MarinStatsLectures-R Programming & Statistics - Invidious

MarinStatsLectures-R Programming & Statistics

These R Programming and Statistics tutorials are originally created for Master's and PhD students at UBC by Professor Mike Marin and are now available to public.These videos provide a comprehensive and completely free set of tutorials for introductory and intermediate concepts in Statistics and Statistics with R.

Our team:

Professor Mike Marin is the content creator for these video tutorials and is currently teaching at The University of British Columbia in The School of Population and Public Health, Biostatistics and Epidemiology Division. During his career he has won many awards for excellence in teaching. You can learn more about him here: spph.ubc.ca/person/mike-marin/

Ladan Hamadani (B.Sc., BA., MPH), is the media manager and content editor of the videos. She makes sure that the amazing content is polished and presented in a way that is easy to follow. She also works as a research analyst in health sector.

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4:09

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

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4:33

9.11 Poisson Regression: Model Assumptions

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3:49

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

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8:18

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

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5:33

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

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9:20

9.7 Poisson Regression: The Model For Count Data

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5:12

9.6 Intro To Data Used In Poisson Regression R Videos

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10:24

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

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3:58

9.4 Why We Work On Log-Scale?

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14:04

9.3 Poisson Regression Connection To Poisson Distribution and Odds Ratios

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20:50

Logistic Regression Example

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5:34

9.2 Comparing Logistic, Poisson, and Survival Analysis

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4:53

9.1 Week 9 Intro and Recap

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9:23

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

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4:57

8.7 Logistic Regression: R Square Type Measures in R

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6:10

8.6 Logistic Regression: R-Square Type Measures

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8:03

8.5 Examining Model Fit

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0:38

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

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9:40

8.3 Effect Modification: Stratifying vs Modelling With Interaction Term

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26:20

8.2 Building Model To Estimate Effect Size in R

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2:44

8.1 Week 8 Intro And Recap

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14:16

3.2 Confounding (Confounder) Explained

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8:54

7.7 Logistic Regression in R: Checking Linearity In R

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9:22

7.6 Logistic Regression: Checking Linearity

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9:56

7.5 Logistic Regression: Model Assumptions

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9:58

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

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17:15

7.3 Effect Modification: Calculating Odds Ratios From Model

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8:51

7.2 Likelihood Ratio Test in R (for LBW Data)

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1:53

7.1 Week 7 Intro and Recap

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3:17

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

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5:19

6.9 Logistic Regression: Checking Mediation In The LBW Data

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7:58

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

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5:02

6.7 Logistic Regression: Checking Confounding in the LBW Data

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4:18

6.6 Likelihood Ratio Test (LRT) in R

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3:40

6.5 Likelihood Ratio Test (LRT) Explained

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12:59

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

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21:41

6.3 Logistic Regression: Using Model Equation To Answer Questions

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6:23

6.2 Logistic Regression Models in R

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4:30

6.1 Week 6 Intro and Recap

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5:37

5.9 Why We Work On Log Scale With Odds Ratios

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10:57

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

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11:09

5.7 Logistic Regression: Interpreting Model Coefficients

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3:50

5.6 Logistic Regression: Estimating Probability of Outcome Using Model Equation

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13:48

5.5 Logistic Regression: The Model Equation

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17:52

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

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10:50

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

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15:59

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

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2:15

5.1 Week 5 Intro and Recap

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11:05

4.6 Model Building and Variable Selection: Validating Predictive Models

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11:58

4.5 Model Building and Variable Selection: Predictive Models

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18:39

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

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16:20

4.3 Model Building and Variable Selection: Effect Size Models

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7:08

4.2 Model Building and Variable Selection: General Comments

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3:40

4.1 Week 4 Intro and Recap

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10:47

1.0 SPPH 500 Course Structure

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905 views

4:16

3.11 Ways Bivariate Analysis Can Mislead Us

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7:43

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

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1:41

3.9 Unnecessary Variables Explained

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5:12

3.8 Independent Predictors (Risk Factors) Explained

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6:59

3.7 Effect Modification (Interaction) Explained

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7.8K views