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Alexandrova, Natalia, Natalia Klimushkina, Elena Leshina, and Maria Surkova. "Correlation and regression modeling of the grain production cost." BIO Web of Conferences 37 (2021): 00006. http://dx.doi.org/10.1051/bioconf/20213700006.
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The study of the efficiency of the grain economy has shown that its level is determined by production costs. Correlation and regression analysis of the factors of the production cost of 100 kg of grain has revealed that its value is determined, first of all, by the level of intensification of the industry and the yield capacity of grain and leguminous crops. According to the obtained result, in order to lower level the production cost of 100 kg of grain, the value of production costs per hectare of area under crops should not exceed 10.5 thousand rubles (the level of intensification) and the yield of grain and leguminous crops should not be less than 17.5 dt/ha. Otherwise, the production cost of 100 kg of grain will be too high, which will lead to a decrease in the profitability of the grain economy.
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Chen, S., X. X. Wang, and D. J. Brown. "Sparse incremental regression modeling using correlation criterion with boosting search." IEEE Signal Processing Letters 12, no. 3 (2005): 198–201. http://dx.doi.org/10.1109/lsp.2004.842250.
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Ostapenko, Ya, and D. Pastukh. "Correlation-regression analysis of factors affecting inventories of production enterprise." 101, no. 101 (December 30, 2021): 124–29. http://dx.doi.org/10.26565/2311-2379-2021-101-12.
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The article highlights the feasibility of correlation-regression analysis and inventory modeling at a manufacturing enterprise using applications. Inventory modeling will help to optimize them and further increase the profitability of the enterprise. The use of applications will speed up and simplify the modeling process and strengthen the analytical component. Among the modern variety of applications for statistical and econometric analysis, it is important to choose an effective software product, simple and easy to use, which does not require significant costs. It is offered to use a free, but no less high-quality R-Studio product, which is easy to use and fast to calculate. On the example of application of the free application program R-Studio the correlation-regression analysis is carried out and the regression model of stocks at the production enterprise of PJSC "Novokramatorsk Machine-Building Plant" is constructed. The influence of internal factors on the company's stocks, such as: net income from sales of products (goods, works, services), net financial result: profit, accounts payable for goods (works, services) and external: GDP and the dollar. According to the simulation results, the greatest influence among internal factors has the net income from sales of products (goods, works, services). Among external factors, GDP is the most influential. The constructed model is adequate, as evidenced by a significant indicator of the Fisher criterion and the coefficient of determination. 90% of the stocks of the studied enterprise depend on the selected factors. The construction of a matrix of correlation coefficients and correlation analysis confirmed the close relationship between the selected factors and their impact on stocks as a result. The example of PJSC "Novokramatorsk Machine-Building Plant" demonstrates the practical usefulness of inventory modeling using computer programs.
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Wang, Yadi, Wenbo Zhang, Minghu Fan, et al. "Regression with adaptive lasso and correlation based penalty." Applied Mathematical Modelling 105 (May 2022): 179–96. http://dx.doi.org/10.1016/j.apm.2021.12.016.
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Xiang, Liwen, Jie Jin, Jinghui Zhang, Changjiang Ma, Bingnan Xiang, and Wenhua Sun. "Modeling the Correlation Relationship of Aqueous Battery Parameters Based on Regression Analysis." IOP Conference Series: Earth and Environmental Science 898, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1755-1315/898/1/012020.
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Abstract This study is a research on the new Aqueous battery. Based on the experimental data, the author selects the charge and discharge capacity, voltage and current of the battery during the charging and discharging process, establishing the correlation model between the parameters of the battery through regression analysis and other methods, and concluding the methods to optimise the performance and power supply capacity of the battery, which can explore a new high-efficiency battery that can replace the ordinary battery and provide new ideas and methods for the development of battery business.
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et al., Jabeen. "Regression modeling and correlation analysis spread of COVID-19 data for Pakistan." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 3 (2022): 71–81. http://dx.doi.org/10.21833/ijaas.2022.03.009.
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This study presents a mathematical analysis of the coronavirus spread in Pakistan by analyzing the (COVID-19) situation in six provinces, including Gilgit Baltistan, Azad Jammu Kashmir and federal capital (seven zones) individually. The influence of each province and the Federal Capital territory is then observed over the other territories. By subdividing the associated data into confirmed cases, death cases, and recovery cases, the dependence of the (COVID-19) situation from one province to the other provinces is investigated. Since the worsening circumstance in the neighboring countries were considered as a catalyst to initiate the outburst in Pakistan, it seemed necessary to have an understanding of the situation in neighboring countries, particularly, Iran, India, and Bangladesh. Exploratory data analysis is utilized to understand the behavior of confirmed cases, death cases, and recovery cases data of (COVID-19) in Pakistan. Also, an understanding of the pandemic spread during different waves of (COVID-19) is obtained. Depending on the individual situation in each of the provinces, it is expected to have a different ARIMA model in each case. Hunt for the most suitable ARIMA models is an essential part of this study. The time-series data forecasts by processing the most suitable ARIMA models to observe the influence of one territory over the other. Moreover, forecasting for the month of August 2021 is performed and a possible correlation with actual data is determined. Linear, multiple regression, and exponential models have been applied and the best-fitted model is acquired. The information obtained from such analysis can be employed to vary possible parameters and variables in the system to achieve optimal performance.
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Holomsha, Nataliia, and Olha Holomsha. "Correlation-regression modeling of competitiveness of ukrainian wheat on the world markets." Ekonomika APK, no. 10 (October 30, 2019): 88–97. http://dx.doi.org/10.32317/2221-1055.201910088.
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Maryna, Litvinova, Andrieieva Nataliia, Zavodyannyi Viktor, Loi Sergii, and Shtanko Olexandr. "APPLICATION OF MULTIPLE CORRELATION ANALYSIS METHOD TO MODELING THE PHYSICAL PROPERTIES OF CRYSTALS (ON THE EXAMPLE OF GALLIUM ARSENIDE)." Eastern-European Journal of Enterprise Technologies 6, no. 12 (102) (2019): 39–45. https://doi.org/10.15587/1729-4061.2019.188512.
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The use of modern applied computer programs expands the possibility of multicomponent statistical analysis in materials science. The procedure for applying the method of multiple correlation and regression analysis for the study and modeling of multifactorial relationships of physical characteristics in crystalline structures is considered. The consideration is carried out using single crystals of undoped gallium arsenide as an example. The statistical analysis involved a complex of seven physical characteristics obtained by non-destructive methods for each of 32 points along the diameter of the crystal plate. The data array is investigated using multiple correlation analysis methods. A computational model of regression analysis is built. Based on it, using the programs Excel, STADIA and SPSS Statistics 17.0, statistical data processing and analytical study of the relationships of all characteristics are carried out. Regression relationships are obtained and analyzed in determining the concentration of the background carbon impurity, residual mechanical stresses, and the concentration of the background silicon impurity. The ability to correctly conduct multiple statistical analysis to model the properties of a GaAs crystal is established. New relationships between the parameters of the GaAs crystal are revealed. It is found that the concentration of the background silicon impurity is related to the vacancy composition of the crystal and the concentration of cents EL2. It is also found that there is no relationship between the silicon concentration and the value of residual mechanical stresses. These facts and the thermal conditions for the formation of point defects during the growth of a single crystal indicate the absence of a redistribution of background impurities during cooling of an undoped GaAs crystal. The use of the multiple regression analysis method in materials science allows not only to model multifactor bonds in binary crystals, but also to carry out stochastic modeling of factor systems of variable composition
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KAZAMBAYEVA, Aigul, Zein AYDYNOV, Zhanbolat DAUKHARIN, and Railash TURCHEKENOVA. "MODELING OF STATE SUPPORT FOR AGRICULTURE." Public Administration and Civil Service 91, no. 4 (2024): 30–43. https://doi.org/10.52123/1994-2370-2024-1277.
