Канал: Jarad Niemi

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

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

Making Awesome Scatterplots with ggplot2 | Learn R

Making Awesome Scatterplots with ggplot2 | Learn R

R packages contain ALL the functionality in R | Learn R

R packages contain ALL the functionality in R | Learn R

The Most Important Data Structure in R | Learn R

The Most Important Data Structure in R | Learn R

The Basics on Factors in R | Learn R

The Basics on Factors in R | Learn R

Data Types and Data Dimensions in R | Learn R

Data Types and Data Dimensions in R | Learn R

Getting Started with R | Learn R

Getting Started with R | Learn R

Monte Hall Problem Explained

Monte Hall Problem Explained

Most Dangerous Setting in R | Learn R

Most Dangerous Setting in R | Learn R

Install R and R Studio | Learn R

Install R and R Studio | Learn R

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

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

Analyses of Two-Factor Unbalanced and Incomplete Designs in R

Analyses of Two-Factor Unbalanced and Incomplete Designs in R

Analysis of a Two-Factor Completely Randomized Design in R

Analysis of a Two-Factor Completely Randomized Design in R

Randomized Complete Block Design (RCBD)

Randomized Complete Block Design (RCBD)

Completely Randomized Design (CRD)

Completely Randomized Design (CRD)

Demonstrating Contrasts using a Potato Scab Example

Demonstrating Contrasts using a Potato Scab Example

Statistical Contrasts for Addressing Specific Scientific Hypotheses

Statistical Contrasts for Addressing Specific Scientific Hypotheses

Regression: F-tests

Regression: F-tests

One-way Analysis of Variance (ANOVA)

One-way Analysis of Variance (ANOVA)

Interpreting Regression p-values as Posterior Probabilities

Interpreting Regression p-values as Posterior Probabilities

Regression Interactions for Vastly More Complex Modeling

Regression Interactions for Vastly More Complex Modeling

Multiple Regression: Higher Order Terms and Additional Explanatory Variables

Multiple Regression: Higher Order Terms and Additional Explanatory Variables

Regression with Categorical Explanatory Variables

Regression with Categorical Explanatory Variables

Simple Linear Regression with a Binary Explanatory Variable

Simple Linear Regression with a Binary Explanatory Variable

Simple Linear Regression: an Example using Logarithms

Simple Linear Regression: an Example using Logarithms

Simple Linear Regression using Logarithms

Simple Linear Regression using Logarithms

Regression diagnostics in (base) R

Regression diagnostics in (base) R

Regression: Uncertainty and Prediction intervals

Regression: Uncertainty and Prediction intervals

Regression: Choosing Explanatory Variables

Regression: Choosing Explanatory Variables

Simple Linear Regression

Simple Linear Regression

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

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

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

Comparing three or more binomial probabilities

Comparing three or more binomial probabilities

Comparing two binomial probabilities

Comparing two binomial probabilities

Bayesian posterior probabilities for one-sided alternative hypotheses

Bayesian posterior probabilities for one-sided alternative hypotheses

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

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

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

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

Why p-values dont mean what you think they mean

Why p-values dont mean what you think they mean

Correspondence between pvalues and confidence intervals

Correspondence between pvalues and confidence intervals

T-tests

T-tests

Hypothesis tests

Hypothesis tests

p-values

p-values

Statistical hypotheses

Statistical hypotheses

I05 Confidence intervals

I05 Confidence intervals

Sampling distributions

Sampling distributions

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

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

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

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

I3 Bayesian parameter estimation with a binomial model example

I3 Bayesian parameter estimation with a binomial model example

Student's t random variables

Student's t random variables

Gamma random variables

Gamma random variables

Exponential random variables

Exponential random variables

Inverse gamma random variables

Inverse gamma random variables

I3 Bayesian parameter estimation with the binomial model as an example

I3 Bayesian parameter estimation with the binomial model as an example

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

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

I2 Likelihood (part 2/3) Likelihood

I2 Likelihood (part 2/3) Likelihood

Statistical modeling - Inference 2 (part 1/3)

Statistical modeling - Inference 2 (part 1/3)

Graphical Statistics - Inference 1.4/4

Graphical Statistics - Inference 1.4/4

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

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

Estimators - Inference 1.2/4

Estimators - Inference 1.2/4

Statistics - Inference 1.1/4

Statistics - Inference 1.1/4

P5 - Multiple random variables

P5 - Multiple random variables

P4-3 Central Limit Theorem - part 3/3 (CLT Astronomy Example)

P4-3 Central Limit Theorem - part 3/3 (CLT Astronomy Example)

P4-2 Central Limit Theorem - part (2/3) Approximating Normal Distributions

P4-2 Central Limit Theorem - part (2/3) Approximating Normal Distributions

P4-1 Central Limit Theorem - part 1/3 (Formal definition)

P4-1 Central Limit Theorem - part 1/3 (Formal definition)

P3-2 Continuous random variables - part 2/3 (Uniform random variables)

P3-2 Continuous random variables - part 2/3 (Uniform random variables)

P3-3 Continuous random variables - part 3/3 (normal random variables)

P3-3 Continuous random variables - part 3/3 (normal random variables)

P3-1 Continuous random variables - part 1/3 (general continuous random variables)

P3-1 Continuous random variables - part 1/3 (general continuous random variables)

P2-5 Discrete random variables - part 5/5 (Poisson random variables)

