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.
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
c and d
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.
Transcribed Image Text:B
D
E
G
H
K
M
1 Square Feet Age of the Home Sales Price
Square Feet Residual Plot
Square Feet Line Fit Plot
2
1,610
70
227,900
3
2,146
284,900
149,900
59
100000
800,000
4
816
70
700,000
50000
600,000
5
2,183
48
309,900
500,000
6
1,046
64
134,900
400,000
5,183
21
440,000
1,000
2,000
3,000
4,000
5,000
6.000
300,000
-50000
8
1,150
62
150,000
200,000
100,000
9
1,068
70
154,900
-100000
10
5,570
50
700,000
-150000
1,000
2,000
3,000
4,000
5,000
6,000
11
2,449
53
257,000
Square Feet
12
1,950
59
239,900
Square Feet
2,630
2,732
13
73
349,900
14
20
339,900
Age of the Home Residual Plot
15
1,908
Age of the Home Line Fit Plot
289,000
399,900
46
100000
16
3,666
17
800,000
17
1,878
19
290,000
50000
18
2,172
62
278,000
600,000
19
20 MMARY OUTPUT
10
20
30
40
50
60.
70
80
400,000
-50000
21
200,000
-100000
ression Statistics
23 Multiple R
0.94020507
-150000
10
20
30
40
50
60
70
80
Age of the Home
R Square
25 ljusted R Squa 0.867412084
26 tandard Erro
24
0.883985573
Age of the Home
49698.41817
27 Observations
17
28
Normal Probability Plot
29
ANOVA
800000
30
df
S
MS
Significance F
31 Regression
2
2.63479E+11 1.3174E+11
53.33732344
2.82868E-07
600000
32
Residual
14
34579058765 2469932769
400000
33
Total
16
2.98058E+11
34
200000
Upper 95%
159992.9769
Standard Error
t Stat
Lower 95.0%
-86656.451
35
Coefficients
P-value
Lower 95%
Upper 95.0%
36
Intercept
36668.26295
57499.75727 0.63771161
0.533953161
-86656.451
159992.9769
20
40
60
80
100
120
37 Square Feet
38 ge of the Hon
99.16082372
10.96828321 9.04068776
3.20882E-07
75.6361959
122.6854515
75.6361959
122.6854515
Sample Percentile
452.6516011
747.433283 0.60560803
0.554466659
-1150.433354
2055.736556
-1150.433354
2055.736556
39
40
41
42 SIDUAL OUTPUT
PROBABILITY OUTPUT
43
44 Observation redicted Sales Pric
Residuals
indard Residuals
Percentile
Sales Price
45
1
228002.8012
-102.801216 -0.0022113
2.941176471
134900
46
2
276173.8351
8726.164883 0.18770542
8.823529412
149900
47
3
149269.1072
630.8928168 0.01357091
14.70588235
150000
48
4
274863.618
35036.38202 0.75365513
20.58823529
154900
49
5
169360.187
-34460.187
-0.7412608
26.47058824
227900
50
6
560124.4959
-120124.496
-2,5839552
32.35294118
239900
51
7
178767.6095
-28767.6095
-0.6188098
38.23529412
257000
52
8
174257.6348
-19357.6348
-0.4163952
44.11764706
278000
53
9
611626.6311
88373.36888 1.90096805
50
284900
54
10
303503.6551
-46503.6551 -1.0003236
55.88235294
289000
55
11
256738.3137
-16838.3137
-0.362203
61.76470588
290000
56
12
330504.7962
19395.20379 0.41720332
67.64705882
309900
57
13
316628.6654
23271.33463 0.50058139
73.52941176
339900
58
14
246689.0883
42310.91174 0.91013495
79.41176471
349900
59
15
407886.9199
-7986.91992
-0.1718038
85.29411765
399900
60
16
231492.6703
58507.32968 1.25853032
91.17647059
440000
61
17
280109.9713
-2109.97134 -0.0453868
97.05882353
700000
62
Residuals
Sales Price
Sales Price
Sales Price
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