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Journal articles on the topic 'Multiple linear regression mode'

Author: Grafiati

Published: 8 September 2021

Last updated: 1 February 2022

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1

Rust, Henning W., Andy Richling, Peter Bissolli, and Uwe Ulbrich. "Linking teleconnection patterns to European temperature – a multiple linear regression model." Meteorologische Zeitschrift 24, no. 4 (July 21, 2015): 411–23. http://dx.doi.org/10.1127/metz/2015/0642.

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2

Islam, M. Qamarul, and Moti L. Tiku. "Multiple Linear Regression Model Under Nonnormality." Communications in Statistics - Theory and Methods 33, no. 10 (January 2, 2005): 2443–67. http://dx.doi.org/10.1081/sta-200031519.

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3

عبد السلام, ايهاب. "Detecting Outliers In Multiple Linear Regression." Journal of Economics and Administrative Sciences 17, no. 64 (December 1, 2011): 9. http://dx.doi.org/10.33095/jeas.v17i64.900.

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It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases : first, in real data; and secondly, after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.

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4

Zhanatauov, S. U. "INVERSE MODEL OF MULTIPLE LINEAR REGRESSION ANALYSIS." Theoretical & Applied Science 60, no. 04 (April 30, 2018): 201–12. http://dx.doi.org/10.15863/tas.2018.04.60.38.

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5

B., Pratikno, Sulaeman I.P., Sopanti D., and Supriyono. "A BEST MODEL ON MULTIPLE LINEAR REGRESSION." International Journal of Engineering and Technology 12, no. 1 (February 29, 2020): 58–63. http://dx.doi.org/10.21817/ijet/2020/v12i1/201201025.

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6

Li, Yao Xiang, and Li Chun Jiang. "Modeling Wood Crystallinity with Multiple Linear Regression." Key Engineering Materials 480-481 (June 2011): 550–55. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.550.

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The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. Crystallinity of larch plantation wood was investigated with near infrared spectroscopy and multiple linear regression. Five typical wave lengths were selected to establish prediction model for wood crystallinity. Full-cross validation was applied to the model development. The model performance is satisfied with prediction correlation coefficient of 0.896 and bias of 0.0004. The results indicated that prediction of wood crystallinity with near infrared spectroscopy and multiple linear regression is feasible, which provides a fast and nondestructive method for wood crystallinity prediction.

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7

Alheety, M. I., and S. D. Gore. "A new estimator in multiple linear regression model." Model Assisted Statistics and Applications 3, no. 3 (September 11, 2008): 187–200. http://dx.doi.org/10.3233/mas-2008-3303.

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8

Kicsiny, Richárd. "Multiple linear regression based model for solar collectors." Solar Energy 110 (December 2014): 496–506. http://dx.doi.org/10.1016/j.solener.2014.10.003.

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9

Islam, M. Qamarul, and Moti L. Tiku. "Multiple linear regression model with stochastic design variables." Journal of Applied Statistics 37, no. 6 (May 11, 2010): 923–43. http://dx.doi.org/10.1080/02664760902939612.

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10

Ferraro, Maria Brigida, and Paolo Giordani. "A multiple linear regression model for imprecise information." Metrika 75, no. 8 (July 23, 2011): 1049–68. http://dx.doi.org/10.1007/s00184-011-0367-3.

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11

Wei, Laisheng. "Empirical Bayes test of regression coefficient in a multiple linear regression model." Acta Mathematicae Applicatae Sinica 6, no. 3 (July 1990): 251–62. http://dx.doi.org/10.1007/bf02019151.

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12

Kim, Tae-Hyoung, Ichiro Maruta, Toshiharu Sugie, Semin Chun, and Minji Chae. "Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4321539.

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This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO) is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

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13

Pramoedyo, Henny, Sativandi Riza, Afiati Oktaviarina, and Deby Ardianti. "GEOGRAPHICALLY WEIGHTED REGRESSION AND MULTIPLE LINEAR REGRESSION FOR TOPSOIL TEXTURE PREDICTION." International Journal of Research -GRANTHAALAYAH 9, no. 2 (February 23, 2021): 64–71. http://dx.doi.org/10.29121/granthaalayah.v9.i2.2021.3112.

