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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 computationSat, 15 Dec 2012 06:25:48 -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/15/t1355570849uf0gsih2430jm9i.htm/, Retrieved Thu, 31 Oct 2024 23:54:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199840, Retrieved Thu, 31 Oct 2024 23:54:04 +0000
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User-defined keywords
Estimated Impact148
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] [Workshop 10: Kend...] [2012-12-09 10:01:58] [7bed0d115000febeef05f886574a3dac]
-    D      [Kendall tau Correlation Matrix] [Workshop 10 Peer ...] [2012-12-15 11:25:48] [02d90269174925f788b5f8bc5e12639b] [Current]
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Dataseries X:
2	7	41	38	13	12	14	12
2	5	39	32	16	11	18	11
2	5	30	35	19	15	11	14
1	5	31	33	15	6	12	12
2	8	34	37	14	13	16	21
2	6	35	29	13	10	18	12
2	5	39	31	19	12	14	22
2	6	34	36	15	14	14	11
2	5	36	35	14	12	15	10
2	4	37	38	15	6	15	13
1	6	38	31	16	10	17	10
2	5	36	34	16	12	19	8
1	5	38	35	16	12	10	15
2	6	39	38	16	11	16	14
2	7	33	37	17	15	18	10
1	6	32	33	15	12	14	14
1	7	36	32	15	10	14	14
2	6	38	38	20	12	17	11
1	8	39	38	18	11	14	10
2	7	32	32	16	12	16	13
1	5	32	33	16	11	18	7
2	5	31	31	16	12	11	14
2	7	39	38	19	13	14	12
2	7	37	39	16	11	12	14
1	5	39	32	17	9	17	11
2	4	41	32	17	13	9	9
1	10	36	35	16	10	16	11
2	6	33	37	15	14	14	15
2	5	33	33	16	12	15	14
1	5	34	33	14	10	11	13
2	5	31	28	15	12	16	9
1	5	27	32	12	8	13	15
2	6	37	31	14	10	17	10
2	5	34	37	16	12	15	11
1	5	34	30	14	12	14	13
1	5	32	33	7	7	16	8
1	5	29	31	10	6	9	20
1	5	36	33	14	12	15	12
2	5	29	31	16	10	17	10
1	5	35	33	16	10	13	10
1	5	37	32	16	10	15	9
2	7	34	33	14	12	16	14
1	5	38	32	20	15	16	8
1	6	35	33	14	10	12	14
2	7	38	28	14	10	12	11
2	7	37	35	11	12	11	13
2	5	38	39	14	13	15	9
2	5	33	34	15	11	15	11
2	4	36	38	16	11	17	15
1	5	38	32	14	12	13	11
2	4	32	38	16	14	16	10
1	5	32	30	14	10	14	14
1	5	32	33	12	12	11	18
2	7	34	38	16	13	12	14
1	5	32	32	9	5	12	11
2	5	37	32	14	6	15	12
2	6	39	34	16	12	16	13
2	4	29	34	16	12	15	9
1	6	37	36	15	11	12	10
2	6	35	34	16	10	12	15
1	5	30	28	12	7	8	20
1	7	38	34	16	12	13	12
2	6	34	35	16	14	11	12
2	8	31	35	14	11	14	14
2	7	34	31	16	12	15	13
1	5	35	37	17	13	10	11
2	6	36	35	18	14	11	17
1	6	30	27	18	11	12	12
2	5	39	40	12	12	15	13
1	5	35	37	16	12	15	14
1	5	38	36	10	8	14	13
2	5	31	38	14	11	16	15
2	4	34	39	18	14	15	13
1	6	38	41	18	14	15	10
1	6	34	27	16	12	13	11
2	6	39	30	17	9	12	19
2	6	37	37	16	13	17	13
2	7	34	31	16	11	13	17
1	5	28	31	13	12	15	13
1	7	37	27	16	12	13	9
1	6	33	36	16	12	15	11
1	5	37	38	20	12	16	10
2	5	35	37	16	12	15	9
1	4	37	33	15	12	16	12
2	8	32	34	15	11	15	12
2	8	33	31	16	10	14	13
1	5	38	39	14	9	15	13
