Basic Statistics

For courses in Political and Social Statistics ? Using the popular SPSS software to de-mystify statistics ? Using SPSS for Windows and Macintosh guides students through basic SPSS techniques

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced

Mathematical methods are essential tools for all physical scientists. This book provides a comprehensive tour of the mathematical knowledge and techniques that are needed by students across the physical sciences. In contrast to more traditional textbooks, all the material is presented in the form of exercises. Within these exercises, basic mathematical theory and its applications in the physical sciences are well integrated. In this way, the mathematical insights that readers acquire are driven by their physical-science insight. This third edition has been completely revised: new material has been added to most chapters, and two completely new chapters on probability and statistics and on inverse problems have been added. This guided tour of mathematical techniques is instructive, applied, and fun. This book is targeted for all students of the physical sciences. It can serve as a stand-alone text, or as a source of exercises and examples to complement other textbooks.

Mathematical methods are essential tools for all physical scientists. This book provides a comprehensive tour of the mathematical knowledge and techniques that are needed by students across the physical sciences. In contrast to more traditional textbooks, all the material is presented in the form of exercises. Within these exercises, basic mathematical theory and its applications in the physical sciences are well integrated. In this way, the mathematical insights that readers acquire are driven by their physical-science insight. This third edition has been completely revised: new material has been added to most chapters, and two completely new chapters on probability and statistics and on inverse problems have been added. This guided tour of mathematical techniques is instructive, applied, and fun. This book is targeted for all students of the physical sciences. It can serve as a stand-alone text, or as a source of exercises and examples to complement other textbooks.

Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data

These three volumes of Kentucky election statistics at last make this basic tool of political research easily accessible to scholars, journalists, teachers, political candidates and others interested

These three volumes of Kentucky election statistics at last make this basic tool of political research easily accessible to scholars, journalists, teachers, political candidates and others interested

Mastering the basic concepts of mathematics is the key to understanding other subjects such as Economics, Finance, Statistics, and Accounting. Mathematics for Finance, Business and Economics is writte

"Statistical Applications for Environmental Analysis and Risk Assessment stresses and explains the importance of a basic knowledge of statistics and statistical analysis in the environmental sciences.

A solid introduction to mathematical modeling for a range of chemical engineering applications, covering model formulation, simplification and validation. It explains how to describe a physical/chemical reality in mathematical language and how to select the type and degree of sophistication for a model. Model reduction and approximation methods are presented, including dimensional analysis, time constant analysis and asymptotic methods. An overview of solution methods for typical classes of models is given. As final steps in model building, parameter estimation and model validation and assessment are discussed. The reader is given hands-on experience of formulating new models, reducing the models and validating the models. The authors assume the knowledge of basic chemical engineering, in particular transport phenomena, as well as basic mathematics, statistics and programming. The accompanying problems, tutorials, and projects include model formulation at different levels, analysis, pa

The book contains all the basic notions of Positron Emission Tomography that span from physiology to radiochemistry, physics, math and statistics. All material was carefully crafted and is now present

Interest in the skew-normal and related families of distributions has grown enormously over recent years, as theory has advanced, challenges of data have grown, and computational tools have made substantial progress. This comprehensive treatment, blending theory and practice, will be the standard resource for statisticians and applied researchers. Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, the book is accessible to the wide range of researchers who use statistical modelling techniques. Guiding readers through the main concepts and results, it covers both the probability and the statistics sides of the subject, in the univariate and multivariate settings. The theoretical development is complemented by numerous illustrations and applications to a range of fields including quantitative finance, medical statistics, environmental risk studies, and industrial and business efficiency. The author's freely available R package sn, available fr

In recent years cosmologists have advanced from largely qualitative models of the Universe to precision modelling using Bayesian methods, in order to determine the properties of the Universe to high accuracy. This timely book is the only comprehensive introduction to the use of Bayesian methods in cosmological studies, and is an essential reference for graduate students and researchers in cosmology, astrophysics and applied statistics. The first part of the book focuses on methodology, setting the basic foundations and giving a detailed description of techniques. It covers topics including the estimation of parameters, Bayesian model comparison, and separation of signals. The second part explores a diverse range of applications, from the detection of astronomical sources (including through gravitational waves), to cosmic microwave background analysis and the quantification and classification of galaxy properties. Contributions from 24 highly regarded cosmologists and statisticians make

If you have basic programming skills and do any kind of data analysis, you need R in your toolkit. This book teaches you how to be productive with this important programming language for statistics by

Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.

Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.

Nielsen and Slatkin (both U. of California-Berkeley) present a textbook they developed in and for a one-semester undergraduate course in population genetics. Students should have some basic knowledge of biology and genetics, they say, and not be afraid of quantitative thinking. Assuming only basic algebra, they introduce some ideas from probability and statistics that are an integral part of modern population genetics. The topics include genetic drift and mutation, population subdivision, selection, the neutral theory and tests of neutrality, and quantitative genetics. Annotation c2013 Book News, Inc., Portland, OR (booknews.com)

Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the stat

This brief introduces people with a basic background in probability theory to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics. The

Stochastic Dynamics of Marine Structures is a text for students and a reference for professionals on the basic theory and methods used for stochastic modelling and analysis of marine structures subjected to environmental loads. The first part of the book provides a detailed introduction to the basic dynamic analysis of structures, serving as a foundation for later chapters on stochastic response analysis. This includes an extensive chapter on the finite element method. A careful introduction to stochastic modelling is provided, which includes such concepts as stochastic process, variance spectrum, random environmental processes, response spectrum, response statistics and short- and long-term extreme value models. The second part of the book offers detailed discussion of limit state design approaches, fatigue design methods, the equations of motion for dynamic structures and numerical solution techniques. The final chapter highlights methods for prediction of extreme values from measure

This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.

