Journal articles: '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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Zhao, Xi, Yong Shi, and Lingfeng Niu. "Kernel based simple regularized multiple criteria linear program for binary classification and regression." Intelligent Data Analysis 19, no. 3 (June 9, 2015): 505–27. http://dx.doi.org/10.3233/ida-150729.

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Amiri, Amirhossein, and Elnaz Moein. "Some Notes on Diagnostic Procedures in Simple and Multiple Linear Regression Profiles Monitoring." Communications in Statistics - Simulation and Computation 42, no. 5 (May 2013): 981–1002. http://dx.doi.org/10.1080/03610918.2011.638425.

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Baird, Grayson L., and Stephen L. Bieber. "The Goldilocks Dilemma: Impacts of Multicollinearity -- A Comparison of Simple Linear Regression, Multiple Regression, and Ordered Variable Regression Models." Journal of Modern Applied Statistical Methods 15, no. 1 (May 1, 2016): 332–57. http://dx.doi.org/10.22237/jmasm/1462076220.

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Guimarães, Bruno V. C., Sérgio L. R. Donato, Ignacio Aspiazú, Alcinei M. Azevedo, and Abner J. de Carvalho. "Regression models for productivity prediction in cactus pear cv. Gigante." Revista Brasileira de Engenharia Agrícola e Ambiental 24, no. 11 (November 2020): 721–27. http://dx.doi.org/10.1590/1807-1929/agriambi.v24n11p721-727.

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ABSTRACT The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.

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Cooper, Paul D. "A Simple and Convenient Method of Multiple Linear Regression To Calculate Iodine Molecular Constants." Journal of Chemical Education 87, no. 7 (July 2010): 687–90. http://dx.doi.org/10.1021/ed100287r.

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Husain, Jakra A., and Ashish Manusmare. "PREDICTIVE MAINTENANCE OF SINGLE PHASE AC MOTOR USING IOT SENSOR DATA AND MACHINE LEARNING (SIMPLE LINEAR REGRESSION AND MULTIPLE LINEAR REGRESSION ALGORITHMS)." International Journal of Engineering Applied Sciences and Technology 04, no. 04 (August 31, 2019): 128–35. http://dx.doi.org/10.33564/ijeast.2019.v04i04.022.

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Návar Cháidez, José de Jesús, Nicolás González Barrientos, José de Jesús Graciano Luna, Virginia Dale, and Bernard Parresol. "Additive biomass equations for pine species of forest plantations of Durango, Mexico." Madera y Bosques 10, no. 2 (September 1, 2016): 17–28. http://dx.doi.org/10.21829/myb.2004.1021272.

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Statistical analysis between three weighted additive biomass equations are presented for planted pine species typical of the coniferous forests of the Western Sierra Madre mountain range of Durango, Mexico. Statistical and graphical analyses were used to select the best single and multiple individual biomass component equation. Linear equations better fitted the biomass components. Therefore, three linear additive procedures were tested: (i) the conventional, (ii) a harmonization, and ( iii) the seemingly-unrelated regression in two types of equations of component biomass estimation using both simple regression and multiple regression techniques. These tests were performed at two scales: (a) each of three pine species and (b) all three species. For both the simple linear and best multiple regression equation, the seemingly-unrelated equations provided more precise biomass component estimates, with tendencies consistent with the conventional non-additive non-linear regression procedures, and provided average biomass component estimates when equations were applied to a data set of 23 sample quadrants.

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Maulud, Dastan, and Adnan M. Abdulazeez. "A Review on Linear Regression Comprehensive in Machine Learning." Journal of Applied Science and Technology Trends 1, no. 4 (December 31, 2020): 140–47. http://dx.doi.org/10.38094/jastt1457.

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Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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Jensen, A. L. "Comparison of theoretical derivations, simple linear regressions, multiple linear regression and principal components for analysis of fish mortality, growth and environmental temperature data." Environmetrics 12, no. 6 (2001): 591–98. http://dx.doi.org/10.1002/env.487.

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Tlas, Mohammed, and Jamal Asfahani. "Multiple-linear regression to best-estimate of gravity parameters related to simple geometrical shaped structures." Contributions to Geophysics and Geodesy 49, no. 3 (September 1, 2019): 303–24. http://dx.doi.org/10.2478/congeo-2019-0016.

