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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 computationFri, 02 Dec 2016 09:55:50 +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/02/t1480669203nem1p9qqcl2azxk.htm/, Retrieved Tue, 07 May 2024 19:12:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297558, Retrieved Tue, 07 May 2024 19:12:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [kendal matrix cor...] [2016-12-02 08:55:50] [c3c00422a8efeb721f46880d0369ae73] [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	NA
4	4	5	4	5	4	NA
3	4	4	4	5	5	16
3	4	4	3	3	4	NA
3	4	5	4	4	4	NA
4	5	4	4	5	5	NA
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	NA
NA	NA	5	NA	5	5	NA
5	5	4	3	4	4	NA
4	4	4	4	5	4	NA
3	4	5	3	4	5	16
4	4	4	4	5	5	NA
4	4	5	4	4	5	16
4	4	5	4	4	4	NA
4	4	5	4	4	5	NA
3	4	4	4	4	4	NA
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	NA	5	NA
2	3	5	4	5	4	13
4	5	2	4	4	4	17
3	4	5	4	4	4	NA
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	NA
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	NA
3	5	5	3	3	4	15
4	4	4	4	5	4	13
2	3	4	2	NA	4	NA
4	5	5	4	4	4	17
5	5	2	4	5	4	NA
5	5	5	4	4	4	NA
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	NA
3	4	4	4	4	3	17
4	4	4	3	5	4	16
4	4	4	4	5	4	NA
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	NA
4	5	5	5	5	4	NA
4	4	3	4	NA	4	NA
4	4	4	4	4	4	NA
4	4	4	5	5	4	NA
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	NA
3	3	3	4	4	4	10
4	4	4	4	5	4	NA
4	4	3	4	5	5	17
4	4	4	4	5	5	NA
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	NA
3	NA	4	4	4	4	17
4	2	3	3	4	4	14
4	4	5	4	4	3	NA
4	4	5	4	4	5	17
4	4	4	4	5	4	NA
4	5	4	4	5	3	17
3	4	4	3	5	5	NA
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	NA
5	3	4	4	5	5	NA
4	4	5	4	4	5	15
5	4	4	4	4	5	13
3	4	4	3	NA	4	NA
5	4	4	5	5	5	NA
4	4	5	3	NA	5	NA
4	4	3	3	4	3	NA
4	4	5	4	4	4	NA
4	4	5	4	4	4	16
3	4	5	4	5	3	NA
4	4	4	4	4	4	NA
4	4	4	3	4	5	NA
3	3	4	3	5	5	12
4	4	4	3	4	4	NA
3	4	5	4	4	4	16
4	4	5	4	3	4	16
5	4	5	1	5	5	NA
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	NA
4	4	4	4	5	4	15
4	5	3	4	4	4	NA
3	4	4	4	4	4	15
4	4	4	3	4	4	16
4	4	4	4	4	5	NA
3	4	3	3	4	4	NA
4	4	4	3	4	3	NA
3	2	4	2	4	4	11
4	4	4	3	5	4	NA
5	4	4	3	5	4	18
2	4	4	3	3	5	NA
3	3	4	4	4	4	11
4	4	4	3	4	4	NA
5	5	4	4	5	4	18
NA	NA	2	NA	NA	NA	NA
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	NA
3	4	5	4	5	4	14
4	4	5	4	4	4	NA
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	NA
3	3	2	3	4	4	NA
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	NA
5	4	5	4	5	4	17
4	4	5	4	3	4	NA
2	3	5	4	4	4	NA
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	NA
5	4	4	3	4	4	NA
3	3	1	4	5	5	NA
4	4	4	4	4	5	16
4	4	4	4	5	4	NA
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=297558&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=297558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297558&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)
SK1SK2SK3SK4SK5SK5TOT
SK110.24-0.0050.0910.027-0.0040.322
