Ans: Both correlation and regression are used in statistics for describing relationships between variables. They have similarities as well as significant differences. The primary use of regression is to develop equations or models which can predict a key response (R) from a set of predictor values (P). Whereas, the main use of correlation is to quickly give us the direction and strength of the relationship between a set of 2 (or more) variables.The table below provides a summary of key similarities and differences between correlation and regression:ContextCorrelationRegressionTells us the direction of the relationshipyesyesCan we interchange variablesyesnoCan predict and be used to optimizeNoyesCan be extended to fit curvilinear relationshipsNoyesGives cause and effectNoIt attempts to establish that.Variable V1 is random YesNoVariable V2 is randomYesYesWhen to useWhen you need a quick summary of the direction and strength of pairwise variablesWhen you want to predict and optimize the numeric response of V2 from V1 where V1 is a variable that impacts V2.[Image will be Uploaded Soon]