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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationSat, 10 Dec 2011 14:13:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/10/t1323544448us9nw7a02i981sj.htm/, Retrieved Sat, 04 May 2024 20:44:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153618, Retrieved Sat, 04 May 2024 20:44:30 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
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]
- RMPD    [Kendall tau Correlation Matrix] [ws 10 - pearson c...] [2011-12-10 19:13:51] [2489a3445a7d2af96337a363cd642931] [Current]
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Dataseries X:
1	0	32	31	13	12	15
1	1	33	34	8	8	11
1	0	38	27	14	12	12
1	0	34	24	14	11	9
1	1	41	34	13	11	14
1	1	39	35	16	13	16
1	1	35	27	14	11	15
1	1	34	30	13	10	16
1	0	47	31	15	7	7
1	1	32	31	13	10	13
1	0	28	28	16	12	15
1	1	44	48	20	15	20
1	1	40	40	17	12	16
1	1	29	31	15	12	16
1	0	30	27	16	12	15
1	1	41	37	16	10	15
1	1	32	29	12	10	17
1	0	33	34	9	8	12
1	0	33	33	15	11	15
1	0	40	37	17	14	13
1	1	38	35	12	12	9
0	1	37	34	10	11	14
1	0	41	35	11	6	16
0	0	32	33	16	12	9
1	1	29	29	16	14	14
1	0	38	31	15	11	14
0	1	35	37	13	8	15
1	0	40	31	14	12	14
1	1	43	40	19	15	17
1	1	31	41	16	13	15
1	0	34	29	17	11	12
1	1	26	34	10	12	16
1	1	28	41	15	7	14
1	1	31	34	14	11	14
0	1	32	36	14	7	14
0	0	29	30	16	12	15
1	1	32	36	17	12	15
1	1	35	31	15	12	16
1	0	31	35	17	13	14
1	0	37	35	14	12	14
1	1	34	33	10	9	17
1	0	35	31	14	9	10
1	0	36	31	16	11	10
1	0	45	35	18	14	12
1	1	39	35	15	12	16
1	1	32	28	16	15	14
1	1	39	27	16	12	17
1	1	34	33	10	6	12
1	0	34	33	8	5	16
0	1	34	35	17	13	15
1	1	37	30	14	11	14
1	1	27	29	12	11	15
1	0	43	30	10	6	14
1	1	40	42	14	12	16
1	1	40	36	12	10	16
1	1	35	36	16	6	17
1	1	37	33	16	12	15
1	1	39	34	15	14	15
1	0	26	33	11	6	6
0	1	29	30	16	11	14
1	0	34	25	8	6	12
1	1	32	40	17	14	10
1	1	38	36	16	12	12
0	1	39	33	15	12	14
1	0	27	35	8	8	18
0	1	40	25	13	10	12
0	1	37	39	14	11	15
0	1	34	32	13	7	8
1	1	36	34	16	12	11
0	0	34	38	12	9	16
0	1	36	29	19	13	14
1	1	32	39	19	14	16
1	1	43	36	12	6	7
1	1	47	32	14	12	16
0	0	24	38	15	6	9
1	1	40	39	13	14	8
1	0	33	32	16	12	15
0	0	38	31	10	10	10
0	1	33	31	15	10	12
0	1	36	30	16	12	11
1	1	39	44	15	11	14
1	0	37	28	11	10	18
1	0	38	36	9	7	12
1	1	36	30	16	12	17
0	1	30	31	12	12	16
1	0	36	32	14	12	11
1	1	41	32	14	10	9
1	1	32	35	13	10	18
0	0	35	33	15	12	14
1	0	41	32	17	12	13
1	0	36	32	14	12	16
1	0	34	27	9	9	10
0	0	35	28	11	8	13
0	1	36	36	9	10	16
1	0	43	35	7	5	9
1	1	36	27	13	10	12
1	0	36	34	15	10	10
0	1	34	31	12	12	16
1	0	36	33	15	11	12
0	0	32	32	14	9	16
0	1	27	33	15	15	15
0	0	32	35	9	8	8
1	1	41	31	16	12	17
1	1	40	33	16	12	13
1	1	30	30	14	10	16
0	0	37	28	14	11	13
0	0	35	31	13	10	15
0	1	39	31	14	11	13
0	0	35	30	16	12	16
0	1	27	38	16	11	14
0	1	37	35	13	10	18
0	0	37	28	12	9	10
0	0	38	37	16	9	13
0	1	38	36	16	11	14
0	1	41	34	16	12	18
0	0	38	27	10	7	9
0	0	39	29	14	12	15
0	0	31	30	12	11	15
0	0	39	35	12	12	11
0	1	32	32	12	6	17
0	1	35	32	12	9	10
0	1	45	39	19	15	13
