MARC Record from harvard_bibliographic_metadata

LEADER: 05388cam a2200517 i 4500
001 014109647-0
005 20140905184548.0
008 140530t20142014enka b 001 0 eng
010 $a 2014395338
016 7 $a101627793$2DNLM
020 $a9780199946648 (alk. paper)
020 $a0199946647 (alk. paper)
035 0 $aocn859383632
040 $aNLM$beng$cNLM$erda$dYDXCP$dBTCTA$dBDX$dVET$dNEH$dNKM$dOHS$dJHE$dCDX$dCLU$dDLC
042 $anlmcopyc$alccopycat
050 00 $aR853.S7$bM68 2014
060 00 $a2014 C-339
060 10 $aWA 950
082 00 $a610.72/4$223
100 1 $aMotulsky, Harvey.
245 10 $aIntuitive biostatistics :$ba nonmathematical guide to statistical thinking /$cHarvey Motulsky, GraphPad Software, Inc.
250 $aThird edition.
264 1 $aNew York :$bOxford University Press,$c[2014]
264 4 $c©2014
300 $axxxv, 540 pages :$billustrations ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
504 $aIncludes bibliographical references (pages 517-528) and index.
505 0 $aPt. A, Intro ducting statistics : -- 1. Statistics and probability are not intuitive -- 2. The complexities of probability -- 3. From sample to population.
505 0 $aPt. B, Introducing confidence intervals : -- 4. Confidence interval of a proportion -- 5. Confidence interval of survival data -- 6. Confidence interval of counted data (Poisson distribution).
505 0 $aPt. C, Continuous variables : -- 7. Graphing continuous data -- 8. Types of variables -- 9. Quantifying scatter -- 10. The Gaussian distribution -- 11. The lognormal distribution and geometric mean -- 12. Confidence interval of a mean -- 13. The theory of confidence intervals -- 14. Error bars.
505 0 $aPt. D, P Values and statistical significance : -- 15. Introducing P values -- 16. Statistical significance and hypothesis testing -- 17. Relationship between confidence intervals and statistical significance -- 18. Interpreting a result that is statistically significant -- 19. Interpreting a result that is not statistically significant -- 20. Statistical power -- 21. Testing for equivalence or noninferiority.
505 0 $aPt. E, Challenges in statistics : -- 22. Multiple comparisons concepts -- 23. The ubiquity of multiple comparisons -- 24. Normality tests -- 25. Outliers -- 26. Choosing a sample size.
505 0 $aPt. F, Statistical tests : -- 27. Comparing proportions -- 28. Case-controlled studies -- 29. Comparing survival curves -- 30. Comparing two means : unpaired T test -- 31. Comparing two paired groups -- 32. Correlation.
505 0 $aPt. G, Fitting models to data : -- 33. Simple linear regression -- 34. Introducing models -- 35. Comparing models -- 36. Nonlinear regression -- 37. Multiple regression -- 38. Logistic and proportional hazards regression.
505 0 $aPt. H, The rest of statistics : -- 39. Analysis of variance -- 40. Multiple comparison tests after ANOVA -- 41. Nonparametric methods -- 42. Sensitivity, specificity, and receiver-operator characteristic curves -- 43. Meta-analysis.
505 0 $aPt. I, Putting it all together : -- 44. The key concepts of statistics -- 45. Statistical traps to avoid -- 46. Capstone example -- 47. Review problems -- 48. Answers to review problems.
505 0 $aPt. J, Appendices : -- Appendix A: Statistics with GraphPad -- Appendix B: Statistics with Excel -- Appendix C: Statistics with R -- Appendix D: Values of the t distribution needed to compute confidence intervals -- Appendix E: A review of logarithms -- Appendix F: Choosing a statistical test.
520 $a"Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. New to this edition: Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. Chapter 21 contrasts testing for equivalence with testing for differences. Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. Chapters 24 and 25 review testing for normality and outliers. Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. New "Q and A" sections throughout the book review key concepts"--Provided by publisher.
650 22 $aBiometry$xmethods.
650 22 $aBiomedical Research$xmethods.
650 12 $aBiostatistics$xmethods.
650 0 $aMedicine$xResearch$xStatistical methods.
988 $a20140708
906 $0OCLC