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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 07:08:02 -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/t1225372127oazi7plw2hewrqs.htm/, Retrieved Sun, 19 May 2024 16:36:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20002, Retrieved Sun, 19 May 2024 16:36:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact207

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    D    [Mean Plot] [Q2:Mean plot] [2008-10-30 13:08:02] [8758b22b4a10c08c31202f233362e983] [Current]
F R  D      [Mean Plot] [Task 4] [2008-10-30 17:30:27] [1ce0d16c8f4225c977b42c8fa93bc163]
F             [Mean Plot] [Task 4] [2008-11-03 22:07:25] [76963dc1903f0f612b6153510a3818cf]
F    D      [Mean Plot] [Task 5] [2008-10-30 17:41:38] [1ce0d16c8f4225c977b42c8fa93bc163]
-             [Mean Plot] [Task 5] [2008-11-03 22:09:37] [76963dc1903f0f612b6153510a3818cf]
F    D      [Mean Plot] [Task 5(2)] [2008-10-30 17:43:37] [1ce0d16c8f4225c977b42c8fa93bc163]
F    D        [Mean Plot] [Task 5] [2008-11-03 22:14:36] [76963dc1903f0f612b6153510a3818cf]
F    D      [Mean Plot] [Task 5(3)] [2008-10-30 17:45:21] [1ce0d16c8f4225c977b42c8fa93bc163]
F    D      [Mean Plot] [Task 5(4)] [2008-10-30 17:46:49] [1ce0d16c8f4225c977b42c8fa93bc163]
F           [Mean Plot] [Q2] [2008-11-03 21:58:42] [76963dc1903f0f612b6153510a3818cf]
Feedback Forum
2008-11-10 17:00:19 [Matthieu Blondeau] [reply] 
De x-as geeft de maanden van het jaar weer, boven de grafiek staat het aantal blocks, dit zijn er 5 dus elke punt op de grafiek stelt het gemiddelde voor van die bepaalde maand over 5 jaar heen. Ik heb niet vermeld dat het verschil tussen de 6de en de 7de maand significant is door te kijken naar de notched box plots. Hier vallen de betrouwbaarheidsintervallen niet meer samen in elkaar.

2008-11-11 14:45:47 [Liese Tormans] [reply] 
Aan de hand van deze grafiek gaan we kijken of we kunnen spreken van seizoenaliteit.
Als we naar de grafiek zien, zien we een uitschieter rond de maand 7 en maand 10.Zoals de student al gezegd heeft kunnen we spreken van seizoenaliteit.
De student had misschien nog iets duidelijker kunnen zijn, door te onderzoeken of het verschil toevallig of significant is. Dit kunnen we aflezen aan de notched boxplot. We kunnen zien dat in maand 7 en maand 10 de notches sterk afwijken van een denkbeeldige horizontale door het midden. De andere notches liggen dus op ongeveer dezelfde lijn terwijl maand 7 en 10 er sterk van afwijken. We kunnen dus spreken van een significant verschil.
2008-11-11 19:44:58 [Joachim Van Hemelen] [reply] 
De student geeft niet aan dat het gebruik van notched box plots nodig is om de seizoenaltiteit van de mean plot te bevestigen. Enkel door de mean plot aan de “notched box plots – periodic subseries” te toetsen, kan er vastgesteld worden dat significante verschillen zijn tussen de opeenvolgende maanden die niet aan toeval te wijten zijn (wat wijst op seizoenaliteit).

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,20
88,60
94,30
98,30
86,40
80,60
104,10
108,20
93,40
71,90
94,10
94,90
96,40
91,10
84,40
86,40
88,00
75,10
109,70
103,00
82,10
68,00
96,40
94,30
90,00
88,00
76,10
82,50
81,40
66,50
97,20
94,10
80,70
70,50
87,80
89,50
99,60
84,20
75,10
92,00
80,80
73,10
99,80
90,00
83,10
72,40
78,80
87,30
91,00
80,10
73,60
86,40
74,50
71,20
92,40
81,50
85,30
69,90
84,20
90,70
100,30

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20002&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20002&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20002&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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()