1. 1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University

2. 2 2 Slide © 2009 Thomson South-Western. All Rights Reserved Chapter 6 Continuous Probability Distributions n Uniform Probability Distribution f ( x ) x x Uniform x Normal x x Exponential n Normal Probability Distribution n Exponential Probability Distribution

3. 3 3 Slide © 2009 Thomson South-Western. All Rights Reserved Continuous Probability Distributions n A continuous random variable can assume any value in an interval on the real line or in a collection of intervals. n It is not possible to talk about the probability of the random variable assuming a particular value. n Instead, we talk about the probability of the random variable assuming a value within a given interval.

4. 4 4 Slide © 2009 Thomson South-Western. All Rights Reserved Continuous Probability Distributions n The probability of the random variable assuming a value within some given interval from x 1 to x 2 is defined to be the area under the graph of the probability density function between x 1 and x 2. f ( x ) x x Uniform x1 x1x1 x1 x1 x1x1 x1 x2 x2x2 x2 x2 x2x2 x2 x Normal x1 x1x1 x1 x1 x1x1 x1 x2 x2x2 x2 x2 x2x2 x2 x1 x1x1 x1 x1 x1x1 x1 x2 x2x2 x2 x2 x2x2 x2 Exponential x x x1 x1x1 x1 x1 x1x1 x1 x2 x2x2 x2 x2 x2x2 x2

5. 5 5 Slide © 2009 Thomson South-Western. All Rights Reserved Uniform Probability Distribution where: a = smallest value the variable can assume b = largest value the variable can assume b = largest value the variable can assume f ( x ) = 1/( b – a ) for a < x < b f ( x ) = 1/( b – a ) for a < x < b = 0 elsewhere = 0 elsewhere f ( x ) = 1/( b – a ) for a < x < b f ( x ) = 1/( b – a ) for a < x < b = 0 elsewhere = 0 elsewhere n A random variable is uniformly distributed whenever the probability is proportional to the interval’s length. n The uniform probability density function is:

6. 6 6 Slide © 2009 Thomson South-Western. All Rights Reserved Var( x ) = ( b - a ) 2 /12 E( x ) = ( a + b )/2 Uniform Probability Distribution n Expected Value of x n Variance of x

7. 7 7 Slide © 2009 Thomson South-Western. All Rights Reserved Uniform Probability Distribution n Example: Slater's Buffet Slater customers are charged for the amount of Slater customers are charged for the amount of salad they take. Sampling suggests that the amount of salad taken is uniformly distributed between 5 ounces and 15 ounces.

8. 8 8 Slide © 2009 Thomson South-Western. All Rights Reserved n Uniform Probability Density Function f ( x ) = 1/10 for 5 < x < 15 f ( x ) = 1/10 for 5 < x < 15 = 0 elsewhere = 0 elsewhere f ( x ) = 1/10 for 5 < x < 15 f ( x ) = 1/10 for 5 < x < 15 = 0 elsewhere = 0 elsewhere where: x = salad plate filling weight x = salad plate filling weight Uniform Probability Distribution

9. 9 9 Slide © 2009 Thomson South-Western. All Rights Reserved n Expected Value of x E( x ) = ( a + b )/2 E( x ) = ( a + b )/2 = (5 + 15)/2 = (5 + 15)/2 = 10 = 10 E( x ) = ( a + b )/2 E( x ) = ( a + b )/2 = (5 + 15)/2 = (5 + 15)/2 = 10 = 10 Var( x ) = ( b - a ) 2 /12 Var( x ) = ( b - a ) 2 /12 = (15 – 5) 2 /12 = (15 – 5) 2 /12 = 8.33 = 8.33 Var( x ) = ( b - a ) 2 /12 Var( x ) = ( b - a ) 2 /12 = (15 – 5) 2 /12 = (15 – 5) 2 /12 = 8.33 = 8.33 Uniform Probability Distribution n Variance of x

10. 10 Slide © 2009 Thomson South-Western. All Rights Reserved n Uniform Probability Distribution for Salad Plate Filling Weight f(x)f(x) f(x)f(x) x x 1/10 Salad Weight (oz.) Uniform Probability Distribution 5 5 10 15 0 0

11. 11 Slide © 2009 Thomson South-Western. All Rights Reserved f(x)f(x) f(x)f(x) x x 1/10 Salad Weight (oz.) 5 5 10 15 0 0 P(12 < x < 15) = 1/10(3) =.3 What is the probability that a customer What is the probability that a customer will take between 12 and 15 ounces of salad? will take between 12 and 15 ounces of salad? Uniform Probability Distribution 12

12. Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n The normal probability distribution is the most important distribution for describing a continuous random variable. n It is widely used in statistical inference. n It has been used in a wide variety of applications including: including: Heights of people Heights of people Test scores Test scores Rainfall amounts Rainfall amounts Scientific measurements Scientific measurements

13. 13 Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n Normal Probability Density Function  = mean  = standard deviation  = 3.14159 = PI() (Excel) e = 2.71828 = EXP(1) (Excel) where:

14. 14 Slide © 2009 Thomson South-Western. All Rights Reserved The distribution is symmetric; its skewness The distribution is symmetric; its skewness measure is zero. measure is zero. The distribution is symmetric; its skewness The distribution is symmetric; its skewness measure is zero. measure is zero. Normal Probability Distribution n Characteristics x

15. 15 Slide © 2009 Thomson South-Western. All Rights Reserved The entire family of normal probability The entire family of normal probability distributions is defined by its mean  and its distributions is defined by its mean  and its standard deviation . standard deviation . The entire family of normal probability The entire family of normal probability distributions is defined by its mean  and its distributions is defined by its mean  and its standard deviation . standard deviation . Normal Probability Distribution n Characteristics Standard Deviation  Mean  x

16. 16 Slide © 2009 Thomson South-Western. All Rights Reserved The highest point on the normal curve is at the The highest point on the normal curve is at the mean, which is also the median and mode. mean, which is also the median and mode. The highest point on the normal curve is at the The highest point on the normal curve is at the mean, which is also the median and mode. mean, which is also the median and mode. Normal Probability Distribution n Characteristics x

17. 17 Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n Characteristics -10025 The mean can be any numerical value: negative, The mean can be any numerical value: negative, zero, or positive. zero, or positive. The mean can be any numerical value: negative, The mean can be any numerical value: negative, zero, or positive. zero, or positive. x

18. 18 Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n Characteristics  = 15  = 25 The standard deviation determines the width of the curve: larger values result in wider, flatter curves. The standard deviation determines the width of the curve: larger values result in wider, flatter curves. x

19. 19 Slide © 2009 Thomson South-Western. All Rights Reserved Probabilities for the normal random variable are Probabilities for the normal random variable are given by areas under the curve. The total area given by areas under the curve. The total area under the curve is 1 (.5 to the left of the mean and under the curve is 1 (.5 to the left of the mean and.5 to the right)..5 to the right). Probabilities for the normal random variable are Probabilities for the normal random variable are given by areas under the curve. The total area given by areas under the curve. The total area under the curve is 1 (.5 to the left of the mean and under the curve is 1 (.5 to the left of the mean and.5 to the right)..5 to the right). Normal Probability Distribution n Characteristics.5.5 x

20. 20 Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n Characteristics of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean. of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean.68.26%68.26% +/- 1 standard deviation of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean. of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean. 95.44%95.44% +/- 2 standard deviations of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean. of values of a normal random variable of values of a normal random variable are within of its mean. are within of its mean.99.72%99.72% +/- 3 standard deviations

21. 21 Slide © 2009 Thomson South-Western. All Rights Reserved Normal Probability Distribution n Characteristics x  – 3   – 1   – 2   + 1   + 2   + 3  68.26% 95.44% 99.72%

22. 22 Slide © 2009 Thomson South-Western. All Rights Reserved Standard Normal Probability Distribution A random variable having a normal distribution A random variable having a normal distribution with a mean of 0 and a standard deviation of 1 is with a mean of 0 and a standard deviation of 1 is said to have a standard normal probability said to have a standard normal probability distribution. distribution. A random variable having a normal distribution A random variable having a normal distribution with a mean of 0 and a standard deviation of 1 is with a mean of 0 and a standard deviation of 1 is said to have a standard normal probability said to have a standard normal probability distribution. distribution. n Characteristics

23. 23 Slide © 2009 Thomson South-Western. All Rights Reserved  0 z The letter z is used to designate the standard The letter z is used to designate the standard normal random variable. normal random variable. The letter z is used to designate the standard The letter z is used to designate the standard normal random variable. normal random variable. Standard Normal Probability Distribution n Characteristics

24. 24 Slide © 2009 Thomson South-Western. All Rights Reserved n Converting to the Standard Normal Distribution Standard Normal Probability Distribution We can think of z as a measure of the number of standard deviations x is from .

25. 25 Slide © 2009 Thomson South-Western. All Rights Reserved is used to compute the z value is used to compute the z value given a cumulative probability. given a cumulative probability. is used to compute the z value is used to compute the z value given a cumulative probability. given a cumulative probability. NORMSINVNORMSINV NORM S INV is used to compute the cumulative is used to compute the cumulative probability given a z value. probability given a z value. is used to compute the cumulative is used to compute the cumulative probability given a z value. probability given a z value.NORMSDISTNORMSDIST NORM S DIST Using Excel to Compute Standard Normal Probabilities n Excel has two functions for computing probabilities and z values for a standard normal distribution: The “S” in the function names reminds us that they relate to the standard normal probability distribution.

