
So let's do it. Let's calculate mean

and standard deviation. And to do that, let's

think back to our example with stores and

sales. And let's say the question we want to

answer is, is there any correlation between the

day of the week and how much money

people spend on various items? And what's interesting

about this design pattern is that all the

mapper has to do is, I'll put the day of the week as a key, so maybe Monday, and

the value of a sale, maybe $5.20 as a value.

That's it. What does that leave for the reducer? Well,

it leaves all the math for the reducer. And the

general reason for this rule of thumb, for what the

mapper and reducer are doing, comes from the fact that

oftentimes with these, with these summary statistics, you sort of need

to know all of the statistics or all of

the parent data before you can make any calculations. So

we don't want to jump the gun and have the mapper

do calculations before it's ready. So why don't you go

ahead and calculate the mean and standard deviation for

sales for each day of the week, to help us

try to answer this question. If there's any correlation between

the day of the week and how much people spend.