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Statistics is easy!
Author
Title
Statistics is easy! [electronic resource] / Dennis Shasha, Manda Wilson.
ISBN
9781598297782 (electronic bk.)
9781598297775 (pbk.)
Published
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2008.
Physical Description
1 online resource (vi, 70 p. : ill.) : digital file.
Local Notes
Access is available to the Yale community.
Notes
Part of: Synthesis digital library of engineering and computer science.
Title from PDF t.p. (viewed on October 15, 2008).
Series from website.
Access and use
Access restricted by licensing agreement.
Summary
Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then systematically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. The ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers.
Variant and related titles
Synthesis digital library of engineering and computer science.
Other formats
Also available in print.
Format
Books / Online
Language
English
Added to Catalog
April 25, 2013
Series
Synthesis lectures on mathematics and statistics ; #1
System details note
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Bibliography
Includes bibliographical references (p. 69-70).
Contents
The basic idea
Bias corrected confidence intervals
Pragmatic considerations when using resampling
Terminology
The essential stats
Mean
Why and when
Calculate
Example
Pseudocode & code
Difference between two means
Why and when
Calculate
Example
Pseudocode & code
Chi-squared
Why and when
Calculate with example
Pseudocode & code
Calculate with example for multiple variables
Pseudocode & code
Fisher S exact test
Why and when
Calculate with example
Pseudocode & code
One-way ANOVA
Why and when
Calculate with example
Statistics is easy
Pseudocode & code
Multi-way ANOVA
Why and when
Calculate with example
Pseudocode & code
Linear regression
Why and when
Calculate with example
Pseudocode & code
Linear correlation
Why and when
Calculate & example
Pseudocode & code
Multiple regression
Multiple testing
Why and when
Family wise error rate
False discovery rate
Case study: New Mexico's 2004 presidential ballots
Take a close look at the data
What questions do we want to ask
How do we attempt to answer this question
Next: effect of ethnicity for each machine type
We have used the following techniques
What did we find out?
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