소장자료
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020 | ▼a1439812829 (hardcover : alk. paper)▲ | ||
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100 | 1 | ▼aPadgett, Lakshmi V.▲ | |
245 | 1 | 0 | ▼aPractical statistical methods :▼ba SAS programming approach /▼cLakshmi V. Padgett.▲ |
260 | ▼aBoca Raton, FL :▼bCRC Press,▼c2019.▲ | ||
300 | ▼axiii, 290 p. :▼bill. ;▼c25 cm.▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
505 | 0 | 0 | ▼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 | 0 | 0 | ▼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 | 0 | 0 | ▼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 | 0 | 0 | ▼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 | 0 | 0 | ▼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 | 0 | 0 | ▼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.▲ |
650 | 0 | ▼aSAS (Computer program language)▲ | |
650 | 0 | ▼aMathematical statistics▼xData processing.▲ | |
650 | 0 | ▼aProbabilities▼xData processing.▲ | |
999 | ▼c정영주▲ |
Practical statistical methods :a SAS programming approach
자료유형
국외단행본
서명/책임사항
Practical statistical methods : a SAS programming approach / Lakshmi V. Padgett.
발행사항
Boca Raton, FL : CRC Press , 2019.
형태사항
xiii, 290 p. : ill. ; 25 cm.
서지주기
Includes bibliographical references and index.
내용주기
1. Introduction -- 1.1. Types of Data -- 1.2. Descriptive Statistics/Data Summaries -- 1.3. Graphical and Tabular Representation -- 1.4. Population and Sample -- 1.5. Estimation and Testing Hypothesis -- 1.6. Normal Distribution -- 1.7. Nonparametric Methods -- 1.8. Some Useful Concepts -- 2. Qualitative Data -- 2.1. One Sample -- 2.1.1. Binary Data -- 2.1.2. t Categorical Responses -- 2.2. Two Independent Samples -- 2.2.1. Two Proportions -- 2.2.2. Odds Ratio and Relative Risk -- 2.2.3. Logistic Regression with One Dichotomous Explanatory Variable -- 2.2.4. Cochran-Mantel-Haenszel Test for a 2 x 2 Table -- 2.2.5. t Categorical Responses -- 2.3. Paired Two Samples -- 2.3.1. Binary Responses -- 2.3.2. t Categorical Responses -- 2.4. k Independent Samples -- 2.4.1. k Proportions -- 2.4.2. Logistic Regression When the Explanatory Variable Is Not Dichotomous
2.4.3. CMH Test -- 2.4.4. t Categorical Responses -- 2.5. Cochran's Test -- 2.6. Ordinal Data -- 2.6.1. Row Mean Score Test -- 2.6.2. Cochran-Armitage Test -- 2.6.3. Measures of Association -- 2.6.4. Ridit Analysis -- 2.6.5. Weighted Kappa -- 2.6.6. Ordinal Logistic Regression -- 2.6.6.1. Two Samples -- 2.6.6.2. k Samples -- 3. Continuous Normal Data -- 3.1. One Sample -- 3.2. Two Samples -- 3.2.1. Independent Samples -- 3.2.1.1. Means -- 3.2.1.2. Variances -- 3.2.2. Paired Samples -- 3.3. k Independent Samples -- 3.3.1. One-Way Analysis of Variance -- 3.3.1.1. Variance -- 3.3.2. Covariance Analysis -- 3.4. Multivariate Methods -- 3.4.1. Correlation, Partial, and Intraclass Correlation -- 3.4.2. Hotelling's T2 -- 3.4.2.1. One Sample -- 3.4.2.2. Two Samples -- 3.4.3. One-Way Multivariate Analysis of Variance -- 3.4.4. Profile Analysis -- 3.4.5. Discriminant Functions -- 3.4.6. Cluster Analysis -- 3.4.7. Principal Components
