Statistics with R
Build statistical reasoning and practical data analysis skills in R, tailored for beginners in the behavioral sciences. Progress from R programming and descriptive statistics to hypothesis testing, linear models, ANOVA, and Bayesian inference.
What This Course Covers
Statistics with R is structured into 17 chapters that build on each other progressively:
Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.
How to Study Statistics with R on Lambdio
Lambdio's AI-powered platform adapts to how Data Science courses are best learned. Here's our recommended approach:
Statistics with R is a procedural, code-driven subject, so Standard Mode is the right way to learn it. Learning R syntax, the conventions of data frames and factors, and the sequence of steps in a t-test or an ANOVA requires structured explanation, worked examples, and immediate practice with comprehension checks, all of which Standard Mode delivers. Socratic Mode is a poor fit here because discovering R syntax or the mechanics of a chi-square test through leading questions alone would be slow and frustrating when a direct demonstration is clearer. The course's Medium difficulty reflects its breadth rather than its mathematical depth: it ranges from basic programming to factorial ANOVA and Bayesian inference, so a balanced Medium priority keeps a large and cumulative body of material fresh without overloading the review schedule. Use Standard review to consolidate procedures and the reasoning behind them, and Quiz Mode for quick check-ins on distribution properties, test assumptions, and terminology before an exam or before starting a related course such as Regression Analysis with R.
Interactive Quiz
Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:
What You'll Be Able to Do After This Course
- ✓Explain why statistics is essential in the behavioral sciences and recognize common reasoning biases such as belief bias and Simpson's Paradox
- ✓Operationalize theoretical constructs and classify variables by their scale of measurement and role in a study
- ✓Write and run R code using objects, vectors, data frames, factors, functions, loops, and conditionals
- ✓Summarize data with measures of central tendency, variability, distribution shape, standard scores, and correlation
- ✓Create and customize histograms, boxplots, scatterplots, and bar charts to explore and communicate data
- ✓Apply the frequentist and Bayesian interpretations of probability and work with common probability distributions in R
- ✓Construct and interpret confidence intervals and explain the behavior of sampling distributions and the Central Limit Theorem
- ✓Conduct and interpret hypothesis tests, including t-tests, chi-square tests, and ANOVA, with appropriate effect sizes and assumption checks
- ✓Fit, diagnose, and compare simple and multiple linear regression models, including factorial designs
- ✓Apply Bayesian reasoning and use Bayes factors as an alternative approach to statistical inference
Frequently Asked Questions
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