The accompanying data file contains 40 observations on the response variable y along with the predictor variables x1 and x2. Use the holdout method to compare the predictability of the linear model with the exponential model using the first 30 observations for training and the remaining 10 observations for validation. y x1 x2 533.86 20 30 104.84 15 20 64.89 20 23 159.61 16 21 43.06 13 16 4.27 13 13 736.56 15 30 64.89 20 23 10.64 20 22 76.90 18 20 4.89 11 13 80.90 11 16 224.17 12 19 45.75 16 25 8.13 17 17 319.97 13 30 48.61 19 25 564.67 12 27 111.87 11 25 152.39 13 24 13.34 18 14 28.80 15 22 37.56 13 15 105.62 17 26 44.05 18 21 451.65 17 28 10.34 18 21 32.70 12 13 19.21 14 12 14.02 15 16 2.45 16 12 2.48 20 15 50.34 17 21 29.31 17 20 33.75 16 12 196.28 17 29 943.12 13 30 7.25 10 12 89.73 15 25 32.91 12 18 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. 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.
The accompanying data file contains 40 observations on the response variable y along with the predictor variables x1 and x2. Use the holdout method to compare the predictability of the linear model with the exponential model using the first 30 observations for training and the remaining 10 observations for validation. y x1 x2 533.86 20 30 104.84 15 20 64.89 20 23 159.61 16 21 43.06 13 16 4.27 13 13 736.56 15 30 64.89 20 23 10.64 20 22 76.90 18 20 4.89 11 13 80.90 11 16 224.17 12 19 45.75 16 25 8.13 17 17 319.97 13 30 48.61 19 25 564.67 12 27 111.87 11 25 152.39 13 24 13.34 18 14 28.80 15 22 37.56 13 15 105.62 17 26 44.05 18 21 451.65 17 28 10.34 18 21 32.70 12 13 19.21 14 12 14.02 15 16 2.45 16 12 2.48 20 15 50.34 17 21 29.31 17 20 33.75 16 12 196.28 17 29 943.12 13 30 7.25 10 12 89.73 15 25 32.91 12 18 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. 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.
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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The accompanying data file contains 40 observations on the response variable y along with the predictor variables x1 and x2. Use the holdout method to compare the predictability of the linear model with the exponential model using the first 30 observations for training and the remaining 10 observations for validation.
| y | x1 | x2 |
| 533.86 | 20 | 30 |
| 104.84 | 15 | 20 |
| 64.89 | 20 | 23 |
| 159.61 | 16 | 21 |
| 43.06 | 13 | 16 |
| 4.27 | 13 | 13 |
| 736.56 | 15 | 30 |
| 64.89 | 20 | 23 |
| 10.64 | 20 | 22 |
| 76.90 | 18 | 20 |
| 4.89 | 11 | 13 |
| 80.90 | 11 | 16 |
| 224.17 | 12 | 19 |
| 45.75 | 16 | 25 |
| 8.13 | 17 | 17 |
| 319.97 | 13 | 30 |
| 48.61 | 19 | 25 |
| 564.67 | 12 | 27 |
| 111.87 | 11 | 25 |
| 152.39 | 13 | 24 |
| 13.34 | 18 | 14 |
| 28.80 | 15 | 22 |
| 37.56 | 13 | 15 |
| 105.62 | 17 | 26 |
| 44.05 | 18 | 21 |
| 451.65 | 17 | 28 |
| 10.34 | 18 | 21 |
| 32.70 | 12 | 13 |
| 19.21 | 14 | 12 |
| 14.02 | 15 | 16 |
| 2.45 | 16 | 12 |
| 2.48 | 20 | 15 |
| 50.34 | 17 | 21 |
| 29.31 | 17 | 20 |
| 33.75 | 16 | 12 |
| 196.28 | 17 | 29 |
| 943.12 | 13 | 30 |
| 7.25 | 10 | 12 |
| 89.73 | 15 | 25 |
| 32.91 | 12 | 18 |
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.
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.
3.
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