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Proportions

Proportions. A proportion is the fraction of individuals having a particular attribute. Example: 2092 adult passengers on the Titanic; 654 survived. Proportion of survivors = 654/2092  0.3. Probability that two out of three randomly chosen passengers survived the Titanic.

jessamine-tillman
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Proportions

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  1. Proportions

  2. A proportion is the fraction of individuals having a particular attribute.

  3. Example: 2092 adult passengers on the Titanic;654 survived Proportion of survivors = 654/2092  0.3

  4. Probability that two out of three randomly chosen passengers survived the Titanic

  5. Binomial distribution The binomial distribution describes the probability of a given number of "successes" from a fixed number of independent trials.

  6. n trials; p probability of success

  7. n! = n * n-1 * n-2 *... 3 * 2 * 1 6! = 6 * 5 * 4 * 3 * 2 *1 = 7200!=11!=1

  8. Probability that two out of three randomly chosen passengers survived the Titanic

  9. Probability that two out of three randomly chosen passengers survived the Titanic Probability of 1 death Number of ways to get 2 survivors out of 3 passengers Probability of 2 survivors

  10. Example: Paradise flycatchers A population of paradise flycatchers has 80% brown males and 20% white. Your field assistant captures 5 male flycatchers at random. What is the chance that 3 of those are brown and 2 are white?

  11. p = 0.8 n = 5 X = 3 Pr[3] =

  12. Try at home: What is the probability that 3 or more are brown?

  13. Try at home: What is the probability that 3 or more are brown?

  14. Properties of the binomial distribution: Number of successes

  15. Binomial distribution for p = 0.8, n = 5

  16. Proportion of successes in a sample The hat (^) shows that this is an estimate of p.

  17. Properties of sample proportions Mean: p Variance:

  18. Estimating a proportion

  19. Standard error of the estimate of a proportion

  20. A larger sample has a lower standard error

  21. The law of large numbers The greater the sample size, the closer an estimate of a proportion is likely to be to its true value. Sample size

  22. 95% confidence interval for a proportion This is the Agresti-Coull confidence interval

  23. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists?

  24. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists?

  25. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists? What is the 95% confidence interval for this estimate? = 30/87, or 0.345

  26. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists? What is the 95% confidence interval for this estimate?

  27. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists? What is the 95% confidence interval for this estimate?

  28. Example: The daughters of radiologists 30 out of 87 offspring of radiologists are males, and the rest female. What is the best estimate of the proportion of sons among radiologists? What is the 95% confidence interval for this estimate?

  29. Hypothesis testing on proportions The binomial test

  30. Binomial test The binomial test uses data to test whether a population proportion p matches a null expectation for the proportion. H0: The relative frequency of successes in the population is p0 . HA: The relative frequency of successes in the population is not p0 .

  31. Example An example: Imagine a student takes a multiple choice test before starting a statistics class. Each of the 10 questions on the test have 5 possible answers, only one of which is correct. This student gets 4 answers right. Can we deduce from this that this student knows anything at all about statistics?

  32. Hypotheses • H0: Student got correct answers randomly. • HA: Student got more answers correct than random.

  33. Hypotheses H0: Student got correct answers randomly. H0: p0 = 0.2.

  34. N =10, p0 =0.2

  35. P = 0.12 This is greater than the a value of 0.05, so we would not reject the null hypothesis. It is plausible that the student had four answers correct just by guessing randomly.

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