Statistical Data Analysis Using SAS (2nd Ed., 2nd ed. 2018) Intermediate Statistical Methods Springer Texts in Statistics Series
Langue : Anglais
Auteurs : Marasinghe Mervyn G., Koehler Kenneth J.
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.
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.
Introduction to the SAS Language
1.1 Introduction
SAS Example A1
1.2 Basic Language: Rules and Syntax
Data Values
SAS Data Sets
Variables
Observations
SAS Names
SAS Variable Lists
SAS Statements
Syntax of SAS Statements
Missing Values
SAS Programming Statements
1.3 Creating SAS Data Sets
SAS Example A2
SAS Example A3
1.4 The INPUT Statement
List Input
Formatted Input
Column INPUT
Combining INPUT Styles
1.5 SAS Data Step Programming Statements and Their Uses
Assignment Statements
Example 1.5.1
SAS Functions: Conditional Execution
Example 1.5.2
Example 1.5.3
Example 1.5.4
Example 1.5.5
Example 1.5.6
SAS Example A4
Repetit
ive ComputationExample 1.5.7
Example 1.5.8
Example 1.5.9
Example 1.5.10
1.6 Data Step Processing
SAS Example A5
SAS Example A6
SAS Example A7
1.7 More on INPUT Statement
1.7.1 Use of pointer controls
1.7.2 The trailing @ line-hold specifier
SAS Example A8
1.7.3 The trailing @@ line-hold specifier
Example 1.7.1
1.7.4 Use of RETAIN statement
SAS Example A9
1.7.5 The use of line pointer controls
Example 1.7.2
1.8 Using SAS Procedures
The Proc Step
Specifying Options in the PROC Statement
Procedure Information Statements
Example 1.8.1
Output 1
Example A10Output 2
Variable Attribute Statements
The FORMAT statement
The LABEL statement
The LENGTH statement
SAS
SAS Example A11
1.9 Exercises
2 More on SAS Programming and Some Applications
2.1 More on the DATA and PROC Steps
2.1.1 Reading data from _les
The INFILE Statement
The FILENAME Statement
Example 2.1.1
Some In_le Statement Options
2.1.2 Combining SAS data sets
SAS Example B1
The SET Statement
2.1.3 Saving and retrieving permanent SAS data sets
SAS Example B2
SAS Example B3
2.1.4 User-defined informats and formats
Example 2.1.2
SAS Example B4
Example 2.1.3
2.1.5 Creating SAS data sets in procedure steps
SAS Example B5
<2.2 SAS Procedures for Descriptive StatisticsSAS Example B6
SAS Example B7
2.2.1 The UNIVARIATE procedure
Some PROC Statement Options
Some CL
ASS Statement OptionsSAS Example B8
2.2.2 The FREQ procedure
Some TABLES Statement Options
/GRAPH SAS Example B9
Phi coefficient
Contingency coefficient, C
Cramer's V
Gamma , Kendall's tau-b, Somers' D Proportional Reduction in Error (PRE) Measures
Pearson correlation coefficient, r2 and Spearman rank-order correlation coefficient
SAS Example B10
2.3 Some Useful Base SAS Procedures
2.3.1 The TABULATE procedure
SAS Example B11
SAS Example B12
2.3.2 The REPORT procedure
SAS Example B13
SAS Example B14
SAS Example B15
2.4 Exercises
3 Introduction to SAS Graphics
3.1 Introduction
Template-based graphics (ODS graphics)
ODS Statistical Graphics procedures
SAS Example C1
Traditional SAS graphics via SAS
3.2 Template-based graphics (SAS/ODS graphics)
SAS Example C2
SAS Example C3
SAS Example C4
3.3 SAS Statistical Graphics procedures
3.3.1 The SGPLOT procedure
Some SCATTER Statement Options
Some ELLIPSE Statement Options
SAS Example C5
Some HISTOGRAM Statement Options
Some DENSITY Statement Options
SAS Example C6
Some VBOX Statement Options
SAS Example C7
Some VLINE Statement Options
SAS Example C8
3.3.2 The SGPANEL procedure
Some PANELBY Statement Options
SAS Example C9
Some VBAR Statement Options
SAS Example C10
Some DOT Statement Options
