ISSN: 2661-3115 (Online)

 

   Publication Frequency: Annual

 

   Publishing Model: Open Access
 

 

About the Journal

 

Insight - Statistics is a reference journal. The article will include references and figures. Creativity, quality and clarity will be the criteria for selecting published materials.


The proliferation of literature and the long delay in publishing have made it difficult for researchers and practitioners to keep up with new developments outside their professional fields. The purpose of statistics and probability letters is to help alleviate this problem. Concise communication enables the reader to quickly and easily digest a large amount of information and to keep up with the latest developments in all areas of statistics.


Key results and central ideas must be presented in a clear and concise manner. Theories and methods can be published either by omission or by mere sketches, but only if sufficient supporting material is provided to verify the results. Experiences and calculations with important values will be published. We also plan to publish applications and case studies to demonstrate innovative use of existing technologies, or interesting innovative ideas for data collection, modeling, or reasoning.

 

Latest Articles

  • Open Access

    Original Research Articles

    Article ID: 703

    Prediction of UCS values using basic geotechnical soil parameters via regression and Artificial Neural Networks ANN

    by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

    Insight - Statistics, Vol.8, No.1, 2025; 46 Views, 23 PDF Downloads

    Unconfined Compressive Strength (UCS) test is a widely used lab procedure for assessing soil’s undrained shear strength. However, conventional lab testing is time-, cost-, and labor-intensive. This study evaluates predictive models for UCS using basic soil parameters. Soil mixtures were prepared and tested through several laboratory experiments, including Atterberg’s limits, particle size distribution, water content, bulk density using Harvard miniature compaction apparatus, and UCS. A total of 152 soil samples were utilized to train the prediction models. To achieve that, multi-linear regression (MLR), multi-nonlinear regression (MNLR), and backpropagation Artificial Neural Networks (ANN) were employed to relate the dependent variable UCS (predicted) to the independent geotechnical parameters (predictors). Results showed that the best model to predict the UCS values for soil using its soil parameters is the ANN-based model with R 2 of 83% and ASE (Averaged Square Error) of 0.0029, followed by the nonlinear regression model with R 2 = 49.2% and ASE of 3.63, and finally the MLR model with R 2 = 44.5% and ASE of 3.92.

  • Open Access

    Original Research Articles

    Article ID: 740

    Sample selection in social science research: A holistic approach to methodological rigor

    by Mohammad Rashed Hasan Polas

    Insight - Statistics, Vol.8, No.1, 2025; 37 Views, 18 PDF Downloads

    The present study investigates the crucial elements of sample selection in social science research, thoroughly examining the nuances of sampling techniques, categories, and factors. The paper offers a thorough overview of the procedures involved in sampling strategies, with a particular emphasis on non-probability and probability approaches. It also discusses the critical role that sample size determination plays, taking into account variables like cost, ethics, statistical power, accuracy, and generalizability in addition to type I and type II errors. The paper also closely examines how several elements, such as research objectives, design, analytical instruments, and resource constraints, affect the choice of the ideal sample size. The topic of choosing the right data analysis software and how it affects choices about sample size is covered in detail. In the last section of the study, the ideas of power, effect size, and minimum sample size in statistical analysis are thoroughly explored, with a focus on partial least squares structural equation modelling (PLS-SEM).

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Announcements

Call for Papers for the Special Issue: Insight-Statistics

2019-05-14

Trace, organic, and radioactive  elements enter the environment (soil, aquifer, plant, lakes, seas, algae, and plants) via industrial, agricultural, and sanitary waste waters. Uncontaminated environment was recorded. The statistical investigation can identify the sources of pollution. The subdivision of the environment into zones based on chemical, physical, hydrological, and hydrogeological parameters achieved by selected statistical application.  

The Lead Guest Editor

Mohamed Abdelfattah Elkashouty

Read more about Call for Papers for the Special Issue: Insight-Statistics

Call for Papers for the Special Issue: Insight-Statistics

2019-04-22

Any kind of single navigation system has its limitations and cannot provide absolutely reliable and accurate navigation information in any case. In this situation, the integrated navigation system based on fusion method becomes an effective way to improve the robustness and accuracy of the navigation system. However, developing a reliable and high-accuracy integrated navigation system is a challenging task involving multi-sensors data fusion, nonlinear filtering algorithm, optimal estimation and intelligent information processing. 

The Lead Guest Editor

Shesheng Gao

Read more about Call for Papers for the Special Issue: Insight-Statistics