A Logarithmic Square Root Regression Model for The Average Blood Glucose Levels of The Drug Induced Diabetic Experimental Rats Treated with The Cissampelos Pareira L. (Menispermaceae) Root Extract
DOI:
https://doi.org/10.56705/ijodas.v6i2.237Keywords:
Diabetes, Cissampelos Pareire, Blood Glucose, Regression, Residual Probability Plot, Leverage, Kolmogorov-Smirnov StatisticAbstract
We formulate a nonlinear regression model for elucidating the average values of the blood glucose levels of the experimental rats categorized under the division Group 1 Normal Control (G1NC) in a recent study conducted by Ankit Kumar et al. (see, Ankit Kumar, Ravindra Semwal, Ashutosh Chauhan, Ruchi Badoni Semwal, Subhash Chandra, Debabrata Sircar, Partha Roy and Deepak Kumar Semwal, Evaluation of antidiabetic effect of Cissampelos pareira L. (Menispermaceae) root extract in streptozotocin-nicotinamide-induced diabetic rats via targeting SGLT2 inhibition, Phytomedicine Plus 2 (2022) 100374, 11pp., https://doi.org/10.1016/j.phyplu.2022.100374). By treating the recorded average blood glucose levels of the rats of the group G1NC (response), as a function of the number of days of the experiment (predictor), our projected model involves a linear relation between the logarithm of the response and the square root of the predictor and it explains about 85.6239% of the variability in the response.
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