Introductory Statistics And Analytics
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Introductory Statistics and Analytics
Concise, thoroughly class-tested primer that features basicstatistical concepts in the concepts in the context of analytics,resampling, and the bootstrap A uniquely developed presentation of key statistical topics,Introductory Statistics and Analytics: A ResamplingPerspective provides an accessible approach to statisticalanalytics, resampling, and the bootstrap for readers with variouslevels of exposure to basic probability and statistics. Originallyclass-tested at one of the first online learning companies in thediscipline, www.statistics.com, the book primarily focuses onapplications of statistical concepts developed via resampling, witha background discussion of mathematical theory. This featurestresses statistical literacy and understanding, which demonstratesthe fundamental basis for statistical inference and demystifiestraditional formulas. The book begins with illustrations that have the essentialstatistical topics interwoven throughout before moving on todemonstrate the proper design of studies. Meeting all of theGuidelines for Assessment and Instruction in Statistics Education(GAISE) requirements for an introductory statistics course,Introductory Statistics and Analytics: A ResamplingPerspective also includes: Over 300 “Try It Yourself” exercises andintermittent practice questions, which challenge readers atmultiple levels to investigate and explore key statisticalconcepts Numerous interactive links designed to provide solutions toexercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing fieldof data science Multiple discussions of various software systems, such asMicrosoft Office Excel®, StatCrunch, and R, to develop andanalyze data Areas of concern and/or contrasting points-of-view indicatedthrough the use of “Caution” icons Introductory Statistics and Analytics: A ResamplingPerspective is an excellent primary textbook for courses inpreliminary statistics as well as a supplement for courses inupper-level statistics and related fields, such as biostatisticsand econometrics. The book is also a general reference for readersinterested in revisiting the value of statistics.
An Introduction to Statistical Learning
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.
Introductory Statistics and Analytics
Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.