Description (summarizing) analysis is the most complex and the most valuable type of data analysis.
Answers
In this scenario, I was specifically interested in electricity generation by source type and by U.S. state. The EIA provides this information as well as overall total electricity generation values. When facing raw values such as these, ask yourself, “What can I compare these values to in order to better understand their significance?” This is a great question to ask because your audience will understand information much more easily if it’s compared to other information.[1] Tare a number of statistics you can calculate to answer this question:
Totals: summing values to get a big picture perspective is often handy.
Percent of totals: excellent for comparing segmented data against overall totals.
Amount change: a good option to compare how much values have changed.
Percent change: a good way to compare the size by which values have changed.
Averages: these include mean and median averages.
Based on the EIA’s data, I decided that comparing electricity generated by source type to the overall total electricity generated was much more meaningful. So I calculated percentages for each source type against the total number of megawatt hours. That way I could gauge how much of each state’s total electricity was generated by coal, wind, natural gas, and so on. The results were much easier to understand and particularly enlightening!
Once the data was mapped out, I saw that a large percentage of the Midwest’s electrical generation was due to wind energy – an interesting result considering that neighboring states have strongly adhered to coal and oil.