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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, 10 Dec 2016 11:14:50 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/10/t14813651143fxjp9v3p6jexxu.htm/, Retrieved Fri, 01 Nov 2024 03:34:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298624, Retrieved Fri, 01 Nov 2024 03:34:47 +0000
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Original text written by user:
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Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [kendal correlation] [2016-12-10 10:14:50] [ca14e1566745fb922befb698831e7d61] [Current]
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Dataseries X:
4	2	4	3	5	4	13
5	3	3	4	5	4	16
4	4	5	4	5	4	17
3	4	3	3	4	4	15
4	4	5	4	5	4	16
3	4	4	4	5	5	16
3	4	4	3	3	4	16
3	4	5	4	4	4	16
4	5	4	4	5	5	17
4	5	5	4	5	5	17
4	4	2	4	5	4	17
4	4	5	3	5	4	15
4	4	4	3	4	5	16
3	3	5	4	4	5	14
4	4	5	4	2	5	16
3	4	5	4	4	5	17
3	4	5	4	4	5	16
5	5	4	3	4	4	14
4	4	4	4	5	4	17
3	4	5	3	4	5	16
4	4	4	4	5	5	15
4	4	5	4	4	5	16
4	4	5	4	4	4	15
4	4	5	4	4	5	17
3	4	4	4	4	4	14
3	4	4	3	5	5	16
4	4	4	4	4	4	15
2	4	5	4	5	5	16
5	4	4	4	4	4	16
4	3	5	4	4	4	13
4	5	5	4	5	5	15
5	4	5	4	4	5	17
4	3	5	4	5	5	15
2	3	5	4	5	4	13
4	5	2	4	4	4	17
3	4	5	4	4	4	15
4	3	5	3	4	5	14
4	3	3	4	4	4	14
4	4	5	4	4	4	18
5	4	4	4	4	4	15
4	5	5	4	5	5	17
3	3	4	4	4	4	13
5	5	5	3	5	5	16
5	4	5	3	4	4	15
4	4	4	3	4	5	15
4	4	4	4	4	4	16
3	5	5	3	3	4	15
4	4	4	4	5	4	13
2	3	4	2	5	4	12
4	5	5	4	4	4	17
5	5	2	4	5	4	18
5	5	5	4	4	4	17
4	3	5	4	5	5	11
4	3	4	3	4	5	14
4	4	5	4	4	4	13
3	4	4	3	3	4	15
3	4	4	4	4	3	17
4	4	4	3	5	4	16
4	4	4	4	5	4	15
5	5	3	4	5	5	17
2	4	4	4	5	5	16
4	4	4	4	5	5	16
3	4	4	4	2	4	16
4	4	5	4	5	5	15
4	2	4	4	4	4	12
4	4	4	3	5	3	17
4	4	4	3	5	4	14
5	4	5	3	3	5	14
3	4	4	3	5	5	16
3	4	4	3	4	5	15
4	5	5	5	5	4	15
4	4	3	4	4	4	13
4	4	4	4	4	4	13
4	4	4	5	5	4	17
3	4	3	4	4	4	15
4	4	4	4	5	4	16
3	4	5	3	5	5	14
3	3	5	4	4	5	15
4	3	5	4	4	4	17
4	4	5	4	4	5	16
3	3	3	4	4	4	10
4	4	4	4	5	4	16
4	4	3	4	5	5	17
4	4	4	4	5	5	17
5	4	4	4	4	4	20
5	4	3	5	4	5	17
4	4	5	4	5	5	18
3	4	5	4	4	5	15
3	4	4	4	4	4	17
4	2	3	3	4	4	14
4	4	5	4	4	3	15
4	4	5	4	4	5	17
4	4	4	4	5	4	16
4	5	4	4	5	3	17
3	4	4	3	5	5	15
4	4	5	4	4	5	16
5	4	3	4	4	5	18
5	4	5	5	4	5	18
4	5	4	4	5	5	16
3	4	5	4	4	5	16
5	3	4	4	5	5	17
4	4	5	4	4	5	15
5	4	4	4	4	5	13
3	4	4	3	4	4	15
5	4	4	5	5	5	17
4	4	5	3	4	5	16
4	4	3	3	4	3	16
4	4	5	4	4	4	15
4	4	5	4	4	4	16
3	4	5	4	5	3	16
4	4	4	4	4	4	13
4	4	4	3	4	5	15
3	3	4	3	5	5	12
4	4	4	3	4	4	18
3	4	5	4	4	4	16
4	4	5	4	3	4	16
5	4	5	1	5	5	17
5	4	5	4	5	5	16
4	4	4	4	4	3	14
4	4	5	3	4	4	15
3	4	4	3	4	5	14
