Relevant bibliographies by topics / Method of correlation–regression analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Method of correlation–regression analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Method of correlation–regression analysis"

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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&nbsp; 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&nbsp;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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Dissertations / Theses on the topic "Method of correlation–regression analysis"

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Park, Soyoun. "Penalized method based on representatives and nonparametric analysis of gap data." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37307.

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When there are a large number of predictors and few observations, building a regression model to explain the behavior of a response variable such as a patient's medical condition is very challenging. This is a "p ≫n " variable selection problem encountered often in modern applied statistics and data mining. Chapter one of this thesis proposes a rigorous procedure which groups predictors into clusters of "highly-correlated" variables, selects a representative from each cluster, and uses a subset of the representatives for regression modeling. The proposed Penalized method based on Representatives (PR) extends the Lasso for the p ≫ n data and highly correlated variables, to build a sparse model practically interpretable and maintain prediction quality. Moreover, we provide the PR-Sequential Grouped Regression (PR-SGR) to make computation of the PR procedure efficient. Simulation studies show the proposed method outperforms existing methods such as the Lasso/Lars. A real-life example from a mental health diagnosis illustrates the applicability of the PR-SGR. In the second part of the thesis, we study the analysis of time-to-event data called a gap data when missing time intervals (gaps) possibly happen prior to the first observed event time. If a gap occurs prior to the first observed event, then the first observed event may or may not be the first true event. This incomplete knowledge makes the gap data different from the well-studied regular interval censored data. We propose a Non-Parametric Estimate for the Gap data (NPEG) to estimate the survival function for the first true event time, derive its analytic properties and demonstrate its performance in simulations. We also extend the Imputed Empirical Estimating method (IEE), which is an existing nonparametric method for the gap data up to one gap, to handle the gap data with multiple gaps.
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Jankech, Aleš. "Vliv přítomnosti zeleně na cenu nemovitostí." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15812.

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My Diploma thesis is focused on economic evaluation of gratuitous goods, especially on hedonic price method. The core of this paper is theoretical description and practical usage of HPM. Main theme is "Influence of plantscape on flat price.
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Andersson, Gustaf. "Generalised linear factor score regression : a comparison of four methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412851.

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Factor score regression has recently received growing interest as an alternative for structural equation modelling. Two issues causing uncertainty for researchers are addressed in this thesis. Firstly, more knowledge is needed on how different approaches to calculating factor score estimates compare when estimating factor score regression models. Secondly, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. This thesis examines how factor scoring methods compare when estimating regression coefficients in generalised linear factor score regression. An evaluation is made of the regression, correlation-preserving, total sum, and weighted sum method in ordinary, logistic, and Poisson factor score regression. In contrast to previous studies, both the mean and variance of loading coefficients and the degree of inter-factor correlation are varied in the simulations. A meta-analysis demonstrates that the choice of factor scoring method can substantially influence research conclusions. The regression and correlation-preserving method outperform the other two methods in terms of coefficient and standard error bias, accuracy, and empirical Type I error rates. Moreover, the regression method generally has the best performance. It is also noticed that performance can differ notably across the considered regression models.
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Lacko, Matej. "Analýza ekonomických ukazatelů vybrané firmy pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-378337.

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The diploma thesis deals with the analysis of economic indicators of Technos a.s., using statistical methods and the evaluation of the current financial situation. The work contains a theoretical and practical part. The theoretical part describes selected economic indicators, regression analysis, time series and correlation analysis. In the practical part, the analysis of selected economic indicators will be carried out and then statistical methods will be used to determine the prediction for the next year and to reveal the dependence between the individual indicators. The last part of the thesis deals with proposals that will improve the financial situation of the company.
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Shalaginova, Daria. "Posouzení vybraných ukazatelů společnosti pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-414494.

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Master’s thesis is aimed at assessing the selected financial indicators of the company using statistical methods. Based on the results, the current situation of the company is evaluated. The thesis consists of three parts. The first part contains the necessary theoretical bases for processing the analytical part. The second part is devoted to the analysis of selected indicators, which are then applied statistical methods to the prediction of the future development of these indicators and findings, here between these indicators there is a dependence. At the end of this part, there is an evaluation of the analyzed indicators. The third part presents appropriate proposals for solutions to existing problems caused by indicators that deviate from the recommended values.
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Šebestová, Monika. "Posouzení finanční výkonnosti podniku pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224470.

