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

## 23 July 2020 - 24 July 2020

£275.00 - £850.00### Event Navigation

## This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.

This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.

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 oliverhooker@psstatistics.com 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.

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

“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists

“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio

“3” August 6th – 7th Introduction to mixed models using R an d Rstudio

“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio)

“5” September 3rd – 4th Data wrangling using R and Rstudio

ONLINE COURSE – Data wrangling using R and Rstudio (DWRS01) This course will be delivered live

“6” October 1st – 2nd Introduction to Nonlinear Regression using Generalized Additive Models

https://www.psstatistics.com/course/introduction to nonlinear-regression-using-generalized-additive-models-gamr01

“7” Date to be confirmed Introduction to time series analysis using R

### Intended Audience

This course is aimed at anyone who is interested in advanced statistical modelling as it is practiced widely throughout academic scientific research, as well as widely throughout the public and private sectors.

Venue – Delivered remotely

Time zone – Western European Time +1

Availability – 20 places

Duration – 2 days

Contact hours – Approx. 15 hours

ECT’s – Equal to 2 ECT’s

Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PS statistics) will be credited.