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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 computationWed, 11 Dec 2013 14:55:57 -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/2013/Dec/11/t1386792002ojleb9kcrmavdbz.htm/, Retrieved Fri, 29 Mar 2024 09:23:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232157, Retrieved Fri, 29 Mar 2024 09:23:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [WS 10] [2013-12-11 19:55:57] [3ab494e3ec4169588a52211572c2f14a] [Current]
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Dataseries X:
41 38 13 12 14 1 1
39 32 16 11 18 1 1
30 35 19 15 11 1 1
31 33 15 6 12 0 1
34 37 14 13 16 1 1
35 29 13 10 18 1 1
39 31 19 12 14 1 1
34 36 15 14 14 1 1
36 35 14 12 15 1 1
37 38 15 9 15 1 1
38 31 16 10 17 0 1
36 34 16 12 19 1 1
38 35 16 12 10 0 1
39 38 16 11 16 1 1
33 37 17 15 18 1 1
32 33 15 12 14 0 1
36 32 15 10 14 0 1
38 38 20 12 17 1 1
39 38 18 11 14 0 1
32 32 16 12 16 1 1
32 33 16 11 18 0 1
31 31 16 12 11 1 1
39 38 19 13 14 1 1
37 39 16 11 12 1 1
39 32 17 12 17 0 1
41 32 17 13 9 1 1
36 35 16 10 16 0 1
33 37 15 14 14 1 1
33 33 16 12 15 1 1
34 33 14 10 11 0 1
31 31 15 12 16 1 1
27 32 12 8 13 0 1
37 31 14 10 17 1 1
34 37 16 12 15 1 1
34 30 14 12 14 0 1
32 33 10 7 16 0 1
29 31 10 9 9 0 1
36 33 14 12 15 0 1
29 31 16 10 17 1 1
35 33 16 10 13 0 1
37 32 16 10 15 0 1
34 33 14 12 16 1 1
38 32 20 15 16 0 1
35 33 14 10 12 0 1
38 28 14 10 15 1 1
37 35 11 12 11 1 1
38 39 14 13 15 1 1
33 34 15 11 15 1 1
36 38 16 11 17 1 1
38 32 14 12 13 0 1
32 38 16 14 16 1 1
32 30 14 10 14 0 1
32 33 12 12 11 0 1
34 38 16 13 12 1 1
32 32 9 5 12 0 1
37 35 14 6 15 1 1
39 34 16 12 16 1 1
29 34 16 12 15 1 1
37 36 15 11 12 0 1
35 34 16 10 12 1 1
30 28 12 7 8 0 1
38 34 16 12 13 0 1
34 35 16 14 11 1 1
31 35 14 11 14 1 1
34 31 16 12 15 1 1
35 37 17 13 10 0 1
36 35 18 14 11 1 1
30 27 18 11 12 0 1
39 40 12 12 15 1 1
35 37 16 12 15 0 1
38 36 10 8 14 0 1
31 38 14 11 16 1 1
34 39 18 14 15 1 1
38 41 18 14 15 0 1
34 27 16 12 13 0 1
39 30 17 9 12 1 1
37 37 16 13 17 1 1
34 31 16 11 13 1 1
28 31 13 12 15 0 1
37 27 16 12 13 0 1
33 36 16 12 15 0 1
35 37 16 12 15 1 1
37 33 15 12 16 0 1
32 34 15 11 15 1 1
33 31 16 10 14 1 1
38 39 14 9 15 0 1
33 34 16 12 14 1 1
29 32 16 12 13 1 1
33 33 15 12 7 1 1
31 36 12 9 17 1 1
36 32 17 15 13 1 1
35 41 16 12 15 1 1
32 28 15 12 14 1 1
29 30 13 12 13 1 1
39 36 16 10 16 1 1
37 35 16 13 12 1 1
35 31 16 9 14 1 1
37 34 16 12 17 0 1
32 36 14 10 15 0 1
38 36 16 14 17 1 1
37 35 16 11 12 0 1
36 37 20 15 16 1 1
32 28 15 11 11 0 1
33 39 16 11 15 1 1
40 32 13 12 9 0 1
38 35 17 12 16 1 1
41 39 16 12 15 0 1
36 35 16 11 10 0 1
43 42 12 7 10 1 1
30 34 16 12 15 1 1
31 33 16 14 11 1 1
32 41 17 11 13 1 1
37 34 12 10 18 1 1
37 32 18 13 16 0 1
33 40 14 13 14 1 1
34 40 14 8 14 1 1
33 35 13 11 14 1 1
38 36 16 12 14 1 1
33 37 13 11 12 0 1
31 27 16 13 14 1 1
38 39 13 12 15 1 1
37 38 16 14 15 1 1
36 31 15 13 15 1 1
31 33 16 15 13 1 1
39 32 15 10 17 0 1
44 39 17 11 17 1 1
33 36 15 9 19 1 1
35 33 12 11 15 1 1
32 33 16 10 13 0 1
28 32 10 11 9 0 1
40 37 16 8 15 1 1
27 30 12 11 15 0 1
37 38 14 12 15 0 1
