Search results for: penalized logistic regression

Authors: Claudia García-García, Catalina B. García-García, Román Salmerón-Gómez

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Much economic-environmental research includes the analysis of possible interactions by using Moderated Regression Analysis (MRA), which is a specific application of multiple linear regression analysis. This methodology allows analyzing how the effect of one of the independent variables is moderated by a second independent variable by adding a cross-product term between them as an additional explanatory variable. Due to the very specification of the methodology, the moderated factor is often highly correlated with the constitutive terms. Thus, great multicollinearity problems arise. The appearance of strong multicollinearity in a model has important consequences. Inflated variances of the estimators may appear, there is a tendency to consider non-significant regressors that they probably are together with a very high coefficient of determination, incorrect signs of our coefficients may appear and also the high sensibility of the results to small changes in the dataset. Finally, the high relationship among explanatory variables implies difficulties in fixing the individual effects of each one on the model under study. These consequences shifted to the moderated analysis may imply that it is not worth including an interaction term that may be distorting the model. Thus, it is important to manage the problem with some methodology that allows for obtaining reliable results. After a review of those works that applied the MRA among the ten top journals of the field, it is clear that multicollinearity is mostly disregarded. Less than 15% of the reviewed works take into account potential multicollinearity problems. To overcome the issue, this work studies the possible application of recent methodologies to MRA. Particularly, the raised regression is analyzed. This methodology mitigates collinearity from a geometrical point of view: the collinearity problem arises because the variables under study are very close geometrically, so by separating both variables, the problem can be mitigated. Raise regression maintains the available information and modifies the problematic variables instead of deleting variables, for example. Furthermore, the global characteristics of the initial model are also maintained (sum of squared residuals, estimated variance, coefficient of determination, global significance test and prediction). The proposal is implemented to data from countries of the European Union during the last year available regarding greenhouse gas emissions, per capita GDP and a dummy variable that represents the topography of the country. The use of a dummy variable as the moderator is a special variant of MRA, sometimes called “subgroup regression analysis.” The main conclusion of this work is that applying new techniques to the field can improve in a substantial way the results of the analysis. Particularly, the use of raised regression mitigates great multicollinearity problems, so the researcher is able to rely on the interaction term when interpreting the results of a particular study.

Keywords: multicollinearity, MRA, interaction, raise

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Authors: S. M. Ashikur Elahee, Md. Zahidur Rahman, Md. Shofiqur Rahman

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This study evaluated the impact of different water interventions under WASH program on access of household's to safe drinking water. Following survey method, the study was carried out in two Upazila of South-west coastal region of Bangladesh namely Koyra from Khulna and Shymnagar from Satkhira district. Being an explanatory study, a total of 200 household's selected applying random sampling technique were interviewed using a structured interview schedule. The predicted probability suggests that around 62 percent household's are out of year-round access to safe drinking water whereby, only 25 percent household's have access at SPHERE standard (913 Liters/per person/per year). Besides, majority (78 percent) of the household's have not accessed at both indicators simultaneously. The distance from household residence to the water source varies from 0 to 25 kilometer with an average distance of 2.03 kilometers. The study also reveals that the increase in monthly income around BDT 1,000 leads to additional 11 liters (coefficient 0.01 at p < 0.1) consumption of safe drinking water for a person/year. As expected, lining up time has significant negative relationship with dependent variables i.e., for higher lining up time, the probability of getting access for both SPHERE standard and year round access variables becomes lower. According to ordinary least square (OLS) regression results, water consumption decreases at 93 liters for per person/year of a household if one member is added to that household. Regarding water consumption intensity, ordered logistic regression (OLR) model shows that one-minute increase of lining up time for water collection tends to reduce water consumption intensity. On the other hand, as per OLS regression results, for one-minute increase of lining up time, the water consumption decreases by around 8 liters. Considering access to Deep Tube Well (DTW) as a reference dummy, in OLR, the household under Pond Sand Filter (PSF), Shallow Tube Well (STW), Reverse Osmosis (RO) and Rainwater Harvester System (RWHS) are respectively 37 percent, 29 percent, 61 percent and 27 percent less likely to ensure year round access of water consumption. In line of health impact, different type of water born diseases like diarrhea, cholera, and typhoid are common among the coastal community caused by microbial impurities i.e., Bacteria, Protozoa. High turbidity and TDS in pond water caused by reduction of water depth, presence of suspended particle and inorganic salt stimulate the growth of bacteria, protozoa, and algae causes affecting health hazard. Meanwhile, excessive growth of Algae in pond water caused by excessive nitrate in drinking water adversely effects on child health. In lieu of ensuring access at SPHERE standard, we need to increase the number of water interventions at reasonable distance, preferably a half kilometer away from the dwelling place, ensuring community peoples involved with its installation process where collectively owned water intervention is found more effective than privately owned. In addition, a demand-responsive approach to supply of piped water should be adopted to allow consumer demand to guide investment in domestic water supply in future.

