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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_variability.wasp
Title produced by softwareVariability
Date of computationThu, 13 Dec 2012 05:13:03 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/13/t1355393596gtog0iazwbm5wv4.htm/, Retrieved Mon, 29 Apr 2024 03:52:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199129, Retrieved Mon, 29 Apr 2024 03:52:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Variability] [] [2012-12-13 10:13:03] [da7c95f16c64cb676a9e51b1b6b79fc0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
39
35
35
32
NA
49
51
34
38
37
39
42
38
21
32
49
76
49
49
37
50
31
49
44
42
42
50
26
79
46
61
43
68
50
41
32
32
44
49
32
41
33
12
32
32
46
78
49
41
113
NA
28
33
NA
20
31
33
8
12
83
37
80
52
21
25
41
43
53
NA
NA
12
32
33
NA
37
27
29
67
NA
NA
18
54
36
67
51
25
27
29
30
30
34
34
71
NA
57
50
NA
21
23
23
30
41
50
53
53
54
54
55
56
56
65
67
68
72
77
78
78
NA
NA
NA
NA
NA
NA
NA
NA
113
113
18
2
8
8
29
50
10
72
16
35
21
24
34
44
16
18
18
24
26
28
28
40
41
52
52
53
53
53
53
53
53
54
54
55
57
58
58
59
59
60
61
65
65
68
70
76
NA
NA
NA
NA
NA
NA
NA
NA
NA
29
59
NA
NA
NA
NA
NA
NA
10
12
12
14
17
17
17
17
20
22
23
23
23
23
23
23
23
24
24
25
25
25
26
26
27
27
27
27
28
29
30
30
31
32
32
32
32
32
32
33
33
33
34
36
36
37
37
38
38
38
39
40
40
40
41
41
42
43
43
44
44
44
45
45
46
46
47
47
48
48
48
49
51
53
53
53
54
54
55
55
56
57
57
57
58
58
59
59
61
63
63
64
64
65
66
66
67
68
68
72
73
74
75
NA
NA
NA
NA
NA
NA
NA
NA
5
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
8
8
8
9
9
9
9
9
9
10
10
10
11
11
11
11
11
11
11
11
11
11
12
12
12
12
12
12
12
13
13
13
13
13
14
15
15
15
15
15
15
15
15
16
16
16
16
16
16
16
16
16
16
16
16
16
17
17
17
17
17
17
17
17
17
18
18
18
18
18
19
19
19
20
20
21
21
22
22
22
22
22
23
23
23
23
23
24
24
24
24
24
25
25
25
26
26
26
26
26
27
27
28
29
29
29
29
29
30
30
30
30
31
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
33
33
33
33
33
33
34
34
34
35
35
35
35
36
36
36
37
37
37
37
37
37
37
37
37
38
38
38
38
39
40
41
41
42
42
42
44
44
45
46
46
46
47
47
47
48
48
48
48
49
49
49
50
50
50
50
50
50
50
50
50
51
51
51
51
52
53
53
53
54
54
55
55
55
56
56
56
56
56
57
57
58
58
58
59
60
60
60
60
60
60
61
61
62
62
63
63
63
63
64
64
65
65
65
66
67
67
68
68
68
68
68
68
69
69
69
70
70
70
71
72
73
74
74
77
80
80
81
82
83
84
113
113
113
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
8
9
10
11
12
16
17
31
39
42
42
42
43
43
44
48
50
53
67
68
69
69
71
77
113
NA
NA
NA
NA
10
14
14
17
17
22
23
28
29
36
37
40
41
42
43
44
45
45
47
47
47
48
50
10
12
12
17
17
19
20
23
24
24
24
25
25
30
38
39
40
40
40
42
43
43
44
45
46
46
48
48
50
51
51
52
52
52
54
54
55
56
57
60
62
63
63
67
70
71
76
82
NA
16
17
17
17
24
25
27
29
32
32
32
34
37
41
41
45
52
54
55
58
79
NA
NA
8
9
11
14
14
14
16
16
17
17
17
17
20
20
23
23
24
24
27
27
27
28
28
29
30
30
30
30
31
33
33
33
34
34
35
36
38
39
40
40
40
41
41
42
42
45
46
46
46
46
46
46
47
48
49
49
50
50
51
51
52
