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.
Variance is a basic summary statistic that reflects the degree of spread for a set of data from that data’s average value. The wider the spread, the higher the variance.
Summary statistics are convenient because they allow us to talk meaningfully about phenomena with large underlying data sets (think retail sales at Amazon.com, Google search requests, and bank credit book stress tests) without being bogged down by the full details.
The variance calculations are very similar to those used for other common summary statistics so by understanding variance you build a foundation for other common statistical measures.
You’ll also find that correlation among data sets (stock prices, mortgage defaults) involves a modest extension of variance calculations.
Sure, improper use of statistics is a partial cause of the ongoing financial crisis. But, to understand the mistakes made, and to know where the solid ground of fact transitions to the marshy swamp of judgment, you need to learn the fundamentals of statistics.
Exercise:
Unit sales for new product ABC have varied in the first seven months of this year as follows:
|
Month |
Jan |
Feb |
Mar |
Apr |
May |
Jun |
Jul |
|
Unit Sales |
428 |
391 |
459 |
161 |
410 |
367 |
466 |
What is the (population) variance of the data?
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).
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