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Abstract. The article examines the impact of state support through subsidies on the outcomes of agricultural production in the Republic of Kazakhstan. The purpose of the study is to analyze the dynamics of subsidies and model the correlation between the subsidy size and gross agricultural output. The study draws on gross crop and livestock production data, as well as on the amount of state support from 2008 to 2022. The analysis showed a significant increase in gross agricultural output and the subsidy size over the specified period, which demonstrates a significant development of the agricultural sector and increased government support. Conclusion of the article also highlights the importance of continuing and expanding government support programs to ensure sustainable growth of the agricultural sector and increase its competitiveness. The article also examines successful cases of state support for agriculture in developed countries, which allows to offer recommendations for optimizing existing support programs in Kazakhstan. Keywords: state support, subsidies, agriculture, gross output, crop production, livestock, correlation, regression analysis.Удалить Keywords: state support, subsidies, agriculture, gross output, crop production, livestock, correlation, regression analysis.
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Et. al., P. D. Sheena Smart,. "Regression Tree Based Correlation Technique in Spatial Data Classification." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 6184–95. http://dx.doi.org/10.17762/turcomat.v12i10.5458.
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Data mining is the process of discovering useful patterns from large geo-spatial datasets with the help of machine learning methods. . The machine learning methods plays an important role for data analytics modeling and visualization. Geo-spatial data is a significant task in many application domains, such as environmental science, geographic information science, and social networks. However, the existing spatial pattern discovery and prediction techniques failed to predict the event accurately with minimum error and time consumption. . In this paper, a novel Pearson Correlated Regression Tree-based Affine Projective spatial data Classification (PCRT-APSDC) technique is proposed to improve the spatial data classification and minimize error based on the Affine Projective classification technique. The proposed algorithm employs a fuzzy rule procedure that constructs the regression tree. The fuzzy rule is applied for linking the inputs (i.e. spatial data) with the outputs (i.e. classification results). Our goal is to classify the data into two subsets such as fired region and non-fired region. Experimental evaluation is carried out using a forest fire dataset with different factors such as classification accuracy, false-positive rate, and classification time. The results confirm that the proposed technique predicts the fired region with increased spatial data classification accuracy and minimized time as well as false-positive rate than the state-of-the-art methods.
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Wang, Yi, Sisi Fang, Bo Xu, and Boqian Li. "Correlation Analysis of Pyrolysis Yield Using a Linear Regression Model." Academic Journal of Science and Technology 12, no. 1 (2024): 278–82. http://dx.doi.org/10.54097/6bdvn553.
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The paper focuses on the impact of pyrolysis combination and mixture ratio on the yield of various pyrolysis products, such as tar, water, coke residue, and syngas, in the field of catalytic reaction analysis for pyrolysis product generation. Initially, data preprocessing was carried out, outlier detection and missing value processing were respectively performed, and a model was established to explain the relationship between mixing ratio and yield. Descriptive statistical analysis was employed to gain an initial understanding of the overall data situation. Subsequently, statistical indices such as mean, range, and standard deviation of pyrolysis products for each combination were quantified to unveil the extent of influence of different mixing ratios on yield. The correlation analysis and linear regression model were then used to establish the relationship model between mixture ratio and yield, with further explanation of the correlation and presentation of the functional expression. Finally, mathematical formulas and graphs were utilized to analyze the linear trend between product and mixing ratio under various pyrolysis combinations. This study offers robust data analysis and modeling support for comprehending the impact of different mixing ratios on pyrolysis product yield.
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Li, Yao Xiang, and Li Chun Jiang. "Modeling Wood Crystallinity with Multiple Linear Regression." Key Engineering Materials 480-481 (June 2011): 550–55. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.550.
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The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. Crystallinity of larch plantation wood was investigated with near infrared spectroscopy and multiple linear regression. Five typical wave lengths were selected to establish prediction model for wood crystallinity. Full-cross validation was applied to the model development. The model performance is satisfied with prediction correlation coefficient of 0.896 and bias of 0.0004. The results indicated that prediction of wood crystallinity with near infrared spectroscopy and multiple linear regression is feasible, which provides a fast and nondestructive method for wood crystallinity prediction.
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Choi, Soo-Min, Hyo Choi, and Woojin Paik. "Multivariate Regression Modeling for Coastal Urban Air Quality Estimates." Applied Sciences 13, no. 19 (2023): 10556. http://dx.doi.org/10.3390/app131910556.
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Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data of PM and gas from Beijing (China)). Multiple correlation coefficients between the predicted and measured PM10, PM2.5, NO2, SO2, CO and O3 concentrations were 0.957, 0.906, 0.886, 0.795, 0.864 and 0.932 before the yellow sand event at Kangnung, 0.936, 0.982, 0.866, 0.917, 0.887 and 0.916 during the event and 0.919, 0.945, 0.902, 0.857, 0.887 and 0.892 after the event. As the significance levels (p) from multi-regression analyses were less than 0.001, all correlation coefficients were very significant. Partial correlation coefficients presenting the contribution of 15 input variables to 6 output variables using the models were presented for the three periods in detail. Scatter plots and their hourly distributions between the predicted and measured values showed the quite good accuracy of the modeling performance for the current time forecasting of six output values and their high applicability.
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Koshova, Oksana, Olena Olkhovska, Tetyana Chilikina, and Serhiy Shulyar. "PECULIARITIES OF SOFTWARE DEVELOPMENT FOR BUSINESS PROCESS MODELING AND RESEARCH USING CORRELATION-REGRESSION ANALYSIS." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University 146, no. 3 (2024): 86–91. http://dx.doi.org/10.32782/1995-0519.2024.3.12.
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Nadiya, Yavorska. "Modeling the impact of environmental investments on the environment state." Technology audit and production reserves 5, no. 4(55) (2020): 44–47. https://doi.org/10.15587/2706-5448.2020.215648.
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<em>The object of research is the level of environmental pollution by the example of Ukraine. The paper investigates the relationship between the volume of capital investment and a decrease in the level of environmental pollution. The methodological basis of the study is the fundamental foundations of economic theory, environmental protection, environmental economics, statistics and econometrics. To develop a statistical model of the relationship between environmental investment and environmental pollution, a correlation analysis is carried out using the paired regression equation, where a hypothesis is put forward that the relationship between all possible values of factorial and effective indicators is linear. The parameters of the constructed models are estimated by the least squares method and the statistical significance of the models is checked.</em> <em>The research results indicate the presence of a close inverse relationship between the volumes of capital investments for the protection of atmospheric air on the volume of emissions of pollutants into the air. This is due to the fact that the linear correlation coefficient is:</em> <em>–0.826, and the value of the coefficient of determination (0.6818) shows the decisive influence of capital investments on emissions. Checking the statistical significance of the model makes it possible to recognize the constructed econometric model of the effect on the volume of emissions of pollutants into the air as statistically reliable. The resulting model can be used to predict the volume of emissions of pollutants into the air and provides an opportunity to address issues of optimizing investment and environmental policies.</em> <em>On the other hand, an econometric model is obtained for the effect on the amount of recycled waste, which is characterized by a noticeable direct relationship (linear correlation coefficient – 0.595) and shows that only 35.44 % of recycled waste is directly related to the volume of capital investments. Checking the statistical significance shows the unreliability of the model of influence on the amount of disposed waste. Although the resulting model can’t be used for forecasting, it can be used in further studies to identify other factors influencing waste disposal.</em>
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Zhou, Junjie, Haiping Yu, and Liu Yang. "Analysis of the Correlation between Genetic Traits and Pathogenic Sites Based on Multiple Linear Regression Model." Journal of Physics: Conference Series 2650, no. 1 (2023): 012009. http://dx.doi.org/10.1088/1742-6596/2650/1/012009.
