1. BPS - 5th Ed. Chapter 11 1 Sampling Distributions

2. Chapter 11 2 BPS - 5th Ed. Sampling Terminology Parameter Parameter fixed, unknown number that describes the population fixed, unknown number that describes the population Statistic Statistic known value calculated from a sample known value calculated from a sample a statistic is often used to estimate a parameter a statistic is often used to estimate a parameter Variability Variability different samples from the same population may yield different values of the sample statistic different samples from the same population may yield different values of the sample statistic Sampling Distribution Sampling Distribution tells what values a statistic takes and how often it takes those values in repeated sampling tells what values a statistic takes and how often it takes those values in repeated sampling

3. Chapter 11 3 BPS - 5th Ed. Parameter vs. Statistic A properly chosen sample of 1600 people across the United States was asked if they regularly watch a certain television program (Real Housewives of New Jersey), and 24% said yes. The parameter of interest here is the true proportion of all people in the U.S. who watch the program, while the statistic is the value 24% obtained from the sample of 1600 people.

4. Chapter 11 4 BPS - 5th Ed. Parameter vs. Statistic u The mean of a population is denoted by µ – this is a parameter. u The mean of a sample is denoted by – this is a statistic. is used to estimate µ. u The true proportion of a population with a certain trait is denoted by p – this is a parameter. u The proportion of a sample with a certain trait is denoted by (“p-hat”) – this is a statistic. is used to estimate p.

5. Chapter 11 5 BPS - 5th Ed. The Law of Large Numbers Consider sampling at random from a population with true mean µ. As the number of (independent) observations sampled increases, the mean of the sample gets closer and closer to the true mean of the population. ( gets closer to µ )

6. Chapter 11 6 BPS - 5th Ed. The Law of Large Numbers Gambling The “house” in a gambling operation is not gambling at all The “house” in a gambling operation is not gambling at all the games are defined so that the gambler has a negative expected gain per play (the true mean gain after all possible plays is negative) the games are defined so that the gambler has a negative expected gain per play (the true mean gain after all possible plays is negative) each play is independent of previous plays, so the law of large numbers guarantees that the average winnings of a large number of customers will be close the (negative) true average each play is independent of previous plays, so the law of large numbers guarantees that the average winnings of a large number of customers will be close the (negative) true average

7. Chapter 11 7 BPS - 5th Ed. Sampling Distribution The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size (n) from the same population The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size (n) from the same population to describe a distribution we need to specify the shape, center, and spread to describe a distribution we need to specify the shape, center, and spread we will discuss the distribution of the sample mean (x-bar) in this chapter we will discuss the distribution of the sample mean (x-bar) in this chapter

8. Chapter 11 8 BPS - 5th Ed. Case Study Does This Cheese Smell Bad? As cheese ages bacteria grows causing “off- odors”. Cheese makers want to know the odor threshold – the lowest concentration of a smell that the human nose can detect. Different people have different thresholds, and of interest is the mean threshold in the population of all adults.

9. Chapter 11 9 BPS - 5th Ed. Case Study Suppose the mean threshold of all adults is  =25 micrograms of bacteria per ounce of cheese, with a standard deviation of  =7 micrograms per ounce and the threshold values follow a bell-shaped (normal) curve. Does This Cheese Smell Bad?

10. Chapter 11 10 BPS - 5th Ed. Where should 95% of all individual threshold values fall? mean plus or minus two standard deviations mean plus or minus two standard deviations 25  2(7) = 11 25 + 2(7) = 39 95% should fall between 11 & 39 95% should fall between 11 & 39 What about the mean (average) of a sample of n adults? What values would be expected? What about the mean (average) of a sample of n adults? What values would be expected?

11. Chapter 11 11 BPS - 5th Ed. Sampling Distribution What about the mean (average) of a sample of n adults? What values would be expected? What about the mean (average) of a sample of n adults? What values would be expected? Answer this by thinking: “What would happen if we took many samples of n subjects from this population?” (let’s say that n=10 subjects make up a sample) Answer this by thinking: “What would happen if we took many samples of n subjects from this population?” (let’s say that n=10 subjects make up a sample) take a large number of samples of n=10 subjects from the population take a large number of samples of n=10 subjects from the population calculate the sample mean (x-bar) for each sample calculate the sample mean (x-bar) for each sample make a histogram (or stemplot) of the values of x- bar make a histogram (or stemplot) of the values of x- bar examine the graphical display for shape, center, spread examine the graphical display for shape, center, spread

12. Chapter 11 12 BPS - 5th Ed. Case Study Mean threshold of all adults is  =25 micrograms per ounce, with a standard deviation of  =7 micrograms per ounce and the threshold values follow a bell-shaped (normal) curve. Many (1000) repetitions of sampling n=10 adults from the population were simulated and the resulting histogram of the 1000 x-bar values is on the next slide. Does This Cheese Smell Bad?

13. Chapter 11 13 BPS - 5th Ed. Case Study Does This Cheese Smell Bad?

14. Chapter 11 14 BPS - 5th Ed. Mean and Standard Deviation of Sample Means OR “Means of the Means” If numerous samples of size n are taken from a population with mean  and standard deviation , then the mean of the sampling distribution of is  (the population mean) and the standard deviation is: (  is the population s.d.)

15. Chapter 11 15 BPS - 5th Ed. Mean and Standard Deviation of Sample Means  Since the mean of is , we say that is an unbiased estimator of  u Individual observations have standard deviation , but sample means from samples of size n have standard deviation. Averages are less variable than individual observations.

16. Chapter 11 16 BPS - 5th Ed. Sampling Distribution of Sample Means If individual observations have the N(µ,  ) distribution, then the sample mean of n independent observations has the N(µ,  / ) distribution. “If measurements in the population follow a Normal distribution, then so does the sample mean.”

