Relevant bibliographies by topics / Simple and multiple linear regression

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

Academic literature on the topic 'Simple and multiple linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Simple and multiple linear regression"

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

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Hanley, James A. "Simple and multiple linear regression: sample size considerations." Journal of Clinical Epidemiology 79 (November 2016): 112–19. http://dx.doi.org/10.1016/j.jclinepi.2016.05.014.

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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 with different datasets and compares to the linear least squares regression. Sector Based Linear Regression (SBLR) is the multidimensional generalization of the mathematical background of a point cloud processing algorithm called Fitting Disc method, which has been already used in practice to process LiDAR data. A robust regression method can be used also in many other fields.
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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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Wang, Xiao Ying, and Ying Ge Chen. "Mine Location Algorithm Based on Multiple Linear Regression." Applied Mechanics and Materials 58-60 (June 2011): 1830–35. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1830.

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This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.
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Sun, Hui, Yang Yang Chen, and Zhi Qing Fan. "Study the Residential Land Demand by Ridge Regression and Multiple Linear Regression." Key Engineering Materials 467-469 (February 2011): 1250–55. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1250.

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Land is an important natural resource for human’s living and developing. Residential land demand forecast is the core content of urban land use planning. To improve the forecast accuracy, we fully considered the influencing factors and had chosen urban population, GDP, fixed asset investment, and real estate sales as the main influencing indicators. Here we use the Ridge Regression Method to determine the linear relationship between the variables, and supplemented by multiple linear regression. We chose Tianjin as the target city to calculate the residential land demand. The results show that the method is simple, easy, and suitable for urban residential land demand prediction.
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Liebscher, Stefan, and Walter Krämer. "Some simple LM tests against multiple changes of variance in linear regression." Allgemeines Statistisches Archiv 84, no. 1 (April 2000): 33–40. http://dx.doi.org/10.1007/s101820050004.

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Kowal, Robert. "Characteristics and Properties of a Simple Linear Regression Model." Folia Oeconomica Stetinensia 16, no. 1 (December 1, 2016): 248–63. http://dx.doi.org/10.1515/foli-2016-0016.

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Abstract A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.
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Mokhort, Hennadii. "Multiple Linear Regression Model of Meningococcal Disease in Ukraine: 1992–2015." Computational and Mathematical Methods in Medicine 2020 (February 11, 2020): 1–7. http://dx.doi.org/10.1155/2020/5105120.

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Estimating the rates of invasive meningococcal disease (IMD) from epidemiologic data remains critical for making public health decisions. In Ukraine, such estimations have not been performed. We used epidemiological data to develop a national database. These data were used to estimate the population susceptible to IMD and identify the prevalence of asymptomatic carriers of N. meningitidis using simple epidemiological models of meningococcal disease that may be used by the national policy makers. The goal was to create simple, easily understood analysis of patterns of the infection within Ukraine that would capture the major features of the infection dynamics. Studies used nationally reported data during 1992–2015. A logic model identified the prevalence of carriage and the proportion of the population susceptible to IMD as key drivers of IMD incidence. Multiple linear regression models for all ages (total population) and for children ≤14 years old were fit to national-level data. Linear models with the incidence of IMD as an outcome were highly associated with carriage and estimated susceptible population in both total population and children (R2 = 0.994 and R2 = 0.978, respectively). The susceptibility rate to IMD in the study total population averaged 0.0034 ± 0.0009% annually. At the national level, IMD can be characterized by the simple interaction between the prevalence of asymptomatic carriage and the proportion of the susceptible population. IMD association with prevalence rates of carriage and the proportion of susceptible population is sufficiently strong for national-level planning of intervention strategies for IMD.
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Bidabad, Bijan. "New Algorithms for L1 Norm Regression." Bangladesh Journal of Multidisciplinary Scientific Research 1, no. 1 (June 12, 2019): 1–18. http://dx.doi.org/10.46281/bjmsr.v1i1.311.

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In this paper, we propose four algorithms for L1 norm computation of regression parameters, where two of them are more efficient for simple and multiple regression models. However, we start with restricted simple linear regression and corresponding derivation and computation of the weighted median problem. In this respect, a computing function is coded. With discussion on the m parameters model, we continue to expand the algorithm to include unrestricted simple linear regression, and two crude and efficient algorithms are proposed. The procedures are then generalized to the m parameters model by presenting two new algorithms, where the algorithm 4 is selected as more efficient. Various properties of these algorithms are discussed.
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Dissertations / Theses on the topic "Simple and multiple linear regression"

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Tong, Fan. "Capacity demand and climate in Ekerö : Development of tool to predict capacity demand underuncertainty of climate effects." Thesis, KTH, Elektroteknisk teori och konstruktion, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-152522.