4	4	4	4	4	4	16
4	4	4	4	5	4	15
4	5	3	4	4	4	17
3	4	4	4	4	4	15
4	4	4	3	4	4	16
4	4	4	4	4	5	16
3	4	3	3	4	4	15
4	4	4	3	4	3	15
3	2	4	2	4	4	11
4	4	4	3	5	4	16
5	4	4	3	5	4	18
2	4	4	3	3	5	13
3	3	4	4	4	4	11
4	4	4	3	4	4	16
5	5	4	4	5	4	18
4	5	5	4	4	4	15
5	5	5	5	5	4	19
4	5	5	4	5	5	17
4	4	4	3	4	5	13
3	4	5	4	5	4	14
4	4	5	4	4	4	16
4	4	2	4	4	4	13
4	4	3	4	5	5	17
4	4	4	4	5	5	14
5	4	5	3	5	4	19
4	3	5	4	4	4	14
4	4	5	4	4	4	16
3	3	2	3	4	4	12
4	5	5	4	4	3	16
4	4	4	3	4	4	16
4	4	4	4	4	5	15
3	4	5	3	5	5	12
4	4	5	4	4	5	15
5	4	5	4	5	4	17
4	4	5	4	3	4	13
2	3	5	4	4	4	15
4	4	4	4	4	5	18
4	3	4	3	5	5	15
4	4	4	4	4	3	18
4	5	5	5	4	4	15
5	4	3	4	4	4	15
5	4	4	3	4	4	16
3	3	1	4	5	5	13
4	4	4	4	4	5	16
4	4	4	4	5	4	13
2	3	4	5	5	4	16




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298624&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298624&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298624&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Correlations for all pairs of data series (method=kendall)
V1V2V3V4V5V6TVDC
V110.265-0.010.140.103-0.0010.31
V20.26510.1040.1440.06-0.0210.39
V3-0.010.10410.12-0.0570.1430.046
V40.140.1440.1210.081-0.0190.216
V50.1030.06-0.0570.08110.1430.16
V6-0.001-0.0210.143-0.0190.14310.044
TVDC0.310.390.0460.2160.160.0441

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & V1 & V2 & V3 & V4 & V5 & V6 & TVDC \tabularnewline
V1 & 1 & 0.265 & -0.01 & 0.14 & 0.103 & -0.001 & 0.31 \tabularnewline
V2 & 0.265 & 1 & 0.104 & 0.144 & 0.06 & -0.021 & 0.39 \tabularnewline
V3 & -0.01 & 0.104 & 1 & 0.12 & -0.057 & 0.143 & 0.046 \tabularnewline
V4 & 0.14 & 0.144 & 0.12 & 1 & 0.081 & -0.019 & 0.216 \tabularnewline
V5 & 0.103 & 0.06 & -0.057 & 0.081 & 1 & 0.143 & 0.16 \tabularnewline
V6 & -0.001 & -0.021 & 0.143 & -0.019 & 0.143 & 1 & 0.044 \tabularnewline
TVDC & 0.31 & 0.39 & 0.046 & 0.216 & 0.16 & 0.044 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298624&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]V1[/C][C]V2[/C][C]V3[/C][C]V4[/C][C]V5[/C][C]V6[/C][C]TVDC[/C][/ROW]
[ROW][C]V1[/C][C]1[/C][C]0.265[/C][C]-0.01[/C][C]0.14[/C][C]0.103[/C][C]-0.001[/C][C]0.31[/C][/ROW]
[ROW][C]V2[/C][C]0.265[/C][C]1[/C][C]0.104[/C][C]0.144[/C][C]0.06[/C][C]-0.021[/C][C]0.39[/C][/ROW]
[ROW][C]V3[/C][C]-0.01[/C][C]0.104[/C][C]1[/C][C]0.12[/C][C]-0.057[/C][C]0.143[/C][C]0.046[/C][/ROW]
[ROW][C]V4[/C][C]0.14[/C][C]0.144[/C][C]0.12[/C][C]1[/C][C]0.081[/C][C]-0.019[/C][C]0.216[/C][/ROW]
[ROW][C]V5[/C][C]0.103[/C][C]0.06[/C][C]-0.057[/C][C]0.081[/C][C]1[/C][C]0.143[/C][C]0.16[/C][/ROW]
