With inspiring real-life stories and thought-provoking questionnaires, this workbook will help you achieve financial fitness as you daily work out those newly defined money muscles. Ramsey will motivate you to immediate action, so you can: Set up an emergency fund (believe me, you're going to need it) Pay off your home mortgage―it is possible. Prepare for college funding (your kids will love you for it) Maximize your retirement investing so you can live your golden years in financial peace Build wealth like crazy! With incentive exercises that really do exercise your spending and saving habits, Ramsey will get your mind and your money working to make your life free of fiscal stress and strain. It's a no-nonsense plan that will not only make over your money habits, but it will also completely transform your life.

    It presents the ideas and terminology essential to an understanding of balance sheets, income statements, and statements of cash flows. Every frame requires students to solve a problem involving accounting information - e.g., selecting a correct word from two choices, providing an answer, making a journal entry, or preparing a complete balance sheet.

    Scaling Up: How a Few Companies Make It...and Why the Rest Don't is the first major revision of this business classic. In Scaling Up, Harnish and his team share practical tools and techniques for building an industry-dominating business. These approaches have been honed from over three decades of advising tens of thousands of CEOs and executives and helping them navigate the increasing complexities (and weight) that come with scaling up a venture. This book is written so everyone -- from frontline employees to senior executives -- can get aligned in contributing to the growth of a firm. There's no reason to do it alone, yet many top leaders feel like they are the ones dragging the rest of the organization up the S-curve of growth. The goal of this book is to help you turn what feels like an anchor into wind at your back -- creating a company where the team is engaged; the customers are doing your marketing; and everyone is making money. To accomplish this, Scaling Up focuses on the four major decision areas every company must get right: People, Strategy, Execution, and Cash. The book includes a series of new one-page tools including the updated One-Page Strategic Plan and the Rockefeller Habits ChecklistTM, which more than 40,000 firms around the globe have used to scale their companies successfully -- many to $1 billion and beyond. Running a business is ultimately about freedom. Scaling Up shows business leaders how to get their organizations moving in sync to create something significant and enjoy the ride.

    The new edition continues to reflect the conversational style and clarity that has made this a leader in the market. It explores the key concepts and principles while using the PepsiCo financial statement to clearly show how the information is applied in the real world. The book has also been updated with the latest data as to reflect today's business environment.

    Here's how to handle them.If you read nothing else on managing people, read these 10 articles (featuring “Leadership That Gets Results,” by Daniel Goleman). We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you maximize your employees' performance.HBR's 10 Must Reads on Managing People will inspire you to:• Tailor your management styles to fit your people• Motivate with more responsibility, not more money• Support first-time managers• Build trust by soliciting input• Teach smart people how to learn from failure• Build high-performing teams• Manage your bossThis collection of best-selling articles includes: featured article "Leadership That Gets Results" by Daniel Goleman, "One More Time: How Do You Motivate Employees?" "The Set-Up-to-Fail Syndrome," "Saving Your Rookie Managers from Themselves," "What Great Managers Do," "Fair Process: Managing in the Knowledge Economy," "Teaching Smart People How to Learn," "How (Un)ethical Are You?" "The Discipline of Teams," and "Managing Your Boss."

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    More than a quick fix or another "how-to" guide, the book offers an entirely new way of attaining productivity that users at any level of expertise can put into action right away. This is "bit literacy," a method for working more productively in the digital age, with less stress. Mark Hurst - who has reached hundreds of thousands of readers through his Good Experience e-mail newsletter, Uncle Mark technology guides, thisisbroken.com, and other websites - has revealed the way to survive, and thrive, in the digital age: "Let the bits go.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    From inside the corner office at Lehman Brothers to secret meetings in South Korea, and the corridors of Washington, Too Big to Fail is the definitive story of the most powerful men and women in finance and politics grappling with success and failure, ego and greed, and, ultimately, the fate of the world’s economy. “We’ve got to get some foam down on the runway!” a sleepless Timothy Geithner, the then-president of the Federal Reserve of New York, would tell Henry M. Paulson, the Treasury secretary, about the catastrophic crash the world’s financial system would experience. Through unprecedented access to the players involved, Too Big to Fail re-creates all the drama and turmoil, revealing neverdisclosed details and elucidating how decisions made on Wall Street over the past decade sowed the seeds of the debacle. This true story is not just a look at banks that were “too big to fail,” it is a real-life thriller with a cast of bold-faced names who themselves thought they were too big to fail.

    Prahalad describes how the world's low-income markets offer opportunity for the world's wealthiest companies to seek fortune and bring prosperity to the poor.

    It states that securities prices in financial markets must equal fundamental values, either because all investors are rational or because arbitrage eliminates pricing anomalies. This bookdescribes an alternative approach to the study of financial markets: behavioral finance. This approach starts with an observation that the assumptions of investor rationality and perfect arbitrage are overwhelmingly contradicted by both psychological and institutional evidence. In actualfinancial markets, less than fully rational investors trade against arbitrageurs whose resources are limited by risk aversion, short horizons, and agency problems. The book presents models of such markets. These models explain the available financial data more accurately than the efficient marketshypothesis, and generate new predictions about security prices. By summarizing and expanding the research in behavioral finance, the book builds a new theoretical and empirical foundation for the economic analysis of real-world markets.

    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 effectively learning math and science—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 math. 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. A Mind for Numbers shows us that we all have what it takes to excel in math, and learning it is not as painful as some might think!