scholarly journals Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO

2019 ◽  
Vol 16 (14) ◽  
pp. 2492 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Cheng ◽  
Cheng
Keyword(s):  
Policy Development ◽  
Sediment Concentration ◽  
Ordinary Least Squares ◽  
Complex Problem ◽  
Regression Equation ◽  
Management Policy ◽  
Major Influence ◽  
Chaohu Lake ◽  
Regression Equations ◽  
Lasso Regression

Black blooms are a serious and complex problem for lake bays, with far-reaching implications for water quality and drinking safety. While Fe(II) and S(−II) have been reported as the most important triggers of this phenomenon, little effort has been devoted in investigating the relationships between Fe(II) and S(−II) and the host of potentially important aquatic factors. However, a model involving many putative predictors and their interactions will be oversaturated and ill-defined, making ordinary least squares (OLS) estimation unfeasible. In such a case, sparsity assumption is typically required to exclude the redundant predictors from the model, either through variable selection or regularization. In this study, Bayesian least absolute shrinkage and selection operator (LASSO) regression was employed to identify the major influence variables from 11 aquatic factors for Fe(II), S(−II), and suspended sediment concentration (SSC) in the Chaohu Lake (Eastern of China) bay during black bloom maintenance. Both the main effects and the interactions between these factors were studied. The method successfully screened the most important variables from many items. The determination coefficients (R2) and adjusted determination coefficients (Adjust R2) showed that all regression equations for Fe(II), S(-II), and SSC were in good agreement with the situation observed in the Chaohu Lake. The outcome of correlation and LASSO regression indicated that total phosphorus (TP) was the single most important factor for Fe(II), S(-II), and SSC in black bloom with explanation ratios (ERs) of 76.1% , 37.0%, and 12.9%, respectively. The regression results showed that the interaction items previously deemed negligible have significant effects on Fe(II), S(−II), and SSC. For the Fe(II) equation, total nitrogen (TN) × dissolved oxygen (DO) and chlorophyll a (CHLA) × oxidation reduction potential (ORP), which contributed 10.6% and 13.3% ERs, respectively, were important interaction variables. TP emerged in each key interaction item of the regression equation for S(−II). Water depth (DEP) × Fe(II) (30.7% ER) was not only the main interaction item, but DEP (5.6% ER) was also an important single factor for the SSC regression equation. It also indicated that the sediment in shallow bay is an important source for SSC in water. The uncertainty of these relationships was also estimated by the posterior distribution and coefficient of variation (CV) of these items. Overall, our results suggest that TP concentration is the most important driver of black blooms in a lake bay, whereas the other factors, such as DO, DEP, and CHLA act in concert with other aquatic factors. There results provide a basis for the further control and management policy development of black blooms.

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

scholarly journals PENGARUH MOTIVASI KERJA DALAM MENINGKATKAN PRODUKTIVITAS KERJA KARYAWAN PADA PT. SARIGUNA PRIMA TIRTA PULAU SARI KECAMATAN BATI-BATI

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Syarkani Syarkani
Keyword(s):  
Data Analysis ◽  
Work Motivation ◽  
Work Productivity ◽  
Complex Problem ◽  
Regression Equation ◽  
T Test ◽  
Coefficient Of Determination ◽  
Significance Level ◽  
Purposive Sampling ◽  
Simple Regression