2	5	33	34	16	12	14	12
2	6	29	32	16	12	13	15
2	4	33	33	15	12	7	22
2	5	31	36	12	9	17	13
2	5	36	32	17	15	13	15
2	5	35	41	16	12	15	13
2	5	32	28	15	12	14	15
2	6	29	30	13	12	13	10
2	6	39	36	16	10	16	11
2	5	37	35	16	13	12	16
2	6	35	31	16	9	14	11
1	5	37	34	16	12	17	11
1	7	32	36	14	10	15	10
2	5	38	36	16	14	17	10
1	6	37	35	16	11	12	16
2	6	36	37	20	15	16	12
1	6	32	28	15	11	11	11
2	4	33	39	16	11	15	16
1	5	40	32	13	12	9	19
2	5	38	35	17	12	16	11
1	7	41	39	16	12	15	16
1	6	36	35	16	11	10	15
2	9	43	42	12	7	10	24
2	6	30	34	16	12	15	14
2	6	31	33	16	14	11	15
2	5	32	41	17	11	13	11
1	6	32	33	13	11	14	15
2	5	37	34	12	10	18	12
1	8	37	32	18	13	16	10
2	7	33	40	14	13	14	14
2	5	34	40	14	8	14	13
2	7	33	35	13	11	14	9
2	6	38	36	16	12	14	15
2	6	33	37	13	11	12	15
2	9	31	27	16	13	14	14
2	7	38	39	13	12	15	11
2	6	37	38	16	14	15	8
2	5	33	31	15	13	15	11
2	5	31	33	16	15	13	11
1	6	39	32	15	10	17	8
2	6	44	39	17	11	17	10
2	7	33	36	15	9	19	11
2	5	35	33	12	11	15	13
1	5	32	33	16	10	13	11
1	5	28	32	10	11	9	20
2	6	40	37	16	8	15	10
1	4	27	30	12	11	15	15
1	5	37	38	14	12	15	12
2	7	32	29	15	12	16	14
1	5	28	22	13	9	11	23
1	7	34	35	15	11	14	14
2	7	30	35	11	10	11	16
2	6	35	34	12	8	15	11
1	5	31	35	8	9	13	12
2	8	32	34	16	8	15	10
1	5	30	34	15	9	16	14
2	5	30	35	17	15	14	12
1	5	31	23	16	11	15	12
2	6	40	31	10	8	16	11
2	4	32	27	18	13	16	12
1	5	36	36	13	12	11	13
1	5	32	31	16	12	12	11
1	7	35	32	13	9	9	19
2	6	38	39	10	7	16	12
2	7	42	37	15	13	13	17
1	10	34	38	16	9	16	9
2	6	35	39	16	6	12	12
2	8	35	34	14	8	9	19
2	4	33	31	10	8	13	18
2	5	36	32	17	15	13	15
2	6	32	37	13	6	14	14
2	7	33	36	15	9	19	11
2	7	34	32	16	11	13	9
2	6	32	35	12	8	12	18
2	6	34	36	13	8	13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199840&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199840&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199840&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'Sir Maurice George Kendall' @ kendall.wessa.net







Correlations for all pairs of data series (method=kendall)
genderageConnectedSeparateLearningSoftwareHappinessDepression
gender10.1150.030.1940.0950.1660.1420.056
age0.11510.1120.090.007-0.055-0.0230.013
Connected0.030.11210.2410.1590.0550.127-0.128
Separate0.1940.090.24110.0670.0790.11-0.03
Learning0.0950.0070.1590.06710.3910.12-0.189
Software0.166-0.0550.0550.0790.39110.024-0.05
Happiness0.142-0.0230.1270.110.120.0241-0.372
Depression0.0560.013-0.128-0.03-0.189-0.05-0.3721

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & gender & age & Connected & Separate & Learning & Software & Happiness & Depression \tabularnewline
gender & 1 & 0.115 & 0.03 & 0.194 & 0.095 & 0.166 & 0.142 & 0.056 \tabularnewline
age & 0.115 & 1 & 0.112 & 0.09 & 0.007 & -0.055 & -0.023 & 0.013 \tabularnewline
Connected & 0.03 & 0.112 & 1 & 0.241 & 0.159 & 0.055 & 0.127 & -0.128 \tabularnewline
Separate & 0.194 & 0.09 & 0.241 & 1 & 0.067 & 0.079 & 0.11 & -0.03 \tabularnewline
Learning & 0.095 & 0.007 & 0.159 & 0.067 & 1 & 0.391 & 0.12 & -0.189 \tabularnewline
Software & 0.166 & -0.055 & 0.055 & 0.079 & 0.391 & 1 & 0.024 & -0.05 \tabularnewline
Happiness & 0.142 & -0.023 & 0.127 & 0.11 & 0.12 & 0.024 & 1 & -0.372 \tabularnewline
Depression & 0.056 & 0.013 & -0.128 & -0.03 & -0.189 & -0.05 & -0.372 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199840&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]gender[/C][C]age[/C][C]Connected[/C][C]Separate[/C][C]Learning[/C][C]Software[/C][C]Happiness[/C][C]Depression[/C][/ROW]
[ROW][C]gender[/C][C]1[/C][C]0.115[/C][C]0.03[/C][C]0.194[/C][C]0.095[/C][C]0.166[/C][C]0.142[/C][C]0.056[/C][/ROW]
[ROW][C]age[/C][C]0.115[/C][C]1[/C][C]0.112[/C][C]0.09[/C][C]0.007[/C][C]-0.055[/C][C]-0.023[/C][C]0.013[/C][/ROW]
[ROW][C]Connected[/C][C]0.03[/C][C]0.112[/C][C]1[/C][C]0.241[/C][C]0.159[/C][C]0.055[/C][C]0.127[/C][C]-0.128[/C][/ROW]
[ROW][C]Separate[/C][C]0.194[/C][C]0.09[/C][C]0.241[/C][C]1[/C][C]0.067[/C][C]0.079[/C][C]0.11[/C][C]-0.03[/C][/ROW]
[ROW][C]Learning[/C][C]0.095[/C][C]0.007[/C][C]0.159[/C][C]0.067[/C][C]1[/C][C]0.391[/C][C]0.12[/C][C]-0.189[/C][/ROW]
[ROW][C]Software[/C][C]0.166[/C][C]-0.055[/C][C]0.055[/C][C]0.079[/C][C]0.391[/C][C]1[/C][C]0.024[/C][C]-0.05[/C][/ROW]
[ROW][C]Happiness[/C][C]0.142[/C][C]-0.023[/C][C]0.127[/C][C]0.11[/C][C]0.12[/C][C]0.024[/C][C]1[/C][C]-0.372[/C][/ROW]
[ROW][C]Depression[/C][C]0.056[/C][C]0.013[/C][C]-0.128[/C][C]-0.03[/C][C]-0.189[/C][C]-0.05[/C][C]-0.372[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199840&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199840&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)
genderageConnectedSeparateLearningSoftwareHappinessDepression
gender10.1150.030.1940.0950.1660.1420.056
age0.11510.1120.090.007-0.055-0.0230.013
Connected0.030.11210.2410.1590.0550.127-0.128
Separate0.1940.090.24110.0670.0790.11-0.03
Learning0.0950.0070.1590.06710.3910.12-0.189
Software0.166-0.0550.0550.0790.39110.024-0.05
Happiness0.142-0.0230.1270.110.120.0241-0.372
Depression0.0560.013-0.128-0.03-0.189-0.05-0.3721







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
gender;age0.07930.12550.1149
p-value(0.3161)(0.1116)(0.1113)
gender;Connected0.06450.03570.0304
p-value(0.4149)(0.652)(0.6505)
gender;Separate0.23980.22830.1941
p-value(0.0021)(0.0035)(0.0038)
gender;Learning0.11890.10770.0948
p-value(0.1319)(0.1724)(0.1717)
gender;Software0.16340.18980.166
p-value(0.0377)(0.0156)(0.016)
gender;Happiness0.18180.16430.1422
p-value(0.0206)(0.0367)(0.0371)
gender;Depression0.04480.06540.0561
p-value(0.5711)(0.4082)(0.4065)
age;Connected0.14780.14810.1122
p-value(0.0606)(0.0599)(0.0675)
age;Separate0.10570.11240.0898
p-value(0.1807)(0.1544)(0.1433)
age;Learning0.05060.00980.0075
p-value(0.5228)(0.9018)(0.9065)
age;Software-0.0602-0.0681-0.0552
p-value(0.4463)(0.389)(0.3822)
age;Happiness0.0166-0.0337-0.0235
p-value(0.8337)(0.6707)(0.707)
age;Depression0.03090.01790.0133
p-value(0.6964)(0.8208)(0.8298)
Connected;Separate0.36820.32430.2413
p-value(0)(0)(0)
Connected;Learning0.21370.20760.1586
p-value(0.0063)(0.008)(0.0072)
Connected;Software0.06220.07480.0549
p-value(0.4317)(0.3439)(0.3491)
Connected;Happiness0.14270.17170.1274
p-value(0.0701)(0.0289)(0.0283)
Connected;Depression-0.1203-0.1676-0.128
p-value(0.1274)(0.033)(0.026)
Separate;Learning0.07960.08780.0665
p-value(0.3139)(0.2666)(0.2597)
Separate;Software0.06790.11110.0787
p-value(0.3906)(0.1594)(0.1795)
Separate;Happiness0.15370.15430.1097
p-value(0.0509)(0.05)(0.0589)
Separate;Depression-0.0783-0.036-0.0297
p-value(0.3219)(0.6492)(0.606)
Learning;Software0.54560.48810.3906
p-value(0)(0)(0)
Learning;Happiness0.18290.15530.1199
p-value(0.0198)(0.0484)(0.0461)
Learning;Depression-0.2327-0.2515-0.1892
p-value(0.0029)(0.0012)(0.0015)
Software;Happiness0.0740.03120.0241
p-value(0.3492)(0.6933)(0.6863)
Software;Depression-0.1349-0.0649-0.0499
p-value(0.087)(0.4121)(0.3988)
Happiness;Depression-0.5442-0.4738-0.3724
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
gender;age & 0.0793 & 0.1255 & 0.1149 \tabularnewline
p-value & (0.3161) & (0.1116) & (0.1113) \tabularnewline
gender;Connected & 0.0645 & 0.0357 & 0.0304 \tabularnewline
p-value & (0.4149) & (0.652) & (0.6505) \tabularnewline
gender;Separate & 0.2398 & 0.2283 & 0.1941 \tabularnewline
p-value & (0.0021) & (0.0035) & (0.0038) \tabularnewline
gender;Learning & 0.1189 & 0.1077 & 0.0948 \tabularnewline
p-value & (0.1319) & (0.1724) & (0.1717) \tabularnewline
gender;Software & 0.1634 & 0.1898 & 0.166 \tabularnewline
p-value & (0.0377) & (0.0156) & (0.016) \tabularnewline
gender;Happiness & 0.1818 & 0.1643 & 0.1422 \tabularnewline
p-value & (0.0206) & (0.0367) & (0.0371) \tabularnewline
gender;Depression & 0.0448 & 0.0654 & 0.0561 \tabularnewline
p-value & (0.5711) & (0.4082) & (0.4065) \tabularnewline
age;Connected & 0.1478 & 0.1481 & 0.1122 \tabularnewline
p-value & (0.0606) & (0.0599) & (0.0675) \tabularnewline
age;Separate & 0.1057 & 0.1124 & 0.0898 \tabularnewline
p-value & (0.1807) & (0.1544) & (0.1433) \tabularnewline
age;Learning & 0.0506 & 0.0098 & 0.0075 \tabularnewline
p-value & (0.5228) & (0.9018) & (0.9065) \tabularnewline
age;Software & -0.0602 & -0.0681 & -0.0552 \tabularnewline