52
54
54
54
56
57
59
60
61
67
70
73
76
77
77
82
NA
NA
7
9
11
12
12
13
13
13
14
15
15
16
17
18
20
20
21
21
22
22
23
23
24
25
26
26
28
28
28
28
29
29
31
31
31
32
32
32
32
33
33
34
34
34
35
35
35
35
36
37
37
37
37
37
38
39
39
39
40
41
41
41
41
41
42
42
43
44
44
45
45
46
47
47
47
47
50
50
50
50
50
50
50
51
51
51
52
52
53
54
54
55
55
56
57
57
58
58
58
58
58
59
59
59
59
59
60
61
61
62
62
63
64
65
65
65
66
67
67
68
68
68
68
70
70
70
71
72
72
72
73
73
74
74
75
75
77
78
85
NA
NA
NA
NA
NA
12
12
15
16
20
20
22
23
25
27
27
31
36
40
40
43
43
44
44
44
45
46
48
50
51
52
56
57
66
67
7
11
17
18
18
20
21
22
23
24
25
25
25
28
28
34
34
34
36
36
36
37
39
44
46
48
48
49
50
51
51
51
52
54
54
56
56
57
57
58
62
63
63
64
66
66
66
67
67
68
72
74
84
NA
NA
NA
NA
12
15
16
17
20
21
26
28
29
35
35
37
39
39
43
46
48
48
49
50
52
53
56
56
58
58
60
61
65
68
74
77
81
NA
NA
12
15
15
16
16
16
17
18
21
21
22
22
27
29
32
39
39
44
45
46
47
48
48
48
49
51
53
56
58
59
66
68
71
113
NA
NA
NA
NA
NA
6
8
9
12
13
13
14
14
14
14
14
16
16
17
18
18
19
19
19
20
21
21
21
22
22
23
24
25
25
25
26
27
27
28
28
28
29
30
31
31
32
33
34
35
36
37
39
39
40
40
40
41
42
42
43
46
47
48
48
48
48
49
49
49
50
50
50
51
52
53
53
53
54
54
54
55
55
56
57
58
60
62
63
64
64
67
67
70
82
83
84
NA
NA
NA
10
12
13
13
13
15
16
16
17
17
17
20
21
21
21
23
23
23
23
24
25
25
26
27
27
28
28
28
29
29
29
30
30
30
31
31
32
32
33
33
33
33
34
34
35
35
36
36
36
37
37
37
38
38
38
39
39
39
40
41
41
42
42
42
43
43
44
44
44
44
45
45
45
45
45
47
47
47
47
47
47
48
48
48
49
50
50
51
52
52
52
52
53
53
54
54
55
55
55
55
56
57
57
60
60
62
63
63
64
64
65
65
67
68
68
68
69
69
70
71
74
75
81
113
113
113
NA
14
16
29
29
39
39
69
29
34
45
59
NA
56
59
46
51
26
58
66
20
20
26
33
52
54
57
66
67
NA
23
31
61
67
NA
32
26
44
45
52
52
52
53
61
NA
10
19
21
40
45
47
51
57
53
34
NA
9
14
18
31
35
52
54
56
58
NA
23
27
28
31
39
53
42
55
54
NA
28
43
68
15
24
26
36
45
73
38
53
61
29
16
48
NA
30
31
44
58
38
31
16
16
26
28
45
47
51
54
55
56
60
10
28
31
60
66
61
62
72
72
28
43
NA
27
68
68
NA
NA
13
14
23
43
47
49
53
53
54
14
20
22
24
28
32
35
36
36
44
44
45
45
45
48
49
50
50
52
54
58
59
71
15
15
16
20
23
24
26
35
35
36
36
36
38
38
39
41
45
46
47
48
48
48
54
58
60
66
76
78
NA
12
13
14
15
16
18
18
18
20
21
21
23
25
30
33
37
39
43
46
52
52
59
59
64
69
74
11
46
49
52
NA
9
10
13
15
17
18
18
19
20
21
22
23
24
24
25
28
32
33
36
37
45
45
50
54
54
63
66
66
67
14
17
18
19
21
21
21
23
23
24
32
33
38
40
42
47
47
48
52
53
59
70
10
11
12
13
13
13
14
14
14
15
16
16
16
16
17
18
18
18
18
18
18
18
19
19
20
21
21
22
23
23
24
24
25
25
26
26
27
28
29
29
30
30
30
31
32
33
33
33
33
33
36
36
36
37
40
41
42
42
43
44
45
45
45
45
45
46
49
49
49
50
52
52
52
53
53
54
54
55
58
59
59
60
62
63
63
66
68
69
69
70
72
79
NA
27
28
37
39
40
14
18
25
32
38
46
51
51
52
54
64
42
44
55
25
32
50
8
9
9
9
10
11
12
14
15
15
16
16
16
17
17
17
17
17
18
18
18
19
19
19
19
19
19
19
20
20
20
20
21
21
21
21
22
23
23
24
24
25
25
25
26
26
26
27
28
28
29
29
29
30
31
31
31
32
32
34
34
