Research & Reviews : Journal of Statistics

Latest articles

• Published Subscription Original Research 25 Jul 2026

  Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models

  By Pavitr Jain, Vikas Sharma, Vivek Verma, Avnish Soni, Arpit Kumar, Ritish Sharma

  Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article →

• Published Subscription Original Research 25 Jul 2026

  Transforming Punjab's Education System: A Critical Analysis of Automation and Artificial Intelligence

  By Lovejit Kaur, Randhir Kumar, Farah Qudsia, Biju R., M. Suvarchala Manasa, Bose Joseph Thekkumkattil

  Abstract: The rapid advancement of Artificial Intelligence (AI) and automation technologies is transforming education systems across the globe. In Punjab, one of India's leading educational regions, AI-driven tools and automated technologies are increasingly being adopted to improve teaching, learning, assessment, administration, and institutional management. This study critically examines the impact of AI and automation on Punjab's education system by exploring their opportunities, challenges, and future prospects. The research is based on …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 31–42 Read article →

• Published Open access Original Research 10 Jul 2026

  Linear Programming for Profit Optimization in Small-Scale Manufacturing: A Python-Based Simplex and Machine Learning Approach

  By Shipra Aggarwal

  Abstract: Profit maximization under resource constraints is a classic challenge. Small manufacturers face tight margins and scarce capital every day. This paper tackles that problem using four Python-based methods. The case study is Bintang Bakery in Bandar Lampung, Indonesia. The bakery makes three bread types and faces 18 resource constraints. Data comes from Anggoro et al. Methods tested include LP revised simplex, Differential Evolution, PSO, and ANN Surrogate. General-purpose scipy minimizers …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 01–11 Read article →

• Published Subscription Original Research 15 Jun 2026

  Box Plots: An Introduction to the Visual EDA Wonder

  By Vimal Rajaseharan, Sabarieswar V.

  Abstract: A box plot, also known as a box-and-whisker plot, is a widely used statistical graphic that provides a concise summary of the distribution of numerical data. By displaying key descriptive measures such as the median, quartiles, interquartile range (IQR), minimum and maximum values, and potential outliers, box plots allow researchers to quickly understand the spread and central tendency of a dataset. They are especially useful when comparing distributions across multiple …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 20–30 Read article →

• Published Subscription Original Research 15 Jun 2026

  A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning

  By Narendra Janardhanrao Padole, Nitin S. Shrirao

  Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article →

• Published Subscription Review Article 30 Apr 2026

  Statistical and AI Approaches to Measure Sustainability Performance of Enterprises

  By Ruchi Jain, kirti Verma, Madhulika Shukla, Parth Khare

  Abstract: Measuring sustainability performance has become a critical priority for enterprises facing increasing regulatory pressure, stakeholder expectations, and global sustainability challenges. Traditional assessment methods, largely based on static indicators and manual reporting, often struggle to capture the multidimensional, dynamic, and data-intensive nature of sustainability. This study explores the integration of statistical and artificial intelligence (AI) approaches to evaluate and enhance the sustainability performance of enterprises in a more robust, accurate, and …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 30–36 Read article →

• Published Subscription Review Article 30 Apr 2026

  Computer Data Processing and Empirical Economics

  By V. Basil Hans, Sajimon P.P.

  Abstract: This is an article on empirical economics and computer data processing that discusses how computational technology have changed empirical research methods. It analyses how computer systems have changed data management, statistical modelling, and econometric analysis, focussing on large-scale data processing, algorithmic efficiency, and repeatability. Forecasting, policy simulation, and panel data analysis are key uses. The paper also addresses data quality, computational, and ethical issues in economic data use. The paper …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 09–20 Read article →

• Published Subscription Original Research 30 Apr 2026

  An Analytical Study on Learning Difficulties in Estimation Theory Among Undergraduate Engineering Students

  By Sri Raksha MS, Subhasshini M, Pon Sweatha S, Tarunprakash S, Prabakaran R

  Abstract: Estimation Theory is a critical mathematical foundation for all engineering disciplines, enabling learners to model uncertainty, analyse signals, and derive optimal estimators. However, undergraduate students frequently struggle with its abstract properties, complex derivations, and prerequisite statistical concepts. This study investigates the key learning difficulties faced by students across multiple engineering branches using diagnostic tests, structured questionnaires, and interviews. The findings reveal that students commonly experience challenges related to weak probability …

  Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 37–41 Read article →

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