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Basic Experimental Strategies and Data Analysis for Science and Engineering

Basic Experimental Strategies and Data Analysis for Science and Engineering

Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform when to quit and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments give the greatest degree of reliability to their conclusions and keep the risk of overlooking something of practical importance to a minimum. Features:Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledgeFeatures a review of the necessary statistical prerequisitesPresents a set of tables that allow readers to quickly access various experimental designsIncludes a roadmap for where and when to use various experimental design strategiesShows compelling examples of each method discussedIllustrates how to reproduce results using several popular software packages on a supplementary websiteFollowing the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand follow the design to conduct the experiments and analyze and interpret the resulting data. John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students. | Basic Experimental Strategies and Data Analysis for Science and Engineering

GBP 44.99
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Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians epidemiologists and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination calibration and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research. | Medical Risk Prediction Models With Ties to Machine Learning

GBP 48.99
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Programming for Hybrid Multi/Manycore MPP Systems

Programming for Hybrid Multi/Manycore MPP Systems

Ask not what your compiler can do for you ask what you can do for your compiler. John Levesque Director of Cray’s Supercomputing Centers of ExcellenceThe next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10’s to 100’s of MIMD (multiple instruction multiple data) processing units with SIMD (single instruction multiple data) parallel instructions. Since a standard affordable memory architecture will not be able to supply the bandwidth required by these cores new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures covering their advantages and disadvantages. The next section of the book explores the other important component of the target—the compiler. The compiler will ultimately convert the input language to executable code on the target and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next along with hybridization of a program. The last part of the book includes several major applications and examines future hardware advancements and how the application developer may prepare for those advancements.

GBP 44.99
1

Linear Regression Models Applications in R

Linear Regression Models Applications in R

Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material the author explains how to estimate simple and multiple LRMs in R including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model adjusting for measurement error understanding the effects of influential observations and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model including how to understand and interpret its results test assumptions and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results assumptions and other features. Does not assume a background in calculus or linear algebra rather an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social behavioral health sciences and related disciplines taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior. | Linear Regression Models Applications in R

GBP 66.99
1