Courses for Policy, Projects & Programmes

Introduction to Regressions Analysis Using ‘R’ Software


Regression is a very powerful method of quantitative analysis that allows us to investigate the relationship between an outcome of interest and a number of explanatory variables. From this we can make inferences while accounting for confounding factors and use this to make predictions. This course will introduce the theory behind linear regression analysis and show how R can be used to fit and interpret regression models. Please note that this course is only recommended for those that have learned the basics of R elsewhere. The course assumes a basic knowledge of R programming and statistics (e.g. confidence intervals and p-values). Please get in touch if you’d like information on our ‘Intro to R’ online or face-to-face course.

Learning Objectives

  1. Understand the fundamental principles and concepts of regression analysis, including its purpose, assumptions, and the relationship between outcome variables and explanatory variables.

  2. Gain proficiency in using R software for regression analysis, including loading data, preparing variables, and fitting regression models.

  3. Interpret regression model outputs in R, including understanding coefficient estimates, significance levels, and measures of goodness-of-fit.

  4. Explore different types of regression models, such as simple linear regression, multiple linear regression, and logistic regression, and understand their applications and limitations.

  5. Learn how to assess model assumptions, including checking for linearity, independence, homoscedasticity, and normality, and apply appropriate remedial measures if assumptions are violated.

  6. Develop skills in using regression models for prediction and inference, including making predictions based on the model and interpreting the impact of explanatory variables on the outcome variable.

  7. Apply regression analysis techniques to real-world datasets, including identifying and addressing confounding factors, drawing meaningful conclusions, and effectively communicating the results.

  8. Gain hands-on experience through practical exercises and examples in R, enabling participants to apply the knowledge and skills gained in the course to their own data analysis projects.

For more information, to book or to register your interest in future course dates

Contact Us