tutorials
569 PAGES · SIXTEEN TOPIC AREAS
tutorials
Comprehensive tutorials covering probability, inference, regression, Bayesian statistics, survival analysis, bioinformatics, and machine learning, with runnable R examples throughout.
Explore by topic
31 TUTORIALS
Statistical Foundations
Foundational concepts: probability, sampling, inference framework.
25 TUTORIALS
Descriptive Statistics
Summarising and exploring data before modelling.
40 TUTORIALS
Probability Theory
Axioms, distributions, expectations, and the algebra of random variables.
45 TUTORIALS
Inferential Statistics
Hypothesis tests, confidence intervals, and the logic of inference.
30 TUTORIALS
Sample Size & Power
Planning studies with adequate statistical power.
35 TUTORIALS
Data Visualisation
Publication-quality graphics with ggplot2 and friends.
51 TUTORIALS
Regression & Modelling
Linear models, GLMs, mixed models, and beyond.
30 TUTORIALS
Multivariate Methods
PCA, clustering, factor analysis, discriminant analysis.
30 TUTORIALS
Time-Series Analysis
ARIMA, state-space, spectral, and forecasting methods.
35 TUTORIALS
Bayesian Statistics
Bayesian inference, MCMC, and probabilistic programming.
30 TUTORIALS
Survival Analysis
Censored time-to-event data: Kaplan-Meier, Cox, AFT, competing risks.
50 TUTORIALS
Bioinformatics
Genomics pipelines from FASTQ to differential expression and beyond.
46 TUTORIALS
Machine Learning
Tree ensembles, neural networks, calibration, interpretability.
36 TUTORIALS
Clinical Biostatistics
RCT design, adaptive trials, diagnostic accuracy, agreement.
25 TUTORIALS
Meta-Analysis
Effect-size pooling, heterogeneity, bias, network meta-analysis.
30 TUTORIALS
Experimental Design
Factorials, response surfaces, mixture designs, robust design.
Other sections
INTERACTIVE
Decision Tree
Interactive wizard and static chart guiding the choice of statistical test.
APPS
Shiny Apps
Sixteen interactive Shiny applications, one per topic area.