Linear Regression | Dr. Nitin Mishra
What is Linear Regression:
Linear regression is a statistical method that aims to model the relationship between a dependent variable and one or more independent variables. It assumes that there is a linear relationship between the dependent variable and the independent variable(s). In simple linear regression, there is only one independent variable, while in multiple linear regression, there are multiple independent variables.
How does Linear Regression work:
Linear regression works by fitting a line or a hyperplane that best represents the relationship between the dependent variable and the independent variable(s). The line or hyperplane is chosen in such a way that the sum of the squared differences between the observed values and the predicted values is minimized. This is known as the least-squares method.
Types of Linear Regression:
There are two main types of linear regression: simple linear regression and multiple linear regression. Simple linear regression involves only one independent variable, while multiple linear regression involves two or more independent variables.
Applications of Linear Regression:
Linear regression is widely used in various fields, including finance, economics, social sciences, engineering, and natural sciences. It is used for predicting future outcomes based on past data, understanding the relationship between variables, and identifying trends.
Advantages and Disadvantages of Linear Regression:
The main advantage of linear regression is that it is a simple and easy-to-understand method for modeling the relationship between variables. It is also computationally efficient and can handle a large number of data points. However, it has some limitations, such as the assumption of linearity, the presence of outliers, and the possibility of overfitting.