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Walkthrough video of installing tinytex in R/RStudio, to make it possible to Render PDF documents from Quarto.

6:37 computing R^2 from the ANOVA summary 9:34 hypothesis testing by hand, finding a p-value in R 15:50 confidence interval for the slope in R using the confint() function 18:31 confidence interval for the slope by hand 21:21 finding beta1hat by hand using standard deviations and correlation 23:26 finding beta0hat by hand using means 25:08 prediction and confidence intervals for particular x va...

Focuses on determining which cells provided the largest contribution to the chi-square statistic, using the residuals() function.

Focuses on inference for a difference of two means, in other words a relationship between a quantitative and a categorical variable. Uses the t.test() function. Also covers inference for paired data, using a mean of differences.

This video focuses on inference for two proportions, meaning two categorical variables. Uses the prop.test() function.

Following Tiffany Timbers' instructions for installing Python and conda: - github.com/ttimbers/intro-to-reticulate/blob/main/setup-instructions/macos_install_python.md - github.com/ttimbers/intro-to-reticulate/blob/main/setup-instructions/windows_install_python.md As well as for installing reticulate: - github.com/ttimbers/intro-to-reticulate/blob/main/setup-instructions/setup-after-installing-...

Continuation of inference for a single sample. Review of summary statistics and data visualization for one quantitative variable. Making a confidence interval for a single mean "by hand" as well as with the t.test() function.

Introduces the idea of inference for a single sample using distributional approximations (normal and t distributions). Standard error formulas for confidence intervals for a single proportion and single mean. Review of summary statistics and data viz for a single categorical variable, how to create confidence intervals for a single proportion using prop.test().

Linear regression as a descriptive technique in R. Focuses on the lm() and summary() commands, and making predictions using simple linear regression models.

Exploratory data analysis for two quantitative variables, including scatterplots with gf_point(), scatterplot matrices with ggpairs(), and correlation and correlation matrices using cor().

More exploratory data analysis for one and two categorical variables. Summary statistics (frequency and relative frequency tables) using the tally() function.

How to export images for use in the Spring 2022 labs. Short overview: - knit the document - look for the plots folder - check the box next to the plots you want to download - go to More and then Export - download the .zip file - unzip the file on your computer - upload to Canvas!

Categorical variables pre-lab for Spring 2022. Covers opening the RMarkdown document on rstudio.cloud, and creating bar charts of one and two categorical variables using the gf_bar() function.

This video walks you through the second pre-lab for the Spring 2022 semester. The sampling lab is mostly a "thinking lab," so there's very little code.

Describing data pre-lab (video 1 of 1)

Integrating Computation in Statistics: Instructional Decisions for Teaching R

Checking conditions on real models (2 of 2)

Assessing conditions, again (1 of 2)

More multiple regression (2 of 2)

Introduction to multiple regression (1 of 2)

The National Atlas of the United States of America

Visualizing time and timeseries data

More considerations for visualizing spatial data

Visualizing spatial data: points, lines, and polygons

Visualizing more variables

Multiple testing

Type I and Type II errors

Determining statistical significance

Introduction to randomization distributions