Introduction to Probability and Statistics                                    6th Week (4/12)Special Probability Distribut...
Chebyshev’s Inequality                    Chebyshev’s inequality guarantees that in any                    data sample or ...
Chebyshev’s Inequality
Law of Large Numbers             The law of large numbers             (LLN) is a theorem that             describes the re...
Law of Large Numbers
Law of Large NumbersWhy is it important?
Other Measures of Central Tendency
Other Measures of Central Tendency
Other Measures of Central Tendency
Percentiles
Percentiles: A Practical Example
Other Measures of Dispersion
Skewness
Kurtosis
Skewness, Kurtosis, and Moment
Discrete Probability DistributionWhat kinds of PD do we have to know to     solve real-world problems?
Discrete Uniform Distribution• Consider a case with rolling a fair dice•   Each random variable has same probability → Uni...
Discrete Uniform Distribution•   Probability density    function :•   Expectation:•   Variance :
•   Example      Suppose that we have a box containing 45 numbered balls. In this case, we          randomly select a ball...
(Discrete) Binomial DistributionBernoulli experiment: Only two kinds of results are possible                              ...
(Discrete) Binomial DistributionBinomial Distribution
(Discrete) Binomial DistributionSome Properties of the Binomial Distribution
(Discrete) Binomial DistributionSome Properties of the Binomial Distribution (1) (2) (3) (4)
(Discrete) Binomial DistributionSome Properties of the Binomial Distribution                μ = n/2 을 중심으로 좌우대칭 :         ...
(Discrete) Binomial Distribution
(Discrete) Binomial Distribution
Example Two factories, S and L, produce smart phones and their failure ratios are 5%. If you buy 7 phones from S and 13 ph...
Criteria for a Binomial Probability ExperimentAn experiment is said to be a binomial experimentprovided1. The experiment i...
Notation Used in the          Binomial Probability Distribution• There are n independent trials of the experiment• Let p d...
EXAMPLE Identifying Binomial ExperimentsWhich of the following are binomial experiments?(a) A player rolls a pair of fair ...
EXAMPLE Constructing a Binomial Probability        DistributionAccording to the Air Travel Consumer Report,the 11 largest ...
(Discrete) Multinomial Distribution
(Discrete) Geometric DistributionRepeat Bernoulli experiments until the first success. => Number of Trial is X            ...
(Discrete) Geometric DistributionRepeat Bernoulli experiments until the first success. => Number of Trial is X            ...
(Discrete) Geometric Distribution
(Discrete) Negative Binomial DistributionRepeat Bernoulli experiments until the rth success.                              ...
(Discrete) Negative Binomial DistributionRepeat Bernoulli experiments until the rth success.
(Discrete) Negative Binomial Distribution
(Discrete) Hypergeometric Distribution It is similar to the binomial distribution. But the difference is the method of sam...
(Discrete) Hypergeometric DistributionA box contains N balls, where r balls are white (r<N)Suppose that we randomly select...
(Discrete) Hypergeometric Distribution
(Discrete) Hypergeometric Distribution
Total 50 chips are in a box. Among those, 4 are out of order (failedchips). If you select 5 chips:(1) Probability distribu...
Multivariate Hypergeometric Distribution                                              개                                   ...
In a box, there are 3 red balls, 2 blue balls, and 5 yellow balls. You    select 4 balls.(1) Joint probability function fo...
(Discrete) Poisson Distribution-   Describe an event that rarely happens.-   All events in a specific period are mutually ...
(Discrete) Poisson DistributionIt is often used as a model for the number of events (such as the number oftelephone calls ...
(Discrete) Poisson Distribution                                           .Ex.1. On an average Friday, a waitress gets no ...
(Discrete) Poisson Distribution                                             .Ex. 3. A small life insurance company has det...
(Discrete) Poisson Distribution
Characteristics of Poisson Distribution   E(X) increases with parameter µ (or λ).   The graph becomes broadened with incre...
