Design and analysis of experiments : classical and regression approaches with SAS /: classical and regression approaches with SAS. (2008)

• Record Type: Book
• Further Information: Note: Leonard C. Onyiah.
• Other Names: Onyiah, Leonard C
• Contents: Introductory Statistical Inference and Regression Analysis ; Elementary Statistical Inference; Regression Analysis; Experiments, the Completely Randomized Design (CRD)—Classical and Regression Approaches ; Experiments; Experiments to Compare Treatments; Some Basic Ideas; Requirements of a Good Experiment; One-Way Experimental Layout or the CRD: Design and Analysis; Analysis of Experimental Data (Fixed Effects Model); Expected Values for the Sums of Squares; The Analysis of Variance (ANOVA) Table; Follow-Up Analysis to Check for Validity of the Model; Checking Model Assumptions; Applications of Orthogonal Contrasts; Regression Models for the CRD (One-Way Layout); Regression Models for ANOVA for CRD Using Orthogonal Contrasts; Regression Model for Example 2.2 Using Orthogonal Contrasts Coding (Helmert Coding); Regression Model for Example 2.3 Using Orthogonal Contrasts Coding; Two-Factor Factorial Experiments and Repeated Measures Designs (RMDs) ; The Full Two-Factor Factorial Experiment (Two-Way ANOVA with Replication)—Fixed Effects Model; Two-Factor Factorial Effects (Random Effects Model); Two-Factor Factorial Experiment (Mixed Effects Model); One-Way RMD; Mixed Randomized Complete Block Design (RCBD) (Involving Two Factors); Regression Approaches to the Analysis of Responses of Two-Factor Experiments and RMDs ; Regression Models for the Two-Way ANOVA (Full Two-Factor Factorial Experiment); The Regression Model for Two-Factor Factorial Experiment Using Reference Cell Coding for Dummy Variables; Use of SAS for the Analysis of Responses of Mixed Models; Use of PROC Mixed in the Analysis of Responses of RMD in SAS; Residual Analysis for the Vitamin Experiment; Regression Model of the Two-Factor Factorial Design Using Orthogonal Contrasts; Use of PROC Mixed in SAS to Estimate Variance Components When Levels of Factors Are Random; Designs with Randomization Restriction—Randomized Complete Block, Latin Squares, and Related Designs ; RCBD; Testing for Differences in Block Means; Estimation of a Missing Value in the RCBD; Latin Squares; Some Expected Mean Squares; Replications in Latin Square Design; The Graeco–Latin Square Design; Estimation of Parameters of the Model and Extracting Residuals; Regression Models for Randomized Complete Block, Latin Squares, and Graeco–Latin Square Designs ; Regression Models for the RCBD; SAS Analysis of Responses of Example 5.1 Using Dummy Regression (Effects Coding Method); Dummy Variables Regression Model for the RCBD (Reference Cell Method); Application of Dummy Variables Regression Model to Example 5.2 (Effects Coding Method); Regression Model for RCBD of Example 5.2 (Reference Cell Coding); Regression Models for the Latin Square Design; Dummy Variables Regression Analysis for Example 5.5 (Reference Cell Method); Regression Model for Example 5.7 Using Effects Coding Method to Define Dummy Variables; Dummy Variables Regression Model for Example 5.7 (Reference Cell Coding Method); Regression Model for the Graeco–Latin Square Design; Regression Model for Graeco–Latin Square (Reference Cell Method); Regression Model for the RCBD Using Orthogonal Contrasts; Factorial Designs—The 2k and 3k Factorial Designs ; Advantages of Factorial Designs; The 2k and 3k Factorial Designs; Contrasts for Factorial Effects in 22 and 23 Factorial Designs; The General 2k Factorial Design; Factorial Effects in 2k Factorial Designs; The 3k Factorial Designs; Extension to k Factors at Three Levels; Regression Models for 2k and 3k Factorial Designs ; Regression Models for the 22 Factorial Design Using Effects Coding Method; Regression Model for Example 7.1 Using Reference Cell to Define Dummy Variables; General Regression Models for the Three-Way Factorial Design; The General Regression Model for a Three-Way ANOVA (Reference Cell Coding Method); Regression Models for the Four-Factor Factorial Design Using Effects Coding Method; Regression Analysis for a Four-Factor Factorial Experiment Using Reference Cell Coding to Define Dummy Variables; Dummy Variables Regression Models for Experiment in 3k Factorial Designs; Fitting Regression Model for Example 7.5 (Effects Coding Method); Fitting Regression Model 8.22 (Reference Cell Coding Method) to Responses of Experiment of Example 7.5; Fractional Replication and Confounding in 2k and 3k Factorial Designs ; Construction of the 2k− 1 Fractional Factorial Design; Contrasts of the 2k− 1 Fractional Factorial Design; The General 2k− p Fractional Factorial Design; Resolution of a Fractional Factorial Design; Fractional Replication in 3k Factorial Designs; The General 3k− p Factorial Design; Confounding in 2k and 3k Factorial Designs; Confounding in 2k Factorial Designs; Confounding in 3k Factorial Designs; Partial Confounding in Factorial Designs; Balanced Incomplete Blocks, Lattices, and Nested Designs ; The Balanced Incomplete Block Design; Comparison of Two Treatments; Orthogonal Contrasts in Balanced Incomplete Block Designs; Lattice Designs; Partially Balanced Lattices; Nested or Hierarchical Designs; Designs with Nested and Crossed Factors; Methods for Fitting Response Surfaces and Analysis of Covariance ; Method of Steepest Ascent; Designs for Fitting Response Surfaces; Fitting a First-Order Model to the Response Surface; Fitting and Analysis of the Second-Order Model; Analysis of Covariance (ANCOVA); One-Way ANCOVA; Other Covariance Models; Multivariate Analysis of Variance (MANOVA) ; Link between ANOVA and MANOVA; One-Way MANOVA; MANOVA—The Randomized Complete Block Experiment; Multivariate Two-Way Experimental Layout with Interaction; Two-Stage Multivariate Nested or Hierarchical Design; The Multivariate Latin Square Design ; Appendix: Statistical Tables ; Index ; Exercises and References appear at the end of each chapter.
• Publisher Details: Place of publication not identified : Chapman and Hall/CRC
• Publication Date: 2008
• Extent: 1 online resource, illustrations
• Subjects: 519.536; Regression analysis -- Data processing; Analysis of variance -- Data processing
• Languages: English
• ISBNs: 9781420060553; 1420060554
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.