Statistical Modelling

  • Course level: All Levels


This Statistical Modelling course in statistical inference and is a further examination of statistics and data analysis beyond an introductory course. Topics include t-tools and permutation-based alternatives including bootstrapping, multiple-group comparisons, analysis of variance, linear regression, model checking, and refinement.

Statistical computing and the simulation-based emphasis are covered as well as basic programming in the R statistical package. Thinking statistically, evaluating assumptions, and developing tools for real-life applications are emphasized.


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What Will I Learn?

  • Learn bootstrapping
  • Learn multiple-group comparisons
  • Learn analysis of variance
  • And much more

Topics for this course

23 Lessons

Introduction to Statistical Modeling

1. Introduction00:00:00
2. One-Way Analysis of Variance (ANOVA) Recap00:00:00
3. Two-Way ANOVA – Assessing Two Effects in Same Model00:00:00
4. Two-Way ANOVA – Allowing for Structure in The Data00:00:00
5. Paired T-Test Using Two-Way ANOVA & ANOVA With More Effects00:00:00
6. Regression Recap00:00:00
7. General Linear Models (GLMs) – Introduction00:00:00
8. GLM Fitting Categorical & Continuous Effects00:00:00
9. Using GLMs to Adjust for Confounding Variables & Using GLMS for Prediction00:00:00
10. GLMS – general points00:00:00
11. GLMS – checking model assumptions00:00:00
12. GLMs – General Points00:00:00
13. Models for Other Data Types00:00:00
14. Logistic Regression00:00:00
15. Logistic Regression Example – Assessing Risk Factors00:00:00
16. Logistic Regression Example Continued – Predicting Risk00:00:00
17. Logistic Regression – General Points00:00:00
18. Ordinal Logistic Regression00:00:00
19. Survival Analysis00:00:00
20. Repeated Measures Data00:00:00
21 . Mixed (or Multilevel) Models00:00:00
22. Choice of Software Package00:00:00
23. Self-Learning Resources00:00:00
Statistical Modelling
30 £

Enrolment validity: Lifetime