32 29 15 12 16 1 1
28 22 13 9 11 0 1
34 35 15 11 14 0 1
30 35 11 10 11 1 1
35 34 12 8 15 1 1
31 35 11 9 13 0 1
32 34 16 8 15 1 1
30 37 15 9 16 0 1
30 35 17 15 14 1 1
31 23 16 11 15 0 1
40 31 10 8 16 1 1
32 27 18 13 16 1 1
36 36 13 12 11 0 1
32 31 16 12 12 0 1
35 32 13 9 9 0 1
38 39 10 7 16 1 1
42 37 15 13 13 1 1
34 38 16 9 16 0 1
35 39 16 6 12 1 1
38 34 14 8 9 1 1
33 31 10 8 13 1 1
32 37 13 6 14 1 1
33 36 15 9 19 1 1
34 32 16 11 13 1 1
32 38 12 8 12 1 1
27 26 13 10 10 0 0
31 26 12 8 14 0 0
38 33 17 14 16 0 0
34 39 15 10 10 1 0
24 30 10 8 11 0 0
30 33 14 11 14 0 0
26 25 11 12 12 1 0
34 38 13 12 9 1 0
27 37 16 12 9 0 0
37 31 12 5 11 0 0
36 37 16 12 16 1 0
41 35 12 10 9 0 0
29 25 9 7 13 1 0
36 28 12 12 16 1 0
32 35 15 11 13 0 0
37 33 12 8 9 1 0
30 30 12 9 12 0 0
31 31 14 10 16 1 0
38 37 12 9 11 1 0
36 36 16 12 14 1 0
35 30 11 6 13 0 0
31 36 19 15 15 0 0
38 32 15 12 14 0 0
22 28 8 12 16 1 0
32 36 16 12 13 1 0
36 34 17 11 14 0 0
39 31 12 7 15 1 0
28 28 11 7 13 0 0
32 36 11 5 11 0 0
32 36 14 12 11 1 0
38 40 16 12 14 1 0
32 33 12 3 15 1 0
35 37 16 11 11 1 0
32 32 13 10 15 1 0
37 38 15 12 12 0 0
34 31 16 9 14 1 0
33 37 16 12 14 1 0
33 33 14 9 8 0 0
30 30 16 12 9 0 0
24 30 14 10 15 0 0
34 31 11 9 17 0 0
34 32 12 12 13 0 0
33 34 15 8 15 1 0
34 36 15 11 15 1 0
35 37 16 11 14 1 0
35 36 16 12 16 0 0
36 33 11 10 13 0 0
34 33 15 10 16 0 0
34 33 12 12 9 1 0
41 44 12 12 16 0 0
32 39 15 11 11 0 0
30 32 15 8 10 0 0
35 35 16 12 11 1 0
28 25 14 10 15 0 0
33 35 17 11 17 1 0
39 34 14 10 14 1 0
36 35 13 8 8 0 0
36 39 15 12 15 1 0
35 33 13 12 11 0 0
38 36 14 10 16 0 0
33 32 15 12 10 1 0
31 32 12 9 15 0 0
32 36 8 6 16 1 0
31 32 14 10 19 0 0
33 34 14 9 12 0 0
34 33 11 9 8 0 0
34 35 12 9 11 0 0
34 30 13 6 14 1 0
33 38 10 10 9 0 0
32 34 16 6 15 0 0
41 33 18 14 13 1 0
34 32 13 10 16 1 0
36 31 11 10 11 0 0
37 30 4 6 12 0 0
36 27 13 12 13 0 0
29 31 16 12 10 1 0
37 30 10 7 11 0 0
27 32 12 8 12 0 0
35 35 12 11 8 0 0
28 28 10 3 12 0 0
35 33 13 6 12 0 0
29 35 12 8 11 0 0
32 35 14 9 13 0 0
36 32 10 9 14 1 0
19 21 12 8 10 1 0
21 20 12 9 12 1 0
31 34 11 7 15 0 0
33 32 10 7 13 0 0
36 34 12 6 13 1 0
33 32 16 9 13 1 0
37 33 12 10 12 0 0
34 33 14 11 12 0 0
35 37 16 12 9 0 0
31 32 14 8 9 1 0
37 34 13 11 15 1 0
35 30 4 3 10 1 0
27 30 15 11 14 1 0
34 38 11 12 15 0 0
40 36 11 7 7 0 0
29 32 14 9 14 0 0
38 34 15 12 8 0 0
34 33 14 8 10 1 0
21 27 13 11 13 0 0
36 32 11 8 13 0 0
38 34 15 10 13 1 0
30 29 11 8 8 0 0
35 35 13 7 12 0 0
30 27 13 8 13 1 0
36 33 16 10 12 1 0
34 38 13 8 10 0 0
35 36 16 12 13 1 0
34 33 16 14 12 0 0
32 39 12 7 9 0 0
33 29 7 6 15 1 0
33 32 16 11 13 0 0
26 34 5 4 13 1 0
35 38 16 9 13 0 0
21 17 4 5 15 0 0
38 35 12 9 15 0 0
35 32 15 11 14 0 0
33 34 14 12 15 1 0
37 36 11 9 11 0 0
38 31 16 12 15 0 0
34 35 15 10 14 1 0
27 29 12 9 13 0 0
16 22 6 6 12 1 0
40 41 16 10 16 0 0
36 36 10 9 16 0 0
42 42 15 13 9 1 0
30 33 14 12 14 1 0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232157&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]9 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=232157&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232157&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Correlations for all pairs of data series (method=kendall)
