Every STAT 301 concept — with the R code and a picture of the idea.
From t-tests and ANOVA up to the math core (linear algebra, derivatives & optimization) and the machine-learning methods (PCA, LDA, clustering, gradient boosting). Each card gives the plain-English idea, when to use it, the hypotheses, the exact R functions, and how to read the R output.
🧭 Which method should I use?
Answer a few questions. The inferential branch follows your course's decision tree (DV type → number of factors → levels → repeated vs. independent); the analytics branch routes you to PCA, LDA, clustering or gradient boosting.
⚖️ Compare the methods
Inputs, outputs, what's being tested, and the R function for each method side by side.
⇄ STAT 301 vs STAT 353 — what's the difference?
Same instructor, same 4-step hypothesis-testing framework, same decision tree. The big split is software and orientation: 353 is applied environmental statistics in SPSS; 301 is the mathematical + machine-learning version in R.
✍️ Turn your R output into a conclusion
Same instructor, same Four Steps of Hypothesis Testing and phrasing — Reject H₀ / Fail to reject H₀, "There is sufficient evidence that…", always addressing the original claim — just read from R output instead of SPSS.
🛠️ Conclusion builder
Pick the test, type what R printed (statistic, df, p-value), and it drafts the 4-step conclusion.
🚫 The mistakes that cost marks
⌨️ R cheat-sheet
The one-line R call for each method. Open the Concept Explorer for the full script and how to read the output.
🔖 Glossary
The words and R objects that show up across the modules.