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Author

*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Wed, 17 Dec 2014 20:44:26 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418849075kr2b9hofau7ef5k.htm/, Retrieved Thu, 16 May 2024 10:29:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270666, Retrieved Thu, 16 May 2024 10:29:38 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [Kendall tau corre...] [2014-12-13 09:24:32] [6c2f6c6ea910808786c6eeaf4a8f7882]
- R PD  [Kendall tau Correlation Matrix] [] [2014-12-15 17:49:48] [95c11abf048d3a1e472aeccb09199113]
- R  D      [Kendall tau Correlation Matrix] [] [2014-12-17 20:44:26] [d100ddac424efc880e37824ffef4fe9f] [Current]

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Dataseries X:

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Histogram

Boxplots

11	8	7	18	12	20
15	18	18	23	20	25
19	18	20	23	20	19
16	12	9	22	14	18
24	24	19	22	25	24
15	16	12	19	15	20
17	19	16	25	20	20
19	16	17	28	21	24
19	15	9	16	15	21
28	28	28	28	28	28
26	21	20	21	11	10
15	18	16	22	22	22
26	22	22	24	22	19
16	19	17	24	27	27
24	22	12	26	24	23
25	25	18	28	23	24
22	20	20	24	24	24
15	16	12	20	21	25
21	19	16	26	20	24
22	18	16	21	19	21
27	26	21	28	25	28
26	24	15	27	16	28
26	20	17	23	24	22
22	19	17	24	21	26
21	19	17	24	22	26
22	23	18	22	25	21
20	18	15	21	23	26
21	16	20	25	20	23
20	18	13	20	21	20
22	21	21	21	22	24
21	20	12	26	25	25
8	15	6	23	23	24
22	19	13	21	19	20
18	27	6	27	27	23
20	19	19	27	21	24
24	7	12	25	19	25
17	20	14	23	25	23
20	20	13	25	16	21
23	19	12	23	24	23
20	19	17	19	24	21
22	20	19	22	18	18
19	18	10	24	28	24
15	14	10	19	15	18
20	17	11	21	17	21
22	17	11	27	18	23
17	8	10	25	26	25
14	9	7	25	18	22
24	22	22	23	22	22
17	20	12	17	19	23
23	20	18	28	17	24
25	22	20	25	26	25
16	22	9	20	21	22
18	22	16	25	26	24
20	16	14	21	21	21
18	14	11	24	12	24
23	24	20	28	20	25
24	21	17	20	20	23
23	20	14	19	24	27
13	20	8	24	24	27
20	18	16	21	22	23
20	14	11	24	21	18
19	19	10	23	20	20
22	24	15	18	23	23
22	19	15	27	19	24
15	16	10	25	24	26
17	16	10	20	21	20
19	16	18	21	16	23
20	14	10	23	17	22
22	22	22	27	23	23
21	21	16	24	20	17
21	15	10	27	19	20
16	14	7	24	18	22
20	15	16	23	18	18
21	14	16	24	21	19
20	20	16	21	20	19
23	21	22	23	17	16
15	17	13	22	20	24
18	14	5	27	25	26
22	19	18	24	15	14
16	16	10	25	17	25
17	13	8	19	17	23
24	26	16	24	24	18
13	13	8	25	21	22
19	18	16	23	22	26
20	15	14	23	18	25
22	18	15	25	22	26
19	21	9	26	20	26
21	17	21	26	21	24
15	18	7	16	21	22
21	20	17	23	20	21
24	18	18	26	18	22
22	25	16	25	25	28
20	20	16	23	23	22
21	19	14	26	21	26
19	18	15	22	20	20
14	12	8	20	21	24
25	22	22	27	20	21
11	16	5	20	22	23
17	18	13	22	15	23
22	23	22	24	24	23
20	20	18	21	22	22
22	20	15	24	21	23
15	16	11	26	17	21
23	22	19	24	23	27
