Probability and statistics Books

Book SynopsisAbout our author The late Neil A. Weiss received his Ph.D. from UCLA and subsequently accepted an assistant professor position at Arizona State University (ASU), where he was ultimately promoted to the rank of full professor. Dr. Weiss taught statistics, probability, and mathematics, from the freshman level to the advanced graduate level, for more than 30 years. In recognition of his excellence in teaching, Dr. Weiss received the Dean's Quality Teaching Award from the ASU College of Liberal Arts and Sciences. He also was runner-up twice for the Charles Wexler Teaching Award in the ASU School of Mathematical and Statistical Sciences. Dr. Weiss's comprehensive knowledge and experience ensure that his texts are mathematically and statistically accurate, as well as pedagogically sound. In addition to his numerous research publications, Dr. Weiss was the author of A Course in Probability (Addison-Wesley, 2006). He alsoTable of Contents PART I: Introduction 1. The Nature of Statistics PART II: Descriptive Statistics 2. Organizing Data 3. Descriptive Measures PART III: Probability, Random Variables, and Sampling Distributions 4. Probability Concepts 5. Discrete Random Variables 6. The Normal Distribution 7. The Sampling Distribution of the Sample Mean PART IV: Inferential Statistics 8. Confidence Intervals for One Population Mean 9. Hypothesis Tests for One Population Mean 10. Inferences for Two Population Means 11. Inferences for Population Standard Deviations 12. Inferences for Population Proportions 13. Chi-Square Procedures PART V: Regression, Correlation, and ANOVA 14. Descriptive Methods in Regression and Correlation 15. Inferential Methods in Regression and Correlation 16. Analysis of Variance (ANOVA) PART VI: Multiple Regression and Model Building; Experimental Design and ANOVA MODULE A: Multiple Regression Analysis MODULE B: Model Building in Regression MODULE C: Design of Experiments and Analysis of Variance Answers to Selected Exercises Index Appendix A: Statistical Tables Appendix B: Answers to Selected Exercises

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Book SynopsisAbout our author Mario F. Triola is a Professor Emeritus of Mathematics at Dutchess Community College, where he has taught statistics for over 30 years. Marty also is the author of Elementary Statistics, 14th Edition, Elementary Statistics Using Excel, 7th Edition and Elementary Statistics Using the TI-83/84 Plus Calculator, 5th Edition. He is a coauthor of Biostatistics for the Biological and Health Sciences, 2nd Edition, Statistical Reasoning for Everyday Life, 5th Edition and Business Statistics. Essentials of Statistics is currently available as a Global Edition, and it has been translated into several foreign languages. Marty designed the original Statdisk statistical software, and he has w

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Book SynopsisExam board: Cambridge Assessment International EducationLevel: A LevelSubject: MathematicsFirst teaching: September 2018First exams: Summer 2020Reinforce learning and deepen understanding of the key concepts covered in the latest syllabus; an ideal course companion or homework book for use throughout the course.- Develop and strengthen skills and knowledge with a wealth of additional exercises that perfectly supplement the Student's Book. - Build confidence with extra practice for each lesson to ensure that a topic is thoroughly understood before moving on. - Ensure students know what to expect with hundreds of rigorous practice and exam-style questions. - Keep track of students' work with ready-to-go write-in exercises. - Save time with all answers available for free online: www.hoddereducation.co.uk/cambridgeextras.This book covers the syllabus content for Probability and Statistics 1, including representation of data, permutations and combinations, probability, discrete random variables and the normal distribution. This title has not been through the Cambridge Assessment International Education endorsement process.Available in this series:Five textbooks fully covering the latest Cambridge International AS & A Level Mathematics syllabus (9709) are accompanied by a Workbook, and Student and Whiteboard eTextbooks.Pure Mathematics 1: Student Textbook (ISBN 9781510421721), Student eTextbook (ISBN 9781510420762), Whiteboard eTextbook (ISBN 9781510420779), Workbook (ISBN 9781510421844)Pure Mathematics 2 and 3: Student Textbook (ISBN 9781510421738), Student eTextbook (ISBN 9781510420854), Whiteboard eTextbook (ISBN 9781510420878), Workbook (ISBN 9781510421851)Mechanics: Student Textbook (ISBN 9781510421745), Student eTextbook (ISBN 9781510420953), Whiteboard eTextbook (ISBN 9781510420977), Workbook (ISBN 9781510421837)Probability & Statistics 1: Student Textbook (ISBN 9781510421752), Student eTextbook (ISBN 9781510421066), Whiteboard eTextbook (ISBN 9781510421097), Workbook (ISBN 9781510421875)Probability & Statistics 2: Student Textbook (ISBN 9781510421776), Student eTextbook (ISBN 9781510421158), Whiteboard eTextbook (ISBN 9781510421165), Workbook (9781510421882)

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Book SynopsisSuitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books Trade ReviewFrom the reviews: "[the material is] superbly motivated with interest-grabbing examples... exercises excellent and plentiful." Edward Williams, University of Michigan-Dearborn, USA "... it is a notoriously hard task to introduce probability and statistics with a mix of intuition and mathematics to keep students motivated. Therefore, I very much welcome this book and recommend it as course material." Sara van de Geer, Leiden University, The Netherlands "This textbook provides a well-written first course in probability and statistics...It is a book that has been written based on the long teaching experience of the authors and I would certainly recommend it for university coursework." Short Book Reviews of the International Statistical Institute, December 2005 "This book has numerous quick exercises to give direct feedback to the students. … A website at www.springeronline.com/978-1-85233-896-1 gives access to the data files used in the text … . This will be a key text for undergraduates in computer science, physics, mathematics, chemistry, biology and business studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects." (Rainer Beedgen, Zentralblatt MATH, Vol. 1079, 2006) "The book is designed for a one-semester introductory course in probability and statistics basics for engineering students. … It can also be used by students in other more mathematically oriented majors such as applied mathematics with more emphasis on the mathematics and additional coverage in topics such as combinatorics, conditional expectation, and generating functions. … More elaborate exercises and real datasets are given at the end of each chapter." (Arthur B. Yeh, Technometrics, Vol. 49 (3), August, 2007)Table of ContentsWhy probability and statistics?.- Outcomes, events, and probability.- Conditional probability and independence.- Discrete random variables.- Continuous random variables.- Simulation.- Expectation and variance.- Computations with random variables.- Joint distributions and independence.- Covariance and correlation.- More computations with more random variables.- The Poisson process.- The law of large numbers.- The central limit theorem.- Exploratory data analysis: graphical summaries.- Exploratory data analysis: numerical summaries.- Basic statistical models.- The bootstrap.- Unbiased estimators.- Efficiency and mean squared error.- Maximum likelihood.- The method of least squares.- Confidence intervals for the mean.- More on confidence intervals.- Testing hypotheses: essentials.- Testing hypotheses: elaboration.- The t-test.- Comparing two samples.

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Book SynopsisHow does chance enter our world? And why is so much not predictable?In an understandable, exciting and amusing narrative, the author takes us into the world of chemistry, quantum physics and biology. Touching on astronomy and philosophy, we witness a rewarding journey of discovery. In the process, he develops a completely new view of chance based on the laws of nature. Here, the omnipresent non-equilibrium plays an extremely decisive role, because it generates the complex structures in our world. Finally, on this basis, he presents an equally simple and captivating hypothesis on the nature of time.This non-fiction book provides a deep insight into the fascination of research, the agonizing search for fundamental understanding, and the struggle for scientific knowledge.Table of ContentsChance takes its course.- Chance is everywhere.- Creativity is chance in the brain.- "Balance is good, non-balance is bad" - is it true?- Almost despairing of science.- The birth of chance in complex systems.- What is there when time flows, and where does it flow to?- Our perception of time.