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The master’s thesis deals with the assessment of the financial performance of a company using statistical methods. The theoretical part describes the problems of time series, regression analysis, correlation analysis and financial analysis. In the practical part statistical methods are used to reveal the dependence between the indicators and the prediction of the future development of the company in the coming years. On the basis of established results the company is compared with two important rivals, and there are made some suggestions how to improve its economic situation.
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Rešková, Petra. "Posouzení vybraných ukazatelů společnosti pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-414492.

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Master´s thesis deals with the assessment of selected financial indicators of the company using a statistical methods. The first part is focused on the theoretical description of financial indicators, time series analysis as well as regression and correlation analysis. The practical part contains a statistical analysis of selected indicators with subsequent prediction of indicators for the next two years. The practical part also contains a comparison of selected indicators with the industry average and a correlation analysis to determine the dependence of selected indicators. The last part contains suggestions to improve the situation of the company.
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Bednářová, Veronika. "Posouzení vybraných ukazatelů společnosti pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2021. http://www.nusl.cz/ntk/nusl-443126.

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The diploma thesis is focused on the assessment of selected indicators using statistical methods. The first part is devoted to theoretical background, which describes financial indicators, time series analysis and regression and correlation analysis. The second part deals with the analysis of selected indicators and statistical analysis, which predicts the values of indicators for the next two years. Then correlation analysis is created, which determines the dependence between selected financial indicators. The last part is devoted to proposals leading to the improvement of the current situation of the company.
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Hofmanová, Aneta. "Posouzení vybraných ukazatelů společnosti pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-377969.

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Master's thesis deals with the assessment of selected financial indicators of the company through a financial analysis and statistical methods, on the basis of which then evaluates the current situation of the company. The thesis is divided into three parts. The theoretical part contains the issues necessary for the analytical part. The analytical part is focused on the analysis of selected indicators and the subsequent application of statistical methods to predict their future development and to detect dependencies between the indexes. The last part formulates possible solutions to problems caused by financial indicators that do not reach the required values.
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Johanides, Petr. "Posouzení vybraných ukazatelů firmy pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241622.

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Master’s thesis assesses the financial and economic situation of the joint-stock company Composite Components. There are theoretical aspects including financial, regression and correlation analysis in the first part of the thesis. Financial indicators are computed and then subjected to the mentioned analyses in order to get prognoses for following years. The created suggestions which are in the end of the thesis are based on the indicators and prognoses.
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Books on the topic "Method of correlation–regression analysis"

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. University of Wisconsin Press, 1993.

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. University of Wisconsin Press, 1994.

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1923-, Cohen Jacob, and Cohen Jacob 1923-, eds. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. L. Erlbaum Associates, 2003.

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Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. W.H. Freeman, 1989.

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Atkinson, A. C. Plots, transformations and regression: An introduction to graphical methods of diagnostic analysis. Oxford University Press, 1985.

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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

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L, Sjoquist David, and Stephan Paula E, eds. Understanding regression analysis: An introductory guide. Sage Publications, 1986.

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Keppel, Geoffrey. Data analysis for research designs. Freeman, 1995.

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Vogt, W., and R. Johnson. Correlation and Regression Analysis. SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446286104.

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Duan, Naihua. Slicing regression: A link-free regression method. RAND, 1991.

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Book chapters on the topic "Method of correlation–regression analysis"

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Davison, A. C. "Regression and Correlation." In Methods of Environmental Data Analysis. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9512-9_3.

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Davison, A. C. "Regression and Correlation." In Methods of Environmental Data Analysis. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2920-6_3.

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Rassel, Gary, Suzanne Leland, Zachary Mohr, and Elizabethann O’Sullivan. "Regression Analysis and Correlation." In Research Methods for Public Administrators. Routledge, 2020. http://dx.doi.org/10.4324/9780429319860-14.

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Reddy, M. Venkataswamy. "Correlation Analysis and Regression Analysis." In Statistical Methods in Psychiatry Research and SPSS. Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-12.

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Skinner, James, Aaron C. T. Smith, Daniel Read, Lauren M. Burch, and Jacqueline Mueller. "Correlation and Regression Analysis Techniques." In Research Methods for Sport Management, 2nd ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003397335-23.

