Relevant bibliographies by topics / Multiple linear regression mode

Contents

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

Academic literature on the topic 'Multiple linear regression mode'

Author: Grafiati
Published: 8 September 2021
Last updated: 1 February 2022

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Journal articles on the topic "Multiple linear regression mode"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Multiple linear regression mode"

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Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.

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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regression model acquired through a model reduction procedure showed that mostly structural covariates such as the academic background of pupils, percentage of female pupils and the percentage with Swedish background had a statistically significant impact on the academic performances of the students. R2 adjusted of the final model was 0.5289. The multiple regression model was discussed by referencing to previous research. In addition, the strategic management performance framework known as Balanced Scorecard which was introduced by Robert S. Kaplan and David P. Norton was used to discuss relevant key performance indicators to achieve the strategic objectives of schools.
Detta examensarbete, inom ämnet för matematisk statistik och industriell ekonomi, genomfördes med syftet att analysera avgångsbetygen för år 9 i den svenska skolan. Syftet var att förstå vilka variabler som hade en statistisk signifikant påverkan på elevers avgångsbetyg, så kommuner kan förstå vilka variabler som är viktiga för att förbättra de genomsnittliga skolresultaten. En regressionsanalys utfördes, på data från Skolverket, för att se vilka variabler som var statistiskt signifikanta. Den slutgiltiga regressionsmodellen, erhållen genom iterativ reducering av variabler, visade att främst strukturella kovariat, som akademisk bakgrund hos elever, andel kvinnliga studenter och andel studenter med svensk bakgrund hade en signifikant betydelse på studenters akademiska resultat. Justerad R2 var 0.5289 för den slutgiltiga modellen. I diskussionen utvärderades modellen utifrån tidigare forskning. Vidare användes teorin om balanserat styrkort, utvecklat av Robert S. Kaplan och David P. Norton, för att diskutera relevanta nyckeltal för att uppnå strategiska mål för skolan.
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Gustafsson, Alexander, and Sebastian Wogenius. "Modelling Apartment Prices with the Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146735.

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This thesis examines factors that are of most statistical significance for the sales prices of apartments in the Stockholm City Centre. Factors examined are address, area, balcony, construction year, elevator, fireplace, floor number, maisonette, monthly fee, penthouse and number of rooms. On the basis of this examination, a model for predicting prices of apartments is constructed. In order to evaluate how the factors influence the price, this thesis analyses sales statistics and the mathematical method used is the multiple linear regression model. In a minor case-study and literature review, included in this thesis, the relationship between proximity to public transport and the prices of apartments in Stockholm are examined. The result of this thesis states that it is possible to construct a model, from the factors analysed, which can predict the prices of apartments in Stockholm City Centre with an explanation degree of 91% and a two million SEK confidence interval of 95%. Furthermore, a conclusion can be drawn that the model predicts lower priced apartments more accurately. In the case-study and literature review, the result indicates support for the hypothesis that proximity to public transport is positive for the price of an apartment. However, such a variable should be regarded with caution due to the purpose of the modelling, which differs between an individual application and a social economic application
Denna uppsats undersöker faktorer som är av störst statistisk signifikans för priset vid försäljning av lägenheter i Stockholms innerstad. Faktorer som undersöks är adress, yta, balkong, byggår, hiss, kakelugn, våningsnummer, etage, månadsavgift, vindsvåning och antal rum. Utifrån denna undersökning konstrueras en modell för att predicera priset på lägenheter. För att avgöra vilka faktorer som påverkar priset på lägenheter analyseras försäljningsstatistik. Den matematiska metoden som används är multipel linjär regressionsanalys. I en mindre litteratur- och fallstudie, inkluderad i denna uppsats, undersöks sambandet mellan närhet till kollektivtrafik och priset på läagenheter i Stockholm.   Resultatet av denna uppsats visar att det är möjligt att konstruera en modell, utifrån de faktorer som undersöks, som kan predicera priset på läagenheter i Stockholms innerstad med en förklaringsgrad på 91 % och ett två miljoner SEK konfidensintervall på 95 %. Vidare dras en slutsats att modellen preciderar lägenheter med ett lägre pris noggrannare. I litteratur- och fallstudien indikerar resultatet stöd för hypotesen att närhet till kollektivtrafik är positivt för priset på en lägenhet. Detta skall dock betraktas med försiktighet med anledning av syftet med modelleringen vilket skiljer sig mellan en individuell tillämpning och en samhällsekonomisk tillämpning.
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Alt, Raimund. "Multiple hypotheses testing in the linear regression model with applications to economics and finance /." Göttingen : Cuvillier, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=013081924&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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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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Tao, Jinxin. "Comparison Between Confidence Intervals of Multiple Linear Regression Model with or without Constraints." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/404.

