000 04461nam a2200577 i 4500
001 6812562
003 IEEE
005 20200413152859.0
006 m eo d
007 cr cn |||m|||a
008 101013s2011 caua foab 000 0 eng d
020 _a9781608455713 (electronic bk.)
020 _z9781608455706 (pbk.)
024 7 _a10.2200/S00295ED1V01Y201009MAS008
_2doi
035 _a(CaBNVSL)gtp00544198
035 _a(OCoLC)673043000
040 _aCaBNVSL
_cCaBNVSL
_dCaBNVSL
050 4 _aQA278.8
_b.S527 2011
082 0 4 _a519.5
_222
100 1 _aShasha, Dennis Elliott.
245 1 0 _aStatistics is easy!
_h[electronic resource] /
_cDennis Shasha, Manda Wilson.
250 _a2nd ed.
260 _aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool Publishers,
_cc2011.
300 _a1 electronic text (xii, 162 p. : ill.) :
_bdigital file.
490 1 _aSynthesis lectures on mathematics and statistics,
_x1938-1751 ;
_v# 8
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (p. 75-76).
505 0 _a1. The basic idea --
505 0 _a2. Pragmatic considerations when using resampling --
505 0 _a3. Terminology --
505 0 _a4. The essential stats -- 4.1. Mean -- Why and when -- Calculate -- Example -- Pseudocode & code -- 4.2. Difference between two means -- Why and when -- Calculate -- Example -- Pseudocode & code -- 4.3. Chi-squared -- Why and when -- Calculate with example -- Pseudocode & code -- Calculate with example for multiple variables -- Pseudocode & code -- 4.4. Fisher's exact test -- Why and when -- Calculate with example -- Pseudocode & code -- 4.5. One-way ANOVA -- Why and when -- Calculate with example -- Statistics is easy -- Pseudocode & code -- 4.6. Multi-way ANOVA -- Why and when -- Calculate with example -- Pseudocode & code -- 4.7. Linear regression -- Why and when -- Calculate with example -- Pseudocode & code -- 4.8. Linear correlation -- Why and when -- Calculate & example -- Pseudocode & code -- 4.9. Multiple regression -- 4.10. Multiple testing -- Why and when -- Family wise error rate -- False discovery rate --
505 0 _a5. Case study: New Mexico's 2004 presidential ballots -- 5.1. 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? --
505 0 _aReferences -- Bias corrected confidence intervals -- Appendix B.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 _aStatistics 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.
530 _aAlso available in print.
588 _aTitle from PDF t.p. (viewed on October 13, 2010).
650 0 _aNonparametric statistics.
650 0 _aResampling (Statistics)
700 1 _aWilson, Manda.
730 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on mathematics and statistics,
_x1938-1751 ;
_v# 8.
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812562
999 _c561787
_d561787