1. 1 1 Slide © 2001 South-Western/Thomson Learning  Anderson  Sweeney  Williams Anderson  Sweeney  Williams  Slides Prepared by JOHN LOUCKS  CONTEMPORARYBUSINESSSTATISTICS WITH MICROSOFT  EXCEL CONTEMPORARYBUSINESSSTATISTICS

2. 2 2 Slide Chapter 6 Continuous Probability Distributions n Uniform Probability Distribution n Normal Probability Distribution n Exponential Probability Distribution

3. 3 3 Slide Continuous Probability Distributions Continuous Probability Distributions  x f(x)f(x)f(x)f(x)

4. 4 4 Slide 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. 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.

5. 5 5 Slide Uniform Probability Distribution A random variable is uniformly distributed whenever the probability is proportional to the interval’s length. n Uniform Probability Density Function f ( x ) = 1/( b - a ) for a < x < b f ( x ) = 1/( b - a ) for a < x < b = 0 elsewhere = 0 elsewhere n Expected Value of x E( x ) = ( a + b )/2 n Variance of x Var( x ) = ( b - a ) 2 /12 Var( x ) = ( b - a ) 2 /12 where: a = smallest value the variable can assume where: a = smallest value the variable can assume b = largest value the variable can assume b = largest value the variable can assume

6. 6 6 Slide Example: Slater's Buffet n Uniform Probability Distribution 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. Probability Density FunctionProbability Density Function f ( x ) = 1/10 for 5 < x < 15 f ( x ) = 1/10 for 5 < x < 15 = 0 elsewhere = 0 elsewhere where: where: x = salad plate filling weight

7. 7 7 Slide Example: Slater's Buffet n Uniform Probability Distribution What is the probability that a customer will take between 12 and 15 ounces of salad? f(x)f(x) f(x)f(x) x x 5 5 10 15 12 1/10 Salad Weight (oz.) P(12 < x < 15) = 1/10(3) =.3

8. 8 8 Slide Example: Slater's Buffet n Expected Value of x E( x ) = ( a + b )/2 = (5 + 15)/2 = (5 + 15)/2 = 10 = 10 n Variance of x Var( x ) = ( b - a ) 2 /12 Var( x ) = ( b - a ) 2 /12 = (15 – 5) 2 /12 = (15 – 5) 2 /12 = 8.33 = 8.33

9. 9 9 Slide Normal Probability Distribution n Graph of the Normal Probability Density Function  x f(x)f(x)f(x)f(x)

10. 10 Slide Normal Probability Distribution n Characteristics of the Normal Probability Distribution The shape of the normal curve is often illustrated as a bell-shaped curve.The shape of the normal curve is often illustrated as a bell-shaped curve. Two parameters,  (mean) and  (standard deviation), determine the location and shape of the distribution.Two parameters,  (mean) and  (standard deviation), determine the location and shape of the distribution. The highest point on the normal curve is at the mean, which is also the median and mode.The highest point on the normal curve is at the mean, which is also the median and mode. The mean can be any numerical value: negative, zero, or positive.The mean can be any numerical value: negative, zero, or positive. … continued

11. 11 Slide Normal Probability Distribution n Characteristics of the Normal Probability Distribution The normal curve is symmetric.The normal curve is symmetric. 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. The total area under the curve is 1 (.5 to the left of the mean and.5 to the right).The total area under the curve is 1 (.5 to the left of the mean and.5 to the right). Probabilities for the normal random variable are given by areas under the curve.Probabilities for the normal random variable are given by areas under the curve.

12. 12 Slide Normal Probability Distribution n % of Values in Some Commonly Used Intervals 68.26% of values of a normal random variable are within +/- 1 standard deviation of its mean. 68.26% of values of a normal random variable are within +/- 1 standard deviation of its mean. 95.44% of values of a normal random variable are within +/- 2 standard deviations of its mean. 95.44% of values of a normal random variable are within +/- 2 standard deviations of its mean. 99.72% of values of a normal random variable are within +/- 3 standard deviations of its mean. 99.72% of values of a normal random variable are within +/- 3 standard deviations of its mean.

13. 13 Slide Normal Probability Distribution n Normal Probability Density Function where:  = mean  = mean  = standard deviation  = standard deviation  = 3.14159  = 3.14159 e = 2.71828 e = 2.71828

14. 14 Slide Standard Normal Probability Distribution n A random variable that has a normal distribution with a mean of zero and a standard deviation of one is said to have a standard normal probability distribution. n The letter z is commonly used to designate this normal random variable. n Converting to the Standard Normal Distribution We can think of z as a measure of the number of standard deviations x is from . We can think of z as a measure of the number of standard deviations x is from .

