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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 computationMon, 27 Oct 2008 12:08:24 -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/27/t1225131013qh4b0sjiraeuurm.htm/, Retrieved Sun, 19 May 2024 13:58:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19354, Retrieved Sun, 19 May 2024 13:58:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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] [Prijsindexcijfer ...] [2008-10-27 18:08:24] [6aa66640011d9b98524a5838bcf7301d] [Current]
-   PD    [Univariate Explorative Data Analysis] [reproductie] [2008-11-02 19:10:57] [5e74953d94072114d25d7276793b561e]
Feedback Forum
2008-10-29 16:14:04 [Nathalie Koulouris] [reply] 
Om de autocorrelatie te onderzoeken had de student gebruik moeten maken van de Lag plot.
2008-11-02 16:42:45 [Kristof Augustyns] [reply] 
Alles is hier bijna juist, maar weer werd hier de 'lag plot' vergeten door die naar '12' of '36' te brengen.
https://automated.biganalytics.eu/rwasp_edauni.wasp?parent=t1225131013qh4b0sjiraeuurm
De assumptions zijn ook allemaal goed geïnterpreteerd.
2008-11-02 19:21:45 [Annelies Michiels] [reply] 
Hier werden dezelfde fouten gemaakt als bij vraag 1 Q2.
Hier moet dus ook gebruik worden gemaakt van de lag plot:
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/02/t12256531528cm0p3j5ci7z731.htm

Assumptie 1:
Deze assumptie moet via de lag plot worden berekent. Als we deze grafiek bekijken zien we dat er een heel sterke correlatie is. Dit wil zeggen dat we duidelijk niet kunnen spreken van randomness.

Assumptie 2:
Als we het histogram, density-plot, Q-Q plot bekijken zien we dat er duidelijk geen sprake is van een normaalverdeling.

Assumptie 3:
Als we kijken naar de run sequency plot, zien we duidelijk dat er een stijgend verloop is, we kunnen dus duidelijk zien dat C hier geen constante is.

Assumptie 4:
Als we kijken naar de run sequency plot zien we dat de spreiding hier niet gelijk is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
88,3
88,6
91
91,5
95,4
98,7
99,9
98,6
100,3
100,2
100,4
101,4
103
109,1
111,4
114,1
121,8
127,6
129,9
128
123,5
124
127,4
127,6
128,4
131,4
135,1
134
144,5
147,3
150,9
148,7
141,4
138,9
139,8
145,6
147,9
148,5
151,1
157,5
167,5
172,3
173,5
187,5
205,5
195,1
204,5
204,5
201,7
207
206,6
210,6
211,1
215
223,9
238,2
238,9
229,6
232,2
222,1
221,6
227,3
221
213,6
243,4
253,8
265,3
268,2
268,5
266,9

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19354&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19354&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19354&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Descriptive Statistics
# observations70
minimum88.3
Q1123.625
median148.2
mean164.137142857143
Q3210.975
maximum268.5

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 70 \tabularnewline
minimum & 88.3 \tabularnewline
Q1 & 123.625 \tabularnewline
median & 148.2 \tabularnewline
mean & 164.137142857143 \tabularnewline
Q3 & 210.975 \tabularnewline
maximum & 268.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19354&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]70[/C][/ROW]
[ROW][C]minimum[/C][C]88.3[/C][/ROW]
[ROW][C]Q1[/C][C]123.625[/C][/ROW]
[ROW][C]median[/C][C]148.2[/C][/ROW]
[ROW][C]mean[/C][C]164.137142857143[/C][/ROW]
[ROW][C]Q3[/C][C]210.975[/C][/ROW]
[ROW][C]maximum[/C][C]268.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19354&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19354&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
# observations70
minimum88.3
Q1123.625
median148.2
mean164.137142857143
Q3210.975
maximum268.5


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)
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')