Paola Zaninotto, Giorgio Di Gessa, Andrea Aparicio-Castro and Meredith Martyn
This online course gives you an overview of commonly used regression methods to examine the relationship between an outcome of interest and an explanatory variable. You will be introduced to classical linear regression and generalised linear models (e.g. logistic, Poisson, ordinal/multinomial models) depending on the distribution of the outcome. The course covers the basic concept, formulation, interpretation, and validation of the models. Real-world data will be used to demonstrate the practical applications of these models.
By the end of this course you will be able to:
There will be four sessions, each consisting of a pre-recorded lecture (length varies) and a 1.5-hour live computer practical session in R or Stata.
Linear Regression for continuous outcomes
Logistic Regression for binary outcomes
Poisson Regression for count data
Ordinal/Multinomial Regression for categorical outcomes
Monday 13th February
Lecture 1 followed by
Practical 1 in either Stata or R
Tuesday 14th February
Lecture 2 followed by
Practical 2 in either Stata or R
Wednesday 15th February
Lecture 3 followed by
Practical 3 in either Stata or R
Thursday 16th February
Lecture 4 followed by
Practical 4 in either Stata or R
An understanding of basic statistical concepts (i.e. descriptive statistics mean standard deviation confidence intervals etc), quantitative data structures and types of variables.
This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.