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The load forecasting has become an important role in the operation of power system, and several models by using different techniques have been applied to solve these problems. In the literature, the linear regression models are considered as a traditional approach to predict power consumption, and more recently, the artificial neural network (ANN) models have received more attention for a great number of successful and practical applications. This report introduces both linear regression and ANN models to predict the power consumption for Fortum in Ekerö. The characteristics of power consumption of different kinds of consumers are analyzed, together with the effects of weather parameters to power consumption. Further, based on the gained information, the numerical models of load forecasting are built and tested by the historical data. The predictions of power consumption are focus on three cases separately: total power consumption in one year, daily peak power consumption during winter and hourly power consumption. The processes of development of the models will be described, such as the choice of the variables, the transformations of the variables, the structure of the models and the training cases of ANN model. In addition, two linear regression models will be built according to the number of input variables. They are simple linear regression with one input variable and multiple linear regression with several input variables. Comparison between the linear regression and ANN models will be carried out. In the end, it finds out that the linear regression obtains better results for all the cases in Ekerö. Especially, the simple linear regression outperforms in prediction of total power consumption in one year, and the multiple linear regression is better in prediction of daily peak load during the winter.
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.
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Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.
In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.
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Silva, Mariellen Vital da [UNESP]. "Um modelo matemático para estudo de otimização do consumo de energia elétrica." Universidade Estadual Paulista (UNESP), 2007. http://hdl.handle.net/11449/87261.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção.
In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production.
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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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Silva, Mariellen Vital da. "Um modelo matemático para estudo de otimização do consumo de energia elétrica /." Ilha Solteira : [s.n.], 2007. http://hdl.handle.net/11449/87261.

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Resumo: Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção.
Abstract: In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production.
Orientador: Francisco Villarreal Alvarado
Coorientador: Antonio Padilha Feltrin
Banca: Evaristo Bianchini Sobrinho
Banca: José Carlos de Melo Vieira Júnior
Mestre
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Karsburg, Roberta Machado, and Roberta Machado Karsburg. "Precipitação e velocidade do vento na oscilação dos níveis d’água do canal São Gonçalo-RS." Universidade Federal de Pelotas, 2016. http://repositorio.ufpel.edu.br:8080/handle/prefix/3693.

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Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul - FAPERGS
O canal São Gonçalo se configura como um importante curso d’água, pertencente a bacia hidrográfica Mirim-São Gonçalo, que tem área de 25.000 km² e situa-se na região costeira no estado do Rio Grande do Sul. Aliado a isso, ele une a laguna dos Patos, a qual mantém conexão direta com o oceano Atlântico, à lagoa Mirim, considerada como um grande reservatório de água doce no sul do Brasil. Está situado em uma região de planície, de baixas declividades e apresenta grande complexidade e sensibilidade às oscilações dos níveis d’água, tanto em função do regime e direção de ventos como do regime de chuvas. Com isso, este trabalho teve como objetivo avaliar a influência do vento e da precipitação na oscilação do nível d`água à jusante da barragem eclusa do canal São Gonçalo-RS. A hipótese foi que a velocidade e direção do vento, juntamente com a precipitação, influenciam à oscilação dos níveis do canal São Gonçalo. Para alcançar os objetivos, foram empregados métodos de regressão linear múltipla e simples entre o nível d’água, precipitação e velocidades do vento à 2 e 7 m de altura e velocidade máxima. Para determinar a significância dos modelos de regressões lineares, foi utilizado o teste t de Student, para a verificação quais variáveis que são influenciadoras da oscilação do nível. Por fim, para indicar o grau de precisão dos modelos de regressões lineares avaliados, aplicou-se a metodologia do erro relativo médio quadrático. As direções de ventos que mostraram-se mais influenciadoras no processo de oscilação dos níveis d’água à jusante do canal São Gonçalo, foram as de sudeste (SE), sul (S) e oeste (O). A variável com maior influência no processo de oscilação dos níveis d’água à jusante da barragem eclusa do canal São Gonçalo, foi a velocidade máxima do vento (VMáx).
São Gonçalo is an important watercourse, belonging to the Mirim-São Gonçalo hydrographic basin, which has an area of 25,000 km² and is located in the coastal region of the state of Rio Grande do Sul. The Patos lagoon, which maintains a direct connection with the Atlantic Ocean, to Mirim lagoon, considered as a large reservoir of fresh water in southern Brazil. It’s situated in a lowland lowland region and presents great complexity and sensitivity to the fluctuations of water levels, both due to the regime and direction of the winds and the rainfall regime. The objective of this work was to evaluate the influence of wind and precipitation on the oscillation of the water level downstream of the São Gonçalo channel dam. The hypothesis was that the velocity and direction of the wind, along with the precipitation, influence the oscillation of the levels of the São Gonçalo channel. To achieve the objectives, multiple linear simple regression methods were used between water level, precipitation and wind velocities at 2 and 7 m in height and at maximum velocity. To determine the significance of the linear regression models, the Student's t test was used to verify which variables are influencing the level oscillation. Finally, to indicate the degree of precision of the linear regression models evaluated, the methodology of the relative mean square error was applied. The directions of the winds that were most influential in the process of oscillation of the water levels downstream of the São Gonçalo channel were those of the southeast (SE), south (S) and west (O). The variable with the greatest influence on the oscillation process of the water levels downstream of the São Gonçalo channel dam was the maximum wind speed.
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Brodbeck, William Joseph. "The Effect of Readability on Simple Linear Regression." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1591867761661656.

