I ran a logit model using statsmodel api
available in Python. I have few questions on how to make sense of these
1) What's the difference between summary
and summary2
output?
2) Why is the AIC
and BIC
score in the range of 2k-3k? I read online that lower values of AIC
and BIC
indicates good model. Is my model doing good? Is there any optimal range for AIC
and BIC
?
3) As you can see covariance Type
is non-robust
. What is it and should I be concerned about it?
4) Is there any other field/item in the output
that I should pay attention to?
5) You can see below how certain significant variables like X2
,X8
,X45
have very low coefficients. How can they be significant and still have very low or near to zero coefficient? Is it normal?
This is the output that I got
Summary output
Dep. Variable: vae_flag No. Observations: 3298
Model: Logit Df Residuals: 3241
Method: MLE Df Model: 56
Date: Mon, 30 Dec 2019 Pseudo R-squ.: 0.3347
Time: 21:18:36 Log-Likelihood: -1392.2
converged: True LL-Null: -2092.7
Covariance Type: nonrobust LLR p-value: 3.894e-256
Summary2 output
Model: Logit Pseudo R-squared: 0.335
Dependent Variable: op_flag AIC: 2898.4259
Date: 2019-12-30 21:18 BIC: 3246.1870
No. Observations: 3298 Log-Likelihood: -1392.2
Df Model: 56 LL-Null: -2092.7
Df Residuals: 3241 LLR p-value: 3.8937e-256
Converged: 1.0000 Scale: 1.0000
No. Iterations: 7.0000
Significant variables
coef std err z P>|z| [0.025 0.975]
x2 0.0321 0.060 11.227 0.000 0.558 0.794
x6 2.2996 0.095 24.332 0.000 2.114 2.485
x7 -1.8795 0.082 -22.835 0.000 -2.041 -1.718
x8 0.0002 0.058 2.116 0.034 0.009 0.237
x16 0.2693 0.059 4.564 0.000 0.154 0.385
x33 -0.3138 0.139 -2.254 0.024 -0.587 -0.041
x34 0.4644 0.137 3.392 0.001 0.196 0.733
x45 0.0088 0.052 2.306 0.021 0.018 0.221
x52 -0.1755 0.087 -2.007 0.045 -0.347 -0.004
x55 -0.0982 0.050 -1.965 0.049 -0.196 -0.000