Keywords: access, impact, safe drinking water, Sphere standard, water interventions

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Authors: Bersufekad Wubie

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Introduction: Near visual impairment is presenting near visual acuity of the eye worse than N6 at a 40 cm distance. Teachers' regular duties, such as reading books, writing on the blackboard, and recognizing students' faces, need good near vision. If a teacher has near-visual impairment, the work output is unsatisfactory. Objective: The study was aimed to assess the prevalence and associated factors near vision impairment among school teachers at Gondar city Northwest Ethiopia, August 2022. Methods: To select 567 teachers in Gondar city schools, an institutional-based cross-sectional study design with a multistage sampling technique were used. The study was conducted in selected schools from May 1 to May 30, 2022. Trained data collectors used well-structured Amharic and English language questionnaires and ophthalmic instruments for examination. The collected data were checked for completeness and entered into Epi data version 4.6, then exported to SPSS version 26 for further analysis. A binary and multivariate logistic regression model was fitted. And associated factors of the outcome variable. Result: The prevalence of near visual impairment was 64.6%, with a confidence interval of 60.3%–68.4%. Near visual impairment was significantly associated with age >= 35 years (AOR: 4.90 at 95% CI: 3.15, 7.65), having prolonged years of teaching experience (AOR: 3.29 at 95% CI: 1.70, 4.62), having a history of ocular surgery (AOR: 1.96 at 95% CI: 1.10, 4.62), smokers (AOR: 2.21 at 95% CI: 1.22, 4.07), history of ocular trauma (AOR : 1.80 at 95%CI:1.11,3.18 and uncorrected refractive error (AOR:2.01 at 95%CI:1.13,4.03). Conclusion and recommendations: This study showed the prevalence of near vision impairment among school teachers was high, and it is not a problem of the presbyopia age group alone; it also happens at a young age. So teachers' ocular health should be well accommodated in the school's eye health.

Keywords: Gondar, near visual impairment, school, teachers

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Authors: Atefeh Esmailnejad, Mohammad Abbaszadeh Hasiri

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Coxiella burnetii is a gram-negative obligatory intracellular bacterium that causes Q fever, a significant zoonotic disease. Sheep, cattle, and goats are the most commonly reported reservoirs for the bacteria, but infected cats and dogs have also been implicated in the transmission of the disease to human. The aim of present study was to investigate the presence of antibodies against Coxiella burnetii among companion dogs in Fars province, South of Iran. A total of 181 blood samples were collected from asymptomatic dogs, mostly referred to Veterinary Hospital of Shiraz University for regular vaccination. The IgG antibody detection against Coxiella burnetii was made by indirect Enzyme-linked Immunosorbent Assay (ELISA), employing phase I and II Coxiella burnetii antigens. A logistic regression model was developed to analyze multiple risk factors associated with seropositivity. An overall seropositivity of 7.7% (n=14) was observed. Prevalence was significantly higher in adult dogs above five years (18.18 %) compared with dogs between 1 and five years (7.86 %) and less than one year (6.17%) (P=0.043). Prevalence was also higher in male dogs (11.21 %) than in female (2.7 %) (P=0.035). There were no significant differences in the prevalence of positive cases and breed, type of housing, type of food and exposure to other farm animals (P>0.05). The results of this study showed the presence of Coxiella burnetii infection among the companion dogs population in Fars province. To our knowledge, this is the first study regarding Q fever in dogs carried out in Iran. In areas like Iran, where human cases of Q fever are not common or remain unreported, the public health implications of Q fever seroprevalence in dogs are quite significant.

Keywords: Coxiella burnetii, dog, Iran, Q fever

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Authors: Al Omari Moahmmed Ahmed

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In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator.

