MARC Record from marc_columbia

LEADER: 03912pam a22003134a 4500
001 5505144
005 20221110050753.0
008 050225t20062006caua 000 0 eng
010 $a 2005005273
020 $a1412924731 (acid-free paper)
020 $a1412913144 (pbk. : acid-free paper)
035 $a(OCoLC)ocm57893254
035 $a(NNC)5505144
035 $a5505144
040 $aDLC$cDLC$dYDX$dBAKER$dOrLoB-B
042 $apcc
050 00 $aQA276$b.K253 2006
082 00 $a519.5$222
100 1 $aKeller, Dana K.$0http://id.loc.gov/authorities/names/n2005013943
245 14 $aThe tao of statistics :$ba path to understanding (with no math) /$cDana K. Keller ; illustrated by Helen Cardiff.
260 $aThousand Oaks, Calif. :$bSage Publications,$c[2006], ©2006.
300 $axi, 153 pages :$billustrations ;$c22 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
505 00 $g1.$tThe beginning - the question -- $g2.$tAmbiguity - statistics -- $g3.$tFodder - data -- $g4.$tData - measurement -- $g5.$tData structure - levels of measurement -- $g5.A.$tNominal -- $g5.B.$tOrdinal -- $g5.C.$tInterval -- $g5.D.$tRatio -- $g6.$tSimplifying - groups and clusters -- $g7.$tCounts - frequencies -- $g8.$tPictures - graphs -- $g9.$tScatterings - distributions -- $g10.$tBell-shaped - the normal curve -- $g11.$tLopsidedness - skewness -- $g12.$tAverages - central tendencies -- $g12.A.$tMean -- $g12.B.$tMedian -- $g12.C.$tMode -- $g13.$tTwo types - descriptive and inferential -- $g14.$tFoundations - assumptions -- $g15.$tLeeway - robustness -- $g16.$tConsistency - reliability -- $g17.$tTruth - validity -- $g18.$tUnpredictability - randomness -- $g19.$tRepresentativeness - samples -- $g20.$tMistakes - error -- $g21.$tReal or not - outliers -- $g22.$tImpediments - confounds -- $g23.$tNuisances - covariates -- $g24.$tBackground - independent variables -- $g25.$tTargets - dependent variables -- $g26.$tInequality - standard deviations and variance -- $g27.$tProve - no, falsify -- $g28.$tNo difference - the null hypothesis -- $g29.$tReductionism - models -- $g30.$tRisk - probability -- $g31.$tUncertainty - p values -- $g32.$tExpectations - chi-square -- $g33.$tImportance vs. difference - substantive vs. statistical significance -- $g34.$tStrength - power -- $g35.$tLikely range - confidence intervals -- $g36.$tAssociation - correlation -- $g37.$tPredictions - multiple regression -- $g38.$tAbundance - multivariate analysis -- $g39.$tDifferences - t tests and analysis of variance -- $g39.A.$tANOVA -- $g39.B.$tANCOVA -- $g39.C.$tMANOVA -- $g39.D.$tMANCOVA -- $g40.$tDifferences that matter - discriminant analysis -- $g41.$tBoth sides loaded - canonical covariance analysis -- $g42.$tNesting - hierarchical models -- $g43.$tCohesion - factor analysis -- $g44.$tOrdered events - path analysis -- $g45.$tDigging deeper - structural equation models -- $g46.$tFiddling - modifications and new techniques -- $g47.$tEpilogue.
520 1 $a"The Tao of Statistics: A Path To Understanding (With No Math) provides a new approach to statistics through impressionistic illustrations, verse, and accompanying text. Unlike other introductions, this text explains what statistics mean and how they are used, rather than how to calculate them. Author Dana K. Keller takes readers by the hand and walks them through the basic concepts, as well as some of the most complex statistical models in use." "The book shows how life seen through statistical eyes never looks quite the same again. The author invites professionals and students to journey down this path and experience a growing sense of wonderment at the patterns and relationships that emerge from the chaos."--BOOK JACKET.
650 0 $aStatistics.$0http://id.loc.gov/authorities/subjects/sh85127580
856 41 $3Table of contents$uhttp://www.loc.gov/catdir/toc/ecip058/2005005273.html
852 00 $bmat$hQA276$i.K253 2006