ConnectedSeparateLearningSoftwareHappinessGenderPop
Connected10.2790.1640.1330.1060.0460.126
Separate0.27910.1790.1580.0740.1340.124
Learning0.1640.17910.4850.1750.1720.317
Software0.1330.1580.48510.1180.1730.277
Happiness0.1060.0740.1750.11810.180.236
Gender0.0460.1340.1720.1730.1810.197
Pop0.1260.1240.3170.2770.2360.1971

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Connected & Separate & Learning & Software & Happiness & Gender & Pop \tabularnewline
Connected & 1 & 0.279 & 0.164 & 0.133 & 0.106 & 0.046 & 0.126 \tabularnewline
Separate & 0.279 & 1 & 0.179 & 0.158 & 0.074 & 0.134 & 0.124 \tabularnewline
Learning & 0.164 & 0.179 & 1 & 0.485 & 0.175 & 0.172 & 0.317 \tabularnewline
Software & 0.133 & 0.158 & 0.485 & 1 & 0.118 & 0.173 & 0.277 \tabularnewline
Happiness & 0.106 & 0.074 & 0.175 & 0.118 & 1 & 0.18 & 0.236 \tabularnewline
Gender & 0.046 & 0.134 & 0.172 & 0.173 & 0.18 & 1 & 0.197 \tabularnewline
Pop & 0.126 & 0.124 & 0.317 & 0.277 & 0.236 & 0.197 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232157&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Connected[/C][C]Separate[/C][C]Learning[/C][C]Software[/C][C]Happiness[/C][C]Gender[/C][C]Pop[/C][/ROW]
[ROW][C]Connected[/C][C]1[/C][C]0.279[/C][C]0.164[/C][C]0.133[/C][C]0.106[/C][C]0.046[/C][C]0.126[/C][/ROW]
[ROW][C]Separate[/C][C]0.279[/C][C]1[/C][C]0.179[/C][C]0.158[/C][C]0.074[/C][C]0.134[/C][C]0.124[/C][/ROW]
[ROW][C]Learning[/C][C]0.164[/C][C]0.179[/C][C]1[/C][C]0.485[/C][C]0.175[/C][C]0.172[/C][C]0.317[/C][/ROW]
[ROW][C]Software[/C][C]0.133[/C][C]0.158[/C][C]0.485[/C][C]1[/C][C]0.118[/C][C]0.173[/C][C]0.277[/C][/ROW]
[ROW][C]Happiness[/C][C]0.106[/C][C]0.074[/C][C]0.175[/C][C]0.118[/C][C]1[/C][C]0.18[/C][C]0.236[/C][/ROW]
[ROW][C]Gender[/C][C]0.046[/C][C]0.134[/C][C]0.172[/C][C]0.173[/C][C]0.18[/C][C]1[/C][C]0.197[/C][/ROW]
[ROW][C]Pop[/C][C]0.126[/C][C]0.124[/C][C]0.317[/C][C]0.277[/C][C]0.236[/C][C]0.197[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232157&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232157&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=kendall)
ConnectedSeparateLearningSoftwareHappinessGenderPop
Connected10.2790.1640.1330.1060.0460.126
Separate0.27910.1790.1580.0740.1340.124
Learning0.1640.17910.4850.1750.1720.317
Software0.1330.1580.48510.1180.1730.277
Happiness0.1060.0740.1750.11810.180.236
Gender0.0460.1340.1720.1730.1810.197
Pop0.1260.1240.3170.2770.2360.1971







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Connected;Separate0.52440.37410.2786
p-value(0)(0)(0)
Connected;Learning0.29810.21660.1644
p-value(0)(2e-04)(2e-04)
Connected;Software0.20060.17960.1331
p-value(6e-04)(0.0022)(0.0023)
Connected;Happiness0.11440.14350.1058
p-value(0.0525)(0.0148)(0.0142)
Connected;Gender0.06030.05480.0465
p-value(0.3081)(0.3537)(0.3528)
Connected;Pop0.19710.14930.1265
p-value(8e-04)(0.0112)(0.0115)
Separate;Learning0.29660.23920.1791
p-value(0)(0)(0)
