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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationThu, 11 Dec 2014 10:27:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/11/t14182937214a0ec2n1smjxbds.htm/, Retrieved Thu, 16 May 2024 15:10:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265680, Retrieved Thu, 16 May 2024 15:10:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [pearson correlati...] [2014-12-11 10:27:55] [d71ad52285d92a63edfc83f9fb1da7a1] [Current]
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Dataseries X:
12.9 0 0 21 26 50 21 149 18 68
7.4 0 0 23 51 68 26 152 7 55
12.2 0 1 22 57 62 22 139 31 39
12.8 0 0 21 37 54 22 148 39 32
7.4 0 1 21 67 71 18 158 46 62
6.7 0 1 21 43 54 23 128 31 33
12.6 0 1 21 52 65 12 224 67 52
14.8 0 0 21 52 73 20 159 35 62
13.3 0 1 23 43 52 22 105 52 77
11.1 0 1 22 84 84 21 159 77 76
8.2 0 1 25 67 42 19 167 37 41
11.4 0 1 21 49 66 22 165 32 48
6.4 0 1 23 70 65 15 159 36 63
10.6 0 1 22 52 78 20 119 38 30
12 0 0 21 58 73 19 176 69 78
6.3 0 0 21 68 75 18 54 21 19
11.3 1 0 25 62 72 15 91 26 31
11.9 0 1 21 43 66 20 163 54 66
9.3 0 0 21 56 70 21 124 36 35
9.6 1 1 20 56 61 21 137 42 42
10 0 0 24 74 81 15 121 23 45
6.4 0 1 23 65 71 16 153 34 21
13.8 0 1 21 63 69 23 148 112 25
10.8 0 0 24 58 71 21 221 35 44
13.8 0 1 23 57 72 18 188 47 69
11.7 0 1 21 63 68 25 149 47 54
10.9 0 1 22 53 70 9 244 37 74
16.1 1 1 20 57 68 30 148 109 80
13.4 1 0 18 51 61 20 92 24 42
9.9 0 1 21 64 67 23 150 20 61
11.5 0 0 22 53 76 16 153 22 41
8.3 0 0 22 29 70 16 94 23 46
11.7 0 0 21 54 60 19 156 32 39
6.1 0 1 23 51 77 25 146 7 63
9 0 1 21 58 72 25 132 30 34
9.7 0 1 25 43 69 18 161 92 51
10.8 0 1 22 51 71 23 105 43 42
10.3 0 1 22 53 62 21 97 55 31
10.4 0 0 20 54 70 10 151 16 39
12.7 1 1 21 56 64 14 131 49 20
9.3 0 1 21 61 58 22 166 71 49
11.8 0 0 21 47 76 26 157 43 53
5.9 0 1 22 39 52 23 111 29 31
11.4 0 1 21 48 59 23 145 56 39
13 0 1 24 50 68 24 162 46 54
10.8 0 1 22 35 76 24 163 19 49
12.3 1 1 22 30 65 18 59 23 34
11.3 0 0 21 68 67 23 187 59 46
11.8 0 1 22 49 59 15 109 30 55
7.9 1 1 19 61 69 19 90 61 42
12.7 0 0 22 67 76 16 105 7 50
12.3 1 1 23 47 63 25 83 38 13
11.6 1 1 20 56 75 23 116 32 37
6.7 1 1 20 50 63 17 42 16 25
10.9 0 1 23 43 60 19 148 19 30
12.1 1 1 20 67 73 21 155 22 28
13.3 0 1 23 62 63 18 125 48 45
10.1 0 1 21 57 70 27 116 23 35
5.7 1 0 22 41 75 21 128 26 28
14.3 0 1 21 54 66 13 138 33 41
8 1 0 21 45 63 8 49 9 6
13.3 1 1 19 48 63 29 96 24 45
9.3 0 1 22 61 64 28 164 34 73
12.5 0 0 21 56 70 23 162 48 17
7.6 0 0 21 41 75 21 99 18 40
15.9 0 1 21 43 61 19 202 43 64
9.2 0 0 21 53 60 19 186 33 37
9.1 1 1 21 44 62 20 66 28 25
11.1 0 0 21 66 73 18 183 71 65
13 0 1 22 58 61 19 214 26 100
14.5 0 1 22 46 66 17 188 67 28
12.2 1 0 18 37 64 19 104 34 35
12.3 0 0 21 51 59 25 177 80 56
11.4 0 0 23 51 64 19 126 29 29
8.8 1 0 19 56 60 22 76 16 43
14.6 1 1 19 66 56 23 99 59 59
7.3 0 1 23 45 66 26 157 58 52
12.6 0 0 21 37 78 14 139 32 50
NA 0 1 21 59 53 28 78 47 3
13 0 0 21 42 67 16 162 43 59
12.6 1 1 21 38 59 24 108 38 27
13.2 0 0 20 66 66 20 159 29 61
9.9 1 0 19 34 68 12 74 36 28
7.7 0 1 21 53 71 24 110 32 51
10.5 1 0 19 49 66 22 96 35 35
13.4 1 0 19 55 73 12 116 21 29
10.9 1 0 19 49 72 22 87 29 48
4.3 1 1 20 59 71 20 97 12 25
10.3 1 0 19 40 59 10 127 37 44
11.8 1 1 19 58 64 23 106 37 64
11.2 1 1 19 60 66 17 80 47 32
11.4 1 0 20 63 78 22 74 51 20
8.6 1 0 19 56 68 24 91 32 28
13.2 1 0 18 54 73 18 133 21 34
12.6 1 1 19 52 62 21 74 13 31
5.6 1 1 21 34 65 20 114 14 26
9.9 1 1 18 69 68 20 140 -2 58
8.8 1 0 18 32 65 22 95 20 23
7.7 1 1 19 48 60 19 98 24 21
9 1 0 21 67 71 20 121 11 21
7.3 1 1 20 58 65 26 126 23 33
11.4 1 1 24 57 68 23 98 24 16
13.6 1 1 22 42 64 24 95 14 20
7.9 1 1 21 64 74 21 110 52 37
10.7 1 1 21 58 69 21 70 15 35
10.3 1 0 19 66 76 19 102 23 33
8.3 1 1 19 26 68 8 86 19 27
9.6 1 1 20 61 72 17 130 35 41
14.2 1 1 18 52 67 20 96 24 40
8.5 1 0 19 51 63 11 102 39 35
13.5 1 0 19 55 59 8 100 29 28
4.9 1 0 20 50 73 15 94 13 32
6.4 1 0 21 60 66 18 52 8 22
9.6 1 0 18 56 62 18 98 18 44
11.6 1 0 19 63 69 19 118 24 27
11.1 1 1 19 61 66 19 99 19 17
4.35 0 1 22 52 51 23 48 23 12
12.7 0 1 22 16 56 22 50 16 45
18.1 0 1 22 46 67 21 150 33 37
17.85 0 1 20 56 69 25 154 32 37
