Search results for: panel data regression analysis

Authors: C. Casini, V. Da Deppo, P. Zuppella, P. Chioetto, A. Slemer, F. Frassetto, M. Romoli, F. Landini, M. Pancrazzi, V. Andretta, E. Antonucci, A. Bemporad, M. Casti, Y. De Leo, M. Fabi, S. Fineschi, F. Frassati, C. Grimani, G. Jerse, P. Heinzel, K. Heerlein, A. Liberatore, E. Magli, G. Naletto, G. Nicolini, M.G. Pelizzo, P. Romano, C. Sasso, D. Spadaro, M. Stangalini, T. Straus, R. Susino, L. Teriaca, M. Uslenghi, A. Volpicelli

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Solar Orbiter, launched on February 9^th 2020, is an ESA/NASA mission conceived to study the Sun. The payload is composed of 10 instruments, among which there is the Metis coronagraph. A coronagraph aims at taking images of the solar corona: the occulter element simulates a total solar eclipse. This work presents some of the results obtained in the visible light band (580-640 nm) using a flat field panel source. The flat field panel gives a uniform illumination; consequently, it has been used during the on-ground calibration for several purposes: evaluating the response of each pixel of the detector (linearity); and characterizing the Field of View of the coronagraph. As a conclusion, a major result is the verification that the requirement for the Field of View (FoV) of Metis is fulfilled. Some investigations are in progress in order to verify that the performance measured on-ground did not change after launch.

Keywords: solar orbiter, Metis, coronagraph, flat field panel, calibration, on-ground, performance

Procedia PDF Downloads 82

Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

Procedia PDF Downloads 309

Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

Procedia PDF Downloads 825

Authors: Hyeondal Jeong, Yoonjung Baek

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This study examines the relationship between ethical leadership and individual creativity and focused on mediating effects of psychological safety. In order to clarify the mechanism of ethical leadership, psychological safety of the members was set as a mediator. Using data gathered from a sample of 150 employees. For data analysis, exploratory factor analysis, correlation analysis, hierarchical regression analysis and Sobel-Test were performed. The results showed that ethical leadership had a positive effect on psychological safety and individual creativity, and psychological safety had a positive mediating effect. Since the mediating effect of psychological safety has been confirmed, we need to find ways to improve the psychological safety of the members in terms of organizational management. Psychological safety has a positive effect on individual creativity, which can have a positive impact on innovation throughout the organization.

Keywords: ethical leadership, creativity, psychological safety, ethics management, innovative behaviors

Procedia PDF Downloads 223

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 65

Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana

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Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.

Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker

Procedia PDF Downloads 549

Authors: Kanae Suzuki

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Handheld video games are popular communication tools among Japanese elementary school students today. This study aims to examine the effects of the use of handheld video games on interpersonal relationships of the students in real and virtual worlds. A two-wave panel survey was conducted for students of ten elementary schools at an interval of approximately six months. The survey questionnaire included questions about the average amount of time spent playing a handheld video game during the past one month, the frequency of communication with players during game play, and the interpersonal relationships, such as the number of real and virtual friends the students have. A multiple regression model was constructed for 324 students to examine causal relationships. The results indicated that the more frequently the students communicated with other players while playing games, the number of the real friends tended to increase. In contrast, no significant effect of the total time spent playing games was found on interpersonal relationships. The findings suggested that communication during game play is an important factor for improving interpersonal relationships of this age group.

Keywords: communication, real friend, social adjustment, virtual friend

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Authors: Runglaksamee Rodkam

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The purpose of this research was to study the influence of accountants’ potential performance on their working process, a case study of Government Savings Banks in the northeast of Thailand. The independent variables included accounting knowledge, accounting skill, accounting value, accounting ethics, and accounting attitude, while the dependent variable included the success of the working process. A total of 155 accountants working for Government Savings Banks were selected by random sampling. A questionnaire was used as a tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analyses. The findings revealed that the majority of accountants were female with an age between 35-40 years old. Most of the respondents had an undergraduate degree with ten years of experience. Moreover, the factors of accounting knowledge, accounting skill, accounting a value and accounting ethics and accounting attitude were rated at a high level. The findings from regression analysis of observation data revealed a causal relationship in that the observation data could explain at least 51 percent of the success in the accountants’ working process.

