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David Adedia

Publications (4)

Leverages, Outliers and the Performance of Robust Regression Estimators

David Adedia, Atinuke Adebanji, Eric Okyere & James Kwaku Agyen · Journal of Advances in Mathematics and Computer Science · 2016

This study compared the performance of some robust regression methods and the Ordinary Least Squares Estimator (OLSE). The estimators were compared using varied levels of leverages and vertical outliers in the predictors and the dependent variables. An anthropometric dataset on t...

Open access Research Article 10.9734/BJMCS/2016/24281

Ordinary Least Squares and Robust Estimators in Linear Regression: Impacts of Outliers, Error and Response Contaminations

David Adedia, Atinuke Adebanji, Moremi Labeodan & Shola Adeyemi · Journal of Advances in Mathematics and Computer Science · 2015

The Ordinary Least Squares Estimator (OLSE) is the best method for linear regression if the classical assumptions are satis ed for estimating weights. When these assumptions are violated, the robust methods give more reliable estimates while the OLSE is strongly a ected adversely...

Open access Research Article 10.9734/BJMCS/2016/22876

Sensitivity Analysis of the Quadratic Discriminant Function for Predicting Pregnancy Outcomes

Killian Asampana Asosega, David Adedia & Atinuke O. Adebanji · Journal of Scientific Research and Reports · 2020

The prevalence rate of stillbirth is ten times higher in developing countries relative to developed countries with a 2016 rate of 18 percent in Ghana. This study employed the Quadratic Discriminant Function for discriminating and classifying of pregnancy outcomes based on some pr...

Open access Research Article 10.9734/jsrr/2020/v26i230225

Statistical Analysis of Misfiling Causes in Higher Education: A Test Construction

Basintale D. Iddrisu, Kwadzokpui Precious Kwablah, Emmanuel Barima Agyemang Prempeh, Johnson S. Azorlibu, Solomon D. Akrong, Cynthia Sena Kpeglo, David Adedia, Atinuke O. Adebanji & Simon Kojo Appiah · Asian Journal of Education and Social Studies · 2022

Introduction: Misfiling remains a substantial challenge in data management in various institutions of higher learning. It has been asserted that one in every ten documents or files stored becomes immediately lost forever, due to inadequate filing techniques and practices. The res...

Open access Research Article 10.9734/ajess/2022/v33i330793