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Statistics for Data Science
by James D. MillerGet your statistics basics right before diving into the world of data science About This Book • No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; • Implement statistics in data science tasks such as data cleaning, mining, and analysis • Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn • Analyze the transition from a data developer to a data scientist mindset • Get acquainted with the R programs and the logic used for statistical computations • Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more • Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis • Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks • Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples
Statistics for Data Science and Analytics
by Peter C. Bruce Peter Gedeck Janet DobbinsIntroductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration 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. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and setsExperiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary dataSpecialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from themStatistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.
Statistics for Data Science and Policy Analysis
by Azizur RahmanThis book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation. This book explores the theme of effective policy methods through the use of big data, accurate estimates and modern computing tools and statistical modelling.
Statistics For Dummies
by Deborah J. RumseyThe fun and easy way to get down to business with statisticsStymied by statistics? No fear ? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.Tracks to a typical first semester statistics courseUpdated examples resonate with today's studentsExplanations mirror teaching methods and classroom protocolPacked with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.
Statistics for Earth and Environmental Scientists
by Larry Drew John SchuenemeyerA comprehensive treatment of statistical applications for solving real-world environmental problemsA host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems.The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling--all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including:Interval estimation and Methods for analyzinghypothesis testing of means time series dataSpatial statisticsMultivariate analysisDiscrete distributionsExperimental designMost statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples.Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.
Statistics For Economics class 11 - NCERT - 23
by National Council of Educational Research and TrainingThe "STATISTICS FOR ECONOMICS" textbook by NCERT for Class XI is an essential guide for students venturing into the realm of economic data analysis. This resource demystifies the complex world of statistics, making it accessible for budding economists. Beginning with an introduction to the significance of statistics in economics, it delves into the techniques of data collection, organization, and presentation. The textbook elucidates central tendencies, dispersions, and correlations, highlighting their relevance in economic scenarios. Students are also introduced to index numbers and their applications in economic data interpretation. Special attention is given to the use of graphical and tabular methods for data presentation, providing students with practical tools to understand and analyze economic trends. By the end of the book, students are well-equipped with foundational statistical concepts, tailored specifically for economic applications, preparing them for advanced studies and real-world economic data analysis.
Statistics for Empowerment and Social Engagement: Teaching Civic Statistics to Develop Informed Citizens
by Jim Ridgway“This book is a remarkable achievement” Gerd GigerenzerThis book offers practical approaches to working in a new field of knowledge - Civic Statistics - which sets out to engage with, and overcome well documented and long-standing problems in teaching quantitative skills. The book includes 23 peer-reviewed chapters, written in coordination by an international group of experts from ten countries. The book aims to support and enhance the work of teachers and lecturers working both at the high school and tertiary (university) levels. It is designed to promote and improve the critical understanding of quantitative evidence relevant to burning social issues – such as epidemics, climate change, poverty, migration, natural disasters, inequality, employment, and racism.Effective citizen engagement with social issues requires active participation and a broad understanding of data and statistics about societal issues. However, many statistics curricula are not designed to teach relevant skills nor to improve learners' statistical literacy. Evidence about social issues is provided to the public via print and digital media, official statistics offices, and other information channels, and a great deal of data is accessible both as aggregated summaries and as individual records. Chapters illustrate the approaches needed to teach and promote the knowledge, skills, dispositions, and enabling processes associated with critical understanding of Civic Statistics presented in many forms. These include: statistical analysis of authentic multivariate data; use of dynamic data visualisations; deconstructing texts about the social and economic well-being of societies and communities. Chapters discuss: the development of curricula and educational resources; use of emerging technologies and visualizations; preparation of teachers and teaching approaches; sources for relevant datasets and rich texts about Civic Statistics; ideas regarding future research, assessment, collaborations between different stakeholders; and other systemic issues.
Statistics for Engineering and the Sciences
by William M. Mendenhall Terry L. SincichPrepare Your Students for Statistical Work in the Real WorldStatistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statist
Statistics for Engineering and the Sciences Student Solutions Manual
by William M. Mendenhall Terry L. Sincich Nancy S. BoudreauA companion to Mendenhall and Sincich’s Statistics for Engineering and the Sciences, Sixth Edition, this student resource offers full solutions to all of the odd-numbered exercises.