20	19	19	24	22	23
22	23	21	27	23	26
16	6	4	25	16	27
25	19	17	27	18	27
18	24	10	19	25	23
19	19	13	22	18	23
25	15	15	22	14	23
21	18	11	25	20	28
22	18	20	23	19	24
21	22	13	24	18	20
22	23	18	24	22	23
23	18	20	23	21	22
20	17	15	22	14	15
6	6	4	24	5	27
15	22	9	19	25	23
18	20	18	25	21	23
24	16	12	26	11	20
22	16	17	18	20	18
21	17	12	24	9	22
23	20	16	28	15	20
20	23	17	23	23	21
20	18	14	19	21	25
18	13	13	19	9	19
25	22	20	27	24	25
16	20	16	24	16	24
20	20	15	26	20	22
14	13	10	21	15	28
22	16	16	25	18	22
26	25	21	28	22	21
20	16	15	19	21	23
17	15	16	20	21	19
22	19	19	26	21	21
22	19	9	27	20	25
20	24	19	23	24	23
17	9	7	18	15	28
22	22	23	23	24	14
17	15	14	21	18	23
22	22	10	23	24	24
21	22	16	22	24	25
25	24	12	21	15	15
11	12	10	14	19	23
19	21	7	24	20	26
24	25	20	26	26	21
17	26	9	24	26	26
22	19	14	26	18	15
22	21	12	22	23	23
17	14	10	20	13	15
26	28	19	20	16	16
19	16	16	20	19	20
20	21	11	18	22	20
19	16	15	18	21	20
21	16	14	25	11	21
24	25	11	28	23	28
21	21	14	23	18	19
19	22	15	20	19	21
13	9	7	22	15	22
24	20	22	27	8	27
28	19	19	24	15	20
27	24	22	23	21	17
22	22	11	20	25	26
23	22	19	22	14	21
19	12	9	21	21	24
18	17	11	24	18	21
23	18	17	26	18	25
21	10	12	24	12	22
22	22	17	18	24	17
17	24	10	17	17	14
15	18	17	23	20	23
21	18	13	21	24	28
20	23	11	21	22	24
26	21	19	24	15	22
19	21	21	22	22	24
28	28	24	24	26	25
21	17	13	24	17	21
19	21	16	24	23	22
22	21	13	23	19	16
21	20	15	21	21	18
20	18	15	24	23	27
19	17	11	19	19	17
11	7	7	19	18	25
17	17	13	23	16	24
19	14	13	25	23	21
20	18	12	24	13	21
17	14	8	21	18	19
21	23	7	18	23	27
21	20	17	23	21	28
12	14	9	20	23	19
23	17	18	23	16	23
22	21	17	23	17	25
22	23	17	23	20	26
21	24	18	23	18	25
20	21	12	27	20	25
18	14	14	19	19	24
21	24	22	25	26	24
24	16	19	25	9	24
22	21	21	21	23	22
20	8	10	25	9	21
17	17	16	17	13	17
19	18	11	22	27	23
16	17	15	23	22	17
19	16	12	27	12	25
23	22	21	27	18	19
8	17	22	5	6	8
22	21	20	19	17	14
23	20	15	24	22	22
15	20	9	23	22	25
17	19	15	28	23	28
21	8	14	25	19	25
25	19	11	27	20	24
18	11	9	16	17	15
23	15	18	23	18	25
20	13	12	25	24	24
21	18	11	26	20	28
21	19	14	24	18	24
24	23	10	23	23	25
22	20	18	24	27	23
22	22	11	27	25	26
23	19	14	25	24	26
17	16	16	19	12	22
15	11	11	19	16	25
24	11	8	14	16	20
22	21	16	24	24	22
19	14	13	20	23	26
18	21	12	21	24	20
21	20	17	28	24	26
20	21	23	26	26	26
19	20	14	19	19	21
19	19	10	23	28	21
16	19	16	23	23	24
18	18	11	21	21	21
23	20	16	26	19	18
22	21	19	25	23	23
23	22	17	25	23	26
20	19	12	24	20	23
24	23	17	23	18	25
25	16	11	22	20	20
25	23	19	27	28	25
20	18	12	26	21	26
23	23	8	23	25	19
21	20	17	22	18	21
23	20	13	26	24	23
23	23	17	22	28	24
11	13	7	17	9	6
21	21	23	25	22	22
27	26	18	22	26	21
19	18	13	28	28	28
21	19	17	22	18	24
16	18	13	21	23	14
22	19	13	21	22	17
21	18	8	24	15	20
22	19	16	26	24	28
16	13	14	26	12	19
18	10	13	24	12	24
