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Background: The Gyr breed is widely used in Colombian low tropic dairy production systems. During the last 10 years, the Asociación Colombiana de Criadores de Ganado Cebú† - ASOCEBU, has been leading a dairy milk control program which led to the creation of a dataset that permits to carry out the first analysis of milk yield in Gyr cattle in the country using records from several herds. Objectives: To study milk production dynamics of Gyr cattle in the Colombian low tropic through the estimation of lactation curves and four derived production parameters: total milk yield between 5 and 305 days (TMY305), peak milk yield (PMY), days at peak (DP) and persistency (P). Methods: 13,798 daily milk yield records from 1,510 cows performing in 103 herds were used; the total number of lactations was 2,480. Four models were considered: Wood, Wiltmink, Papajcsik & Bordero, and a second-degree polynomial. Mean square error, mean absolute error, mean square error of prediction, Akaike and Bayesian information criteria were used to select the model better describing each lactation using the majority rule, that is, the model selected by most criteria was the chosen one. The shape of each fitted lactation curve was checked using basic results from calculus which permitted the classification of the estimated curves into two groups: typical and atypical; only typical functions were used to compute the four aforementioned production parameters. Results: The second-order polynomial was the model most frequently selected, while the Papajcsik & Bordero model had the lowest frequency. Average TMY305, PMY, DP and P were 3,489.86 kg, 17.28 kg, 57.17 days, and 0.83, respectively, with coefficients of variation: 0.27, 0.21, 0.41, and 0.16. Conclusions: This study permitted to identify individuals with outstanding phenotypic performance. To the best of our knowledge, this is the first study of this kind involving thousands of lactations from Gyr cows performing in several regions of Colombian low tropic.

Objective This work sought to determine the association between personal factors and not drinking alcoholic beverages in alcohol-dependent individuals on recovery process. Methods This was a cross-sectional quantitative study. The sample was comprised by 119 adult belonging to 50 Alcoholics Anonymous groups in Saltillo, Coahuila (Mexico). The sampling was simple random, by conglomerates (AA groups). To gather the information, a Personal Data Card was used along with a history on alcohol consumption and the instruments Scale on Social Readjustment Classification, Spiritual Perspective Scale, Schwartz Values Survey, and the Alcohol Use Disorders Identification Test (AUDIT). Results The time without alcohol consumption was related positively with age (r=0.59) and spirituality (r=0.29) and negatively with stressful events (r=-0.31). The Multiple Linear Regression Model explained 32.5% of the variance, with age being the variable remaining in the model and which affected not drinking alcoholic beverages. Conclusion Values and spirituality favor not drinking alcoholic beverages in individuals in the process of recovering from the dependence, while exposure to stressful events increases vulnerability to alcohol consumption. Descriptors: alcoholics anonymous; linear models; social values; alcohol drinking; surveys and questionnaires.

Offering deep insight into the connections between design choice and the resulting statistical analysis, this text explores how experiments are designed using the language of linear statistical models. It presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models. The text describes specific forms or classes of experimental designs, incorporates actual experiments drawn from the scientific and technical literature, and includes many end-of-chapter exercises. Calculations are performed using R, with commands provided in an appendix. A solutions manual is available upon qualified course adoption.

Praise for the First Edition \"The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.\" -Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Evidence for severe declines in large predatory fishes is increasing around the world. Because of its long history of intense fishing, the Mediterranean Sea offers a unique perspective on fish population declines over historical timescales. We used a diverse set of records dating back to the early 19th and mid 20th century to reconstruct long-term population trends of large predatory sharks in the northwestern Mediterranean Sea. We compiled 9 time series of abundance indices from commercial and recreational fishery landings, scientific surveys, and sighting records. Generalized linear models were used to extract instantaneous rates of change from each data set, and a meta-analysis was conducted to compare population trends. Only 5 of the 20 species we considered had sufficient records for analysis. Hammerhead (Sphyrna spp.), blue (Prionace glauca), mackerel (Isurus oxyrinchus and Lamna nasus), and thresher sharks (Alopias vulpinus) declined between 96 and 99.99% relative to their former abundance. According to World Conservation Union (IUCN) criteria, these species would be considered critically endangered. So far, the lack of quantitative population assessments has impeded shark conservation in the Mediterranean Sea. Our study fills this critical information gap, suggesting that current levels of exploitation put large sharks at risk of extinction in the Mediterranean Sea. Possible ecosystem effects of these losses involve a disruption of top-down control and a release of midlevel consumers.

