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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, 21 Sep 2021 21:41:47 +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/2021/Sep/21/t1632253358csc82182p9e28gp.htm/, Retrieved Sat, 27 Apr 2024 21:02:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319513, Retrieved Sat, 27 Apr 2024 21:02:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [] [2021-09-21 19:41:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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40,500
27,500
31,500
60,500
47,500
2,694,420
1,675,000
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490,531
44,500
935,321
1,283,190
1,453,640
1,453,640
169,769
491,182
212,400
1,066,550
5,123,210
107,589
50,000
2,515,000
134,926
77,799
638,394
2,539,000
330,314
1,251,320
846,000
20,000
1,100,000
648,000
638,500
195,000
667,000
692,864
1,105,000
226,641
1,708,000
594,500
679,000
667,047
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239,000
26,304
934,000
390,453
428,000
140,000
1,296,520
85,500
538,000
6,305,000
399,050
1,091,950
248,000
182,200
6,556,000
771,000
4,450,000
1,120,820
1,934,000
4,000,000
707,001
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1,298,750
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163,000
233,500
204,000
1,396,000
475,882
359,900
612,264
92,300
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97,250
366,000
259,538
473,000
300,000
1,797,000
1,631,000
981,000
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4,146,000
60,250
167,987
198,604
1,025,130
222,000
879,166
1,900,000
1,460,000
914,874
2,885,000
1,235,770
4,207,960
711,250
2,347,000
1,811,350
202,532
872,074
125,520
785,000
82,500
5,200,000
2,325,000
420,385
66,000
2,885,000
1,153,000
1,499,340
431,492
350,000
3,424,470
1,413,670
5,013,090
91,260
567,000
6,950,420
278,900
899,000
1,038,240
2,146,550
381,157
2,202,810
2,862,000
214,733
720,482
402,500
849,330
596,833
210,000
1,083,750
187,940
1,453,000
1,212,700
800,430
318,032
2,100,000
399,217
40,000
679,000
4,000,000
1,810,000
2,065,890
2,615,000
352,000
146,000
176,000
3,447,010
115,000
1,026,600
708,000
226,054
1,222,000
434,724
254,832
868,964
875,000
1,313,000
1,215,000
2,187,000
1,766,000
1,158,000
13,089,000
1,754,420
981,469
69,400
956,550
3,034,000
59,000
116,807
683,000
967,000
782,400
862,500
926,247
1,595,000
1,153,470
430,000
2,025,420
6,774,000
2,467,400
1,328,000
36,600
93,000
279,200
544,847
178,500
2,382,290
840,000
342,000
853,000
113,000
65,000
160,797
434,000
Dataseries Y:
Download CSV
Histogram 
715
955,646
1,375,649
500,000
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105,568,000
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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
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14,456,000
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3,583,600
84,020,000
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336,389,000
48,889,000
37,514,000
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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
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657,238,000
147,144,000
407,657,000
1,392,842,000
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28,041,000
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663,656,000
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552,456,000
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350,201,000
374,145,000
248,815,000
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251,355,000
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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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680,041,000
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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 time0 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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319513&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319513&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319513&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 time0 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()