Statistics with R
Learn the theory. Apply it instantly. Right in your browser.
8 modules that bridge statistical concepts and R programming — no RStudio, no installs, no accounts. Every concept is immediately applied in live R code.
📋 Before You Start
Helpful: Introduction to Statistics — for the statistical theory. This course teaches R alongside the theory, but familiarity with concepts like mean, variance, and hypothesis testing helps.
Helpful: R Practice Labs — for R syntax. You can follow along with zero prior R experience — all code is provided and explained. But if you want more R fundamentals first, start there.
Bottom line: All code is written for you. You can follow along without prior R knowledge. The more stats background you have, the richer the experience.
Powered by WebR — R runs right here, in your browser
No accounts. No downloads. No RStudio. No cloud IDE. R runs directly in your browser using WebR — a full R runtime compiled to WebAssembly. You write code, click Run, and see real output and plots on the page. Everything stays local. Nothing is sent to a server.
Descriptive Statistics in R
Compute mean, median, mode, variance, standard deviation, and IQR in R. Understand when each measure is appropriate. Visualize distributions with histograms.
Start Module →Probability Distributions
Explore normal, uniform, binomial, and Poisson distributions. Use R's d/p/q/r functions to calculate probabilities, quantiles, and generate random samples.
Start Module →Sampling & The Central Limit Theorem
Simulate sampling from populations. Watch the CLT in action as sample means converge to a normal distribution regardless of the population shape.
Start Module →Confidence Intervals
Build confidence intervals manually and with t.test(). Understand what "95% confident" really means. Simulate 100 intervals and see how many contain the truth.
Start Module →Hypothesis Testing
Run one-sample, two-sample, and paired t-tests. Interpret p-values correctly. Simulate Type I errors to understand false positives at the 5% level.
Start Module →Correlation & Simple Regression
Measure correlation with cor(). Fit linear regression with lm(). Interpret slope, intercept, and R². Explore Anscombe's Quartet — always visualize!
Start Module →Multiple Regression
Extend regression to multiple predictors. Interpret coefficients while holding others constant. Compare models with adjusted R². Check residual assumptions.
Start Module →Capstone: Full Statistical Analysis
You're a research analyst. Apply all 7 modules to a student performance dataset: descriptive stats, normality checks, t-tests, correlation, regression, and a written report.
Start Module →What you’ll be able to do
After completing all 8 modules, you’ll have real, practical skills — not just theory.
Summarize any dataset
Run descriptive statistics, identify distributions, and spot outliers in R.
Interpret p-values correctly
Know what p < 0.05 really means — and what it doesn't mean.
Build regression models
Fit simple and multiple regression models, interpret every coefficient.
Calculate confidence intervals
Quantify uncertainty and communicate findings with appropriate precision.
Write analysis reports
Combine code, statistics, and plain-English interpretation in one workflow.
Use R without RStudio
Run real R analyses in the browser — a transferable skill for any R environment.
What makes this course different
Theory + Immediate Application
Every concept is applied in R within the same module — no separate "lab" session needed.
No RStudio Needed
R runs live in your browser using WebR. Nothing to install, configure, or maintain.
ND-Friendly Design
Short sections, one idea at a time, clear structure, and brain breaks built into every module.
Bridges Two Courses
Connects the statistics theory from Intro Stats with the R skills from R Practice Labs.
Every module follows the same structure
Consistent format means you always know what comes next — less cognitive load, more learning.
Before You Start
Prerequisites and learning goals
The Concept
Statistical idea explained plainly
The Formula
Key equations shown simply
In R
See it work in a read-only example
Your Turn
2–3 interactive exercises
Brain Break
A moment to pause and process
Key Takeaway
One clear sentence to remember
Technical notes
- All modules load WebR, which compiles R to WebAssembly — allow 15–20 seconds on first load.
- Modules with plots (06, 07, 08) render ggplot2 and base R charts directly on the page.
- Everything runs locally in your browser tab. Nothing is sent to a server.
- Works best in Chrome or Firefox. Safari should work too.
- Your code is NOT saved between page refreshes. Copy anything you want to keep.