Fishing pressure has increased the extinction risk of many elasmobranch (shark and ray) species. Although many countries have established no-take marine reserves, a paucity of monitoring data means it is still unclear if reserves are effectively protecting these species. We examined data collected by a small group of divers over the past 21 years at one of the world's oldest marine protected areas (MPAs), Cocos Island National Park, Costa Rica. We used mixed effects models to determine trends in relative abundance, or probability of occurrence, of 12 monitored elasmobranch species while accounting for variation among observers and from abiotic factors. Eight of 12 species declined significantly over the past 2 decades. We documented decreases in relative abundance for 6 species, including the iconic scalloped hammerhead shark (Sphyrna lewini) (−45%), whitetip reef shark (Triaenodon obesus) (−77%), mobula ray (Mobula spp.) (−78%), and manta ray (Manta birostris) (−89%), and decreases in the probability of occurrence for 2 other species. Several of these species have small home ranges and should be better protected by an MPA, which underscores the notion that declines of marine megafauna will continue unabated in MPAs unless there is adequate enforcement effort to control fishing. In addition, probability of occurrence at Cocos Island of tiger (Galeocerdo cuvier), Galapagos (Carcharhinus galapagensis), blacktip (Carcharhinus limbatus), and whale (Rhincodon typus) sharks increased significantly. The effectiveness of MPAs cannot be evaluated by examining single species because population responses can vary depending on life history traits and vulnerability to fishing pressure. La presión de la pesca ha incrementado el riesgo de extinción de muchas especies de elasmobranquios (tiburones y rayas). Aunque muchos países han establecido áreas de no-pesca, una escasez de datos de monitoreo implica que todavía no está claro si estas reservas están protegiendo efectivamente a estas especies. Examinamos datos colectados por un pequeño grupo de buzos a lo largo de los últimos 21 años en una de las áreas marinas protegidas (AMP) más vieja del mundo: Parque Nacional Isla de Cocos, Costa Rica. Usamos modelos de efectos mixtos para determinar tendencias en la abundancia relativa, o probabilidad de caso, de doce especies monitoreadas de elasmobranquios compensando la variación entre observadores y de factores abióticos. Ocho de las doce especies declinaron significativamente a lo largo de las últimas dos décadas. Documentamos disminuciones en la abundancia relativa de seis especies, incluidos el tiburón martillo (Sphyrna lewini) (−45%), el tiburón de arrecife de punta blanca (Triaenodon obesus) (−77%), la manta (Mobula spp.) (−78%) y la mantarraya (Manta birostris) (−89%); así como disminuciones en la probabilidad de caso de otras dos especies. Muchas de estas especies tienen extensiones de hábitat pequeñas y deberían estar mejor protegidas por una AMP, lo que enfatiza la noción de que las declinaciones de megafauna marina continuarán sin cesar en las AMP a menos de que exista un esfuerzo adecuado de control de pesca. Además de esto, la probabilidad de ocurrencia en la Isla de Cocos de de los tiburones tigre (Galeocerdo cuvier), de las Galápagos (Carcharhinus galapagensis), de punta negra (Carcharhinus limbatus) y ballena (Rhincodon typus) incrementó significativamente. La efectividad de las AMP no puede ser evaluada examinando a una sola especie porque las respuestas poblacionales pueden variar dependiendo de las características de la historia de vida y de la vulnerabilidad a la presión de la pesca.

Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The authors enable a clear comparison between general and generalized linear models and cover Gaussian-based hierarchical models and hierarchical generalized linear models. They illustrate the methods with many real-world examples and use R throughout to solve the problems. Ancillaries are available on the book's website.

Provides timely applications, modifications, and extensions of experimental designs for a variety of disciplines Design and Analysis of Experiments, Volume 3: Special Designs and Applications continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research. Featuring contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes: Genetic cross experiments, microarray experiments, and variety trials Clinical trials, group-sequential designs, and adaptive designs Fractional factorial and search, choice, and optimal designs for generalized linear models Computer experiments with applications to homeland security Robust parameter designs and split-plot type response surface designs Analysis of directional data experiments Throughout the book, illustrative and numerical examples utilize SAS®, JMP®, and R software programs to demonstrate the discussed techniques. Related data sets and software applications are available on the book's related FTP site. Design and Analysis of Experiments, Volume 3 is an ideal textbook for graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, and business.

Oxidative stress is a pathophysiological mechanism potentially involved in psychiatric disorders. The objective of this study was to assess the relationship between total antioxidant status (TAS) and the functional status of patients with a first episode of psychosis at the onset of the disease. For this purpose, a sample of 70 patients aged between 9 and 17 years with a first episode of psychosis were followed up for a period of two years. Blood samples were drawn to measure TAS levels at three time points: at baseline, at one year, and at two years. Clinical symptoms and functioning were also assessed at the same time points using various scales. Linear regression analysis was performed to investigate the relationship between TAS and clinical status at each assessment, adjusting for potential confounding factors. The distribution of clinical variables was grouped in different percentiles to assess the dose-response in the relation between clinical variables and TAS. At baseline, patient's score on Children's Global Assessment Scale (CGAS) was directly and significantly associated with TAS with a monotonic increase in percentiles, and surprising this association was reversed after one and two years of follow-up with a monotonic decrease. In summary at the onset of the illness, TAS is positively related to clinical status, whereas as the illness progresses this correlation is reversed and becomes negative. This may be the result of an adaptive response.

Este trabajo investiga la hipótesis de convergencia en el PIB per cápita de los Estados de la República Mexicana para el periodo 1970-2012, a través de un modelo no lineal con coeficientes variantes de un solo factor en el tiempo propuesto por Phillips y Sul (2007). Este enfoque tiene la virtud de ser extremadamente flexible para modelar un gran número de sendas de transición a la convergencia, además de que no requiere ningún tipo de supuesto sobre la no estacionariedad de las series del panel. Encontramos la evidencia de convergencia relativa entre los estados de seis grupos o clubs de convergencia y si se excluyen los estados considerados como petroleros, los resultados son prácticamente los mismos. Abstract: This work shows the results of our research on the convergence hypothesis among the GDP of the Mexican states from 1970 to 2012 through the one factor non-linear model with time-varying coefficients as propounded by Phillips and Sul (2007). This highly flexible approach is fitted to model a number of transition paths to convergence, no requiring any special assumption about the non stationary of the panel series under analysis. We found evidence of relative convergence with state among six groups or convergence clubs and if the oil producer’s states are excluded of the sample, the results hold independently.