Analysis of variance, design, and regression : linear modeling for unbalanced data /: linear modeling for unbalanced data. ([2016])

• Record Type: Book
• Further Information: Note: Ronald Christensen, University of New Mexico, Albuquerque, USA.
• Authors: Christensen, Ronald, 1951-
• Contents: Introduction; Probability; Random variables and expectations; Continuous distributions; The binomial distribution; The multinomial distribution One Sample; Example and introduction; Parametric inference about μ ; Prediction intervals; Model testing; Checking normality; Transformations; Inference about σ2 General Statistical Inference ; Model-based testing; Inference on single parameters: assumptions; Parametric tests; Confidence intervals; P values; Validity of tests and confidence intervals; Theory of prediction intervals; Sample size determination and power; The shape of things to come Two Samples; Two correlated samples: Paired comparisons; Two independent samples with equal variances; Two independent samples with unequal variances; Testing equality of the variances Contingency Tables ; One binomial sample; Two independent binomial samples; One multinomial sample; Two independent multinomial samples; Several independent multinomial samples; Lancaster–Irwin partitioning Simple Linear Regression; An example; The simple linear regression model; The analysis of variance table; Model-based inference; Parametric inferential procedures; An alternative model; Correlation; Two-sample problems; A multiple regression; Estimation formulae for simple linear regression Model Checking; Recognizing randomness: Simulated data with zero correlation; Checking assumptions: Residual analysis; Transformations Lack of Fit and Nonparametric Regression ; Polynomial regression; Polynomial regression and leverages; Other basis functions; Partitioning methods; Splines; Fisher’s lack-of-fit test Multiple Regression: Introduction ; Example of inferential procedures; Regression surfaces and prediction; Comparing regression models; Sequential fitting; Reduced models and prediction; Partial correlation coefficients and added variable plots; Collinearity; More on model testing; Additive effects and interaction; Generalized additive models; Final comment Diagnostics and Variable Selection ; Diagnostics; Best subset model selection; Stepwise model selection; Model selection and case deletion; Lasso regression Multiple Regression: Matrix Formulation ; Random vectors; Matrix formulation of regression models; Least squares estimation of regression parameters; Inferential procedures; Residuals, standardized residuals, and leverage; Principal components regression One-Way ANOVA ; Example; Theory; Regression analysis of ANOVA data; Modeling contrasts; Polynomial regression and one-way ANOVA; Weighted least squares Multiple Comparison Methods ; "Fisher’s" least significant difference method; Bonferroni adjustments; Scheffé’s method; Studentized range methods; Summary of multiple comparison procedures Two-Way ANOVA ; Unbalanced two-way analysis of variance; Modeling contrasts; Regression modeling; Homologous factors ACOVA and Interactions ; One covariate example; Regression modeling; ACOVA and two-way ANOVA; Near replicate lack-of-fit tests Multifactor Structures ; Unbalanced three-factor analysis of variance; Balanced three-factors; Higher-order structures Basic Experimental Designs ; Experiments and causation; Technical design considerations; Completely randomized designs; Randomized complete block designs; Latin square designs; Balanced incomplete block designs; Youden squares; Analysis of covariance in designed experiments; Discussion of experimental design Factorial Treatments ; Factorial treatment structures; Analysis; Modeling factorials; Interaction in a Latin square; A balanced incomplete block design; Extensions of Latin squares Dependent Data ; The analysis of split-plot designs; A four-factor example; Multivariate analysis of variance; Random effects models Logistic Regression: Predicting Counts ; Models for binomial data; Simple linear logistic regression; Model testing; Fitting logistic models; Binary data; Multiple logistic regression; ANOVA type logit models; Ordered categories Log-Linear Models: Describing Count Data ; Models for two-factor tables; Models for three-factor tables; Estimation and odds ratios; Higher-dimensional tables; Ordered categories; Offsets; Relation to logistic models; Multinomial responses; Logistic discrimination and allocation Exponential and Gamma Regression: Time-to-Event Data ; Exponential regression; Gamma regression Nonlinear Regression ; Introduction and examples; Estimation; Statistical inference; Linearizable models Appendix A: Matrices and Vectors; Appendix B: Tables Exercises appear at the end of each chapter.
• Publisher Details: Boca Raton : CRC Press, Taylor & Francis Group
• Publication Date: 2016
• Copyright Date: 2016
• Extent: 1 online resource (603 pages), illustrations
• Subjects: 519.5; Mathematical statistics; Analysis of variance; Regression analysis; Experimental design; Analysis of variance; Experimental design; Mathematical statistics; Regression analysis; MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General; Electronic books
• Languages: English
• ISBNs: 9781498774055; 1498774059
• Related ISBNs: 9781498730143; 1498730140
• Notes: Note: Includes bibliographical references.; Note: Print version record.
• 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.