(Discrete) Poisson DistributionProbability mass function   Cumulative distribution function
(Discrete) Poisson Distribution
(Discrete) Poisson Distribution
(Discrete) Poisson Distribution                 Comparison of the Poisson                 distribution (black dots) and th...
Discrete Probability Distributions:                   Summary• Uniform Distribution• Binomial Distributions• Multinomial D...
Continuous Probability DistributionsWhat kinds of PD do we have to know to     solve real-world problems?
(Continuous) Uniform Distribution
(Continuous) Uniform Distributions In a Period [a, b], f(x) is constant. f(x) E(x):
 Var(X  ) : F(X) :
If X ∼ U(0, 1) and Y = a + (b - a) X,(1)Distribution function for Y(2)Probability function for Y(3)Expectation and Varianc...
(2)(3)(4)
(Continuous) Uniform Distributions
(Continuous) Exponential Distribution▶ Analysis of survival rate▶ Period between first and second earthquakes▶ Waiting tim...
(Continuous) Exponential Distribution
From a survey, the frequency of traffic accidents X is given by                                         -3x               ...
• Survival function :• Hazard rate, Failure rate:
A patient was told that he can survive average of 100 days. Suppose that theprobability function is given by(1) What is th...
(Continuous) Exponential Distribution ⊙ Relation with Poisson Process(1) If an event occurs according to Poisson process w...
(Continuous) Gamma Distribution
(Continuous) Gamma Distribution                   α : shape parameter, α > 0                   β : scale parameter, β > 0α...
(Continuous) Gamma Distribution
(Continuous) Gamma Distribution  ⊙ Relation with Exponential DistributionExponential distribution is a special gamma distr...
If the time to observe an traffic accident (X) in a region have the followingprobability distribution                     ...
(Continuous) Chi Square DistributionA special gamma distribution α = r/2, β = 2 PD E(X) Var(X)
(Continuous) Chi Square Distribution
(Continuous) Chi Square Distribution
(Continuous) Chi Square Distribution
(Continuous) Chi Square Distribution
(Continuous) Chi Square DistributionA random variable X follows a Chi Square Distribution with a degree offreedom of 5, Ca...
(Continuous) Chi Square DistributionWhy do we have to be bothered?
6주차
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6주차

  1. 1. Introduction to Probability and Statistics 6th Week (4/12)Special Probability Distributions (1)
  2. 2. Chebyshev’s Inequality Chebyshev’s inequality guarantees that in any data sample or probability distribution, "nearly all" values are close to the mean The precise statement being that no more than 1/k2 of the distribution’s values can be more than k standard deviations away from the mean. The inequality has great utility because it can be applied to completely arbitrary distributions (unknown except for mean and variance), for example it can be used to prove the weak law ofPafnuty Lvovich large numbers.Chebyshev (1821 –1894)
  3. 3. Chebyshev’s Inequality
  4. 4. Law of Large Numbers The law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.
  5. 5. Law of Large Numbers
  6. 6. Law of Large NumbersWhy is it important?
  7. 7. Other Measures of Central Tendency
  8. 8. Other Measures of Central Tendency
  9. 9. Other Measures of Central Tendency
  10. 10. Percentiles
  11. 11. Percentiles: A Practical Example
  12. 12. Other Measures of Dispersion
  13. 13. Skewness
  14. 14. Kurtosis
  15. 15. Skewness, Kurtosis, and Moment
  16. 16. Discrete Probability DistributionWhat kinds of PD do we have to know to solve real-world problems?