16.6 1 0 19 52 57 30 109 37 108
12.6 1 1 20 55 56 17 68 14 10
17.1 0 1 22 50 55 27 194 52 68
19.1 0 0 21 59 63 23 158 75 72
16.1 0 1 21 60 67 23 159 72 143
13.35 0 0 21 52 65 18 67 15 9
18.4 0 0 21 44 47 18 147 29 55
14.7 0 1 21 67 76 23 39 13 17
10.6 0 1 21 52 64 19 100 40 37
12.6 0 1 21 55 68 15 111 19 27
16.2 0 1 22 37 64 20 138 24 37
13.6 0 1 24 54 65 16 101 121 58
18.9 1 1 21 72 71 24 131 93 66
14.1 0 1 22 51 63 25 101 36 21
14.5 0 1 20 48 60 25 114 23 19
16.15 0 0 21 60 68 19 165 85 78
14.75 0 1 24 50 72 19 114 41 35
14.8 0 1 25 63 70 16 111 46 48
12.45 0 1 22 33 61 19 75 18 27
12.65 0 1 21 67 61 19 82 35 43
17.35 0 1 21 46 62 23 121 17 30
8.6 0 1 22 54 71 21 32 4 25
18.4 0 0 23 59 71 22 150 28 69
16.1 0 1 24 61 51 19 117 44 72
11.6 1 1 20 33 56 20 71 10 23
17.75 0 1 22 47 70 20 165 38 13
15.25 0 1 25 69 73 3 154 57 61
17.65 0 1 22 52 76 23 126 23 43
15.6 0 0 22 55 59 14 138 26 22
16.35 0 0 21 55 68 23 149 36 51
17.65 0 0 21 41 48 20 145 22 67
13.6 0 1 21 73 52 15 120 40 36
11.7 0 0 22 51 59 13 138 18 21
14.35 0 0 22 52 60 16 109 31 44
14.75 0 0 22 50 59 7 132 11 45
18.25 0 1 21 51 57 24 172 38 34
9.9 0 0 22 60 79 17 169 24 36
16 0 1 23 56 60 24 114 37 72
18.25 0 1 21 56 60 24 156 37 39
16.85 0 0 21 29 59 19 172 22 43
14.6 1 1 21 66 62 25 68 15 25
13.85 1 1 19 66 59 20 89 2 56
18.95 0 1 21 73 61 28 167 43 80
15.6 0 0 21 55 71 23 113 31 40
14.85 1 0 19 64 57 27 115 29 73
11.75 1 0 18 40 66 18 78 45 34
18.45 1 0 19 46 63 28 118 25 72
15.9 1 1 21 58 69 21 87 4 42
17.1 0 0 22 43 58 19 173 31 61
16.1 0 1 22 61 59 23 2 -4 23
19.9 1 0 19 51 48 27 162 66 74
10.95 1 1 20 50 66 22 49 61 16
18.45 1 0 19 52 73 28 122 32 66
15.1 1 1 21 54 67 25 96 31 9
15 1 0 19 66 61 21 100 39 41
11.35 1 0 20 61 68 22 82 19 57
15.95 1 1 21 80 75 28 100 31 48
18.1 1 0 19 51 62 20 115 36 51
14.6 1 1 21 56 69 29 141 42 53
15.4 0 1 21 56 58 25 165 21 29
15.4 0 1 21 56 60 25 165 21 29
17.6 1 1 19 53 74 20 110 25 55
13.35 0 1 25 47 55 20 118 32 54
19.1 0 0 21 25 62 16 158 26 43
15.35 1 1 20 47 63 20 146 28 51
7.6 0 0 25 46 69 20 49 32 20
13.4 1 0 19 50 58 23 90 41 79
13.9 1 0 20 39 58 18 121 29 39
19.1 0 1 22 51 68 25 155 33 61
15.25 1 0 19 58 72 18 104 17 55
12.9 1 1 20 35 62 19 147 13 30
16.1 1 0 19 58 62 25 110 32 55
17.35 1 0 19 60 65 25 108 30 22
13.15 1 0 18 62 69 25 113 34 37
12.15 1 0 19 63 66 24 115 59 2
12.6 1 1 21 53 72 19 61 13 38
10.35 1 1 19 46 62 26 60 23 27
15.4 1 1 20 67 75 10 109 10 56
9.6 1 1 20 59 58 17 68 5 25
18.2 1 0 19 64 66 13 111 31 39
13.6 1 0 19 38 55 17 77 19 33
14.85 1 1 22 50 47 30 73 32 43
14.75 0 0 21 48 72 25 151 30 57
14.1 1 0 19 48 62 4 89 25 43
14.9 1 0 19 47 64 16 78 48 23
16.25 1 0 19 66 64 21 110 35 44
19.25 0 1 23 47 19 23 220 67 54
13.6 1 1 19 63 50 22 65 15 28
13.6 0 0 20 58 68 17 141 22 36
15.65 1 0 19 44 70 20 117 18 39
12.75 0 1 22 51 79 20 122 33 16
14.6 1 0 19 43 69 22 63 46 23
9.85 0 1 25 55 71 16 44 24 40
12.65 1 1 19 38 48 23 52 14 24
11.9 1 1 20 56 66 16 62 23 29
19.2 1 0 19 45 73 0 131 12 78
16.6 1 1 19 50 74 18 101 38 57
11.2 1 1 20 54 66 25 42 12 37
15.25 0 1 20 57 71 23 152 28 27
11.9 0 0 21 60 74 12 107 41 61
13.2 1 0 19 55 78 18 77 12 27
16.35 0 0 21 56 75 24 154 31 69
12.4 0 1 23 49 53 11 103 33 34
15.85 1 1 19 37 60 18 96 34 44
14.35 0 0 21 43 50 14 154 41 21
18.15 0 1 22 59 70 23 175 21 34
11.15 1 1 20 46 69 24 57 20 39
15.65 1 0 18 51 65 29 112 44 51
17.75 0 0 21 58 78 18 143 52 34
7.65 1 0 20 64 78 15 49 7 31
12.35 0 1 21 53 59 29 110 29 13
15.6 0 1 21 48 72 16 131 11 12
19.3 0 0 21 51 70 19 167 26 51
15.2 1 0 19 47 63 22 56 24 24
17.1 0 0 21 59 63 16 137 7 19
15.6 1 1 19 62 71 23 86 60 30
18.4 0 1 21 62 74 23 121 13 81
19.05 0 0 21 51 67 19 149 20 42
18.55 0 0 22 64 66 4 168 52 22
19.1 0 0 21 52 62 20 140 28 85
13.1 1 1 22 67 80 24 88 25 27
12.85 0 1 22 50 73 20 168 39 25
9.5 0 1 22 54 67 4 94 9 22
4.5 0 1 22 58 61 24 51 19 19
11.85 1 0 21 56 73 22 48 13 14
13.6 0 1 22 63 74 16 145 60 45
11.7 0 1 23 31 32 3 66 19 45
12.4 1 1 19 65 69 15 85 34 28
13.35 0 0 22 71 69 24 109 14 51
11.4 1 0 21 50 84 17 63 17 41
14.9 1 1 19 57 64 20 102 45 31
19.9 1 0 19 47 58 27 162 66 74
17.75 0 1 20 54 60 23 128 24 24
11.2 1 1 20 47 59 26 86 48 19
14.6 1 1 18 57 78 23 114 29 51
