Regression Models for 2-Dimensional Cartesian Coordinates Prediction: A Case study at University of Mines and Technology (UMaT), Tarkwa-Ghana.

The aim of this research is to study and analyze statistical models applicable in bringing out arelationship between global coordinates and cartesian planimetric coordinates of some known controlstations in the University of Mines and Technology (UMaT) campus. To achieve the aims of this research,the Global Position System (GPS) latitudes and longitudes of selected control stations with knowncartesian planimetric coordinates were determined using the Handheld GPS receiver at different epoch(morning and evening). Linear Regression analysis was then conducted to establish the correlationbetween global and cartesian planimetric coordinates of the selected control stations and regressionmodels generated to show the results. The correlation coefficient r, a t-test for non -zero slope, t-test oncorrelation coefficient, graphical residual analysis, test of normality, comparing model predictions toobserved data, were used to evaluate and check the adequacy of the models. The obtained resultsindicated that the proposed linear regression models are suitable for predictions at 95% confidenceinterval and do not violate any of the statistical assumptions of a linear model. However, the proposedregression models for the evening observation gave better prediction accuracy than the morning. Acomputer programming algorithm and a designed interface was created for the proposed regressionmodels established using Microsoft C++ standard edition 6.0, thus making it easier in applying themodels in making cartesian planimetric coordinates prediction at different epoch at UMaT.