In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression, analyzing whether the regression residuals are random, and checking whether the model's predictive performance deteriorates substantially when applied to data that were not used in model estimation.
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In statistics, regressionvalidation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables...
Look up validation or validate in Wiktionary, the free dictionary. Validation may refer to: Data validation, in computer science, ensuring that data inserted...
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...
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, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
(e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used...
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...
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding...
(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance...
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...
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...
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....
Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regressionvalidation, the following topics relate to goodness of fit:...
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic...
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...
case, the "regression" effect is statistically likely to occur, but in the second case, it may occur less strongly or not at all. Regression toward the...
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...
Through regression analysis, one can derive the equation for the curve or straight line and obtain the correlation coefficient. Simple linear regression is...
used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the...
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