Abstract: Employee work motivation in an organization can be considered simple and can also be a complex problem, because basically humans are easy to be motivated by giving what they want. The purpose of this study was to determine and prove the effect of work motivation on employee work productivity at PT. Sariguna Prima Tirta Pulau Sari Bati-Bati District, Tanah Laut Regency. The main data was obtained through questionnaires to respondents as many as 44 people who were determined by purposive sampling, then supported by the results of observations and direct interviews with respondents. Data analysis was carried out with the help of the SPSS program. It was concluded that the results of the Simple Regression Equation in this study were Y = 18.165 + 0.719 X. Based on the results of the t test, it was found that the work motivation variable had a positive and significant effect on work productivity, because at the 5% significance level t counted greater than t table (8.510 > 2 , 02). The coefficient of determination (r2) is 0.633 which means the ability of the work motivation variable (X) can explain the variable work productivity (Y) of 63.3% while the remaining 36.7% is influenced by other variables not examined in this study. Keywords: motivation, productivity, Bati-Bati Subdistrict Abstrak: Motivasi kerja karyawan dalam suatu organisasi dapat dianggap sederhana dan dapat pula menjadi masalah yang komplek, karena pada dasarnya manusia mudah untuk dimotivasi dengan memberikan apa yang menjadi keinginannya. Tujuan penelitian ini adalah untuk mengetahui dan membuktikan pengaruh motivasi kerja terhadap produktivitas kerja karyawan pada PT. Sariguna Prima Tirta Pulau Sari Kecamatan Bati-Bati Kabupaten Tanah Laut. Data utama diperoleh melalui penyebaran angket kepada responden sebanyak 44 orang yang ditetapkan secara purposive sampling, kemudian didukung dengan hasil observasi dan wawancara langsung dengan responden. Analisis data dilakukan dengan bantuan program SPSS. Disimpulkan bahwa hasil Persamaan Regresi Sederhana dalam penelitian ini adalah Y = 18,165 + 0,719 X. Berdasarkan hasil uji t diketahui variabel motivasi kerja berpengaruh positif dan signifikan terhadap poduktivitas kerja, karena pada taraf signifikasi 5% t hitung lebih besar dari t tabel (8,510 > 2,02).  Koefisien determinasi (r2) sebesar 0,633 yang berarti kemampuan variabel motivasi kerja (X) dapat menjelaskan variabel produktivitas kerja  (Y)  sebesar 63,3% sedangkan sisanya 36,7% dipengaruhi oleh variabel lain yang tidak diteliti dalam penelitian ini. Kata kunci : motivasi,  produktivitas, Kecamatan Bati-Bati

scholarly journals Prediksi Tinggi Badan Ditentukan dari Panjang Rentang Lengan dan Panjang Telapak Kaki Pada Mongoloid Feminina Khususnya Etnis Jawa

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Nur Mujaddidah Mochtar
Keyword(s):  
Clinical Medicine ◽  
Pearson Correlation ◽  
Body Height ◽  
Regression Equation ◽  
The Body ◽  
Regression Equations ◽  
Foot Length ◽  
Arm Span ◽  
Knee Height ◽  
The Mean

Background: There are various circumstances where measurements are not actually possible, replacement parameters can be used to estimate body height. Many characteristics of body height measurement and how to measure it. These include anthropometric measurements that can be used for the identification of medicolegal-forensic processes. Body height in clinical medicine and in the field of scientific research can be easily estimated using various anthropometric parameters such as arm span, knee height, foot length and foot breadth, and others. The arm span and foot length has proved to be one of the most reliable predictors. This study was conducted to estimate of body height from arm span and foot length using the regression equation and to determine the correlation between the body height and arm span and foot length.Methods: This study was conducted at Universitas Muhammadiyah Surabaya with 182 Javanese female students. Stature, arm span and foot length measured directly using anthropometric technique and measuring tape. The data obtained were then analyzed with SPSS version 16. The regression equation was derived for the estimate of body height and the relationship between stature, arm span and foot length determined by the Pearson correlation.               Results: We found that the mean body height of Javanese women was 1534,45 ± 47,623  mm, mean of arm span 1543,25 ± 60,468 mm and the mean of foot length 226,14 ± 9,586 mm. The correlation between stature and arm span was positive and significant (r = 0,715  , p <0,05). The correlation between stature and foot length was positive and significant (r = 0,726 , p <0,05). The correlation between stature and arm span and foot length was positive and significant (r = 0,798, p <0,05).               Conclusion: Body height correlates well with the arm span and foot length so that it can be used as a reliable marker for high estimates using regression equations.

scholarly journals PROBLEMS FOR ANTI-CRISIS ANALYTICAL PROCEDURES OF FINANCIAL STABILITY OF TRADE ENTERPRISES

2020 ◽  
Author(s):  
Ludmila Anipko ◽  
◽  
Irina Klimovych ◽  
Keyword(s):  
Financial Stability ◽  
Regression Equation ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Financial Condition ◽  
Financial Audit ◽  
Analytical Procedures ◽  
Financial Monitoring ◽  
Complex Indicators

