A New York Times bestseller"Brilliant, funny…the best math teacher you never had." ―San Francisco ChronicleOnce considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal―and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

This resource emphasizes statistical inference and sound decision-making through its extensive coverage of data collection and analysis. As in earlier editions, it helps develop statistical thinking and promotes inference assessment- from the vantage point of both the consumer and the producer. Includes new Three-phased Examples that contain three components: "problem," "solution," and "look back." Provides Now Work exercises that follow each example, suggesting an end-of-section exercise that is similar in style and concept to the example. Offers new Chapter Summary Notes along with end-of- chapter material. Provides new Critical Thinking Challenges. A comprehensive resource for anyone who needs to improve their understanding of statistics.

Over Half a Million Copies Sold--an Honest-to-Goodness Bestseller Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform.

Drawing upon over 40 years of experience, the authors of this highly accessible book provide business professionals with a clear and methodical approach to essential statistical procedures. The ninth edition clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. It features a new emphasis on expressions involving sums of squares and degrees of freedom as well as a stronger stress on the importance of variability. This accessible approach will help business professionals tackle such perennially mystifying topics as the standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and estimates of effect size.

Be prepared for exam day with Barron's. Trusted content from AP experts! Barron's AP Statistics Premium: 2021-2022 includes in-depth content review and online practice. It's the only book you'll need to be prepared for exam day. Written by Experienced Educators Learn from Barron's--all content is written and reviewed by AP experts Build your understanding with comprehensive review tailored to the most recent exam Get a leg up with tips, strategies, and study advice for exam day--it's like having a trusted tutor by your side Be Confident on Exam Day Sharpen your test-taking skills with 9 full-length practice tests--6 in the book, including a diagnostic test to target your studying, and 3 more online Strengthen your knowledge with in-depth review covering all Units on the AP Statistics Exam Reinforce your learning with numerous practice quizzes throughout the book Interactive Online Practice Continue your practice with 3 full-length practice tests on Barron's Online Learning Hub Simulate the exam experience with a timed test option Deepen your understanding with detailed answer explanations and expert advice Gain confidence with automated scoring to check your learning progress

“Witty, compelling, and just plain fun to read . . ." —Evelyn Lamb, Scientific AmericanThe Freakonomics of math—a math-world superstar unveils the hidden beauty and logic of the world and puts its power in our handsThe math we learn in school can seem like a dull set of rules, laid down by the ancients and not to be questioned. In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life, but rather touches everything we do—the whole world is shot through with it.Math allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Armed with the tools of mathematics, we can see through to the true meaning of information we take for granted: How early should you get to the airport? What does “public opinion” really represent? Why do tall parents have shorter children? Who really won Florida in 2000? And how likely are you, really, to develop cancer?How Not to Be Wrong presents the surprising revelations behind all of these questions and many more, using the mathematician’s method of analyzing life and exposing the hard-won insights of the academic community to the layman—minus the jargon. Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God.Ellenberg pulls from history as well as from the latest theoretical developments to provide those not trained in math with the knowledge they need. Math, as Ellenberg says, is “an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength.” With the tools of mathematics in hand, you can understand the world in a deeper, more meaningful way. How Not to Be Wrong will show you how.

Title: Statistics for Business and Economics + CD (P.N.I.E)Author: McClave, Benson, SincichEdition: 12th editionISBN-13: 9781292023298Format: Soft Cover / PaperbackBRAND NEW, Color Printed in Acid Free Paper.Written in English.Different Book Cover Design and Different ISBN from US edition.

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"One of the more momentous books of the decade." —The New York Times Book ReviewNate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder of the website FiveThirtyEight.Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

There is a NEW EDITION. The Fourth Edition was released in May 2019.\nBlack-and-white paperback.\nThe OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for about $20.\nOpenIntro has grown through the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide videos, labs for R and SAS, teaching resources like slides, and many other helpful resources.

