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Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 10 Dec 2010 12:29:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/10/t1291985761tnxo1nx3rh4x4z9.htm/, Retrieved Mon, 29 Apr 2024 10:26:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107629, Retrieved Mon, 29 Apr 2024 10:26:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
F   PD    [Kendall tau Correlation Matrix] [Workshop 10 - Pea...] [2010-12-10 12:29:09] [708f372e2a7a3c78ea31b4de2d1213f8] [Current]
Feedback Forum
2010-12-16 12:31:49 [Stefanie Van Esbroeck] [reply
Ik zie net dat je eigenlijk twee keer dezelfde berekening hebt geblogd. Je kan de grafiek even goed aflezen bij de vorige berekening. De berekening blijft uiteraard correct uitgevoerd. Bij je interpretatie maak je hier weer een theoretische aaleiding in verband met het model wat erg goed gedaan is. Maar eigenlijk interpreteer je hier niet veel. Je merkt enkel goed op dat de PE en PC geen normaalverdeling volgen. Hier had je ook een interpretatie moeten geven van de p-waarde en van de regressielijn om zo een zekere conclusie te kunnen vormen en om een volledige interpretatie te verkrijgen. Zo zien we dat we niet mogen betrouwen op de p-waarde omdat die gelijk is aan 0.00 (zie p-waarde 'tussen' de beide variabelen). Deze assumptie is dus ook niet voldaan. Als we dan kijken naar de scatterplot erboven dan zien we dat de regressielijn wijst op een positief verband (die is positief hellend) Maar de puntenwolk ligt niet mooi op de lijn verdeeld zijn. Je conclusie over het feit dat de assumpties niet voldaan zijn, is dus wel een goede conclusie enkel had je hem dus meer moeten motiveren.

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Dataseries X:
0	1	24	14	11	12	24	26
1	1	25	11	7	8	25	23
1	0	17	6	17	8	30	25
0	1	18	12	10	8	19	23
1	0	16	10	12	7	22	29
1	1	20	10	11	4	25	25
1	1	16	11	11	11	23	21
1	1	18	16	12	7	17	22
1	1	17	11	13	7	21	25
0	1	23	13	14	12	19	24
1	1	30	12	16	10	19	18
1	1	18	12	10	8	16	15
0	1	15	11	11	8	23	22
0	1	12	4	15	4	27	28
1	1	21	9	9	9	22	20
0	1	20	8	17	7	22	24
1	1	27	15	11	9	23	21
0	1	34	16	18	11	21	20
1	1	21	9	14	13	19	21
0	1	31	14	10	8	18	23
0	1	19	11	11	8	20	28
1	1	16	8	15	9	23	24
1	1	20	9	15	6	25	24
0	1	21	9	13	9	19	24
0	1	22	9	16	9	24	23
1	1	17	9	13	6	22	23
0	1	24	10	9	6	25	29
1	1	25	16	18	16	26	24
1	1	26	11	18	5	29	18
1	1	25	8	12	7	32	25
1	1	17	9	17	9	25	21
0	1	32	16	9	6	29	26
0	1	33	11	9	6	28	22
0	0	32	12	18	12	28	22
0	1	25	12	12	7	29	23
0	1	29	14	18	10	26	30
1	1	22	9	14	9	25	23
0	1	18	10	15	8	14	17
1	1	17	9	16	5	25	23
0	1	20	10	10	8	26	23
0	1	15	12	11	8	20	25
1	1	20	14	14	10	18	24
0	1	33	14	9	6	32	24
1	1	23	14	17	7	25	21
0	1	26	16	5	4	23	24
0	1	18	9	12	8	21	24
1	1	20	10	12	8	20	28
1	1	11	6	6	4	15	16
0	1	28	8	24	20	30	20
1	1	26	13	12	8	24	29
1	1	22	10	12	8	26	27
0	1	17	8	14	6	24	22
0	1	12	7	7	4	22	28
0	1	17	9	12	9	24	25
1	0	19	12	14	7	24	28
0	1	18	13	8	9	24	24
0	1	10	10	11	5	19	23
0	1	29	11	9	5	31	30
0	1	31	8	11	8	22	24
0	1	9	13	10	6	19	25
1	0	20	11	11	8	25	25
1	1	28	8	12	7	20	22
1	1	19	9	9	7	21	23
1	1	29	15	18	11	23	23
1	1	26	9	15	6	25	25
1	1	23	10	12	8	20	21
0	1	13	14	13	6	21	25
1	1	21	12	14	9	22	24
0	1	19	12	10	8	23	29
1	1	28	11	13	6	25	22
1	1	23	14	13	10	25	27
1	0	18	6	11	8	17	26
0	1	21	12	13	8	19	22
1	1	20	8	16	10	25	24
1	1	21	10	11	5	26	24
1	1	28	12	16	14	27	22
0	1	26	14	14	8	17	24
1	1	10	5	8	6	19	24
0	0	16	11	9	5	17	23
0	1	22	10	15	6	22	20
0	1	19	9	11	10	21	27
1	1	31	10	21	12	32	26
0	1	31	16	14	9	21	25
1	1	29	13	18	12	21	21
0	1	19	9	12	7	18	21
1	1	22	10	13	8	18	19
0	1	15	7	12	6	19	21
1	1	20	9	19	10	20	16
0	1	23	14	11	10	20	29
1	1	24	9	13	10	19	15
1	1	25	14	15	11	22	21
1	1	13	8	12	7	14	19
1	1	28	8	16	12	18	24
1	0	25	7	18	11	35	17
1	1	9	6	8	11	29	23
0	1	17	11	9	6	20	19
0	1	25	14	15	9	22	24
1	1	15	8	6	6	20	25
0	1	19	20	8	7	19	25
1	0	15	8	10	4	22	24
1	1	20	11	11	8	24	26
1	1	18	10	14	9	21	26
1	1	33	14	11	8	26	25
1	1	16	9	12	8	16	21
0	1	17	9	11	5	23	26
1	1	16	8	9	4	18	23
0	1	21	10	12	8	16	23
0	1	26	13	20	10	26	22
1	1	18	12	13	9	21	13
1	1	22	13	12	13	22	15
1	1	30	14	9	9	23	14
1	1	24	14	24	20	21	10
1	1	29	16	11	6	27	24
1	1	31	9	17	9	25	19
1	0	20	9	11	7	21	20
1	1	20	7	11	9	26	22
1	1	28	16	16	8	24	24
1	1	17	9	13	6	19	21
0	1	28	14	11	8	24	24
1	1	31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107629&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107629&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
GeslachtBrowserCMDAPEPCPSO
Geslacht1-0.133-0.017-0.1620.1530.1320.033-0.271
Browser-0.13310.0990.179-0.0240.064-0.115-0.082
CM-0.0170.09910.4920.3380.3570.37-0.056
DA-0.1620.1790.49210.0610.235-0.0210.019
PE0.153-0.0240.3380.06110.6130.205-0.249
PC0.1320.0640.3570.2350.61310.074-0.298
PS0.033-0.1150.37-0.0210.2050.07410.235
O-0.271-0.082-0.0560.019-0.249-0.2980.2351

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Geslacht & Browser & CM & DA & PE & PC & PS & O \tabularnewline
Geslacht & 1 & -0.133 & -0.017 & -0.162 & 0.153 & 0.132 & 0.033 & -0.271 \tabularnewline
