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Journal articles on the topic 'Multiple Linear Stepwise Regression'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Omar Gan, Sarimah, and Sabri Ahmad. "ESTIMATION OF TRADE BALANCE USING MULTIPLE LINEAR REGRESSION MODEL." Labuan Bulletin of International Business and Finance (LBIBF) 16 (November 30, 2018): 44–52. http://dx.doi.org/10.51200/lbibf.v16i.1642.

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This study aims to evaluate the performance of multiple linear regression in estimating trade balance, so that a regression model for estimating the trade balance can be developed based on the important variables that have been identified. The performance of four regression methods including enter, stepwise regression, backward deletion, and forward selection is measured by mean absolute error, standard deviation, and Pearson correlation at the validation stage. The study concludes that multiple linear regression model developed by stepwise method is the best model for the trade balance estimation. The model considers the following six significant variables: Exports of palm oil, imports of tubes, pipes, and fittings of iron or steel, exports of crude petroleum, imports of petroleum products, exports of plywood plain, and imports of rice. The regression model achieves a moderate value of model estimated accuracy (76.10%), mean absolute error (0.257), standard deviation (0.308), and linear correlation (0.851).

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Albalawi, Itidal Ali, Mohammad A. Abdulhakeem, and Waleed B. ALShammari. "APPLICATION OF STEPWISE MULTIPLE REGRESSION TO SUPERSATURATED DESIGNS DATA OF WATER POLLUTION IN SAUDI ARABIA." JP Journal of Biostatistics 24, no. 3 (2024): 487–515. http://dx.doi.org/10.17654/0973514324027.

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Background. In data analysis issues, stepwise multiple linear regression is seen to be a very helpful computational method. Multiple linear regression (MLR), logistic regression, ordinal regression and multinominal regression are all examples of regressions. Multiple linear regressions are a type of linear regression analysis that is used to examine the relationship between a single response variable (the dependent variable) and two or more controlled variables (the independent variables). Objective. This research aims to comprehensively investigate and address the multifaceted issue of water pollution. Methods and materials. Data were obtained using an online questionnaire form, where the questionnaire was sent to the target group. There were four general steps to build questionnaire model in MLR. The general steps were supersaturated designs, checking assumptions, choosing appropriate multiple variables analysis, and interpreting the output. Each model was analyzed separately using the chosen analysis methods using a program Statistical Package for the Social Sciences 20. Results. The stepwise regression method for all applications reveals that five controlled variables were chosen using SPSS 20: , , , and . Conclusion. (increase in population), (inorganic materials such as (copper, mercury, etc.), (rainwater), (waste chemicals) and (waste of living organisms) are the actual causes of water pollution.

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

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AbstractIn the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q2 = 0.626) considering the leave-oneout prediction statistics.

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Sulistianingsih, Erna, Suparti Suparti, and Dwi Ispriyanti. "PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH MENGGUNAKAN METODE REGRESI RIDGE DAN REGRESI STEPWISE." Jurnal Gaussian 11, no. 3 (2022): 468–77. http://dx.doi.org/10.14710/j.gauss.11.3.468-477.

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The Human Development Index (HDI) is an important indicator in measuring the success of national development. Central Java with a high population can be considered as an obstacle and a driver of development. To find out the factors that affect HDI, it is necessary to make a model. One of the statistical methods that can be used is multiple linear regression analysis. However, in modeling multiple linear regression there are assumptions that must be met, namely linearity, normality, homoscedasticity, non-autocorrelation, and non-multicollinearity. If the non-multicollinearity assumption is not met, then another alternative is needed to estimate the regression parameters. Several methods that can be used are ridge regression and stepwise regression methods. The best model selection is done by looking at the smallest Mean Square Error (MSE) value. In this study, ridge and stepwise regression were applied to Central Java HDI data in 2021 and the factors that influence it, namely life expectancy at birth, expected years of schooling, average length of schooling, per capita expenditure, percentage of poor people, and unemployment open. Based on the Variance Inflation Factor (VIF) value of more than 10, it can be concluded that there is a multicollinearity violation. Modeling with stepwise regression produces the best model, with the smallest MSE value. The R square model value of 0,99 indicates that the model is included in the criteria for a strong model.

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Wang, Chunxiao, Jie Sun, Yongping Li, Jing Zhao, and Baochuan Tian. "A Comparison of Stepwise Cluster Analysis and Multiple Linear Regression for Hydrological Simulation." Journal of Physics: Conference Series 2224, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2224/1/012026.

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Abstract Investigating the dynamic characteristic of hydrological processes is of vital significance for environmental protection. In this study, the stepwise cluster analysis (SCA) method was used for monthly streamflow simulation in a hypothetical case. According to SCA, a cluster tree was formulated through training the data of monthly temperature, precipitation and streamflow from 2004 to 2010. Then, the generated tree was used to reproduce monthly streamflow in calibration period (i.e., 2004-2010) and validation period (i.e., 2011-2013). A comparison of SCA and multiple linear regression (MLR) was conducted to reflect the complex relationship of meteorological parameters (e.g., precipitation) and hydrological parameters. Model performance was assessed using Nash-Sutcliffe efficiencies (NSE), the determination coefficient (R 2), the root-mean-square error (RMSE) and the mean absolute error (MAE). NSE and R2 obtained from SCA are higher than that obtained from MLR. RMSE and MAE obtained from SCA are smaller than that obtained from MLR, indicating a better coincidence between simulated streamflow and the observed values in SCA. Results indicated that SCA has advantage in revealing the nonlinear relationship among precipitation, temperature and streamflow.

