Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

Patrick.Wessarwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Tue, 21 Dec 2010 18:57:23 +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/2010/Dec/21/t1292957875xm1pasp6w1pvtmu.htm/, Retrieved Fri, 17 May 2024 23:22:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113832, Retrieved Fri, 17 May 2024 23:22:24 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

114

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] [] [2010-12-12 17:09:17] [de55ccbf69577500a5f46ed42a101114]
-   PD    [Kendall tau Correlation Matrix] [] [2010-12-21 17:13:21] [de55ccbf69577500a5f46ed42a101114]
-    D        [Kendall tau Correlation Matrix] [] [2010-12-21 18:57:23] [6b31f806e9ccc1f74a26091056f791cb] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

0.903		2.544		2.217		3.567		1.061
0.903		2.502		2.176		3.536		1.041
0.903		2.480		2.146		3.538		1.021
0.903		2.632		2.297		3.638		1.000
0.903		2.643		2.332		3.635		0.929
0.903		2.658		2.352		3.646		1.000
0.903		2.583		2.230		3.552		1.000
0.903		2.531		2.204		3.557		0.903
0.903		2.658		2.352		3.489		1.000
0.602		2.053		1.978		3.375		1.176
0.778		2.299		1.987		3.443		1.190
0.602		1.987		1.663		3.264		1.312
0.602		2.041		1.940		3.427		1.243
0.602		2.017		1.978		3.376		1.243
0.602		2.083		2.053		3.349		1.097
0.903		2.556		2.332		3.664		1.146
0.903		2.487		2.301		3.641		1.176
0.903		2.483		2.286		3.675		1.267
0.602		1.987		1.944		3.328		1.161
0.602		2.053		1.978		3.348		1.146
0.778		2.398		2.000		3.522		1.190
0.778		2.365		2.000		3.517		1.190
0.903		2.544		2.217		3.624		1.079
0.903		2.502		2.176		3.612		1.114
0.903		2.602		2.230		3.676		1.079
0.903		2.602		2.243		3.711		1.079
0.602		2.146		1.857		3.382		1.279
0.778		2.398		2.000		3.516		1.176
0.602		2.086		1.934		3.346		1.146
0.602		2.064		1.954		3.327		1.146
0.602		1.944		1.881		3.315		1.161
0.602		1.851		1.813		3.249		1.279
0.602		1.987		1.778		3.263		1.279
0.602		1.959		1.845		3.291		1.312
0.602		1.989		1.903		3.328		1.230
0.602		2.086		1.934		3.348		1.217
0.903		2.544		2.217		3.631		1.079
0.903		2.502		2.176		3.616		1.130
0.903		2.545		2.185		3.616		1.114
0.903		2.632		2.318		3.666		1.041
0.903		2.544		2.190		3.653		1.130
0.903		2.602		2.279		3.646		1.097
0.477		1.845		1.987		3.367		1.130
0.903		2.487		2.114		3.613		1.146
0.903		2.480		2.146		3.633		1.204
0.602		2.083		2.049		3.467		1.161
0.602		2.083		1.881		3.400		1.255
0.602		2.086		1.934		3.379		1.204
0.602		2.079		1.987		3.399		1.161
0.602		1.991		1.903		3.335		1.176
0.903		2.544		2.243		3.613		1.114
0.903		2.483		2.176		3.565		1.061
0.903		2.480		2.137		3.607		1.161
0.903		2.502		2.176		3.577		1.097
0.903		2.602		2.176		3.650		1.079
0.903		2.545		2.199		3.640		1.114
0.903		2.643		2.332		3.675		1.041
0.903		2.658		2.352		3.695		1.041
0.778		2.352		2.021		3.494		1.217
0.778		2.398		2.000		3.516		1.255
0.778		2.398		1.944		3.480		1.217
0.778		2.297		1.978		3.463		1.204
0.903		2.602		2.176		3.699		1.146
0.903		2.544		2.255		3.653		1.097
0.778		2.365		2.000		3.445		1.176
0.602		2.146		1.857		3.380		1.290
0.602		2.033		1.973		3.376		1.217
0.602		2.086		1.929		3.364		1.267
0.778		2.190		2.029		3.393		1.146
0.903		2.544		2.161		3.611		1.114
0.903		2.602		2.362		3.631		0.978
0.602		2.064		1.875		3.334		1.190
0.602		2.057		1.959		3.412		1.146
0.903		2.502		2.176		3.531		1.041
0.602		2.083		2.041		3.425		1.146
0.903		2.544		2.255		3.564		1.041
0.778		2.297		1.978		3.492		1.217
0.778		2.365		2.000		3.463		1.204
0.602		2.086		1.903		3.389		1.217
