Free Statistics

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Author's title

Author*Unverified author*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationMon, 19 May 2014 14:40:49 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/May/19/t14005249337q9avlc8zlqjsy8.htm/, Retrieved Wed, 15 May 2024 11:20:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234963, Retrieved Wed, 15 May 2024 11:20:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Mean Plot] [] [2014-03-03 19:53:52] [1a8a9c3e9efac5cdf721943be7ce7ff8]
- R PD    [Mean Plot] [] [2014-05-19 18:40:49] [9093db98244a413fd54e2b1bc98c9896] [Current]
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Dataseries X:
71.97
72.32
74.07
77.95
81.75
80.81
74.1
71.37
75.21
76.9
74.44
74.76
76.23
76.97
78.4
78.6
80.08
81.12
80.31
84.59
81.34
80.95
80.48
75.26
76.32
78.92
80.47
83.14
85.42
81.53
87.31
86.01
85.1
79.91
78.6
78.6
79.37
82.89
84.43
85.32
87.71
84.68
80.62
84.79
85.49
81.68
77.69
78.31
79.18
80.91
83.91
86.3
89.76
85.11
83.81
85.36
85.89
82.59
80.87
80.27
81.36
84.81
90.3
95.43
97.59
97.8
99.48
97.52
104.39
97.74
91.37
92.42
96.9
101.58
105.46
110.06
107.9
102.87
96.28
98.59
103.22
98.6
91.79
93.83
95.17
95.19
99.44
109.18
109.15
109.72
108.41
102.96
107.64
97.28
97.25
91.84
94.12
97.86
98.83
102.29
104.49
102.11
102.14
101.28
101.21
94.2
88.47
88.08
88.02
92.95
97.05
101.44
100.34
99.98
94.17
94.54
95.12
98.04
93.72
93.83
93.03
95.81
99.1
100.12
100.67
103.87
102.39
107.21
105.71
99.79
96.12
96.17
97.23
98.08
99.84
99.72
99.92
102.7
102.06
102.36
102.43
100.6
98.4
98.61
103.03
104.7
107.45
109.67
110.54
112.05
113.19
114.2
112.56
107.36
103.93
103.83
104.74
107.5
109.53
109.42
108.6
110.72
105.1
105.19
102.55
101.25
101.56
101.62
101.7
102.94
104.37
106.93
107.82
110.83
106.86
109.46
108.8
108.69
107.77
108.64
108.5
113.84
114.59
116.27
113.63
112.29
110.31
108.47
110.67
109.1
107.02
108.12
106.69
109.87
110.82
114.14
113.31
115.16
111.06
111.13
115.96
117.57
114.69
119.42
118.4
123.32
123.39
127.04
129.35
127.12
122.1
120.22
121.53
119.01
114.27
114.46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234963&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234963&T=0

As an alternative you can also use a 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'Gertrude Mary Cox' @ cox.wessa.net



Parameters (Session):
par1 = 17 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
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))
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()