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Oliveira, Cinthia Pereira de, Rinaldo Luiz Caraciolo Ferreira, José Antônio Aleixo da Silva, et al. "Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil." Forests 12, no. 4 (2021): 473. http://dx.doi.org/10.3390/f12040473.

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In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and carbon estimates from forest inventory data and LiDAR technology in a dry tropical forest in Brazil. We use forest inventory data in two areas together with data from the LiDAR flyby, generating estimates of local biomass and carbon levels obtained from local species. We approach three types of models for data analysis: Multiple linear regression with principal components (PCA), conventional multiple linear regression and stepwise multiple linear regression. The best fit total above ground biomass (TAGB) and total above ground carbon (TAGC) model was the stepwise multiple linear regression, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics. Our finding provides important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration.

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Aredo, Victor, Lía Ethel Velásquez Castillo, and Nikol Siche. "Beef marbling measurement using spectral imaging: A multiple linear regression approach." Manglar 20, no. 4 (2023): 333–39. http://dx.doi.org/10.57188/manglar.2023.038.

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This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology.

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Nouman, Shakeel. "Multiple and stepwise regression of reproduction efficiency on linear type traits in Sahiwal cows." International Journal of Livestock Production 4, no. 1 (2013): 14–17. http://dx.doi.org/10.5897/ijlp12.029.

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Simmons, R. W., and P. Pongsakul. "Preliminary Stepwise Multiple Linear Regression Method to Predict Cadmium and Zinc Uptake in Soybean." Communications in Soil Science and Plant Analysis 35, no. 13-14 (2005): 1815–28. http://dx.doi.org/10.1081/lcss-200026798.

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Le, Xubo, Xueyan Wang, Haibo Li, Bingye Zhang, Xing Zhao, and Xiyong Zou. "Capacity Prediction of VRLA Batteries Based on Stepwise Regression Analysis." Journal of Physics: Conference Series 2659, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2659/1/012014.

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Abstract Effective maintenance of valve-regulated lead-acid (VRLA) battery groups within substations is critical for DC system reliability. Therefore, assessing battery health and remaining capacity is essential. In this study, we focus on predicting the VRLA battery group remaining capacity and propose a prediction model based on stepwise regression. This model utilizes operational data, combining the charging and discharging characteristics of VRLA batteries. Stepwise regression selects the optimal independent variable subset, mitigating poor predictions from using all variables in multiple linear regression. Experimental results validate the accurate prediction of lead-acid battery capacity decline, offering effective forecasts. This model is expected to be widely applied in the capacity prediction of DC power systems in substations.

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

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Simanjuntak, Presli Panusunan, and Afif Fatchur Rozzy. "PERBANDINGAN PREDIKSI CURAH HUJAN KUMULATIF BULANAN DENGAN VARIASI WAKTU JEDA (TIME LAG)." ORBITA: Jurnal Kajian, Inovasi dan Aplikasi Pendidikan Fisika 9, no. 1 (2023): 94. http://dx.doi.org/10.31764/orbita.v9i1.13950.

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ABSTRAKPeningkatkan akurasi pada prediksi curah hujan kumulatif bulanan sangat penting dilakukan terutama pada wilayah NON ZOM (Non Zona Musim), mengingat curah hujan mempengaruhi terhadap berbagai sektor kehidupan. Salah satu cara untuk meningkatkan akurasi prediksi curah hujan kumulatif bulanan adalah dengan menggunakan metode regresi linier. Terdapat banyak pilihan metode regresi linier untuk prediksi curah hujan, metode yang sering digunakan antara lain adalah regresi linier berganda dan regresi stepwise. Data curah hujan dasarian yang digunakan adalah 3 titik pos hujan dari tahun 1996 – 2016 dengan prediktor suhu muka laut dan air mampu curah (precipitable water). Penelitian ini menggunakan variasi waktu jeda (time lag) untuk mendapatkan nilai prediksi curah hujan kumulatif terbaik berdasarkan nilai RMSE pada waktu jeda tertentu. Masing – masing metode regresi kemudian digunakan untuk melakukan simulasi prediksi curah hujan kumulatif bulanan tahun 2017 – Februari 2019. Hasil prediksi menggunakan metode regresi linier berganda secara umum menujukkan nilai RMSE terendah pada waktu jeda 1 bulan. Untuk hasil prediksi menggunakan metode regresi stepwise secara umum nilai RMSE terendah terjadi pada waktu jeda simultan. Kata kunci: curah hujan; stepwise; time lag; RMSE ABSTRACTIncreasing accuracy in predicting monthly cumulative rainfall is very important especially in the NON ZOM area, considering that rainfall affects various sectors of life. One way to improve the accuracy of predictions of monthly cumulative rainfall is to use the linear regression method. There are many choices of linear regression methods for rainfall prediction, methods that are often used include multiple linear regression and stepwise regression. The dasarian rainfall data used are 3 rain post points from 1996-2016 with predictors of sea surface temperature and precipitable water. This study uses a time lag to get the best cumulative rainfall prediction value based on the RMSE value at a certain time interval. Each regression method is then used to simulate monthly cumulative rainfall prediction for 2017 - February 2019. Prediction results using multiple linear regression methods generally show the lowest RMSE value at 1month time lag. For prediction results using the stepwise regression method in general the lowest RMSE value occurs at the time of simultaneous time lag. Keywords: rainfall; stepwise; time lag; RMSE

