2.10:
Scatter Plot
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.
2.10:
Scatter Plot
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
- High values of one variable occurring with high values of the other variable or low values of one variable occurring with low values of the other variable.
- High values of one variable occurring with low values of the other variable.
One can determine the strength of the relationship by looking at the scatter plot and seeing how close the points are to a line, a power function, an exponential function, or to some other type of function. For a linear relationship, there is an exception. Consider a scatter plot where all the points fall on a horizontal line providing a "perfect fit." The horizontal line would, in fact, show no relationship.
When looking at a scatterplot, one must notice the overall pattern and any deviations, if any.