Fourth – correlations
Before you can write this report, you need to have a set of correlated data points that tell your story. * dependencies — Which numbers depend on each other? That is, a change in one creates a change in another or others. * trade-offs — An improvement in one number causes its opposite in another number.
Discussing these correlations will be the bulk of your analysis of what happened during the month. They’ll provide the reasons for your proposal to improve the following month. As you see the correlations, you’ll begin to see the story of your month, the “hiSTORY” of the month. Some correlations will be irrelevant to that story. Others will tell it in pictures. The text of your report will explain those pictures.
Don’t look at the graphs and charts as an extra, optional add-on. Look at them as THE way to tell your story.
Examples: What correlates with weather? What correlates with profits? What correlates with daily sales?
These will rarely be one-cause, one-effect correlations, even for as simple an operation as a lemonade stand.
Which chart or graph is right for you? Tell impactful stories with data
Fifth – correlations in subsets of data

Correlations
How are you going to structure this story? That is, in what order are you going to discuss the correlations? * by chronology, perhaps week by week or phase by phase. * by types of weather: all the sunny days, all the rainy days, etc. * by ingredients. If you see that your ordering of lemons correlates highly with other decisions, then you can make subsets based on lemons in inventory. * by profits or sales. How to the seven highest-profit/sales days compare to the seven lowest-profit/sales days?
After you decide the structure, as with the patterns above: * Make a separate spreadsheet with only the data for each subset by copying your original onto a new sheet. * Rename the sheet for the sort * Do the sorting * Make the new set of graphs, that is, run all your correlations on each subset.