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Abstract A new interpretative approach is proposed to best-estimate of gravity parameters related to simple geometrical shaped structures such as a semi-infinite vertical cylinder, an infinite horizontal cylinder, and a sphere like structures. The proposed technique is based on the multiple-linear regression oriented towards estimating the model parameters, e.g., the depth from the surface to the center of the buried structure (sphere or infinite horizontal cylinder) or the depth from the surface to the top of the buried object (semi-infinite vertical cylinder), the amplitude coefficient, and the horizontal location from residual gravity anomaly profile. The validity of the proposed approach is firstly demonstrated through testing different synthetic data set corrupted and contaminated by a white Gaussian random noise level. The theoretical synthetic obtained results obviously show that the estimated parameters values, derived by the proposed technique are close to the assumed true parameters values. This approach is applied on five real field residual gravity anomalies taken from Cuba, Sweden, Iran, USA, and Germany, where the efficacy of this new approach is consequently proven. A comparable and acceptable agreement is noticed between the results derived by this proposed approach and those obtained from the real field data information.

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Hermawati, Rahmi, and Sri Handayani. "The Influence of Work Stress and Discipline on Work Performance of Emplyee at PT. Surya Menara Pratama Jakarta Selatan." PINISI Discretion Review 1, no. 2 (March 18, 2020): 55. http://dx.doi.org/10.26858/pdr.v1i2.13045.

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The final measure of success of an HR Department is work performance. The purpose of this study was to determine how much influence work stress has on work performance of empolyee at PT Surya Menara Pratama, South Jakarta, to find out how much influence discipline has on employee performance at PT Surya Menara Pratama, South Jakarta, and to determine the Effect of Work Stress and Discipline Towards Employee Job Performance at PT Surya Menara Pratama, South Jakarta. The research method used is a quantitative method with descriptive explanation. The population in this study were employees of PT Surya Menara Pratama South Jakarta with a sample of 60 and the sampling technique used was the Simple Random Sampling technique. Furthermore, the analysis method used in this study is multiple linear regression analysis consisting of descriptive analysis of the questionnaire, validity test, reliability test, classic assumption test, coefficient of determination test correlation coefficient test, simple regression test, multiple linear regression test, t test (test partial), f test (simultaneous test). The results of simple linear regression analysis show that work stress has a significant effect on work performance with a tcount of 3.902, a significance value of 0.000, a regression coefficient of 0.436 and a regression equation Y = 24.167 + 0.436X1. The results of simple linear regression analysis show that work discipline has a significant effect on work performance with a tcount of 3,500, a significance value of 0.001, a regression coefficient of 0.400 and a regression equation Y = 25.804 + 0.400X2. The results of multiple linear regression analysis show that work stress and work discipline simultaneously have a significant effect on work performance with a Fcount of 10.923, a significance value of 0.000, a coefficient of determination of 0.252 and a regression equation Y = 17.293 + 0.332X1 + 0.273X2.

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Jurisevic, Nebojsa, Dusan Gordic, and Arso Vukicevic. "Assessment of predictive models for the estimation of heat consumption in kindergartens." Thermal Science, no. 00 (2021): 84. http://dx.doi.org/10.2298/tsci201026084j.

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The service sector remains the only economic sector that has recorded an increase (3.8%) in energy consumption during the last decade, and it is projected to grow more than 50% in the following decades. Among the public buildings, educational are especially important since they have high abundance, great retrofit potential in terms of energy savings and impact in promoting a culture of energy efficiency. Since predictive models have shown high potential in optimizing usage of energy in buildings, this study aimed to assess their application for both finding the most influential factors on heat consumption in public kindergarten and heat consumption prediction. Two linear (Simple and Multiple Linear Regression) and two non-linear (Decision Tree and Artificial Neural Network) predictive models were utilized to estimate monthly heat consumption in 11 public kindergartens in the city of Kragujevac, Serbia. Top-performing and most complex to develop was the Artificial Neural Network predictive model. Contrary to that, Simple Linear Regression was the least precise but the most simple to develop. It was found that Multiple Linear Regression and Decision Tree were relatively simple to develop and interpret, where in particular the Multiple Linear Regression provided relatively satisfying results with a good balance of precision and usability. It was concluded that the selection of proper predictive methods depends on data availability, and technical abilities of those who utilize and create them, often offering the choice between simplicity and precision.

