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Journal articles on the topic 'Regression straight line'
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1
Giordano, José Luis. "On reporting uncertainties of the straight-line regression parameters." European Journal of Physics 20, no. 5 (September 1, 1999): 343–49. http://dx.doi.org/10.1088/0143-0807/20/5/307.
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Schwartz, Lowell M. "Rejection of a deviant point from a straight-line regression." Analytica Chimica Acta 178 (1985): 355–59. http://dx.doi.org/10.1016/s0003-2670(00)86289-3.
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Schaalje, G. Bruce, and Richard A. Butts. "Some Effects of Ignoring Correlated Measurement Errors in Straight Line Regression and Prediction." Biometrics 49, no. 4 (December 1993): 1262. http://dx.doi.org/10.2307/2532270.
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Verrall, R. J. "Graduation by dynamic regression methods." Journal of the Institute of Actuaries 120, no. 1 (1993): 153–70. http://dx.doi.org/10.1017/s002026810003688x.
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AbstractThis paper extends the theory of graduation by parametric formulae to include dynamic estimation methods. This is an application of the Kalman filter and allows the parameters of the curve fitted to vary with age. The amount of variation is determined by the amount of smoothing required, and the method can be regarded as a combination of curve fitting and sequential smoothing, each of which has been used separately for performing graduations. In practice, a dynamic straight line can always be used for the graduation and the method has a sensible logical interpretation.
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Clarke, Allan J., and Stephen Van Gorder. "On Fitting a Straight Line to Data when the “Noise” in Both Variables Is Unknown*." Journal of Atmospheric and Oceanic Technology 30, no. 1 (January 1, 2013): 151–58. http://dx.doi.org/10.1175/jtech-d-12-00067.1.
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Abstract In meteorology and oceanography, and other fields, it is often necessary to fit a straight line to some points and estimate its slope. If both variables corresponding to the points are noisy, the slope as estimated by the ordinary least squares regression coefficient is biased low; that is, for a large enough sample, it always underestimates the true regression coefficient between the variables. In the common situation when the relative size of the noise in the variables is unknown, an appropriate regression coefficient is plus or minus the ratio of the standard deviations of the variables, the sign being determined by the sign of the correlation coefficient. For this case of unknown noise, the authors here obtain the probability density function (pdf) for the true regression coefficient divided by the appropriate regression coefficient just mentioned. For the case when the number of data is very large, a simple analytical expression for this pdf is obtained; for a finite number of data points the relevant pdfs are obtained numerically. The pdfs enable the authors to provide tables for confidence intervals for the true regression coefficient. Using these tables, the end result of this analysis is a simple practical way to estimate the true regression coefficient between two variables given their standard deviations, the sample correlation, and the number of independent data.
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Emancipator, K., and M. H. Kroll. "A quantitative measure of nonlinearity." Clinical Chemistry 39, no. 5 (May 1, 1993): 766–72. http://dx.doi.org/10.1093/clinchem/39.5.766.
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Abstract Quantitative measures of the nonlinearity of an analytical method are defined as follows: the "(dimensional) nonlinearity" of a method is the square root of the mean of the square of the deviation of the response curve from a straight line, where the straight line is chosen to minimize the nonlinearity. The "relative nonlinearity" is defined as the dimensional nonlinearity divided by the difference between the maximum and minimum assayed values. These definitions may be used to develop practical criteria for linearity that are still objective. Calculation of the nonlinearity requires a method of curve-fitting. In this article, we use polynomial regression to demonstrate calculations, but the definition of nonlinearity also accommodates alternative nonlinear regression procedures.
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Jehlička, Vladimír, and Vladimír Mach. "Determination of Estimates of Parameters of Calibration Regression Straight Line with Objective Elimination of Remote Measurements." Collection of Czechoslovak Chemical Communications 60, no. 12 (1995): 2064–73. http://dx.doi.org/10.1135/cccc19952064.
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An alternative algorithm of calculation of estimates of parameters of calibration regression straight line with elimination of remote measurements has been elaborated for a function f(x) composed of linear-nonlinear or nonlinear-linear parts or, as the case may be, of nonlinear-linear-nonlinear parts. This algorithm makes it possible to objectively eliminate remote measurements and determine the interval <x1;x2> in which is located the linear part of dependence of output measured quantity y = f(x) with normal distribution N(f(x), σ2) on the input, i.e., independent variable x. For the procedure used for testing of remoteness of experimental points, a relation has been derived for calculation of the critical value of deviation of the point tested. The algorithm is finished by the calculation of parameters of the corresponding regression straight line and other statistical characteristics. On the basis of the algorithm suggested, a program has been assembled whose reliability was verified on a series of both model and practical examples.
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Wehr, Richard, and Scott R. Saleska. "The long-solved problem of the best-fit straight line: application to isotopic mixing lines." Biogeosciences 14, no. 1 (January 3, 2017): 17–29. http://dx.doi.org/10.5194/bg-14-17-2017.
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Abstract. It has been almost 50 years since York published an exact and general solution for the best-fit straight line to independent points with normally distributed errors in both x and y. York's solution is highly cited in the geophysical literature but almost unknown outside of it, so that there has been no ebb in the tide of books and papers wrestling with the problem. Much of the post-1969 literature on straight-line fitting has sown confusion not merely by its content but by its very existence. The optimal least-squares fit is already known; the problem is already solved. Here we introduce the non-specialist reader to York's solution and demonstrate its application in the interesting case of the isotopic mixing line, an analytical tool widely used to determine the isotopic signature of trace gas sources for the study of biogeochemical cycles. The most commonly known linear regression methods – ordinary least-squares regression (OLS), geometric mean regression (GMR), and orthogonal distance regression (ODR) – have each been recommended as the best method for fitting isotopic mixing lines. In fact, OLS, GMR, and ODR are all special cases of York's solution that are valid only under particular measurement conditions, and those conditions do not hold in general for isotopic mixing lines. Using Monte Carlo simulations, we quantify the biases in OLS, GMR, and ODR under various conditions and show that York's general – and convenient – solution is always the least biased.
