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Author's title

Author*Unverified author*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 14 Apr 2020 05:40:55 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/14/t1586838053tr84gdh61szewgu.htm/, Retrieved Wed, 21 Apr 2021 11:06:07 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 21 Apr 2021 11:06:07 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
21.7630	21.0000	22.5100	1.1270	1.0890	33.5000
21.7500	21.8000	22.5000	1.1000	1.0580	32.6000
21.7760	24.2000	22.5000	1.1200	1.0410	31.7000
21.9730	21.6000	22.4550	1.0720	1.0680	30.0000
21.8670	21.5000	22.4880	1.1190	1.0930	27.3000
21.9060	22.4000	22.5000	1.1010	1.1120	29.6000
22.0880	22.8000	22.5000	1.0960	1.0650	33.6000
21.7220	22.4000	22.5000	1.0850	1.0760	34.6000
21.7220	22.4000	22.5000	1.0850	1.0760	34.6000
21.9310	21.8000	22.5000	1.0950	1.1030	31.5000
21.7580	21.2000	22.5000	1.0870	1.0850	35.2000
21.7930	21.0000	22.5000	1.0760	1.0580	33.7000
21.9480	21.2000	22.4490	1.0890	1.0760	30.3000
21.7720	21.9000	22.4820	1.0950	1.0670	27.7000
22.0070	19.9000	22.5000	1.1080	1.1110	31.3000
21.9360	21.8000	22.5000	1.1080	1.0680	22.7000
21.7350	22.8000	22.5000	1.0990	1.0810	33.6000
21.7350	22.8000	22.5000	1.0990	1.0810	33.6000
21.8320	21.5000	22.5000	1.0840	1.1340	35.6000
21.7560	21.3000	22.5000	1.1060	1.1440	33.3000
21.7090	20.6000	22.5000	1.0500	1.1410	33.6000
21.9510	21.0000	22.4520	1.1310	1.0800	28.2000
21.7920	22.0000	22.4850	1.0950	1.0990	26.3000
22.0970	20.4000	22.5000	1.0940	1.0610	31.1000
21.8810	22.1000	22.5000	1.0640	1.1010	32.7000
21.8140	21.5000	22.4790	1.0860	1.0940	28.5000
21.9010	22.6000	22.4850	1.0990	1.0850	14.2000
21.8260	19.8000	22.5100	1.0770	1.0800	34.1000
21.7950	20.9000	22.5000	1.1200	1.0650	32.3000
21.7570	20.0000	22.5000	1.1000	1.0720	34.2000
21.9470	21.9000	22.4490	1.1290	1.0980	27.8000
21.9030	22.6000	22.4850	1.0970	1.0960	27.1000
22.0450	22.5000	22.5000	1.1080	1.0970	29.4000
22.0940	23.5000	22.5000	1.0940	1.0660	30.5000
21.7820	24.0000	22.4820	1.1150	1.0840	30.1000
21.8550	21.7000	22.5000	1.1390	1.0870	33.8000
21.7840	20.4000	22.5000	1.1030	1.0770	32.7000
21.8930	20.5000	22.4880	1.1100	1.1620	29.9000
