The accompanying data file contains 40 observations on the response variable y along with the predictor variables x and d. Consider two linear regression models where Model 1 uses the variables x and dand Model 2 extends the model by including the interaction variable xd. Use the holdout method to compare the predictability of the models using the first 30 observations for training and the remaining 10 observations for validation. Click here for the Excel Data File a-1. Use the training set to estimate Models 1 and 2. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Constant X d xd RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round intermediate calculations and round final answers to 2 decimal places. Model 1 (No interaction) Model 2 2.00 4.18 41.52 0.00 9.01 because its RMSE is Model 2 (Interaction) a-3. Which model is better for making predictions? Model 2 (Interaction) lower 0.05 2.05 -5.37 3.02 8.32 4 A 1 y 2 70 3 102 4 76 2 5 83 = 6 61 7 62 2 8 67 908 9 98 77 10 84 11 101 12 51 13 108 14 32 - 15 71 16 101 17 90 18 112 19 88 20 110 21 95 21 93 22 44 2244 23 51 2 pl 24 112 25 113 113 26 52 34 27 59 28 100 20 30 29 78 30 00 30 90 31 59 32 59 33 53 24 110 34 119 35 109 36 68 37 104 38 45 39 67 40 65 41 74 42 43 44 X 11 19 12 14 17 17 12 13 20 20 15 16 11 15 16 20 16 = 13 " 15 17 15 19 13 18 17 14 14 19 19 17 * 17 * 13 115 10 16 14 77 16 77 16 7 15 Ar 15 77 19 7 18 11 19 18 17 15 14 B d 1 1 1 1 5 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 1 0 0 10 |1 с
The accompanying data file contains 40 observations on the response variable y along with the predictor variables x and d. Consider two linear regression models where Model 1 uses the variables x and dand Model 2 extends the model by including the interaction variable xd. Use the holdout method to compare the predictability of the models using the first 30 observations for training and the remaining 10 observations for validation. Click here for the Excel Data File a-1. Use the training set to estimate Models 1 and 2. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Constant X d xd RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round intermediate calculations and round final answers to 2 decimal places. Model 1 (No interaction) Model 2 2.00 4.18 41.52 0.00 9.01 because its RMSE is Model 2 (Interaction) a-3. Which model is better for making predictions? Model 2 (Interaction) lower 0.05 2.05 -5.37 3.02 8.32 4 A 1 y 2 70 3 102 4 76 2 5 83 = 6 61 7 62 2 8 67 908 9 98 77 10 84 11 101 12 51 13 108 14 32 - 15 71 16 101 17 90 18 112 19 88 20 110 21 95 21 93 22 44 2244 23 51 2 pl 24 112 25 113 113 26 52 34 27 59 28 100 20 30 29 78 30 00 30 90 31 59 32 59 33 53 24 110 34 119 35 109 36 68 37 104 38 45 39 67 40 65 41 74 42 43 44 X 11 19 12 14 17 17 12 13 20 20 15 16 11 15 16 20 16 = 13 " 15 17 15 19 13 18 17 14 14 19 19 17 * 17 * 13 115 10 16 14 77 16 77 16 7 15 Ar 15 77 19 7 18 11 19 18 17 15 14 B d 1 1 1 1 5 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 1 0 0 10 |1 с
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 accompanying data file contains 40 observations on the response variable y along with the predictor variables x and d. Consider
two linear regression models where Model 1 uses the variables x and dand Model 2 extends the model by including the interaction
variable xd. Use the holdout method to compare the predictability of the models using the first 30 observations for training and the
remaining 10 observations for validation.
Click here for the Excel Data File
a-1. Use the training set to estimate Models 1 and 2.
Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.
Predictor Variable Model 1 (No interaction)
Constant
X
d
xd
RMSE
a-2. Calculate the RMSE of the two models in the validation set.
Note: Do not round intermediate calculations and round final answers to 2 decimal places.
Model 2
2.00
4.18
41.52
0.00
Model 1 (No interaction) Model 2 (Interaction)
9.01
because its RMSE is
Model 2 (Interaction)
a-3. Which model is better for making predictions?
lower
0.05
2.05
-5.37
3.02
8.32
1
1 1 y
2 70
3 102
4
4 76
5 83
585
6
6
7 62
02
61
01
8 67
or
990
9
98
10 84
11 101
12 51
13 108
122
14 32
15 71
16 101
17 90
18 112
19 88
20 110
21 95
2
22 44
23 51
24 112
25 113
26 52
27 59
28 100
28 100
29 78
29
78
30 00
30 90
21 50
31 59
22 50
32 59
4 34
33 53
24 119
34 119
35 109
35 109
25 50
36
68
27 104
| 37 104
38 45
39 67
40 65
41 74
42
43
44
45
A
X
11
19
12
14
17
13
129
20
16
tu
|11
15
15
16
16
tre
13
15
17
15
19
13
18
17
14
19
17
17
13
10
16
14
14
1
16
16
16
15
15
15
10
19
18
11
11
10
19
18
17
15
14
B
d
1
1
E
1
F
1
lo
0
To 0
0
1
1
1
10
1
0
1
1
1
=
1
5
1
5
1
1
0
0
1
1
0
1
1
1
1
0
0
0
1
1
0
1
0
0
0
1
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 4 steps
Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman