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1
McGilchrist, C. A. "Regression analysis of dependent error models." Bulletin of the Australian Mathematical Society 34, no. 2 (1986): 199–209. http://dx.doi.org/10.1017/s0004972700010066.
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A method of analysing the general linear regression model is described, for the case where the observations are correlated. For many applications the correlations are structured, with neighbouring observations being more strongly correlated than those some distance apart in time or space. Such correlation structures may often be assumed to belong to some class of models indexed by a small number of parameters. Estimation and inference procedures which are able to cope with a wide range of correlation models, are described and the methods are applied to problems which occur in biometry.
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Jensen, A. L. "Functional Regression and Correlation Analysis." Canadian Journal of Fisheries and Aquatic Sciences 43, no. 9 (1986): 1742–45. http://dx.doi.org/10.1139/f86-218.
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In fisheries, many applications of regression analysis are based on functional relations, but application of predictive regression results in two regression equations. Ricker proposed application of a method developed by Teissier to estimate the geometric mean functional relation when the parameters of a functional relation are of biological significance. Functional regression results in a single equation relating variables as opposed to the two equations that result when predictive regression is applied. The geometric mean functional relation also is given by bivariate normal correlation analysis when the correlation coefficient is 1. Bivariate normal correlation analysis provides a model for functional regression. An equation for variation of observed values about the functional regression line is obtained, and functional regression is compared with predictive regression. If the model assumptions are met, the one equation of functional regression is less precise for prediction than the two equations of predictive regression. However, the confidence intervals for the estimates of the slopes for functional and predictive regression are nearly the same.
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Hamdamov, Ahad Hamroyevich, Alimardon Toxir o'g'li To'rayev, and O'g'iloy Norpo'lat qizi Shamsiyeva. "Statistical Analysis of the Relationship Between Speed and Stopping Distance in Transportation Movements." Multidisciplinary Journal of Science and Technology 5, no. 2 (2025): 818–25. https://doi.org/10.5281/zenodo.14942327.
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This article explores the statistical relationship between speed and stopping distance in transportation movements. The study aims to analyze how variations in vehicle speed influence the distance required to bring a vehicle to a complete stop. Using real-world data, various statistical methods, such as regression analysis, were applied to determine the strength and nature of the correlation. The findings suggest that the stopping distance increases significantly with speed, highlighting the importance of safe driving practices and road design in minimizing accidents. The results also provide valuable insights for transportation engineers and policymakers to optimize traffic safety measures.
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Vakhabov, V. V., and M. A. Hidoyatova. "The method of correlation analysis in agriculture." E3S Web of Conferences 401 (2023): 05053. http://dx.doi.org/10.1051/e3sconf/202340105053.
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The mass data obtained from scientific and practical experiments are mostly probable-random. The methods of mathematical statistics are used for their processing; they include correlation, regression methods, dispersion analysis methods, and others. In this paper, we propose a correlation-regression analysis of the results of an experiment using a specific example from agriculture. The processing method and analysis of experimental results described in the paper are scientific-methodological and will be useful for the specialists involved in scientific research.
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İsgаndаrov, E., and A. Rzabayli. "INTERPRETATION OF GRAVITY DATA BY CORRELATION METHODS." Danish scientific journal, no. 69 (February 24, 2023): 5–10. https://doi.org/10.5281/zenodo.7688727.
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<strong>Abstract</strong> The article is devoted to the issue of interpretation of gravimetric data by correlation methods. As you know, there can be a correlation between geological and geophysical data, or, as they say, a statistical relationship. This connection can be studied by methods of mathematical statistics. Thus, correlations are established between geophysical and geological parameters, for example, between gravity anomalies and the depth of the geological boundary of interest. Such a correlation analysis is first carried out on a reference area with known values of geophysical and geological parameters. On the basis of the analogy principle, using the correlation links established on the standard, geological characteristics are predicted by geophysical parameters within a certain (forecast) territory. Statistical methods for constructing structural maps based on gravity data are mainly used in the modification of multidimensional regression analysis (MRA) and correlation methods for transforming anomalies (COMT). Software for the correlation analysis of gravity data has been developed at the Department of Geophysics of the ASOIU. The article presents the mathematical foundations and results of the forecast of the structural scheme of the surface of the Upper Cretaceous deposits of the Northern Saatly area of the Middle Kura depression of Azerbaijan.
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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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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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Tippett, Michael K., Timothy DelSole, Simon J. Mason, and Anthony G. Barnston. "Regression-Based Methods for Finding Coupled Patterns." Journal of Climate 21, no. 17 (2008): 4384–98. http://dx.doi.org/10.1175/2008jcli2150.1.
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Abstract There are a variety of multivariate statistical methods for analyzing the relations between two datasets. Two commonly used methods are canonical correlation analysis (CCA) and maximum covariance analysis (MCA), which find the projections of the data onto coupled patterns with maximum correlation and covariance, respectively. These projections are often used in linear prediction models. Redundancy analysis and principal predictor analysis construct projections that maximize the explained variance and the sum of squared correlations of regression models. This paper shows that the above pattern methods are equivalent to different diagonalizations of the regression between the two datasets. The different diagonalizations are computed using the singular value decomposition of the regression matrix developed using data that are suitably transformed for each method. This common framework for the pattern methods permits easy comparison of their properties. Principal component regression is shown to be a special case of CCA-based regression. A commonly used linear prediction model constructed from MCA patterns does not give a least squares estimate since correlations among MCA predictors are neglected. A variation, denoted least squares estimate (LSE)-MCA, is suggested that uses the same patterns but minimizes squared error. Since the different pattern methods correspond to diagonalizations of the same regression matrix, they all produce the same regression model when a complete set of patterns is used. Different prediction models are obtained when an incomplete set of patterns is used, with each method optimizing different properties of the regression. Some key points are illustrated in two idealized examples, and the methods are applied to statistical downscaling of rainfall over the northeast of Brazil.
