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Design of Experiments and Hypothesis Testing: A Practical Approach for Engineers and Researchers offers a comprehensive, application-focused guide to planning, conducting, and analyzing experiments in engineering and scientific research. Structured into four parts, the book begins with the foundations of statistical thinking, covering experimental design principles, statistical inference, and data measurement scales. It then explores key DOE techniques, including full factorial designs, Response Surface Methodology (RSM), and Taguchi methods for robust design. The hypothesis testing section explains statistical errors, ANOVA, and non-parametric tests, equipping readers to make sound, data-driven decisions. The final section addresses multi-objective optimization, introducing Pareto fronts, trade-offs, and real-world engineering applications. This book bridges the gap between theory and practice, making complex statistical concepts accessible. It is an essential resource for students, researchers, and practicing engineers seeking to enhance process quality, optimize performance, and innovate through data-driven problem-solving.