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Sampling Methods and Sampling Distributions

Sampling Methods and Sampling Distributions. Chapter . GOALS. EXPLAIN WHY SAMPLES ARE USED. DEFINE AND CONSTRUCT A SAMPLING DISTRIBUTION OF SAMPLE MEANS. EXPLAIN THE CENTRAL LIMIT THEOREM CALCULATE CONFIDENCE INTERVALS FOR MEANS AND PROPORTIONS.

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Sampling Methods and Sampling Distributions

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  1. Sampling Methods and Sampling Distributions Chapter

  2. GOALS • EXPLAIN WHY SAMPLES ARE USED. • DEFINE AND CONSTRUCT A SAMPLING DISTRIBUTION OF SAMPLE MEANS. • EXPLAIN THE CENTRAL LIMIT THEOREM • CALCULATE CONFIDENCE INTERVALS FOR MEANS AND PROPORTIONS. • DETERMINE HOW LARGE A SAMPLE SHOULD BE FOR BOTH MEANS AND PROPORTIONS.

  3. WHY SAMPLE THE POPULATION? • The destructive nature of certain tests. • The physical impossibility of checking all items in the population. • The cost of studying all the items in a population is often prohibitive. • The adequacy of sample results. • To contact the whole population would often be time-consuming.

  4. PROBABILITY SAMPLING • What is a Probability Sample? • A sample selected in such a way that each item or person in the population being studied has a known (nonzero) likelihood of being included in the sample. • Simple Random Sample:A sample formulated so that each item or person in the population has the same chance of being included.

  5. SIMPLE RANDOM SAMPLING VIA EXCEL • Given a list of elements, select a random subset. • Tools • Uniform Random Number Generator • Sort Function • How?

  6. PROBABILITY SAMPLING (continued) • Systematic Random Sampling:The items or individuals of the population are arranged in some way-alphabetically or by some other method. A random starting point is selected, and then every kth member of the population is selected for the sample. • Stratified Random Sampling:A population is first divided into subgroups, called strata, and a sample is selected from each stratum.

  7. PROBABILITY SAMPLING (continued) • Sampling Error:The difference between a sample statistic and its corresponding parameter. • For example …

  8. SAMPLING DISTRIBUTION OF THE SAMPLE MEANS • A probability distribution consisting of a list of all possible sample means of a given sample size selected from a population, and the probability of occurrence associated with each sample mean. • EXAMPLE :A law firm has five partners. At their weekly partners meeting each reported the number of hours they charged clients for their professional services last week. The results are given on the next slide.

  9. EXAMPLE (continued) • Two partners are randomly selected. How many different samples are possible?

  10. EXAMPLE (continued) • This is the combination of 5 objects taken 2 at a time. That is, 5C2 = (5!)/[(2!)(3!)] = 10. • List the possible samples of size 2 and compute the mean.

  11. EXAMPLE (continued) • Organize the sample means into a sampling distribution. The sampling distribution is shown below.

  12. EXAMPLE (continued) • Compute the mean of the sample means and compare it with the population mean. • The population mean, m = (22 + 26 + 30 + 26 + 22)/5 = 25.2. • The mean of the sample means = [(22)(1) + (24)(4) + (26)(3) + (28)(2)]/10 = 25.2. • Observe that the mean of the sample means is equal to the population mean.

  13. CENTRAL LIMIT THEOREM • For a population with a mean m and a variance s2, the sampling distribution of the means of all possible samples of size ngenerated from the population will be approximately normally distributed - with the mean of the sampling distribution equal to m and the variance equal to s2/n- assuming that the sample size is sufficiently large.

  14. POINT ESTIMATES • One value (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 9.4. Thus a point estimate for the weekly mean number of defectives is 9.4

  15. INTERVAL ESTIMATES • An Interval Estimatestates 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 parameterbeing estimated.

  16. 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.

  17. 95% 99% -2.58 -1.96 0 1.96 2.58

  18. STANDARD ERROR OF THE SAMPLE MEANS • This is the standard deviation of the sampling distribution of the sample means. • The standard errorof the sample means is computed by: is the symbol for the standard error of the sample means. sis the standard deviation of the population. n is the size of the sample.

  19. STANDARD ERROR OF THE SAMPLE MEANS (continued) • Ifsis not known and n = 30 or more (considered a large sample), the standard deviation of the sample, designated by s, is used to approximate the population standard deviation, s. The formula for the standard error then becomes: • What happens as n gets larger?

