A Modern Introduction to Probability and Statistics


Book Description

Suitable 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




Business Statistics for Contemporary Decision Making


Book Description

Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace.




Introduction to Business Statistics


Book Description

Highly praised for its clarity and great examples, Weiers' INTRODUCTION TO BUSINESS STATISTICS, 6E introduces fundamental statistical concepts in a conversational language that connects with today's students. Even those intimidated by statistics quickly discover success with the book's proven learning aids, outstanding illustrations, non-technical terminology, and hundreds of current examples drawn from real-life experiences familiar to students. A continuing case and contemporary applications combine with more than 100 new or revised exercises and problems that reflect the latest changes in business today with an accuracy you can trust. You can easily introduce today's leading statistical software and teach not only how to complete calculations by hand and using Excel, but also how to determine which method is best for a particular task. The book's student-oriented approach is supported with a wealth of resources, including the innovative new CengageNOW online course management and learning system that saves you time while helping students master the statistical skills most important for business success.







An Introduction to Modern Business Statistics


Book Description

Using the computer to eliminate rote computation and facilitate learning, this book inspires and motivates readers to learn statistics by showing them its great practical importance to their careers. In every chapter, the authors include an ample number of examples and vignettes that illustrate and emphasize skills that enable students to interpret data effectively and to convert data into usable information. This approach enhances students' abilities to make better decisions, thus preparing them to exert greater influence in their future careers. To reinforce the idea that statistics is the linkage that transforms data into useful information, thereby enhancing planning and decision making, almost every numbered example includes introductory language that articulates the importance of the illustration in a functional area of business. The authors use Microsoft Excel, MINITAB, and JMP IN statistical software to execute statistical methods--presenting computer outputs and interpretation first; then illustrating the method using statistical tables. In addition, to promote the learning of fundamentals, the authors also take the users through many methods step-by-step, using examples with very small data sets. Chapter appendices provide clear, detailed instructions on the use of Excel, MINITAB, and JMP IN. Users are not just purchasing a textbook--every new copy of the book is packaged with a student software and data disk. This disk contains Data Analysis Plus Add-ins for Microsoft Excel, as well as all the data sets used in the book formatted for Excel, MINITAB, JMP IN, and ASCII. In addition to the many examples and exercises they included in the First Edition, the authors add approximately 120 exercises based on published articles in academic journals, the popular media, or widely available sources of data. Many of these exercises contain large data sets, and many are revisited is subsequent chapters.




Introduction to Modern Time Series Analysis


Book Description

This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.




Introductory Statistics 2e


Book Description

Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory 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.




An Introduction to Statistical Learning


Book Description

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.




Practical Business Statistics


Book Description

Practical Business Statistics, 5/e was written in response to instructors not wanting a formula driven, mathematically encyclopedic book. The use of computer applications means some topics no longer require coverage in detail. This allows future managers to know how to use and understand statistics. The text does this by using examples with real data that relate to the functional areas of business such as finance, accounting, and marketing. It de-emphasizes the theoretical, and presents the material in a well-written, easy style designed to motivate students. The emphasis is on understanding and applications as opposed to mathematical precision and formula detail.




Modern Applied Statistics with S-PLUS


Book Description

A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets, with many of the methods discussed being modern approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification. This second edition is intended for users of S-PLUS 3.3, or later, and covers both Windows and UNIX. It treats the recent developments in graphics and new statistical functionality, including bootstraping, mixed effects linear and non-linear models, factor analysis, and regression with autocorrelated errors. The authors have written several software libraries which enhance S-PLUS, and these, plus all the datasets used, are available on the Internet.