Introduction to Statistical Quality Control
Quality Improvement in the Modern Business Environment.STAISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT.Modeling Process Quality.Inferences About Process Quality.BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS.Methods and Philosophy of Statistical Process Control.Control Charts for Variables.Control Charts for Attributes.Process and Measurement Systems System Capability Analysis.OTHER STATISTICAL PROCESS MONITORING AND CONTROL TECHNIQUES.Cumulative Sum and Exponentially Weighted Moving Average Control Charts.Other Univariate SPC Techniques.Multivariate Process Monitoring and Control.Engineering Process Control and SPC.PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS.Factorial and Fractional Factorial Designs for Process Design and Improvement.Process Optimization with Designed Experiments.ACCEPTANCE SAMPLING.Lot--by--Lot Acceptance Sampling for Attributes.Other Acceptance Sampling Techniques.Appendix.Bibliography.Answers to Selected Exercises.Index.
Statistics for experimenters : an introduction to design, data analysis, and model building
Science and Statistics. COMPARING TWO TREATMENTS. Use of External Reference Distribution to Compare Two Means. Random Sampling and the Declaration of Independence. Randomization and Blocking with Paired Comparisons. Significance Tests and Confidence Intervals for Means, Variances, Proportions and Frequences. COMPARING MORE THAN TWO TREATMENTS. Experiments to Compare k Treatment Means. Randomized Block and Two--Way Factorial Designs. Designs with More Than One Blocking Variable. MEASURING THE EFFECTS OF VARIABLES. Empirical Modeling. Factorial Designs at Two Levels. More Applications of Factorial Designs. Fractional Factorial Designs at Two Levels. More Applications of Fractional Factorial Designs. BUILDING MODELS AND USING THEM. Simple Modeling with Least Squares (Regression Analysis). Response Surface Methods. Mechanistic Model Building. Study of Variation. Modeling Dependence: Times Series. Appendix Tables. Index.
Introduction to Statistical Quality Control
Quality Improvement in the Modern Business Environment.STAISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT.Modeling Process Quality.Inferences About Process Quality.BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS.Methods and Philosophy of Statistical Process Control.Control Charts for Variables.Control Charts for Attributes.Process and Measurement Systems System Capability Analysis.OTHER STATISTICAL PROCESS MONITORING AND CONTROL TECHNIQUES.Cumulative Sum and Exponentially Weighted Moving Average Control Charts.Other Univariate SPC Techniques.Multivariate Process Monitoring and Control.Engineering Process Control and SPC.PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS.Factorial and Fractional Factorial Designs for Process Design and Improvement.Process Optimization with Designed Experiments.ACCEPTANCE SAMPLING.Lot--by--Lot Acceptance Sampling for Attributes.Other Acceptance Sampling Techniques.Appendix.Bibliography.Answers to Selected Exercises.Index.
Quick and easy analysis of unreplicated factorials
Box and Meyer (1986) introduced a method for assessing the sizes of contrasts in unreplicated factorial and fractional factorial designs. This is a useful technique, and an associated graphical display popularly known as a Bayes plot makes it even more effective. This article presents a competing technique that is also effective and is computationally simple. An advantage of the new method is that the results are given in terms of the original units of measurement. This direct association with the data may make the analysis easier to explain.
Applied Chemometrics for Scientists
Preface. 1 Introduction. 1.1 Development of Chemometrics. 1.2 Application Areas. 1.3 How to Use this Book. 1.4 Literature and Other Sources of Information. References. 2 Experimental Design. 2.1 Why Design Experiments in Chemistry? 2.2 Degrees of Freedom and Sources of Error. 2.3 Analysis of Variance and Interpretation of Errors. 2.4 Matrices, Vectors and the Pseudoinverse. 2.5 Design Matrices. 2.6 Factorial Designs. 2.7 An Example of a Factorial Design. 2.8 Fractional Factorial Designs. 2.9 Plackett-Burman and Taguchi Designs. 2.10 The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction. 2.11 Central Composite Designs. 2.12 Mixture Designs. 2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils. 2.14 Simplex Optimization. 2.15 Leverage and Confidence in Models. 2.16 Designs for Multivariate Calibration. References. 3 Statistical Concepts. 3.1 Statistics for Chemists. 3.2 Errors. 3.3 Describing Data. 3.4 The Normal Distribution. 3.5 Is a Distribution Normal? 3.6 Hypothesis Tests. 3.7 Comparison of Means: the t-Test. 3.8 F-Test for Comparison of Variances. 3.9 Confidence in Linear Regression. 3.10 More about Confidence. 