Keywords: influence, potential performance, success, working process

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Authors: Naragain Phumchusri, Kittitach Pongpairoj

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Overbooking is a very useful revenue management technique that could help reduce costs caused by either undersales or oversales. In this paper, we propose an overbooking model for two types of cars that can minimize the total cost for car rental service. With two types of cars, there is an upgrade possibility for lower type to upper type. This makes the model more complex than one type of cars scenario. We have found that convexity can be proved in this case. Sensitivity analysis of the parameters is conducted to observe the effects of relevant parameters on the optimal solution. Model simplification is proposed using multiple linear regression analysis, which can help estimate the optimal overbooking level using appropriate independent variables. The results show that the overbooking level from multiple linear regression model is relatively close to the optimal solution (with the adjusted R-squared value of at least 72.8%). To evaluate the performance of the proposed model, the total cost was compared with the case where the decision maker uses a naïve method for the overbooking level. It was found that the total cost from optimal solution is only 0.5 to 1 percent (on average) lower than the cost from regression model, while it is approximately 67% lower than the cost obtained by the naïve method. It indicates that our proposed simplification method using regression analysis can effectively perform in estimating the overbooking level.

Keywords: overbooking, car rental industry, revenue management, stochastic model

Procedia PDF Downloads 149

Authors: Christos E. Lampropoulos, Maria Konsta, Tamta Sirbilatze, Ifigenia Apostolou, Vicky Dradaki, Konstantina Panouria, Irini Dri, Christina Kordali, Vaggelis Lambas, Georgios Mavras

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Objectives: Full Mini Nutritional Assessment (MNA) questionnaire is one of the most useful tools in diagnosis of malnutrition in hospitalized patients, which is related to increased morbidity and mortality. The purpose of our study was to assess the nutritional status of elderly, hospitalized patients and examine the hypothesis that MNA may predict mortality and extension of hospitalization. Methods: One hundred fifty patients (78 men, 72 women, mean age 80±8.2) were included in this cross-sectional study. The following data were taken into account in analysis: anthropometric and laboratory data, physical activity (International Physical Activity Questionnaires, IPAQ), smoking status, dietary habits, cause and duration of current admission, medical history (co-morbidities, previous admissions). Primary endpoints were mortality (from admission until 6 months afterwards) and duration of admission. The latter was compared to national guidelines for closed consolidated medical expenses. Logistic regression and linear regression analysis were performed in order to identify independent predictors for mortality and extended hospitalization respectively. Results: According to MNA, nutrition was normal in 54/150 (36%) of patients, 46/150 (30.7%) of them were at risk of malnutrition and the rest 50/150 (33.3%) were malnourished. After performing multivariate logistic regression analysis we found that the odds of death decreased 20% per each unit increase of full MNA score (OR=0.8, 95% CI 0.74-0.89, p < 0.0001). Patients who admitted due to cancer were 23 times more likely to die, compared to those with infection (OR=23, 95% CI 3.8-141.6, p=0.001). Similarly, patients who admitted due to stroke were 7 times more likely to die (OR=7, 95% CI 1.4-34.5, p=0.02), while these with all other causes of admission were less likely (OR=0.2, 95% CI 0.06-0.8, p=0.03), compared to patients with infection. According to multivariate linear regression analysis, each increase of unit of full MNA, decreased the admission duration on average 0.3 days (b:-0.3, 95% CI -0.45 - -0.15, p < 0.0001). Patients admitted due to cancer had on average 6.8 days higher extension of hospitalization, compared to those admitted for infection (b:6.8, 95% CI 3.2-10.3, p < 0.0001). Conclusion: Mortality and extension of hospitalization is significantly increased in elderly, malnourished patients. Full MNA score is a useful diagnostic tool of malnutrition.

