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In this fascinating book, mathematician Ed Beltrami takes a close enough look at randomness to make it mysteriously disappear. The results of coin tosses, it turns out, are determined from the start, and only our incomplete knowledge makes them look random. "Random" sequences of numbers are more elusive, but Godels undecidability theorem informs us that we will never know. Those familiar with quantum indeterminacy assert that order is an illusion, and that the world is fundamentally random. Yet randomness is also an illusion. Perhaps order and randomness, like waves and particles, are only two sides of the same (tossed) coin.
Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems. There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said π? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc. Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/~eugened. This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.
Organizations need research, and managers have to be able to commission, judge and use others' research as well as conduct research themselves to inform business decisions. Business Research Methods helps you understand the challenges of carrying out worthwhile research into significant issues and develop a wide range of research-related professional skills. Guiding you through the process of selecting, carrying out and reporting on a successful research project, it breaks down the research process, from exploring the literature and crafting a research proposal to practical research management and addressing the transferable skills of project management and communication. Business Research Methods places research firmly in the real world, exploring why research is done and how to ensure that projects are meaningful for organizations. Examples and case studies, including examples of students' projects, give learners with little or no work experience a meaningful context in which to relate their own projects. Online supporting resources for lecturers include an instructor's manual with additional activities and supporting handouts, lecture slides and figures and tables from the text. Resources for students include web links, templates, quizzes, activities, examples of practice and sample questionnaire results for students.
Research Design and Methods: A Process Approach takes students through the research process, from getting and developing a research idea, to designing and conducting a study, through analyzing and reporting data. Information on the research process is presented in a lively and engaging way, highlighting the numerous decisions, both big and small, that must be made when designing and conducting successful research.
Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.
Handbook of Combinatorics
Employee selection has long stood at the practical forefront of industrial/organizational psychology. Today's social, business, and economic climates require ongoing adaptations by those who select organizations' personnel, and research on the topic helps gauge the impact of these adaptations and their implications for human performance and potential. The Oxford Handbook of Personnel Assessment and Selection codifies the wealth of new research surrounding employee selection (web-based assessments, social networking, globalization of organizations), situating them alongside more traditional practices to establish the best and most relevant research for both professionals and academics. Comprising chapters from authors in both the private sector and academia, this volume is organized into seven parts: (1) historical and social context of the field of assessment and selection; (2) research strategies; (3) individual difference constructs that underlie effective performance; (4) measures of predictor constructs; (5) employee performance and outcome assessment; (6) societal and organizational constraints on selection practice; and (7) implementation and sustainability of selection systems. While providing a comprehensive review of current research and practice, the purpose of this handbook is to provide an up-to-date profile of each of the areas addressed and highlight current questions that deserve additional attention from researchers and practitioners. This compendium is essential reading for industrial/organizational psychologists and human resource managers.
Any student wishing to solve problems via mathematical modelling will find that this book provides an excellent introduction to the subject.