p-value & (0.4463) & (0.389) & (0.3822) \tabularnewline
age;Happiness & 0.0166 & -0.0337 & -0.0235 \tabularnewline
p-value & (0.8337) & (0.6707) & (0.707) \tabularnewline
age;Depression & 0.0309 & 0.0179 & 0.0133 \tabularnewline
p-value & (0.6964) & (0.8208) & (0.8298) \tabularnewline
Connected;Separate & 0.3682 & 0.3243 & 0.2413 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Connected;Learning & 0.2137 & 0.2076 & 0.1586 \tabularnewline
p-value & (0.0063) & (0.008) & (0.0072) \tabularnewline
Connected;Software & 0.0622 & 0.0748 & 0.0549 \tabularnewline
p-value & (0.4317) & (0.3439) & (0.3491) \tabularnewline
Connected;Happiness & 0.1427 & 0.1717 & 0.1274 \tabularnewline
p-value & (0.0701) & (0.0289) & (0.0283) \tabularnewline
Connected;Depression & -0.1203 & -0.1676 & -0.128 \tabularnewline
p-value & (0.1274) & (0.033) & (0.026) \tabularnewline
Separate;Learning & 0.0796 & 0.0878 & 0.0665 \tabularnewline
p-value & (0.3139) & (0.2666) & (0.2597) \tabularnewline
Separate;Software & 0.0679 & 0.1111 & 0.0787 \tabularnewline
p-value & (0.3906) & (0.1594) & (0.1795) \tabularnewline
Separate;Happiness & 0.1537 & 0.1543 & 0.1097 \tabularnewline
p-value & (0.0509) & (0.05) & (0.0589) \tabularnewline
Separate;Depression & -0.0783 & -0.036 & -0.0297 \tabularnewline
p-value & (0.3219) & (0.6492) & (0.606) \tabularnewline
Learning;Software & 0.5456 & 0.4881 & 0.3906 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Happiness & 0.1829 & 0.1553 & 0.1199 \tabularnewline
p-value & (0.0198) & (0.0484) & (0.0461) \tabularnewline
Learning;Depression & -0.2327 & -0.2515 & -0.1892 \tabularnewline
p-value & (0.0029) & (0.0012) & (0.0015) \tabularnewline
Software;Happiness & 0.074 & 0.0312 & 0.0241 \tabularnewline
p-value & (0.3492) & (0.6933) & (0.6863) \tabularnewline
Software;Depression & -0.1349 & -0.0649 & -0.0499 \tabularnewline
p-value & (0.087) & (0.4121) & (0.3988) \tabularnewline
Happiness;Depression & -0.5442 & -0.4738 & -0.3724 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199840&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]gender;age[/C][C]0.0793[/C][C]0.1255[/C][C]0.1149[/C][/ROW]
[ROW][C]p-value[/C][C](0.3161)[/C][C](0.1116)[/C][C](0.1113)[/C][/ROW]
[ROW][C]gender;Connected[/C][C]0.0645[/C][C]0.0357[/C][C]0.0304[/C][/ROW]
[ROW][C]p-value[/C][C](0.4149)[/C][C](0.652)[/C][C](0.6505)[/C][/ROW]
[ROW][C]gender;Separate[/C][C]0.2398[/C][C]0.2283[/C][C]0.1941[/C][/ROW]
[ROW][C]p-value[/C][C](0.0021)[/C][C](0.0035)[/C][C](0.0038)[/C][/ROW]
[ROW][C]gender;Learning[/C][C]0.1189[/C][C]0.1077[/C][C]0.0948[/C][/ROW]
[ROW][C]p-value[/C][C](0.1319)[/C][C](0.1724)[/C][C](0.1717)[/C][/ROW]
