The county assessor is studying housing demand and is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that the most important variable affecting selling price (measured in thousands of dollars) is the size of house (measured in hundreds of square feet). She randomly selects 15 houses and measures both the selling price and size, as shown in the following table. Complete the table and then use it to determine the estimated regression line. Size Observation (x 100 sq. ft.) i I₁ 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total O8.074 10.181 O8.358 20.2 27 O 0.327 30 O 0.316 O 0.398 30 Regression Parameters Slope (8) Intercept (α) 21.4 21.6 O Yes 25.2 O No 37.2 14.4 15 22.4 23.9 26.6 30.7 357.60 What is the standard error of the estimate (se)? Selling Price (x $1,000) Y 265.2 279.6 311.2 328.0 352.0 281.2 288.4 292.8 358.0 263.2 272.4 291.2 299.6 307.6 320.4 4,510.80 In words, for each hundred square feet, the expected selling price of a house Estimations Fili 3,182.40 5,647.92 8,402.40 9,840.00 10,560.00 What is the estimate of the standard deviation of the estimated slope (st)? 6,017.68 6,229.44 7,378.56 13,317.60 3,790.08 4,086.00 6,522.88 7,160.44 8,182.16 9,836.28 1,² 144.00 408.04 729.00 900.00 900.00 457.96 466.56 635.04 1,383.84 207.36 225.00 501.76 571.21 707.56 942.49 9,179.82 34² 70,331.04 78,176.16 96,845.44 107,584.00 123,904.00 79,073.44 83,174.56 85,731.84 128,164.00 69,274.24 74,201.76 84,797.44 89,760.16 94,617.76 102,656.16 Can the assessor reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., B=0) between the price and size variables? (Hint: €0.025,13 = 2.160) by $
The county assessor is studying housing demand and is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that the most important variable affecting selling price (measured in thousands of dollars) is the size of house (measured in hundreds of square feet). She randomly selects 15 houses and measures both the selling price and size, as shown in the following table. Complete the table and then use it to determine the estimated regression line. Size Observation (x 100 sq. ft.) i I₁ 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total O8.074 10.181 O8.358 20.2 27 O 0.327 30 O 0.316 O 0.398 30 Regression Parameters Slope (8) Intercept (α) 21.4 21.6 O Yes 25.2 O No 37.2 14.4 15 22.4 23.9 26.6 30.7 357.60 What is the standard error of the estimate (se)? Selling Price (x $1,000) Y 265.2 279.6 311.2 328.0 352.0 281.2 288.4 292.8 358.0 263.2 272.4 291.2 299.6 307.6 320.4 4,510.80 In words, for each hundred square feet, the expected selling price of a house Estimations Fili 3,182.40 5,647.92 8,402.40 9,840.00 10,560.00 What is the estimate of the standard deviation of the estimated slope (st)? 6,017.68 6,229.44 7,378.56 13,317.60 3,790.08 4,086.00 6,522.88 7,160.44 8,182.16 9,836.28 1,² 144.00 408.04 729.00 900.00 900.00 457.96 466.56 635.04 1,383.84 207.36 225.00 501.76 571.21 707.56 942.49 9,179.82 34² 70,331.04 78,176.16 96,845.44 107,584.00 123,904.00 79,073.44 83,174.56 85,731.84 128,164.00 69,274.24 74,201.76 84,797.44 89,760.16 94,617.76 102,656.16 Can the assessor reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., B=0) between the price and size variables? (Hint: €0.025,13 = 2.160) by $
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Transcribed Image Text:The county assessor is studying housing demand and is interested in developing a regression model to estimate the market value (i.e., selling price) of
residential property within her jurisdiction. The assessor suspects that the most important variable affecting selling price (measured in thousands of
dollars) is the size of house (measured in hundreds of square feet). She randomly selects 15 houses and measures both the selling price and size, as
shown in the following table.
Complete the table and then use it to determine the estimated regression line.
Observation
i
1
2
3
4
5
6
7
8
9
10
i
11
12
13
14
15
Total
O 8.074
Regression Parameters
Slope (B)
Intercept (α)
10.181
8.358
O 0.327
Size
(x 100 sq. ft.)
O 0.316
12
20.2
27
O 0.398
30
30
O Yes
21.4
21.6
25.2
37.2
14.4
15
22.4
23.9
26.6
30.7
357.60
O No
What is the standard error of the estimate (Se)?
In words, for each hundred square feet, the expected selling price of a house
Selling Price
(x $1,000)
Ii
Yi
y
265.2 253.4
12
2 20.2 279.6
286.2
3 27 311.2 313.4
4
30 328.0 325.4
5
30 352.0 325.4
6 21.4
281.2
291.0
7 21.6 288.4 291.8
8 25.2 292.8 306.2
9 37.2 358.0 354.2
10 14.4 263.2 263.0
11 15 272.4 265.4
12 22.4 291.2 295.0
13 23.9 299.6 301.0
14 26.6 307.6 311.8
15 30.7 320.4 328.2
Yi
265.2
279.6
What is the estimate of the standard deviation of the estimated slope (sb)?
311.2
328.0
352.0
Total
281.2
288.4
292.8
358.0
263.2
272.4
291.2
299.6
307.6
320.4
4,510.80
Estimations
The coefficient of determination (²) is
O 175.3 to 216.1
O 179.6 to 211.8
O245.0 to 285.8
FiYi
3,182.40
5,647.92
2,860.1
1,422.8
1,247.5
32.7
8,402.40
9,840.00
10,560.00
6,017.68
6,229.44
7,378.56
13,317.60
3,790.08
4,086.00
6,522.88
7,160.44
8,182.16
9,836.28
Can the assessor reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., B=0) between the price and size
variables? (Hint: to.025,13 = 2.160)
0.1
122.8
755.2
Complete the following worksheet and then use it to calculate the coefficient of determination.
(9,- y)²
(3k - y)²
(Yi - y)²
2,239.2
139.2
1,261.7
210.8
43.6
446.1
160.8
4.8
109.8
609.1
6.8
744.2
609.1
2,629.6
94.5
79.6
30.0
707.6
96.0
11.6
1,²
144.00
408.04
729.00
900.00
900.00
457.96
466.56
635.04
1,383.84
207.36
179.6
14.4
0.0
49.0
14.4
225.00
501.76
571.21
707.56
942.49
9,179.82
2.0
17.6
60.8
381.0
151.8
62.7
3/²
70,331.04
78,176.16
96,845.44
107,584.00
123,904.00
79,073.44
83,174.56
85,731.84
3,281.0
1,407.8
802.0
128,164.00
69,274.24
90.6
1.3
47.3
387.3
74,201.76
84,797.44
89,760.16
94,617.76
102,656.16
▼by $
The F-ratio is
▼, which means that the assessor
relationship between the selling price and the area of the house. (Hint: The critical value of F0.05,1,13 is 4.67.)
▼reject, at the 5% level of significance, the null hypothesis that there is no
Which of the following is an approximate 95% prediction interval for the selling price of a house having an area (size) of 15 (hundred) square feet?
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