Best Practices in Logistic Regression
Best Practices in Logistic Regression
复制标题
逻辑回归的最佳实践
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
J. Osborne
中科院分区：
文献类型：
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作者：
J. Osborne
1. 二元逻辑回归的概念性介绍
2. 深入了解逻辑回归
3. 进行简单逻辑回归
4. 逻辑回归中检验假设和清理数据的概念与实践介绍
5. 逻辑回归中的连续变量（以及为什么不应该将它们转换为分类变量！）
6. 处理逻辑回归中无序分类预测变量
7. 逻辑回归中的曲线效应
8. 逻辑回归中的多个预测变量（包括交互效应）
9. 概率单位回归简述
10. 逻辑回归与重复：样本量、波动性的故事，以及为什么重采样无法挽救有偏差的样本，但数据清理和独立重复可以
11. 逻辑回归分析中的缺失数据、样本量、功效和普遍性
12. 多项逻辑回归和有序逻辑回归：对具有两个以上类别的因变量进行建模
13. 具有二元结果的分层线性模型：多层逻辑回归
1. A Conceptual Introduction to Bivariate Logistic Regression 2. Under the Hood with Logistic Regression 3. Performing Simple Logistic Regression 4. Conceptual and Practical Introduction to Testing Assumptions and Cleaning Data for Logistic Regression 5. Continuous Variables In Logistic Regression (And Why You Should Not Convert Them To Categorical Variables!) 6. Dealing with Unordered Categorical Predictors in Logistic Regression 7. Curvilinear Effects in Logistic Regression 8. Multiple Predictors in Logistic Regression (Including Interaction Effects) 9. A Brief Overview of Probit Regression 10. Logistic Regression and Replication: A Story Of Sample Size, Volatility, and Why Resampling Cannot Save Biased Samples but Data Cleaning And Independent Replication Can 11. Missing Data, Sample Size, Power, and Generalizability of Logistic Regression Analyses 12. Multinomial and Ordinal Logistic Regression: Modeling Dependent Variables with More Than Two Categories 13. Hierarchical Linear Models with Binary Outcomes: Multilevel Logistic Regression