Relevant bibliographies by topics / Linear multiple stepwise regression analysis

Contents

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

Academic literature on the topic 'Linear multiple stepwise regression analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Linear multiple stepwise regression analysis"

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Park, ManKi, HyeRan Yoon, KyoungHo Kim, and JungHwan Cho. "Quantitative analysis by diffuse reflectance infrared Fourier transform and linear stepwise multiple regression analysis I —Simultaneous quantitation of ethenzamide, isopropylantipyrine, caffeine, and allylisopropylacetylurea in tablet by DRIFT and linear stepwise multiple regression analysis—." Archives of Pharmacal Research 11, no. 2 (1988): 99–113. http://dx.doi.org/10.1007/bf02857712.

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Ray, Supratim, Chandana Sengupta, and Kunal Roy. "QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters." Open Chemistry 6, no. 2 (2008): 267–76. http://dx.doi.org/10.2478/s11532-008-0014-7.

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AbstractIn the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q2 = 0.626) considering the leave-oneout prediction statistics.
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Liu, Yingxia, Gerard B. M. Heuvelink, Zhanguo Bai, et al. "Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression." Field Crops Research 264 (May 2021): 108098. http://dx.doi.org/10.1016/j.fcr.2021.108098.

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Ghasemi, Jahan B., Parvin Zohrabi, and Habibollah Khajehsharifi. "Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis." Monatshefte für Chemie - Chemical Monthly 141, no. 1 (2009): 111–18. http://dx.doi.org/10.1007/s00706-009-0225-4.

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Zhan, Xinhua, Xiao Liang, Guohua Xu, and Lixiang Zhou. "Influence of plant root morphology and tissue composition on phenanthrene uptake: Stepwise multiple linear regression analysis." Environmental Pollution 179 (August 2013): 294–300. http://dx.doi.org/10.1016/j.envpol.2013.04.033.

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Jia, Renfu, Shibiao Fang, Wenrong Tu, and Zhilin Sun. "Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8957530.

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This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR) and principal component analysis (PCA) to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has
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Oliveira, Cinthia Pereira de, Rinaldo Luiz Caraciolo Ferreira, José Antônio Aleixo da Silva, et al. "Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil." Forests 12, no. 4 (2021): 473. http://dx.doi.org/10.3390/f12040473.

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In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and carbon estimates from forest inventory data and LiDAR technology in a dry tropical forest in Brazil. We use forest inventory data in two areas together with data from the LiDAR flyby, generating estimates of local biomass and carbon levels obtained from local species. We approach three types of models for data analysi
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Kokaly, R. "Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression." Remote Sensing of Environment 67, no. 3 (1999): 267–87. http://dx.doi.org/10.1016/s0034-4257(98)00084-4.

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Boulet, Sebastien, Elsa Boudot, and Nicolas Houel. "Relationships between each part of the spinal curves and upright posture using Multiple stepwise linear regression analysis." Journal of Biomechanics 49, no. 7 (2016): 1149–55. http://dx.doi.org/10.1016/j.jbiomech.2016.02.054.

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Hanif, Aswar. "Menggunakan Stepwise Linear Regression Untuk Menentukan Faktor Yang Mempengaruhi Produktivitas Tenaga Kerja." Jurnal Informatika 5, no. 1 (2018): 73–80. http://dx.doi.org/10.31311/ji.v5i1.2701.

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Abstrak
 Semakin lama masa kerja, semakin banyak pengalaman yang dimiliki seseorang atas pekerjaannya. Seorang yang memiliki tingkat kehadiran yang tinggi, dianggap sebagai pekerja yang baik. Kedua faktor ini membentuk asumsi bahwa masa kerja dan tingkat kehadiran, secara positif atau negatif, mempengaruhi produktivitas pekerja. Dikarenakan besarnya pengaruh produktivitas pekerja terhadap kesehatan sebuah perusahaan, kegiatan menganalisis produktivitas tenaga kerja perusahaan, seharusnya tidak didasarkan pada asumsi-asumsi, meskipun asumsi tersebut bisa diterima. Menggunakan Regresi Linie
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Dissertations / Theses on the topic "Linear multiple stepwise regression analysis"

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Hu, Qing. "Predictor Selection in Linear Regression: L1 regularization of a subset of parameters and Comparison of L1 regularization and stepwise selection." Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-051107-154052/.

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Kinns, David Jonathan. "Multiple case influence analysis with particular reference to the linear model." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368427.

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Galijasevic, Amar, and Josef Tegbaru. "Can IPO first day returns be predicted? A multiple linear regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254293.

