Blending theory with practice, the book helps pre-service and in-service teachers develop the knowledge, skills, attitudes, and beliefs they'll need to construct learning environments that make it possible for all students to reach their potential.

    This title is aimed at courses in applied econometrics, political methodology, and sociological methods or a one-year graduate course in econometrics for social scientists.

    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.

    By using objects early to solve interesting problems and defining objects later in the course, Building Java Programs develops programming knowledge for a broad audience. Introduction to Java Programming, Primitive Data and Definite Loops, Introduction to Parameters and Objects, Conditional Execution, Program Logic and Indefinite Loops, File Processing, Arrays, Defining Classes, Inheritance and Interfaces, ArrayLists, Java Collections Framework, Recursion, Searching and Sorting, Graphical User Interfaces. For all readers interested in introductory programming.

    This book also takes into account the period of emerging adulthood (ages 18-25), an area sometimes neglected but of particular interest to many students who see themselves reflected in the research. Looking for additional resources to help you understand the material and succeed in this course? MyDevelopmentLab contains study tools such as flashcards, self tests, videos, as well as MyVirtulTeen which allows you to raise your own virtual teenager, focusing on the ages 10 through 18. MyDevelpmentLab is available at www.mydevelopmentlab.com.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems."

    The authors have eliminated unnecessary mathematical detail and concentrate on the intuition and insights that will be useful to practitioners throughout their careers as new ideas and challenges emerge from the financial marketplace. Essentials maintains the theme of asset allocation (authors discuss asset pricing and trading then apply these theories to portfolio planning in real-world securities markets that are governed by risk/return relationships).

    Froeb/McCann's MANAGERIAL ECONOMICS: A PROBLEM SOLVING APPROACH, 2E covers traditional material using a problem-based pedagogy built around common business mistakes. Models are used sparingly, and then only to the extent that they help students figure out why mistakes are made, and how to fix them. This edition's succinct, fast-paced presentation and challenging, interactive applications place students in the role of a decision maker who has to identify mistakes that reduce profits, and propose solutions to bring profits back up. The lively book provides an excellent ongoing reference for students pursuing business careers. New chapters and updates highlight mistakes that precipitated the financial crisis. With MANAGERIAL ECONOMICS, 2E your students are taught to use economics to not only identify profitable decisions, but also how to implement them within an organization.

    The book's hallmark values-accuracy, currency, and passion for teaching and learning-have made Campbell/Reece the most successful book for readers for seven consecutive editions. More than 6 million readers have benefited from "BIOLOGY's"clear explanations, carefully crafted artwork, and student-friendly narrative style.Introduction: Themes in the Study of Life, The Chemical Context of Life, Water and the Fitness of the Environment, Carbon and the Molecular Diversity of Life, The Structure and Function of Large Biological Molecules, A Tour of the Cell, Membrane Structure and Function, An Introduction to Metabolism, Cellular Respiration: Harvesting Chemical Energy, Photosynthesis, Cell Communication, The Cell Cycle, Meiosis and Sexual Life Cycles, Mendel and the Gene Idea, The Chromosomal Basis of Inheritance, The Molecular Basis of Inheritance, From Gene to Protein, Control of Gene Expression, Viruses, Biotechnology, Genomes and Their Evolution, Descent with Modification: A Darwinian View of Life, The Evolution of Populations, The Origin of Species, The History of Life on Earth, Phylogeny and the Tree of Life, Bacteria and Archaea, Protists, Plant Diversity I: How Plants Colonized Land, Plant Diversity II: The Evolution of Seed Plants, Fungi, An Introduction to Animal Diversity, Invertebrates, Vertebrates, Plant Structure, Growth, and Development, Transport in Vascular Plants, Soil and Plant Nutrition, Angiosperm Reproduction and Biotechnology, Plant Responses to Internal and External Signals, Basic Principles of Animal Form and Function, Animal Nutrition, Circulation and Gas Exchange, The Immune System, Osmoregulation and Excretion, Hormones and the Endocrine System, Animal Reproduction, Animal Development, Neurons, Synapses, and Signaling, Nervous Systems, Sensory and Motor Mechanisms, Animal Behavior, An Introduction to Ecology and the Biosphere, Population Ecology, Community Ecology, Ecosystems, Conservation Biology and Restoration Ecology.For readers interested in learning the basics of Biology.

    It provides a transition from elementary calculus to advanced courses in real and complex function theory and introduces the reader to some of the abstract thinking that pervades modern analysis.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    Dealing with measure theory and Lebesque integration, this is an introductory graduate text.

    This text is highly regarded because of its engaging and understandable introduction to statistics. The author's commitment to providing student-friendly guidance through the material and giving students opportunities to apply their newly learned skills in a real-world context has made Elementary Statistics the #1 best-seller in the market.