Keywords: non-informative prior, Bayesian method, type-I censoring, Gauss quardature

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Oanthata Jester Sealetsa, Richie Moalosi

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Musculoskeletal Disorders (MSDs) are reported to be amongst the leading contributing factors of low productivity in many industries across the world, and the most affected being New Emerging Economies (NEC) such as Botswana. This is due to lack of expertise and resources to deal with existing ergonomics challenges. This study was aimed to evaluate occupational postures and the prevalence of musculoskeletal disorders among diamond polishers in a diamond company in Botswana. A case study was conducted with about 106 diamond polishers in Gaborone, Botswana. A case study was chosen because it can investigate and explore an issue thoroughly and deeply, and record behaviour over time so changes in behaviour can be identified. The Corlett and Bishop Body Map was used to determine frequency of MSDs symptoms in different body parts of the workers. This was then followed by the use of the Rapid Entire Body Assessment (REBA) to evaluate the occupational postural risks of MSDs. Descriptive statistics, chi square, and logistic regression were used for data analysis. The results of the study reveal that workers experienced pain in the upper back, lower back, shoulders, neck, and wrists with the most pain reported in the upper back (44.6%) and lower back (44.2%). However, the mean REBA score of 6.07 suggests that sawing, bruiting and polishing were the most dangerous processes in diamond polishing. The study recommends that a redesign of the diamond polishing workstations is necessary to accommodate the anthropometry characteristic of Batswana (people from Botswana) to prevent the development of MSDs.

Keywords: assessment, Botswana, diamond polishing, ergonomics, musculoskeletal disorders, occupational postural risks

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Authors: Nicholas Agyepong, Paul Gyan

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Inappropriate prescribing of antibiotic is a common healthcare concern globally resulted in an increased risk of adverse reactions and the emergence of antimicrobial resistance. The wrong antibiotic prescribing habits may lead to ineffective and unsafe treatment, worsening of disease condition, and thus increase in health care costs. The study was to examine the antibiotic prescribing pattern and associated risk factors at Regional Hospital in the Bono region of Ghana. A retrospective cross-sectional study was conducted to describe the current prescribing practices at the Hospital from January 2014 to December, 2021. A systematic random sampling method was used to select the participants for the study. STATA version 16 software was used for data management and analysis. Descriptive statistics and logistic regression analysis were used to analyze the data. Statistical significance set at p<0.05. Antibiotic consumption was equivalent to 11 per 1000 inhabitants consuming 1 DDD per day. Most common prescribed antibiotic was amoxicillin/clavulanic acid (14.39%) followed by erythromycin (11.44%), and ciprofloxacin (11.36%). Antibiotics prescription have been steadily increased over the past eight years (2014: n=59,280 to 2021: n=190,320). Prescribers above the age of 35 were more likely to prescribe antibiotics than those between the ages of 20 and 25 (COR=21.00; 95% CI: 1.78 – 48.10; p=0.016). Prescribers with at least 6 years of experience were also significantly more likely to prescribe antibiotics than those with at most 5 years of experience (COR=14.17; 95% CI: 2.39 – 84.07; p=0.004). Thus, the establishment of an antibiotic stewardship program in the hospitals is imperative, and further studies need to be conducted in other facilities to establish the national antibiotic prescription guideline.

Keywords: antibiotic, antimicrobial resistance, prescription, prescribers

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Authors: Antoine Vikkey Hinson, Menonli Adjobimey, Gemayel Ahmed Biokou, Rose Mikponhoue

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Introduction: Construction industry is a critical concern with regard to Health and Safety Service worldwide. World health Organization revealed that work-related disease and trauma were held responsible for the death of one million nine hundred thousand people in 2016. The aim of this study it was to determine the prevalence and factors associated with the occurrence of work accidents in a construction industry in Benin. Method: It was a descriptive cross-sectional and analytical study. Data analysis was performed with R software 4.1.1. In multivariate analysis, we performed a binary logistic regression. OR adjusted (ORa) association measures and their 95% confidence interval [CI95%] were presented for the explanatory variables used in the final model. The significance threshold for all tests selected was 5% (p < 0.05) Result: In this study, 472 workers were included, and, of these, 452 (95.7%) were men corresponding to a sex ratio of 22.6. The average age of the workers was 33 years ± 8.8 years. Workers were mostly laborers (84.7%), and had declared having inadequate personal protective equipment (50.6%, n=239). The prevalence of work accidents is 50.8%. Collision with a rolling stock (25.8%), cut (16.2%), and stumbling (16.2%) were the main types of work accidents on the construction site. Four factors were associated with contributing to work accidents. Fatigue or exhaustion (ORa : 1.53[1.03 ; 2.28]); The use of dangerous tools (ORa : 1.81 [1.22 ; 2.71]); The various laborers’ jobs (ORa : 4.78 [2.62 ; 9.21]); and seniority in the company ≥ 4 years (ORa : 2.00 [1.35 ; 2.96]). Conclusion: This study allowed us to identify the associated factors. It is imperative to implement a rigorous policy of occupational health and security mostly the continuing training for workers safe, the supply of appropriate work tools and protective

