2. Chapter 18 – Part II Sampling Distribution for the Sample Mean

3. Review One Quantitative Variable – Age – Height

4. Review Population parameters – ____________________________________

5. Review Sample statistics – _______________________________________

6. Example 1994 Senators Population Parameter – _______________________________________ Sample of 10 Senators – _______________________________________

7. Example Sample 1 2 3 4 5

8. SRS characteristics Value of ___________________________ Changes from sample to sample. Different from population parameter. – _______________________________

9. Long Term Behavior of Sample Mean Repeat taking SRS of size n = 10. – ______________________________________ ___________________________________

10. Sampling Distribution of Sample Mean Summarize _________________________ __________________________________ – Center (Mean) – Spread (Standard Deviation) – Shape

11. Sampling Distribution of Sample Mean Mean (Center)

12. Sampling Distribution for Sample Mean Standard Deviation (Spread)

13. Sampling Distribution for Sample Mean Distribution of Population Values for Quantitative Variable is Normal Three conditions 1. Sample must be random sample 2. Sample must be independent values 3. Sample must be less than 10% of population.

14. Sampling Distribution for Sample Mean When Population Values Come from a Normal Distribution

15. Sampling Distribution for Sample Mean Distribution of Population Values for Quantitative Variables is non-normal. Three conditions 1. Sample must be random sample 2. Sample must be independent values 3. Sample must be less than 10% of population.

16. Sampling Distribution for Sample Mean When Population Values Come from a Non- Normal Distribution.

17. Central Limit Theorem As the sample size n increases, the mean of n independent values has a sampling distribution that tends toward a ______________________________.

18. Central Limit Theorem Major Result in Statistics! When Population Values Come from a non- Normal Distribution, Central Limit Theorem still applies when sample size is large.

19. How large does n need to be? Depends on shape of population distribution – Symmetric: – Skewed:

20. 68-95-99.7 Rule for Sampling Distribution of Sample Mean In 68% of all samples, the sample mean will be between

21. 68-95-99.7 Rule for Sampling Distribution of Sample Mean In 95% of all samples, the sample mean will be between

22. 68-95-99.7 Rule for Sampling Distribution of Sample Mean In 99.7% of all samples, the sample mean will be between

23. Example #1 The height of women follows a normal distribution with mean 65.5 inches and standard deviation 2.5 inches. If you take a sample of size 25 from a large population of women, use the 68-95-99.7 rule to describe the sample mean heights.

24. Example #1 (cont.)

27. Probabilities with the Sample Mean We can also find probabilities with the sample mean statistic.

28. Example #1 Ithaca, New York, gets an average of 35.4 inches of rainfall per year with a standard deviation of 4.2 inches. Assume yearly rainfall follows a normal distribution.

29. Example #1 – cont. What is the probability a single year will have more than 40 inches of rain?

30. Example #1 – cont.

31. What is the probability that over a four year period the mean rainfall will be less than 30 inches?

32. Example #1 – cont.

33. Example #2 Carbon monoxide emissions for a certain kind of car vary with mean 2.9 gm/mi and standard deviation 0.4 gm/mi. A company has 80 cars in its fleet. Estimate the probability that the mean emissions for the fleet is between 2.95 and 3.0 gm/mi.

34. Example #2 – cont.

36. Example #3 Grocery store receipts show that customer purchases are skewed to the right with a mean of $32 and a standard deviation of $20.

37. Example #3 – cont. – Can you determine the probability the next customer will spend at least $40?

38. Example #3 – cont. – Can you determine the probability the next 50 customers will spend an average of at least $40?

39. Example #3 – cont.