Probability and statistics Books

Book SynopsisMedical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patientâs individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest.Features: All you need to know to correctly make an online risk calculator from scratch. Discrimination, calibration, and predictive performance with censored data and competing risks. R-code and illustrative examples. InterpreTrade Review"Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Overall, the book offers a well-written, complete and illustrative overview of clinical prediction models with clear stances and directions on the modelling methods, choices and strategies. I find this a very welcome and much needed addition to the literature because prediction is the backbone of medical decision-making; few books are dedicated to modelling strategies and artificial intelligence is ascending in medical research. I thereby highly recommend this book for anyone who would be interested in performing predictive modelling for prognostic or diagnostic research." -Evangelos I. Kritsotakis, International Society for Clinical Biostatistics, 72, 2021 Table of Contents Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.

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Book SynopsisPractical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.Trade Review"I think overall the book is pitched perfectly and the step by step approach with code will act as an excellent training resources as well as reference guide.”-Ruth Weir, City, University of London"Overall, this is a great book! It is written in an accessible style, is up to date and covers the foundational material one would want a student to understand. As an experienced R user, I was delighted to learn something. Staying abreast of the fast-developing packages is nearly a full-time job, so I see this book as highly useful to many readers. The authors do a great job illustrating the main concepts of import but also pointing readers to places to follow up for more detailed treatments.”-Michael Townsley, Professor of Criminology and Criminal Justice, Griffith UniversityTable of Contents1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis

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Book SynopsisBig Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples.Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples Table of ContentsPreface Author Bios Acknowledgements List of Figures List of Tables Introduction to Big Data Systems 1.1 INTRODUCTION: REVIEW OF BIG DATA SYSTEMS1.2 UNDERSTANDING BIG DATA 1.3 TYPE OF DATA: TRANSACTIONAL OR ANALYTICAL1.4 REQUIREMENTS AND CHALLENGES OF BIG DATA 1.5 CONCLUDING REMARKS 1.6 FURTHER READING 1.7 EXERCISE QUESTIONS Architecture and Organization of Big Data Systems 2.1 ARCHITECTURE FOR BIG DATA SYSTEMS 2.2 ORGANIZATION OF BIG DATA SYSTEMS: CLUSTERS2.3 CLASSIFICATION OF CLUSTERS: DISTRIBUTED MEMORY VS. SHARED MEMORY2.4 CONCLUDING REMARKS 2.5 FURTHER READING 2.6 EXERCISE QUESTIONS Cloud Computing for Big Data 3.1 CLOUD COMPUTING 3.2 VIRTUALIZATION 3.3 PROCESSOR VIRTUALIZATION 3.4 CONTAINERIZATION 3.5 VIRTUALIZATION OR CONTAINERIZATION 3.6 FOG COMPUTING 3.7 EXAMPLES 3.8 CONCLUDING REMARKS 3.9 FURTHER READING 3.10 EXERCISE QUESTIONS HADOOP: An Efficient Platform for Storing and Processing Big Data 4.1 REQUIREMENTS FOR PROCESSING AND STORING BIG DATA 4.2 HADOOP - THE BIG PICTURE 4.3 HADOOP DISTRIBUTED FILE SYSTEM 4.4 MAPREDUCE 4.5 HBASE 4.6 CONCLUDING REMARKS 4.7 FURTHER READING 4.8 EXERCISE QUESTIONS Enhancements in Hadoop 5.1 ISSUES WITH HADOOP 5.2 YARN 5.3 PIG 5.4 HIVE 5.5 DREMEL 5.6 IMPALA 5.7 DRILL 5.8 DATA TRANSFER 5.9 AMBARI 5.10 CONCLUDING REMARKS 5.11 FURTHER READING 5.12 EXERCISE QUESTIONS Spark 6.1 LIMITATIONS OF MAPREDUCE 6.2 INTRODUCTION TO SPARK 6.3 SPARK CONCEPTS 6.4 SPARK SQL 6.5 SPARK MLLIB 6.6 STREAM BASED SYSTEM 6.7 SPARK STREAMING 6.8 CONCLUDING REMARKS 6.9 FURTHER READING 6.10 EXERCISE QUESTIONS NoSQL Systems 7.1 INTRODUCTION 7.2 HANDLING BIG DATA SYSTEMS - PARALLEL RDBMS 7.3 EMERGENCE OF NOSQL SYSTEMS 7.4 KEY-VALUE DATABASE 7.5 DOCUMENT-ORIENTED DATABASE 7.6 COLUMN-ORIENTED DATABASE 7.7 GRAPH DATABASE 7.8 CONCLUDING REMARKS 7.9 FURTHER READING 7.10 EXERCISE QUESTIONS NewSQL Systems 8.1 INTRODUCTION8.2 TYPES OF NEWSQL SYSTEMS 8.3 FEATURES 8.4 NEWSQL SYSTEMS: CASE STUDIES 8.5 CONCLUDING REMARKS 8.6 FURTHER READING8.7 EXERCISE QUESTIONS Networking for Big Data 9.1 NETWORK ARCHITECTURE FOR BIG DATA SYSTEMS9.2 CHALLENGES AND REQUIREMENTS 9.3 NETWORK PROGRAMMABILITY AND SOFTWARE DEFINED NETWORKING 9.4 LOW LATENCY AND HIGH SPEED DATA TRANSFER9.5 AVOIDING TCP INCAST - ACHIEVING LOW LATENCYAND HIGH THROUGHPUT 9.6 FAULT TOLERANCE9.7 CONCLUDING REMARKS 9.8 FURTHER READING 9.9 EXERCISE QUESTIONS Security for Big Data 10.1 INTRODUCTION 10.2 SECURITY REQUIREMENTS 10.3 SECURITY: ATTACK TYPES AND MECHANISMS 10.4 ATTACK DETECTION AND PREVENTION 10.5 CONCLUDING REMARKS 10.6 FURTHER READING 10.7 EXERCISE QUESTIONS Privacy for Big Data 11.1 INTRODUCTION 11.2 UNDERSTANDING BIG DATA AND PRIVACY 11.3 PRIVACY VIOLATIONS AND THEIR IMPACT 11.4 TYPES OF PRIVACY VIOLATIONS 11.5 PRIVACY PROTECTION SOLUTIONS AND THEIR LIMITATIONS 11.6 CONCLUDING REMARKS 11.7 FURTHER READING 11.8 EXERCISE QUESTIONS High Performance Computing for Big Data 12.1 INTRODUCTION 12.2 SCALABILITY: NEED FOR HPC 12.3 GRAPHIC PROCESSING UNIT 12.4 TENSOR PROCESSING UNIT 12.5 HIGH SPEED INTERCONNECTS 12.6 MESSAGE PASSING INTERFACE 12.7 OPENMP 12.8 OTHER FRAMEWORKS 12.9 CONCLUDING REMARKS 12.10 FURTHER READING 12.11 EXERCISE QUESTIONS Deep Learning with Big Data 13.1 INTRODUCTION 13.2 FUNDAMENTALS 13.3 NEURAL NETWORK 13.4 TYPES OF DEEP NEURAL NETWORK 13.5 BIG DATA APPLICATIONS USING DEEP LEARNING13.6 CONCLUDING REMARKS 13.7 FURTHER READING 13.8 EXERCISE QUESTIONS Big Data Case Studies 14.1 GOOGLE EARTH ENGINE 14.2 FACEBOOK MESSAGES APPLICATION 14.3 HADOOP FOR REAL-TIME ANALYTICS 14.4 BIG DATA PROCESSING AT UBER 14.5 BIG DATA PROCESSING AT LINKEDIN 14.6 DISTRIBUTED GRAPH PROCESSING AT GOOGLE 14.7 FUTURE TRENDS 14.8 CONCLUDING REMARKS 14.9 FURTHER READING 14.10 EXERCISE QUESTIONS Bibliography Index