Keywords: duration of admission, malnutrition, mini nutritional assessment score, prognostic factors for mortality

Procedia PDF Downloads 293

Authors: Shehu Usman Hassan

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Published accounting information in financial statements are required to provide various users - shareholders, employees, suppliers, creditors, financial analysts, stockbrokers and government agencies – with timely and reliable information useful for making prudent, effective and efficient decisions. The widespread failure in the financial information quality has created the need to improve the financial information quality and to strengthen the control of managers by setting up good firms structures. This paper investigates firm attributes from perspective of structure, monitoring, performance elements of listed deposit money banks in Nigeria. The study adopted correlational research design with balanced panel data of 14 banks as sample of the study using multiple regression as a tool of analysis. The result reveals that firms attributes (leverage, profitability, liquidity, bank size and bank growth) has as significant influence on earnings quality of listed deposit money banks in Nigeria after the adoption of IFRS, while the pre period shows that the selected firm attributes has no significant impact on earnings quality. It is therefore concluded that the adoption of IFRS is right and timely.

Keywords: earnings quality, firm attributes, listed deposit money bank, Nigeria

Procedia PDF Downloads 487

Authors: Muhammad Umar Kibiya

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The study aims to examine whether regulatory changes have an impact on the financial reporting quality of Microfinance Banks in Nigeria. The research employed a panel data analysis technique, using data from 2018 to 2022. The sample includes 72 microfinance banks, using regression analyses to examine the relationship between variables. The findings indicate that Board Chairmanship has a positive and significant effect on financial reporting quality. It also reveals that share ownership has a negative and significant impact on financial reporting quality. The results suggest that regulatory changes have a positive and significant influence on financial reporting quality. Thus, findings have important implications for microfinance banks in Nigeria. It suggests that having a strong and competent board chairperson can enhance financial reporting quality, leading to more transparent and reliable information for stakeholders. Furthermore, the study highlights the importance of regulatory changes in improving financial reporting practices in the microfinance banking sector. The study contributes to the extant literature by providing empirical evidence on the relationship between board chairmanship, share ownership, financial reporting quality, and regulatory changes in microfinance banks. It further supports the concept that governance mechanisms and regulatory reforms play a crucial role in ensuring transparency and accountability within the microfinance banking sector. It recommends that microfinance banks should appoint experienced and qualified individuals as board chairpersons to enhance financial reporting quality. Furthermore, policymakers and regulatory authorities should continue to implement and enforce regulations that promote transparent financial reporting practices in microfinance banks.

Keywords: board chairman, share ownership, financial reporting quality, microfinance, regulatory changes

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

Procedia PDF Downloads 337

Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

Procedia PDF Downloads 395

Authors: Ya-Li Tsai

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Nitrogen is a vital element crucial for crop growth, significantly influencing crop yield. In rice cultivation, farmers often apply substantial nitrogen fertilizer to maximize yields. However, excessive nitrogen application increases the risk of lodging and pest infestation, leading to yield losses. Additionally, conventional flooded irrigation methods consume significant water resources, necessitating precise agricultural and intelligent water management systems. In this study, it leveraged physiological data and field images captured by unmanned aerial vehicles, considering fertilizer treatment and irrigation as key factors. Statistical models incorporating rice physiological data, yield, and vegetation indices from image data were developed. Missing physiological data were addressed using multiple imputation and regression methods, and regression models were established using principal component analysis and stepwise regression. Target nitrogen accumulation at key growth stages was identified to optimize fertilizer application, with the difference between actual and target nitrogen accumulation guiding recommendations for ear dressing dosage. Field experiments conducted in 2022 validated the recommended ear dressing dosage, demonstrating no significant difference in final yield compared to traditional fertilizer levels under alternate wetting and drying irrigation. These findings highlight the efficacy of applying recommended dosages based on fertilizer decision models, offering the potential for reduced fertilizer use while maintaining yield in rice cultivation.

Keywords: intelligent fertilizer management, nitrogen top and ear dressing fertilizer, rice, yield optimization

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Authors: Diep Thanh Tung

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This study aims to measure technical efficiency and examine productivity performance of livestock production in regions of Vietnam based on a panel data of 2008–2012. After four years, although there are improvements in efficiency of some regions, low technical efficiency, poor performance of productivity and its compositions are dominant features in almost regions. Households which much depend on livestock income in agricultural income or agricultural income in total income are more vulnerable than the others in term of livestock production.