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During the last three years the Swedish stock market has showed a strong upwards movement from the lows of 2016. At the same time the IPO activity has been large and a lot of the offerings have had a positive return during the first day of trading in the market. The goal of this study is to analyze if there is any particular IPO specific data that has a correlation with the first day return and if it can be used to predict the first day return for future IPO’s. If any regressors were shown to have correlation with the first day return, the goal is also to find a subset of regressors with even
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Kareflod, Michaela, and Jennifer Ljungquist. "A Study of Hot el Occupancy : Using Multiple Linear Regression and Market Strategy Analysis." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189017.

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This paper is based on collaboration between a company called StayAt HotelApart AB and two KTH students. It examines which factors that are influencing the hotel’s occupancy and how this may be increased by enhancing the market strategy. The aim is to provide a foundation for strategy development to the company. The study is performed by connecting applied mathematics with industrial management. The mathematical part is based on a multiple linear regression on occupancy with historical data from 2011 to 2016 mainly collected from StayAt.  The analysis of the market strategy is performed by mea
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Yeasmin, Mahbuba 1965. "Multiple maxima of likelihood functions and their implications for inference in the general linear regression model." Monash University, Dept. of Econometrics and Business Statistics, 2003. http://arrow.monash.edu.au/hdl/1959.1/5821.

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Goicoechea, Saioa, and Patricia López. "Modeling the air change rate in a naturally ventilated historical church : MultipleLinear Regression analysis." Thesis, Högskolan i Gävle, Akademin för teknik och miljö, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13640.

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In this thesis the air infiltration through the envelope of a naturally ventilated stone church located in Bergby (Gävle, Sweden) is studied. The project is focused on Multiple Linear Regression (MLR) modeling the air change rate (ACH) inside the church hall and studying the factors (stack effect and wind effect) that influence the air infiltration. The weather parameters outside the building were recorded in a weather station and the properties of the air inside the church was analyzed with different methods. Infrared thermography techniques and thermistors were used to measure the temperatur
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Högbom, Johannes, and August Regnell. "Analysis of Performance Measures affecting the economic success on the PGA Tour using multiple linear regression." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275678.

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This bachelor thesis examined the relationship between performance measures and prize money earnings on the PGA Tour. Using regression analysis and data from seasons 2004 through 2019 retrieved from the PGA Tour website this thesis examined if prize money could be predicted. Starting with 102 covariates, comprehensibly covering all aspects of the game, the model was reduced to 13 with Driving Distance being most prominent, favouring simplicity resulting in an R2Adjusted of 0.6918. The final model was discussed in regard to relevance, reliability and usability. This thesis further analysed how
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Brandner, Hanna. "Idenitfying the Influential Factors of the Temporal Variation of Water Consumption : A Case Study using Multiple Linear Regression Analysis." Thesis, KTH, Vattendragsteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192650.

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This thesis is a part of the water development project conducted by Svenskt Vatten, which is the Swedish Water and Wastewater Association (SWWA) as well as Tyréns, a consultancy company with offices based in Stockholm, Sweden. Prior to this thesis work, a quality assessment was conducted for some of the locations provided by municipalities in Sweden. This thesis builds upon the revised water consumption data, and also continues to work with validating and modifying the water measurement data in order to proceed with the next step of the water development project, which is to identify any trend
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Prudencio, Gerald, Diego Pino, Luis Arauzo, and Carlos Raymundo. "Productivity analysis of LHD equipment using the multiple linear regression method in an underground mine in Peru." International Institute of Informatics and Systemics, IIIS, 2019. http://hdl.handle.net/10757/656294.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The current study is based on a multiple linear regression analysis with an objective to formulate an equation related to the productivity analysis of LHD equipment using independent variables such as the effective utilization of the equipment. To identify the independent variables, main productive factors, such as the actual capacity of the buckets, the transport cycles in the cleaning process, and the performance by means of curves, were anal
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Buzatoiu, Roxana. "Long Term Forecasting of Industrial Electricity Consumption Data With GRU, LSTM and Multiple Linear Regression." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289632.

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Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Lin
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Books on the topic "Linear multiple stepwise regression analysis"

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On stepwise procedures for some multiple inference problems. Alqvist & Wiksell International, 1989.

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J, Niccolucci Michael, Schuster Ervin G, and Intermountain Research Station (Ogden, Utah), eds. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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Gelman, Andrew, and Deborah Nolan. Multiple regression and nonlinear models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0010.