Keywords: prevalence, work accident, associated factors, construction, benin

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Authors: Lito M. Amit, Venecio U. Ultra, Young Woong Song

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The purpose of this study was to document the prevalence and types of work-related health and safety problems among Filipino migrant workers and the link between their migration status and occupational health and safety (OHS) problems. We conducted a survey among 116 Filipino migrant workers who were both legal and undocumented. To assess the various forms of occupational health problems, we utilized the Korean occupational stress scale (KOSS), Nordic musculoskeletal questionnaire (NMQ) and a validated health and safety questionnaire. A focus group discussion (FGD) was also conducted to record relevant information that was limited by the questionnaires. Descriptive data were presented in frequency with percentages, mean, and standard deviation. Chi-square tests and logistic regression analyses were performed to estimate the degree of association between variables (p < 0.05). Among the eight subscales of KOSS, inadequate social support (2.48), organizational injustice (2.57), and lack of reward (2.52) were experienced by workers. There was a 44.83% prevalence of musculoskeletal disorders with arm/elbow having the highest rate, followed by shoulder and low back regions. Inadequate social support and discomfort in organizational climate and overall MSDs prevalence showed significant relationships with migration status (p < 0.05). There was a positive association between migration status and seven items under language and communication. A positive association was seen between migration status and some of the OHS problems of Filipino migrant workers in Korea. Undocumented workers in this study were seen to be more vulnerable to those stressors compared to those employed legally.

Keywords: Filipino workers, migration status, occupational health and safety, undocumented workers

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Chen-Chen Lo, Yin-Tzu Lin, Kang Lo

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Background: Driving fatigue related to inadequate or disordered sleep accounts for a major percentage of traffic accidents. Obstructive sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. However, the effects of OSAS severity on driving drowsiness remain unclear. Objective: The aim of this study is to investigate the relationship between OSAS severity and driving fatigue. Methodologies: The physical condition while driving was obtained from the questionnaires to classify the state of driving fatigue. OSAS severity was quantified as the polysomnography, and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). The severity of OSAS was diagnosed by the apnea and hypopnea index (AHI) with the American Academy of Sleep Medicine (AASM) guideline. The logistic regression model was used to examine the associations after adjusted age, gender, neck circumstance, waist circumstance, and body mass index (BMI). Results: There were 880 subjects recruited in this study, who had been done polysomnography for evaluating severity for OSAS as well as completed the driver condition questionnaire. 752 subjects were diagnosed with OSA, and 484 subjects had fatigue driving behavior in the past week. Patients diagnosed with OSAS had a 9.42-fold higher odds ratio (p < 0.01, 95% CI = 5.41 – 16.42) of driving drowsiness for cohorts with a normal degree. Conclusion: We observe the considerable correlation between OSAS and driving fatigue. For the purpose of promoting traffic safety, OSAS should be monitored and treated.

Keywords: obstructive sleep apnea syndrome, driving fatigue, polysomnography, apnea and hypopnea index

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Prof. Ammal Mokhtar Metwally, Samia M. Sami, Nihad A. Ibrahim, Fatma A. Shaaban, Iman I. Salama

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Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available.

