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*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

Thu, 15 Dec 2011 08:46:49 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t13239568540c11z2472065bkz.htm/, Retrieved Wed, 08 May 2024 21:57:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155404, Retrieved Wed, 08 May 2024 21:57:33 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

131

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...] [2010-12-10 16:13:49] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Kendall tau Correlation Matrix] [] [2010-12-13 18:15:16] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-           [Kendall tau Correlation Matrix] [] [2010-12-25 21:43:55] [2e1e44f0ae3cb9513dc28781dfdb387b]
-    D          [Kendall tau Correlation Matrix] [Workshop 10] [2011-12-15 13:46:49] [b625935f05df8270d3a5abfea0142dde] [Current]

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Dataseries X:

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2	41	38	13	12	14	1
2	39	32	16	11	18	1
2	30	35	19	15	11	0
1	31	33	15	6	12	0
2	34	37	14	13	16	1
2	35	29	13	10	18	1
2	39	31	19	12	14	1
2	34	36	15	14	14	0
2	36	35	14	12	15	0
2	37	38	15	6	15	1
1	38	31	16	10	17	0
2	36	34	16	12	19	0
1	38	35	16	12	10	0
2	39	38	16	11	16	0
2	33	37	17	15	18	0
1	32	33	15	12	14	1
1	36	32	15	10	14	1
2	38	38	20	12	17	1
1	39	38	18	11	14	0
2	32	32	16	12	16	0
1	32	33	16	11	18	1
2	31	31	16	12	11	1
2	39	38	19	13	14	0
2	37	39	16	11	12	1
1	39	32	17	9	17	1
2	41	32	17	13	9	1
1	36	35	16	10	16	1
2	33	37	15	14	14	0
2	33	33	16	12	15	0
1	34	33	14	10	11	1
2	31	28	15	12	16	1
1	27	32	12	8	13	1
2	37	31	14	10	17	0
2	34	37	16	12	15	0
1	34	30	14	12	14	0
1	32	33	7	7	16	1
1	29	31	10	6	9	1
1	36	33	14	12	15	0
2	29	31	16	10	17	0
1	35	33	16	10	13	1
1	37	32	16	10	15	1
2	34	33	14	12	16	1
1	38	32	20	15	16	1
1	35	33	14	10	12	1
2	38	28	14	10	12	0
2	37	35	11	12	11	0
2	38	39	14	13	15	0
2	33	34	15	11	15	1
2	36	38	16	11	17	1
1	38	32	14	12	13	0
2	32	38	16	14	16	0
1	32	30	14	10	14	1
1	32	33	12	12	11	0
2	34	38	16	13	12	0
1	32	32	9	5	12	0
2	37	32	14	6	15	0
2	39	34	16	12	16	0
2	29	34	16	12	15	0
1	37	36	15	11	12	0
2	35	34	16	10	12	0
1	30	28	12	7	8	0
1	38	34	16	12	13	1
2	34	35	16	14	11	1
2	31	35	14	11	14	1
2	34	31	16	12	15	0
1	35	37	17	13	10	0
2	36	35	18	14	11	0
1	30	27	18	11	12	0
2	39	40	12	12	15	1
1	35	37	16	12	15	1
1	38	36	10	8	14	0
2	31	38	14	11	16	0
2	34	39	18	14	15	0
1	38	41	18	14	15	0
1	34	27	16	12	13	1
2	39	30	17	9	12	1
2	37	37	16	13	17	0
2	34	31	16	11	13	1
1	28	31	13	12	15	1
1	37	27	16	12	13	1
1	33	36	16	12	15	0
1	37	38	20	12	16	0
2	35	37	16	12	15	0
1	37	33	15	12	16	1
2	32	34	15	11	15	0
2	33	31	16	10	14	0
1	38	39	14	9	15	1
2	33	34	16	12	14	1
