Say I am running a store and I have the daily customer count for the last 5 years.
I note that the average customer count on Black Fridays is three times higher than the average for the rest of the year.
I build a table with two columns:
IsDayBlackFriday(0 or 1),
and run linear regression where the independent variable is customer count and the independent variable is whether it is a Black Friday or not. As expected, the p-value for the Black Friday variable is extremely low, but the R-squared val for the model is also quite low. I note that I am not bothered by it since I am aware that there are many other factors that determine the demand, and I am really looking for some statistical validation of the correlation between the customer count and one particular day in a year, and not trying to build a predictive model for customer count.
My questions are:
Can I conclude that on Black Fridays, I should hire 3 times more people as compared to the rest of the year to cover the demand, assuming the staffing requirement increases linearly with the customer count? For example, if on normal days, I have a staff count of 5, will hiring 15 people be a "statistically informed" decision?
If not, what can I really infer from the facts given above that I can translate to an informed decision? Should the staffing question be approached differently?
Thank you for your answers Chris and ERT.
I think what I am interested in right now is getting a more solid understanding about the relation between 1) the p-value of the Black Friday binary variable in the regression results and 2) the percentage increase in the customer count that we can compute separately. More particularly, if the percentage increase is 200% (3 times higher value) and the p-value is low, can we say that for the next Black Friday, we can expect the customer count to be three times as high as the average we observed for the rest of the year? Or is that an overreach and the most we can say is that the variables are strongly correlated but we cannot predict anything since our R-squared value is low.
The hiring staff part is a detail in which I am not interested in at the moment (sorry if that wasn't clear from the original post) and was just used as an example of where the insight could be applied.
Chris, you mentioned that regression can help us quantify how busier it is on a holiday as opposed to the rest of the year. Can you elaborate on that?