# Question

One of the biggest factors in determining the value of a home is the square footage. The following data represent the square footage and asking price (in thousands of dollars) for a random sample of homes for sale in Naples, Florida, in December 2010.

Square Footage, x ... Asking Price ($000s), y

1148 .......... 154

1096 .......... 159.9

1142 .......... 169

1288 .......... 169.9

1322 .......... 170

1466 .......... 179.9

1344 .......... 180

1544 .......... 189

1494 .......... 189.9

(a) Which variable is the explanatory variable?

(b) Draw a scatter diagram of the data.

(c) Determine the linear correlation coefﬁcient between square footage and asking price.

(d) Is there a linear relation between the square footage and asking price?

(e) Find the least-squares regression line treating square footage as the explanatory variable.

(f) Interpret the slope.

(g) Is it reasonable to interpret the y-intercept? Why?

(h) One home that is 1092 square feet is listed at $189,900. Is this home’s price above or below average for a home of this size? What might be some reasons for this price?

Square Footage, x ... Asking Price ($000s), y

1148 .......... 154

1096 .......... 159.9

1142 .......... 169

1288 .......... 169.9

1322 .......... 170

1466 .......... 179.9

1344 .......... 180

1544 .......... 189

1494 .......... 189.9

(a) Which variable is the explanatory variable?

(b) Draw a scatter diagram of the data.

(c) Determine the linear correlation coefﬁcient between square footage and asking price.

(d) Is there a linear relation between the square footage and asking price?

(e) Find the least-squares regression line treating square footage as the explanatory variable.

(f) Interpret the slope.

(g) Is it reasonable to interpret the y-intercept? Why?

(h) One home that is 1092 square feet is listed at $189,900. Is this home’s price above or below average for a home of this size? What might be some reasons for this price?

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