SK20.2410.1560.1610.1250.0410.429
SK3-0.0050.15610.024-0.0590.1340.018
SK40.0910.1610.0241-0.051-0.0490.23
SK50.0270.125-0.059-0.05110.1320.118
SK5-0.0040.0410.134-0.0490.13210.025
TOT0.3220.4290.0180.230.1180.0251

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & SK1 & SK2 & SK3 & SK4 & SK5 & SK5 & TOT \tabularnewline
SK1 & 1 & 0.24 & -0.005 & 0.091 & 0.027 & -0.004 & 0.322 \tabularnewline
SK2 & 0.24 & 1 & 0.156 & 0.161 & 0.125 & 0.041 & 0.429 \tabularnewline
SK3 & -0.005 & 0.156 & 1 & 0.024 & -0.059 & 0.134 & 0.018 \tabularnewline
SK4 & 0.091 & 0.161 & 0.024 & 1 & -0.051 & -0.049 & 0.23 \tabularnewline
SK5 & 0.027 & 0.125 & -0.059 & -0.051 & 1 & 0.132 & 0.118 \tabularnewline
SK5 & -0.004 & 0.041 & 0.134 & -0.049 & 0.132 & 1 & 0.025 \tabularnewline
TOT & 0.322 & 0.429 & 0.018 & 0.23 & 0.118 & 0.025 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297558&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]SK1[/C][C]SK2[/C][C]SK3[/C][C]SK4[/C][C]SK5[/C][C]SK5[/C][C]TOT[/C][/ROW]
[ROW][C]SK1[/C][C]1[/C][C]0.24[/C][C]-0.005[/C][C]0.091[/C][C]0.027[/C][C]-0.004[/C][C]0.322[/C][/ROW]
[ROW][C]SK2[/C][C]0.24[/C][C]1[/C][C]0.156[/C][C]0.161[/C][C]0.125[/C][C]0.041[/C][C]0.429[/C][/ROW]
[ROW][C]SK3[/C][C]-0.005[/C][C]0.156[/C][C]1[/C][C]0.024[/C][C]-0.059[/C][C]0.134[/C][C]0.018[/C][/ROW]
[ROW][C]SK4[/C][C]0.091[/C][C]0.161[/C][C]0.024[/C][C]1[/C][C]-0.051[/C][C]-0.049[/C][C]0.23[/C][/ROW]
[ROW][C]SK5[/C][C]0.027[/C][C]0.125[/C][C]-0.059[/C][C]-0.051[/C][C]1[/C][C]0.132[/C][C]0.118[/C][/ROW]
[ROW][C]SK5[/C][C]-0.004[/C][C]0.041[/C][C]0.134[/C][C]-0.049[/C][C]0.132[/C][C]1[/C][C]0.025[/C][/ROW]
[ROW][C]TOT[/C][C]0.322[/C][C]0.429[/C][C]0.018[/C][C]0.23[/C][C]0.118[/C][C]0.025[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297558&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)
SK1SK2SK3SK4SK5SK5TOT
SK110.24-0.0050.0910.027-0.0040.322
SK20.2410.1560.1610.1250.0410.429
SK3-0.0050.15610.024-0.0590.1340.018
SK40.0910.1610.0241-0.051-0.0490.23
SK50.0270.125-0.059-0.05110.1320.118
SK5-0.0040.0410.134-0.0490.13210.025
TOT0.3220.4290.0180.230.1180.0251







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
SK1;SK20.25230.26880.2396
p-value(0.0105)(0.0063)(0.0074)
SK1;SK3-0.0413-0.0063-0.0051
p-value(0.6805)(0.9499)(0.9547)
SK1;SK40.0780.09690.0914
p-value(0.4356)(0.3326)(0.3194)
SK1;SK50.00740.0280.0267
p-value(0.9411)(0.7799)(0.772)
SK1;SK5-0.004-0.0047-0.004
p-value(0.9686)(0.9629)(0.9651)
SK1;TOT0.37720.38270.3219
p-value(1e-04)(1e-04)(1e-04)
SK2;SK30.14430.170.1557
p-value(0.1478)(0.0876)(0.0849)
SK2;SK40.23220.1720.1607
p-value(0.0189)(0.084)(0.0821)
SK2;SK50.09530.13550.1254
p-value(0.3409)(0.1746)(0.1759)
SK2;SK50.03860.04210.0408
p-value(0.7004)(0.6747)(0.6588)
SK2;TOT0.54360.50510.4287
p-value(0)(0)(0)
SK3;SK4-0.01660.02530.0237
p-value(0.8685)(0.8009)(0.798)
SK3;SK5-0.0652-0.0635-0.0586
p-value(0.515)(0.5259)(0.5287)
SK3;SK50.14020.14490.1339
