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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 computationMon, 03 Nov 2008 13:25:30 -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/03/t12257439701ta0hrgr1z45ggb.htm/, Retrieved Sun, 19 May 2024 10:07:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21194, Retrieved Sun, 19 May 2024 10:07:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191

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] [2008-11-03 10:05:52] [a7f04e0e73ce3683561193958d653479]
F R  D      [Mean Plot] [Task 4 - Trimmed 5%] [2008-11-03 20:25:30] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
Feedback Forum
2008-11-08 10:13:24 [Astrid Sniekers] [reply] 
Het antwoord van de student is niet correct. De vraag was om te kijken of bij het trimmen de resultaten van vraag Q2 en Q3 anders zouden zijn. We kunnen besluiten dat er een verschil is met de resultaten van vraag Q2 en Q3. Dit komt omdat we door het trimmen van de tijdreeks, de outliers uit onze tijdreeks hebben gehaald. Bijgevolg wordt de spreiding kleiner.
2008-11-09 14:14:27 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Hier moesten de grafieken van Q2 en Q3 vgl worden na reproductie.

Wanneer men 5% wegneemt van de twee staarten verkleint het interal en worden de outliers gereduceerd. Ze beinvloeden de gegevens maw nietmeer. Doordat het interval kleiner wordt zal ook de spreiding afnemen.



2008-11-10 14:25:53 [Matthieu Blondeau] [reply] 
De student geeft een foutief antwoord. Hij/zij vergelijkt niet met Q2 en Q3. We bekomen een ander resultaat door de 5% bovenste en onderste waarden weg te laten, dit zijn de outliers. Hierdoor wordt de spreiding kleiner.
2008-11-11 09:28:10 [Jens Peeters] [reply] 
Het antwoord van de student is inderdaad niet correct. Door de 5% grootste en kleinste waarden weg te filteren wordt de spreiding juist kleiner aangezien de outliers verdwijnen. Bovendien vergelijk je niet met Q2 en Q3.
2008-11-11 16:07:11 [Yara Van Overstraeten] [reply] 
De redenering was hier inderdaad foutief. Met het wegnemen van de 5% hoogste en laagste waarden wordt inderdaad de spreiding kleiner in plaats van groter.

Herberekening Q2:

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/11/t1226418745ecaau6m2a0p17kp.htm

Herberekening Q3:

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/11/t1226418925nil06900a6s2tpl.htm

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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'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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21194&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21194&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21194&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'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):
x <- x[x>quantile(x,0.05) & xpar1 <- 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()