1. Session 8b
Decision Models Prof. Juran

2. Decision Models -- Prof. Juran
Overview
Hypothesis Testing
Review of the Basics
Single Parameter
Differences Between Two Parameters
Independent Samples
Matched Pairs
Goodness of Fit
Simulation Methods
Decision Models Prof. Juran
Decision Models Prof. Juran

3. Basic Hypothesis Testing Method
Formulate Two Hypotheses
Select a Test Statistic
Derive a Decision Rule
Calculate the Value of the Test Statistic; Invoke the Decision Rule in light of the Test Statistic
Decision Models Prof. Juran
Decision Models Prof. Juran

4. Decision Models -- Prof. Juran

5. Hypothesis Testing: Gardening Analogy
Dirt
Rocks
Applied Regression Prof. Juran
Applied Regression Prof. Juran

6. Hypothesis Testing: Gardening Analogy
Applied Regression Prof. Juran
Applied Regression Prof. Juran

7. Hypothesis Testing: Gardening Analogy
Applied Regression Prof. Juran
Applied Regression Prof. Juran

8. Hypothesis Testing: Gardening Analogy
Applied Regression Prof. Juran
Applied Regression Prof. Juran

9. Hypothesis Testing: Gardening Analogy
Screened out stuff:
Correct decision or Type I Error?
Stuff that fell through:
Correct decision or Type II Error?
Applied Regression Prof. Juran
Applied Regression Prof. Juran

10. Decision Models -- Prof. Juran
The p-value of a test is the probability of observing a sample at least as “unlikely” as ours.
In other words, it is the “minimum level” of significance that would allow us to reject H0.
Small p-value = unlikely H0
Decision Models Prof. Juran
Decision Models Prof. Juran

11. Example: Buying a Laundromat
A potential entrepreneur is considering the purchase of a coin-operated laundry. The present owner claims that over the past 5 years the average daily revenue has been $675. The buyer would like to find out if the true average daily revenue is different from $675.
A sample of 30 selected days reveals a daily average revenue of $625 with a standard deviation of $75.
Decision Models Prof. Juran
Decision Models Prof. Juran

12. Decision Models -- Prof. Juran

13. Decision Models -- Prof. Juran
Test Statistic:
Decision Rule, based on alpha of 1%: Reject H0 if the test statistic is greater than or less than
Decision Models Prof. Juran
Decision Models Prof. Juran

14. Decision Models -- Prof. Juran

15. Decision Models -- Prof. Juran
We reject H0. There is sufficiently strong evidence against H0 to reject it at the 0.01 level. We conclude that the true mean is different from $675.
Decision Models Prof. Juran
Decision Models Prof. Juran

16. Example: Reliability Analysis
Decision Models Prof. Juran
Decision Models Prof. Juran

17. Decision Models -- Prof. Juran
The brand manager wants to begin advertising for this product, and would like to claim a mean time between failures (MTBF) of 45 hours.
The product is only in the prototype phase, so the design engineer uses Crystal Ball simulation to estimate the product’s reliability characteristics. Extracted data:
Decision Models Prof. Juran
Decision Models Prof. Juran

18. Decision Models -- Prof. Juran
H0 here is that the product lasts 45 hours (on the average).
There is sufficiently strong evidence against H0 to reject it at any reasonable significance level. We conclude that the true MTBF is less than 45 hours.
Decision Models Prof. Juran
Decision Models Prof. Juran

19. Example: Effects of Sales Campaigns
In order to measure the effect of a storewide sales campaign on nonsale items, the research director of a national supermarket chain took a random sample of 13 pairs of stores that were matched according to average weekly sales volume.
One store of each pair (the experimental group) was exposed to the sales campaign, and the other member of the pair (the control group) was not.
Decision Models Prof. Juran
Decision Models Prof. Juran

20. Decision Models -- Prof. Juran
The following data indicate the results over a weekly period:
Decision Models Prof. Juran
Decision Models Prof. Juran

21. Decision Models -- Prof. Juran
Is the campaign effective?
Basically this is asking: Is there a difference between the average sales from these two populations (with and without the campaign)?
Decision Models Prof. Juran
Decision Models Prof. Juran

22. Decision Models -- Prof. Juran
Two Methods
Independent Samples
General Method
Matched Pairs
Useful Only in Specific Circumstances
More Powerful Statistically
Requires Logical One-to-One Correspondence between Pairs
Decision Models Prof. Juran
Decision Models Prof. Juran

23. Independent Samples Method
Decision Models Prof. Juran
Decision Models Prof. Juran

24. Decision Models -- Prof. Juran
We do not reject the null hypothesis. The campaign made no significant difference in sales.
Decision Models Prof. Juran
Decision Models Prof. Juran

25. Decision Models -- Prof. Juran

26. Decision Models -- Prof. Juran
Matched-Pairs Method
Decision Models Prof. Juran
Decision Models Prof. Juran

27. Decision Models -- Prof. Juran
This time we do reject the null hypothesis, and conclude that the campaign actually did have a significant positive effect on sales.
Decision Models Prof. Juran
Decision Models Prof. Juran

