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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edauni.wasp
Title produced by softwareUnivariate Explorative Data Analysis
Date of computationFri, 11 Dec 2009 06:29:26 -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/2009/Dec/11/t1260538208a51kq9atfzxsdpa.htm/, Retrieved Mon, 29 Apr 2024 02:24:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66179, Retrieved Mon, 29 Apr 2024 02:24:03 +0000
QR Codes:

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)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
F RMPD    [Univariate Data Series] [] [2009-10-14 08:30:28] [74be16979710d4c4e7c6647856088456]
- RMP       [Univariate Explorative Data Analysis] [WS3] [2009-10-19 07:33:07] [eaf42bcf5162b5692bb3c7f9d4636222]
-  M D          [Univariate Explorative Data Analysis] [paper assumpties ...] [2009-12-11 13:29:26] [78d370e6d5f4594e9982a5085e7604c6] [Current]
- RMP             [Central Tendency] [paper assumpties ...] [2009-12-11 14:14:56] [eaf42bcf5162b5692bb3c7f9d4636222]
-    D            [Univariate Explorative Data Analysis] [paper assumpties ...] [2009-12-11 14:38:50] [eaf42bcf5162b5692bb3c7f9d4636222]
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Dataseries X:
4716.99
4926.65
4920.10
5170.09
5246.24
5283.61
4979.05
4825.20
4695.12
4711.54
4727.22
4384.96
4378.75
4472.93
4564.07
4310.54
4171.38
4049.38
3591.37
3720.46
4107.23
4101.71
4162.34
4136.22
4125.88
4031.48
3761.36
3408.56
3228.47
3090.45
2741.14
2980.44
3104.33
3181.57
2863.86
2898.01
3112.33
3254.33
3513.47
3587.61
3727.45
3793.34
3817.58
3845.13
3931.86
4197.52
4307.13
4229.43
4362.28
4217.34
4361.28
4327.74
4417.65
4557.68
4650.35
4967.18
5123.42
5290.85
5535.66
5514.06
5493.88
5694.83
5850.41
6116.64
6175.00
6513.58
6383.78
6673.66
6936.61
7300.68
7392.93
7497.31
7584.71
7160.79
7196.19
7245.63
7347.51
7425.75
7778.51
7822.33
8181.22
8371.47
8347.71
8672.11
8802.79
9138.46
9123.29
9023.21
8850.41
8864.58
9163.74
8516.66
8553.44
7555.20
7851.22
7442.00
7992.53
8264.04
7517.39
7200.40
7193.69
6193.58
5104.21
4800.46
4461.61
4398.59
4243.63
4293.82




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66179&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66179&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66179&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Descriptive Statistics
# observations108
minimum2741.14
Q14155.81
median4872.65
mean5519.66259259259
Q37259.3925
maximum9163.74

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 108 \tabularnewline
minimum & 2741.14 \tabularnewline
Q1 & 4155.81 \tabularnewline
median & 4872.65 \tabularnewline
mean & 5519.66259259259 \tabularnewline
Q3 & 7259.3925 \tabularnewline
maximum & 9163.74 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66179&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]108[/C][/ROW]
[ROW][C]minimum[/C][C]2741.14[/C][/ROW]
[ROW][C]Q1[/C][C]4155.81[/C][/ROW]
[ROW][C]median[/C][C]4872.65[/C][/ROW]
[ROW][C]mean[/C][C]5519.66259259259[/C][/ROW]
[ROW][C]Q3[/C][C]7259.3925[/C][/ROW]
[ROW][C]maximum[/C][C]9163.74[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66179&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66179&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics
# observations108
minimum2741.14
Q14155.81
median4872.65
mean5519.66259259259
Q37259.3925
maximum9163.74



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
library(lattice)
bitmap(file='pic1.png')
plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(x)
grid()
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~x,col='black',main=paste('Density Plot bw = ',par1),bw=par1)
} else {
densityplot(~x,col='black',main='Density Plot')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot1.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main='Lag plot (k=1), lowess, and regression line')
lines(lowess(z))
abline(lm(z))
dev.off()
if (par2 > 1) {
bitmap(file='lagplotpar2.png')
dum <- cbind(lag(x,k=par2),x)
dum
dum1 <- dum[(par2+1):length(x),]
dum1
z <- as.data.frame(dum1)
z
mylagtitle <- 'Lag plot (k='
mylagtitle <- paste(mylagtitle,par2,sep='')
mylagtitle <- paste(mylagtitle,'), and lowess',sep='')
plot(z,main=mylagtitle)
lines(lowess(z))
dev.off()
}
bitmap(file='pic5.png')
acf(x,lag.max=par2,main='Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(x,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(x,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')