Chapter 5: Regression - University of Utah

Chapter 5: Regression - University of Utah

CHAPTER 15: Sampling Distributions Basic Practice of Statistics 7th Edition Lecture PowerPoint Slides In Chapter 15, we cover

Parameters and statistics Statistical estimation and the law of large numbers Sampling distributions The sampling distribution of The central limit theorem Sampling distributions and statistical significance Sampling Terminology

(Parameters and statistics) As we begin to use sample data to draw conclusions about a wider population, we must be clear about whether a number describes a sample or a population. A parameter is a fixed unknown number that describes the population. Example: population mean A statistic is a number that can be computed from the sample data without making use of any unknown parameters. In practice, we often use a statistic to estimate an unknown parameter. Example: Sample mean

Remember p and s: parameters come from populations and statistics come from samples. We write (the Greek letter mu) for the mean of the population and (the Greek letter sigma) for the standard deviation of the population. We write (x-bar) for the mean of the sample and for the standard deviation of the sample. Parameter vs Statistic A properly chosen sample of 1600 people across the United States was asked if they regularly watch a certain television program, 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. Parameter vs. Statistic The mean of a population is denoted by this is a parameter. The mean of a sample is denoted by this is a statistic. is used to estimate .

The true proportion of a population with a certain trait is denoted by p this is a parameter. The proportion of a sample with a certain trait is denoted by (p-hat) this is a statistic. is used to estimate p. Parameters and Statistics (1 of 2) A parameter is a number that describes the

_________. a) population b) sample c) statistic d) None of the answer options is correct. Parameters and Statistics (2 of 2) A ___________ is a number that can be computed from the sample data to estimate an unknown population parameter. a) population b) parameter c) statistic

d) None of the answer options is correct. Statistical estimation The process of statistical inference involves using information from a sample to draw conclusions about a wider population. Different random samples yield different statistics. We need to be able to describe the sampling distribution of possible statistic values in order to perform statistical inference. We can think of a statistic as a random variable because it takes numerical values that describe the outcomes of the random sampling process. Therefore, we can examine its probability distribution using concepts we learned in earlier chapters.

Population Sample Collect data from a representative sample Make an inference about the population The law of large numbers If is rarely exactly right and varies from sample to sample, why is it nonetheless a reasonable estimate of

the population mean ? Here is one answer: If we keep taking larger and larger samples, the statistic is guaranteed to get closer and closer to the parameter . ( gets closer to ) LAW OF LARGE NUMBERS Draw observations at random from any population with finite mean . As the number of observations drawn increases, the mean of the observed values tends to get closer and closer to the mean of the population. The Law of Large Numbers

(Gambling) 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 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 the (negative) true average

Law of Large Numbers (1 of 2) As the sample size gets larger, the sample mean gets closer to the ________. a) population b) population c) population d) population mean variance standard deviation median

Law of Large Numbers (2 of 2) ______________ assures us that, if we measure enough subjects, the statistic will eventually get very close to the unknown parameter . a) Benfords law b) The law of large numbers c) Conditional probability d) The continuous probability model Sampling distributions

The law of large numbers assures us that if we measure enough subjects, the statistic will eventually get very close to the unknown parameter . If we took every one of the possible samples of a certain size, calculated the sample mean for each, and graphed all of those values, wed have a sampling distribution. If we use software to imitate chance behavior to carry out tasks such as exploring sampling distributions, this is called simulation. The population distribution of a variable is the distribution of values of the

variable among all individuals in the population. The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size from the same population. Be careful: The population distribution describes the individuals that make up the population. A sampling distribution describes how a statistic varies in many samples from the population. Population distributions vs. sampling distributions

The sampling distribution of (part I) When we choose many SRSs from a population, the sampling distribution of the sample mean is centered at the population mean and is less spread out than the population distribution. MEAN AND STANDARD DEVIATION OF A SAMPLE MEAN Suppose that is the mean of an SRS of size drawn from a large population with mean and standard deviation . Then the sampling distribution of has mean and standard deviation Because the mean of the statistic is always equal to the mean of the population (that is, the sampling distribution of is centered

at ), we say the statistic is an unbiased estimator of the parameter . Note: on any particular sample, may fall above or below . Case Study Does This Wine Smell Bad? Dimethyl sulfide (DMS) is sometimes present in wine, causing off-odors. Winemakers want to know the odor threshold the lowest concentration of DMS that the human nose can detect. Different people have different thresholds, and of interest is the mean threshold in the population of all adults.

Case Study Does This Wine Smell Bad? Suppose the mean threshold of all adults is =25 micrograms of DMS per liter of wine, with a standard deviation of =7 micrograms per liter and the threshold values follow a bell-shaped (normal) curve. Where should 95% of all individual threshold values fall?

mean plus or minus two standard deviations 25 2(7) = 11 25 + 2(7) = 39 95% should fall between 11 & 39 What about the mean (average) of a sample of 10 adults? What values would be expected? Sampling Distribution What about the mean (average) of a

sample of 10 adults? What values would be expected? Answer this by thinking: What would happen if we took many samples of 10 subjects from this population? take a large number of samples of 10 subjects from the population calculate the sample mean (x-bar) for each sample make a histogram of the values of x-bar

examine the graphical display for shape, center, spread Case Study Does This Wine Smell Bad? Mean threshold of all adults is =25 micrograms per liter, with a standard deviation of =7 micrograms per liter and the threshold values follow a bell-shaped (normal) curve. Many (1000) samples of n=10 adults from the population were taken and the resulting histogram of the 1000 x-bar values is on the next slide.