Keywords: low birth weight, neonatal mortality, anthropometric measures, practical screening

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Authors: Jarrod Sio Jyh Lih

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This paper discusses the factors contributing to student achievement in literacy and numeracy in primary schools within Sibu division. The study involved 694 level 1 primary schoolteachers. Using descriptive statistics, the study observed high levels of practice for teacher efficacy, instructional leadership and professional learning communities (PLCs). The differences between gender, teaching experience and academic qualification were analyzed using the t-test and one-way analysis of variance (ANOVA). The study reported significant differences in respondent perceptions based on teaching experience vis-à-vis teacher efficacy. Here, the post hoc Tukey test revealed that efficaciousness grows with experience. A correlation test observed positive and significant correlations between all independent variables. Binary logistic regression was applied to predict the independent variables’ influence on student achievement. The findings revealed that a dimension of instructional leadership – ‘monitoring student progress’ - emerged as the best predictor of student achievement for literacy and numeracy. The result indicated the students were more than 4 times more likely to achieve the national key performance index for both literacy and numeracy when student progress was monitored. In conclusion, ‘monitoring student progress’ had a positive influence on students’ achievement for literacy and numeracy, hence making it a possible course of action for school heads. However, more comprehensive studies are needed to ascertain its consistency within the context of Malaysia.

Keywords: efficacy, instructional, literacy, numeracy

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Authors: Muhammad Kamran Hanif Khan, Fahad Mushtaq

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Globally, the death rate from cardiovascular disease has risen over the past 20 years, but especially in low and middle-income countries (LMICS), reports the World Health Organization (WHO). Around 17.5 million deaths, or 31% of all deaths worldwide in 2012, were attributed to CVD, 80% of which occurred in low- and middle-income nations, and eighty five percent of all worldwide disability is attributable to cardiovascular disease. This study assessed the dietary habit and Cardiovascular Risk factors among the patients of coronary artery disease against matched controls. The research was a case-control study. Sample size for this case-control study was 410 CAD cases and 410 healthy controls. The case-control ratio was 1:1. Patients diagnosed with coronary artery disease were recruited from the outpatient departments and emergency rooms of four hospitals in Pakistan. The ages of people who were diagnosed with coronary artery disease were not significantly different from (mean 57.97 7.39 years) the healthy controls (mean 57.12 6.73 years). In order to determine the relationship between food consumption and the two binary outcomes, logistic regression analysis was carried out. Chicken (0.340 (0.245-0.47), p-value 0.0001), beef (0.38 (0.254-0.56), p-value 0.0001), eggs (0.297 (0.208-0.426), p-value 0.0001), and junk food (0.249 (0.167-0.372), p-value 0.0001)) were protective, while yogurt consumption more than twice weekly was risk. Conclusion: In conclusion, poor dietary habits are closely linked to the risk of CAD. Investigations based on dietary trends offer vital and practical knowledge about societal patterns.

Keywords: dietary habbits, cardiovasculardisease, CVD risk factors, hypercholesterolemia

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Authors: Wilma Coetzee

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Statistics lecturers experience that many students who are taking a service course in statistics do not like statistics. Students in these courses tend to struggle and do not perform well. This research takes a look at the student’s perspective, with the aim to determine how to change the teaching of statistics so that students will enjoy it more and perform better. Questionnaires were used to determine the perspectives of first year service statistics students at a South African university. Factors addressed included motivation to study, attitude toward statistics, statistical anxiety, mathematical abilities and tendency to procrastinate. Logistic regression was used to determine what contributes to students performing badly in statistics. The results show that the factors that contribute the most to students performing badly are: statistical anxiety, not being motivated and having had mathematical literacy instead of mathematics in secondary school. Two open ended questions were included in the questionnaire: 'I will enjoy statistics more if…' and 'I will perform better in statistics if…'. The answers to these questions were analyzed using qualitative methods. Frequent themes were identified for each of the questions. A simulation study incorporating bootstrapping was done to determine the saturation of the themes. The majority of the students indicated that they would perform better in statistics if they studied more, managed their time better, had a flare for mathematics and if the lecturer was able to explain difficult concepts better. They also want more active learning. To ensure that students enjoy statistics more, they want an active learning experience. They want fun activities, more interaction with the lecturer and with one another, more computer based problems, and more challenges. They want a better understanding of the subject, want to understand the relevance of statistics to their future career and want excellent lecturers. These findings can be used to direct the improvement of the tuition of statistics.

