Winner of the National Business Book Award

From the New York Times bestselling author of The Organized Mind and This Is Your Brain on Music, a primer to the critical thinking that is more necessary now than ever

We are bombarded with more information each day than our brains can process--especially in election season. It's raining bad data, half-truths, and even outright lies. New York Times bestselling author Daniel J. Levitin shows how to recognize misleading announcements, statistics, graphs, and written reports, revealing the ways lying weasels can use them.

It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, and distortions from reliable information? Levitin groups his field guide into two categories--statistical information and faulty arguments--ultimately showing how science is the bedrock of critical thinking. Infoliteracy means understanding that there are hierarchies of source quality and bias that variously distort our information feeds via every media channel, including social media. We may expect newspapers, bloggers, the government, and Wikipedia to be factually and logically correct, but they so often aren't. We need to think critically about the words and numbers we encounter if we want to be successful at work, at play, and in making the most of our lives. This means checking the plausibility and reasoning--not passively accepting information, repeating it, and making decisions based on it. Readers learn to avoid the extremes of passive gullibility and cynical rejection. Levitin's charming, entertaining, accessible guide can help anyone wake up to a whole lot of things that aren't so. And catch some weasels in their tracks

Practical Statistics for Educators, Seventh Edition, is a clear and easy-to follow book written specifically for education students in introductory statistics and action research courses. It is also an invaluable resource and guidebook for educational practitioners who wish to study their own settings and for those involved in program evaluation. The book's focus is on essential concepts in educational statistics, understanding when to use various statistical tests, and learning how to interpret results. This book introduces education students and practitioners to the use of parametric and nonparametric statistics in education, and basic concepts in statistics are explained in clear language. Formulas and equations are used sparingly, and readers are not required to do any computations. The book also includes a discussion of testing, test score interpretation, reliability, and validity. A chapter on survey design and analysis provides readers with examples that demonstrate how the different statistical tests introduced in the book can be used to analyze survey data. An extensive study guide at the end of the book provides an opportunity to review all the information that was presented in the book; the guide includes an answer key with a clear explanation of each correct answer. Throughout this text, examples taken from the field of education serve to illustrate the various concepts, terms, statistical tests, and data interpretations.

Make better decisions with this deconstructed guide to data analyticsWant to add data analytics to your skill stack? Having trouble finding where to start?
Cell by cell, bit by bit, this book teaches you the vocabulary, tools, and basic algorithms to think like a data scientist. Using the Lego set approach, each chapter adds and connects individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.

Who is This Book For?
This book is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this book is for you.

Practical Approach
This book takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the book, you will have the practical knowledge to tackle real data problems in your organization or daily life.

What You Will Learn
- How to recognize the common data types every data scientist needs to master
- Where to store your data, including Big Data
- New trends in data analytics, including what is Alternative Data and why few people know about it!
- How to explain the distinction between Data Mining, Machine Learning, and Analytics to your colleagues
- When and how to use Regression Analysis, Classification, Clustering, Association Analysis, and Natural Language Processing
- How to make better business decisions using Data Visualization and Business Intelligence

A volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching Series Editor Ron Serlin, University of Wisconsin (sponsored by the Educational Statisticians, SIG) Multilevel Modeling of Educational Data, co-edited by Ann A. O'Connell, Ed.D., and D. Betsy McCoach, Ph.D., is the next volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching (Information Age Publishing), sponsored by the Educational Statisticians' Special Interest Group (Ed-Stat SIG) of the American Educational Research Association. The use of multilevel analyses to examine effects of groups or contexts on individual outcomes has burgeoned over the past few decades. Multilevel modeling techniques allow educational researchers to more appropriately model data that occur within multiple hierarchies (i.e.- the classroom, the school, and/or the district). Examples of multilevel research problems involving schools include establishing trajectories of academic achievement for children within diverse classrooms or schools or studying school-level characteristics on the incidence of bullying. Multilevel models provide an improvement over traditional single-level approaches to working with clustered or hierarchical data; however, multilevel data present complex and interesting methodological challenges for the applied education research community. In keeping with the pedagogical focus for this book series, the papers this volume emphasize applications of multilevel models using educational data, with chapter topics ranging from basic to advanced. This book represents a comprehensive and instructional resource text on multilevel modeling for quantitative researchers who plan to use multilevel techniques in their work, as well as for professors and students of quantitative methods courses focusing on multilevel analysis. Through the contributions of experienced researchers and teachers of multilevel modeling, this volume provides an accessible and practical treatment of methods appropriate for use in a first and/or second course in multilevel analysis. A supporting website links chapter examples to actual data, creating an opportunity for readers to reinforce their knowledge through hands-on data analysis. This book serves as a guide for designing multilevel studies and applying multilevel modeling techniques in educational and behavioral research, thus contributing to a better understanding of and solution for the challenges posed by multilevel systems and data.