3.11 Consequences of Outliers and How to Deal with Them. 3.12 Detection of Outliers. 3.13 Shewhart Charts. 3.14 More about Control Charts. References. 4 Sequential Methods. 4.1 Sequential Data. 4.2 Correlograms. 4.3 Linear Smoothing Functions and Filters. 4.4 Fourier Transforms. 4.5 Maximum Entropy and Bayesian Methods. 4.6 Fourier Filters. 4.7 Peakshapes in Chromatography and Spectroscopy. 4.8 Derivatives in Spectroscopy and Chromatography. 4.9 Wavelets. References. 5 Pattern Recognition. 5.1 Introduction. 5.2 Principal Components Analysis. 5.3 Graphical Representation of Scores and Loadings. 5.4 Comparing Multivariate Patterns. 5.5 Preprocessing. 5.6 Unsupervised Pattern Recognition: Cluster Analysis. 5.7 Supervised Pattern Recognition. 5.8 Statistical Classification Techniques. 5.9 K Nearest Neighbour Method. 5.10 How Many Components Characterize a Dataset? 5.11 Multiway Pattern Recognition. References. 6 Calibration. 6.1 Introduction. 6.2 Univariate Calibration. 6.3 Multivariate Calibration and the Spectroscopy of Mixtures. 6.4 Multiple Linear Regression. 6.5 Principal Components Regression. 6.6 Partial Least Squares. 6.7 How Good is the Calibration and What is the Most Appropriate Model? 6.8 Multiway Calibration. References. 7 Coupled Chromatography. 7.1 Introduction. 7.2 Preparing the Data. 7.3 Chemical Composition of Sequential Data. 7.4 Univariate Purity Curves. 7.5 Similarity Based Methods. 7.6 Evolving and Window Factor Analysis. 7.7 Derivative Based Methods. 7.8 Deconvolution of Evolutionary Signals. 7.9 Noniterative Methods for Resolution. 7.10 Iterative Methods for Resolution. 8 Equilibria, Reactions and Process Analytics. 8.1 The Study of Equilibria using Spectroscopy. 8.2 Spectroscopic Monitoring of Reactions. 8.3 Kinetics and Multivariate Models for the Quantitative Study of Reactions 8.4 Developments in the Analysis of Reactions using On-line Spectroscopy. 8.5 The Process Analytical Technology Initiative. References. 9 Improving Yields and Processes Using Experimental Designs. 9.1 Introduction. 9.2 Use of Statistical Designs for Improving the Performance of Synthetic Reactions. 9.3 Screening for Factors that Influence the Performance of a Reaction. 9.4 Optimizing the Process Variables. 9.5 Handling Mixture Variables using Simplex Designs. 9.6 More about Mixture Variables. 10 Biological and Medical Applications of Chemometrics. 10.1 Introduction. 10.2 Taxonomy. 10.3 Discrimination. 10.4 Mahalanobis Distance. 10.5 Bayesian Methods and Contingency Tables. 10.6 Support Vector Machines. 10.7 Discriminant Partial Least Squares. 10.8 Micro-organisms. 10.9 Medical Diagnosis using Spectroscopy. 10.10 Metabolomics using Coupled Chromatography and Nuclear Magnetic Resonance. References. 11 Biological Macromolecules. 11.1 Introduction. 11.2 Sequence Alignment and Scoring Matches. 11.3 Sequence Similarity. 11.4 Tree Diagrams. 11.5 Phylogenetic Trees. References. 12 Multivariate Image Analysis. 12.1 Introduction. 12.2 Scaling Images. 12.3 Filtering and Smoothing the Image. 12.4 Principal Components for the Enhancement of Images. 12.5 Regression of Images. 12.6 Alternating Least Squares as Employed in Image Analysis. 12.7 Multiway Methods In Image Analysis. References. 13 Food. 13.1 Introduction. 13.2 How to Determine the Origin of a Food Product using Chromatography. 13.3 Near Infrared Spectroscopy. 13.4 Other Information. 13.5 Sensory Analysis: Linking Composition to Properties. 13.6 Varimax Rotation. 13.7 Calibrating Sensory Descriptors to Composition. References. Index.
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Preface. Introduction. Building Empirical Models. Two--Level Factorial Designs. Two--Level Fractional Factorial Designs. Process Improvement with Steepest Ascent. The Analysis of Second--Order Response Surfaces. Experimental Designs for Fitting Response Surfaces--I. Experimental Designs for Fitting Response Surfaces--II. Advanced Response Surface Topics I. Advanced Response Surface Topics II. Robust Parameter Design and Process Robustness Studies. Experiments with Mixtures. Other Mixture Design and Analysis Techniques. Continuous Process Improvement with Evolutionary Operation. Appendix 1: Variable Selection and Model--Building in Regression. Appendix 2: Multicollinearity and Biased Estimation in Regression. Appendix 3: Robust Regression. Appendix 4: Some Mathematical Insights into Ridge Analysis. Appendix 5: Moment Matrix of a Rotatable Design. Appendix 6: Rotatability of a Second--Order Equiradial Design. Appendix 7: Relationship Between D--Optimality and the Volume of a Joint Confidence Ellipsoid on s. Appendix 8: Relationship Between Maximum Prediction Variance in a Region and the Number of Parameters. Appendix 9: The Development of Equation (8.21). Appendix 10: Determination of Data Augmentation Result (Choice of xr+1 for the Sequential Development of a D--Optimal Design). Index.
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