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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 computationSun, 02 Nov 2008 08:46:36 -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/02/t1225640898ab5tw0i9p6hxfyw.htm/, Retrieved Sun, 19 May 2024 11:39:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20627, Retrieved Sun, 19 May 2024 11:39:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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] [Mean Plot] [2008-11-02 15:46:36] [21d7d81e7693ad6dde5aadefb1046611] [Current]
F R         [Mean Plot] [Trimming 5% at bo...] [2008-11-03 19:44:38] [bc937651ef42bf891200cf0e0edc7238]
-    D      [Mean Plot] [Mean Plot Uitvoer...] [2008-11-03 19:58:45] [bc937651ef42bf891200cf0e0edc7238]
-    D      [Mean Plot] [Mean Plot invoer ...] [2008-11-03 20:04:39] [bc937651ef42bf891200cf0e0edc7238]
- RMPD      [Linear Regression Graphical Model Validation] [Regression] [2008-11-03 20:33:31] [bc937651ef42bf891200cf0e0edc7238]
-    D        [Linear Regression Graphical Model Validation] [Regression 2] [2008-11-03 20:40:26] [bc937651ef42bf891200cf0e0edc7238]
-    D          [Linear Regression Graphical Model Validation] [Regression 3] [2008-11-03 20:55:17] [bc937651ef42bf891200cf0e0edc7238]
-    D            [Linear Regression Graphical Model Validation] [Regression 3] [2008-11-03 21:03:12] [bc937651ef42bf891200cf0e0edc7238]
F RMPD            [Kendall tau Correlation Matrix] [Kendall tau Corre...] [2008-11-03 21:18:54] [bc937651ef42bf891200cf0e0edc7238]
Feedback Forum
2008-11-11 13:40:18 [256f97d8b7c07ed49f142eff724c6520] [reply] 
U heeft de periode index op 12 staan, je bekomt hier dus een tijdreeks van 12 maanden. Je kan hier best op 60 plaatsen zodat je een langere termijn tendens krijgt.
2008-11-11 14:46:32 [Dorien Peeters] [reply] 
Ik heb hier toch op een andere manier gewerkt. In tegenstelling tot de student heb ik de breedte niet ingesteld op 12 maar op 36, zo kan je de hypothese testen over 3 jaren en niet over slechts 1 jaar.
mean plot. Hoe kunnen we nu zien of we seizoenaliteit hebben? Het gemiddelde van september is helemaal anders dan dat van maart, april,mei,... => de stijging en daling kunnen wijzen op seizoenaliteit (dit is ook wat de student zei, nl.'dat er een invloed is van seizoenaliteit maar dit kan je zien adhv de plotse stijging rond juli en oktober)Maar indien we de breedte instellen op 36 maanden ipv 12,bekomen we rond de maand oktober een daling. Rond maand 22 en 31 komt deze daling terug en steeds intenser. De stijging komt terug in maand 7,17 en 30. De stijging en dalingen kunnen wijzen op seizoenaliteit.
Notched box splot(deze ontbrak bij de uitleg van de student). Deze volgt dezelfde tendens als de mean plot.(dus de verandering is NIET te wijten aan toevalligheid)Bij de notched plot zien we ook spreiding, soms grote matches(we zien betrouwbaarheidsinterval) april=twijfelgeval, ligt op de rand=>significant verschil tussen augustus en september.

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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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20627&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20627&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20627&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'Gwilym Jenkins' @ 72.249.127.135


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))
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()