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Bain: Bayesian informative hypotheses evaluation [8] for t-tests, ANOVA, ANCOVA, linear regression and structural equation modeling. BSTS: Bayesian take on linear Gaussian state space models suitable for time series analysis. Circular Statistics: Basic methods for directional data. Cochrane meta-analyses: Analyse Cochrane medical datasets.
Interactive help is available. The program provides methods that are appropriate for matched and independent t-tests, [2] survival analysis, [5] matched [6] and unmatched [7] [8] studies of dichotomous events, the Mantel-Haenszel test, [9] and linear regression. [3]
Curve fitting can be performed with user-defined or built-in linear and nonlinear functions, including multi-peak fitting, based on the GNU Scientific Library. The plots can be exported to several bitmap formats, PDF, EPS or SVG. Note windows support in-place evaluation of mathematical expressions or an optional scripting interface to Python.
In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...
The extension to multiple and/or vector-valued predictor variables (denoted with a capital X) is known as multiple linear regression, also known as multivariable linear regression (not to be confused with multivariate linear regression). [10] Multiple linear regression is a generalization of simple linear regression to the case of more than one ...
It is one approach to handling the "errors in variables" problem, and is also sometimes used even when the covariates are assumed to be error-free. Linear Template Fit (LTF) [7] combines a linear regression with (generalized) least squares in order to determine the best estimator. The Linear Template Fit addresses the frequent issue, when the ...
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
Deming regression (total least squares) also finds a line that fits a set of two-dimensional sample points, but (unlike ordinary least squares, least absolute deviations, and median slope regression) it is not really an instance of simple linear regression, because it does not separate the coordinates into one dependent and one independent ...