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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationTue, 12 Sep 2017 01:52:01 +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/Sep/12/t1505173936967e22ekyui2u1y.htm/, Retrieved Wed, 15 May 2024 12:26:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307722, Retrieved Wed, 15 May 2024 12:26:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [] [2017-09-11 23:52:01] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2,382,290
1,595,000
4,000,000
3,424,470
2,885,000
2,100,000
1,120,820
1,222,000
4,146,000
1,934,000
2,202,810
3,447,010
1,811,350
5,123,210
544,847
1,153,470
1,026,600
1,296,520
1,797,000
1,019,000
853,000
667,000
1,675,000
1,754,420
708,000
1,460,000
875,000
342,000
1,396,000
638,500
868,964
1,091,950
932,159
1,708,000
648,000
683,000
667,047
210,000
248,000
13,089,000
6,774,000
2,187,000
4,000,000
2,467,400
840,000
967,000
1,083,750
956,550
5,200,000
4,450,000
2,025,420
981,469
1,153,000
1,031,210
1,283,190
935,321
594,500
402,500
1,251,320
707,001
1,105,000
1,298,750
113,000
434,724
567,000
1,235,770
352,000
711,250
381,157
1,631,000
5,013,090
3,034,000
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1,313,000
1,158,000
849,330
879,166
679,000
862,500
1,025,130
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679,000
914,874
638,394
1,453,640
1,453,640
2,862,000
2,615,000
6,556,000
2,325,000
1,215,000
6,305,000
1,810,000
1,328,000
2,347,000
2,065,890
1,212,700
4,207,960
2,146,550
239,000
782,400
785,000
2,885,000
1,413,670
2,515,000
475,882
473,000
430,000
350,000
115,000
1,900,000
1,038,240
259,538
1,480,630
926,247
1,945,000
596,833
720,482
872,074
390,453
226,054
202,532
934,000
330,314
178,500
1,499,340
612,264
538,000
366,000
167,987
214,733
187,940
2,694,420
771,000
300,000
140,000
899,000
1,453,000
359,900
146,000
222,000
226,641
204,000
85,500
318,032
212,400
278,900
233,500
434,000
97,250
800,430
692,864
428,000
420,385
116,807
69,400
66,000
36,600
1,766,000
254,832
50,000
82,500
399,050
169,769
47,500
59,000
182,200
176,000
91,260
20,000
195,000
399,217
60,250
490,531
491,182
125,520
163,000
93,000
40,000
846,000
431,492
1,100,000
2,539,000
81,564
107,589
50,000
134,926
26,304
31,500
102,909
27,500
77,799
44,500
60,500
65,000
4,000
279,200
198,604
92,300
160,797
40,500
Dataseries Y:
Download CSV
Histogram 
715
955,646
1,375,649
500,000
4,706,424
1,043,000
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105,568,000
678,367,000
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54,839,000
3,117,144
324,610,000
1,088,642,000
182,023,000
182,023,000
5,829,596
48,692,339
10,254,799
225,438,000
1,668,100,000
47,133,850
7,833,000
374,800,000
13,641,221
38,806,307
62,463,000
714,151,000
25,526,452
305,288,000
27,322,000
1,600,000
1,437,977,000
27,601,000
827,633,000
22,011,000
174,407,000
118,105,000
449,764,000
24,153,310
263,312,000
214,452,000
635,487,000
66,179,000
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80,139,887
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200,700,000
59,913,000
75,543,000
14,456,000
655,968,000
3,583,600
84,020,000
5,317,858,000
52,956,000
336,389,000
48,889,000
37,514,000
6,273,516,000
128,819,000
1,401,307,000
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912,508,000
11,459,000,000
83,814,000
307,002,000
67,885,000
233,388,000
11,525,000
47,915,000
172,660,729
429,686,000
116,171,000
21,856,000
147,470,000
10,200,000
199,795,000
29,471,835
155,002,000
101,186,000
80,944,000
41,129,205
531,872,000
657,238,000
147,144,000
407,657,000
1,392,842,000
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28,041,000
3,154,230
663,656,000
26,921,000
552,456,000
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666,931,000
350,201,000
374,145,000
248,815,000
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251,355,000
1,762,400,000
811,130,000
25,136,000
161,930,000
5,827,041
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34,700,000
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111,597,000
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57,389,000
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115,427,000
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16,598,200
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275,386,000
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330,947,000
241,821,000
53,095,000,000
525,930,000
420,795,000
30,594,342
742,838,000
3,219,184,000
9,765,000
3,026,000
92,673,000
2,405,711,000
106,442,000
183,224,468
327,579,000
2,293,231,000
616,318,000
56,078,000
737,423,000
12,119,000,000
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780,290,000
5,482,439
34,469,000
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292,693,000
27,777,678
3,349,512,000
1,421,529,000
144,415,000
325,562,000
42,566,440
200,000
4,477,670
13,980,000

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=307722&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=307722&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307722&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


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()