Канал: Brian Caffo

DS4PH Git github and version control

DS4PH Git github and version control

How I'm using Zoom for teaching

How I'm using Zoom for teaching

Followup on zoom for teaching, what the recordings page looks like

Followup on zoom for teaching, what the recordings page looks like

04 Basic regression using pytorch

04 Basic regression using pytorch

03 What are those NN diagrams 3

03 What are those NN diagrams 3

02 What are those neural network diagrams 2

02 What are those neural network diagrams 2

01 Neural network diagrams, getting started

01 Neural network diagrams, getting started

Brian Caffo Live Stream

Brian Caffo Live Stream

On causation and manipulation

On causation and manipulation

The geometric mean and logging data

The geometric mean and logging data

Bayesianism versus Frequentism rediscussed via statistical pragmatism

Bayesianism versus Frequentism rediscussed via statistical pragmatism

How to create and serve a simple web page with github pages.

How to create and serve a simple web page with github pages.

Getting gigantum running on a digital ocean server

Getting gigantum running on a digital ocean server

Deep learning in public health and personalized medicine

Deep learning in public health and personalized medicine

Methods in Biostatistics with R short intro 720HD

Methods in Biostatistics with R short intro 720HD

Methods in Biostatistics with R

Methods in Biostatistics with R

A trick for running Rstudio on paperspace through SSH

A trick for running Rstudio on paperspace through SSH

How to use docker to run an rustdio server on digital ocean

How to use docker to run an rustdio server on digital ocean

On applying to and accepting offers from PhD/Master's programs

On applying to and accepting offers from PhD/Master's programs

Trying out gnu-root debian

Trying out gnu-root debian

Try out the new Rstudio server

Try out the new Rstudio server

Why are P-values uniformly distributed under H0?

Why are P-values uniformly distributed under H0?

Will AI eat statistics?

Will AI eat statistics?

How can I get started in Bayesian data analysis?

How can I get started in Bayesian data analysis?

Silly little tip, register your IP with a DNS hosting service

Silly little tip, register your IP with a DNS hosting service

Setting up keras, rstudio server and shiny

Setting up keras, rstudio server and shiny

What math prereqs do I need for biostat grad school; how long will it take me to get them

What math prereqs do I need for biostat grad school; how long will it take me to get them

Why aren't you settings your chrome settings as a search engine?

Why aren't you settings your chrome settings as a search engine?

Clearing the docket

Clearing the docket

How to embed a shiny app

How to embed a shiny app

What do people mean when they say a statistic is "Robust to normality"?

What do people mean when they say a statistic is "Robust to normality"?

R versus Python, sort of, not really

R versus Python, sort of, not really

Should I rescale my regression variables?

Should I rescale my regression variables?

Post model selection inference

Post model selection inference

What R IDE should I use

What R IDE should I use

Screencastify

Screencastify

Review of Jeff Leek's blog post on chromebook data science

Review of Jeff Leek's blog post on chromebook data science

Which regression

Which regression

Review of wevideo

Review of wevideo

What are GAMs

What are GAMs

What is GEE (Episode 27)

What is GEE (Episode 27)

Short evaluation of Paperspace

Short evaluation of Paperspace

What is a P-Value? (episode 24)

What is a P-Value? (episode 24)

What should I use to serve R applications over the internet? (episode 23)

What should I use to serve R applications over the internet? (episode 23)

Week 22 What are the steps of a data science experiment

Week 22 What are the steps of a data science experiment

Week 21 Is regression an ML technique?

Week 21 Is regression an ML technique?

Week 20 Setting Up an Rstudio Server on Digital Ocean or EC2

Week 20 Setting Up an Rstudio Server on Digital Ocean or EC2

Week 18 the future of MOOCs

Week 18 the future of MOOCs

Week 17, Mediation versus Moderation

Week 17, Mediation versus Moderation

week 16 will data science eventually take over the field of statistics?

week 16 will data science eventually take over the field of statistics?

week 15 what mathematics should I know to apply to a stats/biostats PhD program?

week 15 what mathematics should I know to apply to a stats/biostats PhD program?

Week 14, professors and startups

Week 14, professors and startups

Week 13, robust variance estimation

Week 13, robust variance estimation

Week 12 what is Chi-Squared testing / Filtering

Week 12 what is Chi-Squared testing / Filtering

Week 11, question olio

Week 11, question olio

Week 10 What's too big? Also, excuses for no video last week

Week 10 What's too big? Also, excuses for no video last week

Week 9 Machine Learning versus classical statistics

Week 9 Machine Learning versus classical statistics

Week 8, should I get a PhD, if so in what?

Week 8, should I get a PhD, if so in what?