0.602		1.851		1.813		3.264		1.322
0.778		2.398		2.000		3.578		1.230
0.778		2.412		2.041		3.560		1.255
0.903		2.480		2.146		3.617		1.146
0.903		2.544		2.176		3.672		1.161
0.903		2.480		2.146		3.666		1.204
0.903		2.483		2.176		3.629		1.190
0.602		1.898		1.826		3.293		1.190
0.602		1.987		1.892		3.362		1.161
0.602		1.919		1.785		3.302		1.279
0.602		1.954		1.875		3.327		1.161
0.602		2.064		1.875		3.351		1.146
0.602		2.079		1.987		3.396		1.176
0.602		1.898		1.826		3.301		1.204
0.778		2.352		1.978		3.514		1.204
0.778		2.398		1.857		3.499		1.290
0.903		2.602		2.230		3.669		1.061
0.903		2.544		2.161		3.647		1.146
0.903		2.545		2.170		3.668		1.130
0.778		2.364		2.041		3.592		1.322
0.778		2.412		2.041		3.572		1.279
0.778		2.352		1.978		3.578		1.279
0.903		2.418		2.041		3.508		1.130
0.903		2.480		2.111		3.501		1.079
0.602		2.146		1.919		3.421		1.230
0.778		2.365		2.000		3.464		1.204
0.602		2.127		1.982		3.432		1.130
0.602		1.954		1.851		3.347		1.217
0.778		2.233		1.987		3.475		1.161
0.602		2.061		1.978		3.430		1.176
0.602		2.079		1.944		3.471		1.230
0.602		2.083		2.061		3.427		1.130
0.602		1.959		1.724		3.254		1.243
0.602		2.064		1.908		3.346		1.228
0.602		2.146		1.964		3.410		1.173
0.602		2.004		1.919		3.343		1.185
0.903		2.484		2.146		3.625		1.114
0.903		2.483		2.079		3.598		1.143
0.903		2.545		2.182		3.625		1.107
0.778		2.398		2.021		3.525		1.161
0.778		2.301		1.908		3.479		1.246
0.602		1.929		1.716		3.309		1.346
0.602		1.991		1.778		3.335		1.344
0.778		2.352		2.000		3.562		1.248
0.778		2.398		2.041		3.562		1.210
0.778		2.412		1.978		3.504		1.250
0.602		1.929		1.845		3.299		1.230
0.602		1.987		1.875		3.333		1.215
0.602		2.114		2.009		3.498		1.196
0.903		2.502		2.176		3.595		1.121
0.778		2.225		2.079		3.582		1.223
0.903		2.544		2.255		3.641		1.083
0.903		2.480		2.114		3.588		1.176
0.903		2.502		2.176		3.575		1.146
0.602		2.045		1.903		3.333		1.170
0.602		1.898		1.763		3.261		1.270
0.602		1.929		1.845		3.289		1.225
0.903		2.484		2.161		3.589		1.097
0.903		2.502		2.161		3.617		1.137
0.778		2.364		2.021		3.535		1.228
0.778		2.352		2.000		3.560		1.248
0.903		2.602		2.255		3.625		1.045
0.903		2.544		2.230		3.620		1.057
0.903		2.545		2.173		3.637		1.161
0.602		1.987		1.892		3.288		1.161
0.602		1.987		1.875		3.355		1.260
0.602		2.146		1.949		3.440		1.199
0.602		1.991		1.919		3.317		1.201
0.602		1.987		1.826		3.298		1.215
0.778		2.164		1.987		3.449		1.161
0.602		2.083		2.041		3.415		1.107
0.602		1.954		1.681		3.298		1.332
0.602		1.991		1.820		3.255		1.158
0.602		1.929		1.845		3.316		1.270
0.903		2.502		2.146		3.572		1.121
0.903		2.480		2.143		3.553		1.107
0.778		2.301		1.978		3.499		1.260
0.778		2.301		1.929		3.472		1.199
0.778		2.352		2.000		3.535		1.236
0.778		2.365		1.954		3.507		1.236
0.778		2.301		1.929		3.487		1.223
0.778		2.352		2.041		3.559		1.272
0.903		2.484		2.161		3.535		1.121
0.778		2.364		2.217		3.537		1.127
0.903		2.502		2.146		3.611		1.137
0.602		1.991		1.833		3.333		1.217
0.602		2.076		1.987		3.362		1.167
0.602		2.021		1.875		3.348		1.161
0.602		2.179		1.929		3.456		1.246
0.699		2.117		2.013		3.452		1.201
0.778		2.212		2.097		3.497		1.134
0.778		2.212		2.124		3.533		1.199
0.602		1.949		1.851		3.299		1.173
0.602		1.991		1.833		3.329		1.220
0.778		2.301		1.929		3.476		1.260
0.602		2.146		1.944		3.461		1.238
0.778		2.352		2.041		3.526		1.220
0.903		2.484		2.114		3.584		1.188
0.903		2.545		2.140		3.597		1.121
0.903		2.502		2.130		3.583		1.182
0.903		2.545		2.152		3.608		1.155
0.903		2.427		2.097		3.557		1.176
0.602		1.949		1.851		3.284		1.146
0.602		1.934		1.813		3.296		1.182