Focusing on what actuaries need in practice, this introductory account provides readers with essential tools for handling complex problems and explains how simulation models can be created, used and re-used (with modifications) in related situations. The book begins by outlining the basic tools of modelling and simulation, including a discussion of the Monte Carlo method and its use. Part II deals with general insurance and Part III with life insurance and financial risk. Algorithms that can be implemented on any programming platform are spread throughout and a program library written in R is included. Numerous figures and experiments with R-code illustrate the text. The author's non-technical approach is ideal for graduate students, the only prerequisites being introductory courses in calculus and linear algebra, probability and statistics. The book will also be of value to actuaries and other analysts in the industry looking to update their skills.

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on compu

A concise, easily accessible introduction to descriptive and inferential techniquesStatistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers

本書對致力於公共衛生領域中，學習如何使用統計觀念的所有學生和研究者是一本很好的入門書。它首先先將統計主要的觀念清楚地解釋，並運用一些範例來做說明。若能仔細閱讀，相信讀者及能聰明並巧妙的使用一般的統計套裝軟體。它可以幫助任何背景的公共衛生學生和新的研究人員，更有信心地詮釋和清楚地解釋他們的研究發現。

Going beyond?the?common poker strategy book, this one-of-a-kind guide utilizes basic memory techniques designed to enable a player to easily keep track of poker statistics during a live game. A variet

Nicholson (mathematics and statistics, U. of Calgary, Canada) updates the 2007 edition of his self-contained introduction to the basic structures of abstract algebra: groups, rings, and fields. It is

Collecting, Managing, and Assessing Data Using Sample Surveys provides a thorough, step-by-step guide to the design and implementation of surveys. Beginning with a primer on basic statistics, the first half of the book takes readers on a comprehensive tour through the basics of survey design. Topics covered include the ethics of surveys, the design of survey procedures, the design of the survey instrument, how to write questions and how to draw representative samples. Having shown readers how to design surveys, the second half of the book discusses a number of issues surrounding their implementation, including repetitive surveys, the economics of surveys, web-based surveys, coding and data entry, data expansion and weighting, the issue of non-response, and the documenting and archiving of survey data. The book is an excellent introduction to the use of surveys for graduate students as well as a useful reference work for scholars and professionals.

Accessible to students in statistics and industrial engineering, this text presents a systematic introduction to traditional and modern SPC methods. Requiring some background in basic linear algebra,

Collecting, Managing, and Assessing Data Using Sample Surveys provides a thorough, step-by-step guide to the design and implementation of surveys. Beginning with a primer on basic statistics, the first half of the book takes readers on a comprehensive tour through the basics of survey design. Topics covered include the ethics of surveys, the design of survey procedures, the design of the survey instrument, how to write questions and how to draw representative samples. Having shown readers how to design surveys, the second half of the book discusses a number of issues surrounding their implementation, including repetitive surveys, the economics of surveys, web-based surveys, coding and data entry, data expansion and weighting, the issue of non-response, and the documenting and archiving of survey data. The book is an excellent introduction to the use of surveys for graduate students as well as a useful reference work for scholars and professionals.

This text covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. Interesting, relevant applications

Richard Stanley's two-volume basic introduction to enumerative combinatorics has become the standard guide to the topic for students and experts alike. This thoroughly revised second edition of Volume 1 includes ten new sections and more than 300 new exercises, most with solutions, reflecting numerous new developments since the publication of the first edition in 1986. The author brings the coverage up to date and includes a wide variety of additional applications and examples, as well as updated and expanded chapter bibliographies. Many of the less difficult new exercises have no solutions so that they can more easily be assigned to students. The material on P-partitions has been rearranged and generalized; the treatment of permutation statistics has been greatly enlarged; and there are also new sections on q-analogues of permutations, hyperplane arrangements, the cd-index, promotion and evacuation and differential posets.

This detailed introduction to distribution theory uses no measure theory, making it suitable for students in statistics and econometrics as well as for researchers who use statistical methods. Good backgrounds in calculus and linear algebra are important and a course in elementary mathematical analysis is useful, but not required. An appendix gives a detailed summary of the mathematical definitions and results that are used in the book. Topics covered range from the basic distribution and density functions, expectation, conditioning, characteristic functions, cumulants, convergence in distribution and the central limit theorem to more advanced concepts such as exchangeability, models with a group structure, asymptotic approximations to integrals, orthogonal polynomials and saddlepoint approximations. The emphasis is on topics useful in understanding statistical methodology; thus, parametric statistical models and the distribution theory associated with the normal distribution are cover

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided fro

For readers who are already familiar with both basic statistics and the R free statistical software package, Ekstrom (statistics, U. of Copenhagen) demonstrates how to deal with some problems that new

Quantitative risk assessments cannot eliminate risk, nor can they resolve trade-offs. They can, however, guide principled risk management and reduction - if the quality of assessment is high and decision makers understand how to use it. This book builds a unifying scientific framework for discussing and evaluating the quality of risk assessments and whether they are fit for purpose. Uncertainty is a central topic. In practice, uncertainties about inputs are rarely reflected in assessments, with the result that many safety measures are considered unjustified. Other topics include the meaning of a probability, the use of probability models, the use of Bayesian ideas and techniques, and the use of risk assessment in a practical decision-making context. Written for professionals, as well as graduate students and researchers, the book assumes basic probability, statistics and risk assessment methods. Examples make concepts concrete, and three extended case studies show the scientific framew

“Getting Started with a SIMPLIS Approach” is particularly appropriate for those users who are not experts in statistics, but have a basic understanding of multivariate analysis that would allow them t

Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics and explores the nature of statistical tests, the tests that produces the p-values and basic aspects