scholarly journals Multinomial Logistic Regression to Estimate and Predict the Job Opportunities for People with Disabilities in Chile

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 356
Author(s):  
Nelson Lay-Raby ◽  
Hanns de la Fuente-Mella ◽  
Omar Lameles-Corvalán
Keyword(s):  
Logistic Regression ◽  
Educational Level ◽  
Applied Research ◽  
Multinomial Logistic Regression ◽  
People With Disabilities ◽  
Multinomial Logistic Regression Model ◽  
Job Opportunities ◽  
Degree Of Disability ◽  
Job Opportunity ◽  
Additional Income

In Chile there is a growing interest from society in improving the access of people with disabilities to the labor market. However, applied research on this topic is not abundant. The purpose of this research is to estimate the job opportunities of people with disabilities in Chile. For this, the data from the second Chilean national disability study were used to make a Multinomial Logistic Regression Model that would help to predict the probability of certain variables that influence job opportunity. For the generated model, variables related to the additional income of people (subsidies or extra income), educational level attained, pursuit of studies, and the degree of disability itself were found. It was determined how some variables affect the employment opportunity, particularly, variables related to continuity and access to studies.

scholarly journals Perceptions and Adaptations to Climate Change in Southern Mali

2021 ◽  
Author(s):  
Tiémoko Soumaoro
Keyword(s):  
Climate Change ◽  
Logistic Regression ◽  
Educational Level ◽  
Multinomial Logistic Regression ◽  
Probit Model ◽  
Adaptation To Climate Change ◽  
Impact Of Climate Change ◽  
Main Determinants ◽  
The Impact

This study aims to determine the impact of climate change on market garden production in the extreme south of Mali through the perception and adaptation of market gardeners to climatic phenomena. The study used two models, namely the probit selection and Heckman results models and multinomial logistic regression, based on data collected from producers. A total of 194 producers were surveyed. The results of Heckman's probit model indicate that experience in agriculture and the educational level of the producers are the two main determinants of producers' perception and simultaneous adaptation to climate change. Among these variables agricultural experience is both positively and negatively correlated with perception.

scholarly journals A Statistical Study of Crime in Ghana

2020 ◽  
pp. 6-16
Author(s):  
Bosson-Amedenu Senyefia ◽  
Eyiah-Bediako Francis ◽  
Kusi Prince
Keyword(s):  
Logistic Regression ◽  
Educational Level ◽  
Multinomial Logistic Regression ◽  
Religious Affiliation ◽  
Secondary Data ◽  
Age Group ◽  
Multinomial Logistic Regression Analysis ◽  
Equality Of Means ◽  
Homogeneity Of Variance ◽  
The Mean

Understanding the dynamics, patterns, and probabilities associated with the correlates of crime is a promising way to managing crime. In this study, a multinomial logistic regression was used to predict the propensity of individuals for committing particular crimes. The secondary data of 6702 prisoners was collated from Ghana Prisons Service for the purpose of the study. ANOVA and Brown-Forsythe robust tests of equality of means were employed, where the assumptions for homogeneity of variance were sustained and violated respectively. Pearson’s correlation matrix was also used in the analysis. Our findings showed that religious affiliation and educational level of convicts significantly affected the odds that they would commit a particular crime. Multinomial logistic regression analysis indicated that illiteracy significantly affected the odds that one would commit the crimes of manslaughter, rape, theft, causing harm, and issuing death threats. On the other hand, religious affiliation of an offender significantly affected the odds to commit the crime of murder. Educational level (r= -0.25; p< 0.05) and religious affiliation (r= -0.26; p<0.05) correlated negatively with crime. There were no significant differences in the mean score of crime across educational and religious levels. However, there were significant differences in the mean score of crime across age and gender. The mean difference from the post-hoc analysis showed a pattern of an initial rise in crime among the younger age group (8-25 years), a subsequent decline in the age group of 26-35, and a final surge in individuals beyond 35 years that did not surpass the initial peak. Females (M: 6.89, SD: 1.253) were found to have lower crime incidence than males (M: 7.43, SD: 3.008) for all crimes considered in this study. We recommend that Ghana’s Prison Service consider incorporating further demographic information of inmates in order to support research; which could help identify avenues for the amelioration of crime locally.

scholarly journals Burden and predictors ofStaphylococcus aureusandS. pseudintermediusinfections among dogs presented at an academic veterinary hospital in South Africa (2007–2012)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3198 ◽  
Author(s):  
Daniel N. Qekwana ◽  
James Wabwire Oguttu ◽  
Fortune Sithole ◽  
Agricola Odoi
Keyword(s):  
South Africa ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Risk Of Infection ◽  
Multinomial Logistic Regression Model ◽  
Tract Infections ◽  
Veterinary Hospital ◽  
Isolated Species