0.602		2.083		1.903		3.427		1.176
0.602		2.149		1.851		3.504		1.394
0.903		2.415		1.954		3.534		1.346
0.602		2.021		1.845		3.332		1.173
0.602		1.929		1.813		3.305		1.283
0.602		2.179		1.954		3.427		1.204
0.778		2.238		2.061		3.414		1.053
0.602		2.179		1.954		3.408		1.121
0.602		1.991		1.881		3.331		1.167
0.602		1.991		1.845		3.326		1.190
0.602		1.934		1.813		3.305		1.215
0.602		2.146		1.944		3.458		1.258
0.602		2.179		1.954		3.478		1.303
0.602		1.987		1.892		3.340		1.199
0.602		2.127		1.954		3.433		1.190
0.602		2.076		1.964		3.386		1.176
0.602		2.033		1.875		3.355		1.182
0.602		2.193		2.021		3.447		1.158
0.602		1.929		1.813		3.324		1.283
0.699		2.083		1.826		3.470		1.299
0.602		1.959		1.826		3.267		1.140
0.602		1.949		1.792		3.266		1.185
0.602		2.086		1.944		3.398		1.179
0.602		2.130		1.924		3.396		1.196
0.602		2.179		1.924		3.421		1.215
0.778		2.238		2.041		3.435		1.100
0.602		2.130		1.924		3.377		1.111
0.602		1.934		1.806		3.273		1.215
0.602		1.908		1.778		3.246		1.207
0.602		1.929		1.813		3.296		1.288
0.602		1.949		1.792		3.312		1.238
0.602		2.021		1.799		3.345		1.173
0.602		1.991		1.813		3.311		1.210
0.602		2.021		1.869		3.340		1.152
0.602		2.076		2.000		3.417		1.170
0.602		2.149		1.903		3.509		1.310
0.778		2.164		2.079		3.467		1.140
0.778		2.364		2.041		3.533		1.199
0.778		2.301		1.944		3.486		1.233
0.778		2.352		1.929		3.540		1.220
0.602		2.049		1.944		3.422		1.270
0.602		2.049		1.944		3.379		1.255
0.602		2.130		1.924		3.402		1.204
0.602		2.179		1.954		3.437		1.255
0.602		2.021		1.869		3.297		1.185
0.602		1.959		1.833		3.294		1.246
0.602		2.021		1.799		3.327		1.167
0.602		2.079		1.944		3.334		1.161
0.602		2.029		1.875		3.343		1.161
0.602		1.959		1.826		3.293		1.196
0.778		2.258		2.041		3.469		1.215
0.778		2.418		1.929		3.479		1.230
0.602		2.158		1.982		3.426		1.143
0.602		2.179		1.954		3.470		1.238
0.602		2.146		1.934		3.446		1.193
0.602		2.130		1.924		3.361		1.064
0.602		2.079		1.898		3.419		1.270
0.602		1.857		1.839		3.208		1.255
0.602		1.881		1.716		3.217		1.230
0.602		1.898		1.763		3.244		1.230
0.602		1.908		1.778		3.246		1.204
0.602		1.851		1.813		3.249		1.279
0.602		1.959		1.724		3.254		1.255
0.602		1.959		1.724		3.254		1.230
0.602		1.991		1.820		3.255		1.146
0.602		1.959		1.778		3.255		1.204
0.602		1.987		1.851		3.261		1.079
0.602		1.898		1.763		3.261		1.279
0.602		1.987		1.778		3.263		1.279
0.602		1.987		1.663		3.264		1.322
0.602		1.851		1.813		3.264		1.322
0.602		1.949		1.792		3.266		1.176
0.602		1.959		1.826		3.267		1.146
0.602		1.833		1.690		3.271		1.301
0.602		1.934		1.806		3.273		1.204
0.602		1.991		1.903		3.282		1.146
0.602		1.949		1.851		3.284		1.146
0.602		1.954		1.845		3.287		1.146
0.602		1.954		1.845		3.287		1.146
0.602		1.987		1.892		3.288		1.176
0.602		1.929		1.845		3.289		1.230
0.602		1.987		1.663		3.290		1.322
0.602		1.898		1.826		3.290		1.279
0.602		1.959		1.845		3.291		1.322
0.602		1.898		1.826		3.293		1.204
0.602		1.959		1.826		3.293		1.176
0.602		1.959		1.826		3.293		1.204
0.602		1.949		1.778		3.294		1.279
0.602		1.959		1.833		3.294		1.255
0.602		1.934		1.813		3.296		1.176
0.602		1.929		1.813		3.296		1.279
0.602		2.021		1.869		3.297		1.176
0.602		1.954		1.681		3.298		1.342
0.602		1.987		1.826		3.298		1.204
0.602		1.892		1.716		3.298		1.279
0.602		1.959		1.833		3.298		1.204
0.602		1.949		1.851		3.299		1.176
0.602		1.929		1.845		3.299		1.230
0.602		1.959		1.826		3.300		1.204
0.602		1.898		1.826		3.301		1.204
0.602		1.919		1.785		3.302		1.279
0.602		1.934		1.813		3.305		1.204
0.602		1.929		1.813		3.305		1.279
