How to practice for the R quiz (mosaic and formula interface)
Nicholas Horton
How to practice for the R quiz (mosaic and formula interface)
5:50
Sheriff Emeritus Robert Garvey closing the 199th commencement ceremony for the Class of 2020
Nicholas Horton
Sheriff Emeritus Robert Garvey closing the 199th commencement ceremony for the Class of 2020
1:32
Retired Sheriff Robert Garvey closing the 200th commencement of Amherst College May 30, 2021
Nicholas Horton
Retired Sheriff Robert Garvey closing the 200th commencement of Amherst College May 30, 2021
0:44
Creating a data package in RStudio using devtools (class example from STAT495F20)
Nicholas Horton
Creating a data package in RStudio using devtools (class example from STAT495F20)
20:19
Shiny restaurants challenge
Nicholas Horton
Shiny restaurants challenge
1:05
Shiny babynames challenge
Nicholas Horton
Shiny babynames challenge
0:32
Shiny restaurant violations example
Nicholas Horton
Shiny restaurant violations example
7:04
Shiny babynames example
Nicholas Horton
Shiny babynames example
4:28
Mine Cetinkaya Rundel git reflections for JSM 2020 panel
Nicholas Horton
Mine Cetinkaya Rundel git reflections for JSM 2020 panel
7:25
Maria Tackett (Duke University): including GitHub as a learning outcome for her course
Nicholas Horton
Maria Tackett (Duke University): including GitHub as a learning outcome for her course
2:22
Joan Garfield (University of Minnesota) tribute to George Whitfield Cobb, recorded May 15, 2020
Nicholas Horton
Joan Garfield (University of Minnesota) tribute to George Whitfield Cobb, recorded May 15, 2020
4:03
Congratulations to the 2020 Amherst College Mathematics and Statistics graduates
Nicholas Horton
Congratulations to the 2020 Amherst College Mathematics and Statistics graduates
3:38
Ron Wasserstein Amherst College virtual talk "Moving to a World Beyond P less than 0.05”
Nicholas Horton
Ron Wasserstein Amherst College virtual talk "Moving to a World Beyond P less than 0.05”
1:00:50
Chapter 7 of IS5 in R: Linear Regression
Nicholas Horton
Chapter 7 of IS5 in R: Linear Regression
4:41
Chapter 6 of IS5 in R: Scatterplots, Association, and Correlation
Nicholas Horton
Chapter 6 of IS5 in R: Scatterplots, Association, and Correlation
4:06
Chapter 5 of IS5 in R: Standard Deviation as a Ruler and the Normal Model
Nicholas Horton
Chapter 5 of IS5 in R: Standard Deviation as a Ruler and the Normal Model
3:58
Chapter 4 of IS5 in R: Understanding and Comparing Distributions
Nicholas Horton
Chapter 4 of IS5 in R: Understanding and Comparing Distributions
2:47
Chapter 3 of IS5 in R: Relationships Between Categorical Variables
Nicholas Horton
Chapter 3 of IS5 in R: Relationships Between Categorical Variables
4:32
Chapter 2 of IS5 in R: Displaying and Describing Data
Nicholas Horton
Chapter 2 of IS5 in R: Displaying and Describing Data
5:48
Chapter 1 of IS5 in R: Stats Starts Here
Nicholas Horton
Chapter 1 of IS5 in R: Stats Starts Here
3:44
Chapter 0 of IS5 in R: overview of the materials
Nicholas Horton
Chapter 0 of IS5 in R: overview of the materials
1:51
Statistics and the SAT Math sample questions
Nicholas Horton
Statistics and the SAT Math sample questions
6:14
Amherst College Stats Comps template Amherst College Stats Comps template
Nicholas Horton
Amherst College Stats Comps template Amherst College Stats Comps template
4:15
Using the Amherst College Stats Comps template
Nicholas Horton
Using the Amherst College Stats Comps template
4:46
Modern Data Science with R: Overview of "Supervised learning"
Nicholas Horton
Modern Data Science with R: Overview of "Supervised learning"
2:58
Modern Data Science with R: Overview of "Text as Data"
Nicholas Horton
Modern Data Science with R: Overview of "Text as Data"
2:30
sdm4 overview of chapter 29 (Multiple regression wisdom)
Nicholas Horton
sdm4 overview of chapter 29 (Multiple regression wisdom)
4:08
sdm4 overview of chapter 28 (Multiple regression part 2 of 2: inferential models)
Nicholas Horton
sdm4 overview of chapter 28 (Multiple regression part 2 of 2: inferential models)
1:56
sdm4 overview chapter28 (Multiple regression part 1 of 2: descriptive models)
Nicholas Horton
sdm4 overview chapter28 (Multiple regression part 1 of 2: descriptive models)
4:12
sdm4 overview of chapter 27 (Multiway analysis of variance)
Nicholas Horton
sdm4 overview of chapter 27 (Multiway analysis of variance)
2:14
sdm4 overview of chapter 26 (Analysis of variance)
Nicholas Horton
sdm4 overview of chapter 26 (Analysis of variance)
3:51
sdm4 overview of chapter 25 (inferences for regression)
Nicholas Horton
sdm4 overview of chapter 25 (inferences for regression)
5:36
sdm4 overview of chapter 24 (Comparing counts)
Nicholas Horton
sdm4 overview of chapter 24 (Comparing counts)
4:21
sdm4 overview of chapter 23 (Paired samples and blocking)
Nicholas Horton
sdm4 overview of chapter 23 (Paired samples and blocking)
2:39
sdm4 overview of chapter22 (Comparing groups)
Nicholas Horton
sdm4 overview of chapter22 (Comparing groups)