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Vikhot, A. N., and V. A. Lutoev. "Analysis of Vibration Field Parameters of the City by the Correlation-Regression Method." Вестник Пермского университета. Геология 20, no. 1 (2021): 49–55. http://dx.doi.org/10.17072/psu.geol.20.1.49.
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Analysis of the parameters of the vibration field of the city was carried out on the example of the area of Syktyvkar. The vibroseismic monitoring data were used as a source material. We obtained the estimates of the mathematical expectation, variances and standard deviations, checked the normal distribution of random variables. The values of the correlation coefficient and correlation ratio were determined applying the method of correlation-regression analysis and carrying out the necessary calculations. Distribution diagrams were also constructed and approximating functions and estimated equations were obtained. This approach can be used to predict the parameters of the vibration field on the city territory and made it possible to give recommendations on its application as selection rationale of construction sites and environmental survey in the field of man-induced impact.
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SOYAN, Shonchalai Ch, and Orgaadai E. TUTATCHIKOVA. "Forecasting the Tyva Republic population based on correlation and regression analysis." Economic Analysis: Theory and Practice 20, no. 7 (2021): 1278–95. http://dx.doi.org/10.24891/ea.20.7.1278.
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Subject. The article deals with the forecast of the population of the Tyva Republic in the short term, based on correlation and regression analysis. Objectives. The aim is to forecast the population of the Tyva Republic, using the correlation and regression analysis. Methods. The study draws on the correlation and regression analysis, as one of the methods of multivariate statistical analysis, in which the form and intensity of relationship are presented in the form of mathematical equations and formulas. We also apply methods of comparison, dynamics, table and image format of visualization of the study results. Results. The correlation and regression analysis provides forecast data relating to the population of the Tyva Republic for 2020, which is very close to the actual population for this year. The analysis of indicators for development of the population revealed an annual increase, despite the declining fertility rates and unstable trend in mortality. The paper estimates the parameters of the regression equation, which describes the relationship between fertility, mortality, migration and population size. The findings may help create programs for demographic policy, socio-economic development of the territory, improvement of living standards in the region. Conclusions. The use of correlation and regression analysis will serve as a fairly reliable method to solve the problem. The study unveils significant factors that affect the growth or decline of population in the region.
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Yi, Juntai. "Analysis of Tianjin real estate market: multiple linear regression prediction method." Transactions on Economics, Business and Management Research 12 (September 28, 2024): 33–37. http://dx.doi.org/10.62051/a4tma782.
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In this paper, the price prediction of Tianjin real estate market is studied, and the key factors affecting housing prices are systematically analyzed by using the method of multiple linear regression prediction. The research data comes from the real estate transaction data of Tianjin from 2018 to 2022, covering independent variable information such as housing area, housing age, geographical location and supporting facilities. By constructing multiple linear regression model, it is found that housing area and supporting facilities have a significant positive impact on housing prices, while housing age and distance from the city center have a significant negative impact on housing prices. Specifically, for every 1 square meter increase in housing area, house prices are expected to rise by 450 yuan; Every year the age of the house increases, the house price is expected to drop by 200 yuan; Every kilometer away from the city center, house prices are expected to drop in 800 yuan; Every time the score of supporting facilities is increased by 1 point, the house price is expected to increase by 1200 yuan. The goodness-of-fit test of the model shows that the R value is 0.85, which shows that the model has strong explanatory power. In addition, through the correlation analysis of independent variables, it is found that there is a positive correlation between housing area and supporting facilities, but the correlation with other variables is weak. This study not only enriches the theoretical research of the real estate market, but also provides valuable decision-making reference for the government, enterprises and consumers, and helps the healthy and stable development of the real estate market in Tianjin.
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Vlasenko, Maria, Evgenia Prikhodko, and Maria Berezikova. "Correlation and Regression Analysis as a Tool for Forecasting Organizational Profits." Ideas and Ideals 16, no. 4-2 (2024): 261–80. https://doi.org/10.17212/2075-0862-2024-16.4.2-261-280.
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The planning process is important for the rhythmic functioning of an organization. Planning at each stage of the production process leads to forecasting financial results, in particular the profit from sales of the organization. As a rule, in the process of forecasting profit from sales, only internal factors or processes that influence the organization’s activities are taken into account. The purpose of this paper is to substantiate the use of correlation and regression analysis as a tool for forecasting profits from a sales organization. The paper considers two approaches to forecasting profit from sales, taking into account, among other things, the influence of external factors affecting organizations whose main activity is related to the rental and management of its own or leased real estate. The first approach to profit forecasting is based on the direct counting method and economic and mathematical algorithms. The second approach is the use of correlation and regression analysis, based on the influence of selected external factors. Profit forecasting using correlation-regression analysis showed that the average rental price of commercial real estate has a negative relationship with sales profit, and the amount of electricity consumed has a positive relationship with profit from sales. Based on the results of the correlation analysis, it can be concluded that changes in the rental price of commercial real estate and the volume of electricity consumed affect the company’s sales profit. The value of the forecast sales profit obtained under the optimistic scenario, calculated by the direct calculation method, is close to the forecast values calculated by the methods of correlation and regression analysis, which proves the importance of using this forecasting tool.
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Song, Chuan-Dong, Jian Li, Yan-Yan Hou, Qing-Hui Liu, and Zhuo Wang. "Quantum canonical correlation analysis algorithm." Laser Physics Letters 20, no. 10 (2023): 105203. http://dx.doi.org/10.1088/1612-202x/acee63.