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Book SynopsisThis second edition textbook offers a practical introduction to probability for undergraduates at all levels with different backgrounds and views towards applications. Calculus is a prerequisite for understanding the basic concepts, however the book is written with a sensitivity to students’ common difficulties with calculus that does not obscure the thorough treatment of the probability content. The first six chapters of this text neatly and concisely cover the material traditionally required by most undergraduate programs for a first course in probability. The comprehensive text includes a multitude of new examples and exercises, and careful revisions throughout. Particular attention is given to the expansion of the last three chapters of the book with the addition of one entirely new chapter (9) on ’Finding and Comparing Estimators.’ The classroom-tested material presented in this second edition forms the basis for a second course introducing mathematical statistics.Table of ContentsProbability Space.- Conditional probabilities.- Discrete random variables.- Binomial random variables.- Poisson random variables.- Simulations of discrete random variables.- Combinatorics.- Continuous random variables.- The sample average and sample.- Estimating and testing proportions.- Estimating and testing means.- Small samples.- Chi-squared tests.- Design of experiments.- The cumulative distribution function.- Continuous joint distributions.- Covariance and independence.- Conditional distribution and expectation.- The bivariate normal distribution.- Sums of Bernoulli random variables.- Coupling random variables.- The moment generating function.- The chi-squared, Student and F distributions.- Sampling from a normal distribution.- Finding estimators.- Comparing estimators.- Best unbiased estimators.- Bayes’ estimator.- Multiple linear regression.- List of common discrete distributions.- List of common continuous distributions.- Further reading.- Normal table.- Student table.- Chi-squared table.- Index.

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Book SynopsisThis book presents a concise and focused introduction to engineering statistics, emphasizing topics and concepts that a practicing engineer is mostly likely to use: the display of data, confidence intervals, hypothesis testing, fitting straight lines to data, and designing experiments to find the impact of process changes on a system or its output. It introduces the language of statistics, derives equations with sufficient detail so that there is no mystery as to how they came about, makes extensive use of tables to collect and summarize important formulas and concepts, and utilizes enhanced graphics that are packed with visual information to illustrate the meaning of the equations and their usage. The book can be used as an introduction to the subject, to refresh one’s knowledge of engineering statistics, to complement course materials, as a study guide, and to provide a resource in laboratories where data acquisition and analysis are performed.Created specifically for the book are 16 interactive graphics (IGs) that can be used to replicate all numerical calculations appearing in the book and many of its figures, numerically evaluate all formulas appearing in tables, solve all exercises, and determine probabilities and critical values for commonly used probability distributions. After downloading a free program, the IGs are ready to use and are self-explanatory in the context of the material.Table of Contents- 1. Descriptive Statistics and Discrete Probability Distributions. - 2. Continuous Probability Distributions, Confidence Intervals, and Hypothesis Testing. - 3. Regression Analysis and the Analysis of Variance. - 4. Experimental Design. - Appendix A: Moment Generating Function.

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Book SynopsisTable of Contents1. Introduction to Probability Theory 2. Random Variables 3. Conditional Probability and Conditional Expectation 4. Markov Chains 5. The Exponential Distribution and the Poisson Process 6. Continuous-Time Markov Chains 7. Renewal Theory and Its Applications 8. Queueing Theory 9. Reliability Theory 10. Brownian Motion and Stationary Processes 11. Simulation 12. Coupling 13. Martingales

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Book SynopsisOtt and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.Table of ContentsPART 1: INTRODUCTION. 1. Statistics and the Scientific Method. Introduction. Why Study Statistics? Some Current Applications of Statistics. A Note to the Student. Summary. Exercises. PART 2: COLLECTING DATA. 2. Using Surveys and Scientific Studies to Collect Data. Introduction and Abstract of Research Study. Observational Studies. Sampling Designs for Surveys. Experimental Studies. Designs for Experimental Studies. Research Study: Exit Polls versus Election Results. Summary. Exercises. PART 3: SUMMARIZING DATA. 3. Data Description. Introduction and Abstract of Research Study. Calculators, Computers, and Software Systems. Describing Data on a Single Variable: Graphical Methods. Describing Data on a Single Variable: Measures of Central Tendency. Describing Data on a Single Variable: Measures of Variability. The Boxplot. Summarizing Data from More Than One Variable: Graphs and Correlation. Research Study: Controlling for Student Background in the Assessment of Teaching. Summary and Key Formulas. Exercises. 4. Probability And Probability Distributions. Introduction and Abstract of Research Study. Finding the Probability of an Event. Basic Event Relations and Probability Laws. Conditional Probability and Independence. Bayes' Formula. Variables: Discrete and Continuous. Probability Distributions for Discrete Random Variables. Two Discrete Random Variables: The Binomial and the Poisson. Probability Distributions for Continuous Random Variables. A Continuous Probability Distribution: The Normal Distribution. Random Sampling. Sampling Distributions. Normal Approximation to the Binomial. Evaluating Whether or Not a Population Distribution Is Normal. Research Study: Inferences about Performance Enhancing Drugs among Athletes. Minitab Instructions. Summary and Key Formulas. Exercises. PART 4: ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS. 5. Inferences about Population Central Values. Introduction and Abstract of a Research Study. Estimation of ��. Choosing the Sample Size for Estimating ��. A Statistical Test for ��. Choosing the Sample Size for ��. The Level of Significance of a Statistical Test. Inferences about �� for a Normal Population, �� Unknown. Inferences about �� when Population in Nonnormal and n is small: Bootstrap Methods. Inferences about the Median. Research Study: Percent Calories from Fat. Summary and Key Formulas. Exercises. 6. Inferences Comparing Two Population Central Values. Introduction and Abstract of a Research Study. Inferences about ��1 ��� ��2: Independent Samples. A Nonparametric Alternative: The Wilcoxon Rank Sum Test. Inferences about ��1 ��� ��2: Paired Data. A Nonparametric Alternative: The Wilcoxon Signed-Rank Test. Choosing Sample Sizes for Inferences about ��1 ��� ��2. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 7. Inferences about Population Variances. Introduction and Abstract of a Research Study. Estimation and Tests for a Population Variance. Estimation and Tests for Comparing Two Population Variances. Tests for Comparing t > 2 Population Variances. Research Study: Evaluation of Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 8. Inferences About More Than Two Population Central Values Introduction and Abstract of a Research Study. A Statistical Test About More Than Two Population Means: An Analysis of Variance. The Model for Observations in a Completely Randomized Design. Checking on the AOV Conditions. An Alternative Analysis: Transformations of the Data. A Nonparametric Alternative: The Kruskal-Wallis Test. Research Study: Effect on Timing on the Treatment of Port-Wine Stains with Lasers. Summary and Key Formulas. Exercises. 9. Multiple Comparisons. Introduction and Abstract of Research Study. Linear Contrasts. Which Error Rate Is Controlled? Fisher's Least Significant Difference. Tukey's W Procedure. Student-Neuman-Keuls Procedure. Dunnett's Procedure: Comparison of Treatments to a Control. Scheff��'s S Method. A Nonparametric Multiple-Comparison Procedure. Research Study: Are Interviewers' Decisions Affected by Different Handicap Types? Summary and Key Formulas. Exercises. 10. Categorical Data. Introduction and Abstract of Research Study. Inferences about a Population Proportion ���. Inferences about the Difference between Two Population Proportions, ���1 ��� ���2. Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test. Tests for Independence and Homogeneity. Measuring Strength of Relaxation. Odds and Odd Ratios. Combining Sets of 2 ��� 2 Contingency Tables (optional). Research Study: Does Gender Bias Exist in the Selection of Students for Vocational Education? Summary and Key Formulas. Exercises. PART 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING. 11. Linear Regression and Correlation. Introduction and Abstract of Research Study. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New y Values Using Regression. Examining Lack of Fit in Linear Regression. The Inverse Regression Problem (Calibration). Correlation. Research Study: Two Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 12. Multiple Regression and the General Linear Model. Introduction and Abstract of Research Study. The General Linear Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of Regression Coefficients. Forecasting Using Multiple Regression. Comparing the Slopes of Several Regression Lines. Logistic Regression. Some Multiple Regression Theory (Optional). Research Study: Designing an Electric Drill. Summary and Key Formulas. Exercises. 13. Further Regression Topics. Introduction and Abstract of Research Study. Selecting the Variables (Step 1). Formulating the Model (Step 2). Checking Model Assumptions (Step 3). Research Study: Construction Costs for Nuclear Power Plants. Summary and Key Formulas. Exercises. PART 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE. 14. Analysis of Variance for Completely Randomized Designs. Introduction and Abstract of Research Study. Completely Randomized Design with Single Factor. Factorial Treatment Structure. Factorial Treatment Structures with an Unequal Number of Replications. Estimation of Treatment Differences and Comparisons of Treatment Means. Determining the Number of Replications. Research Study: Development of a Low-Fat Processed Meat. Summary and Key Formulas. Exercises. 15. Analysis of Variance for Blocked Designs. Introduction and Abstract of Research Study. Randomized Complete Block Design. Latin Square Design. Factorial Treatment Structure in a Randomized Complete Block Design. A Nonparametric Alternative���Friedman's Test. Research Study: Control of Leatherjackets. Summary and Key Formulas. Exercises. 16. Analysis of Covariance. Introduction and Abstract of Research Study. A Completely Randomized Design with One Covariate. The Extrapolation Problem. Multiple Covariates and More Complicated Designs. Research Study: Evaluations of Cool-Season Grasses for Putting Greens. Summary. Exercises. 17. Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models. Introduction and Abstract of Research Study. A One-Factor Experiment with Random Treatment Effects. Extensions of Random-Effects Models. Mixed-Effects Models. Rules for Obtaining Expecting Mean Squares. Nested Factors. Research Study: Factors Affecting Pressure Drops Across Expansion Joints . Summary. Exercises. 18. Split-Plot, Repeated Measures, and Crossover Designs. Introduction and Abstract of Research Study. Split-Plot Designs. Single-Factor Experiments with Repeated Measures on One of the Factors. Two-Factor Experiments with Repeated Measures on One of the Factors. Crossover Design. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 19. Analysis of Variance for Some Unbalanced Designs. Introduction and Abstract of Research Study. A Randomized Block Design with One or More Missing Observations. A Latin Square Design with Missing Data. Balanced Incomplete Block (BIB) Designs. Research Study: Evaluation of the Consistency of Property Assessment. Summary and Key Formulas. Exercises. PART 7: COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES 20. Communicating and Documenting the Results of a Study or Experiment. Introduction. The Difficulty of Good Communication. Communication Hurdles: Graphical Distortions. Communication Hurdles: Biased Samples. Communication Hurdles: Sample Size. The Statistical Report. Documentation and Storage of Results. Summary. Exercises.

