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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 computationSun, 09 Dec 2012 06:32:38 -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/09/t1355052779cwsofez6h8sl0a3.htm/, Retrieved Thu, 31 Oct 2024 23:47:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197811, Retrieved Thu, 31 Oct 2024 23:47:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMP     [Kendall tau Correlation Matrix] [WS10-Kendall Tau ...] [2012-12-09 11:32:38] [8dfbd7005bbb2cc4a26b500e2e7611ce] [Current]
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Dataseries X:
1	1	41	38	13	12	14
1	1	39	32	16	11	18
1	1	30	35	19	15	11
1	0	31	33	15	6	12
1	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	0	38	31	16	10	17
1	1	36	34	16	12	19
1	0	38	35	16	12	10
1	1	39	38	16	11	16
1	1	33	37	17	15	18
1	0	32	33	15	12	14
1	0	36	32	15	10	14
1	1	38	38	20	12	17
1	0	39	38	18	11	14
1	1	32	32	16	12	16
1	0	32	33	16	11	18
1	1	31	31	16	12	11
1	1	39	38	19	13	14
1	1	37	39	16	11	12
1	0	39	32	17	12	17
1	1	41	32	17	13	9
1	0	36	35	16	10	16
1	1	33	37	15	14	14
1	1	33	33	16	12	15
1	0	34	33	14	10	11
1	1	31	31	15	12	16
1	0	27	32	12	8	13
1	1	37	31	14	10	17
1	1	34	37	16	12	15
1	0	34	30	14	12	14
1	0	32	33	10	7	16
1	0	29	31	10	9	9
1	0	36	33	14	12	15
1	1	29	31	16	10	17
1	0	35	33	16	10	13
1	0	37	32	16	10	15
1	1	34	33	14	12	16
1	0	38	32	20	15	16
1	0	35	33	14	10	12
1	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	0	38	32	14	12	13
1	1	32	38	16	14	16
1	0	32	30	14	10	14
1	0	32	33	12	12	11
1	1	34	38	16	13	12
1	0	32	32	9	5	12
1	1	37	35	14	6	15
1	1	39	34	16	12	16
1	1	29	34	16	12	15
1	0	37	36	15	11	12
1	1	35	34	16	10	12
1	0	30	28	12	7	8
1	0	38	34	16	12	13
1	1	34	35	16	14	11
1	1	31	35	14	11	14
1	1	34	31	16	12	15
1	0	35	37	17	13	10
1	1	36	35	18	14	11
1	0	30	27	18	11	12
1	1	39	40	12	12	15
1	0	35	37	16	12	15
1	0	38	36	10	8	14
1	1	31	38	14	11	16
1	1	34	39	18	14	15
1	0	38	41	18	14	15
1	0	34	27	16	12	13
1	1	39	30	17	9	12
1	1	37	37	16	13	17
1	1	34	31	16	11	13
1	0	28	31	13	12	15
1	0	37	27	16	12	13
1	0	33	36	16	12	15
1	1	35	37	16	12	15
1	0	37	33	15	12	16
1	1	32	34	15	11	15
1	1	33	31	16	10	14
1	0	38	39	14	9	15
1	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	0	37	34	16	12	17
1	0	32	36	14	10	15
1	1	38	36	16	14	17
1	0	37	35	16	11	12
1	1	36	37	20	15	16
1	0	32	28	15	11	11
1	1	33	39	16	11	15
1	0	40	32	13	12	9
1	1	38	35	17	12	16
1	0	41	39	16	12	15
1	0	36	35	16	11	10
1	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	0	37	32	18	13	16
1	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	0	33	37	13	11	12
1	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	0	39	32	15	10	17
1	1	44	39	17	11	17
1	1	33	36	15	9	19
1	1	35	33	12	11	15
1	0	32	33	16	10	13
1	0	28	32	10	11	9
1	1	40	37	16	8	15
1	0	27	30	12	11	15
1	0	37	38	14	12	15
1	1	32	29	15	12	16
1	0	28	22	13	9	11
1	0	34	35	15	11	14
1	1	30	35	11	10	11
1	1	35	34	12	8	15
1	0	31	35	11	9	13
1	1	32	34	16	8	15
1	0	30	37	15	9	16
1	1	30	35	17	15	14
1	0	31	23	16	11	15
1	1	40	31	10	8	16
1	1	32	27	18	13	16
1	0	36	36	13	12	11
1	0	32	31	16	12	12
1	0	35	32	13	9	9
1	1	38	39	10	7	16
1	1	42	37	15	13	13
1	0	34	38	16	9	16
1	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
0	0	27	26	13	10	10
0	0	31	26	12	8	14
0	0	38	33	17	14	16
0	1	34	39	15	10	10
0	0	24	30	10	8	11
0	0	30	33	14	11	14
0	1	26	25	11	12	12
0	1	34	38	13	12	9
0	0	27	37	16	12	9
0	0	37	31	12	5	11
0	1	36	37	16	12	16
0	0	41	35	12	10	9
0	1	29	25	9	7	13
0	1	36	28	12	12	16
0	0	32	35	15	11	13
0	1	37	33	12	8	9
0	0	30	30	12	9	12
0	1	31	31	14	10	16
0	1	38	37	12	9	11
0	1	36	36	16	12	14
0	0	35	30	11	6	13
0	0	31	36	19	15	15
0	0	38	32	15	12	14
0	1	22	28	8	12	16
0	1	32	36	16	12	13
0	0	36	34	17	11	14
0	1	39	31	12	7	15
0	0	28	28	11	7	13
0	0	32	36	11	5	11
0	1	32	36	14	12	11
0	1	38	40	16	12	14
0	1	32	33	12	3	15
0	1	35	37	16	11	11
0	1	32	32	13	10	15
0	0	37	38	15	12	12
0	1	34	31	16	9	14
0	1	33	37	16	12	14
0	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	1	33	34	15	8	15
0	1	34	36	15	11	15
0	1	35	37	16	11	14
0	0	35	36	16	12	16
0	0	36	33	11	10	13
0	0	34	33	15	10	16
0	1	34	33	12	12	9
0	0	41	44	12	12	16
0	0	32	39	15	11	11
0	0	30	32	15	8	10
0	1	35	35	16	12	11
0	0	28	25	14	10	15
0	1	33	35	17	11	17
0	1	39	34	14	10	14
0	0	36	35	13	8	8
0	1	36	39	15	12	15
0	0	35	33	13	12	11
0	0	38	36	14	10	16
0	1	33	32	15	12	10
0	0	31	32	12	9	15
0	1	32	36	8	6	16
0	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	1	34	30	13	6	14
0	0	33	38	10	10	9
0	0	32	34	16	6	15
0	1	41	33	18	14	13
0	1	34	32	13	10	16
0	0	36	31	11	10	11
0	0	37	30	4	6	12
0	0	36	27	13	12	13
0	1	29	31	16	12	10
0	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	1	36	32	10	9	14
0	1	19	21	12	8	10
0	1	21	20	12	9	12
0	0	31	34	11	7	15
0	0	33	32	10	7	13
0	1	36	34	12	6	13
0	1	33	32	16	9	13
0	0	37	33	12	10	12
0	0	34	33	14	11	12
0	0	35	37	16	12	9
0	1	31	32	14	8	9
0	1	37	34	13	11	15
0	1	35	30	4	3	10
0	1	27	30	15	11	14
0	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	1	34	33	14	8	10
0	0	21	27	13	11	13
0	0	36	32	11	8	13
0	1	38	34	15	10	13
0	0	30	29	11	8	8
0	0	35	35	13	7	12
0	1	30	27	13	8	13
0	1	36	33	16	10	12
0	0	34	38	13	8	10
0	1	35	36	16	12	13
0	0	34	33	16	14	12
0	0	32	39	12	7	9
0	1	33	29	7	6	15
0	0	33	32	16	11	13
0	1	26	34	5	4	13
0	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	1	33	34	14	12	15
0	0	37	36	11	9	11
0	0	38	31	16	12	15
0	1	34	35	15	10	14
0	0	27	29	12	9	13
0	1	16	22	6	6	12
0	0	40	41	16	10	16
0	0	36	36	10	9	16
0	1	42	42	15	13	9
0	1	30	33	14	12	14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197811&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197811&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197811&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'Gertrude Mary Cox' @ cox.wessa.net







Correlations for all pairs of data series (method=kendall)
PopGenderConnectedSeparateLearningSoftwareHappiness
Pop10.1970.1260.1240.3170.2770.236
Gender0.19710.0460.1340.1720.1730.18
Connected0.1260.04610.2790.1640.1330.106
Separate0.1240.1340.27910.1790.1580.074
Learning0.3170.1720.1640.17910.4850.175
Software0.2770.1730.1330.1580.48510.118
Happiness0.2360.180.1060.0740.1750.1181

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Pop & Gender & Connected & Separate & Learning & Software & Happiness \tabularnewline
Pop & 1 & 0.197 & 0.126 & 0.124 & 0.317 & 0.277 & 0.236 \tabularnewline
Gender & 0.197 & 1 & 0.046 & 0.134 & 0.172 & 0.173 & 0.18 \tabularnewline
Connected & 0.126 & 0.046 & 1 & 0.279 & 0.164 & 0.133 & 0.106 \tabularnewline
Separate & 0.124 & 0.134 & 0.279 & 1 & 0.179 & 0.158 & 0.074 \tabularnewline
Learning & 0.317 & 0.172 & 0.164 & 0.179 & 1 & 0.485 & 0.175 \tabularnewline
Software & 0.277 & 0.173 & 0.133 & 0.158 & 0.485 & 1 & 0.118 \tabularnewline
Happiness & 0.236 & 0.18 & 0.106 & 0.074 & 0.175 & 0.118 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197811&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Pop[/C][C]Gender[/C][C]Connected[/C][C]Separate[/C][C]Learning[/C][C]Software[/C][C]Happiness[/C][/ROW]
[ROW][C]Pop[/C][C]1[/C][C]0.197[/C][C]0.126[/C][C]0.124[/C][C]0.317[/C][C]0.277[/C][C]0.236[/C][/ROW]
[ROW][C]Gender[/C][C]0.197[/C][C]1[/C][C]0.046[/C][C]0.134[/C][C]0.172[/C][C]0.173[/C][C]0.18[/C][/ROW]
[ROW][C]Connected[/C][C]0.126[/C][C]0.046[/C][C]1[/C][C]0.279[/C][C]0.164[/C][C]0.133[/C][C]0.106[/C][/ROW]
[ROW][C]Separate[/C][C]0.124[/C][C]0.134[/C][C]0.279[/C][C]1[/C][C]0.179[/C][C]0.158[/C][C]0.074[/C][/ROW]
[ROW][C]Learning[/C][C]0.317[/C][C]0.172[/C][C]0.164[/C][C]0.179[/C][C]1[/C][C]0.485[/C][C]0.175[/C][/ROW]
[ROW][C]Software[/C][C]0.277[/C][C]0.173[/C][C]0.133[/C][C]0.158[/C][C]0.485[/C][C]1[/C][C]0.118[/C][/ROW]
[ROW][C]Happiness[/C][C]0.236[/C][C]0.18[/C][C]0.106[/C][C]0.074[/C][C]0.175[/C][C]0.118[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197811&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197811&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)
PopGenderConnectedSeparateLearningSoftwareHappiness
Pop10.1970.1260.1240.3170.2770.236
Gender0.19710.0460.1340.1720.1730.18
Connected0.1260.04610.2790.1640.1330.106
Separate0.1240.1340.27910.1790.1580.074
Learning0.3170.1720.1640.17910.4850.175
Software0.2770.1730.1330.1580.48510.118
Happiness0.2360.180.1060.0740.1750.1181







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Pop;Gender0.19660.19660.1966
p-value(8e-04)(8e-04)(9e-04)
Pop;Connected0.19710.14930.1265
p-value(8e-04)(0.0112)(0.0115)
Pop;Separate0.15780.1460.1238
p-value(0.0073)(0.0132)(0.0134)
Pop;Learning0.36270.36460.3174
p-value(0)(0)(0)
Pop;Software0.32570.31870.2774
p-value(0)(0)(0)
Pop;Happiness0.27590.27350.2358
p-value(0)(0)(0)
Gender;Connected0.06030.05480.0465
p-value(0.3081)(0.3537)(0.3528)
Gender;Separate0.13950.15820.1342
p-value(0.0179)(0.0072)(0.0074)
Gender;Learning0.15830.19770.1721
p-value(0.0071)(7e-04)(8e-04)
Gender;Software0.16810.1990.1732
p-value(0.0042)(7e-04)(7e-04)
Gender;Happiness0.21410.20840.1797
p-value(3e-04)(4e-04)(4e-04)
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)
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)
Learning;Software0.65470.59860.4848
p-value(0)(0)(0)
Learning;Happiness0.21830.23330.1749
p-value(2e-04)(1e-04)(1e-04)
Software;Happiness0.16140.15850.1179
p-value(0.006)(0.007)(0.0078)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Pop;Gender & 0.1966 & 0.1966 & 0.1966 \tabularnewline
p-value & (8e-04) & (8e-04) & (9e-04) \tabularnewline
Pop;Connected & 0.1971 & 0.1493 & 0.1265 \tabularnewline
p-value & (8e-04) & (0.0112) & (0.0115) \tabularnewline
Pop;Separate & 0.1578 & 0.146 & 0.1238 \tabularnewline
p-value & (0.0073) & (0.0132) & (0.0134) \tabularnewline
Pop;Learning & 0.3627 & 0.3646 & 0.3174 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Pop;Software & 0.3257 & 0.3187 & 0.2774 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Pop;Happiness & 0.2759 & 0.2735 & 0.2358 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Gender;Connected & 0.0603 & 0.0548 & 0.0465 \tabularnewline
p-value & (0.3081) & (0.3537) & (0.3528) \tabularnewline
Gender;Separate & 0.1395 & 0.1582 & 0.1342 \tabularnewline
p-value & (0.0179) & (0.0072) & (0.0074) \tabularnewline
Gender;Learning & 0.1583 & 0.1977 & 0.1721 \tabularnewline
p-value & (0.0071) & (7e-04) & (8e-04) \tabularnewline
Gender;Software & 0.1681 & 0.199 & 0.1732 \tabularnewline
p-value & (0.0042) & (7e-04) & (7e-04) \tabularnewline
Gender;Happiness & 0.2141 & 0.2084 & 0.1797 \tabularnewline
p-value & (3e-04) & (4e-04) & (4e-04) \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
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
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
Software;Happiness & 0.1614 & 0.1585 & 0.1179 \tabularnewline
p-value & (0.006) & (0.007) & (0.0078) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197811&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]Pop;Gender[/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]
[ROW][C]Pop;Connected[/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]Pop;Separate[/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]Pop;Learning[/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]Pop;Software[/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]Pop;Happiness[/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;Connected[/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]Gender;Separate[/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]Gender;Learning[/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]Gender;Software[/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]Gender;Happiness[/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]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]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]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]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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197811&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197811&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
Pop;Gender0.19660.19660.1966
p-value(8e-04)(8e-04)(9e-04)
Pop;Connected0.19710.14930.1265
p-value(8e-04)(0.0112)(0.0115)
Pop;Separate0.15780.1460.1238
p-value(0.0073)(0.0132)(0.0134)
Pop;Learning0.36270.36460.3174
p-value(0)(0)(0)
Pop;Software0.32570.31870.2774
p-value(0)(0)(0)
Pop;Happiness0.27590.27350.2358
p-value(0)(0)(0)
Gender;Connected0.06030.05480.0465
p-value(0.3081)(0.3537)(0.3528)
Gender;Separate0.13950.15820.1342
p-value(0.0179)(0.0072)(0.0074)
Gender;Learning0.15830.19770.1721
p-value(0.0071)(7e-04)(8e-04)
Gender;Software0.16810.1990.1732
p-value(0.0042)(7e-04)(7e-04)
Gender;Happiness0.21410.20840.1797
p-value(3e-04)(4e-04)(4e-04)
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)
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)
Learning;Software0.65470.59860.4848
p-value(0)(0)(0)
Learning;Happiness0.21830.23330.1749
p-value(2e-04)(1e-04)(1e-04)
Software;Happiness0.16140.15850.1179
p-value(0.006)(0.007)(0.0078)



Parameters (Session):
par1 = kendall ;
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')
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)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')