35
35
36
36
37
37
37
38
41
42
43
43
43
46
47
47
47
48
49
49
50
50
51
52
52
52
52
53
53
53
53
54
54
54
54
54
54
55
55
56
57
58
62
64
64
65
65
71
72
73
74
75
76
76
77
83
113
113
NA
NA
NA
NA
NA
NA
NA
NA
9
11
11
12
12
13
13
13
14
14
14
14
14
14
15
15
15
15
15
16
16
16
16
16
16
16
16
16
16
16
17
17
17
17
17
17
17
17
17
18
18
18
18
18
18
18
19
19
19
19
19
19
19
19
20
20
20
20
20
21
21
21
21
21
21
21
21
21
21
21
22
22
22
22
22
22
22
22
22
22
23
23
23
23
23
24
24
24
24
24
24
24
24
24
24
25
25
25
25
26
26
26
26
26
26
26
26
26
27
27
27
27
27
27
27
27
28
28
28
28
28
28
28
28
28
29
29
29
29
29
29
29
29
29
29
29
30
30
30
30
30
31
31
31
31
31
31
32
32
32
32
32
32
32
32
32
33
33
33
33
33
33
33
34
34
34
34
34
34
34
34
34
35
35
35
35
35
35
36
36
37
37
37
37
37
37
37
37
37
38
38
38
38
38
38
38
38
38
39
39
39
39
39
40
40
40
40
40
41
41
41
41
41
41
41
41
41
42
42
42
42
42
42
42
42
42
42
43
43
43
43
43
43
43
44
44
44
44
44
44
44
44
44
44
45
45
45
45
45
46
46
46
46
46
46
46
46
46
46
46
47
47
47
47
47
47
47
48
48
48
48
49
49
49
50
50
50
50
50
50
50
50
51
51
51
51
52
52
52
52
52
52
52
53
53
53
53
53
53
53
53
53
54
54
54
55
55
55
55
55
55
55
55
56
56
56
56
57
57
57
58
58
58
59
59
59
59
59
59
59
59
60
60
61
61
62
62
62
62
62
63
64
64
64
64
65
65
65
65
66
66
66
66
66
67
67
67
67
67
68
68
68
68
68
69
69
70
71
71
72
72
72
72
73
73
74
75
75
76
76
76
76
77
78
78
79
80
80
83
86
113
113
113
113
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
9
9
10
11
11
14
14
15
17
17
17
18
18
18
20
20
20
20
20
20
22
24
25
25
25
25
26
26
27
27
27
27
28
28
29
29
29
30
31
31
31
33
34
34
35
35
35
36
36
36
36
37
38
38
38
39
39
39
40
41
41
41
42
42
42
42
43
43
43
43
44
45
46
46
46
47
47
47
47
48
48
48
49
49
49
52
52
55
55
55
55
59
60
62
63
63
64
65
65
65
65
67
67
67
73
77
79
82
NA
NA
NA
NA
NA
NA
4
8
16
17
17
17
19
19
24
24
24
27
29
31
31
31
32
33
33
36
36
38
38
40
40
41
42
42
42
44
44
45
45
45
46
46
46
46
48
48
48
48
49
51
52
53
55
56
57
58
58
58
59
59
59
62
62
63
64
70
6
8
9
11
13
13
15
16
16
17
19
19
19
20
20
20
21
21
21
22
22
22
23
24
26
26
27
27
27
28
28
28
29
29
29
30
30
30
32
33
33
35
35
35
36
36
37
37
38
38
38
38
38
40
41
41
43
43
43
43
44
44
44
44
45
45
45
47
47
47
47
49
49
49
49
49
49
52
52
52
54
54
54
55
55
55
57
57
57
58
58
59
59
60
61
61
61
61
65
65
65
66
67
68
68
69
69
76
77
77
113
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
7
12
13
13
14
14
14
15
15
16
16
17
17
17
17
17
18
18
18
18
19
20
21
22
22
23
23
23
26
26
26
29
29
30
30
30
30
31
32
32
34
35
35
36
37
39
39
40
40
42
42
43
44
44
44
44
44
45
45
45
46
46
46
46
47
47
47
47
47
48
48
48
48
48
49
49
52
53
53
53
53
56
56
59
61
62
64
65
65
65
68
71
72
72
73
75
75
76
79
80
NA
NA
6
6
9
13
13
16
16
16
16
17
17
17
18
18
18
19
19
19
21
22
22
22
23
24
24
24
24
24
24
24
24
25
25
25
25
26
26
26
26
27
29
29
29
31
32
33
33
34
34
34
34
35
37
38
38
39
40
41
41
42
42
43
43
44
44
45
46
46
47
49
49
51
51
51
52
53
54
54
54
54
55
55
56
56
56
57
58
58
59
59
59
59
59
60
60
61
61
62
62
64
64
65
66
67