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Guan, Tianyuan. "Sample Size Calculations in Simple Linear Regression: A New Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667392849137.

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Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.

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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were created and their functionality was tested, but only seven models were better than chance i.e. more than 50 % in the right direction. To determine the most suitable model out of the remaining seven, Akaike’s Information Criterion (AIC), was applied. The covariates used in the final model were; Dow Jones closing price, Shanghai opening price, conjuncture, oil price, share’s opening price, share’s highest price, share’s lowest price, lending rate, reports, positive/negative insider trading, payday, positive/negative price target, number of completed transactions during one day, OMX Stockholm closing price, TCW index, increasing closing price three days in a row and decreasing closing price three days in a row. The maximum average deviation between the predicted closing price and the real closing price of all the 44 shares predicted were 6,60 %. In predicting the correct direction (increase or decrease) of the 44 shares an average of 61,72 % were achieved during the time period 2012-02-22 to 2013-02-20. If investing 50.000 SEK in each company i.e. a total investment of 2.2 million SEK, the total yield when using the regression model during the year 2012-02-22 to 2013-02-20 would have been 259.639 SEK (11,80 %) compared to 184.171 SEK (8,37 %) if the shares were never to be traded with during the same period of time. Of the 44 companies analysed, 31 (70,45 %) of them were profitable when using the regression model during the year compared to 30 (68,18 %) if the shares were never to be sold during the same period of time. The difference in yield in percentage between the model and keeping the shares for the year was 40,98 %.
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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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Books on the topic "Simple and multiple linear regression"

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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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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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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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Quantitative methods in business: Unit 8 : Simple linear regression. Milton Keynes: Open University, 2003.

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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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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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Book chapters on the topic "Simple and multiple linear regression"

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de Micheaux, Pierre Lafaye, Rémy Drouilhet, and Benoit Liquet. "Simple and Multiple Linear Regression." In Statistics and Computing, 455–501. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9020-3_14.

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Baltagi, Badi H. "Simple Linear Regression." In Springer Texts in Business and Economics, 29–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54548-1_3.

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Holmes, William H., and William C. Rinaman. "Simple Linear Regression." In Statistical Literacy for Clinical Practitioners, 341–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_13.

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Sheather, Simon J. "Simple Linear Regression." In Springer Texts in Statistics, 15–43. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_2.

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Park, Sung H. "Simple Linear Regression." In International Encyclopedia of Statistical Science, 1327–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_517.

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Baltagi, Badi H. "Simple Linear Regression." In Econometrics, 51–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04693-7_3.

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Gooch, Jan W. "Simple Linear Regression." In Encyclopedic Dictionary of Polymers, 996. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15375.

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Baltagi, Badi H. "Simple Linear Regression." In Econometrics, 41–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-00516-3_3.