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Regression analysis is one of the most applied statistical techniques. The sta- tistical inference of a linear regression model with a monotone constraint had been discussed in early analysis. A natural question arises when it comes to the difference between the cases of with and without the constraint. Although the comparison be- tween confidence intervals of linear regression models with and without restriction for one predictor variable had been considered, this discussion for multiple regres- sion is required. In this thesis, I discuss the comparison of the confidence intervals between a multiple linear regression model with and without constraints.
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Januario, Ana Paula Ferrari. "Análise estatística da produção de vitelão Mertolengo." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29316.

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The work was intended to support the Association of mertolenga cattle breed in its breeding process and decision making, namely in modeling the cost per day of production of the male mertolenga cattle, and in identifying the variables that favor the sale of the animal as a product with a protected designation of origin (PDO) seal. The database contained information on 716 male animals, of which 54 % went to the slaughter that guarantees the PDO seal. We also had data on the cost structure production of the animals from when it enters into the CTR to slaughter, in addition to the individual characteristics of each animal, in particular, of its estimated breeding value. To obtain the cost-per-day production model, multiple linear regression models and other generalized linear models were used. For the classi cation of the animal as a PDO slaughter destination, a logistic regression model was used. When we comparing the generalized linear models tested, the multiple linear regression model was con rmed as the best technique to explain the cost per day of production. For this model, it was found that information such as weight at entry as well as di erent estimated breeding value positively in uence the cost of production. With regard to logistic regression, weight at entry, age at entry and genetic values referring to maternal capacity and calving interval are factors that enhance the animal being sold under the PDO seal; Sumário: Com o trabalho desenvolvido nesta dissertação, pretendeu-se apoiar a Associação de produtores de bovinos da raça mertolenga no seu processo de recria e nas tomadas de decisão, nomeadamente na modelação do custo por dia de produção de bovinos machos da raça mertolenga, e na identificação das variáveis que favorecem a venda do animal como um produto com selo de denominação de origem protegida (DOP). A base de dados continha a informação de 716 animais machos, dos quais 54% foram para o abate que garante o selo DOP, dados referentes _a estrutura de custo de produção dos animais desde a entrada no CTR até o abate, além das características individuais de cada animal, em particular, dos seus valores genéticos. Para obter o modelo do custo por dia de produção, utilizou-se modelos de regressão linear múltipla e outros modelos lineares generalizados. Para a classificação do animal por destino de abate DOP, utilizou-se um modelo de regressão logística. Quando se comparou os diferentes modelos lineares generalizados testados, confirmou-se o modelo de regressão linear multipla como o mais adequado para explicar o custo por dia de produção. Para este modelo, verificou-se que informações como o peso à entrada bem como diferentes valores genéticos infuenciam de forma positiva o custo de produção. No que diz respeito a regressão logística, o peso à entrada, a idade à entrada e os valores genéticos referentes à capacidade maternal e intervalo entre partos são fatores potenciadores do animal ser vendido
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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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Fridgeirsdottir, Gudrun A. "The development of a multiple linear regression model for aiding formulation development of solid dispersions." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52176/.

