Statistics

While qualitative methods have been rigorously critiqued and reformulated, the population statistics relied on by virtually all research on Indigenous Peoples continue to be taken for granted as straightforward, transparent numbers. Drawing on a diverse new author team, this book dismantles that persistent positivism with a forceful critique, then fills the void with a new paradigm for Indigenous quantitative methods using concrete examples of research projects from first world Indigenous Peoples in the United States, Australia, Aotearoa New Zealand and Canada. Concise and accessible, it is an ideal supplementary text as well as a core component of the methodological toolkit for anyone conducting Indigenous research or using Indigenous population statistics.

Data plays a vital role in different parts of our lives. In the world of big data, and policy determined by a variety of statistical artifacts, discussions around the ethics of data gathering, manipulation and presentation are increasingly important. Ethics in Statistics aims to make a significant contribution to that debate. The processes of gathering data through sampling, summarising of the findings, and extending results to a population, need to be checked via an ethical prospective, as well as a statistical one. Statistical learning without ethics can be harmful for mankind.This edited collection brings together contributors in the field of data science, data analytics and statistics, to share their thoughts about the role of ethics in different aspects of statistical learning.

The book is designed for basic courses in probability and statistics, and is aimed primarily at non-specialists and beginner level students. The book is divided into 2 sections, respectively. Probability: Includes a primer on set theory, basic probability theory definitions and calculations, combinatorial analysis, random variables and distribution laws Statistics: Covers basic concepts of descriptive statistics Key features · Simple, clear language for easy comprehension of key concepts.

Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of "kitchen sink" formulas.

The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills.

A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical inference for a small sample under one methodological umbrella. Two competing approaches are offered: maximum concentration (MC) and mode (MO) statistics combined under one methodological umbrella, which is why the symbolic equation M=MC+MO. M-statistics defines an estimator as the limit point of the MC or MO exact optimal confidence interval when the confidence level approaches zero, the MC and MO estimator, respectively. Neither mean nor variance plays a role in M-statistics theory.

Packed with lessons, examples, and practice problems to help you slay your stats course. Develop confidence and understanding in statistics with easy-to-understand (even fun) explanations of key concepts. Plus, you'll get access to online chapter quizzes and other resources that will turn you into a stats master. This book teaches you how to interpret graphs, determine probability, critique data, and so much more. Written by an expert author and serious statistics nerd, Statistics AIO For Dummies explains everything in terms anyone can understand. Get a grasp of basic statistics concepts required in every statistics course.

Medical journals and researchers are increasingly recognizing the need for improved statistical rigor in medical science. In Applied Medical Statistics, renowned statistician and researcher Dr. Jingmei Jiang delivers a clear, coherent, and accessible introduction to basic statistical concepts, ideal for medical students and medical research practitioners. The book will help readers master foundational concepts in statistical analysis and assist in the development of a critical understanding of the basic rationale of statistical analysis techniques.

Nature evolves mainly in a statistical way. Different strategies, formulas, and conformations are continuously confronted in the natural processes. Some of them are selected and then the evolution continues with a new loop of confrontation for the next generation of phenomena and living beings. Failings are corrected without a previous program or design. The new options generated by different statistical and random scenarios lead to solutions for surviving the present conditions. This is the general panorama for all scrutiny levels of the life cycles. Over three sections, this book examines different statistical questions and techniques in the context of machine learning and clustering methods, the frailty models used in survival analysis, and other studies of statistics applied to diverse problems.

In the last twenty years, sports organizations have become a data-driven business. Before this, most decisions in sports were qualitatively driven by subject-matter experts. In the years since numerous teams found success with "Money Ball" analytical perspectives, the industry has sought to advance its analytical acumen to improve on- and off-field outcomes. The increasing demand for data to inform decisions for coaches, scouts, and players before and during sporting events has led to intriguing efforts to build upon this quantitative approach. As this methodology for assessing performance has matured and grown in importance, so too has the open-source R software emerged as one of the leading analytical software packages. In fact, R is a top 10 programming language that is useful in academia and industry for statistics, machine learning, and rapid prototyping.

The only biostatistics resource written specifically for MSN and DNP nursing students, this text provides a unique graphic representation of data to help students understand difficult analytic information. Modelled on an effective and proven method of learning--graphical illustrations first, technical details second--the text helps students understand appropriate research design, measurement, and analysis of how results are calculated. Providing a gentle progression into the complexities of statistics, it builds upon concepts step-by-step, helping students to reinforce prior knowledge before moving on the new information. Chapters initially focus on the practical application of content to illustrate how statistics are used, followed by working examples--addressing both research and evidence-based content and epidemiology and population health content--to enhance understanding and underscore the importance of the methodology. The book integrates the latest changes from the ASA related to statistical significance tested and addresses recent deliberations about sample size. Abundant Instructor Resources include PowerPoints, a test bank, data sets, information on how to obtain online programming classes, and sample lesson plans and activities.

