Measures of Association
In this video we have provided a new measure, demonstrated it’s calculation, and summarised our statistics.
Our new measure of correlation is useful for determining whether there is a linear relationship between two variables. One common pitfall encountered by analysts reporting correlation is to confuse it with causation. It is very important to remember that a strong correlation does not mean that one variable causes the other. Two variables might be correlated while actually having very little to do with each other. The example given in the above video was of the strong correlation between the number of films Nicholas Cage has appeared in and number of deaths by drowning. Even if the correlation is strong, we don’t believe either of these are causing the other. Many other examples of strong correlations between unrelated variables can be found here.
Now you have a collection of useful statistics in your data analyst toolbox. You can describe the central location, spread, shape, and association of data. In addition to the graphical techniques from activities one and two, you’re quickly gaining the key tools of data analysis.
Check your knowledge of this activity in the next step’s quiz before you move onto our final activity for this week, where we discuss the importance of ethics in data analysis.
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