SAS Example C11
3.3.3 The SGSCATTER procedure
Some MATRIX Statement Options
SAS Example C12
Attribute Map Data Sets
3.4 ODS
Graphics from other SAS procedures SAS Example C13
SAS Example C14
SAS Example C15
SAS Example C16
3.5 Exercises
4 Statistical Analysis of Regression Models
4.1 An Introduction to Simple Linear Regression
Estimation of Parameters
Statistical Inference
4.1.1 Simple linear regression using PROC REG
SAS Example D1
4.1.2 Lack of t test
SAS Example D2
4.1.3 Diagnostic use of case statistics
SAS Example D3
4.1.4 Prediction of new y values using regression
SAS Example D4
4.2 An Introduction to Multiple Regression Analysis
Multiple Regression Model
Estimation of Parameters
Matrix Notation
4.2.1 Multiple regression analysis using PROC REG
SAS Example D5
4.2.2 Case statistics and residual analysis
Residuals Hat Matrix
Con_dence Interval for the Mean E(yi)
Prediction Interval for yi
Studentized Residuals
Externally Studentized Residuals
Leverage
Inuence Statistics:Cook's D
Inuence Statistics:DFFITS
Inuence Statistics:DFBETAS
SAS Example D5 (continued)
4.2.3 Residual plots
SAS Example D6
4.2.4 Examining relationships among regression variables
Multicollinearity
SAS Example D7
4.3 Types of Sums of Squares Computed in PROC REG
4.3.1 Model comparison technique and extra sum of squares
Reduction Notation
4.3.2 Types of sums of squares in SAS
Definition: 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 Method
Other Stepwise Methods
Coefficient of Multiple Correlation R2
All-Subsets Methods
Adjusted R2
Mallows' Cp Statistic
The AIC Criterion
The BIC and the SBC Criteria
4.4.1 Subset selection using PROC REG
SAS Example D9
SAS Example D10
riance ModelsSAS Example D11
SAS Example D12
4.4.2 Other options available in PROC REG for model selection
4.5 Model Selection using PROC GLMSELECT: Validation and Cross-Validation
SAS Example D13
SAS Example D14
4.6 Exercises
5 Analysis of Va
5.1 Introduction
5.1.1 Treatment Structure
5.1.2 Experimental Designs
5.1.3 Linear Models
5.2 One-Way Classification
Model
Estimation
Testing Hypotheses
Preplanned or a Priori Comparisons of Means
Example 5.2.1
Pairwise Comparisons of Means
Multiple Comparisons of Pairs of Means
5.2.1 Using PROC ANOVA to analyze one-way classiffcations
SAS Example E1
5.2.2 Making preplanned (or a priori) comparisons using PROC GLM
SAS Example E2
5.2.3 Testing orthogonal polynomials using contrasts
Example 5.2.2
SAS Example E3
5.3 One-Way Analysis of Covariance
Model
Estimation
Testing Hypotheses
5.3.1 Using PROC GLM to perform one-way covariance analysis
SAS Example E4
5.3.2 One-way cov
ariance analysis: Testing for equal slopesSAS Example E5
5.4 A Two-Way Factorial in a Completely Randomized Design
Model
Hypotheses Testing
Estimation
5.4.1 Analysis of a two-way factorial using PROC GLM
SAS Example E6
5.4.2 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 E8
5.7 Two-Way Classi_cation: Randomized Complete Block Design
Model
Estimation
Testing Hypotheses
5.7.1 Using PROC GLM to analyze a RCBD
SAS Example E9
5.7.2 Using PROC GLM to test for nonadditivity
SAS Example E10
5.8 Exercises
6 Analysis of Variance: Random and Mixed Effects Models
6.1 Introduction
6.2 One-Way Random Effects Model Model
Estimation and Hypothesis Testing
6.2.1 Using PROC GLM to analyze one-way random effects models
SAS Example F1
6.2.2 Using PROC MIXED to analyze one-way random effects models
SAS Example F2
SAS Example F3
SAS Example F4
6.3 Two-Way Crossed Random E_ects Model
Model
