The Central Limit Theorem

 If a person scores in the bottom 10 of test scores, what is the maximum score they could have received?

G. The Central Limit Theorem

The Central Limit Theorem shows why normal distributions are so common and so useful. Essentially, it says that if a large sample is drawn, the sample averages for any random variable have a normal distribution. More specifically: Central Limit Theorem – If the following conditions are true:  x is a random variable with a known mean  and known standard deviation   A random sample of n values of the random variable x is drawn  Either x is normally distributed or 30 n  Then for the random variable x which represents the sample mean for our sample of n values, the following are also true:  The distribution of x is approximately normal with greater values of n giving a closer approximation.  The mean of x denoted by x  is equal to the mean of x: x     The standard deviation of x denoted by x  is equal to the standard deviation of x divided by the square root of the sample size: x n    Example: If the random variable h represents the height of a randomly selected woman, then h is normally distributed with a mean of 63.5   inches and a standard deviation of 2.75   inches. If random samples of 16 women are selected, then the Central Limit Theorem can be applied. The sample mean h is a normally distributed random variable with 63.5 h   and 2.75 16 0.6875 h    We can now calculate the probability of selecting a sample of 16 women whose average height is more than 65 inches. We are interested in . Converting 65 to a z-score we have: 65 63.5 1.5 2.18 0.6875 0.6875 h h h z         The probability in the Standard Normal Table for this value is .9854. Thus . So there is only a 1.43 chance that we would choose 16 women at random with an average height of at least 65”. Note that was calculated in one of the exercises above. It is far more likely to find a single woman who is taller than 65” than a group of 16 women whose average height is more than 65”. Exercises:  Find 64.5 P h   Find 63 64.5 P h   Example: If w represents the weight of an American man chosen at random, then w is a continuous random variable with a right-skewed distribution with a mean of lbs. and a standard deviation of lbs. If a random sample of 43 men is selected, and w is the average weight of the 43 men, find the following probabilities:  Find 175 185 P w    Find 170 190 P w    Find 162 P w  Example: Consider the lottery example introduced in Chapter 4 Notes. An instant lottery ticket is purchased for 2. The possible prizes are 0, 2, 20, 200, and 1000. Let Z be the random variable representing the amount won on the ticket, and suppose Z has the following distribution: Z 2 20 200 1000 P Z .7489 .2 .05 .001 .0001 We determined the mean of Z to be 1.70   , and the standard deviation of Z to be 12.57   . 65 P h  65 1 .9857 .0143 P h     65 .2912 P h   180   20   If a player purchases 1000 random tickets, then we apply the Central Limit Theorem to Z the average amount won per ticket. 1.70 Z   and 12.57 1000 0.3975 Z    . We can now determine the probability that the player gains money. In order for the player to win money on the 1000 tickets, Z must exceed 2. To find 2 P Z  we calculate the z- score for 2 Z  : 2 1.7 .3 .75 .3975 .3975 Z Z Z z         The standard normal table gives a value of .7734 for the z-score .75, and so: 2 1 .7734 .2266 P Z     . Exercise: Repeat the above with 10,000 tickets.

CHAPTER 6 POINT ESTIMATION OF PARAMETERS