Applied Data Analysis for Public Policy Studies
Syllabus
1
Introduction to
R
1.1
Getting Started
1.2
Starting R and RStudio
1.3
Basic Calculations
1.4
Getting Help
1.5
Installing Packages
1.6
Code
vs Output in this Book
1.7
ScPoApps
Package
1.8
Data Types
1.9
Data Structures
1.10
Data Frames
1.11
Programming Basics
2
Working With Data
2.1
Summary Statistics
2.2
Plotting
2.3
Summarizing Two Variables
2.4
The
tidyverse
3
Linear Regression
3.1
How are
x
and
y
related?
3.2
Ordinary Least Squares (OLS) Estimator
3.3
Predictions and Residuals
3.4
Correlation, Covariance and Linearity
3.5
Analysing
\(Var(y)\)
3.6
Assessing the
Goodness of Fit
3.7
An Example: A Log Wage Equation
3.8
Scaling Regressions
3.9
A Particular Rescaling: The
\(\log\)
Transform
4
Multiple Regression
4.1
All Else Equal
4.2
Multicolinearity
4.3
Log Wage Equation
4.4
How To Make Predictions
5
Categorial Variables
5.1
The Binary Regressor Case
5.2
Dummy and Continuous Variables
5.3
Categorical Variables in
R
:
factor
5.4
Interactions
5.5
(Unobserved) Individual Heterogeneity
6
Regression Inference
6.1
Sampling
6.2
Taking Eleven Samples From The Population
6.3
Handover to
Moderndive
6.4
Uncertainty in Regression Estimates
6.5
What is
true
? What are Statistical Models?
6.6
The Classical Regression Model (CRM)
6.7
Standard Errors in Theory
7
Causality
7.1
Directed Acyclical Graphs (DAG)
7.2
Smoking in a DAG
7.3
Randomized Control Trials (RCT) Primer
7.4
The Potential Outcomes Model
7.5
Omitted Variable Bias and DAGs
7.6
STAR Experiment
8
STAR Experiment
8.1
The STAR Experiment
8.2
PO as Regression
8.3
Implementing STAR
9
Regression Discontinuity Design
9.1
RDD Setup
9.2
Clicking on Heaven’s Door
10
Projects
10.1
Trade Exercise
References
Published with bookdown
Applied Data Analysis for Public Policy Studies
Chapter 10
Projects
This chapter contains several empirical projects.
10.1
Trade Exercise
Trade exercise