Estimation and Hypothesis Testing
6.3.1 Using PROC GLM and PROC MIXED to analyze
two-way crossed random effects models
SAS Example F5
SAS Example F6
SAS Example F7
6.3.2 Randomized complete block design: Blocking when treatment factors are random
6.4 Two-Way Nested Random Effects Model
Model
Estimation and Hypothesis Testing
6.4.1 Using PROC GLM to analyze two-way nested random effects models
SAS Example F8
6.4.2 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 design
Model
Estimation and Hypothesis Testing
SAS Example F10
SAS Example F11
6.5.2 Two-way mixed effects model: Crossed classification
Model
A Special Comment
Estimation and Hypothesis Testing
SAS Example F12
SAS Example F13
6.5.3 Two-way mixed effects model: Nested classification
Model
Estimation and Hypothesis Testing
SAS Example F14
SAS Example F15
6.6 Models with Random and Nested Effects for More Complex Experiments
6.6.1 Models for nested factorials
SAS Example F16
6.6.2 Models for split-plot expe
riments6.6.3 Analys
is of split-plot experiments using PROC GLM SAS Example F17
6.6.4 Analysis of split-plot experiments using PROC MIXED
SAS Example F18
6.7 Exercises
7 Beyond Regression and Analysis of Variance
7.1 Introduction
7.2 Non-linear Models
7.2.1 Introduction
7.2.2 Growth Curve Models
SAS Example G1
7.2.3 Pharmacokinetic Models
SAS Example G2
7.2.4 Models for Toxicology Assays
SAS Example G3
SAS Example G4
7.3 Generalized Linear Models
7.3.1 Introduction
7.3.2 Logistic Regression
SAS Example G5
7.3.3 Poisson Regression and Log-linear Models
SAS Example G6
7.3.4 Models for Over-dispersion
SAS Example G7
SAS Example G8
7.4 Generalized Estimating Equations (GEE
)7.4.1 Dealing with
Over-Dispersion7.4.2 Logistic and Poisson Regression for Repeated
Measures Studies
SAS Example G9
7.4.3 Logistic and Poisson Regression for Nested
Experiments
SAS Example G10
7.4.4 Robust Estimation of Standard Errors (Sandwich Estimators)
SAS Example G11
e G17SAS Example G12
7.5 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 G13
7.5.3 Application to More Complex Experiments
SAS Example G14
7.5.4 Models with Spatial Variability
SAS Example G15
7.6 Non-linear Models with Random Effects
7.6.1 Growth Curve Model with Random Effects
SAS Example G16
7.6.2 Non-linear Models with random Coefficients
SAS Exampl
A SAS Templates
A.1 Introduction
A.2 Simple Template Modification
B Tables
References
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. He has taught a data analysis course based on SAS for over 35 years and offered workshops and short-courses on various aspects of SAS including traditional SAS programming, SAS Enterprise Guide, SAS Enterprise Miner, and JMP for many years to both university audiences and non-academic participants.
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.
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.
Integrates SAS programming with complex data analysis applications
Focuses on using SAS and statistical aspects of models and methods of analysis
Introduction to SAS - beyond the basics and illustrated with numerous worked examples
Advanced material suitable for a second course in applied statistics with every method explained using a SAS analysis to illustrate a real-world problem
15-20 problems in every chapter
End of chapter exercises
Solutions to exercises
Downloadable SAS code and data sets
Date de parution : 04-2018
Ouvrage de 679 p.
17.8x25.4 cm
Thèmes de Statistical Data Analysis Using SAS :
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