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Louis, Olivia, Yves Fierens, Maria Strantza, Robert Luypaert, Johan de Mey, and Erik Cattrysse. "Using Magnetic Resonance for Predicting Femoral Strength: Added Value with respect to Bone Densitometry." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/801518.

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Background and Purpose. To evaluate the added value of MRI with respect to peripheral quantitative computed tomography (pQCT) and dual energy X-ray absorptiometry (DXA) for predicting femoral strength.Material and Methods. Bone mineral density (BMD) of eighteen femur specimens was assessed with pQCT, DXA, and MRI (using ultrashort echo times (UTE) and the MicroView software). Subsequently biomechanical testing was performed to assess failure load. Simple and multiple linear regression were used with failure load as the dependent variable.Results. Simple linear regression allowed a prediction of failure load with either pQCT, DXA, or MRI in anr2range of 0.41–0.48. Multiple linear regression with pQCT, DXA, and MRI yielded the best prediction (r2=0.68).Conclusions. The accuracy of MRI, using UTE and MicroView software, to predict femoral strength compares well with that of pQCT or DXA. Furthermore, the inclusion of MRI in a multiple-regression model yields the best prediction.

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Pertiwi, Ajeng Rachma, Syaiful Iqbal, and Zaki Baridwan. "Effect of fairness and knowledge on tax compliance for Micro, Small, and Medium Enterprises (MSMEs)." International Journal of Research in Business and Social Science (2147- 4478) 9, no. 1 (January 3, 2020): 143–50. http://dx.doi.org/10.20525/ijrbs.v9i1.590.

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This study aims to empirically examine the effect of tax fairness and tax knowledge on tax compliance for Micro, Small and Medium Enterprises (MSMEs). Azmi and Perumal (2008) identified five of tax fairness dimensions: general fairness, exchanges with the government, special provisions, tax rates structure, and self-interest. Tax knowledge related to tax calculation, tax payment, and tax reporting. This study used a survey method by distributing the questionnaire. The sample used was Micro, Small and Medium Enterprises (MSMEs) in Malang city through the cluster random sampling method. The data obtained were 107 respondents who were processed with the help of SPSS 24. Two regression analyses used in this study are multiple linear regression analysis and simple linear regression analysis. The results of the study using multiple linear regression analysis showed that the three dimensions of tax fairness that affect MSMEs compliance are general fairness, tax rates structure, and self-interest. The results from the simple linear regression analysis show that tax knowledge influences MSMEs' compliance.

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Nitasari, Desi. "Pengaruh Suku Bunga Deposito, Nilai Tukar dan Inflasi Terhadap Harga Obligasi Pemerintah yang Terdaftar di Bursa Efek Indonesia Periode 2011-2017." Permana : Jurnal Perpajakan, Manajemen, dan Akuntansi 10, no. 1 (February 28, 2018): 1–20. http://dx.doi.org/10.24905/permana.v10i1.61.

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The purpose of this study is to 1). To know the effect of deposit interest rate on government bond price, 2). To know the effect of exchange rate on the price of government bonds, 3) To know the effect of inflation on the price of government bonds, 4). To know the effect of deposit interest rate, exchange rate, and inflation simultaneously to government bonds. The method used in this study is multiple regression. While the data analysis methods used are classical assumption test, simple linear regression analysis, test of simple linear regression coefficient, multiple linear regression analysis, multiple linear regression coefficient test, coefficient of determination analysis. Based on the results of simple regression analysis analysis of deposit interest rates on government bond prices obtained sig value. amounted to 0.342> 0.05, so it can be concluded that there is no effect of deposit rates on the price of government bonds listed on the Indonesia Stock Exchange period 2011-2017. From the results of simple regression analysis of the exchange rate against the price of government bonds obtained sig value. amounted to 0.060> 0.05, so it can be concluded that there is no effect of exchange rate on the price of government bonds listed on the Indonesia Stock Exchange period 2011 -2017. From the results of simple regression analysis of inflation analysis on the price of government bonds obtained sig value. amounted to 0.046 <0.05, so it can be concluded that there is inflationary influence on the price of government bonds listed on the Indonesia Stock Exchange period 20112017. From the results of simultaneous testing known significance value of 0.047. Because the probability value of sig nificance of 0,047 <0,05 can be interpreted that there is influence of deposit interest rate, exchange rate, and inflation simultaneously to government bond price listed in Bursa Efek Indonesia period 2011-2017.