[ROW][C]gender;Software[/C][C]0.1634[/C][C]0.1898[/C][C]0.166[/C][/ROW]
[ROW][C]p-value[/C][C](0.0377)[/C][C](0.0156)[/C][C](0.016)[/C][/ROW]
[ROW][C]gender;Happiness[/C][C]0.1818[/C][C]0.1643[/C][C]0.1422[/C][/ROW]
[ROW][C]p-value[/C][C](0.0206)[/C][C](0.0367)[/C][C](0.0371)[/C][/ROW]
[ROW][C]gender;Depression[/C][C]0.0448[/C][C]0.0654[/C][C]0.0561[/C][/ROW]
[ROW][C]p-value[/C][C](0.5711)[/C][C](0.4082)[/C][C](0.4065)[/C][/ROW]
[ROW][C]age;Connected[/C][C]0.1478[/C][C]0.1481[/C][C]0.1122[/C][/ROW]
[ROW][C]p-value[/C][C](0.0606)[/C][C](0.0599)[/C][C](0.0675)[/C][/ROW]
[ROW][C]age;Separate[/C][C]0.1057[/C][C]0.1124[/C][C]0.0898[/C][/ROW]
[ROW][C]p-value[/C][C](0.1807)[/C][C](0.1544)[/C][C](0.1433)[/C][/ROW]
[ROW][C]age;Learning[/C][C]0.0506[/C][C]0.0098[/C][C]0.0075[/C][/ROW]
[ROW][C]p-value[/C][C](0.5228)[/C][C](0.9018)[/C][C](0.9065)[/C][/ROW]
[ROW][C]age;Software[/C][C]-0.0602[/C][C]-0.0681[/C][C]-0.0552[/C][/ROW]
[ROW][C]p-value[/C][C](0.4463)[/C][C](0.389)[/C][C](0.3822)[/C][/ROW]
[ROW][C]age;Happiness[/C][C]0.0166[/C][C]-0.0337[/C][C]-0.0235[/C][/ROW]
[ROW][C]p-value[/C][C](0.8337)[/C][C](0.6707)[/C][C](0.707)[/C][/ROW]
[ROW][C]age;Depression[/C][C]0.0309[/C][C]0.0179[/C][C]0.0133[/C][/ROW]
[ROW][C]p-value[/C][C](0.6964)[/C][C](0.8208)[/C][C](0.8298)[/C][/ROW]
[ROW][C]Connected;Separate[/C][C]0.3682[/C][C]0.3243[/C][C]0.2413[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Connected;Learning[/C][C]0.2137[/C][C]0.2076[/C][C]0.1586[/C][/ROW]
[ROW][C]p-value[/C][C](0.0063)[/C][C](0.008)[/C][C](0.0072)[/C][/ROW]
[ROW][C]Connected;Software[/C][C]0.0622[/C][C]0.0748[/C][C]0.0549[/C][/ROW]
[ROW][C]p-value[/C][C](0.4317)[/C][C](0.3439)[/C][C](0.3491)[/C][/ROW]
[ROW][C]Connected;Happiness[/C][C]0.1427[/C][C]0.1717[/C][C]0.1274[/C][/ROW]
[ROW][C]p-value[/C][C](0.0701)[/C][C](0.0289)[/C][C](0.0283)[/C][/ROW]
[ROW][C]Connected;Depression[/C][C]-0.1203[/C][C]-0.1676[/C][C]-0.128[/C][/ROW]
[ROW][C]p-value[/C][C](0.1274)[/C][C](0.033)[/C][C](0.026)[/C][/ROW]
[ROW][C]Separate;Learning[/C][C]0.0796[/C][C]0.0878[/C][C]0.0665[/C][/ROW]
[ROW][C]p-value[/C][C](0.3139)[/C][C](0.2666)[/C][C](0.2597)[/C][/ROW]
[ROW][C]Separate;Software[/C][C]0.0679[/C][C]0.1111[/C][C]0.0787[/C][/ROW]
[ROW][C]p-value[/C][C](0.3906)[/C][C](0.1594)[/C][C](0.1795)[/C][/ROW]
[ROW][C]Separate;Happiness[/C][C]0.1537[/C][C]0.1543[/C][C]0.1097[/C][/ROW]
[ROW][C]p-value[/C][C](0.0509)[/C][C](0.05)[/C][C](0.0589)[/C][/ROW]
[ROW][C]Separate;Depression[/C][C]-0.0783[/C][C]-0.036[/C][C]-0.0297[/C][/ROW]
[ROW][C]p-value[/C][C](0.3219)[/C][C](0.6492)[/C][C](0.606)[/C][/ROW]
[ROW][C]Learning;Software[/C][C]0.5456[/C][C]0.4881[/C][C]0.3906[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Happiness[/C][C]0.1829[/C][C]0.1553[/C][C]0.1199[/C][/ROW]
