Statistics - Effects (between predictor variable)

Thomas Bayes


Effect between predictor variable

See also: Statistics - Effect Size

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Card Puncher Data Processing
R - Multiple Linear Regression

Multiple linear regression with R functions such as lm Unstandardized Multiple Regression Regression analyses, standardized (in the z scale). The point is a short-cut to select all variables....
Card Puncher Data Processing
R - Simple Linear Regression

simple linear regression with R function such as lm Unstandardized Simple Regression Regression analyses, standardized (in the z scale). In simple regression, the standardized regression coefficient...
Thomas Bayes
Statistics - (Interaction|Synergy) effect

In a multiple regression, is assumed that the effect on the target of increasing one unit of one predictor (is independent|has no influence) on the other predictor. If this is not the case, sharing a...
Thomas Bayes
Statistics - (Significance | Significant) Effect

Fisher found with the P-value a way of judging the “significance” of experimental data entirely objectively. effect
Thomas Bayes
Statistics - Effect Size

Effect size tells you the magnitude of the effect. Glass coined the term “meta-analysis”. 1978: Gene V. Glass statistically aggregate the findings of 375 psychotherapy outcome studies Glass (and...
Thomas Bayes
Statistics - Generalized Linear Models (GLM) - Extensions of the Linear Model

The Generalized Linear Model is an extension of the linear model that allows for lots of different,non-linear models to be tested in the context of regression. GLM is the mathematical framework used in...
Thomas Bayes
Statistics - Non-linear (effect|function|model)

Non-linear effect The truth is almost never linear but often the linearity assumption is good enough. (Linearity is an approximation) When its not (increasing in complexity): polynomials, step functions,...

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