2	29	32	16	12	13	0
2	33	33	15	12	7	0
2	31	36	12	9	17	1
2	36	32	17	15	13	1
2	35	41	16	12	15	1
2	32	28	15	12	14	0
2	29	30	13	12	13	1
2	39	36	16	10	16	1
2	37	35	16	13	12	1
2	35	31	16	9	14	1
1	37	34	16	12	17	0
1	32	36	14	10	15	0
2	38	36	16	14	17	1
1	37	35	16	11	12	0
2	36	37	20	15	16	0
1	32	28	15	11	11	0
2	33	39	16	11	15	0
1	40	32	13	12	9	1
2	38	35	17	12	16	1
1	41	39	16	12	15	0
1	36	35	16	11	10	0
2	43	42	12	7	10	1
2	30	34	16	12	15	1
2	31	33	16	14	11	1
2	32	41	17	11	13	0
1	32	33	13	11	14	0
2	37	34	12	10	18	1
1	37	32	18	13	16	0
2	33	40	14	13	14	0
2	34	40	14	8	14	0
2	33	35	13	11	14	0
2	38	36	16	12	14	0
2	33	37	13	11	12	1
2	31	27	16	13	14	1
2	38	39	13	12	15	1
2	37	38	16	14	15	0
2	33	31	15	13	15	0
2	31	33	16	15	13	1
1	39	32	15	10	17	1
2	44	39	17	11	17	0
2	33	36	15	9	19	1
2	35	33	12	11	15	1
1	32	33	16	10	13	1
1	28	32	10	11	9	1
2	40	37	16	8	15	0
1	27	30	12	11	15	0
1	37	38	14	12	15	1
2	32	29	15	12	16	1
1	28	22	13	9	11	1
1	34	35	15	11	14	0
2	30	35	11	10	11	0
2	35	34	12	8	15	0
1	31	35	8	9	13	1
2	32	34	16	8	15	1
1	30	34	15	9	16	0
2	30	35	17	15	14	0
1	31	23	16	11	15	1
2	40	31	10	8	16	1
2	32	27	18	13	16	1
1	36	36	13	12	11	1
1	32	31	16	12	12	1
1	35	32	13	9	9	0
2	38	39	10	7	16	0
2	42	37	15	13	13	0
1	34	38	16	9	16	1
2	35	39	16	6	12	1
2	35	34	14	8	9	0
2	33	31	10	8	13	0
2	36	32	17	15	13	1
2	32	37	13	6	14	0
2	33	36	15	9	19	0
2	34	32	16	11	13	0
2	32	35	12	8	12	0
2	34	36	13	8	13	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155404&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155404&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155404&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	1 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Correlations for all pairs of data series (method=pearson)
	Gender	Connected	Separate	Learning	Software	Happiness	Population
Gender	1	0.064	0.24	0.119	0.163	0.182	-0.077
Connected	0.064	1	0.368	0.214	0.062	0.143	0.022
Separate	0.24	0.368	1	0.08	0.068	0.154	-0.196
Learning	0.119	0.214	0.08	1	0.546	0.183	-0.062
Software	0.163	0.062	0.068	0.546	1	0.074	-0.036
Happiness	0.182	0.143	0.154	0.183	0.074	1	0.043
Population	-0.077	0.022	-0.196	-0.062	-0.036	0.043	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Gender & Connected & Separate & Learning & Software & Happiness & Population \tabularnewline
Gender & 1 & 0.064 & 0.24 & 0.119 & 0.163 & 0.182 & -0.077 \tabularnewline
Connected & 0.064 & 1 & 0.368 & 0.214 & 0.062 & 0.143 & 0.022 \tabularnewline
Separate & 0.24 & 0.368 & 1 & 0.08 & 0.068 & 0.154 & -0.196 \tabularnewline
Learning & 0.119 & 0.214 & 0.08 & 1 & 0.546 & 0.183 & -0.062 \tabularnewline
Software & 0.163 & 0.062 & 0.068 & 0.546 & 1 & 0.074 & -0.036 \tabularnewline
