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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationSat, 01 Nov 2008 08:24:03 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/01/t1225549484mdqjaznzc54zx8n.htm/, Retrieved Sun, 19 May 2024 10:49:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20405, Retrieved Sun, 19 May 2024 10:49:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD  [Mean Plot] [workshop 4 deel 1...] [2008-10-31 09:40:26] [077ffec662d24c06be4c491541a44245]
F         [Mean Plot] [] [2008-11-01 13:19:15] [4c8dfb519edec2da3492d7e6be9a5685]
F    D        [Mean Plot] [] [2008-11-01 14:24:03] [6d40a467de0f28bd2350f82ac9522c51] [Current]
F    D          [Mean Plot] [Task 5 - Bob Leysen] [2008-11-02 16:03:09] [57850c80fd59ccfb28f882be994e814e]
F    D            [Mean Plot] [opdracht 4 task 5] [2008-11-03 17:56:26] [077ffec662d24c06be4c491541a44245]
-    D          [Mean Plot] [task 5] [2008-11-02 16:22:21] [73d6180dc45497329efd1b6934a84aba]
F RMPD          [Star Plot] [Star Plot - Bob L...] [2008-11-02 16:44:14] [57850c80fd59ccfb28f882be994e814e]
F   P             [Star Plot] [Q2 -part 2] [2008-11-02 18:39:32] [73d6180dc45497329efd1b6934a84aba]
F   P             [Star Plot] [Star plot - Stefa...] [2008-11-03 19:45:53] [393f8bd7ec1141df13b2cdc1ba8ed059]
-    D              [Star Plot] [Verbetering Q2] [2008-11-05 17:50:39] [2d4aec5ed1856c4828162be37be304d9]
-                     [Star Plot] [Verbetering] [2008-11-09 11:11:25] [79c17183721a40a589db5f9f561947d8]
-   P             [Star Plot] [Part 2 - Q2] [2008-11-03 19:55:23] [547636b63517c1c2916a747d66b36ebf]
-   P             [Star Plot] [Q2- Jens Peeters] [2008-11-11 10:44:11] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Critical Value] [Q1] [2008-11-11 12:00:37] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - p-value] [Q2] [2008-11-11 12:25:49] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Type II Error] [Q3] [2008-11-11 12:43:52] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Sample Size] [Q4] [2008-11-11 12:54:50] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Population Mean with known Variance - Confidence Interval] [Q5] [2008-11-11 13:03:28] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Sample Mean with known Variance - Confidence Interval] [Q6] [2008-11-11 13:11:57] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Bivariate Kernel Density Estimation] [Q1-1] [2008-11-11 14:13:42] [b47fceb71c9525e79a89b5fc6d023d0e]
F RM D            [Partial Correlation] [Q1-2] [2008-11-11 14:17:22] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Trivariate Scatterplots] [Q1-3] [2008-11-11 14:19:10] [b47fceb71c9525e79a89b5fc6d023d0e]
F    D          [Mean Plot] [task 5] [2008-11-02 17:55:34] [73d6180dc45497329efd1b6934a84aba]
Feedback Forum
2008-11-05 17:51:49 [Ken Van den Heuvel] [reply] 
Interpreteer je dit niet een beetje verkeerd?

Je stelt: 'We nemen maw wel enkele outliers waar maar kunnen niet spreken van seizonaliteiten daar de outliers geen vast patroon vertonen.'

De mean plot maakt eigenlijk het gemiddelde van de waarden die op dezelfde periode (maand) slaan en plot deze. Als de gemiddelde dus hoog liggen, zien we dus ook een hoge piek op de plot en vice versa.

Als ik nu naar jouw plot kijk, dan valt het toch op dat er tussen 2 en 6 een relatief hoog gemiddelde is en dan plot in 7 een dieptepunt.
Maw, de waarden in periode 7 vertonen dieptes in de opeenvolgende jaren. Er is dus naar mijn mening wel seizoenaliteit...elke keer in periode 7 gaat de uitvoer van BLUE dus achteruit.

Je had dan ook nog kunnen proberen dit fenomeen te verklaren. Gezien er geen data vermeld staan bij je gegevens in de tabel kan ik geen bijhorende maanden kleven op de periodes van de plot.
Mocht je dit doen kan je misschien wel een heel logische verklaring vinden voor deze periodieke terugval? Mogelijks zit het kalverseizoen hier voor iets tussen?
2008-11-09 12:54:50 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
De interpretatie van de gegevens gebeurde inderdaad niet correct.

De mean plot geeft weer in welke periode het gemiddelde een forse daling of stijging ondergaat. In periode 7 kennen de data (Uitvoer van de BLEU Levende dieren en producten van het dierenrijk) telkens een forse terugval. Er is maw sprake van seizonaliteit.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
299,63
305,945
382,252
348,846
335,367
373,617
312,612
312,232
337,161
331,476
350,103
345,127
297,256
295,979
361,007
321,803
354,937
349,432
290,979
349,576
327,625
349,377
336,777
339,134
323,321
318,86
373,583
333,03
408,556
414,646
291,514
348,857
349,368
375,765
364,136
349,53
348,167
332,856
360,551
346,969
392,815
372,02
371,027
342,672
367,343
390,786
343,785
362,6
349,468
340,624
369,536
407,782
392,239

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20405&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20405&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20405&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Figures (Output of Computation)

Input Parameters & R Code

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()