Test Equating, Scaling, and Linking Methods and Practices (Statistics for Social and Behavioral Sciences)

Mathematics

Test Equating, Scaling, and Linking: Methods and Practices (Statistics for Social and Behavioral Sciences) by Michael J. Kolen
The Gamma Function (Dover Books on Mathematics) by Emil Artin
Design OF Experiments by Examples Using Matlab by Pérez C.
Statistical Inference on Residual Life (Statistics for Biology and Health) by Jong-Hyeon Jeong
Machine Learning With Sas Enterprise Miner by C. Pérez

Test Equating, Scaling, and Linking: Methods and Practices (Statistics for Social and Behavioral Sciences) by Michael J. Kolen

English | 14 Jan. 2014 | ISBN: 1493903160 | 594 Pages | EPUB | 4.91 MB

This book provides an introduction to test equating, scaling and linking, including those concepts and practical issues that are critical for developers and all other testing professionals. In addition to statistical procedures, successful equating, scaling and linking involves many aspects of testing, including procedures to develop tests, to administer and score tests and to interpret scores earned on tests. Test equating methods are used with many standardized tests in education and psychology to ensure that scores from multiple test forms can be used interchangeably. Test scaling is the process of developing score scales that are used when scores on standardized tests are reported. In test linking, scores from two or more tests are related to one another. Linking has received much recent attention, due largely to investigations of linking similarly named tests from different test publishers or tests constructed for different purposes. In recent years, researchers from the education, psychology and statistics communities have contributed to the rapidly growing statistical and psychometric methodologies used in test equating, scaling and linking. In addition to the literature covered in previous editions, this new edition presents coverage of significant recent research.

The Gamma Function (Dover Books on Mathematics) by Emil Artin

English | 28 Jan. 2015 | ISBN: 0486789780 | ASIN: B00T2QF51C, B0006BMA10 | 52 Pages | AZW3 | 4.96 MB

This brief monograph on the gamma function was designed by the author to fill what he perceived as a gap in the literature of mathematics, which often treated the gamma function in a manner he described as both sketchy and overly complicated. Author Emil Artin, one of the twentieth century’s leading mathematicians, wrote in his Preface to this book, “I feel that this monograph will help to show that the gamma function can be thought of as one of the elementary functions, and that all of its basic properties can be established using elementary methods of the calculus.”
Generations of teachers and students have benefitted from Artin’s masterly arguments and precise results. Suitable for advanced undergraduates and graduate students of mathematics, his treatment examines functions, the Euler integrals and the Gauss formula, large values of x and the multiplication formula, the connection with sin x, applications to definite integrals, and other subjects.

Design OF Experiments by Examples Using Matlab by Pérez C.

English | 18 Oct. 2017 | ISBN: 1974098109 | ASIN: B076KSNJGK | 204 Pages | AZW3 | 4.85 MB

MATLAB Model-Based Calibration Toolbox provides apps and design tools for optimally calibrating complex experimental designs models. This Toolbox use un Apps that support the entire workflow: designing experiments, fitting statistical models to engine data, and producing optimal calibrations. Also support Design-of-Experiments methodology for reducing testing time through classical, space-filling, and optimal design techniques, This Toolbox accurate engine modeling with data fitting techniques including Gaussian process,radial basis function, and linear regression modeling. Other options are: Boundary modeling to keep optimization results within the engine operating envelope, Generation of lookup tables from optimizations over drive cycles, models, or test data. Also is posible export of performance-optimized models to Simulink for use in simulation and HILtesting

Statistical Inference on Residual Life (Statistics for Biology and Health) by Jong-Hyeon Jeong

English | 21 Jan. 2014 | ISBN: 1493900048 | 216 Pages | EPUB | 1.96 MB

This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.

Machine Learning With Sas Enterprise Miner by C. Pérez

English | 16 Oct. 2017 | ISBN: 1978377371 | ASIN: B076HM8L3F | 412 Pages | AZW3 | 9.25 MB

Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.
This book develops unsupervised learning techniques and supervised learning techniques across examples using SAS Enterprise Miner.