0	0	29	27	14	10	14
0	1	26	34	13	11	16
0	1	35	31	17	14	17
0	0	40	30	16	12	16
0	1	39	36	15	12	16
0	1	35	35	12	12	13
0	1	34	33	8	11	14
0	1	35	36	10	9	13
0	1	33	36	16	11	16
0	0	37	28	10	6	7
0	0	35	31	16	12	15
0	1	38	33	10	12	14
0	1	35	42	18	14	12
0	1	29	35	12	8	7
0	0	0	5	16	10	14
0	0	30	28	10	9	15
0	0	32	31	15	9	10
0	1	43	41	17	11	17
0	0	37	27	14	10	12
0	0	33	32	12	9	13
0	0	41	30	11	10	13
0	0	39	30	15	12	12
0	1	39	33	7	11	11




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153618&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]2 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=153618&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153618&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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net







Correlations for all pairs of data series (method=pearson)
PopGenderConnectedSeparateLearningSoftwareHappiness
Pop10.0340.140.0980.0740.0530.06
Gender0.03410.0690.350.1630.2610.272
Connected0.140.06910.3840.0650.11-0.04
Separate0.0980.350.38410.1760.1390.119
Learning0.0740.1630.0650.17610.640.229
Software0.0530.2610.110.1390.6410.322
Happiness0.060.272-0.040.1190.2290.3221

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Pop & Gender & Connected & Separate & Learning & Software & Happiness \tabularnewline
Pop & 1 & 0.034 & 0.14 & 0.098 & 0.074 & 0.053 & 0.06 \tabularnewline
Gender & 0.034 & 1 & 0.069 & 0.35 & 0.163 & 0.261 & 0.272 \tabularnewline
Connected & 0.14 & 0.069 & 1 & 0.384 & 0.065 & 0.11 & -0.04 \tabularnewline
Separate & 0.098 & 0.35 & 0.384 & 1 & 0.176 & 0.139 & 0.119 \tabularnewline
Learning & 0.074 & 0.163 & 0.065 & 0.176 & 1 & 0.64 & 0.229 \tabularnewline
Software & 0.053 & 0.261 & 0.11 & 0.139 & 0.64 & 1 & 0.322 \tabularnewline
Happiness & 0.06 & 0.272 & -0.04 & 0.119 & 0.229 & 0.322 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153618&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Pop[/C][C]Gender[/C][C]Connected[/C][C]Separate[/C][C]Learning[/C][C]Software[/C][C]Happiness[/C][/ROW]
[ROW][C]Pop[/C][C]1[/C][C]0.034[/C][C]0.14[/C][C]0.098[/C][C]0.074[/C][C]0.053[/C][C]0.06[/C][/ROW]
[ROW][C]Gender[/C][C]0.034[/C][C]1[/C][C]0.069[/C][C]0.35[/C][C]0.163[/C][C]0.261[/C][C]0.272[/C][/ROW]
[ROW][C]Connected[/C][C]0.14[/C][C]0.069[/C][C]1[/C][C]0.384[/C][C]0.065[/C][C]0.11[/C][C]-0.04[/C][/ROW]
[ROW][C]Separate[/C][C]0.098[/C][C]0.35[/C][C]0.384[/C][C]1[/C][C]0.176[/C][C]0.139[/C][C]0.119[/C][/ROW]
[ROW][C]Learning[/C][C]0.074[/C][C]0.163[/C][C]0.065[/C][C]0.176[/C][C]1[/C][C]0.64[/C][C]0.229[/C][/ROW]
[ROW][C]Software[/C][C]0.053[/C][C]0.261[/C][C]0.11[/C][C]0.139[/C][C]0.64[/C][C]1[/C][C]0.322[/C][/ROW]
[ROW][C]Happiness[/C][C]0.06[/C][C]0.272[/C][C]-0.04[/C][C]0.119[/C][C]0.229[/C][C]0.322[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153618&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153618&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)
