# Linear Regression

__Regression__:

The word regression was used by Frances Galton in 1985. It is defined as “**The dependence of one variable upon other variable**”. For example, a weight depends upon the heights. The yield of wheat depends upon the amount of fertilizer. In regression we can estimate the unknown values of one (dependent) variable from known values of the other (independent) variable.

__Linear Regression__:

When the dependence of the variable is represented by a straight line then it is called linear regression, otherwise it is said to be non linear or curvilinear regression.

**For Example, **if is dependent variable and is dependent variable, then the relation is linear regression.

__Regression Line of Y on X__:

Regression lines study the average relationship between two variables. In regression line on , we estimate the average value of *Y* for a given value of .

Where *Y* is dependent and is independent variable. Alternate form of regression line on is:

__Regression Line of X on Y__:

In regression line on we estimate the average value of for a given value of .

.

Where is dependent and is independent variable. Alternate form of regression line on is: