Statistical data analysis using SAS : intermediate statistical methods
著者
書誌事項
Statistical data analysis using SAS : intermediate statistical methods
（Springer texts in statistics）
Springer, c2018
2nd ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data.
The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude.
Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem.
New to this edition:
* Covers SAS v9.2 and incorporates new commands
* Uses SAS ODS (output delivery system) for reproduction of tables and graphics output
* Presents new commands needed to produce ODS output
* All chapters rewritten for clarity
* New and updated examples throughout
* All SAS outputs are new and updated, including graphics
* More exercises and problems
* Completely new chapter on analysis of nonlinear and generalized linear models
* Completely new appendix
Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing.
Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
目次
Introduction to the SAS Language1.1 Introduction SAS Example A1 1.2 Basic Language: Rules and SyntaxData Values SAS Data SetsVariablesObservationsSAS Names SAS Variable ListsSAS StatementsSyntax of SAS Statements Missing ValuesSAS Programming Statements1.3 Creating SAS Data SetsSAS Example A2SAS Example A31.4 The INPUT StatementList InputFormatted Input Column INPUTCombining INPUT Styles1.5 SAS Data Step Programming Statements and Their UsesAssignment StatementsExample 1.5.1SAS Functions: Conditional ExecutionExample 1.5.2 Example 1.5.3Example 1.5.4Example 1.5.5Example 1.5.6SAS Example A4 Repetitive ComputationExample 1.5.7Example 1.5.8Example 1.5.9Example 1.5.101.6 Data Step Processing SAS Example A5SAS Example A6 SAS Example A71.7 More on INPUT Statement1.7.1 Use of pointer controls1.7.2 The trailing @ line-hold specifier SAS Example A8 1.7.3 The trailing @@ line-hold specifierExample 1.7.11.7.4 Use of RETAIN statement SAS Example A9 1.7.5 The use of line pointer controlsExample 1.7.2 1.8 Using SAS ProceduresThe Proc StepSpecifying Options in the PROC StatementProcedure Information Statements Example 1.8.1Output 1Output 2 Variable Attribute StatementsThe FORMAT statementThe LABEL statementThe LENGTH statementSAS Example A10SAS Example A111.9 Exercises
2 More on SAS Programming and Some Applications2.1 More on the DATA and PROC Steps2.1.1 Reading data from _lesThe INFILE StatementThe FILENAME StatementExample 2.1.1Some In_le Statement Options 2.1.2 Combining SAS data setsSAS Example B1The SET Statement2.1.3 Saving and retrieving permanent SAS data setsSAS Example B2SAS Example B3 2.1.4 User-defined informats and formatsExample 2.1.2SAS Example B4 Example 2.1.3 2.1.5 Creating SAS data sets in procedure stepsSAS Example B5<2.2 SAS Procedures for Descriptive StatisticsSAS Example B6 SAS Example B72.2.1 The UNIVARIATE procedure Some PROC Statement OptionsSome CLASS Statement OptionsSAS Example B8 2.2.2 The FREQ procedureSome TABLES Statement Options SAS Example B9Phi coefficientContingency coefficient, CCramer's VGamma , Kendall's tau-b, Somers' D Proportional Reduction in Error (PRE) MeasuresPearson correlation coefficient, r2 and Spearman rank-order correlation coefficientSAS Example B102.3 Some Useful Base SAS Procedures2.3.1 The TABULATE procedure SAS Example B11SAS Example B122.3.2 The REPORT procedureSAS Example B13SAS Example B14 SAS Example B152.4 Exercises
3 Introduction to SAS Graphics3.1 Introduction Template-based graphics (ODS graphics) ODS Statistical Graphics proceduresSAS Example C1Traditional SAS graphics via SAS/GRAPH 3.2 Template-based graphics (SAS/ODS graphics)SAS Example C2SAS Example C3SAS Example C43.3 SAS Statistical Graphics procedures3.3.1 The SGPLOT procedureSome SCATTER Statement OptionsSome ELLIPSE Statement OptionsSAS Example C5 Some HISTOGRAM Statement Options Some DENSITY Statement OptionsSAS Example C6Some VBOX Statement Options SAS Example C7Some VLINE Statement OptionsSAS Example C83.3.2 The SGPANEL procedureSome PANELBY Statement OptionsSAS Example C9 Some VBAR Statement OptionsSAS Example C10Some DOT Statement OptionsSAS Example C113.3.3 The SGSCATTER procedureSome MATRIX Statement OptionsSAS Example C12 Attribute Map Data Sets3.4 ODS Graphics from other SAS procedures SAS Example C13SAS Example C14 SAS Example C15SAS Example C163.5 Exercises
4 Statistical Analysis of Regression Models4.1 An Introduction to Simple Linear RegressionEstimation of ParametersStatistical Inference 4.1.1 Simple linear regression using PROC REGSAS Example D14.1.2 Lack of t testSAS Example D24.1.3 Diagnostic use of case statisticsSAS Example D34.1.4 Prediction of new y values using regression SAS Example D44.2 An Introduction to Multiple Regression AnalysisMultiple Regression Model Estimation of ParametersMatrix Notation4.2.1 Multiple regression analysis using PROC REG SAS Example D5 4.2.2 Case statistics and residual analysisResiduals Hat Matrix Con_dence Interval for the Mean E(yi) Prediction Interval for yiStudentized Residuals Externally Studentized Residuals LeverageInuence Statistics:Cook's DInuence Statistics:DFFITSInuence Statistics:DFBETAS SAS Example D5 (continued) 4.2.3 Residual plotsSAS Example D6 4.2.4 Examining relationships among regression variablesMulticollinearity