Statistical Analysis with R
Apply descriptive and inferential statistics, hypothesis testing, and ANOVA using R's tidyverse workflow, and learn to interpret your results with confidence.
Course Overview
Knowing how to run a statistical test in R is one thing. Knowing which test to run, why, and what the output actually means is another. This course closes that gap. You'll move from summarising a dataset with descriptive statistics to running and correctly interpreting the inferential tests that back up real research and business claims.
You'll cover hypothesis testing, confidence intervals, t-tests, chi-square tests, and one-way and two-way ANOVA, all within R's tidyverse workflow. Each technique is taught alongside the visualisations that support it, so you leave able to both run a test and explain what it shows in plain language.
This course builds directly on Data Analysis with R and leads into Data Modeling with R, where you'll extend these statistical foundations into regression and prediction.
Course at a Glance
What You'll Learn
A step-by-step path from descriptive summaries to defensible, correctly interpreted statistical tests.
Descriptive Statistics in R
Calculate and interpret measures of central tendency and dispersion, and understand common data distributions.
Data Visualisation for Statistics
Build histograms, boxplots, and scatterplots with ggplot2 to explore and communicate what your data is showing.
Probability & Sampling
Cover probability distributions, sampling techniques, and the Central Limit Theorem that underpins inferential statistics.
Hypothesis Testing
Run and interpret t-tests and chi-square tests, and understand p-values and confidence intervals correctly.
ANOVA
Compare means across multiple groups with one-way and two-way ANOVA, including appropriate post-hoc tests.
Applied Statistical Project
Analyse a real research dataset statistically and write up your findings clearly, as you would for a thesis or report.
Who This Course Is For
Built for people who need their statistical results to hold up to scrutiny, not just run without errors.
Prerequisites
Completion of Data Analysis with R, or equivalent, hands-on familiarity with R and the tidyverse (importing, cleaning, and wrangling data), is required before starting this course.
Ready to build these skills?
Reach out to our team to ask questions about this course or express your interest. We'll notify you as soon as the next intake opens.