04:09

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

04:33

9.11 Poisson Regression: Model Assumptions

03:49

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

08:18

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

05:33

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

09:20

9.7 Poisson Regression: The Model For Count Data

05:12

9.6 Intro To Data Used In Poisson Regression R Videos

10:24

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

03:58

9.4 Why We Work On Log-Scale?

14:04

9.3 Poisson Regression Connection To Poisson Distribution and Odds Ratios

20:50

Logistic Regression Example

05:34

9.2 Comparing Logistic, Poisson, and Survival Analysis

04:53

9.1 Week 9 Intro and Recap

09:23

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

04:57

8.7 Logistic Regression: R Square Type Measures in R

06:10

8.6 Logistic Regression: R-Square Type Measures

08:03

8.5 Examining Model Fit

00:38

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

09:40

8.3 Effect Modification: Stratifying vs Modelling With Interaction Term

26:20

8.2 Building Model To Estimate Effect Size in R

02:44

8.1 Week 8 Intro And Recap

14:16

3.2 Confounding (Confounder) Explained

08:54

7.7 Logistic Regression in R: Checking Linearity In R

09:22

7.6 Logistic Regression: Checking Linearity

09:56

7.5 Logistic Regression: Model Assumptions

09:58

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

17:15

7.3 Effect Modification: Calculating Odds Ratios From Model

08:51

7.2 Likelihood Ratio Test in R (for LBW Data)

01:53

7.1 Week 7 Intro and Recap

03:17

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

05:19

6.9 Logistic Regression: Checking Mediation In The LBW Data

07:58

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

05:02

6.7 Logistic Regression: Checking Confounding in the LBW Data

04:18

6.6 Likelihood Ratio Test (LRT) in R

03:40

6.5 Likelihood Ratio Test (LRT) Explained

12:59

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

21:41

6.3 Logistic Regression: Using Model Equation To Answer Questions

06:23

6.2 Logistic Regression Models in R

04:30

6.1 Week 6 Intro and Recap

05:37

5.9 Why We Work On Log Scale With Odds Ratios

10:57

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

11:09

5.7 Logistic Regression: Interpreting Model Coefficients

03:50

5.6 Logistic Regression: Estimating Probability of Outcome Using Model Equation

13:48

5.5 Logistic Regression: The Model Equation

17:52

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

10:50

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

15:59

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

02:15

5.1 Week 5 Intro and Recap

11:05

4.6 Model Building and Variable Selection: Validating Predictive Models

11:58

4.5 Model Building and Variable Selection: Predictive Models

18:39

4.4 Model Building and Variable Selection: Example Effect Size Model in R

16:20

4.3 Model Building and Variable Selection: Effect Size Models

07:08

4.2 Model Building and Variable Selection: General Comments

03:40

4.1 Week 4 Intro and Recap

10:47

1.0 SPPH 500 Course Structure

04:16

3.11 Ways Bivariate Analysis Can Mislead Us

07:43

3.10 Summary of Confounder, Mediator, Collinearity, Effect Modifier, Independent Predictor

01:41

3.9 Unnecessary Variables Explained

05:12

3.8 Independent Predictors (Risk Factors) Explained

06:59

3.7 Effect Modification (Interaction) Explained