*2.6.1.9 Regression modelling*

Regression modelling is an essential statistical component for the analysis of the data. It is employed for the identification and depiction of relationships among various factors. It is also used for the identification of prognostically pertinent risk elements and the determination of risk results for each prognostication [54]. The most commonly used regression techniques are the following: Linear regression, Cox regression, and Logistics regression. Regression modelling is used for the statistical evaluation of the data by enabling three things: (a) Description analysis shows the relationship among the independent variables and the dependent variables and it can be statistically defined. (b) Estimation of the data for the dependent variables can be estimated from defined data of the independent variables. (c) Prediction of risk elements that influence the results can be identified, and individual prediction can be determined [55].
