Regression Analysis
This analysis was conducted to determine the primary factors influencing the
dependent variable. The findings are summarized below:
1. Model Evaluation:
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The model displays a Multiple R of 0.9976, indicating a strong correlation
between the dependent and independent variables.
•
An R-Square of 0.9952 shows that the model explains approximately
99.52% of the variability in the dependent variable.
•
The model is statistically robust, as evidenced by an F-statistic of 6116.69
and a significance level of 8.1 × 10⁻¹³⁵.
2. Significant Factors:
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Number of Deliveries: This variable has the largest impact, with a
coefficient of 63.94 and a p-value of <4.48 × 10⁻³⁵, indicating a strong
and statistically significant relationship. More deliveries directly
correspond to higher costs.
•
Delivery Time: With a coefficient of 0.39 and a p-value of 0.0012, this
variable also contributes significantly to costs, though its influence is less
pronounced compared to the number of deliveries.
3. Variables with Limited Impact:
•
Miles: This variable has a p-value of 0.119, suggesting it does not
significantly influence the dependent variable in this context.
•
Delivery Weight: Similarly, this factor has a p-value of 0.501, indicating a
negligible impact on the model's outcomes.
The analysis highlights Deliveries as the most critical cost driver due to its large
coefficient, strong statistical significance, and substantial contribution to
explaining cost variability. Focusing on optimizing delivery numbers offers a
practical strategy to improve cost efficiency.