Now available from TC Press with a new foreword by Nel Noddings and a new prologue by P. Bruce Uhrmacher and Christy McConnell Moroye, this classic text on qualitative research is ideal for both novice and established researchers. Eisner's seminal work on mind, education, and research explores the ways in which the methods, content, and assumptions in the arts, humanities, and social sciences can help us better understand our schools and classrooms. The Enlightened Eye expands how we think about inquiry in education and broadens our views about what it means to know with the goal of positively influencing the educational experience of those who live and work in our schools. The text includes examples depicting this type of research and how it can be used to evaluate teaching, learning, and the school environment.

Book Features:

• Provides researchers with ethical frameworks promoting diversity and inclusivity.
• Establishes connections between qualitative inquiry in our daily lives and its functions in the practice and study of education.
• Broadens understanding of how we come to know the world.

A lot of students have to learn some statistics as part of their degree. Students in this situation usually find statistics stressful, difficult, and difficult to relate to the rest of their degree. This book is intended to help students in this situation. It explains how statistics can be used as a helpful tool. It describes the key concepts of statistics, and how statistics are used; it's written in clear, accessible language, with numerous examples and illustrative anecdotes, and it's centred around the reader, not around the statistics.

Ready to add Machine Learning to your skill stack?As the second title in the Machine Learning From Scratch series, this book teaches you how to code machine learning models in Python.

By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.

Who is this book for?The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. For a gentle explanation of machine learning theory minus the code, we suggest reading the first book in this series Machine Learning for Absolute Beginners (Third Edition), which is written for a more general audience.

In this step-by-step guide you will learn: - How to code a machine learning prediction model using a range of algorithms including logistic regression, gradient boosting, and decision trees.
- How to install a development environment and use the programming language Python to code 10 different models.
- How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib, and Seaborn.
- How to visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code.

The A/B Testing mindset at a company evolves through four stages. Intuition: In the beginning, intuition drives decisions. The company acknowledges the user, but does not solicit feedback. Data is sparse. Data driven: Decision makers use data to supplement their intuition in cases when they are less confident. The data lacks richness and methods for processing data are crude. Causal statements are drawn from situations that do not warrant them. Data is not respected. A/B Testing: The company unearths the practice of A/B Testing and embarks on the well-trodden path of successful companies. A/B Testing gains followers, however infrastructure is nascent and statistical methods are questionable. The goal is to get a number, not necessarily a correct number. The thought is that a number from an A/B Test must be trustworthy because, well, it's from an A/B Test Sound A/B Testing: The company is educated on the fundamentals of A/B Testing. The company adopts sounds practices, produces trustworthy numbers, and makes informed go/no-go decisions. Regardless where your company is on the journey, this book will guide you to the last stage.

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Statistics for the Social Sciences: Moving Toward an Integrated Approach bridges the educational gap between undergraduate and graduate-level courses in social sciences statistics, providing students with a single, focused way to think about statistics, ensuring they have a firm grasp of fundamental knowledge, and introducing advanced topics and concepts. The book approaches the subject matter from a conceptual and practical standpoint. It teaches students how to read and understand empirical research articles and then effectively design and analyze their own studies.

Over the course of 12 chapters, students learn about descriptive statistics, null hypothesis significance testing, preparatory data analysis, means comparison procedures, multiple independent group means, correlation, and simple regression. Specific sections explore multiple regression and categorical variables in regression. The final chapters focus on testing the moderation hypothesis. Throughout, students are provided with data analysis examples with annotated output to build practical knowledge, as well as discussion questions and exercises to foster critical thinking and direct application.

Innovative and highly accessible, Statistics for the Social Sciences is well suited for upper-level undergraduate or first-year graduate level courses in social sciences statistics.

Simon M. Moon is an associate professor in the Department of Psychology at La Salle University, where he teaches courses in measurement and statistics, research methods, psychometrics, industrial and organizational psychology, and social psychology. He earned a Ph.D. in psychology from The University of Akron and a master's degree in psychology from Yonsei University. Dr. Moon has taught statistics since 2000 at the undergraduate, graduate, and doctoral levels. He has worked on various research and grant projects that require the most up-to-date knowledge within the discipline, and he is a member of APA Division 5, the Division of Evaluation, Measurement, and Statistics, and Division 14, the Society for Industrial and Organizational Psychology.

Indisputably recognized as a classic in its field, Statistics of Extremes was the first book to exclusively evaluate the relevance of maximum and minimum (extreme) values. More than fifty years after publication, it remains relevant and helpful to the contemporary work of statisticians, engineers, and scientists.