This is a study of how such data influenced every aspect of Victorian culture and thought, from the methods of natural science and the struggle against disease, to the development of social administration and the arguments and conflicts between social classes. Numbers were collected in the 1830s by newly-created statistical societies in response to this 'data revolution'. They became a regular aspect of governmental procedure thereafter, and inspired new ways of interrogating both the natural and social worlds. William Farr used them to study cholera; Florence Nightingale deployed them in campaigns for sanitary improvement; Charles Babbage was inspired to design and build his famous calculating engines to process them. The mid-Victorians employed statistics consistently to make the case for liberal reform. In later decades, however, the emergence of the academic discipline of mathematical statistics - statistics as we use them today - became associated with eugenics and a contrary social philosophy.

Provides readers with a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches. The evidential approach is conceptually easier to grasp, and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses. The evidential approach is currently underused, despite its mathematical precision and statistical validity. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations and exercises. Additionally, the companion website complements and expands on the information contained in the book.

This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis - one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria.

Now in its twelfth edition, this international bestseller continues to be a must-have resource for anyone in need of a thorough introduction to statistics in the health sciences. Clear and accessible chapters help students with no previous background in the subject understand fundamental topics including summary statistics for quantitative and binary data, diagnostic and screening tests, populations and samples, survival analysis, correlation and regression, study design, computer modeling, and more. This edition reflects contemporary understanding of medical statistics and emphasizes the importance of statistics in public health, including extensively updated coverage of diagnostic tests and new COVID-related examples. All figures and examples now include code to reproduce them in the R statistical software. New chapters cover the basics for understanding numbers and introduce the use of models in medical statistical analysis.

Throughout Don Benbow's extensive career, teaching mathematics and statistical courses to college students and corporate employees, one common question always popped up: how do you fix statistical errors in quality? Most textbooks and courses tend to emphasize how to perform statistical analysis and give little attention to errors that can occur in the process. This book intends to teach readers how to avoid common pitfalls by providing examples and scenarios based on similar real-world events. The book also provides caveats (or quick tips) to help readers navigate their way through statistical methodology.

Written for those with only a brief introduction to statistics, this book takes you on a statistical journey from how polls are taken to how they can--and should--be used to estimate current popular opinion. Once an understanding of the election process is built, we turn toward testing elections for evidence of unfairness. While holding elections has become the de facto proof of government legitimacy, those electoral processes may hide a dirty little secret of the government illicitly ensuring a favorable election outcome. This book includes these features designed to make your statistical journey more enjoyable: Vignettes of elections, including maps, to provide concrete bases for the material In-chapter cues to help one avoid the heavy math--or to focus on it End-of-chapter problems designed to review and extend that which was covered in the chapter Many opportunities to turn the power of the R statistical environment to the enclosed election data files, as well as to those you find interesting.

With its excellent topical coverage, the focus of this book is on the basic principles and practical applications of the fundamental concepts that are extensively used in various Engineering disciplines as well as in a variety of programs in Life and Social Sciences. The text provides students with the requisite building blocks of knowledge they require to understand and progress in their areas of interest. With a simple, clear-cut style of writing, the intuitive explanations, insightful examples, and practical applications are the hallmarks of this book. The text consists of twelve chapters divided into four parts. Part-I, Probability (Chapters 1 - 3), lays a solid groundwork for probability theory, and introduces applications in counting, gambling, reliability, and security. Part-II, Random Variables (Chapters 4 - 7), discusses in detail multiple random variables, along with a multitude of frequently-encountered probability distributions. Part-III, Statistics (Chapters 8 - 10), highlights estimation and hypothesis testing. Part-IV, Random Processes (Chapters 11 - 12), delves into the characterization and processing of random processes.

Statistics in Nutrition and Dietetics is a clear and accessible volume introducing the basic concepts of the scientific method, statistical analysis, and research in the context of the increasingly evidence-based field of nutrition and dietetics. Focusing on quantitative analysis and drawing on short, practical exercises and real-world examples, this reader-friendly textbook helps students understand samples, principles of measurement, confidence intervals, the theoretical basis and practical application of statistical tests, and more. Includes numerous examples and exercises that demonstrate how to compute the relevant outcome measures for a variety of tests, both by hand and using SPSS Provides access to online resources, including analysis-ready data sets, flow charts, further readings and a range of instructor materials such as PowerPoint slides and lecture notes Ideal for demystifying statistical analysis for undergraduate and postgraduate students.

Statistic: A Concise Mathematical Introduction for Students and Scientists offers a one academic term text that prepares the student to broaden their skills in statistics, probability and inference, prior to selecting their follow-on courses in their chosen fields, whether it be engineering, computer science, programming, data sciences, business or economics.  The book places focus early on continuous measurements, as well as discrete random variables. By invoking simple and intuitive models and geometric probability, discrete and continuous experiments and probabilities are discussed throughout the book in a natural way. Classical probability, random variables, and inference are discussed, as well as material on understanding data and topics of special interest.