26. 26 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Formula Worksheet Using Excel to Compute Standard Normal Probabilities

27. 27 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Value Worksheet Using Excel to Compute Standard Normal Probabilities

28. 28 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Formula Worksheet Using Excel to Compute Standard Normal Probabilities

29. 29 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Value Worksheet Using Excel to Compute Standard Normal Probabilities

30. 30 Slide © 2009 Thomson South-Western. All Rights Reserved Standard Normal Probability Distribution n Example: Pep Zone Pep Zone sells auto parts and supplies including Pep Zone sells auto parts and supplies including a popular multi-grade motor oil. When the stock of this oil drops to 20 gallons, a replenishment order is placed. The store manager is concerned that sales are The store manager is concerned that sales are being lost due to stockouts while waiting for a replenishment order.

31. 31 Slide © 2009 Thomson South-Western. All Rights Reserved It has been determined that demand during It has been determined that demand during replenishment lead-time is normally distributed with a mean of 15 gallons and a standard deviation of 6 gallons. Standard Normal Probability Distribution n Example: Pep Zone The manager would like to know the probability The manager would like to know the probability of a stockout during replenishment lead-time. In other words, what is the probability that demand during lead-time will exceed 20 gallons? P ( x > 20) = ? P ( x > 20) = ?

32. 32 Slide © 2009 Thomson South-Western. All Rights Reserved z = ( x -  )/  z = ( x -  )/  = (20 - 15)/6 = (20 - 15)/6 =.83 =.83 z = ( x -  )/  z = ( x -  )/  = (20 - 15)/6 = (20 - 15)/6 =.83 =.83 n Solving for the Stockout Probability Step 1: Convert x to the standard normal distribution. Step 2: Find the area under the standard normal curve to the left of z =.83. curve to the left of z =.83. Step 2: Find the area under the standard normal curve to the left of z =.83. curve to the left of z =.83. see next slide see next slide Standard Normal Probability Distribution

33. 33 Slide © 2009 Thomson South-Western. All Rights Reserved n Cumulative Probability Table for the Standard Normal Distribution P ( z <.83) Standard Normal Probability Distribution

34. 34 Slide © 2009 Thomson South-Western. All Rights Reserved P ( z >.83) = 1 – P ( z.83) = 1 – P ( z <.83) = 1-.7967 = 1-.7967 =.2033 =.2033 P ( z >.83) = 1 – P ( z.83) = 1 – P ( z <.83) = 1-.7967 = 1-.7967 =.2033 =.2033 n Solving for the Stockout Probability Step 3: Compute the area under the standard normal curve to the right of z =.83. curve to the right of z =.83. Step 3: Compute the area under the standard normal curve to the right of z =.83. curve to the right of z =.83. Probability of a stockout of a stockout P ( x > 20) Standard Normal Probability Distribution

35. 35 Slide © 2009 Thomson South-Western. All Rights Reserved n Solving for the Stockout Probability 0.83 Area =.7967 Area = 1 -.7967 =.2033 =.2033 z Standard Normal Probability Distribution

36. 36 Slide © 2009 Thomson South-Western. All Rights Reserved n Standard Normal Probability Distribution Standard Normal Probability Distribution If the manager of Pep Zone wants the probability If the manager of Pep Zone wants the probability of a stockout during replenishment lead-time to be no more than.05, what should the reorder point be?

37. 37 Slide © 2009 Thomson South-Western. All Rights Reserved n Solving for the Reorder Point 0 Area =.9500 Area =.0500 z z.05 Standard Normal Probability Distribution

38. 38 Slide © 2009 Thomson South-Western. All Rights Reserved n Solving for the Reorder Point Step 1: Find the z -value that cuts off an area of.05 in the right tail of the standard normal in the right tail of the standard normal distribution. distribution. Step 1: Find the z -value that cuts off an area of.05 in the right tail of the standard normal in the right tail of the standard normal distribution. distribution. We look up the complement of the tail area (1 -.05 =.95) Standard Normal Probability Distribution

39. 39 Slide © 2009 Thomson South-Western. All Rights Reserved n Solving for the Reorder Point Step 2: Convert z.05 to the corresponding value of x. x =  + z.05  x =  + z.05   = 15 + 1.645(6) = 24.87 or 25 = 24.87 or 25 x =  + z.05  x =  + z.05   = 15 + 1.645(6) = 24.87 or 25 = 24.87 or 25 A reorder point of 25 gallons will place the probability A reorder point of 25 gallons will place the probability of a stockout during leadtime at (slightly less than).05. of a stockout during leadtime at (slightly less than).05. Standard Normal Probability Distribution

