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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 computationThu, 30 Oct 2008 00:53:19 -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/Oct/30/t12253496522rkgpj20yvsi870.htm/, Retrieved Sun, 19 May 2024 15:21:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19960, Retrieved Sun, 19 May 2024 15:21:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F R PD  [Mean Plot] [vraag 2] [2008-10-29 19:00:24] [c45c87b96bbf32ffc2144fc37d767b2e]
F R P     [Mean Plot] [vraag 4] [2008-10-29 22:25:49] [c45c87b96bbf32ffc2144fc37d767b2e]
F R PD        [Mean Plot] [taak 5] [2008-10-30 06:53:19] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
-   P           [Mean Plot] [herberekening] [2008-11-05 18:47:04] [c45c87b96bbf32ffc2144fc37d767b2e]
-   P           [Mean Plot] [] [2008-11-11 18:34:45] [2a30350413961f11db13c46be07a5f73]
-   P           [Mean Plot] [] [2008-11-11 18:34:45] [2a30350413961f11db13c46be07a5f73]
-   P           [Mean Plot] [] [2008-11-11 18:34:45] [2a30350413961f11db13c46be07a5f73]
- RMPD          [Bivariate Kernel Density Estimation] [] [2008-11-11 20:43:43] [2a30350413961f11db13c46be07a5f73]
- RMPD          [Partial Correlation] [] [2008-11-11 20:56:10] [2a30350413961f11db13c46be07a5f73]
- RMPD          [Bivariate Kernel Density Estimation] [] [2008-11-11 20:58:08] [2a30350413961f11db13c46be07a5f73]
- RMPD          [Trivariate Scatterplots] [] [2008-11-11 21:02:37] [2a30350413961f11db13c46be07a5f73]
- RMPD          [Hierarchical Clustering] [] [2008-11-11 21:15:39] [2a30350413961f11db13c46be07a5f73]
Feedback Forum
2008-11-05 18:59:52 [Michaël De Kuyer] [reply] 
Aangezien ik bij deze vraag me enkel gebaseerd heb op de mean plot en de blockwidth verkeerd heb ingesteld, heb ik de tijdreeks opnieuw gecompute:
http://www.freestatistics.org/blog/date/2008/Nov/05/t1225910920vigchpi6o585bhd.htm . Hieronder volgen enkele besluiten:

- Aan de hand van de notched boxplot dat de waarden in de wintermaand januari (maand 4) significant lager ligt dan die van de zonmermaanden (maanden 7-8-9-10-12).
- Aan de hand van de notched boxplot (in maanden) zien we ook dat er een terugval is in maand elf (dit is juli). Dit is logisch aangezien het dan bouwverlof is.
- Deze twee conclusies leidt ertoe dat er seizonaliteit is in deze dataset.

- Als we kijken naar de evolutie per jaar, zien we dat er een stijging is. Meer zelfs, het niveau van jaar 4 en jaar 5 ligt significant hoger dan jaar 1.
-Jaar 6 laten we buiten beschouwing, aangezien het geen volledig jaar is. Hierover kunnen we geen gegronde uitspraken doen omdat ik seizonlaiteit heb vastgesteld in de tijdreeks.


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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4348
3603
2700
2640
2916
3180
4151
4023
3431
3874
2617
3580
5267
3832
3441
3228
3397
3971
4625
4486
4131
4686
3174
4282
4209
4158
3936
3149
3623
4230
4443
4810
4853
5050
3553
4674
5412
5131
4856
3980
4431
4606
5352
4640
5170
4824
3280
4706
4909
5092
4911
3824
4214
4449
4486
4777
5132
4522
3295
4281
4590
4623
4075
3398
3029

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19960&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19960&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19960&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 65 ;
Parameters (R input):
par1 = 65 ;
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()