  17. 17. Discrete Uniform Distribution• Consider a case with rolling a fair dice• Each random variable has same probability → Uniform distribution
  18. 18. Discrete Uniform Distribution• Probability density function :• Expectation:• Variance :
  19. 19. • Example Suppose that we have a box containing 45 numbered balls. In this case, we randomly select a ball and its number is X: (1) Probability distribution for X (2) Expectation and Variance for X (3) P(X>40)• Solution (1) (2) (3)
  20. 20. (Discrete) Binomial DistributionBernoulli experiment: Only two kinds of results are possible p = 0.85 q = 1- p = 0.15
  21. 21. (Discrete) Binomial DistributionBinomial Distribution
  22. 22. (Discrete) Binomial DistributionSome Properties of the Binomial Distribution
  23. 23. (Discrete) Binomial DistributionSome Properties of the Binomial Distribution (1) (2) (3) (4)
  24. 24. (Discrete) Binomial DistributionSome Properties of the Binomial Distribution μ = n/2 을 중심으로 좌우대칭 : 대칭이항분포 (symmetric binomial distribution) Tail in right Tail in left
  25. 25. (Discrete) Binomial Distribution
  26. 26. (Discrete) Binomial Distribution
  27. 27. Example Two factories, S and L, produce smart phones and their failure ratios are 5%. If you buy 7 phones from S and 13 phones from L, what is the probability to have at least one failed phone? And what is the probability that you have one failed phone? Assume that the failure rates are independent.Solution X (from S) and Y (from L) : X ∼ B (7, 0.05) , Y ∼ B (13, 0.05) , X, Y : Independent X + Y ∼ B (20, 0.05) Only one phone is failed At least one phone is failed
  28. 28. Criteria for a Binomial Probability ExperimentAn experiment is said to be a binomial experimentprovided1. The experiment is performed a fixed number oftimes. Each repetition of the experiment is called atrial.2. The trials are independent. This means theoutcome of one trial will not affect the outcome of theother trials.3. For each trial, there are two mutually exclusiveoutcomes, success or failure.4. The probability of success is fixed for each trial ofthe experiment.
  29. 29. Notation Used in the Binomial Probability Distribution• There are n independent trials of the experiment• Let p denote the probability of success so that1 – p is the probability of failure.• Let x denote the number of successes in nindependent trials of the experiment. So, 0 < x < n.
  30. 30. EXAMPLE Identifying Binomial ExperimentsWhich of the following are binomial experiments?(a) A player rolls a pair of fair die 10 times. The numberX of 7’s rolled is recorded.(b) The 11 largest airlines had an on-time percentage of84.7% in November, 2001 according to the Air TravelConsumer Report. In order to assess reasons fordelays, an official with the FAA randomly selects flightsuntil she finds 10 that were not on time. The number offlights X that need to be selected is recorded.(c ) In a class of 30 students, 55% are female. Theinstructor randomly selects 4 students. The number Xof females selected is recorded.
  31. 31. EXAMPLE Constructing a Binomial Probability DistributionAccording to the Air Travel Consumer Report,the 11 largest air carriers had an on-timepercentage of 84.7% in November, 2001.Suppose that 4 flights are randomly selectedfrom November, 2001 and the number of on-timeflights X is recorded. Construct a probabilitydistribution for the random variable X using atree diagram.
  32. 32. (Discrete) Multinomial Distribution
  33. 33. (Discrete) Geometric DistributionRepeat Bernoulli experiments until the first success. => Number of Trial is X Slot Machine: How many should I try if I get the jackpot?
  34. 34. (Discrete) Geometric DistributionRepeat Bernoulli experiments until the first success. => Number of Trial is X : 성공 : 실패
  35. 35. (Discrete) Geometric Distribution
  36. 36. (Discrete) Negative Binomial DistributionRepeat Bernoulli experiments until the rth success. Crane Game: How many should I try if I want to get three dolls?
  37. 37. (Discrete) Negative Binomial DistributionRepeat Bernoulli experiments until the rth success.