17.6 0 0 21 43 57 17 164 -2 73
14.05 0 1 21 41 60 20 119 51 24
16.1 0 0 20 63 68 22 126 2 61
13.35 0 1 20 63 68 19 132 24 23
11.85 0 1 21 56 73 24 142 40 14
11.95 0 0 21 51 69 19 83 20 54
14.75 1 1 19 50 67 23 94 19 51
15.15 1 0 19 22 60 15 81 16 62
13.2 0 1 21 41 65 27 166 20 36
16.85 1 0 19 59 66 26 110 40 59
7.85 1 1 19 56 74 22 64 27 24
7.7 0 0 24 66 81 22 93 25 26
12.6 1 0 19 53 72 18 104 49 54
7.85 1 1 19 42 55 15 105 39 39
10.95 1 1 20 52 49 22 49 61 16
12.35 1 0 19 54 74 27 88 19 36
9.95 1 1 19 44 53 10 95 67 31
14.9 1 1 19 62 64 20 102 45 31
16.65 1 0 19 53 65 17 99 30 42
13.4 1 1 19 50 57 23 63 8 39
13.95 1 0 19 36 51 19 76 19 25
15.7 1 0 20 76 80 13 109 52 31
16.85 1 1 20 66 67 27 117 22 38
10.95 1 1 19 62 70 23 57 17 31
15.35 1 0 21 59 74 16 120 33 17
12.2 1 1 19 47 75 25 73 34 22
15.1 1 0 19 55 70 2 91 22 55
17.75 1 0 19 58 69 26 108 30 62
15.2 1 1 21 60 65 20 105 25 51
14.6 0 0 22 44 55 23 117 38 30
16.65 1 0 19 57 71 22 119 26 49
8.1 1 1 19 45 65 24 31 13 16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265680&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265680&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265680&T=0

As an alternative you can also use a 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 time3 seconds
R Server'George Udny Yule' @ yule.wessa.net







Correlations for all pairs of data series (method=pearson)
TOTstudieprogrammagenderageAMS.IAMS.ENUMERACYTOTLFMPRHCH
TOT1-0.058-0.131-0.110.024-0.1290.0980.2820.140.331
studieprogramma-0.0581-0.082-0.6730.0050.0490.033-0.52-0.147-0.173
gender-0.131-0.08210.2290.058-0.130.186-0.0510.095-0.107
age-0.11-0.6730.22910.042-0.007-0.0730.2750.1340.051
AMS.I0.0240.0050.0580.04210.3440.0870.070.1440.089
AMS.E-0.1290.049-0.13-0.0070.3441-0.034-0.002-0.047-0.019
NUMERACYTOT0.0980.0330.186-0.0730.087-0.03410.0110.1010.086
LFM0.282-0.52-0.0510.2750.07-0.0020.01110.3650.442
PRH0.14-0.1470.0950.1340.144-0.0470.1010.36510.261
CH0.331-0.173-0.1070.0510.089-0.0190.0860.4420.2611

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & TOT & studieprogramma & gender & age & AMS.I & AMS.E & NUMERACYTOT & LFM & PRH & CH \tabularnewline
TOT & 1 & -0.058 & -0.131 & -0.11 & 0.024 & -0.129 & 0.098 & 0.282 & 0.14 & 0.331 \tabularnewline
studieprogramma & -0.058 & 1 & -0.082 & -0.673 & 0.005 & 0.049 & 0.033 & -0.52 & -0.147 & -0.173 \tabularnewline
gender & -0.131 & -0.082 & 1 & 0.229 & 0.058 & -0.13 & 0.186 & -0.051 & 0.095 & -0.107 \tabularnewline
age & -0.11 & -0.673 & 0.229 & 1 & 0.042 & -0.007 & -0.073 & 0.275 & 0.134 & 0.051 \tabularnewline
AMS.I & 0.024 & 0.005 & 0.058 & 0.042 & 1 & 0.344 & 0.087 & 0.07 & 0.144 & 0.089 \tabularnewline
AMS.E & -0.129 & 0.049 & -0.13 & -0.007 & 0.344 & 1 & -0.034 & -0.002 & -0.047 & -0.019 \tabularnewline
NUMERACYTOT & 0.098 & 0.033 & 0.186 & -0.073 & 0.087 & -0.034 & 1 & 0.011 & 0.101 & 0.086 \tabularnewline
LFM & 0.282 & -0.52 & -0.051 & 0.275 & 0.07 & -0.002 & 0.011 & 1 & 0.365 & 0.442 \tabularnewline
PRH & 0.14 & -0.147 & 0.095 & 0.134 & 0.144 & -0.047 & 0.101 & 0.365 & 1 & 0.261 \tabularnewline
CH & 0.331 & -0.173 & -0.107 & 0.051 & 0.089 & -0.019 & 0.086 & 0.442 & 0.261 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265680&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]TOT[/C][C]studieprogramma[/C][C]gender[/C][C]age[/C][C]AMS.I[/C][C]AMS.E[/C][C]NUMERACYTOT[/C][C]LFM[/C][C]PRH[/C][C]CH[/C][/ROW]
[ROW][C]TOT[/C][C]1[/C][C]-0.058[/C][C]-0.131[/C][C]-0.11[/C][C]0.024[/C][C]-0.129[/C][C]0.098[/C][C]0.282[/C][C]0.14[/C][C]0.331[/C][/ROW]
[ROW][C]studieprogramma[/C][C]-0.058[/C][C]1[/C][C]-0.082[/C][C]-0.673[/C][C]0.005[/C][C]0.049[/C][C]0.033[/C][C]-0.52[/C][C]-0.147[/C][C]-0.173[/C][/ROW]
[ROW][C]gender[/C][C]-0.131[/C][C]-0.082[/C][C]1[/C][C]0.229[/C][C]0.058[/C][C]-0.13[/C][C]0.186[/C][C]-0.051[/C][C]0.095[/C][C]-0.107[/C][/ROW]
[ROW][C]age[/C][C]-0.11[/C][C]-0.673[/C][C]0.229[/C][C]1[/C][C]0.042[/C][C]-0.007[/C][C]-0.073[/C][C]0.275[/C][C]0.134[/C][C]0.051[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.024[/C][C]0.005[/C][C]0.058[/C][C]0.042[/C][C]1[/C][C]0.344[/C][C]0.087[/C][C]0.07[/C][C]0.144[/C][C]0.089[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.129[/C][C]0.049[/C][C]-0.13[/C][C]-0.007[/C][C]0.344[/C][C]1[/C][C]-0.034[/C][C]-0.002[/C][C]-0.047[/C][C]-0.019[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.098[/C][C]0.033[/C][C]0.186[/C][C]-0.073[/C][C]0.087[/C][C]-0.034[/C][C]1[/C][C]0.011[/C][C]0.101[/C][C]0.086[/C][/ROW]