22.0550	25.4000	22.5000	1.0780	1.0920	28.7000
21.7070	27.3000	22.4820	1.1290	0.0910	26.5000
21.8700	21.8000	22.5000	1.1030	1.0950	33.2000
21.9310	21.2000	22.5000	1.1230	1.0790	33.9000
21.7810	21.7000	22.5000	1.0520	1.0410	33.7000
21.9450	22.5000	22.4790	1.1120	1.0930	16.6000
22.4680	23.3000	22.5000	1.0790	1.0750	32.4000
21.9600	25.0000	22.5000	1.0750	1. 092	33.0000
21.8830	23.5000	22.5000	1.0960	1.1050	32.5000
21.8360	19.7000	22.5000	1.0990	1.1110	35.2000
21.8160	22.6000	22.5000	1.0800	1.1230	33.2000
21.8510	22.6000	22.5000	1.1340	1.1250	32.5000
21.8660	21.9000	22.5000	1.1050	1.1220	32.4000
21.7780	23.3000	22.5000	1.1380	1.1230	31.5000
21.8350	22.4000	22.5000	1.0790	1.1200	32.2000
21.8530	21.9000	22.5000	1.1300	1.1040	34.1000
21.7780	22.8000	22.5000	1.0630	1.1510	28.6000
21.7140	24.7000	22.5000	1.1010	1.0770	28.8000
21.7910	24.5000	22.5000	1.1000	1.1180	29.6000
21.7370	24.0000	22.5000	1.1420	1.0760	29.0000
21.7010	25.9000	22.5000	1.1070	1.1010	29.4000
21.8010	22.2000	22.5100	1.1240	1.1030	32.9000
21.6770	23.3000	22.5000	1.0780	1.0560	29.6000
21.5200	24.2000	22.5000	1.0780	1.1100	29.7000
21.5480	22.2000	22.5000	1.1030	1.0710	30.4000
21.5310	22.9000	22.5000	1.0690	1.1160	30.7000
21.5320	23.6000	22.5100	1.0890	1.1040	30.1000
21.5480	21.4000	22.5100	1.1140	1.0740	30.9000
22.3900	21.3000	22.4900	1.1130	1.1240	27.3000
22.3900	22.3000	22.4900	1.1350	1.1050	27.6000
22.4300	22.2000	22.4900	1.1340	1.0300	27.3000
22.1500	20.8000	22.4900	1.1300	1.1310	27.6000
22.3300	19.8000	22.4900	1.1230	1.1670	29.1000
22.3200	21.4000	22.4900	1.1500	1.1010	25.5000
22.1300	21.3000	22.4900	1.1140	1.1320	27.8000
22.4900	21.8000	22.4900	1.0950	1.1520	26.9000
21.8620	23.0000	22.4850	1.1360	1.1530	27.1000
21.9910	22.3000	22.5620	1.1410	1.0980	28.3000
21.9840	22.7000	22.3880	1.1190	1.1120	31.0000
21.8480	22.1000	22.5680	1.0880	1.0850	28.8000
21.9920	21.5000	22.5010	1.1060	1.0770	29.1000
21.8550	22.1000	22.5710	1.1050	1.0690	27.0000
22.0340	22.5000	22.4950	1.0920	1.1090	28.3000
21.8160	25.2000	22.5620	1.1310	1.0670	26.1000
21.9530	22.8000	22.4950	1.1000	1.1100	27.4000
21.8980	21.2000	22.5680	1.0730	1.0980	28.6000
22.0190	24.4000	22.4880	1.0970	1.0990	26.3000
21.8570	22.7000	22.5620	1.0860	1.0820	26.3000




