Reading comma-separated value (csv) files in R | Learn R

Making Awesome Scatterplots with ggplot2 | Learn R

R packages contain ALL the functionality in R | Learn R

The Most Important Data Structure in R | Learn R

The Basics on Factors in R | Learn R

Data Types and Data Dimensions in R | Learn R

Getting Started with R | Learn R

Monte Hall Problem Explained

Most Dangerous Setting in R | Learn R

Install R and R Studio | Learn R

Analysis of a Two-Factor Experiment to Find an Optimal Response in R

Analyses of Two-Factor Unbalanced and Incomplete Designs in R

Analysis of a Two-Factor Completely Randomized Design in R

Randomized Complete Block Design (RCBD)

Completely Randomized Design (CRD)

Demonstrating Contrasts using a Potato Scab Example

Statistical Contrasts for Addressing Specific Scientific Hypotheses

Regression: F-tests

One-way Analysis of Variance (ANOVA)

Interpreting Regression p-values as Posterior Probabilities

Regression Interactions for Vastly More Complex Modeling

Multiple Regression: Higher Order Terms and Additional Explanatory Variables

Regression with Categorical Explanatory Variables

Simple Linear Regression with a Binary Explanatory Variable

Simple Linear Regression: an Example using Logarithms

Simple Linear Regression using Logarithms

Regression diagnostics in (base) R

Regression: Uncertainty and Prediction intervals

Regression: Choosing Explanatory Variables

Simple Linear Regression

Comparing Two Normal Means with Unequal Variances (Bayesian and frequentist)

Comparing Three or More Normal Means with Unequal Variances (Bayesian and frequentist)

Comparing Normal Means with Equal Variances (Bayesian and frequentist)

Comparing three or more binomial probabilities

Comparing two binomial probabilities

Jeffreys-Lindley Paradox: p-values vs posterior model probabilities

Bayesian posterior model probabilities (for two-sided alternative hypotheses and more)

Bayesian posterior probabilities for one-sided alternative hypotheses

Why p-values dont mean what you think they mean

Correspondence between pvalues and confidence intervals

T-tests

Hypothesis tests

p-values

Statistical hypotheses

I05 Confidence intervals

Sampling distributions

I4 Bayesian parameter estimation in the normal model (part 2/2)

I4 Bayesian parameter estimation for a normal model (part 1/2)

I3 Bayesian parameter estimation with a binomial model example

Inverse gamma random variables

Exponential random variables

Gamma random variables

Student's t random variables

I3 Bayesian parameter estimation with the binomial model as an example

Maximum likelihood estimators (MLEs) - I2 Inference part 3/3

I2 Likelihood (part 2/3) Likelihood

Statistical modeling - Inference 2 (part 1/3)

Graphical Statistics - Inference 1.4/4

Properties of Estimators (Unbiased, Consistent) - Inference 1.3/4

Estimators - Inference 1.2/4