scholarly journals Nexus between Poverty and Child Labor: A Case Study in Narayanganj Area of Bangladesh Using Binary Logistic Regression

2021 ◽  
Vol 7 (11) ◽  
pp. 194
Author(s):  
Shamima Akter ◽  
Wasim Akram
Keyword(s):  
Logistic Regression ◽  
Child Labor ◽  
Odds Ratio ◽  
Group Discussion ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Drop Out ◽  
Poor People ◽  
Binary Logistic Regression Model ◽  
Household Heads

<p>The purpose of the study is to see how poverty influences child labor. To carry out the study, moderate poor people have been considered as respondents. Kalibazar and Langalband regions of Narayanganj district have been selected. Random sampling technique and Focused Group Discussion with children have been taken to conduct the study. Data has been collected from 50 Household Heads and 50 Children (male and female). For economic analysis, the Binary Logistic Regression model has been undertaken to see the relationship between poverty and child labor.  The analysis shows that the odds ratio indicates that drop-out children from school are 11.34 times more likely to go for taking the occupation of child labor due to poverty (major cause) than those children who have been dropped out due to other reasons (reference category). The study also shows that the families having no loan are 0.444 times less likely to go for child labor due to major causes (poverty) than that of the families having a loan. Moreover, the odds ratio corresponding to the children who use their income to help their families is 3.26. It means that the children who use their income for family purposes go 3.26 times more likely to take the occupation of child labor due to a major cause (poverty) than those children who do not use their income for family purposes. At the same time, the children who use their income for treatment purposes go 1.45 times more likely to take the occupation of child labor due to poverty (a major cause) than those children who do not use their income for treatment purposes.</p>

scholarly journals Determinants of the Use of Hermetic Storage Bags for Maize Storage among Smallholder Farmers in Northwest Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tigist Kefale Mekonen ◽  
Biruk Yazie Wubetie
Keyword(s):  
Production Systems ◽  
Smallholder Farmers ◽  
Group Discussion ◽  
Binary Logistic Regression ◽  
Simple Random Sampling ◽  
Northwest Ethiopia ◽  
Small Scale ◽  
Binary Logistic Regression Model ◽  
Storage Losses ◽  
Household Heads

In Ethiopia, postharvest losses, mainly storage losses by insects, are very critical problems in agricultural production systems. In particular, maize is highly susceptible to insect and pest attacks in the storage. These storage losses affect the livelihood of small-scale farmers by aggravating their food insecurity and reducing their household income. This critical problem forced the farmers to look forward modern storage technologies like Purdue improved crop storage (PICS) bags, but its adoption is considerably low in the study area. Therefore, the objectives of this study were to examine the determinants to use PICS bags for maize storage. Data were collected through semistructured questionnaire, group discussion, key informants, and direct observation. A total of 392 sample household heads were selected by simple random sampling techniques. Binary logistic regression model, descriptive statistics, and inferential statistics were employed to analyze the data. This study revealed that, about 58% of the respondents replied that the importance of PICS bags in reducing insect damage was high. The results of binary logit model also indicated that educational level, sex, awareness, training, accessibility, perception on the effect of pesticide, social responsibility, and total income of the household positively and significantly affect farmers’ decision to use PICS bags. Moreover, the price of PICS affects negatively and significantly. Therefore, policy makers have to give emphasis for this newly introduced storage technology to address storage loss problems by taking in to account these determinants.

scholarly journals Association of dyslipidaemia with osteoporosis in postmenopausal women

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199955
Author(s):  
Lingnan Zhang ◽  
Qilong Liu ◽  
Xianshang Zeng ◽  
Wenshan Gao ◽  
Yanan Niu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Model ◽  
Postmenopausal Women ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Control Group ◽  
Binary Logistic Regression Model

Objective To assess the association of dyslipidaemia with osteoporosis in postmenopausal women. Methods Data from 160 postmenopausal women with newly diagnosed osteoporosis (osteoporosis group) and 156 healthy controls (control group) were retrospectively reviewed from 2016 to 2020. The primary outcomes were laboratory values assessed by a multivariate binary logistic regression model. Results Factors that greatly increased the risk of being in the osteoporosis group included high low-density lipoprotein (LDL) and low high-density lipoprotein (HDL) levels. The osteoporosis group had lower HDL and higher LDL levels than the control group. A multivariate binary logistic regression model showed that lower HDL and higher LDL levels were the only variables that were significantly associated with osteoporosis (odds ratio 1.86, 95% confidence interval: 3.66–4.25 and odds ratio 1.47, 95% confidence interval: 1.25–2.74, respectively). Conclusion Low HDL and high LDL levels may be associated with the occurrence of osteoporosis in postmenopausal women.

scholarly journals Effects of Different Socio-economic Characteristics of Rural Households on Their Saving Decision in Pabna District of Bangladesh: A Binary Logistic Regression Analysis

2020 ◽  
pp. 14-20
Author(s):  
Md. Al-Amin
Keyword(s):  
Logistic Regression ◽  
Random Sampling ◽  
Rural Households ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Binary Logistic Regression Analysis ◽  
Binary Logistic Regression Model ◽  
Cross Sectional ◽  
Household Saving ◽  
Private Savings