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Abstract Canonical correlation analysis (CCA) is a fundamental technique used to analyze data correlation in various fields, including video and medical data analysis. In this paper, we propose a quantum canonical correlation analysis (QCCA) algorithm. First, we introduce a combined density matrix representation method that transforms CCA into generalized eigenvalue decomposition. Moreover, to address the challenge of performing generalized eigenvalue decomposition in high-dimensional scenarios, we propose a quantum method for extracting the canonical principal axes. In this method, two sets of variables are transformed into a reduced density matrix, so that the product of variable matrices can be accelerated by phase estimation and controlled rotation. Complexity analysis shows that the QCCA algorithm achieves exponential acceleration in variable dimensions n, p and variable size m compared to classical algorithms. The QCCA algorithm serves as a foundation for the subsequent development of quantum algorithms for classification, regression, and other machine learning tasks.
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Chernenko, V. A., I. A. Reznik, and A. A. Voronov. "CORRELATION AND REGRESSION ANALYSIS IN ASSESSING THE INDICATORS OF COMMERCIAL BANKS." ECONOMIC VECTOR 3, no. 30 (2022): 123–26. http://dx.doi.org/10.36807/2411-7269-2022-3-30-123-126.
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A correlation and regression analysis of the evaluation of financial intermediaries' per-formance indicators was carried out. The method of analyzing the capital and assets of commercial banks using correlation coef-ficients and a linear regression equation is applied. The presented algorithm can be used by financial intermediaries to analyze the interrelated parameters of their activities by determining the correlation coefficient, determination and evaluation of the variation of the studied factors.
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Chen, Cheng-Yang, and Yu-Wei Chang. "Missing data imputation using classification and regression trees." PeerJ Computer Science 10 (June 28, 2024): e2119. http://dx.doi.org/10.7717/peerj-cs.2119.
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Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART). Methods We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration. Results The method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the rpart package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the rpart package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.
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ПАХОМОВА, Т. В., Л. А. ВОЛОЩУК, В. А. ШИБАЙКИН, and Д. Н. ГИЛЯЖЕВА. "STATISTICAL DATA PROCESSING USING CORRELATION AND REGRESSION ANALYSIS." Экономика и предпринимательство, no. 9(158) (November 18, 2023): 1192–95. http://dx.doi.org/10.34925/eip.2023.158.09.231.
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Обработка статистических данных уже давно применяется в самых разнообразных видах человеческой деятельности. Вообще говоря, трудно назвать ту сферу, в которой она бы не использовалась. Но, пожалуй, ни в одной области знаний и практической деятельности обработка статистических данных не играет такой исключительно большой роли, как в экономике, имеющей дело с обработкой и анализом огромных массивов информации о социально-экономических явлениях и процессах. Всесторонний и глубокий анализ этой информации, так называемых статистических данных, предполагает использование различных специальных методов, важное место среди которых занимает корреляционный и регрессионный анализы обработки статистических данных. Statistical data processing has long been used in a wide variety of human activities. Generally speaking, it is difficult to name the sphere in which it would not be used. But, perhaps, in no field of knowledge and practice does the processing of statistical data play such an exceptionally large role as in economics, dealing with the processing and analysis of huge amounts of information about socio-economic phenomena and processes. A comprehensive and in-depth analysis of this information, the so-called statistical data, involves the use of various special methods, an important place among which is occupied by correlation and regression analysis of statistical data processing.
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Anil, Kumar Dwivedi, Airan Niyati, and Bhatnagar Rajan. "Reconstruction of Femoral Length from Distal Segmental Morphometry Using Regression Equation Method." International Journal of Pharmaceutical and Clinical Research 15, no. 7 (2023): 1328–34. https://doi.org/10.5281/zenodo.11905454.
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<strong>Introduction: </strong>Stature is one of the most important parameters for identification of an individual. The estimation of stature can be done by the measurement of long bones of the body and using established formulae. Previous studies have documented that femur has highest correlation with the stature and it is commonly used for deriving regression equation. However, the intact femur is not always recovered in medicolegal cases. The reconstruction of stature from fragmentary skeleton form part of forensic anthropological analysis for the purpose of establishment of identity of an individual. <strong>Aim: </strong>To establish a relation, between the dimensions of distal segments of femur and its length and to derive regression equations for the same. <strong>Materials and Methods: </strong>The study included 280 femora (136 Right and 144 left), which were measured for length of femur, bicondylar width, anterior posterior diameter of medial and lateral condyle, transverse diameter of medial and lateral condyle and intercondylar notch width with the help of Osteometric board and Vernier calliper. Then, the data was analysed statistically using student t-test, Pearson’s correlation coefficient and linear regression analysis. A p-value of <0.05 was considered significant and <0.01 highly significant. <strong>Results: </strong>The value of mean length of femur was 412.56<u>+</u>30.34 mm (Right 414.96<u>+</u>30.57 mm, left 410.29<u>+</u>30.05 mm). The length of femur correlates significantly with the dimensions of distal end (p<0.05). The linear regression equation for length of femur from distal femoral dimensions were derived. <strong>Conclusion: </strong>The morphometric data collected from the lower end of femur will be helpful for Orthopaedic surgeons, Sport physician and forensic experts. Regression equation for length of femur from its distal end dimensions derived in the present study will be useful for anthropologist, archaeologist and forensic investigators for determining the length of femur and thereby stature and identity of an individual.
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Prishchenko, Olga, Nadezhda Cheremskaya, and Tetyana Chernogor. "CONSTRUCTION OF MATHEMATICAL MODELS USING THE METHODS OF CORRELATION AND REGRESSION ANALYSIS." Bulletin of the National Technical University "KhPI". Series: Innovation researches in students’ scientific work, no. 2 (December 16, 2021): 29–36. http://dx.doi.org/10.20998/2220-4784.2021.02.05.
