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Introduction to generalised linear models using R and Rstudio (IGLM04)

1 message · Oliver Hooker

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Oliver Hooker
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Introduction to generalised linear models using R and Rstudio (IGLM04)


3 November 2021 - 4 November 2021



https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm04/



Course Overview:

In this two day course, we provide a comprehensive practical and
theoretical introduction to generalized linear models using R. Generalized
linear models are generalizations of linear regression models for
situations where the outcome variable is, for example, a binary, or
ordinal, or count variable, etc. The specific models we cover include
binary, binomial, ordinal, and categorical logistic regression, Poisson and
negative binomial regression for count variables. We will also cover
zero-inflated Poisson and negative binomial regression models. On the first
day, we begin by providing a brief overview of the normal general linear
model. Understanding this model is vital for the proper understanding of
how it is generalized in generalized linear models. Next, we introduce the
widely used binary logistic regression model, which is is a regression
model for when the outcome variable is binary. Next, we cover the ordinal
logistic regression model, specifically the cumulative logit ordinal
regression model, which is used for the ordinal outcome data. We then cover
the case of the categorical, also known as the multinomial, logistic
regression, which is for modelling outcomes variables that are
polychotomous, i.e., have more than two categorically distinct values. On
the second day, we begin by covering Poisson regression, which is widely
used for modelling outcome variables that are counts (i.e the number of
times something has happened). We then cover the binomial logistic and
negative binomial models, which are used for similar types of problems as
those for which Poisson models are used, but make different or less
restrictive assumptions. Finally, we will cover zero inflated Poisson and
negative binomial models, which are for count data with excessive numbers
of zero observations.


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Email oliverhooker at prstatistiucs.com with any questions



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