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Journal articles on the topic 'Multiple linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Genç, S., and M. Mendeş. "Multiple Linear Regression versus Automatic Linear Modelling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1 (2024): 131–36. http://dx.doi.org/10.1590/1678-4162-13071.

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ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave ve

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2

Krzywinski, Martin, and Naomi Altman. "Multiple linear regression." Nature Methods 12, no. 12 (2015): 1103–4. http://dx.doi.org/10.1038/nmeth.3665.

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3

Slinker, Bryan K., and Stanton A. Glantz. "Multiple Linear Regression." Circulation 117, no. 13 (2008): 1732–37. http://dx.doi.org/10.1161/circulationaha.106.654376.

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4

Fletcher, J. "Multiple linear regression." BMJ 338, jan28 3 (2009): b167. http://dx.doi.org/10.1136/bmj.b167.

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5

Bangdiwala, Shrikant I. "Regression: multiple linear." International Journal of Injury Control and Safety Promotion 25, no. 2 (2018): 232–36. http://dx.doi.org/10.1080/17457300.2018.1452336.

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6

Grégoire, G. "Multiple Linear Regression." EAS Publications Series 66 (2014): 45–72. http://dx.doi.org/10.1051/eas/1466005.

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7

Narula, Subhash C., and John F. Wellington. "Multiple criteria linear regression." European Journal of Operational Research 181, no. 2 (2007): 767–72. http://dx.doi.org/10.1016/j.ejor.2006.06.026.

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8

Pandis, Nikolaos. "Multiple linear regression analysis." American Journal of Orthodontics and Dentofacial Orthopedics 149, no. 4 (2016): 581. http://dx.doi.org/10.1016/j.ajodo.2016.01.012.

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9

Marill, Keith A. "Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 94–102. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01379.x.

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10

Smith, D. M., and M. G. Kenward. "Extensions of multiple linear regression." Communications in Statistics - Theory and Methods 29, no. 9-10 (2000): 2033–53. http://dx.doi.org/10.1080/03610920008832594.

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11

Clausel, M., and G. Grégoire. "Practical Session: Multiple Linear Regression." EAS Publications Series 66 (2014): 73–75. http://dx.doi.org/10.1051/eas/1466006.

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12

Fanny Ki, Yuen-Ching. "Multiple shrinkage estimators in multiple linear regression." Communications in Statistics - Theory and Methods 21, no. 1 (1992): 111–36. http://dx.doi.org/10.1080/03610929208830767.

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13

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 (2015): 411–23. http://dx.doi.org/10.1127/metz/2015/0642.

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14

Marill, Keith A. "Advanced Statistics:Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 94–102. http://dx.doi.org/10.1197/j.aem.2003.09.006.

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15

Fearn, Tom. "Partial Least Squares Regression versus Multiple Linear Regression." NIR news 22, no. 4 (2011): 15–16. http://dx.doi.org/10.1255/nirn.1252.

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16

Jerome, Lawrence. "Multiple linear and non-linear regression in Minitab." MSOR Connections 9, no. 3 (2009): 17–20. http://dx.doi.org/10.11120/msor.2009.09030017.

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17

Sun, Yiming, Xinyuan Wang, Chi Zhang, and Mingkai Zuo. "Multiple Regression: Methodology and Applications." Highlights in Science, Engineering and Technology 49 (May 21, 2023): 542–48. http://dx.doi.org/10.54097/hset.v49i.8611.

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Multiple regression is one of the most significant forms of regression and has a wide range of applications. The study of the implementation of multiple regression analysis in different settings contributes to the development of relevant theories and the improvement of models. In this paper, four different kinds of regressions are discussed individually by referring to different articles. The four kinds of regressions discussed are multivariable/multiple linear regression, multivariate multiple linear regression, multinomial logistic regression, and multivariate non-linear regression. As for m

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18

Sterie, Cristina Maria, Gabriela Dalila Stoica, Andreea Daniela Giucă, and Marilena E. Potârniche. "Circular economy indicators – multiple linear regression." Proceedings of the International Conference on Business Excellence 16, no. 1 (2022): 437–45. http://dx.doi.org/10.2478/picbe-2022-0043.