[ROW][C]p-value[/C][C](0.0198)[/C][C](0.0484)[/C][C](0.0461)[/C][/ROW]
[ROW][C]Learning;Depression[/C][C]-0.2327[/C][C]-0.2515[/C][C]-0.1892[/C][/ROW]
[ROW][C]p-value[/C][C](0.0029)[/C][C](0.0012)[/C][C](0.0015)[/C][/ROW]
[ROW][C]Software;Happiness[/C][C]0.074[/C][C]0.0312[/C][C]0.0241[/C][/ROW]
[ROW][C]p-value[/C][C](0.3492)[/C][C](0.6933)[/C][C](0.6863)[/C][/ROW]
[ROW][C]Software;Depression[/C][C]-0.1349[/C][C]-0.0649[/C][C]-0.0499[/C][/ROW]
[ROW][C]p-value[/C][C](0.087)[/C][C](0.4121)[/C][C](0.3988)[/C][/ROW]
[ROW][C]Happiness;Depression[/C][C]-0.5442[/C][C]-0.4738[/C][C]-0.3724[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199840&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199840&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
gender;age0.07930.12550.1149
p-value(0.3161)(0.1116)(0.1113)
gender;Connected0.06450.03570.0304
p-value(0.4149)(0.652)(0.6505)
gender;Separate0.23980.22830.1941
p-value(0.0021)(0.0035)(0.0038)
gender;Learning0.11890.10770.0948
p-value(0.1319)(0.1724)(0.1717)
gender;Software0.16340.18980.166
p-value(0.0377)(0.0156)(0.016)
gender;Happiness0.18180.16430.1422
p-value(0.0206)(0.0367)(0.0371)
gender;Depression0.04480.06540.0561
p-value(0.5711)(0.4082)(0.4065)
age;Connected0.14780.14810.1122
p-value(0.0606)(0.0599)(0.0675)
age;Separate0.10570.11240.0898
p-value(0.1807)(0.1544)(0.1433)
age;Learning0.05060.00980.0075
p-value(0.5228)(0.9018)(0.9065)
age;Software-0.0602-0.0681-0.0552
p-value(0.4463)(0.389)(0.3822)
age;Happiness0.0166-0.0337-0.0235
p-value(0.8337)(0.6707)(0.707)
age;Depression0.03090.01790.0133
p-value(0.6964)(0.8208)(0.8298)
Connected;Separate0.36820.32430.2413
p-value(0)(0)(0)
Connected;Learning0.21370.20760.1586
p-value(0.0063)(0.008)(0.0072)
Connected;Software0.06220.07480.0549
p-value(0.4317)(0.3439)(0.3491)
Connected;Happiness0.14270.17170.1274
p-value(0.0701)(0.0289)(0.0283)
Connected;Depression-0.1203-0.1676-0.128
p-value(0.1274)(0.033)(0.026)
Separate;Learning0.07960.08780.0665
p-value(0.3139)(0.2666)(0.2597)
Separate;Software0.06790.11110.0787
p-value(0.3906)(0.1594)(0.1795)
Separate;Happiness0.15370.15430.1097
p-value(0.0509)(0.05)(0.0589)
Separate;Depression-0.0783-0.036-0.0297
p-value(0.3219)(0.6492)(0.606)
Learning;Software0.54560.48810.3906
p-value(0)(0)(0)
Learning;Happiness0.18290.15530.1199
p-value(0.0198)(0.0484)(0.0461)
Learning;Depression-0.2327-0.2515-0.1892
p-value(0.0029)(0.0012)(0.0015)
Software;Happiness0.0740.03120.0241
p-value(0.3492)(0.6933)(0.6863)
Software;Depression-0.1349-0.0649-0.0499
p-value(0.087)(0.4121)(0.3988)
Happiness;Depression-0.5442-0.4738-0.3724
p-value(0)(0)(0)



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')