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Abstract Current research shows that many phenotypic traits, susceptibility to diseases, and drugs of the human body are related to some pathogenic sites. Locating the position of loci associated with traits or diseases in chromosomes or genes helps researchers understand disease traits and genetic mechanisms and prevent the occurrence of certain genetic diseases. Select 1000 data sets of two types of sample (disease and health) information, including disease information of the sample, 9445 site coding information and gene information of these sites. Two types of methods are used to analyze the correlation between the pathogenic sites and related traits, the chi-square test method is used to find the sites with the highest probability of pathogenicity between single disease and pathogenic sites, and the multiple regression analysis model is used to determine the most likely pathogenic sites between multiple phenotypic traits and sites. Use statistical tools and software to solve the model, and use the error detection rate (FDR) method to conduct multiple comparisons and corrections for each regression parameter. The conclusion of the modeling analysis is consistent with the standard conclusion provided by the 17th Organizing Committee for Mathematical Modeling of Postgraduates. This shows that the comprehensive method of chi-square test and multiple linear regression modeling is reliable, and has universal and reference significance for finding the pathogenic sites of a certain genetic disease and locating the pathogenic genes.
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PROSKUROVYCH, O., V. SOKOLYUK, and M. CHUBENKO. "MODELING SYSTEMS OF FINANCIAL CONTROLLING OF PIDPRISES." Herald of Khmelnytskyi National University. Economic sciences 276, no. 6(1) (2019): 129–35. https://doi.org/10.31891/2307-5740-2019-276-6-138-144.
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The main purpose of this article is to investigate theoretical approaches to the definition of the category "financial controlling", to create a mechanism for implementing a system of financial controlling at industrial enterprises, to increase the efficiency of their activity using correlation-regression analysis based on improving the system of financial controlling of the enterprise. The scientific article analyzes the activity of an industrial enterprise and econometric modelling and prediction of an integral indicator of the level of efficiency of its activity, which allows to improve the system of financial controlling at the enterprise. The results of the analysis of the financial controlling of the manufacturing enterprise indicate its unstable financial condition since 2011. by 2017, and in 2018, the situation has significantly deteriorated and the company is in a financial crisis, it is bankrupt, its solvency is broken, but there is an opportunity to restore it in the future. Correlation and regression analysis of changes in the integral indicator of the level of efficiency of the enterprise activity under the influence of net profit, general economic expenses and expenses on the purchase of equipment and software allowed to determine the influence of individual factors on the dynamics of the effective indicator. In the process of econometric modelling, a model of the integral indicator of activity efficiency for the functioning of the controlling system is constructed, which is adequate in terms of the coefficient of determination and the Fisher criterion. The parameters of this model are valid according to the Student's criterion. On the basis of the results of econometric modelling, the level of the integral indicator of the efficiency of the enterprise activity for the functioning of the controlling system for the future is predicted.
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Smirnova, Galina I., and Maria E. Listopad. "Economic and Mathematical Modeling of Russia’s Economic Security in the Period Under Sanctions." Economic Strategies 152 (March 25, 2020): 32–39. http://dx.doi.org/10.33917/es-2.168.2020.32-39.
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For the study, the time period of introducing economic sanctions against the Russian Federation was selected. In consideration are taken the data of Rosstat in terms of finding the values of indicators of our state’s economic security (2013–2017). A correlation and regression analysis of this system, consisting of 15 indicators, was carried out. An economic-mathematical model of the sanctions impact on the economic security of Russia was compiled. To solve this problem, the authors used a correlation-regression analysis, the regression equation was found and statistical significance of the constructed model was substantiated. The findings were recommended to specialists in the sphere of improving the state’s economic security.
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Ashok, Aparna, and Neeru Bhagat. "Predictive Modeling of Half-Metallicity in Heusler Compound through Machine Learning Modeling." Applied Mechanics and Materials 925 (April 7, 2025): 39–50. https://doi.org/10.4028/p-m05thx.
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Heusler alloys are intermetallic compounds formed in two combinations: Full-Heusler (X2YZ) and Half-Heusler (XYZ). X and Y can be any transition element, and Z belongs to the main group. This shows that there can be a huge variation in the combinations, leading to various properties and applications. We aimed at predicting the combination leading to shape memory properties using machine learning tools and then synthesizing the same. The predictions are done by training the tool with input data. We employed the lattice strain, valence electron concentration ratio, mechanical stress, difference in entropy, and saturation magnetization as input features. The correlation between the martensitic and austenitic temperature was evaluated in terms of regression metrics. The random forest and decision tree modeling were executed. Test scores were obtained using frequency ordering, PCA, linear regression, and correlation matrix to forecast magnetically controlled shape memory effect. The silhouette score matched the transition temperature at which the material showed shape memory behavior. Additionally, from 70% of the training data, a combination of Iron (Fe), Nickel (Ni), and Aluminum (Al) as Full Heusler alloys stimulated the algorithms in gaining the accuracy of predictive modeling by minimizing the error. Through DFT-based bandgap and density of states calculations, the Fe2NiAl Heusler compound is hypothesized to behave as a half-metallic ferromagnet by considering the atomic number, the number of valence electrons, and the local magnetic moment. The experimental validation will be done along with magnetization studies, magneto-transport, and magneto-caloric measurements.
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Zhan, Huan, Yu Cai Dong, Liang Hai Yi, Su Na Cao, Qi Jin, and Yan Xia Liu. "The Fault Diagnose of Advanced Amphibious Assault Vehicle Fan Pump Based on Principal Component Regression." Advanced Materials Research 860-863 (December 2013): 1582–85. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1582.
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The fan pump hydraulic system of amphibious assault vehicle is prone to fault , an pump leakage model is established by principal component regression. This new modeling strategy can effectively extract on the components which have a strong explanatory role of the dependent variable, it achieves regression modeling in multiple arguments correlation conditions, and allows the inclusion of all the original variables. This model get rid of drawback that the least squares regression can not effectively identify and eliminate the influence of the multiple relativity among factors.
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Yavarmasroor, Soheila, Zahra Hojjati Zidashti, Akbar Khodaparast Haghi, and Kaveh Hariri Asli. "Computational Engineering Modeling for Runner Athletes." Journal of Computational Engineering 2013 (September 5, 2013): 1–6. http://dx.doi.org/10.1155/2013/286426.
