Regression analysis is a basic method used in the statistical analysis of data. It's a statistical method that helps to find the relationships between variables. It is basically used for understanding the relationship between input and output numerical variables. We should first identify the dependent variable, which will vary based on the value of the independent variable. For example, the value of the house (dependent variable) varies based on the square footage of the house (independent variable). Regression analysis is very useful for prediction.

In a simple regression problem (a single x and a single y), the form of the model would be as follows:

In higher dimensions, when we have more than one input (x), the line is called a plane or a hyperplane. 

In our example, we predict the price of the house based on the various parameters that may impact the price of the data in that particular area.

The following are some of the important points to be considered while addressing a regression problem:

• The prediction is to be a numeric quantity.
• The input variables can be real-valued or discrete.
• If there are multiple input variables then it is called a multivariate regression problem.
• When the input variables are ordered by time, the regression problem is called a time series forecasting problem.
• Regression should not be confused with classification. Classification is the task of predicting a discrete class label, whereas regression is the task of predicting a continuous quantity.

An algorithm that is capable of learning a regression predictive model is called a regression algorithm.