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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, 06 Nov 2008 09:32:30 -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/06/t1225989278w3uieksmmevl50g.htm/, Retrieved Sun, 19 May 2024 17:04:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22317, Retrieved Sun, 19 May 2024 17:04:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182

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] [Mean Plot] [2008-11-06 16:32:30] [96839c4b6d4e03ef3851369c676780bf] [Current]
-    D    [Mean Plot] [Mean Plot] [2008-12-22 08:12:49] [1a98f534d827b920a5783bf87d2d3cce]
- RM D    [Variance Reduction Matrix] [Variance Reductio...] [2008-12-22 09:53:55] [1a98f534d827b920a5783bf87d2d3cce]
- RM D    [Standard Deviation-Mean Plot] [Standard Deviatio...] [2008-12-22 11:03:46] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [(Partial) Autocorrelation Function] [acf] [2008-12-22 12:36:13] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [(Partial) Autocorrelation Function] [acf] [2008-12-22 12:41:23] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [Spectral Analysis] [Spectral Analysis] [2008-12-22 12:55:16] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [Spectral Analysis] [Spectral Analysis] [2008-12-22 13:13:07] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [Spectral Analysis] [Spectral Analysis] [2008-12-22 13:23:10] [1a98f534d827b920a5783bf87d2d3cce]
- RMPD    [Cross Correlation Function] [Cross Correlation...] [2008-12-22 13:51:00] [1a98f534d827b920a5783bf87d2d3cce]
-   PD      [Cross Correlation Function] [cross correlatie ...] [2009-01-24 06:52:11] [f77c9ab3b413812d7baee6b7ec69a15d]
- RMPD    [Spectral Analysis] [Spectral Analysis] [2008-12-22 19:18:22] [1a98f534d827b920a5783bf87d2d3cce]
Feedback Forum
2008-11-10 16:57:36 [Natalie De Wilde] [reply] 
Goede opmerking ivm de benoeming van de maanden! De grafiek begint inderdaad bij maart. Je had misschien nog het verband tussen de mean plot en de notched box plot kunnen aanhalen door erop te duiden dat de mediaan bij maand 7 hoog is en bij maand 10 laag zoals te zien is in de mean plot.
2008-11-10 17:00:52 [Natalie De Wilde] [reply] 
Voor Q3:De student heeft inderdaad niet met mean plot gewerkt en vermeld bij het gebruik van de notched box plot de overlappingen tussen de inkepingen niet. Het is belangrijk om dit na te kijken en niet af te gaan op veronderstellingen.
2008-11-11 18:04:42 [Peter Van Doninck] [reply] 
De opmerking ivm de maanden is goed gemaakt! Wel spreekt de student al over seizoenaliteit zonder eerst de notched-boxplots erbij te nemen. De conclusie is nogal vaag, want er wordt niet echt aangetoond tussen welke maanden er seizoenaliteit is en welke maanden niet. Wanneer de box-plots armen en benen hebben, dan wordt het betrouwbaarheidsinterval verder nog vergroot.
2008-11-11 18:11:36 [Peter Van Doninck] [reply] 
Een daling van de 6 medianen klopt echter niet! Het zijn er slechts 5. De daling is hier niet significant. Voor verder onderzoek dienen de gegevens ipv de visualisatie onderzocht te worden. De conclusie dat de mediaan daalt klopt echter wel.
2008-11-12 09:51:57 [Marie-Lien Loos] [reply] 
Goed gezien van de maanden. Je had nog het verband tussen de mean plot en notched blocks kunnen vermelden.
2008-11-12 09:57:26 [Marie-Lien Loos] [reply] 
Q3: Er is geen significant verschil te zien over de 5 jaar. (6e mediaan mag niet gebruikt worden). De daling kan aan toeval te wijten zijn, dit moet verder onderzocht worden.

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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 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=22317&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=22317&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22317&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'George Udny Yule' @ 72.249.76.132


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()