- Full Title : Exam Prep for: Elementary Statistics; A Step By Step ...
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**THE INSTANT #1 NEW YORK TIMES BESTSELLER** "An unforgettable—and Hollywood-bound—new thriller... A mix of Hitchcockian suspense, Agatha Christie plotting, and Greek tragedy." —Entertainment Weekly The Silent Patient is a shocking psychological thriller of a woman’s act of violence against her husband—and of the therapist obsessed with uncovering her motive. Alicia Berenson’s life is seemingly perfect. A famous painter married to an in-demand fashion photographer, she lives in a grand house with big windows overlooking a park in one of London’s most desirable areas. One evening her husband Gabriel returns home late from a fashion shoot, and Alicia shoots him five times in the face, and then never speaks another word. Alicia’s refusal to talk, or give any kind of explanation, turns a domestic tragedy into something far grander, a mystery that captures the public imagination and casts Alicia into notoriety. The price of her art skyrockets, and she, the silent patient, is hidden away from the tabloids and spotlight at the Grove, a secure forensic unit in North London. Theo Faber is a criminal psychotherapist who has waited a long time for the opportunity to work with Alicia. His determination to get her to talk and unravel the mystery of why she shot her husband takes him down a twisting path into his own motivations—a search for the truth that threatens to consume him....
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. An accessible introduction to statistics in the criminal justice field. Elementary Statistics in Criminal Justice Research, Fourth Edition, provides an introduction to statistics for students in criminal justice and criminology. Created specifically for students who many not have strong backgrounds in mathematics, the text focuses primarily on the statistical theories and methods that criminal justice students need to understand. This text was adapted from the best-selling Elementary Statistics in Social Research, and provides broad and accessible coverage that will appeal to students and instructors alike.
With interest growing in areas of forestry, conservation and other natural sciences, the need to organize and tabulate large amounts of forestry and natural science information has become a necessary skill. Previous attempts of applying statistical methods to these areas tend to be over-specialized and of limited use; an elementary text using methods, examples and exercises that are relevant to forestry and the natural sciences is long overdue. This book utilises basic descriptive statistics and probability, as well as commonly used statistical inferential tools to introduce topics that are commonplace in a forestry context such as hypothesis texting, design of experiments, sampling methods, nonparametric tests and statistical quality control. It also contains examples and exercises drawn from the fields of forestry, wood science, and conservation.
SAS for Elementary Statistics: Getting Started provides an introduction to SAS programming for those who have experience with introductory statistical methods. It is also an excellent programming supplement for an introductory statistics course. It is appropriate for the beginning programmer with no prior SAS experience and the researcher who would like to refresh SAS programming skills. These lessons are those the author has found successful in the classroom. Strengths of this book include the following: Examples are easy to follow and understand. Chapters have user-friendly text and objectives. Each chapter has clear objectives with SAS syntax and output results given. Objectives are stated as tasks with detailed step-by-step instructions. Programming notes based on the author's experience occur throughout the book. The author assists the reader in making sense of the error messages in the SAS log. Brief reviews of statistical methods are included in chapters accompanying the corresponding SAS procedures. Easy transition from user terminology to SAS terminology is provided. The ability to select or suppress results using Output Delivery System (ODS) is made simple. Reading and writing to external files are among the most used SAS skills, and these concepts are clearly presented. The IMPORT and EXPORT procedures and ODS are used to accomplish these tasks. Statistical Graphics procedures and SAS/GRAPH can be quite challenging to learn, but these are presented in a very achievable format. Basic graph construction is first introduced then readers learn how to add color, pattern, and other enhancements to graphics images.
The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.
Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. Includes step-by-step tutorials to aid in understanding the process and implementation of unique data Presents statistical theory in a simple way without complex mathematical proofs Shows how to analyze data using R software and provides R scripts for all examples and figures
This book addresses the decision making process under uncertainty. The process commonly encountered in all fields of human endeavor is called the diagnostic process in this monograph. The thrust of this book is to help the struggling student, of all ages, in all fields, to cross the threshold from rote to comprehension, thus bridging an intuitive gap left in many a reader’s mind regarding the significance and clinical implication of the accompanying probability data. The text is, in essence, a verbal and graphic portrait of the basic ideas and symbolic structure of probability and statistical inference with particular stress on the Bayesian version. It aims to expound in words, simile, and diagrams the inherent connections obtained between a given event and its sample space or between a given random sample and a hypothesized population. In this sense, no formula is left naked to be absorbed on its face value without the support of a graphic cover. The final result is a firm grasp of the simple concepts that make the infrastructure (not the superstructure) of the subject. Nonetheless, this is not another book on statistics. It certainly is not a textbook geared for the classroom, it contains no problem to solve other than those structured and graphed examples needed to clarify and illustrate the thrust of the point under consideration. The book deals exclusively with the two topics that I tend to believe are the core thesis of statistics, namely, probability and its counterpoint, inference, supported by the necessary exposition of sets. Thus, the book does not include the mandatory and important chapters on analysis of variance, regression, and correlation.
