Complex data with multiple variables can be simplified to deduce the trends and relationship between the variables using the Principal Component Analysis (PCA), which further submits the usage of the variables to establish a Hierachical Cluster Analysis (HCA). Sample bottles were cleaned and rinsed with water from the sampling sites and filled to the brim to avoid any atmospheric oxidation. Five water catchments, 29 boreholes. 16 Open wells, 16 rivers and 16 streams were sampled in this work, and were taken immediately to the Laboratory for physico-chemical analysis. The physico-chemical data in application of the Software Package for Social Sciences (SPSS), converted the multiple variables into interpretable data in a correlation matrix. Pearson’s correlation matrix used in this work, reduced the data in correlation circles that were a range of geometrical projections that ranged from +1 to -1 called the correlation ratios. Variables, with correlation ratios greater than 0.5 or (-0.5) and closer to +1 or (-1) are considered significant, and are used in the Hierachical Cluster Analysis. The HCA is in form of a tree diagram called a dendrogram that arranged the variables in an increasing order of significance, with similar correlated variables put in clusters. HCA is innovative and compliments the PCA that already exists and focuses on analysis of ions expected to greatly impact an effect on water quality from their significance in the water sources. The Hierachical Component Analysis is derived from and viewed as an extension of the Principal Components Analysis.

Complex data with multiple variables can be simplified to deduce the trends and relationship between the variables using the Principal Component Analysis (PCA), which further submits the usage of the variables to establish a Hierachical Cluster Analysis (HCA). Sample bottles were cleaned and rinsed with water from the sampling sites and filled to the brim to avoid any atmospheric oxidation. Five water catchments, 29 boreholes. 16 Open wells, 16 rivers and 16 streams were sampled in this work, and were taken immediately to the Laboratory for physico-chemical analysis. The physico-chemical data in application of the Software Package for Social Sciences (SPSS), converted the multiple variables into interpretable data in a correlation matrix. Pearson’s correlation matrix used in this work, reduced the data in correlation circles that were a range of geometrical projections that ranged from +1 to -1 called the correlation ratios. Variables, with correlation ratios greater than 0.5 or (-0.5) and closer to +1 or (-1) are considered significant, and are used in the Hierachical Cluster Analysis. The HCA is in form of a tree diagram called a dendrogram that arranged the variables in an increasing order of significance, with similar correlated variables put in clusters. HCA is innovative and compliments the PCA that already exists and focuses on analysis of ions expected to greatly impact an effect on water quality from their significance in the water sources. The Hierachical Component Analysis is derived from and viewed as an extension of the Principal Components Analysis.