  38. 38. (Discrete) Negative Binomial Distribution
  39. 39. (Discrete) Hypergeometric Distribution It is similar to the binomial distribution. But the difference is the method of sampling Binomial experiment: Sampling with replacement Hypergeometric experiment: Sampling without replacement Normal shooting Russian rouletteEach trial has same probability Each trial may have different probability
  40. 40. (Discrete) Hypergeometric DistributionA box contains N balls, where r balls are white (r<N)Suppose that we randomly select n balls from the box, what is the numberof white balls (X)?Assumption: Sampling without replacement n 개의 items N개 추출 의 items 개 개 개 개
  41. 41. (Discrete) Hypergeometric Distribution
  42. 42. (Discrete) Hypergeometric Distribution
  43. 43. Total 50 chips are in a box. Among those, 4 are out of order (failedchips). If you select 5 chips:(1) Probability distribution for the failed chip in these selected chips(2) Probability to have one or two failed chips for this case(3) Mathematical expectation and variance (1) Random variable: X(2)(3) N = 50, r = 4, n = 5
  44. 44. Multivariate Hypergeometric Distribution 개 개 개 개 개 개 X1 , X2 , X3 : Joint Probability Function
  45. 45. In a box, there are 3 red balls, 2 blue balls, and 5 yellow balls. You select 4 balls.(1) Joint probability function for X, Y, and Z(2) Probability to select 1 red ball, 1 blue ball, and 2 yellow balls. 4 x개 개 y개 5개 z개 2개 3개 (1) Joint probability function: (2)
  46. 46. (Discrete) Poisson Distribution- Describe an event that rarely happens.- All events in a specific period are mutually independent.- The probability to occur is proportional to the length of the period.- The probability to occur twice is zero if the period is short.
  47. 47. (Discrete) Poisson DistributionIt is often used as a model for the number of events (such as the number oftelephone calls at a business, number of customers in waiting lines, numberof defects in a given surface area, airplane arrivals, or the number ofaccidents at an intersection) in a specific time period. If z > 0 Satisfy the PF condition Probability function :
  48. 48. (Discrete) Poisson Distribution .Ex.1. On an average Friday, a waitress gets no tip from 5 customers. Find theprobability that she will get no tip from 7 customers this Friday.The waitress averages 5 customers that leave no tip on Fridays: λ = 5.Random Variable : The number of customers that leave her no tip this Friday.We are interested in P(X = 7).Ex. 2 During a typical football game, a coach can expect 3.2 injuries. Find theprobability that the team will have at most 1 injury in this game.A coach can expect 3.2 injuries : λ = 3.2.Random Variable : The number of injuries the team has in this game.We are interested in
  49. 49. (Discrete) Poisson Distribution .Ex. 3. A small life insurance company has determined that on the average it receives 6death claims per day. Find the probability that the company receives at least sevendeath claims on a randomly selected day. P(x ≥ 7) = 1 - P(x ≤ 6) = 0.393697Ex. 4. The number of traffic accidents that occurs on a particular stretch of roadduring a month follows a Poisson distribution with a mean of 9.4. Find the probabilitythat less than two accidents will occur on this stretch of road during a randomlyselected month. P(x < 2) = P(x = 0) + P(x = 1) = 0.000860
  50. 50. (Discrete) Poisson Distribution
  51. 51. Characteristics of Poisson Distribution E(X) increases with parameter µ (or λ). The graph becomes broadened with increasing the parameter µ (or λ).
  52. 52. (Discrete) Poisson DistributionProbability mass function Cumulative distribution function
  53. 53. (Discrete) Poisson Distribution
  54. 54. (Discrete) Poisson Distribution
  55. 55. (Discrete) Poisson Distribution Comparison of the Poisson distribution (black dots) and the binomial distribution with n=10 (red line), n=20 (blue line), n=1000 (green line). All distributions have a mean of 5. The horizontal axis shows the number of events k. Notice that as n gets larger, the Poisson distribution becomes an increasingly better approximation for the binomial distribution with the same mean
  56. 56. Discrete Probability Distributions: Summary• Uniform Distribution• Binomial Distributions• Multinomial Distributions• Geometric Distributions• Negative Binomial Distributions• Hypergeometric Distributions• Poisson Distribution
  57. 57. Continuous Probability DistributionsWhat kinds of PD do we have to know to solve real-world problems?