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The article discusses the construction of a mathematical model using the methods of correlation and regression analysis in determining the functional relationship between the quantities. When conducting an experiment, it is often necessary to establish the interdependence between two or more quantities in order to obtain an empirical formula. In some cases, this is a simple task, because these connections are almost obvious or known in advance. As a rule, to establish the relationship between different indicators, factors and characteristics is not a trivial task. There is a need to use some hypothesis in the form of functional dependence. In other words, it is necessary to replace this functional dependence with a fairly simple mathematical expression. Such a mathematical expression can be a linear equation or a polynomial. In order to use such experimental data to determine such a mathematical or functional relationship between variables, the methods of correlation and regression analysis are used. Correlation analysis provides an answer to the statistical hypothesis of the absence or presence of a relationship between variables with some predetermined confidence probability. Determination of the functional dependence between different values on their experimental values is carried out using regression analysis. It is based on the well-known method of least squares. Proposing one or another regression equation, the researcher determines both the very existence of the relationship between variables and its mathematical form. Regression analysis considers the relationship between the dependent quantity and non-dependent variables. This relationship is represented using a mathematical model, that is, an equation that connects the dependent and independent variables. Processing of experimental data using correlation and regression analysis allows us to build a statistical mathematical model in the form of a regression equation. Thus, the methods of correlation and regression analysis are closely related.
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Hou, Ziyi, Xiao Dang, Yezhen Yuan, Bo Tian, and Sili Li. "Research on Intelligent Compaction Technology of Subgrade Based on Regression Analysis." Advances in Materials Science and Engineering 2021 (September 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/4100896.
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A remote monitoring system with the intelligent compaction index CMV as the core is designed and developed to address the shortcomings of traditional subgrade compaction quality evaluation methods. Based on the actual project, the correlation between the CMV and conventional compaction indexes of compaction degree K and dynamic resilient modulus E is investigated by applying the one-dimensional linear regression equation for three types of subgrade fillers, clayey gravel, pulverized gravel, and soil-rock mixed fill, and the scheme of fitting CMV to the mean value of conventional indexes is adopted, which is compared with the scheme of fitting CMV to the single point of conventional indexes in the existing specification. The test results show that the correlation between the CMV and conventional indexes of clayey gravel and pulverized gravel is much stronger than that of soil-rock mixed subgrades, and the correlation coefficient can be significantly improved by fitting CMV to the mean of conventional indexes compared with single-point fitting, which can be considered as a new method for intelligent rolling correlation verification.
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Thafer Ramathan Muttar AL-Badrany and Takwa Abdulsalam Taha AL-Mola. "A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement." Tikrit Journal of Pure Science 21, no. 7 (2023): 185–203. http://dx.doi.org/10.25130/tjps.v21i7.1126.
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Appear in Many Application Areas for Regression Analysis and Presence the case of More Than One Variable Dependent Affected by A variety of Explanatory Variable and at The Same Time The Number of Observation is Relatively Small Compared to The Number of Variables, and Show Here The Problem of not Provide Offers Several Hypotheses Multiple Regression Analysis, More Over Prominence Problem of Multicollinearity between the Explanatory Variables Beside The Correlation between The Explanatory Variables and The Dependent Variables and Reflexive that on the Regression Estimates. and in This Research was Dealing with Problems from this Type Related to Variables Kirkuk Cement Factory, used the Methods, Principal Component PC and Partial Least Squares PLS to Solve the Problems Above, The First Method is Considered as one of the Commonest Methods used in Solving the Problem of Multicollinearity between the Explanatory Variables and the Second Method is Considered as one of the Methods Which Dally Methodically Different in Deduction the Components Dependent on Curing The Correlation the Presence between The Explanatory Variables and The Dependent Variables. Through the Statistical Analysis, Orphan Conduction to The PLS Method it has Succeeded in Establishing the Optimal Regression Model for all Depended Variables, Besides Superiority This Method Whence Ability on the Prediction for Futuristic Values Apiece Dependent Variables and Also Whence Dimension Reduction. The (Minitab, Version, 16.1) is used in the Statistical Analysis for the Data of this Research .
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Shvets, Vitaliy, Marina Maksymenko, and Vadim Kozak. "SIMULATION OF HEAT FLOW TRANSMISSION THROUGH FOILED THERMOPANELS BY THE CORRELATION-REGRESSION ANALYSIS METHOD." Modern technology, materials and design in construction 26, no. 1 (2019): 70–77. http://dx.doi.org/10.31649/2311-1429-2019-1-70-77.
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Bo, Hongjian, Haifeng Li, Boying Wu, Hongwei Li, and Lin Ma. "Long-Term EEG Component Analysis Method Based on Lasso Regression." Algorithms 14, no. 9 (2021): 271. http://dx.doi.org/10.3390/a14090271.
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At present, there are very few analysis methods for long-term electroencephalogram (EEG) components. Temporal information is always ignored by most of the existing techniques in cognitive studies. Therefore, a new analysis method based on time-varying characteristics was proposed. First of all, a regression model based on Lasso was proposed to reveal the difference between acoustics and physiology. Then, Permutation Tests and Gaussian fitting were applied to find the highest correlation. A cognitive experiment based on 93 emotional sounds was designed, and the EEG data of 10 volunteers were collected to verify the model. The 48-dimensional acoustic features and 428 EEG components were extracted and analyzed together. Through this method, the relationship between the EEG components and the acoustic features could be measured. Moreover, according to the temporal relations, an optimal offset of acoustic features was found, which could obtain better alignment with EEG features. After the regression analysis, the significant EEG components were found, which were in good agreement with cognitive laws. This provides a new idea for long-term EEG components, which could be applied in other correlative subjects.
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Carrera-Gallissà, Enric, Xavier Capdevila, and Josep Valldeperas. "Correlation Analysis between a Modified Ring Method and the FAST System." Journal of Engineered Fibers and Fabrics 9, no. 1 (2014): 155892501400900. http://dx.doi.org/10.1177/155892501400900115.
