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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_sdplot.wasp
Title produced by softwareStandard Deviation Plot
Date of computationTue, 22 Mar 2016 06:53:26 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Mar/22/t1458629627uojeeiowyybopv0.htm/, Retrieved Mon, 29 Apr 2024 11:10:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294411, Retrieved Mon, 29 Apr 2024 11:10:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation Plot] [] [2016-03-22 06:53:26] [a8cf284534efea996701e15b66911faf] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92.09
93.77
94.44
94.91
94.78
94.51
94.36
96.6
96.72
96.71
97.44
97.83
98.92
97.98
98.76
99.76
99.87
100.09
100.07
99.46
100.4
101.25
102.29
102.1
105.91
108.95
110.07
109.92
109.87
110.54
110.79
110.32
110.76
110.24
110.27
110.11
110.39
111.05
110.85
110.24
108.7
109.93
109.53
109.83
107.86
104.61
103.61
103.11
102.59
102.91
101.94
101.8
102.25
102.6
102.49
102.13
100.76
100.86
101.12
100.74
99.99
99.39
99.52
99.21
99.38
99.37
99.38
99.26
99.36
99.2
98.53
98.65
99.15
100.17
99.98
100.07
99.94
100.05
99.13
98.74
98.64
98.44
98.81
98.88
99.63
100.08
100.07
100.55
99.98
99.89
99.86
99.61
100.12
100.24
100.1
99.86
97.99
97.57
98.28
97.97
97.99
97.84
97.33
96.7
96.79
96.76
96.23
96.29
96.46
97.23
97.59
97.13
97.37
96.12
96.96
96.7
97
97.15
96.51
96.68

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' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294411&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294411&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294411&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' @ jenkins.wessa.net


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.sd <- array(NA,dim=par1)
arr.range <- array(NA,dim=par1)
arr.iqr <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.sd[j] <- sqrt(var(arr[j,],na.rm=TRUE))
arr.range[j] <- max(arr[j,],na.rm=TRUE) - min(arr[j,],na.rm=TRUE)
arr.iqr[j] <- quantile(arr[j,],0.75,na.rm=TRUE) - quantile(arr[j,],0.25,na.rm=TRUE)
}
overall.sd <- sqrt(var(x))
overall.range <- max(x) - min(x)
overall.iqr <- quantile(x,0.75) - quantile(x,0.25)
bitmap(file='plot1.png')
plot(arr.sd,type='b',ylab='S.D.',main='Standard Deviation Plot',xlab='Periodic Index')
mtext(paste('# blocks = ',np))
abline(overall.sd,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.range,type='b',ylab='range',main='Range Plot',xlab='Periodic Index')
mtext(paste('# blocks = ',np))
abline(overall.range,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.iqr,type='b',ylab='IQR',main='Interquartile Range Plot',xlab='Periodic Index')
mtext(paste('# blocks = ',np))
abline(overall.iqr,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.sd,arr.range,arr.iqr))
names(z) <- list('S.D.','Range','IQR')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Variability',main='Notched Box Plots'))
dev.off()