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 time0 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Correlations for all pairs of data series (method=pearson)
SolenoidResistanceArmingTimingVoltsFiringCXT#1ResistanceFiringCXT#2ResistanceSafeTime
SolenoidResistance1-0.0750.0570.9220.173-0.834
ArmingTiming-0.0751-0.076-0.058-0.5660.123
Volts0.057-0.07610.046-0.005-0.041
FiringCXT#1Resistance0.922-0.0580.04610.199-0.905
FiringCXT#2Resistance0.173-0.566-0.0050.1991-0.18
SafeTime -0.8340.123-0.041-0.905-0.181

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & SolenoidResistance & ArmingTiming & Volts & FiringCXT#1Resistance & FiringCXT#2Resistance & SafeTime
 \tabularnewline
SolenoidResistance & 1 & -0.075 & 0.057 & 0.922 & 0.173 & -0.834 \tabularnewline
ArmingTiming & -0.075 & 1 & -0.076 & -0.058 & -0.566 & 0.123 \tabularnewline
Volts & 0.057 & -0.076 & 1 & 0.046 & -0.005 & -0.041 \tabularnewline
FiringCXT#1Resistance & 0.922 & -0.058 & 0.046 & 1 & 0.199 & -0.905 \tabularnewline
FiringCXT#2Resistance & 0.173 & -0.566 & -0.005 & 0.199 & 1 & -0.18 \tabularnewline
SafeTime
 & -0.834 & 0.123 & -0.041 & -0.905 & -0.18 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]SolenoidResistance[/C][C]ArmingTiming[/C][C]Volts[/C][C]FiringCXT#1Resistance[/C][C]FiringCXT#2Resistance[/C][C]SafeTime
[/C][/ROW]
[ROW][C]SolenoidResistance[/C][C]1[/C][C]-0.075[/C][C]0.057[/C][C]0.922[/C][C]0.173[/C][C]-0.834[/C][/ROW]
[ROW][C]ArmingTiming[/C][C]-0.075[/C][C]1[/C][C]-0.076[/C][C]-0.058[/C][C]-0.566[/C][C]0.123[/C][/ROW]
[ROW][C]Volts[/C][C]0.057[/C][C]-0.076[/C][C]1[/C][C]0.046[/C][C]-0.005[/C][C]-0.041[/C][/ROW]
[ROW][C]FiringCXT#1Resistance[/C][C]0.922[/C][C]-0.058[/C][C]0.046[/C][C]1[/C][C]0.199[/C][C]-0.905[/C][/ROW]
[ROW][C]FiringCXT#2Resistance[/C][C]0.173[/C][C]-0.566[/C][C]-0.005[/C][C]0.199[/C][C]1[/C][C]-0.18[/C][/ROW]
[ROW][C]SafeTime
[/C][C]-0.834[/C][C]0.123[/C][C]-0.041[/C][C]-0.905[/C][C]-0.18[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series (method=pearson)
SolenoidResistanceArmingTimingVoltsFiringCXT#1ResistanceFiringCXT#2ResistanceSafeTime
SolenoidResistance1-0.0750.0570.9220.173-0.834
ArmingTiming-0.0751-0.076-0.058-0.5660.123
Volts0.057-0.07610.046-0.005-0.041
FiringCXT#1Resistance0.922-0.0580.04610.199-0.905
FiringCXT#2Resistance0.173-0.566-0.0050.1991-0.18
SafeTime -0.8340.123-0.041-0.905-0.181