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Book SynopsisModern statistics is very different from the dry and dusty discipline of the popular imagination. In its place is an exciting subject which uses deep theory and powerful software tools to shed light and enable understanding. And it sheds this light on all aspects of our lives, enabling astronomers to explore the origins of the universe, archaeologists to investigate ancient civilisations, governments to understand how to benefit and improve society, and businesses to learn how best to provide goods and services. Aimed at readers with no prior mathematical knowledge, this Very Short Introduction explores and explains how statistics work, and how we can decipher them. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Table of ContentsPreface ; 1. Surrounded by Statistics ; 2. Simple descriptions ; 3. Collecting good data ; 4. Probability ; 5. Estimation and inference ; 6. Statistical models and methods ; 7. Statistical computing ; Further reading ; Index

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Book SynopsisMany of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality.Trade Review"Despite the fact that quite a few high-quality books on the topic of causal inference have recently been published, this book clearly fills an important gap: that of providing a simple and clear primer...Use of counterfactuals [in the final chapter] is elegantly linked to the structural causal models outlined in the previous chapters...[while]intriguing examples are used to introduce and illustrate the main concepts and methods...Several thought provoking study questions, in the form of exercises, are given throughout the presentation, and they can be very helpful for a better understanding of the material and looking further into the subtleties of the concepts introduced. In summary, there is no doubt that a discussion of the basic ideas in causal inference should be included in all introductory courses of statistics. This book could serve as a very useful companion to the lectures." (Mathematical Reviews/MathSciNet April 2017)Table of ContentsAbout the Authors ix Preface xi List of Figures xv About the Companion Website xix 1 Preliminaries: Statistical and Causal Models 1 1.1 Why Study Causation 1 1.2 Simpson’s Paradox 1 1.3 Probability and Statistics 7 1.3.1 Variables 7 1.3.2 Events 8 1.3.3 Conditional Probability 8 1.3.4 Independence 10 1.3.5 Probability Distributions 11 1.3.6 The Law of Total Probability 11 1.3.7 Using Bayes’ Rule 13 1.3.8 Expected Values 16 1.3.9 Variance and Covariance 17 1.3.10 Regression 20 1.3.11 Multiple Regression 22 1.4 Graphs 24 1.5 Structural Causal Models 26 1.5.1 Modeling Causal Assumptions 26 1.5.2 Product Decomposition 29 2 Graphical Models and Their Applications 35 2.1 Connecting Models to Data 35 2.2 Chains and Forks 35 2.3 Colliders 40 2.4 d-separation 45 2.5 Model Testing and Causal Search 48 3 The Effects of Interventions 53 3.1 Interventions 53 3.2 The Adjustment Formula 55 3.2.1 To Adjust or not to Adjust? 58 3.2.2 Multiple Interventions and the Truncated Product Rule 60 3.3 The Backdoor Criterion 61 3.4 The Front-Door Criterion 66 3.5 Conditional Interventions and Covariate-Specific Effects 70 3.6 Inverse Probability Weighing 72 3.7 Mediation 75 3.8 Causal Inference in Linear Systems 78 3.8.1 Structural versus Regression Coefficients 80 3.8.2 The Causal Interpretation of Structural Coefficients 81 3.8.3 Identifying Structural Coefficients and Causal Effect 83 3.8.4 Mediation in Linear Systems 87 4 Counterfactuals and Their Applications 89 4.1 Counterfactuals 89 4.2 Defining and Computing Counterfactuals 91 4.2.1 The Structural Interpretation of Counterfactuals 91 4.2.2 The Fundamental Law of Counterfactuals 93 4.2.3 From Population Data to Individual Behavior – An Illustration 94 4.2.4 The Three Steps in Computing Counterfactuals 96 4.3 Nondeterministic Counterfactuals 98 4.3.1 Probabilities of Counterfactuals 98 4.3.2 The Graphical Representation of Counterfactuals 101 4.3.3 Counterfactuals in Experimental Settings 103 4.3.4 Counterfactuals in Linear Models 106 4.4 Practical Uses of Counterfactuals 107 4.4.1 Recruitment to a Program 107 4.4.2 Additive Interventions 109 4.4.3 Personal Decision Making 111 4.4.4 Sex Discrimination in Hiring 113 4.4.5 Mediation and Path-disabling Interventions 114 4.5 Mathematical Tool Kits for Attribution and Mediation 116 4.5.1 A Tool Kit for Attribution and Probabilities of Causation 116 4.5.2 A Tool Kit for Mediation 120 References 127 Index 133

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Book SynopsisGamblers have been trying to figure out how to game the system since our ancestors first made wagers over dice fashioned from knucklebones: in revolutionary Paris, the 'martingale' strategy was rumoured to lead to foolproof success at roulette ; today, professional gamblers are using cutting-edge techniques to tilt the odds in their favour. Science is giving us the competitive edge over opponents, casinos and bookmakers. But is there such a thing as a perfect bet? The Perfect Bet looks beyond probability and statistics to examine how wagers have inspired a plethora of new disciplines - spanning chaos theory, machine learning and game theory - which are not just revolutionising gambling, but changing our fundamental notions about chance, randomness and luck. Explaining why poker is gaming's last bastion of human superiority over AI, how methods originally developed for the US nuclear programme are helping pundits predict sports results and why a new breed of algorithms are losing banks millions, The Perfect Bet has the inside track on any wager you'd care to place.Trade ReviewThis book is full of magic. It's brimming with clever people and clever ideas... The links between betting and science run deep and wide, allowing Kucharski to cover some thrilling intellectual territory. * New Scientist *Terrific: beautifully written, solidly researched and full of surprises * New York Times Numberplay blog *Elegant and amusing ... anyone planning to enter a casino or place an online bet would be advised to keep this book handy * Wall Street Journal *Kucharski's clear prose and eye for an entertaining historical anecdote give his book an accessible feel ... an enjoyable account. * Racing Post *[An] enjoyable... paean to human ingenuity, and a Robin Hood tale of wealth redistribution. * Daily Telegraph *Great stories of how smart people have used maths, statistics and science to try and beat the odds - legally' -- David Spiegelhalter, Winton Professor for the Public Understanding of Risk, University of CambridgeA wild ride through the history, psychology, mathematics, and technology of gaming - a remarkable look behind the curtain of what most people think is intuitive, but isn't -- Paul Offit, author of Bad FaithWith an entertaining writing style, Adam Kucharski guides us through the history and state of the art of "The Perfect Bet," showing us how mathematics and computers are used to come up with optimal ways to gamble, play games, bluff, and invest our money. Extremely well-written and carefully researched. I highly recommend it. -- Arthur Benjamin, Author of 'The Magic of Maths'A lucid yet sophisticated look at the mathematics of probability as it's played out on gaming tables, arenas, and fields... Gamblers and math buffs alike will enjoy it for its smart approach to real-world problems * Kirkus Reviews *