23	21	19	27	20	21
24	24	15	22	25	21
20	21	15	23	24	26
20	23	8	22	23	24
18	18	14	23	18	26
4	11	7	15	20	25
14	16	11	20	22	23
22	20	17	22	20	24
17	20	19	25	25	24
23	26	17	27	28	26
20	21	12	24	25	23
18	12	12	21	14	20
19	15	18	17	16	16
20	18	16	26	24	24
15	14	15	20	13	20
24	18	20	22	19	23
21	16	16	24	18	23
19	19	12	23	16	18
19	7	10	22	8	21
27	21	28	28	27	25
23	24	19	21	23	23
23	21	18	24	20	26
20	20	19	28	20	26
17	22	8	25	26	24
21	17	17	24	23	23
23	19	16	24	24	21
22	20	18	21	21	23
16	16	12	20	15	20
20	20	17	26	22	23
16	16	13	16	25	24
21	19	18	23	23	23
19	19	13	16	16	16
27	24	6	25	26	18
13	7	10	15	25	28
17	17	12	25	23	26
18	23	10	22	24	21
20	23	13	19	26	20
22	21	15	22	20	21
18	18	8	24	24	26
6	4	4	10	14	15
22	27	4	24	28	16
15	18	9	23	25	21
19	20	10	22	21	25
17	15	12	22	19	22
22	19	21	26	22	19
10	14	6	24	22	24
21	14	11	23	21	24
21	18	17	26	17	23
23	17	10	27	21	22
18	20	16	28	23	27
20	16	12	21	16	22
27	16	12	23	15	24
13	11	11	25	24	26
20	21	14	23	25	26
20	10	11	22	23	28
22	18	19	23	19	20
20	18	16	22	24	23
24	21	21	27	14	24
23	16	16	24	20	23
19	15	11	24	23	26
22	17	12	20	12	20
24	15	8	19	22	20
21	12	9	23	18	12
19	20	14	21	21	21
20	20	13	27	23	28
16	18	7	19	22	24
17	21	17	25	28	24
25	22	8	25	20	24
16	21	9	24	22	22
23	25	15	26	24	26
20	12	11	21	19	23
23	22	20	22	9	10
22	24	13	26	23	27
15	17	7	20	23	24
16	20	8	21	22	26
20	20	20	21	20	18
23	19	15	28	23	22
24	24	19	24	24	24
17	18	17	19	17	16
19	15	18	23	19	23
25	25	19	25	17	21
14	27	5	27	23	28
18	17	11	18	19	19
22	18	13	26	24	18
15	17	8	14	15	27
27	24	19	25	22	17
22	18	14	23	25	20
26	27	24	24	27	24
16	18	11	20	23	24
25	23	12	19	16	20
20	18	9	25	24	24
19	21	10	19	22	25
19	25	22	23	27	25
24	18	18	23	28	26
14	19	8	17	23	25
18	20	15	24	25	26
13	11	10	22	21	14
19	22	10	20	22	19
25	24	20	23	25	23
20	23	17	22	25	25
17	16	12	20	19	23
17	24	17	23	24	19
13	16	10	22	21	23
20	16	15	21	18	24
20	18	14	22	17	21
24	17	8	26	17	21
25	21	17	24	26	24
19	15	10	28	8	23
20	15	16	24	25	22
20	19	13	26	22	21
22	21	17	20	20	23
18	19	16	26	22	25
21	19	13	21	17	17
20	18	14	26	24	27
11	14	6	22	20	28
18	17	16	21	19	24
22	25	18	25	26	27
21	14	16	25	13	22
15	19	15	23	20	23
23	20	18	27	26	24
18	20	20	23	21	24
23	20	19	28	24	26
19	19	16	24	23	21
23	18	11	21	16	23
26	22	24	23	24	18
19	18	13	21	18	25
26	22	17	24	21	24
20	19	14	28	17	27
20	20	16	11	19	20
23	22	18	25	22	25
24	22	16	25	23	23
26	24	16	28	27	25
23	18	9	28	22	24
19	21	5	19	22	23
25	22	11	25	25	19
23	19	10	25	26	25
19	18	16	25	22	28
27	24	17	28	25	26
23	21	15	26	23	24
24	21	13	27	8	25
20	20	12	24	24	28
16	17	12	18	14	23
22	20	16	21	17	15
26	22	22	23	21	18
26	24	19	24	21	24
24	24	23	26	25	27
20	20	6	25	18	25
20	19	19	23	20	24
12	20	7	24	25	26
21	16	9	20	20	19
27	21	16	26	24	23
26	22	19	27	22	21
