Authors :
Chioma Okeke; AbdulmimuniMurtala
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3OZ8UvC
DOI :
https://doi.org/10.5281/zenodo.7392571
Abstract :
Correlation and regression analyses were
conducted on the data generated from the analysis of
soils samples from Bauchi state, in order to comparehow
two metals (Cr and Ni) in these soils can be predicted by
some soil physico-chemical properties (pH, electrical
conductivity, EC; and soil moisture contents, MC).
Resulting coefficient of correlation established a linear
relationship between the study metals and one or more
physico – chemical parameters in all the study sites. Test
results of the suitability of the models to the data
obtained indicated that linear, logarithmic and quadratic
models were found suitable for both metals in the study
sites, although quadratic models gave better predictions.
The Regression equations obtained showstriking
similarities in the prediction pattern of both metals with
respect to their predictor variables. The models obtained
could be used to predict approximately between 41.0 –
84.0% Cr and 50.0 – 82.0% Ni
Keywords :
Chromium, Nickel, soil moisture contents,soil pH, electrical conductivity, regression equations.
Correlation and regression analyses were
conducted on the data generated from the analysis of
soils samples from Bauchi state, in order to comparehow
two metals (Cr and Ni) in these soils can be predicted by
some soil physico-chemical properties (pH, electrical
conductivity, EC; and soil moisture contents, MC).
Resulting coefficient of correlation established a linear
relationship between the study metals and one or more
physico – chemical parameters in all the study sites. Test
results of the suitability of the models to the data
obtained indicated that linear, logarithmic and quadratic
models were found suitable for both metals in the study
sites, although quadratic models gave better predictions.
The Regression equations obtained showstriking
similarities in the prediction pattern of both metals with
respect to their predictor variables. The models obtained
could be used to predict approximately between 41.0 –
84.0% Cr and 50.0 – 82.0% Ni
Keywords :
Chromium, Nickel, soil moisture contents,soil pH, electrical conductivity, regression equations.