Understanding Basic Statistics 8th Edition
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Understanding Basic Statistics
Contains worked solutions to all odd-numbered excercises in the text.
Interpreting Basic Statistics
Interpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. New to the eighth edition: A broader array of basic statistical concepts is covered, especially to better reflect the New Statistics. Journal excerpts have been updated to reflect current styles in statistical reporting. A stronger emphasis on data visualizations has been added. The statistical exercises have been re-organized into units to facilitate ease of use and understanding. About this book Each of the 64 exercises gives a brief excerpt of statistical reporting from a published research article, and begins with guidelines for interpreting the statistics in the excerpt. The questions on the excerpts promote learning by requiring students to interpret information in tables and figures, perform simple calculations to further their interpretations, critique data-reporting techniques, and evaluate procedures used to collect data. Each exercise covers a limited number of statistics, making it easy to coordinate the exercises with lectures and a main textbook. The questions in each exercise are divided into two parts: (1) Factual Questions and (2) Questions for Discussion. The factual questions require careful reading for details, while the discussion questions show that interpreting statistics is more than a mathematical exercise. These questions require students to apply good judgment as well as statistical reasoning in arriving at appropriate interpretations.
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.