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The original ring method and some of its modified versions were examined with a view to developing a straightforward, universal alternative for use by the textile industry. For this purpose, a total of 42 specimens of commercial woven fabrics differing in composition, weave type and aerial weight were studied by using the FAST method and a modified version of the ring method developed by the authors. Correlation between the results of the two methods was found to depend largely on (a) fabric formability, (b) bending rigidity, and (c) maximum extraction force and the time needed to reach it. Regression equations relating the main variables of the two methods via canonical correlations were developed. The proposed modified version of the ring method allows the easy, inexpensive determination of fabric formability, which was previously possible with the FAST method only.
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Mylnikov, S. V. "Regression analysis. At the three-route crossroad." Science Editor and Publisher 9, no. 1 (2024): 2–64. http://dx.doi.org/10.24069/sep-24-19.
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Regression analysis is a rather popular and uncomplicated method of analyzing experimental data. If the results of correlation analysis tell us about the presence of a statistically significant relationship between the studied features, the results of regression analysis represent a graphical description of the nature of this relationship. The article considers the points that authors usually do not pay attention to, but to which the editor should pay attention, and the reviewer simply must do this. Among such points are false predictions, non-Gaussian distribution of residuals, and lack of confidence intervals.
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Pollock, M. A., S. G. Jefferson, J. W. Kane, K. Lomax, G. MacKinnon, and C. B. Winnard. "Method Comparison—A Different Approach." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 29, no. 5 (1992): 556–60. http://dx.doi.org/10.1177/000456329202900512.
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The commonly accepted method of analysing data from method comparison studies is regression analysis, a method which has limitations. This study illustrates the use of a graphical presentation of data, the difference plot, which can be used as an alternative to least squares regression analysis. The data from comparison studies performed on five methods were analysed both by Deming's regression analysis, with calculation of the correlation coefficient, and by the difference plot. The results show that in most cases much more relevant information was obtained from the difference plot.
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Xu, Xin-fang, Li-xing Nie, Li-li Pan, et al. "Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy." Journal of Analytical Methods in Chemistry 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/741571.
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Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.
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Nizamova, G. Z., and M. M. Gaifullina. "CORRELATION AND REGRESSION ANALYSIS OF THE AUTOMOTIVE GASOLINE MARKET." Bulletin USPTU Science education economy Series economy 3, no. 37 (2021): 35–44. http://dx.doi.org/10.17122/2541-8904-2021-3-37-35-44.
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Purpose of the study: to identify the factors affecting the volume of production of motor gasoline. Research methods: analysis and synthesis, systematic approach, as well as methods of correlation and regression analysis. Results of the research: A methodological approach to the use of tools for correlation and regression analysis of the gasoline market is proposed, which includes the following stages: 1) formation of a data array; 2) carrying out correlation analysis, building a correlation matrix, selecting factors into the model using the Correlation tool in the Data Analysis package of MS Excel; 3) conducting a regression analysis, constructing a regression equation, substantiating the obtained dependence using the "Regression" tool in the "Data Analysis" MS Excel package, calculating the elasticity coefficients. It is proposed to use the volume of production of motor gasoline as effective in carrying out the correlation-regression analysis and constructing mathematical models. Among the dependent variables and factors affecting the volume of production of motor gasoline, it is proposed to use variables that characterize four groups of factors: resource (raw material) limitations (the volume of oil production and refining), production capabilities of the industry (through the depth of oil refining and the yield of light oil products that characterize production capacity and set of installations in the industry), price attractiveness of the market (prices of producers and consumers of motor gasoline, world oil prices), export attractiveness (volume and value of exports). Multivariate economic and statistical models of the dependence of the volume of production of motor gasoline on a number of factors have been developed. Based on the results of calculations, a strong correlation was revealed between the volume of production of motor gasoline and the values of primary oil refining, oil production, and export of motor gasoline. The predicted values are located as close as possible to the residual values, which indicates that the resulting regression equation has a high degree of accuracy. Research prospects: the research results can be used to identify significant factors in the development of the motor gasoline market in the Russian Federation.
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UMAR, M. "CORRELATION AND REGRESSION ANALYSIS FOR MORPHOLOGICAL TRAITS OF CORIANDER." Journal of Physical, Biomedical and Biological Sciences 2024, no. 1 (2024): 22. https://doi.org/10.64013/jpbab.v2024i1.22.
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This study, conducted between 2023 and 2024, examines the physical characteristics and genetic variation of Coriandrum sativum, or coriander. A popular herb for culinary and medicinal uses, coriander was studied for its root length, stem length, leaf length, leaf width, and leaf area. Essential oils and extracts from C. sativum show promising antibacterial, antifungal, and antioxidative properties as distinct chemical components in various plant parts. As a consequence, they contribute significantly to food preservation by preventing degradation. This research indicates that Coriandrum sativum is resistant in adverse settings because of the significant positive connections among its morphological characteristics. Regression analysis also reveals the crucial role that shoot length plays in encouraging overall plant growth, providing valuable information that can be used to enhance farming methods and increase crop yields.
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Abadi, Aji Bijaksana, and Syahfrizal Tahcfulloh. "Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis." JOIV : International Journal on Informatics Visualization 6, no. 4 (2022): 763. http://dx.doi.org/10.30630/joiv.6.4.1013.
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Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.
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Tenkovskaya, Lyudmila I. "Correlation regression based forecast of Gazprom PJSC stock quotes." Вестник Пермского университета. Серия «Экономика» = Perm University Herald. ECONOMY 18, no. 1 (2023): 25–52. http://dx.doi.org/10.17072/1994-9960-2023-1-25-52.