This lively and engaging textbook explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The author, David A. Freedman, explains the basic ideas of association and regression, and takes you through the current models that link these ideas to causality. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: • authoritative guidance from a well-known author with wide experience in teaching, research, and consulting • careful analysis of statistical issues in substantive applications • no-nonsense, direct style • versatile structure, enabling the text to be used as a text in a course, or read on its own • text that has been thoroughly class-tested at Berkeley • background material on regression and matrix algebra • plenty of exercises, most with solutions • extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) • many new exercises and examples • reorganized, restructured, and revised chapters to aid teaching and understanding

*Major New York Times Bestseller*More than 2.6 million copies sold*One of The New York Times Book Review's ten best books of the year*Selected by The Wall Street Journal as one of the best nonfiction books of the year*Presidential Medal of Freedom Recipient*Daniel Kahneman's work with Amos Tversky is the subject of Michael Lewis's best-selling The Undoing Project: A Friendship That Changed Our MindsIn his mega bestseller, Thinking, Fast and Slow, Daniel Kahneman, world-famous psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think.System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive biases on everything from playing the stock market to planning our next vacation―each of these can be understood only by knowing how the two systems shape our judgments and decisions.Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives―and how we can use different techniques to guard against the mental glitches that often get us into trouble. Topping bestseller lists for almost ten years, Thinking, Fast and Slow is a contemporary classic, an essential book that has changed the lives of millions of readers.

The ideal supplement and study guide for students preparing for advanced statisticsPacked with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test.

This inexpensive paperback provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. New features in the third edition include: \na new chapter on Factor and Reliability Analysis especially helpful to those who do and/or read survey research, new "Writing it Up" sections demonstrate how to write about and interpret statistics seen in books and journals, a website at http://www.psypress.com/statistics-in-plain-english with PowerPoint presentations, interactive problems (including an overview of the problem's solution for Instructors) with an IBM SPSS dataset for practice, videos of the author demonstrating how to calculate and interpret most of the statistics in the book, links to useful websites, and an author blog, new section on understanding the distribution of data (ch. 1) to help readers understand how to use and interpret graphs, many more examples, tables, and charts to help students visualize key concepts. \n\nStatistics in Plain English, Third Edition is an ideal supplement for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Not a numbers person? No problem! This new edition is aimed at high school and college students who need to take statistics to fulfill a degree requirement and follows a standard statistics curriculum. Readers will find information on frequency distributions; mean, median, and mode; range, variance, and standard deviation; probability; and more. —Emphasizes Microsoft Excel for number-crunching and computations

Wouldn't it be great if there were a statistics book that made histograms, probability distributions, and chi square analysis more enjoyable than going to the dentist? Head First Statistics brings this typically dry subject to life, teaching you everything you want and need to know about statistics through engaging, interactive, and thought-provoking material, full of puzzles, stories, quizzes, visual aids, and real-world examples.Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.Head First Statistics is ideal for high school and college students taking statistics and satisfies the requirements for passing the College Board's Advanced Placement (AP) Statistics Exam. With this book, you'll:\n\nStudy the full range of topics covered in first-year statistics\nTackle tough statistical concepts using Head First's dynamic, visually rich format proven to stimulate learning and help you retain knowledge \nExplore real-world scenarios, ranging from casino gambling to prescription drug testing, to bring statistical principles to life\nDiscover how to measure spread, calculate odds through probability, and understand the normal, binomial, geometric, and Poisson distributions\nConduct sampling, use correlation and regression, do hypothesis testing, perform chi square analysis, and more\n\nBefore you know it, you'll not only have mastered statistics, you'll also see how they work in the real world. Head First Statistics will help you pass your statistics course, and give you a firm understanding of the subject so you can apply the knowledge throughout your life.

NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY THE ECONOMIST“The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow.”—Jason Zweig, The Wall Street JournalEveryone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters."In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course.Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.

This affordable student study guide and workbook to accompany Wendy Steinberg′s Statistics Alive! text will help students get the added review and practice they need to improve their skills and master their Introduction to Statistics course.

A comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series.Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice.Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun.Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.

As the title says, this book covers all the topics for probability & statistics in context of data science. While working on data science projects, I tried to look for a reference book which can give reader holistic view of probability & statistics useful for data science, but I could not find everything at one place. So every time, I used to look for the term or topic at various places and then used to relate it in context of data science. At the end, I started writing about these topics in my blog (https://medium.com/@rathi.ankit) as my notes on probability & statistics which were well received by data science community.This book is for people who are working in data science field and want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.The approach I have taken here is not to reinvent the wheel, so I try to give an intuitive understanding of each topic and if the user wants to dig further on that topic, he can refer to the companion GitHub notebook of this book, scan the QR code given in the book to get the link.

