In regression, the relationship of one variable with another is estimaed by expressing the one in terms of the linear function of the other. It is different from correlation (r) in that, in correlation
the degree to which the two variables vary together is estimated. In both regression and correlation the values are continuous. The functional relationship in regression is a mathematical relationship which enables to predict the value of a variable y which corresponds to a given variable x. The relationship is determined by y=a+bx in which y is the function of x and is called the dependent variable, x the independent variable. By this formula when the independent variable (x) equals zero, the dependent variable equals' a'. This point is the intersection of the
function line with the y axis which is called as ' y-intercept', and * b ' refers to the slope or the gradient of the function y=a+bx. ' b ' is called the regression coefficient and the formula is referred to as regression equation (Sokal & Rohlf, 1973)
