Regression Unfolded

Exploring the Spectrum of Predictive Patterns

AI Textbook - 100+ pages

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Dive into the world of regression models with 'Regression Unfolded: Exploring the Spectrum of Predictive Patterns.' This comprehensive guide is your key to understanding the different types of regression models used in statistics and machine learning. Perfect for beginners who need clear explanations and experts seeking advanced theories, this 12-chapter book provides in-depth insights into linear, logistic, and beyond. Enhance your analytical skills and apply regression techniques to real-world problems as you navigate through systematic explorations and practical examples.

Table of Contents

1. The Essence of Regression
- Understanding Data Relationships
- History and Evolution of Regression
- Major Types and Uses

2. Linear Regression in Depth
- Fundamentals of Linear Relationships
- Assumptions and Properties
- Practical Applications and Case Studies

3. Diving Into Logistic Regression
- From Linear to Logistic: The Transition
- Binary Outcomes and Odds Ratio
- Case Studies: Medical and Social Sciences

4. Polynomial Regression
- Beyond Linearity: Capturing Curvature
- Determining the Degree of Polynomials
- Polynomial Regression in the Wild

5. Ridge and Lasso Regression
- Tackling Multicollinearity
- Balance Between Fit and Complexity
- Ridge vs. Lasso: A Comparative Analysis

6. Elastic Net Regression
- Combining L1 and L2 Penalties
- Parameters Selection and Optimization
- Application in High-Dimensional Data

7. Quantile Regression
- Percentiles in Regression Analysis
- Advantages Over Ordinary Least Squares
- Applications in Economics and Finance

8. Generalized Linear Models
- Expanding Beyond Normality
- Link Functions and Exponential Families
- Model Selection and Evaluation

9. Non-Linear and Non-Parametric Regression
- When Linearity Fails
- Kernel Methods and Splines
- Case Studies: Complex Data Patterns

10. Survival and Hazard Regression
- Time-to-Event Data Analysis
- Cox Proportional-Hazards Model
- Predictive Modeling in Healthcare

11. Mixed Models and Hierarchical Data
- Random Effects and Nested Structures
- Application in Longitudinal Studies
- Software for Mixed Model Analysis

12. Machine Learning and Regression
- Regression Trees and Forests
- Support Vector Regression
- Neural Networks and Deep Learning

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