1. Pemodelan Kualitas Proses Kode Matakuliah: I0092 – Statistik Pengendalian Kualitas Pertemuan : 2

2. 2 Learning Objectives

3. 3 Easy to find percentiles of the data; see page 43 Stem-and-Leaf Display

4. 4 Also called a run chart Marginal plot produced by MINITAB Plot of Data in Time Order

5. 5 Group values of the variable into bins, then count the number of observations that fall into each bin Plot frequency (or relative frequency) versus the values of the variable Histograms – Useful for large data sets

6. 6 Histogram for discrete data

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8. 8 Numerical Summary of Data

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11. 11 The Box Plot (or Box-and-Whisker Plot)

12. 12 Comparative Box Plots

13. 13 Probability Distributions

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15. 15 Will see many examples in the text Sometimes called a probability mass function Sometimes called a probability density function

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23. 23 The Hypergeometric Distribution

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25. 25 Basis is in Bernoulli trials The random variable x is the number of successes out of n Bernoulli trials with constant probability of success p on each trial The Binomial Distribution

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29. 29 Frequently used as a model for count data The Poisson Distribution

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31. 31 The random variable x is the number of Bernoulli trials upon which the rth success occurs The Pascal Distribution

32. 32 When r = 1 the Pascal distribution is known as the geometric distribution The geometric distribution has many useful applications in SQC

33. 33 The Normal Distribution

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37. 37 Original normal distribution Standard normal distribution

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40. 40 Practical interpretation – the sum of independent random variables is approximately normally distributed regardless of the distribution of each individual random variable in the sum

41. 41 The Lognormal Distribution

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44. 44 The Exponential Distribution

45. 45 Relationship between the Poisson and exponential distributions

46. 46 The Gamma Distribution

47. 47 The gamma distribution has many applications in reliability engineering; see Example 2-121, text page 71 When r is an integer, the gamma distribution is the result of summing r independently and identically exponential random variables each with parameter λ

48. 48 The Weibull Distribution

49. 49 When β = 1, the Weibull distribution reduces to the exponential distribution

50. 50 Determining if a sample of data might reasonably be assumed to come from a specific distribution Probability plots are available for various distributions Easy to construct with computer software (MINITAB) Subjective interpretation

51. 51 Normal Probability Plot

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53. 53 Other Probability Plots What is a reasonable choice as a probability model for these data?

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