About Course
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.
Course Content
Introduction to Statistical Modeling
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1. Introduction
00:00 -
14. Logistic Regression
00:00 -
15. Logistic Regression Example – Assessing Risk Factors
00:00 -
16. Logistic Regression Example Continued – Predicting Risk
00:00 -
17. Logistic Regression – General Points
00:00 -
18. Ordinal Logistic Regression
00:00 -
19. Survival Analysis
00:00 -
20. Repeated Measures Data
00:00 -
21 . Mixed (or Multilevel) Models
00:00 -
22. Choice of Software Package
00:00 -
13. Models for Other Data Types
00:00 -
12. GLMs – General Points
00:00 -
11. GLMS – checking model assumptions
00:00 -
2. One-Way Analysis of Variance (ANOVA) Recap
00:00 -
3. Two-Way ANOVA – Assessing Two Effects in Same Model
00:00 -
4. Two-Way ANOVA – Allowing for Structure in The Data
00:00 -
5. Paired T-Test Using Two-Way ANOVA & ANOVA With More Effects
00:00 -
6. Regression Recap
00:00 -
7. General Linear Models (GLMs) – Introduction
00:00 -
8. GLM Fitting Categorical & Continuous Effects
00:00 -
9. Using GLMs to Adjust for Confounding Variables & Using GLMS for Prediction
00:00 -
10. GLMS – general points
00:00 -
23. Self-Learning Resources
00:00
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