In this far-sighted and important work, Gumbel explains the applications of statistical extremes and reinforces its main ideas with generalized exercises. Key topics in this book include:

• Aims and tools
• Order statistics and their exceedances
• Exact distribution of extremes
• Analytical study of extremes
• First asymptotic distribution
• Uses of the first, second, and third asymptotes
• Range

To remain relevant to a wide audience of statisticians, Professor Gumbel relies on elementary language to convey highly technical concepts. With 47 tables and 97 graphs, the focus is on graphical procedures rather than calculations.

Gumbel's original research in the 1940s was developed to predict natural disasters, but as he further refined his theory and tools of extreme statistics, it became clear it has applications in numerous fields.

This is a reprint of the orginal book released in 1968. Our primary goal in this book is to sharpen the skill, sophistication, and in- tuition of the reader in the interpretation of mental test data, and in the construction and use of mental tests both as instruments of psychological theory and as tools in the practical problems of selection, evaluation, and guidance. We seek to do this by exposing the reader to some psychologically meaningful statistical theories of mental test scores. Although this book is organized in terms of test-score theories and models, the practical applications and limitations of each model studied receive substantial emphasis, and these discussions are presented in as nontechnical a manner as we have found possible. Since this book catalogues a host of test theory models and formulas, it may serve as a reference handbook. Also, for a limited group of specialists, this book aims to provide a more rigorous foundation for further theoretical research than has heretofore been available. One aim of this book is to present statements of the assumptions, together with derivations of the implications, of a selected group of statistical models that the authors believe to be useful as guides in the practices of test construction and utilization. With few exceptions we have given a complete proof for each major result presented in the book. In many cases these proofs are simpler, more complete, and more illuminating than those originally offered. When we have omitted proofs or parts of proofs, we have generally provided a reference containing the omitted argument. We have left some proofs as exercises for the reader, but only when the general method of proof has already been demonstrated. At times we have proved only special cases of more generally stated theorems, when the general proof affords no additional insight into the problem and yet is substantially more complex mathematically.

Complete and practical yet easy-to-understand graduate-level statistics instruction with ALL of the problems and examples worked out in the accompanying Excel workbooks. Thoroughly covers all topics of an intense graduate statistics course using nothing but step-by-step, simple explanations. Some of the major topics covered with easy-to-follow explanations and fully described and demonstrated in detail in Excel include: 1) ALL types of t-Tests (1-sample, 2-sample pooled and unpooled, and paired) and z tests including verification of ALL required assumptions * 2) Single-variable and multiple regression (includes verification of ALL required assumptions and ALL underlying formulas used to produce Excel regression output) * 3) Logistic regression (Logit and P(X), MLL, Max Log-Likelihood Function, R Square (Cox and Snell and Nagelkerke), variable significance with Likelihood Ratio, Classification Table, Hosmer-Lemeshow) * 4) Normality Tests (Kolmogorov-Smirnov, Anderson-Darlington, Shapiro-Wilk, Automated Histograms) * 5) Single-factor and two-factor ANOVA with and without replication including verification of ALL required assumptions and ALL underlying formulas used to produce Excel ANOVA output * 6) Post-Hoc tests for ANOVA (Tukey's HSD, Tukey-Kramer, Games-Howell) * 7) ANOVA substitute tests (Welch's ANOVA, Brown-Forsythe F test) * 8) Variance comparison tests (F test, Levene's test, Brown-Forsythe test) * 9) Effect size tests (Eta square, RMSSE, Omega square) * 10) Detailed description of calculating test power using the online utility G*Power for all types of tests) * 11) Nonparametric tests (Mann-Whitney U test alternative for 2-sample t-Tests, Wilcoxon Signed-Rank test alternative for 1-sample and paired t-Tests, Kruskal-Wallis test alternative for 1-way ANOVA, Scheirer-Ray-Hare test alternative for 2-way ANOVA, Sign Test) * 11) Chi-Square tests (Goodness-of-Fit, Independence tests, and population variance tests) * 12) Confidence intervals of population means and of population proportions (includes calculation of min sample size and verification of ALL required assumptions) * 13) Combinations and Permutations (many different examples of each) * 14) Correlations - Pearson and Spearman (includes calculation of r Critical and p value of calculated r) * 15) Covariance * 16) Automated histograms, sorting, and charting created with formulas that automatically re-calculate when data changes * 17) Central Limit Theorem demonstrated in Excel * 18) Lots of problems solved in Excel using the following distributions: Normal, t, Binomial, Negative Binomial, F, Chi-Square, Poisson, Exponential, Uniform, Geometric, Beta, Gamma, Hypergeometric, and Multinomial * 19) Instructions to create user-interactive PDF and CDF graphs in Excel for the following distributions: Normal, t, Binomial, Chi-Square, Poisson, Exponential, Uniform, Beta, Gamma, and Hypergeometric * This book is complete and thorough enough for the professional statistician but simple and clear enough for the new statistics student. The reader of this book will become an Excel Statistical Master

Practical Statistic for Educators, 6th Edition is a clear and easy-to-follow book written specifically for education students in introductory statistics and action research courses. It is also an invaluable resource and guidebook for educational practitioners who wish to study their own settings and for those involved in program evaluation.

