Overview

This course aims to teach you fascinating new statistical skills and a statistical programming language.

Content

In this course, you will be introduced to the fundamental principles of statistics and to ways of conducting your own analysis, including significance tests and linear regression techniques. The excellent free software R studio will be introduced and used for all practical aspects of the course which is a user-friendly interface for R. We will cover:

• An overview of quantitative research study designs
• Types of data
• Graphical displays
• Summaries of data
• Confidence intervals
• Hypothesis testing (parametric and non-parametric tests)
• p-values
• Linear Regression analysis

Learning outcomes

At the end of the course, learners should understand the main principles of collecting good data and producing statistics using the R software package. In particular, learners will be able to:

• Set out a plan of analysis for a research question accounting for all types of data involved and aspects of their question.
• Choose the best way of graphically displaying their data and results.
• Choose the significance tests suitable to answer their question.
• Make appropriate use of statistical inference methods.
• Understand when regression methods are useful.
• Choose the most suitable regression model for their analysis.
• Evaluate the goodness of fit of the fitted model.
• Perform appropriate model diagnostics and predictions.
• Perform all the above analyses in the R studio software package.

Course structure and teaching

This is an online, self-paced course that includes:

• Full electronic notes
• Short lecture videos (recorded outside of the classroom with screen recordings and annotation) that follow closely with the notes.
• All programming code.
• Interactive quizzes for each chapter, and opportunities to collaborate with other learners.
• Support will also be available through a forum, where you can ask questions related to the course materials.

Entry requirements

It's suitable for anyone who is new to quantitative research and wants to read journal articles or conduct their own research including data collection and analysis.

No prior knowledge of R or statistical analysis is required.

Cost and concessions

The standard price is £375 (including VAT).

A 50% discount is available for UCL staff, students and alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and UCL doctoral students. Please also email ich.statscou@ucl.ac.uk to gain a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies. Find out more about CASC's full range of statistics courses.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at UCL. He has a Bachelor’s degree in Mathematics from University of Liverpool, a Master's degree in Medical Statistics from University of Leicester, and a PhD from University of York for his research in statistical methods for random-effects meta-analysis. He's worked as a statistician on a number of clinical trials related to stroke and myeloma at the Clinical Trials Research Unit in Leeds. His specialist areas include statistical methods for meta-analysis, R programming, clinical trial methodology and research design.

Course information last modified: 10 Jul 2025, 15:38