The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. This edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition. Read more

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.But rather than spend $30-$50 USD on a thick textbook, you may want to read this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations. Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.New Updated EditionThis new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.In This Step-By-Step Guide You Will Learn• How to download free datasets • What tools and machine learning libraries you need • Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data • Preparing data for analysis, including k-fold Validation • Regression analysis to create trend lines • k-Means Clustering to find new relationships • The basics of Neural Networks • Bias/Variance to improve your machine learning model • Decision Trees to decode classification, and • How to build your first Machine Learning Model to predict house values using PythonFrequently Asked QuestionsQ: Do I need programming experience to complete this e-book? A: This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see some programming used in this book. Q: I have already purchased the Second Edition of Machine Learning for Absolute Beginners, should I purchase this Third Edition? A: As the same topics from the Second Edition are covered in the Third Edition, you may be better served reading a more advanced title on machine learning. If you have purchased a previous edition of this book and wish to get access to the free video tutorials, please email the author. Q: Does this book include everything I need to become a machine learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.Please feel welcome to join this introductory course by buying a copy, or sending a free sample to your chosen device. Read more

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add "Machine Learning" to your LinkedIn profile?Well, hold on there.... Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.But rather than spend $30-$50 USD on a thick textbook, you may want to listen to this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations.Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.New Updated EditionThis new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition.Disclaimer: If you have passed the "beginner" stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.In this step-by-step guide, you will learn:How to download free datasetsWhat tools and machine learning libraries you needData scrubbing techniques, including one-hot encoding, binning and dealing with missing dataPreparing data for analysis, including k-fold ValidationRegression analysis to create trend linesk-Means Clustering to find new relationshipsThe basics of Neural NetworksBias/Variance to improve your machine learning modelPLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

In their bestselling title MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps you discover the nature of statistics and understand its essential role in scientific research. With the addition of contributor Brendan Ames, MATHEMATICAL STATISTICS WITH APPLICATIONS now includes an enhanced eTextbook. Simulation activities using interactive applets and R embedded within the MindTap Reader help students visualize statistical concepts, and an appendix introducing students to statistical data analysis using R can be found at the end of the eTextbook. Read more

This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.FeaturesThe classic graduate-level textbook on statistical inferenceDevelops elements of statistical theory from first principles of probabilityWritten in a lucid style accessible to anyone with some background in calculusCovers all key topics of a standard course in inferenceHundreds of examples throughout to aid understandingEach chapter includes an extensive set of graduated exercisesStatistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations.This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001. Read more

Written for an engineering audience, this book has a threefold purpose: (1) to present elements of modern probability theory — discrete, continuous, and stochastic; (2) to present elements of information theory with emphasis on its basic roots in probability theory; and (3) to present elements of coding theory.The emphasis throughout the book is on such basic concepts as sets, the probability measure associated with sets, sample space, random variables, information measure, and capacity. These concepts proceed from set theory to probability theory and then to information and coding theories. No formal prerequisites are required other than the usual undergraduate mathematics included in an engineering or science program. However, since these programs may not include a course in probability, the author presents an introductory treatment of probability for those who wish to pursue the general study of statistical theory of communications. The book is divided into four parts: memoryless discrete themes, memoryless continuum, schemes with memory, and an outline of some recent developments. An appendix contains notes to help familiarize the reader with the literature in the field, while the inclusion of many reference tables and an extensive bibliography with some 200 entries makes this an excellent resource for any student in the field. Read more