[ROW][C]LFM[/C][C]0.282[/C][C]-0.52[/C][C]-0.051[/C][C]0.275[/C][C]0.07[/C][C]-0.002[/C][C]0.011[/C][C]1[/C][C]0.365[/C][C]0.442[/C][/ROW]
[ROW][C]PRH[/C][C]0.14[/C][C]-0.147[/C][C]0.095[/C][C]0.134[/C][C]0.144[/C][C]-0.047[/C][C]0.101[/C][C]0.365[/C][C]1[/C][C]0.261[/C][/ROW]
[ROW][C]CH[/C][C]0.331[/C][C]-0.173[/C][C]-0.107[/C][C]0.051[/C][C]0.089[/C][C]-0.019[/C][C]0.086[/C][C]0.442[/C][C]0.261[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265680&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265680&T=1

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series (method=pearson)
TOTstudieprogrammagenderageAMS.IAMS.ENUMERACYTOTLFMPRHCH
TOT1-0.058-0.131-0.110.024-0.1290.0980.2820.140.331
studieprogramma-0.0581-0.082-0.6730.0050.0490.033-0.52-0.147-0.173
gender-0.131-0.08210.2290.058-0.130.186-0.0510.095-0.107
age-0.11-0.6730.22910.042-0.007-0.0730.2750.1340.051
AMS.I0.0240.0050.0580.04210.3440.0870.070.1440.089
AMS.E-0.1290.049-0.13-0.0070.3441-0.034-0.002-0.047-0.019
NUMERACYTOT0.0980.0330.186-0.0730.087-0.03410.0110.1010.086
LFM0.282-0.52-0.0510.2750.07-0.0020.01110.3650.442
PRH0.14-0.1470.0950.1340.144-0.0470.1010.36510.261
CH0.331-0.173-0.1070.0510.089-0.0190.0860.4420.2611







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
TOT;studieprogramma-0.0575-0.0546-0.0448
p-value(0.3323)(0.3572)(0.3563)
TOT;gender-0.1312-0.1256-0.103
p-value(0.0265)(0.0338)(0.034)
TOT;age-0.1103-0.0943-0.0654
p-value(0.0624)(0.1117)(0.1317)
TOT;AMS.I0.02370.01560.0112
p-value(0.6894)(0.7923)(0.7805)
TOT;AMS.E-0.1285-0.1291-0.0883
p-value(0.0298)(0.029)(0.0292)
TOT;NUMERACYTOT0.09830.13420.0932
p-value(0.097)(0.0232)(0.0231)
TOT;LFM0.28180.28050.1985
p-value(0)(0)(0)
TOT;PRH0.13990.12640.0839
p-value(0.0179)(0.0326)(0.0365)
TOT;CH0.33140.30730.2111
p-value(0)(0)(0)
studieprogramma;gender-0.0818-0.0818-0.0818
p-value(0.167)(0.167)(0.1666)
studieprogramma;age-0.673-0.7231-0.6458
p-value(0)(0)(0)
studieprogramma;AMS.I0.00490.0120.01
p-value(0.934)(0.8389)(0.8385)
studieprogramma;AMS.E0.04870.01810.0151
p-value(0.4109)(0.7599)(0.7593)
studieprogramma;NUMERACYTOT0.03270.03870.0326
p-value(0.5808)(0.514)(0.5131)
studieprogramma;LFM-0.5202-0.5592-0.459
p-value(0)(0)(0)
studieprogramma;PRH-0.1467-0.1564-0.1291
p-value(0.0129)(0.0079)(0.0082)
studieprogramma;CH-0.1731-0.174-0.1435
p-value(0.0033)(0.0031)(0.0033)
gender;age0.22880.23680.2115
p-value(1e-04)(1e-04)(1e-04)
gender;AMS.I0.05780.04690.039
p-value(0.3291)(0.4283)(0.4273)
gender;AMS.E-0.1301-0.1171-0.0975
p-value(0.0275)(0.0474)(0.0476)
gender;NUMERACYTOT0.1860.2080.1754
p-value(0.0016)(4e-04)(4e-04)
gender;LFM-0.0507-0.0544-0.0447
p-value(0.3921)(0.3583)(0.3574)
gender;PRH0.0950.07650.0631
p-value(0.1081)(0.1962)(0.1957)
gender;CH-0.1066-0.1297-0.1069
p-value(0.0714)(0.0281)(0.0283)
age;AMS.I0.04180.0011e-04
p-value(0.4808)(0.9861)(0.998)
age;AMS.E-0.00680.04370.0333
p-value(0.9083)(0.461)(0.4485)
age;NUMERACYTOT-0.0729-0.055-0.0417
p-value(0.2182)(0.3529)(0.3494)
age;LFM0.27520.33080.2404
p-value(0)(0)(0)
age;PRH0.13370.10080.0719
p-value(0.0235)(0.0883)(0.0989)
age;CH0.05140.04510.0313
p-value(0.3857)(0.4462)(0.4719)
AMS.I;AMS.E0.34410.34380.2425
p-value(0)(0)(0)
AMS.I;NUMERACYTOT0.08680.0540.0355
p-value(0.1425)(0.3617)(0.3915)
AMS.I;LFM0.070.05850.0412
p-value(0.2373)(0.3238)(0.3066)
AMS.I;PRH0.14430.08710.0593
p-value(0.0144)(0.1409)(0.1432)
AMS.I;CH0.08860.07270.0473
p-value(0.1342)(0.2193)(0.243)
AMS.E;NUMERACYTOT-0.0345-0.0653-0.0463
p-value(0.5608)(0.2703)(0.265)
AMS.E;LFM-0.00170.04080.0267
p-value(0.9775)(0.4912)(0.5098)
AMS.E;PRH-0.0475-0.051-0.0359
p-value(0.423)(0.3893)(0.3772)
AMS.E;CH-0.0190.01770.0124
p-value(0.7485)(0.7652)(0.7603)
NUMERACYTOT;LFM0.01090.03980.0256
p-value(0.8544)(0.5023)(0.5317)
NUMERACYTOT;PRH0.10110.09740.0683
p-value(0.0874)(0.0995)(0.0971)
NUMERACYTOT;CH0.08610.06970.0466
p-value(0.1456)(0.2393)(0.2577)
LFM;PRH0.36460.36780.2547
p-value(0)(0)(0)
