Foundations of Quantitative Analysis for International Business with R
Motivation
1
Introduction
1.1
What Is Statistics?
1.2
For Whom?
1.3
Course Layout
1.4
Introduction to R/RStudio
1.5
Introduction to R/RStudio
1.6
Introduction to R/RStudio
1.7
Reading Data Into R/RStudio
1.8
Working in RStudio
2
R for the impatient
2.1
Useful commands
2.2
Examples
2.3
Arithmetic
2.4
R for the very impatient
3
Exploratory Data Analysis
3.1
Displaying Data
3.2
Measures of Center
3.3
Measures of Spread
3.4
Finding Outliers
3.5
Measures of Association
3.6
Some Advanced Ideas
3.7
Summarizing Data in R
3.8
Pitfalls in Exploratory Data Analysis
3.9
How to get Data from Different Sources
3.9.1
From the Internet to a data frame directly
3.9.2
From the Internet to a file
3.9.3
Get Data from JSON
3.9.4
Get Data from S3 to R
3.9.5
Get Data from Hive to R
4
Probability
4.1
Conditional probability
4.2
Random Variables
4.3
Binomial Distribution
4.4
Normal Distribution
4.5
Sampling Distributions
4.6
Probability Pitfalls
4.7
Using R for Probability
5
Study Design
5.1
Introduction to Study Design
5.2
Survey Design Basics
5.3
Simple Random Sampling
5.4
Stratified and Multistage Sampling
5.5
Discussion on Surveys
5.6
Experimental Design Basics
5.7
Principles of Experimental Design
5.8
Blocked and Paired Designs
5.9
Jennifer Taylor Protagonist Video
5.10
Discussion on Experiments
6
Introduction to Inference
6.1
Variability of Sample Statistics
6.2
Interval Estimates for Proportions and Means
6.3
Introduction to Hypothesis Testing
6.4
Testing a Proportion or Mean
6.5
Two Sample Testing
6.6
Two Sample Testing
6.7
R Code for Statistical Inference
6.8
Pitfalls in Statistical Inference
6.9
Causal inference
7
Linear Models I
7.1
Introduction
7.2
Introduction to Regression Modeling
7.3
Simple Linear Regression
7.4
4 Interpretations and Predictions
7.5
Multiple Linear Regression
7.6
Fitting Least-Squares Regression in R, Example Case Study
7.7
Discussion of Pros and Cons of Using Least-Squares Regression
8
Linear Models II
8.1
Material
8.2
Introduction to Model Building
8.3
Binary and Categorical Predictors
8.4
Quadratic Effects
8.5
Model Diagnostics
8.6
Comparing Models
8.7
Automatic Model Selection
8.8
Model Building Example in R
8.9
Inference vs. Prediction
8.10
New Section
8.10.1
Setting up Stata
8.10.2
Setting up a panel
8.10.3
How to generate variables
8.10.4
How to generate dummies
8.10.5
Running OLS regressions
8.10.6
Running panel regressions
8.10.7
GMM estimations
9
Classification Modeling and Logistic Regression
9.1
The Basics of Logistic Regression
9.2
Inference and Goodness of Fit
9.3
Multiple Logistic Regression
9.4
Comparing Logistic Models
9.5
R Code and Examples
9.6
Critical Assessment
10
Data and Model Biases
11
Times-Series Models
11.1
Introduction
11.2
Independence
11.3
Identical Distribution
11.4
Time Series
11.5
Stationarity (strong, weak, local)
11.6
Dependence Structure
11.7
References
12
Advanced time series
13
Bayesian Statistics
14
Machine Learning in a Nutshell
15
Deep Learing with Keras Tensorflow
16
Data Sources
16.1
For teaching
16.2
For research
References
www.warin.ca
Foundations of Quantitative Analysis for International Business with R
References