In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

data that can be modelled well with a negative binomial distribution via negative binomial regression. Pat Collis is required to sell candy bars to raise...

a single value, as in linear regression. Binary regression is usually analyzed as a special case of binomial regression, with a single outcome ( n = 1...

tabulating the corresponding binomial coefficients in what is now recognized as Pascal's triangle. Mathematics portal Logistic regression Multinomial distribution...

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression, which predicts multiple correlated dependent...

especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...

P(N(D)=k)={\frac {(\lambda |D|)^{k}e^{-\lambda |D|}}{k!}}.} Poisson regression and negative binomial regression are useful for analyses where the dependent (response)...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness...

squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of...

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...

of model capable of using the binomial distribution (binomial regression, logistic regression) or the negative binomial distribution where the assumptions...

(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance...

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

represented using a Poisson distribution or a negative binomial distribution. Hilbe notes that "Poisson regression is traditionally conceived of as the basic count...

coefficient Wald test Bernstein inequalities (probability theory) Binomial regression Binomial proportion confidence interval Chebyshev's inequality Chernoff...

regression; when binary data is converted to count data and modeled as i.i.d. variables (so they have a binomial distribution), binomial regression can...

^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least...

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the...

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable...

Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...