Statistics for Engineers: An Introduction with Examples from Practice
by Hartmut Schiefer Felix SchieferThis book describes how statistical methods can be effectively applied in the work of an engineer in terms that can be readily understood. Application of these methods enables the effort involved in experiments to be reduced, the results of these experiments to be fully evaluated, and statistically sound statements to be made as a result. Products can be developed more efficiently and manufactured more cost-effectively, not to mention with greater process reliability. The overarching aim is to save time, money, and materials. From the examples provided, the nature of the practical application can be clearly grasped in each case.This book is a translation of the original German 1st edition Statistik für Ingenieure by Hartmut Schiefer and Felix Schiefer, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2018. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). The present version has been revised technically and linguistically by the authors in collaboration with a professional translator. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Statistics for Environmental Biology and Toxicology (Interdisciplinary Statistics Ser. #4)
by A.John BailerStatistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.
Statistics for Environmental Science and Management (Chapman & Hall/CRC Applied Environmental Statistics)
by Bryan F.J. ManlyPresenting a nonmathematical approach to this topic, Statistics for Environmental Science and Management introduces frequently used statistical methods and practical applications for the environmental field. This second edition features updated references and examples along with new and expanded material on data quality objectives, the generalized linear model, spatial data analysis, and Monte Carlo risk assessment. Additional topics covered include environmental monitoring, impact assessment, censored data, environmental sampling, the role of statistics in environmental science, assessing site reclamation, and drawing conclusions from data.
Statistics for Epidemiology (Chapman & Hall/CRC Texts in Statistical Science)
by Nicholas P. JewellStatistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."Statistics for E
Statistics For Evidence-Based Practice And Evaluation
by Allen RubinBoth practical and easy to read, Rubin's STATISTICS FOR EVIDENCE-BASED PRACTICE AND EVALUATION provides you with a step-by-step guide that will help you succeed in your course. Practice illustrations and exercises support your ability to study and retain core concepts, while practical examples provide you with the opportunity to see how and when data analysis and statistics are used by helping professionals in the real world.
Statistics for Exercise Science and Health with Microsoft Office Excel
by J. P. VermaThis book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis. Statistics for Exercise Science and Health with Microsoft Office Excel: Aids readers in analyzing their own data using the presented statistical techniques combined with Excel Features comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports science Utilizes Excel to enhance reader competency in data analysis and experimental designs Includes coverage of both binomial and poison distributions with applications in exercise science and health Provides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniques Contains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems
Statistics for Experimenters: Design, Innovation, and Discovery (Second Edition)
by George E.P. Box J. Stuart Hunter William G. HunterThe book intends to make available to experimenters scientific and statistical tools that can greatly catalyze innovation, problem solving, and discovery and illustrate how these tools may be used by and with subject matter specialists as their investigations proceed.
Statistics for Finance (Chapman & Hall/CRC Texts in Statistical Science)
by Erik Lindström Henrik Madsen Jan Nygaard NielsenStatistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Itō’s formula, the Black–Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students’ financial reasoning skills.
Statistics for Fission Track Analysis (Chapman & Hall/CRC Interdisciplinary Statistics)
by Rex F. GalbraithStatistical analyses of the numbers, lengths, and orientations of fission tracks etched in minerals yield dating and thermal history information valuable in geological and geoscience applications, particularly in oil exploration. Fission tracks can be represented mathematically by a stochastic process of randomly oriented line segments in three dim
Statistics for Health Data Science: An Organic Approach (Springer Texts in Statistics)
by Ruth Etzioni Micha Mandel Roman GulatiStudents and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/
Statistics for Imaging, Optics, and Photonics
by Peter BajorskiA vivid, hands-on discussion of the statistical methods in imaging, optics, and photonics applications In the field of imaging science, there is a growing need for students and practitioners to be equipped with the necessary knowledge and tools to carry out quantitative analysis of data. Providing a self-contained approach that is not too heavily statistical in nature, Statistics for Imaging, Optics, and Photonics presents necessary analytical techniques in the context of real examples from various areas within the field, including remote sensing, color science, printing, and astronomy. Bridging the gap between imaging, optics, photonics, and statistical data analysis, the author uniquely concentrates on statistical inference, providing a wide range of relevant methods. Brief introductions to key probabilistic terms are provided at the beginning of the book in order to present the notation used, followed by discussions on multivariate techniques such as: Linear regression models, vector and matrix algebra, and random vectors and matrices Multivariate statistical inference, including inferences about both mean vectors and covariance matrices Principal components analysis Canonical correlation analysis Discrimination and classification analysis for two or more populations and spatial smoothing Cluster analysis, including similarity and dissimilarity measures and hierarchical and nonhierarchical clustering methodsIntuitive and geometric understanding of concepts is emphasized, and all examples are relatively simple and include background explanations. Computational results and graphs are presented using the freely available R software, and can be replicated by using a variety of software packages. Throughout the book, problem sets and solutions contain partial numerical results, allowing readers to confirm the accuracy of their approach; and a related website features additional resources including the book's datasets and figures.Statistics for Imaging, Optics, and Photonics is an excellent book for courses on multivariate statistics for imaging science, optics, and photonics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for professionals working in imaging, optics, and photonics who carry out data analyses in their everyday work.
