Statistics is Easy! 2nd Edition
2nd Edition
Published on 3. October 2010
Book
Paperback/Softback
XII, 162 pages
978-3-031-01272-3 (ISBN)
Description
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 system atically 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. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea/ Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
XII, 162 p.
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 10 mm
Weight
341 gr
ISBN-13
978-3-031-01272-3 (9783031012723)
DOI
10.1007/978-3-031-02400-9
Schweitzer Classification
Persons
Dennis Shasha is a Julius Silver Professor of computer science at the Courant Institute of New York University and an Associate Director of NYU Wireless. In addition to his long fascination with concurrent algorithms, he works on meta-algorithms for machine learning to achieve guaranteed correctness rates; with biologists on pattern discovery for network inference; with physicists and financial people on algorithms for time series; on database tuning; and tree and graph matching. Because he likes to type, he has written six books of puzzles about a mathematical detective named Dr. Ecco, a biography about great computer scientists, and a book about the future of computing. He has also written technical books about database tuning, biological pattern recognition, time series, DNA computing, resampling statistics, and causal inference in molecular networks. He has written the puzzle column for various publications including Scientific American, Dr. Dobb's Journal, and currently the Communications of the ACM. He is a fellow of the ACM and an INRIA International Chair.
Content
The Basic Idea.- Pragmatic Considerations when Using Resampling.- Terminology.- The Essential Stats.- Case Study: New Mexico's 2004 Presidential Ballots.- References.- Bias Corrected Confidence Intervals.- Appendix B.