No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). This text may be used in a one or two course sequence. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

    New 'Consolidation' sections and questions designed to provide a link between GCSE and A-level feature in the text.At the end of each section there are many questions - ideal for consolidation and revision - mainly from past A-level examination papers. Over 15 of these past-paper questions have been added in the Fourth Edition. Answers are included.

    This simple way to manage your household income and expenses includes a stylish cover, coin purse, places for your checkbook and check register, memo pad, debit card holders, and extra cash-management envelopes.

    But all too often, these numbers are wrong. This book is a lively guide to spotting bad statistics and learning to think critically about these influential numbers. Damned Lies and Statistics is essential reading for everyone who reads or listens to the news, for students, and for anyone who relies on statistical information to understand social problems.Joel Best bases his discussion on a wide assortment of intriguing contemporary issues that have garnered much recent media attention, including abortion, cyberporn, homelessness, the Million Man March, teen suicide, the U.S. census, and much more. Using examples from the New York Times, the Washington Post, and other major newspapers and television programs, he unravels many fascinating examples of the use, misuse, and abuse of statistical information.In this book Best shows us exactly how and why bad statistics emerge, spread, and come to shape policy debates. He recommends specific ways to detect bad statistics, and shows how to think more critically about "stat wars," or disputes over social statistics among various experts. Understanding this book does not require sophisticated mathematical knowledge; Best discusses the most basic and most easily understood forms of statistics, such as percentages, averages, and rates.This accessible book provides an alternative to either naively accepting the statistics we hear or cynically assuming that all numbers are meaningless. It shows how anyone can become a more intelligent, critical, and empowered consumer of the statistics that inundate both the social sciences and our media-saturated lives.

    Adobe Photoshop CC Classroom in a Book contains 14 lessons that cover the basics, providing countless tips and techniques to help you become more productive with the program. You can follow the book from start to finish or choose only those lessons that interest you. In addition to learning the key elements of the Photoshop interface, this completely revised CC (2014 release) edition covers new features, including Generator, 3D printing, linked Smart Objects, Blur Gallery, smarter Smart Guides, Perspective Warp, and more. Purchasing this book gives you access to the downloadable lesson files you need to work through the projects in the book, and to electronic book updates covering new features that Adobe releases for Creative Cloud customers. For access, goto www.peachpit.com/redeem and redeem the unique code provided inside this book. "The Classroom in a Book series is by far the best training material on the market. Everything you need to master the software is included: clear explanations of each lesson, step-by-step instructions, and the project files for the students." Barbara Binder, Adobe Certified InstructorRocky Mountain Training

    He takes you inside a typical interview by exploring the various types of case questions and he shares with you the acclaimed Ivy Case System which will give you the confidence to answer even the most sophisticated cases. The book includes over 40 strategy cases, ten case starts exercises and 21 ways to cut costs, plus much, much more!

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    

    This text discusses and explains the traditional, functional approach to management, through planning, organising, leading and controlling.

    

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Maloney delivers a heartpounding tale as fast, cold, and sleek as a 9mm bullet . . .FOR DUTY AND HONORThe unthinkable has happened to operative Dan Morgan. Captured by the Russians. Imprisoned in the Gulag. Tortured by his cruelest, most sadistic enemy. But Morgan knows that every prisoner has a past—and every rival can be used. With the most unlikely of allies, Morgan hatches a plan. To save what’s important, he must risk everything. And that’s when the stakes go sky-high. Dan Morgan’s got to keep fighting. For duty. And honor. And even certain death . . .

    

    Consider: sports teams have an immense amount of detailed, quantifiable information to draw upon, more than in virtually any other industry. They have powerful incentives for making good decisions. Everyone sees the results of their choices, and the consequences for failure are severe. And yet...they keep making the same mistakes over and over again...systematic mistakes you'd think they'd learn how to avoid. Now, two leading sports economists reveal those mistakes in basketball, baseball, football, and hockey, and explain why sports decision-makers never seem to learn their lessons. You'll learn which statistics are connected to wins, and which aren't, and which statistics can and can't predict the future. Along the way, David Berri and Martin Schmidt show why a quarterback's place in the draft tells you nothing about how he'll perform in the NFL...why basketball decision-makers don't focus on the factors that really correlate with NBA success...why famous coaches don't deliver better results...and much more.