The end result of the produce from the manufacturing plant is whether it can be accepted as a good quality product or discarded due to bad quality. This status for each manufacturing exercise is identified in the data using the 'Detergent_Quality' dimension, which is calculated using some weighted algorithm by taking into account the four output quality parameters of the end detergent produced. Our end goal is to find out the reasons why the final product was not accepted, which shows that we need to study why the output quality was bad. The reasons could be many, but how do we identify them? This is when the task of studying relationships is presented to the decision scientist. We have with us plenty of independent variables that are either continuous or categorical. Trying to understand how these independent dimensions eventually contribute to the end output is where we start studying the relationship between them. The entire exercise can be simply defined as bivariate...