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Sim, Siong Fong, Min Xuan Laura Chai, and Amelia Laccy Jeffrey Kimura. "Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)." Journal of Chemistry 2018 (November 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/7182801.

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Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

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Cepowski, Tomasz. "Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage." Management Systems in Production Engineering 25, no. 2 (June 1, 2017): 97–99. http://dx.doi.org/10.1515/mspe-2017-0014.

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Abstract The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

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Wohling, D. L., F. W. Leaney, and R. S. Crosbie. "Deep drainage estimates using multiple linear regression with percent clay content and rainfall." Hydrology and Earth System Sciences 16, no. 2 (February 24, 2012): 563–72. http://dx.doi.org/10.5194/hess-16-563-2012.

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Abstract. Deep drainage estimates are required for effective management of water resources. However, field measurements are time consuming and costly so simple empirical relationships are often used. Relationships developed between clay content of the surface soil and deep drainage have been used extensively in Australia to provide regional estimates of deep drainage but these relationships have been poorly justified and did not include rainfall in the relationships. Here we present a rigorous appraisal of clay content of soils and rainfall as predictors of deep drainage using an extensive database of field observations from across Australia. This study found that annual average rainfall and the average clay content of the top 2 m of the soil are statistically significant predictors of point scale deep drainage. Relationships have been defined for annual, perennial and tree type vegetation as a line of best fit along with 95% confidence intervals. This allows the uncertainty in these deep drainage estimates to be assessed for the first time.

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Jäntschi, Lorentz, Lavinia L. Pruteanu, Alina C. Cozma, and Sorana D. Bolboacă. "Inside of the Linear Relation between Dependent and Independent Variables." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/360752.

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Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors.

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Guimarães, Bruno Vinícius Castro, Sérgio Luiz Rodrigues Donato, Victor Martins Maia, Ignacio Aspiazú, Maria Geralda Vilela Rodrigues, and Pedro Ricardo Rocha Marques. "Simple and multiple linear regressions for harvest prediction of Prata type bananas." African Journal of Agricultural Research 8, no. 48 (December 12, 2013): 6300–6308. http://dx.doi.org/10.5897/ajar2013.7544.

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Darnawi, Jaka Waskito, and Mahben Jalil. "Pengaruh Biaya Promosi dan Biaya Distribusi Terhadap Hasil Penjualan Produk Bawang Goreng Pada UD Bawang Goreng di Desa Pagedangan Kecamatan Adiwerna Kabupaten Tegal." Permana : Jurnal Perpajakan, Manajemen, dan Akuntansi 10, no. 1 (February 28, 2018): 50–64. http://dx.doi.org/10.24905/permana.v10i1.66.

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The purpose of this study is 1). To know the effect of promotion cost to the sale result, 2). To know the effect of distribution cost on sales result, 3). To know the effect of promotion cost and distribution cost together to the sales result. Data collection techniques used in this study is the method of documentation and interviews. While the data analysis method used is classical assumption test, simple linear regression analysis, test of simple linear regression coefficient, multiple linear regression analysis, test of multiple linear regression coefficient, coefficient of determination. Based on the results of simple linear regression analysis calculations obtained results for the promotion variable in obtaining a probability significance value of 0.000 is smaller than 0.05 means there is a significant effect of promotion on sales results concluded that the first hypothesis that reads "Suspected there is the influence of promotional costs on sales results fried onion product "proved true. The result of calculation of simple linear regression analysis obtained result for the distribution variable in obtaining the value of probability significance of 0.000 is smaller than 0,05 meaning there is significant influence of distribution to result of sale can be concluded that second hypothesis which reads "allegedly there influence of distribution cost to result of sale fried onion product "is proven. The result of simultaneous influence test obtained by the significance level of 0,000 <0,05 means there is a significant influence between the cost of promotion and distribution costs together to the sales results can be said the third hypothesis that reads "It is suspected that there is influence of promotion costs and distribution costs together. same to the sale of onion garlic products on UD Bawang Goreng in Pagedangan Village Adiwerna District Tegal Regency "proved true.

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Ding, J., U. Haberlandt, and J. Dietrich. "Estimation of the instantaneous peak flow from maximum daily flow: a comparison of three methods." Hydrology Research 46, no. 5 (October 25, 2014): 671–88. http://dx.doi.org/10.2166/nh.2014.085.