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Gritsenko, S. A., and Yu R. Sadyrtdinova. "Association and heritability of traits of milk productivity and blood in the dairy cows." Glavnyj zootehnik (Head of Animal Breeding), no. 12 (December 1, 2020): 16–23. http://dx.doi.org/10.33920/sel-03-2012-02.
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Numerous studies have shown that there is a certain association between the biochemical parameters of blood and milk. It is of big practical importance to determine the heritability of traits involved in the selection process, which gives breeders the opportunity to choose the direction of selection for the fastest solution of the tasks. The purpose of the researches was to establish the association between the indicators of the biochemical composition of milk and blood in the dairy cows, as well as to determine the heritability of the studied traits. It has been found in the course of researches that in most cases the correlations between the biochemical parameters of blood and milk were insignifi cant and unreliable. It can be seen that there is a slight correlation between the main indicators of blood and milk, which should be taken into account by breeders when conducting breeding to improve the quality of milk. The highest regression association has been observed between the content of carotene, calcium and phosphorus in blood and milk. If you increase these indicators in the blood by the corresponding unit, their content in milk will also increase by 0,90, 0,31 and 0,42, respectively. The heritability of milk productivity traits in most cases was at medium and low levels and ranged from 0,03 to 0,66 (straight-line correlation method) and from 0,03 to 0,44 (straight-line regression method). The heritability of hematological traits in most cases was at an average level and ranged from 0,14 to 0,98 (straight-line correlation method) and from 0,11 to 0,76 (straight-line regression method). The total protein content in the blood (h² =0,98 и 0,76) and the number of monocytes (h² =0,82 и 0,60) depended more on the genotypic features of the animal. The average level of heritability coeffi cients has been observed in the blood content of total lipids, phosphorus, eosinophils, young neutrophils, and lymphocytes.
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Rueda, R., L. G. B. Ruiz, M. P. Cuéllar, and M. C. Pegalajar. "An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling." International Journal of Approximate Reasoning 121 (June 2020): 23–38. http://dx.doi.org/10.1016/j.ijar.2020.03.005.
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Eysseltová, Jitka, and Jana Dosoudilová. "Solubility in the Glycine-H3BO3-H2O System." Collection of Czechoslovak Chemical Communications 59, no. 6 (1994): 1337–41. http://dx.doi.org/10.1135/cccc19941337.
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Solubility isotherms in the title system were measured at 0, 25, 50, and 70 °C. Regression straight line equations interrelating the solubilities of boric acid and glycine were derived. The activity of water in saturated solutions of the system at 25 °C was determined by vapour pressure measurements.
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Christiansen, Bo. "Straight Line Fitting and Predictions: On a Marginal Likelihood Approach to Linear Regression and Errors-In-Variables Models." Journal of Climate 27, no. 5 (February 24, 2014): 2014–31. http://dx.doi.org/10.1175/jcli-d-13-00299.1.
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Abstract Even in the simple case of univariate linear regression and prediction there are important choices to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. A unified probabilistic (i.e., Bayesian with flat priors) treatment of univariate linear regression and prediction is given by taking, as starting point, the general errors-in-variables model. Other versions of linear regression can be obtained as limits of this model. The likelihood of the model parameters and predictands of the general errors-in-variables model is derived by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well-known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference cannot be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. A probabilistic version of classical calibration is also included and it is shown how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.
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Tyrsin, A. N. "Algorithms for descent along nodal straight lines in the problem of estimating regression equations using the least absolute deviations method." Industrial laboratory. Diagnostics of materials 87, no. 5 (May 23, 2021): 68–75. http://dx.doi.org/10.26896/1028-6861-2021-87-5-68-75.
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A problem of estimating linear regression equations by the least absolute deviations method is considered. The exact methods of implementation of the method are significantly inferior in performance to the least square method. The fastest algorithm based on coordinate descent along nodal straight lines has a computational complexity proportional to the square of the number of observations, which limits the practical application of the method to monitoring and diagnostic tasks. The goal of the study is to describe a faster version of the descent along the nodal straight lines, as well as to evaluate the performance. Reduction of the computational costs is achieved due to the fact that instead of calculating the values of the objective function at nodal points, we find the derivative of the objective function in the vicinity of these points along the nodal line. The computational efficiency of gradient descent along nodal straight lines is estimated. For a typical computer, a comparative analysis of the average calculation time for various algorithms of descent along nodal straight lines is performed. A simple example is given to illustrate the implementation of a gradient descent procedure. Along with reduction of the computational costs, we also eliminated the possibility of accumulating computational errors when determining the values of the objective function for large samples. Moreover, gradient descent is quite simple for implementation. This makes it possible to use the method of least absolute deviations as an alternative to the least square method in various practical applications.
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Warsza, Zygmunt, and Jacek Puchalski. "Uncertainty of Measurement in the Linear Regression Method Part 2. Uncertainty Bands of the Regression Straight-Line for the Correlated Data of Y Variable." Pomiary Automatyka Robotyka 24, no. 4 (December 30, 2020): 61–72. http://dx.doi.org/10.14313/par_238/61.
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This is the continuation of authors’ works on the description of the accuracy of various straight-line cases determined from the results of linear regression measurements. In the first work, the essence, criteria and dependencies of the regression method were examined, as well as simulated examples of determining simple uncertainty bands fitted to measured points with uncorrelated ordinates. The GUM Guide was referred to and the B type uncertainty not discussed yet in the literature about the application of the regression method in measurements was taken into account. This work discusses determining the equation of a simple regression and its uncertainty bands from measuring points with ordinates with autocorrelation. This is illustrated by examples with precisely known abscissa and ordinates with different correlation variants, and absolute and relative uncertainty types A and B. Proposed is the extended method for assessing the accuracy of simple regression takes into account both the correlation of the Y variable data and the impact of type B uncertainty in routine measurements.
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Jeon, Jun-Seo, Seung-Rae Lee, Min-Jun Kim, and Seok Yoon. "Suggestion of a Scale Factor to Design Spiral-Coil-Type Horizontal Ground Heat Exchangers." Energies 11, no. 10 (October 12, 2018): 2736. http://dx.doi.org/10.3390/en11102736.