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Liu, Yingxia, Gerard B. M. Heuvelink, Zhanguo Bai, et al. "Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression." Field Crops Research 264 (May 2021): 108098. http://dx.doi.org/10.1016/j.fcr.2021.108098.

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Sun, Qikai, and Jiaming Yao. "Comprehensive Evaluation and Stepwise Regression Model of Light Pollution in Topsis Based on Entropy Weight Method." Highlights in Science, Engineering and Technology 59 (July 15, 2023): 175–83. http://dx.doi.org/10.54097/hset.v59i.10079.

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Light pollution is a phenomenon that affects our environment. It is of great significance to make a quantitative analysis of light pollution and establish an evaluation system for energy saving, environment improvement and social and economic benefits. In order to establish a reasonable evaluation system, based on a comprehensive evaluation model of light pollution risk level based on the entropy weight method of Topsis analysis, combined a multiple linear regression model to test the significance of the indicator data and performed the White's test and the VIF test for multiple covariance on the model.Then this paper used the standard inverse stepwise regression model to solve the linear regression model, constructed a comprehensive evaluation model of light pollution.

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Wu, Di. "A Study on Evaluation and Effectiveness Prediction of Medical English Teaching Combined with Multivariate Analysis Modeling." Journal of Combinatorial Mathematics and Combinatorial Computing 127a (April 15, 2025): 1667–81. https://doi.org/10.61091/jcmcc127a-097.

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The article evaluates and predicts the effectiveness of medical English teaching through stepwise regression analysis in multivariate analysis method. It constructs an analytical prediction model of medical English teaching evaluation based on multivariate regression analysis. After initially establishing the evaluation index system of medical English teaching, effective evaluation indexes are screened out through stepwise regression, an effective evaluation index system of medical English teaching is constructed, and multiple regression equations for the quality of medical English teaching (students’ performance in medical English) are established. The prediction model of medical English teaching quality is constructed by eliminating the influential factors and abnormal data that do not have significance through multiple linear regression analysis. Teaching quality prediction equations were constructed by choosing teaching content, teaching method, teaching organization, teaching expression, teaching attitude, and overall effect of teaching. Among them, teaching content and teaching expression were significant, and the final prediction model of medical English teaching quality was Y=0.1958+0.1142*teaching content+0.7232* teaching expression. The 79.26% of students’ medical English performance can be explained by the multivariate linear regression analysis mode

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

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Abstrak Semakin lama masa kerja, semakin banyak pengalaman yang dimiliki seseorang atas pekerjaannya. Seorang yang memiliki tingkat kehadiran yang tinggi, dianggap sebagai pekerja yang baik. Kedua faktor ini membentuk asumsi bahwa masa kerja dan tingkat kehadiran, secara positif atau negatif, mempengaruhi produktivitas pekerja. Dikarenakan besarnya pengaruh produktivitas pekerja terhadap kesehatan sebuah perusahaan, kegiatan menganalisis produktivitas tenaga kerja perusahaan, seharusnya tidak didasarkan pada asumsi-asumsi, meskipun asumsi tersebut bisa diterima. Menggunakan Regresi Linier Berganda, sebuah model persamaan dihasilkan dari data-data mengenai tenaga kerja. Tapi, karena nilai Koefisien Determinasi yang dihasilkan kurang memuaskan, dilakukan analisis ulang terhadap data. Kali ini menggunakan Regresi Linier Stepwise. Analisis kedua ini dapat menghasilkan nilai Koefisien Determinasi yang lebih tinggi dari nilai sebelumnya, meskipun harus diterima bahwa nilai yang baru ini masih terlalu rendah. Meskipun begitu, beberapa fakta mengenai sistem kerja perusahaan dan latar belakang tenaga kerjanya, dapat dijadikan penjelasan mengenai hasil analisis yang telah dilakukan.  Kata kunci: produktivitas, regresi linier stepwise  Abstract  The longer the employment length, the more experience a person has on his or her job. A person who has a high attendance at work, is considered a good worker. These two factors form the assumption that both employment length and work attendance, influence laborer productivity, either positively or negatively. As Labor productivity holds a lot of weight in relation to a company’s health, conducting an analysis of a company's productivity, must not be based on assumptions, even though it’s an acceptable one. Using multiple linear regression, a model was generated from data about the workforce. But, because the Coefficient of Determination value was less than satisfactory, an analysis was performed again on the data, this time using Stepwise Regression. The second analysis managed to produce a higher Coefficient of Determination value than the previous one, but it must accepted that the value remains too low. Though a few facts about the company’s work system and labours history could provide some explanation on this result.  Keyword: productivity, stepwise linear regression

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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 (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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Roy, Banibrata, Kyle Perry, Ira Ripstein, and Barry Cohen. "Predictive value of grade point average (GPA), Medical College Admission Test (MCAT), internal examinations (Block) and National Board of Medical Examiners (NBME) scores on Medical Council of Canada qualifying examination part I (MCCQE-1) scores." Canadian Medical Education Journal 7, no. 1 (2016): e47-e56. http://dx.doi.org/10.36834/cmej.36616.

