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Description of Statistical Computation

Author's title

Apakah Terdapat Hubungan antara indeks Persepsi Korupsi dengan Indeks Demok...

Author*Unverified author*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationThu, 04 Apr 2019 10:00:46 +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/2019/Apr/04/t1554366008upf6pzgls0589pt.htm/, Retrieved Thu, 02 May 2024 07:36:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318771, Retrieved Thu, 02 May 2024 07:36:06 +0000
QR Codes:

Original text written by user:Terdapat hubungan positif antara persepsi korupsi di suatu negara dengan tingkat demokrasinya
IsPrivate?No (this computation is public)
User-defined keywordsIndeks Persepsi Korupsi Indeks Demokrasi Tahun 2018
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [Apakah Terdapat H...] [2019-04-04 08:00:46] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
84
64
65
81
60
65
23
24
75
64
74
56
25
60
32
71
62
39
65
58
59
83
43
80
75
19
74
81
33
61
64
73
81
44
65
52
53
41
41
80
12
69
70
66
65
65
84
76
27
66
15
28
69
63
42
20
59
55
73
72
84
63
80
71
24
54
24
59
62
72
82
27
39
48
56
27
51
69
73
59
71
71
42
72
59
68
83
41
19
63
75
68
53
68
46
73
49
72
67
63
55
57
77
71
47
69
18
13
75
66
73
86
16
48
67
63
72
71
65
64
40
36
38
53
72
44
51
55
61
70
15
50
40
57
43
42
62
84
57
62
15
15
87
37
75
64
64
65
70
59
57
59
80
74
68
30
20
29
30
77
82
67
86
65
78
Dataseries Y:
Download CSV
Histogram 
29,7
59,8
35
36,2
70,2
47,9
90,9
82,9
26,5
27,1
55,7
31,3
77,8
57,4
53
57
49,8
78,1
69,7
70,3
47,5
23,3
78,8
35,9
32,8
91,5
15,2
16,1
79,7
33,2
69,6
37,1
33,1
80,7
41,5
65,7
30
75,9
76,9
14,9
92,2
28,7
65,4
62,7
33,6
59,6
19,2
23,7
79,7
33,5
91,4
78
36,1
43,1
55
86,8
66,3
72,9
56
31,4
19,8
66,7
49,1
56,3
61,5
66,3
95,8
72,3
63,9
24,5
40,6
91,5
77,9
77,1
70,2
79,9
39,3
29,4
51,1
38,5
51,1
23,7
73,8
46,3
66,4
53,5
21,9
75
88,1
58,7
52,2
54,9
68,8
54,1
82,1
38,2
82,2
61,9
58,5
65
57,4
49,9
38,5
38,3
62,5
51,8
88,9
92,6
36,3
37,6
44,4
10,8
98,7
30,4
41,7
70,5
60,3
62,4
66
67,1
66,7
78,4
31,9
63,8
29,4
33,5
19,3
61,5
64,1
46,6
63,8
71
75
72,4
80
80,8
61,9
21,5
69,8
30,3
93,9
90,3
14,3
77,3
19,3
54,1
46,3
71,9
31
71,6
64,1
43,7
17,2
52
56,9
27,6
85,3
79,6
83,8
20,1
31,6
30,8
19,5
56,1
31,6

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318771&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]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318771&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318771&T=0

As an alternative you can also use a QR Code:  
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 time1 seconds
R ServerBig Analytics Cloud Computing Center






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean57.272727272727354.8290909090909
Biased Variance367.91955922865485.864123415978
Biased Standard Deviation19.181229346125122.0423257261111
Covariance-323.84822616408
Correlation-0.761320584747334
Determination0.579609032760022
T-Test-14.9911409226595
p-value (2 sided)1.73569708422792e-32
p-value (1 sided)8.67848542113962e-33
95% CI of Correlation[-0.818856948536498, -0.688637441395562]
Degrees of Freedom163
Number of Observations165

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 57.2727272727273 & 54.8290909090909 \tabularnewline
Biased Variance & 367.91955922865 & 485.864123415978 \tabularnewline
Biased Standard Deviation & 19.1812293461251 & 22.0423257261111 \tabularnewline
Covariance & -323.84822616408 \tabularnewline
Correlation & -0.761320584747334 \tabularnewline
Determination & 0.579609032760022 \tabularnewline
T-Test & -14.9911409226595 \tabularnewline
p-value (2 sided) & 1.73569708422792e-32 \tabularnewline
p-value (1 sided) & 8.67848542113962e-33 \tabularnewline
95% CI of Correlation & [-0.818856948536498, -0.688637441395562] \tabularnewline
Degrees of Freedom & 163 \tabularnewline
Number of Observations & 165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318771&T=1

[TABLE]
[ROW][C]Pearson Product Moment Correlation - Ungrouped Data[/C][/ROW]
[ROW][C]Statistic[/C][C]Variable X[/C][C]Variable Y[/C][/ROW]
[ROW][C]Mean[/C][C]57.2727272727273[/C][C]54.8290909090909[/C][/ROW]
[ROW][C]Biased Variance[/C][C]367.91955922865[/C][C]485.864123415978[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]19.1812293461251[/C][C]22.0423257261111[/C][/ROW]
[ROW][C]Covariance[/C][C]-323.84822616408[/C][/ROW]
[ROW][C]Correlation[/C][C]-0.761320584747334[/C][/ROW]
[ROW][C]Determination[/C][C]0.579609032760022[/C][/ROW]
[ROW][C]T-Test[/C][C]-14.9911409226595[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]1.73569708422792e-32[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]8.67848542113962e-33[/C][/ROW]
[ROW][C]95% CI of Correlation[/C][C][-0.818856948536498, -0.688637441395562][/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]163[/C][/ROW]
[ROW][C]Number of Observations[/C][C]165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318771&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318771&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean57.272727272727354.8290909090909
Biased Variance367.91955922865485.864123415978
Biased Standard Deviation19.181229346125122.0423257261111
Covariance-323.84822616408
Correlation-0.761320584747334
Determination0.579609032760022
T-Test-14.9911409226595
p-value (2 sided)1.73569708422792e-32
p-value (1 sided)8.67848542113962e-33
95% CI of Correlation[-0.818856948536498, -0.688637441395562]
Degrees of Freedom163
Number of Observations165






Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 15.599, p-value = 0.00041
alternative hypothesis: greater

> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 7.6735, p-value = 0.02156
alternative hypothesis: greater

> ad.x
	Anderson-Darling normality test
data:  x
A = 3.985, p-value = 5.825e-10

> ad.y
	Anderson-Darling normality test
data:  y
A = 1.5243, p-value = 0.0006081


\begin{tabular}{lllllllll}
\hline
Normality Tests \tabularnewline

> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 15.599, p-value = 0.00041
alternative hypothesis: greater

 \tabularnewline

> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 7.6735, p-value = 0.02156
alternative hypothesis: greater

 \tabularnewline

> ad.x
	Anderson-Darling normality test
data:  x
A = 3.985, p-value = 5.825e-10

 \tabularnewline

> ad.y
	Anderson-Darling normality test
data:  y
A = 1.5243, p-value = 0.0006081

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318771&T=2

[TABLE]
[ROW][C]Normality Tests[/C][/ROW]
[ROW][C]
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 15.599, p-value = 0.00041
alternative hypothesis: greater

[/C][/ROW]
[ROW][C]
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 7.6735, p-value = 0.02156
alternative hypothesis: greater

[/C][/ROW]
[ROW][C]
> ad.x
	Anderson-Darling normality test
data:  x
A = 3.985, p-value = 5.825e-10

[/C][/ROW]
[ROW][C]
> ad.y
	Anderson-Darling normality test
data:  y
A = 1.5243, p-value = 0.0006081

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318771&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318771&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 15.599, p-value = 0.00041
alternative hypothesis: greater

> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 7.6735, p-value = 0.02156
alternative hypothesis: greater

> ad.x
	Anderson-Darling normality test
data:  x
A = 3.985, p-value = 5.825e-10

> ad.y
	Anderson-Darling normality test
data:  y
A = 1.5243, p-value = 0.0006081


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
library(psychometric)
x <- x[!is.na(y)]
y <- y[!is.na(y)]
y <- y[!is.na(x)]
x <- x[!is.na(x)]
bitmap(file='test1.png')
histx <- hist(x, plot=FALSE)
histy <- hist(y, plot=FALSE)
maxcounts <- max(c(histx$counts, histx$counts))
xrange <- c(min(x),max(x))
yrange <- c(min(y),max(y))
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
par(mar=c(4,4,1,1))
plot(x, y, xlim=xrange, ylim=yrange, xlab=xlab, ylab=ylab, sub=main)
par(mar=c(0,4,1,1))
barplot(histx$counts, axes=FALSE, ylim=c(0, maxcounts), space=0)
par(mar=c(4,0,1,1))
barplot(histy$counts, axes=FALSE, xlim=c(0, maxcounts), space=0, horiz=TRUE)
dev.off()
lx = length(x)
makebiased = (lx-1)/lx
varx = var(x)*makebiased
vary = var(y)*makebiased
corxy <- cor.test(x,y,method='pearson', na.rm = T)
cxy <- as.matrix(corxy$estimate)[1,1]
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistic',1,TRUE)
a<-table.element(a,'Variable X',1,TRUE)
a<-table.element(a,'Variable Y',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.element(a,mean(y))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Biased Variance',header=TRUE)
a<-table.element(a,varx)
a<-table.element(a,vary)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Biased Standard Deviation',header=TRUE)
a<-table.element(a,sqrt(varx))
a<-table.element(a,sqrt(vary))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Covariance',header=TRUE)
a<-table.element(a,cov(x,y),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',header=TRUE)
a<-table.element(a,cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Determination',header=TRUE)
a<-table.element(a,cxy*cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-Test',header=TRUE)
a<-table.element(a,as.matrix(corxy$statistic)[1,1],2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value (2 sided)',header=TRUE)
a<-table.element(a,(p2 <- as.matrix(corxy$p.value)[1,1]),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value (1 sided)',header=TRUE)
a<-table.element(a,p2/2,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'95% CI of Correlation',header=TRUE)
a<-table.element(a,paste('[',CIr(r=cxy, n = lx, level = .95)[1],', ', CIr(r=cxy, n = lx, level = .95)[2],']',sep=''),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degrees of Freedom',header=TRUE)
a<-table.element(a,lx-2,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of Observations',header=TRUE)
a<-table.element(a,lx,2)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
library(moments)
library(nortest)
jarque.x <- jarque.test(x)
jarque.y <- jarque.test(y)
if(lx>7) {
ad.x <- ad.test(x)
ad.y <- ad.test(y)
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Normality Tests',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('jarque.x'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('jarque.y'),'
',sep=''))
a<-table.row.end(a)
if(lx>7) {
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('ad.x'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('ad.y'),'
',sep=''))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
library(car)
bitmap(file='test2.png')
qqPlot(x,main='QQplot of variable x')
dev.off()
bitmap(file='test3.png')
qqPlot(y,main='QQplot of variable y')
dev.off()