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.You'll find advice on:Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan How to think about p values, significance, insignificance, confidence intervals, and regression Choosing the right sample size and avoiding false positives Reporting your analysis and publishing your data and source code Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know.The first step toward statistics done right is Statistics Done Wrong.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.\nTwo of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Get your statistics basics right before diving into the world of data science Key Features 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 Book DescriptionData 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. 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 programmaticallyStyle and approachStep by step comprehensive guide with real world examples Who This Book Is ForThis 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. Table of Contents Transitioning from Data Developer to Data Scientist Declaring the Objectives A Developer's Approach to Data Cleaning Data Mining and the Database Developer Statistical Analysis for the Database Developer Database Progression to Database Regression Regularization for Database Improvement Database Development and Assessment Databases and Neural Networks Boosting your Database Database Classification using Support Vector Machines Database Structures and Machine Learning

From the author of Expecting Better, The Family Firm, and The Unexpected an economist's guide to the early years of parenting.“Both refreshing and useful. With so many parenting theories driving us all a bit batty, this is the type of book that we need to help calm things down.” —LA Times“The book is jampacked with information, but it’s also a delightful read because Oster is such a good writer.” —NPRWith Expecting Better, award-winning economist Emily Oster spotted a need in the pregnancy market for advice that gave women the information they needed to make the best decision for their own pregnancies. By digging into the data, Oster found that much of the conventional pregnancy wisdom was wrong. In Cribsheet, she now tackles an even greater challenge: decision-making in the early years of parenting.As any new parent knows, there is an abundance of often-conflicting advice hurled at you from doctors, family, friends, and strangers on the internet. From the earliest days, parents get the message that they must make certain choices around feeding, sleep, and schedule or all will be lost. There's a rule—or three—for everything. But the benefits of these choices can be overstated, and the trade-offs can be profound. How do you make your own best decision?Armed with the data, Oster finds that the conventional wisdom doesn't always hold up. She debunks myths around breastfeeding (not a panacea), sleep training (not so bad!), potty training (wait until they're ready or possibly bribe with M&Ms), language acquisition (early talkers aren't necessarily geniuses), and many other topics. She also shows parents how to think through freighted questions like if and how to go back to work, how to think about toddler discipline, and how to have a relationship and parent at the same time.Economics is the science of decision-making, and Cribsheet is a thinking parent's guide to the chaos and frequent misinformation of the early years. Emily Oster is a trained expert—and mom of two—who can empower us to make better, less fraught decisions—and stay sane in the years before preschool.

'A statistical national treasure' Jeremy Vine, BBC Radio 2'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever.In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial.'Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world' Nature

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data- Wiley India- EMC Education Services-2015-EDN-1

A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem.Key Features Develop a Conceptual and Mathematical understanding of Statistics Get an overview of Statistical Applications in Python Learn how to perform Hypothesis testing in Statistics Understand why Statistics is important in Machine Learning Learn how to process data in PythonDescriptionThis book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.What you will learnUnderstand the basics of Statistics Get to know more about Descriptive Statistics Understand and learn advanced Statistics techniques Learn how to apply Statistical concepts in Python Understand important Python packages for Statistics and Machine LearningWho this book is forThis book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.Table of Contents1. Introduction to Statistics2. Descriptive Statistics3. Probability4. Random Variables5. Parameter Estimations6. Hypothesis Testing7. Analysis of Variance8. Regression9. Non Parametric Statistics10. Data Analysis using Python11. Introduction to Machine LearningAbout the AuthorsHimanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management.LinkedIn Profile: https://www.linkedin.com/in/himanshu-singh-2264a350/Blog links: https://medium.com/@himanshuit3036Facebook Profile: https://www.facebook.com/silli23

