• About this course
  • Drop-in Sessions
  • 1 Introduction to Quantitative Analysis
    • 1.1 Seminar
      • 1.1.1 Getting Started
      • 1.1.2 RStudio
      • 1.1.3 Courseware Setup
      • 1.1.4 Console
      • 1.1.5 Functions
      • 1.1.6 Getting Help
      • 1.1.7 The Assignment Operator
      • 1.1.8 Sequences
      • 1.1.9 Scripts
      • 1.1.10 data frames
      • 1.1.11 Plots
      • 1.1.12 Exercises
    • 1.2 Solutions
      • 1.2.1 Exercise 3)
      • 1.2.2 Exercise 4)
      • 1.2.3 Exercise 5)
      • 1.2.4 Exercise 6)
      • 1.2.5 Exercise 7)
      • 1.2.6 Exercise 8)
      • 1.2.7 Exercise 9)
      • 1.2.8 Exercise 10)
      • 1.2.9 Exercise 11)
  • 2 Descriptive Statistics
    • 2.1 Seminar
      • 2.1.1 Loading data
      • 2.1.2 Central Tendency
      • 2.1.3 Factor Variables
      • 2.1.4 Dispersion
      • 2.1.5 Visualizing Data
      • 2.1.6 Additional Resources
      • 2.1.7 Exercises
    • 2.2 Solutions
      • 2.2.1 Exercise 2
      • 2.2.2 Exercise 3
      • 2.2.3 Exercise 4
      • 2.2.4 Exercise 5
      • 2.2.5 Exercise 6
      • 2.2.6 Exercise 7
      • 2.2.7 Exercise 8
      • 2.2.8 Exercise 9
      • 2.2.9 Exercise 10
      • 2.2.10 Exercise 11
      • 2.2.11 Exercise 12
      • 2.2.12 Exercise 13
  • 3 T-test for Difference in Means and Hypothesis Testing
    • 3.1 Seminar
      • 3.1.1 Loading Dataset in CSV Format
      • 3.1.2 T-test (one sample hypothesis test)
      • 3.1.3 T-test (difference in means)
      • 3.1.4 Exercises
      • 3.1.5 Optional Exercises that require reading Extra Info below
      • 3.1.6 Advanced Exercises
      • 3.1.7 Extra Info
    • 3.2 Solutions
      • 3.2.1 Optional Exercises that require reading Extra Info below
      • 3.2.2 Advanced Exercises
  • 4 Bivariate linear regression models
    • 4.1 Seminar
      • 4.1.1 Packages
      • 4.1.2 Fitted values
      • 4.1.3 Additional Resources
      • 4.1.4 Exercises
    • 4.2 Solutions
  • 5 Multiple linear regression models (I)
    • 5.1 Seminar
      • 5.1.1 Loading, Understanding and Cleaning our Data
      • 5.1.2 Estimating a Bivariate Regression
      • 5.1.3 Multivariate Regression
      • 5.1.4 Joint Significance Test (F-statistic)
      • 5.1.5 Predicting outcome conditional on institutional quality
      • 5.1.6 Additional Resources
      • 5.1.7 Exercises
      • 5.1.8 Midterm Preparation
    • 5.2 Solutions
  • 6 Interactions, Non-Linearities, Fixed Effects
    • 6.1 Seminar
      • 6.1.1 Loading Data
      • 6.1.2 Interactions: Continuous and Binary
      • 6.1.3 Non-Linearities
      • 6.1.4 Fixed Effects
      • 6.1.5 Exercises
      • 6.1.6 Extra Info: Dummy Variables Repetition
    • 6.2 Solutions
      • 6.2.1 Question 1
      • 6.2.2 Question 2
      • 6.2.3 Question 3
      • 6.2.4 Question 4
      • 6.2.5 Question 5
      • 6.2.6 Question 6
      • 6.2.7 Question 7
      • 6.2.8 Question 6
  • 7 Regression Assumptions
    • 7.1 Seminar
      • 7.1.1 Required Packages
      • 7.1.2 Omitted Variable Bias
      • 7.1.3 Detecting non-linearity
      • 7.1.4 Heteroskedasticity
      • 7.1.5 Exercises
    • 7.2 Solutions
  • 8 Binary Dependent Varible Models (I)
    • 8.1 Seminar
      • 8.1.1 Required Packages
      • 8.1.2 Loading Data
      • 8.1.3 Linear regression with a Binary Dependent Variable
      • 8.1.4 Logistic Regression Model
      • 8.1.5 Odds-ratios
      • 8.1.6 Predicted probabilities
      • 8.1.7 Exercises
    • 8.2 Solutions
  • 9 Binary Dependent Variable Models (II)
    • 9.1 Seminar
      • 9.1.1 Loading Data
      • 9.1.2 Models with Binary Dependent Variables and Interactions
      • 9.1.3 Predicted Probabilities and Predictive Power
      • 9.1.4 Joint hypothesis testing
      • 9.1.5 Exercises
      • 9.1.6 Final Preparation
    • 9.2 Solutions
  • Optional Material
    • Central Limit Theorem
    • Visualizing Distributions
    • Linear Regression
  • Resources
    • Downloading R and RStudio
    • Online Learning
    • Graphics and Visualizations
    • Documentation
    • Other Resources
    • R Humor
  • Datasets
    • Seminars and Assignments
    • R Packages
    • Governmental and Nongovernmental Organizations
    • Collections
    • Books
  • Frequently Asked Questions
    • Exporting a Plot from R

Introduction to Quantitative Methods

Datasets

Seminars and Assignments

  • PUBLG100
  • Stock and Watson, 2003
  • Polity Project
    • User’s Manual

R Packages

  • R Datasets Package

Governmental and Nongovernmental Organizations

  • Centers for Disease Control and Prevention
  • IMF
  • OECD
  • UK Data Service
  • US Data.gov
  • World Bank

Collections

  • Consortium of European Social Science Data Archives
  • Datahub
  • Harvard Dataverse
  • ICPSR at University of Michigan
  • StatSci.org
  • UC Irvine
  • UK Data Archive

Books

  • An Introduction to Statistical Learning
  • Econometric Analysis
  • Introduction to Econometrics