This project will only use the Closing Values. Assume that the closing prices of the stock form a normally distributed data set. This means that you need to use Excel to find the mean and standard deviation and then use those numbers and the methods you learned in sections 5.2 and 5.3 of our text book for Normal distributions to answer the questions.

Complete this assignment within a single Excel file. Show your work or explain how you obtained each of your answers. Answers with no work and no explanation will receive no credit.

- If a person bought 1 share of Google stock within the last year, what is the probability that the stock on that day closed at less than the mean for that year? Hint: You do not want to calculate the mean to answer this one. The probability would be the same for any normal distribution.
**(4 points)** - If a person bought one share of Google stock within the last year, what is the probability that the stock on that day closed at more than $400?
**(6 points)** - If a person bought 1 share of Google stock within the last year, what is the probability that the stock on that day closed within $45 of the mean for that year?
**(6 points)** - Suppose a person within the last year claimed to have bought Google stock at closing at $362.50 per share. What is the probability that the stock closed at $362.50 or less on a randomly selected business day?
**(6 points)** - At what prices would Google have to close at in order for it to be considered statistically unusual? You should have a low and high value. Be sure to use the definition of unusual from the textbook.
**(6 points)** - What are Quartile 1, Quartile 2, and Quartile 3 in this data set? Use Excel to find these values. This is the only question that you should answer without using anything about the Normal distribution.
**(6 points)**

Is the normality assumption that was made at the beginning valid? Why or why not? Hint: Does this distribution have the properties of a normal distribution as described in our textbook? It does not need to be perfect. Real data sets are never perfect. However, it should be close. One option would be to construct a histogram like we did in Project 1 and see if it has the right shape. If you go this route, something in the range of 10 to 12 classes would be a good number.