Question
In: Statistics and Probability
Problem 1 14.59 Is the number of square feet of living space a good predictor of...
Problem 1
14.59 Is the number of square feet of living space a good predictor of a house’s selling price? The following data show the square footage and selling price for fifteen houses in Winston Salem, North Carolina (Zillow.com, April 5, 2015).
|
Size (1000s sq. ft) |
Selling Price ($1000s) |
|
1.26 |
117.5 |
|
3.02 |
299.9 |
|
1.99 |
139.0 |
|
0.91 |
45.6 |
|
1.87 |
129.9 |
|
2.63 |
274.9 |
|
2.60 |
259.9 |
|
2.27 |
177.0 |
|
2.30 |
175.0 |
|
2.08 |
189.9 |
|
1.12 |
95.0 |
|
1.38 |
82.1 |
|
1.80 |
169.0 |
|
1.57 |
96.5 |
|
1.45 |
114.9 |
- What is the coefficient of determination?
- What is the sample correlation coefficient?
- At 95% confidence level, construct a confidence interval for the mean value of the selling price of a 2000 square foot house in Winston Salem, North Carolina.
- At 99% confidence level, construct a prediction interval for the selling price of a 2500 square foot house in Winston Salem, North Carolina.
Solutions
Expert Solution
--------------------------------------------------------------------------------------------------------------------------------------------------
-----------------------------------------------------------------------------------------------------------
orchestra answered 1 year agoRelated Solutions
The data relating the square feet for the living space and the selling price of 12...
The data relating the square feet for the living space and the
selling price of 12 residential properties given in example 3.5 are
reproduced here.
X
Y
1360
178.5
1940
275.7
1750
239.5
1550
229.8
1790
195.6
1750
210.3
2230
360.5
1600
205.2
1450
188.6
1870
265.7
2210
325.3
1480
168.8
What is the y-intercept of the best fitting line?
1.
-106.6
2.
0.196
3.
-0.196
4.
106.6
The data relating the square feet for the living space and the selling price of 12...
The data relating the square feet for the living space and the
selling price of 12 residential properties given in example 3.5 are
reproduced here.
X
Y
1360
178.5
1940
275.7
1750
239.5
1550
229.8
1790
195.6
1750
210.3
2230
360.5
1600
205.2
1450
188.6
1870
265.7
2210
325.3
1480
168.8
What is the y-intercept of the best fitting line?
1.
-106.6
2.
0.196
3.
-0.196
4.
106.6
Descriptive Statistic Price Living Area (Square Feet) Number of Bedrooms Number of Bathrooms Age (Years) Mean...
Descriptive Statistic
Price
Living Area (Square Feet)
Number of Bedrooms
Number of Bathrooms
Age (Years)
Mean
160695.775
1733.874
3.136
1.8805
27.871
Standard Error
2132.773719
17.45410352
0.022891658
0.019322088
1.012996802
Median
148829.5
1656
3
2
18
Mode
139079
1480
3
2.5
1
Standard Deviation
67444.22686
551.9472165
0.723897802
0.611018079
32.03377158
Sample Variance
4548723737
304645.7299
0.524028028
0.373343093
1026.162522
Kurtosis
3.889356216
-0.420418335
-0.363359978
-1.004482917
5.291745807
Skewness
1.371395188
0.478357102
0.010298402
0.018867528
2.173487195
Range
552807
2785
4
3
208
Minimum
26049
672
1
1
0
Maximum
578856...
. A merchant has 9000 square feet of floor space for storing units of products 1...
. A merchant has 9000 square feet of floor space for storing
units of products 1 and 2. To gain the advantage of a good price he
must purchase his stock now. A unit of product 1 costs the merchant
$6 and requires 2 square feet of space, while a unit of product 2
costs the merchant $3 and requires 3 square feet of space. The
merchant must stock at least 500 units of product 1. He expects to
make...
An income property has 5000 square feet of leasable space, and rents for $8.00 per square...
An income property has 5000 square feet
of leasable space, and rents for $8.00 per square foot per year.
Vacancy and credit loss are estimated at 4% and operating expenses
at 38% of potential gross income. Lenders require a 1.2 Debt
Coverage Ratio. Mortgage is available at terms of 25 years, at 15%,
payable annually. The investor requires a 14% rate of return on the
projects before tax equity cash flow.
What would be your maximum offering price based on...
The following data was collected to explore how the number of square feet in a house,...
The following data was collected to explore how the number of
square feet in a house, the number of bedrooms, and the age of the
house affect the selling price of the house. The dependent variable
is the selling price of the house, the first independent variable
(x1x1) is the square footage, the second independent variable
(x2x2) is the number of bedrooms, and the third independent
variable (x3x3) is the age of the house.
Effects on Selling Price of Houses...
The following data was collected to explore how the number of square feet in a house,...
The following data was collected to explore how the number of
square feet in a house, the number of bedrooms, and the age of the
house affect the selling price of the house. The dependent variable
is the selling price of the house, the first independent variable
(x1) is the square footage, the second independent variable (x2) is
the number of bedrooms, and the third independent variable (x3) is
the age of the house. Effects on Selling Price of Houses...
The following data was collected to explore how the number of square feet in a house,...
The following data was collected to explore how the number of
square feet in a house, the number of bedrooms, and the age of the
house affect the selling price of the house. The dependent variable
is the selling price of the house, the first independent variable
(x1x1) is the square footage, the second independent variable
(x2x2) is the number of bedrooms, and the third independent
variable (x3x3) is the age of the house.
Square Feet Number of Bedrooms
Age ...
The following data was collected to explore how the number of square feet in a house,...
The following data was collected to explore how the number of
square feet in a house, the number of bedrooms, and the age of the
house affect the selling price of the house. The dependent variable
is the selling price of the house, the first independent variable
(x1x1) is the square footage, the second independent variable
(x2x2) is the number of bedrooms, and the third independent
variable (x3x3) is the age of the house.
Effects on Selling Price of Houses...
The following data was collected to explore how the number of square feet in a house,...
The following data was collected to explore how the number of
square feet in a house, the number of bedrooms, and the age of the
house affect the selling price of the house. The dependent variable
is the selling price of the house, the first independent variable
(x1) is the square footage, the second independent variable (x2) is
the number of bedrooms, and the third independent variable (x3) is
the age of the house. Effects on Selling Price of Houses...
ADVERTISEMENT
ADVERTISEMENT
Latest Questions
- 1. What is the historical role of investment bankers ? Has this role changed ? Why...
- How were the original Internet requirements met through its design? What are the two main requirements...
- discuss crowdfunding on platforms: What features of the services incorporate virality? What are some of the...
- A textbook of mass 2.08 kg rests on a frictionless, horizontal surface. A cord attached to...
- Describe a time when someone did not really listen to or understand a point you were...
- Enumerations Create an advanced enumeration to represent months (JANUARY – DECEMBER) called enuMonths Add internal properties...
- What is the minimum size of the automorphism group of a simple directed graph having more...
ADVERTISEMENT