Question
In: Statistics and Probability
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 |
Solutions
Expert Solution
The regression line that we want to fit is
where 
is the
y-intercept of the regression line
is the slope
coefficient of the regression line
is a random error
We will estimate the coefficients manually, without using any software
First we calculate the following
sample emans
Sum of squares
The estimate of slope is
The estimate of intercept is
ans: The y-intercept of the best fitting line is 1: -106.06 (Note that it is not -106.6, that must be a typo)
orchestra answered 2 years 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
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...
Suppose the following data were collected relating the selling price of a house to square footage...
Suppose the following data were collected relating the selling
price of a house to square footage and whether or not the house is
made out of brick. Use statistical software to find the regression
equation. Is there enough evidence to support the claim that on
average brick houses are more expensive than other types of houses
at the 0.050.05 level of significance? If yes, type the regression
equation in the spaces provided with answers rounded to two decimal
places. Else,...
Suppose the following data were collected relating the selling price of a house to square footage...
Suppose the following data were collected relating the selling
price of a house to square footage and whether or not the house is
made out of wood. Use statistical software to find the regression
equation. Is there enough evidence to support the claim that on
average wood houses are more expensive than other types of houses
at the 0.05 level of significance? If yes, type the regression
equation in the spaces provided with answers rounded to two decimal
places. Else,...
Suppose the following data were collected relating the selling price of a house to square footage...
Suppose the following data were collected relating the selling
price of a house to square footage and whether or not the house is
made out of wood. Use statistical software to find the regression
equation. Is there enough evidence to support the claim that on
average wood houses are more expensive than other types of houses
at the 0.05 level of significance? If yes, type the regression
equation in the spaces provided with answers rounded to two decimal
places. Else,...
Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 1125 2...
Effects on Selling Price of Houses
Square Feet
Number of Bedrooms
Age
Selling Price
1125
2
1
121500
1461
3
4
123600
1527
3
8
158100
1719
4
9
214800
1745
4
9
215500
2197
4
11
255000
2414
4
13
257200
28302830
4
14
262200
30153015
5
14
282400
Determine if a statistically significant linear relationship
exists between the independent and dependent variables at the 0.01
level of significance. If the relationship is statistically
significant, identify the multiple regression...
Selling Price Living Area (Sq Feet) No. Bathrooms No Bedrooms Age (Years) $240,000 &nbs
Selling Price Living
Area (Sq Feet) No. Bathrooms No
Bedrooms Age (Years)
$240,000
2,022
2.5
3
20
$235,000
1,578
2
3
20
$500,075
3,400
3
3
20
$240,000
1,744
2.5
3
20
$270,000
2,560
2.5
3
20
$225,000
1,398
2.5
3
20
$280,000
2,494
2.5
3
20
$225,000
2,208
2.5
4
20
$248,220
2,550
2.5
3
20
$275,000
1,812
2.5
2
20
$137,000
1,290
1
2
20
$150,000
1,172
2
2
20
$649,000
4,128
3.5
3
20
$195,000
1,816
2.5 ...
(House Selling Price) The data below show the selling price, square footage, bedrooms, and age of...
(House Selling Price) The data below show the selling price,
square footage, bedrooms, and age of houses that have sold in a
neighborhood in the last six months. Selling price Square footage
Bedrooms Age 64,000 1,670 2 30 59,000 1,339 2 25 61,500 1,712 3 30
79,000 1,840 3 40 87,500 2,300 3 18 92,500 2,234 3 30 95,000 2,311
3 19 113,000 2,377 3 7 115,000 2,736 4 10 138,000 2,500 3 1 142,500
2,500 4 3 144,000 2,479...
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...
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...
ADVERTISEMENT
ADVERTISEMENT
Latest Questions
- ***Convert the C++ to Python*** #include <iostream> using std::cout; using std::cin; using std::endl; int charClass; char...
- Make-or-Buy Decision Fremont Computer Company has been purchasing carrying cases for its portable computers at a...
- The following costs were incurred for the single product produced during the first year of operations...
- Non-constant growth model: Do=$2.00 required return on equity= 5% g= 9% n=1,2 g=7% n=3,4 g=3% n=5...
- . Find all x ∈ Z143 such that x2Ξ 7 (mod 143). Please explain all steps...
- #Python: A palindrome is a sequence of characters that reads the same backwards as forwards. For...
- Explain an example in which a variable has two different addresses at different places in a...
ADVERTISEMENT