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Book SynopsisThis book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy Table of Contents1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

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Book SynopsisA Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn progrTable of ContentsAssumptions about the reader’s backgroundBook overview Introduction to R/Python Programming Calculator Variable and TypeFunctions Control flowsSome built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneous data.table and pandasSQL Get started with data.table and pandas Indexing & selecting data Add/Remove/UpdateGroup by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence intervalHypothesis testing Basics of linear regression Ridge regression Optimization in PracticeConvexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous

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Book SynopsisA unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data.Table of ContentsIntroduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

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Book SynopsisI The Methodology of Statistical Consulting.- 1 Introduction to Statistical Consulting.- 2 Communication.- 3 Methodological Aspects.- 4 A Consulting Project from A to Z.- II Case Studies.- 5 Introduction to the Case Studies.- 6 Case Studies from Group I.- 7 Case Studies from Group II.- 8 Case Studies from Group III.- 9 Additional Case Studies.- A Resources.- A.1 References.- A.2 Datasets for Case Studies in Part II.- A.3 Statistical Consulting Course.- A.3.1 Course Description.- A.3.2 List of Topics by Week.- A.3.3 Reference List.- B Statistical Software.- B.1 SAS.- B.1.1 The SAS Setup.- B.1.2 Details on the DATA Step.- B.1.3 SAS Procedures.- B.1.4 Further Details of SAS.- B.2 S-PLUS.- B.2.1 S-PLUS Preliminaries.- B.2.2 The S-PLUS Setup.- B.2.3 Basic S-PLUS Commands.- B.2.4 Efficient Use of S-PLUS.- B.2.5 S-PLUS Statistical Procedures.- B.2.6 S-PLUS Glossary.- C Statistical Addendum.- C.1 Univariate Distributions.- C.2 Multivariate Distributions.- C.3 Statistical Tests.- C.4 Sample SizTrade ReviewFrom the reviews: THE AMERICAN STATISTICIAN "Although there are other books that effectively tackle the individual aspects described above, this book seems to be the most ideally suited to teaching a well-rounded statistics course at the undergraduate of graduate level…[It] gives informative and self-contained discussions for the many aspects of consulting in balanced proportions that would make using the book for a textbook delightfully straightforward. The collection of case studies is diverse in disciplines considered and level of difficulty, and seems to focus on interesting problems that students will find highly motivating…a valuable resource for statistical consultants, both beginning and established…a prime candidate for use as a stand-alone textbook…since it contains a desirable balance of materials with statistical methodology, oral and written communication skills, and rich case studies…It will make a solid long-term reference for students. Also, for instructors of more traditional senior undergraduate and junior graduate courses, it provides useful case studies to illustrate standard methods in realistic settings that can easily be implemented."Table of ContentsIntroduction to Statistical Consulting * Communication * Methodological Aspects * A Consulting Project from A to Z * Introduction to Case Studies * Case Studies from Group I * Case Studies from Group II * Case Studies from Group III * Additional Case Studies

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Book SynopsisThis classic textbook is suitable for a first course in the theory of statistics for students with a background in calculus, multivariate calculus, and the elements of matrix algebra.Table of ContentsPreface, 1 Preliminaries, 2 Probability, 3 Random Variables, 4 Expectations, 5 Limit Theorems, 6 Some Parametric Families, 7 Sampling and Reduction of Data, 8 Estimation, 9 Testing Hypotheses, 10 Analysis of Categorical Data, 11 Sequential Analysis, 12 Multivariate Distributions, 13 Nonpararnetric Tests, 14 Linear Models and Analysis of Variance, 15 Decision Theory, Tables, References and Further Reading, Answers to Problems, Index