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Book SynopsisA Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.New to the Third Edition 40% more content with more explanation and examples throughout New chapter on sampling featuring simulation-based methods Model assessment methods discussed Explanation chapter expanded to include introductory ideas about causation Model interpretation in the presence of transformation Crossvalidation for model s

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Book SynopsisThis book provides in-depth coverage of Probability & Statistics 1 for Cambridge International AS and A Level Mathematics 9709, for examination from 2020 onwards. With a clear focus on mathematics in life and work, this text builds the key mathematical skills and knowledge that will open up a wide range of careers and further study.Exam Board: Cambridge Assessment International EducationFirst teaching: 2018 First examination: 2020This student book is part of a series of nine books covering the complete syllabus for Cambridge International AS and A Level Mathematics (9709) and Further Mathematics (9231), for first teaching from September 2018 and examination from 2020. This title is endorsed by Cambridge Assessment International Education.Written by expert authors, this Student Book: covers the complete content of Probability & Statistics 1 with clear references to what you will learn at the start of each chapter, and coverage that clearly and directly matches the Cambridge syllabus set

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Book SynopsisThe book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decision­making problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics. Table of Contents1. A New Version of the Generalized Rayleigh Distribution with Copula, Properties, Applications and Different Methods of Estimation 2. Expanding the Burr X Model: Properties, Copula, Real Data Modeling and Different Methods of Estimation 3. Transmuted Burr Type X Model with Applications to Life Time Data 4. Monitoring Patients Blood Level through Enhanced Control Chart 5. Goodness of Fit in Parametric and Non-parametric Econometric Models 6. Stochastic Models for Cancer Progression and its Optimal Programming for Control with Chemotherapy 7. A New Unrelated Question Model with Two Questions Per Card 8. Hybrid of Simple Model and a New Unrelated Question Model for Two Sensitive Characteristics 9. Hybrid of Crossed Model and a New Unrelated Question Model for Two Sensitive Characteristics 10. Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling 11. Ratio and Regression Type Estimators for a New Measure of Coefficient of Dispersion Relative to the Empirical Mode 12. Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling 13. An Inventory Model for Substitutable Deteriorating Products under Fuzzy and Cloud Fuzzy Demand Rate 14. Co-ordinated Selling Price and Replenishment Policies for Duopoly Retailers under Quadratic Demand and Deteriorating Nature of Items15. Quadratic Programming Approach for the Optimal Multi-objective Transportation Problem 16. Analyzing Multi-Objective Fixed-Charge Solid Transportation Problem under Rough and Fuzzy-Rough Environments 17. Overall Shale Gas Water Management: A Neutrosophic Optimization Approach 18. Memory Effect on an EOQ Model with Price Dependant Demand and Deterioration 19. Optimality Conditions of an Unconstrained Imprecise Optimization Problem via Interval Order Relation 20. Power Comparison of Different Goodness of Fit Tests for Beta Generalized Weibull Distribution 21. On the Transmuted Modified Lindley Distribution: Theory and Applications to Lifetime Data 22. Adjusted Bias and Risk for Estimating Treatment Effect after Selection with an Application in Idiopathic Osteoporosis 23. Validity Judgement of an EOQ Model using Phi-coefficient 24. Uncertain Chance-Constrained Multi-Objective Geometric Programming Problem 25. Optimal Decision Making for the Prediction of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients

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Book SynopsisThis book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysTable of Contents1. Data, Stats and Stories - An Introduction 2. Python Programming Primer 3. Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & Pandas 4. The Measure of All Things - Statistics 5. Definitely Maybe: Probability and Distributions 6. Alluring Arguments and Ugly Facts - Statistical Modelling and Hypothesis Testing 7. Delightful Details - Data Visualisation 8. Dazzling Data Designs - Creating Charts A. Variance: Population v Sample B. Sum of First n Integers C. Sum of Squares of the First n Integers D. The Binomial Coefficient E. The Hypergeometric Distribution F. The Poisson Distribution G. The Normal Distribution H. Skewness and Kurtosis I. Kruskal-Wallis Test - No Ties