[ROW][C]V6[/C][C]-0.001[/C][C]-0.021[/C][C]0.143[/C][C]-0.019[/C][C]0.143[/C][C]1[/C][C]0.044[/C][/ROW]
[ROW][C]TVDC[/C][C]0.31[/C][C]0.39[/C][C]0.046[/C][C]0.216[/C][C]0.16[/C][C]0.044[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298624&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298624&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)
V1V2V3V4V5V6TVDC
V110.265-0.010.140.103-0.0010.31
V20.26510.1040.1440.06-0.0210.39
V3-0.010.10410.12-0.0570.1430.046
V40.140.1440.1210.081-0.0190.216
V50.1030.06-0.0570.08110.1430.16
V6-0.001-0.0210.143-0.0190.14310.044
TVDC0.310.390.0460.2160.160.0441







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
V1;V20.27920.28970.2646
p-value(3e-04)(1e-04)(2e-04)
V1;V3-0.0183-0.0114-0.0102
p-value(0.8145)(0.8834)(0.8843)
V1;V40.12360.14860.1403
p-value(0.1116)(0.0553)(0.0494)
V1;V50.09880.11050.1035
p-value(0.2042)(0.1552)(0.1488)
V1;V64e-04-8e-04-5e-04
p-value(0.9958)(0.9921)(0.9944)
V1;TVDC0.37460.36550.31
p-value(0)(0)(0)
V2;V30.11520.1130.1044
p-value(0.1383)(0.1461)(0.1418)
V2;V40.19390.15310.1438
p-value(0.012)(0.0482)(0.0475)
V2;V50.04730.06510.0604
p-value(0.5438)(0.4032)(0.4073)
V2;V6-0.0148-0.0226-0.0205
p-value(0.8497)(0.7722)(0.7781)
V2;TVDC0.49710.44860.3896
p-value(0)(0)(0)
V3;V40.06090.12960.1197
p-value(0.434)(0.0951)(0.0965)
V3;V5-0.0734-0.0613-0.0566
p-value(0.3456)(0.4315)(0.434)
V3;V60.130.15470.1432
p-value(0.0939)(0.0459)(0.0474)
V3;TVDC0.09480.05470.0464
p-value(0.2231)(0.4824)(0.4782)
V4;V50.05850.08420.0807
p-value(0.4525)(0.2796)(0.274)
V4;V6-0.0235-0.0204-0.0193
p-value(0.7631)(0.7936)(0.7935)
V4;TVDC0.22510.2470.2156
p-value(0.0035)(0.0013)(0.0013)
V5;V60.12050.15030.1428
p-value(0.121)(0.0525)(0.0537)
V5;TVDC0.13480.18650.16
p-value(0.0825)(0.0158)(0.0171)
V6;TVDC0.02210.05050.044
p-value(0.7766)(0.5165)(0.5116)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
V1;V2 & 0.2792 & 0.2897 & 0.2646 \tabularnewline
p-value & (3e-04) & (1e-04) & (2e-04) \tabularnewline
V1;V3 & -0.0183 & -0.0114 & -0.0102 \tabularnewline
p-value & (0.8145) & (0.8834) & (0.8843) \tabularnewline
V1;V4 & 0.1236 & 0.1486 & 0.1403 \tabularnewline
p-value & (0.1116) & (0.0553) & (0.0494) \tabularnewline
V1;V5 & 0.0988 & 0.1105 & 0.1035 \tabularnewline
p-value & (0.2042) & (0.1552) & (0.1488) \tabularnewline
V1;V6 & 4e-04 & -8e-04 & -5e-04 \tabularnewline
p-value & (0.9958) & (0.9921) & (0.9944) \tabularnewline
V1;TVDC & 0.3746 & 0.3655 & 0.31 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V2;V3 & 0.1152 & 0.113 & 0.1044 \tabularnewline