0.602		1.959		1.833		3.306		1.255
0.602		1.929		1.716		3.309		1.342
0.602		1.991		1.813		3.311		1.204
0.602		1.991		1.833		3.311		1.279
0.602		1.949		1.792		3.312		1.230
0.602		1.991		1.799		3.312		1.230
0.602		1.944		1.881		3.315		1.176
0.602		1.987		1.826		3.315		1.255
0.602		1.929		1.845		3.316		1.279
0.602		1.898		1.845		3.317		1.301
0.602		1.991		1.919		3.317		1.204
0.602		1.954		1.681		3.319		1.342
0.602		1.987		1.944		3.322		1.230
0.602		1.954		1.875		3.324		1.204
0.602		1.934		1.813		3.324		1.255
0.602		1.929		1.813		3.324		1.279
0.602		1.991		1.845		3.326		1.204
0.602		2.064		1.954		3.327		1.146
0.477		1.845		1.954		3.327		1.146
0.602		1.954		1.875		3.327		1.176
0.602		2.021		1.799		3.327		1.176
0.602		1.991		1.845		3.327		1.230
0.602		1.991		1.903		3.328		1.230
0.602		1.959		1.839		3.328		1.176
0.602		1.987		1.716		3.328		1.398
0.602		1.987		1.944		3.328		1.176
0.602		1.987		1.944		3.328		1.176
0.602		1.991		1.833		3.329		1.230
0.602		1.991		1.881		3.331		1.176
0.602		1.987		1.826		3.331		1.255
0.602		2.021		1.845		3.332		1.176
0.602		2.045		1.903		3.333		1.176
0.602		1.991		1.833		3.333		1.230
0.602		1.987		1.875		3.333		1.204
0.602		2.064		1.875		3.334		1.204
0.602		2.079		1.944		3.334		1.176
0.602		1.991		1.903		3.335		1.176
0.602		1.991		1.778		3.335		1.342
0.602		1.987		1.875		3.337		1.204
0.602		1.987		1.892		3.340		1.204
0.602		1.982		1.839		3.340		1.255
0.602		1.987		1.892		3.340		1.146
0.602		2.021		1.869		3.340		1.146
0.602		2.021		1.845		3.342		1.114
0.602		2.004		1.919		3.343		1.176
0.602		2.029		1.875		3.343		1.176
0.602		2.029		1.875		3.344		1.146
0.602		2.021		1.799		3.345		1.176
0.602		1.991		1.919		3.346		1.230
0.602		2.086		1.934		3.346		1.146
0.602		2.064		1.908		3.346		1.230
0.602		1.954		1.851		3.347		1.230
0.602		2.086		1.934		3.348		1.230
0.602		2.053		1.978		3.348		1.146
0.602		2.021		1.875		3.348		1.176
0.602		2.083		2.053		3.349		1.114
0.602		2.033		1.845		3.351		1.230
0.602		2.064		1.875		3.351		1.146
0.602		1.987		1.732		3.353		1.380
0.602		1.991		1.898		3.353		1.255
0.602		2.146		1.954		3.355		1.204
0.602		1.991		1.954		3.355		1.204
0.602		1.987		1.875		3.355		1.255
0.602		2.033		1.875		3.355		1.176
0.602		2.053		1.978		3.358		1.204
0.602		1.987		1.944		3.358		1.279
0.602		1.987		1.964		3.359		1.230
0.602		2.029		1.857		3.360		1.230
0.602		2.130		1.924		3.361		1.079
0.602		2.086		1.982		3.362		1.204
0.602		1.987		1.892		3.362		1.176
0.602		2.076		1.987		3.362		1.176
0.602		2.086		1.929		3.364		1.279
0.477		1.845		1.987		3.367		1.146
0.602		1.954		1.681		3.368		1.380
0.602		2.033		1.875		3.371		1.230
0.602		2.130		1.924		3.375		1.114
0.602		2.053		1.978		3.375		1.176
0.602		2.017		1.978		3.376		1.255
0.602		2.033		1.973		3.376		1.230
0.602		1.991		1.813		3.377		1.322
0.602		2.130		1.924		3.377		1.114
0.602		2.033		1.968		3.379		1.204
0.602		2.086		1.934		3.379		1.204
0.602		2.049		1.944		3.379		1.255
0.602		2.146		1.857		3.380		1.301
0.602		2.076		1.987		3.381		1.176
0.602		2.146		1.857		3.382		1.279
0.602		2.146		1.954		3.382		1.301
0.477		1.845		2.000		3.384		1.114
0.602		2.029		1.954		3.386		1.176
0.602		2.076		1.964		3.386		1.176
0.602		2.086		1.903		3.389		1.230
0.602		2.029		1.934		3.392		1.204
0.778		2.190		2.029		3.393		1.146
0.602		2.079		1.987		3.396		1.176
0.602		2.130		1.924		3.396		1.204
0.602		2.086		1.944		3.398		1.176
0.602		2.079		1.987		3.399		1.176
0.602		2.083		1.881		3.400		1.255
0.602		2.127		1.978		3.401		1.176
0.602		2.130		1.924		3.402		1.204
0.602		2.146		1.875		3.405		1.230
0.602		2.079		1.875		3.405		1.255