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Book SynopsisIn their bestselling title MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps you discover the nature of statistics and understand its essential role in scientific research.With the addition of contributor Brendan Ames, MATHEMATICAL STATISTICS WITH APPLICATIONS now includes an enhanced eTextbook. Simulation activities using interactive applets and R embedded within the MindTap Reader help students visualize statistical concepts, and an appendix introducing students to statistical data analysis using R can be found at the end of the eTextbook.Table of Contents1. What Is Statistics? Introduction. Characterizing a Set of Measurements: Graphical Methods. Characterizing a Set of Measurements: Numerical Methods. How Inferences Are Made. Theory and Reality. Summary. 2. Probability. Introduction. Probability and Inference. A Review of Set Notation. A Probabilistic Model for an Experiment: The Discrete Case. Calculating the Probability of an Event: The Sample-Point Method. Tools for Counting Sample Points. Conditional Probability and the Independence of Events. Two Laws of Probability. Calculating the Probability of an Event: The Event-Composition Methods. The Law of Total Probability and Bayes''''s Rule. Numerical Events and Random Variables. Random Sampling. Summary. 3. Discrete Random Variables and Their Probability Distributions. Basic Definition. The Probability Distribution for Discrete Random Variable. The Expected Value of Random Variable or a Function of Random Variable. The Binomial Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution (Optional). The Hypergeometric Probability Distribution. Moments and Moment-Generating Functions. Probability-Generating Functions (Optional). Tchebysheff''''s Theorem. Summary. 4. Continuous Random Variables and Their Probability Distributions. Introduction. The Probability Distribution for Continuous Random Variable. The Expected Value for Continuous Random Variable. The Uniform Probability Distribution. The Normal Probability Distribution. The Gamma Probability Distribution. The Beta Probability Distribution. Some General Comments. Other Expected Values. Tchebysheff''''s Theorem. Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional). Summary. 5. Multivariate Probability Distributions. Introduction. Bivariate and Multivariate Probability Distributions. Independent Random Variables. The Expected Value of a Function of Random Variables. Special Theorems. The Covariance of Two Random Variables. The Expected Value and Variance of Linear Functions of Random Variables. The Multinomial Probability Distribution. The Bivariate Normal Distribution (Optional). Conditional Expectations. Summary. 6. Functions of Random Variables. Introductions. Finding the Probability Distribution of a Function of Random Variables. The Method of Distribution Functions. The Methods of Transformations. Multivariable Transformations Using Jacobians. Order Statistics. Summary. 7. Sampling Distributions and the Central Limit Theorem. Introduction. Sampling Distributions Related to the Normal Distribution. The Central Limit Theorem. A Proof of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distributions. Summary. 8. Estimation. Introduction. The Bias and Mean Square Error of Point Estimators. Some Common Unbiased Point Estimators. Evaluating the Goodness of Point Estimator. Confidence Intervals. Large-Sample Confidence Intervals Selecting the Sample Size. Small-Sample Confidence Intervals for u and u1-u2. Confidence Intervals for o2. Summary. 9. Properties of Point Estimators and Methods of Estimation. Introduction. Relative Efficiency. Consistency. Sufficiency. The Rao-Blackwell Theorem and Minimum-Variance Unbiased Estimation. The Method of Moments. The Method of Maximum Likelihood. Some Large-Sample Properties of MLEs (Optional). Summary. 10. Hypothesis Testing. Introduction. Elements of a Statistical Test. Common Large-Sample Tests. Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test. Relationships Between Hypothesis Testing Procedures and Confidence Intervals. Another Way to Report the Results of a Statistical Test: Attained Significance Levels or p-Values. Some Comments on the Theory of Hypothesis Testing. Small-Sample Hypothesis Testing for u and u1-u2. Testing Hypotheses Concerning Variances. Power of Test and the Neyman-Pearson Lemma. Likelihood Ration Test. Summary. 11. Linear Models and Estimation by Least Squares. Introduction. Linear Statistical Models. The Method of Least Squares. Properties of the Least Squares Estimators for the Simple Linear Regression Model. Inference Concerning the Parameters BI. Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression. Predicting a Particular Value of Y Using Simple Linear Regression. Correlation. Some Practical Examples. Fitting the Linear Model by Using Matrices. Properties of the Least Squares Estimators for the Multiple Linear Regression Model. Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression. Prediction a Particular Value of Y Using Multiple Regression. A Test for H0: Bg+1 + Bg+2 = . = Bk = 0. Summary and Concluding Remarks. 12. Considerations in Designing Experiments. The Elements Affecting the Information in a Sample. Designing Experiment to Increase Accuracy. The Matched Pairs Experiment. Some Elementary Experimental Designs. Summary. 13. The Analysis of Variance. Introduction. The Analysis of Variance Procedure. Comparison of More than Two Means: Analysis of Variance for a One-way Layout. An Analysis of Variance Table for a One-Way Layout. A Statistical Model of the One-Way Layout. Proof of Additivity of the Sums of Squares and E (MST) for a One-Way Layout (Optional). Estimation in the One-Way Layout. A Statistical Model for the Randomized Block Design. The Analysis of Variance for a Randomized Block Design. Estimation in the Randomized Block Design. Selecting the Sample Size. Simultaneous Confidence Intervals for More than One Parameter. Analysis of Variance Using Linear Models. Summary. 14. Analysis of Categorical Data. A Description of the Experiment. The Chi-Square Test. A Test of Hypothesis Concerning Specified Cell Probabilities: A Goodness-of-Fit Test. Contingency Tables. r x c Tables with Fixed Row or Column Totals. Other Applications. Summary and Concluding Remarks. 15. Nonparametric Statistics. Introduction. A General Two-Sampling Shift Model. A Sign Test for a Matched Pairs Experiment. The Wilcoxon Signed-Rank Test for a Matched Pairs Experiment. The Use of Ranks for Comparing Two Population Distributions: Independent Random Samples. The Mann-Whitney U Test: Independent Random Samples. The Kruskal-Wallis Test for One-Way Layout. The Friedman Test for Randomized Block Designs. The Runs Test: A Test for Randomness. Rank Correlation Coefficient. Some General Comments on Nonparametric Statistical Test. 16. Introduction to Bayesian Methods for Inference. Introduction. Bayesian Priors, Posteriors and Estimators. Bayesian Credible Intervals. Bayesian Tests of Hypotheses. Summary and Additional Comments. Appendix 1. Matrices and Other Useful Mathematical Results. Matrices and Matrix Algebra. Addition of Matrices. Multiplication of a Matrix by a Real Number. Matrix Multiplication. Identity Elements. The Inverse of a Matrix. The Transpose of a Matrix. A Matrix Expression for a System of Simultaneous Linear Equations. Inverting a Matrix. Solving a System of Simultaneous Linear Equations. Other Useful Mathematical Results. Appendix 2. Common Probability Distributions, Means, Variances, and Moment-Generating Functions. Discrete Distributions. Continuous Distributions. Appendix 3. Tables. Binomial Probabilities. Table of e-x. Poisson Probabilities. Normal Curve Areas. Percentage Points of the t Distributions. Percentage Points of the F Distributions. Distribution of Function U. Critical Values of T in the Wilcoxon Matched-Pairs, Signed-Ranks Test. Distribution of the Total Number of Runs R in Sample Size (n1,n2); P(R < a). Critical Values of Pearman''s Rank Correlation Coefficient. Random Numbers. Answer to Exercises. Index. R Appendix. Students are introduced to statistical data analysis and shown how to use R to conduct all of the major statistical procedures from the textbook.

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Book SynopsisAstronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulations, and now also includes Bayesian inference, Bayes factors and Markov chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables, solutions to problems, and other resources are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced undTrade Review"Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced undergraduate and graduate students and professional astronomers." -Mathematical ReviewsTable of Contents1. Decision; 2. Probability; 3. Statistics and expectations; 4. Correlation and association; 5. Hypothesis-testing; 6. Data modelling and parameter-estimation: basics; 7. Data modelling and parameter-estimation: advanced topics; 8. Detection and surveys; 9. Sequential data - 1D statistics; 10. Statistics of large-scale structure; 11. Epilogue: statistics and our Universe; Appendices; References; Index.

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Book SynopsisIntroduction to Stochastic Finance with Market Examples, Second Edition presents an introduction to pricing and hedging in discrete and continuous-time financial models, emphasizing both analytical and probabilistic methods. It demonstrates both the power and limitations of mathematical models in finance, covering the basics of stochastic calculus for finance, and details the techniques required to model the time evolution of risky assets. The book discusses a wide range of classical topics including BlackScholes pricing, American options, derivatives, term structure modeling, and change of numéraire. It also builds up to special topics, such as exotic options, stochastic volatility, and jump processes.New to this Edition New chapters on Barrier Options, Lookback Options, Asian Options, Optimal Stopping Theorem, and Stochastic Volatility Contains over 235 exercises and 16 problems with complete solutions available online from the iTable of ContentsIntroduction. 1. Assets, Portfolios, and Arbitrage. 1.1. Portfolio Allocation and Short Selling. 1.2. Arbitrage. 1.3. Risk-Neutral Probability Measures. 1.4. Hedging of Contingent Claims. 1.5. Market Completeness. 1.6. Example: Binary Market. Exercises. 2. Discrete-Time Market Model. 2.1. Discrete-Time Compounding. 2.2. Arbitrage and Self-Financing Portfolios. 2.3. Contingent Claims. 2.4. Martingales and Conditional Expectations. 2.5. Market Completeness and Risk-Neutral Measures. 2.6. The Cox-Ross-Rubinstein (CRR) Market Model. Exercises. 3. Pricing and Hedging in Discrete Time. 3.1. Pricing Contingent Claims. 3.2. Pricing Vanilla Options in the CRR Model. 3.3. Hedging Contingent Claims. 3.4. Hedging Vanilla Options. 3.5. Hedging Exotic Options. 3.6. Convergence of the CRR Model. Exercises. 4. Brownian Motion and Stochastic Calculus. 4.1. Brownian Motion. 4.2. Three Constructions of Brownian Motion. 4.3. Wiener Stochastic Integral. 4.4. Itô Stochastic Integral. 4.5. Stochastic Calculus. Exercises. 5. Continuous-Time Market Model. 5.1. Asset Price Modeling. 5.2. Arbitrage and Risk-Neutral Measures. 5.3. Self-Financing Portfolio Strategies. 5.4. Two-Asset Portfolio Model. 5.5. Geometric Brownian Motion. Exercises. 6. Black-Scholes Pricing and Hedging. 6.1. The Black-Scholes PDE. 6.2. European Call Options. 6.3. European Put Options. 6.4. Market Terms and Data. 6.5. The Heat Equation. 6.6. Solution of the Black-Scholes PDE. Exercises. 7. Martingale Approach to Pricing and Hedging. 7.1. Martingale Property of the Itô Integral. 7.2. Risk-neutral Probability Measures. 7.3. Change of Measure and the Girsanov Theorem. 7.4. Pricing by the Martingale Method. 7.5. Hedging by the Martingale Method. Exercises. 8. Stochastic Volatility. 8.1. Stochastic Volatility Models. 8.2. Realized Variance Swaps. 8.3. Realized Variance Options. 8.4. European Options - PDE Method. 8.5. Perturbation Analysis. Exercises. 9. Volatility Estimation. 9.1. Historical Volatility. 9.2. Implied Volatility. 9.3. Local Volatility. 9.4. The VIX® Index. Exercises. 10. Maximum of Brownian motion. 10.1. Running Maximum of Brownian Motion. 10.2. The Reflection Principle. 10.3. Density of the Maximum of Brownian Motion. 10.4. Average of Geometric Brownian Extrema. Exercises. 11. Barrier Options. 11.1. Options on Extrema. 11.2. Knock-Out Barrier. 11.3. Knock-In Barrier. 11.4. PDE Method. 11.5. Hedging Barrier Options. Exercises. 12. Lookback Options. 12.1. The Lookback Put Option. 12.2. PDE Method. 12.3. The Lookback Call Option. 12.4. Delta Hedging for Lookback Options. Exercises. 13. Asian Options. 13.1. Bounds on Asian Option Prices. 13.2. Hartman-Watson Distribution. 13.3. Laplace Transform Method. 13.4. Moment Matching Approximations. 13.5. PDE Method. Exercises. 14. Optimal Stopping Theorem. 14.1. Filtrations and Information Flow. 14.2. Submartingales and Supermartingales. 14.3. Optimal Stopping Theorem. 14.4. Drifted Brownian Motion. Exercises. 15. American Options. 15.1. Perpetual American Put Options. 15.2. PDE Method for Perpetual Put Options. 15.3. Perpetual American Call Options. 15.4. Finite Expiration American Options. 15.5. PDE Method with Finite Expiration. Exercises. 16. Change of Numéraire and Forward Measures. 16.1. Notion of Numéraire. 16.2. Change of Numéraire. 16.3. Foreign Exchange. 16.4. Pricing Exchange Options. 16.5. Hedging by Change of Numéraire. Exercises. 17. Short Rates and Bond Pricing. 17.1. Vasicek model. 17.2. Affine Short Rate Models. 17.3. Zero-Coupon and Coupon Bonds. 17.4. Bond Pricing PDE. Exercises. 18. Forward Rates. 18.1. Construction of Forward Rates. 18.2. LIBOR/SOFR Swap Rates. 18.3. The HJM Model. 18.4. Yield Curve Modeling. 18.5. Two-Factor Model. 18.6. The BGM Model. Exercises. 19. Pricing of Interest Rate Derivatives. 19.1. Forward Measures and Tenor Structure. 19.2. Bond Options. 19.3. Caplet Pricing. 19.4. Forward Swap Measures. 19.5. Swaption Pricing. Exercises. 20. Stochastic Calculus for Jump Processes. 20.1. The Poisson Process. 20.2. Compound Poisson Process. 20.3. Stochastic Integrals and Itô Formula with Jumps. 20.4. Stochastic Differential Equations with Jumps. 20.5. Girsanov Theorem for Jump Processes. Exercises. 21. Pricing and Hedging in Jump Models. 21.1. Fitting the Distribution of Market Returns. 21.2. Risk-Neutral Probability Measures. 21.3. Pricing in Jump Models. 21.4. Exponential Lévy Models. 21.5. Black-Scholes PDE with Jumps. 21.6. Mean-Variance Hedging with Jumps. Exercises. 22. Basic Numerical Methods. 22.1. Discretized Heat Equation. 22.2. Discretized Black-Scholes PDE. 22.3. Euler Discretization. 22.4. Milshtein Discretization. Exercises. Bibliography. Index