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Selvamuthu, Dharmaraja, and Dipayan Das. "Analysis of Correlation and Regression." In Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1736-1_6.

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Selvamuthu, Dharmaraja, and Dipayan Das. "Analysis of Correlation and Regression." In Introduction to Probability, Statistical Methods, Design of Experiments and Statistical Quality Control. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9363-5_11.

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Navarro, Danielle, and David Foxcroft. "12. Correlation and linear regression." In Learning Statistics with jamovi. Open Book Publishers, 2025. https://doi.org/10.11647/obp.0333.12.

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This chapter delves into correlation and linear regression, foundational techniques in statistical analysis for exploring relationships between continuous predictors and outcomes. Beginning with the concept of correlations, the chapter examines how variables relate to each other in data, using scatterplots and descriptive statistics to visualise and interpret the strength and direction of relationships. Pearson’s correlation coefficient r is introduced as a measure ranging from -1 (perfect negative correlation) to +1 (perfect positive correlation), alongside practical guidance on its calculation and interpretation. The chapter also highlights limitations of Pearson’s r, such as its assumption of linearity, and introduces Spearman’s rank correlation for assessing monotonic relationships. Throughout, examples are used that underscore practical applications, supported by scatterplots and jamovi analyses. The latter sections explore linear regression models, a sophisticated extension of correlation analysis. These models estimate the relationship between predictors and outcomes using a regression line, quantified through coefficients such as slope and intercept. Techniques for evaluating model fit are detailed, including R squared and hypothesis testing for coefficients. The chapter extends the discussion to multiple linear regression, examining how multiple predictors interact within a model. It concludes by addressing critical topics such as assumptions (linearity, independence, normality, equality of variance), diagnostics (residual analysis, outlier detection, collinearity), and model selection methods like AIC and hierarchical regression. Practical, hands-on use of jamovi is emphasised throughout, making the concepts accessible and applicable.
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Ma, Jianfeng, Tongfei Shang, Jingwei Yang, and Jiaqing Zhao. "Support Vector Regression Prediction Method of Text Data Based on Correlation Analysis." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_74.

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Lütolf, Meret. "Data and Method." In The Balancing Act of Working Mothers and Caring Fathers. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-47716-5_8.

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Abstract This chapter primarily focuses on data, beginning with an overview of existing data sets, followed by a presentation of the survey data used in the analyses. Section 8.1, therefore, discusses the current state of existing datasets. The lack of a comprehensive dataset that would allow for in-depth analyses of the intra-family division of paid and unpaid work, is explained based on Lütolf and Stadelmann-Steffen (2023). Section 8.2 presents the new data collected from a comprehensive survey. The section illustrates a newly developed method for measuring the allocation of time within families and, furthermore, covers the experimental part of the survey. It presents the specific conjoint module of the survey, briefly explains the method more generally and discusses the concrete operationalisation of the variables used in the conjoint analysis. Section 8.3 discusses the operationalisation used for the regression analyses that determine the correlation between the duration of fathers’ leave and the intra-household distribution of unpaid work, estimated in Chapter 10. The section describes the measurement of the duration of leave and introduces the caregap– the variable to assess the equality of the distribution of unpaid work within a couple.
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Conference papers on the topic "Method of correlation–regression analysis"

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Niembro, Antonio Martinez, and Ernesto L. Gudiño Ortiz. "Cathodic Protection Design Using the Regression and Correlation Method." In CORROSION 1997. NACE International, 1997. https://doi.org/10.5006/c1997-97556.

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Abstract A computerized statistical method which calculates the current demand requirement based on potential measurements for cathodic protection systems is introduced. The method uses the regression and correlation analysis of statistical measurements of current and potentials of the piping network. This approach involves four steps: field potential measurements, statistical determination of the current required to achieve full protection, installation of more cathodic protection capacity with distributed anodes around the plant and examination of the protection potentials. The procedure is described and recommendations for the improvement of the existing and new cathodic protection systems are given.
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Čakanišin, Adrián, and Mária Halenárová. "Monte Carlo as a Method for Examining of Business Changes in Tourism in Slovakia." In 25th International Joint Conference Central and Eastern Europe in the Changing Business Environment. Vydavateľstvo EKONÓM, 2025. https://doi.org/10.53465/ceecbe.2025.9788022552257.50-63.

