Assessment of Regression Model for Rainfall in Saudi Arabia (1979-2011)  Using Dummy Variables

Manahil  Eltayeb; Sulafa  Hag-elsafi

doi:10.35516/hum.v50i3.5404

Authors

Manahil Eltayeb Department of Quantitative Analysis, College of Business administration, King Saud University, Riyadh, Saudi Arabia https://orcid.org/0000-0001-6686-0220
Sulafa Hag-elsafi Department of Geography, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi Arabia https://orcid.org/0000-0003-2805-6084

DOI:

https://doi.org/10.35516/hum.v50i3.5404

Keywords:

Rainfall, dummy variable, T- test, F- test, Saudi Arabia

Abstract

Objectives: This study aims to analyze rainfall in Saudi Arabia by designing models based on data from 20 stations across the Kingdom from 1979 to 2011.

Methods: The analysis employed a multiple linear regression model with rainfall as the dependent variable and annual quarters as the independent variables. Dummy variables were utilized in the analysis. The regression model provided valuable insights into the impact of rainfall rates in different quarters across Saudi Arabia. Monthly data was collected from each region of the Kingdom during the study period and categorized into five groups based on average rainfall: Group 1 (5-15 mm), Group 2 (15-25 mm), Group 3 (25-35 mm), Group 4 (35-45 mm), and Group 5 (45-70 mm). Each group was represented by a separate regression model. To reduce the number of dummy variables in the model, the monthly data was converted to quarterly data.

Results: A significant finding of this study is that all models were statistically significant, indicating that rainfall distribution is influenced by the annual quarters. Furthermore, it was observed that the average rainfall in most quarters across different regions was statistically significant, except for the fourth quarter in Group 5 and the third quarter in Groups 1, 2, and 4.

Conclusions: The inclusion of dummy variables as independent variables in the multiple linear regression model proved to be a novel and effective approach for analyzing rainfall time series. The results can serve as a foundation for future studies, enabling prediction and informed decision-making based on the findings.

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