Practical statistical methods a SAS programming approach
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...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Boca Raton, Fla. u.a.
CRC Press
c 2011
London Taylor & Francis distributor c 2011 |
| Schlagworte: | |
| Online Zugang: | Inhaltsverzeichnis Volltext |
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Tag hinzufügen | Zusammenfassung: | 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. |
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| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIII, 290 S. Ill., graph. Darst. 25 cm |
| ISBN: | 1439812829 1-4398-1282-9 9781439812822 978-1-4398-1282-2 |