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STAT 101 Dr. Kari Lock Morgan 10/23/12

STAT 101 Dr. Kari Lock Morgan 10/23/12. Inference for Proportions: Normal Distribution. Sections 6.1-6.3 , 6.7-6.9 Single Proportion, p Distribution (6.1) Intervals and tests (6.2, 6.3) Difference in proportions, p 1 – p 2 One proportion or two ? (6.7) Distribution (6.7)

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STAT 101 Dr. Kari Lock Morgan 10/23/12

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  1. STAT 101 Dr. Kari Lock Morgan 10/23/12 Inference for Proportions: Normal Distribution • Sections 6.1-6.3, 6.7-6.9 • Single Proportion, p • Distribution (6.1) • Intervals and tests (6.2, 6.3) • Difference in proportions, p1 – p2 • One proportion or two? (6.7) • Distribution (6.7) • Intervals and tests (6.8, 6.9)

  2. Central Limit Theorem! For a sufficiently large sample size, the distribution of sample statistics for a mean or a proportion is normal

  3. Interval Using N(0,1) IF SAMPLE SIZES ARE LARGE… A confidence interval can be calculated by where z*is a N(0,1) percentile depending on the level of confidence.

  4. Tests Using N(0,1) • IF SAMPLE SIZES ARE LARGE… • A p-value is the area in the tail(s) of a N(0,1) beyond

  5. Standard Errors • Today, we’ll learn formulas for the standard errors.

  6. SE of a Proportion • The standard error for a sample proportion can be calculated by *Notice the sample size in the denominator… as the sample size increases, the standard error decreases

  7. Paul the Octopus • If he is truly guessing randomly, then p = 0.5 so the SE of his sample proportion correct out of 8 guesses is

  8. Paul the Octopus This is the same value we get from a randomization distribution… www.lock5stat.com/statkey

  9. Paul the Octopus • If Paul really does have psychic powers, and can guess the correct team every time, then p = 1, and

  10. Distribution of

  11. CLT for a Proportion • If counts for each category are at least 10 (np ≥ 10 and n(1 – p) ≥ 10), then

  12. Standard Error • One small problem… if we are doing inference for p, we don’t know p! • For confidence intervals, use your best guess for p:

  13. Confidence Interval for a Single Proportion

  14. Obama vs Romney On 10/17/12, a random sample of 500 North Carolina likely voters were polled. 260 said they plan to vote for Mitt Romney. Give a 95% CI for the proportion of likely voters in North Carolina that support Mitt Romney. http://www.rasmussenreports.com/public_content/politics/elections/election_2012/election_2012_presidential_election/north_carolina/election_2012_north_carolina_president

  15. Obama vs Romney Counts are greater than 10 in each category For a 95% confidence interval, z* = 2 We are 95% confident that between 47.5% and 56.6% of likely voters in North Carolina support Romney.

  16. Obama vs Romney

  17. Obama vs Romney

  18. Other Levels of Confidence www.lock5stat.com/statkey Technically, for 95% confidence, z* = 1.96, but 2 is much easier to remember, and close enough

  19. z* on TI-83 P% -z* z* • 2ndDISTR  3: invNorm(  Proportion below z* • (for a 95% CI, the proportion below z* is 0.975)

  20. Margin of Error • For a single proportion, what is the margin of error? CI = statistic  margin of error

  21. Margin of Error • You can choose your sample size in advance, depending on your desired margin of error! • Given this formula for margin of error, solve for n.

  22. Margin of Error

  23. Margin of Error Suppose we want to estimate a proportion with a margin of error of 0.03 with 95% confidence. How large a sample size do we need? About 100 About 500 About 1000 About 5000

  24. Hypothesis Testing For hypothesis testing, we want the distribution of the sample proportion assuming the null hypothesis is true What to use for p?

  25. Hypothesis Testing The p-value is the area in the tail(s) beyond z in a N(0,1)

  26. Baseball Home Field Advantage Of the 2430 Major League Baseball (MLB) games played in 2009, the home team won in 54.9% of the games. If we consider 2009 as a representative sample of all MLB games, is this evidence of a home field advantage in Major League Baseball? (a) Yes (b) No (c) No idea The p-value is very small, so we have very strong evidence of a home field advantage.

  27. Baseball Home Field Advantage Counts are greater than 10 in each category Based on this data, there is strong evidence of a home field advantage in major league baseball.

  28. Baseball Home Field Advantage

  29. p-value on TI-83 • 2nd • DISTR • 3: normalcdf( • lower bound, upper bound • Hint: if you want greater than 2, just put 2, 100 (or some other large number)

  30. One Proportion or Two? • Two proportions: there are two separate categorical variables • One proportion: there is only one categorical variable

  31. One Proportion or Two? • Of residents in the triangle area on Saturday, was the proportion of people cheering for Duke or UNC greater? How much greater? • Inference for one proportion • Inference for two proportions • (Note: assume no one will be cheering for both) This is one categorical variable: which team each person will be cheering for on Saturday night.

  32. One Proportion or Two? • Who was more likely to be wearing a blue shirt on Saturday night, a UNC fan or a Duke fan? • Inference for one proportion • Inference for two proportions This is two categorical variables: which team each person will be cheering for on Saturday night, and whether each person is wearing a blue shirt.

  33. Standard Error for

  34. CLT for If counts within each category (each cell of the two-way table) are at least 10

  35. Metal Tags and Penguins Are metal tags detrimental to penguins? A study looked at the 10 year survival rate of penguins tagged either with a metal tag or an electronic tag. 20% of the 167 metal tagged penguins survived, compared to 36% of the 189 electronic tagged penguins. Give a 90% confidence interval for the difference in proportions. Source: Saraux, et. al. (2011). “Reliability of flipper-banded penguins as indicators of climate change,” Nature, 469, 203-206.

  36. Metal Tags and Penguins We are 90% confident that the survival rate is between 0.09 and 0.237 lower for metal tagged penguins, as opposed to electronically tagged.

  37. Metal Tags and Penguins www.lock5stat.com/statkey

  38. Hypothesis Testing What should we use for p1 and p2 in the formula for SE for hypothesis testing?

  39. Pooled Proportion Overall sample proportion across both groups. It will be in between the two observed sample proportions.

  40. Hypothesis Testing The p-value is the area in the tail(s) beyond z in a N(0,1)

  41. Metal Tags and Penguins 20% of the 167 metal tagged penguins survived, compared to 36% of the 189 electronic tagged penguins. Are metal tags detrimental to penguins? (a) Yes (b) No (c) Cannot tell from this data Yes. The p-value is very small.

  42. Metal Tags and Penguins Are metal tags detrimental to penguins?

  43. Metal Tags and Penguins This is very strong evidence that metal tags are detrimental to penguins.

  44. Metal Tags and Penguins www.lock5stat.com/statkey

  45. Accuracy • The accuracy of intervals and p-values generated using simulation methods (bootstrapping and randomization) depends on the number of simulations (more simulations = more accurate) • The accuracy of intervals and p-values generated using formulas and the normal distribution depends on the sample size (larger sample size = more accurate) • If the distribution of the statistic is truly normal and you have generated many simulated randomizations, the p-values should be very close

  46. Summary • For a single proportion: • For a difference in proportions:

  47. To Do • Read Sections 6.1, 6.2, 6.3, 6.7, 6.8, 6.9 • Do Homework 5 (due Tuesday, 10/30)

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