Best Selling Books in Mathematics - Applied

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release date: Jun 14, 2016
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Misbehaving: The Making of Behavioral Economics

Winner of the Nobel Prize in Economics

Get ready to change the way you think about economics.

Nobel laureate Richard H. Thaler has spent his career studying the radical notion that the central agents in the economy are humans―predictable, error-prone individuals. Misbehaving is his arresting, frequently hilarious account of the struggle to bring an academic discipline back down to earth―and change the way we think about economics, ourselves, and our world.

Traditional economics assumes rational actors. Early in his research, Thaler realized these Spock-like automatons were nothing like real people. Whether buying a clock radio, selling basketball tickets, or applying for a mortgage, we all succumb to biases and make decisions that deviate from the standards of rationality assumed by economists. In other words, we misbehave. More importantly, our misbehavior has serious consequences. Dismissed at first by economists as an amusing sideshow, the study of human miscalculations and their effects on markets now drives efforts to make better decisions in our lives, our businesses, and our governments.

Coupling recent discoveries in human psychology with a practical understanding of incentives and market behavior, Thaler enlightens readers about how to make smarter decisions in an increasingly mystifying world. He reveals how behavioral economic analysis opens up new ways to look at everything from household finance to assigning faculty offices in a new building, to TV game shows, the NFL draft, and businesses like Uber.

Laced with antic stories of Thaler’s spirited battles with the bastions of traditional economic thinking, Misbehaving is a singular look into profound human foibles. When economics meets psychology, the implications for individuals, managers, and policy makers are both profound and entertaining.

Shortlisted for the Financial Times & McKinsey Business Book of the Year Award

release date: Jul 18, 2003
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MyMathLab: Student Access Kit
This access kit will provide you with a code to get into MyMathLab, a personalized interactive learning environment, where you can learn mathematics and statistics at your own pace and measure your progress. In order to use MyMathLab, you will need a CourseID provided by your instructor; MyMathLab is not a self-study product and does require you to be in an instructor-led course. This product is for the national MyMathLab access kit. If your school has a custom MyMathLab course the printed access card will not work.   MyMathLab includes:

 

Interactive tutorial exercises: MyMathLab's homework and practice exercises are correlated to the exercises in the relevant textbook, and they regenerate algorithmically to give you unlimited opportunity for practice and mastery. Most exercises are free-response and provide an intuitive math symbol palette for entering math notation. Exercises include guided solutions, sample problems, and learning aids for extra help at point-of-use, and they offer helpful feedback when students enter incorrect answers.

 

eBook with multimedia learning aids: MyMathLab courses include a full eBook with a variety of multimedia resources available directly from selected examples and exercises on the page. You can link out to learning aids such as video clips and animations to improve their understanding of key concepts.

 

Study plan for self-paced learning: MyMathLab's study plan helps you monitor your own progress, letting you see at a glance exactly which topics you need to practice. MyMathLab generates a personalized study plan for you based on your test results, and the study plan links directly to interactive, tutorial exercises for topics you haven't yet mastered. You can regenerate these exercises with new values for unlimited practice, and the exercises include guided solutions and multimedia learning aids to give students the extra help they need.

 

NOTE:  Access codes can only be used one time. If you purchased a used book that claimed that it included an access code, your code may already have been used and it will not work again. In this case, you must purchase a new access code. 

 

 For Customer Technical Support go to http://247pearsoned.custhelp.com

 

Phone Support  800-677-6337

 

Please note the packaging on this product has changed, whether you receive the current cover or earlier cover the product is still the same. 

 

release date: Jun 07, 2011
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Outliers: The Story of Success
In this stunning new book, Malcolm Gladwell takes us on an intellectual journey through the world of "outliers"--the best and the brightest, the most famous and the most successful. He asks the question: what makes high-achievers different?

His answer is that we pay too much attention to what successful people are like, and too little attention to where they are from: that is, their culture, their family, their generation, and the idiosyncratic experiences of their upbringing. Along the way he explains the secrets of software billionaires, what it takes to be a great soccer player, why Asians are good at math, and what made the Beatles the greatest rock band.

Brilliant and entertaining, Outliers is a landmark work that will simultaneously delight and illuminate.
release date: Sep 01, 2017
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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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.

Two 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.

release date: Jan 05, 2017
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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.

You’ll learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true "signals" in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results
release date: Sep 05, 2017
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Longlisted for the National Book Award
New York Times Bestseller

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric


We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.

O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

— Longlist for National Book Award (Non-Fiction)
— Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology)
— Kirkus, Best Books of 2016
New York Times, 100 Notable Books of 2016 (Non-Fiction)
The Guardian, Best Books of 2016
— WBUR's "On Point," Best Books of 2016: Staff Picks
— Boston Globe, Best Books of 2016, Non-Fiction
release date: Feb 01, 2017
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Barron's AP Statistics, 9th Edition
This manual's in-depth preparation for the AP Statistics exam features the 35 absolutely best AP Statistics exam hints found anywhere, and includes:
  • A diagnostic test and five full-length and up-to-date practice exams
  • All test questions answered and explained
  • Additional multiple-choice and free-response questions with answers
  • A 14-chapter subject review, covering all test topics
  • A new review chapter highlighting statistical insights into social issues
  • a new chapter on the Investigative Task, which counts as one-eighth of the exam
  • A guide to basic uses of TI, Casio, and HP graphing calculators
BONUS ONLINE PRACTICE TEST: Students who purchase this book will also get FREE access to one additional full-length online AP Statistics test with all questions answered and explained.
release date: Jul 31, 2014
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A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra)
The companion book to COURSERA®'s wildly popular massive open online course "Learning How to Learn"

Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a new skill set, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating material. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life.
 
In A Mind for Numbers, Dr. Oakley lets us in on the secrets to learning effectively—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. The learning strategies in this book apply not only to math and science, but to any subject in which we struggle. We all have what it takes to excel in areas that don't seem to come naturally to us at first, and learning them does not have to be as painful as we might think!
release date: Dec 19, 2016
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Microsoft Excel Data Analysis and Business Modeling (5th Edition)

Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel’s newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet models to make sure you’re fully up to speed.


Solve real business problems with Excel—and build your competitive advantage

  • Quickly transition from Excel basics to sophisticated analytics
  • Summarize data by using PivotTables and Descriptive Statistics
  • Use Excel trend curves, multiple regression, and exponential smoothing
  • Master advanced functions such as OFFSET and INDIRECT
  • Delve into key financial, statistical, and time functions
  • Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts)
  • Make charts more effective by using Power View
  • Tame complex optimizations by using Excel Solver
  • Run Monte Carlo simulations on stock prices and bidding models
  • Work with the AGGREGATE function and table slicers
  • Create PivotTables from data in different worksheets or workbooks
  • Learn about basic probability and Bayes’ Theorem
  • Automate repetitive tasks by using macros

 

release date: Jul 14, 2020
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

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