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.

scholarly journals Using the Econometric Models for Identification of Risk Factors for Albanian Smes (Case Study: Smes of Gjirokastra Region)

2021 ◽  
Vol 18 ◽  
pp. 163-170
Author(s):  
Lorenc Koçiu ◽  
Kledian Kodra
Keyword(s):  
Logistic Regression ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Qualitative Data ◽  
Linear Models ◽  
Binary Logistic Regression ◽  
Random Variable ◽  
Econometric Models ◽  
Binary Logistic Regression Model

Using the econometric models, this paper addresses the ability of Albanian Small and Medium-sizedEnterprises (SMEs) to identify the risks they face. To write this paper, we studied SMEs operating in theGjirokastra region. First, qualitative data gathered through a questionnaire was used. Next, the 5-level Likertscale was used to measure it. Finally, the data was processed through statistical software SPSS version 21,using the binary logistic regression model, which reveals the probability of occurrence of an event when allindependent variables are included. Logistic regression is an integral part of a category of statistical models,which are called General Linear Models. Logistic regression is used to analyze problems in which one or moreindependent variables interfere, which influences the dichotomous dependent variable. In such cases, the latteris seen as the random variable and is dependent on them. To evaluate whether Albanian SMEs can identifyrisks, we analyzed the factors that SMEs perceive as directly affecting the risks they face. At the end of thepaper, we conclude that Albanian SMEs can identify risk

scholarly journals The Relationship Between Tactical Positioning and the Race Outcome in 800-M Running at the 2016 Olympic Games and 2017 IAAF World Championship

2020 ◽  
Vol 71 (1) ◽  
pp. 299-305
Author(s):  
Fernando González-Mohíno ◽  
Jesús Santos del Cerro ◽  
Andrew Renfree ◽  
Inmaculada Yustres ◽  
José Mª González-Ravé
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Olympic Games ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Significance Test ◽  
Final Position ◽  
The Difference ◽  
The Relationship

AbstractThe purpose of this analysis was to quantify the probability of achieving a top-3 finishing position during 800-m races at a global championship, based on dispersion of the runners during the first and second laps and the difference in split times between laps. Overall race times, intermediate and finishing positions and 400 m split times were obtained for 43 races over 800 m (21 men’s and 22 women’s) comprising 334 individual performances, 128 of which resulted in higher positions (top-3) and 206 the remaining positions. Intermediate and final positions along with times, the dispersion of the runners during the intermediate and final splits (SS1 and SS2), as well as differences between the two split times (Dsplits) were calculated. A logistic regression model was created to determine the influence of these factors in achieving a top-3 position. The final position was most strongly associated with SS2, but also with SS1 and Dsplits. The Global Significance Test showed that the model was significant (p < 0.001) with a predictive ability of 91.08% and an area under the curve coefficient of 0.9598. The values of sensitivity and specificity were 96.8% and 82.5%, respectively. The model demonstrated that SS1, SS2 and Dplits explained the finishing position in the 800-m event in global championships.

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 Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models

2012 ◽  
Vol 94 (6) ◽  
pp. 307-317 ◽  
Author(s):  
M. FELLEKI ◽  
D. LEE ◽  
Y. LEE ◽  
A. R. GILMOUR ◽  
L. RÖNNEGÅRD
Keyword(s):  
Random Effects ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Estimation Algorithm ◽  
Least Square ◽  
Residual Variance ◽  
Hierarchical Generalized Linear Models ◽  
Genetic Components ◽  
The Difference ◽  
Variance Component Estimates

SummaryThe possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being −0·52 for IRWLS and −0·62 in Sorensen & Waagepetersen (2003).

scholarly journals Reflections on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

2020 ◽  
Author(s):  
Kellyn F Arnold ◽  
Vinny Davies ◽  
Marc de Kamps ◽  
Peter W G Tennant ◽  
John Mbotwa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Generalized Linear Models ◽  
Functional Form ◽  
Linear Models ◽  
Causal Explanation ◽  
Causal Modelling ◽  
Prediction And Prevention ◽  
Modern Methods ◽  
The Difference ◽  
Health Applications

Abstract Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research.

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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