1. Chapter 18: The Central Limit Theorem Objective: To apply the Central Limit Theorem to the Normal Model CHS Statistics

2.  Before, we took the individual values and compared them to others using the Normal model  Today, we will work with data in which each value represents the mean of some other sample Link to the Past Section

3.  The sampling distribution of a mean is the probability distribution of sample means, with all samples having the same sample size n.  In general, the sampling distribution of any particular statistic is the probability distribution of that statistic. Sampling Distributions

4. The Central Limit Theorem (CLT)

5. Practical Rules Commonly Used: 1.For sample sizes larger than 30 (within context), the distribution of the sample means can be approximated reasonably well by a Normal distribution. The approximation gets better as the sample size n becomes larger. 2.If the original probability is itself Normally distributed, then the sample means will be Normally distributed for any sample size n (not just the values of n larger than 30). The Central Limit Theorem (cont.)

6.  In a study of work patterns in one population, the mean family times devoted to child care is 23.08 hours with a standard deviation of 15.58 hours. If 55 subjects are randomly selected from this population, find the probability that the mean of this sample group is between 23.08 hours and 25 hours. Example 1

7. We used women's weights, which are Normally distributed with a mean of 143 lbs. and a standard deviation of 29 lbs. Find the probability that:  If 1 woman is randomly selected, her weight is greater than 150 lbs.  If 36 different women are randomly selected, their mean weight is greater than 150 lbs. Example 2

8.  Pp. 432-438 # 27, 28, 34, 37, 39, 44 – 48 Even  Check your answers using the solutions manual online. Assignment