9.12 Poisson Regression: Why are model coefficients the log rate ratio?

9.11 Poisson Regression: Model Assumptions

9.10 Poisson Regression in R: Fitting a Model To Rate Data (with offset) in R

9.9 Poisson Regression: The Model For Rate Data (what is an offset?)

9.8 Poisson Regression in R: Fitting a Model To Count Data in R

9.7 Poisson Regression: The Model For Count Data

9.6 Intro To Data Used In Poisson Regression R Videos

9.5 Poisson Regression: Counts vs Rates, Individual vs Aggregated Data

9.4 Why We Work On Log-Scale?

9.3 Poisson Regression Connection To Poisson Distribution and Odds Ratios

Logistic Regression Example

9.2 Comparing Logistic, Poisson, and Survival Analysis

9.1 Week 9 Intro and Recap

8.8 Extensions of The Logistic Regression Model: Multinomial, Ordinal, and Conditional Logistic

8.7 Logistic Regression: R Square Type Measures in R

8.6 Logistic Regression: R-Square Type Measures

8.5 Examining Model Fit

8.4 Effect Modification: Stratifying vs. Modelling It With Interaction Term in R

8.3 Effect Modification: Stratifying vs Modelling With Interaction Term

8.2 Building Model To Estimate Effect Size in R

8.1 Week 8 Intro And Recap

3.2 Confounding (Confounder) Explained

7.7 Logistic Regression in R: Checking Linearity In R

7.6 Logistic Regression: Checking Linearity

7.5 Logistic Regression: Model Assumptions

7.4 Effect Modification in R: Calculating Odds Ratios and Comparing With Stratification In R

7.3 Effect Modification: Calculating Odds Ratios From Model

7.2 Likelihood Ratio Test in R (for LBW Data)

7.1 Week 7 Intro and Recap

6.10 Logistic Regression in R: Checking Mediation In The LBW Data In R