Keywords: data envelopment analysis, meta-frontier, Malmquist, technical efficiency, livestock production

Procedia PDF Downloads 673

Authors: Y. H. Mugahed Amran, Raizal S. M. Rashid, Farzad Hejazi, Nor Azizi Safiee, A. A. Abang Ali

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Experimental and analytical studies were accomplished to examine the structural behavior of precast foamed concrete sandwich panel (PFCSP) under vertical in-plane shear load. PFCSP full-scale specimens with total number of six were developed with varying heights to study an important parameter slenderness ratio (H/t). The production technique of PFCSP and the procedure of test setup were described. The results obtained from the experimental tests were analysed in the context of in-plane shear strength capacity, load-deflection profile, load-strain relationship, slenderness ratio, shear cracking patterns and mode of failure. Analytical study of finite element analysis was implemented and the theoretical calculations of the ultimate in-plane shear strengths using the adopted ACI318 equation for reinforced concrete wall were determined aimed at predicting the in-plane shear strength of PFCSP. The decrease in slenderness ratio from 24 to 14 showed an increase of 26.51% and 21.91% on the ultimate in-plane shear strength capacity as obtained experimentally and in FEA models, respectively. The experimental test results, FEA models data and theoretical calculation values were compared and provided a significant agreement with high degree of accuracy. Therefore, on the basis of the results obtained, PFCSP wall has the potential use as an alternative to the conventional load-bearing wall system.

Keywords: deflection curves, foamed concrete (FC), load-strain relationships, precast foamed concrete sandwich panel (PFCSP), slenderness ratio, vertical in-plane shear strength capacity

Procedia PDF Downloads 188

Authors: Tahani Salman Alangari

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Student success can be impacted by internal factors such as their emotional well-being and external factors such as organizational support and instructional support in the classroom. This study is to identify at least one factor that forecasts student engagement. It is a cross-sectional, conducted on 6206 teachers and encompassed three years of data collection and observations of math instruction in approximately 50 schools and 300 classrooms. A multiple linear regression revealed that a model predicting student engagement from emotional support, classroom organization, and instructional support was significant. Four linear regression models were tested using hierarchical regression to examine the effects of independent variables: emotional support was the highest predictor of student engagement while instructional support was the lowest.

Keywords: student engagement, emotional support, organizational support, instructional support, well-being

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Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

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Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

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Authors: Majid A. AlSayari

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The study reported here investigated social cognitive theory (SCT) in the context of Vocational Rehab (VR) for people with disabilities. The prime purpose was to increase knowledge of VR phenomena and make recommendations for improving VR services. The sample consisted of 242 persons with Spinal Cord Injuries (SCI) who completed questionnaires. A further 32 participants were Trainers. Analysis of questionnaire data was carried out using factor analysis, multiple regression analysis, and thematic analysis. The analysis suggested that, in motivational terms, and consistent with research carried out in other academic contexts, self-efficacy was the best predictor of VR performance. The author concludes that that VR self-efficacy predicted VR training performance.

Keywords: people with physical disabilities, social cognitive theory, self-efficacy, vocational training

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Authors: H. Abbasi, K. Sennah

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A Structural Insulated Panel (SIP) is a structural element contains of foam insulation core sandwiched between two oriented-strand boards (OSB), plywood boards, steel sheets or fibre cement boards. Superior insulation, exceptional strength and fast insulation are the specifications of a SIP-based structure. There are also many other benefits such as less total construction costs, speed of construction, less expensive HVAC equipment required, favourable energy-efficient mortgages comparing to wood-framed houses. This paper presents the experimental analysis on selected foam-timber SIPs to study their structural behaviour when used as walls in residential construction under lateral loading. The experimental program has also taken several stud panels in order to compare the performance of SIP with conventional wood-frame system. The results of lateral tests performed in this study established a database that can be used further to develop design tables of SIP wall subjected to lateral loading caused by wind or earthquake. A design table for walls subjected to lateral loading was developed. Experimental results proved that the tested SIPs are ‘as good as’ the conventional wood-frame system.