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The literature indicates that relatively little research is available to describe the relationship between functional running tasks and characteristics of individuals who perform these tasks. As a main purpose, the present work is to define the computational modeling for anthropometric characteristics of athletes. Thus the dynamic model presented by this 100-meter running test can play an important role in talent and coaching. The research question was formed by classification and comparison of statures of sportswomen with other anthropometric classes. On the other hand, the present work compares the anthropometric data for runner velocity (running time) against runner weight. The method of research is regression statistical analysis method. In this work, the regression method is based on the univariable ANOVA variance with repeated measures and t-test for independent samples. Data analysis was performed by using the software SPSS13. The results of the 100-meter running test of sportswomen showed good correlation between the parameters. As a dynamic modeling selection, the logarithmic function showed suitable correlation on scatter diagram. Consequently, the results of this work will help to reduce the risk of sportswomen activities. Therefore it can be recommended for medical professionals and athletic talent.
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Nguyen, Loc. "Extreme Bound Analysis Based on Correlation Coefficient for Optimal Regression Model." Sumerianz Journal of Scientific Research, no. 61 (February 26, 2023): 9–13. http://dx.doi.org/10.47752/sjsr.61.9.13.
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Regression analysis is an important tool in statistical analysis, in which there is a demand of discovering essential independent variables among many other ones, especially in case that there is a huge number of random variables. Extreme bound analysis is a powerful approach to extract such important variables called robust regressors. In this research, I propose a so-called Regressive Expectation Maximization with RObust regressors (REMRO) algorithm as an alternative method beside other probabilistic methods for analyzing robust variables. By the different ideology from other probabilistic methods, REMRO searches for robust regressors forming optimal regression model and sorts them according to descending ordering given their fitness values determined by two proposed concepts of local correlation and global correlation. Local correlation represents sufficient explanatories to possible regressive models and global correlation reflects independence level and stand-alone capacity of regressors. Moreover, REMRO can resist incomplete data because it applies Regressive Expectation Maximization (REM) algorithm into filling missing values by estimated values based on ideology of expectation maximization (EM) algorithm. From experimental results, REMRO is more accurate for modeling numeric regressors than traditional probabilistic methods like Sala-I-Martin method but REMRO cannot be applied in case of nonnumeric regression model yet in this research.
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Yang, Jie, Jing Ma, De-xiu Hu, Lu Wang, Ji-na Yin, and Jie Ren. "Sediment Deposition Risk Analysis and PLSR Model Research for Cascade Reservoirs Upstream of the Yellow River." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/696015.
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It is difficult to effectively identify and eliminate the multiple correlation influence among the independent factors by least-squares regression. Focusing on this insufficiency, the sediment deposition risk of cascade reservoirs and fitting model of sediment flux into the reservoir are studied. The partial least-squares regression (PLSR) method is adopted for modeling analysis; the model fitting is organically combined with the non-model-style data content analysis, so as to realize the regression model, data structure simplification, and multiple correlations analysis among factors; meanwhile the accuracy of the model is ensured through cross validity check. The modeling analysis of sediment flux into the cascade reservoirs of Long-Liu section upstream of the Yellow River indicates that partial least-squares regression can effectively overcome the multiple correlation influence among factors, and the isolated factor variables have better ability to explain the physical cause of measured results.
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Hua, X. G., Y. Q. Ni, J. M. Ko, and K. Y. Wong. "Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique." Journal of Computing in Civil Engineering 21, no. 2 (2007): 122–35. http://dx.doi.org/10.1061/(asce)0887-3801(2007)21:2(122).
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Adiatma. "Seemingly Unrelated Regression Spatial Autoregressive Bayesian Modeling on Heteroscedasticity Case." Journal of Physics: Conference Series 2123, no. 1 (2021): 012047. http://dx.doi.org/10.1088/1742-6596/2123/1/012047.
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Abstract The phenomenon encountered occasionally on complications involving spatial data, is that there is a tendency of heteroscedasticity since every region has distinct characteristics. Thus, it requires the approach which is more appropriate with the problem by using the Bayesian method. Bayesian method on spatial autoregressive model to contend the heteroscedasticity by applying prior distribution on variance parameter of error. To detect heteroscedasticity, it is shown from several responses correlating with the predictors. The method abled to estimate some responses is Seemingly Unrelated Regression (SUR). SUR is an econometrics model that used to be being utilized in solving some regression equations in which of them has their own parameter and appears to be uncorrelated. However, by correlation of error in differential equations, the correlation would occur among them. With the condition of the Bayesian SUR spatial autoregressive model, it is able to overcome heteroscedasticity cases from the vision of spatial. Further, the model involves four kinds of parameter priors’ distributions estimated by using the process of MCMC.
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Asnaashari, A., E. A. McBean, I. Shahrour, and B. Gharabaghi. "Prediction of watermain failure frequencies using multiple and Poisson regression." Water Supply 9, no. 1 (2009): 9–19. http://dx.doi.org/10.2166/ws.2009.020.
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An important concern for water utilities managers is the prediction of failure frequency of watermains. To provide insight, reliance can be structured based upon modeling of historical data. In this research two regression-based models are employed, namely multiple and Poisson regression models. The models are derived based on 10 years of historical data collected for the city of Sanandaj in Iran. Several tests to validate each of the models are described. The comparison of correlation coefficients for multiple and Poisson models, besides violating initial assumptions, show that multiple regression-based modeling is inadequate.
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G.A., Salimova. "CORRELATION-REGRESSIVE MODELING OF THE EMPLOYMENT LEVEL OF THE POPULATION." Russian Electronic Scientific Journal 54, no. 4 (2024): 192–202. https://doi.org/10.31563/2308-9644-2024-54-4-192-202.
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The article presents the results of constructing correlation and regression analysis models of the dependence of the employment level of the population in the regions of the Central and Volga Federal Districts of the Russian Federation on factors in a dynamic aspect. Based on the results of the correlation and regression analysis, a significant influence of factors on the employment level of the population of the regions was revealed for almost all years. The combined influence of the selected factors on the employment level of the population increases until 2020, then sharply decreases in 2021 and almost recovers in 2022. Constructing dependence models by year allows us to identify reserves for increasing the employment level due to the factors included in the model. Thus, there are potential reserves for increasing the employment level due to the factors included in the model in 15 analyzed regions. The level of innovative activity of organizations had a significant impact on the employment level in 2020 and 2021.
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Narbaitz, Roberto M., and Yassine Djebbar. "Nonparametric modeling of mass transfer coefficients for air stripping packed towers." Canadian Journal of Civil Engineering 23, no. 2 (1996): 549–59. http://dx.doi.org/10.1139/l96-059.
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Existing parametric correlations have been found to have difficulties in predicting the removal of trace levels of volatile organic chemicals by modern air stripping towers. In this study, a new approach using a nonparametric kernel regression method was used to predict the mass transfer coefficient, KLa, of air stripping towers. Although only four variables were used, the predictions are already improved more than 50% as compared with Onda correlation, the best existing parametric correlation. The proposed technique shows a dependency of KLa on the liquid flow rate which is in good agreement with established theory. Previous parametric approaches were unable to model this relationship correctly. Key words: mass transfer coefficient, air stripping tower, volatile organic compound, nonparametric kernel regression.
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Кадасев, D. Kadasev, Коротнев, and V. Korotnev. "MATHEMATICAL MODELING OF TRAFFIC FLOWS ON THE ROAD NETWORK CITY." Alternative energy sources in the transport-technological complex: problems and prospects of rational use of 3, no. 1 (2016): 236–40. http://dx.doi.org/10.12737/17887.
