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

Power Calculations in Hypothesis Testing | Statistics Tutorial #17 | MarinStatsLectures

Statistics Course Overview | Best Statistics Course | MarinStatsLectures

How to Install Packages in R | R Tutorial 1.13 | MarinStatsLectures

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

Interpreting Interaction in Linear Regression with R | R Tutorial 5.10 | MarinStatsLectures

Bar Charts and Pie Charts in R | R Tutorial 2.1 | MarinStatsLectures

Margin of Error & Sample Size for Confidence Interval | Statistics Tutorial #11| MarinStatsLectures

2.5 Multiple Linear Regression in R: Two X Variables (Visualizing The Data in R)

Paired t-Test in R with Examples | R Tutorial 4.7 | MarinStatsLectures

Importing/Reading Excel data into R using RStudio (readxl) | R Tutorial 1.5b | MarinStatsLectures