  58. 58. (Continuous) Uniform Distribution
  59. 59. (Continuous) Uniform Distributions In a Period [a, b], f(x) is constant. f(x) E(x):
  60. 60.  Var(X ) : F(X) :
  61. 61. If X ∼ U(0, 1) and Y = a + (b - a) X,(1)Distribution function for Y(2)Probability function for Y(3)Expectation and Variance for Y(4) Centered value for Y (1) Since y = a + (b - a) x so 0 ≤ y ≤ b,
  62. 62. (2)(3)(4)
  63. 63. (Continuous) Uniform Distributions
  64. 64. (Continuous) Exponential Distribution▶ Analysis of survival rate▶ Period between first and second earthquakes▶ Waiting time for events of Poisson distribution For any positive α
  65. 65. (Continuous) Exponential Distribution
  66. 66. From a survey, the frequency of traffic accidents X is given by -3x f(x) = 3e (0 ≤ x)(1)Probability to observe the second accident after one month of the firstaccident?(2)Probability to observe the second accident within 2 months(3)Suppose that a month is 30 days, what is the average day of theaccident?(1)(2)(3) μ=1/3, accordingly 10 days.
  67. 67. • Survival function :• Hazard rate, Failure rate:
  68. 68. A patient was told that he can survive average of 100 days. Suppose that theprobability function is given by(1) What is the probability that he dies within 150 days.(2) What is the probability that he survives 200 days λ=0.01 이므로 분포함수와 생존함수 : -x/100 -x/100 F(x)=1-e , S(x)=e(1) 이 환자가 150 일 이내에 사망할 확률 : -1.5 P(X < 150) = F(150) = 1-e = 1-0.2231 = 0.7769(2) 이 환자가 200 일 이상 생존할 확률 -2.0 P(X ≥ 200) = S(200) = e = 0.1353
  69. 69. (Continuous) Exponential Distribution ⊙ Relation with Poisson Process(1) If an event occurs according to Poisson process with the ratio λ , the waiting time between neighboring events (T) follows exponential distribution with the exponent of λ.
  70. 70. (Continuous) Gamma Distribution
  71. 71. (Continuous) Gamma Distribution α : shape parameter, α > 0 β : scale parameter, β > 0α=1 Γ (1, β) = E(1/β)
  72. 72. (Continuous) Gamma Distribution
  73. 73. (Continuous) Gamma Distribution ⊙ Relation with Exponential DistributionExponential distribution is a special gamma distribution with α = 1.IF X1 , X2 , … , Xn have independent exponential distribution with the sameexponent 1/β, the sum of these random variables S= X1 + X2 + … +Xn results ina gamma distribution, Γ(n, β).
  74. 74. If the time to observe an traffic accident (X) in a region have the followingprobability distribution -3x f(x) = 3e , 0<x<∞Estimate the probability to observe the first two accidents between the firstand second months. Assume that the all accidents are independent. X1 : Time for the first accident X2 : Time between the first and second accidents Xi ∼ Exp(1/3) , I = 1, 2 S = X1 + X2 : Time for two accidents S ∼ Γ(2, 1/3) Probability function for S :Answer:
  75. 75. (Continuous) Chi Square DistributionA special gamma distribution α = r/2, β = 2 PD E(X) Var(X)
  76. 76. (Continuous) Chi Square Distribution
  77. 77. (Continuous) Chi Square Distribution
  78. 78. (Continuous) Chi Square Distribution
  79. 79. (Continuous) Chi Square Distribution
  80. 80. (Continuous) Chi Square DistributionA random variable X follows a Chi Square Distribution with a degree offreedom of 5, Calculate the critical value to satisfy P(X < x0 )=0.95 Since P(X < x0 )=0.95, P(X > x0 )=0.05. From the table, find the point with d.f.=5 and α=0.05
  81. 81. (Continuous) Chi Square DistributionWhy do we have to be bothered?

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