"Modern Statistics: Intuition, Math, Python, R" is an authoritative and comprehensive textbook designed to guide university students and professionals through the intricate world of statistics, data science, and machine learning. This extensive 700-page volume, with its impressive array of 390 figures and over 35,000 lines of code (all code and a sample chapter are freely available on github), offers a unique blend of theoretical and practical knowledge, making it an indispensable resource for those seeking to deepen their understanding of statistical methodologies and their applications in the modern digital era.Beginning with foundational concepts, the book delves into what data are and how they can be visualized, setting the stage for more complex subjects. It covers key areas such as descriptive statistics, data simulation, transformations, and data quality assessment and improvement, providing a robust framework for data analysis. The exploration of probability theory, sampling, and distributions paves the way for an in-depth discussion of hypothesis testing, including a detailed examination of the t-test family.The book places a strong emphasis on the practical implementation of statistical techniques, with extensive Python and R code examples that bring to life the concepts of correlations, confidence intervals, ANOVA, and regression analysis. The chapters on permutation tests, power and sample size calculations, biases, and data communication underscore the importance of robust statistical practice and effective data storytelling in research and professional settings.This textbook is not only a rich source of statistical knowledge but also a practical guide to applying these concepts using the latest software tools. It bridges the gap between statistical theory and real-world data analysis, ensuring readers are equipped to tackle complex data challenges with confidence.Whether you are a student embarking on your statistical journey or a seasoned professional looking to refine your skills, "Modern Statistics: Intuition, Math, Python, R" is a vital addition to your educational toolkit.Targeted at those who appreciate the rigor of statistical analysis and the nuances of data interpretation, this book is a testament to the ever-evolving field of statistics and its pivotal role in the age of data science and machine learning.*Note about the ebook version: Formatting a textbook with equations and figures is challenging. Everything should be clear and legible in this ebook version, but the print version will have the best formatting. Read more

The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics.Key Features:All R code and data sets available at Harvard Dataverse.Solutions manual available for instructors.Includes over 100 exercises.This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments. Read more

There has been considerable attention to making the methodologies of structural equation modeling available to researchers, practitioners, and students along with commonly used software. Structural Equation Modelling Using R/SAS aims to bring it all together to provide a concise point-of-reference for the most commonly used structural equation modeling from the fundamental level to the advanced level. This book is intended to contribute to the rapid development in structural equation modeling and its applications to real-world data. Straightforward explanations of the statistical theory and models related to structural equation models are provided, using a compilation of a variety of publicly available data, to provide an illustration of data analytics in a step-by-step fashion using commonly used statistical software of R and SAS. This book is appropriate for anyone who is interested in learning and practicing structural equation modeling, especially in using R and SAS. It is useful for applied statisticians, data scientists and practitioners, applied statistical analysts and scientists in public health, and academic researchers and graduate students in statistics, whilst also being of use to R&D professionals/practitioners in industry and governmental agencies.Key Features: Extensive compilation of commonly used structural equation models and methods from fundamental to advanced levelsStraightforward explanations of the theory related to the structural equation modelsCompilation of a variety of publicly available dataStep-by-step illustrations of data analysis using commonly used statistical software R and SASData and computer programs are available for readers to replicate and implement the new methods to better understand the book contents and for future applicationsHandbook for applied statisticians and practitioners Read more

Political scientists often ask themselves what might have been if history had unfolded differently: if Stalin had been ousted as General Party Secretary or if the United States had not dropped the bomb on Japan. Although scholars sometimes scoff at applying hypothetical reasoning to world politics, the contributors to this volume--including James Fearon, Richard Lebow, Margaret Levi, Bruce Russett, and Barry Weingast--find such counterfactual conjectures not only useful, but necessary for drawing causal inferences from historical data. Given the importance of counterfactuals, it is perhaps surprising that we lack standards for evaluating them. To fill this gap, Philip Tetlock and Aaron Belkin propose a set of criteria for distinguishing plausible from implausible counterfactual conjectures across a wide range of applications. The contributors to this volume make use of these and other criteria to evaluate counterfactuals that emerge in diverse methodological contexts including comparative case studies, game theory, and statistical analysis. Taken together, these essays go a long way toward establishing a more nuanced and rigorous framework for assessing counterfactual arguments about world politics in particular and about the social sciences more broadly. Read more

