Abstract
Actually, multiple linear regression is widely used in data processing in industry, organizational behavior, marketing, management, and social sciences. This paper presents a way to analyze and situate the impact of a set of factors on diesel fuel consumption, which allows company managers to make the right choice in terms of vehicle purchase at the tactical level and driver behavior at the operational level. It follows a methodological approach based on statistical methods; these methods make it possible to extract regression models with several predictors; the impact of each predictor is analyzed and processed using the identified statistical indicators. This article presents a correlational study of factors related to vehicle characteristics and driver behavior as a result of multiple regression models with 118 traffic data records for 28 vehicles to analyze the causal relationship between factors and energy consumption in a Moroccan industrial enterprise. The analysis presented shows that many of the factors have an impact on the energy consumption of road freight transport. The R, R-squared, and student test values indicate a strong relationship between fuel consumption and different factors studied.
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