Search results for ""Author James W. Hardin""

Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

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Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as GLMs with Stata’s glm command offers some advantages. For example, model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution.This text thoroughly covers GLMs, both theoretically and computationally, with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares, another method of parameter estimation, are a consequence of ML estimation using Fisher scoring.

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In 1912, R. A. S. Macalister published reports on his PEF excavations at Tell Gezer in central Israel, including notice of having traced more than 1,400 meters (almost a full mile) of defense walls around the site. Now, a century later, a detailed reassessment of these fortifications is provided in the publication of Gezer VII: The Middle Bronze and Later Fortifications in Fields II, IV, and VIII, edited by Joe D. Seger and James W. Hardin. This volume features work at Gezer sponsored by Hebrew Union College and Harvard University between 1968 and 1974, reporting on excavations at Macalister’s “Southern Gate” (Field IV) and along his “Inner” and “Outer” wall systems both on the southern (Field II) and northern (Field VIII) flanks of the site. These excavations produced much new data, enabling a confident dating of the Southern Gate complex and the Inner Wall system to the latter part of the Middle Bronze period (1700–1500 B.C.E.) and of the Outer Wall to the Late Bronze II and subsequent Hellenistic eras. Among a rich array of cultural remains, intramural occupation of the Middle Bronze Age yielded a gold jewelry hoard and early evidence of alphabetic writing.

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