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Description of Statistical Computation

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 computationSun, 21 Nov 2010 15:28:28 +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/2010/Nov/21/t1290353226ymv0s2ymebzjxa4.htm/, Retrieved Thu, 02 May 2024 02:01:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98369, Retrieved Thu, 02 May 2024 02:01:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
-    D  [Univariate Explorative Data Analysis] [Workshop 7 mini-t...] [2010-11-21 11:57:56] [87d60b8864dc39f7ed759c345edfb471]
F           [Univariate Explorative Data Analysis] [ws 7 - eerste reg...] [2010-11-21 15:28:28] [a948b7c78e10e31abd3f68e640bbd8ba] [Current]
Feedback Forum
2010-11-27 09:08:05 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Deze run sequence plot is correct berekend, maar de interpretatie is volgens mij niet helemaal correct. De student spreekt van een daling en dan weer een stijging in de grafiek, maar naar mijn mening is deze daling of stijging helemaal niet zo sterk aanwezig.
We kunnen wel vaststellen dat er enorme pieken en dalen waar te nemen zijn, maar dit doorheen de hele grafiek.

Ik ben dan ook van mening dat deze grafiek niet echt het beste bewijs is om zich op te baseren om een maandeffect of een trend te gaan onderzoeken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24
25
17
18
18
16
20
16
18
17
23
30
23
18
15
12
21
15
20
31
27
34
21
31
19
16
20
21
22
17
24
25
26
25
17
32
33
13
32
25
29
22
18
17
20
15
20
33
29
23
26
18
20
11
28
26
22
17
12
14
17
21
19
18
10
29
31
19
9
20
28
19
30
29
26
23
13
21
19
28
23
18
21
20
23
21
21
15
28
19
26
10
16
22
19
31
31
29
19
22
23
15
20
18
23
25
21
24
25
17
13
28
21
25
9
16
19
17
25
20
29
14
22
15
19
20
15
20
18
33
22
16
17
16
21
26
18
18
17
22
30
30
24
21
21
29
31
20
16
22
20
28
38
22
20
17
28
22
31

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98369&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]5 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=98369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98369&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations159
minimum9
Q118
median21
mean21.5849056603774
Q325
maximum38

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 159 \tabularnewline
minimum & 9 \tabularnewline
Q1 & 18 \tabularnewline
median & 21 \tabularnewline
mean & 21.5849056603774 \tabularnewline
Q3 & 25 \tabularnewline
maximum & 38 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98369&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]159[/C][/ROW]
[ROW][C]minimum[/C][C]9[/C][/ROW]
[ROW][C]Q1[/C][C]18[/C][/ROW]
[ROW][C]median[/C][C]21[/C][/ROW]
[ROW][C]mean[/C][C]21.5849056603774[/C][/ROW]
[ROW][C]Q3[/C][C]25[/C][/ROW]
[ROW][C]maximum[/C][C]38[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98369&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics
# observations159
minimum9
Q118
median21
mean21.5849056603774
Q325
maximum38


Figures (Output of Computation)

Input Parameters & R Code

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')