advanced statistics from an elementary point of view

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Endocrine Manifestations Of Systemic Autoimmune Diseases

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ISBN : 0080559328
Genre : Medical
File Size : 75. 50 MB
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This book is one of the first to evaluate the role of Steroids in autoimmune rheumatic diseases from the basic mechanisms to the clinical involvements and focuses on the importance of steroidal hormones in the pathogenesis and therapeutic management of the autoimmune rheumatic diseases. In particular, the chapters analyze the mechanisms of action and the involvement of adrenal steroids (glucocorticoids) in the neuroendocrine immune system, including effects on the elderly. The perturbations of the HPA axis as a source of altered steroidal synthesis will be discussed and related to some interesting pathological conditions that commonly complicate the autoimmune rheumatic diseases such as psychosis or fibromyalgia. Concerning the role of gonadal steroids (sex hormones), several chapters will discuss clinical and epidemiological evidences of their role, as well as their effects as risk factors in autoimmune rheumatic diseases, including a section on pediatrics. *The premier issue evaluating the role of steroids in autoimmune rheumatic diseases from the basic mechanisms to the clinical involvements *Documents the latest research and indicate recent and coming new therapeutic-biological approaches to the therapy *The book will present therapeutic perspectives concerning the new glucocorticoids, and the effects of biological drugs on their synthesis

Effective Grant Writing And Program Evaluation For Human Service Professionals

Author : Francis K. O. Yuen
ISBN : 9780470564431
Genre : Business & Economics
File Size : 32. 86 MB
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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.

Probability And Statistical Inference

Author : Nitis Mukhopadhyay
ISBN : 0824703790
Genre : Mathematics
File Size : 62. 15 MB
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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.

Statistik Workshop F R Programmierer

Author : Allen B. Downey
ISBN : 9783868993431
Genre : Computers
File Size : 89. 65 MB
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Statistik Ii F R Dummies

Author : Deborah Rumsey
ISBN : 9783527708437
Genre :
File Size : 53. 1 MB
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Es gibt Qualen, verdammte Qualen und Statistik, so sehen es viele Studenten. Mit "Statistik II für Dummies" lernen Sie so leicht wie möglich. Deborah Rumsey zeigt Ihnen, wie Sie Varianzanalysen und Chi-Quadrat-Tests machen, wie Sie mit Regressionen arbeiten, ein Modell erstellen, Korrelationen bilden und vieles mehr. So lernen Sie die Methoden, die Sie brauchen, und erhalten das Handwerkszeug, erfolgreich Ihre Statistikprüfungen zu bestehen.

A Computational Approach To Statistical Learning

Author : Taylor Arnold
ISBN : 9781351694759
Genre : Business & Economics
File Size : 75. 77 MB
Format : PDF
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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.

Modern Elementary Statistics With Theoretical Supplement And Basic Programming

Author : Donald W. Zimmerman
ISBN : UCAL:B2505001
Genre : Mathematics
File Size : 67. 31 MB
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Journal Of The American Statistical Association

Author :
ISBN : UCSC:32106019241352
Genre : Statistics
File Size : 65. 66 MB
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Doing Data Science

Author : Cathy O'Neil
ISBN : 9781449363895
Genre : Computers
File Size : 62. 42 MB
Format : PDF, Kindle
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Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Big Data Analytics

Author : Vasudha Bhatnagar
ISBN : 9783319036892
Genre : Computers
File Size : 24. 40 MB
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This book constitutes the thoroughly refereed conference proceedings of the Second International Conference on Big Data Analytics, BDA 2013, held in Mysore, India, in December 2013. The 13 revised full papers were carefully reviewed and selected from 49 submissions and cover topics on mining social media data, perspectives on big data analysis, graph analysis, big data in practice.

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