Regression Evolution of Regression The term ‘Regression’ was first coined and used in by Francis Galton while studying the relationship between the height of fathers and sons. The average height of children born of parents of a given height tended to move or “regress” toward the average height in the population as a whole. Galton’s law of universal regression was confirmed by his friend Karl Pearson, who collected more than a thousand records of heights of members of family groups. The literal meaning of the word “regression” is “Stepping back towards the average”.
Regression is the study of the relationship between the variables. If Y is the dependent variable and X is independent variable, the linear relationship between the variable is called the regression equation of Y on X, The regression equation is used to Francis Galton - - Introduction to Statistical Methods and Econometrics estimate the value of Y corresponding to the known value of X. The line describing this tendency to regress or going back was called by Galton a “ Regression Line ”. Difference between Correlation and Regression S.No Correlation Regression Correlation is the relationship between two or more variables, which vary with the other in the same or the opposite direction Regression means going back and it is a mathematical measure showing the average relationship between two variables Both the variables X and Y are random variables Both the variables may be random variables It finds out the degree of relationship between two variables and not the cause and effect relationship.
It indicates the cause and effect relationship between the variables and establishes functional relationship. It is used for testing and verifying the relation between two variables and gives limited information Besides verification it is used for the prediction of one value, in relation to the other given value. The coefficient of correlation is a relative measure. The range of relationship lies between – and + Regression coefficient is also relative measure.
If we know the value of the independent variable, we can find the value of