17	19	8	21	16	22
20	19	15	21	20	23
18	13	10	19	21	23
28	22	18	25	22	20
24	20	19	23	15	20
24	21	12	25	21	25
24	21	16	26	25	28
12	15	12	18	16	19
26	23	20	27	28	21
23	22	19	23	22	21
13	15	10	20	19	25
23	20	16	22	17	18
16	23	12	22	23	22
23	21	15	23	28	21
18	18	15	18	19	21
25	23	17	25	24	25
18	16	13	21	16	20
18	18	14	21	19	22
21	18	18	28	19	27
7	10	4	19	12	23
19	17	11	21	16	25
21	20	10	23	15	28
17	13	7	22	17	25
22	25	20	27	23	24
15	18	10	23	21	27
20	20	18	27	20	19
19	18	14	23	19	24
10	19	11	21	20	22
18	11	12	22	20	23
25	17	16	26	23	21
23	22	19	23	22	21
25	21	18	26	20	20
23	19	16	28	24	26
21	20	9	28	21	28
23	21	15	26	23	23
19	22	14	24	22	19
22	20	17	23	21	23
23	21	14	28	26	18
15	15	11	21	16	21
23	22	11	28	28	28
23	21	19	21	24	22
24	28	25	28	28	28
20	20	20	24	14	20
23	20	15	24	16	23
24	23	17	28	22	25
17	18	12	21	18	16
21	15	10	26	23	23
19	19	24	22	18	18
23	21	16	25	22	22
22	19	9	20	13	21
14	16	16	19	20	19
19	17	8	23	24	20
21	26	11	26	24	27
23	20	13	28	25	27
16	13	14	24	23	20
23	19	12	25	24	26
19	21	14	24	22	25
19	21	16	25	24	23
22	24	19	27	24	24
26	23	17	28	24	27
22	20	20	23	25	28
24	23	11	19	27	26
24	24	19	27	27	27
11	8	6	15	14	23
21	19	16	27	21	28
21	18	14	21	17	22
22	20	14	26	23	23
22	21	16	25	25	27
19	16	11	26	20	18
18	17	14	24	21	22
19	21	16	25	24	23
27	27	22	27	27	25
14	12	7	14	12	14
15	17	17	24	26	21
20	17	16	25	22	26
22	18	18	23	24	28
26	24	22	24	24	22
20	18	13	22	20	24
13	18	11	16	22	28
26	24	19	26	23	24
19	18	14	26	22	26
20	19	15	19	21	18
18	19	15	19	13	19
20	24	15	28	21	26
21	15	15	24	20	26
26	22	19	20	18	12
25	17	22	21	19	24
20	20	18	26	25	26
21	22	10	24	24	23

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'George Udny Yule' @ yule.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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270666&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270666&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270666&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 Output	view raw output of R engine
Computing time	4 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Correlations for all pairs of data series (method=pearson)
	I1	I2	I3	E1	E2	E3
I1	1	0.567	0.558	0.459	0.223	0.034
I2	0.567	1	0.476	0.331	0.481	0.091
I3	0.558	0.476	1	0.28	0.175	-0.029
E1	0.459	0.331	0.28	1	0.295	0.38
E2	0.223	0.481	0.175	0.295	1	0.357
E3	0.034	0.091	-0.029	0.38	0.357	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & I1 & I2 & I3 & E1 & E2 & E3 \tabularnewline
I1 & 1 & 0.567 & 0.558 & 0.459 & 0.223 & 0.034 \tabularnewline
I2 & 0.567 & 1 & 0.476 & 0.331 & 0.481 & 0.091 \tabularnewline
I3 & 0.558 & 0.476 & 1 & 0.28 & 0.175 & -0.029 \tabularnewline
E1 & 0.459 & 0.331 & 0.28 & 1 & 0.295 & 0.38 \tabularnewline
