Hello,
I am new to SAS, could you please help me how to generate exactly like in the below image results using proc logistic regression. I want to generate the odds ratio by categories in the variable and Confidence Intervals for the categories except the first category in the variables. I am trying to predict a binary outcome and need the odds ratio table exactly like below. Please can someone help me with it. Below is the sample dataset.
Target
Education
Health_insurance
Living_arrangement
Age
Sex
0
-0.08264447
-0.677142541
0.979974336
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
1.055157965
-0.385145422
3.014553778
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
0.915907427
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
1.055157965
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
1.055157965
-0.385145422
3.014553778
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
1
-0.57348838
1.055157965
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
1
-0.08264447
1.055157965
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
1
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
1.055157965
-0.385145422
-0.607323603
0.9539654
0
-0.57348838
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
0.915907427
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
1.055157965
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
3.014553778
-1.04615533
1
-0.08264447
-0.677142541
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
1.055157965
-0.385145422
3.014553778
0.9539654
1
-0.08264447
1.055157965
-0.385145422
3.014553778
-1.04615533
1
-0.08264447
1.055157965
-0.385145422
0.915907427
0.9539654
1
-0.08264447
1.055157965
0.979974336
-0.607323603
0.9539654
0
-0.08264447
1.055157965
-0.385145422
3.014553778
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
1.055157965
0.979974336
0.915907427
-1.04615533
1
-0.08264447
-0.677142541
0.979974336
-0.607323603
-1.04615533
1
8.99941821
5.578245149
6.983458922
-0.607323603
0.9539654
1
-0.57348838
1.055157965
0.979974336
0.915907427
0.9539654
1
-0.08264447
-0.677142541
0.979974336
-0.607323603
0.9539654
0
-0.08264447
1.055157965
0.979974336
-0.607323603
0.9539654
1
-0.08264447
-0.677142541
-0.385145422
3.014553778
0.9539654
1
-0.08264447
1.055157965
0.979974336
-0.607323603
0.9539654
1
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
1.055157965
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
0.979974336
-0.607323603
0.9539654
1
-0.08264447
1.055157965
0.979974336
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
0.979974336
0.915907427
0.9539654
1
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
1.055157965
-0.385145422
3.014553778
-1.04615533
1
-0.08264447
1.055157965
0.979974336
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
1
-0.08264447
-0.677142541
0.979974336
-0.607323603
-1.04615533
0
-0.08264447
1.055157965
-0.385145422
0.915907427
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
1
-0.08264447
-0.677142541
-0.385145422
-0.607323603
0.9539654
0
-0.08264447
-0.677142541
-0.385145422
-0.607323603
-1.04615533
1
-0.57348838
-0.677142541
0.979974336
-0.607323603
-1.04615533
0
-0.08264447
-0.677142541
-0.385145422
0.915907427
0.9539654
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