ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live

https://www.psstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm01/

23 July 2020 - 24 July

TIME ZONE – Western European Time +1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email [log in to unmask] for full details or to discuss how we can accommodate you).

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.
Email [log in to unmask] with any questions

This is one module of a five module series – you do not need to attend them all but they are designed to compliment each other. Please see the links below

July 23rd – 24th Introduction to generalised linear models using R and Rstudio
https://www.psstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm01/

August 6th – 7th Introduction to mixed models using R and Rstudio
https://www.psstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr02/

August 20th – 21st Data visualization using GG plot 2 (R and R studio)
https://www.psstatistics.com/course/introduction-to-data-visualization-using-gg-plot-2-r-and-rstudio-dvgg01/

September 3rd – 4th Data wrangling using R and Rstudio
https://www.psstatistics.com/course/introduction-data-wrangling-using-r-and-rstudio-dwrs01/

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