67
69
71
71
75
113
NA
NA
NA
NA
7
8
8
8
8
8
9
9
10
10
10
10
10
10
11
11
11
11
11
12
12
12
13
13
13
13
13
13
13
14
14
15
15
15
15
15
15
16
16
16
16
17
17
17
17
17
17
17
17
17
17
18
18
18
18
18
18
18
18
19
19
19
19
19
19
20
20
20
20
21
21
21
21
22
22
22
22
23
23
23
23
23
23
23
23
24
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
26
26
26
26
26
26
26
27
27
27
27
27
27
27
27
27
27
28
28
28
28
28
29
29
29
29
29
29
29
30
30
30
30
30
30
30
30
30
30
30
30
30
31
31
31
31
32
32
32
32
32
32
32
32
33
33
33
33
33
33
33
33
33
34
34
34
34
34
35
35
35
35
35
35
35
35
36
36
36
36
36
36
36
36
36
36
36
37
37
37
37
37
37
37
38
38
38
38
38
38
38
38
38
39
39
39
39
39
39
39
40
40
40
40
40
40
40
40
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
44
44
44
44
44
44
44
44
44
44
44
44
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
47
47
47
47
47
47
48
48
48
48
48
48
48
48
48
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
50
50
50
50
50
50
50
50
50
50
51
51
51
51
51
51
51
52
52
52
52
52
52
52
53
53
53
53
53
53
53
53
53
54
54
54
55
55
55
55
55
55
55
56
56
56
56
56
56
57
57
58
58
58
58
58
58
59
59
59
59
59
59
60
60
60
60
60
60
61
61
61
62
63
63
63
64
64
64
64
64
65
65
65
65
65
65
66
66
66
66
66
67
67
67
67
67
67
68
68
68
69
69
69
70
70
70
70
70
70
70
73
73
74
75
75
77
77
77
77
77
78
79
79
79
79
80
80
80
81
82
82
113
NA
NA
NA
NA
NA
NA
NA
NA
NA
36
15
24
32
38
38
43
47
47
47
47
48
50
50
62
68
68
74
NA
25
57
21
28
32
39
46
66
NA
7
9
10
12
13
13
18
19
22
23
31
32
35
39
41
47
48
50
56
57
86
NA
NA
NA
NA
NA
8
8
12
12
13
16
17
18
18
18
18
18
19
20
22
24
24
24
29
30
31
32
33
34
35
36
37
37
37
37
38
42
44
46
48
51
64
64
65
66
66
68
77
79
113
NA
NA
9
12
12
14
16
17
17
18
19
19
19
20
21
23
23
24
24
24
26
26
28
29
31
32
33
35
37
38
38
42
42
42
45
47
47
50
52
52
55
56
57
58
60
64
65
66
69
7
12
12
14
20
22
23
24
29
29
29
31
34
34
35
38
38
38
38
39
40
40
41
41
42
44
51
51
55
58
NA
7
14
17
39
41
54
5
17
17
21
22
22
22
23
23
27
29
30
35
36
36
41
42
43
44
46
47
48
50
50
53
59
68
NA
NA
8
19
24
25
25
26
29
31
31
32
35
35
37
37
37
38
39
40
40
43
45
47
48
48
49
53
53
57
58
58
66
67
67
76
77
NA
11
11
11
15
18
21
22
27
28
30
31
37
40
42
42
44
44
48
51
55
77
8
15
15
16
17
17
17
18
18
19
21
21
23
23
25
27
29
29
29
30
31
31
32
32
32
33
33
35
37
38
39
40
42
43
43
43
44
45
45
45
46
46
47
47
49
50
51
53
53
55
56
56
57
57
57
57
57
58
59
60
61
68
68
68
113
113
113
NA
NA
NA
NA
10
16
19
20
21
24
24
26
33
34
36
37
40
46
48
50
54
57
NA
12
13
17
33
34
36
37
38
41
42
43
43
43
43
43
45
46
46
48
51
62
63
NA
NA
NA
NA
NA
9
13
13
13
15
15
15
16
16
16
17
18
18
18
19
19
19
20
20
21
21
21
21
21
21
21
21
22
22
22
23
23
23
23
23
23
24
24
24
24
24
25
25
25
26
26
26
26
26
26
27
27
27
27
27
28
28
28
28
30
31
31
32
32
32
33
33
34
35
35
35
35
36
36
38
39
39
39
40
40
40
40
41
42
42
43
44
44
45
46
46
46
47
47
47
48
50
50
50
50
51
52
53
53
55
55
56
56
56
58
59
63
63
63
64
64
68
70
71
72
73
77
81
113
113
113
NA
NA
NA
8
8
12
13
13
14
15
15
16
17
17
17
17
18
18
18
18