LFM;CH0.44160.43140.3024
p-value(0)(0)(0)
PRH;CH0.26120.22140.1574
p-value(0)(2e-04)(1e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
TOT;studieprogramma & -0.0575 & -0.0546 & -0.0448 \tabularnewline
p-value & (0.3323) & (0.3572) & (0.3563) \tabularnewline
TOT;gender & -0.1312 & -0.1256 & -0.103 \tabularnewline
p-value & (0.0265) & (0.0338) & (0.034) \tabularnewline
TOT;age & -0.1103 & -0.0943 & -0.0654 \tabularnewline
p-value & (0.0624) & (0.1117) & (0.1317) \tabularnewline
TOT;AMS.I & 0.0237 & 0.0156 & 0.0112 \tabularnewline
p-value & (0.6894) & (0.7923) & (0.7805) \tabularnewline
TOT;AMS.E & -0.1285 & -0.1291 & -0.0883 \tabularnewline
p-value & (0.0298) & (0.029) & (0.0292) \tabularnewline
TOT;NUMERACYTOT & 0.0983 & 0.1342 & 0.0932 \tabularnewline
p-value & (0.097) & (0.0232) & (0.0231) \tabularnewline
TOT;LFM & 0.2818 & 0.2805 & 0.1985 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
TOT;PRH & 0.1399 & 0.1264 & 0.0839 \tabularnewline
p-value & (0.0179) & (0.0326) & (0.0365) \tabularnewline
TOT;CH & 0.3314 & 0.3073 & 0.2111 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
studieprogramma;gender & -0.0818 & -0.0818 & -0.0818 \tabularnewline
p-value & (0.167) & (0.167) & (0.1666) \tabularnewline
studieprogramma;age & -0.673 & -0.7231 & -0.6458 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
studieprogramma;AMS.I & 0.0049 & 0.012 & 0.01 \tabularnewline
p-value & (0.934) & (0.8389) & (0.8385) \tabularnewline
studieprogramma;AMS.E & 0.0487 & 0.0181 & 0.0151 \tabularnewline
p-value & (0.4109) & (0.7599) & (0.7593) \tabularnewline
studieprogramma;NUMERACYTOT & 0.0327 & 0.0387 & 0.0326 \tabularnewline
p-value & (0.5808) & (0.514) & (0.5131) \tabularnewline
studieprogramma;LFM & -0.5202 & -0.5592 & -0.459 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
studieprogramma;PRH & -0.1467 & -0.1564 & -0.1291 \tabularnewline
p-value & (0.0129) & (0.0079) & (0.0082) \tabularnewline
studieprogramma;CH & -0.1731 & -0.174 & -0.1435 \tabularnewline
p-value & (0.0033) & (0.0031) & (0.0033) \tabularnewline
gender;age & 0.2288 & 0.2368 & 0.2115 \tabularnewline
p-value & (1e-04) & (1e-04) & (1e-04) \tabularnewline
gender;AMS.I & 0.0578 & 0.0469 & 0.039 \tabularnewline
p-value & (0.3291) & (0.4283) & (0.4273) \tabularnewline
gender;AMS.E & -0.1301 & -0.1171 & -0.0975 \tabularnewline
p-value & (0.0275) & (0.0474) & (0.0476) \tabularnewline
gender;NUMERACYTOT & 0.186 & 0.208 & 0.1754 \tabularnewline
p-value & (0.0016) & (4e-04) & (4e-04) \tabularnewline
gender;LFM & -0.0507 & -0.0544 & -0.0447 \tabularnewline
p-value & (0.3921) & (0.3583) & (0.3574) \tabularnewline
gender;PRH & 0.095 & 0.0765 & 0.0631 \tabularnewline
p-value & (0.1081) & (0.1962) & (0.1957) \tabularnewline
gender;CH & -0.1066 & -0.1297 & -0.1069 \tabularnewline
p-value & (0.0714) & (0.0281) & (0.0283) \tabularnewline
age;AMS.I & 0.0418 & 0.001 & 1e-04 \tabularnewline
p-value & (0.4808) & (0.9861) & (0.998) \tabularnewline
age;AMS.E & -0.0068 & 0.0437 & 0.0333 \tabularnewline
p-value & (0.9083) & (0.461) & (0.4485) \tabularnewline
age;NUMERACYTOT & -0.0729 & -0.055 & -0.0417 \tabularnewline
p-value & (0.2182) & (0.3529) & (0.3494) \tabularnewline
age;LFM & 0.2752 & 0.3308 & 0.2404 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
age;PRH & 0.1337 & 0.1008 & 0.0719 \tabularnewline
p-value & (0.0235) & (0.0883) & (0.0989) \tabularnewline
age;CH & 0.0514 & 0.0451 & 0.0313 \tabularnewline
p-value & (0.3857) & (0.4462) & (0.4719) \tabularnewline
AMS.I;AMS.E & 0.3441 & 0.3438 & 0.2425 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I;NUMERACYTOT & 0.0868 & 0.054 & 0.0355 \tabularnewline
p-value & (0.1425) & (0.3617) & (0.3915) \tabularnewline
AMS.I;LFM & 0.07 & 0.0585 & 0.0412 \tabularnewline
p-value & (0.2373) & (0.3238) & (0.3066) \tabularnewline
AMS.I;PRH & 0.1443 & 0.0871 & 0.0593 \tabularnewline
p-value & (0.0144) & (0.1409) & (0.1432) \tabularnewline
AMS.I;CH & 0.0886 & 0.0727 & 0.0473 \tabularnewline
p-value & (0.1342) & (0.2193) & (0.243) \tabularnewline
AMS.E;NUMERACYTOT & -0.0345 & -0.0653 & -0.0463 \tabularnewline
p-value & (0.5608) & (0.2703) & (0.265) \tabularnewline
AMS.E;LFM & -0.0017 & 0.0408 & 0.0267 \tabularnewline
