Biostatistics
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Academic Integrity
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Lecture 1: Introduction
Tutorial 1: The R environment
Lecture 2: Statistical Hypothesis Testing
Tutorial 2: Statistical Hypothesis Testing
Lecture 3: Stat. Hyp. Testing - part 2
Reference Search
Lecture 4: Estimators and Factorial ANOVA
Lecture 5: Factorial ANOVA part 2
Tutorial 3: Factorial ANOVA
Lecture 6: Post-Hoc Tests
Lecture 7: Non-parametric tests
Tutorial 4: Multiple Testing
Lecture 8: Heteroscedasticity
Lecture 9: ANCOVA
Tutorial 5: Rank-transformation and Heteroscedasticity
Lecture 10: Types of Sum of Squares
Lecture 11: Multiple regression
Tutorial 6: ANCOVA
Lecture 12: Multiple regression part 2
Lecture 13: Multiple regression part 3
Tutorial 7: Multiple regression in practice
Lecture 14: A new look into Heteroscedasticity
Tutorial 8: Heteroscedasticity and GLMs
Lecture 15: Mixed models part 1
Lecture 16: Mixed models part 2
Tutorial 9: Mixed models - part 1
Lecture 17: Mixed models part 3
Lecture 18: Machine Learning - K-means
Tutorial 10: Mixed models - part 2
Lecture 19: Machine Learning - trees
Lecture 20: PCA part 1
Tutorial 11: Machine Learning
Lecture 21: PCA part 2
Lecture 22: Multivariate Response Models
Tutorial 12: PCA and RDA
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Advanced Statistics for Biological Sciences - BIOL422 & BIOL680 - 2025

Lecture 21: PCA part 2

April 2, 2026

Principal Component Analysis (PCA) - part 2

Lecture

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