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Background: To determine whether the pre-medical Grade Point Average (GPA), Medical College Admission Test (MCAT), Internal examinations (Block) and National Board of Medical Examiners (NBME) scores are correlated with and predict the Medical Council of Canada Qualifying Examination Part I (MCCQE-1) scores.Methods: Data from 392 admitted students in the graduating classes of 2010-2013 at University of Manitoba (UofM), College of Medicine was considered. Pearson’s correlation to assess the strength of the relationship, multiple linear regression to estimate MCCQE-1 score and stepwise linear regression to investigate the amount of variance were employed.Results: Complete data from 367 (94%) students were studied. The MCCQE-1 had a moderate-to-large positive correlation with NBME scores and Block scores but a low correlation with GPA and MCAT scores. The multiple linear regression model gives a good estimate of the MCCQE-1 (R2 =0.604). Stepwise regression analysis demonstrated that 59.2% of the variation in the MCCQE-1 was accounted for by the NBME, but only 1.9% by the Block exams, and negligible variation came from the GPA and the MCAT.Conclusions: Amongst all the examinations used at UofM, the NBME is most closely correlated with MCCQE-1.

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Jahan, Mashrat, Keya Das, Linnet Barman, and Preetilata Burman. "Modeling of Lemon Production: A Stepwise Regression Approach." Journal of Bangladesh Agricultural University 21, no. 4 (2023): 542. http://dx.doi.org/10.5455/jbau.157099.

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Lemon, which is both tropical and subtropical fruit, was formerly used for only entertaining the guests, but today, as a result of its many different uses; it is highly popular with both consumers and producers. Bangladesh is home to extensive lemon groves due to the fruit's high nutritional value, favorable growing conditions, and widespread demand on both domestic and international markets. This study examined the combined impact of farm specific factor and climatic variable on lemon production in Bangladesh. Here the stepwise regression technique with various diagnostic plots in R studio was used to assess the suitable multiple linear regression model and an effective autoregressive integrated moving average (ARIMA) model-based time series analysis was produced for future projection of lemon. The study found a positive link between lemon output and price but a negative correlation with climate variables. Annually, one substantial drop in the lemon cultivars as a result of climatic stress is initiated due to the most significant aspects of which are temperatures and rainfall. Result found, with each millimeter of increased AMR, output diminished by 0.0006419 times on average. The production drops by 0.3564144 times on average for every unit increase in annual mean temperature (AMT). Price increases for lemons may be traced back to covid-19, when production was increased in response to increasing demand about the health benefits of lemons. Also, the investigation found ARIMA (1,1,1) model with drift to the best for future projections of lemon production.

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Sapkota, Kamal Raj. "Study on QSPR Method for Theoretical Calculation of Boiling Point of Some organic Compounds." Himalayan Physics 3 (January 1, 2013): 93–95. http://dx.doi.org/10.3126/hj.v3i0.7316.

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Quantitative structure-property relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of boiling point using a set of 25 organic compounds. The molecular descriptors used to represent molecular structure include topological indices and constitutional descriptors. Forward stepwise regression was used to construct the QSPR models. Multiple linear regressions is utilized to construct the linear prediction model. The prediction result agrees well with the experimental value of these properties.The Himalayan PhysicsVol. 3, No. 3, July 2012Page: 93-95

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Yanke, Aldino, Nofrida Elly Zendrato, and Agus M. Soleh. "Handling Multicollinearity Problems in Indonesia's Economic Growth Regression Modeling Based on Endogenous Economic Growth Theory." Indonesian Journal of Statistics and Its Applications 6, no. 2 (2022): 228–44. http://dx.doi.org/10.29244/ijsa.v6i2p214-230.

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One of the multiple linear regression applications in economics is Indonesia’s economic growth model based on the theory of endogenous economic growth. Endogenous economic theory is the development of classical theory which cannot explain how the economy grows in the long run. The regression model based on the theory of endogenous economic growth used many independent variables, which caused multicollinearity problems. In this study, the multiple linear regression model using the least-squares estimation method and some methods to handle the multicollinearity problem was implemented. Variable selection methods (backward, forward, and stepwise), principal component regression (PCR), partial least square (PLS), and regularization methods (Ridge, Lasso, and Elastic Net) were applied to solve the multicollinearity problem. Variable selection method with backward, forward, and stepwise has not been able to overcome the problem of multicollinearity. In contrast, Principal Component Regression, PLS regression, and regularization regression methods overcame the multicollinearity problem. We used "leave one out cross-validation" (LOOCV) to determine the best method for handling multicollinearity problems with the smallest mean square of error (MSE). Based on the MSE value, the best method to overcome the multicollinearity problem in the economic growth model based on endogenous economic growth theory was the Lasso regression method.