p-value & (0.9775) & (0.4912) & (0.5098) \tabularnewline
AMS.E;PRH & -0.0475 & -0.051 & -0.0359 \tabularnewline
p-value & (0.423) & (0.3893) & (0.3772) \tabularnewline
AMS.E;CH & -0.019 & 0.0177 & 0.0124 \tabularnewline
p-value & (0.7485) & (0.7652) & (0.7603) \tabularnewline
NUMERACYTOT;LFM & 0.0109 & 0.0398 & 0.0256 \tabularnewline
p-value & (0.8544) & (0.5023) & (0.5317) \tabularnewline
NUMERACYTOT;PRH & 0.1011 & 0.0974 & 0.0683 \tabularnewline
p-value & (0.0874) & (0.0995) & (0.0971) \tabularnewline
NUMERACYTOT;CH & 0.0861 & 0.0697 & 0.0466 \tabularnewline
p-value & (0.1456) & (0.2393) & (0.2577) \tabularnewline
LFM;PRH & 0.3646 & 0.3678 & 0.2547 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM;CH & 0.4416 & 0.4314 & 0.3024 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PRH;CH & 0.2612 & 0.2214 & 0.1574 \tabularnewline
p-value & (0) & (2e-04) & (1e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265680&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]TOT;studieprogramma[/C][C]-0.0575[/C][C]-0.0546[/C][C]-0.0448[/C][/ROW]
[ROW][C]p-value[/C][C](0.3323)[/C][C](0.3572)[/C][C](0.3563)[/C][/ROW]
[ROW][C]TOT;gender[/C][C]-0.1312[/C][C]-0.1256[/C][C]-0.103[/C][/ROW]
[ROW][C]p-value[/C][C](0.0265)[/C][C](0.0338)[/C][C](0.034)[/C][/ROW]
[ROW][C]TOT;age[/C][C]-0.1103[/C][C]-0.0943[/C][C]-0.0654[/C][/ROW]
[ROW][C]p-value[/C][C](0.0624)[/C][C](0.1117)[/C][C](0.1317)[/C][/ROW]
[ROW][C]TOT;AMS.I[/C][C]0.0237[/C][C]0.0156[/C][C]0.0112[/C][/ROW]
[ROW][C]p-value[/C][C](0.6894)[/C][C](0.7923)[/C][C](0.7805)[/C][/ROW]
[ROW][C]TOT;AMS.E[/C][C]-0.1285[/C][C]-0.1291[/C][C]-0.0883[/C][/ROW]
[ROW][C]p-value[/C][C](0.0298)[/C][C](0.029)[/C][C](0.0292)[/C][/ROW]
[ROW][C]TOT;NUMERACYTOT[/C][C]0.0983[/C][C]0.1342[/C][C]0.0932[/C][/ROW]
[ROW][C]p-value[/C][C](0.097)[/C][C](0.0232)[/C][C](0.0231)[/C][/ROW]
[ROW][C]TOT;LFM[/C][C]0.2818[/C][C]0.2805[/C][C]0.1985[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]TOT;PRH[/C][C]0.1399[/C][C]0.1264[/C][C]0.0839[/C][/ROW]
[ROW][C]p-value[/C][C](0.0179)[/C][C](0.0326)[/C][C](0.0365)[/C][/ROW]
[ROW][C]TOT;CH[/C][C]0.3314[/C][C]0.3073[/C][C]0.2111[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]studieprogramma;gender[/C][C]-0.0818[/C][C]-0.0818[/C][C]-0.0818[/C][/ROW]
[ROW][C]p-value[/C][C](0.167)[/C][C](0.167)[/C][C](0.1666)[/C][/ROW]
[ROW][C]studieprogramma;age[/C][C]-0.673[/C][C]-0.7231[/C][C]-0.6458[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]studieprogramma;AMS.I[/C][C]0.0049[/C][C]0.012[/C][C]0.01[/C][/ROW]
[ROW][C]p-value[/C][C](0.934)[/C][C](0.8389)[/C][C](0.8385)[/C][/ROW]
[ROW][C]studieprogramma;AMS.E[/C][C]0.0487[/C][C]0.0181[/C][C]0.0151[/C][/ROW]
[ROW][C]p-value[/C][C](0.4109)[/C][C](0.7599)[/C][C](0.7593)[/C][/ROW]
[ROW][C]studieprogramma;NUMERACYTOT[/C][C]0.0327[/C][C]0.0387[/C][C]0.0326[/C][/ROW]
[ROW][C]p-value[/C][C](0.5808)[/C][C](0.514)[/C][C](0.5131)[/C][/ROW]
[ROW][C]studieprogramma;LFM[/C][C]-0.5202[/C][C]-0.5592[/C][C]-0.459[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]studieprogramma;PRH[/C][C]-0.1467[/C][C]-0.1564[/C][C]-0.1291[/C][/ROW]
[ROW][C]p-value[/C][C](0.0129)[/C][C](0.0079)[/C][C](0.0082)[/C][/ROW]
[ROW][C]studieprogramma;CH[/C][C]-0.1731[/C][C]-0.174[/C][C]-0.1435[/C][/ROW]
[ROW][C]p-value[/C][C](0.0033)[/C][C](0.0031)[/C][C](0.0033)[/C][/ROW]
[ROW][C]gender;age[/C][C]0.2288[/C][C]0.2368[/C][C]0.2115[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]gender;AMS.I[/C][C]0.0578[/C][C]0.0469[/C][C]0.039[/C][/ROW]
[ROW][C]p-value[/C][C](0.3291)[/C][C](0.4283)[/C][C](0.4273)[/C][/ROW]
[ROW][C]gender;AMS.E[/C][C]-0.1301[/C][C]-0.1171[/C][C]-0.0975[/C][/ROW]
[ROW][C]p-value[/C][C](0.0275)[/C][C](0.0474)[/C][C](0.0476)[/C][/ROW]
[ROW][C]gender;NUMERACYTOT[/C][C]0.186[/C][C]0.208[/C][C]0.1754[/C][/ROW]
[ROW][C]p-value[/C][C](0.0016)[/C][C](4e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]gender;LFM[/C][C]-0.0507[/C][C]-0.0544[/C][C]-0.0447[/C][/ROW]
[ROW][C]p-value[/C][C](0.3921)[/C][C](0.3583)[/C][C](0.3574)[/C][/ROW]
[ROW][C]gender;PRH[/C][C]0.095[/C][C]0.0765[/C][C]0.0631[/C][/ROW]
[ROW][C]p-value[/C][C](0.1081)[/C][C](0.1962)[/C][C](0.1957)[/C][/ROW]
[ROW][C]gender;CH[/C][C]-0.1066[/C][C]-0.1297[/C][C]-0.1069[/C][/ROW]