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis practical book walks intermediate to advanced Tableau users through ways that statistics can help you incorporate decision science into the visualizations you create. Data analysts, business analysts, and business intelligence specialists will greatly benefit from this book.

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Book SynopsisA complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python soTrade Review'Introductory Econometrics for Finance covers a variety of financial applications and illustrates how econometrics methods can be used for each topic. Researchers and practitioners in finance will find this book invaluable. The new fourth edition is expanded with important topics of state space models and extreme value theory. Moreover, a free companion website with various software programs is essential for performing actual empirical analysis. I constantly recommend this text to Masters and undergraduate finance students.' Elena Goldman, Pace University, New York'This is a good book introducing the general field of financial econometrics to students, assuming they have no prior knowledge of econometrics. Undergraduate, as well as beginning graduate, students should find the wide range of topics covered useful for not only getting a good toehold into the literature, but also to be able to apply the methods to data right away.' Prasad V. Bidarkota, Florida International University'Professor Brooks' book provides extraordinarily comprehensive treatment of econometric techniques with application to Finance. The unique feature of this book is the presentation of rich real-world case study examples. This is an ideal text book for MS in Finance, MBA with concentration in Finance and Seniors majoring in Finance. It is also an ideal text book for financial professional training and self-study.' George H. K. Wang, George Mason University, Virginia'Chris Brooks' book is a rather unique offering in the space of financial econometrics because it is specifically targeted to finance students who do not necessarily have prior knowledge of econometric techniques. It's a first yet comprehensive resource to enable students to familiarize with concepts and tackle a broad range of empirical applications.' Walter Distaso, Imperial College London'This new edition of Introductory Econometrics for Finance manages to give even further strength to its exhaustive, fine blend of contents and delivery, of methods and of interesting, relevant applications. This classical but always lively written textbook manages to make modern econometric approaches accessible to a wide audience of senior undergraduates and of graduate students first approaching econometrics, and at the same time leads a more experienced reader to ponder the power of statistics through a number of detailed case studies. The additional, advanced material on the Kalman filter and extreme value theory makes this textbook an invaluable classroom tool for a first approach to financial econometrics.' Massimo Guidolin, Università Commerciale Luigi Bocconi, Milan'This is one of the most readable books on financial econometrics. It will be very useful for students of finance and economics. It covers a wide variety of topics that are of interest to researchers and practitioners, in both academia and industry.' Yong Bao, Purdue University, IndianaTable of ContentsPreface to the fourth edition; 1. Introduction and mathematical foundations; 2. Statistical foundations and dealing with data; 3. A brief overview of the classical linear regression; 4. Further development of classical linear regression; 5. Classical linear regression model assumptions; 6. Univariate time-series modelling and forecasting; 7. Multivariate models; 8. Modelling volatility and correlation; 10. Switching and state space models; 11. Panel data; 12. Limited dependent variable models; 13. Simulation methods; 14. Additional econometric techniques for financial research; 15. Conducting empirical research; Appendix 1. Sources of data used in this book and the accompanying software manuals; Appendix 2. Tables of statistical distributions; Glossary; References; Index.

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Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the OCR AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the Statistics content for AS and A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

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Book SynopsisHighly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems.New to the Third Edition New chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines Many more exercises and examples using both Stata and SAS More than 60 new figures After introducing study design and reviewTrade Review"This text, like its predecessors, hits the mark. … The author writes extremely well and the text is resplendent with exercises. It would be a crime if Epidemiology: Study Design and Data Analysis were never used as a text! … I wish a text like this had been available for my coursework. Enhancing its value as a text, it will be extremely useful as a reference book for its intended audience—researchers and applied statisticians. … the only excuse for an epidemiologist or applied statistician not to have it on his or her bookshelf is that he or she has not seen or heard of it. Make this book your next purchase!"—Gregory E. Gilbert, The American Statistician, November 2014Praise for Previous Editions:"As a text in quantitative epidemiology, this book also works nicely as a text in biostatistics…The presentation style is relaxed, the examples are helpful, and the level of technical difficulty makes the material approachable without oversimplification…It is sufficiently broad and deep in coverage to compete with standard texts in the field and has the added bonus of emphasizing study design. Methods and issues related to designs commonly used in a wide variety of health sciences are included…"-Ken Hess, Department of Biomathematics and Biostatistics, Anderson Cancer Center"The second edition of this epidemiology text is strengthened to cater to the two audiences the author has in mind: applied statisticians wishing to learn how their statistical expertise can be used in the epidemiology field and statistic-curious researchers who want to understand how statistical techniques can be used to solve epidemiological problems. …The result is a book that will invariably appeal to the intended audience, one with practical applications of techniques and interpretations of results in an epidemiological context. …The book is most certainly an ambitious attempt at covering a broad array of the most important epidemiologic study designs and analytical methods. This is further enforced by the addition of the meta-analysis chapter. …This book will be valuable to statisticians in applying their discipline to epidemiology. Mark Woodward's excellent second edition will effectively serve post-graduate or advanced undergraduate students studying epidemiology, as well as statisticians or researchers who are regularly confronted with epidemiological questions."-Journal of the American Statistical Association"This book provides very good coverage of major issues in the design of epidemiological studies, and a decent, but very quick, tour of commonly used statistical models for such studies."-Short Book Reviews Publication of the International Statistical Institute, K.S. Brown, University of Waterloo, Canada"Amazingly, Woodward manages to describe quite sophisticated models and analysis with nothing more complicated than summation signs. …I highly recommend it."-Statistics in Medicine, 2006"The second edition of this concisely written book covers all statistical methods being of relevance for the planning and analysis of epidemiological studies where the author avoids unnecessary mathematical details for the sake of comprehensibility. The presented statistical principles are always carefully discussed in the context of epidemiological concepts, for instance depending on the different study designs. Detailed practical examples coming from real studies as far as possible illustrate their application. …The book can be highly recommended to researchers in epidemiology who want to understand better the statistical principles being typically applied in this field and to statisticians who want to understand more about statistics in epidemiology, but also to graduate students in epidemiology, public health, medical research and statistics."-Biometrics, Sept. 2005"I think anyone with an interest in both biostatistics and epidemiology will want a copy this book on their bookshelf … it is a first-rate reference book." "I find Professor Woodward's text the most complete and practical introduction to the design and analysis of epidemiological studies I've encountered… an excellent text for either a course introducing epidemiologists to statistical thought and methods or a course introducing statisticians to epidemiological thought and methods… students appreciate having a readable textbook replete with understandable examples and worked exercises…offers a complete introduction to statistical and epidemiological methods in the study of disease in human populations. All of the standard topics are included, and the second edition even has a chapter on meta-analysis. …This book can be used as a text to introduce epidemiological methods to graduate students in statistics who have no background in epidemiology, or vice versa…Professor Woodward is to be congratulated on a job well done."-Dan McGee, Dept of Statistics, Florida State UniversityTable of ContentsFundamental Issues. Basic Analytical Procedures. Assessing Risk Factors. Confounding and Interaction. Cohort Studies. Case-Control Studies. Intervention Studies. Sample Size Determination. Modeling Quantitative Outcome Data. Modeling Binary Outcome Data. Modeling Follow-Up Data. Meta-Analysis. Risk Scores and Clinical Decision Rules. Computer-Intensive Methods. Appendices. Index.