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Book SynopsisStatistics for Sport and Exercise Studies guides the student through the full research process, from selecting the most appropriate statistical procedure, to analysing data, to the presentation of results, illustrating every key step in the process with clear examples, case-studies and data taken from real sport and exercise settings.Every chapter includes a range of features designed to help the student grasp the underlying concepts and relate each statistical procedure to their own research project, including definitions of key terms, practical exercises, worked examples and clear summaries. The book also offers an in-depth and practical guide to using SPSS in sport and exercise research, the most commonly used data analysis software in sport and exercise departments. In addition, a companion website includes more than 100 downloadable data sets and work sheets for use in or out of the classroom, full solutions to exercises contained in the book, plus over 1,300 PoTable of Contents1. Data, Information and Statistics 2. Using this book 3. Descriptive Statistics 4. Standardized scores 5. Probability 6. Data distributions 7. Hypothesis testing 8. Correlation 9. Linear Regression 10. t Tests 11. Analysis of Variances 12. Factorial ANOVA 13. Multivariate ANOVA 14. Nonparametric tests 15. Chi Square 16. Statistical Classification 17. Cluster Analysis 18. Data Reduction using Principal Components Analysis 19. Reliability 20. Statistical Power

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Book SynopsisTable of Contents1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems

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Book SynopsisBasic Statistics for Social Research offers an introduction to core general statistical concepts and methods. It covers procedural aspects of the application of statistical methods for data-description; and hypothesis-testing; distributions, tabulations, central tendency, variability, independence, correlation and regression.Table of ContentsTables and Figures ix Preface xv About the Authors xix Part I Univariate Description 1 Chapter 1 Using Statistics 3 Why Study Statistics? 4 Tasks for Statistics: Describing, Inferring, Testing, Predicting 4 Statistics in the Research Process 9 Basic Elements of Research: Units of Analysis and Variables 14 Chapter 2 Displaying One Distribution 25 Summarizing Variation in One Variable 26 Frequency Distributions for Nominal Variables 26 Frequency Distributions for Ordinal Variables 32 Frequency Distributions for Interval/Ratio Variables 38 Summarizing Data Using Excel 43 Chapter 3 Central Tendency 81 The Basic Idea of Central Tendency 82 The Mode 83 The Median 88 The Mean 95 Chapter 4 Dispersion 113 The Basic Idea of Dispersion 114 Dispersion of Categorical Data 115 Dispersion of Interval/Ratio Data 121 Chapter 5 Describing the Shape of a Distribution 149 The Basic Ideas of Distributional Shape 150 The Shape of Nominal and Ordinal Distributions 152 Unimodality 158 Skewness 163 Kurtosis 169 Some Common Distributional Shapes 175 Chapter 6 The Normal Distribution 187 Introduction to the Normal Distribution 188 Properties of Normal Distributions 189 The Standard Normal, or Z, Distribution 192 Working with Standard Normal (Z) Scores 194 Finding Areas “Under the Curve” 197 Part II Inference and Hypothesis Testing 209 Chapter 7 Basic Ideas of Statistical Inference 211 Introduction to Statistical Inference 212 Sampling Concepts 214 Central Tendency Estimates 219 Assessing Confidence in Point Estimates 229 Chapter 8 Hypothesis Testing for One Sample 247 Hypothesis Testing 248 The Testing Process 250 Tests about One Mean 258 Tests about One Proportion 267 Chapter 9 Hypothesis Testing for Two Samples 279 Comparing Two Groups 280 Comparing Two Groups’ Means 280 Comparing Two Groups’ Proportions 289 Non independent Samples 296 Using Excel for Two-Sample Tests 301 Interpreting Group Differences 302 Chapter 10 Multiple Sample Tests of Proportions: Chi-Squared 313 Comparing Proportions across Several Groups 314 Testing for Multiple Group Differences 315 Describing Group Differences 327 Chapter 11 Multiple Sample Tests for Means: One-Way ANOVA 337 Comparing Several Group Means with Analysis of Variance 338 Analyzing Variance and the F-Test 339 Analyzing Variance 342 The F-Test 350 Comparing Means 356 Part III Association and Prediction 369 Chapter 12 Association with Categorical Variables 371 The Concept of Statistical Association 372 Association with Nominal Variables 375 Association with Ordinal Variables 391 Chapter 13 Association of Interval/Ratio Variables 425 Visualizing Interval/Ratio Association 426 Significance Testing for Interval/Ratio Association 434 Chapter 14 Regression Analysis 453 Predicting Outcomes with Regression 454 Simple Linear Regression 454 Applying Simple Regression Analysis 465 Multiple Regression 469 Applying Multiple Regression 474 Chapter 15 Logistic Regression Analysis 489 Predicting with Nonlinear Relationships 490 Logistic Regression 492 The Logistic Regression Model 492 Interpreting Effects in Logistic Regression 493 Estimating Logistic Regression Models with Maximum Likelihood 495 Applying Logistic Regression 496 Assessing Partial Effects 498 Extending Logistic Regression 499 Appendix Chi-Squared Distribution: Critical Values for Commonly Used Alpha=0.05 and Alpha=0.01 505 F-Distribution: Critical Values for Commonly Used Alpha=0.05 and Alpha=0.01 507 Standard Normal Scores (Z-Scores), and Cumulative Probabilities (Proportion of Cases Having Scores below Z) 511 Student’s t-Distribution: Critical Values for Commonly Used Alpha Levels 517 Index 519

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Book SynopsisThis paperback, which comprises the first part of Introduction to Measure and Probability by J. F. C. Kingman and S. J. Taylor, gives a self-contained treatment of the theory of finite measures in general spaces at the undergraduate level. It sets the material out in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability. The theory of measure and integration is presented for general spaces, with Lebesgue measure and the Lebesgue integral considered as important examples whose special properties are obtained. The introduction to functional analysis which follows covers the material to probability theory and also the basic theory of L2-spaces, important in modern physics. A large number of examples is included; these form an essential part of the development.Table of ContentsPreface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and properties of measure; 5. Definitions and properties of the integral; 6. Related Spaces and measures; 7. The space of measurable functions; 8. Linear functionals; 9. Structure of measures in special spaces; Index of notation; General index.