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Land resource management requires extensive land mapping. Conventional soil mapping takes a long time and is expensive; therefore, geographic information system data as a predictor in soil texture modeling can be used as an alternative solution to shorten time and reduce costs. Through digital elevation model data, topographic variability can be obtained as an independent variable in predicting soil texture. Geographically weighted regression is used to observe the effects of spatial heterogeneity. This study uses a data set of 50 observation points, each of which had soil particle-size fraction attributes and eight local morphological variables. The covariates used in this study are eastness aspects, northness aspects, slope, unsphericity curvature, vertical curvature, horizontal curvature, accumulation curvature, and elevation. Prediction using geographically weighted regression shows more results compared to multiple linear regression models. The spatial location can affect product Y, with the R2 value of 0.81 in the sand fraction, 0.57 in the silt fraction, and 0.33 in the clay fraction.

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14

Kicsiny, R. "Simplified multiple linear regression based model for solar collectors." Hungarian Agricultural Engineering, no. 28 (2015): 11–14. http://dx.doi.org/10.17676/hae.2015.28.11.

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15

Zone-Ching Lin and Wen-Jang Wu. "Multiple linear regression analysis of the overlay accuracy model." IEEE Transactions on Semiconductor Manufacturing 12, no. 2 (May 1999): 229–37. http://dx.doi.org/10.1109/66.762881.

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16

Suparta, W., and W. S. Putro. "Using multiple linear regression model to estimate thunderstorm activity." IOP Conference Series: Materials Science and Engineering 185 (March 2017): 012023. http://dx.doi.org/10.1088/1757-899x/185/1/012023.

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17

Singh, R. S. "Empirical Bayes estimation in a multiple linear regression model." Annals of the Institute of Statistical Mathematics 37, no. 1 (December 1985): 71–86. http://dx.doi.org/10.1007/bf02481081.

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18

Cui, Faqiang. "Face value big data multiple linear regression model analysis." Journal of Physics: Conference Series 1941, no. 1 (June 1, 2021): 012079. http://dx.doi.org/10.1088/1742-6596/1941/1/012079.

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19

Lee, Chia-Ying, Michael K. Tippett, Suzana J. Camargo, and Adam H. Sobel. "Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity." Monthly Weather Review 143, no. 3 (February 27, 2015): 933–54. http://dx.doi.org/10.1175/mwr-d-14-00171.1.

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Abstract The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

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20

Beigzadeh, Bahareh, Mehdi Bahrami, Mohammad Javad Amiri, and Mohammad Reza Mahmoudi. "A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent." Water Science and Technology 82, no. 8 (September 14, 2020): 1586–602. http://dx.doi.org/10.2166/wst.2020.440.

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Abstract The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R2 of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

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21

Dhaval, Bhatti, and Anuradha Deshpande. "Short-term load forecasting with using multiple linear regression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3911. http://dx.doi.org/10.11591/ijece.v10i4.pp3911-3917.

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In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.

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22

Jo, Eun-Su, Kyu-Tae Lee, Hyun-Seok Jung, Bu-Yo Kim, and Il-Sung Zo. "Calculation of Surface Broadband Emissivity by Multiple Linear Regression Model." Journal of the Korean earth science society 38, no. 4 (August 30, 2017): 269–82. http://dx.doi.org/10.5467/jkess.2017.38.4.269.

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23

Choi, Youngtae, Chun Gun Park, and Kyeong Eun Lee. "Evaluation of outlier detection methods for multiple linear regression model." Journal of the Korean Data And Information Science Sociaty 29, no. 6 (November 30, 2018): 1663–77. http://dx.doi.org/10.7465/jkdi.2018.29.6.1663.

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24

Shaker Reddy, Pundra Chandra, and Alladi Sureshbabu. "An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 473–83. http://dx.doi.org/10.2174/2210327910666191218124350.

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Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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25

Sinnakaudan, S. K., A. Ab Ghani, M. S. Ahmad, and N. A. Zakaria. "Multiple Linear Regression Model for Total Bed Material Load Prediction." Journal of Hydraulic Engineering 132, no. 5 (May 2006): 521–28. http://dx.doi.org/10.1061/(asce)0733-9429(2006)132:5(521).

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26

Tsujiuchi, N., T. Koizumi, and M. Yoneda. "Motion Estimation from EMG Signals using Linear Multiple Regression Model." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 79–80. http://dx.doi.org/10.1299/jsmermd.2004.79_3.

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27

Shen, Pao-sheng. "Estimation in multiple linear regression model with doubly censored data." Journal of Statistical Computation and Simulation 82, no. 4 (April 2012): 503–14. http://dx.doi.org/10.1080/00949655.2010.543681.