Browser & -0.133 & 1 & 0.099 & 0.179 & -0.024 & 0.064 & -0.115 & -0.082 \tabularnewline
CM & -0.017 & 0.099 & 1 & 0.492 & 0.338 & 0.357 & 0.37 & -0.056 \tabularnewline
DA & -0.162 & 0.179 & 0.492 & 1 & 0.061 & 0.235 & -0.021 & 0.019 \tabularnewline
PE & 0.153 & -0.024 & 0.338 & 0.061 & 1 & 0.613 & 0.205 & -0.249 \tabularnewline
PC & 0.132 & 0.064 & 0.357 & 0.235 & 0.613 & 1 & 0.074 & -0.298 \tabularnewline
PS & 0.033 & -0.115 & 0.37 & -0.021 & 0.205 & 0.074 & 1 & 0.235 \tabularnewline
O & -0.271 & -0.082 & -0.056 & 0.019 & -0.249 & -0.298 & 0.235 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107629&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Geslacht[/C][C]Browser[/C][C]CM[/C][C]DA[/C][C]PE[/C][C]PC[/C][C]PS[/C][C]O[/C][/ROW]
[ROW][C]Geslacht[/C][C]1[/C][C]-0.133[/C][C]-0.017[/C][C]-0.162[/C][C]0.153[/C][C]0.132[/C][C]0.033[/C][C]-0.271[/C][/ROW]
[ROW][C]Browser[/C][C]-0.133[/C][C]1[/C][C]0.099[/C][C]0.179[/C][C]-0.024[/C][C]0.064[/C][C]-0.115[/C][C]-0.082[/C][/ROW]
[ROW][C]CM[/C][C]-0.017[/C][C]0.099[/C][C]1[/C][C]0.492[/C][C]0.338[/C][C]0.357[/C][C]0.37[/C][C]-0.056[/C][/ROW]
[ROW][C]DA[/C][C]-0.162[/C][C]0.179[/C][C]0.492[/C][C]1[/C][C]0.061[/C][C]0.235[/C][C]-0.021[/C][C]0.019[/C][/ROW]
[ROW][C]PE[/C][C]0.153[/C][C]-0.024[/C][C]0.338[/C][C]0.061[/C][C]1[/C][C]0.613[/C][C]0.205[/C][C]-0.249[/C][/ROW]
[ROW][C]PC[/C][C]0.132[/C][C]0.064[/C][C]0.357[/C][C]0.235[/C][C]0.613[/C][C]1[/C][C]0.074[/C][C]-0.298[/C][/ROW]
[ROW][C]PS[/C][C]0.033[/C][C]-0.115[/C][C]0.37[/C][C]-0.021[/C][C]0.205[/C][C]0.074[/C][C]1[/C][C]0.235[/C][/ROW]
[ROW][C]O[/C][C]-0.271[/C][C]-0.082[/C][C]-0.056[/C][C]0.019[/C][C]-0.249[/C][C]-0.298[/C][C]0.235[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107629&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)
GeslachtBrowserCMDAPEPCPSO
Geslacht1-0.133-0.017-0.1620.1530.1320.033-0.271
Browser-0.13310.0990.179-0.0240.064-0.115-0.082
CM-0.0170.09910.4920.3380.3570.37-0.056
DA-0.1620.1790.49210.0610.235-0.0210.019
PE0.153-0.0240.3380.06110.6130.205-0.249
PC0.1320.0640.3570.2350.61310.074-0.298
PS0.033-0.1150.37-0.0210.2050.07410.235
O-0.271-0.082-0.0560.019-0.249-0.2980.2351







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Geslacht;Browser-0.1325-0.1325-0.1325
p-value(0.1491)(0.1491)(0.1483)
Geslacht;CM-0.0166-0.0049-0.0041
p-value(0.8569)(0.9577)(0.9575)
Geslacht;DA-0.1623-0.1671-0.1438
p-value(0.0765)(0.0681)(0.0683)
Geslacht;PE0.15310.18970.1618
p-value(0.095)(0.038)(0.0385)
Geslacht;PC0.13150.14280.1237
p-value(0.1521)(0.1197)(0.1192)