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Abstract The aim of this work is to compare EU countries in their efforts to implement the circular economy model and to indicate the EU’s strategic objectives in this area, by analyzing circular economy indicators within the member states. To achieve this, a qualitative and quantitative analysis of the following indicators in THE EUROSTAT database has been carried out: total waste recycling rate, recycling rate of construction and demolition waste, recycling rate of electronic waste, and contribution of recyclable materials to the demand for raw materials in 2019 within the EU. A linear multi

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19

عبد السلام, ايهاب. "Detecting Outliers In Multiple Linear Regression." Journal of Economics and Administrative Sciences 17, no. 64 (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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20

Shieh, Gwowen. "Suppression Situations in Multiple Linear Regression." Educational and Psychological Measurement 66, no. 3 (2006): 435–47. http://dx.doi.org/10.1177/0013164405278584.

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21

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

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22

França, Júlio Cesar Pereira, Tatiane Ferreira do Nascimento Melo, and Tiago Moreira Vargas. "Modelo de regressão linear múltipla e aplicações / Multiple linear regression model and applications." Brazilian Journal of Development 7, no. 7 (2021): 74294–313. http://dx.doi.org/10.34117/bjdv7n7-555.

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23

Bhatti, Dhaval, and Deshpande Anuradha. "Short-term load forecasting with using multiple linear regression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3911–17. https://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 err

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24

Nagy, Gábor. "Sector Based Linear Regression, a New Robust Method for the Multiple Linear Regression." Acta Cybernetica 23, no. 4 (2018): 1017–38. http://dx.doi.org/10.14232/actacyb.23.4.2018.3.

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This paper describes a new robust multiple linear regression method, which based on the segmentation of the N dimensional space to N+1 sector. An N dimensional regression plane is located so that the half (or other) part of the points are under this plane in each sector. This article also presents a simple algorithm to calculate the parameters of this regression plane. This algorithm is scalable well by the dimension and the count of the points, and capable to calculation with other (not 0.5) quantiles. This paper also contains some studies about the described method, which analyze the result

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25

Rahman, S. M. A. Khaleelur, M. Mohamed Sathik, and K. Senthamarai Kannan. "Multiple Linear Regression Models in Outlier Detection." International Journal of Research in Computer Science 2, no. 2 (2012): 23–28. http://dx.doi.org/10.7815/ijorcs.22.2012.018.

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26

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 mul

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27

Breiman, Leo, and Jerome H. Friedman. "Predicting Multivariate Responses in Multiple Linear Regression." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59, no. 1 (1997): 3–54. http://dx.doi.org/10.1111/1467-9868.00054.

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28

Liu, Wei, Mortaza Jamshidian, and Ying Zhang. "Multiple Comparison of Several Linear Regression Models." Journal of the American Statistical Association 99, no. 466 (2004): 395–403. http://dx.doi.org/10.1198/016214504000000395.

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29

Nimon, Kim F., and Frederick L. Oswald. "Understanding the Results of Multiple Linear Regression." Organizational Research Methods 16, no. 4 (2013): 650–74. http://dx.doi.org/10.1177/1094428113493929.

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30

Lin, Anting. "Walmart Sales Prediction Using Multiple Linear Regression." Highlights in Science, Engineering and Technology 107 (August 15, 2024): 124–29. http://dx.doi.org/10.54097/my1nfb95.

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The sales of Walmart are often considered to be related to many factors, and indeed this is the case. This study aims to examine the impact of objectively observable independent variables on Walmart's weekly sales. The paper employs the Z-score standardization technique to normalize the dimensions and utilizes linear regression analysis to process data from 6,435 weekly sales records of Walmart. The model is derived after preprocessing and standardizing the data in this paper. The findings reveal that most variables have either a minor or significant effect on sales. Notably, temperature and F

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31

Weare, Bryan C. "Examples of Additionally Constrained Multiple Linear Regression." Journal of Climate and Applied Meteorology 26, no. 1 (1987): 216–21. http://dx.doi.org/10.1175/1520-0450(1987)026<0216:eoacml>2.0.co;2.

