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'Stats Means Business' is an introductory textbook aimed at Business Studies students who require guidance in the area of statistics. It minimizes technical language, provides clear definition of key terms, and gives emphasis to interpretation rather than technique. 'Stats Means Business' enables readers to: * appreciate the importance of statistical analysis in business * understand statistical techniques * develop judgment in the selection of appropriate statistical techniques * interpret the results of statistical analysis There is an overwhelming need for successful managers to be able to deal competently with numerical information and this text is developed with this in mind by providing worked examples and review questions which are rooted in viable business contexts. Each chapter includes guidance on using Excel and Minitab to produce the analysis described and explained in the chapter. The start of every chapter identifies aims and summarizes content and each is written in an accessible style. Model solutions are provided for three problems in each chapter and further solutions are available on a web site to accompany the book. The book is suitable for first year undergraduate courses, MBA Programmes and anyone who needs support and guidance in the area of statistics.
Stats Means Business is an introductory textbook written for Business, Hospitality and Tourism students who take modules on Statistics or Quantitative research methods. Recognising that most users of this book will have limited if any grounding in the subject, this book minimises technical language, provides clear definition of key terms, and gives emphasis to interpretation rather than technique. Stats Means Business enables readers to: appreciate the importance of statistical analysis in business, hospitality and tourism understand statistical techniques and develop judgement in the selection of appropriate statistical techniques interpret the results of statistical analysis This new edition includes extra content related to Hospitality and Tourism courses, an extension of the interpretation of correlation analysis and a new section on how to design questionnaires. An introductory text and an accessible approach to a difficult subject, Stats Means Business assumes no prior knowledge of statistics and therefore won’t intimidate students Techniques are explained and demonstrated using worked examples and real life applications of theory. Guidance is also given on using EXCEL, Minitab and SPSS Teaching support materials include fully worked solutions for questions in the book, additional review questions and data sets for lecturers to use for tutorials
Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Stats Means Business is an introductory and comprehensive textbook written especially for Hospitality, Business and Tourism students who take statistics or quantitative methods modules. By minimising technical language, providing clear definitions of key terms and giving emphasis to interpretation rather than technique, this book caters to beginners in the subject. This book enables readers to appreciate the importance of statistical analysis in hospitality, tourism and other fields of business, understand statistical techniques, develop judgement in the selection of appropriate statistical techniques and interpret the results of statistical analysis. This new edition has been fully revised and updated to include: New content on business analytics Case studies demonstrating practical applications An extensive selection of new self-test questions Stats Means Business is an ideal, accessible and practical introduction to statistics and quantitative research methods for Hospitality, Business and Tourism students. Visit the companion website at www.routledge.com/cw/buglear for bonus teaching and learning resources.
This book is designed to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS, and basic statistics (descriptive statistics and basic associational statistics). It provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing. It teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors. --
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Stats Means Business is an introductory textbook written for Business, Hospitality and Tourism students who take modules on Statistics or Quantitative research methods. Recognising that most users of this book will have limited if any grounding in the subject, this book minimises technical language, provides clear definition of key terms, and gives emphasis to interpretation rather than technique. Stats Means Business enables readers to: appreciate the importance of statistical analysis in business, hospitality and tourism understand statistical techniques and develop judgement in the selection of appropriate statistical techniques interpret the results of statistical analysis This new edition includes extra content related to Hospitality and Tourism courses, an extension of the interpretation of correlation analysis and a new section on how to design questionnaires. An introductory text and an accessible approach to a difficult subject, Stats Means Business assumes no prior knowledge of statistics and therefore won’t intimidate students Techniques are explained and demonstrated using worked examples and real life applications of theory. Guidance is also given on using EXCEL, Minitab and SPSS Teaching support materials include fully worked solutions for questions in the book, additional review questions and data sets for lecturers to use for tutorials
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.