The sixth edition of Meaningful Statistics introduces students to foundational concepts and demonstrates how statistics are an integral aspect of their everyday lives-from baseball batting averages to reports on the median cost of buying a home to the projected outcomes of an upcoming election. Each chapter begins with a question and scenario that is then explored through statistical concepts, demonstrating to students how research and statistics can help us to answer questions and solve problems. The opening chapter focuses on the process of collecting data and uses this information to explore whether multivitamins are a waste of money. Additional chapters explore linear regression and whether junk food is harmful to a child's IQ; normal distribution and the issue of a tie for Olympic downhill gold; confidence intervals and a simulation of the NBA draft lottery; and more. Students learn about descriptive measures for populations and samples; probability and random variables; and sampling distributions, with each concept corresponding to real-world examples. Closing chapters cover the testing of hypotheses, tests using the chi-square distribution; and inferences with two or more populations. For the sixth edition, exercises and examples have been updated throughout. Designed to bring key concepts to life, Meaningful Statistics is an ideal resource for courses in mathematics and statistics.

Pultrusion: State-of-the-Art Process Models with Applications, Second Edition is a detailed guide to pultrusion, providing methodical coverage of process models and computation simulation, governing principles and science, and key challenges to help readers enable process optimization and scale-up. This new edition has been revised and expanded to include the latest advances, state-of-the-art process models, and governing principles. The main challenges in pultrusion, such as the process induced residual stresses, shape distortions, thermal history, species conversion, phase changes, impregnation of the reinforcements and pulling force are described, with related examples are provided. Moreover, strategies for having a reliable and optimized process using probabilistic approaches and optimization algorithms are summarized. Another focus of this book is on the thermo-chemical and mechanical analyses of the pultrusion process for industrial profiles.

Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using Mplus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of Mplus results using real data sets Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using Mplus.

This book studies the Dutch mathematician Simon Stevin (1548-1620) as a new type of 'man of knowledge'. Traditionally, Stevin is best known for his contributions to the 'Archimedean turn'. This innovative volume moves beyond this conventional image by bringing many other aspects of his work into view, by analysing the connections between the multiple strands of his thinking and by situating him in a broader European context. Like other multi-talents ('polymaths') in his time (several of whom are discussed in this volume), Stevin made an important contribution to the transformation of the ideal of knowledge in early modern Europe. This book thus provides new insights into the phenomenon of 'polymaths' in general and in the case of Stevin in particular.

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.