Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationSun, 16 Dec 2012 07:10:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t13556598227272kd0x2q7k0pa.htm/, Retrieved Fri, 29 Mar 2024 12:34:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200273, Retrieved Fri, 29 Mar 2024 12:34:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Mean Plot] [Colombia Coffee] [2008-01-07 13:38:24] [74be16979710d4c4e7c6647856088456]
- RMPD    [Mean Plot] [Paper] [2012-12-09 16:34:01] [9d44b52ac7f20a3e9be7c3c8470fe2cd]
-    D        [Mean Plot] [paper] [2012-12-16 12:10:01] [97e5c69206415429213a02c19f23a896] [Current]
Feedback Forum

Post a new message
Dataseries X:
414.89
444.50
481.29
491.09
419.70
432.88
438.01
412.84
402.91
416.20
411.80
393.60
381.70
388.34
370.89
385.96
394.26
381.37
377.40
377.70
347.71
347.70
340.20
341.00
342.00
319.54
302.79
299.10
313.50
326.80
316.00
316.50
317.20
330.40
323.35
325.85
321.50
321.90
347.48
338.89
345.70
340.44
342.40
342.70
348.34
376.66
417.73
423.51
397.56
390.92
408.26
401.12
408.91
438.35
460.23
449.59
450.52
461.15
460.38
465.35
467.57
486.24
476.58
442.07
443.61
451.55
451.07
451.33
437.63
431.28
413.46
406.78
420.17
419.05
404.01
387.51
390.15
384.40
371.05
367.60
375.04
365.14
361.75
366.88
394.26
409.39
410.11
416.81
393.06
374.24
369.05
352.33
362.53
394.73
389.32
380.74
381.73
376.95
383.64
363.83
363.34
358.38
356.95
366.72
367.69
356.31
348.74
358.69
360.17
361.73
354.45
353.91
344.34
338.62
337.24
340.81
352.72
343.06
345.43
344.38
335.02
334.82
329.01
329.31
330.08
342.15
367.18
371.89
392.19
378.84
355.28
364.18
373.83
383.30
386.88
381.91
384.13
377.27
381.43
385.64
385.49
380.36
391.58
389.77
384.39
379.29
378.55
376.64
382.12
391.03
385.22
387.56
386.23
383.67
383.06
383.14
385.31
387.44
399.45
404.76
396.21
392.85
391.93
385.27
383.47
387.35
383.14
381.07
377.85
369.00
355.11
346.58
351.81
344.47
343.84
340.76
324.10
324.01
322.82
324.87
306.04
288.74
289.10
297.49
295.94
308.29
299.10
292.32
292.87
284.11
288.98
295.93
294.12
291.68
287.08
287.33
285.96
282.62
276.44
261.31
256.08
256.69
264.74
310.72
293.18
283.07
284.32
299.86
286.39
279.69
275.19
285.73
281.59
274.47
273.68
270.00
266.01
271.45
265.49
261.87
263.03
260.48
272.36
270.23
267.53
272.39
283.42
283.06
276.16
275.85
281.51
295.50
294.06
302.68
314.49
321.18
313.29
310.26
319.14
316.56
319.07
331.92
356.86
358.97
340.55
328.18
355.68
356.35
351.02
359.77
378.95
378.92
389.91
406.95
413.79
404.88
406.67
403.26
383.78
392.37
398.09
400.51
405.28
420.46
439.38
442.08
424.03
423.35
433.85
429.23
421.87
430.66
424.48
437.93
456.05
469.90
476.67
510.10
549.86
555.00
557.09
610.65
675.39
596.15
633.71
632.59
598.19
585.78
627.83
629.79
631.17
664.75
654.90
679.37
667.31
655.66
665.38
665.41
712.65
754.60
806.25
803.20
889.60
922.30
968.43
909.71
888.66
889.49
939.77
839.03
829.93
806.62
760.86
816.09
858.69
943.00
924.27
890.20
928.65
945.67
934.23
949.38
996.59
1043.16
1127.04
1134.72
1117.96
1095.41
1113.34
1148.69
1205.43
1232.92
1192.97
1215.81
1270.98
1342.02
1369.89
1390.55
1356.40
1372.73
1424.00
1479.76
1512.60
1528.66
1572.21
1757.21
1770.95
1665.21
1738.11
1641.84
1652.21
1742.14
1673.77
1649.69
1591.19
1598.76
1589.90
1630.31
1744.81
1746.58
1721.64




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200273&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200273&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np+1))
darr <- array(NA,dim=c(par1,np+1))
ari <- array(0,dim=par1)
dx <- diff(x)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
darr[j,ari[j]] <- dx[i]
if (j == par1) j = 0
}
ari
arr
darr
arr.mean <- array(NA,dim=par1)
arr.median <- array(NA,dim=par1)
arr.midrange <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.mean[j] <- mean(arr[j,],na.rm=TRUE)
arr.median[j] <- median(arr[j,],na.rm=TRUE)
arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2
}
overall.mean <- mean(x)
overall.median <- median(x)
overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2
bitmap(file='plot1.png')
plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.mean,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.median,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.midrange,0)
dev.off()
bitmap(file='plot4.png')
z <- data.frame(t(arr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries'))
dev.off()
bitmap(file='plot4b.png')
z <- data.frame(t(darr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries'))
dev.off()
bitmap(file='plot5.png')
z <- data.frame(arr)
names(z) <- c(1:np)
(boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks'))
dev.off()
bitmap(file='plot6.png')
z <- data.frame(cbind(arr.mean,arr.median,arr.midrange))
names(z) <- list('mean','median','midrange')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots'))
dev.off()