Keywords: structural insulated panel, experimental study, lateral load, design tables

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Sennanda Musa, Ahmed Mutunzi Kitunzi, Gerald Kasigwa, Ismail Kintu

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This paper is an analysis of the empirical relationship between project financing by Islamic Development Bank (IsDB) and the poverty trends in the context of countries benefiting from IsDB. Specifically, the study seeks to find out whether there is a statistically significant relationship between the project financing dollar amounts by IsDB (PF) and the GNI Per Capita, PPP of 57 countries for the years 2002 to 2021. The research is a longitudinal, desk-top triangulation of correlation, regression, hypothesis-testing employing the linear dynamic panel data GMM model as an estimator of the empirical relationships between the key variables of the study. The study results show that there is a significant positive relationship between the PF dollar amounts from the IsDB and the GNI Per Capita, PPP in these 57 countries. Therefore, countries that receive higher PF dollar amounts from the IsDB, generally have more GNI Per Capita, PPP (less poverty) than their counterparts. It is, therefore, recommendable for countries to formulate policies that facilitate Islamically financed projects to mitigate poverty. This paper develops policy discussions regarding allocation of political attention to the policy topics on poverty mitigation, and their relation to financing projects Islamically, thus generate information on policy choices regarding the Islamic financing alternative.

Keywords: gross-national-income, IsDB-project-financing, public policy, poverty

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Authors: Tariqul Islam, Shaheen Mollah

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The study's main objective is to assess the relationship between psychological adjustment and parental acceptance-rejection, teacher acceptance-rejection, and best friend acceptance-rejection among secondary school students. This study was conducted on a sample of 300 (6th through 10th-grade students) recruited from over ten schools in Dhaka. While the schools were selected purposively, the respondents within each school were selected conveniently. The collected data were analyzed using Pearson product-moment correlation, hierarchical regression, and simultaneous regression analysis. The results showed that psychological adjustment is positively correlated with paternal, maternal, teacher, and best friend acceptance. The paternal acceptance was significantly connected with maternal acceptance. The teacher and best friend acceptance are correlated substantially with paternal and maternal acceptance. The hierarchical multiple regressions indicated that maternal, paternal, teacher, and best friend acceptance-rejection contributed significantly to students' psychological adjustment. The results revealed substantial independent contributions of maternal, paternal, teacher, and best friend acceptance on the students' psychological adjustment. The simultaneous regression analysis indicates that the maternal and best friend acceptances (but not paternal acceptance) were significant predictors of psychological adjustments. It showed that 41.7% variability in psychological adjustment could be explained by paternal, maternal, and best friend acceptance. The findings of the present study are exciting. They may contribute to developing insight in parents and best friends for behaving properly with their offspring and friend, respectively, for better psychological adjustment.

Keywords: adjustment, parenting, rejection, acceptance

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Authors: Innocent Pangapanga, Eric Mungatana

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Tropical cyclones–related floods (TCRFs) in Malawi have devastating effects on smallholder agriculture, thereby threatening the food security agenda, which is already constrained by poor agricultural innovations, low use of improved varieties, and unaffordable inorganic fertilizers, and fragmenting landholding sizes. Accordingly, households have engineered and indigenously implemented sustainable landscape management (SLM) innovations to contain the adverse effects of TCRFs on farm productivity. This study, therefore, interrogated the efficacy of SLM adoption on farm productivity under varying TCRFs, while controlling for the potential selection bias and unobservable heterogeneity through the application of the Endogenous Switching Regression Model. In this study, we further investigated factors driving SLM adoption. Substantively, we found TCRFs reducing farm productivity by 31 percent, on the one hand, and influencing the adoption of SLM innovations by 27 percent, on the other hand. The study also observed that households that interacted SLM with TCRFs were more likely to enhance farm productivity by 24 percent than their counterparts. Interestingly, the study results further demonstrated that multiple adoptions of SLM-related innovations, including intercropping, agroforestry, and organic manure, enhanced farm productivity by 126 percent, suggesting promoting SLM adoption as a package to appropriately inform existing sustainable development goals’ agricultural productivity initiatives under intensifying TCRFs in the country.