Household saving ensures a smooth future by softening the potential insecurities arise from uncertainty at the cost of present consumption. Moreover, the volume of national investment determines the actual health of an economy which is intensively associated with national savings. This study aimed at determining the effects of different socio-economic characteristics of rural households on their saving decision in Pabna district of Bangladesh. This research used a set of cross-sectional data from 250 households from three upazilas in Pabna district namely Pabna Sadar, Iswardi and Sujanagar on the relevant variables for the empirical analysis. A multistage random sampling technique involving simple, purposive and stratified random sampling was used to draw the sample. The study employed a binary logistic regression model to assess the influences of different socio-economic and demographic characteristics of rural household on their saving decision. The findings of the current study asserted that gender, family size and dependency ratio of household have significant and negative effects on their decision to start saving or not to start saving. Contrarily, the effects of the variables age, education level, marital status, income, secondary earner and liabilities on the decision of households to participate in saving were positive and significant. Moreover, the results revealed that social status has a strong but insignificant effect, but the variables access to bank and credit facilities have almost no significant effect on the household saving decisions. Since, private savings is essential for both the micro and macro level of an economy, therefore the study tried to suggest some recommendations with a view to increase private savings.

scholarly journals Factors for the Quality of the Brazilian Car Rental Service

2021 ◽  
pp. 216-226
Author(s):  
Gessica Mina Kim Jesus ◽  
Gladys Dorotea Cacsire Barriga
Keyword(s):  
Logistic Regression ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Car Rental ◽  
Main Factors ◽  
First Time ◽  
Tourist Information

The purpose of this study is to identify the main factors that influence the quality of the car rental service in Brazil, from the perspective of customers. It is the first time that the main factors for the quality of the car rental service have been investigated considering the perspective of Brazilian customers. We adopted a survey based on a questionnaire. The results were analyzed using the binary logistic regression model. The main factors for the quality were: the company offers local maps and tourist information along with the car (odds ratio = 1.56); the company has cars adapted for people with special needs (odds ratio = 1.39); the company supplies cars with a choice of steering (odds ratio = 1.568); the company has employees that are well dressed and look good (odds ratio = 1.58); the company makes all tools and documents available (odds ratio = 1.75); and the company offers compensation if inconveniences arise for customers (odds ratio = 1.51). This study shows how companies can direct their efforts to reduce complaints and improve the satisfaction and quality of the car rental service in Brazil.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

scholarly journals Appraisal of Issues Influencing Soil-Steps Application for Sustainable Farmland Management in High-Potential District, Ethiopia

2018 ◽  
Vol 7 (3) ◽  
pp. 102
Author(s):  
Mehretie Belay
Keyword(s):  
Smallholder Farmers ◽  
Group Discussion ◽  
Household Head ◽  
Binary Logistic Regression ◽  
Northwest Ethiopia ◽  
Study Data ◽  
High Potential ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Chi Square Test

Soil damage by moving water is a somber predicament on farmlands in highland Ethiopia. Sizeable number of trial in farmland preservation has been executed to handle the crisis during the last tens of years. However, the attempts have not been vibrant to trim-down the danger to an attractive extent. This paper evaluates factors contributing to application of soil-steps (bunds) as sustainable farmland management technology (SFLMT) by smallholder farmers in one of the high-potential districts of northwest Ethiopia named Dangila Woreda (District). Mixed method triangulation designs involving concurrent acquisition and interpretation of quantitative and qualitative data were used in the study. Data were acquired from randomly chosen 201 farming households during the harvest seasons of 2011 and 2012. Ordered questionnaire, participatory field observation, key informant interview and focus group discussion were mechanisms employed during the data acquisition. Descriptive statistics (means, standard deviations and percentiles), Chi-square test, t-test and the binary logistic regression model were used to analyze the quantitative data. The qualitative information was textually narrated to augment the quantitative results. Findings of the investigation confirm that age of the household head, the number of household members, slope of the farmland, the size of the farmland held, households’ participation in indigenous labour-sharing activities and the number of farm tools owned were significantly increasing the building of soil-steps as SFLMT in the study district. Involvement in off-farm activities and pest invasions were considerably hindering farmers from building soil-steps on their farmlands. The results in general indicated that households’ access to livelihood assets are key promoters for farmers’ implementation of soil-steps on their farmlands. Local resource preservation and improvement trials should thus ponder on convalescing farmers’ material endowments to improve their capability to use soil-steps as SFLMT in their farming activities.

scholarly journals A predictive model for early diagnosis of keratoconus

2020 ◽  
Author(s):  
GRACIA CASTRO-LUNA ◽  
ANTONIO PÉREZ-RUEDA
Keyword(s):  
Logistic Regression ◽  
Predictive Model ◽  
Early Stage ◽  
Corneal Thickness ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Bivariate Analysis ◽  
Diagnostic Model ◽  
Binary Logistic Regression Model ◽  
Cross Sectional

Abstract Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.

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