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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 computationSat, 01 Nov 2008 05:57:06 -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/Nov/01/t12255407051jfi9yxlgierst8.htm/, Retrieved Sun, 19 May 2024 10:43:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20366, Retrieved Sun, 19 May 2024 10:43:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact222

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 R  D    [Mean Plot] [] [2008-11-01 11:57:06] [19ef54504342c1b076371d395a2ab19f] [Current]
F           [Mean Plot] [Mean plot Worksho...] [2008-11-02 13:28:30] [ffbe22449df335faef31f462015daa42]
F           [Mean Plot] [Task 4] [2008-11-03 19:14:31] [2b46c8b774ad566be9a33a8da3812a44]
F           [Mean Plot] [] [2008-11-03 20:14:38] [888addc516c3b812dd7be4bd54caa358]
-             [Mean Plot] [] [2008-11-03 20:51:28] [2a0ad3a9bcadca2da0acb91636601c6c]
-               [Mean Plot] [] [2008-11-03 20:52:54] [2a0ad3a9bcadca2da0acb91636601c6c]
-             [Mean Plot] [] [2008-11-10 12:00:26] [888addc516c3b812dd7be4bd54caa358]
F RMPD      [Testing Mean with known Variance - Critical Value] [Q1] [2008-11-06 16:23:57] [b518240a1c80d4f939bf8b3e34f77cec]
F RMPD      [Testing Mean with known Variance - p-value] [Q2] [2008-11-06 16:35:28] [b518240a1c80d4f939bf8b3e34f77cec]
F RMPD      [Testing Mean with known Variance - Type II Error] [Q3] [2008-11-06 16:40:38] [b518240a1c80d4f939bf8b3e34f77cec]
F RMPD      [Testing Mean with known Variance - Sample Size] [Q4] [2008-11-06 16:44:37] [b518240a1c80d4f939bf8b3e34f77cec]
F RMPD      [Testing Population Mean with known Variance - Confidence Interval] [Q5] [2008-11-06 16:48:09] [b518240a1c80d4f939bf8b3e34f77cec]
F RMPD      [Testing Sample Mean with known Variance - Confidence Interval] [Q6] [2008-11-06 16:52:07] [b518240a1c80d4f939bf8b3e34f77cec]
Feedback Forum
2008-11-06 14:57:17 [Nathalie Koulouris] [reply] 
De student heeft de R-code correct aangepast. Door de staarten af te knippen, is de spreiding verkleind en zijn de outliers verdwenen.
2008-11-07 13:17:07 [Siem Van Opstal] [reply] 
Juiste berekening en correcte conclusie. Er blijft nog 95% van de grafiek over, de 5% outliers vallen dus weg. Zo verkleint de spreiding en dat merk je op de grafiek.
2008-11-08 13:01:55 [Ruben Jacobs] [reply] 
Een correcte aanpassing van de berekening. De spreiding wordt kleiner omdat je omdat je aan elk van de staarten 2,5% afknipt. Dit kan je ook opmerken op de grafiek maar de seizoenaliteit blijft nog altijd behouden.
2008-11-09 15:43:46 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Correcte interpretatie en conclusie van de student.

De staarten worden aan beide kanten aangepast en het interval wordt dus kleiner gemaakt. De outliers worden gereduceerd en oefenen geen invloed meer uit op de data. De spreiding neemt af.

2008-11-11 11:49:46 [90714a39acc78a7b2ecd294ecc6b2864] [reply] 
De staarten worden afgekapt, je behoudt 95%. M.a.w., de outliers worden afgekapt waardoor de spreing kleiner wordt.

Post a new message

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 time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20366&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]4 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=20366&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20366&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


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()