Award-winning prof brings you from first-year classes to the frontiers of systems and synthetic biology, epidemic modeling, and imaging. Physical Models of Living Systems is a university textbook that integrates those cutting-edge topics with classic results in statistical inference, control theory, biophysical chemistry and mechanobiology, immunology, and neuroscience, as well as guiding you to create your own stochastic simulations. Instead of offering a huge pile of facts, the discovery-style exposition frequently asks you to reflect on "How could anything like that happen at all?" and then shows how scientists have incrementally peeled back the layers of mystery surrounding these beautiful mechanisms. Working through this book will give you an appreciation for how science has advanced in the past, and the skills and frameworks needed to push forward in the future.Intended for intermediate-level undergraduates in any science or engineering major, the only prerequisite for this course is first-year physics. Supplementary sections make the book also suitable as the basis of a graduate-level course. This low-cost second edition expands the first one with four new chapters as well as adding add many clarifications and updates. Dozens of exercises are included at all levels of complexity, many involving computer work. Throughout, the goal is for you to gain the fluency needed to derive every result for yourself. Along the way, you will acquire several research skills that are often not addressed in traditional courses:Basic modeling skills, including dimensional analysis, identification of variables, and ODE formulation;Probabilistic modeling skills, including stochastic simulation;Data analysis methods, including maximum likelihood and Bayesian methods;Computer programming using a general-purpose platform like MATLAB or Python, with short codes written from scratch;Dynamical systems, particularly feedback control, with phase portrait methods.All of these basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including:Virus dynamics;Bacterial genetics and evolution of drug resistance;Statistical inference;Superresolution microscopy and cryo-electron microscopy;Stochastic simulation, for example of gene expression;Synthetic biology;Epidemic modeling;Naturally evolved cellular control circuits, including homeostasis, genetic switches, and the mitotic clock;Excitable media. Read more

In general terms, the shape of an object, data set, or image can be de­ fined as the total of all information that is invariant under translations, rotations, and isotropic rescalings. Thus two objects can be said to have the same shape if they are similar in the sense of Euclidean geometry. For example, all equilateral triangles have the same shape, and so do all cubes. In applications, bodies rarely have exactly the same shape within measure­ ment error. In such cases the variation in shape can often be the subject of statistical analysis. The last decade has seen a considerable growth in interest in the statis­ tical theory of shape. This has been the result of a synthesis of a number of different areas and a recognition that there is considerable common ground among these areas in their study of shape variation. Despite this synthesis of disciplines, there are several different schools of statistical shape analysis. One of these, the Kendall school of shape analysis, uses a variety of mathe­ matical tools from differential geometry and probability, and is the subject of this book. The book does not assume a particularly strong background by the reader in these subjects, and so a brief introduction is provided to each of these topics. Anyone who is unfamiliar with this material is advised to consult a more complete reference. As the literature on these subjects is vast, the introductory sections can be used as a brief guide to the literature. Read more

To practice engineering effectively, engineers must need to have a working knowledge of statistical concepts and methods. What they do not need is a background heavy on statistical theory and number crunching.Statistical Methods for Industrial Process Control provides the practical statistics foundation engineers can immediately apply to the work they do every day, regardless of their industry or specialty. The author illustrates statistical concepts and methods with authentic semiconductor manufacturing process examples-integrated circuit fabrication is an exceedingly rich medium for communicating statistical concepts. However, once learned, these concepts and methods can easily be extended and applied to a variety of other industries.The text emphasizes the application of statistical tools, rather than statistical theory. Modern advances in statistical software have made tedious computations and formula memorization unnecessary. Therefore, the author demonstrates software use throughout the book and supplies MINITAB examples and SAS programs. Review problems at the end of each chapter challenge and deepen readers' understanding of the material. Statistical Methods for Industrial Process Control addresses topics that support the work engineers do, rather than educate them as statisticians, and these topics also reflect modern usage. It effectively introduces novice engineers to a fascinating industry and enables experienced engineers to build upon their existing knowledge and learn new skills. Read more