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This article describes a practical method of constructing mathematical models of traffic flow, the most suitable for a particular city highway. The initial data are: instant speed, time, distance, flux density, intensity of movement of vehicles. Using the obtained data, built regression model, and conducted correlation analysis. The choice of the mathematical model that most faithfully describes the transport process was made on the basis of the correlation coefficient
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Panda, JP. "A review of pressure strain correlation modeling for Reynolds stress models." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 234, no. 8 (2019): 1528–44. http://dx.doi.org/10.1177/0954406219893397.
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Most investigations of turbulent flows in academic studies and industrial applications use turbulence models. Out of the different turbulence modeling approaches Reynolds stress models have the highest potential to replicate complex turbulent flow phenomena at a reasonable computational expense. The Reynolds stress modeling framework is constituted by individual closures that approximate the effects of separate turbulence processes like dissipation, turbulent transport, pressure strain correlation, etc. Owing to its complexity and importance in flow evolution the modeling of the pressure strain correlation mechanism is considered the crucial challenge for the Reynolds stress modeling framework. In the present work, the modeling of the pressure strain correlation for homogeneous turbulent flows is reviewed. The importance of the pressure strain correlation and its effects on flow evolution via energy transfer are established. The fundamental challenges in pressure stain correlation modeling are analyzed and discussed. Starting from the governing equations we outline the theory behind models for both the slow and rapid pressure strain correlation. Established models for both these are introduced and their successes and shortcomings are illustrated using theoretical analysis, computational fluid dynamics simulations, and comparisons against experimental and numerical studies. Recent advances and developments in this field are presented and discussed. The application of machine learning algorithms such as Deep Neural Networks, Random Forests, and Gradient Boosted Regression Trees is summarized and examined. We report fundamental problems in the application of machine learning algorithms for pressure strain correlation modeling. Finally, challenges and hurdles for pressure strain correlation modeling are outlined and explained in detail to guide future investigations.
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Atajanov, M. K., and Q. R. Qutlimuratov. "MODELING THE SELECTION OF A SUSTAINABLE BICYCLE TRANSPORT SERVICE." Journal of Science and Innovative Development 6, no. 4 (2023): 69–79. http://dx.doi.org/10.36522/2181-9637-2023-4-8.
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This article investigates the impact of travel information on transport choice behavior and the impact of travel time display. To this end, we estimate linear regression models using data from the Mobility Survey, which consists of observational data on transport mode choice. Based on the results obtained through the conducted questionnaire surveys, many influencing factors on the choice of transport were studied through the correlation equation. Using the applied Logit model, the maximum likelihood method was used for the selection of the mode of movement in the evaluation of the objective function of passengers in inter-address movement. The objective function in the choice of which of the available alternatives was preferred by each passenger in moving to the intended destination was evaluated. The important variable attributes of the alternatives that influence the choice of inter-destination transportation means of passengers are identified. The choice model represents the probability of choosing each alternative through the calculated Logit model. A linear regression equation was developed using the coefficients obtained on the basis of the correlation matrix. The results of the regression model were obtained as part of the interaction of the transport choices of passengers.
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Jiang, Benben, and Xiaoxiang Zhu. "Latent variable modeling approach for fault detection and identification of process correlations." Transactions of the Institute of Measurement and Control 41, no. 6 (2018): 1740–49. http://dx.doi.org/10.1177/0142331218788126.
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Techniques for monitoring process correlation structures remain to be explored, whereas significant progress has already been achieved on the monitoring of process variables. In particular, typical methods for monitoring correlation structure changes are strictly based on the process information described by the covariance matrix, and lack the ability to effectively monitor underlying structure changes. In this paper, a new approach for fault detection and identification (FDI) of process structural changes is developed, which utilizes the regression technique of latent variable modeling (LVM) to abstract principal parameters as lower-dimensional representations of the parameters in the entire dimensionality. Apart from the enhanced performance of handling the underlying connective structure information, the proposed approach can also improve fault monitoring performance owing to the more accurate confidence intervals of the regression coefficients provided in the LVM step. The effectiveness of the proposed method for the detection and identification of correlation structure changes is demonstrated for both single faults and multiple faults in the simulation studies. In addition, the relationship between the FDI of process variables and correlation structures is discussed.
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Karabıyık, Ümit. "UPPER-SECONDARY STUDENTS' PROBLEM-POSING AND MATHEMATICAL MODELING SKILLS IN THE CONTEXT OF STEM EDUCATION AND 21ST CENTURY SKILLS." Problems of Education in the 21st Century 83, no. 1 (2025): 81–100. https://doi.org/10.33225/pec/25.83.81.
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Mathematical problem-posing and modeling are essential skills in developing students' analytical thinking and problem-solving abilities. This study aims to examine correlation between 9th-grade students' problem-posing and mathematical modeling skills within the learning domain of numbers and algebra. Additionally, it evaluates students' mathematical modeling skills in relation to their 8th-grade mathematics scores from the Upper-secondary Entrance Examination (LGS). The research employs a quantitative approach, utilizing the relational survey technique. The study sample consists of 24 ninth-grade students from a private Upper-secondary school affiliated with the Ministry of National Education of the Republic of Turkey, selected through an accessible sampling method. The data were obtained from the students' examination results, problem-posing activities, and mathematical modeling questions. The data were examined using t-tests, Kruskal-Wallis tests, correlation analysis, and regression analysis. The findings indicated a significant relationship between ninth-grade students' problem-solving skills and mathematical modeling abilities. This relationship was found to be positive and moderate. The simple regression analysis of correlation between the two skills showed that the scores obtained from the problem-posing activities significantly predicted the scores obtained from the mathematical modeling questions. It was observed that problem-solving skills positively influenced mathematical modeling skills. In addition, it was concluded that there was no significant difference between students' LGS mathematics scores and their mathematical modeling skills, and that students with different mathematics score ranges showed similar performance in modeling questions. As a result, this study offers practical suggestions for improving education from the perspective of STEM education and 21st century skills. Keywords: mathematics education, STEM education, algebraic thinking, problem-posing, mathematical modeling
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Yunus, M., Asep Saefuddin, and Agus M. Soleh. "PEMODELAN STATISTICAL DOWNSCALING DENGAN LASSO DAN GROUP LASSO UNTUK PENDUGAAN CURAH HUJAN." Indonesian Journal of Statistics and Its Applications 4, no. 4 (2020): 649–60. http://dx.doi.org/10.29244/ijsa.v4i4.724.
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One of the rainfall prediction techniques is the Statistical Downscaling Modeling (SDS). SDS modeling is one of the applications of modeling with covariates conditions that are generally large and not independent. The problems that will be encountered is the problem of ill-conditional data i.e multicollinearity and the high correlation between variables. The case of highly correlated data causes a linear regression coefficient estimators obtained to have a large variance. This research was conducted to make the statistical downscaling modeling using the lasso and group lasso for the prediction of rainfall. Group of the covariate scenario is applied based on the adjacent area, the high correlation between covariates and correlation between covariates and responses, and also the addition of dummy variables. Scenario six (grouping which is done by considering the covariates that have a positive correlation to the response is divided into 3 groups, 1 individual and the covariates that are negatively correlated with the response are divided into 2 groups, 1 individual) is better than the other scenarios in linear modeling without a dummy. Then, linear modeling with a dummy is better than without a dummy for both techniques. In linear modeling with a dummy, the Group lasso technique can be considered more in SDs modeling, because the difference in the RMSEP statistical value and the correlation coefficient value is significant.
