Experimentation in mathematics : computational paths to discovery / by Borwein, Jonathan M., Bailey, David H., Borwein, Jonathan M., Girgensohn, Roland, 1964-

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Applied Regression Analysis and Experimental Design / by Brook, Richard J.

Table of Contents: “…PrefaceFitting a Model to DataIntroductionHow to Fit a LineResidualsTransformations to Obtain LinearityFitting a Model Using Vectors and MatricesDeviations from MeansAn Example- Value of a Postage Stamp Over TimeProblems Goodness of Fit of the ModelIntroductionCoefficient Estimates for Univariate RegressionCoefficient Estimates for Mulitvariate RegressionANOVA TablesThe F TestThe Coefficient of DeterminationPredicted Values of Y and Confidence IntervalsResidualsReduced ModelsPure Error and Lack of FitExample- Lactation CurveProblemsWhich Variable Should Be Included in the ModelIntroductionOrthogonal Predictor VariablesLinear Transformations of the Predictor VariablesAdding Nonorthogonal Variables SequentiallyCorrelation FormVariable Selection- All Possible RegressionsVariable Selection- Sequential MethodsQualitative (Dummy) VariablesAggregation of DataProblemsPeculiarities of ObservationsIntroductionSensitive or High Leverage PointsOutliersWeighted Least SquaresMore on TransformationsEigenvalues and Principal ComponentsRidge RegressionPrior InformationCleaning up DataProblemsThe Experimental Design ModelIntroductionWhat Makes an Experiment The Linear ModelTests of HypothesisTesting of AssumptionsProblemsAssessing the Treatment MethodsIntroductionSpecific Hypothesis ContrastsFactorial AnalysisUnpredicted EffectsConclusionProblemsBlockingIntroductionStructure of Experimental UnitsBalanced Incomplete Block DesignsConfoundingMiscellaneous TricksProblems Extensions to the ModelIntroductionHierarchic DesignsRepeated MeasuresCovariance AnalysisUnequal ReplicationModeling the DataProblemsAppendix A Review of Vectors an.…”
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Design of Experiments for Generalized Linear Models / by Russell, Kenneth G.

Table of Contents: “…Generalized Linear Models Background Material The Theory Underlying Design The Binomial Distribution The Poisson Distribution Several Other Distributions Bayesian experimental design.…”
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Multivariate analysis, design of experiments, and survey sampling /

Table of Contents: “…chapter 6 Corrected Confidence Sets for Sequentially Designed Experiments: Examples /…”
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Quality by experimental design / by Barker, Thomas B.

Table of Contents: “…part Part I: The Philosophy of Experimentation --…”
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Framing social interaction : continuities and cracks in Goffmanђ́ةs frame analysis by Persson, Anders, 1956-

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Quality by experimental design / by Barker, Thomas B., Milivojevich, Andrew

Table of Contents: “…part Section I: The Philosophy of Experimentation -- chapter 1 Why Design Experiments? -- chapter 2 Organizing the Experiment -- chapter 3 The Neglected Response Variable -- part Section II Statistical Experimental Design -- chapter 4 The Factorial Two-Level Design and General Factorial Designs -- chapter 5 Fractional Factorials at Two Levels -- chapter 6 Multilevel Designs -- chapter 7 Three-Level Designs -- chapter 8 Blocking in Factorial Designs -- chapter 9 Randomized Block and Latin Square -- chapter 10 Nested Designs -- chapter 11 Evolutionary Operation -- part Section III Sorting the Signal from the Noise -- chapter 12 Simple Analysis -- chapter 13 Analysis of Means by Using the Variance -- chapter 14 Yates Analysis: Analysis of 2k and 2k−p Designs -- chapter 15 Matrix Algebra -- chapter 16 Least Squares Analysis -- chapter 17 Putting ANOVA and Least Squares to Work -- chapter 18 ANOVA for Blocked and Nested Designs -- part Section IV: The Derivation of Empirical Equations from Statistically Designed Experiments -- chapter 19 Case History of an Experimental Investigation -- part Section V Utilization of Empirical Equations -- chapter 20 Robust Design -- chapter 21 Monte Carlo Simulation and Tolerance Design -- chapter 22 Case History Completed: The Utilization of the Equation -- part Section VI: Special Topics in Experimental Design Mixture Experiments -- chapter 23 Introduction to Mixture Experiments -- chapter 24 Simplex Lattice Design -- chapter 25 The Simplex Centroid Design -- chapter 26 Constrained Mixtures -- chapter 27 Statistical Tables and Graphs.…”
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Depression - a nurse's experience : shadows of life / by Burton, Veronica, Stewart, Moira

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Statistical Methods in Agriculture and Experimental Biology.

Table of Contents: “…Introduction--The Need for Statistics--The Use of Computers in Statistics--Probability and Distributions--Probability--Populations and Samples--Means and Variances--The Normal Distribution--Sampling Distributions--Estimation and Hypothesis Testing--Estimation of the Population Mean--Testing Hypotheses About the Population Mean--Population Variance Unknown--Comparison of Samples--A Pooled Estimate of Variance--A Simple Experiment--Randomization and Replication--Analysis of a Completely Randomized Design With Two…”
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Exploring Chaos : theory and experiment / by Davies, Brian

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Competitive innovation and improvement : statistical design and control by Dey, Kieron A.

Subjects: “…Experimental design Case studies. sh 85046441…”
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Introduction to experimental biophysics - a laboratory guide / by Nadeau, Jay L.

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Fungi : experimental methods in biology / by Maheshwari, Ramesh

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Changing clinical care : experiences and lessons of systematisation / by Gray, Andrew, Degeling, Pieter, McEwen, Abayomi

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Cooking as a chemical reaction : culinary science with experiments / by Ozilgen, Z. Sibel

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Accelerating health care transformation with lean and innovation : the virginia mason experience / by Plsek, Paul E.

Table of Contents: “…chapter 2 Strategy as Innovation, Strategy for Innovation -- chapter 3 Integrating Innovation and Lean in Practice -- chapter 4 Learning to Be Innovative -- chapter 5 Supportive Culture for Innovation -- chapter 6 Supportive Infrastructure for Innovation -- chapter 7 Bringing Innovation to Daily Work -- chapter 8 Using Patient Experience to Drive Innovation -- chapter 9 Looking Ahead: The Future of Innovation and Lean at the Virginia Mason Medical Center.…”
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Educational game design fundamentals : a journey to creating intrinsically motivating learning experiences / by Kalmpourtzis, George

Table of Contents: “…chapter Introduction: The Journey Begins -- chapter 1 Designing Learning Experiences -- chapter 2 The Magical Powers of the Game Designer -- chapter 3 Pedagogy and Games -- chapter 4 The Game Core -- chapter 5 Where to Start? …”
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Cyclic and computer generated designs by John, J. A., Williams, E. R. (Emlyn Rhys), 1949-

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Analysis of Variance, Design, and Regression : Linear Modeling for Unbalanced Data, Second Edition by Christensen, Ronald

Table of Contents: “…Introduction Probability Random variables and expectations Continuous distributions The binomial distribution The multinomial distribution ----One Sample Example and introduction Parametric inference about Prediction intervals Model testing Checking normality Transformations Inference about 2 ----General Statistical InferenceModel-based testing Inference on single parameters: assumptions Parametric tests Confidence intervalsP 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 LancasterIrwin 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 Fishers 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 "Fishers" least significant difference method Bonferroni adjustments Scheffs 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 varianceRandom 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.…”
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Confidence intervals on variance components by Burdick

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