Linear Regression is a statistical method used in predictive modelling. It is used to determine the strength of relationship between one dependent variable and one or more independent variables. It is an extension of correlation analysis and can be used to explain the relationship between two or more variables. The linear regression model is based on a linear or polynomial equation and can be easily implemented using computers.
Linear regression is used extensively in fields such as economics, finance, and engineering. It can be used to determine prices of an asset, study consumer behaviour and identify demand for a product. In engineering, it can be used to study the relationship between a change in temperature and a change in pressure, and to predict if a particular design will be successful.
The process of linear regression involves fitting the best-fitting line through the given data points. This line is called the regression line and is used to make predictions about the values of future data points. Linear regression relies on assumptions about the data that need to be maintained for the model to work. In particular, the errors must be normally distributed, the relationships must be linear and no influential points should be present.
Linear regression is an extremely useful tool in predicting future values when the relationship between the variables is linear. It is an accessible and powerful tool for making sense of complex data. It is also popular with statisticians because of its ease of use and effectiveness at making predictions about the data.
