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The Hosmer–Lemeshow test is a statistical test for goodness of fit and ... The Hosmer–Lemeshow statistic is calculated using the formula given in the introduction ...
The general formula for G is G = 2 ∑ i O i ⋅ ln ( O i E i ) , {\displaystyle G=2\sum _{i}{O_{i}\cdot \ln \left({\frac {O_{i}}{E_{i}}}\right)},} where O i {\textstyle O_{i}} and E i {\textstyle E_{i}} are the same as for the chi-square test, ln {\textstyle \ln } denotes the natural logarithm , and the sum is taken over all non-empty bins.
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model.
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]
In probability theory and statistics, the Conway–Maxwell–Poisson (CMP or COM–Poisson) distribution is a discrete probability distribution named after Richard W. Conway, William L. Maxwell, and Siméon Denis Poisson that generalizes the Poisson distribution by adding a parameter to model overdispersion and underdispersion.
Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.
In statistics, the Wald test (named after Abraham Wald) assesses constraints on statistical parameters based on the weighted distance between the unrestricted estimate and its hypothesized value under the null hypothesis, where the weight is the precision of the estimate.
Let be a discrete random variable with probability mass function depending on a parameter .Then the function = = (=),considered as a function of , is the likelihood function, given the outcome of the random variable .