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Spiral-coil-type horizontal ground heat exchangers (GHEs) have been increasingly used in ground source heat pump (GSHP) systems due to their higher heat transfer performance. Many attempts have been made to investigate the heat transfer mechanism and establish design methods for the spiral-coil-type ground heat exchangers. Nevertheless, a universal design method for horizontal GHEs has not been reported due to its complexity. In contrast to the spiral-coil-type horizontal GHEs, straight-line-type horizontal GHEs have been widely adopted since they are easy to design for use in industry. In this study, a scale factor model, which could be used to design the coil-type exchanger based on the design length of a straight-line-type heat exchanger, was presented. The ratio of the mean thermal transfer energy between the straight-line-type and spiral-coil-type heat exchangers was numerically investigated by considering weather condition, configuration of GHE, and thermal properties of the ground. Using the numerical results for a total of 108 cases, artificial neural network and linear regression methods were employed for the model development. The proposed model of the scale factor may provide an alternative way to design the spiral-coil-type horizontal GHEs.
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Zielińska, Anetta. "Finansowe skutki wypadków przy pracy." Roczniki Naukowe Ekonomii Rolnictwa i Rozwoju Obszarów Wiejskich 105, no. 1 (April 15, 2018): 101–10. http://dx.doi.org/10.22630/rnr.2018.105.1.9.
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The paper describes the economic conditions of prevention and occupational accidents. The study covers large enterprises of the food industry in the Lodz region. The straight line regression model between the considered variables was developed and statistically evaluated.. It can be concluded that the health and safety costs incurred by enterprises in the food sector, although they grow, are generally insufficient and incurred too late.
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Ramnath, Vishal. "Comparison of straight line curve fit approaches for determining parameter variances and covariances." International Journal of Metrology and Quality Engineering 11 (2020): 14. http://dx.doi.org/10.1051/ijmqe/2020011.
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Pressure balances are known to have a linear straight line equation of the form y = ax + b that relates the applied pressure x to the effective area y, and recent work has investigated the use of Ordinary Least Squares (OLS), Weighted Least Squares (WLS), and Generalized Least Squares (GLS) regression schemes in order to quantify the expected values of the zero-pressure area A0 = b and distortion coefficient λ = a/b in pressure balance models of the form y = A0(1 + λx). The limitations with conventional OLS, WLS and GLS approaches is that whilst they may be used to quantify the uncertainties u(a) and u(b) and the covariance cov(a, b), it is technically challenging to analytically quantify the covariance term cov(A0, λ) without additional Monte Carlo simulations. In this paper, we revisit an earlier Weighted Total Least Squares with Correlation (WTLSC) algorithm to determine the variances u2(a) and u2(b) along with the covariance cov(a, b), and develop a simple analytical approach to directly infer the corresponding covariance cov(A0, λ) for pressure metrology uncertainty analysis work. Results are compared to OLS, WLS and GLS approaches and indicate that the WTLSC approach may be preferable as it avoids the need for Monte Carlo simulations and additional numerical post-processing to fit and quantify the covariance term, and is thus simpler and more suitable for industrial metrology pressure calibration laboratories. Novel aspects is that a Gnu Octave/Matlab program for easily implementing the WTLSC algorithm to calculate parameter expected values, variances and covariances is also supplied and reported.
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ALONSO, CÉSAR L., JOSÉ LUIS MONTAÑA, JORGE PUENTE, and CRUZ ENRIQUE BORGES. "A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS." International Journal on Artificial Intelligence Tools 18, no. 05 (October 2009): 757–81. http://dx.doi.org/10.1142/s0218213009000391.
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Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.
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Daut, Ismail, Mohd Irwan Yusoff, Safwati Ibrahim, Muhamad Irwanto, and Gomesh Nsurface. "Relationship between the Solar Radiation and Surface Temperature in Perlis." Advanced Materials Research 512-515 (May 2012): 143–47. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.143.
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Statistical models for predicting the solar radiation have been developed. In any prediction of the solar radiation, an understanding of its characteristics is of fundamental importance. This study presents an investigation of a relationship between solar radiation and surface temperature in Perlis, Northern Malaysia for the year of 2006. To achieve this, the data are presented in daily averaged maximum and minimum surface temperature, and daily averaged solar radiation. Since the scatter plots represent the straight line, the linear regression model was selected to estimate the solar radiation. It was found that the linear correlation coefficient value is 0.7473 shows that a strong linear relationship between solar radiation and surface temperature. The analysis of variance R2 is 0.5585 that is; about 56 percent of the variability in temperature is accounted for by the straight-line fit to solar radiation. Based on the results, the fitted model is adequate to represent the estimation of solar radiation.
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Varga, József, and Zsolt Szabo. "Modified Regression Model for the Logan Plot." Journal of Cerebral Blood Flow & Metabolism 22, no. 2 (February 2002): 240–44. http://dx.doi.org/10.1097/00004647-200202000-00012.
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Logan's graphical model is a robust estimation of the total distribution volume (DVt) of reversibly bound radiopharmaceuticals, but the resulting DVt values decrease with increasing noise. The authors hypothesized that the noise dependence can be reduced by a linear regression model that minimizes the sum of squared perpendicular rather than vertical ( y) distances between the data points and fitted straight line. To test the new method, 15 levels of simulated noise (repeated 2,000 times) were added to synthetic tissue activity curves, calculated from two different sets of kinetic parameters. Contrary to the traditional method, there was no ( P > 0.05) or dramatically decreased noise dependence with the perpendicular model. Real dynamic 11C (+) McN5652 serotonin transporter binding data were processed either by applying Logan analysis to average counts of large areas or by averaging the Logan slopes of individual-voxel data. There were no significant differences between the parameters when the perpendicular regression method was used with both approaches. The presented experiments show that the DVt calculated from the Logan plot is much less noise dependent if the linear regression model accounts for errors in both the x and y variables, allowing fast creation of unbiased parametric images from dynamic positron-emission tomography studies.