A Cookbook that will help you implement Machine Learning algorithms and techniques bybuilding real-world projectsKey Features Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning.DescriptionMachine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background.What will you learnUnderstand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case.Who this book is forThis cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners.Table of Contents1. Boston Crime2. World Happiness Report3. Iris Species4.Credit Card Fraud Detection5.Heart Disease UCIAbout the AuthorRehan Guha —A Researcher by the day and an Artist by night.Our Author is a Scholar -lecturer, an Innovator, and also a Humanitarian -Philanthropist.He started his life as a Coder, Developer, and now he is into research in the field of Machine Learning and Algorithms but also has a keen interest in General Science, Technology, Invention & Innovation.Psychology and Socioeconomics are his special subject of interest.The author holds a graduation degree from the Institute of Engineering & Management, Kolkata, and a Postgraduate certification on Deep Learning from the Indian Institute of Technology, Kharagpur (IIT-K)-AICTE approved FDP course.If we talk about Rehan's area of interest, it lies in Optimization Problems, Explainable AI, Deep Learning Architecture, Algorithms, Complexity, Algorithmic Thinking, et cetera… He has multiple publications through Journals and Open Publications, along with his publications he has filed multiple patents for his Innovations and Inventions. At an early age, one of his patents was also demonstrated to the Indian Army.In Rehan’s career, he has been involved with a variety of Business Verticals, starting from Banking, Consulting, Law, Insurance, Freight & Logistics, and Telcom.

Product description Statistics for Machine Learning About the Author Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Statistics for Beginners in Data ScienceStatistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists.If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist.As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used.Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python’s focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short—Python is simply a high-priority data science tool.How Is This Book Different?The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better.The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python.The main benefit of purchasing this book is you get quick access to all the extra content provided with this book—Python codes, exercises, references, and PDFs—on the publisher’s website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1.Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren’t new to Python and data science, you’ll find the hands-on projects in this book immensely helpful. The topics covered include: Introduction to Statistics Getting Familiar with Python Data Exploration and Data Analysis Pandas, Matplotlib, and Seaborn for Statistical Visualization Exploring Two or More Variables and Categorical Data Statistical Tests and ANOVA Confidence Interval Regression Analysis Classification Analysis Click the BUY button and download the book now to start learning and coding Python for Data Science.

Frequentist and Bayesian Statistics Crash Course for BeginnersData and statistics are the core subjects of Machine Learning (ML). The reality is the average programmer may be tempted to view statistics with disinterest. But if you want to exploit the incredible power of Machine Learning, you need a thorough understanding of statistics. The reason is a Machine Learning professional develops intelligent and fast algorithms that learn from data. Frequentist and Bayesian Statistics Crash Course for Beginners presents you with an easy way of learning statistics fast. Contrary to popular belief, statistics is no longer the exclusive domain of math Ph.D.s. It’s true that statistics deals with numbers and percentages. Hence, the subject can be very dry and boring. This book, however, transforms statistics into a fun subject. Frequentist and Bayesian statistics are two statistical techniques that interpret the concept of probability in different ways. Bayesian statistics was first introduced by Thomas Bayes in the 1770s. Bayesian statistics has been instrumental in the design of high-end algorithms that make accurate predictions. So even after 250 years, the interest in Bayesian statistics has not faded. In fact, it has accelerated tremendously. Frequentist Statistics is just as important as Bayesian Statistics. In the statistical universe, Frequentist Statistics is the most popular inferential technique. In fact, it’s the first school of thought you come across when you enter the statistics world. How Is This Book Different?AI Publishing is completely sold on the learning by doing methodology. We have gone to great lengths to ensure you find learning statistics easy. The result: you will not get stuck along your learning journey. This is not a book full of complex mathematical concepts and difficult equations. You will find that the coverage of the theoretical aspects of statistics is proportionate to the practical aspects of the subject. The book makes the reading process easier by presenting you with three types of box-tags in different colors. They are: Requirements, Further Readings, and Hands-on Time. The final chapter presents two mini-projects to give you a better understanding of the concepts you studied in the previous eight chapters. The main feature is you get instant access to a treasure trove of all the related learning material when you buy this book. They include PDFs, Python codes, exercises, and references—on the publisher’s website. You get access to all this learning material at no extra cost. You can also download the Machine Learning datasets used in this book at runtime. Alternatively, you can access them through the Resources/Datasets folder. The quick course on Python programming in the first chapter will be immensely helpful, especially if you are new to Python. Since you can access all the Python codes and datasets, a computer with the internet is sufficient to get started.The topics covered include: A Quick Introduction to Python for Statistics Starting with Probability Random Variables and Probability Distributions Descriptive Statistics: Measure of Central Tendency and Spread Exploratory Analysis: Data Visualization Statistical Inference Frequentist Inference Bayesian Inference Hands-on Projects Click the BUY NOW button and start your Statistics Learning journey.

