我对来自Titanic数据集的变量执行了逻辑回归（使用“LOGIT”）。使用的公式是：
survived ~ age + sex + pclass
我得到的结果如下：
==================== Summary() ====================
Logit Regression Results
==============================================================================
Dep. Variable: survived No. Observations: 714
Model: Logit Df Residuals: 710
Method: MLE Df Model: 3
Date: Mon, 20 Jul 2020 Pseudo R-squ.: 0.3289
Time: 14:29:27 Log-Likelihood: -323.65
converged: True LL-Null: -482.26
Covariance Type: nonrobust LLR p-value: 1.860e-68
===============================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 5.0560 0.502 10.069 0.000 4.072 6.040
sex[T.male] -2.5221 0.207 -12.168 0.000 -2.928 -2.116
age -0.3693 0.076 -4.841 0.000 -0.519 -0.220
pclass -1.2885 0.139 -9.253 0.000 -1.561 -1.016
===============================================================================
==================== Summary2() ====================
Results: Logit
=================================================================
Model: Logit Pseudo R-squared: 0.329
Dependent Variable: survived AIC: 655.2909
Date: 2020-07-20 14:29 BIC: 673.5745
No. Observations: 714 Log-Likelihood: -323.65
Df Model: 3 LL-Null: -482.26
Df Residuals: 710 LLR p-value: 1.8597e-68
Converged: 1.0000 Scale: 1.0000
No. Iterations: 6.0000
------------------------------------------------------------------
Coef. Std.Err. z P>|z| [0.025 0.975]
------------------------------------------------------------------
Intercept 5.0560 0.5021 10.0692 0.0000 4.0719 6.0402
sex[T.male] -2.5221 0.2073 -12.1676 0.0000 -2.9284 -2.1159
age -0.3693 0.0763 -4.8415 0.0000 -0.5188 -0.2198
pclass -1.2885 0.1393 -9.2528 0.0000 -1.5615 -1.0156
=================================================================
编辑：我想用通俗的术语解释结果。我想确定随着每个预测变量的变化而变化的生存几率。为了澄清，我想知道：
与女性相比，男性存活的几率是多少？
人的年龄每增加 1 年，几率如何变化？
我知道这是一个非常基本的问题，但重要的是要有可靠的知识。