p-value(0.1599)(0.1463)(0.1489)
SK3;TOT0.03930.02090.0178
p-value(0.6951)(0.8349)(0.8312)
SK4;SK5-0.0364-0.0538-0.0514
p-value(0.7162)(0.5914)(0.5888)
SK4;SK5-0.0406-0.0518-0.049
p-value(0.6851)(0.6053)(0.6053)
SK4;TOT0.26050.26490.23
p-value(0.0082)(0.0071)(0.007)
SK5;SK50.10590.13860.1319
p-value(0.2893)(0.1649)(0.1653)
SK5;TOT0.09010.14190.1184
p-value(0.3678)(0.1549)(0.1661)
SK5;TOT9e-040.02540.0246
p-value(0.9925)(0.7998)(0.7732)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
SK1;SK2 & 0.2523 & 0.2688 & 0.2396 \tabularnewline
p-value & (0.0105) & (0.0063) & (0.0074) \tabularnewline
SK1;SK3 & -0.0413 & -0.0063 & -0.0051 \tabularnewline
p-value & (0.6805) & (0.9499) & (0.9547) \tabularnewline
SK1;SK4 & 0.078 & 0.0969 & 0.0914 \tabularnewline
p-value & (0.4356) & (0.3326) & (0.3194) \tabularnewline
SK1;SK5 & 0.0074 & 0.028 & 0.0267 \tabularnewline
p-value & (0.9411) & (0.7799) & (0.772) \tabularnewline
SK1;SK5 & -0.004 & -0.0047 & -0.004 \tabularnewline
p-value & (0.9686) & (0.9629) & (0.9651) \tabularnewline
SK1;TOT & 0.3772 & 0.3827 & 0.3219 \tabularnewline
p-value & (1e-04) & (1e-04) & (1e-04) \tabularnewline
SK2;SK3 & 0.1443 & 0.17 & 0.1557 \tabularnewline
p-value & (0.1478) & (0.0876) & (0.0849) \tabularnewline
SK2;SK4 & 0.2322 & 0.172 & 0.1607 \tabularnewline
p-value & (0.0189) & (0.084) & (0.0821) \tabularnewline
SK2;SK5 & 0.0953 & 0.1355 & 0.1254 \tabularnewline
p-value & (0.3409) & (0.1746) & (0.1759) \tabularnewline
SK2;SK5 & 0.0386 & 0.0421 & 0.0408 \tabularnewline
p-value & (0.7004) & (0.6747) & (0.6588) \tabularnewline
SK2;TOT & 0.5436 & 0.5051 & 0.4287 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
SK3;SK4 & -0.0166 & 0.0253 & 0.0237 \tabularnewline
p-value & (0.8685) & (0.8009) & (0.798) \tabularnewline
SK3;SK5 & -0.0652 & -0.0635 & -0.0586 \tabularnewline
p-value & (0.515) & (0.5259) & (0.5287) \tabularnewline
SK3;SK5 & 0.1402 & 0.1449 & 0.1339 \tabularnewline
p-value & (0.1599) & (0.1463) & (0.1489) \tabularnewline
SK3;TOT & 0.0393 & 0.0209 & 0.0178 \tabularnewline
p-value & (0.6951) & (0.8349) & (0.8312) \tabularnewline
SK4;SK5 & -0.0364 & -0.0538 & -0.0514 \tabularnewline
p-value & (0.7162) & (0.5914) & (0.5888) \tabularnewline
SK4;SK5 & -0.0406 & -0.0518 & -0.049 \tabularnewline
p-value & (0.6851) & (0.6053) & (0.6053) \tabularnewline
SK4;TOT & 0.2605 & 0.2649 & 0.23 \tabularnewline
p-value & (0.0082) & (0.0071) & (0.007) \tabularnewline
SK5;SK5 & 0.1059 & 0.1386 & 0.1319 \tabularnewline
p-value & (0.2893) & (0.1649) & (0.1653) \tabularnewline
SK5;TOT & 0.0901 & 0.1419 & 0.1184 \tabularnewline
p-value & (0.3678) & (0.1549) & (0.1661) \tabularnewline
SK5;TOT & 9e-04 & 0.0254 & 0.0246 \tabularnewline
p-value & (0.9925) & (0.7998) & (0.7732) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297558&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]SK1;SK2[/C][C]0.2523[/C][C]0.2688[/C][C]0.2396[/C][/ROW]