The focus of the book is on essential concepts in educational statistics, understanding when to use various statistical tests, and how to interpret results. This book introduces educational students and practitioners to the use of statistics in education and basic concepts in statistics are explained in clear language. All of the examples used to explain the use of statistics in educational research are taken from the field of education and serve to illustrate the various concepts, terms, statistical tests, and data interpretations that are discussed in the book. Formulas and equations are used sparingly and readers are not required to do any computations. The book also includes a discussion of testing, test score interpretation, reliability, and validity. A chapter on survey design and analysis provide the book readers with examples which demonstrate how the different statistical tests introduced in the book can be used to analyzed survey data.

Chapter previews are provided, as well as succinct end-of-chapter summaries. The book's glossary of main terms and concepts helps readers navigate the book and easily find useful information. Review exercises are included at the end of the book to allow readers to practice and apply their newly-acquired knowledge and skills.

En la actualidad vivimos en un mundo donde la informaci n adquiere vida propia y se difunde m s r pido que nunca. Para algunas personas, sus vidas dependen de obtener un flujo constante de informaci n, ya que afecta sus vidas de la manera m s reveladora. La noticia diaria es el ejemplo m s simple de este fen meno. Es verdaderamente el alma de la sociedad y nos ayuda a evolucionar hacia personas mejor informadas con cada d a que pasa.Como se puede decir, la informaci n en general nos llega desde todas las direcciones. Su influencia en los negocios, la pol tica y el gobierno, la banca, los campos STEM (ciencia, tecnolog a, ingenier a y matem tica) e incluso las ciencias sociales no puede ser subestimada. Pero su viaje comienza con la recopilaci n de datos sin procesar por medio de un experimento o estudio.Al decir esto, este libro debe servir como una gu a de referencia para aquellos que desean ir m s all de lo b sico y llegar a un territorio que requiere una comprensi n s lida de los conceptos estad sticos avanzados. A n as , como principiante, ser prudente avanzar con cuidado para que pueda consolidar su comprensi n antes de comenzar a utilizar estos m todos avanzados.Finalmente, incluso si el tema es un poco profundo, no sea tan serio que se olvide de divertirse con la herramienta SPSS y el estudio de estad sticas en general.Finalmente, comencemos a procesar n meros sin m s demora.

In the course of biosurfactant production, it would be economical to carry out optimization procedures so as to determine the best combination of substrate concentration and culture conditions that would lead to maximum biosurfactant yield. This will ensure that wastage of resources such as nutrient and production time is avoided.This book provides a guide for carrying out the various steps in the optimization of biosurfactant production. Directions for performing the calculations involved and plotting the response surfaces using the Microsoft excel platform are provided in this book. Although there are softwares available for generating response plots from mere input of experimental data, having a feel of the calculations involved will provide the experimenter with the knowledge of the procedures involved in the optimization process.Topics covered in this book include (1) Culturing microorganisms for biosurfactant production, (2) Screening for biosurfactant production, (3) Procedures in optimization of biosurfactant production, and (4) Extraction of biosurfactants. These will be valuable to researchers and industrialist venturing into biosurfactant production.

College tuition is higher now than at any point in history, and current and former students are accruing record amounts of debt, over a trillion dollars, by some estimates. How can something as essential as education cost so much? Is education a right or is it a privilege? Supporting Social Studies curriculum, this anthology analyzes the patterns of tuition from conflicting points of view. Should students be responsible for paying their way through college, or does the community share the load and benefits of higher education? Readers will learn about the varied factors and key ideas catalyzing the rising cost of college and some novel solutions to the problem.

La presentaci n de la informaci n en este texto, se hace mediante gr ficos y tablas, elementos estad sticos que se constituyen en una poderosa herramienta para el an lisis e interpretaci n de los datos, estos permiten una percepci n r pida de la informaci n presentada al expresar visualmente en forma conjunta los hechos m s importantes. Todo el documento contiene definiciones, procedimientos, ejemplos, ejercicios, c digos en R que explican c mo obtener cada una de los estad grafos calculados,

This book presents a disciplined, qualitative exploration of case study methods by drawing from naturalistic, holistic, ethnographic, phenomenological and biographic research methods.

Robert E. Stake uses and annotates an actual case study to answer such questions as: How is the case selected? How do you select the case which will maximize what can be learned? How can what is learned from one case be applied to another? How can what is learned from a case be interpreted? In addition, the book covers: the differences between quantitative and qualitative approaches; data-gathering including document review; coding, sorting and pattern analysis; the roles of the researcher; triangulation; and reporting.