QUESTION 3 STA4820/014/0/2024 (24) The Chief executive officer (CEO) of a company that manufactures drywall wants to analyse the variables that affect demand for his product. Drywall is used to construct walls in houses and offices. Consequently, the CEO decides to develop a regression model in which the dependent variable is monthly sales of drywall (in hundreds of 4 × 8 sheets) and the independent variables are as follows: Five-year bond rates (Bond in percentage points), Vacancy rate in apartments (A Vacancy in percentage points), Vacancy rate in office buildings (O Vacancy in percentage points). To fit a multiple regression model, he used monthly observations from the past 2 years (in Table 4). Table 4 Drywall Permits Bond A Vacancy O Vacancy 328 49 8.35 2.98 13.43 376 79 8.08 5.6 14.51 373 79 7.9 2.25 14.24 144 50 7.69 4.26 14.3 194 37 7 2.6 11.64 220 53 7.32 2.97 10.61 126 22 8.4 5.35 18.45 301 69 8.28 3.13 18.52 54 21 8 5.6 10.29 252 46 8.95 4.81 11.91 381 79 8.21 5.88 17.75 173 30 7.24 2.98 18.16 152 38 7.35 5.69 17.14 351 73 7.27 4.86 16.11 233 55 7.08 5.68 18.54 35 12 7.76 4.46 19.46 290 62 8.21 2.23 19.26 5 12 7.76 5 17.28 335 60 7.2 2.42 15.15 280 49 7.57 3.25 19.94 101 14 8.44 3.61 15.47 297 66 8.43 2.13 12.75 309 62 8.14 4.35 12.24 233 40 8.81 2.31 18.65 a. Find the regression equation. b. (1) What is the estimate sigma (square root of the error variance)? Can you use this statistic to assess the model's goodness of fit? If so, how? ༅སེ་༠ (2) C. What is the coefficient of determination, and what does it tell you about the regression model's goodness of fit? (3) d. Test the goodness of fit of the model? (3) 5
QUESTION 3 STA4820/014/0/2024 (24) The Chief executive officer (CEO) of a company that manufactures drywall wants to analyse the variables that affect demand for his product. Drywall is used to construct walls in houses and offices. Consequently, the CEO decides to develop a regression model in which the dependent variable is monthly sales of drywall (in hundreds of 4 × 8 sheets) and the independent variables are as follows: Five-year bond rates (Bond in percentage points), Vacancy rate in apartments (A Vacancy in percentage points), Vacancy rate in office buildings (O Vacancy in percentage points). To fit a multiple regression model, he used monthly observations from the past 2 years (in Table 4). Table 4 Drywall Permits Bond A Vacancy O Vacancy 328 49 8.35 2.98 13.43 376 79 8.08 5.6 14.51 373 79 7.9 2.25 14.24 144 50 7.69 4.26 14.3 194 37 7 2.6 11.64 220 53 7.32 2.97 10.61 126 22 8.4 5.35 18.45 301 69 8.28 3.13 18.52 54 21 8 5.6 10.29 252 46 8.95 4.81 11.91 381 79 8.21 5.88 17.75 173 30 7.24 2.98 18.16 152 38 7.35 5.69 17.14 351 73 7.27 4.86 16.11 233 55 7.08 5.68 18.54 35 12 7.76 4.46 19.46 290 62 8.21 2.23 19.26 5 12 7.76 5 17.28 335 60 7.2 2.42 15.15 280 49 7.57 3.25 19.94 101 14 8.44 3.61 15.47 297 66 8.43 2.13 12.75 309 62 8.14 4.35 12.24 233 40 8.81 2.31 18.65 a. Find the regression equation. b. (1) What is the estimate sigma (square root of the error variance)? Can you use this statistic to assess the model's goodness of fit? If so, how? ༅སེ་༠ (2) C. What is the coefficient of determination, and what does it tell you about the regression model's goodness of fit? (3) d. Test the goodness of fit of the model? (3) 5
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:QUESTION 3
STA4820/014/0/2024
(24)
The Chief executive officer (CEO) of a company that manufactures drywall wants to analyse the
variables that affect demand for his product. Drywall is used to construct walls in houses and offices.
Consequently, the CEO decides to develop a regression model in which the dependent variable is
monthly sales of drywall (in hundreds of 4 × 8 sheets) and the independent variables are as follows:
Five-year bond rates (Bond in percentage points), Vacancy rate in apartments (A Vacancy in
percentage points), Vacancy rate in office buildings (O Vacancy in percentage points). To fit a multiple
regression model, he used monthly observations from the past 2 years (in Table 4).
Table 4
Drywall Permits
Bond A Vacancy O Vacancy
328
49
8.35
2.98
13.43
376
79
8.08
5.6
14.51
373
79
7.9
2.25
14.24
144
50
7.69
4.26
14.3
194
37
7
2.6
11.64
220
53
7.32
2.97
10.61
126
22
8.4
5.35
18.45
301
69
8.28
3.13
18.52
54
21
8
5.6
10.29
252
46
8.95
4.81
11.91
381
79
8.21
5.88
17.75
173
30
7.24
2.98
18.16
152
38
7.35
5.69
17.14
351
73
7.27
4.86
16.11
233
55
7.08
5.68
18.54
35
12
7.76
4.46
19.46
290
62
8.21
2.23
19.26
5
12
7.76
5
17.28
335
60
7.2
2.42
15.15
280
49
7.57
3.25
19.94
101
14
8.44
3.61
15.47
297
66
8.43
2.13
12.75
309
62
8.14
4.35
12.24
233
40
8.81
2.31
18.65
a.
Find the regression equation.
b.
(1)
What is the estimate sigma (square root of the error variance)? Can you use this statistic to
assess the model's goodness of fit? If so, how?
༅སེ་༠
(2)
C.
What is the coefficient of determination, and what does it tell you about the regression model's
goodness of fit?
(3)
d.
Test the goodness of fit of the model?
(3)
5
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