20
22
22
22
24
24
24
24
24
26
27
27
28
29
29
29
31
32
33
33
34
34
35
36
36
36
37
38
39
39
41
41
42
42
43
43
43
44
45
46
46
46
46
46
47
48
48
48
50
50
50
50
53
53
55
55
55
59
59
60
61
64
65
68
71
74
6
7
9
10
10
10
14
15
15
15
15
17
18
18
18
18
19
19
19
19
21
21
23
24
24
25
25
26
27
27
28
28
28
30
34
37
38
39
39
40
41
41
41
43
44
44
46
46
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time120 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 120 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199129&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]120 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199129&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199129&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time120 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
num <- 50
res <- array(NA,dim=c(num,3))
q1 <- function(data,n,p,i,f) {
np <- n*p;
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q2 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q3 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
q4 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- (data[i]+data[i+1])/2
} else {
qvalue <- data[i+1]
}
}
q5 <- function(data,n,p,i,f) {
np <- (n-1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i+1]
} else {
qvalue <- data[i+1] + f*(data[i+2]-data[i+1])
}
}
q6 <- function(data,n,p,i,f) {
np <- n*p+0.5
i <<- floor(np)
f <<- np - i
qvalue <- data[i]
}
q7 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- f*data[i] + (1-f)*data[i+1]
}
}
q8 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
if (f == 0.5) {
qvalue <- (data[i]+data[i+1])/2
} else {
if (f < 0.5) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
}
}
iqd <- function(x,def) {
x <-sort(x[!is.na(x)])
n<-length(x)
if (def==1) {
qvalue1 <- q1(x,n,0.25,i,f)
qvalue3 <- q1(x,n,0.75,i,f)
}
if (def==2) {
qvalue1 <- q2(x,n,0.25,i,f)
qvalue3 <- q2(x,n,0.75,i,f)
}
if (def==3) {
qvalue1 <- q3(x,n,0.25,i,f)
qvalue3 <- q3(x,n,0.75,i,f)
}
if (def==4) {
qvalue1 <- q4(x,n,0.25,i,f)
qvalue3 <- q4(x,n,0.75,i,f)
}
if (def==5) {
qvalue1 <- q5(x,n,0.25,i,f)
qvalue3 <- q5(x,n,0.75,i,f)
}
if (def==6) {
qvalue1 <- q6(x,n,0.25,i,f)
qvalue3 <- q6(x,n,0.75,i,f)
}
if (def==7) {
qvalue1 <- q7(x,n,0.25,i,f)
qvalue3 <- q7(x,n,0.75,i,f)
}
if (def==8) {
qvalue1 <- q8(x,n,0.25,i,f)
qvalue3 <- q8(x,n,0.75,i,f)
}
iqdiff <- qvalue3 - qvalue1
return(c(iqdiff,iqdiff/2,iqdiff/(qvalue3 + qvalue1)))
}
range <- max(x) - min(x)
lx <- length(x)
biasf <- (lx-1)/lx
varx <- var(x)
bvarx <- varx*biasf
sdx <- sqrt(varx)
mx <- mean(x)
bsdx <- sqrt(bvarx)
x2 <- x*x
mse0 <- sum(x2)/lx
xmm <- x-mx
xmm2 <- xmm*xmm
msem <- sum(xmm2)/lx
axmm <- abs(x - mx)
medx <- median(x)
axmmed <- abs(x - medx)
xmmed <- x - medx
xmmed2 <- xmmed*xmmed
msemed <- sum(xmmed2)/lx
qarr <- array(NA,dim=c(8,3))
for (j in 1:8) {
qarr[j,] <- iqd(x,j)
}
sdpo <- 0
adpo <- 0
for (i in 1:(lx-1)) {
for (j in (i+1):lx) {
ldi <- x[i]-x[j]
aldi <- abs(ldi)
sdpo = sdpo + ldi * ldi
