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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?

Conducting Hypothesis Tests

Learn and understand the conduct of hypothesis tests.

We'll cover the following

The infer package workflow

Let’s learn how to seamlessly modify our previously seen infer code for constructing confidence intervals to conduct hypothesis tests. The basic outline of the workflow is almost identical, except for an additional hypothesize() step between the specify() and generate() steps, as seen in the figure below.

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