Binary regression, the Glossary

Table of Contents

1. 25 relations: Binary classification, Binary data, Binomial regression, Bioassay, Correlation, Count data, Dependent and independent variables, Discounted cash flow, Discrete choice, Errors and residuals, Exponential family, Fractional model, Generalized linear model, Item response theory, Latent variable model, Linear probability model, Linear regression, Logistic regression, Logit, Normal distribution, Probability distribution, Probit model, Regression analysis, Statistical parameter, Statistics.

Binary classification

Binary classification is the task of classifying the elements of a set into one of two groups (each called class).

See Binary regression and Binary classification

Binary data

Binary data is data whose unit can take on only two possible states.

See Binary regression and Binary data

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 the number of successes in a series of independent Bernoulli trials, where each trial has probability of success.

See Binary regression and Binomial regression

Bioassay

A bioassay is an analytical method to determine the potency or effect of a substance by its effect on living animals or plants (in vivo), or on living cells or tissues (in vitro).

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Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.

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Count data

In statistics, count data is a statistical data type describing countable quantities, data which can take only the counting numbers, non-negative integer values, and where these integers arise from counting rather than ranking.

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Dependent and independent variables

A variable is considered dependent if it depends on an independent variable. Binary regression and dependent and independent variables are regression analysis.

See Binary regression and Dependent and independent variables

Discounted cash flow

The discounted cash flow (DCF) analysis, in financial analysis, is a method used to value a security, project, company, or asset, that incorporates the time value of money.

See Binary regression and Discounted cash flow

Discrete choice

In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport.

See Binary regression and Discrete choice

Errors and residuals

In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "true value" (not necessarily observable). Binary regression and errors and residuals are regression analysis.

See Binary regression and Errors and residuals

Exponential family

In probability and statistics, an exponential family is a parametric set of probability distributions of a certain form, specified below.

See Binary regression and Exponential family

Fractional model

In applied statistics, fractional models are, to some extent, related to binary response models. Binary regression and fractional model are regression analysis.

See Binary regression and Fractional model

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression.

See Binary regression and Generalized linear model

Item response theory

In psychometrics, item response theory (IRT) (also known as latent trait theory, strong true score theory, or modern mental test theory) is a paradigm for the design, analysis, and scoring of tests, questionnaires, and similar instruments measuring abilities, attitudes, or other variables.

See Binary regression and Item response theory

Latent variable model

A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent variables.

See Binary regression and Latent variable model

Linear probability model

In statistics, a linear probability model (LPM) is a special case of a binary regression model.

See Binary regression and Linear probability model

Linear regression

In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables).

See Binary regression and Linear regression

Logistic regression

In statistics, the 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 variables.

See Binary regression and Logistic regression

Logit

In statistics, the logit function is the quantile function associated with the standard logistic distribution.

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Normal distribution

In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.

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Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment.

See Binary regression and Probability distribution

Probit model

In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married.

See Binary regression and Probit model

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features').

See Binary regression and Regression analysis

Statistical parameter

In statistics, as opposed to its general use in mathematics, a parameter is any quantity of a statistical population that summarizes or describes an aspect of the population, such as a mean or a standard deviation.

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Statistics

Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.

See Binary regression and Statistics

References

[1] https://en.wikipedia.org/wiki/Binary_regression

Also known as Binary response model, Binary response model with latent variable, Binary response models, Heteroskedasticity and nonnormality in the binary response model with latent variable.