MARC record from Internet Archive

LEADER: 09453cam a2200697Ia 4500
001 15083019
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006 m o d
007 cr |||||||||||
008 111011s2011 flua ob 001 0 eng d
035 $a(OCoLC)ocn756675787
035 $a(NNC)15083019
040 $aCUS$beng$epn$cCUS$dN$T$dYDXCP$dE7B$dOCLCF$dOCLCO$dCRCPR$dOCLCQ$dIDEBK$dOCLCQ$dEBLCP$dIDB$dOCLCQ$dUAB$dNJR$dU3W$dOCLCQ$dNLE$dINT$dAU@$dOCLCQ$dUKMGB$dWYU$dYDX$dTYFRS$dLEAUB$dOCLCQ$dUKAHL$dOCLCQ$dK6U$dOCLCO
015 $aGBB7A8628$2bnb
016 7 $a015701391$2Uk
016 7 $a018389783$2Uk
019 $a1065993916
020 $a9781439812549$q(electronic bk.)
020 $a1439812543$q(electronic bk.)
020 $z9781439812822$q(hardcover ;$qalk. paper)
020 $z1439812829$q(hardcover ;$qalk. paper)
020 $z9780415804318
020 $z0415804310
020 $z1439812543
035 $a(OCoLC)756675787$z(OCoLC)1065993916
037 $aTANDF_205592$bIngram Content Group
050 4 $aQA276.45.S27$bP33 2011
072 7 $aMAT$x029000$2bisacsh
082 04 $a519.50285$223
049 $aZCUA
100 1 $aPadgett, Lakshmi V.
245 10 $aPractical statistical methods :$ba SAS programming approach /$cLakshmi V. Padgett.
260 $aBoca Raton, FL :$bCRC Press,$c©2011.
300 $a1 online resource (xiii, 290 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
504 $aIncludes bibliographical references and index.
505 00 $g1.$tIntroduction$g1.1.$tTypes of Data$g1.2.$tDescriptive Statistics/Data Summaries --$g1.3.$tGraphical and Tabular Representation --$g1.4.$tPopulation and Sample --$g1.5.$tEstimation and Testing Hypothesis --$g1.6.$tNormal Distribution --$g1.7.$tNonparametric Methods --$g1.8.$tSome Useful Concepts --$g2.$tQualitative Data --$g2.1.$tOne Sample --$g2.1.1.$tBinary Data --$g2.1.2.$tt Categorical Responses --$g2.2.$tTwo Independent Samples --$g2.2.1.$tTwo Proportions --$g2.2.2.$tOdds Ratio and Relative Risk --$g2.2.3.$tLogistic Regression with One Dichotomous Explanatory Variable --$g2.2.4.$tCochran-Mantel-Haenszel Test for a 2 x 2 Table --$g2.2.5.$tt Categorical Responses --$g2.3.$tPaired Two Samples --$g2.3.1.$tBinary Responses --$g2.3.2.$tt Categorical Responses --$g2.4.$tk Independent Samples --$g2.4.1.$tk Proportions --$g2.4.2.$tLogistic Regression When the Explanatory Variable Is Not Dichotomous.
505 00 $g2.4.3.$tCMH Test --$g2.4.4.$tt Categorical Responses --$g2.5.$tCochran's Test --$g2.6.$tOrdinal Data --$g2.6.1.$tRow Mean Score Test --$g2.6.2.$tCochran-Armitage Test --$g2.6.3.$tMeasures of Association --$g2.6.4.$tRidit Analysis --$g2.6.5.$tWeighted Kappa --$g2.6.6.$tOrdinal Logistic Regression --$g2.6.6.1.$tTwo Samples --$g2.6.6.2.$tk Samples --$g3.$tContinuous Normal Data --$g3.1.$tOne Sample --$g3.2.$tTwo Samples --$g3.2.1.$tIndependent Samples --$g3.2.1.1.$tMeans --$g3.2.1.2.$tVariances --$g3.2.2.$tPaired Samples --$g3.3.$tk Independent Samples --$g3.3.1.$tOne-Way Analysis of Variance --$g3.3.1.1.$tVariance --$g3.3.2.$tCovariance Analysis --$g3.4.$tMultivariate Methods --$g3.4.1.$tCorrelation, Partial, and Intraclass Correlation --$g3.4.2.$tHotelling's T2 --$g3.4.2.1.$tOne Sample --$g3.4.2.2.$tTwo Samples --$g3.4.3.$tOne-Way Multivariate Analysis of Variance --$g3.4.4.$tProfile Analysis --$g3.4.5.$tDiscriminant Functions --$g3.4.6.$tCluster Analysis --$g3.4.7.$tPrincipal Components.
