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"The most comprehensive book available for the Logic Reasoning section of the LSAT. This book will provide you with an advanced system for attacking any Logical Reasoning question that you may encounter on the LSAT."--
"Learn how to identify question types, simplify arguments, and eliminate wrong answers efficiently and confidently. Practice the logic skills tested by the GMAT and master proven methods for solving all Critical Reasoning problems"--Page 4 of cover.
Offering a new take on the LSAT logical reasoning section, the Manhattan Prep Logical Reasoning LSAT Strategy Guide is a must-have resource for any student preparing to take the exam. Containing the best of Manhattan Prep’s expert strategies, this book will teach you how to untangle the web of LSAT logical reasoning questions confidently and efficiently. Avoiding an unwieldy and ineffective focus on memorizing sub-categories and steps, the Logical Reasoning LSAT Strategy Guide encourages a streamlined method that engages and improves your natural critical-thinking skills. Beginning with an effective approach to reading arguments and identifying answers, this book trains you to see through the clutter and recognize the core of an argument. It also arms you with the tools needed to pick apart the answer choices, offering in-depth explanations for every single answer – both correct and incorrect – leading to a complex understanding of this subtle section. Each chapter in the Logical Reasoning LSAT Strategy Guide uses real LSAT questions in drills and practice sets, with explanations that take you inside the mind of an LSAT expert as they work their way through the problem. Further practice sets and other additional resources are included online and can be accessed through the Manhattan Prep website. Used by itself or with other Manhattan Prep materials, the Logical Reasoning LSAT Strategy Guide will push you to your top score.
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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
This book investigates the role of inference in argumentation, considering how arguments support standpoints on the basis of different loci. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics (AMT). A prominent feature of the AMT is that it distinguishes, within each and every single argumentation, between an inferential-procedural component, on which the reasoning process is based; and a material-contextual component, which anchors the argument in the interlocutors’ cultural and factual common ground. The AMT explains how these components differ and how they are intertwined within each single argument. This model is introduced in Part II of the book, following a careful reconstruction of the enormously rich tradition of studies on inference in argumentation, from the antiquity to contemporary authors, without neglecting medieval and post-medieval contributions. The AMT is a contemporary model grounded in a dialogue with such tradition, whose crucial aspects are illuminated in this book.