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Book SynopsisThis monograph is the first unified treatment of an indispensable technique for comparing statistical tests, especially in nonparametric statistics. It presents powerful new methods to evaluate explicitly different kinds of efficiencies. Many Russian results are published here for the first time in English.Trade Review'It is an excellent book. I believe that every mathematical statistician should have this book in his or her collection … I enjoyed reading this book. I am sure that others will also like it.' Ramalingam Shanmugam, SIAM ReviewsTable of ContentsIntroduction; 1. Asymptotic efficiency of statistical tests and mathematical means for its computation; 2. Asymptotic efficiency of nonparametric goodness-of-fit tests; 3. Asymptotic efficiency of nonparametric homogeneity tests; 4. Asymptotic efficiency of nonparametric symmetry tests; 5. Asymptotic efficiency of nonparametric independence tests; 6. Local asymptotic optimality of nonparametric tests and the characterisation of distributions.

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Book SynopsisWritten for advanced undergraduates and graduate students in mathematics and related disciplines, this book explains the main approaches to statistical inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize material on computational topics with basic mathematical theory. Each chapter includes instructive problems.Trade Review'This is a delightful book! It gives a well-written exposure to inference issues in statistics, very suitable for a first-year graduate course … The authors present the material in a very good pedagogical manner. The examples are excellent, and the exercises are very instructive … very much up to date and includes recent developments in the field.' MAA Reviews'This is a solid book, ideal for advanced classes in the mathematical justification for statistical inference.' Journal of Recreational Mathematics'I wish that I had had such a textbook during my student days … this new book presents the core ideas of statistical inference in the unifying framework of decision theory and includes a fruitful discussion of the different foundational standpoints (Bayesian, Fisherian and frequentist) … [it is] sufficiently precise to satisfy a mathematician and yet omitting too much technical detail that could hide the core of the ideas. Carefully selected examples from a rainbow of application areas such as baseball, coal-mining disasters or gene expression data make it even more enjoyable to read … this book is a very nice graduate level textbook.' Journal of the Royal Statistical Society'[This] book gives a clear and comprehensive account of the basic elements of statistical theory. It should make a good text for an advanced course on statistical inference … Students will find it informative and challenging.' ISI Short Book Reviews'Essentials of Statistical Inference is a book worth having.' Jane L. Harvill, Journal of the American Statistical Association'The book is comprehensively written without dwelling in unnecessary details.' Iris Pigeot, Biometrics'… gives a clear and comprehensive account of the basic elements of statistical theory … a good text for an advanced course on statistical inference.' Publication of the International Statistical Institute'The text presents the main concepts and results underlying different frameworks of inference, with particular emphasis on the contrasts among frequentist, Fisherian, and Bayesian approaches. It provides a depiction of basic material on these main approaches to inference, as well as more advanced material on recent developments in statistical theory, including higher-order likelihood inference, bootstrap methods, conditional inference, and predictive inference.' Zentralblatt MATHTable of Contents1. Introduction; 2. Decision theory; 3. Bayesian methods; 4. Hypothesis testing; 5. Special models; 6. Sufficiency and completeness; 7. Two-sided tests and conditional inference; 8. Likelihood theory; 9. Higher-order theory; 10. Predictive inference; 11. Bootstrap methods.

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Book SynopsisA textbook for students with some background in probability that develops quickly a rigorous theory of Markov chains and shows how actually to apply it, e.g. to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice.Trade Review'This is an admirable book, treating the topic with mathematical rigour and clarity, mixed with helpful informality; and emphasising numerous applications to a wide range of subjects.' D. V. Lindley, The Mathematical Gazette'My overall impression of this book is very positive … this is the best introduction to the subject that I have come across.' Contemporary Physics'An instructor looking for a suitable text, at the level of a Master of Mathematics degree, can use this book with confidence and enthusiasm.' John Haigh, University of Sussex'We recently based a seminar on this book … it is well suited for an elementary, technically modest, but still rigorous introduction into the heart of a lively and relevant area of stochastic processes.' M. Scheutzow, Zentralblatt MATHTable of ContentsIntroduction; 1. Discrete-time Markov chains; 2. Continuous-time Markov chains I; 3. Continuous-time Markov chains II; 4. Further theory; 5. Applications; Appendix; Probability and measure; Index.

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Book SynopsisThis lively and engaging book explains the basic ideas of association and regression, and tells you the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own.Trade Review'At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal modeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book.' Persi Diaconis, Stanford University'This book is outstanding for the clarity of its thought and writing. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and provides a welcome antidote to the standard formulaic approach to statistics.' Erich L. Lehmann, University of California, Berkeley'In Statistical Models, David Freedman explains the main statistical techniques used in causal modeling - and where the skeletons are buried. Complex statistical ideas are clearly presented and vividly illustrated with interesting examples. Both newcomers and practitioners will benefit from reading this book.' Alan Krueger, Princeton University'Regression techniques are often applied to observational data with the intent of drawing causal conclusions. In what circumstances is this justified? What are the assumptions underlying the analysis? Statistical Models answers these questions. The book is essential reading for anybody who uses regression to do more than summarize data. The treatment is original, and extremely well written. Critical discussions of research papers from the social sciences are most insightful. I highly recommend this book to anybody who engages in statistical modeling, or teaches regression, and most certainly to all of my students.' Aad van der Vaart, Vrije Universiteit Amsterdam'A pleasure to read, Statistical Models shows the field's most elegant writer at the height of his powers. While most textbooks hurry past core assumptions in order to explicate technique, this book places the spotlight on the core assumptions, challenging readers to think critically about how they are invoked in practice.' Donald Green, Yale University'Statistical Models, a modern introduction to the subject, discusses graphical models and simultaneous equations among other topics. There are plenty of instructive exercises and computer labs. Especially valuable is the critical assessment of the main 'philosophers's stones' in applied statistics. This is an inspiring book and a very good read, for teachers as well as students.' Gesine Reinert, Oxford University'Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation.' Mathematical ReviewsTable of Contents1. Observational studies and experiments; 2. The regression line; 3. Matrix algebra; 4. Multiple regression; 5. Multiple regression: special topics; 6. Path models; 7. Maximum likelihood; 8. The bootstrap; 9. Simultaneous equations; 10. Issues in statistical modeling.