Separate;Software0.23040.21250.1578
p-value(1e-04)(3e-04)(3e-04)
Separate;Happiness0.08540.10380.0737
p-value(0.1482)(0.0786)(0.0879)
Separate;Gender0.13950.15820.1342
p-value(0.0179)(0.0072)(0.0074)
Separate;Pop0.15780.1460.1238
p-value(0.0073)(0.0132)(0.0134)
Learning;Software0.65470.59860.4848
p-value(0)(0)(0)
Learning;Happiness0.21830.23330.1749
p-value(2e-04)(1e-04)(1e-04)
Learning;Gender0.15830.19770.1721
p-value(0.0071)(7e-04)(8e-04)
Learning;Pop0.36270.36460.3174
p-value(0)(0)(0)
Software;Happiness0.16140.15850.1179
p-value(0.006)(0.007)(0.0078)
Software;Gender0.16810.1990.1732
p-value(0.0042)(7e-04)(7e-04)
Software;Pop0.32570.31870.2774
p-value(0)(0)(0)
Happiness;Gender0.21410.20840.1797
p-value(3e-04)(4e-04)(4e-04)
Happiness;Pop0.27590.27350.2358
p-value(0)(0)(0)
Gender;Pop0.19660.19660.1966
p-value(8e-04)(8e-04)(9e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Connected;Separate & 0.5244 & 0.3741 & 0.2786 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Connected;Learning & 0.2981 & 0.2166 & 0.1644 \tabularnewline
p-value & (0) & (2e-04) & (2e-04) \tabularnewline
Connected;Software & 0.2006 & 0.1796 & 0.1331 \tabularnewline
p-value & (6e-04) & (0.0022) & (0.0023) \tabularnewline
Connected;Happiness & 0.1144 & 0.1435 & 0.1058 \tabularnewline
p-value & (0.0525) & (0.0148) & (0.0142) \tabularnewline
Connected;Gender & 0.0603 & 0.0548 & 0.0465 \tabularnewline
p-value & (0.3081) & (0.3537) & (0.3528) \tabularnewline
Connected;Pop & 0.1971 & 0.1493 & 0.1265 \tabularnewline
p-value & (8e-04) & (0.0112) & (0.0115) \tabularnewline
Separate;Learning & 0.2966 & 0.2392 & 0.1791 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Separate;Software & 0.2304 & 0.2125 & 0.1578 \tabularnewline
p-value & (1e-04) & (3e-04) & (3e-04) \tabularnewline
Separate;Happiness & 0.0854 & 0.1038 & 0.0737 \tabularnewline
p-value & (0.1482) & (0.0786) & (0.0879) \tabularnewline
Separate;Gender & 0.1395 & 0.1582 & 0.1342 \tabularnewline
p-value & (0.0179) & (0.0072) & (0.0074) \tabularnewline
Separate;Pop & 0.1578 & 0.146 & 0.1238 \tabularnewline
p-value & (0.0073) & (0.0132) & (0.0134) \tabularnewline
Learning;Software & 0.6547 & 0.5986 & 0.4848 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Happiness & 0.2183 & 0.2333 & 0.1749 \tabularnewline
p-value & (2e-04) & (1e-04) & (1e-04) \tabularnewline
Learning;Gender & 0.1583 & 0.1977 & 0.1721 \tabularnewline
p-value & (0.0071) & (7e-04) & (8e-04) \tabularnewline
Learning;Pop & 0.3627 & 0.3646 & 0.3174 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Software;Happiness & 0.1614 & 0.1585 & 0.1179 \tabularnewline
p-value & (0.006) & (0.007) & (0.0078) \tabularnewline
Software;Gender & 0.1681 & 0.199 & 0.1732 \tabularnewline
p-value & (0.0042) & (7e-04) & (7e-04) \tabularnewline
Software;Pop & 0.3257 & 0.3187 & 0.2774 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Happiness;Gender & 0.2141 & 0.2084 & 0.1797 \tabularnewline
p-value & (3e-04) & (4e-04) & (4e-04) \tabularnewline
Happiness;Pop & 0.2759 & 0.2735 & 0.2358 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Gender;Pop & 0.1966 & 0.1966 & 0.1966 \tabularnewline