505 00 $g3.4.8.$tFactor Analysis --$g3.4.9.$tCanonical Correlation --$g3.5.$tMultifactor ANOVA --$g3.5.1.$tCrossed Factors --$g3.5.2.$tTukey 1 df for Nonadditivity --$g3.5.3.$tNested Factors --$g3.6.$tVariance Components --$g3.7.$tSplit Plot Designs --$g3.8.$tLatin Square Design --$g3.9.$tTwo-Treatment Crossover Design --$g4.$tNonparametric Methods --$g4.1.$tOne Sample --$g4.1.1.$tSign Test --$g4.1.2.$tWilcoxon Signed-Rank Test --$g4.1.3.$tKolmogorov Goodness of Fit --$g4.1.4.$tCox and Stuart Test --$g4.2.$tTwo Samples --$g4.2.1.$tWilcoxon-Mann-Whitney Test --$g4.2.2.$tMood's Median Test --$g4.2.3.$tKolmogorov-Smirnov --$g4.2.4.$tEquality of Variances --$g4.3.$tk Samples --$g4.3.1.$tKruskal-Wallis Test --$g4.3.2.$tMedian Test --$g4.3.3.$tJonckheere Test --$g4.4.$tTransformations --$g4.5.$tFriedman Test --$g4.6.$tAssociation Measures --$g4.6.1.$tSpearman Rank Correlation --$g4.6.2.$tKendall's Tau --$g4.6.3.$tKappa Statistic --$g4.7.$tCensored Data.
505 00 $g4.7.1.$tKaplan-Meier Survival Distribution Function --$g4.7.2.$tWilcoxon (Gehan) and Log-Rank Test --$g4.7.3.$tLife-Table (Acturial Method) --$g5.$tRegression --$g5.1.$tSimple Regression --$g5.2.$tPolynomial Regression --$g5.3.$tMultiple Regressions --$g5.3.1.$tMulticollinearity --$g5.3.2.$tDummy Variables --$g5.3.3.$tInteraction --$g5.3.4.$tVariable Selection --$g5.4.$tDiagnostics --$g5.4.1.$tOutliers --$g5.4.2.$tInfluential Observations --$g5.4.3.$tDurbin-Watson Statistic --$g5.5.$tWeighted Regression --$g5.6.$tLogistic Regression --$g5.6.1.$tDichotomous Logistic Regression --$g5.6.2.$tMultinomial Logistic Model --$g5.6.3.$tCumulative Logistic Model --$g5.7.$tPoisson Regression --$g5.8.$tRobust Regression --$g5.9.$tNonlinear Regression --$g5.10.$tPiecewise Regression --$g5.11.$tAccelerated Failure Time (AFT) Model --$g5.12.$tCox Regression --$g5.12.1.$tProportional Hazards Model --$g5.12.2.$tProportional Hazard Assumption --$g5.12.3.$tStratified Cox Model.
505 00 $g5.12.4.$tTime-Varying Covariates --$g5.12.5.$tCompeting Risks --$g5.13.$tParallelism of Regression Equations --$g5.14.$tVariance-Stabilizing Transformations --$g5.15.$tRidge Regression --$g5.16.$tLocal Regression (LOESS) --$g5.17.$tResponse Surface Methodology: Quadratic Model --$g5.18.$tMixture Designs and Their Analysis --$g5.19.$tAnalysis of Longitudinal Data: Mixed Models --$g6.$tMiscellaneous Topics --$g6.1.$tMissing Data --$g6.2.$tDiagnostic Errors and Human Behavior --$g6.2.1.$tIntroduction --$g6.2.2.$tIndependent Samples --$g6.2.2.1.$tTwo Independent Samples --$g6.2.2.2.$tk Independent Samples --$g6.2.3.$tTwo Dependent Samples --$g6.2.4.$tFinding the Threshold for a Screening Variable --$g6.2.5.$tAnalyzing Response Data with Errors --$g6.2.6.$tResponders' Anonymity --$g6.3.$tDensity Estimation --$g6.3.1.$tParametric Density Estimation --$g6.3.2.$tNonparametric Univariate Density Estimation --$g6.3.3.$tBivariate Kernel Estimator --$g6.4.$tRobust Estimators.
505 00 $g6.5.$tJackknife Estimators --$g6.6.$tBootstrap Method --$g6.7.$tPropensity Scores --$g6.8.$tInterim Analysis and Stopping Rules --$g6.8.1.$tStopping Rules --$g6.8.2.$tConditional Power --$g6.9.$tMicroarrays and Multiple Testing --$g6.9.1.$tMicroarrays --$g6.9.2.$tMultiple Testing --$g6.10.$tStability of Products --$g6.11.$tGroup Testing --$g6.12.$tCorrespondence Analysis --$g6.13.$tClassification Regression Trees --$g6.14.$tMultidimensional Scaling --$g6.15.$tPath Analysis --$g6.16.$tChoice-Based Conjoint Analysis --$g6.16.1.$tAvailability Designs and Cross Effects --$g6.16.2.$tPareto-Optimal Choice Sets --$g6.16.3.$tMixture-Amount Designs --$g6.17.$tMeta-Analysis --$g6.17.1.$tHomogeneity of the Effect Sizes --$g6.17.2.$tCombining the p-Values.
588 0 $aPrint version record.
520 $aPractical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.
650 0 $aSAS (Computer program language)
650 0 $aMathematical statistics$xData processing.
650 0 $aProbabilities$xData processing.
650 6 $aSAS (Langage de programmation)
650 6 $aStatistique mathématique$xInformatique.
650 6 $aProbabilités$xInformatique.
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aMathematical statistics$xData processing.$2fast$0(OCoLC)fst01012133
650 7 $aProbabilities$xData processing.$2fast$0(OCoLC)fst01077741
650 7 $aSAS (Computer program language)$2fast$0(OCoLC)fst01738546
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aPadgett, Lakshmi V.$tPractical statistical methods.$dBoca Raton, FL : CRC Press, ©2011$z9781439812822$w(DLC) 2011019323$w(OCoLC)731666359
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15083019$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS