Statistical Complexity

Use Excel 2016’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2016 to perform core statistical tasks every business professional, student,

In this new textbook, a number of unusual applications are discussed in addition to the usual topics covered in a course on Statistical Physics. Examples are: statistical mechanics of powders, Peierls

Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables ar

Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring i

This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for

Complexity theory including the concepts of chaos and emergence has been considered one of the most revolutionary products of the 20th century having influence on science, technology and economics amo

Although complexity surrounds us, its inherent uncertainty, ambiguity, and contradiction can at first make complex systems appear inscrutable. Ecosystems, for instance, are nonlinear, self-organi

Although complexity surrounds us, its inherent uncertainty, ambiguity, and contradiction can at first make complex systems appear inscrutable. Ecosystems, for instance, are nonlinear, self-organi

A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the S

Descriptive complexity theory establishes a connection between the computational complexity of algorithmic problems (the computational resources required to solve the problems) and their descriptive complexity (the language resources required to describe the problems). This groundbreaking book approaches descriptive complexity from the angle of modern structural graph theory, specifically graph minor theory. It develops a 'definable structure theory' concerned with the logical definability of graph theoretic concepts such as tree decompositions and embeddings. The first part starts with an introduction to the background, from logic, complexity, and graph theory, and develops the theory up to first applications in descriptive complexity theory and graph isomorphism testing. It may serve as the basis for a graduate-level course. The second part is more advanced and mainly devoted to the proof of a single, previously unpublished theorem: properties of graphs with excluded minors are decid

Two central problems in computer science are P vs NP and the complexity of matrix multiplication. The first is also a leading candidate for the greatest unsolved problem in mathematics. The second is of enormous practical and theoretical importance. Algebraic geometry and representation theory provide fertile ground for advancing work on these problems and others in complexity. This introduction to algebraic complexity theory for graduate students and researchers in computer science and mathematics features concrete examples that demonstrate the application of geometric techniques to real world problems. Written by a noted expert in the field, it offers numerous open questions to motivate future research. Complexity theory has rejuvenated classical geometric questions and brought different areas of mathematics together in new ways. This book will show the beautiful, interesting, and important questions that have arisen as a result.

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of inte

The question of complexity, as in what makes one language more 'complex' than another, is a long-established topic of debate amongst linguists. Recently, this issue has been complemented with the view that languages are complex adaptive systems, in which emergence and self-organization play major roles. However, few students of the phenomenon have gone beyond the basic assessment of the number of units and rules in a language (what has been characterized as 'bit complexity') or shown some familiarity with the science of complexity. This book reveals how much can be learned by overcoming these limitations, especially by adopting developmental and evolutionary perspectives. The contributors include specialists of language acquisition, evolution and ecology, grammaticization, phonology, and modeling, all of whom approach languages as dynamical, emergent, and adaptive complex systems.

Statistical Analysis with R For Dummies fills a huge gap in the world of statistical analysis, as well as in the R community. Most guides to R, whether in books or online, focus on R functions and pro

The advent of social complexity has been a longstanding debate among social scientists. Existing theories and approaches involving the origins of social complexity include environmental circumscriptio

"Small Dynamic Complexity Classes" was awarded the E.W. Beth Dissertation Prize 2016 for outstanding dissertations in the fields of logic, language, and information. The thesis studies the foundations

For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for t

This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the ha

Conrad Carlberg's Statistical Analysis with R and Microsoft® Excel is the first complete guide to performing modern statistical analyses with Excel, R, or both. Drawing on his immense experience helpi

Ideal for non-math majors, Advanced and Multivariate Statistical Methods teaches students to interpret, present, and write up results for each statistical technique without overemphasizing advanced ma

Suitable for graduate students in chemical physics, statistical physics, and physical chemistry, this text develops an innovative, probabilistic approach to statistical mechanics. The treatment employ

This work addresses the topic of philosophical complexity, which shares certain assumptions with scientific complexity, cybernetics, and General Systems Theory, but which is also developing as a subje

Statistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state

Geo-mathematical modelling: models from complexity science Sir Alan Wilson, Centre for Advanced Spatial Analysis, University College London Mathematical and computer models for a complexity scienc

Today’s leaders and managers have to operate in an environment characterised by complexity. What does coping with this ever- and faster-increasing degree of complexity require of them? It means that i

Statistical Aspects of the Microbiological Examination of Foods, Third Edition, updates some important statistical procedures following intensive collaborative work by many experts in microbiology and

An ideal introduction to statistical neurocomputing, this book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving mul

The last four decades have witnessed the emergence of CTC – Complexity Theories of Cities – a domain of research that applies the various complexity theories to the study of cities.Studies in this dom

"A Modern Course in Statistical Physics" is a textbook that provides a grounding in the foundations of equilibrium and nonequilibrium statistical physics, and focuses on the universal nature

While most introductions to statistical mechanics are either too mathematical or too physical, Colin Thompson's book combines mathematical rigor with familiar physical materials. Following introductor

One striking feature of modern political and social development has been the construction of social systems encompassing more and more groups. The increase in social complexity, the authors of this vo

Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statis

If only today's technology were simpler! It's the universal lament, but it's wrong. In this provocative and informative book, Don Norman writes that the complexity of our technology must mirror the co

Statistical Thermodynamics of Semiconductor Alloys is the consideration of thermodynamic properties and characteristics of crystalline semiconductor alloys by the methods of statistical thermodynamics

Strategies, Leadership and Complexity in Crisis and Emergency Operations will bring together the main themes of strategy, operational leadership, organizational dynamics and complexity in the context

This book discusses computational complexity of High Efficiency Video Coding (HEVC) encoders with coverage extending from the analysis of HEVC compression efficiency and computational complexity to th

This engaging book helps readers identify and then discard 52 misconceptions about data and statistical summaries. The focus is on major concepts contained in typical undergraduate and graduate course

The description for this book, Elementary Statistical Analysis, will be forthcoming.

Complexity theories gained prominence in the 1990s with a focus on self-organising and complex adaptive systems. Since then, complexity theory has become one of the fastest growing topics in both the