MarinStatsLectures-R Programming & Statistics

4.5 Model Building and Variable Selection: Predictive Models

9.3 Poisson Regression Connection To Poisson Distribution and Odds Ratios

9.11 Poisson Regression: Model Assumptions

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

9.1 Week 9 Intro and Recap

8.5 Examining Model Fit

7.6 Logistic Regression: Checking Linearity

3.2 Confounding (Confounder) Explained

8.6 Logistic Regression: R-Square Type Measures

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

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

9.7 Poisson Regression: The Model For Count Data

Logistic Regression Example

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

8.1 Week 8 Intro And Recap

3.7 Effect Modification (Interaction) Explained

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

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

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

9.6 Intro To Data Used In Poisson Regression R Videos

9.2 Comparing Logistic, Poisson, and Survival Analysis

8.2 Building Model To Estimate Effect Size in R

Install R and RStudio

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

Hypothesis Testing: Null & Alternative Hypothesis I Statistics 101 #2 | MarinStatsLectures

Variables and Types of Variables | Statistics Tutorial | MarinStatsLectures

6.9 Logistic Regression: Checking Mediation In The LBW Data

4.5 Model Building and Variable Selection: Predictive Models

Bootstrapping and Resampling in Statistics with Example| Statistics Tutorial #12 |MarinStatsLectures

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

6.1 Week 6 Intro and Recap

7.2 Likelihood Ratio Test in R (for LBW Data)

4.6 Model Building and Variable Selection: Validating Predictive Models

6.2 Logistic Regression Models in R

7.1 Week 7 Intro and Recap

Confidence Interval Concept Explained | Statistics Tutorial #7 | MarinStatsLectures

5.7 Logistic Regression: Interpreting Model Coefficients

Setting Up Working Directory in R | R Tutorial 1.11 | MarinStatsLectures

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

6.7 Logistic Regression: Checking Confounding in the LBW Data

6.3 Logistic Regression: Using Model Equation To Answer Questions

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

9.4 Why We Work On Log-Scale?

8.7 Logistic Regression: R Square Type Measures in R

5.6 Logistic Regression: Estimating Probability of Outcome Using Model Equation

1.5 Linear Regression: Introduction to Data Used in R

4.3 Model Building and Variable Selection: Effect Size Models

Importing , Checking and Working with Data in R | R Tutorial 1.7 | MarinStatsLectures

What is a Hypothesis Test and a P-Value? | Puppet Master of Statistics

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

Getting started with R: Basic Arithmetic and Coding in R | R Tutorial 1.3 | MarinStatsLectures