Forecasting Rebar Product Sales Using a Linear Regression Model: A Case Study of the Bar Rolling Mill at the Libyan Iron and Steel Company (LISCO)
DOI:
https://doi.org/10.65405/.v10i37.703الكلمات المفتاحية:
Linear Regression, Sales Forecasting, Rebar Products, Libyan Iron and Steel Company.الملخص
This the study investigates the use of linear regression to forecast rebar sales for the Libyan Iron and Steel Company from November 2023 to January 2025. Employing a descriptive analytical and case study approach, 12 months of sales data were analyzed using Excel and statistical methods. Results indicated the linear model was statistically significant (F=4.9253, p=0.05074) but with only a medium correlation (R=0.5745). The model explained 33% of variance, with a high standard error of 7666.63, suggesting low predictive accuracy. Recommendations include: adding more independent variables, exploring alternative models (multiple regression, time series), expanding data samples, and conducting market studies to improve forecasting and optimize sales strategies. These efforts are to improve sales forecasting.
التنزيلات
المراجع
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منشور
إصدار
القسم
الرخصة
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