[ROW][C]p-value[/C][C](0.0105)[/C][C](0.0063)[/C][C](0.0074)[/C][/ROW]
[ROW][C]SK1;SK3[/C][C]-0.0413[/C][C]-0.0063[/C][C]-0.0051[/C][/ROW]
[ROW][C]p-value[/C][C](0.6805)[/C][C](0.9499)[/C][C](0.9547)[/C][/ROW]
[ROW][C]SK1;SK4[/C][C]0.078[/C][C]0.0969[/C][C]0.0914[/C][/ROW]
[ROW][C]p-value[/C][C](0.4356)[/C][C](0.3326)[/C][C](0.3194)[/C][/ROW]
[ROW][C]SK1;SK5[/C][C]0.0074[/C][C]0.028[/C][C]0.0267[/C][/ROW]
[ROW][C]p-value[/C][C](0.9411)[/C][C](0.7799)[/C][C](0.772)[/C][/ROW]
[ROW][C]SK1;SK5[/C][C]-0.004[/C][C]-0.0047[/C][C]-0.004[/C][/ROW]
[ROW][C]p-value[/C][C](0.9686)[/C][C](0.9629)[/C][C](0.9651)[/C][/ROW]
[ROW][C]SK1;TOT[/C][C]0.3772[/C][C]0.3827[/C][C]0.3219[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]SK2;SK3[/C][C]0.1443[/C][C]0.17[/C][C]0.1557[/C][/ROW]
[ROW][C]p-value[/C][C](0.1478)[/C][C](0.0876)[/C][C](0.0849)[/C][/ROW]
[ROW][C]SK2;SK4[/C][C]0.2322[/C][C]0.172[/C][C]0.1607[/C][/ROW]
[ROW][C]p-value[/C][C](0.0189)[/C][C](0.084)[/C][C](0.0821)[/C][/ROW]
[ROW][C]SK2;SK5[/C][C]0.0953[/C][C]0.1355[/C][C]0.1254[/C][/ROW]
[ROW][C]p-value[/C][C](0.3409)[/C][C](0.1746)[/C][C](0.1759)[/C][/ROW]
[ROW][C]SK2;SK5[/C][C]0.0386[/C][C]0.0421[/C][C]0.0408[/C][/ROW]
[ROW][C]p-value[/C][C](0.7004)[/C][C](0.6747)[/C][C](0.6588)[/C][/ROW]
[ROW][C]SK2;TOT[/C][C]0.5436[/C][C]0.5051[/C][C]0.4287[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]SK3;SK4[/C][C]-0.0166[/C][C]0.0253[/C][C]0.0237[/C][/ROW]
[ROW][C]p-value[/C][C](0.8685)[/C][C](0.8009)[/C][C](0.798)[/C][/ROW]
[ROW][C]SK3;SK5[/C][C]-0.0652[/C][C]-0.0635[/C][C]-0.0586[/C][/ROW]
[ROW][C]p-value[/C][C](0.515)[/C][C](0.5259)[/C][C](0.5287)[/C][/ROW]
[ROW][C]SK3;SK5[/C][C]0.1402[/C][C]0.1449[/C][C]0.1339[/C][/ROW]
[ROW][C]p-value[/C][C](0.1599)[/C][C](0.1463)[/C][C](0.1489)[/C][/ROW]
[ROW][C]SK3;TOT[/C][C]0.0393[/C][C]0.0209[/C][C]0.0178[/C][/ROW]
[ROW][C]p-value[/C][C](0.6951)[/C][C](0.8349)[/C][C](0.8312)[/C][/ROW]
[ROW][C]SK4;SK5[/C][C]-0.0364[/C][C]-0.0538[/C][C]-0.0514[/C][/ROW]
[ROW][C]p-value[/C][C](0.7162)[/C][C](0.5914)[/C][C](0.5888)[/C][/ROW]
[ROW][C]SK4;SK5[/C][C]-0.0406[/C][C]-0.0518[/C][C]-0.049[/C][/ROW]
[ROW][C]p-value[/C][C](0.6851)[/C][C](0.6053)[/C][C](0.6053)[/C][/ROW]
[ROW][C]SK4;TOT[/C][C]0.2605[/C][C]0.2649[/C][C]0.23[/C][/ROW]
[ROW][C]p-value[/C][C](0.0082)[/C][C](0.0071)[/C][C](0.007)[/C][/ROW]
[ROW][C]SK5;SK5[/C][C]0.1059[/C][C]0.1386[/C][C]0.1319[/C][/ROW]
[ROW][C]p-value[/C][C](0.2893)[/C][C](0.1649)[/C][C](0.1653)[/C][/ROW]
[ROW][C]SK5;TOT[/C][C]0.0901[/C][C]0.1419[/C][C]0.1184[/C][/ROW]
[ROW][C]p-value[/C][C](0.3678)[/C][C](0.1549)[/C][C](0.1661)[/C][/ROW]
[ROW][C]SK5;TOT[/C][C]9e-04[/C][C]0.0254[/C][C]0.0246[/C][/ROW]
[ROW][C]p-value[/C][C](0.9925)[/C][C](0.7998)[/C][C](0.7732)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297558&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297558&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
SK1;SK20.25230.26880.2396