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
SolenoidResistance;ArmingTiming-0.0750.0162-0.0121
p-value(0.4927)(0.8825)(0.8696)
SolenoidResistance;Volts0.05743e-04-0.0312
p-value(0.5999)(0.9979)(0.6852)
SolenoidResistance;FiringCXT#1Resistance0.92230.77280.5483
p-value(0)(0)(0)
SolenoidResistance;FiringCXT#2Resistance0.17270.34170.2386
p-value(0.1117)(0.0013)(0.0012)
SolenoidResistance;SafeTime -0.8341-0.7931-0.5531
p-value(0)(0)(0)
ArmingTiming;Volts-0.0762-0.2439-0.157
p-value(0.4853)(0.0236)(0.0419)
ArmingTiming;FiringCXT#1Resistance-0.0579-0.0269-0.0389
p-value(0.5963)(0.8057)(0.6049)
ArmingTiming;FiringCXT#2Resistance-0.5659-0.0028-0.0121
p-value(0)(0.9796)(0.8696)
ArmingTiming;SafeTime 0.1232-0.1056-0.0482
p-value(0.2583)(0.3331)(0.5138)
Volts;FiringCXT#1Resistance0.0461-0.0396-0.032
p-value(0.6733)(0.7176)(0.6839)
Volts;FiringCXT#2Resistance-0.0048-0.1446-0.103
p-value(0.9649)(0.1842)(0.182)
Volts;SafeTime -0.04060.09810.0998
p-value(0.7103)(0.3691)(0.1948)
FiringCXT#1Resistance;FiringCXT#2Resistance0.19920.36650.2578
p-value(0.066)(5e-04)(6e-04)
FiringCXT#1Resistance;SafeTime -0.9045-0.8534-0.6607
p-value(0)(0)(0)
FiringCXT#2Resistance;SafeTime -0.18-0.4344-0.3057
p-value(0.0973)(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
SolenoidResistance;ArmingTiming & -0.075 & 0.0162 & -0.0121 \tabularnewline
p-value & (0.4927) & (0.8825) & (0.8696) \tabularnewline
SolenoidResistance;Volts & 0.0574 & 3e-04 & -0.0312 \tabularnewline
p-value & (0.5999) & (0.9979) & (0.6852) \tabularnewline
SolenoidResistance;FiringCXT#1Resistance & 0.9223 & 0.7728 & 0.5483 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SolenoidResistance;FiringCXT#2Resistance & 0.1727 & 0.3417 & 0.2386 \tabularnewline
p-value & (0.1117) & (0.0013) & (0.0012) \tabularnewline
SolenoidResistance;SafeTime
 & -0.8341 & -0.7931 & -0.5531 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
ArmingTiming;Volts & -0.0762 & -0.2439 & -0.157 \tabularnewline
p-value & (0.4853) & (0.0236) & (0.0419) \tabularnewline
ArmingTiming;FiringCXT#1Resistance & -0.0579 & -0.0269 & -0.0389 \tabularnewline
p-value & (0.5963) & (0.8057) & (0.6049) \tabularnewline
ArmingTiming;FiringCXT#2Resistance & -0.5659 & -0.0028 & -0.0121 \tabularnewline
p-value & (0) & (0.9796) & (0.8696) \tabularnewline
ArmingTiming;SafeTime
 & 0.1232 & -0.1056 & -0.0482 \tabularnewline
p-value & (0.2583) & (0.3331) & (0.5138) \tabularnewline
Volts;FiringCXT#1Resistance & 0.0461 & -0.0396 & -0.032 \tabularnewline
p-value & (0.6733) & (0.7176) & (0.6839) \tabularnewline
Volts;FiringCXT#2Resistance & -0.0048 & -0.1446 & -0.103 \tabularnewline
p-value & (0.9649) & (0.1842) & (0.182) \tabularnewline
Volts;SafeTime
 & -0.0406 & 0.0981 & 0.0998 \tabularnewline
p-value & (0.7103) & (0.3691) & (0.1948) \tabularnewline
FiringCXT#1Resistance;FiringCXT#2Resistance & 0.1992 & 0.3665 & 0.2578 \tabularnewline
p-value & (0.066) & (5e-04) & (6e-04) \tabularnewline
FiringCXT#1Resistance;SafeTime
 & -0.9045 & -0.8534 & -0.6607 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
FiringCXT#2Resistance;SafeTime
 & -0.18 & -0.4344 & -0.3057 \tabularnewline
p-value & (0.0973) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]SolenoidResistance;ArmingTiming[/C][C]-0.075[/C][C]0.0162[/C][C]-0.0121[/C][/ROW]
[ROW][C]p-value[/C][C](0.4927)[/C][C](0.8825)[/C][C](0.8696)[/C][/ROW]
[ROW][C]SolenoidResistance;Volts[/C][C]0.0574[/C][C]3e-04[/C][C]-0.0312[/C][/ROW]
[ROW][C]p-value[/C][C](0.5999)[/C][C](0.9979)[/C][C](0.6852)[/C][/ROW]
[ROW][C]SolenoidResistance;FiringCXT#1Resistance[/C][C]0.9223[/C][C]0.7728[/C][C]0.5483[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SolenoidResistance;FiringCXT#2Resistance[/C][C]0.1727[/C][C]0.3417[/C][C]0.2386[/C][/ROW]
