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
Chapter 1
[Not Done:] Introduction
Introduction
intro example
Models are always wrong.
Detective analogy