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Modeling Spatial Spillovers of Divorce in Senegal Using Spatial Durbin Model: A Maximum Likelihood Estimation Approach
Alassane Aw,
Emmanuel Nicolas Cabral
Issue:
Volume 8, Issue 1, January 2019
Pages:
1-6
Received:
16 December 2018
Accepted:
5 January 2019
Published:
24 January 2019
Abstract: Spatial Durbin Model (SDM) is one of the family of spatial autoregressive models. In this paper, we use the SDM model to determine the spatial spillovers of divorce in Senegal. The variable of interest is the rate of divorce and the explanatory variables are the rate of illiteracy and the average age at marriage in Senegal. The model parameters are estimated by the maximum likelihood technique. The estimation of the autoregressive parameter is performed using numerical optimization of the concentrated log-likelihood of the SDM model. The results obtained showed that the rate of illiteracy and the average age at marriage have a real impact on the rate of divorce in Senegal. We also note that the departments of the country that are closed are more similar than the distant departments in relation to the divorce data. Direct and indirect effects were used to measure changes in the rate of divorce as a result of changes in the rate of illiteracy and the average age at marriage.
Abstract: Spatial Durbin Model (SDM) is one of the family of spatial autoregressive models. In this paper, we use the SDM model to determine the spatial spillovers of divorce in Senegal. The variable of interest is the rate of divorce and the explanatory variables are the rate of illiteracy and the average age at marriage in Senegal. The model parameters are...
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Application of Principal Component Analysis to Crime Data, Case Study: Mathare Slums, Nairobi County in Kenya
Wafula Mike Erick,
Samson Wangila Wanyonyi,
Chris Muchwanju
Issue:
Volume 8, Issue 1, January 2019
Pages:
7-17
Received:
8 January 2019
Accepted:
28 January 2019
Published:
21 February 2019
Abstract: The study focuses on statistical analysis of causes of crimes in Mathare slums, Nairobi county using data collected via questionnaires in April 2018.The Correlation analysis was carried out to explain the association between the causes of crimes while the principal component analysis (PCA) was used to reduce the dimensionality of the data sets. The Correlation analysis indicates a fairly strong positive relationship between unemployment and drugs and substance abuse which means that their variables can be used to predict one another. PCA analysis reveals that three PCs (drugs and substance abuse, unemployment and neglect from parents) that explains about 52.6% of the total variability of the causes of crimes against person are suggested to be retained. Similarly, two PCs (drugs and substance abuse and unemployment) that explain about 42.2% of the total variability of the causes of crimes against property are suggested to be retained. Generally, the causes of crimes against person and property in Mathare slums are not unique.
Abstract: The study focuses on statistical analysis of causes of crimes in Mathare slums, Nairobi county using data collected via questionnaires in April 2018.The Correlation analysis was carried out to explain the association between the causes of crimes while the principal component analysis (PCA) was used to reduce the dimensionality of the data sets. The...
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Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
Issue:
Volume 8, Issue 1, January 2019
Pages:
18-25
Received:
19 January 2019
Accepted:
20 February 2019
Published:
6 March 2019
Abstract: This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.
Abstract: This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated....
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Determinant of Solid-Waste Management in Debre Birhan Town
Issue:
Volume 8, Issue 1, January 2019
Pages:
26-30
Received:
14 December 2018
Accepted:
27 February 2019
Published:
27 March 2019
Abstract: Most of the developed countries recognized that solid waste management is very crucial for survival (economically) in addition to secure the safety of environment and human health. However, the developing countries like Ethiopia, let alone use its economic benefits, because of various reasons they are dumping of wastes in unauthorized sites, which easily expose to harsh hazards, like environmental pollution and health problem. Hence, the overall objective of the study is to describe and analyze the household solid waste management current situation and examine the influence of demographics, socio-cultural and institutional factors on the factor influencing of solid waste management at household level in the town. The data used in this study for the analysis were obtained from statistical survey by self-administered questioner and direct personal interview method with reference to a total of 1166 households which were selected through simple random sampling. Logistic regression model was used to identify factors that influencing solid waste management at household level in the study area. Though all households have temporary storage in their home, they did not store wastes separately based on its nature. The empirical analyses, using the logistic regression model, shows that, household head educational level, household’s willingness to pay for waste collector, household’s awareness on solid waste management service are the major determinants of household solid waste management in the study area. Moreover, the qualitative analyses, using the interview, show that manpower, budget, and facilities such as container, adequate vehicles, waste gown, and gloves are the other major factors of solid waste management at household level in debre Birhan town.
Abstract: Most of the developed countries recognized that solid waste management is very crucial for survival (economically) in addition to secure the safety of environment and human health. However, the developing countries like Ethiopia, let alone use its economic benefits, because of various reasons they are dumping of wastes in unauthorized sites, which ...
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Class of Difference Cum Ratio–Type Estimator in Double Sampling Using Two Auxiliary Variables with Some Known Population Parameters
Akingbade Toluwalase Janet,
Okafor Fabian Chinemelu
Issue:
Volume 8, Issue 1, January 2019
Pages:
31-38
Received:
2 February 2019
Accepted:
12 March 2019
Published:
1 April 2019
Abstract: In this paper, a class of double sampling difference cum ratio - type estimator using two auxiliary variables was proposed for estimating the finite population mean of the variable of interest. The expression for the bias and the mean square error of the proposed estimators are derived; in addition, some members of the class of the estimator are identified. The conditions under which the proposed estimators perform better than the sample mean and the existing double sampling ratio type estimators are derived. The empirical analysis showed that the proposed class of estimator performs better than the existing estimators considered in this study.
Abstract: In this paper, a class of double sampling difference cum ratio - type estimator using two auxiliary variables was proposed for estimating the finite population mean of the variable of interest. The expression for the bias and the mean square error of the proposed estimators are derived; in addition, some members of the class of the estimator are id...
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