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tara.harris.9 tara.harris.9
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2 weeks ago
Use the following to answer the questions below:

A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on 48 houses that had recently sold in the area. They want to predict the selling price of homes (in thousands of dollars) based on the size of the home (in square feet).

The regression equation is Price (in thousands) = 17.1 + 0.0643 Size (sq. ft.)

PredictorCoefSE CoefTP
Size (sq. ft.)0.064270.012245.250.000
S = 48.5733   R-Sq = 37.5%        R-Sq(adj) = 36.1%

Predicted Values for New Observations

Size (sq. ft.)FitSE Fit95% CI95% PI
2000145.617.07(131.38, 159.83)(46.80, 244.41)

A scatterplot with a regression line shows the relationship between size and price. The horizontal axis is labeled, Size (square feet) and has markings from 1000 to 3500 in increments of 500. The vertical axis is labeled, Price (in thousands) and has markings from 100 to 350 in increments of 50. A regression line starts from (900, 80), increases toward the right, and ends at (3500, 230). The dots are randomly scattered, such that a few dots lie above the regression line, a few dots lie below the regression line, and a few dots lie on the regression line. The dots are plotted between the points, 900 to 3500 on the horizontal axis and between the points, 40 to 340 on the vertical axis. The concentration of the dots is more between the points 900 and 3000 on the horizontal axis and between the points, 40 and 250 on the vertical axis. Two outliers are at the point, (3250, 345), and (3470, 160). All values are approximate.

Construct a 95% confidence interval for the population slope.

▸ 0.0395 to 0.0891

▸ 0.0397 to 0.0889

▸ 0.0392 to 0.0894

▸ 0.0389 to 0.0897
Statistics: Unlocking the Power of Data

Statistics: Unlocking the Power of Data

Edition: 3rd
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tara.harris.9 Author

2 weeks ago
I appreciate what you did here, answered it right Smiling Face with Open Mouth


2 hours ago
Thanks for your help!!
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