12.4 Application: 2021 NJ Election Results.11.6 Application Programming Interfaces.10.3.1 Relationship to Confidence Intervals.10.1 Hypothesis Testing Overview of Process.9.1.2 Prediction/Classification process.8.8.2 Application: Forecasting Election Results.
8.7.1 Example: Butterfly Ballot in Florida.8.6 Application: Predicting Campaign Donations.8.5.1 Regression with Multiple Predictors.8.3.2 Making predictions with regression.7.4.1 Choose an approach: Let’s analyze some polls.7.4 Example: Using polls to predict the 2020 election results.7.3 Example: Forecasting 2020 US Election based on 2016 Results.5.3.1 Applying 3 Identification Strategies.5.3 Application: Economic Effects of Basque Terrorism.5.2.1 Three Common Identification Strategies.4.11 Common R plotting functions and arguments.4.9 Causal claims from before vs. after comparisons.4.4.3 Calculating the Average Treatment Effect.4.4.2 Using ifelse to create new variable.4.4 Application: Changing Minds on Gay Marriage.4.1 Application: Social Status and Economic Views.3.8 Creating New Variables using Conditional statements.3.3 Application: Is there racial discrimination in the labor market?.
3.1.3 Fundamental Problem of Causal Inference.3.1 What separates causation from correlation?.2.6 Comparing Presidential vs. Midterm turnout.2.4.2 Measuring the Turnout in the US Elections.2.3 Functions to summarize univariate data.1.3.5 Executing Commands in your R script.1.3 First Time Working in R and RStudio.