Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationMon, 03 Nov 2008 12:43:52 -0700
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/03/t1225741885skkwqinm89ne294.htm/, Retrieved Sun, 19 May 2024 05:48:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21109, Retrieved Sun, 19 May 2024 05:48:21 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176
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    D  [Mean Plot] [Task 5] [2008-11-02 14:02:33] [4300be8b33fd3dcdacd2aa9800ceba23]
F    D      [Mean Plot] [Task 5] [2008-11-03 19:43:52] [541f63fa3157af9df10fc4d202b2a90b] [Current]
Feedback Forum
2008-11-10 11:03:30 [Steffi Van Isveldt] [reply
Ik zou hier eerder naar de mean plot kijken ipv naar de sequential blocks. Wanneer je dan de analyse opnieuw maakt voor een groter aantal jaar, kan je betere conclusies trekken.
  2008-11-11 21:12:13 [Koen De Winter] [reply
Zoals eerder aangehaald (zie task 1 Q3 van deze student) mag je met de mean plot niet over meerdere jaren heen kijken. Een analyse over meerder jaren heen doe je met de notched box plot - sequential blocks.
2008-11-11 21:18:48 [Koen De Winter] [reply
Ik zou niet teveel kijken naar die terugval in het 5e jaar. Deze is geenszins significant. De terugval kan aan toeval te wijten zijn. De mean plot en de mogelijk daaruit vloeiende seizoensgebondenheid lijkt mij interessanter om te onderzoeken.

Er is een duidelijke daling te zien in juli en augustus (zomervakantie/bouwverlof).
Over wat gaat de tijdreeks eigenlijk?! Zonder omkadering kunnen er moeilijk conclusies worden getrokken.
2008-11-11 21:26:07 [Koen De Winter] [reply
Vervaardiging van transportmiddelen zie ik in het document. Dus ik neem mijn woorden terug.

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Dataseries X:
91,2
99,2
108,2
101,5
106,9
104,4
77,9
60
99,5
95
105,6
102,5
93,3
97,3
127
111,7
96,4
133
72,2
95,8
124,1
127,6
110,7
104,6
112,7
115,3
139,4
119
97,4
154
81,5
88,8
127,7
105,1
114,9
106,4
104,5
121,6
141,4
99
126,7
134,1
81,3
88,6
132,7
132,9
134,4
103,7
119,7
115
132,9
108,5
113,9
142
97,7
92,2
128,8
134,9
128,2
114,8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21109&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21109&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21109&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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132



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))
ari <- array(0,dim=par1)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
if (j == par1) j = 0
}
ari
arr
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='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()