The sampling distribution of (illustrated) Does This Wine Smell Bad? =26.42 S RS size 10 =24 .28 S RS size 10 =25 .22 S RS size10

Population, mean } Sampling distribution, mean The sampling distribution of (part II)

Because the standard deviation of the sampling distribution of is , the averages are less variable than individual observations, and averages are less variable than the results of small samples. Not only is the standard deviation of the distribution of smaller than the standard deviation of individual observations, it gets smaller as we take larger samples. The results of large samples are less variable than the results of small samples. Note: While the standard deviation of the distribution of gets smaller, it does so at the rate of , not . To cut the sampling distributions standard deviation in half, for instance, you must take a sample four times as large, not just twice as large.

Case Study Does This Wine Smell Bad? Mean threshold of all adults is =25 with a standard deviation of =7, and the threshold values follow a bell-shaped (normal) curve. (Population distribution)

The sampling distribution of (part III) We have described the center and variability of the sampling distribution of a sample mean , but not its shape. The shape of the sampling distribution depends on the shape of the population distribution. In one important case there is a simple relationship between the two distributions: if the population distribution is Normal, then so is the sampling distribution of the sample mean. SAMPLING DISTRIBUTION OF A SAMPLE MEAN If individual observations have the distribution, then the

sample mean of an SRS of size has the distribution. Sampling Distribution (1 of 3) The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size from different populations. a) true b) false Sampling Distributions (2 of 3) ____________ is correct on the average in

many samples. How close the estimator falls to the parameter in most samples is determined by the __________ of the sampling distribution. a) An unbiased estimator; spread/variability b) The law of large numbers; mean c) An unbiased estimator; mean d) The law of large numbers; median Sampling Distributions (3 of 3) For any population mean , the standard

deviation of the distribution of gets smaller as we take larger samples. Thus, the results of large samples are less variable than the results of small samples. a) true b) false Standard Deviation of Sample Mean In 2012, high school seniors reported drinking an average of 3.4 alcoholic drinks with a variance of 4.1, a substantial drop since the late 1990s. A

simple random sample of 100 high school seniors is to be taken. What is the standard deviation of , the sample mean number of drinks per student? a) SQR [3.4/100] b) SQR [4.1] c) 4.1/3.4 d) SQR [4.1/100] The central limit theorem Most population distributions are not Normal. What is the shape of the sampling distribution of sample means when the population

distribution isnt Normal? A remarkable fact is that as the sample size increases, the distribution of sample means changes its shape: it looks less like that of the population and more like a Normal distribution! Draw an SRS of size from any population with mean and finite standard deviation . The central limit theorem says that when n is large, the sampling distribution of the sample mean is approximately Normal: is approximately The central limit theorem allows us to use Normal probability calculations to answer questions about sample means from many

observations, even when the population distribution is not Normal. Central limit theorem: example (part I) Based on service records from the past year, the time (in hours) that a technician requires to complete preventative maintenance on an air conditioner follows the distribution that is strongly right-skewed and whose most likely outcomes are close to 0. The mean time is hour and the standard deviation is . Your company will service an SRS of 70 air conditioners. You have budgeted 1.1 hours per unit. Will this be enough?

The central limit theorem states that the sampling distribution of the mean time spent working on the 70 units has: and a Normal distribution shape. Central limit theorem: example (part II) Your company will service an SRS of 70 air conditioners. You have budgeted 1.1 hours per unit. Will this be enough? The sampling distribution of the mean time spent working is approximately since . Exact density curve for

Normal curve from the central limit theorem If you budget 1.1 hours per unit, there is a 20% chance the technicians will not complete the work within the budgeted time. Central Limit Theorem: Sample Size

How large must n be for the CLT to hold? depends on how far the population distribution is from Normal 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 recall: if the population is Normal, any sample size will work (n1) Central Limit Theorem: Sample Size and Distribution of n=1 n=2 n=10 n=25

Central Limit Theorem (1 of 2) As the sample size increases, the distribution of (sample mean) changes shape: It looks less like that of the population and more like a Normal distribution. When the sample is large enough, the distribution of is very close to Normal. This is true no matter what the shape of the population distribution, as long as the population has a finite standard deviation . This famous fact of probability theory is called:

a) the b) the c) the d) the law of large numbers. Normal theorem. central limit theorem. distributive theorem. Central Limit Theorem (2 of 2) A sample of size 64 is taken from a distribution with mean 100 and standard

deviation 24. The sample mean will have a distribution that is approximately ______________ with standard deviation ____________. a) Normal; 24/64 b) binomial; 24/64 c) Normal; 24/8 d) binomial; 24/8

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