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Three different methods are compared to estimate the instantaneous peak flow (IPF) from the corresponding maximum daily flow (MDF), as the daily data are more often available at gauges of interest and often with longer recording periods. In the first approach, simple linear regression is applied to calculate IPF from MDF values using probability weighted moments and quantile values. In the second method, the use of stepwise multiple linear regression analysis allows to identify the most important catchment descriptors of the study basin. The resulting equation can be applied to transfer MDF into IPF. With the third method, the temporal scaling properties of annual maximum flow series are investigated based on the hypothesis of piece wise simple scaling combined with the generalized extreme value distribution. The scaling formulas developed from three 15 min stations in the Aller-Leine river basin of Germany are transferred to all daily stations to estimate the IPF. The method based on stepwise multiple linear regression gives the best results compared with the other two methods. The simple regression method is the easiest to apply given sufficient peak flow data, while the scaling method is the most efficient method with regard to data use.

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Moya-Laraño, J., and G. Corcobado. "Plotting partial correlation and regression in ecological studies." Web Ecology 8, no. 1 (June 4, 2008): 35–46. http://dx.doi.org/10.5194/we-8-35-2008.

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Abstract. Multiple regression, the General linear model (GLM) and the Generalized linear model (GLZ) are widely used in ecology. The widespread use of graphs that include fitted regression lines to document patterns in simple linear regression can be easily extended to these multivariate techniques in plots that show the partial relationship of the dependent variable with each independent variable. However, the latter procedure is not nearly as widely used in ecological studies. In fact, a brief review of the recent ecological literature showed that in ca. 20% of the papers the results of multiple regression are displayed by plotting the dependent variable against the raw values of the independent variable. This latter procedure may be misleading because the value of the partial slope may change in magnitude and even in sign relative to the slope obtained in simple least-squares regression. Plots of partial relationships should be used in these situations. Using numerical simulations and real data we show how displaying plots of partial relationships may also be useful for: 1) visualizing the true scatter of points around the partial regression line, and 2) identifying influential observations and non-linear patterns more efficiently than using plots of residuals vs. fitted values. With the aim to help in the assessment of data quality, we show how partial residual plots (residuals from overall model + predicted values from the explanatory variable vs. the explanatory variable) should only be used in restricted situations, and how partial regression plots (residuals of Y on the remaining explanatory variables vs. residuals of the target explanatory variable on the remaining explanatory variables) should be the ones displayed in publications because they accurately reflect the scatter of partial correlations. Similarly, these partial plots can be applied to visualize the effect of continuous variables in GLM and GLZ for normal distributions and identity link functions.

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Aphiphan, Phiraphat, Uma Seeboonruang, and Somyot Kaitwanidvilai. "Cluster and regression analysis for predicting salinity in groundwater." MATEC Web of Conferences 192 (2018): 02007. http://dx.doi.org/10.1051/matecconf/201819202007.

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Groundwater salinity is a major problem particularly in the northeastern region of Thailand. Saline groundwater can cause widespread saline soil problem resulting in reducing agricultural productivity as in the Lower Nam Kam River Basin. In order to better manage the salinity problem, it is important to be able to predict the groundwater salinity. The objective of this research was to create a cluster-regression model for predicting the groundwater salinity. The indicator of groundwater salinity in this study was electrical conductivity because it was simple to measure in field. Ninety-eight parameters were measured including precipitation, surface water levels, groundwater levels and electrical conductivity. In this study, the highest groundwater salinity at 3 wells was predicted using the combined cluster and multiple linear regression analysis. Cross correlation and cluster analysis were applied in order to reduce the number of parameters to effectively predict the quality. After the parameter selection, multiple linear regression was applied and the modeling results obtained were R2 of 0.888, 0.918, and 0.692, respectively. This linear regression model technique can be applied elsewhere in the similar situation.

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Thomson, K. J., and K. G. Willis. "Errors in Variables: A Problem in Regression and its Solution." Environment and Planning A: Economy and Space 18, no. 5 (May 1986): 687–93. http://dx.doi.org/10.1068/a180687.

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It is not uncommon in socioeconomic analysis to measure variables with error, as in a 10% census. The estimation of linear regression coefficients using such ‘errors-in-variables’ models requires modification of the usual ordinary least squares techniques. The underlying theory both for simple and for multiple regression models is explained, and followed up with a numerical example based on a structure plan model of car ownership.