28. Decision Models -- Prof. Juran

29. Decision Models -- Prof. Juran
TSB Problem Revisited
Decision Models Prof. Juran
Decision Models Prof. Juran

30. Decision Models -- Prof. Juran
Independent Samples
Decision Models Prof. Juran
Decision Models Prof. Juran

31. Decision Models -- Prof. Juran

32. Decision Models -- Prof. Juran

33. Decision Models -- Prof. Juran
Matched Pairs
Decision Models Prof. Juran
Decision Models Prof. Juran

34. Decision Models -- Prof. Juran

35. Decision Models -- Prof. Juran

36. Goodness-of-Fit Tests
Determine whether a set of sample data have been drawn from a hypothetical population
Same four basic steps as other hypothesis tests we have learned
An important tool for simulation modeling; used in defining random variable inputs
Decision Models Prof. Juran
Decision Models Prof. Juran

37. Example: Barkevious Mingo
Financial analyst Barkevious Mingo wants to run a simulation model that includes the assumption that the daily volume of a specific type of futures contract traded at U.S. commodities exchanges (represented by the random variable X) is normally distributed with a mean of 152 million contracts and a standard deviation of 32 million contracts. (This assumption is based on the conclusion of a study conducted in 2013.) Barkevious wants to determine whether this assumption is still valid.
Decision Models Prof. Juran
Applied Regression Prof. Juran

38. Decision Models -- Prof. Juran
He studies the trading volume of these contracts for 50 days, and observes the following results (in millions of contracts traded):
Decision Models Prof. Juran
Applied Regression Prof. Juran

39. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

40. Decision Models -- Prof. Juran
Here is a histogram showing the theoretical distribution of 50 observations drawn from a normal distribution with μ = 152 and σ = 32, together with a histogram of Mingo’s sample data:
Decision Models Prof. Juran
Applied Regression Prof. Juran

41. The Chi-Square Statistic
Decision Models Prof. Juran
Applied Regression Prof. Juran

42. Decision Models -- Prof. Juran
Essentially, this statistic allows us to compare the distribution of a sample with some expected distribution, in standardized terms. It is a measure of how much a sample differs from some proposed distribution.
A large value of chi-square suggests that the two distributions are not very similar; a small value suggests that they “fit” each other quite well.
Decision Models Prof. Juran
Applied Regression Prof. Juran

43. Decision Models -- Prof. Juran
Like Student’s t, the distribution of chi-square depends on degrees of freedom.
In the case of chi-square, the number of degrees of freedom is equal to the number of classes (a.k.a. “bins” into which the data have been grouped) minus one, minus the number of estimated parameters.
Decision Models Prof. Juran
Applied Regression Prof. Juran

44. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

45. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

46. Decision Models -- Prof. Juran
Note: It is necessary to have a sufficiently large sample so that each class has an expected frequency of at least 5. We need to make sure that the expected frequency in each bin is at least 5, so we “collapse” some of the bins, as shown here.
Decision Models Prof. Juran
Applied Regression Prof. Juran

47. =CHIINV(alpha, d.f.) = CHIINV(0.05, 3) = 7.815
The number of degrees of freedom is equal to the number of bins minus one, minus the number of estimated parameters. We have not estimated any parameters, so we have d.f. = 4 – 1 – 0 = 3.
The critical chi-square value can be found either by using a chi-square table or by using the Excel function:
=CHIINV(alpha, d.f.) = CHIINV(0.05, 3) = 7.815
We will reject the null hypothesis if the test statistic is greater than
Decision Models Prof. Juran
Applied Regression Prof. Juran

48. Decision Models -- Prof. Juran
Our test statistic is not greater than the critical value; we cannot reject the null hypothesis at the 0.05 level of significance.
It would appear that Barkevious is justified in using the normal distribution with μ = 152 and σ = 32 to model futures contract trading volume in his simulation.
Decision Models Prof. Juran
Applied Regression Prof. Juran

49. In this case, we calculate the p-value using the Excel function:
The p-value of this test has the same interpretation as in any other hypothesis test, namely that it is the smallest level of alpha at which H0 could be rejected.
In this case, we calculate the p-value using the Excel function:
= CHIDIST(test stat, d.f.) = CHIDIST(7.439,3) =
Decision Models Prof. Juran
Applied Regression Prof. Juran

50. Example: Catalog Company
If we want to simulate the queueing system at this company, what distributions should we use for the arrival and service processes?
Decision Models Prof. Juran
Applied Regression Prof. Juran

51. Decision Models -- Prof. Juran
Arrivals
Decision Models Prof. Juran
Applied Regression Prof. Juran

52. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

53. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

54. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

55. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

56. Decision Models -- Prof. Juran
Services
Decision Models Prof. Juran
Applied Regression Prof. Juran

57. Decision Models -- Prof. Juran
Applied Regression Prof. Juran

58. Decision Models -- Prof. Juran

59. Decision Models -- Prof. Juran

60. Decision Models -- Prof. Juran

61. Decision Models -- Prof. Juran

62. Decision Models -- Prof. Juran

63. Other uses for the Chi-Square statistic
The chi-square technique can often be employed for purposes of estimation or hypothesis testing when the z or t statistics are not appropriate. In addition to the goodness-of-fit application described above, there are at least three other important uses for chi-square:
Tests of the independence of two qualitative population variables.
Tests of the equality or inequality of more than two population proportions.
Inferences about a population variance, including the estimation of a confidence interval for a population variance from sample data.
Decision Models Prof. Juran
Applied Regression Prof. Juran

64. Decision Models -- Prof. Juran
Summary
Hypothesis Testing
Review of the Basics
Single Parameter
Differences Between Two Parameters
Independent Samples
Matched Pairs
Goodness of Fit
Simulation Methods
Decision Models Prof. Juran
Decision Models Prof. Juran