Statistical Modelling

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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.

 

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What Will You Learn?

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

Course Content

Introduction to Statistical Modeling

  • 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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