• Statistics and Analytics for the Social and Computing Sciences
  • Preface
    • Outline of notes
  • Getting Started
    • How to use R Markdown
    • Coding Best Practices
      • Ensuring a Reproducible workflow
      • Other Coding Best Practices
      • Analysis Best Practices
  • 1 [Not Done:] Introduction
  • 2 Handling Data
    • 2.1 Basics of Data Wrangling
    • 2.2 Wrangling in the tidyverse
      • The %>% operator
      • Wide-to-long: pivot_longer()
      • mutate()
      • Long-to-wide: pivot_wider()
    • 2.3 A data cleaning pipeline for research projects
  • 3 [Not Done:] Descriptive Statistics
  • 4 [Not Done:] Data Visualization
  • 5 The Linear Model I: Linear Regression
    • 5.1 Basics of Linear Regression
    • 5.2 Running a regression
      • 5.2.1 Structure your dataset
      • 5.2.2 Visualize
      • 5.2.3 Running the linear model
    • 5.3 Ordinary Least Squares Regression
      • 5.3.1 Ordinary Least Squares Derivation
    • 5.4 Interpreting the output of a regression model
      • 5.4.1 The coefficient table
      • 5.4.2 Goodness-of-fit statistics
    • 5.5 Examples: Simple Regression
    • 5.6 Multiple Linear Regression
    • 5.7 Standardized Coefficients
    • 5.8 Categorical Independent Variables
      • 5.8.1 Dummy Coding
      • 5.8.2 Dummy Coding with 3 levels
      • 5.8.3 The Reference Group
      • 5.8.4 Interpreting categorical and continuous independent variables
    • 5.9 Assumptions behind Linear Regression
      • 5.9.1 Residual plots
    • 5.10 Exercises: Linear Model I
  • 6 The Linear Model II: Logistic Regression
    • 6.1 Basics of Logistic Regression
    • 6.2 Running a Logistic Regression
    • 6.3 Examples: Logistic Regression
    • 6.4 Exercises: Linear Model II
  • 7 [Not Done:] The Linear Model III: Interactions
  • 8 [Not Done:] The Linear Model IV: Model Selection
  • 9 [Not Done:] The Linear Model V: Mixed Effects Linear Models
  • 10 [Not Done:] Simulations (I)
    • 10.1 [Not Done:] Monte Carlo simulations
    • 10.2 [Not Done:] The Bootstrap
  • 11 [Not Done:] Simulations (II): Statistics in Machine Learning
  • 12 [Not Done:] Data Mining
  • 13 Introduction to Time-Series Data
    • 13.1 Time Series Basics
    • 13.2 Smoothing-based models
      • 13.2.1 Simple Moving Average Model
      • 13.2.2 Exponential Smoothing Models
    • 13.3 Regression-based forecasting models
  • 14 Optimization I: Linear Optimization
    • 14.1 What is Linear Optimization
    • 14.2 Objective Functions & Decision Variables
      • Jean the Farmer: Objective Function
    • 14.3 Constraints
      • Jean the Farmer: Constraints
    • 14.4 Solving the Optimization Model
    • 14.5 Using R to solve Linear Optimization
    • 14.6 Sensitivity Analysis
      • Varying objective function coefficients
      • Varying Constraint Values (Shadow Prices)
      • Binding vs non-binding constraints
      • Summarizing sensitivity analyses
    • 14.7 Examples: Linear Optimization
    • 14.8 Linear Optimization Summary
    • 14.9 Exercises: Linear Optimization
  • 15 Optimization II: Integer-valued Optimization
    • 15.1 Integer-valued decision variables
    • 15.2 From real-valued to integer solutions
      • 15.2.1 LP-Relaxation
      • 15.2.2 Specifying Integer Constraints
    • 15.3 Logical Constraints
      • 15.3.1 How to specify logical constraints
      • 15.3.2 Logical Constraints Example: Planning university courses
    • 15.4 Integer Optimization Summary
    • 15.5 Exercises: Integer Optimization
  • Published with bookdown

Statistics and Analytics for the Social and Computing Sciences

10.2 [Not Done:] The Bootstrap

see https://github.com/desmond-ong/doBootstrap/blob/master/doBootstrapPrimer.pdf