Happiness & 0.182 & 0.143 & 0.154 & 0.183 & 0.074 & 1 & 0.043 \tabularnewline
Population & -0.077 & 0.022 & -0.196 & -0.062 & -0.036 & 0.043 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155404&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Gender[/C][C]Connected[/C][C]Separate[/C][C]Learning[/C][C]Software[/C][C]Happiness[/C][C]Population[/C][/ROW]
[ROW][C]Gender[/C][C]1[/C][C]0.064[/C][C]0.24[/C][C]0.119[/C][C]0.163[/C][C]0.182[/C][C]-0.077[/C][/ROW]
[ROW][C]Connected[/C][C]0.064[/C][C]1[/C][C]0.368[/C][C]0.214[/C][C]0.062[/C][C]0.143[/C][C]0.022[/C][/ROW]
[ROW][C]Separate[/C][C]0.24[/C][C]0.368[/C][C]1[/C][C]0.08[/C][C]0.068[/C][C]0.154[/C][C]-0.196[/C][/ROW]
[ROW][C]Learning[/C][C]0.119[/C][C]0.214[/C][C]0.08[/C][C]1[/C][C]0.546[/C][C]0.183[/C][C]-0.062[/C][/ROW]
[ROW][C]Software[/C][C]0.163[/C][C]0.062[/C][C]0.068[/C][C]0.546[/C][C]1[/C][C]0.074[/C][C]-0.036[/C][/ROW]
[ROW][C]Happiness[/C][C]0.182[/C][C]0.143[/C][C]0.154[/C][C]0.183[/C][C]0.074[/C][C]1[/C][C]0.043[/C][/ROW]
[ROW][C]Population[/C][C]-0.077[/C][C]0.022[/C][C]-0.196[/C][C]-0.062[/C][C]-0.036[/C][C]0.043[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155404&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155404&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)
	Gender	Connected	Separate	Learning	Software	Happiness	Population
Gender	1	0.064	0.24	0.119	0.163	0.182	-0.077
Connected	0.064	1	0.368	0.214	0.062	0.143	0.022
Separate	0.24	0.368	1	0.08	0.068	0.154	-0.196
Learning	0.119	0.214	0.08	1	0.546	0.183	-0.062
Software	0.163	0.062	0.068	0.546	1	0.074	-0.036
Happiness	0.182	0.143	0.154	0.183	0.074	1	0.043
Population	-0.077	0.022	-0.196	-0.062	-0.036	0.043	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
Gender;Connected	0.0645	0.0357	0.0304
p-value	(0.4149)	(0.652)	(0.6505)
Gender;Separate	0.2398	0.2283	0.1941
p-value	(0.0021)	(0.0035)	(0.0038)
Gender;Learning	0.1189	0.1077	0.0948
p-value	(0.1319)	(0.1724)	(0.1717)
Gender;Software	0.1634	0.1898	0.166
p-value	(0.0377)	(0.0156)	(0.016)
Gender;Happiness	0.1818	0.1643	0.1422
p-value	(0.0206)	(0.0367)	(0.0371)
Gender;Population	-0.0767	-0.0767	-0.0767
p-value	(0.332)	(0.332)	(0.3305)
Connected;Separate	0.3682	0.3243	0.2413
p-value	(0)	(0)	(0)
Connected;Learning	0.2137	0.2076	0.1586
p-value	(0.0063)	(0.008)	(0.0072)
Connected;Software	0.0622	0.0748	0.0549
p-value	(0.4317)	(0.3439)	(0.3491)
Connected;Happiness	0.1427	0.1717	0.1274
p-value	(0.0701)	(0.0289)	(0.0283)
Connected;Population	0.022	0.0291	0.0247
p-value	(0.7809)	(0.7135)	(0.7123)
Separate;Learning	0.0796	0.0878	0.0665
p-value	(0.3139)	(0.2666)	(0.2597)
Separate;Software	0.0679	0.1111	0.0787
p-value	(0.3906)	(0.1594)	(0.1795)