PopGenderConnectedSeparateLearningSoftwareHappiness
Pop10.0340.140.0980.0740.0530.06
Gender0.03410.0690.350.1630.2610.272
Connected0.140.06910.3840.0650.11-0.04
Separate0.0980.350.38410.1760.1390.119
Learning0.0740.1630.0650.17610.640.229
Software0.0530.2610.110.1390.6410.322
Happiness0.060.272-0.040.1190.2290.3221







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Pop;Gender0.03380.03380.0338
p-value(0.6862)(0.6862)(0.6848)
Pop;Connected0.14030.10460.0882
p-value(0.0923)(0.2104)(0.2093)
Pop;Separate0.09820.060.0509
p-value(0.2398)(0.4733)(0.4714)
Pop;Learning0.07350.09110.0785
p-value(0.3794)(0.2759)(0.2744)
Pop;Software0.05280.10140.0887
p-value(0.5282)(0.225)(0.2238)
Pop;Happiness0.05990.07650.0658
p-value(0.4741)(0.3606)(0.3588)
Gender;Connected0.06930.03760.0317
p-value(0.4075)(0.6537)(0.6522)
Gender;Separate0.35040.35050.297
p-value(0)(0)(0)
Gender;Learning0.16340.14110.1216
p-value(0.0495)(0.0904)(0.0904)
Gender;Software0.26120.24150.2113
p-value(0.0015)(0.0034)(0.0038)
Gender;Happiness0.2720.28690.2469
p-value(9e-04)(5e-04)(6e-04)
Connected;Separate0.38360.16290.1162
p-value(0)(0.0503)(0.0512)
Connected;Learning0.06510.09060.069
p-value(0.4368)(0.2787)(0.2549)
Connected;Software0.11050.14830.1187
p-value(0.1858)(0.0751)(0.0538)
Connected;Happiness-0.0401-0.0602-0.0407
p-value(0.6317)(0.472)(0.5013)
Separate;Learning0.17640.16750.1188
p-value(0.0338)(0.0441)(0.0511)
Separate;Software0.13880.10930.0822
p-value(0.096)(0.1908)(0.1839)
Separate;Happiness0.11860.10410.0768
p-value(0.1554)(0.2126)(0.2064)
Learning;Software0.640.6480.5314
p-value(0)(0)(0)
Learning;Happiness0.22890.18310.1349
p-value(0.0056)(0.0275)(0.0293)
Software;Happiness0.32220.25170.1991
p-value(1e-04)(0.0023)(0.0015)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Pop;Gender & 0.0338 & 0.0338 & 0.0338 \tabularnewline
p-value & (0.6862) & (0.6862) & (0.6848) \tabularnewline
Pop;Connected & 0.1403 & 0.1046 & 0.0882 \tabularnewline
p-value & (0.0923) & (0.2104) & (0.2093) \tabularnewline
Pop;Separate & 0.0982 & 0.06 & 0.0509 \tabularnewline
p-value & (0.2398) & (0.4733) & (0.4714) \tabularnewline
Pop;Learning & 0.0735 & 0.0911 & 0.0785 \tabularnewline
p-value & (0.3794) & (0.2759) & (0.2744) \tabularnewline
Pop;Software & 0.0528 & 0.1014 & 0.0887 \tabularnewline
p-value & (0.5282) & (0.225) & (0.2238) \tabularnewline
Pop;Happiness & 0.0599 & 0.0765 & 0.0658 \tabularnewline
p-value & (0.4741) & (0.3606) & (0.3588) \tabularnewline
Gender;Connected & 0.0693 & 0.0376 & 0.0317 \tabularnewline
p-value & (0.4075) & (0.6537) & (0.6522) \tabularnewline
Gender;Separate & 0.3504 & 0.3505 & 0.297 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Gender;Learning & 0.1634 & 0.1411 & 0.1216 \tabularnewline
p-value & (0.0495) & (0.0904) & (0.0904) \tabularnewline
Gender;Software & 0.2612 & 0.2415 & 0.2113 \tabularnewline
p-value & (0.0015) & (0.0034) & (0.0038) \tabularnewline
Gender;Happiness & 0.272 & 0.2869 & 0.2469 \tabularnewline
