1. The Normal Probability Distribution

2. What is a distribution? A collection of scores, values, arranged to indicate how common various values, or scores are. Mean (population, sample) Standard deviation (population, sample) Median Mode

3. Scores in our class

4. CHARACTERISTICS OF A NORMAL DISTRIBUTION Theoretically, curve extends to - infinity Theoretically, curve extends to + infinity Mean, median, and mode are equal Tail Normal curve is symmetrical - two halves identical -

5. AREAS UNDER THE NORMAL CURVE About 68 percent of the area under the normal curve is within plus one and minus one standard deviation of the mean. This can be written as  ± 1 . About 95 percent of the area under the normal curve is within plus and minus two standard deviations of the mean, written  ± 2 . Practically all (99.74 percent) of the area under the normal curve is within three standard deviations of the mean, written  ± 3 .

6.  Between:  68.26%  95.44%  99.97% Between:  68.26%  95.44%  99.97%

7. Normal Distributions with Equal Means but Different Standard Deviations.    3.9  = 5.0   3.9  = 5.0

8. Normal Probability Distributions with Different Means and Standard Deviations.  = 5,  = 3  = 9,  = 6  = 14,  = 10  = 5,  = 3  = 9,  = 6  = 14,  = 10

9. What is this good for?? describes the data and how it clusters, arranges around a mean. it’s good for us because it can allow us to make statistical inferences

10. CHARACTERISTICS OF A NORMAL PROBABILITY DISTRIBUTION 0 1 standard normal distribution A normal distribution with a mean of 0 and a standard deviation of 1 is called the standard normal distribution. z value: z value: The distance between a selected value, designated X, and the population mean , divided by the population standard deviation, . Disguised under z-score, normal scores, standardized score

11. What is it good for? Indicates how many standard deviations an observation is above/below the mean It’s good, because it allows us to compare observations from other normal distributions Is a 3.00 GPA UNLV student as good as a 3.00 GPA UCF student?

12. EXAMPLE 1 The monthly incomes of recent high school graduates in a large corporation are normally distributed with a mean of $2,000 and a standard deviation of $200. What is the z value for an income X of $2,200? $1,700? For X = $2,200 and since z = (X -  then z =.

13. EXAMPLE 1 (continued) For X = $1,700 and since z = (X -  then A z value of +1.0 indicates that the value of $2,200 is ___ standard deviation ______ the mean of $2,000. A z value of – 1.5 indicates that the value of $1,700 is ____ standard deviation ______ the mean of $2,000.

14. EXAMPLE 2 The daily water usage per person in Toledo, Ohio is normally distributed with a mean of 20 gallons and a standard deviation of 5 gallons. About 68% of the daily water usage per person in Toledo lies between what two values?  ± 1  That is, about 68% of the daily usage per person will lie between __________________ gallons. Similarly for 95% and 99%, the intervals will be __________________________________________.

15. POINT ESTIMATES Point estimate: one number (called a point) that is used to estimate a population parameter. Examples of point estimates are the sample mean, the sample standard deviation, the sample variance, the sample proportion, etc. EXAMPLE: The number of defective items produced by a machine was recorded for five randomly selected hours during a 40-hour work week. The observed number of defectives were 12, 4, 7, 14, and 10. So the sample mean is ____. Thus a point estimate for the weekly mean number of defectives is 9.4.

16. INTERVAL ESTIMATES Interval Estimate: states the range within which a population parameter probably lies. The interval within which a population parameter is expected to occur is called a confidence interval. The two confidence intervals that are used extensively are the 95% and the 99%. A 95%confidence interval means that about 95% of the similarly constructed intervals will contain the parameter being estimated.

17. INTERVAL ESTIMATES (continued) Another interpretation of the 95% confidence interval is that 95% of the sample means for a specified sample size will lie within 1.96 standard deviations of the hypothesized population mean. For the 99% confidence interval, 99% of the sample means for a specified sample size will lie within 2.58 standard deviations of the hypothesized population mean.

18. Determining Sample Size for Probability Samples Financial, Statistical, and Managerial Issues The larger the sample, the smaller the sampling error, but larger samples cost more. Budget Available Rules of Thumb

19. Typical Sample Sizes Number ofConsumer researchBusiness research* subgroupNationalSpecialNationalSpecial analysespopulationpopulationpopulationpopulation None/few200-500100-50020-10020-50 Average500-1000200-100050-20050-100 Many1000-2000500-1000200-500100-250

20. Sample Size Determination Sample size depends on Allowable Error/level of precision/ sampling error (E) Acceptable confidence in standard errors (Z) Population standard deviation (  )

21. Sample size determination Problem involving means: Sample Size (n) = Z 2  2 / E 2 where: Z = level of confidence expressed in standard errors  = population standard deviation E = acceptable amount of sampling error

22. Sample size determination Problem involving proportions: Sample Size (n) = Z 2 [P(1-P)] / E 2

23. Sampling Exercise Let us assume we have a population of 5 people whose names and ages are given below: Abe24 Bob30 Cara36 Don42 Emily 36

24. Average of all samples of size = 1 Abe24 Bob30 Cara36 Don42 Emily48 Average of all possible “size = 1” samples= 36

25. Average of all samples of size = 2 Abe, Bob(24+30)/2 = 27 Abe, Cara30 Abe, Don33 Bob, Cara33 Abe, Emily36 Bob, Don36 Bob, Emily39 Cara, Don39 Cara, Emily 42 Don, Emily 45 Average of all possible “size = 2” samples= 36

26. Average of all samples of size = 3 Abe, Bob, Cara30 Abe, Bob, Don32 Abe, Bob, Emily34 Abe, Cara, Don34 Abe, Cara, Emily36 Bob, Cara, Don36 Bob, Cara, Emily38 Abe, Don, Emily38 Bob, Don, Emily40 Cara, Don, Emily42 Average of all possible “size = 3” samples= 36

27. Average of all samples of size = 4 Abe, Bob, Cara, Don33 Abe, Bob, Cara, Emily34.5 Abe, Bob, Don, Emily36 Abe, Cara, Don, Emily37.5 Bob, Cara, Don, Emily 39 Average of all possible “size = 4” samples= 36

28. Average of all samples of size = 3 Abe, Bob, Cara, Don, Emily 36 Average of all possible “size = 5” samples= 36

29. What can be learned? What is the average of the average of the sample for a given size? Does the mean of any individual sample equal to the population mean? Range of values for each sample size category?

30. Sampling Distribution Population distribution: A frequency distribution of all the elements of a population. Sample distribution: A frequency distribution of all the elements of an individual sample. Sampling distribution- a frequency distribution of the means of many samples.

31. Normal Distribution Central Limit Theorem - Central Limit Theorem—distribution of a large number of sample means or sample proportions will approximate a normal distribution, regardless of the distribution of the population from which they were drawn

32. The Standard Error of the Mean Applies to the standard deviation of a distribution of sample means.  x x =  n √

33. The Standard Error of the Distribution of Proportions Applies to the standard deviation of a distribution of sample proportions. Sampling Distribution of the Proportion P (1-P) SpSp = n √ where: Sp = standard error of sampling distribution proportion P = estimate of population proportion n = sample size

34. Sample size determination – adjusting for population size Make an adjustment in the sample size if the sample size is more than 5 percent of the size of the total population. Called the Finite Population Correction (FPC).  x x =  n √ N - n √ N - 1