1st Edition
Regression and Machine Learning for Education Sciences Using R
This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making.
Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning. With each chapter, the discussion and development of each statistical concept and data analytical technique are presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts.
Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial. The dataset and examples used in the book will be from the educational setting, and students will find that this text provides good preparation for studying more statistical and data analytical materials.
A brief introduction to R and R Studio
Part 1: Regression models: Foundation of machine learning
Chapter 01: First thing first: Simple regression
1.1. Introduction
1.2. An example
1.3. What is the regression model
1.4. How to interpret the regression model
1.5. What is the sum of squares and r2 in the regression model
1.6. What are the predicted values and the residuals?
1.7. How to estimate regression line and what method is used?
1.8. Inference about regression coefficients
1.9. Regression with categorical independent variable
1.10. Summary
Hands-on practice
Chapter 02: Beyond simple: Multiple regression analysis
2.1. Introduction
2.2. An example
2.3. What is a multiple regression model
2.4. How to interpret the results from multiple regression analysis
2.5. Assessing the importance of multiple independent variables
2.6. Recap on categorical independent variables
2.7. How the multiple regression model is estimated
2.8. Summary
Hands-on practice
Chapter 03: It takes two to tangle: Regression with interaction
3.1. Introduction
3.2. An example
3.3. The difference between regression model with and without interaction
3.4. The meaning of βi associated with an interaction term
3.5. Interpretation of interaction
3.6. Summary
Hands-on practice
Chapter 04: Are we thinking correctly: Checking assumptions of regression model
4.1. Introduction
4.2. What are the assumptions of the regression model
4.3. How to check the assumptions
4.4. Summary
Hands-on practice
Chapter 05: I am not straight but robust: Curvilinear Robust and Quantile regression
5.1. An example
5.2. What is curvilinear regression?
5.3. Piecewise regression
5.4. Robust regression
5.5. Quantile regression
5.6. Summary
Hands-on practice
Chapter 06: Predicting the class probability: Logistic regression
6.1. An example
6.2. What is logistic regression
6.3. Interpreting the results from the logistic regression
6.4. The logistic regression model with interaction
6.5. Multinomial logistic regression
6.6. Assumptions of the logistic regression model
6.7. Summary
Hands-on practice
Part 2: Machine learning: Classification and predictive modeling
Chapter 07: Introduction to machine learning
7.1. Big data, data science, and data mining
7.2. What is machine learning
7.3. Data preprocessing: A critical step in machine learning
7.4. Machine learning algorithms
7.5. Data splitting for validation
7.6. Summary
Chapter 08. Machine learning algorithms and process
8.1. Introduction to caret package
8.2. Steps in performing machine learning
8.2.1 Detailed discussion of each step of machine learning
8.3. Summary
Chapter 09. Let me regulate: Regularized Machine learning
9.1. Data preprocessing
9.2. Linear regression using machine learning
9.3 Lass, ridge, and elastic net regression models
9.4. Multivariate adaptive regression spline
9.5. Regression tree
9.6. Summary
Hands-on practice
Chapter 10. Finding ways in the forest: Prediction with Random Forest
10.1. Random forest
10.2 Basic principles
10.3 Randomization
10.4. Single tree with CART
10.5 Bagging
10.6 Tuning parameters
10.7. Variable importance
10.8. Example
10.9. Adaptive boosting (AdaBoost) with decision trees
10.10. Gradient boosting with decision trees
10.11. Summary
Hands-on practice
Chapter 11. I can divide better: Classification with support vector machine
11.1. What is Support Vector Machine
11.2. Tuning parameters
11.3. Multiclass classification
11.4 Estimated class probabilities
11.5. Other classification methods
11.6. An example of SVM classification
11.7. An example of SVM regression
11.8. Summary
Hands-on practice
Chapter 12. Work like a human brain: Artificial neural network
12.1. What are artificial neural networks?
12.2. Types of artificial neural networks
12.3. Single-layer feedforward neural network
12.4. Multilayer feedforward neural networks
12.5. Recurrent neural networks
12.6 An Example
12.7. Summary
Hands-on practice
Chapter 13. Desire to find causal relations: Bayesian network
13.1. Bayesian network and causal discovery
13.2. Construction of Bayesian network
13.3. Example
13.4. Summary
Hands-on practice
Chapter 14. We want to see the relationships: Multivariate data visualization
14.1. Commonly used data visualization methods
14.2. Multidimensional scaling visual method for classification
14.3. Example
14.4. Summary
Hands-on practice
Biography
Cody Dingsen is a professor in the Department of Educational Sciences & Professional Programs at the University of Missouri-St. Louis. His research interests include Multidimensional Scaling models for change and preference, psychometrics, data science, cognition and learning, emotional development, and biopsychosocial development.