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Book SynopsisShows the effectiveness of abstract analysis for solving fundamental problems of stochastic theory, specifically the use of functional analytic methods for elucidating stochastic processes.Trade Review"More than 20 original papers reflect Rao's broad scientific interests in probability, stochastic processes, Banach space theory, measure theory and (stochastic) differential equations. …The volume is completed with a biography and bibliography of M. M. Rao, a remarkable collection of personal reminiscences (written by his former students) adds a human dimension to this fine book."-EMS Newsletter, June 2005Table of ContentsBiography of M. M. Rao, Published Writings of M. M. Rao, Ph.D. Theses Completed Under the Direction of M. M. Rao, Contributors, For M. M. Rao, An Appreciation of My Teacher, M. M. Rao, 1001 Words About Rao, A Guide to Life, Mathematical and Otherwise, Rao and the Early Riverside Years, On M. M. Rao, Reflections on M. M. Rao, 1: Stochastic Analysis and Function Spaces, 2: Applications of Sinkhorn Balancing to Counting Problems, 3: Zakai Equation of Nonlinear Filtering with Ornstein-Uhlenbeck Noise: Existence and Uniqueness, 4: Hyperfunctionals and Generalized Distributions, 5: Process-Measures and Their Stochastic Integral, 6: Invariant Sets for Nonlinear Operators, 7: The Immigration-Emigration with Catastrophe Model, 8: Approximating Scale Mixtures, 9: Cyclostationary Arrays: Their Unitary Operators and Representations, 10: Operator Theoretic Review for Information Channels, 11: Pseudoergodicity in Information Channels, 12: Connections Between Birth-Death Processes, 13: Integrated Gaussian Processes and Their Reproducing Kernel Hilbert Spaces, 14: Moving Average Representation and Prediction for Multidimensional Harmonizable Processes, 15: Double-Level Averaging on a Stratified Space, 16: The Problem of Optimal Asset Allocation with Stable Distributed Returns, 17: Computations for Nonsquare Constants of Orlicz Spaces, 18: Asymptotically Stationary and Related Processes, 19: Superlinearity and Weighted Sobolev Spaces, 20: Doubly Stochastic Operators and the History of Birkhoff s Problem 111, 21: Classes of Harmonizable Isotropic Random Fields, 22: On Geographically-Uniform Coevolution: Local Adaptation in Non-fluctuating Spatial Patterns, 23: Approximating the Time Delay in Coupled van der Pol Oscillators with Delay Coupling

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Book SynopsisOften it is more instructive to know ''what can go wrong'' and to understand ''why a result fails'' than to plod through yet another piece of theory. In this text, the authors gather more than 300 counterexamples - some of them both surprising and amusing - showing the limitations, hidden traps and pitfalls of measure and integration. Many examples are put into context, explaining relevant parts of the theory, and pointing out further reading. The text starts with a self-contained, non-technical overview on the fundamentals of measure and integration. A companion to the successful undergraduate textbook Measures, Integrals and Martingales, it is accessible to advanced undergraduate students, requiring only modest prerequisites. More specialized concepts are summarized at the beginning of each chapter, allowing for self-study as well as supplementary reading for any course covering measures and integrals. For researchers, it provides ample examples and warnings as to the limitations of general measure theory. This book forms a sister volume to René Schilling''s other book Measures, Integrals and Martingales (www.cambridge.org/9781316620243).Trade Review'This book is an admirable counterpart, both to the first author's well-known text Measures, Integrals and Martingales (Cambridge, 2005/2017), and to the books on counter-examples in analysis (Gelbaum and Olmsted), topology (Steen and Seebach) and probability (Stoyanov). To paraphrase the authors' preface: in a good theory, it is valuable and instructive to probe the limits of what can be said by investigating what cannot be said. The task is thus well-conceived, and the execution is up to the standards one would expect from the books of the first author and of their papers. I recommend it warmly.' N. H. Bingham, Imperial College'… an excellent reference text and companion reader for anyone interested in deepening their understanding of measure theory.' John Ross, MAA Reviews'… the unique nature of the book makes it an essential acquisition for any university with a doctoral program in pure mathematics … Essential.' M. Bona, Choice Connect'The book is well written, the demonstrations are clear and the bibliographic references are competent. We appreciate this work as extremely useful for those interested in measure theory and integration, starting with beginners and extending even to advanced researchers in the field.' Liviu Constantin Florescu, Mathematical Reviews/MathSciNet'Counterexamples in Measure and Integration is an ideal companion to help better understand canonically problematic examples in analysis … This collection of counterexamples is an excellent resource to researchers who rely on measure and integration theory. It would be helpful for students studying for their analysis qualifying exam as it draws on common misconceptions and enables readers to build intuition about why a given counterexample works and how conditions can be changed to make a particular statement hold.' Katelynn Kochalski, Notices of the AMS'This is a remarkable book covering Measure and Integration, perhaps one of the most important parts of Mathematics. It is written in a master style by following the best traditions in writing this kind of books. The authors are passionate about the topic. Look at the great care with which each of the counterexamples is presented. It is done in a way to help maximally the reader. The names of the counterexamples are chosen very carefully. Any name can be considered as a 'door' behind which is a treasure!' Jordan M. Stoyanov, zbMATH'… compendia of counterexamples remain a useful and thought-provoking resource, and this new text is a high-quality example in an analytic direction.' Dominic Yeo, The Mathematical GazetteTable of ContentsPreface; User's guide; List of topics and phenomena; 1. A panorama of Lebesgue integration; 2. A refresher of topology and ordinal numbers; 3. Riemann is not enough; 4. Families of sets; 5. Set functions and measures; 6. Range and support of a measure; 7. Measurable and non-measurable sets; 8. Measurable maps and functions; 9. Inner and outer measure; 10. Integrable functions; 11. Modes of convergence; 12. Convergence theorems; 13. Continuity and a.e. continuity; 14. Integration and differentiation; 15. Measurability on product spaces; 16. Product measures; 17. Radon–Nikodým and related results; 18. Function spaces; 19. Convergence of measures; References; Index.