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Gayfullina, M. M., and G. Z. Nizamova. "Correlation and regression analysis of the investment attractiveness of the petroleum refining industry." UPRAVLENIE 9, no. 3 (2021): 27–38. http://dx.doi.org/10.26425/2309-3633-2021-9-3-27-38.
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The article presents the results of the analysis of the investment attractiveness of the petroleum refining industry using correlation and regression methods. It has been suggested to evaluate the level of investment attractiveness of the petroleum refining industry through capital productivity. A system of indicators affecting the investment attractiveness of the petroleum refining has been formed in the context of resource and production, financial, economic and social groups of factors. This methodology of correlation and regression analysis for modeling factors affecting investment attractiveness has been presented. The methodology includes the construction of a pair correlation, the selection of factors, the construction of a generalised correlation matrix using the “Correlation” tool in the “Data Analysis” package Microsoft Excel, the regression analysis based on the finally selected factors, the construction of the regression equation, the justification of the obtained dependence using the “Regression” tool in the “Data Analysis” package MS Excel.According to the results of calculations for the type of economic activity “Production of coke and petroleum products” in the Russian Federation in dynamics for 2012 –2019, a strong correlation has been revealed between the output-capital ratio and such factors as the oil refining depth, profit from sales and labor productivity.The results of the study can be used to identify significant factors affecting the investment attractiveness of the petroleum refining industry in order to further optimise them.
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Yang, Miao, Yuquan Qiu, Jinnai Dong, Lingxiao Wu, and Mengjiao Shen. "Modeling of Soil Moisture Data by ARMA Time Series." Journal of Physics: Conference Series 2650, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2650/1/012016.
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Abstract The use of known data to predict future environmental parameters plays a crucial role in agriculture. In this paper, we propose a novel time series prediction method that combines the Auto-Regressive Moving Average Model (ARMA) and Gradient Boost Regression Tree(GBR) to forecast future soil moisture values. Firstly, the optimal number of decomposition modes for AMRA is determined by using Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) plots. Secondly, according to statistics of XIlin Gol grassland offered by the Huawei Cup Mathematical Modeling Contest in 2022, the data including soil evaporation, precipitation, and soil moisture in the past ten years, are used as input parameters of ARMA to predict the precipitation and soil evaporation from 2022 to 2023. Then, the superiority of GBR was verified by comparing algorithms such as Support Vector Regression (SVR) and Random Forest(RF). Finally, GBR was used to realize the prediction for different soil moisture values from 2022 to 2023.
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Firomsa, Worku, Fikadu Fufa, and Yerosan Feyisa. "Numerical Modeling for Prediction of Compression Index from Soil Index Properties in Jimma town, Ethiopia." U.Porto Journal of Engineering 8, no. 6 (2022): 102–20. http://dx.doi.org/10.24840/2183-6493_008.006_0008.
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In this study, correlations are developed to predict compression index (Cc) from index parameters so that one can be able to model Jimma soils with compression index using simple laboratory tests. Undisturbed and disturbed soil samples from twelve different locations in Jimma town were collected. Laboratory tests like specific gravity, grain size analysis, Atterberg limit, and one-dimensional consolidation test for a total of twenty-four test samples were conducted. From one-dimensional consolidation tests, compressibility soil parameters (Cc and Cs) are determined. From the results of limited tests, an indicative good correlation is observed between Cc and LL, PL, and PI. However, a Poor correlation is developed between Cc and PL when related to the other parameters. The developed correlations will be important inputs in modeling Jimma clay soils with regression model and Artificial neural networks (ANN) analysis using simple index tests. In addition, the results of this study can serve as a basis for further study of such correlations on different clay soils in the country. In this study, regression analysis was used to explore the significance of individual independent (index) soil properties. Regression model and correlation of compression index for liquid limit, plastic limit, and plasticity index were obtained from the linear regression analysis and ANN. This correlation will be helpful for geotechnical engineers in developing the coefficient of compression (Cc) value of expansive/clay soil from index properties. Finally, based on the general findings of the study, suitable recommendations have been forwarded.
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GUBAREV, R. V., L. G. CHEREDNICHENKO, A. I. BORODIN, and E. I. DZIUBA. "COMPARATIVE ANALYSIS OF THE EFFECTIVENESSOF CORRELATION-REGRESSION AND NEURAL NETWORK MODELING IN PREDICTING ENERGY EMISSIONSOF CARBON DIOXIDE IN RUSSIA." Moscow University Economics Bulletin 58, no. 3 (2023): 217–38. http://dx.doi.org/10.55959/msu0130-0105-6-58-3-11.
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Effective national cap-and-trade system involves accurate projections of greenhouse gas emissions for the national economy as a whole and by industry. The main source of carbon dioxide emissions in most countries of the world (including Russia) is the energy sector with 9traditional fuels (coal, gas and oil). The objective of the paper is to forecast energy emissionsof carbon dioxide in the Russian Federation by applying adequate economic and mathematical modelling methods. To achieve it, two hypotheses are consistently put forward and tested: the possibility of building a medium-term forecast of the indicator as a result of correlation and regression analysis and the one based on the formation of a Bayesian ensemble of artificial neural networks. Both hypotheses are confirmed in the empirical study. However, the second method provides a higher degree of accuracy in approximating statistical data. Therefore, within the framework of this article, the formation of medium-term forecasts of energy carbondioxide emissions in Russia is made with the help of neural network modeling. Highly accurate forecasting provides a scientific basis for effective policymakers' decisions in decarbonisation of the national economy.
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Peng, Yong, Leijie Zhang, Wanzeng Kong, Feiwei Qin, and Jianhai Zhang. "Low rank spectral regression via matrix factorization for efficient subspace learning." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3401–12. http://dx.doi.org/10.3233/jifs-191752.
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Subspace learning aims to obtain the corresponding low-dimensional representation of high dimensional data in order to facilitate the subsequent data storage and processing. Graph-based subspace learning is a kind of effective subspace learning methods by modeling the data manifold with a graph, which can be included in the general spectral regression (SR) framework. By using the least square regression form as objective function, spectral regression mathematically avoids performing eign-decomposition on dense matrices and has excellent flexibility. Recently, spectral regression has obtained promising performance in diverse applications; however, it did not take the underlying classes/tasks correlation patterns of data into consideration. In this paper, we propose to improve the performance of spectral regression by exploring the correlation among classes with low-rank modeling. The newly formulated low-rank spectral regression (LRSR) model is achieved by decomposing the projection matrix in SR by two factor matrices which were respectively regularized. The LRSR objective function can be handled by the alternating direction optimization framework. Besides some analysis on the differences between LRSR and existing related models, we conduct extensive experiments by comparing LRSR with its full rank counterpart on benchmark data sets and the results demonstrate its superiority.
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A, Tanko. "A Machine Learning Approach to Modeling Pore Pressure." Petroleum & Petrochemical Engineering Journal 4, no. 1 (2020): 1–6. http://dx.doi.org/10.23880/ppej-16000213.
