One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and seling price (in thousands of dollars) for a random sample of homes for sale in a certain region. Complete parts (a) through (n) below. * Clck the icon to view the housing data. (a) Which variable is the explanatory variable? O Square Footage O Seling Price (b) Draw a scatter diagram of the data. Choose the correct scatter diagram below. OA OB. Oc. OD. 4500 700 700 1000 1000 150 1000 1000 150 150 1000 (e) Determine the linear corelation coefficient between square footage and asking price. (Round to three decimal places as needed.) (4) Is there a linear relation between square footage and asking price? O Yes O No
One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and seling price (in thousands of dollars) for a random sample of homes for sale in a certain region. Complete parts (a) through (n) below. * Clck the icon to view the housing data. (a) Which variable is the explanatory variable? O Square Footage O Seling Price (b) Draw a scatter diagram of the data. Choose the correct scatter diagram below. OA OB. Oc. OD. 4500 700 700 1000 1000 150 1000 1000 150 150 1000 (e) Determine the linear corelation coefficient between square footage and asking price. (Round to three decimal places as needed.) (4) Is there a linear relation between square footage and asking price? O Yes O No
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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![One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and selling price (in thousands of dollars) for a
random sample of homes for sale in a certain region. Complete parts (a) through (h) below.
1 Click the icon to view the housing data.
(a) Which variable is the explanatory variable?
Square Footage
O Selling Price
(b) Draw a scatter diagram of the data. Choose the correct scatter diagram below.
O A.
OB.
Oc.
OD.
Ay
4500-
700-
Ax
4500-
700-
1000
1000
150
1000
1000
150
150 IPI>
1000
4500
4500
700
4500
(c) Determine the linear correlation coefficient between square footage and asking price.
r=
(Round to three decimal places as needed.)
(d) Is there a linear relation between square footage and asking price?
O Yes
O No
(e) Find the least-squares regression line treating square footage as the explanatory variable.
]x+ (C
(Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
(f) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
O A. For a house that is 0 square feet, the predicted selling price is
|thousand dollars.
(Round to three decimal places as needed.)
O B. For every additional thousand dollars in selling price, the square footage increases by
| square feet, on average.
(Round to three decimal places as needed.)
OC. For a house that is sold for $0, the predicted square footage is
(Round to three decimal places as needed.)
OD. For every additional square foot, the selling price increases by
thousand dollars, on average.
(Round to three decimal places as needed.)
O E. It is not appropriate to interpret the slope.
(9) Is it reasonable to interpret the y-intercept? Why? Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
O A. No-a house of
(Type an integer or a simplified fraction.)
OB. No-a house of
square feet is not possible.
square feet is not possible and outside the scope of the model.
(Type an integer or a simplified fraction.)
OC. Noa house of
square feet is outside the scope of the model
(Type an integer or a simplified fraction.)
OD. Yes-a house of
(Type an integer or a simplified fraction.)
O E. More information about the houses is necessary before deciding.
| square feet is possible and within the scope of the model.
(h) One home that is 1466 square feet is sold for $210 thousand. Is this home's price above or below average for a home of this size?
The home's price is (1)
the average price. The average price of a home that is 1466 square feet is $
thousand.
(Round to the nearest whole number as needed.)
1: Data Table
TT
Square Footage, x
Selling Price ($000s), y e
2157
372.6
3065
357.9
1117
188.2
2012
343.8
3004
603.9
2815
377.1
4097
624.9
2166
370
2561
417.5
1628
285.6
1773
269.1
3650
664.2
(1) O above
O below](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb6027075-0c99-4785-814c-8d659b5323ef%2F8a6775ac-74a5-42ae-b71a-555f6f6bfcae%2Fkfth26_processed.jpeg&w=3840&q=75)
Transcribed Image Text:One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and selling price (in thousands of dollars) for a
random sample of homes for sale in a certain region. Complete parts (a) through (h) below.
1 Click the icon to view the housing data.
(a) Which variable is the explanatory variable?
Square Footage
O Selling Price
(b) Draw a scatter diagram of the data. Choose the correct scatter diagram below.
O A.
OB.
Oc.
OD.
Ay
4500-
700-
Ax
4500-
700-
1000
1000
150
1000
1000
150
150 IPI>
1000
4500
4500
700
4500
(c) Determine the linear correlation coefficient between square footage and asking price.
r=
(Round to three decimal places as needed.)
(d) Is there a linear relation between square footage and asking price?
O Yes
O No
(e) Find the least-squares regression line treating square footage as the explanatory variable.
]x+ (C
(Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
(f) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
O A. For a house that is 0 square feet, the predicted selling price is
|thousand dollars.
(Round to three decimal places as needed.)
O B. For every additional thousand dollars in selling price, the square footage increases by
| square feet, on average.
(Round to three decimal places as needed.)
OC. For a house that is sold for $0, the predicted square footage is
(Round to three decimal places as needed.)
OD. For every additional square foot, the selling price increases by
thousand dollars, on average.
(Round to three decimal places as needed.)
O E. It is not appropriate to interpret the slope.
(9) Is it reasonable to interpret the y-intercept? Why? Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
O A. No-a house of
(Type an integer or a simplified fraction.)
OB. No-a house of
square feet is not possible.
square feet is not possible and outside the scope of the model.
(Type an integer or a simplified fraction.)
OC. Noa house of
square feet is outside the scope of the model
(Type an integer or a simplified fraction.)
OD. Yes-a house of
(Type an integer or a simplified fraction.)
O E. More information about the houses is necessary before deciding.
| square feet is possible and within the scope of the model.
(h) One home that is 1466 square feet is sold for $210 thousand. Is this home's price above or below average for a home of this size?
The home's price is (1)
the average price. The average price of a home that is 1466 square feet is $
thousand.
(Round to the nearest whole number as needed.)
1: Data Table
TT
Square Footage, x
Selling Price ($000s), y e
2157
372.6
3065
357.9
1117
188.2
2012
343.8
3004
603.9
2815
377.1
4097
624.9
2166
370
2561
417.5
1628
285.6
1773
269.1
3650
664.2
(1) O above
O below
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