Once 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 book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without.Understand Lean Startup, analytics fundamentals, and the data-driven mindsetLook at six sample business models and how they map to new ventures of all sizesFind the One Metric That Matters to youLearn how to draw a line in the sand, so you’ll know it’s time to move forwardApply Lean Analytics principles to large enterprises and established products
Students do no programming, and hence need no previous detailed knowledge of this. The program has a very convenient, self-explanatory user interface based on the Java software technology. The book provides a recapitulation of the basic quantum mechanical formula, a manual to the IQ program, and a complete course with more than 300 tested problems. Fully automatic demonstration sessions are provided as introduction to interactive work.
Physics topics covered include free particles, bound states and scattering in various potentials in one and three space dimensions, two-particle systems, properties of special functions of mathematical physics.
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.
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 advantageQuickly 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
This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI.Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction Simplifies overall content while still making the more technical applications available to those readers who want to dig deeper Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas Offers practical methods for measuring a variety of "intangibles" Provides an online database (www.howtomeasureanything.com) of downloadable, practical examples worked out in detailed spreadsheets
Written by recognized expert Douglas Hubbard—creator of Applied Information Economics—How to Measure Anything, Third Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.
It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected.
In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge.
The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans.
The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What happens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others?Who knows—maybe there’s a bot learning to do your job this minute.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
How can you use Excel and Power BI to gain real insights into your information? As you examine your data, how do you write a formula that provides the numbers you need? The answers to both of these questions lie with the data model. This book introduces the basic techniques for shaping data models in Excel and Power BI. It’s meant for readers who are new to data modeling as well as for experienced data modelers looking for tips from the experts. If you want to use Power BI or Excel to analyze data, the many real-world examples in this book will help you look at your reports in a different way–like experienced data modelers do. As you’ll soon see, with the right data model, the correct answer is always a simple one!
By reading this book, you will:
• Gain an understanding of the basics of data modeling, including tables, relationships, and keys
• Familiarize yourself with star schemas, snowflakes, and common modeling techniques
• Learn the importance of granularity
• Discover how to use multiple fact tables, like sales and purchases, in a complex data model
• Manage calendar-related calculations by using date tables
• Track historical attributes, like previous addresses of customers or manager assignments
• Use snapshots to compute quantity on hand
• Work with multiple currencies in the most efficient way
• Analyze events that have durations, including overlapping durations
• Learn what data model you need to answer your specific business questions
About This Book
• For Excel and Power BI users who want to exploit the full power of their favorite tools
• For BI professionals seeking new ideas for modeling data
Updated throughout, this edition contains new chapters assessing the current options landscape, discussing margin collateral issues, and introducing Cohen’s exceptionally valuable OVI indicators.
The Bible of Options Strategies, Second Editionis practical from start to finish: modular, easy to navigate, and thoroughly cross-referenced, so you can find what you need fast, and act before your opportunity disappears. Cohen systematically covers every key area of options strategy: income strategies, volatility strategies, sideways market strategies, leveraged strategies, and synthetic strategies.
Even the most complex techniques are explained with unsurpassed clarity – making them accessible to any trader with even modest options experience. More than an incredible value, this is the definitive reference to contemporary options trading: the one book you need by your side whenever you trade. For all options traders with at least some experience.
The authors show how to use marketing dashboards to view market dynamics from multiple perspectives, maximize accuracy, and "triangulate" to optimal solutions. You’ll discover high-value metrics for virtually every facet of marketing: promotional strategy, advertising, and distribution; customer perceptions; market share; competitors’ power; margins and pricing; products and portfolios; customer profitability; sales forces, channels, and more.
For every metric, the authors present real-world pros, cons, and tradeoffs — and help you understand what the numbers really mean. Last but not least, they show you how to build comprehensive models to support planning — and optimize every marketing decision you make.
Marketing Metrics, Third Edition will be invaluable to all marketing executives, practitioners, analysts, consultants, and advanced students interested in quantifying marketing performance.