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Book SynopsisDo you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures.Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get to grips with. This new edition has been fully updated.Whether you want to understand the statistics that you are bombarded with every day or are a student or professional coming to statistics from a wide range of disciplines, Statistics: An Introduction covers it all.

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Book SynopsisThis affordable student study guide and workbook to accompany Wendy J. Steinberg and Matthew Price’s Statistics Alive!, Third Edition, helps students get the added review and practice they need to improve their skills and master their Introduction to Statistics course. Bundle and SAVE! Student Study Guide to Accompany Statistics Alive!, Third Edition + Main Text ISBN: 978-1-0718-3088-8Table of ContentsModule 1. Math Review, Vocabulary, and Symbols Module 2. Measurement Scales Module 3. Frequency and Percentile Tables Module 4. Graphs and Plots Module 5. Mode, Median, and Mean Module 6. Range, Variance, and Standard Deviation Module 7. Percent Area and the Normal Curve Module 8. z Scores Module 9. Score Transformations and Their Effects Module 10. Probability Definitions and Theorems Module 11. The Binomial Distribution Module 12. Sampling, Variables, and Hypotheses Module 13. Errors and Significance Module 14. The z Score as a Hypothesis Test Module 15. Standard Error of the Mean Module 16. Normal Deviate Z Test Module 17. One-Sample t Test Module 18. Interpreting and Reporting One-Sample t: Error, Confidence, and Parameter Estimates Module 19. Standard Error of the Difference Between the Means Module 20. t Test With Independent Samples and Equal Sample Sizes Module 21. t Test With Unequal Sample Sizes Module 22. t Test With Related Samples Module 23. Interpreting and Reporting Two-Sample t: Error, Confidence, and Parameter Estimates Module 24. ANOVA Logic: Sums of Squares, Partitioning, and Mean Squares Module 25. One-Way ANOVA: Independent Samples and Equal Sample Sizes Module 26. Tukey HSD Test Module 27. Scheffé Test Module 28. Main Effects and Interaction Effects Module 29. Factorial ANOVA Module 30. One-Variable Chi-Square: Goodness of Fit Module 31. Two-Variable Chi-Square: Test of Independence Module 32. Measures of Effect Size Module 33. Power and the Factors Affecting It Module 34. Relationship Strength and Direction Module 35. Pearson r Module 36. Correlation Pitfalls Module 37. Linear Prediction Module 38. Standard Error of Prediction Module 39. Introduction to Multiple Regression Module 40. Selecting the Appropriate Analysis

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Book SynopsisWhether you are new to Stata graphics or a seasoned veteran, this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 1,200 illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any type of statistical data. Each graph is displayed in full color with simple and clear instructions that illustrate how to create and customize graphs using Stata commands. Whether you use this book as a learning tool or a quick reference, you will have the power of Stata graphics at your fingertips.Table of Contents1. Introduction 2. Twoway graphs 3. Scatterplot matrix graphs 4. Bar graphs 5. Box plots 6. Dot plots 7. Pie charts 8. Options available for most graphs 9. Standard options available for all graphs 10. Styles for changing the look of graphs 11. Appendix

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Book SynopsisUncertainty is everywhere. It lurks in every consideration of the future - the weather, the economy, the sex of an unborn child - even quantities we think that we know such as populations or the transit of the planets contain the possibility of error. It's no wonder that, throughout that history, we have attempted to produce rigidly defined areas of uncertainty - we prefer the surprise party to the surprise asteroid. We began our quest to make certain an uncertain world by reading omens in livers, tea leaves, and the stars. However, over the centuries, driven by curiosity, competition, and a desire be better gamblers, pioneering mathematicians and scientists began to reduce wild uncertainties to tame distributions of probability and statistical inferences. But, even as unknown unknowns became known unknowns, our pessimism made us believe that some problems were unsolvable and our intuition misled us. Worse, as we realized how omnipresent and varied uncertainty is, we encountered chaos, quantum mechanics, and the limitations of our predictive power. Bestselling author Professor Ian Stewart explores the history and mathematics of uncertainty. Touching on gambling, probability, statistics, financial and weather forecasts, censuses, medical studies, chaos, quantum physics, and climate, he makes one thing clear: a reasonable probability is the only certainty.Trade ReviewIntriguing ... a challenging but rewarding trip through a quantum world of uncertainties. * Publisher's Weekly *Praise for Ian Stewart: Stewart is Britain's most brilliant and prolific populariser of maths -- Alex BelosThis is not pure maths. It is maths contaminated with wit, wisdom, and wonder ... He guides us on a mind-boggling journey from the ultra trivial to the profound. Thoroughly entertaining * New Scientist *Humbling and inspiring. Stewart shows with his typical clarity how the power of pure thought has shaped our world for over two millennia. -- Jim Al-Khalili, FRSThis is a superb Cabinet of Mathematical Curiosities that deserves a place with the classics of the genre * Mathematics Today *'With captivating stories and his signature clarity, Ian Stewart shows us how math makes the world - and the rest of the universe - go round. -- Steven Strogatz, Professor of Mathematics, Cornell University, and author of The Joy of XStewart has served up the instructive equivalent of a Michelin-starred tasting menu, or perhaps a smorgasbord of appetisers. And of course, appetisers are designed to give you an appetite for more. -- Tim Radford * Guardian *

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Book SynopsisThis book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).Table of Contents1. Getting started.2. Managing your data.3. Data visualization.4. Spatiotemporal data visualization and basic crime analysis.5. Descriptive statistics: measures of central tendency.6. Descriptive statistics: measures of dispersion.7. Statistical inference in criminal justice research.8. Defining the observed significance level of a test.9. Hypothesis testing using the binomial distribution.10. Chi-square: a test commonly used for nominal-level measures.11. The normal distribution and its application to tests of statistical significance.12. Comparing means in two samples.13. Analysis of variance.14. Measures of association for nominal and ordinal variables.15. Measuring association for interval data.16. Introduction to regression analysis.

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Book SynopsisIntroduction to Statistics.- Types of Data.- Data Collection Methods (Sampling Theory).- Measures of Central Tendency.- Measures of Dispersion.- Measures of Positions.- Outliers.- Introduction to Distributions.- Skewness, Kurtosis and Modality.- Data Visualisation.- Confidence Intervals.- Hypothesis Testing.- Correlation and Linear Regression.- Statistical Project - Steps and Process.- Appendix A - Partioning of the Ordinary Least Square Variance.- Appendix B - Big-O and Little-o Notation.

