*QUESTION #1 (65 WORDS)*

1: Find a recent example of one of these two types of misleading graphs (mentioned above) and inform us about it.

Provide the link you obtained information – DO NOT copy and paste the graph, because that violates the copyright laws.

The usual way to measure spread numerically is called Standard Deviation.

*NOTE* Here is a different example to think about standard deviation: Investment returns such as for stocks can be measured by their means and standard deviations. Some people would rather have a stock investment with an average return of 10% and a standard deviation of 2%, while others would prefer one with an average return of 10% and a standard deviation of 30%. The first stock is safe, while the second is risky. There is no right answer, and your choice depends on your wealth, your tolerance toward risk and uncertainty, how quickly you might need the cash, etc. But you see the need for standard deviation to distinguish the two stocks.

*QUESTION #2 (65 WORDS)*

*Here is a video showing the calculation of standard deviation of 4 numbers: http://youtu.be/nrLaHuBzHDI

2: Why do we care about standard deviation?

*QUESTION #3 (70 WORDS)*

*NOTE* A book says, “A disadvantage of the mean is that just one extreme value (outlier) can change the value of the mean substantially.” Here is a real example of this:

The University of Virginia maintains a history of the starting salaries of its undergraduate business school graduates. One Virginia graduate claims, “when my brother and I graduated, we significantly raised the average starting salary of our class.” The speaker was Tiki Barber, whose first job along with his twin brother Ronde was as a professional football player. Tiki’s starting salary was 325,000 and his signing bonus was 700,000, while Ronde’s starting salary was 250,000 and his bonus was 400,000; clearly these salaries are not representative of the rest of their class. So this would be a case where the mean might not be representative of the class.

3: If you were the university administrator assembling annual starting salary data, and you didn’t want to have to explain why next year’s data (without any professional football players) looked so different than the year with the football players would you use mean, median, or mode for each year? Why?