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As poor solubility continues to be problem for new chemical entities (NCEs) in medicines development the use and interest in solid dispersions as a formulation-based solution has grown. Solid dispersions, where a drug is typically dispersed in a molecular state within an amorphous water-soluble polymer, present a good strategy to significantly enhance the effective drug solubility and hence bioavailability of drugs. The main drawback of this formulation strategy is the inherent instability of the amorphous form. With the right choice of polymer and manufacturing method, sufficient stability can be accomplished. However, finding the right combination of carrier and manufacturing method can be challenging, being labour, time and material costly. Therefore, a knowledge based support tool based upon a statistically significant data set to help with the formulation process would be of great value in the pharmaceutical industry. Here, 60 solid dispersion formulations were produced using ten, poorly soluble, chemically diverse APIs, three commonly used polymers and two manufacturing methods (spray drying and hot-melt extrusion). A long term stability study, up to one year, was performed on all formulations at accelerated conditions. Samples were regularly checked for the onset of crystallisation during the period, using mainly, polarised light microscopy. The stability data showed a large variance in stability between, methods, polymers and APIs. No obvious trends could be observed. Using statistical modelling, the experimental data in combination with calculated and predicted physicochemical properties of the APIs, several multiple linear regression (MLR) models were built. These had a good adjusted R2 and most showed good predictability in leave-one-out cross validations. Additionally, a validation on half of the models (eg. those based on spray-drying models) using an external dataset showed excellent predictability, with the correct ranking of formulations and accurate prediction of stability. In conclusion, this work has provided important insight into the complex correlations between the physical stability of amorphous solid dispersions and factors such as manufacturing method, carrier and properties of the API. Due to the expansive number of formulations studied here, which is far greater than previously published in the literature in a single study, more general conclusions can be drawn about these correlations than has previously been possible. This thesis has shown the potential of using well-founded statistical models in the formulation development of solid dispersion and given more insight into the complexity of these systems and how stability of these is dependent on multiple factors.
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Huschens, Stefan. "Einführung in die Ökonometrie." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-222629.

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Die Kapitel 1 bis 6 im ersten Teil dieses Skriptes beruhen auf einer Vorlesung Ökonometrie I, die zuletzt im WS 2001/02 gehalten wurde, die Kapitel 7 bis 16 beruhen auf einer Vorlesung Ökonometrie II, die zuletzt im SS 2006 gehalten wurde. Das achte Kapitel enthält eine komprimierte Zusammenfassung der Ergebnisse aus dem Teil Ökonometrie I.
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Löwe, Rakel, and Ida Schneider. "Automatic Differential Diagnosis Model of Patients with Parkinsonian Syndrome : A model using multiple linear regression and classification tree learning." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413638.

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Parkinsonian syndrome is an umbrella term including several diseases with similar symptoms. PET images are key when differential diagnosing patients with parkinsonsian syndrome. In this work two automatic diagnosing models are developed and evaluated, with PET images as input, and a diagnosis as output. The two devoloped models are evaluated based on performance, in terms of sensitivity, specificity and misclassification error. The models consists of 1) regression model and 2) either a decision tree or a random forest. Two coefficients, alpha and beta, are introduced to train and test the models. The coefficients are the output from the regression model. They are calculated with multiple linear regression, with the patient images as dependent variables, and mean images of four patient groups as explanatory variables. The coefficients are the underlying relationship between the two. The four patient groups consisted of 18 healthy controls, 21 patients with Parkinson's disease, 17 patients with dementia with Lewi bodies and 15 patients with vascular parkinsonism. The models predict the patients with misclassification errors of 27% for the decision tree and 34% for the random forest. The patient group which is easiest to classify according to both models is healthy controls. The patient group which is hardest to classify is vascular parkinsonism. These results implies that alpha and beta are interesting outcomes from PET scans, and could, after further development of the model, be used as a guide when diagnosing in the models developed.
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Books on the topic "Multiple linear regression mode"

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Shelton, Katherine Lesley. An illustration of heteroscedasticity in the multiple linear regression model. [s.l: The author], 1985.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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Zorn, Troy G. Utility of species-specific, multiple linear regression models for prediction of fish assemblages in rivers of Michigan's lower peninsula. Lansing, MI: Michigan Dept. of Natural Resources, Fisheries Division, 2004.

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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 results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.
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Higham, Ronald P. A multiple linear regression model for predicting zone A retention by military occupational specialty. 1986.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia. Springer, 2020.

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Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429066665.