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This chapter covers multiple regression and links statistical inference to general topics such as lurking variables that arose earlier. Many examples can be used to illustrate multiple regression, but we have found it useful to come to class prepared with a specific example, with computer output (since our students learn to run the regressions on the computer). We have found it is a good strategy to simply use a regression analysis from some published source (e.g., a social science journal) and go through the model and its interpretation with the class, asking students how the regression resul
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Center, Lewis Research, ed. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys. National Aeronautics and Space Administration, Lewis Research Center, 1985.

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Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).
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Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges
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Book chapters on the topic "Linear multiple stepwise regression analysis"

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Zhou, Yiqian, Rehman Qureshi, and Ahmet Sacan. "Analysis of Paired miRNA-mRNA Microarray Expression Data Using a Stepwise Multiple Linear Regression Model." In Bioinformatics Research and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59575-7_6.

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Armstrong, Richard A., and Anthony C. Hilton. "Stepwise Multiple Regression." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch26.

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Armstrong, Richard A., and Anthony C. Hilton. "Multiple Linear Regression." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch25.

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Vehkalahti, Kimmo, and Brian S. Everitt. "Multiple Linear Regression." In Multivariate Analysis for the Behavioral Sciences. CRC Press, 2018. http://dx.doi.org/10.1201/9781351202275-4.

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Navarra, Antonio, and Valeria Simoncini. "Multiple Linear Regression Methods." In A Guide to Empirical Orthogonal Functions for Climate Data Analysis. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3702-2_8.

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Thrane, Christer. "Linear regression with several independent variables Multiple regression." In Applied Regression Analysis. Routledge, 2019. http://dx.doi.org/10.4324/9780429443756-4.

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Sakawa, Masatoshi, and Hitoshi Yano. "Interactive Decision Making for Multiobjective Fuzzy Linear Regression Analysis." In Multiple Criteria Decision Making. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2666-6_18.

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Ji, Nan, Jincai Chang, and Yuanyuan Luo. "Multiple Linear Regression Analysis Based on Software R." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40618-8_51.

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Subgroup Analysis using Multiple Linear Regression: Confounding, Interaction, Synergism." In Statistics Applied to Clinical Trials. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0337-7_9.

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Silva, Jesús, Omar Bonerge Pineda Lezama, and Darwin Solano. "Multiple Linear Regression Analysis of Factors Affecting the Consumption." In Inventive Computation Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33846-6_96.

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Conference papers on the topic "Linear multiple stepwise regression analysis"

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Liu, Pudong, Runhe Shi, Hong Wang, Kaixu Bai, and Wei Gao. "Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model." In SPIE Optical Engineering + Applications, edited by Wei Gao, Ni-Bin Chang, and Jinnian Wang. SPIE, 2014. http://dx.doi.org/10.1117/12.2060530.

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Lan, Yuqing, and Shuhang Guo. "Multiple Stepwise Regression Analysis on Knowledge Evaluation." In 2008 International Conference on Management of e-Commerce and e-Government. IEEE, 2008. http://dx.doi.org/10.1109/icmecg.2008.65.

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Zhou, Yiqian, Rehman Qureshi, and Ahmet Sacan. "Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data." In 2012 7th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2012. http://dx.doi.org/10.1109/hibit.2012.6209046.

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Yiqian Zhou, J. Gerhart, and A. Sacan. "Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112544.

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Xiao, Randong, Jiajia Zhu, Zilong Zhao, Haitao Yu, and Yong Du. "A Passenger Flow Prediction Method for Bus Lines Based on Multiple Stepwise Regression Analysis." In 2021 11th International Conference on Information Science and Technology (ICIST). IEEE, 2021. http://dx.doi.org/10.1109/icist52614.2021.9440559.

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Aiyin, Wang, and Xu Yanmei. "Multiple Linear Regression Analysis of Real Estate Price." In 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2018. http://dx.doi.org/10.1109/icris.2018.00145.

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"Transmission loss modelling and analysis with multiple linear regression." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.g2.appalasamy.

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Abuella, Mohamed, and Badrul Chowdhury. "Solar power probabilistic forecasting by using multiple linear regression analysis." In SoutheastCon 2015. IEEE, 2015. http://dx.doi.org/10.1109/secon.2015.7132869.

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Ulgen, Toygar, and Gokturk Poyrazoglu. "Predictor Analysis for Electricity Price Forecasting by Multiple Linear Regression." In 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, 2020. http://dx.doi.org/10.1109/speedam48782.2020.9161866.

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li, yi, Xiaofei Lu, and Li Zhang. "Precision analysis of atmospheric transmittance based on multiple linear regression." In Fourth Seminar on Novel Optoelectronic Detection Technology and Application, edited by Weiqi Jin and Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2309758.

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