Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationSun, 19 Aug 2018 16:43:46 +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/2018/Aug/19/t1534689904ikvnlky85zguznv.htm/, Retrieved Thu, 02 May 2024 20:05:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315104, Retrieved Thu, 02 May 2024 20:05:01 +0000
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Original text written by user:
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User-defined keywords
Estimated Impact109
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [] [2018-08-19 14:43:46] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
-17.846
-0.9285
-0.1113
-0.2341
-14.481
307.780
0.1602
-12.487
503.249
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11.062
27.282
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0.4838
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34.552
504.692
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100.950
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529.500
160.532
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704.818
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95.361
12.192
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0.9340
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233.782
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336.791
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173.737
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0.6541
0.7627
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35.990
18.242
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0.2368
527.020
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35.363
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10.450
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129.343
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0.1749
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199.746
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115.794
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543.052
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14.342
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38.278
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17.034
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21.156
25.294
29.352
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58.950
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20.921
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28.466
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388.066
345.805
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60.903
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27.556
0.4581
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547.124
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566.284
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86.464
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17.385
13.767
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31.237
NaN
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541.896
16.527
0.5872
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11.808
0.4049
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0.1137
21.268
196.180
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443.011
1.129.122
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0.5935
0.2559
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294.409
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0.7816
23.284
0.0223
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0.0591
-11.694
0.2914
NaN
10.792
0.3111
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-15.039
12.959
15.479
0.1662
0.5174
0.9803
146.794
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92.934
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327.635
0.1208
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67.669
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22.824
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0.7068
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0.4099
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0.4806
263.512
0.0369
-0.2961
0.6034
-15.752
-12.581
334.256
10.880
12.845
0.2308
461.831
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13.195
0.1740
13.631
0.9709
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0.7305
32.802
0.6558
-0.7043
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19.016
127.950
0.1209
0.0434
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15.108
-0.3555
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10.587
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0.5935
145.961
0.8202
0.3518
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23.989
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10.665
-14.428
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555.812
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0.1125
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21.068
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102.409
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0.8662
181.269
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17.224
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237.043
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15.402
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19.642
279.578
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0.8609
108.109
75.797
542.514
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0.0274
18.936
32.641
0.0752
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462.632
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0.3373
556.699
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16.472
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15.404
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34.057
566.471
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313.039
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0.7998
32.772
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0.8607
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99.705
0.2679
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20.610
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29.704
21.789
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10.216
11.670
12.579
12.453
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124.015
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0.8128
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29.718
142.180
0.1373
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0.6774
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361.339
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0.5446
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0.1058
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0.7721
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0.1055
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0.1264
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0.3014
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425.365
0.5653
31.912
0.5662
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535.813
0.6174
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0.6835
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55.321
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426.973
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0.2236
0.7329
14.459
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292.181
0.5269
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0.1780
437.486
10.450
19.139
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0.1293
0.6076
0.6039
32.280
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0.7088
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0.0518
13.161
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21.566
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355.022
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121.290
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0.5313
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0.3415
0.1056
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11.950
0.3989
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10.078
19.459
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0.1215
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41.861
0.1438
10.016
74.446
0.0980
0.7112
NaN
0.6386
0.4691
15.801
-14.060
10.345
-0.7023
0.0132
0.3962
-0.3756
16.772
-0.4266
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274.477
-13.580
0.4382
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0.3023
0.9603
0.2339
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-0.3352
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616.024
-0.5394
-0.8674
0.3845
-0.8446
NaN
-0.1512
73.592
0.4040
-0.8535
10.910
-11.847
-10.480
612.973
-0.9586
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-13.017
12.077
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10.152
13.454
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0.8362
0.9288
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37.357
15.229
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141.451
444.370
106.325
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0.4319
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999.780
0.3603
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424.942
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268.991
359.701
17.217
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25.743
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0.6608
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0.5248
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16.298
0.1362
0.0849
0.8610
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0.0548
0.1875
0.4825
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13.784
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0.2724
12.982
-10.367
108.060
16.108
-0.4856
373.486
-0.6757
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521.051
-0.7192
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205.906
0.0736
299.339
-11.879
757.278
0.7348
22.522
0.8158
-0.7938
0.3135
11.601
39.904
0.1370
175.815
-0.0567
0.7599
0.9416
0.3232
11.726
-0.9103
-16.997
0.1617
-0.1752
-10.613
-13.507
0.2480
-0.9252
-0.2087
572.239
-0.1161
0.6118
-0.6879
74.964
-12.647
0.8551
-0.4499
0.1515
-12.178
10.779
24.812
-0.2183
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87.872
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0.5822
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0.1661
-0.0987
0.7255
0.2315
-0.9089
329.244
54.096
10.475
-17.693
-13.597
0.4647
-0.4156
11.285
-18.345
-0.0536
-0.5004
0.4619
54.989
-0.5648
Dataseries Y:
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-0.2633
-0.1144
-0.2610
-0.3311
-0.2509
-0.1395
-0.1654
0.0628
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-0.0090
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0.0168
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0.1224
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0.0950
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0.1646
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0.0567
-0.2353
-0.3013
-0.1566
-0.2495
-0.2437
-0.2224
-0.2449
-0.1013
-0.0637
-0.2600
-0.0982
-0.2173
-0.2371
-0.1634
-0.1707
-0.2744
-0.3169
-0.2423
-0.1634
0.0398
-0.1657
0.0530
0.4219
0.0354
-0.0350
-0.3642
-0.0793
-0.0734
-0.2512
-0.0438
-0.0935
-0.2060
103.157
-0.0353
-0.1582
0.5525
0.5856
-0.2538
-0.3564
-0.1382
-0.2088
-0.2235
-0.3622
-0.2571
0.0303
0.6346
-0.0900
-0.1885
0.0392
-0.2560
-0.0987
0.8727
-0.2992
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-0.2018
0.5690
-0.2827
0.0950
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-0.0569
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-0.3235
-0.1878
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0.6469
12.898
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-0.2102
-0.2034
-0.2866
-0.1703
-0.1776
-0.0468
0.2406
-0.2753
-0.2456
0.3465
-0.2457
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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=315104&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=315104&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315104&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 time1 seconds
R ServerBig Analytics Cloud Computing Center



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()