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Mainali, Janardan, Heejun Chang, and Yongwan Chun. "A review of spatial statistical approaches to modeling water quality." Progress in Physical Geography: Earth and Environment 43, no. 6 (June 26, 2019): 801–26. http://dx.doi.org/10.1177/0309133319852003.
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We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those clustering in the error (residual spatial autocorrelation), used multi-scale processes, and improved the model performance. The water-quality-based regression modeling approaches are shifting from straight-line distance-based spatial relations to upstream–downstream relations. Calculation of spatial autocorrelation and residual spatial autocorrelation was dependent upon the type of spatial regression used. The weights matrix is used as available in the software and most of the studies did not attempt to modify it. Different scale processes like certain distance from rivers versus consideration of entire watersheds are dealt with separately in most of the studies. Generally, the capacity of the predictor variables to predict the response variable significantly improves when spatial regressions are used. We identify new research directions in terms of spatial considerations, weights matrix construction, inclusion of multi-scale processes, and identification of predictor variables in such models.
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Warsza, Zygmunt, and Jacek Puchalski. "Uncertainty of Measurement in the Linear Regression Method. Part 1. Straight-line and its Uncertainty Bands for Uncorrelated Measurement Data." Pomiary Automatyka Robotyka 24, no. 3 (September 30, 2020): 79–91. http://dx.doi.org/10.14313/par_237/79.
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McLean, Noah M. "Straight line regression through data with correlated uncertainties in two or more dimensions, with an application to kinetic isotope fractionation." Geochimica et Cosmochimica Acta 124 (January 2014): 237–49. http://dx.doi.org/10.1016/j.gca.2013.08.035.
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ZECHMAN, L. G., and I. J. PFLUG. "Heat Penetration Rates of Natural Convection Heating Liquids in Metal Containers." Journal of Food Protection 57, no. 9 (September 1, 1994): 802–7. http://dx.doi.org/10.4315/0362-028x-57.9.802.
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Heat penetration (HP) test data have been obtained for water in 13 sizes of metal cans and for 16 fluids with different physical properties in 211 × 400 metal cans. Data were collected until the slowest heating zone was less than 0.1 C° below the heating medium temperature. When conventional HP graphs were plotted, these natural convection heating liquids formed curves instead of straight lines. A straight line was fitted by least squares regression to the data between (T1 - T) values from 40.0 to 4.0, 10.0 to 1.0, 4.0 to 0.4, and 1.0 to 0.2 C° and the f-values determined. The results show that the f-value of fluids that heat by convection increase with heating time, indicating that the true shape of the heat penetration data on a semilogarithmic graph is a curve.
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ZECHMAN, L. G., and I. J. PFLUG. "Heat Penetration Rates of Natural Convection Heating Liquids in Metal Containers." Journal of Food Protection 58, no. 1 (January 1, 1995): 108–14. http://dx.doi.org/10.4315/0362-028x-58.1.108.
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Heat penetration (HP) test data have been obtained for water in 13 sizes of metal cans and for 16 fluids with different physical properties in 211 × 400 metal cans. Data were collected until the slowest heating zone was less than 0.1 C° below the heating medium temperature. When conventional HP graphs were plotted, these natural convection heating liquids formed curves instead of straight lines. A straight line was fitted by least squares regression to the data between (T1 - T) values from 40.0 to 4.0, 10.0 to 1.0, 4.0 to 0.4, and 1.0 to 0.2 C° and the f-values determined. The results show that the f-value of fluids that heat by convection increase with heating time, indicating that the true shape of the heat penetration data on a semilogarithmic graph is a curve.
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Wen, Hua Dian, Xiao Ting Xiao, Li Guang Tan, Ying Chen, and Kai Zhang. "The Influence of Process Force on the Wall Thickness." Applied Mechanics and Materials 166-169 (May 2012): 3131–35. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.3131.
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Multi-way tubes on air conditioning condenser are considered as research objective. The ordinal hydro-forming method and finite element simulation method are utilized to analyze the influence of process force on wall thickness such as internal pressure, axial force ,balance force and their matching relationship. The uniform experimental design method and regression method are utilized to solve the prediction model of wall thickness. The results show that the slope of straight line loading path has a significant effect on branch height, the greater the slope is, and the higher the height is. What’s more, the fold line loading path can effectively improve the branch height while the shock loading path can effectively improve the wall thickness‘s uniformity.
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Chrosciewicz, Z. "Prediction of forest-floor moisture content on jack pine cutovers." Canadian Journal of Forest Research 19, no. 2 (February 1, 1989): 239–43. http://dx.doi.org/10.1139/x89-033.
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Moisture contents of organic forest-floor materials were studied by strata on a clear-cut jack pine (Pinusbanksiana Lamb.) site in relation to slash distribution and changes in both duff moisture code and fine fuel moisture code, the two weather-based components of the Canadian Forest Fire Weather Index System. The best-fitting curvilinear regression (Y = a + b/X) of the duff moisture code showed distinctive patterns of variation, so that both the surface and subsurface forest-floor strata were consistently moister under slash cover than in slash openings, and for each of these two slash conditions, the subsurface forest-floor strata were consistently moister than the surface strata. Combinations of these materials down to mineral soil showed intermediate moisture contents both in slash openings and under slash cover. The straight line regression (Y = a + bX) of the fine fuel moisture code was the best-fitting moisture content relationship for the surface forest-floor materials, but only in slash openings; under slash cover, the curvilinear regression (Y = a + b/X) of the duff moisture code was more suitable. The coefficients of correlation, r, as derived from these varied regressions, were all very highly significant at P < 0.001.
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Pytela, Oldřich. "Chemometric Analysis of Substituent Effects. I. Substituent Effects on the Dissociation of Monosubstituted Benzoic Acids." Collection of Czechoslovak Chemical Communications 59, no. 1 (1994): 159–74. http://dx.doi.org/10.1135/cccc19940159.