15. 15 Slide Using Excel to Compute Normal Probabilities n Excel has two functions for computing probabilities and z values for a standard normal distribution: NORMSDIST is used to compute the cumulative probability given a z value.NORMSDIST is used to compute the cumulative probability given a z value. NORMSINV is used to compute the z value given a cumulative probability.NORMSINV is used to compute the z value given a cumulative probability. (The letter S in the above function names reminds us that they relate to the standard normal probability distribution.)

16. 16 Slide Using Excel to Compute Normal Probabilities n Formula Worksheet

17. 17 Slide Using Excel to Compute Normal Probabilities n Value Worksheet

18. 18 Slide Using Excel to Compute Normal Probabilities n Formula Worksheet

19. 19 Slide Using Excel to Compute Normal Probabilities n Value Worksheet

20. 20 Slide Example: Pep Zone n Standard Normal Probability Distribution 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 being lost due to stockouts while waiting for an order. It has been determined that leadtime demand is normally distributed with a mean of 15 gallons and a standard deviation of 6 gallons. The manager would like to know the probability of a stockout, P( x > 20).

21. 21 Slide n Standard Normal Probability Distribution The Standard Normal table shows an area of.2967 for the region between the z = 0 and z =.83 lines below. The shaded tail area is.5 -.2967 =.2033. The probability of a stock- out is.2033. z = ( x -  )/  = (20 - 15)/6 = (20 - 15)/6 =.83 =.83 0.83 Area =.2967 Area =.5 Area =.5 -.2967 =.2033 =.2033 z Example: Pep Zone

22. 22 Slide n Using the Standard Normal Probability Table Example: Pep Zone

23. 23 Slide n Standard Normal Probability Distribution If the manager of Pep Zone wants the probability of a stockout to be no more than.05, what should the reorder point be? Let z.05 represent the z value cutting the.05 tail area. Example: Pep Zone Area =.05 Area =.5 Area =.45 0 z.05

24. 24 Slide n Using the Standard Normal Probability Table We now look-up the.4500 area in the Standard Normal Probability table to find the corresponding z.05 value. z.05 = 1.645 is a reasonable estimate. z.05 = 1.645 is a reasonable estimate. Example: Pep Zone

25. 25 Slide n Standard Normal Probability Distribution The corresponding value of x is given by x =  + z.05   = 15 + 1.645(6) = 24.87 = 24.87 A reorder point of 24.87 gallons will place the probability of a stockout during leadtime at.05. Perhaps Pep Zone should set the reorder point at 25 gallons to keep the probability under.05. Example: Pep Zone

26. 26 Slide 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.

27. 27 Slide Using Excel to Compute Normal Probabilities n Formula Worksheet for Pep Zone Example

28. 28 Slide Using Excel to Compute Normal Probabilities n Value Worksheet for Pep Zone Example Note: P( x > 20) =.7977 here using Excel, while our previous manual approach using the z table yielded.7967 due to our rounding of the z value.

29. 29 Slide Exponential Probability Distribution n Exponential Probability Density Function for x > 0,  > 0 for x > 0,  > 0 where:  = mean e = 2.71828 e = 2.71828 n Cumulative Exponential Distribution Function where: x 0 = some specific value of x

30. 30 Slide Using Excel to Compute Exponential Probabilities n Formula Worksheet

31. 31 Slide Using Excel to Compute Exponential Probabilities n Value Worksheet

32. 32 Slide n Exponential Probability Distribution The time between arrivals of cars at Al’s Carwash 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. P ( x < 2) = 1 - 2.71828 -2/3 = 1 -.5134 =.4866 Example: Al’s Carwash

33. 33 Slide Example: Al’s Carwash n Graph of the Probability Density Function x x f(x)f(x) f(x)f(x).1.3.4.2 1 2 3 4 5 6 7 8 9 10 P ( x < 2) = area =.4866 Time Between Successive Arrivals (mins.)

34. 34 Slide Using Excel to Compute Exponential Probabilities n Excel’s EXPONDIST function can be used to compute exponential probabilities. n The function has three arguments: First – the value of the random variable xFirst – the value of the random variable x Second – 1/  (the inverse of the mean number of occurrences in an interval)Second – 1/  (the inverse of the mean number of occurrences in an interval) Third – “TRUE” or “FALSE” (we will always enter “TRUE” because we’re seeking a cumulative probability)Third – “TRUE” or “FALSE” (we will always enter “TRUE” because we’re seeking a cumulative probability)

35. 35 Slide Using Excel to Compute Exponential Probabilities n Formula Worksheet

36. 36 Slide Using Excel to Compute Exponential Probabilities n Value Worksheet

37. 37 Slide Relationship between the Poisson and Exponential Distributions (If) the Poisson distribution provides an appropriate description of the number of occurrences per interval (If) the exponential distribution provides an appropriate description of the length of the interval between occurrences

38. 38 Slide End of Chapter 6