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Quantitative methods in social research --- Mathematical statistics --- Psychology --- Regression Analysis --- Research. --- Regression Analysis. --- Regression (Psychology) --- 303 --- 519.242 --- -Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Behavioral sciences --- Mental philosophy --- Mind --- Science, Mental --- Human biology --- Philosophy --- Soul --- Mental health --- Regression --- Regressions (Psychology) --- Regression Diagnostics --- Statistical Regression --- Analyses, Regression --- Diagnostics, Regression --- Regression Analyses --- Regression, Statistical --- Regressions, Statistical --- Statistical Regressions --- Methoden bij sociaalwetenschappelijk onderzoek --- Experimental design. Optimal designs. Block designs --- Research --- Regression analysis. --- 519.242 Experimental design. Optimal designs. Block designs --- 303 Methoden bij sociaalwetenschappelijk onderzoek --- Regression analysis --- Psychological research --- Psychological Regression --- Psychology Regression --- Psychology - Research.

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The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies .

Medical statistics. --- Medicine --- Evidence-based medicine --- Research --- Statistical methods. --- Statistics. --- Internal medicine. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Internal Medicine. --- Medicine, Internal --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics . --- Models, Statistical. --- Regression Analysis. --- Analysis, Regression --- Regression Diagnostics --- Statistical Regression --- Analyses, Regression --- Diagnostics, Regression --- Regression Analyses --- Regression, Statistical --- Regressions, Statistical --- Statistical Regressions --- Statistics as Topic --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Health Workforce --- Statistics --- Health --- Health statistics

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Mathematical statistics --- Psychometrics --- Analysis of variance --- Regression analysis --- Psychométrie --- Analyse de variance --- Analyse de régression --- Regression Analysis --- Analysis of Variance. --- Psychometrics. --- Regression Analysis. --- 519.233.4 --- #SBIB:303H523 --- #SBIB:303H522 --- Analysis, Regression --- Regression Diagnostics --- Statistical Regression --- Analyses, Regression --- Diagnostics, Regression --- Regression Analyses --- Regression, Statistical --- Regressions, Statistical --- Statistical Regressions --- Psychometric --- Statistics as Topic --- Analysis, Variance --- Variance Analysis --- ANOVA --- Analyses, Variance --- Variance Analyses --- Variance analysis. Covariance analysis --- Methoden sociale wetenschappen: associatie, correlatie --- Methoden sociale wetenschappen: handboeken statistische analyse --- Analysis of variance. --- Regression analysis. --- 519.233.4 Variance analysis. Covariance analysis --- Psychométrie --- Analyse de régression --- Analysis of Variance --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Measurement --- Scaling --- Methodology --- Analyse de covariance

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The raw materials industry is widely considered to be too environmentally costly, and causing more losses than benefits. The responsible solving of the problems caused by this industry is not “exporting” its operations to less developed countries, but addressing all recognized hazards with dedicated technological developments. Such an approach is presented by the authors of this book. The contributions deal with the optimization of processes in the raw materials industry, obtaining energy from alternative fuels, researching the environmental aspects of industrial activities. This book determines some guidelines for the sustainable raw materials industry, describing methods of the optimized use of mined deposits and the recovery of materials, reductions in energy consumption and the recuperation of energy, minimizations in the emissions of pollutants, the perfection of quieter and safer processes, and the facilitation of modern materials-, water-, and energy-related techniques and technologies.

