I\'m looking to improve the AIC of this Logistic Regression Model below by remov

Question

I'm looking to improve the AIC of this Logistic Regression Model below by removing and keeping variables. Which ones should stay and which ones would go? Why?

The Canadian Immigration and Refugee Board decide whether to allow or deny refugee status to refugee claimants. Claimants who have been denied refugee status may ask the Federal Court of Appeal for permission to appeal the negative ruling. A judge then either gives or denies leave to appeal the ruling. Imagine that you are an immigration and refugee activist and that you have collected the following data on cases requesting leave to appeal the negative ruling of the Board. The data is as follows: judge: Names of judge hearing case. A factor with levels: Desjardins, Heald, Hugessen, Iacobucci, MacGuigan, Mahoney, Marceau, Pratte, Stone, and Urie. merit: Judgment of merit of the case by an independent (not the judge) rater. A factor with levels: no, case has no merit yes, case has some merit (leave to appeal should be granted). decision: Judge's decision. A factor with levels: no, leave to appeal not granted; yes, leave to appeal granted language: Language of case. A factor with levels: English, French. location: Location of original refugee claim. A factor with levels: Montreal, other, Toronto success: success rate, for all cases from the applicant's originating nation. . Suppose that you have run a logistic regression model with decision as the target variable, with the model developed so that the probability of "yes" is modeled (Prob l is a certainty of a "yes" decision). The following output is generated: Coefficients: 0.68266 0.53655 0.5289 0.72730 0.51916 1.36324 0.446962 0.011062 0.004630 0.000205 0.005280** 0.115413 0.072435 0.000779*** 0.002832* 0.924373 0.747785 0.077981 judgeHeald judgelacobucci judgeMacGuigan 1.28781 1.07194 2.00107 1.66145 -0.07157 -0.19384 0.5348 0.59673 0.59556 0.55652 0.75393 0.60281 0.67761 0.60813 0.27475 0.30155 judgePratte judgeStone 5 2 5 locationOther locationToronto meritYes 0.94914 3 3 3.16e-07** 1.60878

Explanation / Answer

These are the variables that should stay in the model according to the logistic regression output which has been provided here - judgeHeald, judgeHugessen, judgeIacobucci, judgeMacGuigan, judgePratte, judgeStone, meritYes and success.
Remaining variables can be dropped from the model in order to improve the AIC.
The reason is that the variables, which are staying in the model, have p-values less than alpha = 0.05 (level of significance). These values have been provided in the Pr(>|z|) column in the output. So, by observing these values and keeping in mind the cutoff, I prepared the list of variables which are to be kept.

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