Many statistical analyses can be undertaken to examine the relationship between two continuous variables within a group of subjects. Two of the main purposes of such analyses are: Correlation coefficients are used in statistics to measure how strong a relationship is between two variables. To assess whether the two variables are associated. There is no distinction between the two variables and no causation is implied, simply association. To enable the value of one variable to be predicted from any known value of the other variable. One variable is regarded as a response to the other predictor (explanatory) variable and the value of the predictor variable is used to predict what the response would be. Correlation coefficient is a measure of association between two variables, and it ranges between –1 and 1. If the two variables are in perfect linear relationship, the correlation coefficient will be either 1 or –1. The sign depends on whether the variables are positively or negatively related. The correlation coefficient is 0 if there is no linear relationship between the variables. Two different types of correlation coefficients are in use. One is called the Pearson product moment correlation coefficient, and the other is called the Spearman rank…