Generalized Linear Models I: Logistic Regression

2001 ◽  
pp. 205-222 ◽  
Author(s):  
Brian Everitt ◽  
Sophia Rabe-Hesketh
Keyword(s):  
Logistic Regression ◽  
Generalized Linear Models ◽  
Linear Models

Optimally hounded score functions for generalized linear models with applications to logistic regression

Biometrika ◽  
1986 ◽  
Vol 73 (2) ◽  
pp. 413-424 ◽  
Author(s):  
LEONARD A. STEFANSKI ◽  
RAYMOND J. CARROLL ◽  
DAVID RUPPERT
Keyword(s):  
Logistic Regression ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Score Functions

scholarly journals COMPARAÇÃO DE TÉCNICAS ESTATÍSTICAS PARA ANALISAR A RELAÇÃO ENTRE DOENÇAS RESPIRATÓRIAS E CONCENTRAÇÕES DE POLUENTES ATMOSFÉRICOS

2013 ◽  
Vol 35 (1) ◽  
pp. 98
Author(s):  
Angela Radünz Lazzari
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Generalized Linear Models ◽  
Respiratory Diseases ◽  
Linear Models ◽  
Ordinal Logistic Regression ◽  
Atmospheric Pollutants ◽  
Meteorological Variables ◽  
Data Set

Air pollution is a risk factor for the population health. Its harmful effects on the population are observed even when the atmospheric pollutants are within the parameters set out in specific legislation, and they develop mainly through respiratory diseases. The aim of this study was to analyze the relationship between the concentrations of air pollutants and the incidence of respiratory diseases in the city of Porto Alegre, in 2005 and 2006. Applied multiple linear regression analysis, ordinal logistic regression and generalized linear models were used in the work. The results show good adjustment by the three techniques. The ordinal logistic regression detected only positive influence of air temperature and relative humidity in hospitalizations for respiratory diseases. Multiple linear regression related negatively hospitalizations with meteorological variables and positively with the particulate matter (PM10). The generalized linear model detected negative influence of meteorological variables and positive of pollutants, tropospheric ozone (O3) and PM10 in hospitalizations. Comparing the three statistical techniques to analyze the same data set, it can be concluded that all of them had a model with good fit to the data, but the technique of generalized linear models showed higher sensitivity in capturing the influence of pollutants, except in ordinal logistic regression and multiple linear regression.

scholarly journals Test time assessment on student's performance of statistics subjects by generalized linear models

2021 ◽  
Vol 10 (9) ◽  
pp. e8310917883
Author(s):  
Esttefani Duarte Brum ◽  
Gilberto Rodrigues Liska ◽  
Alisson Darós Santos
Keyword(s):  
Logistic Regression ◽  
Generalized Linear Models ◽  
Logistic Regression Model ◽  
Linear Models ◽  
Test Time ◽  
The Feminine ◽  
Student’S Performance ◽  
The Difference ◽  
Development Evaluation ◽  
Time Assessment

Can the time it takes a student to complete a test influence his / her performance? To answer this question, the logistic regression model was considered. In its development, evaluation was considered as a way of quantifying the performance of a student reflecting his degree of knowledge in a given content. For this we use records of the initial and final moments when developing an evaluation. The records of time spent were obtained from five different undergraduate classes, with subjects taught by the same teacher, with the same theoretical content, at the same university. The results confirm statistically that each additional minute that the student remains taking the test, implies in greater chances of obtaining good performance, as well as differences of performance between the feminine and masculine genders, although not statistically different, demonstrating that feminine students have greater chances of reaching the average. The model also confirms, according to the odds ratios that during the evaluations the students' performance decreases, having the best score in the first test. Through the references consulted, we understand that the difference in the grades of each student is influenced by several factors, the result of their own experiences.

Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression

Biometrika ◽  
1986 ◽  
Vol 73 (2) ◽  
pp. 413 ◽  
Author(s):  
Leonard A. Stefanski ◽  
Raymond J. Carroll ◽  
David Ruppert
Keyword(s):  
Logistic Regression ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Score Functions
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