Separate;Happiness	0.1537	0.1543	0.1097
p-value	(0.0509)	(0.05)	(0.0589)
Separate;Population	-0.1959	-0.1981	-0.1684
p-value	(0.0125)	(0.0115)	(0.0119)
Learning;Software	0.5456	0.4881	0.3906
p-value	(0)	(0)	(0)
Learning;Happiness	0.1829	0.1553	0.1199
p-value	(0.0198)	(0.0484)	(0.0461)
Learning;Population	-0.0615	-0.0407	-0.0358
p-value	(0.4366)	(0.6069)	(0.6054)
Software;Happiness	0.074	0.0312	0.0241
p-value	(0.3492)	(0.6933)	(0.6863)
Software;Population	-0.0363	-0.058	-0.0507
p-value	(0.6461)	(0.4636)	(0.4619)
Happiness;Population	0.0432	0.0413	0.0357
p-value	(0.585)	(0.6022)	(0.6007)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Gender;Connected & 0.0645 & 0.0357 & 0.0304 \tabularnewline
p-value & (0.4149) & (0.652) & (0.6505) \tabularnewline
Gender;Separate & 0.2398 & 0.2283 & 0.1941 \tabularnewline
p-value & (0.0021) & (0.0035) & (0.0038) \tabularnewline
Gender;Learning & 0.1189 & 0.1077 & 0.0948 \tabularnewline
p-value & (0.1319) & (0.1724) & (0.1717) \tabularnewline
Gender;Software & 0.1634 & 0.1898 & 0.166 \tabularnewline
p-value & (0.0377) & (0.0156) & (0.016) \tabularnewline
Gender;Happiness & 0.1818 & 0.1643 & 0.1422 \tabularnewline
p-value & (0.0206) & (0.0367) & (0.0371) \tabularnewline
Gender;Population & -0.0767 & -0.0767 & -0.0767 \tabularnewline
p-value & (0.332) & (0.332) & (0.3305) \tabularnewline
Connected;Separate & 0.3682 & 0.3243 & 0.2413 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Connected;Learning & 0.2137 & 0.2076 & 0.1586 \tabularnewline
p-value & (0.0063) & (0.008) & (0.0072) \tabularnewline
Connected;Software & 0.0622 & 0.0748 & 0.0549 \tabularnewline
p-value & (0.4317) & (0.3439) & (0.3491) \tabularnewline
Connected;Happiness & 0.1427 & 0.1717 & 0.1274 \tabularnewline
p-value & (0.0701) & (0.0289) & (0.0283) \tabularnewline
Connected;Population & 0.022 & 0.0291 & 0.0247 \tabularnewline
p-value & (0.7809) & (0.7135) & (0.7123) \tabularnewline
Separate;Learning & 0.0796 & 0.0878 & 0.0665 \tabularnewline
p-value & (0.3139) & (0.2666) & (0.2597) \tabularnewline
Separate;Software & 0.0679 & 0.1111 & 0.0787 \tabularnewline
p-value & (0.3906) & (0.1594) & (0.1795) \tabularnewline
Separate;Happiness & 0.1537 & 0.1543 & 0.1097 \tabularnewline
p-value & (0.0509) & (0.05) & (0.0589) \tabularnewline
Separate;Population & -0.1959 & -0.1981 & -0.1684 \tabularnewline
p-value & (0.0125) & (0.0115) & (0.0119) \tabularnewline
Learning;Software & 0.5456 & 0.4881 & 0.3906 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Learning;Happiness & 0.1829 & 0.1553 & 0.1199 \tabularnewline
p-value & (0.0198) & (0.0484) & (0.0461) \tabularnewline