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Book SynopsisA graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.

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Book SynopsisPsychological Statistics: The Basics walks the reader through the core logic of statistical inference and provides a solid grounding in the techniques necessary to understand modern statistical methods in the psychological and behavioral sciences. This book is designed to be a readable account of the role of statistics in the psychological sciences. Rather than providing a comprehensive reference for statistical methods, Psychological Statistics: The Basics gives the reader an introduction to the core procedures of estimation and model comparison, both of which form the cornerstone of statistical inference in psychology and related fields. Instead of relying on statistical recipes, the book gives the reader the big picture and provides a seamless transition to more advanced methods, including Bayesian model comparison.Psychological Statistics: The Basics not only serves as an excellent primer for beginners but it is also the perfectTrade Review"This book cuts to the very heart of the core principles of statistical inference and does so in a way that is accessible and easily digestible. I honestly wish a book like this one had existed when I was a student – I would have clutched it hard and never let it go!" -- Dr. Ruth Horry, Senior Lecturer in Psychology, Swansea University, UK"I see this book as a very useful resource, not only for those who have just started their journey at the university, but also for senior students to experience "aha!" moments while recapping the basics from a unique and nicely presented perspective. I am looking forward to recommending this book to my students as soon as it is available." -- Dr. Krzysztof Cipora, Lecturer in Mathematical Cognition, Loughborough University, UK"As a new graduate student, I was suddenly faced with academic papers presenting statistical methods. But with hardly any statistical understanding myself, I struggled to do this well. I longed for a book that I could easily refer back to. This is that book. The explanations are very accessible, the examples are relatable, and the book is concise. I thoroughly recommend it." -- Jennifer Read, Graduate Student in Education, University of Derby, UK"If you want to understand why we use statistics in psychology, this is the book for you!" -- Dawn Short, Ph.D. student in Psychology, Abertay University, UK"This is an accessible and helpful educational tool that students with a variety of backgrounds will enjoy. The author incorporates clear examples and is able to frame advanced concepts in a simple and straightforward way." -- Dr. Dawn Weatherford, Associate Professor of Psychology, Texas A&M University - San Antonio, USATable of Contents1. A (Very) Brief Introduction to Statistical Inference 2. Describing the Observed Data 3. Modeling the Observed Data 4. How Likely is the Observed Data? 5. Comparing Statistical Models 6. Introduction to the t-test 7. Bayesian Model Comparison 8. Recap and Next Steps

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Book SynopsisLibrarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there's a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through eTable of Contents1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills

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Book SynopsisBayesian Inference: Theory, Methods, Computations provides a comprehensive coverage of the fundamentals of Bayesian inference from all important perspectives, namely theory, methods and computations.All theoretical results are presented as formal theorems, corollaries, lemmas etc., furnished with detailed proofs. The theoretical ideas are explained in simple and easily comprehensible forms, supplemented with several examples. A clear reasoning on the validity, usefulness, and pragmatic approach of the Bayesian methods is provided. A large number of examples and exercises, and solutions to all exercises, are provided to help students understand the concepts through ample practice. The book is primarily aimed at first or second semester master students, where parts of the book can also be used at Ph.D. level or by research community at large. The emphasis is on exact cases. However, to gain further insight into the core concepts, an entire chapter is dedicated to

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Book SynopsisThe book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout. Trade Review"This clean and fun book covers a wide range of topics on statistical communication, programming, and modeling in a way that should be a useful supplement to any statistics course or self-learning program. I absolutely love this book!"- Andrew Gelman, Columbia University"An excellent book. Communication and reproducibility are of increasing concern in statistics, and this book covers these topics and more in a practical, appealing, and truly unique way."- Daniela Witten, University of Washington"Many data science texts tell you how to perform perfunctory calculations. Instead, Telling Stories with Data tells you how to engage in the mindset and process of analysis. By arming students with the computational, statistical and philosophical skills needed to use data in sense-making and story-telling, this book stands out from the pack as uniquely actionable and empowering."- Emily Riederer, Capital One"This is not another statistics book. It is much better than that. It is a book about doing quantitative research, about scientific justification, about quality control, about communication and epistemic humility. It's a valuable supplement to any methods curriculum, and useful for self-learners as well."- Richard McElreath, Max Planck Institute for Evolutionary Anthropology"Telling Stories with Data is a thoughtful guide to using data to learn and affect positive change. The book includes each stage of the process and can serve as a long-lasting companion to many data scientists and future data story tellers."- Christopher Peters, Zapier“A clever career choice is to pick a field where your skills are complementary with a growing resource. In the coming decades, those who are adept in analysing data will flourish. That means crunching statistics and telling compelling stories. Rohan Alexander’s book will help you do both.”- Andrew Leigh, Member of the Australian Parliament and author of Randomistas: How Radical Researchers Are Changing Our World"Every data analyst has to tell stories with data, and yet traditional textbooks focus on statistical methods alone. Telling Stories with Data teaches the entire data science workflow, including data acquisition, communication, and reproducibility. I highly recommend this unique book!"- Kosuke Imai, Harvard University"This is an extraordinary, wonderful, book, full of wise advice for anyone starting in data science. Intermixing concepts and code means the ideas are immediately made concrete, and the emphasis on reproducible workflows brings a welcome dose of rigor to a rapidly developing field."- David Spiegelhalter, The University of CambridgeTable of Contents1. Telling stories with data 2. Drinking from a fire hose 3. Reproducible workflows Part 1. Foundations 4. Writing research 5. Static communication Part 2. Communication 6. Farm data 7. Gather data 8. Hunt data Part 3. Acquisition 9. Clean and prepare 10. Store and share Part 4. Preparation 11. Exploratory data analysis 12. Linear models 13. Generalized linear models 14. Causality from observational data 15. Multilevel regression with post-stratification 16. Text as data 17. Concluding remarks

