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- 2. Random Variable A random variable x takes on a defined set of values with different probabilities.
- 3. Random variables can be discrete or continuous Discrete random variables have a countable number of outcomes
- 4. Probability functions A probability function maps the possible values of x against their respective probabilities of
- 5. Discrete example: roll of a die
- 6. Probability mass function (pmf)
- 7. Cumulative distribution function (CDF)
- 8. Cumulative distribution function
- 9. Examples 1. What’s the probability that you roll a 3 or less? P(x≤3)=1/2 2. What’s the
- 10. Practice Problem Which of the following are probability functions? a. f(x)=.25 for x=9,10,11,12 b. f(x)= (3-x)/2
- 11. Answer (a) a. f(x)=.25 for x=9,10,11,12 1.0
- 12. Answer (b) b. f(x)= (3-x)/2 for x=1,2,3,4
- 13. Answer (c) c. f(x)= (x2+x+1)/25 for x=0,1,2,3
- 14. Practice Problem: The number of ships to arrive at a harbor on any given day is
- 15. Practice Problem: You are lecturing to a group of 1000 students. You ask them to each
- 16. Important discrete distributions in epidemiology… Binomial Yes/no outcomes (dead/alive, treated/untreated, smoker/non-smoker, sick/well, etc.) Poisson Counts (e.g.,
- 17. Continuous case The probability function that accompanies a continuous random variable is a continuous mathematical function
- 18. Continuous case For example, recall the negative exponential function (in probability, this is called an “exponential
- 19. Continuous case: “probability density function” (pdf) The probability that x is any exact particular value (such
- 20. For example, the probability of x falling within 1 to 2:
- 21. Cumulative distribution function As in the discrete case, we can specify the “cumulative distribution function” (CDF):
- 22. Example
- 23. Example 2: Uniform distribution The uniform distribution: all values are equally likely The uniform distribution: f(x)=
- 24. Example: Uniform distribution What’s the probability that x is between ¼ and ½? P(½ ≥x≥ ¼
- 25. Practice Problem 4. Suppose that survival drops off rapidly in the year following diagnosis of a
- 26. Answer The probability of dying within 1 year can be calculated using the cumulative distribution function:
- 27. Expected Value and Variance All probability distributions are characterized by an expected value and a variance
- 28. For example, bell-curve (normal) distribution:
- 29. Expected value, or mean If we understand the underlying probability function of a certain phenomenon, then
- 30. Example: expected value Recall the following probability distribution of ship arrivals:
- 31. Expected value, formally Discrete case: Continuous case:
- 32. Empirical Mean is a special case of Expected Value… Sample mean, for a sample of n
- 33. Expected value, formally Discrete case: Continuous case:
- 34. Extension to continuous case: uniform distribution x p(x) 1 1
- 35. Symbol Interlude E(X) = µ these symbols are used interchangeably
- 36. Expected Value Expected value is an extremely useful concept for good decision-making!
- 37. Example: the lottery The Lottery (also known as a tax on people who are bad at
- 38. Lottery Calculate the probability of winning in 1 try: The probability function (note, sums to 1.0):
- 39. Expected Value The probability function Expected Value E(X) = P(win)*$2,000,000 + P(lose)*-$1.00 = 2.0 x 106
- 40. Expected Value If you play the lottery every week for 10 years, what are your expected
- 41. Gambling (or how casinos can afford to give so many free drinks…) A roulette wheel has
- 42. **A few notes about Expected Value as a mathematical operator: If c= a constant number (i.e.,
- 43. E(c) = c E(c) = c Example: If you cash in soda cans in CA, you
- 44. E(cX)=cE(X) E(cX)=cE(X) Example: If the casino charges $10 per game instead of $1, then the casino
- 45. E(c + X)=c + E(X) E(c + X)=c + E(X) Example, if the casino throws in
- 46. E(X+Y)= E(X) + E(Y) E(X+Y)= E(X) + E(Y) Example: If you play the lottery twice, you
- 47. Practice Problem If a disease is fairly rare and the antibody test is fairly expensive, in
- 48. Answer (a) a. Suppose a particular disease has a prevalence of 10% in a third-world population
- 49. Answer (b) b. What if you pool only 10 samples at a time? E(X) = (.90)10
- 50. Answer (c) c. 5 samples at a time? E(X) = (.90)5 (1) + [1-.905] (6) =
- 51. Practice Problem If X is a random integer between 1 and 10, what’s the expected value
- 52. Answer If X is a random integer between 1 and 10, what’s the expected value of
- 53. Expected value isn’t everything though… Take the show “Deal or No Deal” Everyone know the rules?
