In-depth Before the Regression: A Complete Guide to Validating Data for Linear Models
Paperback
Series: In-Depth, Book 4
Publisher: Independently Published
Published: Jul 9 2026
Pages: 410
Weight: 2.08
Height: 0.84 Width: 8.50 Depth: 11.00
Language: English
- The assumptions that make linear regression valid
- Exploratory Data Analysis before model building
- Correlation analysis and multicollinearity detection
- Matrix algebra underlying regression estimation
- Model diagnostics and residual analysis
- Detection of influential observations and outliers
- Heteroscedasticity, autocorrelation and normality assessment
- Information criteria including AIC and BIC
- Model selection and comparison strategies
- Robust regression techniques, including Huber Regression and RANSAC
- Modern model interpretability using SHAP and LIME
- Practical Python implementations throughout the book
- Real-world illustrations using the widely studied Advertising dataset Unlike many texts that present formulas without context, this book carefully builds intuition before introducing mathematics. Every concept is connected to practical decision-making, allowing readers to understand the consequences of violating assumptions, selecting inappropriate models or misinterpreting statistical output. The emphasis throughout is on understanding before computation. Once the reasoning becomes clear, the software naturally follows. The book also bridges the gap between classical statistics and modern machine learning by showing how traditional regression principles remain essential in today's explainable AI landscape. Readers will appreciate how rigorous statistical thinking complements contemporary AI tools rather than competing with them. Designed for self-study as well as university courses, the book balances theoretical rigour with practical implementation. Mathematical derivations are accompanied by intuitive explanations, diagrams, infographics and executable Python code, making complex concepts accessible without sacrificing precision. If you have ever wondered:
- Why does linear regression actually work?
- How do experts decide whether a model can be trusted?
- Which diagnostic tests matter most?
- When should robust regression replace ordinary least squares?
- How can modern explainable AI techniques be applied to regression models?
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