Aims: This research work investigated the best regression technique in handling multicollinearity using the Ridge, Least Absolute Shrinkage and Selection Operator (LASSO) and Bridge regression models in comparison to Analysis and Prediction. Study Design: Two sets of secondary data on Body Size and Heart Rate gotten from the Lulu Briggs Health Center, University of Port Harcourt were used for comparison for model fit and in handling multicollinearity between the regression techniques. Tables were used to present Comparisons made using MSE, RMSE, VIF, AIC and BIC for efficiency. Scatter plots were employed to show fitted regression models. R Software was used to perform data analysis. Methodology: The data were tested for the presence of Multicollinearity using VIF respectively, before proceeding to apply Ridge, LASSO and Bridge regression techniques to solve the problem of multicollinearity. Then comparison was made in analysis and prediction between the regression techniques. Results: The results from the study show that, for analysis on body size, we found that none of the Regression Techniques handled the problem of multicollinearity, even though the degree of multicollinearity present in the data set reduces, with VIF values of 11.36762 for Ridge, 10.8042 for LASSO, and Bridge which are 10.95578, 11.24945, 12.22628 and 12.14645 respectively. For Heart Rate analysis, we see that all the regularized regression techniques handled the problem of multicollinearity. The results show that the Bridge regression technique performed better with a VIF of 1.744461 when

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Wnt/β‐catenin signaling pathway plays a role in cancer development, organogenesis, and embryogenesis. The abnormal activation promotes cancer stem cell renewal, proliferation, and differentiation. In the present study, molecular docking simulation and ADMET studies were carried out on selected bioactive compounds in search of β‐catenin protein inhibitors for drug discovery against cancer. Blind docking simulation was performed using PyRx software on Autodock Vina. β‐catenin protein (PDB ID: 1jdh) and 313 bioactive compounds (from PubChem database) with selected standard anticancer drugs were used for molecular docking. The ADMET properties of the best‐performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained from the molecular docking study showed that glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside‐1, diosmin, and rutin had the best binding interactions with β‐catenin protein based on their binding affinities. Glycyrrhizic acid and solanine had the same and lowest binding energy of −8.5 kcal/mol. This was followed by polyphyllin I with −8.4 kcal/mol, and crocin, hypericin, and tubeimoside‐1 which all had a binding energy of 8.1 kcal/mol. Other top‐performing compounds include diosmin and rutin with binding energy of −8.0 kcal/mol. The ADMET study revealed that the following compounds glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside‐1, diosmin, rutin, and baicalin all violated Lipinski's rule of 5 which implies poor oral bioavailability. However, based on the binding energy score, it was suggested that these pharmacologically active compounds are potential molecules to be tested against cancer.

This research work focuses on determining the difference between the health habits of countries in Africa and Europe, especially in females. It is crucial because it could help enlighten women on the dangers of bad health habits. Multivariate Hotelling T- square test is adopted to determine the significant difference between the two continents, Africa and Europe, having Cancer deaths caused by alcohol consumption, smoking prevalence, and Obesity prevalence as the variables and the correlation between the variables. The result showed that there is indeed a significant difference between the bad health habits in the two continents in the females. Correlation analysis was also carried out to determine the relationship between the variables, and the results showed that the relationship between the variables was little. More methods were adopted using comparison of Rate between the means, and the sum of the same variables, to determine which continent was more affected by the bad health habits, and it was figured out to be Europe.

Statistical Theory and Analysis in Bioassay is a seven-chapter monograph tailored essentially to meet the needs of graduate students, practitioners, and researchers in the fields of medicine, pharmacology, biosciences/life sciences, and related fields that employs the tools of biostatistics in bioassays and analysis. In this wise, we have taken time to discuss in detail relevant topics; principles, methods, and applications. Practice exercises are also included where necessary. The earlier chapters gave background information, definition of terms, purpose, types, structure, and relative potency in bioassay. An important theorem, Fieller’s theorem is proved in detail with illustrative examples. The last two chapters are on dose-response relationship, fitting in parallel-line assays and estimation. We are convinced that this monograph will meet the expectations of the readers while constructive criticisms that will improve another edition will be appreciated.