E2 & 0.223 & 0.481 & 0.175 & 0.295 & 1 & 0.357 \tabularnewline
E3 & 0.034 & 0.091 & -0.029 & 0.38 & 0.357 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270666&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]I1[/C][C]I2[/C][C]I3[/C][C]E1[/C][C]E2[/C][C]E3[/C][/ROW]
[ROW][C]I1[/C][C]1[/C][C]0.567[/C][C]0.558[/C][C]0.459[/C][C]0.223[/C][C]0.034[/C][/ROW]
[ROW][C]I2[/C][C]0.567[/C][C]1[/C][C]0.476[/C][C]0.331[/C][C]0.481[/C][C]0.091[/C][/ROW]
[ROW][C]I3[/C][C]0.558[/C][C]0.476[/C][C]1[/C][C]0.28[/C][C]0.175[/C][C]-0.029[/C][/ROW]
[ROW][C]E1[/C][C]0.459[/C][C]0.331[/C][C]0.28[/C][C]1[/C][C]0.295[/C][C]0.38[/C][/ROW]
[ROW][C]E2[/C][C]0.223[/C][C]0.481[/C][C]0.175[/C][C]0.295[/C][C]1[/C][C]0.357[/C][/ROW]
[ROW][C]E3[/C][C]0.034[/C][C]0.091[/C][C]-0.029[/C][C]0.38[/C][C]0.357[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270666&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270666&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)
	I1	I2	I3	E1	E2	E3
I1	1	0.567	0.558	0.459	0.223	0.034
I2	0.567	1	0.476	0.331	0.481	0.091
I3	0.558	0.476	1	0.28	0.175	-0.029
E1	0.459	0.331	0.28	1	0.295	0.38
E2	0.223	0.481	0.175	0.295	1	0.357
E3	0.034	0.091	-0.029	0.38	0.357	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
I1;I2	0.5666	0.5477	0.4223
p-value	(0)	(0)	(0)
I1;I3	0.5579	0.5317	0.407
p-value	(0)	(0)	(0)
I1;E1	0.4586	0.4141	0.3095
p-value	(0)	(0)	(0)
I1;E2	0.2226	0.1969	0.1461
p-value	(0)	(0)	(0)
I1;E3	0.0341	0.022	0.0161
p-value	(0.4482)	(0.6236)	(0.6201)
I2;I3	0.4763	0.4473	0.344
p-value	(0)	(0)	(0)
I2;E1	0.3309	0.2998	0.2219
p-value	(0)	(0)	(0)
I2;E2	0.4805	0.4845	0.3659
p-value	(0)	(0)	(0)
I2;E3	0.0912	0.1109	0.0815
p-value	(0.0419)	(0.0132)	(0.012)
I3;E1	0.2799	0.2677	0.1959
p-value	(0)	(0)	(0)
I3;E2	0.1749	0.1583	0.116
p-value	(1e-04)	(4e-04)	(3e-04)
I3;E3	-0.029	-0.0249	-0.0172
p-value	(0.5181)	(0.5794)	(0.5934)
E1;E2	0.2949	0.2943	0.216
p-value	(0)	(0)	(0)
E1;E3	0.38	0.3611	0.2726
p-value	(0)	(0)	(0)
E2;E3	0.3574	0.3431	0.2531
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
I1;I2 & 0.5666 & 0.5477 & 0.4223 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I1;I3 & 0.5579 & 0.5317 & 0.407 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I1;E1 & 0.4586 & 0.4141 & 0.3095 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I1;E2 & 0.2226 & 0.1969 & 0.1461 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I1;E3 & 0.0341 & 0.022 & 0.0161 \tabularnewline
p-value & (0.4482) & (0.6236) & (0.6201) \tabularnewline
I2;I3 & 0.4763 & 0.4473 & 0.344 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I2;E1 & 0.3309 & 0.2998 & 0.2219 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I2;E2 & 0.4805 & 0.4845 & 0.3659 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I2;E3 & 0.0912 & 0.1109 & 0.0815 \tabularnewline
