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The general linear model is a generalization of multiple linear regression to the case of more than one dependent variable. If Y, B, and U were column vectors, the matrix equation above would represent multiple linear regression. Hypothesis tests with the general linear model can be made in two ways: multivariate or as several independent ...
The better the linear regression (on the right) fits the data in comparison to the simple average (on the left graph), the closer the value of R 2 is to 1. The areas of the blue squares represent the squared residuals with respect to the linear regression. The areas of the red squares represent the squared residuals with respect to the average ...
The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham [1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data. [2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3 ...
R packages are collections of functions, documentation, and data that expand R. [16] For example, packages add report features such as RMarkdown, Quarto, [17] knitr and Sweave. Packages also add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests ...
All major statistical software packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some spreadsheet applications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression ...
Pseudo-R-squared values are used when the outcome variable is nominal or ordinal such that the coefficient of determination R2 cannot be applied as a measure for goodness of fit and when a likelihood function is used to fit a model. In linear regression, the squared multiple correlation, R2 is used to assess goodness of fit as it represents the ...
In statistics, linear regression is a model that estimates the linear relationship between a scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable). A model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple ...
The Posit Package Manager (formerly RStudio Package Manager) is a similar tool produced by the developers of RStudio which, in addition to CRAN snapshots, includes an archive of R packages from Bioconductor and Python packages from the Python Package Index. [26] It also distributes pre-compiled binary packages for Linux (only Windows and macOS ...