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Afantitis, Antreas, Georgia Melagraki, Haralambos Sarimveis, Panayiotis A. Koutentis, John Markopoulos, and Olga Igglessi-Markopoulou. "A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis." Molecular Diversity 10, no. 3 (August 2006): 405–14. http://dx.doi.org/10.1007/s11030-005-9012-2.

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Susanto, Annisa Husninadiyah, and Ida Bagus Gede Surya Abadi. "The Influence of Emotional Intelligence and Teacher Workload on Teacher Performance." Indonesian Journal Of Educational Research and Review 4, no. 1 (May 15, 2021): 34. http://dx.doi.org/10.23887/ijerr.v4i1.32925.

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There are still many teachers who have not been able to act professionally. Lack of emotional intelligence and motivation of teachers to complete tasks hampers teacher performance. This research was an ex post facto research. The population in the study involved 46 respondents. The techniques used to collect data are observation, interviews, and questionnaires. The instrument used in analyzing the data is a questionnaire. The data analysis method used consists of classical assumption tests and hypothesis testing. Classical assumption test consisted of residual normality test, heteroscedasticity test, multicollinearity test, and SPSS-assisted linearity test. Hypothesis testing used simple linear regression analysis and multiple linear regression analysis assisted by SPSS. The study results based on simple linear regression analysis and multiple linear regression analysis showed that (1) there was no effect of emotional intelligence on teacher performance. (2) There was an effect of workload on teacher performance with a contribution of 98%. (3) There is an influence of emotional intelligence and teacher workload on teacher performance with a contribution of 14.3%. It can be concluded that there is an influence of emotional intelligence and teacher workload on the performance of elementary school teachers.

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Park, Yeonseok, Anthony Choi, and Keonwook Kim. "Single-Channel Multiple-Receiver Sound Source Localization System with Homomorphic Deconvolution and Linear Regression." Sensors 21, no. 3 (January 23, 2021): 760. http://dx.doi.org/10.3390/s21030760.

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The conventional sound source localization systems require the significant complexity because of multiple synchronized analog-to-digital conversion channels as well as the scalable algorithms. This paper proposes a single-channel sound localization system for transport with multiple receivers. The individual receivers are connected by the single analog microphone network which provides the superimposed signal over simple connectivity based on asynchronized analog circuit. The proposed system consists of two computational stages as homomorphic deconvolution and machine learning stage. A previous study has verified the performance of time-of-flight estimation by utilizing the non-parametric and parametric homomorphic deconvolution algorithms. This paper employs the linear regression with supervised learning for angle-of-arrival prediction. Among the circular configurations of receiver positions, the optimal location is selected for three-receiver structure based on the extensive simulations. The non-parametric method presents the consistent performance and Yule–Walker parametric algorithm indicates the least accuracy. The Steiglitz–McBride parametric algorithm delivers the best predictions with reduced model order as well as other parameter values. The experiments in the anechoic chamber demonstrate the accurate predictions in proper ensemble length and model order.

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Tillyer, C. R., S. Rakhorst, and C. M. Colley. "Multicomponent analysis for alkaline phosphatase isoenzyme determination by multiple linear regression." Clinical Chemistry 40, no. 5 (May 1, 1994): 803–10. http://dx.doi.org/10.1093/clinchem/40.5.803.

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Abstract Alkaline phosphatase (EC 3.1.3.1) isoenzymes in serum may be determined by multicomponent analysis of the enzyme activities in the presence of multiple inhibitors. To determine inhibition coefficients of the isoenzymes, we used multiple linear regression analysis to compare alkaline phosphatase activities in the presence of known inhibitors with electrophoretically determined isoenzyme activities in plasma and serum samples. All possible combinations of exactly determined and overdetermined linear systems of inhibitors were ranked according to their prediction error to select an optimum set. The best multicomponent system for prediction included the use of levamisole, phenylalanine, and heat inhibition at 56 degrees C and 65 degrees C to determine bone, hepatic, intestinal, and placental isoenzymes. Consideration of the hepatic isoenzyme as liver and macromolecular fractions resulted in significantly worse predictions. Error analysis involving repeat determinations and a simplex optimization of the inhibition coefficients indicated that the inaccuracy of the comparison electrophoretic method may have been a major factor affecting poor isoenzyme prediction in some samples.