p-value & (9e-04) & (5e-04) & (6e-04) \tabularnewline
Connected;Separate & 0.3836 & 0.1629 & 0.1162 \tabularnewline
p-value & (0) & (0.0503) & (0.0512) \tabularnewline
Connected;Learning & 0.0651 & 0.0906 & 0.069 \tabularnewline
p-value & (0.4368) & (0.2787) & (0.2549) \tabularnewline
Connected;Software & 0.1105 & 0.1483 & 0.1187 \tabularnewline
p-value & (0.1858) & (0.0751) & (0.0538) \tabularnewline
Connected;Happiness & -0.0401 & -0.0602 & -0.0407 \tabularnewline
p-value & (0.6317) & (0.472) & (0.5013) \tabularnewline
Separate;Learning & 0.1764 & 0.1675 & 0.1188 \tabularnewline
p-value & (0.0338) & (0.0441) & (0.0511) \tabularnewline
Separate;Software & 0.1388 & 0.1093 & 0.0822 \tabularnewline
p-value & (0.096) & (0.1908) & (0.1839) \tabularnewline
Separate;Happiness & 0.1186 & 0.1041 & 0.0768 \tabularnewline
p-value & (0.1554) & (0.2126) & (0.2064) \tabularnewline
Learning;Software & 0.64 & 0.648 & 0.5314 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Happiness & 0.2289 & 0.1831 & 0.1349 \tabularnewline
p-value & (0.0056) & (0.0275) & (0.0293) \tabularnewline
Software;Happiness & 0.3222 & 0.2517 & 0.1991 \tabularnewline
p-value & (1e-04) & (0.0023) & (0.0015) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153618&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]Pop;Gender[/C][C]0.0338[/C][C]0.0338[/C][C]0.0338[/C][/ROW]
[ROW][C]p-value[/C][C](0.6862)[/C][C](0.6862)[/C][C](0.6848)[/C][/ROW]
[ROW][C]Pop;Connected[/C][C]0.1403[/C][C]0.1046[/C][C]0.0882[/C][/ROW]
[ROW][C]p-value[/C][C](0.0923)[/C][C](0.2104)[/C][C](0.2093)[/C][/ROW]
[ROW][C]Pop;Separate[/C][C]0.0982[/C][C]0.06[/C][C]0.0509[/C][/ROW]
[ROW][C]p-value[/C][C](0.2398)[/C][C](0.4733)[/C][C](0.4714)[/C][/ROW]
[ROW][C]Pop;Learning[/C][C]0.0735[/C][C]0.0911[/C][C]0.0785[/C][/ROW]
[ROW][C]p-value[/C][C](0.3794)[/C][C](0.2759)[/C][C](0.2744)[/C][/ROW]
[ROW][C]Pop;Software[/C][C]0.0528[/C][C]0.1014[/C][C]0.0887[/C][/ROW]
[ROW][C]p-value[/C][C](0.5282)[/C][C](0.225)[/C][C](0.2238)[/C][/ROW]
[ROW][C]Pop;Happiness[/C][C]0.0599[/C][C]0.0765[/C][C]0.0658[/C][/ROW]
[ROW][C]p-value[/C][C](0.4741)[/C][C](0.3606)[/C][C](0.3588)[/C][/ROW]
[ROW][C]Gender;Connected[/C][C]0.0693[/C][C]0.0376[/C][C]0.0317[/C][/ROW]
[ROW][C]p-value[/C][C](0.4075)[/C][C](0.6537)[/C][C](0.6522)[/C][/ROW]
[ROW][C]Gender;Separate[/C][C]0.3504[/C][C]0.3505[/C][C]0.297[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Gender;Learning[/C][C]0.1634[/C][C]0.1411[/C][C]0.1216[/C][/ROW]
[ROW][C]p-value[/C][C](0.0495)[/C][C](0.0904)[/C][C](0.0904)[/C][/ROW]
[ROW][C]Gender;Software[/C][C]0.2612[/C][C]0.2415[/C][C]0.2113[/C][/ROW]
[ROW][C]p-value[/C][C](0.0015)[/C][C](0.0034)[/C][C](0.0038)[/C][/ROW]
[ROW][C]Gender;Happiness[/C][C]0.272[/C][C]0.2869[/C][C]0.2469[/C][/ROW]
[ROW][C]p-value[/C][C](9e-04)[/C][C](5e-04)[/C][C](6e-04)[/C][/ROW]
[ROW][C]Connected;Separate[/C][C]0.3836[/C][C]0.1629[/C][C]0.1162[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0.0503)[/C][C](0.0512)[/C][/ROW]
