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The accompanying data file contains salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and O otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. Click here for the Excel Data File -1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. Note: Negative values should be Indicated by a minus sign. Round your answers to 2 decimal places. Model 2 (Interaction) Predictor Variable Model 1 (No interaction) Constant SMI White BMIX White -0.98 -0.73

The accompanying data file contains salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and O otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. Click here for the Excel Data File -1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. Note: Negative values should be Indicated by a minus sign. Round your answers to 2 decimal places. Model 2 (Interaction) Predictor Variable Model 1 (No interaction) Constant SMI White BMIX White -0.98 -0.73

MATLAB: An Introduction with Applications
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 salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and O otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. Click here for the Excel Data File a-1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Model 2 (Interaction) Constant BMI White BMI X White RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round Intermediate calculations and round your final answers to 2 decimal places. Model 1 -0.96 a-3. Which model is better for making predictions? Model 1 (No interaction) Model 2 (Interaction) Average RMSE because its RMSE is -0.73 Tower b-1. Use the k-fold method, with k= 3, to calculate the average RMSE of the two models. Note: Do not round Intermediate calculations and round your final answers to 2 decimal places. Model 1 (No interaction) Model 2 (Interaction) A1 A 1 Salary 2 34 3 43 4 44 5 34 6 34 7 53 8 42 9 37 10 35 11 46 12 46 13 34 14 45 15 33 16 25 17 32 18 46 19 31 20 54 21 37 22 42 23 38 24 45 25 38 26 41 27 39 28 50 29 41 30 38 31 45 32 33 34 35 36 די BMI 33 26 26 35 36 20 20 26 28 22 20 35 26 34 29 30 18 31 19 23 B 27 30 18 26 28 28 20 26 28 21 X✓ fx Salary с White 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 + ⠀ D
Transcribed Image Text:The accompanying data file contains salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and O otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. Click here for the Excel Data File a-1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Model 2 (Interaction) Constant BMI White BMI X White RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round Intermediate calculations and round your final answers to 2 decimal places. Model 1 -0.96 a-3. Which model is better for making predictions? Model 1 (No interaction) Model 2 (Interaction) Average RMSE because its RMSE is -0.73 Tower b-1. Use the k-fold method, with k= 3, to calculate the average RMSE of the two models. Note: Do not round Intermediate calculations and round your final answers to 2 decimal places. Model 1 (No interaction) Model 2 (Interaction) A1 A 1 Salary 2 34 3 43 4 44 5 34 6 34 7 53 8 42 9 37 10 35 11 46 12 46 13 34 14 45 15 33 16 25 17 32 18 46 19 31 20 54 21 37 22 42 23 38 24 45 25 38 26 41 27 39 28 50 29 41 30 38 31 45 32 33 34 35 36 די BMI 33 26 26 35 36 20 20 26 28 22 20 35 26 34 29 30 18 31 19 23 B 27 30 18 26 28 28 20 26 28 21 X✓ fx Salary с White 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 + ⠀ D
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