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Introduction. A scientific research examining the concern of Gazprom PJSC (a leading Russian energy company) stock quote forecasts is relevant because it could determine investment prospects in the stock market of the Russian Federation. The key idea of this work is to find the most profitable and investable Russian company and outline the factors among many others that affect its stock quotes. Purpose . The author attempts to construct multiple linear regression equations reflecting the impact of the economic factors (the prices of the American natural gas, the USD/RUB currency pair, the M2 monetary aggregate in Russia) on the Gazprom PJSC stock quotes. These multiple linear regression equations are used to develop economic and mathematical models with the projected values of the factors. Materials and Methods . The paper refers to the general and special scientific methods – analysis, synthesis, a monographic method, and statistical methods – graphs, tables, trend spotting, correlation and regression analysis. The choice of independent variables in the correlation and regression analysis was driven by several factors: global natural gas prices determine the fi nancial performance of the Russian energy companies; the revenue of the Russian exporters has long been dependent on the USD/RUB currency pair; security quotes rose at the stock markets due to the monetary policy of central banks which strive to build up the monetary supply. Results. The study developed the equations of multiple linear regressions for the selected four periods with different economic scenarios. This proves the relevancy of the issue under analysis. These equations reflect the dependence of the Gazprom PJSC stock quotes from the prices of the American natural gas, the USD/RUB currency pair, and the money supply in Russia. They could give rise to the economic and mathematical models with the projected values of the analyzed factors and their possible correlations. Conclusion . The economic factors in question could have a positive impact on the Gazprom PJSC stock quotes against a weak Russian ruble and the national energy companies refocusing their attention on other markets. This information could be of use for the economic entities to boost their investment performance.
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Kim, Younghwan, and Hongseob Oh. "Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP." Materials 14, no. 17 (2021): 4861. http://dx.doi.org/10.3390/ma14174861.
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In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs.
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Исмиханов, З. Н. "Analysis of the relationship of economic indicators by the method of correlation analysis." Экономика и предпринимательство, no. 5(118) (June 11, 2020): 872–75. http://dx.doi.org/10.34925/eip.2020.118.5.177.
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В статье представлены результаты проверки гипотез о тесной связи показателей уровня жизни регионов России, входящих в Центральный федеральный округ. Оценки меры связей произведены с использованием метода корреляционного анализа - коэффициентов парной и частной корреляции разных порядков. Результаты анализа могут быть использованы в процессах разработки и реализации управленческих решений в социально-экономических системах, а также для построения регрессионных уравнений прогнозирования. The article presents the results of testing hypotheses about the close relationship of living standards of the regions of Russia that are members of the Central Federal District. Estimates of the measure of connections were made using the method of correlation analysis - pairwise and partial correlation coefficients of different orders. The results of the analysis can be used in the processes of developing and implementing managerial decisions in socio-economic systems, as well as for constructing regression forecasting equations.
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PYLYPIUK, Tetiana, and Viktor SHCHYRBA. "DATA MINING METHODS." Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 29 (December 14, 2023): 7–10. http://dx.doi.org/10.32626/2307-4507.2023-29.7-10.
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Research is devoted to Data Mining methods. A comparison of classical and mathematical and statistical methods of data analysis was made. One of the variants of correlation analysis method for intelligent data analysis is proposed and described in an argumentative manner. The question of applying different methodologies for Data Mining is actual. Classically, the following methods of knowledge discovery and analysis are offered in Data Mining: classification; regression; forecasting time sequences (series); clustering; association. As mathematical and statistical methods of analysis in applied research, the most of authors offer such methods as: statistical hypothesis testing, regression models construction and research. Since most real models are not amenable to analysis using classical methods, including regression analysis, the authors propose to use correlational analysis method in Data Mining.
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Fan, Guoliang, Jie Ji, and Hongxia Xu. "Serial correlation test of parametric regression models with response missing at random." Filomat 38, no. 7 (2024): 2521–35. https://doi.org/10.2298/fil2407521f.
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It is well-known that successive residuals maybe correlated with each other, and serial correlation usually result in an inefficient estimate in time series analysis. In this paper, we investigate the serial correlation test of parametric regression models where the response is missing at random. Three test statistics based on the empirical likelihood method are proposed to test serial correlation. It is proved that three proposed empirical likelihood ratios admit limiting chi-square distribution under the null hypothesis of no serial correlation. The proposed test statistics are simple to calculate and convenient to use, and they can test not only zero first-order serial correlation, but also the higher-order serial correlation. A simulation study and a real data analysis are conducted to evaluate the finite sample performance of our proposed test methods.
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Lv, Zhuang, Kaifeng Zhu, Xin He, et al. "Phase Unwrapping Error Correction Based on Multiple Linear Regression Analysis." Sensors 23, no. 5 (2023): 2743. http://dx.doi.org/10.3390/s23052743.
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Fringe projection profilometry (FPP) is prone to phase unwrapping error (PUE) due to phase noise and measurement conditions. Most of the existing PUE-correction methods detect and correct PUE on a pixel-by-pixel or partitioned block basis and do not make full use of the correlation of all information in the unwrapped phase map. In this study, a new method for detecting and correcting PUE is proposed. First, according to the low rank of the unwrapped phase map, multiple linear regression analysis is used to obtain the regression plane of the unwrapped phase, and thick PUE positions are marked on the basis of the tolerance set according to the regression plane. Then, an improved median filter is used to mark random PUE positions and finally correct marked PUE. Experimental results show that the proposed method is effective and robust. In addition, this method is progressive in the treatment of highly abrupt or discontinuous regions.
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Dong, Dingwen. "Decision-Making Optimization of Mine Gas Monitoring Based on Gauss Process Regression and Interval Number Correlation Analysis." Mathematical Problems in Engineering 2021 (November 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/9031448.
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For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.
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Сидорова, О. Е. "Approaches to predicting the amount of overdue debt by the method of correlation-regression analysis." Экономика и предпринимательство, no. 11(124) (December 23, 2020): 1155–58. http://dx.doi.org/10.34925/eip.2020.124.11.227.
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В статье рассматриваются вопросы определения функциональной зависимости между суммами невозвращенных и суммами выданных банковских кредитов и предлагается соответствующее ей значимое уравнение регрессии для прогнозирования размеров просроченной задолженности. In article are considered questions of determination of the functional relationship between the unpaid amounts and the amounts of issued Bank loans and offers the significant regression equation to predict the size of the overdue debt.