There is so much buzz around big data. We all need to know what it is and how it works - that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations, Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform.Discusses how companies need to clearly define what it is they need to know Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them Includes many high-profile case studies from the author's work with some of the world's best known brands
- Create pivot tables from worksheet databases.
- Rearrange pivot tables by dragging, swapping, and nesting fields.
- Customize pivot tables with styles, layouts, totals, and subtotals.
- Combine numbers, dates, times, or text values into custom groups.
- Calculate common statistics or create custom formulas.
- Filter data that you don't want to see.
- Create and customize pivot charts.
- Unlink a pivot table from its source data.
- Control references to pivot table cells.
- Plenty of tips, tricks, and timesavers.
- Fully cross-referenced, linked, and searchable.
1. Pivot Table Basics
2. Nesting Fields
3. Grouping Items
4. Calculations and Custom Formulas
5. Filtering Data
6. Charting Pivot Tables
7. Tricks with Pivot Tables
We’ve all heard that a picture is worth a thousand words, but what if we don’t understand what we’re looking at? Social media has made charts, infographics, and diagrams ubiquitous—and easier to share than ever. We associate charts with science and reason; the flashy visuals are both appealing and persuasive. Pie charts, maps, bar and line graphs, and scatter plots (to name a few) can better inform us, revealing patterns and trends hidden behind the numbers we encounter in our lives. In short, good charts make us smarter—if we know how to read them.
However, they can also lead us astray. Charts lie in a variety of ways—displaying incomplete or inaccurate data, suggesting misleading patterns, and concealing uncertainty—or are frequently misunderstood, such as the confusing cone of uncertainty maps shown on TV every hurricane season. To make matters worse, many of us are ill-equipped to interpret the visuals that politicians, journalists, advertisers, and even our employers present each day, enabling bad actors to easily manipulate them to promote their own agendas.
In How Charts Lie, data visualization expert Alberto Cairo teaches us to not only spot the lies in deceptive visuals, but also to take advantage of good ones to understand complex stories. Public conversations are increasingly propelled by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie demystifies an essential new literacy, one that will make us better equipped to navigate our data-driven world.
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book’s pedagogical program, built on cognitive science principles, reinforces learning:
Boxes provide "evidence-based" advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical applications.
This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed.
Hate math? No sweat. You’ll be amazed at how little you need. Like math? Optional "Equation Blackboard" sections reveal the mathematical foundations of statistics right before your eyes. If you need to understand, evaluate, or use statistics in business, academia, or anywhere else, this is the book you've been searching for!
Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started?
Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others.
You'll learn how to:Identify the metrics you need to measureRun experiments and A/B testsAsk the right questions of your data expertsUnderstand statistical terms and conceptsCreate effective charts and visualizationsAvoid common mistakes
The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to help your business win. You don’t need multimillion-dollar software: All the tools you need are available in Microsoft Excel, and all the knowledge and skills are right here, in this book!
Microsoft Excel MVP Conrad Carlberg shows you how to use Excel predictive analytics to solve real-world problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, showing how to gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS.
You’ll get an extensive collection of downloadable Excel workbooks you can easily adapt to your own unique requirements, plus VBA code—much of it open-source—to streamline several of this book’s most complex techniques.
Step by step, you’ll build on Excel skills you already have, learning advanced techniques that can help you increase revenue, reduce costs, and improve productivity. By mastering predictive analytics, you’ll gain a powerful competitive advantage for your company and yourself.
• Learn both the “how” and “why” of using data to make better tactical decisions
• Choose the right analytics technique for each problem
• Use Excel to capture live real-time data from diverse sources, including third-party websites
• Use logistic regression to predict behaviors such as “will buy” versus “won’t buy”
• Distinguish random data bounces from real, fundamental changes
• Forecast time series with smoothing and regression
• Construct more accurate predictions by using Solver to find maximum likelihood estimates
• Manage huge numbers of variables and enormous datasets with principal components analysis and Varimax factor rotation
• Apply ARIMA (Box-Jenkins) techniques to build better forecasts and understand their meaning
This pocket guide is designed to be a quick, on-the-job reference for anyone interested in making their workplace more effective and efficient. It will provide a solid initial overview of what quality is and how it could impact you and your organization. Use it to compare how you and your organization are doing things, and to see whether whats described in the guide might be useful.