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Forty-six representative sets of data relating to the dissociation of monosubstituted benzoic acids in various solvents were extracted from the literature. The set of substituents included 25 common substituents in the meta position and the same number of substituents in the para position. Hydrogen served as the reference standard. The sets were subjected to regression analysis using conventional empirical models. The Hammett model was found to be valid within the limits of experimental error. The Taft model with the σR0, σR+ and σR- parameters is the best model to account for the substituent effects from the meta or para position solely. The inductive and mesomeric effects of the substituent are also best separated on this parametric scale. By applying the method of conjugated deviations (analysis of latent variables), a single latent variable was found to be sufficient to describe the data variability in all the three data sets analyzed (meta + para in the Hammett model and sets of meta and para substituted derivatives separately). The relationship between the first latent variables from the meta and para positions is isoparametric, the substituents lie on three straight lines intersecting in one point. The first straight line corresponds to substituents with the I effect (CH3, C2H5, tert-C4H9, C6H5, SO2NH2, CN, NO2 and hydrogen as the reference standard), the second straight line corresponds to substituents with I and +M effects (NH2, N(CH3)2, NHCOCH3, CH3O, SH, F, Cl, Br), and the third straight line corresponds to substituents with I and -M effects (CHO, CH3CO, COOR, SO2CH3, CF3). The +M mesomeric effect is twice as strong as the -M effect. These facts were used to propose a new empirical model for the description of substituent effects by means of one substituent constant and one (meta) or two (para) reaction constants. The PLS method revealed that the additional effects contribute about 8% to the data variability in the interpretation of the para substitution through meta substitution.
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Logachov, A. V., O. M. Logachova, and P. A. Karpik. "Mathematical Model of Water Level Dependence in the Ob River from the Discharge of Water to Hydroelectric Power Station." Vestnik NSUEM, no. 2 (July 6, 2020): 177–82. http://dx.doi.org/10.34020/2073-6495-2020-2-177-182.
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The dependence of the average water level in the Ob River at the Novosibirsk water post on the discharge rate to a hydroelectric power station is being investigated. The stochastic approach is used to model this dependence. As a result of the analysis of an array of statistical data a two-component mathematical model has been obtained. The simultaneous use of the Probit probability determination model and the construction of a regression straight line result in a nonlinear relationship between the water level and the discharge rate, which allows for a more accurate flood forecast.
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Pizzella, A., E. Dalla Bontà, E. M. Corsini, L. Coccato, and F. Bertola. "The σc — Vcirc correlation in high and low surface brightness galaxies." Symposium - International Astronomical Union 220 (2004): 339–40. http://dx.doi.org/10.1017/s0074180900183597.
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We investigate the relation between the central velocity dispersion, σc, and the circular velocity, Vcirc, in galaxies. in addition to previously obtained data, we consider an observationally homogeneus sample of 52 high surface brightness and 11 low surface brightness spiral galaxies. We performed a straight line regression analysis in a linear scale, finding a good fit, also for low σc galaxies, always rejected in the previous studies. Low surface brightness galaxies seem to behave differently, showing either higher values of Vcirc or lower values of σc with respect to their high surface brightness counterparts.
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Haslauer, Kristina E., Philippe Schmitt-Kopplin, and Silke S. Heinzmann. "Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis." Metabolites 11, no. 5 (April 29, 2021): 285. http://dx.doi.org/10.3390/metabo11050285.
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Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
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McLeod, RW. "Influence of root-knot nematodes on yield of tobacco in northern New South Wales." Australian Journal of Experimental Agriculture 25, no. 3 (1985): 687. http://dx.doi.org/10.1071/ea9850687.
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Root-knot nematode galling and yield of tobacco were measured in six nematicide trials in northern New South Wales. There were significant differences in the average galling indices of treatments in all trials. Treatment yields differed significantly in only one trial. Straight-line regression analyses of plot yield on plot galling index showed a significant correlation in four trials. These analyses indicate yield losses of 2.6-9.4% for increments of 10 in galling index. [Index = 1.25 X (sum of gall scores for 20 plants), where scores range from 0 (no galls) to 4 (75- 100% of root galled).]
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Das, Biplab, and Pankaj Biswas. "Effect of operating parameters on plate bending by laser line heating." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 10 (October 29, 2015): 1812–19. http://dx.doi.org/10.1177/0954405415612678.
Full textAbstract:
Laser line heating is a plate bending technique. It is produced due to the heating effect of a laser beam when irradiated over a suitable heating path. In this work, the effect on angular deformation under different operating parameters, such as energy (in terms of laser power), scanning speed and number of passes along with the thickness of the substrate material, was studied under straight line scanning schemes. In this experiment, CO2 laser has been used and the substrate material used is the mild steel sheet. The effect of each operating parameter was taken into account to find the optimum process parameter. The design of experiment has been applied with the help of Taguchi method, and the results were obtained and analysed with the help of analysis of variance and Taguchi analysis (signal-to-noise ratio), for the determination of the optimized values of the operating parameters with their effect towards angular deformation in the process. A regression analysis also has also been performed to obtain a suitable co-relationship between bending angle and other operating parameters.
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Kakı, Esra, Nurcan Gögsu, Ahmet Altındal, Bekir Salih, and Özer Bekaroğlu. "Synthesis, characterization and VOCs adsorption kinetics of diethylstilbestrol-substituted metallophthalocyanines." Journal of Porphyrins and Phthalocyanines 23, no. 01n02 (January 2019): 166–74. http://dx.doi.org/10.1142/s1088424619500196.
Full textAbstract:
Compound (4,4[Formula: see text] -hex-3-ene-3,4-diyl)bis(4,1-phenylene)bis(oxy)diphthalonitrile 3 was synthesized by the reaction of 4-nitrophthalonitrile 1 and diethylstilbestrol 2 in dry DMF in presence of dry K2CO3. New mononuclear phthalocyanines 4-6 were obtained from compound 3 by addition of the corresponding metal salts [Co(OAc)2 ⋅ 4H2O, Zn(OAc)2 ⋅ 2H2O and Cu(OAc)2]. The novel compounds were characterized by elemental analysis and FT-IR, UV-vis, 1H-NMR and MALDI-TOF mass spectroscopy techniques. The effects of four main groups of organic vapors on these novel compounds were studied and discussed. The adsorption kinetics of alkanes ([Formula: see text]-hexane and [Formula: see text]-octane), alcohols (methanol and 2-proponal), chlorinated hydrocarbons (dichloromethane and trichloromethane) and amines (diethylamine and triethylamine) on 4-6 were examined using three adsorption kinetic models: the Elovich equation, the pseudo-first-order equations and Ritchie’s equation. Results show that the linear regression analysis with respect to the pseudo-second-order rate equations generates a straight line that best fits the data of adsorption of alcohols and chlorinated hydrocarbons on Pc films. On the other hand, the Elovich equation generates a straight line that best fits the data of adsorption of alkanes and amines.