The interface between statistics and machine learning (ML) is an increasingly popular research subject, as scientists and statisticians discover useful areas where these disciplines overlap. ML deals primarily with learning rules or structure, and while some books on the subject exist, this volume is the only one to integrate ML with statistics. It explores new areas where theory and methods can be shared and demonstrates the benefits to those working in either discipline.Written by leading experts in both fields, Machine Learning and Statistics is a result of the authors' participation in the 1994 European Conference in Machine Learning. This important collection of contributions was adapted from conference workshop material and reworked to address readers of diverse backgrounds and skills. For newcomers to the field, a thorough introduction surveys the various topics and supplies numerous references for further reading.The book's main focus is on classification, the most common area of intersection. The classification process uses information about a new example to assign the example to one of a known number of classes. Such methods typically involve a rule learned from an initial set of data, which is where ML comes into play. Other topics covered include prediction, control, and an introduction to methods of knowledge discovery in databases—a skill that has become especially relevant with the explosion in large-scale databases.Timely, practical, and innovative, this book offers a number of new algorithms and draws on real-world examples including financial and medical applications. It also includes two chapters on loans/credit applications that help identify bad risks and good customers—useful for those working with credit scoring and bad debt analysis.Machine Learning and Statistics is an invaluable resource for researchers involved with artificial intelligence and ML in academia, government, or industry, as well as those working with pattern recognition in statistical departments; for students at the graduate level who seek to expand their horizons; and for anyone who would like to learn more about these cutting-edge methodologies.The first book to explore the theory, methods, and applications of the relationship between machine learning and statistics.Here is the first book to address the growing demand for applications that integrate machine learning and statistics. The only text to focus on the interface between the two disciplines, this volume explores a mutually beneficial relationship that is fast becoming recognized by scientists, engineers, and researchers in data analysis and intelligent systems worldwide.The book shows that machine learning shares several areas of common research with statistics, most notably classification, prediction, and control. It demonstrates that statistical and probability methodologies can be applied in developing different machine learning algorithms and that these algorithms can be used by statisticians to perform classification and forecasting tasks.Offering an accessible treatment geared to a diverse audience, this collection of contributions from an international group of leading researchers in both fieldsDescribes numerous applications drawn from real-world projects in finance, investing, medicine, and other areas Develops new research topics such as probability trees and prediction Outlines new algorithms Covers the latest developments in knowledge discovery in systems, addressing the worldwide exponential growth in databases Examines the prospects of learning rules in an "Occam's razor" fashion, using the simplest representation to learn by analogy Features over eighty illustrations as well as many references for further reading

This is such a excellent book which cover all important topics related to exams and a decent book to upgrade your knowledge for the respective topic very clearly.

This accessible and easy-to-read book provides many examples to illustrate diverse topics in probability and statistics, from initial concepts up to advanced calculations. Special attention is devoted e.g. to independency of events, inequalities in probability and functions of random variables. The book is directed to students of mathematics, statistics, engineering, and other quantitative sciences, in particular to readers who need or want to learn by self-study. The author is convinced that sophisticated examples are more useful for the student than a lengthy formalism treating the greatest possible generality.Contents:Mathematics revisionIntroduction to probabilityFinite sample spacesConditional probability and independenceOne-dimensional random variablesFunctions of random variablesBi-dimensional random variablesCharacteristics of random variablesDiscrete probability modelsContinuous probability modelsGenerating functions in probabilitySums of many random variablesSamples and sampling distributionsEstimation of parametersHypothesis tests

Can studying really be interesting and enjoyable? This book explores attitudes towards studying and offers tips and techniques to turn studying into an interesting, enjoyable activity instead of the dull drudgery that it is for most people. Why study subjects you don't like? How to exercise and diet right to keep your brain alert? How to use mind maps to study during an emergency?Art of Living teachers Khurshed Batliwala and Dinesh Ghodke distill years of learning and teaching young people into this fun, easy-to-read book.

