Carroll, a recognized leader in the field of evolutionary development, to this new edition of Introduction to Genetic Analysis (IGA). The authors’ ambitious new plans for this edition focus on showing how genetics is practiced today. In particular, the new edition renews its emphasis on how genetic analysis can be a powerful tool for answering biological questions of all types.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    An interesting and reader friendly writing style makes for a clear presentation, complete with all the necessary assumptions and mathematical details. Chapter topics include probability concepts and applications, decision models and decision trees, regression models, forecasting, inventory control models, linear programming modeling applications and computer analyses, network models, project management, simulation modeling, and more. For an introduction toquantitative analysis, quantitative management, operations research, or management science-especially for those individuals preparing for work in agricultural economics and health care fields.

    From dominant and recessive inherited traits to the DNA double-helix, you get clear explanations in easy-to-understand terms. Plus, you'll see how people are applying genetic science to fight disease, develop new products, solve crimes . . . and even clone cats.Discover:What geneticists do How traits are passed on How genetic counseling works The basics of cloning The role of DNA in forensics The scoop on the Human Genome Project

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Time-saving study tools help readers arrive at a complete understanding of human anatomy. KEY TOPICS: An Introduction to Anatomy, The Cell, Tissues and Early Embryology, The Integumentary System, The Skeletal System: Osseous Tissue and Skeletal Structure, The Skeletal System: Axial Division, The Skeletal System: Appendicular Division, The Skeletal System: Articulations, The Muscular System: Skeletal Muscle Tissue and Muscle Organization, The Muscular System: Axial Musculature, The Muscular System: Appendicular Musculature, Surface Anatomy and Cross-Sectional Anatomy, The Nervous System: Neural Tissue, The Nervous System: The Spinal Cord and Spinal Nerves, The Nervous System: The Brain and Cranial Nerves, The Nervous System: Pathways And Higher-Order Functions, The Nervous System:  Autonomic Division, The Nervous System: General and Special Senses, The Endocrine System, The Cardiovascular System: Blood, The Cardiovascular System: The Heart, The Cardiovascular System: Vessels and Circulation, The Lymphoid System, The Respiratory System, The Digestive System, The Urinary System, The Reproductive System, The Reproductive System: Embryology and HumanDevelopment. MARKET: For all readers interested in human anatomy.

    Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.

    What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    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.

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

    Sophia Yin, author of the Small Animal Veterinary Nerdbook, is a veterinarian, applied animal behaviorist, author, and speaker. Her passion for animals and their proper handling and restraint led her to spend hundreds of hours on this project - a new book and instructional DVD set focused on the most humane techniques that reduce stress for people and pets. The book and DVD feature: More than 1,600 photos that show practicing veterinarians and students how to handle dogs and cats correctly Explanations of what vets think they re doing right but may actually be doing wrong Three hours of video clips with voiceover narratives that show correct and incorrect handling proceduresBy using this training tool, vets will: Be able to implement the most up-to-date handling techniques Learn how to restrain animals correctly through behavior modification that does not involve coercion, dominance, or other negative training methods Increase efficiency because patients will willingly comply with procedures Stop perpetuating behavior problems in patients Create a safer environment for themselves and and their team with fewer bites, scratches, and back injuries Improve the bond between vet, pet, and clientThis new book and DVD quickly and painlessly teaches how to handle dogs and cats with ease and grace, showing veterinarians how to impress clients and create patients who willingly comply with treatments and procedures. This visual guide is the result of hundreds of hours of research and a dedication to intelligent and compassionate behavior modification in animals. Over 1,600 photos and three hours of video clips make it easy to recognize poor handling methods that can cause behavior issues for a lifetime. Dr. Yin shows the wrong way, and then the right way, to clearly illustrate exactly how and why a pet reacts the way she does.The benefits of low-stress handling are tremendous; practicing these refined handling skills will create a safer work environment, increase efficiency, and ultimately improve the bond between vet, pet, and client."

    '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.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    The first and only true textbook written by professors who teach the undergraduate course, it presents the most current concepts in an experimental context with clinical advances highlighted in boxes, supported by the kind of helpful pedagogical tools that other books do not provide.