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"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--

"This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extedible to cross-section data sets." "An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource."--Jacket

Front Matter -- Introduction / Daniel Pęa, George C Tiao -- Basic Concepts in Univariate Time Series. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, and State-Space Model / G Tunnicliffe Wilson -- Univariate Autoregressive Moving-Average Models / George C Tiao -- Model Fitting and Checking, and the Kalman Filter / G Tunnicliffe Wilson -- Prediction and Model Selection / Daniel Pęa -- Outliers, Influential Observations, and Missing Data / Daniel Pęa -- Automatic Modeling Methods for Univariate Series / Victor G̤mez, Agust̕n Maravall -- Seasonal Adjustment and Signal Extraction Time Series / Victor G̤mez, Agust̕n Maravall -- Advanced Topics in Univariate Time Series. Heteroscedastic Models / Ruey S Tsay -- Nonlinear Time Series Models: Testing and Applications / Ruey S Tsay -- Bayesian Time Series Analysis / Ruey S Tsay -- Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression, and Quantile Regression / Siegfried Heiler -- Neural Network Models / Kurt Hornik, Friedrich Leisch -- Multivariate Time Series. Vector ARMA Models / George C Tiao -- Cointegration in the VAR Model / S̜ren Johansen -- Identification of Linear Dynamic Multiinput/Multioutput Systems / Manfred Deistler -- Index -- Wiley Series in Probability and Statistics.

В статията са представени идеи за връзка между класическа и геометрична вероятност, които биха могли да подпомогнат преподавателите при операционализирането на учебната програма за общообразователна подготовка в ХІ клас. Най-общо казано, стохастично явление с безкрайно (неизброимо) много равновъзможни изходи се разлага на крайно обединение от равновероятни изходи. За това обединение е приложим моделът на класическата вероятност. Предимствата на такава методика са в по-добрата мотивация за въвеждане на новото понятие геометрична вероятност. Засегнати са и философски проблеми относно връзката между континуум и дискретна структура, които за целевата група носят мирогледно послание. Включени са примери от състезателните теми на турнира "Черноризец Храбър".

chapter 1 Geometric data analysis -- chapter 2 Correspondence analysis -- chapter 3 Multiple correspondence analysis -- chapter 4 Passive and supplementary points, supplementary variables and structured data analysis -- chapter 5 MCA and ascending hierarchical cluster analysis -- chapter 6 Constructing spaces -- chapter 7 Analyzing sub-groups: class-specific MCA.

Basic concepts -- Overall estimation of health disparities -- Domain-specific estimates -- Causality, moderation and mediation -- Machine learning based approaches to disparity estimation -- Health disparity estimation under a precision medicine paradigm -- Extended topics.

Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data analysis of CLRV models. Each chapter can be read and studied separately with R coding snippets and template interpretation for easy replication. Along with the doing part, the text provides basic and accessible statistical theories behind these models and uses a narrative style to recount their origins and evolution. This book first scaffolds both Bayesian and frequentist paradigms for regression analysis, and then moves onto different types of categorical and limited response variable models, including binary, ordered, multinomial, count, and survival regression. Each of the middle four chapters discusses a major type of CLRV regression that subsumes an array of important variants and extensions. The discussion of all major types usually begins with the history and evolution of the prototypical model, followed by the formulation of basic statistical properties and an elaboration on the doing part of the model and its extension. The doing part typically includes R codes, results, and their interpretation. The last chapter discusses advanced modeling and predictive techniques--multilevel modeling, causal inference and propensity score analysis, and machine learning--that are largely built with the toolkits designed for the CLRV models previously covered. The online resources for this book, including R and Stan codes and supplementarynotes, can be accessed at https://sites.google.com/site/socjunxu/home/statistics/modernapplied-regressions.

A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects. Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation pre

A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences. Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conferen

Cover; Half Title; Series Page; Title Page; Copyright Page; Table of Contents; Preface; Editors; Contributors; 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment; 1.1 Introduction; 1.2 Smartphone-Driven Assessment of Airport Pavement Condition; 1.2.1 Description of Smartphone Application; 1.2.2 Smartphone Characteristics; 1.3 Case Study of Missouri Airports; 1.3.1 Calibration Study; 1.3.2 Missouri Airport Smartphone Data Collection Methodology; 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport; 1.3.4 Discussion.

This book provides solid sociological foundations to computational social science (CSS). CSS is an emerging research field, and many books with those words in the title are on the market. However, CSS has not become mainstream in sociology, for which there are two reasons. First, CSS does not necessarily solve major research questions in sociology. Second, its sociological foundations are weak. These two reasons are interrelated-that is, CSS cannot solve major research questions because its sociological foundations are weak. Thus, even if it tries to solve those questions, its approaches seem to mainstream sociologists to miss the point. To resolve that shortcoming, this book fills the gap between CSS and sociology, shows that CSS can solve major research questions in sociology, and advances sociology by introducing to it theories and methodologies of CSS

Good graphs make complex problems clear. From the weather forecast to the Dow Jones average, graphs are so ubiquitous today that it is hard to imagine a world without them. Yet they are a modern invention. This book is the first to comprehensively plot humankind's fascinating efforts to visualize data, from a key seventeenth-century precursor--England's plague-driven initiative to register vital statistics--right up to the latest advances. In a highly readable, richly illustrated story of invention and inventor that mixes science and politics, intrigue and scandal, revolution and shopping