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Everything about Logit
Function in statistics
In statistics, the logit or log-odds function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning, especially in data transformations. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: LogitA 5-minute orientation
- Skim a recent paper: Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approachInfluential work since 2022
- Pick up a classic book: Interpreting probability modelsThe most-reprinted book on the subject
- Read the landmark paper: Interaction terms in logit and probit modelsCited 4,605 times
- Go sideways: Logistic regressionA closely related topic
- Go sideways: ProbitA closely related topic
📖 Definitions
Noun The inverse of the sigmoid or logistic function used in mathematics, especially in statistics. The logit of a number p between 0 and 1 is given by the formula:
Noun A logistic regression, which applies such a mathematical function.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Interaction terms in logit and probit models
Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables
Specification Tests for the Multinomial Logit Model
The Mixed Logit model: The state of practice
A latent class model for discrete choice analysis: contrasts with mixed logit
Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level
A Logit Model of Brand Choice Calibrated on Scanner Data
🆕 Influential since 2022
Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach
Machine unlearning: linear filtration for logit-based classifiers
A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition
Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models
📚 Books worth reading
Interpreting probability modelsTim Futing Liao · 1994
Evaluating nonindustrial private landowners for forestry assistance programsSchuster, Ervin G. · 1983- 📕The logit transformation: with special reference to its uses in bioassayWinifred Diana Ashton · 1972
An introduction to the interpretation of quantal responses in biologyPaul Soames Hewlett · 1979
A generalization of the logit transformationTakamitsu Sawa · 1977- 📕Polytomous logit estimation by weighted least squaresDavid J. Weinschrott · 1976
- 📕A computer program for logit-log transformation and linear regression analysis of concentration-dependent biological and chemical responsesG. M. Banowetz · 1987
- 📕A comparison of multivariate logit and translog models for input demand studiesThomas J Lutton · 1982
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Logistic regression In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent…
- Probit In statistics, the probit function converts a probability (a number between 0 and 1) into a score. This score indicates how many standard deviations a…
- Logistic distribution In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic…
- Generalized linear model In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing…
- Binomial regression In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is…
- Probit model In statistics, a probit model is a type of regression where the dependent variable can take only two values. The word is a portmanteau, coming from probability…
- Binary regression In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output…
- Ordered logit In statistics, the ordered logit model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal…