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The goal of logistic regression is to use the dataset to create a predictive model of the outcome variable. As in linear regression, the outcome variables Y i are assumed to depend on the explanatory variables x 1,i... x m,i. Explanatory variables. The explanatory variables may be of any type: real-valued, binary, categorical, etc.
[2] [3] [4] It has an integrated spreadsheet for data input and can import files in several formats (Excel, SPSS, CSV, ...). MedCalc includes basic parametric and non-parametric statistical procedures and graphs such as descriptive statistics , ANOVA , Mann–Whitney test , Wilcoxon test , χ 2 test , correlation , linear as well as non-linear ...
IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set, for example, by minimizing the least absolute errors rather than the least square errors.
Consider the linear regression model = +, =,, …,.That is, = +, where, is the design matrix whose rows correspond to the observations and whose columns correspond to the independent or explanatory variables.
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various ...
In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model.It is used when there is a non-zero amount of correlation between the residuals in the regression model.
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant.
In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks. It resembles the normal distribution in shape but has heavier tails (higher kurtosis).