[ROW][C]p-value[/C][C](0.1117)[/C][C](0.0013)[/C][C](0.0012)[/C][/ROW]
[ROW][C]SolenoidResistance;SafeTime
[/C][C]-0.8341[/C][C]-0.7931[/C][C]-0.5531[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]ArmingTiming;Volts[/C][C]-0.0762[/C][C]-0.2439[/C][C]-0.157[/C][/ROW]
[ROW][C]p-value[/C][C](0.4853)[/C][C](0.0236)[/C][C](0.0419)[/C][/ROW]
[ROW][C]ArmingTiming;FiringCXT#1Resistance[/C][C]-0.0579[/C][C]-0.0269[/C][C]-0.0389[/C][/ROW]
[ROW][C]p-value[/C][C](0.5963)[/C][C](0.8057)[/C][C](0.6049)[/C][/ROW]
[ROW][C]ArmingTiming;FiringCXT#2Resistance[/C][C]-0.5659[/C][C]-0.0028[/C][C]-0.0121[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0.9796)[/C][C](0.8696)[/C][/ROW]
[ROW][C]ArmingTiming;SafeTime
[/C][C]0.1232[/C][C]-0.1056[/C][C]-0.0482[/C][/ROW]
[ROW][C]p-value[/C][C](0.2583)[/C][C](0.3331)[/C][C](0.5138)[/C][/ROW]
[ROW][C]Volts;FiringCXT#1Resistance[/C][C]0.0461[/C][C]-0.0396[/C][C]-0.032[/C][/ROW]
[ROW][C]p-value[/C][C](0.6733)[/C][C](0.7176)[/C][C](0.6839)[/C][/ROW]
[ROW][C]Volts;FiringCXT#2Resistance[/C][C]-0.0048[/C][C]-0.1446[/C][C]-0.103[/C][/ROW]
[ROW][C]p-value[/C][C](0.9649)[/C][C](0.1842)[/C][C](0.182)[/C][/ROW]
[ROW][C]Volts;SafeTime
[/C][C]-0.0406[/C][C]0.0981[/C][C]0.0998[/C][/ROW]
[ROW][C]p-value[/C][C](0.7103)[/C][C](0.3691)[/C][C](0.1948)[/C][/ROW]
[ROW][C]FiringCXT#1Resistance;FiringCXT#2Resistance[/C][C]0.1992[/C][C]0.3665[/C][C]0.2578[/C][/ROW]
[ROW][C]p-value[/C][C](0.066)[/C][C](5e-04)[/C][C](6e-04)[/C][/ROW]
[ROW][C]FiringCXT#1Resistance;SafeTime
[/C][C]-0.9045[/C][C]-0.8534[/C][C]-0.6607[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]FiringCXT#2Resistance;SafeTime
[/C][C]-0.18[/C][C]-0.4344[/C][C]-0.3057[/C][/ROW]
[ROW][C]p-value[/C][C](0.0973)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
SolenoidResistance;ArmingTiming-0.0750.0162-0.0121
p-value(0.4927)(0.8825)(0.8696)
SolenoidResistance;Volts0.05743e-04-0.0312
p-value(0.5999)(0.9979)(0.6852)
SolenoidResistance;FiringCXT#1Resistance0.92230.77280.5483
p-value(0)(0)(0)
SolenoidResistance;FiringCXT#2Resistance0.17270.34170.2386
p-value(0.1117)(0.0013)(0.0012)
SolenoidResistance;SafeTime -0.8341-0.7931-0.5531
p-value(0)(0)(0)
ArmingTiming;Volts-0.0762-0.2439-0.157
p-value(0.4853)(0.0236)(0.0419)
ArmingTiming;FiringCXT#1Resistance-0.0579-0.0269-0.0389
p-value(0.5963)(0.8057)(0.6049)
ArmingTiming;FiringCXT#2Resistance-0.5659-0.0028-0.0121
p-value(0)(0.9796)(0.8696)
ArmingTiming;SafeTime 0.1232-0.1056-0.0482
p-value(0.2583)(0.3331)(0.5138)
Volts;FiringCXT#1Resistance0.0461-0.0396-0.032
p-value(0.6733)(0.7176)(0.6839)
Volts;FiringCXT#2Resistance-0.0048-0.1446-0.103
p-value(0.9649)(0.1842)(0.182)
Volts;SafeTime -0.04060.09810.0998
p-value(0.7103)(0.3691)(0.1948)
FiringCXT#1Resistance;FiringCXT#2Resistance0.19920.36650.2578
p-value(0.066)(5e-04)(6e-04)
FiringCXT#1Resistance;SafeTime -0.9045-0.8534-0.6607
p-value(0)(0)(0)
FiringCXT#2Resistance;SafeTime -0.18-0.4344-0.3057
p-value(0.0973)(0)(0)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.270.40.4
0.020.270.40.4
0.030.270.470.4
0.040.270.470.4
0.050.270.470.47
0.060.270.470.47
0.070.330.470.47
0.080.330.470.47
0.090.330.470.47
0.10.40.470.47

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

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

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

As an alternative you can also use a QR Code:  

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

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.270.40.4
0.020.270.40.4
0.030.270.470.4
0.040.270.470.4
0.050.270.470.47
0.060.270.470.47
0.070.330.470.47
0.080.330.470.47
0.090.330.470.47
0.10.40.470.47



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')