  20. 95% AND THE 99% CONFIDENCE INTERVALS (CI) FOR m • The 95% and the 99% confidence intervals for m are constructed as follows when n ³ 30. • 95% CI for the population meanmis given by • 99% CI formis given by

  21. CONSTRUCTING A GENERAL CONFIDENCE INTERVALS (CI) FOR m • In general, a confidence interval for the mean is computed by: • The Z value is obtained from the standard normal table in Appendix D (look-up confidence/2).

  22. EXAMPLE • The Dean of Students at Penta Tech wants to estimate the mean number of hours worked per week by students. A sample of 49 students showed a mean of 24 hours with a standard deviation of 4 hours. • What is the point estimate of the mean number of hours worked per week by students? • The point estimate is 24 hours (sample mean). • What is the 95% confidence interval for the average number of hours worked per week by the students?

  23. EXAMPLE (continued) • Using formula, we have 24 ± 1.96(4/7) or we have 22.88 to 25.12. • What are the 95% confidence limits? • The endpoints of the confidence interval are the confidence limits. The lower confidence limitis 22.88 and the upper confidence limitis 25.12. • What degree of confidence is being used? • The degree of confidence (level of confidence) is 0.95

  24. EXAMPLE (continued) • Interpret the findings. • If we had time to select 100 samples of size 49 from the population of the number of hours worked per week by students at Penta Tech and compute the sample means and 95% confidence intervals, the population mean of the number of hours worked by the students per week would be found in about 95 out of the 100 confidence intervals. Either a confidence interval contains the population mean or it does not. About 5 out of the 100 confidence intervals would not contain the population mean.

  25. CONFIDENCE INTERVAL FOR A POPULATION PROPORTION • The confidence interval for a population proportion: where is the standard error of the proportion:

  26. The confidence interval is constructed by: where: is the sample proportion. z is the z value for the degree of confidence selected. n is the sample size.

  27. EXAMPLE • Chris Cooper, a financial planner, is studying the retirement plans of young executives. A sample of 500 young executives who owned their own home revealed that 175 planned to sell their homes and retire to Arizona. Develop a 98% confidence interval for the proportion of executives that plan to sell and move to Arizona. • Here n = 500, = 175/500 = 0.35, and z =2.33 • the 98% CI is 0.35 ± 2.33 or 0.35 ± 0.0497. • Interpret?

  28. FINITE-POPULATION CORRECTION FACTOR • A population that has a fixed upper bound is said to be finite. • For a finite population, where the total number of objects is N and the size of the sample is n, the following adjustment is made to the standard errors of the sample means and the proportion. • Standard error of the sample means:

  29. FINITE-POPULATION CORRECTION FACTOR (continued) • Standard error of the sample proportions: • Note: If n/N < 0.05, the finite-population correction factor can be ignored.

  30. EXAMPLE • The Dean of Students at Penta Tech wants to estimate the mean number of hours worked per week by students. A sample of 49 students showed a mean of 24 hours with a standard deviation of 4 hours. Construct a 95% confidence interval for the mean number of hours worked per week by the students if there are only 500 students on campus. • Now n/N = 49/500 = 0.098 > 0.05, so we have to use the finite population correction factor. = [22.9352,25.1065]

  31. SELECTING A SAMPLE SIZE • There are 3 factors that determine the size of a sample, none of which has any direct relationship to the size of the population. They are: 1. The degree of confidence selected. 2. The maximum allowable error. 3. The variation of the population.

  32. SAMPLE SIZE FOR THE MEAN • A convenient computational formula for determining n is: where: E is the allowable error. z is the z score associated with the degree of confidence selected. s is the sample deviation of the pilot survey.

  33. EXAMPLE • A consumer group would like to estimate the mean monthly electric bill for a single family house in July. Based on similar studies the standard deviation is estimated to be $20.00. A 99% level of confidence is desired, with an accuracy of ± $5.00. How large a sample is required? • n = [(2.58)(20)/5]2 = 106.5024 » 107.

  34. SAMPLE SIZE FOR PROPORTIONS • The formula for determining the sample size in the case of a proportion is: is the estimated proportion, based on past experience or a pilot survey. z is the z value associated with the degree of confidence selected. Eis the maximum allowable error the researcher will tolerate.

  35. EXAMPLE • The American Kennel Club wanted to estimate the proportion of children that have a dog as a pet. If the club wanted the estimate to be within 3% of the population proportion, how many children would they need to contact? Assume a 95% level of confidence and that the Club estimated that 30% of the children have a dog as a pet. • n = (0.30)(0.70)(1.96/0.03)2 = 896.3733 » 897.

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