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Book SynopsisThis text introduces bad events (incidents and accidents) named as metaphors. The metaphors, called as safety animals, are named as black swan, gray rhino, gray swans, and invisible gorilla.The book analyzes incidents and accidents from the context of the safety management system in the risky industries including aviation, nuclear, chemical, oil, and petroleum. It further uses mathematical analysis of these events (through statistics and probabilities) and presents preventive and corrective measures in dealing with the same.It comprehensively covers important topics including real-time monitoring, reverse stress testing, change management, predictive maintenance, management system, contingency plans, human factors, behavioral safety, anticipatory failure determination, resilience engineering (RE), resilience management (RM), Swiss cheese model, and probability distribution.Aimed at professionals working in the fields of health and safety, quTable of Contents1. Philosophy of Science as Introduction. 2. The Black Swan events. 3. Analysis of the “Fat-tails” in Safety. 4. What to do with BSe in the Risky Industry?. 5. The Gray Rhino events. 6. Specifics of GRe in Risky Industry. 7. The Invisible Gorilla. 8. Other “Safety Animals”. 9. How to fight “Safety Animals”?. 10. Top Management and “Safety Animals”. 11. Final Words.

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Book SynopsisEvery finance professional wants and needs a competitive edge. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are notâand that is the competitive edge these books offer the astute reader.Published under the collective title of Foundations of Quantitative Finance, this set of ten books develops the advanced topics in mathematics that finance professionals need to advance their careers. These books expand the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses.As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial services industry and two decades in academia where he taught in highly respected graduate programs.Readers should be quantitatively literate and familiar with the development

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Book SynopsisEmerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches.Features: Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art software The connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inference Multilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structures Methods for clustering functional data before and after smoothing are discussed Multiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data and the CONTENT child growth study Step-by-step software implementations are included, along with a supplementary website (www.FunctionalDataAnalysis.com) featuring software, data, and tutorials More than 100 plots for visualization of functional data are presented Functional Data Analysis with R is primarily aimed at undergraduate, master's and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.

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Book SynopsisTo celebrate Data Sketches'' 1-year anniversary, Nadieh Bremer and Shirley Wu have created an exclusive set of high-quality prints and postcards based on their bestselling dataviz book. The high-quality prints and postcards pack is also available in a discounted set including the book Data Sketches. The set ISBN is: 9781032303895.

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Book SynopsisMaking Sense of Statistics, Eighth Edition, is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning.It focuses on presenting the why, as well as the how of statistical concepts, rather than computations and formulas. As such, it is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. This conceptual book is useful for all study levels, from undergraduate to doctoral level across disciplines. Once students understand and feel comfortable with the statistics presented in this book, they should find it easy to master additional statistical concepts.Table of ContentsIntroduction: What is Research?; Part A: The Research Context 1. The Empirical Approach to Knowledge 2. Types of Empirical Research 3. Scales of Measurement 4. Descriptive, Correlational, and Inferential Statistics; Part B: Sampling 5. Introduction to Sampling 6. Variations on Random Sampling 7. Sample Size 8. Standard Error of Mean and Central Limit Theorem; Part C: Descriptive Statistics 9. Frequencies, Percentages, and Proportions 10. Shapes of Distributions 11. The Mean: An Average 12. Mean, Median, and Mode 13. Range and Interquartile Range 14. Standard Deviation 15. Z Score; Part D: Correlational Statistics 16. Correlation 17. Pearson r 18. Scattergram 19. Coefficient of Determination 20. Multiple Correlation; Part E: Inferential Statistics 21. Introduction to Null Hypothesis 22. Decisions About the Null Hypothesis 23. Limits of Significance Testing and Practical Implications; Part F: Means Comparison 24. Introduction to the t Test 25. Independent Samples t Test 26. Dependent Samples t Test 27. One Sample t Test 28. Reporting the Results of t Tests: Display of Outcomes 29. One-Way ANOVA 30. Two-Way ANOVA; Part G: Predictive Significance 31. Chi-Square 32. Effect Size 33. Simple and Multiple Linear Regressions; Appendix A. Computations Appendix B. Notes on Interpreting Pearson r and Linear Regression Appendix C. Table of Random Numbers

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Book SynopsisSurveys are powerful tools for gathering information, uncovering insights, and facilitating decision-making. However, to ensure the accurate interpretation of results, they require specific analysis methods. In this book, readers embark on an in-depth journey into conducting complex survey analysis with the {srvyr} package and tidyverse family of functions from the R programming language. Intended for intermediate R users familiar with the basics of the tidyverse, this book gives readers a deeper understanding of applying appropriate survey analysis techniques using {srvyr}, {survey}, and other related packages. With practical walkthroughs featuring real-world datasets, such as the American National Election Studies and Residential Energy Consumption Survey, readers will develop the skills necessary to perform impactful survey analysis on survey data collected through a randomized sample design. Additionally, this book teaches readers how to interpret and communicate results of surv