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28

Zuccaro, Cataldo. "Mallows’ Cp Statistic and Model Selection in Multiple Linear Regression." Market Research Society. Journal. 34, no. 2 (March 1992): 1–10. http://dx.doi.org/10.1177/147078539203400204.

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29

Rusiman, Mohd Saifullah, Siti Nasuha Md Nor, Suparman Suparman, and Siti Noor Asyikin Mohd Razali. "Robust Method in Multiple Linear Regression Model on Diabetes Patients." Mathematics and Statistics 8, no. 2A (March 2020): 36–39. http://dx.doi.org/10.13189/ms.2020.081306.

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30

Blyth, Karl, and Ammar Kaka. "A novel multiple linear regression model for forecasting S‐curves." Engineering, Construction and Architectural Management 13, no. 1 (January 2006): 82–95. http://dx.doi.org/10.1108/09699980610646511.

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31

Shen, Pao-Sheng. "Estimation of Multiple Linear Regression Model with Twice-Censored Data." Communications in Statistics - Theory and Methods 44, no. 21 (November 2, 2015): 4631–40. http://dx.doi.org/10.1080/03610926.2013.799689.

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32

KUMAR, NEERAJ, GAURANG JOSHI, ROHIT KUMAR, and PANKAJ KUMAR. "Evaporation estimation from meteorological parameters using multiple linear regression model." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 10, no. 2 (October 15, 2017): 503–7. http://dx.doi.org/10.15740/has/ijae/10.2/503-507.

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Satapathy, Suresh Chandra, J. V. R. Murthy, P. V. G. D. Prasad Reddy, B. B. Misra, P. K. Dash, and G. Panda. "Particle swarm optimized multiple regression linear model for data classification." Applied Soft Computing 9, no. 2 (March 2009): 470–76. http://dx.doi.org/10.1016/j.asoc.2008.05.007.

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34

Shi, Shurong, Yi Li, and Chuang Wan. "Robust continuous piecewise linear regression model with multiple change points." Journal of Supercomputing 76, no. 5 (September 7, 2018): 3623–45. http://dx.doi.org/10.1007/s11227-018-2597-x.

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35

Gad, M. A., and H. F. M. Hussein. "REMEDY OF EFFECTS OF MULTICOLLINEARITY IN MULTIPLE LINEAR REGRESSION MODEL." Fayoum Journal of Agricultural Research and Development 33, no. 1 (January 1, 2019): 77–91. http://dx.doi.org/10.21608/fjard.2019.190382.

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36

Zhou, Hua Ren, Yue Hong Qian, Xi Qiang Liu, and Ou Wu. "Multiple Regression Analysis Model on Power Dispatch." Advanced Materials Research 512-515 (May 2012): 953–56. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.953.

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The multiple linear regression method is used, the method of calculating the active power flow and the unit output is discussed , a simple approximate expression is designed, and the corresponding error value is given. a simple calculation rules of congestion cost is given, calculation rules for the actual cost minus the theoretical costs and requirements of the actual costs is as low as possible to avoid blocking; Block can not be avoided, then try to avoid the wind up.

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37

Bazilevskiy, Mikhail P. "Multi-criteria approach to pair-multiple linear regression models constructing." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 21, no. 1 (February 24, 2021): 88–99. http://dx.doi.org/10.18500/1816-9791-2021-21-1-88-99.

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A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

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38

Almedeij, Jaber. "Modeling Pan Evaporation for Kuwait by Multiple Linear Regression." Scientific World Journal 2012 (2012): 1–9. http://dx.doi.org/10.1100/2012/574742.

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Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values.

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39

Wang, Hong, and Wansoo T. Rhee. "An algorithm for estimating the parameters in multiple linear regression model with linear constraints." Computers & Industrial Engineering 28, no. 4 (October 1995): 813–21. http://dx.doi.org/10.1016/0360-8352(95)00011-o.

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40

Klein, Martin D., John Zylstra, and Bimal K. Sinha. "Finite Sample Inference for Multiply Imputed Synthetic Data under a Multiple Linear Regression Model." Calcutta Statistical Association Bulletin 71, no. 2 (November 2019): 63–82. http://dx.doi.org/10.1177/0008068318803814.

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In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05

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41

IVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA, and MADE SUSILAWATI. "ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA." E-Jurnal Matematika 5, no. 2 (May 31, 2016): 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.

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Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.