Statistics for K-8 Educators
by Robert RosenfeldThis book offers an introduction to descriptive and inferential statistics tailored to the teaching and research needs of K-8 educators. Using statistics to tell a story, veteran teacher educator Robert Rosenfeld pushes readers away from simply performing a calculation to truly understanding the statistical concepts themselves. In addition to helping educators develop this statistical habit of mind, Rosenfeld also focuses on developing an understanding of the statistics in published research and on interpreting school data, which can be applied in school assessment and educational research. Features of this must-read resource include: Numerous exercises and activities throughout that are related specifically to the world of educators and are designed to foster conversation and small group discussion. Connections drawn between statistics and the regular mathematics curriculum to aid teachers who do classroom-based action research. A section covering the basic concepts of standardized tests, such as summative versus formative assessment, and standards-based versus norm-referenced tests. Accessibly written and conversational in tone, Statistics for K-8 Eductors provides the technical foundation to help teachers make good sense of quantitative information connected to their classrooms and to their schools.
Statistics for Kids: Model Eliciting Activities to Investigate Concepts in Statistics (Grades 4-6)
by Scott ChamberlinPerhaps the most useful and neglected content area of mathematics is statistics, especially for students in Grades 4-6. Couple that fact with the notion that mathematical modeling is an increasing emphasis in many standards, such as the Common Core State Standards for Mathematics and the NCTM standards, and the necessity for this topic is overdue. In this book, teachers will facilitate learning using model-eliciting activities (MEAs), problem-solving tasks created by mathematics educators to encourage students to investigate concepts in mathematics through the creation of mathematical models. Students will explore statistical concepts including trends, spread of data, standard deviation, variability, correlation, sampling, and more—all of which are designed around topics of interest to students. Grades 4-6
Statistics for Lawyers
by Michael O. Finkelstein Bruce LevinThis classic text, first published in 1990, is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The third edition includes over twenty new sections, including the addition of timely topics, like New York City police stops, exonerations in death-sentence cases, projecting airline costs, and new material on various statistical techniques such as the randomized response survey technique, rare-events meta-analysis, competing risks, and negative binomial regression. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors' calculations and comments are given in the back of the book. As with previous editions, the cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, sampling for insurance audits, and the death penalty. A chapter on epidemiology was added in the second edition. In 1991, the first edition was selected by the University of Michigan Law Review as one of the important law books of the year.
Statistics for Lawyers: Statistics for Social and Behavioral Science (Statistics for Social and Behavioral Sciences #189)
by Michael O. Finkelstein Bruce LevinThis classic text, first published in 1990, is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The fourth edition includes fourteen new sections, four inserts to the statistical text, and six new answer sections, on topics including the following: Use of prior probabilities after DNA database searches; Lipitor and diabetes; Harvard’s affirmative action practices in admissions; New York City garbage trucks; Tests of odds ratio homogeneity; Disparate impact of a pre-employment exam on minority applicants; Liraglutide and pancreatic cancer; Representative sampling; Reversals in death-penalty cases; Technology assisted review in e-discovery; Asbestos and colon cancer; Guilty pleas in the federal courts; The “financing secured” event study; and Average marginal effects. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors' calculations and comments are given in the back of the book. As with previous editions, the cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, sampling for insurance audits, and the death penalty.
Statistics for Linguists: An Introduction Using R
by Bodo WinterStatistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.