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Machine Learning techniques and applications have lately gained a lot of interest in many areas, including spheres of arithmetic, finances, engineering, dialectology, and a lot more. This is owing to the upwelling of ground-breaking and sophisticated machine learning procedures to exceedingly multifaceted complications along with the prevailing advances in high speed computing. Numerous usages of Machine learning in daily life include pattern recognition, automation, data processing and analysis, and so on. The Petroleum industry is not lagging behind also. On the contrary, machine learning approaches have lately been applied to enhance production, forecast recoverable hydrocarbons, augment well placement by means of pattern recognition, optimize hydraulic fracture design, and to help in reservoir characterization. In this paper, three different machine learning models were trained and utilized to explore the feasibility of forecasting pore pressure of a well. The machine learning algorithms include, Simple Linear Regression, Decision Stump and Multilayer Perceptron (ANN). The predictive accuracies of the algorithm was analyzed using statistical measures. Five (5) parameters were utilized as input variables in the models: hydrostatic pressure, overburden pressure, observed and normal sonic velocities and pore pressure. 80% of the data was used in training while the remaining 20% was used for testing of the models. A sensitivity analysis of the five variable was conducted so as to identify correlations of the variables. Results of the sensitivity analysis revealed that both hydrostatic and overburden pressures appear to have the strongest correlation with pore pressure (0.766) and closely followed by normal compacted sonic velocity (0.753). Meanwhile, observed sonic velocity has the least correlation (0.046). The models were appraised by determining their Relative Absolute Errors. Results indicate that Multilayer Perceptron has the best prediction and least Relative Absolute Error of 5.77%. While the Decision Stump model had a Relative absolute error of 54.41%. The Simple Linear Regression had a relative absolute error of 67.93%. By and large, all three models appear to be suitable for modeling pore pressure but the Multilayer Perceptron is the most accurate.
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Tuskov, Andrey A., and Daria A. Goldueva. "ECONOMETRIC MODELING OF THE INTERNATIONAL HAPPINESS INDEX." Krasnoyarsk Science 11, no. 4 (2022): 77–95. http://dx.doi.org/10.12731/2070-7568-2022-11-4-77-95.
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Purpose – offer different variants of building multiple regression models to describe real economic processes.
 Method or methodology of the work: the article uses the econometric method of data analysis.
 Results: it was shown that econometric methods are effective in describing the hidden dependencies of the economic system. The study proposes several ways to build regression models: the classical version using the matrix of pairwise correlation coefficients (correlation pleiades method), the method of inflation factors, denying the presence of “moderately strong” dependence, after which the removal of “unnecessary” factors was performed using Gretl tool “Test for excess variables”, as well as using the principal components method to consider the influence of all factors on the dependent variable. In order to facilitate the interpretation of the results obtained, the article shows the transition from the principal components to the original data. The model is built on a publicly available dataset available to the research community.
 The sphere of application of the results: in practice, the results are useful for planning effective strategies for the development of individual states, as well as a manual for beginner econometricians wishing to learn different approaches to econometric modeling with the use of effective tools.
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Mohylnytska, A., and A. Panfilova. "Estimation and modeling of the influence of weather and climatic conditions on the yield of winter wheat." UKRAINIAN BLACK SEA REGION AGRARIAN SCIENCE 108, no. 4 (2020): 29–36. http://dx.doi.org/10.31521/2313-092x/2020-4(108)-4.
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The article considers the agro-climatic conditions of winter wheat cultivation under different feed options in Ukraine. It gives a detailed analysis of the air temperature change, precipitation, relative humidity and their mixed effect on winter wheat productivity for each phase of field crop development, as well as changes in climate fertility and crop efficiency in the modern climate period (2012-2016). The main sample characteristics of the interaction results of wheat varieties are found and the multifactor regression of yield dependence on hydrometeorological factors is formed. Keywords: yield, agroclimatic conditions; winter wheat; regression; correlation.
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Morozovskiy, Pavel, Ilya Kulish, Denis Muradov, and Kirill Kulakov. "Statistical modeling of residential complex construction project." E3S Web of Conferences 91 (2019): 08001. http://dx.doi.org/10.1051/e3sconf/20199108001.
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The article presents a statistical simulation of the deviation of the project duration from the planned value. Regression analysis was carried out - a method of statistical data processing that allows measuring the relationship between one or more causes (factor characteristics) and the consequence (effective characteristic). The end result is a curve and a correlation coefficient, which with a certain probability will allow us to predict the amount of pecuniary injury in this project.
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Babaei, Ali Akbar, Leila Atari, Mehdi Ahmadi, Kambiz Ahmadiangali, Mirzaman Zamanzadeh, and Nadali Alavi. "Trihalomethanes formation in Iranian water supply systems: predicting and modeling." Journal of Water and Health 13, no. 3 (2015): 859–69. http://dx.doi.org/10.2166/wh.2015.211.
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Trihalomethanes (THMs) were the first disinfection by-products discovered in drinking water and are classified as probable carcinogens. This study measures and models THMs formation at two drinking water distribution systems (WDS1 and WDS2) in Ahvaz City, Iran. The investigation was based on field-scale investigations and an intensive 36-week sampling program, from January to September 2011. The results showed total THM concentrations in the range 17.4–174.8 μg/L and 18.9–99.5 μg/L in WDS1 and WDS2, respectively. Except in a few cases, the THM concentrations in WDS1 and WDS2 were lower than the maximum contaminant level values. Using two-tailed Pearson correlation test, the water temperature, dissolved organic carbon, UV254, bromide ion (Br−), free residual chlorine, and chlorine dose were identified as the significant parameters for THMs formation in WDS2. Water temperature was the only significant parameter for THMs formation in WDS1. Based on the correlation results, a predictive model for THMs formation was developed using a multiple regression approach. A multiple linear regression model showed the best fit according to the coefficients of determination (R2) obtained for WDS1 (R2 = 0.47) and WDS2 (R2 = 0.54). Further correlation studies and analysis focusing on THMs formation are necessary to assess THMs concentration using the predictive models.
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Storelvmo, T., J. E. Kristjansson, G. Myhre, M. Johnsrud, and F. Stordal. "Combined observational and modeling based study of the aerosol indirect effect." Atmospheric Chemistry and Physics Discussions 6, no. 3 (2006): 3757–99. http://dx.doi.org/10.5194/acpd-6-3757-2006.
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Abstract. The indirect effect of aerosols via liquid clouds is investigated by comparing aerosol and cloud characteristics from the Global Climate Model CAM-Oslo to those observed by the MODIS instrument onboard the TERRA and AQUA satellites (http://modis.gsfc.nasa.gov). The comparison is carried out for 15 selected regions ranging from remote and clean to densely populated and polluted. For each region, the regression coefficient and correlation coefficient for the following parameters are calculated: Aerosol Optical Depth vs. Liquid Cloud Optical Thickness, Aerosol Optical Depth vs. Liquid Cloud Droplet Effective Radius and Aerosol Optical Depth vs. Cloud Liquid Water Path. Modeled and observed correlation coefficients and regression coefficients are then compared for a 3-year period starting in January 2001. Additionally, global maps for a number of aerosol and cloud parameters crucial for the understanding of the aerosol indirect effect are compared for the same period of time. Significant differences are found between MODIS and CAM-Oslo both in the regional and global comparison. However, both the model and the observations show a positive correlation between Aerosol Optical Depth and Cloud Optical Depth in practically all regions and for all seasons, in agreement with the current understanding of aerosol-cloud interactions. The correlation between Aerosol Optical Depth and Liquid Cloud Droplet Effective Radius is variable both in the model and the observations. However, the model reports the expected negative correlation more often than the MODIS data. Aerosol Optical Depth is overall positively correlated to Cloud Liquid Water Path both in the model and the observations, with a few regional exceptions.