This revised edition of this unique textbook is specifically designed for statistics and probability courses taught to students of forestry and related disciplines. It introduces probability, statistical techniques, data analysis, hypothesis testing, experimental design, sampling methods, nonparametric tests and statistical quality control, using examples drawn from a forestry, wood science and conservation context. The book now includes several new practical exercises for students to practice data analysis and experimental design themselves. It has been updated throughout, and its scope has been broadened to reflect the evolving and dynamic nature of forestry, bringing in examples from conservation science, recreation and urban forestry.
To request a free 30-day online trial to this product, visit www.sagepub.com/freetrial Research design can be daunting for all types of researchers. At its heart it might be described as a formalized approach toward problem solving, thinking, and acquiring knowledge—the success of which depends upon clearly defined objectives and appropriate choice of statistical tools, tests, and analysis to meet a project's objectives. Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. Key Features Covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research Addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences Provides summaries of advantages and disadvantages of often-used strategies Uses hundreds of sample tables, figures, and equations based on real-life cases Key Themes Descriptive Statistics Distributions Graphical Displays of Data Hypothesis Testing Important Publications Inferential Statistics Item Response Theory Mathematical Concepts Measurement Concepts Organizations Publishing Qualitative Research Reliability of Scores Research Design Concepts Research Designs Research Ethics Research Process Research Validity Issues Sampling Scaling Software Applications Statistical Assumptions Statistical Concepts Statistical Procedures Statistical Tests Theories, Laws, and Principles Types of Variables Validity of Scores The Encyclopedia of Research Design is the perfect instrument for new learners as well as experienced researchers to explore both the original and newest branches of the field.
The methodological needs of environmental studies are unique in the breadth of research questions that can be posed, calling for a textbook that covers a broad swath of approaches to conducting research with potentially many different kinds of evidence. Written specifically for social science-based research into the environment, this book covers the best-practice research methods most commonly used to study the environment and its connections to societal and economic activities and objectives. Over five key parts, Kanazawa introduces quantitative and qualitative approaches, mixed methods, and the special requirements of interdisciplinary research, emphasizing that methodological practice should be tailored to the specific needs of the project. Within these parts, detailed coverage is provided on key topics including the identification of a research project; spatial analysis; ethnography approaches; interview technique; and ethical issues in environmental research. Drawing on a variety of extended examples to encourage problem-based learning and fully addressing the challenges associated with interdisciplinary investigation, this book will be an essential resource for students embarking on courses exploring research methods in environmental studies.
As an undergraduate student, you should carry out a research to be qualified for a bachelor degree. Yet, research can be a major stumbling block for a student to achieve his/her goal. However, research should not hinder you to attain your aim. It only takes a little understanding and practice. This book describes almost everything you need to carry out a research assignment, as well as some techniques, concepts and conventions for writing a scientific paper. And more importantly, it has ample samples and practices. The objective of this book is to guide you step by step, little by little to design your research and finally write your very own thesis proposal. I sincerely wish that you could take advantage of this book and begin your journey to greatness. Happy researching and writing!
Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
This volume presents a collection of peer-reviewed, scientific articles from the 14th International Conference on Information Technology - New Generations, held at Tuscany Suites Hotel in Las Vegas. The proceedings addresses critical areas of information technology including web technology, communications, computing architectures, software engineering, security, and data mining.
Applications of Hypothesis Testing for Environmental Science presents the theory and application of hypothesis testing in environmental science, allowing researchers to carry out suitable tests for decision-making on a variety of issues. This book works as a step-by-step resource to provide understanding of the concepts and applications of hypothesis testing in the field of environmental science. The tests are presented in simplified form without relying on complex mathematical proofs to allow researchers to easily locate the most appropriate test and apply it to real-world situations. Each example is accompanied by a case study showing the application of the method to realistic data. This book provides step-by-step guidance in analyzing and testing various environmental data for researchers, postgraduates and graduates of environmental sciences, as well as academics looking for a book that includes case studies of the applications of hypothesis testing. It will also be a valuable resource for researchers in other related fields and those who are not familiar with the use of statistics who may need to analyze data or perform hypothesis tests in their research. Includes step-by-step tutorials to aid in the understanding of procedures and allowing implementation of suitable tests Presents the theory of hypothesis testing in a simple yet thorough manner without complex mathematical proofs Describes how to implement hypothesis testing in analyzing and interpretation environmental science data
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.