Simple linear and multivariate regression models

Águila, M.M. Rodríguez del; Benítez-Parejo, Nicolás

doi:10.1016/j.aller.2011.02.001

Cited by 27 publications

(7 citation statements)

References 9 publications

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“…The explained and explanatory variables constituted a set of indicators for the achievement of SDG7. The following equation can describe the multiple regression model [48][49][50]:…”

Section: Trend Multiple Regressionmentioning

confidence: 99%

Analysis of the EU-27 Countries Energy Markets Integration in Terms of the Sustainable Development SDG7 Implementation

Rybak

Kolev

2021

Energies

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The article presents the results of research related to the SDG7 sustainable development implementation analysis. The goal is to provide affordable and clean energy. Its implementation will allow for development that will simultaneously provide the possibility of economic growth and the achievement of an optimal level of citizens’ health and life. The research was conducted for the countries of the European Union EU-27. During the analysis, the indicators proposed by Eurostat were used. The research aimed to examine the progress in EU member states’ energy markets integration. In order to carry out the indispensable research, it was necessary to use a spatial information system. Cluster analysis, as well as TSA analysis, were applied. The conducted research made it possible to verify the posed hypotheses and showed that the energy transformation process of the EU-27 countries is so complicated and heterogeneous that it has given rise to new independent and unique clusters. The authors also verified the adopted set of SDG7 achievement indicators using multiple regression. Additional indicators were also proposed that could complement the set and clarify its analyses.

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“…The explained and explanatory variables constituted a set of indicators for the achievement of SDG7. The following equation can describe the multiple regression model [48][49][50]:…”

Section: Trend Multiple Regressionmentioning

confidence: 99%

Analysis of the EU-27 Countries Energy Markets Integration in Terms of the Sustainable Development SDG7 Implementation

Rybak

Kolev

2021

Energies

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“…The Akaike Information Criterion (AIC) was used as selection criterion. AIC calculation is based on minimization of the loss of information function, penalizing for the number of variables introduced that seeks the model that best adjusts to the data with the minimum number of possible variables, thus producing simpler models [ 30 , 31 ]. The model that has been chosen minimizes the AIC.…”

Section: Methodsmentioning

confidence: 99%

Epidemiology of American Tegumentary Leishmaniasis andTrypanosoma cruziInfection in the Northwestern Argentina

Hoyos

Cajal

Juárez

et al. 2016

BioMed Research International

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Background. Endemic areas of tegumentary leishmaniasis (TL) in Salta, Argentina, present some overlap zones with the geographical distribution of Chagas disease, with mixed infection cases being often detected. Objectives. The purpose of this study was to determine the magnitude of Leishmania sp. infection and potential associated risk factors, the serologic prevalence of T. cruzi, and the presence of T. cruzi-Leishmania sp. mixed infection in a region of the northwest of Argentina. Methods. Cross-sectional studies were conducted to detect TL prevalence and T. cruzi seroprevalence. A case-control study was conducted to examine leishmaniasis risk factors. Results. Prevalence of TL was 0.17%, seroprevalence of T. cruzi infection was 9.73%, and mixed infection proportion—within the leishmaniasic patients group—was 16.67%. The risk factors associated with TL transmission were sex, age, exposure to bites at work, staying outdoors more than 10 hours/day, bathing in the river, and living with people who had lesions or were infected during the study. Discussion. The endemic pattern of TL seems to involve exposure of patients to vectors in wild as well as peridomestic environment. Cases of T. cruzi infection are apparently due to migration. Therefore, a careful epidemiological surveillance is necessary due to the contraindication of antimonial administration to chagasic patients.

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“…. , a ni are regression coefficients of each predictor, including a 0i -the ith intercept, and ε i is the i-th residual [46,47]. The regression coefficients indicate the alteration of the dependent variables caused by a unit increase in the predictor variables.…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

“…The regression coefficients indicate the alteration of the dependent variables caused by a unit increase in the predictor variables. These coefficients facilitate a comparative assessment of the relative significance of each predictor for model output, providing insights into their respective impacts within the regression model [47]. The residuals indicate the spread of the true values of the target parameter around the regression line [46].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

Maciejewska,

Azizah,

Szczurek

2024

Applied Sciences

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This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.

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Simple linear and multivariate regression models

Cited by 27 publications

References 9 publications

Analysis of the EU-27 Countries Energy Markets Integration in Terms of the Sustainable Development SDG7 Implementation

Analysis of the EU-27 Countries Energy Markets Integration in Terms of the Sustainable Development SDG7 Implementation

Epidemiology of American Tegumentary Leishmaniasis andTrypanosoma cruziInfection in the Northwestern Argentina

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

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