0.602		2.076		1.987		3.406		1.230
0.602		2.179		1.954		3.408		1.114
0.602		2.127		1.978		3.408		1.146
0.602		2.146		1.857		3.409		1.146
0.602		2.146		1.964		3.410		1.176
0.602		2.049		1.929		3.411		1.204
0.602		2.057		1.959		3.412		1.146
0.602		2.193		1.964		3.412		1.176
0.778		2.301		1.929		3.413		1.204
0.602		2.146		1.892		3.414		1.279
0.778		2.238		2.061		3.414		1.041
0.602		2.083		2.041		3.415		1.114
0.602		2.049		1.944		3.416		1.301
0.602		2.076		2.000		3.417		1.176
0.602		2.193		1.964		3.418		1.146
0.602		2.079		1.898		3.419		1.279
0.778		2.365		2.000		3.421		1.114
0.602		2.179		1.924		3.421		1.204
0.602		2.079		1.869		3.421		1.255
0.602		2.146		1.919		3.421		1.230
0.602		2.049		1.944		3.422		1.279
0.778		2.299		1.954		3.423		1.176
0.602		2.083		2.041		3.425		1.146
0.602		2.158		1.982		3.426		1.146
0.602		2.083		1.903		3.427		1.176
0.602		2.179		1.954		3.427		1.204
0.602		2.083		2.061		3.427		1.146
0.602		2.041		1.940		3.427		1.255
0.602		2.179		1.954		3.428		1.230
0.602		2.061		1.978		3.430		1.176
0.778		2.238		2.061		3.431		1.114
0.602		2.127		1.982		3.432		1.146
0.602		2.127		1.954		3.433		1.204
0.602		2.146		1.944		3.435		1.176
0.602		2.076		1.914		3.435		1.279
0.477		1.903		2.041		3.435		1.146
0.778		2.238		2.041		3.435		1.114
0.602		2.179		1.954		3.437		1.255
0.602		2.179		1.944		3.438		1.204
0.602		2.193		2.021		3.439		1.230
0.602		2.146		1.949		3.440		1.204
0.778		2.299		1.987		3.443		1.204
0.778		2.365		2.000		3.445		1.176
0.602		2.146		1.934		3.446		1.204
0.602		2.083		2.061		3.446		1.204
0.602		2.193		2.021		3.447		1.146
0.778		2.193		2.086		3.448		1.146
0.778		2.164		1.987		3.449		1.176
0.699		2.117		2.013		3.452		1.204
0.778		2.297		1.978		3.452		1.204
0.778		2.365		2.049		3.453		1.176
0.602		2.179		1.929		3.456		1.255
0.602		2.146		1.964		3.457		1.204
0.602		2.083		2.049		3.458		1.204
0.602		2.146		1.944		3.458		1.255
0.602		2.146		1.944		3.461		1.230
0.778		2.225		2.064		3.462		1.114
0.778		2.365		2.000		3.463		1.204
0.778		2.297		1.978		3.463		1.204
0.778		2.225		2.121		3.464		1.041
0.778		2.365		2.000		3.464		1.204
0.778		2.193		2.033		3.467		1.204
0.778		2.164		2.079		3.467		1.146
0.602		2.083		2.049		3.467		1.176
0.778		2.365		2.000		3.469		1.204
0.778		2.258		2.041		3.469		1.204
0.602		2.083		1.991		3.469		1.176
0.602		2.179		1.954		3.470		1.230
0.699		2.083		1.826		3.470		1.301
0.602		2.079		1.944		3.471		1.230
0.778		2.412		2.041		3.472		1.146
0.778		2.301		1.929		3.472		1.204
0.602		2.079		1.940		3.474		1.301
0.778		2.233		1.987		3.475		1.176
0.778		2.301		1.929		3.476		1.255
0.602		2.179		1.954		3.478		1.301
0.778		2.301		1.908		3.479		1.255
0.778		2.418		1.929		3.479		1.230
0.778		2.398		1.944		3.480		1.230
0.778		2.364		2.041		3.483		1.176
0.778		2.301		1.944		3.486		1.230
0.778		2.301		1.929		3.487		1.230
0.778		2.365		1.954		3.489		1.255
0.903		2.658		2.352		3.489		1.000
0.778		2.297		1.978		3.492		1.230
0.778		2.352		2.021		3.494		1.230
0.778		2.398		1.944		3.497		1.176
0.778		2.212		2.097		3.497		1.146
0.602		2.114		2.009		3.498		1.204
0.778		2.301		1.978		3.499		1.255
0.778		2.398		1.857		3.499		1.301
0.778		2.161		1.881		3.500		1.301
0.903		2.480		2.111		3.501		1.079
0.602		2.149		1.851		3.504		1.398
0.778		2.412		1.978		3.504		1.255
0.903		2.480		2.143		3.506		1.041
0.778		2.365		1.954		3.507		1.230
0.778		2.365		1.954		3.507		1.230
0.903		2.418		2.041		3.508		1.146
0.602		2.149		1.903		3.509		1.301
0.778		2.352		2.000		3.510		1.176
0.778		2.364		2.061		3.511		1.176
0.602		2.164		1.826		3.512		1.342
