![]() ![]() You may come across systems called dashboards, which are often part of enterprise resource planning (ERP) systems. ![]() The techniques learned in this book, while focusing mainly on unit operations, are equally applicable though to data from a plant, region, or country. Higher levels of management track statistics from a different point of view, often summarizing data from an entire plant, geographic region, or country. As you move into an industrial environment you will find there are many such systems already in place. This preceding section of the book is only intended to give an overview of the concepts of process monitoring. The industrial practice of process monitoring ¶ Product development and product improvementģ.9. Applications of Process Improvement using Data Analysis of designed experiments using PLS models Variability explained with each component A mathematical/statistical interpretation of PLS Advantages of the projection to latent structures (PLS) method Introduction to Projection to Latent Structures (PLS) Visualization latent variable models with linking and brushing Using indicator variables in a latent variable model Determining the number of components to use in the model with cross-validation Algorithms to calculate (build) PCA models Preprocessing the data before building a model Interpreting loadings and scores together More about the direction vectors (loadings) Extended topics related to designed experiments Blocking and confounding for disturbances Highly fractionated designs: beyond half-fractions Generators: to determine confounding due to blocking Generating the complementary half-fraction Example: analysis of systems with 4 factors Assessing significance of main effects and interactions Example: design and analysis of a three-factor experiment Analysis of a factorial design: interaction effects Analysis of a factorial design: main effects Changing one single variable at a time (COST) Experiments with a single variable at two levels Design and analysis of experiments in context Outliers: discrepancy, leverage, and influence of the observations More than one variable: multiple linear regression (MLR) Summary of steps to build and investigate a linear model Least squares models with a single x-variable The industrial practice of process monitoring ![]() Statistical tables for the normal- and t-distribution The normal distribution and checking for normality General summary: revealing complex data graphically ![]()
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