- 54. Deal or No Deal… This could really be represented as a probability distribution and a non-random
- 55. Expected value doesn’t help…
- 56. How to decide? Variance! If you take the deal, the variance/standard deviation is 0. If you
- 57. Variance/standard deviation “The average (expected) squared distance (or deviation) from the mean” **We square because squaring
- 58. Variance, formally Discrete case: Continuous case:
- 59. Similarity to empirical variance The variance of a sample: s2 =
- 60. Symbol Interlude Var(X) = σ2 these symbols are used interchangeably
- 61. Variance: Deal or No Deal Now you examine your personal risk tolerance…
- 62. Practice Problem A roulette wheel has the numbers 1 through 36, as well as 0 and
- 63. Answer Standard deviation is $.99. Interpretation: On average, you’re either 1 dollar above or 1 dollar
- 64. Handy calculation formula! Handy calculation formula (if you ever need to calculate by hand!):
- 65. Var(x) = E(x-μ)2 = E(x2) – [E(x)]2 (your calculation formula!) Proofs (optional!): E(x-μ)2 = E(x2–2μx +
- 66. For example, what’s the variance and standard deviation of the roll of a die?
- 67. **A few notes about Variance as a mathematical operator: If c= a constant number (i.e., not
- 68. Var(c) = 0 Var(c) = 0 Constants don’t vary!
- 69. Var (c+X)= Var(X) Var (c+X)= Var(X) Adding a constant to every instance of a random variable
- 70. Var (c+X)= Var(X) Var (c+X)= Var(X) Adding a constant to every instance of a random variable
- 71. Var(cX)= c2Var(X) Var(cX)= c2Var(X) Multiplying each instance of the random variable by c makes it c-times
- 72. Var(X+Y)= Var(X) + Var(Y) Var(X+Y)= Var(X) + Var(Y) ONLY IF X and Y are independent!!!!!!!! With
- 73. Example of Var(X+Y)= Var(X) + Var(Y): TPMT TPMT metabolizes the drugs 6- mercaptopurine, azathioprine, and 6-thioguanine
- 74. TPMT activity by genotype Weinshilboum R. Drug Metab Dispos. 2001 Apr;29(4 Pt 2):601-5
- 75. TPMT activity by genotype Weinshilboum R. Drug Metab Dispos. 2001 Apr;29(4 Pt 2):601-5 The variability in
- 76. TPMT activity by genotype Weinshilboum R. Drug Metab Dispos. 2001 Apr;29(4 Pt 2):601-5 There is variability
- 77. Practice Problem Find the variance and standard deviation for the number of ships to arrive at
- 78. Answer: variance and std dev Interpretation: On an average day, we expect 11.3 ships to arrive
- 79. Practice Problem You toss a coin 100 times. What’s the expected number of heads? What’s the
- 80. Answer: expected value Intuitively, we’d probably all agree that we expect around 50 heads, right? Another
- 81. Answer: variance What’s the variability, though? More tricky. But, again, we could do this for 1
- 82. Or use computer simulation… Flip coins virtually! Flip a virtual coin 100 times; count the number
- 83. Coin tosses… Mean = 50 Std. dev = 5 Follows a normal distribution ∴95% of the
- 84. Covariance: joint probability The covariance measures the strength of the linear relationship between two variables The
- 85. The Sample Covariance The sample covariance:
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