Statistics and Research Methods - Part 3: Regression Analysis and Further Methods

Model relationships and predict outcomes using regression

Understand key relationships between variables and make predictions from your data. Regression techniques allow you to model how variables relate to each other and predict outcomes based on those relationships.

This course introduces linear regression, logistic regression, and other advanced methods for analysing complex data. You’ll learn when to use each approach, how to interpret results, and how to assess model quality.

Building on previous statistical knowledge, you’ll develop skills for sophisticated data analysis.

Understand linear regression

Learn the principles of linear regression for modelling relationships between continuous variables. You’ll explore how to fit models, interpret coefficients, and assess how well models explain your data.

Understanding these foundations helps you predict outcomes and understand variable relationships quantitatively.

Apply logistic regression

Discover how logistic regression works for binary outcomes and categorical predictions. You’ll learn when this approach is appropriate and how to interpret results meaningfully.

These skills extend your analytical toolkit beyond continuous outcomes to categorical variables.

Explore advanced methods

Examine additional techniques for handling complex data, including model selection, diagnostics, and dealing with confounding. You’ll learn to assess assumptions and choose appropriate methods for different research questions.

By the end of this course, you’ll have a comprehensive toolkit for quantitative data analysis in research settings.

This course is ideal for students, researchers, and professionals with basic statistics knowledge who want to develop skills in regression analysis and advanced quantitative methods for research.

This course is ideal for students, researchers, and professionals with basic statistics knowledge who want to develop skills in regression analysis and advanced quantitative methods for research.

• Describe the relationship between variables and the use of regression for prediction.
• Identify the purpose of adjusting for confounding variables within a regression model.
• Debate parametric and non-parametric statistical methods.
• Explore the basic principles of bootstrapping and resampling techniques.
• Summarise the outputs of a regression analysis for a general audience.