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Inference

Inference. We want to know how often students in a medium-size college go to the mall in a given year. We interview an SRS of n = 10. If we interviewed lots of SRSs, the “average sample frequency of visits” would be centered around the true “average population frequency of visits.”.

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Inference

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  1. Inference • We want to know how often students in a medium-size college go to the mall in a given year. • We interview an SRS of n = 10. • If we interviewed lots of SRSs, the “average sample frequency of visits” would be centered around the true “average population frequency of visits.”

  2. 49.6896 49.7618 50.0742 49.8520 50.0590 50.3243 49.2806 49.6056 50.4129 49.3963 49.3617 49.7741 49.9237 50.2201 49.2904 50.1797

  3. Inference • Suppose that instead we interviewed an SRS of n = 400. • Our estimates will be more reliable because estimates from other SRSs would be similar … that is, our estimates would be less variable.

  4. 49.9496 50.0165 50.0941 50.0573 50.1674 50.1402 50.0506 50.0838 49.9865 50.0195 49.9752 49.9439 49.9738 49.9966 50.0396 49.9819

  5. Because we didn’t have money for 16 separate samples, we actually only collected data from the first sample, whose sample mean is = 49.9496. • Is the true number actually 50? Is the difference between 50 and 49.9496 purely a fluke? Does this result exclude 50 as a possibility?

  6. The Central Limit Theorem says that if the entire population has a mean m and a standard deviation s, then in repeated samples of size n the sample mean approximately follows a Normal distribution

  7. The first sample had a mean = 49.9496 and a standard deviation = 1.0264.

  8. We know that 95% of all observations fall within ± two standard deviations of the mean. • Likewise, 95% of all sample means fall within ± two standard deviations of the observed sample mean. • So, for 1900 out of 2,000 samples, the interval will contain the true population mean.

  9. 2 x (0.0513) 2 x (0.0513)

  10. Now there are two possibilities. Either • the true population mean is contained in the interval • or this is one of those 5% of samples whose interval does not contain the true value. (49.8470, 50.0522)

  11. C is typically set at 95%, but it’s sometimes chosen to be 90% or 99%. STATA Exercise 1

  12. -z*= - 1.96 if C=95% z* = 1.96 if C=95%

  13. So don’t use 2 when constructing a 95% CI: use 1.96.

  14. If the margin of error is too large… • Reduce s • s is determined by the population: a population with a lot of variability will increase the chance that a sample contain observations very far from the true mean. • This is easier to say than to do.

  15. 16 samples. The s of the population increases from 1 to 4, increasing the spread of the sample and the likelihood of getting m wrong.

  16. If the margin of error is too large… • Increase the sample size (larger n)

  17. If the margin of error is too large… • Be less confident of your estimate …Use a lower confidence level (make C smaller, hence a smaller z*)

  18. If the margin of error is too large… • “We’re 99% sure that the President will receive 51.5% of the votes, with a ±5% margin of error.” • “We’re 95% sure that the President will receive 51.5% of the votes, with a ±3% margin of error.” • “We’re 90% sure that the President will receive 51.5% of the votes, with a ±1% margin of error.”

  19. Cautions • Is it an SRS? • Is the data unbiased (or do we know the bias)? • Are there no outliers that influence the sample mean? • Is n large? If not, is the underlying population Normally distributed? • Do you know the true s? Theorems of mathematical statistics are true; statistical methods are effective only when used with skill.

  20. Cautions • FALSE: “The probability that the true mean falls within is 95%” • This is false because either the interval contains the true population mean (which is not a random variable), with Pr=1, or it doesn’t, with Pr=0. • TRUE: “The probability that the interval is one of the ones that contain the true mean is 95%”

  21. Tests of Significance

  22. Making claims about the population parameters • In our sample, we observed a mean of 49.9496 visits to the mall per year. • Assuming that the true population mean is 50, how likely is it that we observe a sample mean as small as 49.9496, or even smaller? • if the true population mean were 45, how likely is it that we observe a sample mean as large as 49.9496, or even larger?

  23. Making claims about the population parameters

  24. Making claims about the population parameters x -0.0252 2.4748 If the true population mean were 50, how likely is it that we observe a sample mean at least as small as 49.9496? Pr=49% if the true population mean were 45, how likely is it that we observe a sample mean as large as 49.9496? Pr=0.68%

  25. Making claims about the population parameters • We found that if the true mean is 45, the Pr of observing a sample mean as large as 49.9496 is 0.68%. Either • we’ve observed a very rare event (our sample is really unusual) • the true mean is not 45. There’s another number that makes the observed sample more likely. A sample outcome that would be extreme if a hypothesis were trueis evidence that they hypothesis is not true.

  26. A sample outcome that would be extreme if a hypothesis were trueis evidence that they hypothesis is not true.

  27. These are hypothesis about the population. H0: m=45 Ha: m45 This is a two-sided alternative hypothesis

  28. Test Statistics • A test statistic measures compatibility between the null hypothesis and the data. • The z-score can be used as a test statistic because we can compare it against 1.96, the z-score that delimits a 0.95 area under the Normal curve. • 1.96 is called the appropriate “critical value”.

  29. Test Statistics • The Student’s t Distribution is used when n is small. • It approximates the Standard Normal, z-distribution as n gets large.

  30. Test Statistics • We know that 95% of all values are between 2 standard deviations of the mean. • That is, 95% of all values are between the z-score of 1.96 and the z-score of -1.96. • So if we get a sample outcome whose z-score is greater than 1.96 (in absolute value), we know that it it is unlikely to belong to the population of which the null hypothesis is a parameter.

  31. Suppose • n = 110 • s = 26.4 • x = 8.1 • H0: m = 0 • Ha: m 0

  32. Exercise • A company makes cellphones using components from two countries: Ecuador and Canadaguay. Here are data on days of cellphone durability. • Your retail shop buys 100 cellphones because the manufacturer claims they were made in Ecuador. On average, they stop working after 279 days of use. • Is this difference (279 days versus 300 days) significant? Is it a fluke or does it mean something?

  33. Exercise • The null hypothesis is that the phone typically lasts 300 days. • Alternatively, it’s a lower quality phone. • The z-score can tell us how far this observation is from the mean. • Look up in table A the probability of observing a z-score as small as this or smaller.

  34. Exercise • Suppose the parameters were, instead • Now, is this difference (279 days versus 300 days) significant? Is it a fluke or does it mean something?

  35. Exercise • Suppose average durability of the 100 cellphones was, instead, 90 days. • Now, is this difference (90 days versus 300 days) significant? Is it a fluke or does it mean something?

  36. We found that if the true mean is 45, the Pr of observing a sample mean as large as 49.9496 is 0.68%. Notice that here H0: m = 45 Ha: m > 45 This is a one-sided alternative hypothesis

  37. Look this up in Table D, 20-1 degrees of freedom. We have to use the Student’s t because n is small.

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