Statistics for People Who (Think They) Hate Statistics
by Neil J. Salkind
Call Number: HA29.S2365 2000
Publication Date: 2000-04-11
Written for people who want to learn or brush-up on the basics of statistics but question their abilities, this book offers a step-by-step introduction to the topic. The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:
- Difficulty Rating Index for each chapter's material
- Tips for doing and thinking about a statistical technique
- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection
- Steps that break techniques down into a clear sequence of procedures
- SPSS tips for executing each major statistical technique
- Practice exercises at the end of each chapter, followed by worked out solutions.
by Charles Wheelan
Call Number: QA276.W458 2013
Publication Date: 2013-01-07
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
The Tao of Statistics
by Dana K. Keller; Helen Cardiff (Illustrator)
Call Number: QA276.K253 2006
Publication Date: 2005-08-05
The Tao of Statistics: A Path to Understanding (With No Math) provides a new approach to statistics in plain English. Unlike other introductions to statistics, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts, as well as some of the most complex statistical models in use. Professionals and college students who want to be informed about statistics but do not want to spend a lot of time learning to how compute them should not be without this volume.
The Cambridge Dictionary of Statistics
by Brian S. Everitt
Call Number: REF QA276.14.E84 2002
Publication Date: 2002-06-27
If you work with data and need easy access to clear, reliable definitions and explanations of modern statistical and statistics-related concepts, then look no further than this dictionary. Nearly 4000 terms are defined, covering medical, survey, theoretical, and applied statistics, including computational and graphical aspects. Entries are provided for standard and specialized statistical software. In addition, short biographies of over 100 important statisticians are given. Definitions provide enough mathematical detail to clarify concepts and give standard formula when these are helpful. The majority of definitions then give a reference to a book or article where the user can seek further or more specialized information, and many are accompanied by graphical material to aid understanding.
R in a Nutshell
by Joseph Adler
Call Number: QA276.45.R3A3 2010
Publication Date: 2010-01-11
Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.
The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.
Understand the basics of the language, including the nature of R objects
Learn how to write R functions and build your own packages
Work with data through visualization, statistical analysis, and other methods
Explore the wealth of packages contributed by the R community
Become familiar with the lattice graphics package for high-level data visualization
Learn about bioinformatics packages provided by Bioconductor
SPSS Survival Manual
by Julie Pallant
Call Number: HA32.P35 2010
Publication Date: 2010-11-01
In this thoroughly revised edition of her bestselling text, now covering up to version 18 of the SPSS software, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project.
From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with easy to follow step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the results in a report. In this fourth edition all chapters have been updated to accommodate changes to SPSS procedures, screens and output. A number of additional techniques (McNemar's Test, Cochran's Q Test) have been included in the Non_parametric Statistics chapter. For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is THE essential guide. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing.
A Simple Guide to SPSS for Windows
by Lee A. Kirkpatrick; Brooke C. Feeney
Call Number: HA32.K5673 2005
Publication Date: 2004-06-15
Featuring just the right amount of information and instruction, Kirkpatrick and Feeney's no-nonsense, streamlined guide provides a thorough introduction to Versions 12.0 and 13.0 of the powerful SPSS software. With this guide, students can learn how to use SPSS to perform all of the statistical procedures covered by a typical introductory statistics text--from histograms and descriptive statistics through correlation, regression, t-tests, and analysis of variance. Writing for students who need to use SPSS to complete homework problems or to conduct statistical analysis for a research project, Kirkpatrick and Feeney keep their explanations as simple and practical as possible.
R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.
Assuming no previous knowledge of statistics or R, the book includes:
•A comprehensive introduction to the R language.
•An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results.
•Over 300 examples, including detailed explanations of the R scripts used throughout.
•Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences.
•A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods.
•Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept.
An accompanying Wiki website, http://turtle.gis.umn.edu includes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the exercises presented in the book. Visitors are invited to download/upload data and scripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.