Anti-crisis analytical procedures the financial stability of trade enterprises (hereinafter – AP FS) are part of the internal financial audit of economic activity. The system of financial monitoring is practically acceptable for the implementation of AP FS. The developed classification allows to determine the ability of the enterprise to implement AP FS. An analytical method has been developed that allows, based on the analysis of the financial condition and multivariate forecast, to develop measures to ensure the financial stability of the trade enterprise continuously. By interpolation, the study of the current financial situation, and extrapolation – a multivariate forecast, the numerical value of the integrated (complex) indicator that characterizes financial stability is determined by the regression equation, including factors listed in the classification, the significance of which is determined by regression equations. Based on the analysis of the numerical values of the regression coefficients, it is possible to determine the most important factors that affect the financial stability of trade enterprises, and those that have almost no effect on it. Components with significantly small numerical values of the regression coefficients can be generally discarded. This will reduce the number of indicators that affect financial stability and thus, you can reduce the number of components in the regression equation to the two three most important, which allows you to solve the problem of optimization. The expediency of using integrated and complex indicators is shown. The obtained results are only an information basis for the economic administration of the trade enterprise in making management decisions, the formation of long-term plans. The developed approaches to assessing the financial stability of enterprises are universal and can be used for enterprises in other sectors of the economy.

India-BIMSTEC Bilateral Trade Activities

2020 ◽  
pp. 126-145
Author(s):  
Gurpreet Kaur ◽  
Akriti Gupta
Keyword(s):  
Bay Of Bengal ◽  
Fixed Effects ◽  
Bilateral Trade ◽  
Moving Average ◽  
Trade Agreements ◽  
Ordinary Least Squares ◽  
Regression Equations ◽  
Economic Interests ◽  
Auto Regressive ◽  
Regional Dummy

The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) is one of the solutions to converge the economic interests of India's Look East Policy and Thailand's Look West Policy. Its objective is to integrate the regions on both sides of the Bay of Bengal. The development of BIMSTEC countries is indispensable for the forward march of Asia as a whole. This chapter analyzes the India-BIMSTEC trade activities after the establishment of BIMSTEC bloc. Gravity model and Auto-Regressive Integrated Moving Average (ARIMA) are used. The model estimates the sets of regression equations to measure the effects of regional trade agreements using ordinary least squares with nation dummies to capture country-specific fixed effects. The study reveals that all coefficients of regional dummy variables are mostly positive and significant, indicating the agreements that tend to enhance more trade than bilateral trade agreements. The authors state that based on India's trade with the BIMSTEC region, there exists a scope for intraregional trade in the future.

Cross-Validation of Regression Equations to Predict Performance in a Pursuit Tracking Task

1978 ◽  
Vol 22 (1) ◽  
pp. 369-372
Author(s):  
Ricky E. Savage ◽  
Robert C. Williges ◽  
Beverly H. Williges
Keyword(s):  
Cross Validation ◽  
Regression Equation ◽  
Tracking Task ◽  
Training Condition ◽  
Regression Equations ◽  
Training Time ◽  
Pooled Data ◽  
Pursuit Tracking ◽  
Validation Procedure ◽  
Time To Learn

A double, cross-validation procedure was used to validate regression equations which predict training time to learn a two-dimensional pursuit tracking task. Motor skill and information processing tasks were used as predictors. The results yielded a reliable regression equation for each training condition, and these equations were quite similar in cross-validation. Subsequently, a regression equation based on pooled data from the original and cross-validation sample was calculated for each training condition. To establish the usefulness of a regression approach for selecting training strategies, these equations will be used in a future study where students will be matched, mismatched, and randomly assigned to various training alternatives.

scholarly journals Development and Deployment of a Framework to Prioritize Environmental Contamination Issues

2020 ◽  
Vol 12 (22) ◽  
pp. 9393
Author(s):  
Nicholas D. Kim ◽  
Matthew D. Taylor ◽  
Jonathan Caldwell ◽  
Andrew Rumsby ◽  
Olivier Champeau ◽  
...  
Keyword(s):  
Environmental Contamination ◽  
Policy Development ◽  
Management Policy ◽  
Regulatory Agencies ◽  
Policy Context ◽  
Policy Goals ◽  
Wide Range ◽  
Sustainability Policy ◽  
Research And Policy ◽  
Environmental Compartments