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Storelvmo, T., J. E. Kristjánsson, G. Myhre, M. Johnsrud, and F. Stordal. "Combined observational and modeling based study of the aerosol indirect effect." Atmospheric Chemistry and Physics 6, no. 11 (2006): 3583–601. http://dx.doi.org/10.5194/acp-6-3583-2006.
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Abstract. The indirect effect of aerosols via liquid clouds is investigated by comparing aerosol and cloud characteristics from the Global Climate Model CAM-Oslo to those observed by the MODIS instrument onboard the TERRA and AQUA satellites http://modis.gsfc.nasa.gov). The comparison is carried out for 15 selected regions ranging from remote and clean to densely populated and polluted. For each region, the regression coefficient and correlation coefficient for the following parameters are calculated: Aerosol Optical Depth vs. Liquid Cloud Optical Thickness, Aerosol Optical Depth vs. Liquid Cloud Droplet Effective Radius and Aerosol Optical Depth vs. Cloud Liquid Water Path. Modeled and observed correlation coefficients and regression coefficients are then compared for a 3-year period starting in January 2001. Additionally, global maps for a number of aerosol and cloud parameters crucial for the understanding of the aerosol indirect effect are compared for the same period of time. Significant differences are found between MODIS and CAM-Oslo both in the regional and global comparison. However, both the model and the observations show a positive correlation between Aerosol Optical Depth and Cloud Optical Depth in practically all regions and for all seasons, in agreement with the current understanding of aerosol-cloud interactions. The correlation between Aerosol Optical Depth and Liquid Cloud Droplet Effective Radius is variable both in the model and the observations. However, the model reports the expected negative correlation more often than the MODIS data. Aerosol Optical Depth is overall positively correlated to Cloud Liquid Water Path both in the model and the observations, with a few regional exceptions.
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Pettit, Rowland W., and Christopher I. Amos. "Abstract 5041: Multiple outcome linkage disequilibrium score regression for confounding independent genetic correlations." Cancer Research 82, no. 12_Supplement (2022): 5041. http://dx.doi.org/10.1158/1538-7445.am2022-5041.
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Abstract Introduction: Linkage disequilibrium score regression (LDSR) estimates the genetic correlation between traits. Currently genetic correlation is calculated in LDSR by regressing the product of genome wide association study SNP z-scores (Ztrait1 * Ztrait2) against the SNPs calculated linkage disequilibrium scores for a population. The slope of this regression accurately estimates the genetic covariance between the two traits. Genetic covariance is then normalized to a genetic correlation. No method exists for calculating multi-trait genetic correlations. Understanding the genetic correlation between two traits, conditional on the effects of a third trait, would help clarify the correlation in traits, after removing effects from confounding variables. Methods: To estimate genetic correlation between two traits independent of the effects of a third trait, we are developing a multiple outcome linkage disequilibrium score regression. To do this we set up a multivariate outcome regression on the linkage disequilbrium score, jointly modeling the products of z-scores for i) the trait of interest with the outcome of interest and ii) the trait of interest with the confouder. This process is similar to the steps of the usual implementation of LDSR, however in multiple outcome LDSR modeling the model coefficients, or the genetic covariances of the trait and the confounder will covary. This affords the opportunity for testing to jointly determine if the genetic covariances identified are jointly or independently significant. We have implemented this multiple outcome LDSR method and compared the results to standard outcome LDSR using data from the United Kingdom Biobank (UKBB) and the TRICL-OncoArray lung cancer (LC) consortium. We evaluated the UKBB trait of BMI and current smoking status, for their joint genetic correlation on overall LC outcomes. Results: Implementing LDSR, significant pairwise genetic correlation (rg) was observed between the UKBB trait of current smoking status, and the overall LC outcome (rg = 0.62 +/- 0.06, p = 7.17x10-25). Similarly, significant positive genetic correlation was observed between the UKBB trait of BMI and overall LC predisposition (rg = 0.20 +/- 0.03, p = 2.607x10-9). Using multiple outcome LDSR and jointly modeling BMI-LC susceptibility with smoking-LC susceptibility resulted in a BMI-LC genetic correlation of 0.0103, which was highly attenuated, and consistent with prior studies that show the BMI-LC association is modulated through smoking behavior. Conclusions: Multiple outcome linkage disequilibrium score regression allows for multiple-trait genetic covariances to be measured simultaneously and for effect estimates to be partially mitigated for the influence of a confounder. Further validation and sensitivity analysis is warranted to determine appropriate implementations and interpretations of this novel method. Citation Format: Rowland W. Pettit, Christopher I. Amos. Multiple outcome linkage disequilibrium score regression for confounding independent genetic correlations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5041.
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Li, Hairui, Xuemei Liu, Xiaolu Chen, and Xianfeng Huai. "Robust Anomaly Recognition in Hydraulic Structural Safety Monitoring: A Methodology Based on Deconfounding Boosted Regression Trees." Mathematical Problems in Engineering 2023 (August 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/7854792.
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Analyzing monitoring data to recognize structural anomalies is a typical intelligent application of structural safety monitoring, which is of great significance to hydraulic engineering operational management. Many regression modeling methods have been developed to describe the complex statistical relationships between engineering safety monitoring points, which in turn can be used to recognize abnormal data. However, existing studies are devoted to introducing the correlation between adjacent response points to improve prediction accuracy, ignoring the detrimental effects on anomaly recognition, especially the pseudo-regression problem. In this paper, an anomaly recognition method is proposed from the perspective of causal inference to realize the best exploitation of various types of monitoring information in model construction, including four steps of constructing causal graph, regression modeling, model interpretation, and anomaly recognition. In regression modeling stage, two deconfounding machine learning models, two-stage boosted regression trees and copula debiased boosted regression trees, are proposed for recovering the causal effects of correlated response points. The validation was carried out with Shanmen River culvert monitoring data, and experiment results showed that the proposed method in this paper has better anomaly recognition compared to existing regression modeling methods, as shown by lower false alarm rates and lower averaged missing alarm rates under different structural anomaly scenarios.
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Yang, Xiao Li, and Qiong He. "Influence of Modeling Methods for Housing Price Forecasting." Advanced Materials Research 798-799 (September 2013): 885–88. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.885.
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In this work, we estimate Yunnan housing price from 1999 to 2009. Firstly, we analyze the correlation coefficients between housing price and characteristic variables, identify the characteristic variables. Then, we build the forecasting model using four techniques, support vector regression (SVR), radial basis function neural network (RBFNN), partial least square (PLS) and multiple regression analysis (MRA), based on whole variables and characteristic variables. The results show that PLS technique is the best one for housing price forecasting. Its mean absolute percentage error (MAPE) is only 2.45%. SVR and RBFNN are better techniques to obtain a satisfactory forecasting result with almost 5% MAPE. Furthermore, the performance of MRA and SVR can be obviously improved through variables selection.
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Raevsky, O. A., V. Yu Grigorev, A. V. Yarkov, and O. V. Tinkov. "QSAR Modeling Of Mammal Acute Toxicity By Oral Exposure." Biomedical Chemistry: Research and Methods 1, no. 3 (2018): e00066. http://dx.doi.org/10.18097/bmcrm00066.
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7490 organic compounds exhibiting acute oral toxicity in mice were studied. Regression models with satisfactory statistical characteristics have been created using the original AMP (arithmetic mean property) approach. The best models using the training and test sets were characterized by the squared linear correlation coefficient and the standard deviation of 0.5 and 0.45 (in log(1/LD50) units).