[ROW][C]Connected;Learning[/C][C]0.0651[/C][C]0.0906[/C][C]0.069[/C][/ROW]
[ROW][C]p-value[/C][C](0.4368)[/C][C](0.2787)[/C][C](0.2549)[/C][/ROW]
[ROW][C]Connected;Software[/C][C]0.1105[/C][C]0.1483[/C][C]0.1187[/C][/ROW]
[ROW][C]p-value[/C][C](0.1858)[/C][C](0.0751)[/C][C](0.0538)[/C][/ROW]
[ROW][C]Connected;Happiness[/C][C]-0.0401[/C][C]-0.0602[/C][C]-0.0407[/C][/ROW]
[ROW][C]p-value[/C][C](0.6317)[/C][C](0.472)[/C][C](0.5013)[/C][/ROW]
[ROW][C]Separate;Learning[/C][C]0.1764[/C][C]0.1675[/C][C]0.1188[/C][/ROW]
[ROW][C]p-value[/C][C](0.0338)[/C][C](0.0441)[/C][C](0.0511)[/C][/ROW]
[ROW][C]Separate;Software[/C][C]0.1388[/C][C]0.1093[/C][C]0.0822[/C][/ROW]
[ROW][C]p-value[/C][C](0.096)[/C][C](0.1908)[/C][C](0.1839)[/C][/ROW]
[ROW][C]Separate;Happiness[/C][C]0.1186[/C][C]0.1041[/C][C]0.0768[/C][/ROW]
[ROW][C]p-value[/C][C](0.1554)[/C][C](0.2126)[/C][C](0.2064)[/C][/ROW]
[ROW][C]Learning;Software[/C][C]0.64[/C][C]0.648[/C][C]0.5314[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Happiness[/C][C]0.2289[/C][C]0.1831[/C][C]0.1349[/C][/ROW]
[ROW][C]p-value[/C][C](0.0056)[/C][C](0.0275)[/C][C](0.0293)[/C][/ROW]
[ROW][C]Software;Happiness[/C][C]0.3222[/C][C]0.2517[/C][C]0.1991[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0023)[/C][C](0.0015)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153618&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153618&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
Pop;Gender0.03380.03380.0338
p-value(0.6862)(0.6862)(0.6848)
Pop;Connected0.14030.10460.0882
p-value(0.0923)(0.2104)(0.2093)
Pop;Separate0.09820.060.0509
p-value(0.2398)(0.4733)(0.4714)
Pop;Learning0.07350.09110.0785
p-value(0.3794)(0.2759)(0.2744)
Pop;Software0.05280.10140.0887
p-value(0.5282)(0.225)(0.2238)
Pop;Happiness0.05990.07650.0658
p-value(0.4741)(0.3606)(0.3588)
Gender;Connected0.06930.03760.0317
p-value(0.4075)(0.6537)(0.6522)
Gender;Separate0.35040.35050.297
p-value(0)(0)(0)
Gender;Learning0.16340.14110.1216
p-value(0.0495)(0.0904)(0.0904)
Gender;Software0.26120.24150.2113
p-value(0.0015)(0.0034)(0.0038)
Gender;Happiness0.2720.28690.2469
p-value(9e-04)(5e-04)(6e-04)
Connected;Separate0.38360.16290.1162
p-value(0)(0.0503)(0.0512)
Connected;Learning0.06510.09060.069
p-value(0.4368)(0.2787)(0.2549)
Connected;Software0.11050.14830.1187
p-value(0.1858)(0.0751)(0.0538)
Connected;Happiness-0.0401-0.0602-0.0407
p-value(0.6317)(0.472)(0.5013)
Separate;Learning0.17640.16750.1188
p-value(0.0338)(0.0441)(0.0511)
Separate;Software0.13880.10930.0822
p-value(0.096)(0.1908)(0.1839)
Separate;Happiness0.11860.10410.0768
p-value(0.1554)(0.2126)(0.2064)
Learning;Software0.640.6480.5314
p-value(0)(0)(0)
Learning;Happiness0.22890.18310.1349
p-value(0.0056)(0.0275)(0.0293)
Software;Happiness0.32220.25170.1991
p-value(1e-04)(0.0023)(0.0015)



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')