When dealing with numerical data, the most common way to graphically explore the patterns and relationships between variables and draw conclusion about how varaibles relate to one another is by plotting the data points using a scatterplot. A scatterplot uses dots/markers to represent values for two numeric variables where the position of each dot indicates values for an individual data point in the (x,y) coordinates.
In this post, I try to cover the most common methods of transforming a skewed distribution into a normal distribution, and the foundational step that you must consider prior deciding which method to apply.
While studying, for example, the relationship between GDP and life expectancy, you might be interested to know whether there exists any relationship between the two indicators? is it a positive relationship or a negative relationship? and how strong the association is? These questions can be answered by computing the correlation coefficient between the two indicators. Depending on the type of data, different methods of correlation exist. In this post, you will learn the Pearson correlation coefficient and the Spearman correlation coefficient.