. Regression Analysis Introduction: So far we have studied correlation analysis which measures the direction and strength of the relationship between two variables. Here we can estimate or predict the value of one variable from the given value of the other variable. For instance, price and supply are correlated.
We can find out the expected amount of supply for a given price or the required price level for attaining the given amount of supply. The term “ regression” literally means “Stepping back towards the average”. It was first used by British biometrician Sir Francis Galton ( - ), in connection with the inheritance of stature. Galton found that the offsprings of abnormally tall or short parents tend to “regress” or “step back” to the average population height.
But the term “regression” as now used in Statistics is only a convenient term without having any reference to biometry. - - Correlation and Regression analysis Definition . Regression analysis is a mathematical measure of the average relationship between two or more variables in terms of the original units of the data. .
. Dependent and independent variables Definition . In regression analysis there are two types of variables. The variable whose value is to be predicted is called dependent variable and the variable which is used for prediction is called independent variable.
Regression helps us to estimate the value of one variable, provided the value of the other variable is given. The statistical method which helps us to estimate the unknown value of one variable from the known value of the related variable is called Regression. . .
Regression Equations Regression equations are algebraic expressions of the regression lines. Since there are two regression lines, there are two regression equations. The regression equation of X on Y is used to describe the variation in the values of X for given changes in Y and the regression equation of Y on X is used to describe the variation in the values of Y for given changes in X . Regression equations of (i) X on Y (ii) Y on X and their coefficients