This is the first book to show the capabilities of Microsoft Excel to teach biological and life sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of com…
This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of depart…
This book constitutes the refereed proceedings of the 11th Conference on Computability in Europe, CiE 2015, held in Bucharest, Romania, in June/July 2015. The 26 revised papers presented were carefully reviewed and selected from 64 submissions and included together with 10 invited papers in this proceedings. The conference CiE 2015 has six special sessions: two sessions, Representing Streams…
This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to sta…
In this thesis, the author develops for the first time an implementation methodology for arbitrary Gaussian operations using temporal-mode cluster states. The author also presents three experiments involving continuous-variable one-way quantum computations, where their non-classical nature is shown by observing entanglement at the outputs. The experimental basic structure of one-way quantum com…
This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known a…
The essays in this book look at the question of whether physics can be based on information, or – as John Wheeler phrased it – whether we can get “It from Bit”. They are based on the prize-winning essays submitted to the FQXi essay competition of the same name, which drew over 180 entries. The eighteen contributions address topics as diverse as quantum foundations, entropy conservation…
This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures. One studies and uses statistics to learn how to study, analyze, and understand a data set of partic…
This open access book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history. However, this brave new world comes with problems. Several high-profile cases o…
Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data…