Question

A random sample of records of home sales from Feb. 15 to Apr. 30, 1993, from the files maintained by the Albuquerque Board of Realtors gives the Price and Size (in square feet) of 117 homes. A regression to predict Price (in thousands of dollars) from Size has an R2 of 71.4%. The residuals plot indicated that a linear model is appropriate. Complete parts a through c below. a) What are the variables and units in this regression?

A. Size (in square feet) is y and Price (in thousands of dollars) is x.
B. Price (in thousands of dollars) is y and Size (in square feet) is x.
C. Price in dollars) is y and Number of Houses is x.
D. Price in dollars) is y and Size (in square feet) is x.
b) What units does the slope have?
A. The slope has units of square feet per thousands of dollars.
B. The slope has units of thousands of dollars per square foot.
C. The slope has units of dollars per square foot.
D. The slope has units of dollar amount.
c) Do you think the slope is positive or negative? Explain.
A. The slope is positive. The price of a home cannot be negative.
B. The slope is negative. As the size of the home decreases, the price should also decrease.
C. The slope is positive. As the size of the home increases, the price should also increase.
D. The slope is negative. As the size of the home increases, the price should decrease.

Answer 1

The variables in this regression are Size (in square feet) as the independent variable and Price (in thousands of dollars) as the dependent variable. The slope of the regression has units of thousands of dollars per square foot and is positive, indicating that as the size of the home increases, the price should also increase.

Step-by-step explanation:

a) The variables in this regression are Size (in square feet) as the independent variable (x) and Price (in thousands of dollars) as the dependent variable (y).

b) The units of the slope are thousands of dollars per square foot.

c) The slope is positive. As the size of the home increases, the price should also increase.