Q3.  Using Data set 2 about the process times taken for the last xxxxx parts (from data part n1 to part n2). -         n1= 6. , n2= 76 -          Draw a histogram of your data and comment on if it looks like to be close to Normal distribution -          Assume it follows normal distribution, calculate its parameters and draw Quantile-Quantile plot and comment about the plot. -          Draw scatter diagram (XY plot in excel) between two successive data (X axis is Xi is, Y axis is Xi+1) and comment about the dependency of successive data points. -          Calculate the correlation between two successive data (Xi is one column, Xi+1 is the other column of data) and comment about the dependency of successive data points. (You can use the correlation function of excel, or formulae given our classes.) -          Apply Chi-square test to check the goodness-of-fit using the significance level = 0.05. What is the p-value of your test? (You will need to use Norm.inv function of excel to answer this question)

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  Q3.  Using Data set 2 about the process times taken for the last xxxxx parts (from data part n1 to part n2). -         n1= 6. , n2= 76 -          Draw a histogram of your data and comment on if it looks like to be close to Normal distribution -          Assume it follows normal distribution, calculate its parameters and draw Quantile-Quantile plot and comment about the plot. -          Draw scatter diagram (XY plot in excel) between two successive data (X axis is Xi is, Y axis is Xi+1) and comment about the dependency of successive data points. -          Calculate the correlation between two successive data (Xi is one column, Xi+1 is the other column of data) and comment about the dependency of successive data points. (You can use the correlation function of excel, or formulae given our classes.) -          Apply Chi-square test to check the goodness-of-fit using the significance level = 0.05. What is the p-value of your test? (You will need to use Norm.inv function of excel to answer this question)
Data set 2
Part No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Process times
(min)
3.64
4.68
3.64
3.12
4.16
3.12
3.64
5.2
4.16
4.16
4.68
4.68
5.2
3.64
4.16
3.64
4.68
2.6
3.12
3.12
5.2
4.68
5.2
2.6
3.12
4.68
5.2
4.68
3.64
3.64
5.2
2.6
3.12
3.64
4.16
2.6
4.68
4.68
4.68
3.12
5.2
4.16
3.12
5.2
3.12
3.12
5.2
5.2
3.64
5.2
Part No
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Process
times (min)
2.6
4.68
4.16
3.64
3.64
5.2
4.68
3.64
4.16
3.64
4.68
4.68
4.16
4.16
5.2
3.64
2.6
5.2
3.12
3.12
2.6
4.68
3.64
5.2
2.6
3.64
3.64
4.68
5.2
4.68
3.64
2.6
5.2
3.64
3.12
3.64
3.64
3.12
4.68
5.2
4.16
2.6
4.68
4.68
4.68
4.16
5.2
3.64
4.68
5.2
Transcribed Image Text:Data set 2 Part No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Process times (min) 3.64 4.68 3.64 3.12 4.16 3.12 3.64 5.2 4.16 4.16 4.68 4.68 5.2 3.64 4.16 3.64 4.68 2.6 3.12 3.12 5.2 4.68 5.2 2.6 3.12 4.68 5.2 4.68 3.64 3.64 5.2 2.6 3.12 3.64 4.16 2.6 4.68 4.68 4.68 3.12 5.2 4.16 3.12 5.2 3.12 3.12 5.2 5.2 3.64 5.2 Part No 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 Process times (min) 2.6 4.68 4.16 3.64 3.64 5.2 4.68 3.64 4.16 3.64 4.68 4.68 4.16 4.16 5.2 3.64 2.6 5.2 3.12 3.12 2.6 4.68 3.64 5.2 2.6 3.64 3.64 4.68 5.2 4.68 3.64 2.6 5.2 3.64 3.12 3.64 3.64 3.12 4.68 5.2 4.16 2.6 4.68 4.68 4.68 4.16 5.2 3.64 4.68 5.2
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