![](https://crypto4nerd.com/wp-content/uploads/2023/02/1v6oP0YCqdnbXJYhF5VD2TA.jpeg)
Linear Regression is a widely used statistical method for predicting a continuous dependent variable from one or more independent variables. It is a simple yet powerful technique that is widely used in various fields such as economics, finance, marketing, and engineering. However, to achieve accurate results, Linear Regression has several assumptions that must be met. In this blog, we will delve into the assumptions of Linear Regression and why they are important.
- Linearity: The relationship between the independent and dependent variables must be linear. This means that the change in the dependent variable must be proportional to the change in the independent variable. If the relationship between the variables is not linear, the results of the Linear Regression may not be accurate.
- Independence: The observations must be independent of each other. This means that the value of the dependent variable for one observation must not be influenced by the value of the dependent variable for another observation. If the observations are not independent, the results of the Linear Regression may not be accurate.
- Homoscedasticity: The variance of the residuals (the difference between the actual and predicted values) must be constant. This means that the spread of the residuals must be the same for all values of the independent variable. If the variance of the residuals is not constant, the results of the Linear Regression may not be accurate.
- Normality: The residuals must be normally distributed. This means that the distribution of the residuals must be symmetrical and bell-shaped. If the residuals are not normally distributed, the results of the Linear Regression may not be accurate.
It is important to check the assumptions of Linear Regression before using the method to ensure that the results are accurate. The assumptions can be checked using various statistical methods, such as residual plots and statistical tests. If the assumptions are not met, the Linear Regression model can be modified or a different statistical method can be used.
In conclusion, the assumptions of Linear Regression are important for ensuring that the results are accurate. Understanding and checking the assumptions before using the method is crucial for obtaining reliable results and making informed decisions.