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Ando, Akihiko, Michiaki Miyamoto, Kazuhiko Kotani, Kenta Okada, Shoichiro Nagasaka, and Shun Ishibashi. "Cardio-Ankle Vascular Index and Indices of Diabetic Polyneuropathy in Patients with Type 2 Diabetes." Journal of Diabetes Research 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2810914.

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The cardio-ankle vascular index (CAVI) is used to test vascular function and is an arterial stiffness marker and potential predictor of cardiovascular events. This study aimed to analyze the relation between objective indices of diabetic polyneuropathy (DPN) and the CAVI. One hundred sixty-six patients with type 2 diabetes mellitus were included in this study. We used nerve conduction studies (NCSs) and the coefficient of variation of the R-R interval to evaluate DPN. We estimated arteriosclerosis by the CAVI. Simple and multiple linear regression analyses were performed between neuropathy indices and the CAVI. In univariate analysis, the CAVI showed significant associations with sural sensory nerve conduction velocity and median F-wave conduction velocity. Multiple linear regression analysis for the CAVI showed that sural nerve conduction velocity and median F-wave conduction velocity were significant explanatory variables second only to age. In multiple linear regression analysis for sural nerve conduction velocity among neuropathy indices, the CAVI remained the most significant explanatory variable. In multiple linear regression analysis for median nerve F-wave conduction velocity among neuropathy indices, the CAVI remained the second most significant explanatory variable following HbA1c. These results suggest a close relationship between macroangiopathy and DPN.

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Minshew, Hudson, John Selker, Delbert Hemphill, and Richard P. Dick. "NLEAP Computer Model and Multiple Linear Regression Prediction of Nitrate Leaching in Vegetable Systems." HortTechnology 12, no. 2 (January 2002): 250–56. http://dx.doi.org/10.21273/horttech.12.2.250.

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Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

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Morais, Camilo L. M., Ana C. O. Neves, Fabrício G. Menezes, and Kássio M. G. Lima. "Determination of serum protein content using cell phone image analysis." Analytical Methods 8, no. 34 (2016): 6458–62. http://dx.doi.org/10.1039/c6ay01783e.

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This paper presents a simple, fast and inexpensive way to measure serum protein content (albumin and total proteins) by integration of color images acquired with a cell phone camera and multiple linear regression (MLR).

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Xu, Hao, Kang Xiao, Jinlan Yu, Bin Huang, Xiaomao Wang, Shuai Liang, Chunhai Wei, Xianghua Wen, and Xia Huang. "A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression." Membranes 10, no. 8 (July 29, 2020): 171. http://dx.doi.org/10.3390/membranes10080171.

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Membrane fouling is a complicated issue in microfiltration and ultrafiltration. Clearly identifying the dominant fouling mechanisms during the filtration process is of great significance for the phased and targeted control of fouling. To this end, we propose a semi-empirical multiple linear regression model to describe flux decline, incorporating the five fouling mechanisms (the first and second kinds of standard blocking, complete blocking, intermediate blocking, and cake filtration) based on the additivity of the permeate volume contributed by different coexisting mechanisms. A piecewise fitting protocol was established to distinguish the fouling stages and find the significant mechanisms in each stage. This approach was applied to a case study of a microfiltration membrane filtering a model foulant solution composed of polysaccharide, protein, and humic substances, and the model fitting unequivocally revealed that the dominant fouling mechanism evolved in the sequence of initial adaptation, fast adsorption followed by slow adsorption inside the membrane pores, and the gradual growth of a cake/gel layer on the membrane surface. The results were in good agreement with the permeate properties (total organic carbon, ultraviolet absorbance, and fluorescence) during the filtration process. This modeling approach proves to be simple and reliable for identifying the main fouling mechanisms during membrane filtration with statistical confidence.

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Fadilah, Fadilah, Ade Arsianti, Arry Yanuar, Retnosari Andrajati, Rafika Indah Paramita, and Ernie Hernawati Purwaningsih. "Structure Activity Relationship Analysis of Antioxidant Activity of Simple Benzene Carboxylic Acids Group Based on Multiple Linear Regression." Oriental Journal of Chemistry 34, no. 5 (October 1, 2018): 2656–60. http://dx.doi.org/10.13005/ojc/340558.