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The business environment in tourism encompasses a set of factors influencing the establishment, development, and sustainability of businesses in this sector, including economic, legislative, and market conditions. The dynamics of this environment are crucial for the economic stability of the sector. The main objective of this paper is to model the development of business establishments and closures in the tourism sector based on historical data and the influence of selected factors. The data used for this study were obtained from the Statistical Office of the Slovak Republic upon request. To achieve this objective, correlation and regression analysis were employed to examine relationships between economic variables, while a Monte Carlo simulation was used to predict future business activity trends. The results indicated that there are only moderately statistically significant relationships between economic factors and business establishment or closure. Domestic tourists' expenditures showed a weak positive correlation with business formation, whereas expenditures on inbound tourism had the opposite effect. The Monte Carlo simulation suggested that, assuming historical trends continue, the number of newly established businesses will stabilize at around 7,500 per year, while the number of closed businesses will be approximately 6,000 per year. Extreme scenarios demonstrated that economic fluctuations could lead to significant deviations, with the pessimistic scenario predicting a higher number of business closures and the optimistic scenario indicating a more favorable sectoral development.
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Cvetkov, Vasil. "A MYTH CONCERNING THE GEOMETRIC LEVELLING: CLOSING ERRORS AND LOOP CIRCUMFERENCES." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/2.1/s09.19.

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The geometric levelling of the highest order has been the main method for many scientific and engineering tasks for more than 150 years. One of them is the monitoring of the recent vertical movements of the Earth�s crust. In order to understand the low of accumulation of the discrepancies in levelling lines, many investigators have planned and performed various experiments and analysis through the years. As a result, the systematic effects of many sources of measurement errors have been studied, and partly or fully solved. However, the basic assumption about the relationship between the closing errors in levelling loops and the circumferences of the loops has hardly ever been the topic of wide discussions. It seems that this relationship is undoubted. The main objective of this article is to demonstrate that this popular belief about the accumulation of closing errors in the precise levelling is not valid regarding the data of the Third levelling of Bulgaria, the Second and the Third levelling of Finland. Using correlation and regression analysis we found that there is no statistically significant connection between closing errors and loop circumferences in the analyzed levelling networks. The correlation coefficients are below 0.35 considering each network, which means that the determination coefficients are up to 0.10. That is to say, the circumferences of the levelling loops cannot explain more than 10% of the variance of the closing errors. In addition, the p-values of the regression parameter b in the equation |??|=??+??.??0.5 are 0.104, 0.725 and 0.976, in the case of the Third levelling of Bulgaria, the Second and the Third levelling of Finland, respectively. Based on the above facts, we can conclude that some basic assumptions and methods regarding the processing of geometric levelling data should be an object of further modern consideration and revision.
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Price, Kendra T., Janet Buckingham, and James F. Dante. "CPC Performance in Occluded Sites." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07226.

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Abstract The United States Marine Corps has experienced success employing thin water displacing Corrosion Preventive Compounds (CPCs) for the reduction of corrosion in occluded sites (i.e., around hinges and fasteners and under lap seams). However, no definitive test methodology has been established that clearly delineates relative corrosion protection requirements for the use of CPCs in occluded geometries. This work seeks to confirm the performance of commercially available CPCs in occluded sites by establishing a correlation between laboratory results and published field data. These findings will in turn yield a practical means of ranking candidate CPCs as well as a plausible method of testing these CPCs prior to application. Steel lap joint assemblies and galvanically coupled steel and aluminum lap joint assemblies were exposed to GM9540P conditions. A correlation of mass loss data from laboratory and accelerated outdoor exposures is presented for steel lap joints. The Washburn method was used to determine the candidate CPCs relative ability to wick into a crevice. Mass gain data taken from the Washburn method also provided a rough approximation of the amount of CPC that wicks in over time and the thickness of the protective layer formed. A regression analysis was performed on properties associated with wicking in order to determine which properties predominantly influence CPC performance.
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Nahalková Tesárová, Eva, and Anna Križanová. "THE USE REGRESSION AND CORRELATION ANALYSIS IN GENERATIONAL STRATIFICATION AND CONSUMER BEHAVIOUR." In 13th International Scientific Conference „Business and Management 2023“. Vilnius Gediminas Technical University, 2023. http://dx.doi.org/10.3846/bm.2023.949.