SAS Example D7 4.3 Types of Sums of Squares Computed in PROC REG4.3.1 Model comparison technique and extra sum of squaresReduction Notation4.3.2 Types of sums of squares in SASDefinition: Type I (or Sequential) Sums of Squares Definition: Type II (or Partial) Sum of Squares Type I and Type II Sums of Squares in Reduction NotationSAS Example D8 Interactive Model Fitting using PROC REG 4.4 Subset Selection Methods in Multiple Regression Forward Selection Method Backward Elimination Method Stepwise MethodOther Stepwise MethodsCoefficient of Multiple Correlation R2 All-Subsets MethodsAdjusted R2Mallows' Cp Statistic The AIC Criterion The BIC and the SBC Criteria 4.4.1 Subset selection using PROC REGSAS Example D9SAS Example D10 SAS Example D11SAS Example D124.4.2 Other options available in PROC REG for model selection4.5 Model Selection using PROC GLMSELECT: Validation and Cross-ValidationSAS Example D13SAS Example D14 4.6 Exercises
5 Analysis of Variance Models5.1 Introduction5.1.1 Treatment Structure 5.1.2 Experimental Designs5.1.3 Linear Models5.2 One-Way ClassificationModel EstimationTesting Hypotheses Preplanned or a Priori Comparisons of Means Example 5.2.1 Pairwise Comparisons of MeansMultiple Comparisons of Pairs of Means5.2.1 Using PROC ANOVA to analyze one-way classiffcationsSAS Example E15.2.2 Making preplanned (or a priori) comparisons using PROC GLMSAS Example E25.2.3 Testing orthogonal polynomials using contrastsExample 5.2.2SAS Example E35.3 One-Way Analysis of Covariance Model EstimationTesting Hypotheses5.3.1 Using PROC GLM to perform one-way covariance analysisSAS Example E45.3.2 One-way covariance analysis: Testing for equal slopesSAS Example E5 5.4 A Two-Way Factorial in a Completely Randomized DesignModelHypotheses Testing Estimation 5.4.1 Analysis of a two-way factorial using PROC GLMSAS Example E65.4.2 Residual analysis and transformations 5.5 Two-Way Factorial: Analysis of Interaction SAS Example E7 5.6 Two-Way Factorial: Unequal Sample Sizes SAS Example E85.7 Two-Way Classi_cation: Randomized Complete Block DesignModelEstimationTesting Hypotheses5.7.1 Using PROC GLM to analyze a RCBD SAS Example E95.7.2 Using PROC GLM to test for nonadditivitySAS Example E10 5.8 Exercises
6 Analysis of Variance: Random and Mixed Effects Models 6.1 Introduction6.2 One-Way Random Effects Model Model Estimation and Hypothesis Testing6.2.1 Using PROC GLM to analyze one-way random effects modelsSAS Example F16.2.2 Using PROC MIXED to analyze one-way random effects models SAS Example F2 SAS Example F3SAS Example F4 6.3 Two-Way Crossed Random E_ects Model Model Estimation and Hypothesis Testing6.3.1 Using PROC GLM and PROC MIXED to analyzetwo-way crossed random effects models SAS Example F5SAS Example F6SAS Example F76.3.2 Randomized complete block design: Blocking when treatment factors are random 6.4 Two-Way Nested Random Effects Model ModelEstimation and Hypothesis Testing6.4.1 Using PROC GLM to analyze two-way nested random effects models SAS Example F86.4.2 Using PROC MIXED to analyze two-way nested random effects modelsSAS Example F9 6.5 Two-Way Mixed Effects Model 6.5.1 Two-way mixed effects model: Randomized complete blocks designModel Estimation and Hypothesis TestingSAS Example F10 SAS Example F116.5.2 Two-way mixed effects model: Crossed classification Model A Special Comment Estimation and Hypothesis Testing SAS Example F12 SAS Example F136.5.3 Two-way mixed effects model: Nested classification Model Estimation and Hypothesis Testing SAS Example F14SAS Example F15 6.6 Models with Random and Nested Effects for More Complex Experiments6.6.1 Models for nested factorialsSAS Example F166.6.2 Models for split-plot experiments6.6.3 Analysis of split-plot experiments using PROC GLM SAS Example F176.6.4 Analysis of split-plot experiments using PROC MIXEDSAS Example F186.7 Exercises
7 Beyond Regression and Analysis of Variance7.1 Introduction 7.2 Non-linear Models7.2.1 Introduction7.2.2 Growth Curve ModelsSAS Example G1 7.2.3 Pharmacokinetic ModelsSAS Example G2 7.2.4 Models for Toxicology Assays SAS Example G3SAS Example G47.3 Generalized Linear Models7.3.1 Introduction7.3.2 Logistic Regression SAS Example G5 7.3.3 Poisson Regression and Log-linear ModelsSAS Example G67.3.4 Models for Over-dispersion SAS Example G7SAS Example G87.4 Generalized Estimating Equations (GEE)7.4.1 Dealing with Over-Dispersion7.4.2 Logistic and Poisson Regression for RepeatedMeasures Studies SAS Example G97.4.3 Logistic and Poisson Regression for NestedExperiments SAS Example G107.4.4 Robust Estimation of Standard Errors (Sandwich Estimators)SAS Example G11SAS Example G127.5 Generalized Linear Mixed Models 7.5.1 Logistic and Poisson Regression Models with Random Subject Effects 7.5.2 Models for Repeated Measures Studies SAS Example G137.5.3 Application to More Complex ExperimentsSAS Example G147.5.4 Models with Spatial VariabilitySAS Example G15 7.6 Non-linear Models with Random Effects7.6.1 Growth Curve Model with Random EffectsSAS Example G167.6.2 Non-linear Models with random CoefficientsSAS Example G17
APPENDICESA SAS Templates A.1 IntroductionA.2 Simple Template ModificationB TablesReferences
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