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Advanced Statistics from an Elementary Point of View is a highly readable text that communicates the content of a course in mathematical statistics without imposing too much rigor. It clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations. The book provides comprehensive coverage of descriptive statistics; detailed treatment of univariate and bivariate probability distributions; and thorough coverage of probability theory with numerous event classifications. This book is designed for statistics majors who are already familiar with introductory calculus and statistics, and can be used in either a one- or two-semester course. It can also serve as a statistics tutorial or review for working professionals. Students who use this book will be well on their way to thinking like a statistician in terms of problem solving and decision-making. Graduates who pursue careers in statistics will continue to find this book useful, due to numerous statistical test procedures (both parametric and non-parametric) and detailed examples. Comprehensive coverage of descriptive statistics More detailed treatment of univariate and bivariate probability distributions Thorough coverage of probability theory with numerous event classifications
Priced very competitively compared with other textbooks at this level! This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference studies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions develops notions of convergence in probability and distribution spotlights the central limit theorem (CLT) for the sample variance introduces sampling distributions and the Cornish-Fisher expansions concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity explains Basu's Theorem as well as location, scale, and location-scale families of distributions covers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cramér-Rao inequality discusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheffé Theorems focuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals includes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient summarizes Bayesian methods describes the monotone likelihood ratio (MLR) property handles variance stabilizing transformations provides a historical context for statistics and statistical discoveries showcases great statisticians through biographical notes Employing over 1400 equations to reinforce its subject matter, Probability and Statistical Inference is a groundbreaking text for first-year graduate and upper-level undergraduate courses in probability and statistical inference who have completed a calculus prerequisite, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory.
A state-of-the-art guide for developing grants witha strong emphasis on using program outcome measurement to underscore need and accountability Based on the authors' many years of experience in the public and nonprofit sectors, Effective Grant Writing and Program Evaluation for Human Service Professionals integrates the topics of grant proposal writing and program evaluation, offering grant seekers the practical guidance they need to develop quality proposals, obtain funding, and demonstrate service results and accountability. The authors clearly and succinctly illustrate and describe each stage of the grant writing and evaluation process. Problems or issues that arise frequently are highlighted and followed by specific advice. In addition, numerous real-world examples and exercises are included throughout the book to give readers the opportunity for reflection and practice. This timely reference incorporates a strengths perspective, providing: An inside look at the grant writing and evaluation processes, with insights from experienced grant writers, agency administrators, foundation program managers, and grant reviewers Specific examples of successful grant proposals and evaluation plans and instruments serving as models for learning and practice Field-tested individual and group exercises that facilitate the development of grant writing and evaluation skills Discussion of electronic technology in grant writing and evaluation, including writing and submitting grant proposals online, and identifying funding sources This grant writing and program evaluation guide follows a needs-driven, evidence-based, result-oriented, and client-centered perspective. Its authoritative discussion equips human service professionals to effectively develop grants with a strong emphasis on measuring program outcomes.
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

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