p-value & (0.0419) & (0.0132) & (0.012) \tabularnewline
I3;E1 & 0.2799 & 0.2677 & 0.1959 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
I3;E2 & 0.1749 & 0.1583 & 0.116 \tabularnewline
p-value & (1e-04) & (4e-04) & (3e-04) \tabularnewline
I3;E3 & -0.029 & -0.0249 & -0.0172 \tabularnewline
p-value & (0.5181) & (0.5794) & (0.5934) \tabularnewline
E1;E2 & 0.2949 & 0.2943 & 0.216 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
E1;E3 & 0.38 & 0.3611 & 0.2726 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
E2;E3 & 0.3574 & 0.3431 & 0.2531 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270666&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]I1;I2[/C][C]0.5666[/C][C]0.5477[/C][C]0.4223[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I1;I3[/C][C]0.5579[/C][C]0.5317[/C][C]0.407[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I1;E1[/C][C]0.4586[/C][C]0.4141[/C][C]0.3095[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I1;E2[/C][C]0.2226[/C][C]0.1969[/C][C]0.1461[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I1;E3[/C][C]0.0341[/C][C]0.022[/C][C]0.0161[/C][/ROW]
[ROW][C]p-value[/C][C](0.4482)[/C][C](0.6236)[/C][C](0.6201)[/C][/ROW]
[ROW][C]I2;I3[/C][C]0.4763[/C][C]0.4473[/C][C]0.344[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I2;E1[/C][C]0.3309[/C][C]0.2998[/C][C]0.2219[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I2;E2[/C][C]0.4805[/C][C]0.4845[/C][C]0.3659[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I2;E3[/C][C]0.0912[/C][C]0.1109[/C][C]0.0815[/C][/ROW]
[ROW][C]p-value[/C][C](0.0419)[/C][C](0.0132)[/C][C](0.012)[/C][/ROW]
[ROW][C]I3;E1[/C][C]0.2799[/C][C]0.2677[/C][C]0.1959[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]I3;E2[/C][C]0.1749[/C][C]0.1583[/C][C]0.116[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](4e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]I3;E3[/C][C]-0.029[/C][C]-0.0249[/C][C]-0.0172[/C][/ROW]
[ROW][C]p-value[/C][C](0.5181)[/C][C](0.5794)[/C][C](0.5934)[/C][/ROW]
[ROW][C]E1;E2[/C][C]0.2949[/C][C]0.2943[/C][C]0.216[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]E1;E3[/C][C]0.38[/C][C]0.3611[/C][C]0.2726[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]E2;E3[/C][C]0.3574[/C][C]0.3431[/C][C]0.2531[/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=270666&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270666&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
pair	Pearson r	Spearman rho	Kendall tau
I1;I2	0.5666	0.5477	0.4223
p-value	(0)	(0)	(0)
I1;I3	0.5579	0.5317	0.407
p-value	(0)	(0)	(0)
I1;E1	0.4586	0.4141	0.3095
p-value	(0)	(0)	(0)
I1;E2	0.2226	0.1969	0.1461