Week 7 R versus Excel

Week 7 R versus Excel

Week 6, IID assumptions demystified, sort of

Week 6, IID assumptions demystified, sort of

Ask Brian Week 5: ggplot2 versus base R graphics

Ask Brian Week 5: ggplot2 versus base R graphics

Week 4, A rambling rant about Bayes versus frequentist statistics

Week 4, A rambling rant about Bayes versus frequentist statistics

Week 3 Data Scientist Versus Statistician

Week 3 Data Scientist Versus Statistician

Week 2 Biological Significance Versus Statistical Significance

Week 2 Biological Significance Versus Statistical Significance

What are some good ways to learn R?

What are some good ways to learn R?

13 6 Variance of the Residual Variance in an Overfit Model

13 6 Variance of the Residual Variance in an Overfit Model

13 2 Underfitting Bias

13 2 Underfitting Bias

13 4 Residual Variance Bias under an Underfit Model

13 4 Residual Variance Bias under an Underfit Model

13 8 Variance Inflation Factors

13 8 Variance Inflation Factors

13 3 Variance with an Underfit Model

13 3 Variance with an Underfit Model

13 7 Impact of Inclusion of Variables on Standard Errors

13 7 Impact of Inclusion of Variables on Standard Errors

13 5 Bias in an Overfit Model

13 5 Bias in an Overfit Model

13 1 Overfitting and Underfitting

13 1 Overfitting and Underfitting

Plotly in R part 2 of 8

Plotly in R part 2 of 8

Plotly in R part 6 of 8

Plotly in R part 6 of 8

Plotly in R part 3 of 8

Plotly in R part 3 of 8

Plotly in R part 5 of 8

Plotly in R part 5 of 8

Plotly in R part 4 of 8

Plotly in R part 4 of 8

Plotly in R part 8 of 8

Plotly in R part 8 of 8

Plotly in R part 7 of 8

Plotly in R part 7 of 8

Plotly in R part 1 of 8

Plotly in R part 1 of 8

R Markdown slides part 2 of 6

R Markdown slides part 2 of 6

R Markdown slides part 6 of 6

R Markdown slides part 6 of 6

R Markdown slides part 5 of 6

R Markdown slides part 5 of 6

R Markdown slides part 1 of 6

R Markdown slides part 1 of 6

R Markdown slides part 3 of 6

R Markdown slides part 3 of 6

R Markdown slides part 4 of 6

R Markdown slides part 4 of 6

Google Vis Part 1 of 2

Google Vis Part 1 of 2

GoogleVis Part 2 of 2

GoogleVis Part 2 of 2

Using Leaflet in R part 2 of 6

Using Leaflet in R part 2 of 6

Using Leaflet in R part 5 of 6

Using Leaflet in R part 5 of 6

Using Leaflet in R part 1 of 6

Using Leaflet in R part 1 of 6

Using Leaflet in R part 6 of 6

Using Leaflet in R part 6 of 6

Using Leaflet in R part 3 of 6

Using Leaflet in R part 3 of 6

Using Leaflet in R part 4 of 6

Using Leaflet in R part 4 of 6

Shiny lecture 2 part 3 of 6

Shiny lecture 2 part 3 of 6

Shiny lecture 2 part 2 of 6

Shiny lecture 2 part 2 of 6

Shiny lecture 2 part 6 of 6

Shiny lecture 2 part 6 of 6

Shiny lecture 2 part 4 of 6

Shiny lecture 2 part 4 of 6

Shiny Lecture 2 part 5 of 6

Shiny Lecture 2 part 5 of 6

Shiny lecture 2 part 1 of 6

Shiny lecture 2 part 1 of 6

RStudio's Shiny Lecture 1 Part 2 of 5

RStudio's Shiny Lecture 1 Part 2 of 5

RStudio's Shiny Lecture 1 Part 5 of 5

RStudio's Shiny Lecture 1 Part 5 of 5

RStudio's Shiny Lecture 1 Part 4 of 5

RStudio's Shiny Lecture 1 Part 4 of 5

RStudio's Shiny Lecture 1 Part 1 of 5

RStudio's Shiny Lecture 1 Part 1 of 5

RStudio's Shiny Lecture 1 Part 3 of 5

RStudio's Shiny Lecture 1 Part 3 of 5

Shiny Gadgets Part 3 of 3

Shiny Gadgets Part 3 of 3

Shiny Gadgets Part 1 of 3

Shiny Gadgets Part 1 of 3

ShinyGadgets Part 2 of 3

ShinyGadgets Part 2 of 3

episode1: render a totally heavy metal 3d interactive skull, in R

episode1: render a totally heavy metal 3d interactive skull, in R

Puppet Roger Peng Intro

Puppet Roger Peng Intro

Non Significance

Non Significance

Report Writing

Report Writing

Multiplicity

Multiplicity

Negative Controls

Negative Controls

Effect Size, Significance, Modeling

Effect Size, Significance, Modeling

Comparison with Benchmark Effects

Comparison with Benchmark Effects

Version Control

Version Control

Confounding

Confounding

Brian Causality Part 1 New Text Slides

Brian Causality Part 1 New Text Slides