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赵, 冉. "Analysis of the Correlation between Housing Price Data in Boston Based on the Regression Method." Statistics and Application 09, no. 03 (2020): 335–44. http://dx.doi.org/10.12677/sa.2020.93036.
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Chao, Shu-Hsien, and Yi-Hau Chen. "A Novel Regression Analysis Method for Randomly Truncated Strong-Motion Data." Earthquake Spectra 35, no. 2 (2019): 977–1001. http://dx.doi.org/10.1193/022218eqs044m.
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Regression analysis is a basic and essential tool for developing the ground motion prediction equation (GMPE). Generally, the probability of intensity measurement for a given ground motion scenario described by several predictors is assumed to be normally distributed. However, because of the triggering threshold of the strong-motion station, ground motion records below the triggering threshold are truncated (i.e., not recorded), and the truncated intensity levels of spectral accelerations at different periods are random variables. Consequently, the sampling of the ground motion data used in GMPE development is biased, and the observed probability of the intensity measurement is no longer normally distributed. Therefore, a novel two-step maximum-likelihood method is proposed in this paper as a regression tool to overcome this problem in GMPE development. The advantage of the proposed method is that the correlation between records from the same events and those from the same sites as well as the biased sampling problem can be considered simultaneously, and more ground motion data can be considered to derive more reliable analysis results.
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Mohamad, Fani Sulaima, Saharani Sharizad, Ahmad Arfah, Erwani Hassan Elia, and Hasrizal Bohari Zul. "Investigation of energy demand correlation during pandemic using self-organizing map algorithm." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1333–43. https://doi.org/10.11591/ijai.v11.i4.pp1333-1343.
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The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The selforganizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.
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Wiwin, Sulistyo, Sulistyo Wiwin, and Pulungan Reza. "Development of a Spatial Path-Analysis Method for Spatial Data Analysis." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2456–67. https://doi.org/10.11591/ijece.v8i4.pp2456-2467.
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Path analysis is a method for identifying and analyzing direct and indirect relationship between independent and dependent variables. This method was developed by Sewal Wright and initially only used correlation analysis results in identifying the variables’ relationship. So far, path analysis has been mostly used to deal with variables of non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to enhance path analysis to identify the effects of spatial dependencies. This paper proposes a method derived from path analysis that can process data with spatial elements and furthermore can be used to identify and analyze the spatial effects on the data; we call this method spatial path analysis.
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Zhang, Zhenhong, Shunyin Wang, Junru Yan, et al. "Comparing differences and correlation between 24-hour ambulatory blood pressure and office blood pressure monitoring in patients with untreated hypertension." Journal of International Medical Research 49, no. 6 (2021): 030006052110161. http://dx.doi.org/10.1177/03000605211016144.
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Objective We assessed differences and correlations between 24-hour ambulatory blood pressure (ABP) and office blood pressure (OBP) monitoring. Methods We conducted an observational study among 85 untreated patients with essential hypertension and measured 24-hour ABP, OBP, target organ damage (TOD) markers, and metabolism indexes. Variance analysis and the Pearson method were used to compare differences and correlation between the two methods. The Spearman or Pearson method was applied to compare the correlation between TOD markers, blood pressure index, and metabolism index. Linear regression analysis was applied to estimate the quantitative relationship between the blood pressure index and TOD markers. Results There were significant differences in the mean and variance of systolic blood pressure (SBP) and diastolic blood pressure and a positive correlation between ABP and OBP. Correlations between the left ventricular mass index (LVMI) and average ambulatory SBP, daytime ambulatory SBP, nighttime ambulatory SBP, and fasting blood glucose were significant. Correlations between left intima-media thickness (IMT) and average ambulatory SBP, nighttime ambulatory SBP, right IMT, and nighttime ambulatory SBP were significant. In linear regression analysis of the LVMI (y) and ambulatory SBP (x), the equation was expressed as y = 0.637*x. Conclusion Nighttime ambulatory SBP may be an optimal predictor of TOD.
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Xu, Manyi, Wan Li, Jiaheng He, et al. "DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm." International Journal of Molecular Sciences 25, no. 10 (2024): 5267. http://dx.doi.org/10.3390/ijms25105267.
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Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease–drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method’s feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease–drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
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Kang, Qiong. "Correlation Analysis of Stocks and PMI Index Based on Logistic Regression Model." Journal of Sensors 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/1089266.
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In order to explore the correlation between stocks and the PMI index, based on the generalized logistic loss and margin distribution, this paper designs a margin distribution logistic regression model that is easy to optimize, has robustness, and generalization ability, and gives a multiclass margin distribution logistic regression framework. This framework can be used to perform two-classification, multiclassification, and feature selection tasks. Moreover, this paper gives a training algorithm for margin distribution logistic regression on large-scale data sets through the pairwise stochastic gradient descent method. In addition, this paper combines the logistic regression model to construct a correlation analysis model between stocks and PMI index and uses the PMI data of the National Bureau of Statistics as a sample to design experiments to verify the performance of the system model constructed in this paper. From the experimental analysis, it can be seen that the algorithm constructed in this paper has a certain effect, and the strong correlation between PMI and stocks has been further verified.
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Toodehzaeim, Mohammad Hossein, Hossein Aghili, Elham Shariatifar, and Mahboobe Dehghani. "New Regression Equations for Mixed Dentition Space Analysis in an Iranian Population." Journal of Contemporary Dental Practice 14, no. 6 (2013): 1156–60. http://dx.doi.org/10.5005/jp-journals-10024-1467.
