This volume addresses the complexities of real-world data analysis, offering clear guidance through common challenges such as missing data, model specification, variable selection, and hypothesis testing. It also introduces tools for deeper interpretation including marginal effects, interaction (moderation), and mediation analysis to help uncover nuanced relationships in data.
Ideal for both learners deepening their understanding and practitioners refining their techniques, this volume blends statistical theory with applied insight. Readers are equipped to navigate ambiguity, confront data imperfections, and extract actionable meaning.
With practical examples and an emphasis on analytical reasoning, this book supports coursework, exam review, and professional development alike making it an essential resource for building confidence in modern data analysis.
Missing data. - Variable transformation. - Variable selection. - Hypothesis testing.- Marginal effects. - Mediation.
Dr. Mike Nguyen is a professor of marketing and data science whose research is deeply informed by active collaborations with firms in telecommunications, financial institutions, and digital platforms. He teaches data analytics and causal inference at both undergraduate and graduate levels, with an emphasis on business applications in marketing, finance, and strategic decision-making. His work integrates econometrics, machine learning, and experimental design to tackle real-world problems. Dr. Nguyen designs research-based solutions in partnership with industry to inform both theory and practice.