Geslacht;PS0.03310.04990.0423
p-value(0.7194)(0.588)(0.5859)
Geslacht;O-0.2713-0.2297-0.1963
p-value(0.0027)(0.0116)(0.0122)
Browser;CM0.09930.13080.1097
p-value(0.2803)(0.1545)(0.1536)
Browser;DA0.17930.16660.1433
p-value(0.05)(0.069)(0.0691)
Browser;PE-0.0243-0.0101-0.0086
p-value(0.7921)(0.9131)(0.9126)
Browser;PC0.0640.0660.0572
p-value(0.4875)(0.4739)(0.4715)
Browser;PS-0.1151-0.0729-0.0617
p-value(0.2107)(0.4286)(0.4263)
Browser;O-0.0816-0.0898-0.0768
p-value(0.3757)(0.3293)(0.3272)
CM;DA0.49210.48170.3704
p-value(0)(0)(0)
CM;PE0.33770.30420.2339
p-value(2e-04)(7e-04)(4e-04)
CM;PC0.35660.37330.2876
p-value(1e-04)(0)(0)
CM;PS0.36950.35810.2661
p-value(0)(1e-04)(0)
CM;O-0.0564-0.0766-0.055
p-value(0.5408)(0.4054)(0.4034)
DA;PE0.06110.04160.0248
p-value(0.5075)(0.6518)(0.7128)
DA;PC0.23530.23270.1786
p-value(0.0097)(0.0105)(0.009)
DA;PS-0.02070.02210.0181
p-value(0.8222)(0.8109)(0.7866)
DA;O0.01890.06030.0405
p-value(0.8377)(0.513)(0.5479)
PE;PC0.61280.48090.3732
p-value(0)(0)(0)
PE;PS0.20510.14240.1104
p-value(0.0247)(0.1208)(0.0958)
PE;O-0.2492-0.2258-0.1703
p-value(0.0061)(0.0131)(0.0109)
PC;PS0.07360.02630.0206
p-value(0.4241)(0.7752)(0.7594)
PC;O-0.2975-0.2038-0.15
p-value(0.001)(0.0255)(0.0272)
PS;O0.23530.24890.178
p-value(0.0097)(0.0061)(0.0074)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Geslacht;Browser & -0.1325 & -0.1325 & -0.1325 \tabularnewline
p-value & (0.1491) & (0.1491) & (0.1483) \tabularnewline
Geslacht;CM & -0.0166 & -0.0049 & -0.0041 \tabularnewline
p-value & (0.8569) & (0.9577) & (0.9575) \tabularnewline
Geslacht;DA & -0.1623 & -0.1671 & -0.1438 \tabularnewline
p-value & (0.0765) & (0.0681) & (0.0683) \tabularnewline
Geslacht;PE & 0.1531 & 0.1897 & 0.1618 \tabularnewline
p-value & (0.095) & (0.038) & (0.0385) \tabularnewline
Geslacht;PC & 0.1315 & 0.1428 & 0.1237 \tabularnewline
p-value & (0.1521) & (0.1197) & (0.1192) \tabularnewline
Geslacht;PS & 0.0331 & 0.0499 & 0.0423 \tabularnewline
p-value & (0.7194) & (0.588) & (0.5859) \tabularnewline
Geslacht;O & -0.2713 & -0.2297 & -0.1963 \tabularnewline
p-value & (0.0027) & (0.0116) & (0.0122) \tabularnewline
Browser;CM & 0.0993 & 0.1308 & 0.1097 \tabularnewline
p-value & (0.2803) & (0.1545) & (0.1536) \tabularnewline
Browser;DA & 0.1793 & 0.1666 & 0.1433 \tabularnewline
p-value & (0.05) & (0.069) & (0.0691) \tabularnewline
Browser;PE & -0.0243 & -0.0101 & -0.0086 \tabularnewline
p-value & (0.7921) & (0.9131) & (0.9126) \tabularnewline
Browser;PC & 0.064 & 0.066 & 0.0572 \tabularnewline
