Dieter Schott – författare

What do we mean when we say that cities have altered humanity’s interaction with nature? The more people are living in cities, the more nature is said to be "urbanizing": turned into a resource, mobilized over long distances, controlled, transformed and then striking back with a vengeance as "natural disaster". Confronting insights derived from Environmental History, Science and Technology Studies or Political Ecology, Urbanizing Nature aims to counter teleological perspectives on the birth of modern "urban nature" as a uniform and linear process, showing how new technological schemes, new actors and new definitions of nature emerged in cities from the sixteenth to the twentieth century.

What do we mean when we say that cities have altered humanity’s interaction with nature? The more people are living in cities, the more nature is said to be "urbanizing": turned into a resource, mobilized over long distances, controlled, transformed and then striking back with a vengeance as "natural disaster". Confronting insights derived from Environmental History, Science and Technology Studies or Political Ecology, Urbanizing Nature aims to counter teleological perspectives on the birth of modern "urban nature" as a uniform and linear process, showing how new technological schemes, new actors and new definitions of nature emerged in cities from the sixteenth to the twentieth century.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods

This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions.

Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs.

Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods

This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions.

Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs.

Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.