BackgroundStaphylococci are commensals of the mucosal surface and skin of humans and animals, but have been implicated in infections such as otitis externa, pyoderma, urinary tract infections and post-surgical complications. Laboratory records provide useful information to help investigate these infections. Therefore, the objective of this study was to investigate the burdens of these infections and use multinomial regression to examine the associations between variousStaphylococcusinfections and demographic and temporal factors among dogs admitted to an academic veterinary hospital in South Africa.MethodsRecords of 1,497 clinical canine samples submitted to the bacteriology laboratory at a veterinary academic hospital between 2007 and 2012 were included in this study. Proportions of staphylococcal positive samples were calculated, and a multinomial logistic regression model was used to identify predictors of staphylococcal infections.ResultsTwenty-seven percent of the samples tested positive forStaphylococcusspp. The species ofStaphylococcusidentified wereS. pseudintermedius(19.0%),S. aureus(3.8%),S. epidermidis(0.7%) andS. felis(0.1%). The remaining 2.87% consisted of unspeciatedStaphylococcus. Distribution of the species by age of dog showed thatS. pseudintermediuswas the most common (25.6%) in dogs aged 2–4 years whileS. aureuswas most frequent (6.3%) in dogs aged 5–6 years.S. pseudintermedius(34.1%) andS. aureus(35.1%) were the most frequently isolated species from skin samples. The results of the multivariable multinomial logistic regression model identified specimen, year and age of the dog as significant predictors of the risk of infection withStaphylococcus. There was a significant temporal increase (RRR = 1.17; 95% CI [1.06–1.29]) in the likelihood of a dog testing positive forS. pseudintermediuscompared to testing negative. Dogs ≤ 8 years of age were significantly more likely to test positive forS. aureusthan those >8 years of age. Similarly, dogs between 2–8 years of age were significantly more likely to test positive forS. pseudintermediusthan those >8 years of age. In addition, dogs 2–4 years of age (RRR = 1.83; 1.09–3.06) were significantly more likely to test positive forS. pseudintermediuscompared to those <2 years of age. The risk of infection withS. pseudintermediusorS. aureuswas significantly higher in ear canal and skin specimens compared to other specimens.ConclusionsThe findings suggest thatS. pseudintermediusandS. aureuswere the most commonly isolated species from dogs presented at the study hospital. Age of the dog and the location of infection were significant predictors of infection with bothStaphylococcusspecies investigated. Significant increasing temporal trend was observed forS. pseudintermediusbut notS. aureus. This information is useful for guiding clinical decisions as well as future research.

scholarly journals Multinomial Logistic Regression to Estimate and Predict the Perceptions of Individuals and Companies in the Face of the COVID-19 Pandemic in the Ñuble Region, Chile

2020 ◽  
Vol 12 (22) ◽  
pp. 9553
Author(s):  
Benito Umaña-Hermosilla ◽  
Hanns de la Fuente-Mella ◽  
Claudio Elórtegui-Gómez ◽  
Marisela Fonseca-Fuentes
Keyword(s):  
Logistic Regression ◽  
Local Community ◽  
Multinomial Logistic Regression ◽  
Local Level ◽  
The Elderly ◽  
Local Economy ◽  
Economic Policies ◽  
Community Based ◽  
Job Opportunities ◽  
The Face

The Coronavirus Disease 2019 (COVID-19) pandemic is transforming the world we live in, revealing our health, economic, and social weaknesses. In the local economy, the loss of job opportunities, the uncertainty about the future of small and medium-sized companies and the difficulties of families to face the effects of this crisis, invite us to investigate the perception of the local community. Based on a questionnaire applied to 313 citizens and 51 companies, this study explored the perception of these actors on the effects of the pandemic at the local level and determined the main factors that influenced their assessment using a multinomial logistic regression model. The results indicated a systematic concern for issues of employment, job security, and household debt. The variables of age and sex were significant when analyzing the vulnerability of certain groups, especially women and the elderly, to face the effects of the crisis and their role as citizens. At the business level, the focus was on economic policies that support its operational continuity and management capacity to face a changing scenario.

Diachronic frame analysis

2019 ◽  
Vol 11 (1) ◽  
pp. 43-78
Author(s):  
James Law
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Frame Analysis ◽  
Semantic Change ◽  
Frame Semantics ◽  
Multinomial Logistic Regression Model ◽  
Grammatical Category

Abstract Frame Semantics offers a valuable perspective on mechanisms of semantic change, particularly metonymy. However, corpus-based frame analysis has rarely been applied to diachronic data. The potential of this approach is illustrated with a diachronic description of the Purpose frame in French, based on 1,429 tokens of 17 frame-evoking words. Metonymic mappings in the frame allow Means and Medium to replace Agent. A multinomial logistic regression model shows that usage of these mappings has increased since 1600 and is conditioned by genre and the frequency and grammatical category of the frame-evoking word. The approach may inform how metonymy leads to lexicalized semantic change.

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