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32

Wang, Huiwen, Liying Shangguan, Junjie Wu, and Rong Guan. "Multiple linear regression modeling for compositional data." Neurocomputing 122 (December 2013): 490–500. http://dx.doi.org/10.1016/j.neucom.2013.05.025.

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33

Uyanık, Gülden Kaya, and Neşe Güler. "A Study on Multiple Linear Regression Analysis." Procedia - Social and Behavioral Sciences 106 (December 2013): 234–40. http://dx.doi.org/10.1016/j.sbspro.2013.12.027.

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34

Wiens, Douglas P. "Robust minimax designs for multiple linear regression." Linear Algebra and its Applications 127 (1990): 327–40. http://dx.doi.org/10.1016/0024-3795(90)90347-f.

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35

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 (2020): 58–63. http://dx.doi.org/10.21817/ijet/2020/v12i1/201201025.

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36

Huh, Myung-Hoe, and Myoungshic Jhun. "RANDOM PERMUTATION TESTING IN MULTIPLE LINEAR REGRESSION." Communications in Statistics - Theory and Methods 30, no. 10 (2001): 2023–32. http://dx.doi.org/10.1081/sta-100106060.

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37

Naoum, S., and I. K. Tsanis. "Orographic Precipitation Modeling with Multiple Linear Regression." Journal of Hydrologic Engineering 9, no. 2 (2004): 79–102. http://dx.doi.org/10.1061/(asce)1084-0699(2004)9:2(79).

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38

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

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39

Rahmatullah Imon, A. H. M. "Identifying multiple influential observations in linear regression." Journal of Applied Statistics 32, no. 9 (2005): 929–46. http://dx.doi.org/10.1080/02664760500163599.

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40

Jankovic, Slobodan. "The Multivariate Statistical Analysis – Multiple Linear Regression." International Journal on Biomedicine and Healthcare 10, no. 4 (2022): 173. http://dx.doi.org/10.5455/ijbh.2022.10.173-175.

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Background. When processing the results of observational studies we ave to usemultivariate statistical methods that will examine the simultaneous influence of both independent and confounding variables on the outcome, Objective: The aim of this paper is to further explain how a researcher could decide whether multiple linear regression is suitable statistical option for processing his (her) data, and then how to implement it properly. Methods: This article is a narrative review of literature about logic, assumptions, quality check and interpretation of multiple linear regression. Results: Mult

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41

С. И., Носков,, and Базилевский, М. П. "Multiple Lv-estimation of Linear Regression Models." Успехи кибернетики / Russian Journal of Cybernetics, no. 4(12) (December 28, 2022): 32–40. http://dx.doi.org/10.51790/2712-9942-2022-3-4-04.

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для оценки моделей множественной линейной регрессии существует много различных математических методов: наименьших квадратов, модулей, антиробастного оценивания, Lv-оценивания, множественного оценивания. Целью данной работы является обобщение указанных методов оценивания единой функцией потерь. Сначала была сформулирована задача оценивания, в которой в качестве критериев минимизации выступают критерии для антиробастного и Lv-оценивания. Недостатком сформулированной задачи является то, что для ее численного решения затруднительно определять начальные значения параметров, поскольку переменные мог

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42

Davis-Stober, Clintin P., Jason Dana, and David V. Budescu. "A Constrained Linear Estimator for Multiple Regression." Psychometrika 75, no. 3 (2010): 521–41. http://dx.doi.org/10.1007/s11336-010-9162-8.

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43

Lu, Qiqi, and Robert B. Lund. "Simple linear regression with multiple level shifts." Canadian Journal of Statistics 35, no. 3 (2007): 447–58. http://dx.doi.org/10.1002/cjs.5550350308.

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44

Foucart, T. "Multiple Linear Regression on Canonical Correlation Variables." Biometrical Journal 41, no. 5 (1999): 559–72. http://dx.doi.org/10.1002/(sici)1521-4036(199909)41:5<559::aid-bimj559>3.0.co;2-g.

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45

Lemos, Tony, and John H. Kalivas. "Leveraging multiple linear regression for wavelength selection." Chemometrics and Intelligent Laboratory Systems 168 (September 2017): 121–27. http://dx.doi.org/10.1016/j.chemolab.2017.07.011.