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Hodeghatta, Umesh R., and Umesh Nayak. "Simple Linear Regression." In Business Analytics Using R - A Practical Approach, 187–205. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2514-1_8.

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Heiberger, Richard M., and Erich Neuwirth. "Simple Linear Regression." In R Through Excel, 193–212. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0052-4_8.

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Conference papers on the topic "Simple and multiple linear regression"

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Ouarda, T. B. M. J., and O. Seidou. "Simple and Multiple Change Point Detection in Multiple Linear Regression and Application to Hydroclimatic Variables." In World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)409.

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Hidayat, Wahyu, Mursyid Ardiansyah, and Kusrini Kusrini. "Decision Support System for Selection of Staples Food and Food Commodity Price Prediction Post-COVID-19 Using Simple Additive Weighting and Multiple Linear Regression Methods." In 2020 3rd International Conference on Information and Communications Technology (ICOIACT). IEEE, 2020. http://dx.doi.org/10.1109/icoiact50329.2020.9332095.

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Hage, Ilige S., Charbel Y. Seif, Ré-Mi Hage, and Ramsey F. Hamade. "A Verified Non-Linear Regression Model for Elastic Stiffness Estimates of Finite Composite Domains Considering Combined Effects of Volume Fractions, Shapes, Orientations, Locations, and Number of Multiple Inclusions." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86231.

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A non-linear regression model using SAS/STAT (JMP® software; Proc regression module) is developed for estimating the elastic stiffness of finite composite domains considering the combined effects of volume fractions, shapes, orientations, inclusion locations, and number of multiple inclusions. These estimates are compared to numerical solutions that utilized another developed homogenization methodology by the authors (dubbed the generalized stiffness formulation, GSF) to numerically determine the elastic stiffness tensor of a composite domain having multiple inclusions with various combinations of geometric attributes. For each inclusion, these considered variables represent the inclusions’ combined attributes of volume fraction, aspect ratio, orientation, number of inclusions, and their locations. The GSF methodology’s solutions were compared against literature-reported solutions of simple cases according to such well-known techniques as Mori-Tanaka and generalized self-consistent type methods. In these test cases, the effect of only one variable was considered at a time: volume fraction, aspect ratio, or orientation (omitting the number and locations of inclusions). For experimental corroboration of the numerical solutions, testing (uniaxial compression) was performed on test cases of 3D printed test cubes. The regression equation returns estimates of the composite’s ratio of normalized longitudinal modulus (E11) to that of the matrix modulus (Em) or E11/Em when considering any combination of all of the aforementioned inclusions’ variables. All parameters were statistically analyzed with the parameters retained are only those deemed statistically significant (p-values less than 0.05). Values returned by the regression stiffness formulation solutions were compared against values returned by the GSF formulation numerical and against the experimentally found stiffness values. Results show good agreement between the regression model estimates as compared with both numerical and experimental results.
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Li, Y. G., M. F. Abdul Ghafir, L. Wang, R. Singh, K. Huang, X. Feng, and W. Zhang. "Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45289.

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At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.
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Downer, Lee M., and D. Geoff Rideout. "A Design of Experiments Approach to Identifying Damage in Structures Using Modal Frequency." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63965.

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Modal vibration parameters such as frequency, damping ratio and mode shape have long been considered useful for identifying damage in structures. In this paper a generalized approach is presented that allows for damage to be localized and quantified using regression and response surface modeling of modal frequency. Regression models or response surface models are developed to characterize how modal frequencies of structures are affected by variations in parameters such as defect depth, width and location. Design of experiments (DOE) techniques are used in conjunction with experimental modal frequency measurements to solve for defect parameters of test specimens in the field for condition monitoring. Determining defect parameters can be done by inverting and explicitly solving regression model equations, employing software-driven numeric optimization or through a graphical approach that overlays contour lines of multiple response surface models. Either of these methods can be automated. This approach is explored and validated with finite element and theoretical beam models along with a series of physical experiments on cantilevered aluminum rods. The method performs well for detecting simple and distinct defects. Implementation complexity increases when detecting multiple or more variable, less-easily quantifiable defects. In its general form, the method shows promise for damage detection when a specific type of consistent defect is known to occur or for applications such as quality control on production lines and monitoring of deposit buildup in pipes.
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Kim, Taehyun, Gu¨l E. Okudan, and Gu¨rdal Ertek. "Innovation in Product Form and Function: Customer Perception of Their Value." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87701.