17. When do I use the adjusted standard deviation? When evaluating Samples: When evaluating an individual: We can compare an individual to the entire population. For example, what is the probability that one student from MHS will get a 2000 on the SAT? We can compare a sample to the performance of past samples of the population. For example: what is the probability that a sample of 20 students from MHS will get an average of 1800 on the SAT?

18. Chapter 11 18 BPS - 5th Ed. Case Study Mean threshold of all adults is  =25 with a standard deviation of  =7, and the threshold values follow a bell-shaped (normal) curve. Does This Cheese Smell Bad? (Population distribution)

19. Chapter 11 19 BPS - 5th Ed. Central Limit Theorem “No matter what distribution the population values follow, the sample mean will follow a Normal distribution if the sample size is large.” If a random sample of size n is selected from ANY population with mean  and standard deviation , then when n is large the sampling distribution of the sample mean is approximately Normal: is approximately N(µ,  / )

20. Chapter 11 20 BPS - 5th Ed. Central Limit Theorem: Sample Size How large must n be for the CLT to hold? How large must n be for the CLT to hold? depends on how far the population distribution is from Normal depends on how far the population distribution is from Normal the further from Normal, the larger the sample size needed the further from Normal, the larger the sample size needed a sample size of 25 or 30 is typically large enough for any population distribution encountered in practice a sample size of 25 or 30 is typically large enough for any population distribution encountered in practice recall: if the population is Normal, any sample size will work (n≥1) recall: if the population is Normal, any sample size will work (n≥1)

21. Chapter 11 21 BPS - 5th Ed. Central Limit Theorem: Sample Size and Distribution of x-bar n=1 n=25 n=10 n=2

22. Chapter 11 22 BPS - 5th Ed. Statistical Process Control Goal is to make a process stable over time and keep it stable unless there are planned changes Goal is to make a process stable over time and keep it stable unless there are planned changes All processes have variation All processes have variation Statistical description of stability over time: the pattern of variation remains stable (does not say that there is no variation) Statistical description of stability over time: the pattern of variation remains stable (does not say that there is no variation)

23. Chapter 11 23 BPS - 5th Ed. Statistical Process Control A variable described by the same distribution over time is said to be in control A variable described by the same distribution over time is said to be in control To see if a process has been disturbed and to signal when the process is out of control, control charts are used to monitor the process To see if a process has been disturbed and to signal when the process is out of control, control charts are used to monitor the process distinguish natural variation in the process from additional variation that suggests a change distinguish natural variation in the process from additional variation that suggests a change most common application: industrial processes most common application: industrial processes

24. Chapter 11 24 BPS - 5th Ed. Charts Charts There is a true mean  that describes the center or aim of the process There is a true mean  that describes the center or aim of the process Monitor the process by plotting the means (x-bars) of small samples taken from the process at regular intervals over time Monitor the process by plotting the means (x-bars) of small samples taken from the process at regular intervals over time Process-monitoring conditions: Process-monitoring conditions: measure quantitative variable x that is Normal measure quantitative variable x that is Normal process has been operating in control for a long period process has been operating in control for a long period know process mean  and standard deviation  that describe distribution of x when process is in control know process mean  and standard deviation  that describe distribution of x when process is in control

25. Chapter 11 25 BPS - 5th Ed. Control Charts Control Charts Plot the means (x-bars) of regular samples of size n against time Plot the means (x-bars) of regular samples of size n against time Draw a horizontal center line at  Draw a horizontal center line at  Draw horizontal control limits at  ± 3  / Draw horizontal control limits at  ± 3  / almost all (99.7%) of the values of x-bar should be within the mean plus or minus 3 standard deviations almost all (99.7%) of the values of x-bar should be within the mean plus or minus 3 standard deviations Any x-bar that does not fall between the control limits is evidence that the process is out of control Any x-bar that does not fall between the control limits is evidence that the process is out of control

26. Chapter 11 26 BPS - 5th Ed. Case Study Need to control the tension in millivolts (mV) on the mesh of fine wires behind the surface of the screen. Proper tension is 275 mV (target mean  ) Proper tension is 275 mV (target mean  ) When in control, the standard deviation of the tension readings is  =43 mV When in control, the standard deviation of the tension readings is  =43 mV Making Computer Monitors

27. Chapter 11 27 BPS - 5th Ed. Case Study Proper tension is 275 mV (target mean  ). When in control, the standard deviation of the tension readings is  =43 mV. Making Computer Monitors Take samples of n=4 screens and calculate the means of these samples the control limits of the x-bar control chart would be the control limits of the x-bar control chart would be

28. Chapter 11 28 BPS - 5th Ed. Case Study Making Computer Monitors (data)

29. Chapter 11 29 BPS - 5th Ed. Case Study Making Computer Monitors ( chart) (in control)

30. Chapter 11 30 BPS - 5th Ed. Case Study Making Computer Monitors (examples of out of control processes)

31. Chapter 11 31 BPS - 5th Ed. Natural Tolerances For x-bar charts, the control limits for the mean of the process are  ± 3  / For x-bar charts, the control limits for the mean of the process are  ± 3  / almost all (99.7%) of the values of x-bar should be within the mean plus or minus 3 standard deviations almost all (99.7%) of the values of x-bar should be within the mean plus or minus 3 standard deviations When monitoring a process, the natural tolerances for individual products are  ± 3  When monitoring a process, the natural tolerances for individual products are  ± 3  almost all (99.7%) of the individual measurements should be within the mean plus or minus 3 standard deviations almost all (99.7%) of the individual measurements should be within the mean plus or minus 3 standard deviations