[ROW][C]p-value[/C][C](0.0714)[/C][C](0.0281)[/C][C](0.0283)[/C][/ROW]
[ROW][C]age;AMS.I[/C][C]0.0418[/C][C]0.001[/C][C]1e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.4808)[/C][C](0.9861)[/C][C](0.998)[/C][/ROW]
[ROW][C]age;AMS.E[/C][C]-0.0068[/C][C]0.0437[/C][C]0.0333[/C][/ROW]
[ROW][C]p-value[/C][C](0.9083)[/C][C](0.461)[/C][C](0.4485)[/C][/ROW]
[ROW][C]age;NUMERACYTOT[/C][C]-0.0729[/C][C]-0.055[/C][C]-0.0417[/C][/ROW]
[ROW][C]p-value[/C][C](0.2182)[/C][C](0.3529)[/C][C](0.3494)[/C][/ROW]
[ROW][C]age;LFM[/C][C]0.2752[/C][C]0.3308[/C][C]0.2404[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]age;PRH[/C][C]0.1337[/C][C]0.1008[/C][C]0.0719[/C][/ROW]
[ROW][C]p-value[/C][C](0.0235)[/C][C](0.0883)[/C][C](0.0989)[/C][/ROW]
[ROW][C]age;CH[/C][C]0.0514[/C][C]0.0451[/C][C]0.0313[/C][/ROW]
[ROW][C]p-value[/C][C](0.3857)[/C][C](0.4462)[/C][C](0.4719)[/C][/ROW]
[ROW][C]AMS.I;AMS.E[/C][C]0.3441[/C][C]0.3438[/C][C]0.2425[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I;NUMERACYTOT[/C][C]0.0868[/C][C]0.054[/C][C]0.0355[/C][/ROW]
[ROW][C]p-value[/C][C](0.1425)[/C][C](0.3617)[/C][C](0.3915)[/C][/ROW]
[ROW][C]AMS.I;LFM[/C][C]0.07[/C][C]0.0585[/C][C]0.0412[/C][/ROW]
[ROW][C]p-value[/C][C](0.2373)[/C][C](0.3238)[/C][C](0.3066)[/C][/ROW]
[ROW][C]AMS.I;PRH[/C][C]0.1443[/C][C]0.0871[/C][C]0.0593[/C][/ROW]
[ROW][C]p-value[/C][C](0.0144)[/C][C](0.1409)[/C][C](0.1432)[/C][/ROW]
[ROW][C]AMS.I;CH[/C][C]0.0886[/C][C]0.0727[/C][C]0.0473[/C][/ROW]
[ROW][C]p-value[/C][C](0.1342)[/C][C](0.2193)[/C][C](0.243)[/C][/ROW]
[ROW][C]AMS.E;NUMERACYTOT[/C][C]-0.0345[/C][C]-0.0653[/C][C]-0.0463[/C][/ROW]
[ROW][C]p-value[/C][C](0.5608)[/C][C](0.2703)[/C][C](0.265)[/C][/ROW]
[ROW][C]AMS.E;LFM[/C][C]-0.0017[/C][C]0.0408[/C][C]0.0267[/C][/ROW]
[ROW][C]p-value[/C][C](0.9775)[/C][C](0.4912)[/C][C](0.5098)[/C][/ROW]
[ROW][C]AMS.E;PRH[/C][C]-0.0475[/C][C]-0.051[/C][C]-0.0359[/C][/ROW]
[ROW][C]p-value[/C][C](0.423)[/C][C](0.3893)[/C][C](0.3772)[/C][/ROW]
[ROW][C]AMS.E;CH[/C][C]-0.019[/C][C]0.0177[/C][C]0.0124[/C][/ROW]
[ROW][C]p-value[/C][C](0.7485)[/C][C](0.7652)[/C][C](0.7603)[/C][/ROW]
[ROW][C]NUMERACYTOT;LFM[/C][C]0.0109[/C][C]0.0398[/C][C]0.0256[/C][/ROW]
[ROW][C]p-value[/C][C](0.8544)[/C][C](0.5023)[/C][C](0.5317)[/C][/ROW]
[ROW][C]NUMERACYTOT;PRH[/C][C]0.1011[/C][C]0.0974[/C][C]0.0683[/C][/ROW]
[ROW][C]p-value[/C][C](0.0874)[/C][C](0.0995)[/C][C](0.0971)[/C][/ROW]
[ROW][C]NUMERACYTOT;CH[/C][C]0.0861[/C][C]0.0697[/C][C]0.0466[/C][/ROW]
[ROW][C]p-value[/C][C](0.1456)[/C][C](0.2393)[/C][C](0.2577)[/C][/ROW]
[ROW][C]LFM;PRH[/C][C]0.3646[/C][C]0.3678[/C][C]0.2547[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM;CH[/C][C]0.4416[/C][C]0.4314[/C][C]0.3024[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PRH;CH[/C][C]0.2612[/C][C]0.2214[/C][C]0.1574[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](1e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265680&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265680&T=2

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
TOT;studieprogramma-0.0575-0.0546-0.0448
p-value(0.3323)(0.3572)(0.3563)
TOT;gender-0.1312-0.1256-0.103
p-value(0.0265)(0.0338)(0.034)
TOT;age-0.1103-0.0943-0.0654
p-value(0.0624)(0.1117)(0.1317)
TOT;AMS.I0.02370.01560.0112
p-value(0.6894)(0.7923)(0.7805)
TOT;AMS.E-0.1285-0.1291-0.0883
p-value(0.0298)(0.029)(0.0292)
TOT;NUMERACYTOT0.09830.13420.0932
p-value(0.097)(0.0232)(0.0231)
TOT;LFM0.28180.28050.1985
p-value(0)(0)(0)
TOT;PRH0.13990.12640.0839
p-value(0.0179)(0.0326)(0.0365)
TOT;CH0.33140.30730.2111
p-value(0)(0)(0)
studieprogramma;gender-0.0818-0.0818-0.0818
p-value(0.167)(0.167)(0.1666)
studieprogramma;age-0.673-0.7231-0.6458
p-value(0)(0)(0)
studieprogramma;AMS.I0.00490.0120.01
p-value(0.934)(0.8389)(0.8385)
studieprogramma;AMS.E0.04870.01810.0151
p-value(0.4109)(0.7599)(0.7593)
studieprogramma;NUMERACYTOT0.03270.03870.0326
p-value(0.5808)(0.514)(0.5131)
studieprogramma;LFM-0.5202-0.5592-0.459
p-value(0)(0)(0)
studieprogramma;PRH-0.1467-0.1564-0.1291
p-value(0.0129)(0.0079)(0.0082)
studieprogramma;CH-0.1731-0.174-0.1435
p-value(0.0033)(0.0031)(0.0033)
gender;age0.22880.23680.2115
p-value(1e-04)(1e-04)(1e-04)
