Upload the Neth.CSV data file to google colab. Estimate a linear regression model of number of weekly trips per household as a function of the remaining variables (to the exte
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Get it written →Upload the Neth.CSV data file to google colab. Estimate a linear regression model of number of weekly trips per household as a function of the remaining variables (to the extent possible). In the final model identify which variables contribute to an increase in number of weekly trips and which variables contribute to a decrease in the number of weekly trips.
In estimating a linear regression model these are the steps I want you to do:
- Run multiple linear regression by including all variables
- Check for multi-collinearity and remove appropriate variables.
- Check if all variables are significant and remove the variables which are not significant one by one.
- Report the R2 and Adjusted R2 of the final model
- Check if the mean of the residuals is close to zero. Comment on the mean.
- Plot the histogram of the standardized residuals. Comment on whether the standardized residuals look normal.
- Check for outliers.
- Plot the residuals vs fitted values and comment.
The variable definitions are:
• HHSIZE household size
• NCAR number of cars in household
• HEMPSTS number of workers in household
• HSTUDEN number of students in household
• HTTRPS number of weekly trips per household
• NUCHLT12 number of children < 12 years in household
• CITY household residence in city (dummy variable)
• SUBURB household residence in suburb (dummy variable) • RURAL household residence in rural area (dummy variable)• INCOME continuous household income value
• NUCHGT12 number of children >= 12 yrs in household
CE 316: Applied Probability and Statistics in CEE
Homework 8
Due: December 5 at 10:00 PM
Problem 1:
Upload the Neth.CSV data file to google colab. Estimate a linear regression model of number of weekly trips per household as a function of the remaining variables (to the extent possible). In the final model identify which variables contribute to an increase in number of weekly trips and which variables contribute to a decrease in the number of weekly trips.
In estimating a linear regression model these are the steps I want you to do:
• Run multiple linear regression by including all variables
• Check for multi-collinearity and remove appropriate variables.
• Check if all variables are significant and remove the variables which are not significant one by one.
• Report the R2 and Adjusted R2 of the final model
• Check if the mean of the residuals is close to zero. Comment on the mean.
• Plot the histogram of the standardized residuals. Comment on whether the standardized residuals look normal.
• Check for outliers.
• Plot the residuals vs fitted values and comment.
The variable definitions are:
• HHSIZE household size
• NCAR number of cars in household
• HEMPSTS number of workers in household
• HSTUDEN number of students in household
• HTTRPS number of weekly trips per household
• NUCHLT12 number of children < 12 years in household
• CITY household residence in city (dummy variable)
• SUBURB household residence in suburb (dummy variable)
• RURAL household residence in rural area (dummy variable)
• INCOME continuous household income value
• NUCHGT12 number of children >= 12 yrs in household
8-1
,
| hhsize | ncar | hempsts | hstuden | httrps | nuchlt12 | city | suburb | rural | income | nuchgt12 |
| 1 | 4 | 1 | 0 | 18 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 1 | 0 | 31 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 3 | 0 | 1 | 0 | 55 | 1 | 1 | 0 | 0 | 43000 | 0 |
| 1 | 0 | 1 | 0 | 38 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 0 | 22 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 1 | 0 | 34 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 1 | 0 | 13 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 1 | 0 | 25 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 1 | 0 | 20 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 2 | 0 | 0 | 0 | 8 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 3 | 0 | 0 | 0 | 18 | 2 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 10 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 22 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 0 | 50 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 1 | 0 | 12 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 0 | 1 | 32 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 9 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 1 | 0 | 18 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 1 | 0 | 38 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 1 | 0 | 47 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 22 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 1 | 0 | 30 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 2 | 0 | 0 | 0 | 29 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 0 | 1 | 29 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 11 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 1 | 41 | 1 | 1 | 0 | 0 | 12000 | 0 |
| 4 | 0 | 0 | 0 | 47 | 2 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 0 | 0 | 16 | 0 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 0 | 1 | 24 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 5 | 0 | 0 | 0 | 19 | 4 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 15 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 24 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 3 | 0 | 1 | 0 | 35 | 1 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 1 | 0 | 24 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 6 | 0 | 1 | 4 | 52 | 0 | 1 | 0 | 0 | 31000 | 4 |
| 1 | 0 | 0 | 1 | 22 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 14 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 8 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 23 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 17 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 46 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 1 | 0 | 16 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 0 | 1 | 48 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 48 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 29 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 26 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 28 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 2 | 0 | 2 | 0 | 18 | 0 | 1 | 0 | 0 | 31000 | 0 |
| 1 | 0 | 0 | 1 | 25 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 24 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 38 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 22 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 3 | 0 | 0 | 2 | 45 | 0 | 0 | 1 | 0 | 12000 | 2 |
| 1 | 0 | 0 | 0 | 20 | 0 | 0 | 1 | 0 | 20500 | 0 |
| 1 | 0 | 0 | 0 | 14 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 8 | 0 | 1 | 3 | 109 | 3 | 0 | 1 | 0 | 31000 | 3 |
| 2 | 0 | 0 | 0 | 46 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 2 | 0 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 2 | 0 | 0 | 0 | 12 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 3 | 0 | 2 | 0 | 102 | 0 | 0 | 1 | 0 | 20500 | 1 |
| 1 | 0 | 1 | 0 | 30 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 2 | 0 | 0 | 0 | 24 | 0 | 0 | 1 | 0 | 31000 | 1 |
| 1 | 0 | 0 | 0 | 15 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 3 | 0 | 2 | 0 | 24 | 1 | 0 | 1 | 0 | 31000 | 0 |
| 3 | 0 | 2 | 0 | 54 | 0 | 0 | 1 | 0 | 31000 | 1 |
| 7 | 0 | 2 | 2 | 70 | 2 | 0 | 1 | 0 | 31000 | 3 |
| 1 | 0 | 0 | 0 | 11 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 1 | 0 | 0 | 0 | 16 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 12 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 7 | 0 | 0 | 0 | 65 | 5 | 0 | 1 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 0 | 29 | 0 | 0 | 1 | 0 | 31000 | 0 |
| 3 | 0 | 2 | 0 | 38 | 0 | 0 | 1 | 0 | 43000 | 1 |
| 1 | 0 | 0 | 0 | 12 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 20 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 24 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 0 | 15 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 2 | 0 | 2 | 0 | 45 | 0 | 0 | 1 | 0 | 12000 | 0 |
| 1 | 0 | 0 | 1 | 29 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 2 | 0 | 0 | 2 | 62 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 5 | 0 | 1 | 1 | 55 | 2 | 1 | 0 | 0 | 20500 | 1 |
| 4 | 0 | 0 | 1 | 47 | 2 | 1 | 0 | 0 | 20500 | 0 |
| 1 | 0 | 0 | 0 | 29 | 0 | 1 | 0 | 0 | 12000 | 0 |
| 6 | 0 | 0 | 1 | 90 | 3 | 1 | 0 | 0 | 1200
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