query: Latent variables motivation for ordinal and binary logistic regression
positive_passages.docid: 105703
positive_passages.text: How is Logistic Regression related to Logistic Distribution?
negative_passages.docid: 111321
negative_passages.text: When should we discretize/bin continuous independent variables/features and when should not?
negative_passages.docid: 26034
negative_passages.text: Choosing variables to include in a multiple linear regression model
negative_passages.docid: 76276
negative_passages.text: Logistic Regression: Bernoulli vs. Binomial Response Variables
negative_passages.docid: 64602
negative_passages.text: Logistic regression: class probabilities
negative_passages.docid: 109822
negative_passages.text: Problem understanding the logistic regression link function
negative_passages.docid: 2074
negative_passages.text: Multinomial logistic regression vs one-vs-rest binary logistic regression
negative_passages.docid: 710
negative_passages.text: Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
negative_passages.docid: 22005
negative_passages.text: Is the logit function always the best for regression modeling of binary data?
negative_passages.docid: 144448
negative_passages.text: Understanding lack of fit in logistic regression
negative_passages.docid: 9119
negative_passages.text: Multiple logistic regression power analysis
negative_passages.docid: 138321
negative_passages.text: Intuition behind logistic regression
negative_passages.docid: 71272
negative_passages.text: Scoring predictions of an ordinal variable
negative_passages.docid: 147994
negative_passages.text: Bayesian logit model - intuitive explanation?
negative_passages.docid: 91776
negative_passages.text: Understanding which features were most important for logistic regression
negative_passages.docid: 133414
negative_passages.text: Inter-rater reliability for ordinal or interval data
negative_passages.docid: 58384
negative_passages.text: Explaining quantile regression to nonstatisticians