0.778		2.352		1.978		3.514		1.204
0.778		2.365		1.954		3.514		1.255
0.602		2.079		1.944		3.515		1.342
0.778		2.398		2.000		3.516		1.255
0.778		2.398		2.000		3.516		1.176
0.778		2.365		2.000		3.517		1.204
0.778		2.398		1.944		3.519		1.204
0.778		2.398		2.000		3.522		1.204
0.778		2.398		2.000		3.523		1.230
0.778		2.398		2.021		3.525		1.176
0.778		2.352		2.041		3.526		1.230
0.903		2.415		2.041		3.527		1.204
0.778		2.364		2.021		3.529		1.204
0.778		2.352		1.954		3.529		1.279
0.903		2.502		2.176		3.531		1.041
0.778		2.412		2.079		3.533		1.176
0.778		2.212		2.124		3.533		1.204
0.778		2.364		2.041		3.533		1.204
0.903		2.415		1.954		3.534		1.342
0.778		2.364		2.021		3.535		1.230
0.903		2.484		2.161		3.535		1.114
0.778		2.352		2.000		3.535		1.230
0.778		2.398		1.857		3.536		1.322
0.903		2.483		2.176		3.536		1.079
0.903		2.502		2.176		3.536		1.041
0.778		2.352		2.021		3.536		1.204
0.778		2.364		2.217		3.537		1.114
0.903		2.480		2.146		3.538		1.041
0.778		2.398		2.021		3.539		1.204
0.778		2.352		1.929		3.540		1.230
0.903		2.487		2.114		3.545		1.079
0.778		2.398		2.041		3.547		1.204
0.778		2.398		1.991		3.547		1.279
0.699		2.262		1.886		3.548		1.301
0.778		2.364		2.021		3.548		1.279
0.903		2.583		2.230		3.552		1.000
0.903		2.480		2.143		3.553		1.114
0.778		2.398		1.892		3.553		1.322
0.903		2.427		2.097		3.557		1.176
0.903		2.531		2.204		3.557		0.903
0.778		2.352		2.021		3.558		1.230
0.778		2.352		2.041		3.559		1.279
0.778		2.352		2.000		3.560		1.255
0.778		2.412		2.041		3.560		1.255
0.778		2.398		2.041		3.562		1.204
0.778		2.352		2.000		3.562		1.255
0.903		2.544		2.255		3.564		1.041
0.903		2.483		2.176		3.565		1.079
0.903		2.483		2.176		3.565		1.079
0.903		2.544		2.217		3.567		1.079
0.903		2.480		2.111		3.571		1.114
0.903		2.544		2.021		3.571		1.279
0.778		2.412		2.041		3.572		1.279
0.903		2.502		2.146		3.572		1.114
0.903		2.502		2.176		3.575		1.146
0.903		2.602		2.176		3.575		1.000
0.903		2.502		2.176		3.577		1.114
0.778		2.398		2.000		3.578		1.230
0.778		2.352		1.978		3.578		1.279
0.778		2.225		2.079		3.582		1.230
0.903		2.556		2.243		3.582		1.041
0.903		2.502		2.130		3.583		1.176
0.903		2.484		2.114		3.584		1.176
0.903		2.591		2.279		3.585		0.954
0.903		2.480		2.114		3.588		1.176
0.903		2.484		2.161		3.589		1.114
0.903		2.483		2.176		3.590		1.114
0.778		2.398		2.021		3.591		1.279
0.903		2.544		2.097		3.591		1.230
0.778		2.364		2.041		3.592		1.322
0.903		2.502		2.176		3.595		1.114
0.903		2.556		2.176		3.595		1.114
0.903		2.545		2.140		3.597		1.114
0.903		2.483		2.079		3.598		1.146
0.903		2.544		2.161		3.601		1.114
0.903		2.480		2.137		3.607		1.176
0.903		2.545		2.152		3.608		1.146
0.903		2.544		2.161		3.608		1.079
0.903		2.415		2.041		3.609		1.279
0.903		2.502		2.176		3.610		1.146
0.903		2.502		2.146		3.611		1.146
0.903		2.544		2.161		3.611		1.114
0.903		2.502		2.176		3.612		1.114
0.903		2.487		2.114		3.613		1.146
0.903		2.544		2.243		3.613		1.114
0.903		2.545		2.185		3.616		1.114
0.903		2.502		2.176		3.616		1.146
0.903		2.502		2.161		3.617		1.146
0.903		2.480		2.146		3.617		1.146
0.903		2.545		2.185		3.618		1.146
0.903		2.544		2.230		3.620		1.041
0.903		2.502		2.176		3.622		1.114
0.903		2.544		2.217		3.624		1.079
0.903		2.484		2.146		3.625		1.114
0.903		2.545		2.182		3.625		1.114
0.903		2.602		2.255		3.625		1.041
0.903		2.502		2.176		3.627		1.176
0.903		2.483		2.176		3.629		1.204
0.903		2.544		2.217		3.631		1.079
0.903		2.602		2.362		3.631		1.000
0.903		2.480		2.146		3.633		1.204
0.903		2.480		2.114		3.633		1.176
0.903		2.643		2.332		3.635		0.954
0.903		2.602		2.279		3.636		1.079
