Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Mikailalsys Journal of Mathematics and Statistics
Yakubu, J.
Unknown Affiliation
Engineering Mathematics Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Mikailalsys Journal of Mathematics and Statistics

 1 
Methods and Applications of Point Estimation in Inferential Statistics: A Case Study of Energy Consumption Data at ATBU Yakubu, J.; Bishir, A.; Jibril, J.; Adamu, A.; James, K. Y.; Ibrahim, A. I.
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 3 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i3.7467

Abstract

This study employs established point estimation techniques in inferential statistics—including Ordinary Least Squares (OLS), Maximum Likelihood Estimation (MLE), Ridge regression, and Lasso regression—to analyze a 30-month dataset on energy consumption, billing, and revenue collection from Abubakar Tafawa Balewa University (ATBU), Bauchi. The primary objective is to assess the accuracy and efficiency of parameter estimation methods for predicting revenue based on energy billed. Using regression-based models, the study evaluates performance across two sites: the Main Campus and the Permanent Site. Empirical findings demonstrate strong model explanatory power, with R² values of approximately 0.90 and 0.80, respectively, indicating a high degree of reliability in the predictive capacity of the models. OLS is shown to provide unbiased estimates, while regularization techniques such as Ridge and Lasso improve model robustness by addressing multicollinearity and overfitting. The results highlight the practical applicability of statistical modeling in energy revenue forecasting and offer valuable insights for institutional energy management. The study concludes by recommending the integration of regularized regression techniques for more resilient forecasting frameworks in similar energy consumption environments.
Co-Authors Adamu, A. Bishir, A. Ibrahim, A. I. James, K. Y. Jibril, J.