Correlation and Filtering¶
As you learned in Module A, filtering is a useful technique to help make sense of a large dataset. Filtering data helps identify the similarities and differences between groups and describe the relationships between variables. Recall that a filter is a way of selecting a subset of rows based on a set of column conditions. For example, if you have population data over the past ten years for each state, you could use a filter to analyze data only for Florida or only for the year 2017. Often, you can only see important differences or trends by filtering. This is even the case when finding the correlation coefficient that best reflects the data. This is best explained through an example.
Imagine that your friend is a personal trainer who has implemented a new strength training regime to use with her clients. An screenshot of this data is shown below:
Your friend wants to see if the new routine increases the number of push-ups her clients can perform. It’s a great routine and her clients are working hard, so she expects a positive r value showing that her clients can do more push-ups as they progress through the regime. Before you start looking at each of the variables separately, think about what types of variables you are working with by first answering some questions. If you want to review what the different variable types are, you can go back to the section on variables.
As you have learned in the past, you can use the CORREL function to calculate the r value. When calculating the r value, you can see that it is -0.41. However, looking at the scatter plot, it looks like each individual has improved. How is this the case when the overall trend is negative? This is an example of Simpson’s paradox, in which every subset of a population shows the opposite effect to the population itself. If the trainer could filter by participant, she could find the correlation for each participant. Notice that the trends become more apparent once the data is filtered by participant.
This graph shows that each participant has improved, and the correlation coefficient for each individual would be positive. Although r values can be useful, it is important that you closely examine data before making conclusions using just one value.