0.8991 0.133 6.76

S = 88.7959

R-Sq = 76.5%

R-Sq (adj) = 74.9%

The constant term in the regression output represents the y-intercept and the Appraisal coefficient the slope of the regression line.

Then the equation of the least square regression line for predicting selling price from appraised value is:

`\text{Selling Price}=227.0989 + 0.8991\ \text{Appraised Value}`