From the medicine we take, the treatments we receive, the aptitude and psychometric tests given by employers, the cars we drive, the clothes we wear to even the beer we drink, statistics have given shape to the world we inhabit. For the media, statistics are routinely 'damning', 'horrifying', or, occasionally, 'encouraging'. Yet, for all their ubiquity, most of us really don't know what to make of statistics. Exploring the history, mathematics, philosophy and practical use of statistics, Eileen Magnello - accompanied by Bill Mayblin's intelligent graphic illustration - traces the rise of statistics from the ancient Babylonians, Egyptians and Chinese, to the censuses of Romans and the Greeks, and the modern emergence of the term itself in Europe. She explores the 'vital statistics' of, in particular, William Farr, and the mathematical statistics of Karl Pearson and R.A. Fisher.She even tells how knowledge of statistics can prolong one's life, as it did for evolutionary biologist Stephen Jay Gould, given eight months to live after a cancer diagnoses in 1982 - and he lived until 2002. This title offers an enjoyable, surprise-filled tour through a subject that is both fascinating and crucial to understanding our world.

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Self-Controlled Case Series Studies: A Modelling Guide with R provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical technique for investigating associations between outcome events and time-varying exposures. The method only requires information from individuals who have experienced the event of interest, and automatically controls for multiplicative time-invariant confounders, even when these are unmeasured or unknown. It is increasingly being used in epidemiology, most frequently to study the safety of vaccines and pharmaceutical drugs.Key features of the book include:A thorough yet accessible description of the SCCS method, with mathematical details provided in separate starred sections.Comprehensive discussion of assumptions and how they may be verified.A detailed account of different SCCS models, extensions of the SCCS method, and the design of SCCS studies.Extensive practical illustrations and worked examples from epidemiology.Full computer code from the associated R package SCCS, which includes all the data sets used in the book.The book is aimed at a broad range of readers, including epidemiologists and medical statisticians who wish to use the SCCS method, and also researchers with an interest in statistical methodology. The three authors have been closely involved with the inception, development, popularisation and programming of the SCCS method.

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Statistics is a class that is required in many college majors, and it’s an increasingly popular Advanced Placement (AP) high school course. In addition to math and technical students, many business and liberal arts students are required to take it as a fundamental component of their majors. A knowledge of statistical interpretation is vital for many careers.Idiot’s Guides®: Statistics explains the fundamental tenets in language anyone can understand.Content includes:- Calculating descriptive statistics.- Measures of central tendency: mean, median, and mode.- Probability.- Variance analysis.- Inferential statistics.- Hypothesis testing.- Organizing data into statistical charts and tables.

A comprehensive and easy to understand text, this book discusses fundamental theoretical concepts with emphasis on practical applicability. The book begins with the explanation of statistical fundamentals and progresses to discussion of representation and presentation techniques, measures of central tendency, dispersion, skewness, correlation, regression and index numbers.

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This Text Book Is A Comprehensive, User Friendly And Easy To Read Resource On Biostatistics And Research Methodology. It Is Meant For Undergraduate And Post Graduate Students Of Medical And Biomedical Sciences. Health Researchers, Research Supervisors And Faculty Members May Find It Useful As A Reference Book.

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While there are books focusing on parametric tests, the domain of nonparametric tests is mostly unexplored. Data Analysis in Business Research: A Step by Step Nonparametric Approach brings under one umbrella all the major nonparametric statistical tools that can be used by undergraduate and postgraduate students of all disciplines, especially students of Research Methods in Social Sciences and Management Studies, in their dissertation work.Students face difficulty in analyzing data collected from small samples; they end up reporting mere percentage analysis which results in the loss of information collected. Hence there is a need to create awareness among students and researchers about the application of major nonparametric tools that can be applied confidently without worrying about sample size, scale of measurement, normality assumptions or other parameters of that nature. The lucid presentation of the step-by-step procedures, explaining in simple English how to perform each of the major nonparametric tests, is a major attraction of the book. The book, which also has a comprehensive question bank, assumes minimal or little knowledge of statistics on the part of the reader.This book will also be informative for Marketing Research professionals and organisations, consultancies and organisations of economic research.