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A handbook of statistical analyses using SAS / Geoff Der and Brian S. Everitt.

By: Der, Geoff.
Contributor(s): Everitt, Brian.
Material type: TextTextPublisher: Boca Raton : CRC Press, c2009Edition: 3rd ed.Description: xv, 400 p. : ill. ; 24 cm.ISBN: 9781584887843 (hardback : alk. paper); 1584887842 (hardback : alk. paper).Subject(s): Mathematical statistics -- Data processingDDC classification: 519.5 Online resources: Table of contents only | WorldCat details | E-book Fulltext
Contents:
Table of contents Introduction to SAS. Data Description and Simple Inference: Mortality and Water Hardness in the United Kingdom. Simple Inference for Categorical Data: From Sandflies to Organic Particulates in the Air. Analysis of Variance I: Treating Hypertension. Analysis of Variance II: School Attendance among Australian Children. Simple Linear Regression: Alcohol Consumption and Cirrhosis Deaths and How Old Is the Universe? Multiple Regression: Determinants of the Crime Rate in States of the United States. Logistic Regression: Psychiatry Screening, Plasma Proteins, Danish Do-It-Yourself, and Lower Back Pain. Generalized Linear Models: Polyposis and School Attendance among Australian School Children. Generalized Additive Models: Burning Rubber and Air Pollution in the United States. Analysis of Variance of Repeated Measures Visual Acuity. Longitudinal Data I: Treatment of Postnatal Depression. Longitudinal Data II: Linear Mixed Models. Longitudinal Data III: Generalized Estimating Equations and Generalized Mixed Models. Survival Analysis: Gastric Cancer, the Treatment of Heroin Addicts, and Heart Transplants. Principal Components Analysis and Factor Analysis: Olympic Decathlon and Statements about Pain. Cluster Analysis: Air Pollution in the United States. Discriminant Function Analysis: Classifying Tibetan Skulls. Correspondence Analysis: Smoking and Motherhood, Sex and the Single Girl, and European Stereotypes. Appendix. References. Index.
Summary: Summary: Shows how to conduct various statistical analysis using SAS. This book covers inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis.
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"A Chapman & Hall book."

Includes bibliographical references (p. 377-381) and index.

Table of contents Introduction to SAS. Data Description and Simple Inference: Mortality and Water Hardness in the United Kingdom. Simple Inference for Categorical Data: From Sandflies to Organic Particulates in the Air. Analysis of Variance I: Treating Hypertension. Analysis of Variance II: School Attendance among Australian Children. Simple Linear Regression: Alcohol Consumption and Cirrhosis Deaths and How Old Is the Universe? Multiple Regression: Determinants of the Crime Rate in States of the United States. Logistic Regression: Psychiatry Screening, Plasma Proteins, Danish Do-It-Yourself, and Lower Back Pain. Generalized Linear Models: Polyposis and School Attendance among Australian School Children. Generalized Additive Models: Burning Rubber and Air Pollution in the United States. Analysis of Variance of Repeated Measures Visual Acuity. Longitudinal Data I: Treatment of Postnatal Depression. Longitudinal Data II: Linear Mixed Models. Longitudinal Data III: Generalized Estimating Equations and Generalized Mixed Models. Survival Analysis: Gastric Cancer, the Treatment of Heroin Addicts, and Heart Transplants. Principal Components Analysis and Factor Analysis: Olympic Decathlon and Statements about Pain. Cluster Analysis: Air Pollution in the United States. Discriminant Function Analysis: Classifying Tibetan Skulls. Correspondence Analysis: Smoking and Motherhood, Sex and the Single Girl, and European Stereotypes. Appendix. References. Index.

Summary:
Shows how to conduct various statistical analysis using SAS. This book covers inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis.

Applied Statistics

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