3.4.8. Factor Analysis -- 3.4.9. Canonical Correlation -- 3.5. Multifactor ANOVA -- 3.5.1. Crossed Factors -- 3.5.2. Tukey 1 df for Nonadditivity -- 3.5.3. Nested Factors -- 3.6. Variance Components -- 3.7. Split Plot Designs -- 3.8. Latin Square Design -- 3.9. Two-Treatment Crossover Design -- 4. Nonparametric Methods -- 4.1. One Sample -- 4.1.1. Sign Test -- 4.1.2. Wilcoxon Signed-Rank Test -- 4.1.3. Kolmogorov Goodness of Fit -- 4.1.4. Cox and Stuart Test -- 4.2. Two Samples -- 4.2.1. Wilcoxon-Mann-Whitney Test -- 4.2.2. Mood's Median Test -- 4.2.3. Kolmogorov-Smirnov -- 4.2.4. Equality of Variances -- 4.3. k Samples -- 4.3.1. Kruskal-Wallis Test -- 4.3.2. Median Test -- 4.3.3. Jonckheere Test -- 4.4. Transformations -- 4.5. Friedman Test -- 4.6. Association Measures -- 4.6.1. Spearman Rank Correlation -- 4.6.2. Kendall's Tau -- 4.6.3. Kappa Statistic -- 4.7. Censored Data
4.7.1. Kaplan-Meier Survival Distribution Function -- 4.7.2. Wilcoxon (Gehan) and Log-Rank Test -- 4.7.3. Life-Table (Acturial Method) -- 5. Regression -- 5.1. Simple Regression -- 5.2. Polynomial Regression -- 5.3. Multiple Regressions -- 5.3.1. Multicollinearity -- 5.3.2. Dummy Variables -- 5.3.3. Interaction -- 5.3.4. Variable Selection -- 5.4. Diagnostics -- 5.4.1. Outliers -- 5.4.2. Influential Observations -- 5.4.3. Durbin-Watson Statistic -- 5.5. Weighted Regression -- 5.6. Logistic Regression -- 5.6.1. Dichotomous Logistic Regression -- 5.6.2. Multinomial Logistic Model -- 5.6.3. Cumulative Logistic Model -- 5.7. Poisson Regression -- 5.8. Robust Regression -- 5.9. Nonlinear Regression -- 5.10. Piecewise Regression -- 5.11. Accelerated Failure Time (AFT) Model -- 5.12. Cox Regression -- 5.12.1. Proportional Hazards Model -- 5.12.2. Proportional Hazard Assumption -- 5.12.3. Stratified Cox Model
5.12.4. Time-Varying Covariates -- 5.12.5. Competing Risks -- 5.13. Parallelism of Regression Equations -- 5.14. Variance-Stabilizing Transformations -- 5.15. Ridge Regression -- 5.16. Local Regression (LOESS) -- 5.17. Response Surface Methodology: Quadratic Model -- 5.18. Mixture Designs and Their Analysis -- 5.19. Analysis of Longitudinal Data: Mixed Models -- 6. Miscellaneous Topics -- 6.1. Missing Data -- 6.2. Diagnostic Errors and Human Behavior -- 6.2.1. Introduction -- 6.2.2. Independent Samples -- 6.2.2.1. Two Independent Samples -- 6.2.2.2. k Independent Samples -- 6.2.3. Two Dependent Samples -- 6.2.4. Finding the Threshold for a Screening Variable -- 6.2.5. Analyzing Response Data with Errors -- 6.2.6. Responders' Anonymity -- 6.3. Density Estimation -- 6.3.1. Parametric Density Estimation -- 6.3.2. Nonparametric Univariate Density Estimation -- 6.3.3. Bivariate Kernel Estimator -- 6.4. Robust Estimators
6.5. Jackknife Estimators -- 6.6. Bootstrap Method -- 6.7. Propensity Scores -- 6.8. Interim Analysis and Stopping Rules -- 6.8.1. Stopping Rules -- 6.8.2. Conditional Power -- 6.9. Microarrays and Multiple Testing -- 6.9.1. Microarrays -- 6.9.2. Multiple Testing -- 6.10. Stability of Products -- 6.11. Group Testing -- 6.12. Correspondence Analysis -- 6.13. Classification Regression Trees -- 6.14. Multidimensional Scaling -- 6.15. Path Analysis -- 6.16. Choice-Based Conjoint Analysis -- 6.16.1. Availability Designs and Cross Effects -- 6.16.2. Pareto-Optimal Choice Sets -- 6.16.3. Mixture-Amount Designs -- 6.17. Meta-Analysis -- 6.17.1. Homogeneity of the Effect Sizes -- 6.17.2. Combining the p-Values.