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Abstract Aims Prediction of the mesiodistal crown width of unerupted canines and premolars is an important aspect of mixed dentition analysis. The accuracy of Tanaka-Johnston equations, the most commonly method, is questionable when it is applied to different ethnic groups. In this study, we aimed to develop a new regression equation for this prediction in an Iranian population. Materials and methods The dental casts of 120 Iranian subjects with complete permanent dentition were selected. Mesiodistal crown widths of teeth were measured with digital caliper. In the first part of the study, the correlation and linear regression equations between four mandibular incisors and the canine-premolars segments of both arches were developed (modified Tanaka-Johnston equation). In the second part, as a new method, correlation and linear regression equations were developed between the sum of mandibular central incisorsmaxillary first molars and the canine-premolars segments. Results It was found that the correlation coefficients between the sum of mandibular central incisors-maxillary first molars and the maxillary and mandibular canine-premolars segments were higher (r = 0.66, 0.68 respectively) than the one between the four mandibular incisors and the canine-premolars segments (r = 0.58. 0.64). Conclusion New linear regression equations were derived. In this study, the sum of mandibular central incisors and maxillary first molars was better predictor for unerupted canines and premolars. This novel approach allows the prediction of width of unerupted canines and premolars to take place at earlier age. Clinical significance Using the new method, orthodontists could take advantage of mixed dentition analysis at earlier age. Moreover, to test the derived equations on a larger sample size and in other ethnicities is highly recommended. How to cite this article Toodehzaeim MH, Aghili H, Shariatifar E, Dehghani M. New Regression Equations for Mixed Dentition Space Analysis in an Iranian Population. J Contemp Dent Pract 2013;14(6):1156-1160.
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Siagian, Ruben Cornelius, Lulut Alfaris, Ghulab Nabi Ahmad, et al. "Relationship between Solar Flux and Sunspot Activity Using Several Regression Models." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 15, no. 2 (2023): 146–65. http://dx.doi.org/10.25077/jif.15.2.146-165.2023.
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This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12-period timeframe. However, it is essential to note that this study solely focuses on correlation analysis and does not establish a causal relationship. Nonetheless, the findings contribute valuable insights into future variations in solar flux and sunspot numbers, thereby aiding scientists in comprehending and predicting solar activity's potential impact on Earth. The study recommends further research to explore additional factors that may influence the relationship between sunspots and solar flux, extend the research period to enhance the accuracy of solar activity predictions and investigate alternative prediction methods to improve the precision of forecasts.
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Li, Pan, Zhongjie Shen, and Xingwu Zhang. "Evaluation method of degradation index based on AdaBoost regression." Journal of Physics: Conference Series 2031, no. 1 (2021): 012059. http://dx.doi.org/10.1088/1742-6596/2031/1/012059.
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Abstract In the field of bearing monitoring, it has always been a difficult problem in the industry to judge the performance of the degradation index. A degradation index evaluation method is proposed based on AdaBoost regression. In this method, AdaBoost regression method is used for the bearing degradation index correlation evaluation criteria. The comprehensive weighted evaluation of degradation characteristics was obtained by the combination with robustness and monotonicity. Features were optimized according to the evaluation score. The analysis of accelerated life experimental data shows that the degradation feature evaluation method based on AdaBoost regression can effectively select the degradation features with good characterization.
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Chen, Jiong Wei, and Guo Ping Cheng. "Computer Aided Regression Analysis of Synergistic Effect of Energy Industry." Applied Mechanics and Materials 543-547 (March 2014): 4667–70. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.4667.
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In this paper, we try to find out the synergistic effect between energy industry investment and economic development. Based on the objective, with computer aided method, we choose the statistic data of energy industry investment and GDP of China from 2001 to 2011, use econometrics software Eviews 5.0 and least square method to build the linear regression model between energy industry investment and GDP. The result shows that there exists a linear correlation between energy industry investment and GDP; moreover, energy industry investment is more influential than the GDP.
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Livadiotis, George. "Linear Regression with Optimal Rotation." Stats 2, no. 4 (2019): 416–25. http://dx.doi.org/10.3390/stats2040028.
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The paper shows how the linear regression depends on the selection of the reference frame. The slope of the fitted line and the corresponding Pearson’s correlation coefficient are expressed in terms of the rotation angle. The correlation coefficient is found to be maximized for a certain optimal angle, for which the slope attains a special optimal value. The optimal angle, the value of the optimal slope, and the corresponding maximum correlation coefficient were expressed in terms of the covariance matrix, but also in terms of the values of the slope, derived from the fitting at the nonrotated and right-angle-rotated axes. The potential of the new method is to improve the derived values of the fitting parameters by detecting the optimal rotation angle, that is, the one that maximizes the correlation coefficient. The presented analysis was applied to the linear regression of density and temperature measurements characterizing the proton plasma in the inner heliosheath, the outer region of our heliosphere.
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Nur, Idalisa, Rivaie Mohd, Hidayah Mohd Noh Nur, Agos Salim Nasir Mohd, Hafawati Fadhilah Nurul, and Alias Norma. "A new three-term conjugate gradient method with application to regression analysis." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5248–59. https://doi.org/10.11591/ijece.v12i5.pp5248-5259.
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Conjugate gradient (CG) method is well-known for its ability to solve unconstrained optimization (UO.) problems. This article presenting a new CG method with sufficient descent conditions which improves the former method developed by Rvaie, Mustafa, Ismail and Leong (RMIL). The efficacy of the proposed method has been demonstrated through simulations on the Kijang Emas pricing regression problem. The daily data between January 2021 to May 2021 were obtained from Malaysian Ministry of Health and Bank Negara Malaysia. The dependent variable for this study was the Kijang Emas price, and the independent variables were the coronavirus disease (COVID-19) measures (i.e., new cases, R-naught, death cases, new recovered). Data collected were analyzed on its correlation and coefficient determinant, and the influences of COVID-19 on Kijang Emas price was examined through multiple linear regression model. Findings revealed that the suggested technique outperformed the existing CG algorithms in terms of computing efficiency.