Download or read book Event Probabilities Estimated by Regression written by David Alexander Hamilton and published by . This book was released on 1974 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introductory Business Statistics 2e written by Alexander Holmes and published by . This book was released on 2023-12-13 with total page 1801 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Download or read book A Logistic Regression Equation for Estimating the Probability of a Stream Flowing Perennially in Massachusetts written by Gardner C. Bent and published by . This book was released on 2002 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: ... Produced to assist agencies administering the Commonwealth of Massachusetts' River Protection Act of 1996; data was collected on the characteristics of verified perennial or intermittent streams to create an equation that could be used to indicate the probability of a stream flowing perenially; available online at: www.water.usgs.gov/pubs/of/ofr02183 ...
Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Download or read book Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems written by Jerome Morio and published by Woodhead Publishing. This book was released on 2015-11-16 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rare event probability (10-4 and less) estimation has become a large area of research in the reliability engineering and system safety domains. A significant number of methods have been proposed to reduce the computation burden for the estimation of rare events from advanced sampling approaches to extreme value theory. However, it is often difficult in practice to determine which algorithm is the most adapted to a given problem.Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems: A Practical Approach provides a broad up-to-date view of the current available techniques to estimate rare event probabilities described with a unified notation, a mathematical pseudocode to ease their potential implementation and finally a large spectrum of simulation results on academic and realistic use cases. Provides a broad overview of the practical approach of rare event methods. Includes algorithms that are applied to aerospace benchmark test cases Offers insight into practical tuning issues
Download or read book Statistical Methods in the Atmospheric Sciences written by Daniel S. Wilks and published by Academic Press. This book was released on 2011-07-04 with total page 697 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines. - Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting - Many worked examples - End-of-chapter exercises, with answers provided
Download or read book An Automated Low Cloud Prediction System written by Edward B. Geisler and published by . This book was released on 1981 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the Air Force Geophysics Laboratory (AFGL) Weather Test Facility (WTF) at Otis AFB, MA, a network of cloud base height, visibility, and wind measuring instruments were used to explore techniques for the short range prediction of low cloud ceiling. AFGL developed this system in response to the USAF Air Weather Service's requirements to modernize its basic weather support capabilities. This system allowed AFGL to evaluate the ability of statistical forecasting techniques to provide decision assistance significantly improved over the decision assistance currently provided by climatology and persistence. The approach relies upon the use of a hierarchical clustering algorithm to transform the raw cloud base height data into an automated low cloud observation. Four prediction techniques (Regression Estimation of Event Probabilities, Equivalent Markov, climatology, and persistence) yielding probability estimates of low cloud ceiling were evaluated and comparisons made to determine their respective accuracy and reliability. In addition, thresholding techniques were used to convert probability forecasts (unit bias, maximum probability, iterative, and persistence). Analysis of the data collected at the AFGL WTF demonstrates the accuracy and reliability of the automated low cloud prediction system. Regression estimation of event probabilities provided accurate, reliable, high resolution probability forecasts with results superior to climatology, persistence, and Equivalent Markov.
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Download or read book Interpreting Probability Models written by Tim Futing Liao and published by SAGE. This book was released on 1994-06-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Monthly Catalog of United States Government Publications written by and published by . This book was released on 1971 with total page 1388 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Development and Evaluation of an Even and Uneven aged Ponderosa Pine Arizona Fescue Stand Simulator written by David W. Hann and published by . This book was released on 1980 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Test of Methods for Amending and Seeding Spoils at the Blackbird Mine written by Bland Z. Richardson and published by . This book was released on 1981 with total page 894 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advanced Statistics for Physical and Occupational Therapy written by Thomas Gus Almonroeder and published by Taylor & Francis. This book was released on 2022-04-05 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Statistics for Physical and Occupational Therapy explains the basis for statistical analyses that are commonly used to answer clinical research questions related to physical and occupational therapy. This textbook provides a resource to help students and faculty in physical and occupational therapy graduate programs understand the basis for common statistical analyses and be able to apply these techniques in their own research. This textbook provides readers with the basis for common statistical analyses, including t-tests, analysis of variance, regression, and nonparametric tests. Each chapter includes step-by-step tutorials with corresponding example data sets explaining how to conduct these statistical analyses using Statistical Package for the Social Sciences (SPSS) software and the Excel Analysis ToolPak, as well as how to identify and interpret relevant output and report results. Advanced Statistics for Physical and Occupational Therapy is key reading for students in physical therapy, occupational therapy, sport performance, and sport rehabilitation graduate programs as well as students in athletic training courses, applied statistics in sport, and research methods in sport modules. This new text will also be of interest to practicing clinicians who hope to better understand the research they are reading and/or are interested in starting to conduct their own clinical research.
Download or read book Fundamentals of Predictive Analytics with JMP Second Edition written by Ron Klimberg and published by SAS Institute. This book was released on 2017-12-19 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Going beyond the theoretical foundation, this step-by-step book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. --
Download or read book State of the art Methodology of Forest Inventory written by and published by . This book was released on 1990 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayes Rules written by Alicia A. Johnson and published by CRC Press. This book was released on 2022-03-03 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.
