Teaching and Training

Teaching and Training

This section highlights lecture sessions delivered under StatsClinic R Lab, focusing on applied statistics, data science, and biostatistics using real-world datasets in R.

Lecture 4 — Correlation and Simple Linear Regression (Dec 4, 2025)

In this StatsClinic R Lab session, taught by Daniel Olofin, participants are introduced to correlation analysis and simple linear regression using real clinical and public health datasets.

Topics Covered

Correlation

  • Visualizing relationships between variables
  • Computing Pearson correlation coefficient
  • Interpreting correlation strength and direction

Simple Linear Regression

  • Fitting regression models using lm()
  • Introduction to generalized linear models using glm()
  • Interpretation of:
    • Regression coefficients
    • p-values
    • R-squared
    • Residual diagnostics and model assumptions

Additional Concepts

  • Use of binary, categorical, and multi-variable predictors in R
  • Model comparison using AIC
  • Connection to the broader Linear Regression module in the StatsClinic curriculum

Materials:
- Video Link

Lecture 3 — T-Tests and Non-Parametric Alternatives (Nov 23, 2025)

In this session, Daniel Olofin guides students through key hypothesis testing methods in statistical analysis.

Topics Covered

Parametric Tests

  • One-sample t-test
  • Paired t-test
  • Independent samples t-test
  • Introduction to ANOVA

Non-Parametric Alternatives

  • Wilcoxon Signed-Rank Test (paired/one-sample)
  • Wilcoxon Rank-Sum Test (independent samples)

This lecture emphasizes when to apply parametric versus non-parametric methods using practical examples from real datasets.

Materials:
- Video Link 1
- Video Link 2

Lecture 2 — Introduction to R and RStudio (Nov 9, 2025)

This live introductory session, led by Daniel Olofin, introduces participants to the fundamentals of R programming.

Topics Covered

  • Installing and setting up R and RStudio
  • Understanding the RStudio interface
  • Navigating Console, Script Editor, Environment, and Output Panels
  • Writing and executing basic R code

This session is designed to help beginners build confidence in using R for statistical computing and data analysis.

Materials:
- Video Link

Lecture 1 — Introduction to Descriptive Statistics (R4DataScience)

This hands-on tutorial introduces foundational concepts in descriptive statistics and statistical inference using R, designed to support early-stage data science learning.

Learning Outcomes

  • Summarizing datasets using descriptive measures
  • Understanding distributions and variability
  • Basic data visualization in R
  • Introduction to statistical programming workflows

Materials:
- Video Link