Biostatistics Foundations for Public Health
Distributions, estimation, confidence intervals and hypothesis testing — the vocabulary every later guide builds on.
Learning guides
22 in-depth guides across five tracks, plus reference pages on Alberta health geography, cluster detection and writing math. Each guide is a single page with explanations, worked examples and code you can run.
Reference pages
Shorter, practical notes for everyday health data work.
Alberta's official health geographies
Zones, subzones, health status areas, local geographic areas and the rural–urban continuum, and which level to report at.
Read the guideFinding disease clusters with SaTScan
How the scan statistic works, choosing a model, preparing input files, and running it from the interface or R.
Read the guideWriting math in Markdown and LaTeX
Delimiters for each tool, everyday syntax, numbered equations, statistics examples and a live preview.
Read the guideBiostatistics
14 guides · Track hubFrom descriptive statistics to causal inference: a full applied-statistics sequence for health research, each guide with runnable R code.
Common Statistical Tests in Medical Research
Choosing between t-tests, chi-square, nonparametric and paired tests, and reading their output correctly.
Linear Regression in Depth
Model specification, assumptions, diagnostics and interpretation for continuous outcomes.
Logistic Regression in Detail
Binary outcomes, odds ratios, model fit and calibration.
Poisson Regression and Zero-Inflated Models
Count outcomes, rates with offsets, overdispersion and excess zeros.
Survival Analysis in Depth
Censoring, Kaplan–Meier curves, log-rank tests and time-to-event thinking.
AFT and Cox PH Survival Models
Proportional hazards versus accelerated failure time models, and when each fits.
Longitudinal Data Analysis in Depth
Repeated measures, correlation structures, GEE and mixed models over time.
Multilevel Modelling in Depth
Patients within clinics within regions: random effects and partial pooling.
Design of Experiments
Randomization, blocking, factorial designs and the analyses that match them.
Clinical Trials: Design and Common Methods
Trial phases, sample size, randomization, endpoints and analysis populations.
Meta-Analysis in Medicine and Psychology
Effect sizes, fixed and random effects, heterogeneity and publication bias.
Causal Inference in Depth
Potential outcomes, DAGs, confounding and identification strategies.
Propensity Score Matching in Depth
Estimating propensity scores, matching, balance checks and effect estimation.
Epidemiology
3 guides · Track hubA population view of health: disease frequency, study design, bias, and modern causal methods.
Foundations of Epidemiology
Prevalence and incidence, measures of association, bias and confounding, screening and outbreak investigation.
Epidemiologic Research Designs
Study types, sampling, and how to turn a question into a workable protocol.
Advanced Epidemiologic Methods
Estimands, weighting, marginal structural models, missing data and quantitative bias analysis.
Data Visualization
2 guides · Track hubStatic, publication-ready graphics and interactive browser charts in R.
Common ggplot2 Methods
The grammar of graphics end to end: mappings, geoms, scales, facets, themes and export — with 20 rendered charts.
Plotly for R
Interactive traces, hover design, linked views, animation and maps — 23 executed figures.
Reproducible Reporting
1 guide · Track hubNarrative, code and results in one document that rebuilds itself.
R Markdown Practical Guide
YAML, chunks and chunk options, inline R, parameterized reports, and tables with knitr::kable().
Data Platforms & GIS
2 guidesWorking with data where it lives: cloud warehouses and spatial analysis.
Snowflake for Beginners and Data Analytics
Architecture, safe lab setup, SQL analytics, window functions, semi-structured JSON, Time Travel and data sharing.
Getting Started with ArcGIS
Spatial thinking, data models, coordinate systems, ArcGIS Pro workflows, cartography and responsible spatial practice.
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