A 2 day course on Thursday/Friday 1st & 2nd November 2018.

This is the first 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 basic linear model, extends it to the general linear model, then builds up to the concept of a generalized linear model.

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 introduce the software package R as a tool for such statistical analysis.

On this course the focus is on simple linear regression and multiple linear regression where we model a continuous dependent variable. The second related course is Modelling Binary and Count data (GLM II).

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:

The basics: Linear models, General linear models;

LM framework: Simple linear regression, multiple linear regression

Use of continuous and categorical (factor) covariates;

Using interaction terms;

Model building and testing (F-test, ANOVA, Likelihood ratio test, AIC);

Applications of LMs;

What to report;

Using R for LMs.

Diagnostics and transformations