0.903		2.545		2.173		3.637		1.176
0.903		2.632		2.297		3.638		1.000
0.903		2.657		2.342		3.639		0.954
0.903		2.544		2.190		3.639		1.176
0.903		2.545		2.199		3.640		1.114
0.903		2.487		2.301		3.641		1.176
0.903		2.544		2.255		3.641		1.079
0.903		2.502		2.322		3.642		1.146
0.903		2.602		2.243		3.642		1.079
0.903		2.602		2.279		3.646		1.114
0.903		2.658		2.352		3.646		1.000
0.903		2.544		2.161		3.647		1.146
0.903		2.544		2.204		3.649		1.146
0.903		2.502		2.176		3.649		1.146
0.903		2.602		2.243		3.650		1.079
0.903		2.602		2.176		3.650		1.079
0.903		2.502		2.176		3.653		1.176
0.903		2.544		2.255		3.653		1.114
0.903		2.544		2.190		3.653		1.146
0.903		2.556		2.332		3.664		1.146
0.903		2.632		2.318		3.666		1.041
0.903		2.480		2.146		3.666		1.204
0.903		2.556		2.230		3.668		1.114
0.903		2.545		2.170		3.668		1.146
0.903		2.602		2.230		3.669		1.079
0.903		2.544		2.176		3.672		1.176
0.903		2.483		2.286		3.675		1.279
0.903		2.643		2.332		3.675		1.041
0.903		2.602		2.230		3.676		1.079
0.903		2.602		2.223		3.691		1.114
0.903		2.658		2.352		3.695		1.041
0.903		2.632		2.297		3.695		1.079
0.903		2.583		2.255		3.695		1.079
0.903		2.602		2.176		3.699		1.146
0.903		2.602		2.243		3.711		1.079

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113832&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113832&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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Correlations for all pairs of data series (method=kendall)
	cylinders	engine.displacement	horsepower	weight	acceleration
cylinders	1	0.797	0.694	0.743	-0.377
engine.displacement	0.797	1	0.726	0.803	-0.362
horsepower	0.694	0.726	1	0.712	-0.487
weight	0.743	0.803	0.712	1	-0.273
acceleration	-0.377	-0.362	-0.487	-0.273	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & cylinders & engine.displacement & horsepower & weight & acceleration
 \tabularnewline
cylinders & 1 & 0.797 & 0.694 & 0.743 & -0.377 \tabularnewline
engine.displacement & 0.797 & 1 & 0.726 & 0.803 & -0.362 \tabularnewline
horsepower & 0.694 & 0.726 & 1 & 0.712 & -0.487 \tabularnewline
weight & 0.743 & 0.803 & 0.712 & 1 & -0.273 \tabularnewline
acceleration
 & -0.377 & -0.362 & -0.487 & -0.273 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113832&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]cylinders[/C][C]engine.displacement[/C][C]horsepower[/C][C]weight[/C][C]acceleration
[/C][/ROW]
[ROW][C]cylinders[/C][C]1[/C][C]0.797[/C][C]0.694[/C][C]0.743[/C][C]-0.377[/C][/ROW]
[ROW][C]engine.displacement[/C][C]0.797[/C][C]1[/C][C]0.726[/C][C]0.803[/C][C]-0.362[/C][/ROW]
[ROW][C]horsepower[/C][C]0.694[/C][C]0.726[/C][C]1[/C][C]0.712[/C][C]-0.487[/C][/ROW]
[ROW][C]weight[/C][C]0.743[/C][C]0.803[/C][C]0.712[/C][C]1[/C][C]-0.273[/C][/ROW]
[ROW][C]acceleration
[/C][C]-0.377[/C][C]-0.362[/C][C]-0.487[/C][C]-0.273[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113832&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)
	cylinders	engine.displacement	horsepower	weight	acceleration
cylinders	1	0.797	0.694	0.743	-0.377
engine.displacement	0.797	1	0.726	0.803	-0.362
horsepower	0.694	0.726	1	0.712	-0.487
weight	0.743	0.803	0.712	1	-0.273
acceleration	-0.377	-0.362	-0.487	-0.273	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
cylinders;engine.displacement	0.9473	0.9143	0.7966
p-value	(0)	(0)	(0)
cylinders;horsepower	0.834	0.8251	0.6944
p-value	(0)	(0)	(0)
cylinders;weight	0.8866	0.8801	0.7433
p-value	(0)	(0)	(0)
cylinders;acceleration	-0.5073	-0.4806	-0.3772
p-value	(0)	(0)	(0)
