The Monday MBA Math series helps prospective MBA students to self assess their proficiency with the quantitative building blocks of the MBA first year curriculum.
The normal distribution is essentially the familiar symmetrical bell curve that characterizes many phenomena. Even when distributions of interest are asymmetrical, the normal distribution is central to sampling and confidence intervals that help guide efforts to make sense of massive data sets by working with representative data samples. Working with the normal distribution highlights the importance of thinking about intervals measured by units of standard deviations away from the mean.
Hal Varian, Chief Economist at Google, was recently quoted in the New York Times from a McKinsey Quarterly interview as saying "that the sexy job in the next 10 years will be statisticians." Perhaps more on point for MBAs, Tom Davenport and Jeanne Harris are releasing "Analytics at Work: Smarter Decisions, Better Results" this month as a follow up to their influential 2007 book Competing on Analytics. Understanding normal distributions is a core skill in developing the statistical numeracy required to apply analytics to strategy.
Suppose the daily customer volume at a call center has a normal distribution with mean 4,600 and standard deviation 950. What is the probability that the call center will get fewer than 3,400 calls in a day?
Click here to view a standard normal (z-score) table, if you know how to use it.
Solution (with audio commentary): click here
Prof. Peter Regan created the self-paced, online MBA Math quantitative skills course and teaches live MBA courses at Dartmouth (Tuck), Duke (Fuqua), and Cornell (Johnson).