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Book SynopsisThis book offers a rigorous and self-contained presentation of stochastic integration and stochastic calculus within the general framework of continuous semimartingales. The main tools of stochastic calculus, including Itô’s formula, the optional stopping theorem and Girsanov’s theorem, are treated in detail alongside many illustrative examples. The book also contains an introduction to Markov processes, with applications to solutions of stochastic differential equations and to connections between Brownian motion and partial differential equations. The theory of local times of semimartingales is discussed in the last chapter.Since its invention by Itô, stochastic calculus has proven to be one of the most important techniques of modern probability theory, and has been used in the most recent theoretical advances as well as in applications to other fields such as mathematical finance. Brownian Motion, Martingales, and Stochastic Calculus provides a strong theoretical background to the reader interested in such developments.Beginning graduate or advanced undergraduate students will benefit from this detailed approach to an essential area of probability theory. The emphasis is on concise and efficient presentation, without any concession to mathematical rigor. The material has been taught by the author for several years in graduate courses at two of the most prestigious French universities. The fact that proofs are given with full details makes the book particularly suitable for self-study. The numerous exercises help the reader to get acquainted with the tools of stochastic calculus.Trade Review“‘The aim of this book is to provide a rigorous introduction to the theory of stochastic calculus for continuous semi-martingales putting a special emphasis on Brownian motion.’ … If the reader has the background and needs a rigorous treatment of the subject this book would be a good choice. Le Gall writes clearly and gets to the point quickly … .” (Richard Durrett, MAA Reviews, March, 2017) “The purpose of this book is to provide concise but rigorous introduction to the theory of stochastic calculus for continuous semimartingales, putting a special emphasis on Brownian motion. … The book is written very clearly, it is interesting both for its construction and maintenance, mostly it is self-contained. It can be recommended to everybody who wants to study stochastic calculus, including those who is interested to its applications in other fields.” (Yuliya S. Mishura, zbMATH, 2017)Table of ContentsGaussian variables and Gaussian processes.- Brownian motion.- Filtrations and martingales.- Continuous semimartingales.- Stochastic integration.- General theory of Markov processes.- Brownian motion and partial differential equations.- Stochastic differential equations.- Local times.- The monotone class lemma.- Discrete martingales.- References.

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Book SynopsisThe second edition represents an ongoing effort to make probability accessible to students in a wide range of fields such as mathematics, statistics and data science, engineering, computer science, and business analytics. The book is written for those learning about probability for the first time. Revised and updated, the book is aimed specifically at statistics and data science students who need a solid introduction to the basics of probability.While retaining its focus on basic probability, including Bayesian probability and the interface between probability and computer simulation, this edition's significant revisions are as follows:The approach followed in the book is to develop probabilistic intuition before diving into details. The best way to learn probability is by practising on a lot of problems. Many instructive problems together with problem-solving strategies are given. Answers to all problems and worked-out solutions to selected problems are also provided.Henk Tijms is the author of several textbooks in the area of applied probability. In 2008, he had received the prestigious INFORMS Expository Writing Award for his work. He is active in popularizing probability at Dutch high schools.Table of ContentsCombinatorics and Calculus for Probability; Basics of Probability; Useful Probability Distributions; Real-Life Examples of Poisson Probabilities; Monte Carlo Simulation and Probability; A Primer on Markov Chains; Solutions to Selected Problems;

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Book SynopsisThis volume provides a unified mathematical introduction to stationary time series models and to continuous time stationary stochastic processes. The analysis of these stationary models is carried out in time domain and in frequency domain. It begins with a practical discussion on stationarity, by which practical methods for obtaining stationary data are described. The presented topics are illustrated by numerous examples. Readers will find the following covered in a comprehensive manner:At the end, some selected topics such as stationary random fields, simulation of Gaussian stationary processes, time series for planar directions, large deviations approximations and results of information theory are presented. A detailed appendix containing complementary materials will assist the reader with many technical aspects of the book.

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Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Book SynopsisWinner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.Features Integrates working code into the main text. Illustrates concepts through worked data analysis examples. Emphasizes understanding assumptions and how assumptions are reflected in code. Offers more detailed explanations of the mathematics in optional sections. Presents examples of using the dagitty R package to analyze causal graphs. Provides the rethinking R package on the author's website and on GitHub. Trade Review"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."- Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."- Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."- Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." - Nguyet Nguyen, Youngstown State University "As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."- Nathan Green, Journal of the Royal Statistical Society, 2021, https://doi.org/10.1111/rssa.12755"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."- Abhirup Mallik in Technometrics, August 2021"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."~Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."~Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."~Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." ~Nguyet Nguyen, Youngstown State University"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."~Abhirup Mallik in Technometrics, August 2021"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."~ Nathan Green, Journal of the Royal Statistical Society, 2021Table of Contents1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables & The Spurious Waffles. 6. The Haunted DAG & The Causal Terror. 7. Ulysses’ Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.

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Book SynopsisTrade Review"Sports fans will learn much from probability theory and statistical models as they abandon empty clichés (time to throw momentum out of the informed fan's lexicon) and confront institutionalized injustices (such as those built into the protocols for selecting a national champion in college football and for seeding the NCAA's basketball tournament). A rare fusion of sports enthusiasm and numerical acumen." * Booklist *"Who is Wayne Winston? Maybe we should begin by telling you who he is not. He is not some barstool fan or uninformed sportswriter who fuels his opinions with information gleaned from SportsCenter highlights or newspaper box scores. He is a professor of decision sciences at Indiana University's Kelley School of Business, and until this year was the statistical guru for the Dallas Mavericks. He is author of the book Mathletics, which explains what statistics really tell us about sports."---Ken Berger, CBSSports.com"[A] terrific read for anyone trying to model markets statistically and make trading decisions based on statistical data. . . . Reading Winston's book is a mind-opening experience."---Brenda Jubin, Reading the Markets blog"[Huge] and highly interesting."---Mathematics Magazine, Paul J. Campbell

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Book SynopsisTrade Review"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability."---Jonathan Shock, Mathemafrica"Valuable."---J. Brzezinski, Choice

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Book SynopsisThis 3rd edition of Modern Mathematical Statistics with Applications tries to strike a balance between mathematical foundations and statistical practice. The book provides a clear and current exposition of statistical concepts and methodology, including many examples and exercises based on real data gleaned from publicly available sources. Here is a small but representative selection of scenarios for our examples and exercises based on information in recent articles: Use of the “Big Mac index” by the publication The Economist as a humorous way to compare product costs across nations Visualizing how the concentration of lead levels in cartridges varies for each of five brands of e-cigarettes Describing the distribution of grip size among surgeons and how it impacts their ability to use a particular brand of surgical stapler Estimating the true average odometer reading of used Porsche Boxsters listed for sale on www.cars.com Comparing head acceleration after impact when wearing a football helmet with acceleration without a helmet Investigating the relationship between body mass index and foot load while running The main focus of the book is on presenting and illustrating methods of inferential statistics used by investigators in a wide variety of disciplines, from actuarial science all the way to zoology. It begins with a chapter on descriptive statistics that immediately exposes the reader to the analysis of real data. The next six chapters develop the probability material that facilitates the transition from simply describing data to drawing formal conclusions based on inferential methodology. Point estimation, the use of statistical intervals, and hypothesis testing are the topics of the first three inferential chapters. The remainder of the book explores the use of these methods in a variety of more complex settings. This edition includes many new examples and exercises as well as an introduction to the simulation of events and probability distributions. There are more than 1300 exercises in the book, ranging from very straightforward to reasonably challenging. Many sections have been rewritten with the goal of streamlining and providing a more accessible exposition. Output from the most common statistical software packages is included wherever appropriate (a feature absent from virtually all other mathematical statistics textbooks). The authors hope that their enthusiasm for the theory and applicability of statistics to real world problems will encourage students to pursue more training in the discipline. Trade Review“The textbook Modern Mathematical Statistics with Applications can be recommended for applied mathematics and statistics majors as well as prospective scientists, business, social and medical science majors interested in the applying modern statistical methods for their disciplines.” (Maria Ivanchuk, ISCB News, iscb.info, June, 2022)Table of Contents

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