Statistical Inference by Casella and Berger is a rigorous and widely respected textbook that introduces the theory of statistical inference from both frequentist and Bayesian perspectives. The book covers essential topics such as point estimation, hypothesis testing, confidence intervals, and decision theory, with mathematical precision and thorough examples. Designed for upper-level undergradu…
This book offers a comprehensive introduction to the theory and application of linear models, with a strong emphasis on clarity and practical understanding. Designed for graduate students and professionals in statistics and related fields, it covers the fundamental concepts of linear model theory, including least squares estimation, hypothesis testing, and model diagnostics. The text balances t…
This foundational text emphasizes active learning and critical thinking. It integrates interactive in-class and out-of-class activities, data exploration, TI graphing calculator steps, and real-world examples. Suitable for algebra-based statistics courses. Features include hypothesis testing, experimental design, count data analysis, bootstrap methods, and interactive projects.
This textbook introduces the fundamental concepts of probability, random variables, and random signal principles as applied to electrical and computer engineering. Designed for junior and senior engineering students, the book provides clear explanations and practical examples related to noise analysis, linear systems, and digital communications.
This foundational text introduces integral equation techniques with a focus on practical applications in physics and engineering. Designed for upper-level undergraduates and graduate students, it includes worked examples and emphasizes clarity in the development of integral methods.