Master linear regression the way experienced statisticians and data scientists actually think.

Most books teach you how to run a regression. This book teaches you how to understand one.
In-depth Before the Regression is not another introductory guide filled with superficial explanations and software screenshots. It is a comprehensive journey into the mathematical foundations, statistical reasoning, diagnostic techniques, model validation procedures and practical decision-making that separate routine analysis from professional practice.

Whether you are a researcher, postgraduate student, data scientist, engineer, business analyst or AI practitioner, this book equips you with the conceptual depth required to build regression models that are not merely statistically significant, but genuinely reliable and scientifically defensible.

Rather than treating regression as a black box, this book explains every major concept from first principles. You will learn not only what each technique does, but why it exists, when it should be used and how it influences the quality of your conclusions.
Inside the book, you will explore:
• The mathematical intuition behind Ordinary Least Squares (OLS)
• 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?
—then this book was written for you.

Whether you are preparing for research, industry practice, higher education or simply wish to develop a deeper understanding of predictive modelling, In-depth Before the Regression provides the knowledge needed to move beyond running software commands and towards thinking like a statistician.