In: Statistics and Probability
Develop the best logistic regression model that can predict the
wage by using the
combination of any following variables: total unit (X2),
constructed unit (X3), equipment
used (X4), city location (X5) and total cost of a project (X6).
Make sure that you partition
your data with 60% training test, 40% validation test, and default
seed of 12345 before
running the logistic regression (15 points)
Wage - X1 | Total Unit - X2 | Contracted Units - X3 | Equipment Used - X4 | City Location - X5 | Total Cost - X6 |
0 | 50 | 5 | 2 | 1 | 83680 |
1 | 25 | 2 | 3 | 1 | 73604 |
0 | 55 | 1 | 2 | 1 | 101562 |
0 | 68 | 3 | 2 | 1 | 91055 |
1 | 35 | 3 | 2 | 1 | 41790 |
0 | 24 | 2 | 2 | 1 | 75770 |
1 | 12 | 2 | 4 | 1 | 37420 |
0 | 20 | 1 | 2 | 1 | 58000 |
1 | 48 | 2 | 2 | 1 | 97800 |
0 | 36 | 2 | 3 | 1 | 73960 |
0 | 40 | 1 | 2 | 1 | 98720 |
1 | 39 | 4 | 2 | 1 | 54190 |
0 | 26 | 1 | 1 | 1 | 67800 |
1 | 25 | 1 | 4 | 1 | 66760 |
0 | 70 | 3 | 3 | 2 | 88055 |
The attach images details solution solved in R-software given below.
Output:
1.
2
2.
3.
4.
The model is not well fitted because all the explonatory variables are insignificant at 5% of level of significance. i.e(p-value > alpha value)