MarinStatsLectures-R Programming & Statistics

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

8.6 Logistic Regression: R-Square Type Measures

8.7 Logistic Regression: R Square Type Measures in R

8.5 Examining Model Fit

8.3 Effect Modification: Stratifying vs Modelling With Interaction Term

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

8.2 Building Model To Estimate Effect Size in R

8.1 Week 8 Intro And Recap

3.2 Confounding (Confounder) Explained

7.6 Logistic Regression: Checking Linearity

7.7 Logistic Regression in R: Checking Linearity In R

7.5 Logistic Regression: Model Assumptions

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

7.1 Week 7 Intro and Recap

7.3 Effect Modification: Calculating Odds Ratios From Model

7.2 Likelihood Ratio Test in R (for LBW Data)

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

6.2 Logistic Regression Models in R

6.5 Likelihood Ratio Test (LRT) Explained

6.1 Week 6 Intro and Recap

6.7 Logistic Regression: Checking Confounding in the LBW Data

6.6 Likelihood Ratio Test (LRT) in R

6.4 Logistic Regression in R: Using Model To Answer Questions With R

6.8 Logistic Regression in R: Checking Confounding in the LBW Data in R

6.9 Logistic Regression: Checking Mediation In The LBW Data

6.3 Logistic Regression: Using Model Equation To Answer Questions

5.7 Logistic Regression: Interpreting Model Coefficients

5.8 Logistic Regression: Why Do The Model Coefficients Give Us Odds Ratios (OR)?

5.9 Why We Work On Log Scale With Odds Ratios

5.5 Logistic Regression: The Model Equation

5.4 Logistic Regression in R: Understanding The Model Using Data in R

5.3 Logistic Regression: What Is It? (Logistic Regression Explained Conceptually)

5.1 Week 5 Intro and Recap

5.6 Logistic Regression: Estimating Probability of Outcome Using Model Equation

5.2 Logistic Regression: Connecting To Binomial Distribution and Odds Ratios (OR)

4.6 Model Building and Variable Selection: Validating Predictive Models

4.5 Model Building and Variable Selection: Predictive Models