p-value & (0.1383) & (0.1461) & (0.1418) \tabularnewline
V2;V4 & 0.1939 & 0.1531 & 0.1438 \tabularnewline
p-value & (0.012) & (0.0482) & (0.0475) \tabularnewline
V2;V5 & 0.0473 & 0.0651 & 0.0604 \tabularnewline
p-value & (0.5438) & (0.4032) & (0.4073) \tabularnewline
V2;V6 & -0.0148 & -0.0226 & -0.0205 \tabularnewline
p-value & (0.8497) & (0.7722) & (0.7781) \tabularnewline
V2;TVDC & 0.4971 & 0.4486 & 0.3896 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V3;V4 & 0.0609 & 0.1296 & 0.1197 \tabularnewline
p-value & (0.434) & (0.0951) & (0.0965) \tabularnewline
V3;V5 & -0.0734 & -0.0613 & -0.0566 \tabularnewline
p-value & (0.3456) & (0.4315) & (0.434) \tabularnewline
V3;V6 & 0.13 & 0.1547 & 0.1432 \tabularnewline
p-value & (0.0939) & (0.0459) & (0.0474) \tabularnewline
V3;TVDC & 0.0948 & 0.0547 & 0.0464 \tabularnewline
p-value & (0.2231) & (0.4824) & (0.4782) \tabularnewline
V4;V5 & 0.0585 & 0.0842 & 0.0807 \tabularnewline
p-value & (0.4525) & (0.2796) & (0.274) \tabularnewline
V4;V6 & -0.0235 & -0.0204 & -0.0193 \tabularnewline
p-value & (0.7631) & (0.7936) & (0.7935) \tabularnewline
V4;TVDC & 0.2251 & 0.247 & 0.2156 \tabularnewline
p-value & (0.0035) & (0.0013) & (0.0013) \tabularnewline
V5;V6 & 0.1205 & 0.1503 & 0.1428 \tabularnewline
p-value & (0.121) & (0.0525) & (0.0537) \tabularnewline
V5;TVDC & 0.1348 & 0.1865 & 0.16 \tabularnewline
p-value & (0.0825) & (0.0158) & (0.0171) \tabularnewline
V6;TVDC & 0.0221 & 0.0505 & 0.044 \tabularnewline
p-value & (0.7766) & (0.5165) & (0.5116) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298624&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]V1;V2[/C][C]0.2792[/C][C]0.2897[/C][C]0.2646[/C][/ROW]
[ROW][C]p-value[/C][C](3e-04)[/C][C](1e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]V1;V3[/C][C]-0.0183[/C][C]-0.0114[/C][C]-0.0102[/C][/ROW]
[ROW][C]p-value[/C][C](0.8145)[/C][C](0.8834)[/C][C](0.8843)[/C][/ROW]
[ROW][C]V1;V4[/C][C]0.1236[/C][C]0.1486[/C][C]0.1403[/C][/ROW]
[ROW][C]p-value[/C][C](0.1116)[/C][C](0.0553)[/C][C](0.0494)[/C][/ROW]
[ROW][C]V1;V5[/C][C]0.0988[/C][C]0.1105[/C][C]0.1035[/C][/ROW]
[ROW][C]p-value[/C][C](0.2042)[/C][C](0.1552)[/C][C](0.1488)[/C][/ROW]
[ROW][C]V1;V6[/C][C]4e-04[/C][C]-8e-04[/C][C]-5e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.9958)[/C][C](0.9921)[/C][C](0.9944)[/C][/ROW]
[ROW][C]V1;TVDC[/C][C]0.3746[/C][C]0.3655[/C][C]0.31[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V2;V3[/C][C]0.1152[/C][C]0.113[/C][C]0.1044[/C][/ROW]