engine.displacement;horsepower	0.8788	0.8837	0.7264
p-value	(0)	(0)	(0)
engine.displacement;weight	0.9444	0.9473	0.803
p-value	(0)	(0)	(0)
engine.displacement;acceleration	-0.5286	-0.5035	-0.3619
p-value	(0)	(0)	(0)
horsepower;weight	0.8782	0.8849	0.7115
p-value	(0)	(0)	(0)
horsepower;acceleration	-0.7083	-0.6475	-0.4871
p-value	(0)	(0)	(0)
weight;acceleration	-0.4241	-0.4053	-0.2733
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
cylinders;engine.displacement & 0.9473 & 0.9143 & 0.7966 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;horsepower & 0.834 & 0.8251 & 0.6944 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;weight & 0.8866 & 0.8801 & 0.7433 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cylinders;acceleration
 & -0.5073 & -0.4806 & -0.3772 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;horsepower & 0.8788 & 0.8837 & 0.7264 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;weight & 0.9444 & 0.9473 & 0.803 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
engine.displacement;acceleration
 & -0.5286 & -0.5035 & -0.3619 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
horsepower;weight & 0.8782 & 0.8849 & 0.7115 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
horsepower;acceleration
 & -0.7083 & -0.6475 & -0.4871 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
weight;acceleration
 & -0.4241 & -0.4053 & -0.2733 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113832&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]cylinders;engine.displacement[/C][C]0.9473[/C][C]0.9143[/C][C]0.7966[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;horsepower[/C][C]0.834[/C][C]0.8251[/C][C]0.6944[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;weight[/C][C]0.8866[/C][C]0.8801[/C][C]0.7433[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cylinders;acceleration
[/C][C]-0.5073[/C][C]-0.4806[/C][C]-0.3772[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;horsepower[/C][C]0.8788[/C][C]0.8837[/C][C]0.7264[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;weight[/C][C]0.9444[/C][C]0.9473[/C][C]0.803[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]engine.displacement;acceleration
[/C][C]-0.5286[/C][C]-0.5035[/C][C]-0.3619[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]horsepower;weight[/C][C]0.8782[/C][C]0.8849[/C][C]0.7115[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]horsepower;acceleration
[/C][C]-0.7083[/C][C]-0.6475[/C][C]-0.4871[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]weight;acceleration
[/C][C]-0.4241[/C][C]-0.4053[/C][C]-0.2733[/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=113832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113832&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
cylinders;engine.displacement	0.9473	0.9143	0.7966
p-value	(0)	(0)	(0)
cylinders;horsepower	0.834	0.8251	0.6944
p-value	(0)	(0)	(0)
cylinders;weight	0.8866	0.8801	0.7433
p-value	(0)	(0)	(0)
cylinders;acceleration	-0.5073	-0.4806	-0.3772
p-value	(0)	(0)	(0)
engine.displacement;horsepower	0.8788	0.8837	0.7264
p-value	(0)	(0)	(0)
engine.displacement;weight	0.9444	0.9473	0.803
p-value	(0)	(0)	(0)
engine.displacement;acceleration	-0.5286	-0.5035	-0.3619
p-value	(0)	(0)	(0)
horsepower;weight	0.8782	0.8849	0.7115
p-value	(0)	(0)	(0)
horsepower;acceleration	-0.7083	-0.6475	-0.4871
p-value	(0)	(0)	(0)
weight;acceleration	-0.4241	-0.4053	-0.2733
p-value	(0)	(0)	(0)

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):

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