p-value & (0.4875) & (0.4739) & (0.4715) \tabularnewline
Browser;PS & -0.1151 & -0.0729 & -0.0617 \tabularnewline
p-value & (0.2107) & (0.4286) & (0.4263) \tabularnewline
Browser;O & -0.0816 & -0.0898 & -0.0768 \tabularnewline
p-value & (0.3757) & (0.3293) & (0.3272) \tabularnewline
CM;DA & 0.4921 & 0.4817 & 0.3704 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CM;PE & 0.3377 & 0.3042 & 0.2339 \tabularnewline
p-value & (2e-04) & (7e-04) & (4e-04) \tabularnewline
CM;PC & 0.3566 & 0.3733 & 0.2876 \tabularnewline
p-value & (1e-04) & (0) & (0) \tabularnewline
CM;PS & 0.3695 & 0.3581 & 0.2661 \tabularnewline
p-value & (0) & (1e-04) & (0) \tabularnewline
CM;O & -0.0564 & -0.0766 & -0.055 \tabularnewline
p-value & (0.5408) & (0.4054) & (0.4034) \tabularnewline
DA;PE & 0.0611 & 0.0416 & 0.0248 \tabularnewline
p-value & (0.5075) & (0.6518) & (0.7128) \tabularnewline
DA;PC & 0.2353 & 0.2327 & 0.1786 \tabularnewline
p-value & (0.0097) & (0.0105) & (0.009) \tabularnewline
DA;PS & -0.0207 & 0.0221 & 0.0181 \tabularnewline
p-value & (0.8222) & (0.8109) & (0.7866) \tabularnewline
DA;O & 0.0189 & 0.0603 & 0.0405 \tabularnewline
p-value & (0.8377) & (0.513) & (0.5479) \tabularnewline
PE;PC & 0.6128 & 0.4809 & 0.3732 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PE;PS & 0.2051 & 0.1424 & 0.1104 \tabularnewline
p-value & (0.0247) & (0.1208) & (0.0958) \tabularnewline
PE;O & -0.2492 & -0.2258 & -0.1703 \tabularnewline
p-value & (0.0061) & (0.0131) & (0.0109) \tabularnewline
PC;PS & 0.0736 & 0.0263 & 0.0206 \tabularnewline
p-value & (0.4241) & (0.7752) & (0.7594) \tabularnewline
PC;O & -0.2975 & -0.2038 & -0.15 \tabularnewline
p-value & (0.001) & (0.0255) & (0.0272) \tabularnewline
PS;O & 0.2353 & 0.2489 & 0.178 \tabularnewline
p-value & (0.0097) & (0.0061) & (0.0074) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107629&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]Geslacht;Browser[/C][C]-0.1325[/C][C]-0.1325[/C][C]-0.1325[/C][/ROW]
[ROW][C]p-value[/C][C](0.1491)[/C][C](0.1491)[/C][C](0.1483)[/C][/ROW]
[ROW][C]Geslacht;CM[/C][C]-0.0166[/C][C]-0.0049[/C][C]-0.0041[/C][/ROW]
[ROW][C]p-value[/C][C](0.8569)[/C][C](0.9577)[/C][C](0.9575)[/C][/ROW]
[ROW][C]Geslacht;DA[/C][C]-0.1623[/C][C]-0.1671[/C][C]-0.1438[/C][/ROW]
[ROW][C]p-value[/C][C](0.0765)[/C][C](0.0681)[/C][C](0.0683)[/C][/ROW]
[ROW][C]Geslacht;PE[/C][C]0.1531[/C][C]0.1897[/C][C]0.1618[/C][/ROW]
[ROW][C]p-value[/C][C](0.095)[/C][C](0.038)[/C][C](0.0385)[/C][/ROW]
