Regression Analysis
Regression Analysis is the measure of average relationship between two or more
variables in terms of the original units of the data. The technique of correlation is used to
measure statistical relationship which provides information regarding the degree and
direction of relationship between two related series. But, if the two value of one series
(variable) is given and the value of the other series is to be predicted, the technique of
regression analysis is required for this purpose. The credit of using regression technique in
statistics for the first time goes to British Biometrician Sir Francis Galton who used this
term in 1877 while studying the relationship between the height of fathers and sons.
Definition:-
“Regression analysis is a mathematical measure of the average relationship between two or
more variables in terms of the original unit of data.” – According to M.M. Blair
Types of Regression

1. Simple Regression- This distinction is based on the number of variables studied. If
   regression analysis is based only two variables is known as simple regression.
2. Multiple and Partial Regression: When more than two variables are studied, it is a
   problem of either multiple or partial regression.
3. Linear Regression: If the regression line is in the form of a straight line, it indicates
   linear regression.
4. Curvi-linear Regression: If the regression line is not a straight line but a smoothed
   curve, regression is termed as curvi-linear or non-linear.
   The linear function is useful because it is mathematically simple and it can be shown
   to be reasonably close to the approximation of many situations.
   Regression Line: There are two regression lines such as: