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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_grangercausality.wasp
Title produced by softwareBivariate Granger Causality
Date of computationSun, 06 Aug 2017 20:33:13 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/06/t15020444810eb3h9on2n8f2kt.htm/, Retrieved Sat, 11 May 2024 14:28:18 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 11 May 2024 14:28:18 +0200
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Original text written by user:full fearful
IsPrivate?This computation is private
User-defined keywordsGranger, full, fearful
Estimated Impact0
Dataseries X:
1
3
2
2
3
3
3
3
3
3
3
3
3
4
3
4
3
3
3
4
4
3
4
3
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
3
4
3
3
3
3
4
3
3
3
3
0
0
0
0
0
0
0
0
0
0
0
2
3
4
4
2
3
4
3
2
2
4
4
4
3
4
4
4
3
4
4
4
4
4
3
4
4
4
3
3
4
4
3
4
4
3
4
4
4
4
2
4
4
4
3
4
4
4
4
4
4
4
4
3
4
4
3
4
4
4
3
4
2
1
2
1
0
1
1
1
1
1
1
1
1
1
0
1
1
2
1
1
0
2
1
1
1
0
1
1
0
1
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
2
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
0
1
1
0
1
1
1
1
1
1
1
1
1
4
2
1
3
4
2
4
1
3
2
2
3
2
2
3
2
0
1
1
3
2
3
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
4
4
4
4
4
0
4
4
4
4
4
4
4
4
4
4
3
3
3
4
3
3
3
3
4
3
3
3
3
3
4
4
4
4
4
2
3
3
4
4
3
3
3
3
3
3
3
2
3
3
3
3
3
4
3
3
2
2
2
3
2
3
3
2
4
1
2
2
2
2
2
2
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
1
1
1
1
2
1
2
1
1
1
2
1
1
1
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1
1
1
1
1
2
1
1
1
1
3
2
1
1
2
1
1
1
1
1
2
2
4
3
3
3
2
2
1
1
1
1
1
1
1
1
1
0
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
1
1
0
2
1
1
1
1
1
1
2
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
2
1
2
2
2
2
2
2
1
1
2
1
1
1
1
1
1
2
1
2
1
1
1
1
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
1
1
1
0
2
0
0
0
1
1
1
1
1
0
0
1
1
1
1
1
2
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
2
1
0
1
0
1
0
0
0
0
0
2
2
0
1
3
1
4
0
0
3
2
2
4
0
3
4
0
3
1
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
2
2
1
1
2
2
2
1
1
2
2
1
1
2
2
2
2
2
2
1
1
2
1
1
2
1
1
2
1
0
0
1
0
0
1
1
1
1
4
1
3
2
1
3
0
2
1
1
0
0
1
1
1
2
1
1
2
1
0
0
1
0
1
1
0
1
1
1
2
1
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
2
0
0
0
1
1
0
1
0
0
1
0
1
0
0
0
0
1
4
4
3
1
1
1
1
2
1
1
3
3
3
3
3
3
1
1
3
3
4
2
2
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
3
0
1
2
0
0
0
0
3
0
0
1
1
0
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
0
0
0
0
3
0
1
0
1
1
0
1
1
1
1
1
1
0
0
1
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
1
0
0
0
4
4
3
4
1
2
1
2
4
4
3
3
2
4
1
3
3
4
4
4
3
0
3
4
4
0
0
0
1
0
0
0
4
2
0
0
0
1
4
1
0
0
0
0
3
0
0
0
0
2
0
1
1
1
0
0
0
0
0
1
0
0
0
2
0
1
0
2
0
4
1
1
1
1
1
0
1
0
1
1
1
0
4
0
0
0
0
3
0
3
0
0
0
0
0
1
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
3
4
4
4
3
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
3
3
3
2
3
1
1
3
3
4
3
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
3
2
2
1
2
2
2
1
0
1
1
1
0
2
1
1
1
1
0
0
0
1
1
1
1
2
1
1
0
2
2
1
1
1
1
2
1
1
1
1
1
0
1
0
0
0
1
1
1
1
1
0
1
2
0
1
1
1
0
1
0
1
1
1
1
1
0
1
1
1
1
1
1
1
0
1
1
1
1
1
1
3
3
2
3
2
2
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
3
1
1
2
1
1
1
1
1
2
0
1
0
1
0
1
0
1
0
1
2
1
1
1
1
1
2
1
0
0
0
1
1
0
0
1
1
1
1
1
2
1
2
1
1
1
1
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
3
1
0
0
0
0
1
0
0
1
0
0
0
0
0
2
0
0
0
2
0
0
0
1
0
3
4
3
0
4
0
0
1
1
2
2
2
1
1
2
4
3
4
3
4
3
3
4
4
4
3
3
4
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
3
4
2
3
2
2
2
0
3
1
1
0
1
1
1
3
1
3
1
2
2
1
2
1
1
2
0
2
1
1
2
0
1
1
0
1
1
1
1
1
1
2
1
1
1
0
1
0
1
0
1
1
1
1
1
1
2
2
1
1
1
1
4
3
4
2
1
1
3
3
4
2
3
4
3
3
3
4
3
3
3
4
3
2
4
4
4
4
4
4
3
4
3
3
3
3
3
3
3
3
3
2
2
3
4
3
3
2
2
0
2
2
3
3
1
4
4
2
3
2
2
4
2
3
3
2
3
2
2
2
2
3
1
2
1
0
0
0
1
0
0
3
0
2
0
0
0
0
0
0
0
2
1
2
1
1
1
1
Dataseries Y:
2.69
3.08
2.54
3.15
2.85
3.08
2.62
2.77
2.62
2.92
2.92
2.53
2.92
3.54
3.13
3.54
3.31
2.85
2.69
2.73
3.15
2.35
2.73
3.15
2.41
3.31
3.38
2.33
2.76
2.93
2.47
2.47
3.23
3.62
3.23
2.65
2.82
2.47
3.00
2.71
2.13
2.40
3.00
3.40
2.41
2.40
2.69
2.65
2.82
3.54
2.88
2.53
3.15
1.24
0.94
1.59
1.71
1.29
1.29
1.29
1.00
1.47
1.71
1.88
1.88
2.53
2.35
2.76
2.41
2.24
1.94
2.18
2.29
2.12
2.00
2.12
1.94
2.00
2.12
2.29
1.94
2.35
2.41
2.29
2.29
2.41
3.06
2.76
2.59
2.94
2.82
2.65
2.76
2.59
3.24
2.71
2.71
2.71
2.94
2.59
2.94
2.41
2.47
2.65
2.53
2.59
2.94
3.00
2.88
2.29
2.76
2.65
2.59
2.82
2.53
3.53
2.88
2.71
2.88
2.94
2.71
2.47
3.18
2.29
2.41
2.18
0.94
1.47
0.59
0.65
1.53
1.41
1.35
2.06
0.82
0.76
1.06
1.65
0.76
0.65
0.82
1.29
1.71
1.71
1.53
1.12
1.35
1.82
0.88
0.71
0.82
1.00
1.06
1.65
1.00
1.59
1.12
1.29
1.76
1.00
1.29
1.06
1.24
1.12
1.12
1.24
1.59
0.76
1.06
0.88
1.29
1.06
0.94
1.00
0.65
1.41
1.65
1.06
0.71
0.88
1.00
1.59
1.82
1.53
1.47
1.12
1.41
1.29
1.71
1.53
0.82
1.12
1.24
1.47
1.94
1.12
1.59
1.76
1.24
1.65
1.76
1.06
1.65
1.35
1.00
0.00
0.35
0.00
0.00
1.12
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.18
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.12
1.00
1.00
1.00
1.00
1.53
1.24
1.00
1.00
1.18
1.06
1.06
0.76
1.00
1.12
1.47
1.00
1.18
1.12
1.00
1.12
1.18
1.41
1.18
1.18
1.53
1.41
1.53
1.71
1.35
1.29
1.59
1.06
1.00
1.41
1.59
1.59
1.47
1.24
1.47
1.76
1.82
1.47
1.18
1.29
1.12
1.12
2.00
0.82
1.47
1.18
0.76
0.94
1.29
0.82
0.76
1.18
1.00
0.82
1.65
0.88
1.41
1.29
1.82
1.82
1.12
1.59
1.59
1.35
1.76
1.18
1.24
1.59
1.41
1.65
1.41
0.82
1.76
1.35
1.47
1.41
1.53
1.94
1.29
1.35
1.12
0.53
1.12
1.12
1.06
1.12
2.47
2.94
2.76
2.65
2.76
2.76
2.24
2.24
1.88
2.12
2.35
2.41
1.59
1.53
1.35
0.94
1.53
1.47
1.29
1.24
1.35
1.35
1.18
1.00
1.06
1.29
1.41
1.00
1.35
1.29
1.47
1.35
1.53
1.47
1.65
1.35
1.53
0.76
1.59
1.06
1.65
1.35
1.53
1.41
1.47
1.71
1.76
1.59
1.47
1.24
1.24
1.53
1.41
1.41
1.35
1.59
1.65
1.29
1.47
1.24
1.29
1.82
1.12
1.65
1.41
1.12
1.59
0.47
1.47
1.41
1.53
1.18
1.35
1.12
1.06
0.82
0.59
0.76
1.80
1.00
0.60
0.00
1.06
0.53
0.71
0.00
0.41
0.24
0.24
0.47
0.29
0.41
0.18
0.12
0.29
0.65
1.24
0.29
0.35
0.24
0.41
0.41
0.35
0.00
0.00
0.00
0.53
0.29
0.00
0.29
0.41
0.00
0.00
0.18
0.82
1.24
1.65
1.41
0.47
0.53
1.18
0.94
0.59
0.94
0.18
0.76
1.06
0.94
0.82
0.41
0.35
0.18
2.24
1.76
2.29
2.35
2.27
2.18
2.35
2.12
2.12
2.24
2.24
2.00
2.29
1.76
1.59
2.29
2.12
2.18
2.35
2.18
2.29
2.24
2.35
1.87
2.24
2.12
2.29
2.12
1.92
2.12
2.06
2.12
1.82
2.00
2.00
2.18
2.00
1.71
2.18
1.94
2.24
1.94
2.71
2.65
2.12
2.29
2.41
2.24
2.12
2.06
2.18
2.71
3.47
3.47
3.24
3.35
3.00
2.41
1.82
2.00
2.12
1.76
1.59
1.82
1.82
1.82
1.71
1.47
1.65
1.65
1.71
1.71
1.29
0.82
1.00
0.88
1.12
0.88
1.00
0.88
0.76
0.76
0.76
0.59
0.47
0.59
0.29
0.41
0.47
0.29
0.59
0.18
0.29
0.65
0.47
0.24
0.00
0.35
0.53
0.35
1.12
0.35
0.29
0.24
1.18
0.59
0.53
0.53
0.29
0.35
0.29
0.29
0.53
0.41
0.35
0.12
0.06
0.24
0.82
0.12
0.06
0.18
0.07
0.12
0.00
0.24
0.00
0.00
0.24
0.06
0.00
0.06
0.24
0.24
0.12
0.18
0.00
0.24
0.29
0.29
0.41
0.06
0.47
0.53
0.59
0.41
0.35
0.35
0.12
0.12
0.47
0.24
0.00
0.12
0.12
0.00
0.12
0.06
0.00
0.06
0.29
0.18
0.18
0.00
0.12
0.00
0.00
0.06
0.00
0.35
0.12
0.29
0.00
0.06
0.12
0.41
0.41
0.06
0.00
0.24
0.00
0.24
0.06
0.00
1.53
1.53
1.53
1.47
1.76
1.53
1.65
1.41
1.35
1.24
1.00
1.35
1.24
1.00
1.00
1.18
1.00
1.35
1.18
1.00
1.12
1.00
1.06
1.41
1.41
1.24
0.76
0.53
0.53
0.18
0.12
0.20
0.41
0.59
0.53
0.59
0.53
0.88
1.00
1.24
0.94
0.82
0.65
0.53
1.06
0.53
1.24
0.59
1.88
1.06
1.71
1.24
0.88
0.53
0.47
1.06
1.59
0.88
1.00
1.24
2.18
0.82
1.41
1.00
0.53
0.94
0.53
0.71
0.59
0.76
1.00
1.12
0.35
0.12
0.47
0.65
0.76
1.00
0.76
0.29
0.88
1.00
0.88
0.71
1.29
1.76
1.47
1.47
1.53
0.88
2.47
1.88
1.88
2.00
2.76
2.29
3.00
3.06
2.35
2.76
3.24
2.12
1.82
2.71
2.65
2.12
2.12
1.76
3.12
1.41
3.35
3.53
0.00
0.59
2.06
2.94
3.24
0.00
2.18
0.00
2.71
0.71
0.88
1.24
2.94
0.24
3.06
0.00
2.76
2.29
0.00
0.00
3.06
3.00
0.00
3.06
0.00
1.29
1.59
2.12
2.07
1.76
1.53
1.59
1.47
1.53
1.35
1.29
1.29
1.59
1.29
1.35
1.27
1.41
1.47
1.47
1.33
1.35
1.41
1.53
1.41
1.53
1.29
0.27
1.27
0.71
0.06
0.35
0.00
0.12
0.00
0.06
1.18
1.12
0.65
1.53
1.65
1.41
1.41
0.65
1.24
1.29
0.00
1.47
0.59
0.29
0.00
0.00
0.29
0.18
0.12
0.12
1.47
1.35
0.94
0.53
0.00
0.12
0.00
0.18
0.35
0.35
0.00
0.60
0.65
0.65
1.65
1.59
1.94
1.53
1.41
1.29
1.47
1.35
1.59
1.29
1.18
1.12
1.24
1.29
1.29
1.29
1.24
1.18
1.18
1.29
1.18
1.24
1.24
1.12
1.18
1.24
1.12
1.12
1.18
1.12
1.12
1.24
1.18
1.18
1.18
1.18
1.18
1.24
1.12
1.18
1.29
1.12
1.24
1.12
1.00
1.12
1.12
1.12
1.12
1.18
1.12
1.18
1.06
1.18
1.12
0.76
1.00
3.59
3.59
3.53
2.59
3.06
2.35
2.53
2.71
3.35
3.35
2.82
3.24
3.35
2.93
3.41
3.00
2.65
2.41
3.18
2.82
3.00
3.18
2.29
2.59
1.47
1.18
1.35
0.71
0.24
0.12
0.71
0.00
0.00
0.00
0.47
0.00
0.53
0.00
1.47
1.29
1.29
1.35
1.65
0.00
0.00
0.00
2.53
0.00
2.35
2.00
2.29
1.29
0.00
0.00
2.47
1.12
2.47
2.35
0.00
0.00
0.00
0.00
1.53
0.35
0.00
0.00
2.47
2.12
1.41
0.00
0.00
2.06
0.00
0.00
0.00
0.00
2.24
1.82
0.12
0.00
0.00
1.65
0.06
0.53
0.00
0.00
1.82
1.76
0.00
0.00
0.00
0.00
2.24
1.88
1.41
0.00
1.47
0.53
0.12
0.18
0.06
0.24
0.71
0.29
0.35
0.00
0.00
0.88
0.00
0.00
0.00
0.00
0.13
0.35
0.00
0.00
0.00
0.65
0.94
0.35
0.00
0.00
0.00
0.00
0.00
0.24
0.00
0.18
0.00
0.00
0.00
0.00
0.00
0.00
0.24
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.00
0.00
0.00
3.12
2.55
2.00
1.77
1.07
2.24
2.00
1.47
2.53
2.18
1.65
1.93
1.59
1.35
2.12
1.82
2.07
1.73
1.65
1.77
1.82
1.12
1.00
1.06
1.80
0.94
0.88
0.88
0.59
1.59
0.88
0.80
2.06
0.82
1.12
0.94
0.65
0.94
2.24
1.00
0.82
0.65
1.00
0.71
2.00
0.88
0.53
0.59
0.88
0.59
0.94
1.59
0.76
1.00
0.82
1.06
0.94
0.76
1.00
1.18
0.71
0.88
1.00
1.76
0.82
1.06
0.82
0.59
0.94
2.41
1.24
1.06
0.47
0.71
1.06
1.06
1.00
0.88
1.00
0.59
1.06
0.88
1.71
1.06
0.88
0.47
0.88
2.00
1.12
1.47
1.29
1.47
0.35
0.12
1.53
1.82
1.00
0.76
0.00
0.53
0.71
1.12
0.65
0.06
0.00
0.00
0.06
0.18
0.82
0.65
0.00
0.00
0.00
1.24
0.00
0.00
0.08
0.00
1.06
0.00
0.88
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.47
0.00
0.00
0.00
0.53
0.00
0.00
0.00
0.00
0.00
1.47
1.65
1.82
1.82
1.82
1.88
1.65
1.53
1.76
1.82
1.53
1.47
1.59
1.53
1.53
1.71
1.41
1.65
1.59
1.47
1.88
1.71
1.94
1.71
1.88
1.71
1.82
1.76
1.94
1.59
1.76
1.76
1.88
1.71
1.71
1.71
1.47
0.82
1.18
1.47
1.29
1.59
0.41
0.59
0.88
0.65
0.65
0.76
0.59
0.53
0.88
0.53
1.12
0.65
0.29
0.18
0.41
0.24
0.35
0.24
0.24
0.41
0.40
0.12
0.24
0.29
0.00
0.47
0.18
1.00
0.82
0.82
0.71
0.82
0.29
0.59
0.88
0.29
0.12
0.29
0.71
0.41
0.12
0.24
0.24
0.35
0.12
0.53
0.76
0.53
0.29
0.41
0.06
0.41
1.18
0.59
0.35
0.65
1.47
1.06
0.76
0.76
0.76
0.76
1.06
0.94
0.65
0.73
0.59
0.59
0.65
0.82
0.59
0.71
0.47
0.53
0.59
0.71
0.59
0.59
0.59
0.59
0.47
0.82
0.47
0.65
0.53
1.41
0.76
0.65
0.59
0.65
0.65
0.82
0.76
1.76
0.29
0.65
0.59
0.53
0.53
0.53
0.53
0.53
0.53
0.59
0.53
0.65
0.59
0.59
0.53
0.65
0.53
0.82
0.47
0.41
0.53
0.47
0.41
0.53
0.59
0.76
0.59
0.65
0.82
0.82
0.47
0.59
0.59
0.59
0.82
0.88
0.71
1.82
0.59
0.53
0.71
0.65
0.71
0.65
0.76
2.24
1.94
1.53
1.59
1.94
1.53
1.47
0.76
1.53
0.82
1.24
0.76
0.94
0.88
0.59
0.71
0.47
0.59
1.06
1.29
0.71
0.94
0.94
0.35
1.18
1.65
1.24
0.94
0.94
0.71
0.53
0.76
0.29
0.47
0.94
1.00
1.47
1.29
1.06
1.18
2.24
0.76
0.35
0.71
0.47
0.71
1.59
0.94
0.47
0.53
0.41
1.00
0.53
0.41
0.41
0.24
0.41
2.41
0.76
1.00
0.41
0.65
0.82
1.29
0.65
0.41
0.94
0.47
0.71
1.76
0.88
1.47
1.76
1.53
1.59
1.59
0.94
2.76
1.71
1.24
1.82
1.24
0.76
1.18
0.82
0.82
0.59
1.12
0.71
1.12
0.82
0.76
0.41
0.88
0.71
1.24
0.94
1.06
1.00
0.71
0.47
0.76
0.71
0.71
0.18
0.88
0.71
0.76
0.71
0.53
0.41
0.41
0.29
0.35
0.71
0.41
0.53
0.00
0.41
2.71
0.71
0.71
0.47
0.47
0.47
0.41
0.35
0.41
0.41
0.24
0.35
0.59
0.47
0.47
0.06
0.41
0.29
0.47
0.41
0.41
0.71
0.41
0.00
0.65
0.47
1.12
0.71
0.47
0.82
0.59
0.35
0.00
1.12
0.65
0.59
0.41
0.35
0.71
1.29
0.24
0.35
0.76
0.65
0.82
0.41
0.18
0.88
0.24
0.35
0.65
0.59
0.06
0.60
0.29
1.00
0.67
0.47
0.20
0.00
0.60
0.80
0.71
0.00
0.07
0.06
0.82
0.65
1.12
1.87
0.33
1.71
1.41
0.33
0.65
0.12
0.47
0.53
0.59
0.06
0.82
0.35
0.35
1.94
1.47
0.94
0.29
1.12
0.76
1.24
0.65
0.65
0.53
0.88
1.87
1.82
1.18
1.29
0.12
0.12
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.12
0.00
0.00
0.00
0.18
0.00
0.00
0.00
0.24
0.06
0.00
0.00
0.00
0.00
0.18
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.24
0.00
0.00
0.00
0.00
0.00
0.00
1.76
1.88
0.88
1.41
1.35
1.29
1.24
0.88
1.71
1.29
1.82
1.47
1.41
1.53
1.41
1.24
1.76
1.76
3.12
2.18
1.88
1.94
2.53
1.94
1.35
1.35
0.88
1.35
1.29
1.47
1.00
1.00
1.12
1.35
0.88
1.24
0.88
1.24
0.94
1.41
1.41
1.29
1.00
1.18
1.18
1.06
1.18
1.47
0.94
1.06
1.29
1.59
1.18
1.06
1.06
0.88
1.18
1.24
1.06
1.29
0.88
0.94
2.71
1.76
2.06
2.06
2.00
2.00
2.88
2.06
2.06
1.94
2.24
1.41
2.65
1.88
1.94
2.53
1.47
2.88
1.82
2.06
2.24
2.00
2.41
2.76
1.88
2.53
2.29
2.29
1.76
2.18
2.12
2.12
2.47
2.35
1.88
2.18
2.24
2.35
2.35
2.29
2.12
2.24
2.18
2.24
2.18
2.41
2.53
1.82
2.29
1.71
2.06
2.18
2.18
2.29
2.53
2.35
2.18
2.24
2.65
2.29
2.35
2.53
2.35
1.94
2.18
1.94
1.82
1.82
2.29
2.12
0.94
0.71
0.94
0.65
0.29
0.53
1.82
0.88
0.41
1.76
0.94
1.53
0.53
1.06
1.65
0.88
2.00
0.47
0.59
1.29
2.41
1.24
1.59
1.24
1.82
1.65




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Granger Causality Test: Y = f(X)
ModelRes.DFDiff. DFFp-value
Complete model1791
Reduced model1792-10.02290209504288470.879728992274975

\begin{tabular}{lllllllll}
\hline
Granger Causality Test: Y = f(X) \tabularnewline
Model & Res.DF & Diff. DF & F & p-value \tabularnewline
Complete model & 1791 &  &  &  \tabularnewline
Reduced model & 1792 & -1 & 0.0229020950428847 & 0.879728992274975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Granger Causality Test: Y = f(X)[/C][/ROW]
[ROW][C]Model[/C][C]Res.DF[/C][C]Diff. DF[/C][C]F[/C][C]p-value[/C][/ROW]
[ROW][C]Complete model[/C][C]1791[/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Reduced model[/C][C]1792[/C][C]-1[/C][C]0.0229020950428847[/C][C]0.879728992274975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=1

As an alternative you can also use a QR Code:  

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

Granger Causality Test: Y = f(X)
ModelRes.DFDiff. DFFp-value
Complete model1791
Reduced model1792-10.02290209504288470.879728992274975







Granger Causality Test: X = f(Y)
ModelRes.DFDiff. DFFp-value
Complete model1791
Reduced model1792-11.640926364538550.200364258095502

\begin{tabular}{lllllllll}
\hline
Granger Causality Test: X = f(Y) \tabularnewline
Model & Res.DF & Diff. DF & F & p-value \tabularnewline
Complete model & 1791 &  &  &  \tabularnewline
Reduced model & 1792 & -1 & 1.64092636453855 & 0.200364258095502 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Granger Causality Test: X = f(Y)[/C][/ROW]
[ROW][C]Model[/C][C]Res.DF[/C][C]Diff. DF[/C][C]F[/C][C]p-value[/C][/ROW]
[ROW][C]Complete model[/C][C]1791[/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Reduced model[/C][C]1792[/C][C]-1[/C][C]1.64092636453855[/C][C]0.200364258095502[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=2

As an alternative you can also use a QR Code:  

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

Granger Causality Test: X = f(Y)
ModelRes.DFDiff. DFFp-value
Complete model1791
Reduced model1792-11.640926364538550.200364258095502



Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 1 ; par7 = 0 ; par8 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 1 ; par7 = 0 ; par8 = 1 ;
R code (references can be found in the software module):
par8 <- '1'
par7 <- '0'
par6 <- '1'
par5 <- '1'
par4 <- '1'
par3 <- '0'
par2 <- '1'
par1 <- '1'
library(lmtest)
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
par8 <- as.numeric(par8)
ox <- x
oy <- y
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
print(x)
print(y)
(gyx <- grangertest(y ~ x, order=par8))
(gxy <- grangertest(x ~ y, order=par8))
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
(r <- ccf(ox,oy,main='Cross Correlation Function (raw data)',ylab='CCF',xlab='Lag (k)'))
(r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)'))
par(op)
dev.off()
bitmap(file='test2.png')
op <- par(mfrow=c(2,1))
acf(ox,lag.max=round(length(x)/2),main='ACF of x (raw)')
acf(x,lag.max=round(length(x)/2),main='ACF of x (transformed and differenced)')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow=c(2,1))
acf(oy,lag.max=round(length(y)/2),main='ACF of y (raw)')
acf(y,lag.max=round(length(y)/2),main='ACF of y (transformed and differenced)')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Granger Causality Test: Y = f(X)',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Model',header=TRUE)
a<-table.element(a,'Res.DF',header=TRUE)
a<-table.element(a,'Diff. DF',header=TRUE)
a<-table.element(a,'F',header=TRUE)
a<-table.element(a,'p-value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Complete model',header=TRUE)
a<-table.element(a,gyx$Res.Df[1])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Reduced model',header=TRUE)
a<-table.element(a,gyx$Res.Df[2])
a<-table.element(a,gyx$Df[2])
a<-table.element(a,gyx$F[2])
a<-table.element(a,gyx$Pr[2])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Model',header=TRUE)
a<-table.element(a,'Res.DF',header=TRUE)
a<-table.element(a,'Diff. DF',header=TRUE)
a<-table.element(a,'F',header=TRUE)
a<-table.element(a,'p-value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Complete model',header=TRUE)
a<-table.element(a,gxy$Res.Df[1])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Reduced model',header=TRUE)
a<-table.element(a,gxy$Res.Df[2])
a<-table.element(a,gxy$Df[2])
a<-table.element(a,gxy$F[2])
a<-table.element(a,gxy$Pr[2])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')