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A. Ibrahim, Ammar, and Mohammed Khahtan Hasan. "Theoretical Calculations of pKa Values for Substituted Carboxylic Acid." NTU Journal of Pure Sciences 1, no. 1 (2021): 19–26. http://dx.doi.org/10.56286/ntujps.v1i1.142.

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Six different methods of determination have been used for studying ten derivatives of carboxylic compounds. Semi-empirical (AM1 and PM3), Hartree Fock (HF/STO-3G and HF/3-21G), and Density Function Theory (DFT/STO-3G and DFT/6-31G) were employed to calculate many physical theoretical parameters. The calculated data were correlated with experimental values of pKa using different regression(enter, stepwise and simple regression). Depending on the Fisher values, (HF/STO-3G) was shown as the best method for predicted of the pKa data compare to the (PM3) method using enter method. While (HF/3-21G)method has a big value of fisher compared with(PM3) using (stepwise) and (simple) methods. So, Multiple linear regression was performed to obtain the best prediction values using (enter) compare with (stepwise), and the two methods are the best compared with simple regression.

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Ray, Supratim, Chandana Sengupta, and Kunal Roy. "QSAR modeling of antiradical and antioxidant activities of flavonoids using electrotopological state (E-State) atom parameters." Open Chemistry 5, no. 4 (2007): 1094–113. http://dx.doi.org/10.2478/s11532-007-0047-3.

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AbstractIn the present paper QSAR modeling using electrotopological state atom (E-state) parameters has been attempted to determine the antiradical and the antioxidant activities of flavonoids in two model systems reported by Burda et al. (2001). The antiradical property of a methanolic solution of 1, 1-diphenyl-2-picrylhydrazyl (DPPH) and the antioxidant activity of flavonoids in a β-carotenelinoleic acid were the two model systems studied. Different statistical tools used in this communication are stepwise regression analysis, multiple linear regressions with factor analysis as the preprocessing step for variable selection (FA-MLR) and partial least squares analysis (PLS). In both the activities the best equation is obtained from stepwise regression analysis, considering, both equation statistics and predictive ability (antiradical activity: R 2 = 0.927, Q2 = 0.871 and antioxidant activity: R 2 = 0.901, Q2 = 0.841).

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He, Yang, Dongfang Qi, and Vladimir M. Bure. "New application of multiple linear regression method - A case in China air quality." Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 18, no. 4 (2022): 516–26. http://dx.doi.org/10.21638/11701/spbu10.2022.406.

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In this paper, we propose an econometric model based on the multiple linear regression method. This research aims to evaluate the most important factors of the dependent variable. To be more specific, we consider the properties of this model, model quality, parameters test, checking the residual of the model. Then, to ensure that the prediction model is optimal, we use the backward elimination stepwise regression method to get the final model. At the same time, we also need to check the properties in each step. Finally, the results are illustrated by a real case in China air quality. The achieved model was applied to predict the 31 capital cities in Сhina's air quality index (AQI) during 2013-2019 per year. All calculations and tests were achieved by using R-studio. The dependent variable is the China's AQI. The control variables are six pollutant factors and four meteorological factors. In summary, the model shows that the most significant influencing factor of the AQI in China is PM2.5, followed by O3.

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Gómez-Sánchez, Juan Carlos, María Victoria De la Fuente Aragón, and Lorenzo Brian Ros McDonell. "Stepwise multiple linear regression applied to study the influence of pedestrian volumes according to environmental characteristics." Dirección y Organización, no. 75 (December 1, 2021): 52–61. http://dx.doi.org/10.37610/dyo.v0i75.609.

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Desde comienzos de los años 90, la evolución de los factores socioeconómicos, locales y regionales en la sociedad occidental ha hecho que el peatón se convierta en el foco de los planes de movilidad urbanos llevados a cabo hasta la actualidad. Esto se puede comprobar considerando las numerosas investigaciones realizadas en este ámbito donde se evidencia la necesidad de fundamentar la relación existente entre el planeamiento urbano y la salud de las personas. Pese a que se han propuesto numerosas metodologías para analizar estas relaciones, se observa que aquella que se da entre el volumen de peatones en una zona y las características del entorno dan mejores resultados, aunque partan de modelos multivariables difíciles de calcular. El propósito de este artículo pretende recopilar y reducir aquellas variables que afecten de manera significativa a los movimientos poblacionales en función del nivel comercial de la zona de estudio. Para ello, se utilizará entre los diferentes modelos matemáticos existentes, el de regresión lineal escalonado. Finalmente, se concluye que la conectividad y el mobiliario urbano en áreas primarias, la presencia de bares y oficinas en áreas secundarias y la conectividad, densidad residencial y zonas de uso mixto en áreas terciarias son factores clave que deberán ser consideraros en la planificación futura de las zonas estudiadas.

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

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

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Zhou, Yiqian, Rehman Qureshi, and Ahmet Sacan. "Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression." Network Modeling Analysis in Health Informatics and Bioinformatics 1, no. 1-2 (2012): 3–17. http://dx.doi.org/10.1007/s13721-012-0008-4.

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Li, Mingjun, and Junxing Wang. "An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling." Mathematical Problems in Engineering 2019 (April 17, 2019): 1–13. http://dx.doi.org/10.1155/2019/7620948.

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Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

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Shoikhedbrod, Michael. "Prediction of Tumor Metastases, Using Interpolation of a Statistical Histogram of the Time Intervals of Detecting Metastases at Cancer Patients after Conducted Operation." Journal of Computer Science and System Software 1, no. 1 (2024): 32–40. http://dx.doi.org/10.48001/jocsss.2024.1132-40.

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Prediction of tumor metastases, occurring among cancer patients after surgery plays a vital role in the survival of cancer patients. Prediction of tumor metastases also permits to determine the efficiency of conducted after surgery prophylactic treatment. Today, oncologists expect detection of metastases of malignant neoplasms at cancer patients after operation in accordance with the exponential decrease over time of the number of cancer patients at whom metastases were detected after surgery, which is scientifically unfounded and leads to an incorrect determination of the moment of detection of metastases at cancer patient after operation, and therefore to untimely examination of a cancer patient and his preventive treatment. Predicting in statistics is carried out, using polynomial regression, where a certain relationship, called regression, with a certain accuracy between a pair of studied medical symptoms is determined, multiple linear regression, where a certain relationship, called regression, with a certain accuracy between studied symptom with multiple symptoms is determined in linear form and stepwise multiple linear regression, where a certain relationship, called regression, with a certain accuracy is determined also between studied symptom with multiple symptoms in a linear form.

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Huang, Linsheng, Hansu Zhang, Wenjuan Ding, Wenjiang Huang, Tingguang Hu, and Jinling Zhao. "Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset." Journal of Spectroscopy 2019 (November 11, 2019): 1–9. http://dx.doi.org/10.1155/2019/9153195.

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It is highly important to accurately monitor wheat scab and provide technical guidance for the crop pests and diseases. In this study, relevant analysis was performed among spectral reflectance, first-derivate data, and the disease severity data through ASD hyperspectral data. Two sensitive spectral wavelength ranges of 450–488 nm and 500–540 nm were selected. Then, a new wheat scab index (WSI) consisting of the two bands was proposed. The inversion models of the scab severities were comparatively built by unitary linear regression and multiple stepwise regression techniques. The results showed that the WSI had a significant linear relationship with severity of disease compared with other commonly used spectral indices. The fitting R2, testing R2, and RMSE were 0.73, 0.70, and 13.41, respectively. The multiple stepwise regression model established using the WSI, SDg/SDb, NBNDVI, and SDg as independent variables was better than the single-variable model. Our results suggest that WSI can be used to provide scientific guidance for monitoring and precise management of wheat scab disease.

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Noorunnahar, Mst, Rabbani Rushsa, and Keya Rani Das. "Climate Change and Rice Production in Bangladesh: Finding the Best Prospective Factors Using Multiple Linear Regression Modeling Techniques." European Journal of Agriculture and Food Sciences 5, no. 5 (2023): 30–37. http://dx.doi.org/10.24018/ejfood.2023.5.5.724.

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Climatic factors like temperature, rainfall, humidity, CO2 and solar radiation significantly impact agricultural production. Bangladesh is primarily an agriculture-based developing country. Rice (Oryza sativa L.), the main food of Bangladeshi people also provides a significant percentage of their regular, balanced diet. Many studies have been conducted to determine the effects of climate variability and change on rice productivity in Bangladesh. This study aimed to investigate the relationship between rice crop production and climate variables (namely, average temperature, rainfall, CO2, and humidity) and find out the best model that has an actual impact on rice production. Selecting 'potential predictors' from numerous possible variables to influence the forecast variable and investigating the most appropriate model with a subset of the potential predictors are two major difficulties of fitting the multiple linear regression model. Best subset regression and stepwise regression were used to fit the model using R software. Our results revealed that temperature and CO2 were statistically significant for rice production at 5% and 1% levels of significance respectively. From Adjusted R2, climatic parameters account for 17.39 percent of the variation in rice production. Temperature and CO2 are the best predictors, according to model Cp and AIC values, and stepwise regression also supports this finding. The model that had been so successfully fitted was considered to be highly significant, demonstrating its potential for use in reality by the concerned planners and policymakers.

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Quartezani, Waylson Zancanella, Julião Soares de Souza Lima, Talita Aparecida Pletsch, et al. "Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity." June 2019, no. 13(06) 2019 (June 20, 2019): 857–62. http://dx.doi.org/10.21475/ajcs.19.13.06.p1424.

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There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.

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Hou, Yongmei, and Yulin Liu. "The Psychological Entitlement among Undergraduates and Its main Demographic Factors." Global Journal of Arts Humanity and Social Sciences 4, no. 5 (2024): 305–10. https://doi.org/10.5281/zenodo.11173497.

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Objective: To explore the status of psychological entitlement among undergraduates, and analyze the main demographic factors. Method: Totally 768 undergraduates were selected by stratified random sampling from 5 colleges in Guangdong Province. They were investigated with Psychological Entitlement Scale (PES), and a self-compiled general personal information questionnaire. Results: The total average score of PES was (4.46±0.85). The results of multiple linear stepwise regression analysis showed that the total score of PES was positively correlated with scores of four factors like self-assessment of appearance, professional prospects, grade, and subjective helplessness (β= 0.073 to 0.193, all P<0.01), and negatively correlated with whether one is an only child or not (β=- 0.472, P<0.01). Conclusion: The psychological entitlement of college students is at a moderate level, and its influencing factors involve multiple dimensions such as physiological factors, family factors, and individual experiences.

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Liu, Yuantian. "Revealing Relevant Factors of Automobile Emissions Based on Linear Regression." Theoretical and Natural Science 105, no. 1 (2025): 71–79. https://doi.org/10.54254/2753-8818/2025.22574.

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Global warming has emerged as a critical global concern, with the control of greenhouse gas emissions becoming a paramount priority for nations worldwide. Carbon dioxide (CO), a significant greenhouse gas, comprises a substantial portion of vehicle exhaust emissions. To effectively mitigate CO emissions from automobiles, it is imperative to identify and analyze the key determinants influencing these emissions. In this paper, the collected data were fitted into a model through multiple linear regression in R. The variance inflation factor (VIF) detection method was used to detect multicollinearity, and the stepwise regression method was employed to eliminate multicollinearity in the model to study the related factors of CO emissions from automobiles. The findings indicate that engine size, number of cylinders, and combined fuel consumption are primary factors affecting CO emissions from vehicles. Among them, the combined fuel consumption is the most significant influencing factor. These results offer valuable insights for automotive engineers and researchers, guiding efforts to enhance vehicle design and reduce CO emissions.

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Fadil, Sanaa, Imane Sebari, Moulay Mohamed Ajerame, et al. "An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora." Land 13, no. 5 (2024): 688. http://dx.doi.org/10.3390/land13050688.

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Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

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Hou, Yongmei, and Jiaxin Zhu. "The influence of life stress on achievement motivation among college students." International Journal of Arts and Social Science 06, no. 02 (2023): 46–52. https://doi.org/10.5281/zenodo.7759061.

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To explore the current situation of college students' life stress and achievement motivation, and explore the impact of life stress on college students' achievement motivation. Methods A stratified random sampling was used to select 743 undergraduates in Guangdong Province. They were investigated with StudentLife Stress Inventory (SLSI) and Achievement Motivation Scale (AMS). Results ⑴ The total scores of SLSI and AMS in this group were (181.64 ± 22.59) and (4.21 ± 12.28) respectively. ⑵ The students with high, medium and low life stress accounted for 16.7%, 68.9% and 14.4%, respectively. ⑶ Multiple stepwise linear regression analysis showed that the total score of AMS was negatively predicted by the scores of five dimensions: frustration, pressure, physiological response, emotional response and behavioral response (β=-.342 to -.493, all P <.001), while cognitive response positively predicted the total score of AMS (β=.563, P<.001). Conclusion College students have high level of life stress and low achievement motivation. Life stress may be one of the main influencing factors of college students' achievement motivation.

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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 (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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Marvero, Andre, Muhammad Amri, Muhammad Fadhil Irsyad, and Yenni Kurniawati. "Analisis Pemilihan Model Regresi Konversi Metanol Berdasarkan Suhu, Waktu Tinggal, Konsentrasi, Rasio Oksigen, dan Sistem Reaktor." UNP Journal of Statistics and Data Science 3, no. 1 (2025): 47–52. https://doi.org/10.24036/ujsds/vol3-iss1/339.

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This study aims to determine the best regression model that explains the effect of temperature, residence time, methanol concentration, oxygen to methanol ratio, and reactor system on methanol conversion in supercritical water. Preliminary analysis showed a violation of the multicollinearity assumption, which affected the validity of the multiple linear regression model. To overcome this and determine the optimal model, variable selection was performed using the stepwise selection method. This method was evaluated based on predictive power, model accuracy and statistical validity. The results showed that the stepwise method produced an optimal model in predicting conversion. Reactor system and temperature were the most significant variables affecting methanol conversion. The conclusion of this study shows that the variable selection approach with stepwise selection can be effectively used to identify the best regression model, when classical assumptions are met. These findings make an important contribution to the optimization of supercritical water-based chemical processes.

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Machado, M. V., A. M. G. Tommaselli, V. M. Tachibana, R. P. Martins-Neto, and M. B. Campos. "EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 449–54. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-449-2019.

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Abstract. Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R2 adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&amp;thinsp;cm.

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Kokaly, R. "Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression." Remote Sensing of Environment 67, no. 3 (1999): 267–87. http://dx.doi.org/10.1016/s0034-4257(98)00084-4.

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

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Grossman, Y. L., S. L. Ustin, S. Jacquemoud, E. W. Sanderson, G. Schmuck, and J. Verdebout. "Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data." Remote Sensing of Environment 56, no. 3 (1996): 182–93. http://dx.doi.org/10.1016/0034-4257(95)00235-9.

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Khaja, Mohammad Aasif, Shagoofta Rasool Shah, and Ramakar Jha. "Predictive hydraulic conductivity modelling of wide gradation spectrum sandy soils using stepwise multiple linear and LASSO regression." International Journal of Hydrology Science and Technology 19, no. 4 (2025): 375–403. https://doi.org/10.1504/ijhst.2025.146507.