Management and regulatory agencies face a wide range of environmental issues globally. The challenge is to identify and select the issues to assist the allocation of research and policy resources to achieve maximum environmental gain. A framework was developed to prioritize environmental contamination issues in a sustainable management policy context using a nine-factor ranking model to rank the significance of diffuse sources of stressors. It focuses on contamination issues that involve large geographic scales (e.g., all pastoral soils), significant population exposures (e.g., urban air quality), and multiple outputs from same source on receiving environmental compartments comprising air, surface water, groundwater, and sediment. Factor scores are allocated using a scoring scale and weighted following defined rules. Results are ranked enabling the rational comparison of dissimilar and complex issues. Advantages of this model include flexibility, transparency, ability to prioritize new issues as they arise, and ability to identify which issues are comparatively trivial and which present a more serious challenge to sustainability policy goals. This model integrates well as a planning tool and has been used to inform regional policy development.

scholarly journals Comparison of Regression Equation and Friedewald’s Formula with Direct Measurement of Low-density Lipoprotein Cholesterol in Bangladeshi Population

2013 ◽  
Vol 6 (1) ◽  
pp. 143-152
Author(s):  
M. Saiedullah ◽  
N. Chowdhury ◽  
M.A.H. Khan ◽  
S. Hayat ◽  
S. Begum ◽  
...  
Keyword(s):  
Linear Regression ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Regression Equation ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Regression Equations ◽  
Validation Group ◽  
Low Density Lipoprotein Cholesterol ◽  
Bangladeshi Population

Friedewald’s formula (FF) is the most widely used formula in clinical practice to calculate low-density lipoprotein cholesterol (LDLC) from total cholesterol (TC), triglyceride (TG) and high-density lipoprotein cholesterol (HDLC). But this formula frequently underestimates LDLC. The aim of this study was to derive a regression equation (RE) to abolish the underestimation and to compare the performance of RE and FF in Bangladeshi population. RE was derived from 531 lipid profiles (equation derivation group) for the calculation of LDLC by multiple linear regression analysis. The RE was then used to calculate LDLC in another 952 subjects (equation validation group). LDLC calculated by RE and FF were compared with measured LDLC by appropriate statistical analyses. In equation validation group, measured LDLC, LDLC calculated by RE and FF were 2.97±0.81, 2.91±0.80 and 2.72±0.93 mmol/L respectively. Precision (r) was 0.9525 for RE and 0.9193 for FF. Passing & Bablok linear regression equations against measured LDLC were y = 0.9792x + 0.007 for RE and y = 1.1412x – 0.6781 for FF. Accuracy within ±12% of measured LDLC was 79% and 57% for RE and FF, respectively. The derived RE is more accurate than FF for the calculation of LDLC in Bangladeshi population.  Keywords: Lipoprotein cholesterol; Friedewald’s formula; Bangladeshi population.  © 2013 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.  doi: http://dx.doi.org/10.3329/jsr.v6i1.14864 J. Sci. Res. 6 (1), 143-152 (2014)

Optimization on Uniform Design for Preparing Monodisperse P(St/BA/AA) Microsphere by Dispersion Polymerization

2011 ◽  
Vol 225-226 ◽  
pp. 1167-1170
Author(s):  
Jin Xian Lin
Keyword(s):  
Dispersion Polymerization ◽  
Design Method ◽  
Uniform Design ◽  
Regression Equation ◽  
Polymerization Reaction ◽  
Regression Equations ◽  
Stabilizer Concentration ◽  
Mechanism Research ◽  
Predicted Values ◽  
Uniform Design Method

The uniform design method is used to arrange experiment of dispersion polymerization of styrene, butyl acrylate and acryl acid. The stepwise regression technique is adopted to analyze the results. The mathematical models are built by regression equations between the microsphere conversion rate, the size and its distribution. There are many influencing factors, such as, 3 monomer concentrations, stabilizer concentration, initiator concentration, ratio of alcohol and water, temperature established, and their reliabilities evaluated. Although the predicted values from regression equation different from the observed values (relative divergences less than 10%)，the regression equation can be used for designing particles and mechanism research. All above researches can be used for optimizing dispersion polymerization reaction conditions.

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