3. Types of Linear Regression


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.

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