Estimate Accuracy of the Sample Determination Coefficient R2 and R2 for the Multiple Regression Models

Mohammad, Gorgees Shaheed

doi:10.35741/issn.0258-2724.55.1.25

Cited by 5 publications

(3 citation statements)

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“…It takes values in the range 0 ÷ 1. Some sources state that the model t is better the closer the R 2 value is to unity, while this is incorrect, because the sheer number of observation data makes the model R 2 value decrease; then the evaluation should be carried out on the basis of other model validation parameters (Mohammad 2020). For the case under study, for example, by analyzing instantaneous emission graphs and CO 2 emission maps.…”

Section: Resultsmentioning

confidence: 99%

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

Mądziel

2023

Preprint

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One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle and gradient of road to predict the emission of CO2. In this paper, 3 methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.

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Section: Resultsmentioning

confidence: 99%

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

Mądziel

2023

Preprint

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“…Moving on to the multiple correlation test, which focuses on the relationship between the level of social support and student well-being, the coefficient of determination (R-value) and adjusted coefficient of determination are used as measures of goodness-of-fit for multiple linear regression models (Mohammad, 2020). The obtained R-value of 0.884 indicates the significance of social support in influencing student well-being.…”

Section: Discussionmentioning

confidence: 99%

Affirmation Education Program: Bridging the Gap between Inclusion and Integration for Migrant Students

Anggara

2023

Utamax

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Migrant students face unique challenges and obstacles in their educational journey, particularly in terms of social support and overall well-being. This study aims to examine the relationship between social support and the well-being of migrant students participating in the Affirmation of Education Program (ADik) in Papua and the 3T Regions. Using a quantitative approach, data was collected from a sample of 34 active students through surveys and in-depth interviews. The study employed non-probability sampling, ensuring equal opportunity for 66 eligible students. The collected data was analyzed using SPSS, and the instrument's reliability and validity were established through pretesting. The results indicated that migrant students' well-being scores were particularly low in the area of mental well-being. Regression tests revealed that emotional support, network support, and informational support significantly influenced student well-being, while reward support and instrumental support showed no effect. Additionally, the network of religious groups and student diversity were found to play a role in determining student welfare. These findings underscore the importance of social support interventions targeting emotional support, networking, and information to enhance the well-being of migrant students in the ADik program in Papua and the 3T regions. Future programs and policies should prioritize the development of these aspects to improve the overall welfare of migrant students. This study contributes to our understanding of the challenges faced by migrant students and emphasizes the need for inclusive educational initiatives that address their social support needs and promote their well-being

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“…The PLS model was tested with 100 simulations to assess its overall stability and predictive performance (Couture et al, 2016). The effectiveness of the linear fit of the spectral values to the soluble sugar content was evaluated by the R 2 and RMSE, which measure the degree of correlation and deviation between predicted and measured values, respectively (Mohammad, 2020;Karunasingha, 2022). A higher R 2 value closer to 1 and a lower RMSE value closer to 0 indicate better predictive performance of the model (He et al, 2022).…”

Section: Predictive Model Construction and Performance Evaluationmentioning

confidence: 99%

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts

Yang

Gong

Jiang

2023

Front. For. Glob. Change

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Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them. However, their content varies with cultivar, location, and environmental conditions. Traditional methods for determining soluble sugar content in chestnuts are time-consuming, laborious, and destructive. Therefore, there is a pressing need for rapid, non-destructive, and straightforward methods for determining soluble sugars in chestnuts to expedite genetic selection. This study aimed to develop a hyperspectral imaging-based prediction model for soluble sugar content in Chinese chestnuts. Firstly, abnormal samples were eliminated using ensemble partial least squares for outlier detection. We then compared the average original and block scale (BS) spectra, with the latter demonstrating significant differences. The BS pretreatment exhibited two small absorption peaks in the 403.7 ∼ 429.1 nm band and 454.7 ∼ 500 nm band, less fluctuation in the spectral curves from 503.2 to 687.2 nm, and a substantial increase in spectral absorption between 690.6 and 927.8 nm. Subsequently, we developed a partial least squares (PLS) model using BS pretreatment and regularized elimination (rep) variable selection, which showed better accuracy in predicting chestnut soluble sugar content than other variable selection methods. The model fitting accuracy after the spectra treatment was marginally better than that of the original spectra, with a calibration set correlation coefficient (R2) of 0.59 and root mean square error (RMSE) of 1.02, and a validation set R2 of 0.66 and RMSE of 0.94. The wavelengths at 464.3, 503.2, 539.3, 579, and 711.3 nm were identified as critical for developing the soluble sugar content prediction model. The study demonstrated the potential of Near-Infrared Spectroscopy (NIS) as a rapid and non-destructive method for predicting chestnut soluble sugar content, which could be beneficial for quality control and sorting in the food industry.

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Estimate Accuracy of the Sample Determination Coefficient R2 and R2 for the Multiple Regression Models

Cited by 5 publications

References 0 publications

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

Affirmation Education Program: Bridging the Gap between Inclusion and Integration for Migrant Students

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts

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