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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationThu, 30 Oct 2008 08:52:35 -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/t1225378389sxvq8s5hmpwxlvp.htm/, Retrieved Fri, 17 May 2024 03:07:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20071, Retrieved Fri, 17 May 2024 03:07:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

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] [q2 mean plot] [2008-10-30 14:52:35] [4940af498c7c54f3992f17142bd40069] [Current]
-   PD    [Mean Plot] [q2 mean plot goed] [2008-10-30 14:56:11] [85134b6edb9973b9193450dd2306c65b]
F    D    [Mean Plot] [q3 mean plot] [2008-10-30 15:32:57] [85134b6edb9973b9193450dd2306c65b]
F RMPD    [Bootstrap Plot - Central Tendency] [q4 blocked bootstrap] [2008-10-30 15:47:17] [85134b6edb9973b9193450dd2306c65b]
F RM D    [Notched Boxplots] [task 2 notched bo...] [2008-10-30 17:01:13] [85134b6edb9973b9193450dd2306c65b]
F R PD      [Notched Boxplots] [task 3 notched bo...] [2008-10-30 17:22:47] [85134b6edb9973b9193450dd2306c65b]
F RMPD      [Mean Plot] [taak 5 mean plot] [2008-10-30 18:39:42] [85134b6edb9973b9193450dd2306c65b]
-   P         [Mean Plot] [verbetering taak 5] [2008-11-09 13:07:12] [85134b6edb9973b9193450dd2306c65b]
-   P         [Mean Plot] [Oplossing Taak 5] [2008-11-11 13:29:58] [f9b9e85820b2a54b20380c3265aca831]
-   PD    [Mean Plot] [verbetering q2] [2008-11-09 12:28:20] [85134b6edb9973b9193450dd2306c65b]
Feedback Forum
2008-11-09 12:37:57 [Nathalie Boden] [reply] 
Zo zie ik dat ik een # blockwidth van 12 had moeten nemen in plaats van 24, want op een blockwidth van 12 ga je beter de seizonaliteit kunnen aflezen. We zien op de y-as het gemiddelde en op de x-as gaan we werken met de tijdreeks van de maandelijkse productie van de kleding. De eerste observatie is hier de 1ste maand van onze dataset. Om de 12 maanden gaan we het gemiddelde nemen van deze maanden wat we het periodieke gemiddelde noemen. We zien hier dat er in maand 6 en maand 7 onze lijn op de grafiek stijl naar boven gaat. Zo zien we dit ook terug in de median plot, midrange plot. In de periodic subsidiaries (grafiek) zien we de spreiding van de verschillende observaties. We zien dat er een significant verschil is en dat er dus duidelijk sprake is van seizonaliteit van augustus naar september. We kunnen ook besluiten dat er een grote spreiding is van de gegevens.
2008-11-11 13:12:19 [Dries Van Gheluwe] [reply] 
De blockwidth is hier inderdaad verkeerd. Je moest het gewoon vergelijken met het aantal maanden. Er is een duidelijk significant verschil tussen augustus en september (dit is maand 6 en 7 omdat het pas begint in maart.

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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 time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20071&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]3 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=20071&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20071&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 24 ;
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()