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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_edauni.wasp
Title produced by softwareUnivariate Explorative Data Analysis
Date of computationSat, 25 Oct 2008 07:32:53 -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/Oct/25/t1224941823i9mdm6p273y698i.htm/, Retrieved Sat, 18 May 2024 18:34:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18729, Retrieved Sat, 18 May 2024 18:34:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnci
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Explorative Data Analysis] [Investigating dis...] [2007-10-22 19:45:25] [b9964c45117f7aac638ab9056d451faa]
F    D    [Univariate Explorative Data Analysis] [investigating dis...] [2008-10-25 13:32:53] [d8c5724db236abb5950452133b88474d] [Current]
-    D      [Univariate Explorative Data Analysis] [investigating dis...] [2008-10-25 14:07:21] [5de5fb433ddcb9578e0fa830f795b7e9]
-    D        [Univariate Explorative Data Analysis] [Investigation val...] [2008-10-27 22:01:55] [74be16979710d4c4e7c6647856088456]
-               [Univariate Explorative Data Analysis] [Investigation val...] [2008-10-27 22:10:04] [74be16979710d4c4e7c6647856088456]
-    D      [Univariate Explorative Data Analysis] [Investigating dis...] [2008-10-25 14:11:38] [5de5fb433ddcb9578e0fa830f795b7e9]
-    D      [Univariate Explorative Data Analysis] [Investigating dis...] [2008-10-25 14:14:59] [5de5fb433ddcb9578e0fa830f795b7e9]
Feedback Forum
2008-11-03 09:35:56 [Siem Van Opstal] [reply] 
Assumptie 1: Hier had je ook naar de lag plot moeten kijken. De grafieken komen te vooschijn door het aantal lag's in te vullen bij de berekeningen.

Assumptie 2: De student trekt een juiste conclusie. Je had ook naar de normal q-q plot kunnen kijken.

Assumptie 3: Hiervoor moet je naar het run sequence plot kijken. om constant te zijn zou het niet mogen fluctueren en dat is hier wel duidelijk het geval.

Assumptie 4: We moeten zien of er op de run sequence plot een gelijke spreding is. We kunnen duidelijk zien dat dat neit zo is, er is dus geen vaste variatie.
2008-11-03 10:12:09 [Joris Deboel] [reply] 
Net zoals bij de vorige vragen:

Veronderstelling 1: via het lag plot krijg je een duidelijke grafiek.Waar de seizoenaliteit duidelijk is af te lezen.

Veronderstelling 2: een juiste conclusie

Veronderstelling 3: als we naar de run sequency plot kijken zien we duidelijk dat dit geen constante is.

Veronderstelling 4: Geen gelijke spreiding bij het run sequency plot, dus geen vaste variatie.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,8
1,9
2,2
2,1
2,2
2,7
2,8
2,9
3,4
3
3,1
2,5
2,2
2,3
2,1
2,8
3,1
2,9
2,6
2,7
2,3
2,3
2,1
2,2
2,9
2,6
2,7
1,8
1,3
0,9
1,3
1,3
1,3
1,3
1,1
1,4
1,2
1,7
1,8
1,5
1
1,6
1,5
1,8
1,8
1,6
1,9
1,7
1,6
1,3
1,1
1,9
2,6
2,3
2,4
2,2
2
2,9
2,6
2,3
2,3
2,6
3,1
2,8
2,5
2,9
3,1
3,1
3,2
2,5
2,6
2,9

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=18729&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=18729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18729&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






Descriptive Statistics
# observations72
minimum0.9
Q11.7
median2.25
mean2.19444444444444
Q32.7
maximum3.4

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 72 \tabularnewline
minimum & 0.9 \tabularnewline
Q1 & 1.7 \tabularnewline
median & 2.25 \tabularnewline
mean & 2.19444444444444 \tabularnewline
Q3 & 2.7 \tabularnewline
maximum & 3.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18729&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]0.9[/C][/ROW]
[ROW][C]Q1[/C][C]1.7[/C][/ROW]
[ROW][C]median[/C][C]2.25[/C][/ROW]
[ROW][C]mean[/C][C]2.19444444444444[/C][/ROW]
[ROW][C]Q3[/C][C]2.7[/C][/ROW]
[ROW][C]maximum[/C][C]3.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18729&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
# observations72
minimum0.9
Q11.7
median2.25
mean2.19444444444444
Q32.7
maximum3.4


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 0 ; par2 = 0 ;
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)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Lag plot, lowess, and regression line'))
lines(lowess(z))
abline(lm(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')