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We can definitely state that consumer behavior is influenced by many factors. Age is one of the most significant and important demographic indicators, which are reflected in changes in consumer behavior, primarily in the growth and transformation of the needs of individualists. The primary foundation and purpose of the paper is to determine the influence and dependence of individual generational cohorts, respectively age, on the amount of money spent in the purchasing process, which will be enriched by a comparative analysis of the amount of money spent in retail and in the online space. Among the methods that we used in the article, we primarily include the graph analysis method supplemented by correlation analysis and linear regression. Primary data were obtained from a questionnaire survey of Slovak retail consumers. The last method used was the method of comparing results. We consider the model as a whole to be statistically significant in the case of Gen X, who makes purchases online, and the younger Gen Z in both variants of purchases (physical and online). The contribution is a suitable basis for further research.
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Yan, Xiaozhen, Hong Xie, and Wang Tong. "A multiple linear regression data predicting method using correlation analysis for wireless sensor networks." In 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference. IEEE, 2011. http://dx.doi.org/10.1109/csqrwc.2011.6037116.

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Chertov, M., and V. Antsiferova. "SALES FORECAST MODELING BASED ON CORRELATION AND REGRESSION METHODS." In Modern aspects of modeling systems and processes. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2021. http://dx.doi.org/10.34220/mamsp_185-191.

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The presented article explores the methods of correlation and regression analysis, developed its own algorithm for obtaining the initial data of interest, and also presents the calculation algo-rithm for the law of change in sales volumes. Based on the presented calculations, the errors of the correlation coefficients and the results of the regression analysis were calculated.
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Statsenko, Ekaterina. "ANALYSIS OF THE GROWING PROCESS OF SOY BEAN." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9d640096.59446052.

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Prediction of technological processes in various sectors of the food industry using mathematical modeling is becoming increasingly important. The paper describes the effect of various factors on the grain weight after germination by the method of correlation-regression analysis. Using this modeling method, based on the established relationship, it is possible to predict the weight of the grain after germination in various conditions.
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Zhao, Wentao, Majia Zheng, Di Luo, Mengke Xie, Ya Wu, and Weiyi Wang. "Comprehensive Correlation Analysis of Influencing Factors of Shale Gas Productivity Based on Adaptive Lasso Regression." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216455-ms.

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Abstract The shale gas demonstration area in the Sichuan Basin is currently at the stage of large-scale development. The main factors controlling the high production of shale gas wells has been a hot research topic in shale gas development. To obtain the main influencing factors of shale gas productivity, the typical case well area(Z 201)was selected as an example. 12 geological and engineering productivity variables of shale gas well were optimized based on Adaptive Lasso Regression method. In addition, optimization of multiple linear regression model and weight analysis of key parameters affecting productivity were conducted. Finally, the main controlling factors of productivity of shale gas well were identified, and the corresponding productivity prediction equation of shale gas single well was established. The results showed five independent variables (the drilling length of 1 sublayer, fracture density, thickness, brittleness and maximum principal stress) were optimized based on Adaptive Lasso regression, and the drilling length of 1 sublayer was the main factor controlling the high production of gas wells. In addition, the key to improving the shale gas well productivity were the natural brittle fracture development area and the low stress zone. Based on these study results, a production prediction equation for standardized testing of single well in this area was established, which may guide the drilling operation effectively and improve the production rate of shale gas wells. The research results have certain guidance and reference significance for shale gas production capacity construction and development. It can provide an important basis for optimizing shale gas favorable areas and evaluating single well productivity.
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Ranković, Radoslav. "Correlation and regression analysis of the population change index of border municipalities of the Republic of Serbia, 2011–2022." In Population in Post-Yugoslav Countries: (Dis)Similarities and Perspectives. Institute of Social Sciences, 2024. http://dx.doi.org/10.59954/ppycdsp2024.45.

