a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion. c. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? d. What percentage of the variation in the price is explained by these independent variables? Based on this, is a multiple linear regression model a good model for these data? Explain.
a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion. c. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? d. What percentage of the variation in the price is explained by these independent variables? Based on this, is a multiple linear regression model a good model for these data? Explain.
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
a and b
Transcribed Image Text:B
D
E
F
G
H
K
M
1
Sum of Bedrooms and Bathrooms Age of the Home Sales Price
Sum of Bedrooms and Bathrooms
Sum of Bedrooms and Bathrooms Line
2
5
70
227,900
284,900
149,900
Fit Plot
3
59
Residual Plot
4
4
70
400000
800,000
5
6.5
48
309,900
6
5.5
64
134,900
600,000
200000
440,000
150,000
7
10.5
21
8
4
62
400,000
9
70
154,900
10 •
12
200,000
10
50
700,000
-200000
11
6
53
257,000
239,900
349,900
Sum of Bedrooms and Bathrooms
12
7
59
2
4.
6
8
10
12
13
7.5
73
Sum of Bedrooms and Bathrooms
14
20
339,900
Age of the Home Residual Plot
7.5
15
46
289,000
400000
16
Age of the Home Line Fit Plot
6.5
17
399,900
300000
17
7
19
290,000
800,000
18
62
278,000
200000
700,000
19
100000
600,000
20
SUMMARY OUTPUT
500,000
21
20
70
400,000
10
30
40
50
80
-100000
Regression Statistics
Multiple R
R Square
Adjusted R Square
22
300,000
23
0.621120988
-200000
200,000
Age of the Home
100,000
24
0.385791282
25
0.298047179
10
20
30
40
50
60
70
80
26
Standard Error
114352.208
Age of the Home
27
Observations
17
28
29
ANOVA
Normal Probability Plot
30
df
MS
Significance F
F
31
Regression
2
1.14988E+11 57494139965
4.396777334
0.032977053
800000
32
Residual
14
1.8307E+11 13076427484
700000
600000
33
Total
16
2.98058E+11
500000
34
400000
Standard Error
P-value
0.746793667
Upper 95%
35
Coefficients
68063.34627
t Stat
Lower 95%
Lower 95.0%
Upper 95.0%
300000
36
206682.4812 0.329313572
-375226.4881
-375226.4881
Intercept
37 Sum of Bedrooms and Bathrooms
Age of the Home
511353.1807
511353.1807
200000
44956.45037
22249.49977 2.020560051
0.062882492
-2763.980539
92676.88129
-2763.980539
92676.88129
100000
38
-1125.62813
1767.438606 -0.63686972
0.534485638
-4916.406924
2665.150663
-4916.406924
2665.150663
20
40
60
80
100
120
39
40
Sample Percentile
41
42
RESIDUAL OUTPUT
PROBABILITY OUTPUT
43
44
Observation
redicted Sales Pric Residuals andard Residuals
Percentile
Sales Price
45
1
214051.629
13848.37099 0.129464324
2.941176471
134900
46
2
271389.9888
13510.01118 0.126301098
8.823529412
149900
47
3
169095.1786
-19195.1786 -0.17945005
14.70588235
150000
48
4
306250.1234
3649.876563 0.034121616
20.58823529
154900
49
243283.623
-108383.623 -1.01324643
26.47058824
227900
50
6
516467.8845
-76467.8845 -0.71487563
32.35294118
239900
51
178100.2037
-28100.2037
-0.26270049
38.23529412
257000
52
8
214051.629
-59151.629
-0.55299108
44.11764706
278000
53
326477.0924
373522.9076 3.491955176
50
284900
54
10
278143.7576
-21143.7576 -0.19766674
55.88235294
289000
55
11
316346.4392
-76446.4392-0.71467515
61.76470588
290000
56
12
323065.8705
26834.12945 0.250864339
67.64705882
309900
57
13
382724.1615
-42824.1615 -0.40035042
73.52941176
339900
58
14
241066.7041
47933.29586 0.448114204
79.41176471
349900
59
15
341144.5955
58755.40452 0.549286897
85.29411765
399900
60
16
361371.5644
-71371.5644 -0.66723164
91.17647059
440000
61
17
312969.5548
-34969.5548 -0.32692002
97.05882353
700000
62
Residuals
Residuals
Sales Price
Sales Price
Sales Price
Transcribed Image Text:a. State the null and alternative hypothesis in this global test for linear model utility.
b. Give the p-value and your conclusion.
c. Conduct t-tests on each of the beta parameters. What is your conclusion in each case?
d. What percentage of the variation in the price is explained by these independent variables?
Based on this, is a multiple linear regression model a good model for these data? Explain.
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