Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Introduction to Statistical Thought
Top Free Python Books 🌠 - 100% Free or Open Source!
  • Title Introduction to Statistical Thought
  • Author(s) Michael Lavine
  • Publisher: Orange Grove Texts Plus (September 24, 2009); eBook (Updated, February 21, 2013)
  • License(s) CC BY-NC-SA 3.0 US
  • Paperback 434 pages
  • eBook PDF, 475 page, 40.1 MB
  • Languages: English, Belorussian, Polish
  • ISBN-10: 1616100486
  • ISBN-13: 978-1616100483
  • Share This:  

Book Description

The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. By "statistical thinking" is meant a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. By "likelihood emphasis" is meant that the likelihood function and likelihood principle are unifying ideas throughout the text.

Another unusual aspect is the use of statistical software as a pedagogical tool. That is, instead of viewing the computer merely as a convenient and accurate calculating device, the book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts.

The book is written with the statistical language R embedded throughout.

About the Authors
  • Michael Lavine is a Professor of Statistics at University of Massachusetts Amherst.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Statistical Thinking for the 21st Century (Russell A. Poldrack)

    Statistical thinking is increasingly essential to making informed decisions based on uncertain data. This book provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data.

  • Introduction to Statistical Thinking (Benjamin Yakir)

    This book offers a detailed, illustrated breakdown of the fundamentals of statistics. Develop and use formal logical thinking abilities to understand the message behind numbers and charts in science, politics, and economy.

  • Introduction to Statistical Thinking, with R, without Calculus

    This is an introduction to statistics, with R, without calculus, for students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

  • Foundations in Statistical Reasoning (Pete Kaslik)

    This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

  • Statistics Done Wrong: The Woefully Complete Guide (Reinhart)

    Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

  • Lies, Damned Lies: How to Tell the Truth with Statistics

    The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.

  • Think Stats, 2nd Edition: Exploratory Data Analysis in Python

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

  • O'Reilly® Think Bayes: Bayesian Statistics in Python

    If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

  • Applied Statistics with R (David Dalpiaz)

    This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.

Book Categories
:
Other Categories
Resources and Links