This page briefly introduces linear mixed models LMMs as a method for analyzing data that are non independent, multilevel/hierarchical, longitudinal, or correlated. We focus on the general concepts and interpretation…
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Introduction Power analysis is the name given to the process for determining the sample size for a research study. The technical definition of power is that it is the probability…
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  Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear…
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Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when…
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Multinomial logistic regression is used to model nominal outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables. This page…
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Negative binomial regression is for modeling count variables, usually for over-dispersed count outcome variables. This page uses the following packages. Make sure that you can load them before trying to…
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Introduction The following page discusses how to use R’s polr function from package MASS to perform an ordinal logistic regression. For a more mathematical treatment of the interpretation of results…
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Poisson regression is used to model count variables. This page uses the following packages. Make sure that you can load them before trying to run the examples on this page….
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Probit regression, also called a probit model, is used to model dichotomous or binary outcome variables. In the probit model, the inverse standard normal distribution of the probability is modeled…
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Robust regression is an alternative to least squares regression when data are contaminated with outliers or influential observations, and it can also be used for the purpose of detecting influential…
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