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Kryanev, Alexandr, Victor Ivanov, Anastasiya Romanova, Leonid Sevastianov, and David Udumyan. "Extrapolation of Functions of Many Variables by Means of Metric Analysis." EPJ Web of Conferences 173 (2018): 03014. http://dx.doi.org/10.1051/epjconf/201817303014.
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The paper considers a problem of extrapolating functions of several variables. It is assumed that the values of the function of m variables at a finite number of points in some domain D of the m-dimensional space are given. It is required to restore the value of the function at points outside the domain D. The paper proposes a fundamentally new method for functions of several variables extrapolation. In the presented paper, the method of extrapolating a function of many variables developed by us uses the interpolation scheme of metric analysis. To solve the extrapolation problem, a scheme based on metric analysis methods is proposed. This scheme consists of two stages. In the first stage, using the metric analysis, the function is interpolated to the points of the domain D belonging to the segment of the straight line connecting the center of the domain D with the point M, in which it is necessary to restore the value of the function. In the second stage, based on the auto regression model and metric analysis, the function values are predicted along the above straight-line segment beyond the domain D up to the point M. The presented numerical example demonstrates the efficiency of the method under consideration.
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Zhang, Zhuo, and Jian Ping Gao. "Research on Longitudinal Slope of Highway Based on Control by Flow Path Length." Applied Mechanics and Materials 178-181 (May 2012): 1672–75. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1672.
Full textAbstract:
Flow path on the highway is longer, driving exits the more dangerous. Based on the mechanical theory, the calculation models of the flow path length in different sections were built by FDM and multiple linear regression method. The flow path length of different section and different longitudinal slope was studied. The results show that: in addition to straight line and circle curve, when the number of lane is more than 4 and longitudinal slope is greater than 4%, the flow path length at other sections almost exceeded the prescribed value. Made the flow path length as control index, the amendment value on the maximum longitudinal slope of highway is proposed.
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Maksymowych, Roman, and Joseph A. J. Orkwiszewski. "Allometric growth of Xanthium (Compositae) leaves." Acta Societatis Botanicorum Poloniae 66, no. 3-4 (2014): 293–96. http://dx.doi.org/10.5586/asbp.1997.033.
Full textAbstract:
When <em>Xanthium</em> lamina width is plotted versus leaf length during the entire period of growth, a straight line is obtained representing an allometric relationship with a regression correlation coefficient of 0.9973 a lamina width to length ratio of 0.502 ± 0.01 and chordate morphology. On the other hand, gibberellic acid treated plants yield a correlation coefficient of 0.9871 a lamina width to length ratio of 0.372 ± 0.0074 and lanceolate leaves. The fraction of leaf width to leaf length is a measure of lamina width reduction. Gibberellic acid alters the mechanism which controls the balance between the leaf length and the leaf width.
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Mohri, Yoshifumi, Yukoh Sakata, and Makoto Otsuka. "Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method." Journal of Near Infrared Spectroscopy 17, no. 2 (January 1, 2009): 89–100. http://dx.doi.org/10.1255/jnirs.828.
Full textAbstract:
The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.
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Packard, G. C. "Modeling allometric variation: lessons from the metabolic allometry of black carp (Mylopharyngodon piceus)." Canadian Journal of Zoology 97, no. 11 (November 2019): 1078–83. http://dx.doi.org/10.1139/cjz-2019-0092.
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I used linear and nonlinear regression to re-examine published data on the scaling of metabolic rate vs. body mass in an ontogenetic series of black carp (Mylopharyngodon piceus (Richardson, 1846)). My objective was to expose shortcomings of the conventional procedure for fitting statistical models to bivariate observations (i.e., the procedure that is widely attributed to J.S. Huxley) and simultaneously to outline a more general and utilitarian protocol for analyzing bivariate data in studies of allometry. Authors of the original study on carp reported exponents of 0.83 and 0.78 for two-parameter power functions fitted to observations for resting metabolism and maximum metabolism, respectively. However, metabolic scaling in these fishes actually is described best by straight lines having positive intercepts with the Y axis. The allometric exponent is 1 for a straight line, so interpretations from the current analyses differ substantially from those reached in the original investigation. Contemporary theories for the evolution of optimal body size (e.g., the Metabolic Theory of Ecology) are based on patterns of metabolic allometry that have been estimated by the conventional analytical method. Thus, the current investigation raises questions about generally accepted patterns of metabolic allometry and theoretical models based upon them.
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Chen, Lianjun, Xuekai Jiang, Guoming Liu, and Xiangfei Cui. "Pipe pressure of mining wet shot crete flowing in pipes." E3S Web of Conferences 303 (2021): 01035. http://dx.doi.org/10.1051/e3sconf/202130301035.
Full textAbstract:
In order to explore the pressure change law of mining wet shotcrete in pipes, the rheological model was built based on rheology principle, and the computational formula of rheological parameters of wet shotcrete was deduced with the linear regression. 100 m full-scale pipeline platform of wet shotcrete was designed and built to study the relationship of pressure and other factors including flow rate, water cement ratio, mix proportion, and pipe bends. Results show: pipe pressure increases with the increase in flow rate and declines with the increase in water-cement ratio, the pressure may fluctuate with a high water cement ratio which can cause cement overhydration and bleeding separation. It will be more beneficial to transport materials if the continuous grading and straight pipe were considered. According to the tests of mix proportion 1:1.5:2.25, the pressure drop is 0.032 MPa·m−1 and the bend pressure drop is 1.3 times higher than in the straight line. We also conclude that solid phase pressure is bigger than liquid phase pressure and they both decline along the pipe based on FLUENT simulation. Finally, the formula of on-way resistance used in mine production was deduced.
