This book, "Parameter Estimation and Hypothesis Testing in Linear Models," by Karl-Rudolf Koch, provides a comprehensive overview of statistical methods used in linear models. The second, updated, and enlarged edition is a translation of the third German edition, published in 1997. It includes a new chapter on robust estimation of parameters and excludes the section on discriminant analysis, which is covered in another of the author's works. The book aims to offer a self-contained presentation of multidimensional methods for estimating parameters, testing hypotheses, and interval estimation, along with necessary knowledge in vector and matrix algebra and probability theory. It is intended for engineers, students, and professionals applying statistical methods to practical problems. The book covers various topics, including parameter estimation using methods like least squares and maximum-likelihood, hypothesis testing, interval estimation, and robust estimation. It also discusses generalized linear models, variance and covariance component estimation, and multivariate parameter estimation. The text is structured to provide both theoretical foundations and practical examples, making it a valuable resource for understanding and applying statistical inference in linear models. The book includes detailed explanations, theorems, and practical applications, supported by numerous examples and references to enhance understanding.This book, "Parameter Estimation and Hypothesis Testing in Linear Models," by Karl-Rudolf Koch, provides a comprehensive overview of statistical methods used in linear models. The second, updated, and enlarged edition is a translation of the third German edition, published in 1997. It includes a new chapter on robust estimation of parameters and excludes the section on discriminant analysis, which is covered in another of the author's works. The book aims to offer a self-contained presentation of multidimensional methods for estimating parameters, testing hypotheses, and interval estimation, along with necessary knowledge in vector and matrix algebra and probability theory. It is intended for engineers, students, and professionals applying statistical methods to practical problems. The book covers various topics, including parameter estimation using methods like least squares and maximum-likelihood, hypothesis testing, interval estimation, and robust estimation. It also discusses generalized linear models, variance and covariance component estimation, and multivariate parameter estimation. The text is structured to provide both theoretical foundations and practical examples, making it a valuable resource for understanding and applying statistical inference in linear models. The book includes detailed explanations, theorems, and practical applications, supported by numerous examples and references to enhance understanding.