p-value(0.0105)(0.0063)(0.0074)
SK1;SK3-0.0413-0.0063-0.0051
p-value(0.6805)(0.9499)(0.9547)
SK1;SK40.0780.09690.0914
p-value(0.4356)(0.3326)(0.3194)
SK1;SK50.00740.0280.0267
p-value(0.9411)(0.7799)(0.772)
SK1;SK5-0.004-0.0047-0.004
p-value(0.9686)(0.9629)(0.9651)
SK1;TOT0.37720.38270.3219
p-value(1e-04)(1e-04)(1e-04)
SK2;SK30.14430.170.1557
p-value(0.1478)(0.0876)(0.0849)
SK2;SK40.23220.1720.1607
p-value(0.0189)(0.084)(0.0821)
SK2;SK50.09530.13550.1254
p-value(0.3409)(0.1746)(0.1759)
SK2;SK50.03860.04210.0408
p-value(0.7004)(0.6747)(0.6588)
SK2;TOT0.54360.50510.4287
p-value(0)(0)(0)
SK3;SK4-0.01660.02530.0237
p-value(0.8685)(0.8009)(0.798)
SK3;SK5-0.0652-0.0635-0.0586
p-value(0.515)(0.5259)(0.5287)
SK3;SK50.14020.14490.1339
p-value(0.1599)(0.1463)(0.1489)
SK3;TOT0.03930.02090.0178
p-value(0.6951)(0.8349)(0.8312)
SK4;SK5-0.0364-0.0538-0.0514
p-value(0.7162)(0.5914)(0.5888)
SK4;SK5-0.0406-0.0518-0.049
p-value(0.6851)(0.6053)(0.6053)
SK4;TOT0.26050.26490.23
p-value(0.0082)(0.0071)(0.007)
SK5;SK50.10590.13860.1319
p-value(0.2893)(0.1649)(0.1653)
SK5;TOT0.09010.14190.1184
p-value(0.3678)(0.1549)(0.1661)
SK5;TOT9e-040.02540.0246
p-value(0.9925)(0.7998)(0.7732)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.140.190.19
0.020.240.190.19
0.030.240.190.19
0.040.240.190.19
0.050.240.190.19
0.060.240.190.19
0.070.240.190.19
0.080.240.190.19
0.090.240.290.29
0.10.240.290.29

\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 & 0.14 & 0.19 & 0.19 \tabularnewline
0.02 & 0.24 & 0.19 & 0.19 \tabularnewline
0.03 & 0.24 & 0.19 & 0.19 \tabularnewline
0.04 & 0.24 & 0.19 & 0.19 \tabularnewline
0.05 & 0.24 & 0.19 & 0.19 \tabularnewline
0.06 & 0.24 & 0.19 & 0.19 \tabularnewline
0.07 & 0.24 & 0.19 & 0.19 \tabularnewline
0.08 & 0.24 & 0.19 & 0.19 \tabularnewline
0.09 & 0.24 & 0.29 & 0.29 \tabularnewline
0.1 & 0.24 & 0.29 & 0.29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297558&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]0.14[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.02[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.03[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.04[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.05[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.06[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.07[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.08[/C][C]0.24[/C][C]0.19[/C][C]0.19[/C][/ROW]
[ROW][C]0.09[/C][C]0.24[/C][C]0.29[/C][C]0.29[/C][/ROW]
[ROW][C]0.1[/C][C]0.24[/C][C]0.29[/C][C]0.29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297558&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297558&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
0.010.140.190.19
0.020.240.190.19
0.030.240.190.19
0.040.240.190.19
0.050.240.190.19
0.060.240.190.19
0.070.240.190.19
0.080.240.190.19
0.090.240.290.29
0.10.240.290.29



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')