4:28
sdm4 overview of chapter 21 (More about tests and intervals
Nicholas Horton
sdm4 overview of chapter 21 (More about tests and intervals
3:43
sdm4 overview of chapter 20 (Inferences about means)
Nicholas Horton
sdm4 overview of chapter 20 (Inferences about means)
5:12
sdm4 overview of chapter 19 (Testing hypotheses about proportions)
Nicholas Horton
sdm4 overview of chapter 19 (Testing hypotheses about proportions)
5:44
sdm4 overview of chapter 18 (Confidence intervals for proportions-)
Nicholas Horton
sdm4 overview of chapter 18 (Confidence intervals for proportions-)
4:45
sdm4 overview of chapter 17 (Sampling distribution models)
Nicholas Horton
sdm4 overview of chapter 17 (Sampling distribution models)
6:07
sdm4 overview of chapter 16 (Probability models)
Nicholas Horton
sdm4 overview of chapter 16 (Probability models)
4:13
sdm4 overview of chapter 15 (Random variables part 2 of 2)
Nicholas Horton
sdm4 overview of chapter 15 (Random variables part 2 of 2)
1:05
sdm4 overview of chapter 15 (Random variables part 1 of 2)
Nicholas Horton
sdm4 overview of chapter 15 (Random variables part 1 of 2)
4:18
sdm4 overview of chapter 14 (Probability rules)
Nicholas Horton
sdm4 overview of chapter 14 (Probability rules)
3:26
sdm4 overview of chapter 13 (From randomness to probability)
Nicholas Horton
sdm4 overview of chapter 13 (From randomness to probability)
3:36
sdm4 overview of chapter 12 (Experiments and observational studies)
Nicholas Horton
sdm4 overview of chapter 12 (Experiments and observational studies)
3:21
sdm4 overview of chapter 11 (sample surveys)
Nicholas Horton
sdm4 overview of chapter 11 (sample surveys)
1:36
sdm4 overview of chapter 10 (Randomness)
Nicholas Horton
sdm4 overview of chapter 10 (Randomness)
1:18
sdm4 overview of chapter 9 (re-rexpressing data and transformations)
Nicholas Horton
sdm4 overview of chapter 9 (re-rexpressing data and transformations)
1:20
sdm4 overview of chapter 8 (Regression wisdom)
Nicholas Horton
sdm4 overview of chapter 8 (Regression wisdom)
4:56
sdm4 overview of chapter 7 (Linear regression)
Nicholas Horton
sdm4 overview of chapter 7 (Linear regression)
5:52
sdm4 overview of chapter 6 (Scatterplots, association, and correlation)
Nicholas Horton
sdm4 overview of chapter 6 (Scatterplots, association, and correlation)
5:05
sdm4 overview of chapter 5 (Standard deviation as a ruler and the normal model)
Nicholas Horton
sdm4 overview of chapter 5 (Standard deviation as a ruler and the normal model)
5:18
sdm4 overview of chapter 4 (Comparing groups)
Nicholas Horton
sdm4 overview of chapter 4 (Comparing groups)
5:42
sdm4 overview of chapter 3 (Quantitative variables)
Nicholas Horton
sdm4 overview of chapter 3 (Quantitative variables)
6:36
sdm4 overview of chapter 2 (Categorical data)
Nicholas Horton
sdm4 overview of chapter 2 (Categorical data)
4:22
sdm4 overview of chapter 1 (Introduction)
Nicholas Horton
sdm4 overview of chapter 1 (Introduction)
5:08
Changing monitor source in Merrill 131
Nicholas Horton
Changing monitor source in Merrill 131
0:16
Using Github with the Amherst College RStudio server
Nicholas Horton
Using Github with the Amherst College RStudio server
7:32
Deval Patrick's celebratory remarks as Amtrak restores service to Northampton, MA
Nicholas Horton
Deval Patrick's celebratory remarks as Amtrak restores service to Northampton, MA
3:18
Interpretation of multiple regression coefficients (and residual diagnostics)
Nicholas Horton
Interpretation of multiple regression coefficients (and residual diagnostics)
5:12
Working through the More Data in R lab.
Nicholas Horton
Working through the More Data in R lab.
6:45
Getting Started with RStudio at Amherst: Sample Homework in Markdown
Nicholas Horton
Getting Started with RStudio at Amherst: Sample Homework in Markdown
4:24
Getting Started with RStudio at Amherst: more resources and next steps
Nicholas Horton
Getting Started with RStudio at Amherst: more resources and next steps
2:02
Getting Started with RStudio at Amherst: All about add-on packages
Nicholas Horton
Getting Started with RStudio at Amherst: All about add-on packages
2:10
Getting started with RStudio: second steps with R Markdown
Nicholas Horton
Getting started with RStudio: second steps with R Markdown
4:05
Getting Started with RStudio at Amherst: Dealing with files
Nicholas Horton
Getting Started with RStudio at Amherst: Dealing with files
3:05
Getting Started with RStudio at Amherst: First steps with R Markdown
Nicholas Horton
Getting Started with RStudio at Amherst: First steps with R Markdown
3:50
Getting Started with RStudio at Amherst: second steps in R
Nicholas Horton
Getting Started with RStudio at Amherst: second steps in R
3:36
Getting Started with RStudio at Amherst: First Steps in R
Nicholas Horton
Getting Started with RStudio at Amherst: First Steps in R
1:28
Getting Started with RStudio at Amherst: Authentication
Nicholas Horton
Getting Started with RStudio at Amherst: Authentication
0:45