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Book SynopsisThis book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimalTable of ContentsPreface. About the Editors. Chapter 1 Estimation of Uncertainties for Multiserver Queuing Systems with Bernoulli Feedback. Chapter 2 Optimality for Fuzzy Transportation Problem under Ranking Method. Chapter 3 Solution of Bilevel Linear Fractional Transportation Problem with Pythagorean Fuzzy Numbers. Chapter 4 Optimal Production Evaluation of Cotton in Different Soil and Water Conditions in Sundarban of West Bengal under Hesitant Interval Fuzzy Environment Using Projection Measures. Chapter 5 A Novel Approach for Feature Detection in Vector Graphics. Chapter 6 On Uncertain Matrix Games Involving Linguistic Pythagorean Fuzzy Sets. Chapter 7 Cyclic Surgery Scheduling using Variations of Cohort Intelligence. Chapter 8 Cone Method for Uncertain Multiobjective Optimization Problems with Minmax Robustness. Chapter 9 Solving Multi-Index Transportation Problem with Axial Constraints Having Impaired Flow. Chapter 10 STAR Heuristic Method: A Novel Approach and Its Comparative Analysis with CI Algorithm to Solve CBAP in Healthcare. Chapter 11 Development and Optimization of Quadratic Programming Problems with Intuitionistic Fuzzy Parameters. Index

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Book SynopsisComputer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications.This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI.Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and p

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Book SynopsisThe subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet Table of Contents1. Your Working Environment 2. Reading Data in R 3. Financial Data 4. Introduction to R 5. Functions 6. Data Transformation 7. Merging Data Sets 8. Graphing Using Ggplot 9. Returns and Returns-based Statistics 10. Portfolios 11. Modeling Returns and Simulations 12. Linear and Polynomial Regression 13. Fixed Income 14. Principal Component Analysis 15. Options 16. Value at Risk 17. Time Series Analysis 18. Machine Learning 19. Presenting the Results of Your Analyses 20. Appendix: Main Packages Seen in this Book

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Book SynopsisDigital Image Processing with C++: Implementing Reference Algorithms with the CImg Library presents the theory of digital image processing and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language using the free and easy-to-use CImg library.Chapters cover the field of digital image processing in a broad way and propose practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispecTable of ContentsI INTRODUCTION TO Clmg1. Introduction. 2. Getting Started With the CImg Library. 2.1 Objective: subdivide an image into blocks. 2.2 Setup and first program. 2.3 Computing the variations. 2.4 Computing the block decomposition. 2.5 Rendering of the decomposition. 2.6 Interactive visualization. 2.7 Final source code II IMAGE PROCESSING USING CImg3. Point Processing Transformations. 3.1 Image operations. 3.2 Histogram operations. 4. Mathematical Morphology. 4.1 Binary images. 4.2 Gray-level images. 4.3 Some applications. 5. Filtering. 5.1 Spatial filtering. 5.2 Recursive filtering. 5.3 Frequency filtering. 5.4 Diffusion filtering. 6. Feature Extraction. 6.1 Points of interest. 6.2 Hough transform. 6.3 Texture features. 7. Segmentation. 7.1 Edge-based approaches. 7.2 Region-based approaches. 8. Motion Estimation. 8.1 Optical flow: dense motion estimation. 8.2 Sparse estimation. 9. Multispectral Approaches. 9.1 Dimension reduction. 9.2 Color imaging. 10. 3D Visualisation. 10.1 Structuring of 3D mesh objects. 10.2 3D plot of a function z = f (x;y). 10.3 Creating complex 3D objects. 10.4 Visualization of a cardiac segmentation in MRI. 11. And So Many Other Things. 11.1 Compression by transform (JPEG). 11.2 Tomographic reconstruction. 11.3 Stereovision. 11.4 Interactive deformation using RBF. List of CImg Codes.References.Index.

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Book SynopsisR for Quantitative Chemistry is an exploration of how the R language can be applied to a wide variety of problems in what is typically termed "Quantitative Chemistry" or sometimes "Analytical Chemistry". This book will be based upon, in large part, actual experimental data.Table of Contents1. Intro to R 2. Data and Statistics 3. Beer’s Law and Linear Regression 4. Solving Equilibrium 5. Titrations 6. Binding Curves 7. Electrochemistry 8. Fourier Transform and Spectroscopy 9. R Kinetic Analysis 10. Reports in R Markdown

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Book SynopsisSpreadsheet Problem Solving and Programming for Engineers and Scientists provides a comprehensive resource essential to a full understanding of modern spreadsheet skills needed for engineering and scientific computations.Beginning with the basics of spreadsheets and programming, this book builds on the authors' decades of experience teaching spreadsheets and programming to both university students and professional engineers and scientists. Following on from this, it covers engineering economics, key numerical methods, and applied statistics. Finally, this book details the Visual Basic for Applications (VBA) programming system that accompanies Excel.With each chapter including examples and a set of exercises, this book is an ideal companion for all engineering courses and also for self-study. Based on the latest version of Excel (Microsoft Excel for Microsoft 365), it is also compatible with earlier versions of Excel dating back to Version 2013. Including numerTable of ContentsChapter 1 Spreadsheet Basics Chapter 2 Charts and GraphsChapter 3 Engineering and Scientific FormulasChapter 4 Table-based CalculationsChapter 5 Case Studies and TargetingChapter 6 Financial CalculationsChapter 7 Numerical MethodsChapter 8 Applied StatisticsChapter 9 Introduction to VBA and MacrosChapter 10 User-defined FunctionsChapter 11 VBA ProgrammingChapter 12 User InterfacesAppendix A: Matrix Algebra ReviewAppendix B: Shortcut Keys and Key Combinations

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