adpo = adpo + aldi
}
}
denom <- (lx*(lx-1)/2)
sdpo = sdpo / denom
adpo = adpo / denom
gmd <- 0
for (i in 1:lx) {
for (j in 1:lx) {
ldi <- abs(x[i]-x[j])
gmd = gmd + ldi
}
}
gmd <- gmd / (lx*(lx-1))
sumx <- sum(x)
pk <- x / sumx
ck <- cumsum(pk)
dk <- array(NA,dim=lx)
for (i in 1:lx) {
if (ck[i] <= 0.5) dk[i] <- ck[i] else dk[i] <- 1 - ck[i]
}
bigd <- sum(dk) * 2 / (lx-1)
iod <- 1 - sum(pk*pk)
res[1,] <- c('Absolute range','absolute.htm', range)
res[2,] <- c('Relative range (unbiased)','relative.htm', range/sd(x))
res[3,] <- c('Relative range (biased)','relative.htm', range/sqrt(varx*biasf))
res[4,] <- c('Variance (unbiased)','unbiased.htm', varx)
res[5,] <- c('Variance (biased)','biased.htm', bvarx)
res[6,] <- c('Standard Deviation (unbiased)','unbiased1.htm', sdx)
res[7,] <- c('Standard Deviation (biased)','biased1.htm', bsdx)
res[8,] <- c('Coefficient of Variation (unbiased)','variation.htm', sdx/mx)
res[9,] <- c('Coefficient of Variation (biased)','variation.htm', bsdx/mx)
res[10,] <- c('Mean Squared Error (MSE versus 0)','mse.htm', mse0)
res[11,] <- c('Mean Squared Error (MSE versus Mean)','mse.htm', msem)
res[12,] <- c('Mean Absolute Deviation from Mean (MAD Mean)', 'mean2.htm', sum(axmm)/lx)
res[13,] <- c('Mean Absolute Deviation from Median (MAD Median)', 'median1.htm', sum(axmmed)/lx)
res[14,] <- c('Median Absolute Deviation from Mean', 'mean3.htm', median(axmm))
res[15,] <- c('Median Absolute Deviation from Median', 'median2.htm', median(axmmed))
res[16,] <- c('Mean Squared Deviation from Mean', 'mean1.htm', msem)
res[17,] <- c('Mean Squared Deviation from Median', 'median.htm', msemed)
load(file='createtable')
mylink1 <- hyperlink('difference.htm','Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[18,] <- c('', mylink2, qarr[1,1])
mylink2 <- paste(mylink1,hyperlink('method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[19,] <- c('', mylink2, qarr[2,1])
mylink2 <- paste(mylink1,hyperlink('method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[20,] <- c('', mylink2, qarr[3,1])
mylink2 <- paste(mylink1,hyperlink('method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[21,] <- c('', mylink2, qarr[4,1])
mylink2 <- paste(mylink1,hyperlink('method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[22,] <- c('', mylink2, qarr[5,1])
mylink2 <- paste(mylink1,hyperlink('method_6.htm','(Closest Observation)',''),sep=' ')
res[23,] <- c('', mylink2, qarr[6,1])
mylink2 <- paste(mylink1,hyperlink('method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[24,] <- c('', mylink2, qarr[7,1])
mylink2 <- paste(mylink1,hyperlink('method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[25,] <- c('', mylink2, qarr[8,1])
mylink1 <- hyperlink('deviation.htm','Semi Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[26,] <- c('', mylink2, qarr[1,2])
mylink2 <- paste(mylink1,hyperlink('method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[27,] <- c('', mylink2, qarr[2,2])