Learning;Population & -0.0615 & -0.0407 & -0.0358 \tabularnewline
p-value & (0.4366) & (0.6069) & (0.6054) \tabularnewline
Software;Happiness & 0.074 & 0.0312 & 0.0241 \tabularnewline
p-value & (0.3492) & (0.6933) & (0.6863) \tabularnewline
Software;Population & -0.0363 & -0.058 & -0.0507 \tabularnewline
p-value & (0.6461) & (0.4636) & (0.4619) \tabularnewline
Happiness;Population & 0.0432 & 0.0413 & 0.0357 \tabularnewline
p-value & (0.585) & (0.6022) & (0.6007) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155404&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]Gender;Connected[/C][C]0.0645[/C][C]0.0357[/C][C]0.0304[/C][/ROW]
[ROW][C]p-value[/C][C](0.4149)[/C][C](0.652)[/C][C](0.6505)[/C][/ROW]
[ROW][C]Gender;Separate[/C][C]0.2398[/C][C]0.2283[/C][C]0.1941[/C][/ROW]
[ROW][C]p-value[/C][C](0.0021)[/C][C](0.0035)[/C][C](0.0038)[/C][/ROW]
[ROW][C]Gender;Learning[/C][C]0.1189[/C][C]0.1077[/C][C]0.0948[/C][/ROW]
[ROW][C]p-value[/C][C](0.1319)[/C][C](0.1724)[/C][C](0.1717)[/C][/ROW]
[ROW][C]Gender;Software[/C][C]0.1634[/C][C]0.1898[/C][C]0.166[/C][/ROW]
[ROW][C]p-value[/C][C](0.0377)[/C][C](0.0156)[/C][C](0.016)[/C][/ROW]
[ROW][C]Gender;Happiness[/C][C]0.1818[/C][C]0.1643[/C][C]0.1422[/C][/ROW]
[ROW][C]p-value[/C][C](0.0206)[/C][C](0.0367)[/C][C](0.0371)[/C][/ROW]
[ROW][C]Gender;Population[/C][C]-0.0767[/C][C]-0.0767[/C][C]-0.0767[/C][/ROW]
[ROW][C]p-value[/C][C](0.332)[/C][C](0.332)[/C][C](0.3305)[/C][/ROW]
[ROW][C]Connected;Separate[/C][C]0.3682[/C][C]0.3243[/C][C]0.2413[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Connected;Learning[/C][C]0.2137[/C][C]0.2076[/C][C]0.1586[/C][/ROW]
[ROW][C]p-value[/C][C](0.0063)[/C][C](0.008)[/C][C](0.0072)[/C][/ROW]
[ROW][C]Connected;Software[/C][C]0.0622[/C][C]0.0748[/C][C]0.0549[/C][/ROW]
[ROW][C]p-value[/C][C](0.4317)[/C][C](0.3439)[/C][C](0.3491)[/C][/ROW]
[ROW][C]Connected;Happiness[/C][C]0.1427[/C][C]0.1717[/C][C]0.1274[/C][/ROW]
[ROW][C]p-value[/C][C](0.0701)[/C][C](0.0289)[/C][C](0.0283)[/C][/ROW]
[ROW][C]Connected;Population[/C][C]0.022[/C][C]0.0291[/C][C]0.0247[/C][/ROW]
[ROW][C]p-value[/C][C](0.7809)[/C][C](0.7135)[/C][C](0.7123)[/C][/ROW]
[ROW][C]Separate;Learning[/C][C]0.0796[/C][C]0.0878[/C][C]0.0665[/C][/ROW]
[ROW][C]p-value[/C][C](0.3139)[/C][C](0.2666)[/C][C](0.2597)[/C][/ROW]
[ROW][C]Separate;Software[/C][C]0.0679[/C][C]0.1111[/C][C]0.0787[/C][/ROW]
[ROW][C]p-value[/C][C](0.3906)[/C][C](0.1594)[/C][C](0.1795)[/C][/ROW]
[ROW][C]Separate;Happiness[/C][C]0.1537[/C][C]0.1543[/C][C]0.1097[/C][/ROW]
[ROW][C]p-value[/C][C](0.0509)[/C][C](0.05)[/C][C](0.0589)[/C][/ROW]
[ROW][C]Separate;Population[/C][C]-0.1959[/C][C]-0.1981[/C][C]-0.1684[/C][/ROW]