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42

Norddin, Nur Idalisa, Mohd Rivaie Mohd Ali, Nurul Hafawati Fadhilah, Nur Atikah, Anis Shahida, and Nur Hidayah Nohd Noh. "Multiple Linear Regression Model of Rice Production using Conjugate Gradient Methods." MATEMATIKA 35, no. 2 (July 31, 2019): 229–36. http://dx.doi.org/10.11113/matematika.v35.n2.1180.

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Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.

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43

Oghoyafedo, N. K., J. O. Ehiorobo, and Ebuka Nwankwo. "Accident Prediction Model at Unsignalized Intersection Using Multiple Linear Regression Method." July 2017 1, no. 2 (July 2017): 379–89. http://dx.doi.org/10.36263/nijest.2017.02.0047.

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The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

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44

Trivendra, Chandraprakash. "The Price Prediction for used Cars using Multiple Linear Regression Model." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 1801–4. http://dx.doi.org/10.22214/ijraset.2020.5290.

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45

Zhou, Ying Hong, Bang Qi Zhong, and Ren Hong Guo. "The Multiple Linear Regression Model on Compression Strength of Corrugated Boxes." Applied Mechanics and Materials 200 (October 2012): 13–21. http://dx.doi.org/10.4028/www.scientific.net/amm.200.13.

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Using the multiple linear regression model, we analyzed the test results on thousands batches of corrugated boxes samples to seek the relation between compression strength and a series of affecting variables such as bursting strength, edgewise crush strength Puncture resistance, ply adhesive strength, etc. And based on the non-linear regression model results, we discovered the relation between compression strength and other independent variables is more closed to a power function relation. We made the logarithm transformation on best fitted non-linear regression model to establish the predictive formula for the compression strength. To test how well our formula to predict the compression strength based all other factors, we randomly selected several batches lab tested samples both from Guangdong and Hunan Lab, and compared the real test results to the predicted values of our formula and of the traditional formulas such as Kellicutt, Makee and Wolf formula. We found the predicted values of our model are closer to the real tested value than the predicted values of the other three formulas.

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46

F. J, Ogbozige, Toko M. A, and Arawo C.C. "Multiple Linear Regression (MLR) Model: A Tool for Water Quality Interpretation." Momona Ethiopian Journal of Science 12, no. 1 (April 30, 2020): 123–34. http://dx.doi.org/10.4314/mejs.v12i1.8.

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The lack of standard water analysis equipment as well as inadequate trained personnel especially in the developing countries has discouraged many researchers in such countries to execute water quality researches. Hence, this paper presents developed mathematical relationship among some physicochemical parameters in order to aid the determination of the concentrations of certain parameters with the use of minimal equipment. This was achieved by weekly analyzing 7 physicochemical parameters of two sources of potable water (tap water and borehole water) stored in different containers for a period of 6 weeks using standard methods. The storage containers used were black plastic tank, blue plastic tank, green plastic tank, coated steel metal tank, uncoated steel metal tank and clay pot. The parameters examined were turbidity, electrical conductivity (EC), pH, alkalinity, chloride ion (Cl-), dissolved oxygen (DO) and total hardness. Results showed that the relationship between electrical conductivity (EC), alkalinity (Alk), total hardness (TH) and chloride ion (Cl-) is given as; EC = -224.8066493 + 6.244028022(Alk) + 0.28204735(TH) + 0.000518108(Cl-). A programing language was written on the models using Visual Basic.Net (VB.Net) version 2018. Keywords: Water, Physicochemical, Parameters, Function, Equation.

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Klein, Martin Daniel, and Gauri Sankar Datta. "Statistical disclosure control via sufficiency under the multiple linear regression model." Journal of Statistical Theory and Practice 12, no. 1 (August 22, 2017): 100–110. http://dx.doi.org/10.1080/15598608.2017.1350606.

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CUI, Hengjian. "On asymptotics of t-type regression estimation in multiple linear model." Science in China Series A 47, no. 4 (2004): 628. http://dx.doi.org/10.1360/03ys0020.

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刘, 璇. "Evaluation Model of Diabetes Therapeutic Effect Based on Multiple Linear Regression." Statistics and Application 08, no. 03 (2019): 503–7. http://dx.doi.org/10.12677/sa.2019.83056.

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Smith, Timothy A., Alex Caligiuri, and J. Rhet Montana. "Using a Multiple Linear Regression Model to Calculate Stock Market Volatility." International Journal of Mathematics Trends and Technology 57, no. 4 (May 25, 2018): 220–24. http://dx.doi.org/10.14445/22315373/ijmtt-v57p531.

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