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The goal of product design is to obtain the maximum effect with minimum cost in functionality and aesthetic beauty. Consumers are attracted to the designs that reflect their use behaviors and psychological responses more than they are to the simple visual representations. When product functions and qualities are similar across products, customers make their purchasing decision upon aesthetic form. Form presents a significant competitive factor that improves the value of a product. Overall, the purpose of this study is to examine the most important product design factors that affect the market share trends of mobile phone companies. Study uses product characteristics for 1,028 mobile phones released between 2003 and 2008 as a case study. Multiple linear regression analysis is used to select highly correlated variables that influence the market share, and Mallow’s Cp method is used to determine the best-fitting model. The Partial Regression Coefficients are used to evaluate the relative importance of design criteria. The nine mobile phone design features that affect the market share were identified, and the block form style is determined as the most important design factor. Using these approaches, this study demonstrates how investments should be directed in the next mobile phone design process.
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Li, Bingcheng. "Simple linear regression model based data clustering." In Automatic Target Recognition XXIX, edited by Timothy L. Overman and Riad I. Hammoud. SPIE, 2019. http://dx.doi.org/10.1117/12.2518037.

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Wu, Chien-Ho, Jung-Bin Li, and Tsair-Yuan Chang. "SLinRA2S: A Simple Linear Regression Analysis Assisting System." In 2013 IEEE 10th International Conference on e-Business Engineering (ICEBE). IEEE, 2013. http://dx.doi.org/10.1109/icebe.2013.33.

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Susanti, Nadya, Eti Poncorini Pamungkasari, and Rita Benya Adriani. "Association between Receptive Language Skill and Social Communication Skill among Preschool Children: Evidence from Surakarta, Central Java." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.101.

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ABSTRACT Background: Receptive language skills are crucial for the development of cognitive and social/emotional skills. Children with social communication problems are at risk of failure in the academic field, difficulty forming friendships, and social exclusion. This study aimed to examine the association between receptive language skill and social communication skill among preschool children. Subjects and Method: This was a cross sectional study conducted in Surakarta, Central Java, in January 2020. A sample of 200 pre-school children was selected by simple random sampling. The dependent variable was social communication. The independent variables were receptive language skill, parenting style, gender, and birth order. The data were collected by questionnaire and analyzed by a multiple linear regression. Results: Good social communication increased with good receptive language skill (OR= 3.21; 95% CI= 0.01 to 0.04; p= 0.002), first birth order (OR= 3.71; 95% CI= 0.79 to 1.47; p<0.001), democratic parenting style (OR= 5.21; 95% CI= 0.09 to 0.20; p<0.001), and female gender (OR= 5.23; 95% CI= 0.89 to 1.97; p<0.001). Conclusion: Good social communication increases with good receptive language skill, first birth order, democratic parenting style, and female gender. Keywords: social communication, receptive language skill Correspondence: Nadya Susanti. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: nadyasusanti3@gmail.com. Mobile: 081568222014 DOI: https://doi.org/10.26911/the7thicph.03.101
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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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Reports on the topic "Simple and multiple linear regression"

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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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Douglas, Thomas, Merritt Turetsky, and Charles Koven. Increased rainfall stimulates permafrost thaw across a variety of Interior Alaskan boreal ecosystems. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41050.

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Earth’s high latitudes are projected to experience warmer and wetter summers in the future but ramifications for soil thermal processes and permafrost thaw are poorly understood. Here we present 2750 end of summer thaw depths representing a range of vegetation characteristics in Interior Alaska measured over a 5-year period. This included the top and third wettest summers in the 91-year record and three summers with precipitation close to mean historical values. Increased rainfall led to deeper thaw across all sites with an increase of 0.7 ± 0.1 cm of thaw per cm of additional rain. Disturbed and wetland sites were the most vulnerable to rain-induced thaw with ~1 cm of surface thaw per additional 1 cm of rain. Permafrost in tussock tundra, mixed forest, and conifer forest was less sensitive to rain-induced thaw. A simple energy budget model yields seasonal thaw values smaller than the linear regression of our measurements but provides a first-order estimate of the role of rain-driven sensible heat fluxes in high-latitude terrestrial permafrost. This study demonstrates substantial permafrost thaw from the projected increasing summer precipitation across most of the Arctic region.
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