Consider quantitative data on the price of houses and their corresponding ground area. Such quantitative data with two variables are called bivariate data.  

The variable that acts as the cause is called the independent variable, while another variable that shows the response is called the dependent variable. 

This dependence of one variable over the other can be visualized using the scatter plot. Here, the independent variable—the ground area—is represented along the X-axis, and  the dependent variable—the price of houses—is represented along the Y-axis. 

Mark the prices corresponding to the ground area. Then, draw the best fit line such that an almost equal number of points are present above and below this line. These points together form the pattern to identify the correlation between the two variables.  

Notice that the increase in the ground area leads to a rise in the price of houses. Such an increasing trend denotes a positive correlation. 

Conversely, if one observes a decreasing trend, it indicates a negative correlation. No trend means no correlation.