p-value & (8e-04) & (8e-04) & (9e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232157&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]Connected;Separate[/C][C]0.5244[/C][C]0.3741[/C][C]0.2786[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Connected;Learning[/C][C]0.2981[/C][C]0.2166[/C][C]0.1644[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]Connected;Software[/C][C]0.2006[/C][C]0.1796[/C][C]0.1331[/C][/ROW]
[ROW][C]p-value[/C][C](6e-04)[/C][C](0.0022)[/C][C](0.0023)[/C][/ROW]
[ROW][C]Connected;Happiness[/C][C]0.1144[/C][C]0.1435[/C][C]0.1058[/C][/ROW]
[ROW][C]p-value[/C][C](0.0525)[/C][C](0.0148)[/C][C](0.0142)[/C][/ROW]
[ROW][C]Connected;Gender[/C][C]0.0603[/C][C]0.0548[/C][C]0.0465[/C][/ROW]
[ROW][C]p-value[/C][C](0.3081)[/C][C](0.3537)[/C][C](0.3528)[/C][/ROW]
[ROW][C]Connected;Pop[/C][C]0.1971[/C][C]0.1493[/C][C]0.1265[/C][/ROW]
[ROW][C]p-value[/C][C](8e-04)[/C][C](0.0112)[/C][C](0.0115)[/C][/ROW]
[ROW][C]Separate;Learning[/C][C]0.2966[/C][C]0.2392[/C][C]0.1791[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Separate;Software[/C][C]0.2304[/C][C]0.2125[/C][C]0.1578[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](3e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]Separate;Happiness[/C][C]0.0854[/C][C]0.1038[/C][C]0.0737[/C][/ROW]
[ROW][C]p-value[/C][C](0.1482)[/C][C](0.0786)[/C][C](0.0879)[/C][/ROW]
[ROW][C]Separate;Gender[/C][C]0.1395[/C][C]0.1582[/C][C]0.1342[/C][/ROW]
[ROW][C]p-value[/C][C](0.0179)[/C][C](0.0072)[/C][C](0.0074)[/C][/ROW]
[ROW][C]Separate;Pop[/C][C]0.1578[/C][C]0.146[/C][C]0.1238[/C][/ROW]
[ROW][C]p-value[/C][C](0.0073)[/C][C](0.0132)[/C][C](0.0134)[/C][/ROW]
[ROW][C]Learning;Software[/C][C]0.6547[/C][C]0.5986[/C][C]0.4848[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Happiness[/C][C]0.2183[/C][C]0.2333[/C][C]0.1749[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]Learning;Gender[/C][C]0.1583[/C][C]0.1977[/C][C]0.1721[/C][/ROW]
[ROW][C]p-value[/C][C](0.0071)[/C][C](7e-04)[/C][C](8e-04)[/C][/ROW]
[ROW][C]Learning;Pop[/C][C]0.3627[/C][C]0.3646[/C][C]0.3174[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Software;Happiness[/C][C]0.1614[/C][C]0.1585[/C][C]0.1179[/C][/ROW]
[ROW][C]p-value[/C][C](0.006)[/C][C](0.007)[/C][C](0.0078)[/C][/ROW]
[ROW][C]Software;Gender[/C][C]0.1681[/C][C]0.199[/C][C]0.1732[/C][/ROW]
[ROW][C]p-value[/C][C](0.0042)[/C][C](7e-04)[/C][C](7e-04)[/C][/ROW]
[ROW][C]Software;Pop[/C][C]0.3257[/C][C]0.3187[/C][C]0.2774[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Happiness;Gender[/C][C]0.2141[/C][C]0.2084[/C][C]0.1797[/C][/ROW]
[ROW][C]p-value[/C][C](3e-04)[/C][C](4e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]Happiness;Pop[/C][C]0.2759[/C][C]0.2735[/C][C]0.2358[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Gender;Pop[/C][C]0.1966[/C][C]0.1966[/C][C]0.1966[/C][/ROW]
[ROW][C]p-value[/C][C](8e-04)[/C][C](8e-04)[/C][C](9e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232157&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232157&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
