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Introduction to Applied Statistics

2004 Mathematics
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Release : 2004
Genre : Mathematics
Kind : eBook
Book Rating : 944/5 ( reviews)

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Book Synopsis Introduction to Applied Statistics by : James K. Lindsey

Download or read book Introduction to Applied Statistics written by James K. Lindsey. This book was released on 2004. Available in PDF, EPUB and Kindle. Book excerpt: This text is aimed at students in medicine, biology and the social sciences as well as those planning to specialize in applied statistics. It covers the basics of the design and analysis of surveys and experiments and provides an understanding of the basic principles of modeling and inference. Practical advice is provided on how to design a study, collect data, record observations accurately, detect errors, construct appropriate models, and interpret the results. The text contains many illustrative examples and exercises relating statistical principles to research. A companion web site is available with links to data sets, R codes, and an instructor's manual with teaching hints and solutions.

Introduction to Applied Statistics

2009
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Release : 2009
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Introduction to Applied Statistics by : James K. Lindsey

Download or read book Introduction to Applied Statistics written by James K. Lindsey. This book was released on 2009. Available in PDF, EPUB and Kindle. Book excerpt:

Foundations and Applications of Statistics

2018-04-04 Computers
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Release : 2018-04-04
Genre : Computers
Kind : eBook
Book Rating : 482/5 ( reviews)

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Book Synopsis Foundations and Applications of Statistics by : Randall Pruim

Download or read book Foundations and Applications of Statistics written by Randall Pruim. This book was released on 2018-04-04. Available in PDF, EPUB and Kindle. Book excerpt: Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.

Introduction to Applied Statistics

2017-09 Social sciences
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Release : 2017-09
Genre : Social sciences
Kind : eBook
Book Rating : 551/5 ( reviews)

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Book Synopsis Introduction to Applied Statistics by : Mehmet Ali Arslan

Download or read book Introduction to Applied Statistics written by Mehmet Ali Arslan. This book was released on 2017-09. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Applied Statistics is truly a comprehensible book and after studying this book, students from all levels and backgrounds could easily apply statistics to their real-life problems. This book can also guide students in developing hands on experience by offering/utilizing traditional and contemporary solution tools. The following features have been presented/introduced to make this book quick and efficient way of learning and applying statistics. Briefly, this book includes;- clear and concise introduction to descriptive/inferential statistics and analysis- a content designed just for the students from various disciplines- hand calculations using core statistics formulas- worked section example solutions plus end of chapter exercises- key example Excel solutions - key example Minitab solutions

An Introduction to Statistical Learning

2023-08-01 Mathematics
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Release : 2023-08-01
Genre : Mathematics
Kind : eBook
Book Rating : 473/5 ( reviews)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James. This book was released on 2023-08-01. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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