Technology: general issues --- History of engineering & technology --- acid leaching --- battery recycling --- Li-ion batteries --- metal recovery --- raw material sustainable use --- sieving screen --- inertial vibrator --- dual-frequency --- spectrum --- FEM simulation --- biomass ash --- coal ash --- sintering --- mechanical test --- pressure drop test --- slagging --- fouling --- ion flotation --- used batteries --- ecological safety --- recovery --- Zn(II) --- Mn(II) --- belt conveyor --- prosumer --- downhill transport of overburden --- specific energy consumption --- recuperation --- energy recovery rate --- air quality monitoring --- SO2 --- VOC --- H2S --- PM10 --- PM2.5 --- PM1.0 --- outdoor air quality --- air flow aerodynamics --- street canyon --- digestate --- biogas plant --- hydrothermal carbonisation --- membrane processes --- water recovery --- thermal lag --- fossil fuels --- pyrolysis --- TG --- thermal analysis --- power --- powered roof support --- hydraulic leg --- bench testing --- dynamic load --- discrete event simulation --- quarry --- mine machine --- cost of production --- fire-side corrosion --- boiler tube wastage --- diagnostics --- industrial-scale boilers --- non-destructive inspection --- pipe inspection --- wall thickness measurement --- stone waste --- waste generation --- waste recycling --- industrial waste treatment --- sustainable manufacturing --- dimension natural stone processing --- GHG emissions --- stable isotopes --- waste management --- energy recovery --- unburned carbon --- fly ash --- activated carbon --- adsorption kinetics --- statistical regression --- sustainable mining --- heating and energy processes --- raw material sustainable-use fossil fuels --- energy conversion and storage --- air pollution --- emission reduction methods --- purification and removal techniques --- acid leaching --- battery recycling --- Li-ion batteries --- metal recovery --- raw material sustainable use --- sieving screen --- inertial vibrator --- dual-frequency --- spectrum --- FEM simulation --- biomass ash --- coal ash --- sintering --- mechanical test --- pressure drop test --- slagging --- fouling --- ion flotation --- used batteries --- ecological safety --- recovery --- Zn(II) --- Mn(II) --- belt conveyor --- prosumer --- downhill transport of overburden --- specific energy consumption --- recuperation --- energy recovery rate --- air quality monitoring --- SO2 --- VOC --- H2S --- PM10 --- PM2.5 --- PM1.0 --- outdoor air quality --- air flow aerodynamics --- street canyon --- digestate --- biogas plant --- hydrothermal carbonisation --- membrane processes --- water recovery --- thermal lag --- fossil fuels --- pyrolysis --- TG --- thermal analysis --- power --- powered roof support --- hydraulic leg --- bench testing --- dynamic load --- discrete event simulation --- quarry --- mine machine --- cost of production --- fire-side corrosion --- boiler tube wastage --- diagnostics --- industrial-scale boilers --- non-destructive inspection --- pipe inspection --- wall thickness measurement --- stone waste --- waste generation --- waste recycling --- industrial waste treatment --- sustainable manufacturing --- dimension natural stone processing --- GHG emissions --- stable isotopes --- waste management --- energy recovery --- unburned carbon --- fly ash --- activated carbon --- adsorption kinetics --- statistical regression --- sustainable mining --- heating and energy processes --- raw material sustainable-use fossil fuels --- energy conversion and storage --- air pollution --- emission reduction methods --- purification and removal techniques

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Regression Analysis. --- Cross-Sectional Studies. --- Models, Statistical. --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Analysis, Regression --- Regression Diagnostics --- Statistical Regression --- Analyses, Regression --- Diagnostics, Regression --- Regression Analyses --- Regression, Statistical --- Regressions, Statistical --- Statistical Regressions --- Theses --- Regression analysis --- CROSS-SECTIONAL STUDIES --- Models --- statistical --- Regression analysis. --- CROSS-SECTIONAL STUDIES. --- statistical. --- Cross-sectional studies. --- Statistical. --- Analysis, Cross-Sectional --- Cross Sectional Analysis --- Cross-Sectional Survey --- Surveys, Disease Frequency --- Disease Frequency Surveys --- Prevalence Studies --- Analyses, Cross Sectional --- Analyses, Cross-Sectional --- Analysis, Cross Sectional --- Cross Sectional Analyses --- Cross Sectional Studies --- Cross Sectional Survey --- Cross-Sectional Analyses --- Cross-Sectional Analysis --- Cross-Sectional Study --- Cross-Sectional Surveys --- Disease Frequency Survey --- Prevalence Study --- Studies, Cross-Sectional --- Studies, Prevalence --- Study, Cross-Sectional --- Study, Prevalence --- Survey, Cross-Sectional --- Survey, Disease Frequency --- Surveys, Cross-Sectional --- Regression Analysis --- Cross-Sectional Studies --- Models, Statistical