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Zhang, Tao, Shanshan Zhang, Lan Chen, et al. "UHPLC–MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken." Foods 9, no. 9 (2020): 1326. http://dx.doi.org/10.3390/foods9091326.

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To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

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Zhu, Weijia. "High school student GPA prediction by various linear regression models." Theoretical and Natural Science 52, no. 1 (2024): 153–62. http://dx.doi.org/10.54254/2753-8818/52/2024ch0136.

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Academic performance (GPA) is a significant index for high school students in North America. The research aims to develop and validate the best predictive regression model to evaluate the GPA of high school students. In addition, the prediction numeric data of GPA can correspond to specific classification data of GPA (Grade Level) to calculate and compare the models accuracy for predicting Grade Level. The research subjects are high school students from different backgrounds in North America, including their background, study habits, participation in extracurricular activities, etc. The experiment explores the impact of different factors on student GPA and finds that the number of absences from lectures is a key factor in predicting student GPA. Multiple linear regression analysis is used as the main model in the experiment, which may be improved by the stepwise regression methods. The generalization ability of the model is evaluated through cross-validation (CV) methods. Also, the boosting or random forest model is used to be the comparing model for predicting GPA. The experimental result shows that the multiple linear regression model has high accuracy (84%) and reliability (R^2 value is 0.95) in predicting student GPA. The conclusion of the research emphasizes the importance of predicting student GPA in high school education and the potential for guiding educational practice through data analysis. Future work will consider introducing more subjects and variables, such as different subject learning abilities, mental health, and social support, to further improve the predictive accuracy of the model.

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Muazam, A., S. Widyayanti, Kristamtini, and B. S. Daryono. "The stepwise regression analysis method for estimating sorghum production in Karangmojo." IOP Conference Series: Earth and Environmental Science 1377, no. 1 (2024): 012099. http://dx.doi.org/10.1088/1755-1315/1377/1/012099.

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Abstract Sorghum is a multifunctional crop that can be utilized as a source of food, feed, and bioenergy. Sorghum is a plant that can adapt to land with optimal conditions, dry land with minimal nutrients and tolerant to pests and diseases. Sorghum has been widely cultivated in Indonesia, one in Gunungkidul, Yogyakarta. Sorghum production is influenced by several agronomic characteristics. The purpose of the study was to determine agronomic variables that affect sorghum production. The research was conducted in Karangmojo, Gunungkidul, Special Region of Yogyakarta, from October 2022 to March 2023. Sorghum varieties were local varieties as well as national superior varieties which are usually planted by local farmers. A total of 10 variables were analysed for their significance level on sorghum yield. The collected data were then processed using a multiple linear regression model (stepwise) using SPSS 16.0. The results showed that of the 10 agronomic variables observed, two variables contributed to the sorghum production, namely panicle weight and days to harvesting. The regression model from stepwise results is y = 8.884 + 0.036x3 – 0.062x10, with R2 = 0.754. This result indicates that the two independent variables are the main variables in determining sorghum production in Karangmojo.

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Barbosa, Lucas, Caio Sousa, Marcelo Sales, et al. "Celebrating 40 Years of Ironman: How the Champions Perform." International Journal of Environmental Research and Public Health 16, no. 6 (2019): 1019. http://dx.doi.org/10.3390/ijerph16061019.

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We aimed to determine which discipline had the greater performance improvements in the history of Ironman triathlon in Hawaii and also which discipline had the greater influence in overall race time. Data from 1983 to 2018 of the top three women and men of each year who competed in the Ironman World Championship were included. In addition to exploratory data analyses, linear regressions between split times and years of achievement were performed. Further, a stepwise multiple linear regression was applied using total race time as the dependent variable and split times as the independent variables. Both women and men significantly improved their performances from 1983 to 2018 in the Ironman World Championship. Swimming had the largest difference in improvements between men and women (3.0% versus 12.1%, respectively). A negative and significant decrease in each discipline was identified for both women and men, with cycling being the discipline with the greatest reduction. The results from the stepwise multiple regression indicated that cycling was the discipline with the highest influence on overall race time for both sexes. Based on the findings of this study, cycling seems to be the Ironman triathlon discipline that most improved overall race times and is also the discipline with the greatest influence on the overall race time of elite men and women in the Ironman World Championship.

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Ahmad, Azimah, Nur Anisah Mohamed @ A. Rahman, and Zaharah Wahid. "Optimised Reduction of Surgical Gloves Pinholes using Forward Search Method." Sains Malaysiana 50, no. 12 (2021): 3733–44. http://dx.doi.org/10.17576/jsm-2021-5012-22.

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This research investigates the factors that affect the existence of pinholes in surgical gloves during the manufacturing process. Since eight factors affect the existence of pinholes in surgical gloves, a two-level fractional factorial design 28-4 was used to study the main effects and the first-order interactions of the multiple variables. Multiple linear regressions are used to model the data. This paper also examines the presence of influential points in the data using the influential measures in linear regression such as Cook’s Distance, DFFITS, DFBETAS, Studentized Residual, Standardized Residual, Hadi's measure, and the robust forward search. The impact of influential points is further assessed through deletion of potential influential points and model selection using adjusted R2, information criterion, and stepwise selection to see whether these influential points significantly improved the existing model.

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

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This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR) and principal component analysis (PCA) to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

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