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Data-Centric Statistical Inference Using R and Tidyverse

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Getting Started with Data in R

Getting Started How Do We Code in R?Tips on Learning to Code Explore Our First Dataset Exploring Data Frames Test Your Skills on Getting Started with Data in R

Data Visualization

The Grammar of Graphics Scatterplots Overplotting Line Graphs Histograms Facets Boxplots Barplots When to Avoid Pie Charts Recap: Data Visualization Test Your Skills on Data Visualization

Data Wrangling

The Pipe Operator Filter Rows Summarize Variables Group by Rows Mutate Existing Variables Join Data Frames Other Verbs Recap: Data Wrangling Test Your Skills on Data Wrangling

Data Importing and “Tidy” Data

Introduction of Data and Needed Packages Tidy Data Converting to Tidy Data Case Study: Democracy in Guatemala Tidyverse Package Test Your Skills on Data Importing and Tidy Data

Basic Regression

Introduction and Needed Packages

One Numerical Explanatory Variable

EDA for a Numerical Explanatory Variable

Simple Linear Regression for a Numerical Explanatory Variable

Observed/Fitted Values and Residuals on Numerical Variables

One Categorical Explanatory Variable

Exploratory Data Analysis of One Categorical Explanatory Variable

Linear Regression for One Categorical Explanatory Variable

Observed/Fitted Values and Residuals on Categorical Variables

Correlation Is Not Necessarily Causation

The Best-Fitting Line

The get_regression_x() Functions

Test Your Skills on Basic Regression

Multiple Regression

Introduction and Exploratory Data Analysis Interaction Model Parallel Slopes Model Observed/Fitted Values and Residuals Two Numerical Explanatory Variables Regression Plane Model Selection Using Visualizations Correlation Coefficient and Simpson's Paradox Test Your Skills on Multiple Regression

Statistical Inference with the infer Package

Sampling Virtual Sampling Virtual Sampling Using Different Shovels Sampling Framework Statistical Definitions Case Study: Polls Recap: Statistical Inference with infer Test Your Skills on Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Pennies Activity Resampling Understanding Confidence Intervals Constructing Confidence Intervals with the Pennies Sample Comparing with the Original Workflow Percentile Method with the infer Package Interpreting Confidence Intervals Precise and Shorthand Interpretation Case Study: Is Yawning Contagious?Constructing the Confidence Interval with MythBusters Yawn Interpreting the Confidence Interval Recap: Bootstrapping and Confidence Intervals Test Your Skills on Bootstrapping and Confidence Intervals

Hypothesis Testing

Promotions Activity Understanding Hypothesis Tests Conducting Hypothesis Tests Comparison with Confidence Intervals Interpreting Hypothesis Tests Case Study: Are Action or Romance Movies Rated Higher?Theory-Based Hypothesis Tests Recap: Hypothesis Testing Test Your Skills on Hypothesis Testing

Inference for Regression

Regression Refresher Interpreting Regression Tables-I Interpreting Regression Tables-II Conditions for Inference for Regression-I Conditions for Inference for Regression-II Simulation-Based Inference for Regression Simulation-Based Inference Recap: Inference for Regression Test Your Skills on Inference for Regression

Tell a Story with Data

Review: Data Science Pipeline Case Study: Seattle House Prices-I Case Study: Seattle House Prices-II Case Study: Effective Data Storytelling

Appendix

What are R and RStudio?What are R Packages?

EDA for a Numerical Explanatory Variable

Learn about analyzing numerical data to make observations that will help in regression.

Typing out all these summary statistic functions in summarize() would be long and tedious. Instead, let’s use the convenient skim() function from the skimr package. This function takes in a data frame, skims it, and returns the commonly used summary statistics. Let’s take our evals_ch5 data frame, select() only the outcome and explanatory variables teaching score and bty_avg, and pipe them into the skim() function:

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