Regression analysis is a statistical technique that allows us to understand and predict relationships between variables. It helps in identifying how a dependent variable (the outcome you want to predict) is affected by one or more independent variables (the factors that potentially influence the outcome).

Linear regression is the most commonly used type of regression analysis. It assumes a linear relationship between the dependent and independent variables. For example, if we want to predict the housing prices based on factors like the size of the house, the number of bedrooms, and the location, we can apply linear regression to understand how these variables affect the price.

To apply linear regression, we need a dataset with both the dependent variable and the independent variables. Using this dataset, we can estimate the coefficients of the linear equation and make predictions based on the values of the independent variables.

Regression analysis is widely used in areas such as finance, marketing, and healthcare to make predictions and inform decision-making. By understanding the relationships between variables, we can develop models that provide valuable insights and support data-driven strategies.