A 2 day course: Thursday/Friday 8th & 9th November 2018.

This is the second of two related courses that consider generalized linear models as a broad class of statistical models that can be applied to a variety of commonly encountered data analysis problems in the social and biological sciences. 

This course introduces the generalized linear model framework and extends it to the situation when we wish to model binary data as a dependent variable in a logistic regression analysis, and similarly count data in a Poisson regression analysis.

The use of categorical (factor) explanatory variables, continuous covariates and their interactions to build a flexible class of relationships will be considered.

The course will also use the software package R as a tool for such statistical analysis.

Prior knowledge of the material covered in the first course: Introduction to Multiple Linear Regression (GLM I), is a pre-requisite for this course.

Important please note:

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Payment: Once you have registered, please pay at the online shop 

https://online-payments.lancaster-university.co.uk/product-catalogue/courses/mathematics-and-statistics/short-courses-and-cpd/applied-statistics-psc-short-courses-20182019

Accommodation Details: Can be found here.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.

The topics covered will be:

Different GLMs:, regression with binary data, regression with count data;

Use of continuous and categorical (factor) covariates;

Using interaction terms;

Model building and testing;

Applications of GLMs;

What to report;

Model interpretation

Using R for GLMs