The tools of quality described herein are universal. People across the world need to find better, more effective ways to improve the creation and performance of products and services. Since organizational and process improvement is increasingly integrated into all areas of an organization, everyone must understand the basic principles of process control and process improvement. This succinct and concentrated guide can help.
Unlike any other pocket guide on the market, included throughout are direct links to numerous free online resources that not only go deeper but also to show these concepts and tools in action: case studies, articles, webcasts, templates, tutorials, examples from the ASQ Service Divisions Service Quality Body of Knowledge (SQBOK), and much more. This pocket guide serves as a gateway into the wealth of peerless content that ASQ offers.
How to Measure Anything in Cybersecurity Risk exposes the shortcomings of current "risk management" practices, and offers a series of improvement techniques that help you fill the holes and ramp up security. In his bestselling book How to Measure Anything, author Douglas W. Hubbard opened the business world's eyes to the critical need for better measurement. This book expands upon that premise and draws from The Failure of Risk Management to sound the alarm in the cybersecurity realm. Some of the field's premier risk management approaches actually create more risk than they mitigate, and questionable methods have been duplicated across industries and embedded in the products accepted as gospel. This book sheds light on these blatant risks, and provides alternate techniques that can help improve your current situation. You'll also learn which approaches are too risky to save, and are actually more damaging than a total lack of any security.
Dangerous risk management methods abound; there is no industry more critically in need of solutions than cybersecurity. This book provides solutions where they exist, and advises when to change tracks entirely.Discover the shortcomings of cybersecurity's "best practices" Learn which risk management approaches actually create risk Improve your current practices with practical alterations Learn which methods are beyond saving, and worse than doing nothing
Insightful and enlightening, this book will inspire a closer examination of your company's own risk management practices in the context of cybersecurity. The end goal is airtight data protection, so finding cracks in the vault is a positive thing—as long as you get there before the bad guys do. How to Measure Anything in Cybersecurity Risk is your guide to more robust protection through better quantitative processes, approaches, and techniques.
Why did the size of the U.S. economy increase by 3 percent on one day in mid-2013—or Ghana's balloon by 60 percent overnight in 2010? Why did the U.K. financial industry show its fastest expansion ever at the end of 2008—just as the world’s financial system went into meltdown? And why was Greece’s chief statistician charged with treason in 2013 for apparently doing nothing more than trying to accurately report the size of his country’s economy? The answers to all these questions lie in the way we define and measure national economies around the world: Gross Domestic Product. This entertaining and informative book tells the story of GDP, making sense of a statistic that appears constantly in the news, business, and politics, and that seems to rule our lives—but that hardly anyone actually understands.
Diane Coyle traces the history of this artificial, abstract, complex, but exceedingly important statistic from its eighteenth- and nineteenth-century precursors through its invention in the 1940s and its postwar golden age, and then through the Great Crash up to today. The reader learns why this standard measure of the size of a country’s economy was invented, how it has changed over the decades, and what its strengths and weaknesses are. The book explains why even small changes in GDP can decide elections, influence major political decisions, and determine whether countries can keep borrowing or be thrown into recession. The book ends by making the case that GDP was a good measure for the twentieth century but is increasingly inappropriate for a twenty-first-century economy driven by innovation, services, and intangible goods.
Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts.
This book is aimed at business students, undergraduate and graduate, taking an introductory core course. Topics covered include knowledge management, visualization, sampling and hypothesis testing, regression (simple, multiple, and logistic), as well as optimization modeling. It concludes with a brief overview of data mining. Concepts are demonstrated with worked examples.
The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.
The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."
* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloffis a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.