Follow Nobel laureate and legendary teacher Enrico Fermi’s lost course on geophysics. Nobel Prize–winning physicist Enrico Fermi (1901–54) is known for his work on experimental particle and nuclear physics, quantum theory, and statistical mechanics, and for his particular ability to condense complicated problems into approximations for understanding and testing theory in a variety of scientific disciplines. Six of his graduate students went on to win their own Nobel Prizes. Unearthing Fermi’s Geophysics opens a window onto two underrepresented facets of this extraordinary thinker: Fermi’s teaching and his contribution to the field of geophysics. Drawing on Fermi’s handwritten calculations and notes, many of which are reproduced here in photographic facsimile, physicists Gino Segrè and John Stack have reconstructed a coursebook of Fermi’s insights into the physics of a range of geological and atmospheric phenomena. From gravity on Earth to thermodynamics in the atmosphere, the physics of raindrops, the Coriolis effect in hurricanes, tidal physics, earthquakes and seismic waves, Earth’s magnetism, atmospheric electricity, and much more, Unearthing Fermi’s Geophysics reveals the hidden workings of the world above, around, and below us—and of the mind of a great scientist who was able to bring those physical workings to light. Read more

Essentials of Statistics for the Behavioral Sciences is a concise version of Statistics for the Behavioral Sciences by award-winning teacher, author, and advisor Gregory J. Privitera. The Second Edition provides balanced coverage for today’s students, connecting the relevance of core concepts to daily life with new introductory vignettes for every chapter, while speaking to the reader as a researcher when covering statistical theory, computation, and application. Robust pedagogy allows students to continually check their comprehension and hone their skills while working through carefully developed problems and exercises that include current research and seamless integration of IBM® SPSS® Statistics. Readers will welcome Privitera’s thoughtful instruction, conversational voice, and application of statistics to real-world problems. A Complete Teaching & Learning Package.SAGE coursepacks FREE! SAGE coursepacks makes it easy to import our quality instructor and student resource content into your school’s learning management system (LMS). Intuitive and simple to use, SAGE coursepacks allows you to customize course content to meet your students’ needs. . SAGE edge FREE! SAGE edge offers both instructors and students a robust online environment with an impressive array of teaching and learning resources. Study Guide With IBM® SPSS® Workbook Bundle the Second Edition with the accompanying Student Study Guide With IBM® SPSS® Workbook for Essential Statistics for the Behavioral Sciences. Guide for Users of R, SAS®, and Stata® Bundle the Second Edition with the accompanying Essentials of Statistical Analysis "In Focus". WebAssign® This title is available on WebAssign, allowing instructors to produce and manage assignments with their students online using a grade book that allows them to track and monitor students′ progress. Students receive unlimited practice using a combination of multiple choice and algorithmic questions, and are allowed unlimited access to this edition of the textbook in the same course at no additional cost. WebAssign provides instant feedback and links directly to the accompanying eBook section where the concept was covered, allowing students to find the correct solution. Read more

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. Read more

The theme of this book is simple. The price – the number someone puts on a product to help consumers decide to buy that product – comes from data. Specifically, itcomes from statistically modeling the data.This book gives the reader the statistical modeling tools needed to get the number to put on a product. But statistical modeling is not done in a vacuum. Economic and statistical principles and theory conjointly provide the background and framework for the models. Therefore, this book emphasizes two interlocking components of modeling: economic theory and statistical principles.The economic theory component is sufficient to provide understanding of the basic principles for pricing, especially about elasticities, which measure the effects of pricing on key business metrics. Elasticity estimation is the goal of statistical modeling, so attention is paid to the concept and implications of elasticities.The statistical modeling component is advanced and detailed covering choice (conjoint, discrete choice, MaxDiff) and sales data modeling. Experimental design principles, model estimation approaches, and analysis methods are discussed and developed for choice models. Regression fundamentals have been developed for sales model specification and estimation and expanded for latent class analysis. Read more