[ROW][C]p-value[/C][C](0.1383)[/C][C](0.1461)[/C][C](0.1418)[/C][/ROW]
[ROW][C]V2;V4[/C][C]0.1939[/C][C]0.1531[/C][C]0.1438[/C][/ROW]
[ROW][C]p-value[/C][C](0.012)[/C][C](0.0482)[/C][C](0.0475)[/C][/ROW]
[ROW][C]V2;V5[/C][C]0.0473[/C][C]0.0651[/C][C]0.0604[/C][/ROW]
[ROW][C]p-value[/C][C](0.5438)[/C][C](0.4032)[/C][C](0.4073)[/C][/ROW]
[ROW][C]V2;V6[/C][C]-0.0148[/C][C]-0.0226[/C][C]-0.0205[/C][/ROW]
[ROW][C]p-value[/C][C](0.8497)[/C][C](0.7722)[/C][C](0.7781)[/C][/ROW]
[ROW][C]V2;TVDC[/C][C]0.4971[/C][C]0.4486[/C][C]0.3896[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V3;V4[/C][C]0.0609[/C][C]0.1296[/C][C]0.1197[/C][/ROW]
[ROW][C]p-value[/C][C](0.434)[/C][C](0.0951)[/C][C](0.0965)[/C][/ROW]
[ROW][C]V3;V5[/C][C]-0.0734[/C][C]-0.0613[/C][C]-0.0566[/C][/ROW]
[ROW][C]p-value[/C][C](0.3456)[/C][C](0.4315)[/C][C](0.434)[/C][/ROW]
[ROW][C]V3;V6[/C][C]0.13[/C][C]0.1547[/C][C]0.1432[/C][/ROW]
[ROW][C]p-value[/C][C](0.0939)[/C][C](0.0459)[/C][C](0.0474)[/C][/ROW]
[ROW][C]V3;TVDC[/C][C]0.0948[/C][C]0.0547[/C][C]0.0464[/C][/ROW]
[ROW][C]p-value[/C][C](0.2231)[/C][C](0.4824)[/C][C](0.4782)[/C][/ROW]
[ROW][C]V4;V5[/C][C]0.0585[/C][C]0.0842[/C][C]0.0807[/C][/ROW]
[ROW][C]p-value[/C][C](0.4525)[/C][C](0.2796)[/C][C](0.274)[/C][/ROW]
[ROW][C]V4;V6[/C][C]-0.0235[/C][C]-0.0204[/C][C]-0.0193[/C][/ROW]
[ROW][C]p-value[/C][C](0.7631)[/C][C](0.7936)[/C][C](0.7935)[/C][/ROW]
[ROW][C]V4;TVDC[/C][C]0.2251[/C][C]0.247[/C][C]0.2156[/C][/ROW]
[ROW][C]p-value[/C][C](0.0035)[/C][C](0.0013)[/C][C](0.0013)[/C][/ROW]
[ROW][C]V5;V6[/C][C]0.1205[/C][C]0.1503[/C][C]0.1428[/C][/ROW]
[ROW][C]p-value[/C][C](0.121)[/C][C](0.0525)[/C][C](0.0537)[/C][/ROW]
[ROW][C]V5;TVDC[/C][C]0.1348[/C][C]0.1865[/C][C]0.16[/C][/ROW]
[ROW][C]p-value[/C][C](0.0825)[/C][C](0.0158)[/C][C](0.0171)[/C][/ROW]
[ROW][C]V6;TVDC[/C][C]0.0221[/C][C]0.0505[/C][C]0.044[/C][/ROW]
[ROW][C]p-value[/C][C](0.7766)[/C][C](0.5165)[/C][C](0.5116)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298624&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298624&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
V1;V20.27920.28970.2646
p-value(3e-04)(1e-04)(2e-04)
V1;V3-0.0183-0.0114-0.0102
p-value(0.8145)(0.8834)(0.8843)
V1;V40.12360.14860.1403
p-value(0.1116)(0.0553)(0.0494)
V1;V50.09880.11050.1035
p-value(0.2042)(0.1552)(0.1488)
V1;V64e-04-8e-04-5e-04
p-value(0.9958)(0.9921)(0.9944)
V1;TVDC0.37460.36550.31
p-value(0)(0)(0)
V2;V30.11520.1130.1044
p-value(0.1383)(0.1461)(0.1418)
V2;V40.19390.15310.1438
p-value(0.012)(0.0482)(0.0475)
