Part 1

Data before statistics

1What Statistics Is For: Questions, Evidence, Decisions→2Types of Data in Statistics: Categorical to Continuous→3Populations, Samples and Sampling Bias in R→4Sampling Methods in R: Random, Stratified, Cluster→5Descriptive Statistics in R: The 8 Key Numbers→6Mean vs Median, SD vs IQR: Choosing Center and Spread→7Z-Scores and Percentiles in R→8Outlier Detection in R: 4 Methods Compared→

Part 2

Seeing distributions and relationships

9Univariate EDA in R: Check Every Variable First→10Bivariate EDA in R→11Covariance and Correlation in R from First Principles→12Measures of Association in R: Which One to Use→13EDA in R: A 7-Step Framework for Any Dataset→14Automated EDA in R→

Part 3

Probability with simulation first

15Probability in R→16Probability Axioms in R→17Conditional Probability in R: P(A→18Bayes' Theorem in R: A Simulation That Makes It Click→19Random Variables in R: PMF, PDF and CDF Explained→20Expected Value and Variance, Explained With R→

Part 4

The distributions you will actually meet

21Which Distribution When: A Field Guide in R→22Binomial vs Poisson in R: Which Fits Your Count Data→23Geometric & Negative Binomial Distributions in R→24The Normal Distribution in R: Where It Comes From→25Normal, t, F and Chi-Squared: When Each Arises in R→26Sampling Distributions in R: What Really Varies→27Central Limit Theorem in R: See It by Simulation→28Law of Large Numbers vs CLT: The Real Difference→

Part 5

Estimation: the honest guess

29How Statistical Inference Works (No Formulas Yet)→30Point Estimation in R→31Confidence Intervals in R: What They Really Mean→32Bootstrap Confidence Intervals in R (Any Statistic)→33Margin of Error and Sample Size Planning in R→34Maximum Likelihood in R: Intuition to Real Fits→

Part 6

Testing: the machinery and its limits

35Hypothesis Testing in R: The Framework, Explained→36Your First Hypothesis Test in R: Three Ways→37Type I vs Type II Errors: See the Trade-Off in R→38Power Analysis in R: Find the Sample Size You Need→39Sample Size in R: Plan Your N the Right Way→40Effect Size in R: Cohen's d and Friends, Explained→41Statistical vs Practical Significance in R→42Multiple Testing in R: Control False Discoveries→43What p-Values Mean (and What They Never Meant)→

Part 7

Comparing groups (the workhorse part)

44t-Tests in R→45Paired Designs in R: Before-After and Matched Pairs→46Proportion Tests in R: prop.test() vs binom.test()→47Normality and Variance Tests in R: Use With Care→48Chi-Square Tests in R: Which One and How to Run It→49Fisher's Exact Test in R: When and How (Worked Example)→50Odds Ratio vs Relative Risk in R: Which to Report→51One-Way ANOVA in R: A Complete Walkthrough→52ANOVA Post-Hoc Tests in R: Tukey vs Bonferroni→53Two-Way ANOVA in R: Interactions, Interpreted→54ANCOVA in R: Control a Covariate, Gain Power→55Mixed ANOVA in R: Between and Within, Combined→56Repeated Measures ANOVA in R: A Step-by-Step Guide→

Part 8

When assumptions fail

57When to Use Nonparametric Tests in R→58Wilcoxon Signed-Rank Test in R (Worked Example)→59Kruskal-Wallis Test: Nonparametric ANOVA in R→60Spearman and Kendall Correlation in R: When and How→61Permutation Tests in R: Exact p-Values via Randomization→62Bootstrap in R: The boot Package, Step by Step→63Power Analysis by Simulation in R→

Part 9

Linear regression

64Simple Linear Regression in R: Your First lm()→65How to Read lm() Output in R Line by Line→66Multiple Regression in R→67Linear Regression Assumptions in R: The 5 Checks→68Regression Diagnostics in R: The 5 Plots to Check→69Dummy Variables in R: How lm() Handles Factors→70Interaction Effects in R: Test and Interpret Them→71Variable Selection in R: AIC, BIC or Lasso→72Polynomial and Spline Regression in R (How-To)→73Ridge and Lasso Regression in R, Explained Simply→74Robust Regression in R: rlm() When Outliers Bite→75Quantile Regression in R With quantreg (How-To)→

Part 10

Generalized linear models

76ROC, AUC and Odds Ratios for Logistic Models in R→77How to Read Logistic Regression Output in R→78Multinomial and Ordinal Logistic Regression in R→79Poisson vs Negative Binomial Regression in R→80Offsets and Exposure in Poisson Models in R→81Zero-Inflated and Hurdle Models in R→82GLM Diagnostics in R Beyond Gaussian Models→

Part 11

Grouped and hierarchical data

83Multilevel Models in R With brms, Explained→84Random Intercepts and Slopes with lme4 in R→85Crossed vs Nested Random Effects in R→86Mixed Model Inference in R: p-Values and Bootstrap→87GEE vs Mixed Models in R→

Part 12

The working analyst

88Checking Model Assumptions in R: One Workflow→89Multiple Imputation with mice in R→90Measurement Reliability in R: Alpha, ICC, Agreement→91p-Hacking, Forking Paths and Preregistration→92Report Statistics in R→93Statistical Consulting in R→94Which Statistical Test in R? A Decision Flowchart That Answers in 5 Questions→

Part 13

Capstones (industry-grade, per the capstone brief standard)

95Capstone: A Survey Analysis End to End in R→96Capstone: A Clinical-Style Group Comparison in R→97Capstone: A Business Experiment in R→

Part 14

The theory shelf (optional appendix)

98Matrix Operations in R: Multiply, Invert, Transpose→99Solve Linear Systems in R: solve() and qr()→100Eigenvalues and Eigenvectors in R: eigen() Guide→101Singular Value Decomposition in R: svd() Explained→102The Hat Matrix in R: OLS Geometry, Explained Visually→103QR Decomposition in R: Why lm() Uses qr()→104Quadratic Forms and Chi-Squared: Step-by-Step in R→105Matrix Derivatives and the Hessian, Explained in R→106Cholesky Decomposition in R: chol(), Explained→107Moore-Penrose Pseudoinverse in R: ginv() Explained→108Kronecker Products in R: Where They Show Up→109Spectral Decomposition in R: eigen() in Action→110Exponential Family Distributions, Explained Simply→111Sufficient Statistics in R: Worked Examples→112Complete and Ancillary Statistics: Basu's Theorem→113UMVUE Explained Step by Step (Worked Examples in R)→114Cramer-Rao Lower Bound, Explained With Examples in R→115Asymptotic Theory in R: Consistency and Normality→116Neyman-Pearson Lemma, Explained With Examples in R→117Likelihood Ratio Tests and Pivotal Methods, Explained→118Decision Theory Explained: Loss, Risk and Bayes→119Asymptotic Relative Efficiency, Explained With R→120MGFs vs Characteristic Functions, Explained in R→