Keywords: active learning, performance in statistics, statistical anxiety, statistics education

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Authors: Zahra Bahadoran, Parvin Mirmiran, Fereidoun Azizi

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Background: Higher consumption of white rice has been suggested as a risk factor for development of metabolic abnormalities. In this study we investigated the association between consumption of white rice and the 3-year occurrence of metabolic syndrome (MetS) in adults with and without abdominal obesity. Methods: This longitudinal study was conducted within the framework of the Tehran Lipid and Glucose Study on 1476 adults, aged 19-70 years. Dietary intakes were measured, using a 168-food items validated semi-quantitative food frequency questionnaire at baseline. Biochemical and anthropometric measurements were evaluated at both baseline (2006-2008) and after 3-year follow-up (2009-2011). MetS and its components were defined according to the diagnostic criteria proposed by NCEP ATP III, and the new cutoff points of waist circumference for Iranian adults. Multiple logistic regression models were used to estimate the occurrence of the MetS in each quartile of white rice consumption. Results: The mean age of participants was 37.8±12.3 y, and mean BMI was 26.0±4.5 kg/m2 at baseline. The prevalence of MetS in subjects with abdominal obesity was significantly higher (40.9 vs. 16.2%, P<0.01). There was no significant difference in white rice consumption between the two groups. Mean daily intake of white rice was 93±59, 209±58, 262±60 and 432±224 g/d, in the first to fourth quartiles of white rice, respectively. Stratified analysis by categories of waist circumference showed that higher consumption of white rice was more strongly related to the risk of metabolic syndrome in participants who had abdominal obesity (OR: 2.34, 95% CI:1.14-4.41 vs. OR:0.99, 95% CI:0.60-1.65) Conclusion: We demonstrated that higher consumption of white rice may be a risk for development of metabolic syndrome in adults with abdominal obesity.

Keywords: white rice, abdominal obesity, metabolic syndrome, food science, triglycerides

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Authors: Man Kumar Tamang, Uday Narayan Yadav

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Background and objectives: Every country in the world is facing a demographic challenge due to drastic growth of population over 60 years. Adequate diet and nutritional status are important determinants of health in elderly populations. This study aimed to assess the nutritional status among the elderly population and factors associated with malnutrition at the community setting in rural Nepal. Methods: This is a community-based cross-sectional study among elderly of age 60 years or above in the three randomly selected VDCs of Morang district in eastern Nepal, between August and November, 2016. A multi stage cluster sampling was adopted with sample size of 345 of which 339 participated in the study. Nutritional status was assessed by MNA tool and associated socio-economic, demographic, psychological and nutritional factors were checked by binary logistic regression analysis. Results: Among 339 participants, 24.8% were found to be within normal nutritional status, 49.6% were at risk of malnutrition and 24.8% were malnourished. Independent factors associated with malnutrition status among the elderly people after controlling the cofounders in the bivariate analysis were: elderly who were malnourished were those who belonged to backward caste according to traditional Hindu caste system [OR=2.69, 95% CI: 1.17-6.21), being unemployed (OR=3.23, 95% CI: 1.63-6.41),who experienced any mistreatment from caregivers (OR=4.05, 95% CI: 1.90-8.60), being not involved in physical activity (OR=4.67, 95% CI: 1.87-11.66) and those taking medication for any co-morbidities. Conclusion: Many socio-economic, psychological and physiological factors affect nutritional status in our sample population and these issues need to be addressed for bringing improvement in elderly nutrition and health status.

Keywords: elderly, eastern Nepal, malnutrition, nutritional status

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Authors: Isaiah Oluranti Olurinola, Michael Kayode Bolarinwa, Ebenezer Bowale, Ifeoluwa Ogunrinola

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Abstract - While much literature exists on microfinancing and its impact on the development of micro, small and medium-scale enterprises (MSME), yet little is known in respect of the impact of different types of education of owner-managers on the performances as well as innovative possibilities of such enterprises. This paper aims at contributing to the understanding of the impact of different types of education (academic, technical, apprenticeship, etc) that influence the performance of micro, small and medium-sized enterprise (MSME). This study utilises a recent and larger data-set collected in six states and FCT Abuja, Nigeria in the year 2014. Furthermore, the study carries out a comparative analysis of business performance among the different geo-political zones in Nigeria, given the educational attainment of the owner-managers. The data set were enterprise-based and were collected by the Nigerian Institute for Social and Economic Research (NISER) in the year 2014. Six hundred and eighty eight enterprises were covered in the survey. The method of data analysis for this study is the use of basic descriptive statistics in addition to the Logistic Regression model used in the prediction of the log of odds of business performance in relation to any of the identified educational attainment of the owner-managers in the sampled enterprises. An OLS econometric technique is also used to determine the effects of owner-managers' different educational types on the performance of the sampled MSME. Policy measures that will further enhance the contributions of education to MSME performance will be put forward.

