Princeton Review AP Statistics Premium Prep (21版)

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【簡介】 PREMIUM PREP FOR A PERFECT 5! Ace the newly-digital AP Statistics Exam with this comprehensive study guide--including 5 full-length practice tests with answer explanations, timed online practice, and thorough content reviews.The Princeton Review ExpertiseStudy with prep and practice written entirely by AP educatorsLearn test-taking strategies backed by 40+ years of test prep success Focused, supportive lessons designed and perfected by expertsEverything You Need for a High ScoreA step-by-step guide on how to better your score with this bookComprehensive review of all topics on the new digital examCustomize a study plan and target areas of improvement by using our diagnostic answer keyEnd-of-chapter drills for each topic to reinforce learningExclusive online digital flashcards to hone essential conceptsPremium Practice for AP Excellence5 full-length practice tests (2 in the book, 3 online)Detailed answer explanations to help you learn from mistakesOnline tests provided as both digital versions (with timer option to simulate exam experience), and as downloadable PDFs (with interactive elements mimicking the exam interface)Get more via your online student tools--including a list of key terms and concepts, study plans, and exam updates

Fundamentals of Probability and Statistics for Machine Learning (1版)

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【簡介】 An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning.Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceCovers mathematical fundamentals of probability and statistics and ML basicsEmphasizes hands-on learningSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code

Statistics for Chemical Engineers: From Data to Models to Decisions (1版)

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【簡介】 Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data-model-decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.

Introduction to Statistics and Data Analysis for Physicists

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【簡介】 The tools of statistical analysis for experiments in modern physical applications are increasingly sophisticated and specific tools are needed to reliably extract results from complex data. This textbook thus presents a comprehensive treatment of the topic for the practicing physicist, focusing less on mathematical foundations but appealing to intuitive techniques with a large number of examples. This fourth edition is greatly expanded with new sub-topics not covered in standard textbooks. We begin with fundamental probability concepts and measurement errors, continuing to the indispensable Monte Carlo simulation. Likelihood and its underlying likelihood principle are explored, serving as bases for the sections on parameter inference and the treatment of distorted data. Topics like hypothesis testing, the statistics of weighted events, the elimination of nuisance parameters, and deconvolution are updated with new developments. Final chapters introduce other advanced techniques such as statistical learning and bootstrap sampling. Developed and greatly expanded from a graduate course at the University of Siegen, this book serves as an essential resource for all graduate students and researchers seeking a rigorous foundation in statistical methods for experimental physics, especially those in nuclear, particle and astrophysics. 【目錄】

Probability, Statistics, and Reliability for Engineers and Scientists (1版)

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【簡介】 Virtually every engineer and scientist must be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential.The goal of this popular and proven book is to introduce the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purpose of data and uncertainty analysis and modeling in support of decision-making.The primary objectives to the author’s approach include: (1) introducing probability, statistics, reliability, and risk methods to students and practicing professionals in engineering and the sciences; (2) emphasizing the practical use of these methods; and (3) establishing the limitations, advantages, and disadvantages of the methods. The book was developed with an emphasis on solving real-world technological problems that engineers and scientists are asked to solve as part of their professional responsibilities.Upon graduation, engineers and scientists must have a solid academic foundation in methods of data analysis and synthesis, as the analysis and synthesis of complex systems are common tasks that confront even entry-level professionals.The underlying theory, especially the assumptions central to the methods, is presented, but then the proper application of the theory is presented through realistic examples, often using actual data. Every attempt is made to show that methods of data analysis are not independent of each other. Instead, we show that real-world problem-solving often involves applying many of the methods presented in different chapters.Probability, Statistics, and Reliability for Engineers and Scientists, here in its fourth edition, is a very popular textbook. Ultimately, readers will find its content of great value in problem-solving and decision-making, particularly in practical applications.