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MATHEMATICAL APPLICATIONS OF GLOBAL WARMING IN SAUDI ARABIA
Keywords:
multiple linear regression, questionnaire data, statistical analysis, predictor variables, outcome variable, SPSSDOI:
https://doi.org/10.17654/0972361724075Abstract
This study attempts to thoroughly analyze and address the complicated issue of global warming. The questionnaire was distributed to the target group through an online platform, and responses were collected for subsequent analysis. The study utilized statistical package for the social sciences (SPSS) version 20 to perform data analysis. Each multiple linear regression (MLR) model was analyzed separately, allowing for a detailed examination of the relationships between predictor variables and the outcome variable. The stepwise regression method for all applications reveals that three controlled variables chosen using SPSS 20 are $w_9$, $w_12$ and $w_15$. $w_9$ (tropical cyclones), (explosions in general) and (transportation by vehicle) were the actual causes of global warming.
Received: July 5, 2024
Revised: August 3, 2024
Accepted: August 16, 2024
References
P. R. Brewer and J. McKnight, A statistically representative climate change debate: satirical television news, scientific consensus, and public perceptions of global warming, Atlantic Journal of Communication 25(3) (2017), 166-180.
M. Helbling and D. Meierrieks, Global warming and urbanization, Journal of Population Economics 36(3) (2023), 1187-1223.
K. McMonigal, S. Larson, S. Hu and R. Kramer, Historical changes in wind-driven ocean circulation can accelerate global warming, Geophysical Research Letters 50(4) (2023), e2023GL102846.
R. A. Armstrong and A. C. Hilton, Stepwise Multiple Regression, Statistical Analysis in Microbiology: Statnotes, Wiley, 2010, pp. 135-138.
H. L. T. K. Nhung, V. Van Hai, R. Silhavy, Z. Prokopova and P. Silhavy, Parametric software effort estimation based on optimizing correction factors and multiple linear regression, IEEE Access 10 (2021), 2963-2986.
M. L. Thompson, Selection of variables in multiple regression: Part I. A review and evaluation, Internat. Statist. Rev. 46 (1978), 1-19.
A. T. Abdulrahman, R. Alharbi, O. Alamri, D. Alnagar and B. Alruwaili, Application of supersaturated design to study the spread of electronic games, Mathematics and Statistics 9(3) (2021), 278-284.
Alanazi Talal Abdulrahman, Application of edge designs to study the spread of diabetes, JP Journal of Biostatistics 18(2) (2021), 187-198.
A. Talal Abdulrahman, I. Ahmad, D. K. Alnagar, R. A. Ramadan, T. A. Alraqad and Y. M. Jawarneh, Multivariate regression proposal to the gap between university education and labor market: the kingdom’s vision 2030, Mathematical Problems in Engineering 2022(1) (2022), 7219523.
A. T. Abdulrahman and O. Alamri, Robust estimation methods used to study the reasons behind increasing divorce cases in Saudi Society, Mathematical Problems in Engineering 2021(1) (2021), 4027599.
A. Talal Abdulrahman and A. A. Attiya, Mathematical and statistical models with applications of spread of private tutoring in Saudi Arabia, Complexity 2022(1) (2022), 7842971.
A. T. Abdulrahman, Methods for designing experiments to study the actual causes of the housing crisis, International Journal of Analysis and Applications 19(4) (2021), 542-560.
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