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rbowen87 rbowen87
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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 houses that had recently sold in the area. They want to predict the selling price of homes (in thousands of dollars) based on the age of the home (in years). Some summary statistics, partial regression output, and a scatterplot of the relationship (with regression line) are provided.

Use two decimal places when reporting the results from any calculations, unless otherwise specified.

VariableMeanStDev
Price (in thousands)140.8660.78
Age78.6944.71
The regression equation is Price (in thousands) = 193 - 0.665 Age

Analysis of Variance

SourceDFSSMSFP
Regression1415804158014.490.000
Residual Error461320252870
Total47173605
A scatterplot with a regression line shows the relationship between Age and Price. The horizontal axis is labeled, Age and ranges from 0 to 200 in increments of 50. The vertical axis is labeled, Price (in thousands) and has markings from 50 to 350 in increments of 50. A regression line starts from (10, 180), decreases toward the right, and ends at (185, 70). 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, 10 to 185 on the horizontal axis and between the points, 60 to 340 on the vertical axis. The concentration of the dots is more between the points 30 and 150 on the horizontal axis and between the points, 60 and 200 on the vertical axis. Two outliers are at the point, (3250, 345), and (3470, 160). All values are approximate.


Is the linear model effective at predicting the selling price of homes in this portion of northern New York? Use the provided computer output (and α = 0.05) for this test.

▸ Yes

▸ No
Textbook 
Statistics: Unlocking the Power of Data

Statistics: Unlocking the Power of Data


Edition: 3rd
Authors:
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yoorimchiyoorimchi
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