gender;AMS.I0.05780.04690.039
p-value(0.3291)(0.4283)(0.4273)
gender;AMS.E-0.1301-0.1171-0.0975
p-value(0.0275)(0.0474)(0.0476)
gender;NUMERACYTOT0.1860.2080.1754
p-value(0.0016)(4e-04)(4e-04)
gender;LFM-0.0507-0.0544-0.0447
p-value(0.3921)(0.3583)(0.3574)
gender;PRH0.0950.07650.0631
p-value(0.1081)(0.1962)(0.1957)
gender;CH-0.1066-0.1297-0.1069
p-value(0.0714)(0.0281)(0.0283)
age;AMS.I0.04180.0011e-04
p-value(0.4808)(0.9861)(0.998)
age;AMS.E-0.00680.04370.0333
p-value(0.9083)(0.461)(0.4485)
age;NUMERACYTOT-0.0729-0.055-0.0417
p-value(0.2182)(0.3529)(0.3494)
age;LFM0.27520.33080.2404
p-value(0)(0)(0)
age;PRH0.13370.10080.0719
p-value(0.0235)(0.0883)(0.0989)
age;CH0.05140.04510.0313
p-value(0.3857)(0.4462)(0.4719)
AMS.I;AMS.E0.34410.34380.2425
p-value(0)(0)(0)
AMS.I;NUMERACYTOT0.08680.0540.0355
p-value(0.1425)(0.3617)(0.3915)
AMS.I;LFM0.070.05850.0412
p-value(0.2373)(0.3238)(0.3066)
AMS.I;PRH0.14430.08710.0593
p-value(0.0144)(0.1409)(0.1432)
AMS.I;CH0.08860.07270.0473
p-value(0.1342)(0.2193)(0.243)
AMS.E;NUMERACYTOT-0.0345-0.0653-0.0463
p-value(0.5608)(0.2703)(0.265)
AMS.E;LFM-0.00170.04080.0267
p-value(0.9775)(0.4912)(0.5098)
AMS.E;PRH-0.0475-0.051-0.0359
p-value(0.423)(0.3893)(0.3772)
AMS.E;CH-0.0190.01770.0124
p-value(0.7485)(0.7652)(0.7603)
NUMERACYTOT;LFM0.01090.03980.0256
p-value(0.8544)(0.5023)(0.5317)
NUMERACYTOT;PRH0.10110.09740.0683
p-value(0.0874)(0.0995)(0.0971)
NUMERACYTOT;CH0.08610.06970.0466
p-value(0.1456)(0.2393)(0.2577)
LFM;PRH0.36460.36780.2547
p-value(0)(0)(0)
LFM;CH0.44160.43140.3024
p-value(0)(0)(0)
PRH;CH0.26120.22140.1574
p-value(0)(2e-04)(1e-04)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.270.290.29
0.020.330.290.29
0.030.420.360.36
0.040.420.40.4
0.050.420.420.42
0.060.420.420.42
0.070.440.420.42
0.080.470.420.42
0.090.490.440.42
0.10.510.470.47

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Correlation Tests \tabularnewline
Number of significant by total number of Correlations \tabularnewline
Type I error & Pearson r & Spearman rho & Kendall tau \tabularnewline
0.01 & 0.27 & 0.29 & 0.29 \tabularnewline
0.02 & 0.33 & 0.29 & 0.29 \tabularnewline
0.03 & 0.42 & 0.36 & 0.36 \tabularnewline
0.04 & 0.42 & 0.4 & 0.4 \tabularnewline
0.05 & 0.42 & 0.42 & 0.42 \tabularnewline
0.06 & 0.42 & 0.42 & 0.42 \tabularnewline
0.07 & 0.44 & 0.42 & 0.42 \tabularnewline
0.08 & 0.47 & 0.42 & 0.42 \tabularnewline
0.09 & 0.49 & 0.44 & 0.42 \tabularnewline
0.1 & 0.51 & 0.47 & 0.47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265680&T=3

[TABLE]
[ROW][C]Meta Analysis of Correlation Tests[/C][/ROW]
[ROW][C]Number of significant by total number of Correlations[/C][/ROW]
[ROW][C]Type I error[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]0.01[/C][C]0.27[/C][C]0.29[/C][C]0.29[/C][/ROW]
[ROW][C]0.02[/C][C]0.33[/C][C]0.29[/C][C]0.29[/C][/ROW]
[ROW][C]0.03[/C][C]0.42[/C][C]0.36[/C][C]0.36[/C][/ROW]
[ROW][C]0.04[/C][C]0.42[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.05[/C][C]0.42[/C][C]0.42[/C][C]0.42[/C][/ROW]
[ROW][C]0.06[/C][C]0.42[/C][C]0.42[/C][C]0.42[/C][/ROW]
[ROW][C]0.07[/C][C]0.44[/C][C]0.42[/C][C]0.42[/C][/ROW]
[ROW][C]0.08[/C][C]0.47[/C][C]0.42[/C][C]0.42[/C][/ROW]
[ROW][C]0.09[/C][C]0.49[/C][C]0.44[/C][C]0.42[/C][/ROW]
[ROW][C]0.1[/C][C]0.51[/C][C]0.47[/C][C]0.47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265680&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265680&T=3

As an alternative you can also use a QR Code:  

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

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.270.290.29
0.020.330.290.29
0.030.420.360.36
0.040.420.40.4
0.050.420.420.42
0.060.420.420.42
0.070.440.420.42
0.080.470.420.42
0.090.490.440.42
0.10.510.470.47



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
ncorrs <- (n*n -n)/2
mycorrs <- array(0, dim=c(10,3))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
for (iii in 1:10) {
iiid100 <- iii / 100
if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
}
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
for (iii in 1:10) {
iiid100 <- iii / 100
a<-table.row.start(a)
a<-table.element(a,round(iiid100,2),header=T)
a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')