mylink2 <- paste(mylink1,hyperlink('method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[28,] <- c('', mylink2, qarr[3,2])
mylink2 <- paste(mylink1,hyperlink('method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[29,] <- c('', mylink2, qarr[4,2])
mylink2 <- paste(mylink1,hyperlink('method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[30,] <- c('', mylink2, qarr[5,2])
mylink2 <- paste(mylink1,hyperlink('method_6.htm','(Closest Observation)',''),sep=' ')
res[31,] <- c('', mylink2, qarr[6,2])
mylink2 <- paste(mylink1,hyperlink('method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[32,] <- c('', mylink2, qarr[7,2])
mylink2 <- paste(mylink1,hyperlink('method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[33,] <- c('', mylink2, qarr[8,2])
mylink1 <- hyperlink('variation1.htm','Coefficient of Quartile Variation','')
mylink2 <- paste(mylink1,hyperlink('method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[34,] <- c('', mylink2, qarr[1,3])
mylink2 <- paste(mylink1,hyperlink('method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[35,] <- c('', mylink2, qarr[2,3])
mylink2 <- paste(mylink1,hyperlink('method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[36,] <- c('', mylink2, qarr[3,3])
mylink2 <- paste(mylink1,hyperlink('method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[37,] <- c('', mylink2, qarr[4,3])
mylink2 <- paste(mylink1,hyperlink('method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[38,] <- c('', mylink2, qarr[5,3])
mylink2 <- paste(mylink1,hyperlink('method_6.htm','(Closest Observation)',''),sep=' ')
res[39,] <- c('', mylink2, qarr[6,3])
mylink2 <- paste(mylink1,hyperlink('method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[40,] <- c('', mylink2, qarr[7,3])
mylink2 <- paste(mylink1,hyperlink('method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[41,] <- c('', mylink2, qarr[8,3])
res[42,] <- c('Number of all Pairs of Observations', 'pair_numbers.htm', lx*(lx-1)/2)
res[43,] <- c('Squared Differences between all Pairs of Observations', 'squared_differences.htm', sdpo)
res[44,] <- c('Mean Absolute Differences between all Pairs of Observations', 'mean_abs_differences.htm', adpo)
res[45,] <- c('Gini Mean Difference', 'gini_mean_difference.htm', gmd)
res[46,] <- c('Leik Measure of Dispersion', 'leiks_d.htm', bigd)
res[47,] <- c('Index of Diversity', 'diversity.htm', iod)
res[48,] <- c('Index of Qualitative Variation', 'qualitative_variation.htm', iod*lx/(lx-1))
res[49,] <- c('Coefficient of Dispersion', 'dispersion.htm', sum(axmm)/lx/medx)
res[50,] <- c('Observations', '', lx)
res
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variability - Ungrouped Data',2,TRUE)
a<-table.row.end(a)
for (i in 1:num) {
a<-table.row.start(a)
if (res[i,1] != '') {
a<-table.element(a,hyperlink(res[i,2],res[i,1],''),header=TRUE)
} else {
a<-table.element(a,res[i,2],header=TRUE)
}
a<-table.element(a,res[i,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')