2.4.3. CMH Test -- 2.4.4. t Categorical Responses -- 2.5. Cochran's Test -- 2.6. Ordinal Data -- 2.6.1. Row Mean Score Test -- 2.6.2. Cochran-Armitage Test -- 2.6.3. Measures of Association -- 2.6.4. Ridit Analysis -- 2.6.5. Weighted Kappa -- 2.6.6. Ordinal Logistic Regression -- 2.6.6.1. Two Samples -- 2.6.6.2. k Samples -- 3. Continuous Normal Data -- 3.1. One Sample -- 3.2. Two Samples -- 3.2.1. Independent Samples -- 3.2.1.1. Means -- 3.2.1.2. Variances -- 3.2.2. Paired Samples -- 3.3. k Independent Samples -- 3.3.1. One-Way Analysis of Variance -- 3.3.1.1. Variance -- 3.3.2. Covariance Analysis -- 3.4. Multivariate Methods -- 3.4.1. Correlation, Partial, and Intraclass Correlation -- 3.4.2. Hotelling's T2 -- 3.4.2.1. One Sample -- 3.4.2.2. Two Samples -- 3.4.3. One-Way Multivariate Analysis of Variance -- 3.4.4. Profile Analysis -- 3.4.5. Discriminant Functions -- 3.4.6. Cluster Analysis -- 3.4.7. Principal Components
3.4.8. Factor Analysis -- 3.4.9. Canonical Correlation -- 3.5. Multifactor ANOVA -- 3.5.1. Crossed Factors -- 3.5.2. Tukey 1 df for Nonadditivity -- 3.5.3. Nested Factors -- 3.6. Variance Components -- 3.7. Split Plot Designs -- 3.8. Latin Square Design -- 3.9. Two-Treatment Crossover Design -- 4. Nonparametric Methods -- 4.1. One Sample -- 4.1.1. Sign Test -- 4.1.2. Wilcoxon Signed-Rank Test -- 4.1.3. Kolmogorov Goodness of Fit -- 4.1.4. Cox and Stuart Test -- 4.2. Two Samples -- 4.2.1. Wilcoxon-Mann-Whitney Test -- 4.2.2. Mood's Median Test -- 4.2.3. Kolmogorov-Smirnov -- 4.2.4. Equality of Variances -- 4.3. k Samples -- 4.3.1. Kruskal-Wallis Test -- 4.3.2. Median Test -- 4.3.3. Jonckheere Test -- 4.4. Transformations -- 4.5. Friedman Test -- 4.6. Association Measures -- 4.6.1. Spearman Rank Correlation -- 4.6.2. Kendall's Tau -- 4.6.3. Kappa Statistic -- 4.7. Censored Data
4.7.1. Kaplan-Meier Survival Distribution Function -- 4.7.2. Wilcoxon (Gehan) and Log-Rank Test -- 4.7.3. Life-Table (Acturial Method) -- 5. Regression -- 5.1. Simple Regression -- 5.2. Polynomial Regression -- 5.3. Multiple Regressions -- 5.3.1. Multicollinearity -- 5.3.2. Dummy Variables -- 5.3.3. Interaction -- 5.3.4. Variable Selection -- 5.4. Diagnostics -- 5.4.1. Outliers -- 5.4.2. Influential Observations -- 5.4.3. Durbin-Watson Statistic -- 5.5. Weighted Regression -- 5.6. Logistic Regression -- 5.6.1. Dichotomous Logistic Regression -- 5.6.2. Multinomial Logistic Model -- 5.6.3. Cumulative Logistic Model -- 5.7. Poisson Regression -- 5.8. Robust Regression -- 5.9. Nonlinear Regression -- 5.10. Piecewise Regression -- 5.11. Accelerated Failure Time (AFT) Model -- 5.12. Cox Regression -- 5.12.1. Proportional Hazards Model -- 5.12.2. Proportional Hazard Assumption -- 5.12.3. Stratified Cox Model
5.12.4. Time-Varying Covariates -- 5.12.5. Competing Risks -- 5.13. Parallelism of Regression Equations -- 5.14. Variance-Stabilizing Transformations -- 5.15. Ridge Regression -- 5.16. Local Regression (LOESS) -- 5.17. Response Surface Methodology: Quadratic Model -- 5.18. Mixture Designs and Their Analysis -- 5.19. Analysis of Longitudinal Data: Mixed Models -- 6. Miscellaneous Topics -- 6.1. Missing Data -- 6.2. Diagnostic Errors and Human Behavior -- 6.2.1. Introduction -- 6.2.2. Independent Samples -- 6.2.2.1. Two Independent Samples -- 6.2.2.2. k Independent Samples -- 6.2.3. Two Dependent Samples -- 6.2.4. Finding the Threshold for a Screening Variable -- 6.2.5. Analyzing Response Data with Errors -- 6.2.6. Responders' Anonymity -- 6.3. Density Estimation -- 6.3.1. Parametric Density Estimation -- 6.3.2. Nonparametric Univariate Density Estimation -- 6.3.3. Bivariate Kernel Estimator -- 6.4. Robust Estimators
6.5. Jackknife Estimators -- 6.6. Bootstrap Method -- 6.7. Propensity Scores -- 6.8. Interim Analysis and Stopping Rules -- 6.8.1. Stopping Rules -- 6.8.2. Conditional Power -- 6.9. Microarrays and Multiple Testing -- 6.9.1. Microarrays -- 6.9.2. Multiple Testing -- 6.10. Stability of Products -- 6.11. Group Testing -- 6.12. Correspondence Analysis -- 6.13. Classification Regression Trees -- 6.14. Multidimensional Scaling -- 6.15. Path Analysis -- 6.16. Choice-Based Conjoint Analysis -- 6.16.1. Availability Designs and Cross Effects -- 6.16.2. Pareto-Optimal Choice Sets -- 6.16.3. Mixture-Amount Designs -- 6.17. Meta-Analysis -- 6.17.1. Homogeneity of the Effect Sizes -- 6.17.2. Combining the p-Values.
주제
ISBN
9780367382834 (pbk.) 9781439812822 (hardcover : alk. paper) 1439812829 (hardcover : alk. paper) 9780415804318 0415804310
청구기호
519.50285 P123p
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