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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 of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.
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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. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Miksza, Peter, and Kenneth Elpus. Regression. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0010.

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This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including binary outcomes, is discussed. Particular attention is paid to the ability of regression to be used to estimate the effect of a predictor on an outcome while statistically “controlling” for the values of other observed variables.
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Book chapters on the topic "Multiple linear regression mode"

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Ravishanker, Nalini, Zhiyi Chi, and Dipak K. Dey. "Multiple Linear Regression Models." In A First Course in Linear Model Theory, 275–304. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315156651-9.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Fuzzy Multiple Linear Regression." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 11–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_2.

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Baguley, Thom. "Multiple regression and the general linear model." In Serious Stats, 423–71. London: Macmillan Education UK, 2012. http://dx.doi.org/10.1007/978-0-230-36355-7_12.

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Nakahara, Kazutaka. "G0 beam quality and multiple linear regression corrections." In From Parity Violation to Hadronic Structure and more, 119–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26345-4_27.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Water Quality Index Using Fuzzy Regression." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 37–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_5.

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Khine, Kyi Lai Lai, and Thi Thi Soe Nyunt. "Predictive Big Data Analytics Using Multiple Linear Regression Model." In Advances in Intelligent Systems and Computing, 9–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0869-7_2.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Introduction." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 1–10. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_1.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Water Quality Index (WQI)." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 23–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_3.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Data Collection and Study Sites." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 31–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_4.

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Kapić, Zinaid. "Multiple Linear Regression Model for Predicting PM2.5 Concentration in Zenica." In Advanced Technologies, Systems, and Applications V, 335–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54765-3_23.

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Conference papers on the topic "Multiple linear regression mode"

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Castillo-Garit, Juan, Yudith Cañizares-Carmenate, Karel Mena-Ulecia, Yunier Perera-Sardiña, and Francisco Torrens. "Multiple Linear Regression Model of Thermolysin Inhibitors." In MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition. Basel, Switzerland: MDPI, 2017. http://dx.doi.org/10.3390/mol2net-02-03872.

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Kim, Hak Soo, Jae Cheol Lee, and Kyu Tae Park. "Motion estimation method using multiple linear regression model." In Electronic Imaging '97, edited by Jan Biemond and Edward J. Delp III. SPIE, 1997. http://dx.doi.org/10.1117/12.263272.

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Mohammed, Najeebuddin, A. Kusalava Sarma, and Shahid Dhamani. "Multiple Linear Regression Model for Inflation in India." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456277.

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Xu, Yan, and Shuangting Lan. "Time Series Calibration Model for NO2 Based on Multiple Linear Regression." In 2019 International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2019. http://dx.doi.org/10.1109/icemme49371.2019.00068.

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Shuang, Wang. "Research on Enterprise Innovation Performance Based on Multiple Linear Regression Model." In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2020. http://dx.doi.org/10.1109/icemme51517.2020.00057.

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"Non-linear multiple regression analysis for predicting seasonal streamflow using large scale climate mode." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.l3.esha.

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Jinyu, Tian, and Zhao Xin. "Apply multiple linear regression model to predict the audit opinion." In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5267661.

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Shutenko, Oleg, and Serhii Ponomarenko. "Diagnostics of Transformer Oils Using the Multiple Linear Regression Model." In 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP). IEEE, 2020. http://dx.doi.org/10.1109/paep49887.2020.9240875.

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Li, Zhuoshi, Xuejun Cao, Xiaoqi Ding, and Hang Chen. "Prediction Model of Multiple Linear Regression Analysis in Grain Production." In 5th International Conference on Information Engineering for Mechanics and Materials. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icimm-15.2015.233.

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Cunningham, Charles Franklin, Lisa Cooley, Gregory Wozniak, and Jim Pancake. "Using Multiple Linear Regression To Model EURs of Horizontal Marcellus Wells." In SPE Eastern Regional Meeting. Society of Petroleum Engineers, 2012. http://dx.doi.org/10.2118/161343-ms.

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Reports on the topic "Multiple linear regression mode"

1

Kubik, Harold. MLRP, Multiple Linear Regression Program. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada204565.

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