The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject.

    Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either."

    Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, intuitive visual representations of real-world examples are used to show how Bayes' rule is actually a form of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to gain an intuitive understanding of Bayesian analysis. As an aid to understanding, online computer code (in MatLab, Python and R) reproduces key numerical results and diagrams.Stone's book is renowned for its visually engaging style of presentation, which stems from teaching Bayes' rule to psychology students for over 10 years as a university lecturer.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    Rich illustrations, systematic resumes, and extensive citations make it a valuable references source.

    Over the years sports debates have become muddled by many myths that do not match the numbers generated by those playing the games. In The Wages of Wins, the authors use layman's language and easy to follow examples based on their own academic research to debunk many of the most commonly held beliefs about sports.In this updated version of their book, these authors explain why Allen Iverson leaving Philadelphia made the 76ers a better team, why the Yankees find it so hard to repeat their success from the late 1990s, and why even great quarterbacks like Brett Favre are consistently inconsistent. The book names names, and makes it abundantly clear that much of the decision making of coaches and general managers does not hold up to an analysis of the numbers. Whether you are a fantasy league fanatic or a casual weekend fan, much of what you believe about sports will change after reading this book.

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    I’m Tim. I’m a single cell in society’s body. U.S. society, to be specific.So let me explain why we’re here.As a writer and a generally thinky person, I’ve spent a lot of my life thinking about the society I live in, and societies in general. I’ve always imagined society as a kind of giant human—a living organism like each of us, only much bigger.When you’re a single cell in the body of a giant, it’s hard to understand what the giant’s doing, or why it is the way it is, because you can’t really zoom out and look at the whole thing all at once. But we do our best.The thing is, when I’ve recently tried to imagine what society might look like, I haven’t really been picturing this:Giant stick figure: "I am grown up."Based on what I see around me, in person and online, it seems like my society is actually more like this:Giant stick figure throwing a giant tantrum because their chocolate ice cream fell on the ground.Individual humans grow older as they age—but it kind of seems like the giant human I live in has been getting more childish each year that goes by.So I decided to write a blog post about this. But then something else happened.When I told people I was planning to write a post about society, and the way people are acting, and the way the media is acting, and the way the government is acting, and the way everyone else is acting, people kept saying the same thing to me.Don’t do it. Don’t touch it. Write about something else. Anything else. It’s just not worth it.They were right. With so many non-controversial topics to write about, why take on something so loaded and risk alienating a ton of readers? I listened to people’s warnings, and I thought about moving on to something else, but then I was like, “Wait what? I live inside a giant and the giant is having a six-year-old meltdown in the grocery store candy section and that’s a not-okay thing for me to talk about?”It hit me that what I really needed to write about was that—about why it’s perilous to write about society."

    

    The addition of this book is a must for all upper-level Christian school curricula and for college students and adults interested in math or related fields of science and religion. It will serve as a solid refutation for the claim, often made in court, that mathematics is one subject, which cannot be taught from a distinctively Biblical perspective.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

     * Compelling evidence that selling products on Amazon may be the best home-based business opportunity ever created * The Simple Profit Blueprint – How to Make $1,000 Per Day Clear Profit on Amazon with One Single $35 Product You Choose * The biggest obstacle in your way of achieving that goal and how I will help you overcome it (Hint: It's all in your head) * What selling products on Amazon is all about * The common misconceptions about selling products on Amazon * The powerful advantages to selling on Amazon as opposed to trying to sell products online on your own * The PRICELESS benefits to “Partnering” with Amazon * The basics of selling products on Amazon * The basics of finding profitable products to sell on Amazon * The two distinctly different methods to find the products you’ll be selling on Amazon * The “Retail Arbitrage” method of sourcing products to sell on Amazon * The “Private Label” method of sourcing products to sell on Amazon * Two distinctly different methods of Private Labeling products to sell on Amazon * Two distinctly different ways to sell your products on Amazon * Our (my partner and I) experience in our first year as Amazon sellers, exactly how much money we made and what it taught us * Retail Arbitrage vs. Private Label * The two major obstacles to making really big money with Retail Arbitrage * How to sign-up as an Amazon seller today for free and why you'll soon want to pay $39.99 per month to go "Pro" * You're creating "AZ Money Machines" (AMMs instead of ATMs) * How Amazon pays you and the breakdown of the check Amazon would be sending you every two weeks (or depositing directly into your bank account) * How the blueprint creates a self-perpetuating NET annual income of $360,720 * Why you should NOT make your goal $1,000 per day * How to become a cash millionaire in 3 simple steps * How building an enormously successful Amazon business is a simple step-by-step process * How to earn insanely high annualized returns on your product investments * How you can “fail” your way to success * The “Beyond Amazon” Bonus * How to scale your business up with three simple steps to where you are a bona fide cash millionaire * Can you really make more money in one week than you've ever made on your old job in one year? (Simple answer – YES!) * Where to get 100% of the money you need to grow your Amazon business as large as you want – at 0% interest * Where to find MORE money to build your business * Your Free* Training * Your Shortcut to the BIG Money * Your action steps to get started today * Chart your success to $1,000 per day – and more * The "Secret Sauce" that will supercharge your motivation and determination to succeed in this business Tags: Amazon Private Label FBA, FBA, Private Label FBA, Amazon FBA, Amazon FBA Secrets, Sell on Amazon, Retail Arbitrage, Physical Products, Private Label, FBA, Private Label, Home-Based Business, Amazing Selling Machine, Fulfillment by Amazon, Private La

    Good stories are full of life: they engage our emotions and have subtlety and nuance, but they lack rigor and the truths they tell are elusive and subject to debate. As ways of understanding the world around us, numbers and stories seem almost completely incompatible. Once Upon a Number shows that stories and numbers aren't as different as you might imagine, and in fact they have surprising and fascinating connections. The concepts of logic and probability both grew out of intuitive ideas about how certain situations would play out. Now, logicians are inventing ways to deal with real world situations by mathematical means -- by acknowledging, for instance, that items that are mathematically interchangeable may not be interchangeable in a story. And complexity theory looks at both number strings and narrative strings in remarkably similar terms. Throughout, renowned author John Paulos mixes numbers and narratives in his own delightful style. Along with lucid accounts of cutting-edge information theory we get hilarious anecdotes and jokes; instructions for running a truly impressive pyramid scam; a freewheeling conversation between Groucho Marx and Bertrand Russell (while they're stuck in an elevator together); explanations of why the statistical evidence against OJ Simpson was overwhelming beyond doubt and how the Unabomber's thinking shows signs of mathematical training; and dozens of other treats. This is another winner from America's favorite mathematician.

    His Incompleteness Theorem turned not only mathematics but also the whole world of science and philosophy on its head. Equally legendary were Gö's eccentricities, his close friendship with Albert Einstein, and his paranoid fear of germs that eventually led to his death from self-starvation. Now, in the first popular biography of this strange and brilliant thinker, John Casti and Werner DePauli bring the legend to life. After describing his childhood in the Moravian capital of Brno, the authors trace the arc of Gö's remarkable career, from the famed Vienna Circle, where philosophers and scientists debated notions of truth, to the Institute for Advanced Study in Princeton, New Jersey, where he lived and worked until his death in 1978. In the process, they shed light on Gö's contributions to mathematics, philosophy, computer science, artificial intelligence -- even cosmology -- in an entertaining and accessible way.