Statistics and Analytics for the Social and Computing Sciences
Preface
- Outline of notes
Getting Started
- How to use R Markdown
- Coding Best Practices
1 [Not Done:] Introduction
2 Handling Data
3 [Not Done:] Descriptive Statistics
4 [Not Done:] Data Visualization
5 The Linear Model I: Linear Regression
6 The Linear Model II: Logistic Regression
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
14 Optimization I: Linear Optimization
15 Optimization II: Integer-valued Optimization
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Statistics and Analytics for the Social and Computing Sciences

Chapter 10 [Not Done:] Simulations (I)

Monte Carlo: Intro to simulations, randomness

Bootstrap

Permutation Tests

Simulating p values

Power analyses

Central Limit Theorem in simulations