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46

Schmailzl, Wolfgang, Claudio Piemonte, Erika Garutti, and Walter Hansch. "SiPM signal processing via multiple linear regression." Journal of Instrumentation 18, no. 07 (2023): P07010. http://dx.doi.org/10.1088/1748-0221/18/07/p07010.

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Abstract This paper presents a novel approach using multiple linear regression to process transient signals from silicon photomultipliers. The method provides excellent noise suppression and pulse detection in scenarios with a high pulse count rate and superimposed pulses. Insights into its implementation and benchmark results are presented. We also show how this approach can be used to automatically detect the pulse shape from a given transient signal, providing good detection for count rates up to 90 MHz. Experimental data are used to present an application where this algorithm improves char

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47

Sulistiyono, Mulia, Budy Satria, Acihmah Sidauruk, and Raditya Wardhana. "RAINFALL PREDICTION USING MULTIPLE LINEAR REGRESSION ALGORITHM." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 9, no. 1 (2023): 17–22. http://dx.doi.org/10.33480/jitk.v9i1.4203.

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Indonesia is a tropical region with ever-changing weather changes. It is necessary to conduct a research on weather prediction as a decision making regarding weather information that will occur in the future. Rainfall is one of the factors that cause changes in weather in an area. This research was conducted on the climate in the Yogyakarta region in the form of mountains and lowlands causing differences in rainfall. The variables that are used to make predictions are several parameters that affect rainfall, namely temperature, humidity, wind speed and duration of solar radiation. These 5 vari

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48

Al-Taie, Abida. "Multiple non-Linear Regression and its Applications." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (January 14, 2024): 576–84. http://dx.doi.org/10.55562/jrucs.v54i1.626.

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The non-linear slope reflects the features of the parameters of the model to estimate the values of the approved variable, and the number of independent variables are two or more, the non-Linear model requires complex approximation operation in self non-Linear models, which cannot be converted into α sign by performing known conversions.

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49

Yee, Mon Khaing, and Cho Aung. "Forecasting Academic Performance using Multiple Linear Regression." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1187–89. https://doi.org/10.5281/zenodo.3590591.

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Regression is one of the most powerful statistical methods used in educational researches. This paper shows the important instance of regression methodology called Multiple Linear Regression MLR and proposes a framework of the forecasting of the students' test scores, based on Intelligence Quotient IQ and the number of hours that the students studied. This paper was applied the aid of the Statistical Package for Social Sciences SPSS version 23 and PYTHON version 3.7. Yee Mon Khaing | Aung Cho "Forecasting Academic Performance using Multiple Linear Regression" Published in Interna

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50

Yee, Mon Khaing, Myint Yee Myint, and Ei AungRegression is one of the most powerful statistical methods used in business and marketing researches. This paper shows the important instance of regression methodology called Multiple Linear Regression MLR and proposes a. framework of the forecasting of the Stock Index Price based on the Interest Rate and the Unemployment Rate. This paper was applied the aid of the Statistical Package for Social Sciences SPSS version 23 and PYTHON version 3.7. Yee Mon Khaing |. Myint Myint Yee |. Ei Ei Aung "Forecasting Stock Market using Multiple Linear Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd) ISSN: 2456-6470 Volume-3. |. Issue-5. August 2019 URL: https://www.ijtsrd.com/papers/ijtsrd27819.pdf Ei. "Forecasting Stock Market using Multiple Linear Regression." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2174–76. https://doi.org/10.5281/zenodo.3591185.

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Regression is one of the most powerful statistical methods used in business and marketing researches. This paper shows the important instance of regression methodology called Multiple Linear Regression MLR and proposes a framework of the forecasting of the Stock Index Price, based on the Interest Rate and the Unemployment Rate. This paper was applied the aid of the Statistical Package for Social Sciences SPSS version 23 and PYTHON version 3.7. Yee Mon Khaing | Myint Myint Yee | Ei Ei Aung "Forecasting Stock Market using Multiple Linear Regression" Published in International Journal o

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