[ROW][C]p-value[/C][C](0.0125)[/C][C](0.0115)[/C][C](0.0119)[/C][/ROW]
[ROW][C]Learning;Software[/C][C]0.5456[/C][C]0.4881[/C][C]0.3906[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Learning;Happiness[/C][C]0.1829[/C][C]0.1553[/C][C]0.1199[/C][/ROW]
[ROW][C]p-value[/C][C](0.0198)[/C][C](0.0484)[/C][C](0.0461)[/C][/ROW]
[ROW][C]Learning;Population[/C][C]-0.0615[/C][C]-0.0407[/C][C]-0.0358[/C][/ROW]
[ROW][C]p-value[/C][C](0.4366)[/C][C](0.6069)[/C][C](0.6054)[/C][/ROW]
[ROW][C]Software;Happiness[/C][C]0.074[/C][C]0.0312[/C][C]0.0241[/C][/ROW]
[ROW][C]p-value[/C][C](0.3492)[/C][C](0.6933)[/C][C](0.6863)[/C][/ROW]
[ROW][C]Software;Population[/C][C]-0.0363[/C][C]-0.058[/C][C]-0.0507[/C][/ROW]
[ROW][C]p-value[/C][C](0.6461)[/C][C](0.4636)[/C][C](0.4619)[/C][/ROW]
[ROW][C]Happiness;Population[/C][C]0.0432[/C][C]0.0413[/C][C]0.0357[/C][/ROW]
[ROW][C]p-value[/C][C](0.585)[/C][C](0.6022)[/C][C](0.6007)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155404&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155404&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
Gender;Connected	0.0645	0.0357	0.0304
p-value	(0.4149)	(0.652)	(0.6505)
Gender;Separate	0.2398	0.2283	0.1941
p-value	(0.0021)	(0.0035)	(0.0038)
Gender;Learning	0.1189	0.1077	0.0948
p-value	(0.1319)	(0.1724)	(0.1717)
Gender;Software	0.1634	0.1898	0.166
p-value	(0.0377)	(0.0156)	(0.016)
Gender;Happiness	0.1818	0.1643	0.1422
p-value	(0.0206)	(0.0367)	(0.0371)
Gender;Population	-0.0767	-0.0767	-0.0767
p-value	(0.332)	(0.332)	(0.3305)
Connected;Separate	0.3682	0.3243	0.2413
p-value	(0)	(0)	(0)
Connected;Learning	0.2137	0.2076	0.1586
p-value	(0.0063)	(0.008)	(0.0072)
Connected;Software	0.0622	0.0748	0.0549
p-value	(0.4317)	(0.3439)	(0.3491)
Connected;Happiness	0.1427	0.1717	0.1274
p-value	(0.0701)	(0.0289)	(0.0283)
Connected;Population	0.022	0.0291	0.0247
p-value	(0.7809)	(0.7135)	(0.7123)
Separate;Learning	0.0796	0.0878	0.0665
p-value	(0.3139)	(0.2666)	(0.2597)
Separate;Software	0.0679	0.1111	0.0787
p-value	(0.3906)	(0.1594)	(0.1795)
Separate;Happiness	0.1537	0.1543	0.1097
p-value	(0.0509)	(0.05)	(0.0589)
Separate;Population	-0.1959	-0.1981	-0.1684
p-value	(0.0125)	(0.0115)	(0.0119)
Learning;Software	0.5456	0.4881	0.3906
p-value	(0)	(0)	(0)
Learning;Happiness	0.1829	0.1553	0.1199
p-value	(0.0198)	(0.0484)	(0.0461)
Learning;Population	-0.0615	-0.0407	-0.0358
p-value	(0.4366)	(0.6069)	(0.6054)
Software;Happiness	0.074	0.0312	0.0241
p-value	(0.3492)	(0.6933)	(0.6863)
Software;Population	-0.0363	-0.058	-0.0507
p-value	(0.6461)	(0.4636)	(0.4619)
Happiness;Population	0.0432	0.0413	0.0357
p-value	(0.585)	(0.6022)	(0.6007)

Figure 1

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code