Connected;Separate0.52440.37410.2786
p-value(0)(0)(0)
Connected;Learning0.29810.21660.1644
p-value(0)(2e-04)(2e-04)
Connected;Software0.20060.17960.1331
p-value(6e-04)(0.0022)(0.0023)
Connected;Happiness0.11440.14350.1058
p-value(0.0525)(0.0148)(0.0142)
Connected;Gender0.06030.05480.0465
p-value(0.3081)(0.3537)(0.3528)
Connected;Pop0.19710.14930.1265
p-value(8e-04)(0.0112)(0.0115)
Separate;Learning0.29660.23920.1791
p-value(0)(0)(0)
Separate;Software0.23040.21250.1578
p-value(1e-04)(3e-04)(3e-04)
Separate;Happiness0.08540.10380.0737
p-value(0.1482)(0.0786)(0.0879)
Separate;Gender0.13950.15820.1342
p-value(0.0179)(0.0072)(0.0074)
Separate;Pop0.15780.1460.1238
p-value(0.0073)(0.0132)(0.0134)
Learning;Software0.65470.59860.4848
p-value(0)(0)(0)
Learning;Happiness0.21830.23330.1749
p-value(2e-04)(1e-04)(1e-04)
Learning;Gender0.15830.19770.1721
p-value(0.0071)(7e-04)(8e-04)
Learning;Pop0.36270.36460.3174
p-value(0)(0)(0)
Software;Happiness0.16140.15850.1179
p-value(0.006)(0.007)(0.0078)
Software;Gender0.16810.1990.1732
p-value(0.0042)(7e-04)(7e-04)
Software;Pop0.32570.31870.2774
p-value(0)(0)(0)
Happiness;Gender0.21410.20840.1797
p-value(3e-04)(4e-04)(4e-04)
Happiness;Pop0.27590.27350.2358
p-value(0)(0)(0)
Gender;Pop0.19660.19660.1966
p-value(8e-04)(8e-04)(9e-04)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.810.760.76
0.020.860.90.9
0.030.860.90.9
0.040.860.90.9
0.050.860.90.9
0.060.90.90.9
0.070.90.90.9
0.080.90.950.9
0.090.90.950.95
0.10.90.950.95

\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 & 0.81 & 0.76 & 0.76 \tabularnewline
0.02 & 0.86 & 0.9 & 0.9 \tabularnewline
0.03 & 0.86 & 0.9 & 0.9 \tabularnewline
0.04 & 0.86 & 0.9 & 0.9 \tabularnewline
0.05 & 0.86 & 0.9 & 0.9 \tabularnewline
0.06 & 0.9 & 0.9 & 0.9 \tabularnewline
0.07 & 0.9 & 0.9 & 0.9 \tabularnewline
0.08 & 0.9 & 0.95 & 0.9 \tabularnewline
0.09 & 0.9 & 0.95 & 0.95 \tabularnewline
0.1 & 0.9 & 0.95 & 0.95 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232157&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]0.81[/C][C]0.76[/C][C]0.76[/C][/ROW]
[ROW][C]0.02[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.03[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.04[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.05[/C][C]0.86[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.06[/C][C]0.9[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.07[/C][C]0.9[/C][C]0.9[/C][C]0.9[/C][/ROW]
[ROW][C]0.08[/C][C]0.9[/C][C]0.95[/C][C]0.9[/C][/ROW]
[ROW][C]0.09[/C][C]0.9[/C][C]0.95[/C][C]0.95[/C][/ROW]
[ROW][C]0.1[/C][C]0.9[/C][C]0.95[/C][C]0.95[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232157&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232157&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
0.010.810.760.76
0.020.860.90.9
0.030.860.90.9
0.040.860.90.9
0.050.860.90.9
0.060.90.90.9
0.070.90.90.9
0.080.90.950.9
0.090.90.950.95
0.10.90.950.95



Parameters (Session):
Parameters (R input):
par1 = kendall ;
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')