Keywords: tropical cyclones–related floods, sustainable landscape management innovations, farm productivity, endogeneity, endogenous switching regression model, panel data, smallholder agriculture

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Authors: Anne M. Denton, Rahul Gomes, David W. Franzen

Abstract:

High-resolution elevation data are becoming increasingly available, but typical approaches for computing topographic features, like slope and curvature, still assume small sliding windows, for example, of size 3x3. That means that the digital elevation model (DEM) has to be resampled to the scale of the landform features that are of interest. Any higher resolution is lost in this resampling. When the topographic features are computed through regression that is performed at the resolution of the original data, the accuracy can be much higher, and the reported result can be adjusted to the length scale that is relevant locally. Slope and variance are calculated for overlapping windows, meaning that one regression result is computed per raster point. The number of window centers per area is the same for the output as for the original DEM. Slope and variance are computed by performing regression on the points in the surrounding window. Such an approach is computationally feasible because of the additive nature of regression parameters and variance. Any doubling of window size in each direction only takes a single pass over the data, corresponding to a logarithmic scaling of the resulting algorithm as a function of the window size. Slope and variance are stored for each aggregation step, allowing the reported slope to be selected to minimize variance. The approach thereby adjusts the effective window size to the landform features that are characteristic to the area within the DEM. Starting with a window size of 2x2, each iteration aggregates 2x2 non-overlapping windows from the previous iteration. Regression results are stored for each iteration, and the slope at minimal variance is reported in the final result. As such, the reported slope is adjusted to the length scale that is characteristic of the landform locally. The length scale itself and the variance at that length scale are also visualized to aid in interpreting the results for slope. The relevant length scale is taken to be half of the window size of the window over which the minimum variance was achieved. The resulting process was evaluated for 1-meter DEM data and for artificial data that was constructed to have defined length scales and added noise. A comparison with ESRI ArcMap was performed and showed the potential of the proposed algorithm. The resolution of the resulting output is much higher and the slope and aspect much less affected by noise. Additionally, the algorithm adjusts to the scale of interest within the region of the image. These benefits are gained without additional computational cost in comparison with resampling the DEM and computing the slope over 3x3 images in ESRI ArcMap for each resolution. In summary, the proposed approach extracts slope and aspect of DEMs at the lengths scales that are characteristic locally. The result is of higher resolution and less affected by noise than existing techniques.

Keywords: high resolution digital elevation models, multi-scale analysis, slope calculation, window-based regression

Procedia PDF Downloads 104

Authors: Encarnación Álvarez, Rosa M. García-Fernández, Juan F. Muñoz, Francisco J. Blanco-Encomienda

Abstract:

The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: poverty line, poor, risk of poverty, Monte Carlo simulations, sample

Procedia PDF Downloads 401

Authors: M. A. Ahmed, Sidra A. Shaikh, M. W. Akhtar

Abstract:

Solar radiation on horizontal surface over three southern cities of Sindh, namely Karachi, Hyderabad and Nawabshah has been investigated to asses the feasibility of solar energy application for power generation. In the present work, measured data of bright sunshine hour of the region have been used to estimate the global and diffuse solar radiation. The regression coefficient 'a' and 'b' have been calculated using first order Angstrom type co-relation. The result obtained shows that the contribution of direct solar radiation is low and diffuse radiation is high during the monsoon months July and August for Karachi and Hyderabad. The sky remains clear from September to June, whereas for Nawabshah the global radiation remains high throughout the year. The potential of grid quality solar photovoltaic power in Karachi is estimated for 10 square meter area of solar panel.

Keywords: solar potential over Sindh, global and diffuse solar radiation, radiation over three cities of Sindh, solar panels

Procedia PDF Downloads 417

Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

Abstract:

Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

Procedia PDF Downloads 132