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Ritter, Erich, Raid Amin, Kevin Cahn, and Jonathan Lee. "Against Common Assumptions, the World’s Shark Bite Rates Are Decreasing." Journal of Marine Biology 2019 (June 2, 2019): 1–6. http://dx.doi.org/10.1155/2019/7184634.
Full textAbstract:
The trends of the world’s top ten countries relating to shark bite rates, defined as the ratio of the annual number of shark bites of a country and its resident human population, were analyzed for the period 2000-2016. A nonparametric permutation-based methodology was used to determine whether the slope of the regression line of a country remained constant over time or whether so-called joinpoints, a core feature of the statistical software Joinpoint, occurred, at which the slope changes and a better fit could be obtained by applying a straight-line model. More than 90% of all shark bite incidents occurred along the US, Australia, South Africa, and New Zealand coasts. Since three of these coasts showed a negative trend when transformed into bite rates, the overall global trend is decreasing. Potential reasons for this decrease in shark bite rates—besides an increase in the world’s human population, resulting in more beach going people, and a decrease of sharks due to overfishing—are discussed.
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Journal, Baghdad Science. "Observations on the age and growth of Varicorhinus damascinus (Val.) in Tigris river at Salahuldin province – Iraq." Baghdad Science Journal 5, no. 2 (June 1, 2008): 243–48. http://dx.doi.org/10.21123/bsj.5.2.243-248.
Full textAbstract:
Age and growth of Varicorhinus damascinus (Val.) in Tigris river at Salahuldin province have been investigated. Monthly samples were taken during the period from September 1999 to August 2000, using small-meshed gill nets. The age data showed that there were six age groups and the dominant age groups were (III – IV) for both sexes. The results of the present study revealed that the increment in length of V. damascinus at the sites of study showed a tendency to decrease with the increase in age, after the third year of life in both sexes. The length-weight relationship of males and females were calculated and demonstrated in a straight line logarithmic formula, as follows: Log W = 1.5404 + 2.6885 log L for males r = 0.95 (P > 0.05) Log W = 1.6723 + 2.7877 log L for females r = 0.77 (P > 0.05) The values of the regression coefficient (b) indicated that the growth of both sexes of V. damascinus was allometric..
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Curran-Everett, Douglas. "Explorations in statistics: the analysis of ratios and normalized data." Advances in Physiology Education 37, no. 3 (September 2013): 213–19. http://dx.doi.org/10.1152/advan.00053.2013.
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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of Explorations in Statistics explores the analysis of ratios and normalized–or standardized–data. As researchers, we compute a ratio–a numerator divided by a denominator–to compute a proportion for some biological response or to derive some standardized variable. In each situation, we want to control for differences in the denominator when the thing we really care about is the numerator. But there is peril lurking in a ratio: only if the relationship between numerator and denominator is a straight line through the origin will the ratio be meaningful. If not, the ratio will misrepresent the true relationship between numerator and denominator. In contrast, regression techniques–these include analysis of covariance–are versatile: they can accommodate an analysis of the relationship between numerator and denominator when a ratio is useless.
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von Clarmann, T., G. Stiller, U. Grabowski, and J. Orphal. "Technical Note: Trend estimation from irregularly sampled, correlated data." Atmospheric Chemistry and Physics Discussions 9, no. 6 (December 21, 2009): 27675–92. http://dx.doi.org/10.5194/acpd-9-27675-2009.
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Abstract. Estimation of a trend of an atmospheric state variable is often performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straight forward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate the error correlations.
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Betensky, Rebecca A. "A boundary crossing probability for the Bessel process." Advances in Applied Probability 30, no. 03 (September 1998): 807–30. http://dx.doi.org/10.1017/s0001867800008612.
Full textAbstract:
Analytic approximations are derived for the distribution of the first crossing time of a straight-line boundary by a d-dimensional Bessel process and its discrete time analogue. The main ingredient for the approximations is the conditional probability that the process crossed the boundary before time m, given its location beneath the boundary at time m. The boundary crossing probability is of interest as the significance level and power of a sequential test comparing d+1 treatments using an O'Brien-Fleming (1979) stopping boundary (see Betensky 1996). Also, it is shown by DeLong (1980) to be the limiting distribution of a nonparametric test statistic for multiple regression. The approximations are compared with exact values from the literature and with values from a Monte Carlo simulation.
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Betensky, Rebecca A. "A boundary crossing probability for the Bessel process." Advances in Applied Probability 30, no. 3 (September 1998): 807–30. http://dx.doi.org/10.1239/aap/1035228130.
Full textAbstract:
Analytic approximations are derived for the distribution of the first crossing time of a straight-line boundary by a d-dimensional Bessel process and its discrete time analogue. The main ingredient for the approximations is the conditional probability that the process crossed the boundary before time m, given its location beneath the boundary at time m. The boundary crossing probability is of interest as the significance level and power of a sequential test comparing d+1 treatments using an O'Brien-Fleming (1979) stopping boundary (see Betensky 1996). Also, it is shown by DeLong (1980) to be the limiting distribution of a nonparametric test statistic for multiple regression. The approximations are compared with exact values from the literature and with values from a Monte Carlo simulation.
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Omata, Ryo, Yusuke Hattori, Tetsuo Sasaki, Tomoaki Sakamoto, and Makoto Otsuka. "Elucidation of the Molecular Mechanism of Wet Granulation for Pharmaceutical Standard Formulations in a High-Speed Shear Mixer Using Near-Infrared Spectroscopy." Pharmaceuticals 13, no. 9 (August 31, 2020): 226. http://dx.doi.org/10.3390/ph13090226.
