Regression analysis is a statistical method used to examine and model the relationship between two or more variables. It helps us understand how changes in one variable might be associated with changes in another. The goal is to establish a mathematical equation that can predict the value of one variable based on the value(s) of other variable(s). The simplest form is linear regression, where a straight line is fitted to the data, but there are more complex forms as well. This method is widely used in various fields, to analyze and interpret relationships between variables. Example: Imagine you want to understand the relationship between the size of houses and their sale prices. You collect data on different houses, noting their sizes (in any fixed unit) and their sale prices. Now, you can use regression analysis to create a model that helps predict the sale price of a house based on its size. The regression equation might look like: Y = mx + C Sale Price = Slope X Size of House + Intercept 'm' : is the slope of the line, representing how much the sale price is expected to change for each additional unit of size. 'C' : here is the intercept term, representing the predicted sale price when the size of the house is zero (which doesn't make much sense in this context but is part of the mathematical model). With this equation, you can create a model that helps you predict the likely sale price of a house based on its size.