15:57

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

22:07

Making Awesome Scatterplots with ggplot2 | Learn R

16:14

R packages contain ALL the functionality in R | Learn R

17:57

The Most Important Data Structure in R | Learn R

12:18

The Basics on Factors in R | Learn R

26:46

Data Types and Data Dimensions in R | Learn R

19:38

Getting Started with R | Learn R

08:18

Monte Hall Problem Explained

11:23

Most Dangerous Setting in R | Learn R

16:35

Install R and R Studio | Learn R

08:34

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

12:45

Analyses of Two-Factor Unbalanced and Incomplete Designs in R

17:46

Analysis of a Two-Factor Completely Randomized Design in R

13:27

Randomized Complete Block Design (RCBD)

10:00

Completely Randomized Design (CRD)

14:28

Demonstrating Contrasts using a Potato Scab Example

14:25

Statistical Contrasts for Addressing Specific Scientific Hypotheses

15:35

Regression: F-tests

14:35

One-way Analysis of Variance (ANOVA)

11:41

Interpreting Regression p-values as Posterior Probabilities

23:31

Regression Interactions for Vastly More Complex Modeling

16:34

Multiple Regression: Higher Order Terms and Additional Explanatory Variables

11:29

Regression with Categorical Explanatory Variables

10:50

Simple Linear Regression with a Binary Explanatory Variable

13:16

Simple Linear Regression: an Example using Logarithms

17:23

Simple Linear Regression using Logarithms

20:52

Regression diagnostics in (base) R

11:40

Regression: Uncertainty and Prediction intervals

12:44

Regression: Choosing Explanatory Variables

26:07

Simple Linear Regression

08:47

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

04:50

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

06:19

Comparing Normal Means with Equal Variances (Bayesian and frequentist)

07:32

Comparing three or more binomial probabilities

09:37

Comparing two binomial probabilities

07:25

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

16:05

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

03:28

Bayesian posterior probabilities for one-sided alternative hypotheses

22:24

Why p-values dont mean what you think they mean

10:43

Correspondence between pvalues and confidence intervals

08:55

T-tests

16:57

Hypothesis tests

13:00

p-values

14:19

Statistical hypotheses

18:28

I05 Confidence intervals

10:38

Sampling distributions

08:40

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

15:06

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

22:04

I3 Bayesian parameter estimation with a binomial model example

04:14

Inverse gamma random variables

05:45

Exponential random variables

04:44

Gamma random variables

11:52

Student's t random variables

22:04

I3 Bayesian parameter estimation with the binomial model as an example

10:36

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

09:37

I2 Likelihood (part 2/3) Likelihood

06:46

Statistical modeling - Inference 2 (part 1/3)

08:48

Graphical Statistics - Inference 1.4/4

10:22

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

10:05

Estimators - Inference 1.2/4