40. 40 Slide © 2009 Thomson South-Western. All Rights Reserved n Solving for the Reorder Point By raising the reorder point from 20 gallons to By raising the reorder point from 20 gallons to 25 gallons on hand, the probability of a stockout decreases from about.20 to.05. This is a significant decrease in the chance that This is a significant decrease in the chance that Pep Zone will be out of stock and unable to meet a customer’s desire to make a purchase. Standard Normal Probability Distribution

41. 41 Slide © 2009 Thomson South-Western. All Rights Reserved Using Excel to Compute Normal Probabilities n Excel has two functions for computing cumulative probabilities and x values for any normal distribution: NORMDIST is used to compute the cumulative probability given an x value. NORMDIST is used to compute the cumulative probability given an x value. NORMINV is used to compute the x value given a cumulative probability. NORMINV is used to compute the x value given a cumulative probability.

42. 42 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Formula Worksheet Using Excel to Compute Normal Probabilities

43. 43 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Value Worksheet Using Excel to Compute Normal Probabilities Note: P( x > 20) =.2023 here using Excel, while our previous manual approach using the z table yielded.2033 due to our rounding of the z value.

44. 44 Slide © 2009 Thomson South-Western. All Rights Reserved Exponential Probability Distribution n The exponential probability distribution is useful in describing the time it takes to complete a task. Time between vehicle arrivals at a toll booth Time between vehicle arrivals at a toll booth Time required to complete a questionnaire Time required to complete a questionnaire Distance between major defects in a highway Distance between major defects in a highway n The exponential random variables can be used to describe:

45. 45 Slide © 2009 Thomson South-Western. All Rights Reserved n Density Function Exponential Probability Distribution where:  = mean e = 2.71828 e = 2.71828 for x > 0,  > 0

46. 46 Slide © 2009 Thomson South-Western. All Rights Reserved n Cumulative Probabilities Exponential Probability Distribution where: x 0 = some specific value of x x 0 = some specific value of x

47. 47 Slide © 2009 Thomson South-Western. All Rights Reserved Using Excel to Compute Exponential Probabilities The EXPONDIST function can be used to compute The EXPONDIST function can be used to compute exponential probabilities. exponential probabilities. The EXPONDIST function can be used to compute The EXPONDIST function can be used to compute exponential probabilities. exponential probabilities. The EXPONDIST function has three arguments: The EXPONDIST function has three arguments: 1 st The value of the random variable x 1 st The value of the random variable x 2 nd 1/  2 nd 1/  3 rd “TRUE” or “FALSE” 3 rd “TRUE” or “FALSE” the inverse of the mean number of occurrences in an interval in an interval We will always enter “TRUE” because we’re seeking a cumulative probability.

48. 48 Slide © 2009 Thomson South-Western. All Rights Reserved Using Excel to Compute Exponential Probabilities n Excel Formula Worksheet

49. 49 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Value Worksheet Using Excel to Compute Exponential Probabilities

50. 50 Slide © 2009 Thomson South-Western. All Rights Reserved Exponential Probability Distribution n Example: Al’s Full-Service Pump The time between arrivals of cars at Al’s full- The time between arrivals of cars at Al’s full- service gas pump follows an exponential probability distribution with a mean time between arrivals of 3 minutes. Al would like to know the probability that the time between two successive arrivals will be 2 minutes or less.

51. 51 Slide © 2009 Thomson South-Western. All Rights Reserved x x f(x)f(x) f(x)f(x).1.3.4.2 0 1 2 3 4 5 6 7 8 9 10 Time Between Successive Arrivals (mins.) Exponential Probability Distribution P ( x < 2) = 1 - 2.71828 -2/3 = 1 -.5134 =.4866 P ( x < 2) = 1 - 2.71828 -2/3 = 1 -.5134 =.4866 n Example: Al’s Full-Service Pump

52. 52 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Formula Worksheet Using Excel to Compute Exponential Probabilities

53. 53 Slide © 2009 Thomson South-Western. All Rights Reserved n Excel Value Worksheet Using Excel to Compute Exponential Probabilities

54. 54 Slide © 2009 Thomson South-Western. All Rights Reserved Relationship between the Poisson and Exponential Distributions The Poisson distribution provides an appropriate description of the number of occurrences per interval The Poisson distribution provides an appropriate description of the number of occurrences per interval The exponential distribution provides an appropriate description of the length of the interval between occurrences The exponential distribution provides an appropriate description of the length of the interval between occurrences

55. 55 Slide © 2009 Thomson South-Western. All Rights Reserved End of Chapter 6