p-value	(0)	(0)	(0)
I1;E3	0.0341	0.022	0.0161
p-value	(0.4482)	(0.6236)	(0.6201)
I2;I3	0.4763	0.4473	0.344
p-value	(0)	(0)	(0)
I2;E1	0.3309	0.2998	0.2219
p-value	(0)	(0)	(0)
I2;E2	0.4805	0.4845	0.3659
p-value	(0)	(0)	(0)
I2;E3	0.0912	0.1109	0.0815
p-value	(0.0419)	(0.0132)	(0.012)
I3;E1	0.2799	0.2677	0.1959
p-value	(0)	(0)	(0)
I3;E2	0.1749	0.1583	0.116
p-value	(1e-04)	(4e-04)	(3e-04)
I3;E3	-0.029	-0.0249	-0.0172
p-value	(0.5181)	(0.5794)	(0.5934)
E1;E2	0.2949	0.2943	0.216
p-value	(0)	(0)	(0)
E1;E3	0.38	0.3611	0.2726
p-value	(0)	(0)	(0)
E2;E3	0.3574	0.3431	0.2531
p-value	(0)	(0)	(0)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.8	0.8	0.8
0.02	0.8	0.87	0.87
0.03	0.8	0.87	0.87
0.04	0.8	0.87	0.87
0.05	0.87	0.87	0.87
0.06	0.87	0.87	0.87
0.07	0.87	0.87	0.87
0.08	0.87	0.87	0.87
0.09	0.87	0.87	0.87
0.1	0.87	0.87	0.87

\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 & 0.8 & 0.8 & 0.8 \tabularnewline
0.02 & 0.8 & 0.87 & 0.87 \tabularnewline
0.03 & 0.8 & 0.87 & 0.87 \tabularnewline
0.04 & 0.8 & 0.87 & 0.87 \tabularnewline
0.05 & 0.87 & 0.87 & 0.87 \tabularnewline
0.06 & 0.87 & 0.87 & 0.87 \tabularnewline
0.07 & 0.87 & 0.87 & 0.87 \tabularnewline
0.08 & 0.87 & 0.87 & 0.87 \tabularnewline
0.09 & 0.87 & 0.87 & 0.87 \tabularnewline
0.1 & 0.87 & 0.87 & 0.87 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270666&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]0.8[/C][C]0.8[/C][C]0.8[/C][/ROW]
[ROW][C]0.02[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.03[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.04[/C][C]0.8[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.05[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.06[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.07[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.08[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.09[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[ROW][C]0.1[/C][C]0.87[/C][C]0.87[/C][C]0.87[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270666&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270666&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 error	Pearson r	Spearman rho	Kendall tau
0.01	0.8	0.8	0.8
0.02	0.8	0.87	0.87
0.03	0.8	0.87	0.87
0.04	0.8	0.87	0.87
0.05	0.87	0.87	0.87
0.06	0.87	0.87	0.87
0.07	0.87	0.87	0.87
0.08	0.87	0.87	0.87
0.09	0.87	0.87	0.87
0.1	0.87	0.87	0.87

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = kendall ;

Parameters (R input):

par1 = kendall ;

R code (references can be found in the software module):

par1 <- 'pearson'
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')
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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code