The county assessor is studying housing demand and is interested in developing a regression model to estimate the market

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The county assessor is studying housing demand and is interested
in developing a regression model to estimate the market value
(i.e., selling price) of residential property within his
jurisdiction. The assessor suspects that the most important
variable affecting selling price (measured in thousands of dollars)
is the size of house (measured in hundreds of square feet). He
randomly selects 15 houses and measures both the selling price and
size, as shown in the following table.
Complete the table and then use it to determine the estimated
regression line.
Observation
Size
Selling Price
(x 100 sq. ft.)
(x $1,000)
Regression Parameters
Estimations
In words, for each hundred square feet, the expected selling
price of a house by $ .
What is the standard error of the estimate (sese)?
11.763
10.759
11.364
What is the estimate of the standard deviation of the estimated
slope (sbsb)?
0.460
0.420
0.444
Can the assessor reject the hypothesis (at the 0.05 level of
significance) that there is no relationship (i.e., β=0β=0)
between the price and size variables?
(Hint: t0.025,13=2.160t0.025,13=2.160)
Yes
No