adjustment

adjustment

Brian Causality Part 2

Brian Causality Part 2

A/B testing

A/B testing

Experimental Design and Observational Analysis

Experimental Design and Observational Analysis

Bias

Bias

Managing the Data Pull

Managing the Data Pull

Brian Machine Learning vs Traditional Statistics Part 1

Brian Machine Learning vs Traditional Statistics Part 1

Defining Success in Data Science

Defining Success in Data Science

A discussion of the outputs of a data science experiment

A discussion of the outputs of a data science experiment

Brian Machine Learning vs Traditional Statistics Part 2

Brian Machine Learning vs Traditional Statistics Part 2

Machine learning, the basics

Machine learning, the basics

A Crash Course in Data Science: What is Statistics Good For

A Crash Course in Data Science: What is Statistics Good For

Data Science in Real Life: The Perfect Data Science Experience 2

Data Science in Real Life: The Perfect Data Science Experience 2

Data Science in Real Life: Perfect Data Science Experience 1

Data Science in Real Life: Perfect Data Science Experience 1

12 4 residuals

12 4 residuals

12 3 residuals

12 3 residuals

12 1 residuals

12 1 residuals

12 2 residuals

12 2 residuals

11 6 Distributional Results

11 6 Distributional Results

11 7 Distributional Results

11 7 Distributional Results

11 8 Distributional Results

11 8 Distributional Results

11 5 Distributional Results

11 5 Distributional Results

11 3 Distributional Results

11 3 Distributional Results

11 1 Distributional Results

11 1 Distributional Results

11 4 Distributional Results

11 4 Distributional Results

11 2 Distributional Results

11 2 Distributional Results

10 2 Normal Distribution

10 2 Normal Distribution

10 1 Normal Distribution

10 1 Normal Distribution

10 4 Normal Distribution

10 4 Normal Distribution

10 3 Normal Distribution

10 3 Normal Distribution

09 1 Expected Values

09 1 Expected Values

09 2 Expected Values

09 2 Expected Values

09 3 Expected Values

09 3 Expected Values

09 5 Expected Values

09 5 Expected Values

09 4 Expected Values

09 4 Expected Values

09 6 Expected Values

09 6 Expected Values

08 2 residuals

08 2 residuals

08 1 residuals

08 1 residuals

07 4 bases

07 4 bases

07 2 bases

07 2 bases

07 3 bases

07 3 bases

07 1 bases

07 1 bases

06 4 examples

06 4 examples

06 1 examples

06 1 examples

06 2 examples

06 2 examples

06 3 examples

06 3 examples

05 6 least squares corrected

05 6 least squares corrected

04 1 linear regression

04 1 linear regression

04 5 linear regression

04 5 linear regression

04 3 linear regression

04 3 linear regression

04 7 linear regression

04 7 linear regression

04 2 linear regression

04 2 linear regression

04 6 linear regression

04 6 linear regression

04 4 linear regression

04 4 linear regression

05 5 least squares

05 5 least squares

05 3 least squares

05 3 least squares

05 4 least squares

05 4 least squares

05 7 least squares

05 7 least squares

05 1 least squares

05 1 least squares

05 2 least squares

05 2 least squares

03 5 single parameter

03 5 single parameter

03 1 single parameter

03 1 single parameter

03 2 single parameter

03 2 single parameter

03 3 single parameter

03 3 single parameter

03 7 single parameter

03 7 single parameter

03 6 single parameter

03 6 single parameter

03 4 single parameter

03 4 single parameter

02 3 Background

02 3 Background

02 1 Background

02 1 Background

02 6 Background

02 6 Background

02 5 Background

02 5 Background

02 4 Background

02 4 Background

02 2 Background

02 2 Background

Chapter 13 (GLMs) Question 3

Chapter 13 (GLMs) Question 3

Chapter 13 (GLMs) Question 5

Chapter 13 (GLMs) Question 5

Chapter 13 (GLMs) Question 2

Chapter 13 (GLMs) Question 2

Chapter 13 (GLMs) Question 1

Chapter 13 (GLMs) Question 1

Chapter 12 (Multiple) Question 2

Chapter 12 (Multiple) Question 2

Chapter 11 (Resids) Question 3

Chapter 11 (Resids) Question 3

Chapter 11 (Resids) Question 2

Chapter 11 (Resids) Question 2