P2-5 Discrete random variables - part 5/5 (Poisson random variables)

P2-4 Discrete random variables - part 4/5 (Binomial random variables)

P2-4 Discrete random variables - part 4/5 (Binomial random variables)

P2-3 Discrete random variables - part 3/5 (Bernoulli random variables)

P2-3 Discrete random variables - part 3/5 (Bernoulli random variables)

P2-2 Discrete random variables - part 2/5 (general discrete random variables)

P2-2 Discrete random variables - part 2/5 (general discrete random variables)

P2-1 Discrete random variables - part 1/5 (continuous vs discrete)

P2-1 Discrete random variables - part 1/5 (continuous vs discrete)

Succinct Video on Probability including Sets, Conditional Probability, and Statistical Independence

Succinct Video on Probability including Sets, Conditional Probability, and Statistical Independence

COVID-19 Forecast Hub Update - EW27

COVID-19 Forecast Hub Update - EW27

Dr. J's Guide to Combinations (without replacement)

Dr. J's Guide to Combinations (without replacement)

Dr. J's Guide to Permutations

Dr. J's Guide to Permutations

Dr. J's Guide to Permutations with Replacement

Dr. J's Guide to Permutations with Replacement

Dr. J's Guide to Counting

Dr. J's Guide to Counting

COVID-19 Forecast Hub - Introduction

COVID-19 Forecast Hub - Introduction

Research summary for incoming graduate students (in the middle of a COVID-19 pandemic)

Research summary for incoming graduate students (in the middle of a COVID-19 pandemic)

Let's flatten the curve of the impending COVID19 outbreak in the U.S.

Let's flatten the curve of the impending COVID19 outbreak in the U.S.

Dr. J's Guide to Independence of Events

Dr. J's Guide to Independence of Events

Dr. J's Guide to Bayes' rule with an application to diagnostic screening tests

Dr. J's Guide to Bayes' rule with an application to diagnostic screening tests

Dr. J's Guide to Probability

Dr. J's Guide to Probability

Dr. J's Guide to Cardinality

Dr. J's Guide to Cardinality

Dr. J's Guide to Partitions

Dr. J's Guide to Partitions

Dr. J's Guide to Venn Diagrams

Dr. J's Guide to Venn Diagrams

Dr. J's Guide to Set Operations

Dr. J's Guide to Set Operations

Dr. J's Guide to Subsets and Set Comparisons

Dr. J's Guide to Subsets and Set Comparisons

Dr. J's Guide to Sets

Dr. J's Guide to Sets

Channel relaunch

Channel relaunch

Binomial vs Poisson

Binomial vs Poisson

Case Statistics in Regression

Case Statistics in Regression

Variable Selection

Variable Selection

Multiple regression with indicator functions

Multiple regression with indicator functions

Multiple Regression

Multiple Regression

Regression Diagnostics

Regression Diagnostics

Lack-of-fit F-test

Lack-of-fit F-test

Simple Linear Regression

Simple Linear Regression

Contrasts

Contrasts

General F-test

General F-test

Multiple Comparisons

Multiple Comparisons

One-way ANOVA F-test

One-way ANOVA F-test

Hypothesis test decision tree

Hypothesis test decision tree

Statistical Modeling

Statistical Modeling

Finding pvalues

Finding pvalues

Sign and signed rank test

Sign and signed rank test

Rank Sum Test

Rank Sum Test

Paired t-test

Paired t-test

Two-sample t-test

Two-sample t-test

Randomization pvalues

Randomization pvalues

Statistical Inference

Statistical Inference

randomized_experiment_vs_observational_study

randomized_experiment_vs_observational_study

Generalized linear mixed effect model

Generalized linear mixed effect model

A mixed effects model for seedling weights.

A mixed effects model for seedling weights.

Mixed effect models

Mixed effect models

Hierarchical linear models

Hierarchical linear models

Missing data analysis

Missing data analysis

Bayesian analysis of simple random sampling

Bayesian analysis of simple random sampling

Weighted regression from a Bayesian perspective

Weighted regression from a Bayesian perspective

Bayesian survival analysis

Bayesian survival analysis

Bayesian linear regression

Bayesian linear regression

Metropolis-within-Gibbs

Metropolis-within-Gibbs

Gibbs sampling

Gibbs sampling

Metropolis-Hastings algorithm

Metropolis-Hastings algorithm

Accept-Reject method

Accept-Reject method

Inverse CDF method

Inverse CDF method

A basic model for ordinal data

A basic model for ordinal data

Poisson hierarchical models

Poisson hierarchical models

Normal hierarchical models

Normal hierarchical models

Exchangeability of random sequences

Exchangeability of random sequences

Bayesian hierarchical models

Bayesian hierarchical models

Is the posterior proper in Bayesian inference?

Is the posterior proper in Bayesian inference?

Monte Carlo integration

Monte Carlo integration

Bayesian inference for Poisson data

Bayesian inference for Poisson data

Noninformative prior distributions

Noninformative prior distributions

Conjugacy of prior distributions

Conjugacy of prior distributions

Bayesian parameter estimation the hard way

Bayesian parameter estimation the hard way

Bayesian parameter estimation 2

Bayesian parameter estimation 2

Bayesian parameter estimation

Bayesian parameter estimation

Bayesian statistics

Bayesian statistics

Bayes rule

Bayes rule