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The border municipalities of the Republic of Serbia are most often seen as peripheral in the geographical, demographic, social and economic sense. Serbia, like other post-Yugoslav countries, underwent crucial changes during the 1990s. They encouraged the intensification of unequal regional development in the centre–periphery relation. Although some border territories of European countries deviate from this pattern, the border effect has led to numerous negative consequences of the polarization process in Serbia, especially in those border municipalities with a longer peripheral status. Since depopulation is one of the biggest development problems in Serbia, the population change index 2011–2022 was studied using methods of correlation and regression analysis of demographic and social-economic indicators. Correlation analysis found that there is a medium or strong relationship with six indicators, while regression analysis showed that even those negligible variables in correlation analysis, such as the number of employees in the primary sector, can be significant. By applying the multiple regression method, it was determined that the population change index of 44 border municipalities can be predicted using three independent variables: the rate of natural increase, the number of marriages and the number of employees in the primary sector. The obtained regression model proved to be significant, considering the satisfactory value of the adjusted coefficient of multiple determination (adj R²=0.707) and the p-values of the independent variables below the threshold value (p &lt; 0.05). As an outlier, the municipality of Čajetina stands out, for which the adopted model predicted a decrease in the number of inhabitants by 12.8% (the actual decrease is about 1.1%), while the absolute residual values for the other border municipalities range in the interval of 0.05 (City of Sombor) to 7.83 (municipality of Tutin). The aim of the paper is reflected in the importance of the application of selected statistical methods in demographic analysis.
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Reports on the topic "Method of correlation–regression analysis"

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Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee, and Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-029.

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This study investigates the use of macrotexture as an indicator of the percent embedment (PE) of aggregate in a chip seal and ultimately, as a quality assurance tool for chip seals. The study included an extensive field- and controlled-testing program from 24 chip seal sections constructed in Illinois. Surface texture measurements were acquired using a high-speed texture profiler and a stationary laser texture device. The analysis showed that stationary texture measurements were more consistent and reliable for estimating PE and characterizing chip seals in the field. Moreover, the ground truth PE values were estimated using an image analysis algorithm implemented on side-view images of cores extracted in the field. The ground truth PE values were estimated using four approaches: the average elevation method, percent embedment of each aggregate method, the peak method, and the aggregate circumference method. The analysis showed that the correlations between the different PE estimation methods are relatively weak, indicating the various methods provide different information and may relate to different characteristics. The general regression models for PE values estimated using the average elevation method and the mean profile depth (MPD) acquired using laser texture scans and the average least dimension (ALD) yielded the highest R2 value of 0.50. The model showed a consistent decreasing trend between PE and MPD estimated using laser texture scans and side-view images. Moreover, the model matched the expected behavior that PE should reach 100% as MPD reaches 0. Finally, four models were recommended correlating PE estimated using the average elevation and each aggregate methods to the MPD (mm) estimated from laser texture scans and ALD (mm) estimated from side-view images.
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Jimenez Mori, Raul Alberto, and Jorge Enrique Mercado Díaz. Energy Intensity: A Decomposition and Counterfactual Exercise for Latin American Countries. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0011485.

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This paper investigates trends in energy intensity in Latin American countries over the last 40 years. It applies the Fisher Ideal Index to decompose the energy intensity into the relative contributions of energy efficiency and the activity mix, and then analyzes the determinants of these energy indexes through panel data regression techniques. Finally, the paper compares the performance of Latin American countries to that of a similar set of countries chosen through the synthetic control method. The authors find that the energy intensity in Latin American countries has decreased about 20 percent, closing the gap with respect to its synthetic counterfactual. In both Latin American countries and its synthetic control, efficiency improvements drive these changes, while the activity mix component does not represent a clear source of change. The regression analysis shows that per capita income, petroleum prices, fuel-energy mix, and GDP growth are main determinants of energy intensity and efficiency, while there are no clear correlations with the activity component.
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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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Steed, Chad A., J. Edward SwanII, Patrick J. Fitzpatrick, and T. J. Jankun-Kelly. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1035521.

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Herman, Matthew Joseph. Two-Dimensional Correlation Method for Polymer Analysis. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1172208.

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Qian, Zhi-Wang, Xiao-Lei Du, Yu-Feng Shi, and Ying Yu. Correlation between Economic Security and Medical and Health Service with Regression Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.01.17.

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Qian, Zhi-Wang, Xiao-Lei Du, Yu-Feng Shi, and Ying Yu. Correlation between Economic Security and Medical and Health Service with Regression Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/grabs2018.1.17.

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Sun, T. C. Using Regression Analysis Method to Develop a Material Outgassing Model. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1499976.

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p&lt;0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p&lt;0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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