In Simple Linear Regression, we try to find the relationship between a single independent variable (input) and a corresponding dependent variable (output). This can be expressed in the form of a straight line.
The same equation of a line can be re-written as:
Y represents the output or dependent variable.
β0 and β1 are two unknown constants that represent the intercept and coefficient (slope) respectively.
ε (Epsilon) is the error term.
The following is a sample graph of a Simple Linear Regression Model :
Applications of Simple Linear Regression include :
1) Predicting crop yields based on the amount of rainfall: Yield is dependent variable while the amount of rainfall is independent variable.
2) Marks scored by student based on number of hours studied (ideally) : Here marks scored is dependent and number of hours studied is independent.
3) Predicting the Salary of a person based on years of experience : Thus Experience become the independent variable while Salary becomes the dependent variable.