Keywords: Business Performance, Education, Microfinancing, Micro, Small and Medium Scale Enterprises

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Authors: Aleme Mekuria, Samuel Mathewos

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Background: Teenage pregnancy has long been a worldwide social, economic and educational concern for the developed, developing and underdeveloped countries. Studies on adolescent sexuality and pregnancy are very limited in our country. Therefore, this study aims at assessing the prevalence of teenage pregnancy and its determinants among school adolescents of Arba Minch town. Methods: Institution- based, cross-sectional study was conducted from 20-30 March 2014. Systematic sampling technique was used to select a total of 578 students from four schools of the town. Data were collected by trained data collectors using a pre-tested, self-administered structured questionnaire. The analysis was made using the software SPSS version 20.0 statistical packages. Multivariate logistic regression was used to identify the predictors of teenage pregnancy. Results: The prevalence of teenage pregnancy among school adolescents of Arba Minch town was 7.7%. Being grade11(AOR=4.6;95%CI:1.4,9.3) and grade12 student (AOR=5.8;95% CI:1.3,14.4), not knowing the correct time to take emergency contraceptives(AOR=3.3;95%CI:1.4,7.4), substance use(AOR=3.1;95%CI:1.1,8.8), living with either of biological parents (AOR=3.3;95%CI:1.1,8.7) and poor parent-daughter interaction (AOR=3.1;95%CI:1.1,8.7) were found to be significant predictors of teenage pregnancy. Conclusion: This study revealed a high level of teenage pregnancy among school adolescents of Arba Minch town. A significant number of adolescent female school students were at risk of facing the challenges of teenage pregnancy in the study area. School-based reproductive health education and strong parent-daughter relationships should be strengthened.

Keywords: adolescent, Arba minch, risk factors, school, southern Ethiopia, teenage pregnancy

Procedia PDF Downloads 323

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Ghada Abo-Zaid

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Introduction: Oil is one of the most income source in Bahrain. Low oil price influence on the economy growth and the investment rate in Bahrain. For example, the economic growth was 3.7% in 2012, and it reduced to 2.9% in 2015. Investment rate was 9.8% in 2012, and it is reduced to be 5.9% and -12.1% in 2014 and 2015, respectively. The inflation rate is increased to the peak point in 2013 with 3.3 %. Objectives: The objectives here are to build statistical models to examine the effect of the interest rate inflation rate on the growth economy in Bahrain from 2000 to 2018. Methods: This study based on 18 years, and the multiple regression model is used for the analysis. All of the missing data are omitted from the analysis. Results: Regression model is used to examine the association between the Growth national product (GNP), the inflation rate, and real interest rate. We found that (i) Increase the real interest rate decrease the GNP. (ii) Increase the inflation rate does not effect on the growth economy in Bahrain since the average of the inflation rate was almost 2%, and this is considered as a low percentage. Conclusion: There is a positive impact of the real interest rate on the GNP in Bahrain. While the inflation rate does not show any negative influence on the GNP as the inflation rate was not large enough to effect negatively on the economy growth rate in Bahrain.

Keywords: growth national product, egypt, regression model, interest rate

Procedia PDF Downloads 141

Authors: Ibrahim Ahmed, Richard Amoateng, Akhil Jain, Mohamed Ahmed

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Alcoholic cardiomyopathy (ACM) is a type of acquired cardiomyopathy caused by chronic alcohol consumption. Frequently ACM is associated with arrhythmias such as atrial flutter. Our aim was to characterize the patient demographics and investigate the effect of atrial flutter (AF) on ACM. This was a retrospective cohort study using the Nationwide Inpatient Sample database to identify admissions in adults with principal and secondary diagnoses of alcoholic cardiomyopathy and atrial flutter from 2019. Multivariate linear and logistic regression models were adjusted for age, gender, race, household income, insurance status, Elixhauser comorbidity score, hospital location, bed size, and teaching status. The primary outcome was all-cause mortality, and secondary outcomes were the length of stay (LOS) and total charge in USD. There was a total of 21,855 admissions with alcoholic cardiomyopathy, of which 1,635 had atrial flutter (AF-ACM). Compared to Non-AF-ACM cohort, AF-ACM cohort had fewer females (4.89% vs 14.54%, p<0.001), were older (58.66 vs 56.13 years, p<0.001), fewer Native Americans (0.61% vs2.67%, p<0.01), had fewer smaller (19.27% vs 22.45%, p<0.01) & medium-sized hospitals (23.24% vs28.98%, p<0.01), but more large-sized hospitals (57.49% vs 48.57%, p<0.01), more Medicare (40.37% vs 34.08%, p<0.05) and fewer Medicaid insured (23.55% vs 33.70%, p=<0.001), fewer hypertension (10.7% vs 15.01%, p<0.05), and more obesity (24.77% vs 16.35%, p<0.001). Compared to Non-AF-ACM cohort, there was no difference in AF-ACM cohort mortality rate (6.13% vs 4.20%, p=0.0998), unadjusted mortality OR 1.49 (95% CI 0.92-2.40, p=0.102), adjusted mortality OR 1.36 (95% CI 0.83-2.24, p=0.221), but there was a difference in LOS 1.23 days (95% CI 0.34-2.13, p<0.01), total charge $28,860.30 (95% CI 11,883.96-45,836.60, p<0.01). In patients admitted with ACM, the presence of AF was not associated with a higher all-cause mortality rate or odds of all-cause mortality; however, it was associated with 1.23 days increase in LOS and a $28,860.30 increase in total hospitalization charge. Native Americans, older age and obesity were risk factors for the presence of AF in ACM.

Keywords: alcoholic cardiomyopathy, atrial flutter, cardiomyopathy, arrhythmia

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

Procedia PDF Downloads 427

Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

Procedia PDF Downloads 74

Authors: Carolyne Ndungu, Wesley Too, Diana Kassaman

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Diabetes is a global public health problem with an increasing morbidity and mortality rate across the globe. It is reported that 422 million people worldwide have diabetes with type 2 diabetes more common in people of African descent. Whilst prevalence of diabetes is four times more than it was in the last three decades, making it the world's ninth greatest cause of mortality, treatment of complications resulting from poor glycemic control is still high, contributing to poverty level in sub-Saharan. Poor treatment adherence has also been identified as a major contributing factor poor glycemic control among diabetic patients and still remains a significant challenge especially among patients living in rural Kenya. This study therefore seeks to identify gaps, barriers and challenges towards medication non-adherence among diabetic patients on follow-up at Kiambu County Referral Hospital, Kenya. Methods: A cross- sectional descriptive study was carried out at Gatundu Level five Hospital in Kiambu County. The study population consisted of adult patients with type two diabetes mellitus (T2DM) on follow up, at the Diabetes clinic between the month of June to July 2022. Systematic sampling of 200 participants was carried out. Ethical approvals from relevant authorities were done and ethical aspects of the study were also observed. Data analysis is ongoing using logistic regression analysis. Results, recommendations -contribution of this study will be highlighted within the next one month.

Keywords: adherence, diabetes, medication, Kenya

Procedia PDF Downloads 115

Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

Procedia PDF Downloads 300

Authors: Jingjue Bao, Ye Li, Yujie Qi

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The explosive growth of ride-hailing has reshaped residents' travel behavior and plays a crucial role in urban mobility within the built environment. Contributing to the research of the spatial variation of ride-hailing demand and its relationship to the built environment and socioeconomic factors, this study utilizes multi-source data from Haikou, China, to construct a Multi-scale Geographically Weighted Regression model (MGWR), considering spatial scale heterogeneity. The regression results showed that MGWR model was demonstrated superior interpretability and reliability with an improvement of 3.4% on R2 and from 4853 to 4787 on AIC, compared with Geographically Weighted Regression model (GWR). Furthermore, to precisely identify the surrounding environment of sampling point, DeepLabv3+ model is employed to segment street-level images. Features extracted from these images are incorporated as variables in the regression model, further enhancing its rationality and accuracy by 7.78% improvement on R2 compared with the MGWR model only considered region-level variables. By integrating multi-scale geospatial data and utilizing advanced computer vision techniques, this study provides a comprehensive understanding of the spatial dynamics between ride-hailing demand and the urban built environment. The insights gained from this research are expected to contribute significantly to urban transportation planning and policy making, as well as ride-hailing platforms, facilitating the development of more efficient and effective mobility solutions in modern cities.

Keywords: travel behavior, ride-hailing, spatial relationship, built environment, street-level image

Procedia PDF Downloads 56