Logo

Beginning Statistics by Douglas S. Shafer, Zhiyi Zhang

Beginning Statistics
by

Publisher: lardbucket.org
Number of pages: 716

Description:
This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students. Our motivation for writing it is twofold: 1.) to provide a low-cost alternative to many existing popular textbooks on the market; and 2.) to provide a quality textbook on the subject with a focus on the core material of the course in a balanced presentation.

Home page url

Download or read it online for free here:
Download link
(33MB, PDF)

Similar books

Book cover: Linear Regression Using R: An Introduction to Data ModelingLinear Regression Using R: An Introduction to Data Modeling
by - University of Minnesota
The book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.
(3331 views)
Book cover: Dynamic Programming and Bayesian Inference: Concepts and ApplicationsDynamic Programming and Bayesian Inference: Concepts and Applications
by - InTech
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. The purpose of this volume is to provide some applications of Bayesian optimization and dynamic programming.
(5565 views)
Book cover: Introductory StatisticsIntroductory Statistics
by - Wiley
The popular introduction to statistics for students of economics or business. Presents an approach that is generally available only in much more advanced texts, yet uses the simplest mathematics consistent with a sound presentation.
(13403 views)
Book cover: Introductory Statistics NotesIntroductory Statistics Notes
by - University of Alabama
It is important to know how to understand statistics so that we can make the proper judgments when a person presents us with an argument backed by data. Data are numbers with a context. We must always keep the meaning of our data in mind.
(8372 views)