Full textAbstract:
The granulation process of pharmaceutical standard formulation in a high-speed shear wet granulation (HSWG) was measured by in-line near-infrared spectroscopy (NIRS) and agitation power consumption (APC) methods. The F-1, F-2, and F-3 formulations (500 g) contained 96% w/w α-lactose monohydrate (LA), potato starch (PS), and a LA:PS = 7:3 mixture, respectively, and all the formulations contained 4% w/w hydroxypropyl cellulose. While adding purified water at 10 mL/min, the sample powder was mixed. The calibration models to measure the amount of binding water (Wa) and APC of the HSWG formulations were established based on NIRS of the samples measured for 60 min by partial least-squares regression analysis (PLS). Molecular interaction related to APC between the particle surface and binding liquor was analyzed based on NIRS. The predicted values of Wa and APC for all formulations were superimposed with the measured values on a straight line, respectively. The regression vector (RV) of the calibration model for Wa indicated the chemical information of all the water in the samples. In contrast, the RV for APC suggested that APC changes in the processes are related to powder aggregation because of surface tension of binding water between particles.
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Strzelecki, Przemysław, and Janusz Sempruch. "Experimental Method for Plotting S-N Curve with a Small Number of Specimens." Polish Maritime Research 23, no. 4 (December 1, 2016): 129–37. http://dx.doi.org/10.1515/pomr-2016-0079.
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Abstract The study presents two approaches to plotting an S-N curve based on the experimental results. The first approach is commonly used by researchers and presented in detail in many studies and standard documents. The model uses a linear regression whose parameters are estimated by using the least squares method. A staircase method is used for an unlimited fatigue life criterion. The second model combines the S-N curve defined as a straight line and the record of random occurrence of the fatigue limit. A maximum likelihood method is used to estimate the S-N curve parameters. Fatigue data for C45+C steel obtained in the torsional bending test were used to compare the estimated S-N curves. For pseudo-random numbers generated by using the Mersenne Twister algorithm, the estimated S-N curve for 10 experimental results plotted by using the second model, estimates the fatigue life in the scatter band of the factor 3. The result gives good approximation, especially regarding the time required to plot the S-N curve.
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Caimmi, R. "A simple multistage closed-(box+reservoir) model of chemical evolution." Serbian Astronomical Journal, no. 183 (2011): 37–61. http://dx.doi.org/10.2298/saj1183037c.
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Simple closed-box (CB) models of chemical evolution are extended on two respects, namely (i) simple closed-(box+reservoir) (CBR) models allowing gas outflow from the box into the reservoir (Hartwick 1976) or gas inflow into the box from the reservoir (Caimmi 2007) with rate proportional to the star formation rate, and (ii) simple multistage closed-(box+reservoir) (MCBR) models allowing different stages of evolution characterized by different inflow or outflow rates. The theoretical differential oxygen abundance distribution (TDOD) predicted by the model maintains close to a continuous broken straight line. An application is made where a fictitious sample is built up from two distinct samples of halo stars and taken as representative of the inner Galactic halo. The related empirical differential oxygen abundance distribution (EDOD) is represented, to an acceptable extent, as a continuous broken line for two viable [O/H]-[Fe/H] empirical relations. The slopes and the intercepts of the regression lines are determined, and then used as input parameters to MCBR models. Within the errors (-+?), regression line slopes correspond to a large inflow during the earlier stage of evolution and to low or moderate outflow during the subsequent stages. A possible inner halo - outer (metal-poor) bulge connection is also briefly discussed. Quantitative results cannot be considered for applications to the inner Galactic halo, unless selection effects and disk contamination are removed from halo samples, and discrepancies between different oxygen abundance determination methods are explained.
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Vasilyeva, Natalia, Elmira Fedorova, and Alexandr Kolesnikov. "Big Data as a Tool for Building a Predictive Model of Mill Roll Wear." Symmetry 13, no. 5 (May 12, 2021): 859. http://dx.doi.org/10.3390/sym13050859.
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Big data analysis is becoming a daily task for companies all over the world as well as for Russian companies. With advances in technology and reduced storage costs, companies today can collect and store large amounts of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will ultimately be faced by all companies seeking to maintain their competitiveness and place in the market. An approach to the study of metallurgical processes using the analysis of a large array of operational control data is considered. Using the example of steel rolling production, the development of a predictive model based on processing a large array of operational control data is considered. The aim of the work is to develop a predictive model of rolling mill roll wear based on a large array of operational control data containing information about the time of filling and unloading of rolls, rolled assortment, roll material, and time during which the roll is in operation. Preliminary preparation of data for modeling was carried out, which includes the removal of outliers, uncharacteristic and random measurement results (misses), as well as data gaps. Correlation analysis of the data showed that the dimensions and grades of rolled steel sheets, as well as the material from which the rolls are made, have the greatest influence on the wear of rolling mill rolls. Based on the processing of a large array of operational control data, various predictive models of the technological process were designed. The adequacy of the models was assessed by the value of the mean square error (MSE), the coefficient of determination (R2), and the value of the Pearson correlation coefficient (R) between the calculated and experimental values of the mill roll wear. In addition, the adequacy of the models was assessed by the symmetry of the values predicted by the model relative to the straight line Ypredicted = Yactual. Linear models constructed using the least squares method and cross-validation turned out to be inadequate (the coefficient of determination R2 does not exceed 0.3) to the research object. The following regressions were built on the basis of the same operational control database: Linear Regression multivariate, Lasso multivariate, Ridge multivariate, and ElasticNet multivariate. However, these models also turned out to be inadequate to the object of the research. Testing these models for symmetry showed that, in all cases, there is an underestimation of the predicted values. Models using algorithm composition have also been built. The methods of random forest and gradient boosting are considered. Both methods were found to be adequate for the object of the research (for the random forest model, the coefficient of determination is R2 = 0.798; for the gradient boosting model, the coefficient of determination is R2 = 0.847). However, the gradient boosting algorithm is recognized as preferable thanks to its high accuracy compared with the random forest algorithm. Control data for symmetry in reference to the straight line Ypredicted = Yactual showed that, in the case of developing the random forest model, there is a tendency to underestimate the predicted values (the calculated values are located below the straight line). In the case of developing a gradient boosting model, the predicted values are located symmetrically regarding the straight line Ypredicted = Yactual. Therefore, the gradient boosting model is preferred. The predictive model of mill roll wear will allow rational use of rolls in terms of minimizing overall roll wear. Thus, the proposed model will make it possible to redistribute the existing work rolls between the stands in order to reduce the total wear of the rolls.