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A multivariate analysis of the quantitative relationship of antioxidant structure and activity of a series of benzoic acid derivatives based on computational chemical properties was calculated. The parameters were obtained from the optimized structure of ionization pKa and hydrophobic ClogP while the compound activity was obtained from the literature. Analysis of the relationship between antioxidant activity and chemical properties of the compound was performed with the SPSS 21 program. The analysis result gives the best equation model as follows: Log 1 / IC50 = –1.514 + 0,516 log P + 0.087 pKa (n = 10 r = 0,962 SE = 0,301 Fcalc./Ftable = 1,422).

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Lui, Kung-Jong, and Duane Steffey. "A note on the application of simple linear regression methods for trend detection at multiple sites and visits." Statistics in Medicine 12, no. 12 (June 1993): 1125–39. http://dx.doi.org/10.1002/sim.4780121203.

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Hone, J. "Accuracy of the Multiple-Regression Method for Estimating Population-Density in Strip Transects." Wildlife Research 13, no. 2 (1986): 121. http://dx.doi.org/10.1071/wr9860121.

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Two experiments examined the accuracy of the multiple regression method for estimating population density. In experiment I , in a balanced design, an observer counted objectsin simulated strip transects. Multiple regression analyses yielded accurate estimates when true density was low, and overestimated density when true density was high. Regression equations calculated at each level of true density varied from linear to quadratic. A simple polynomial model accurately estimated true density. In experiment 2 an aerial survey of sheep showed that estimated density was highly significantly (P <0.001) different from true density. The results suggest that greater use should be made of established criteria for robust estimation of true density in transect studies.

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Martauli, Desy, Amri Amir, and Candra Mustika. "Analisis inflasi di lihat dari permintaan dan penawaran di Indonesia Tahun 2000-2018." e-Journal Perdagangan Industri dan Moneter 8, no. 1 (April 1, 2020): 1–10. http://dx.doi.org/10.22437/pim.v8i1.7189.

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This study aims to determine the analysis of inflation in terms of demand and supply in Indonesia in 2000-2018, the variables studied are the exchange rate, loan interest rates, world oil prices, public consumption. The type of time series data with the analytical method used in this study is using simple linear regression analysis and multiple linear regression (OLS) methods. The results of the trend of each variable inflation, exchange rate, interest rates on loans, world oil prices and public consumption fluctuate and have a tendency to increase with average inflation of 2.71%, the exchange rate of Rp. 14,143, the loan interest rate of 12.15%, the price of world oil is 91.67% and Indonesian people's consumption is 6,850,384 billion rupiah. The results of simple linear regression and multiple linear regression are shown through the simultaneous test (F test) that the exchange rate, loan interest rate, world oil price, and public consumption have a positive and significant effect on inflation in Indonesia. The results of the partial test (t-test) show that the loan interest rate and world oil prices have a positive and significant effect on inflation in Indonesia and public consumption has a negative and significant effect on inflation in Indonesia, while the exchange rate has a positive and significant effect on inflation in Indonesia. Keywords: Inflation, Exchange rate, Loan interest rate, World oil price, Community consumption

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Smedegård, Ole Øiene, Thomas Jonsson, Bjørn Aas, Jørn Stene, Laurent Georges, and Salvatore Carlucci. "The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway." Energies 14, no. 16 (August 7, 2021): 4825. http://dx.doi.org/10.3390/en14164825.

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This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range.

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ENNOURI, KARIM, HANEN BEN HASSEN, and NABIL ZOUARI. "Optimization of Bioinsecticides Overproduction by Bacillus thuringiensis subsp. kurstaki Using Linear Regression." Polish Journal of Microbiology 62, no. 3 (2013): 287–93. http://dx.doi.org/10.33073/pjm-2013-037.

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A multiple linear regression analyses were performed to screen for the significant factors simultaneously influencing production of deltaendotoxin, proteolytic activities and spore formation by a Bacillus thuringiensis kurstaki strain. Investigated factors included: pH of the medium, available oxygen and inoculum size. It was observed that oxygen availability was the most influencing setting on both deltaendotoxins production and spores counts, followed by initial pH of the medium and inoculum size. On other hand, pH of medium was found to be the most significant parameter for proteolytic activity, followed by inoculum size and dissolved oxygen. Our results suggested that the first order with two-factor interaction model seemed to be more satisfactory than simple first order model for optimization of delta-endotoxin overproduction. The coefficients of determination (R') indicated a better adequacy of the second order models to justify the obtained data. Based on results, relationships between delta-endotoxins production, proteolytic activities and spores counts were established. Our results can help to balance delta-endotoxins production and its stability.

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Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

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

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