[ROW][C]Geslacht;PC[/C][C]0.1315[/C][C]0.1428[/C][C]0.1237[/C][/ROW]
[ROW][C]p-value[/C][C](0.1521)[/C][C](0.1197)[/C][C](0.1192)[/C][/ROW]
[ROW][C]Geslacht;PS[/C][C]0.0331[/C][C]0.0499[/C][C]0.0423[/C][/ROW]
[ROW][C]p-value[/C][C](0.7194)[/C][C](0.588)[/C][C](0.5859)[/C][/ROW]
[ROW][C]Geslacht;O[/C][C]-0.2713[/C][C]-0.2297[/C][C]-0.1963[/C][/ROW]
[ROW][C]p-value[/C][C](0.0027)[/C][C](0.0116)[/C][C](0.0122)[/C][/ROW]
[ROW][C]Browser;CM[/C][C]0.0993[/C][C]0.1308[/C][C]0.1097[/C][/ROW]
[ROW][C]p-value[/C][C](0.2803)[/C][C](0.1545)[/C][C](0.1536)[/C][/ROW]
[ROW][C]Browser;DA[/C][C]0.1793[/C][C]0.1666[/C][C]0.1433[/C][/ROW]
[ROW][C]p-value[/C][C](0.05)[/C][C](0.069)[/C][C](0.0691)[/C][/ROW]
[ROW][C]Browser;PE[/C][C]-0.0243[/C][C]-0.0101[/C][C]-0.0086[/C][/ROW]
[ROW][C]p-value[/C][C](0.7921)[/C][C](0.9131)[/C][C](0.9126)[/C][/ROW]
[ROW][C]Browser;PC[/C][C]0.064[/C][C]0.066[/C][C]0.0572[/C][/ROW]
[ROW][C]p-value[/C][C](0.4875)[/C][C](0.4739)[/C][C](0.4715)[/C][/ROW]
[ROW][C]Browser;PS[/C][C]-0.1151[/C][C]-0.0729[/C][C]-0.0617[/C][/ROW]
[ROW][C]p-value[/C][C](0.2107)[/C][C](0.4286)[/C][C](0.4263)[/C][/ROW]
[ROW][C]Browser;O[/C][C]-0.0816[/C][C]-0.0898[/C][C]-0.0768[/C][/ROW]
[ROW][C]p-value[/C][C](0.3757)[/C][C](0.3293)[/C][C](0.3272)[/C][/ROW]
[ROW][C]CM;DA[/C][C]0.4921[/C][C]0.4817[/C][C]0.3704[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CM;PE[/C][C]0.3377[/C][C]0.3042[/C][C]0.2339[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](7e-04)[/C][C](4e-04)[/C][/ROW]
[ROW][C]CM;PC[/C][C]0.3566[/C][C]0.3733[/C][C]0.2876[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CM;PS[/C][C]0.3695[/C][C]0.3581[/C][C]0.2661[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](1e-04)[/C][C](0)[/C][/ROW]
[ROW][C]CM;O[/C][C]-0.0564[/C][C]-0.0766[/C][C]-0.055[/C][/ROW]
[ROW][C]p-value[/C][C](0.5408)[/C][C](0.4054)[/C][C](0.4034)[/C][/ROW]
[ROW][C]DA;PE[/C][C]0.0611[/C][C]0.0416[/C][C]0.0248[/C][/ROW]
[ROW][C]p-value[/C][C](0.5075)[/C][C](0.6518)[/C][C](0.7128)[/C][/ROW]
[ROW][C]DA;PC[/C][C]0.2353[/C][C]0.2327[/C][C]0.1786[/C][/ROW]
[ROW][C]p-value[/C][C](0.0097)[/C][C](0.0105)[/C][C](0.009)[/C][/ROW]
[ROW][C]DA;PS[/C][C]-0.0207[/C][C]0.0221[/C][C]0.0181[/C][/ROW]
[ROW][C]p-value[/C][C](0.8222)[/C][C](0.8109)[/C][C](0.7866)[/C][/ROW]
[ROW][C]DA;O[/C][C]0.0189[/C][C]0.0603[/C][C]0.0405[/C][/ROW]
[ROW][C]p-value[/C][C](0.8377)[/C][C](0.513)[/C][C](0.5479)[/C][/ROW]
[ROW][C]PE;PC[/C][C]0.6128[/C][C]0.4809[/C][C]0.3732[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PE;PS[/C][C]0.2051[/C][C]0.1424[/C][C]0.1104[/C][/ROW]