V2;V50.04730.06510.0604
p-value(0.5438)(0.4032)(0.4073)
V2;V6-0.0148-0.0226-0.0205
p-value(0.8497)(0.7722)(0.7781)
V2;TVDC0.49710.44860.3896
p-value(0)(0)(0)
V3;V40.06090.12960.1197
p-value(0.434)(0.0951)(0.0965)
V3;V5-0.0734-0.0613-0.0566
p-value(0.3456)(0.4315)(0.434)
V3;V60.130.15470.1432
p-value(0.0939)(0.0459)(0.0474)
V3;TVDC0.09480.05470.0464
p-value(0.2231)(0.4824)(0.4782)
V4;V50.05850.08420.0807
p-value(0.4525)(0.2796)(0.274)
V4;V6-0.0235-0.0204-0.0193
p-value(0.7631)(0.7936)(0.7935)
V4;TVDC0.22510.2470.2156
p-value(0.0035)(0.0013)(0.0013)
V5;V60.12050.15030.1428
p-value(0.121)(0.0525)(0.0537)
V5;TVDC0.13480.18650.16
p-value(0.0825)(0.0158)(0.0171)
V6;TVDC0.02210.05050.044
p-value(0.7766)(0.5165)(0.5116)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.190.190.19
0.020.240.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.330.38
0.060.240.430.43
0.070.240.430.43
0.080.240.430.43
0.090.290.430.43
0.10.330.480.48

\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 & 0.19 & 0.19 & 0.19 \tabularnewline
0.02 & 0.24 & 0.24 & 0.24 \tabularnewline
0.03 & 0.24 & 0.24 & 0.24 \tabularnewline
0.04 & 0.24 & 0.24 & 0.24 \tabularnewline
0.05 & 0.24 & 0.33 & 0.38 \tabularnewline
0.06 & 0.24 & 0.43 & 0.43 \tabularnewline
0.07 & 0.24 & 0.43 & 0.43 \tabularnewline
0.08 & 0.24 & 0.43 & 0.43 \tabularnewline
0.09 & 0.29 & 0.43 & 0.43 \tabularnewline
0.1 & 0.33 & 0.48 & 0.48 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298624&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]0.19[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.02[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.03[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.04[/C][C]0.24[/C][C]0.24[/C][C]0.24[/C][/ROW]
[ROW][C]0.05[/C][C]0.24[/C][C]0.33[/C][C]0.38[/C][/ROW]
[ROW][C]0.06[/C][C]0.24[/C][C]0.43[/C][C]0.43[/C][/ROW]
[ROW][C]0.07[/C][C]0.24[/C][C]0.43[/C][C]0.43[/C][/ROW]
[ROW][C]0.08[/C][C]0.24[/C][C]0.43[/C][C]0.43[/C][/ROW]
[ROW][C]0.09[/C][C]0.29[/C][C]0.43[/C][C]0.43[/C][/ROW]
[ROW][C]0.1[/C][C]0.33[/C][C]0.48[/C][C]0.48[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298624&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298624&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
0.010.190.190.19
0.020.240.240.24
0.030.240.240.24
0.040.240.240.24
0.050.240.330.38
0.060.240.430.43
0.070.240.430.43
0.080.240.430.43
0.090.290.430.43
0.10.330.480.48



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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')