[ROW][C]p-value[/C][C](0.0247)[/C][C](0.1208)[/C][C](0.0958)[/C][/ROW]
[ROW][C]PE;O[/C][C]-0.2492[/C][C]-0.2258[/C][C]-0.1703[/C][/ROW]
[ROW][C]p-value[/C][C](0.0061)[/C][C](0.0131)[/C][C](0.0109)[/C][/ROW]
[ROW][C]PC;PS[/C][C]0.0736[/C][C]0.0263[/C][C]0.0206[/C][/ROW]
[ROW][C]p-value[/C][C](0.4241)[/C][C](0.7752)[/C][C](0.7594)[/C][/ROW]
[ROW][C]PC;O[/C][C]-0.2975[/C][C]-0.2038[/C][C]-0.15[/C][/ROW]
[ROW][C]p-value[/C][C](0.001)[/C][C](0.0255)[/C][C](0.0272)[/C][/ROW]
[ROW][C]PS;O[/C][C]0.2353[/C][C]0.2489[/C][C]0.178[/C][/ROW]
[ROW][C]p-value[/C][C](0.0097)[/C][C](0.0061)[/C][C](0.0074)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107629&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107629&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
Geslacht;Browser-0.1325-0.1325-0.1325
p-value(0.1491)(0.1491)(0.1483)
Geslacht;CM-0.0166-0.0049-0.0041
p-value(0.8569)(0.9577)(0.9575)
Geslacht;DA-0.1623-0.1671-0.1438
p-value(0.0765)(0.0681)(0.0683)
Geslacht;PE0.15310.18970.1618
p-value(0.095)(0.038)(0.0385)
Geslacht;PC0.13150.14280.1237
p-value(0.1521)(0.1197)(0.1192)
Geslacht;PS0.03310.04990.0423
p-value(0.7194)(0.588)(0.5859)
Geslacht;O-0.2713-0.2297-0.1963
p-value(0.0027)(0.0116)(0.0122)
Browser;CM0.09930.13080.1097
p-value(0.2803)(0.1545)(0.1536)
Browser;DA0.17930.16660.1433
p-value(0.05)(0.069)(0.0691)
Browser;PE-0.0243-0.0101-0.0086
p-value(0.7921)(0.9131)(0.9126)
Browser;PC0.0640.0660.0572
p-value(0.4875)(0.4739)(0.4715)
Browser;PS-0.1151-0.0729-0.0617
p-value(0.2107)(0.4286)(0.4263)
Browser;O-0.0816-0.0898-0.0768
p-value(0.3757)(0.3293)(0.3272)
CM;DA0.49210.48170.3704
p-value(0)(0)(0)
CM;PE0.33770.30420.2339
p-value(2e-04)(7e-04)(4e-04)
CM;PC0.35660.37330.2876
p-value(1e-04)(0)(0)
CM;PS0.36950.35810.2661
p-value(0)(1e-04)(0)
CM;O-0.0564-0.0766-0.055
p-value(0.5408)(0.4054)(0.4034)
DA;PE0.06110.04160.0248
p-value(0.5075)(0.6518)(0.7128)
DA;PC0.23530.23270.1786
p-value(0.0097)(0.0105)(0.009)
DA;PS-0.02070.02210.0181
p-value(0.8222)(0.8109)(0.7866)
DA;O0.01890.06030.0405
p-value(0.8377)(0.513)(0.5479)
PE;PC0.61280.48090.3732
p-value(0)(0)(0)
PE;PS0.20510.14240.1104
p-value(0.0247)(0.1208)(0.0958)
PE;O-0.2492-0.2258-0.1703
p-value(0.0061)(0.0131)(0.0109)
PC;PS0.07360.02630.0206
p-value(0.4241)(0.7752)(0.7594)
PC;O-0.2975-0.2038-0.15
p-value(0.001)(0.0255)(0.0272)
PS;O0.23530.24890.178
p-value(0.0097)(0.0061)(0.0074)



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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')