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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 computationMon, 27 Oct 2008 18:31:04 -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/28/t1225153941nhejfs76uxf1coy.htm/, Retrieved Sun, 19 May 2024 18:21:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19724, Retrieved Sun, 19 May 2024 18:21:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191

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] [Q7: Investigate t...] [2008-10-28 00:31:04] [1d70db93c36870279a28f714be132c6e] [Current]
Feedback Forum
2008-11-03 10:17:23 [Chi-Kwong Man] [reply] 
Idem als uitleg taak 1 Q2, hier wordt naar de verkeerde grafieken gekeken voor de assumpties.
2008-11-03 21:36:33 [Bonifer Spillemaeckers] [reply] 
Asumptie 1 : Voor deze asumptie moeten we niet naar de Run Sequence Plot kijken maar naar de Autocorrelation Function-grafiek. Wanneer we bij de berekeningen de lags instellen op 36 kunnen we een periode van 3 jaar bekijken. We bemerken dat niet alle lijnen binnen het betrouwbaarheidsinterval liggen. Kijken we naar de maanden 12, 24 en 36 dan liggen de lijnen opmerkelijk ver buiten het betrouwbaarheidsinterval. Dit duidt op een positieve seizoenale correlatie.

Asumptie 4 : Om te zien of er een vaste variatie is te bemerken binnen de waarden, kijken we naar het Run Sequence Plot. We bemerken in het begin (t.e.m. 30) een terugkerend patroon, verder in de grafiek verlopen de waarden gelijkmatig op- en neergaand met opvallend minder uitschieters.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92,7
122,8
115,4
128,3
120,2
118,9
114,7
114,6
121,7
129,7
115,2
100,7
89,8
128,7
105,9
111,7
130,3
108
109,6
139
123,6
131
120,7
81,1
88,4
128,9
120,8
95
132
117,9
102,4
117,4
113,5
108
127,6
86,9
76,1
128,8
104,1
121,2
140,2
116
115
112,3
128,9
131,2
128,7
85,8
78,2
128,4
105,5
120,3
135,4
107,1
96,9
95,1
113,1
104,5
106,3
66,6
87,8
117,3
102,1
98,9
130,2
102,4
89,9
95,9
95,4
116,2
115,7
74,1
75,2
107,7
103,7
107,1
113,5
94,7
90,9
97
111
109,6
110,1
77,7
78
113,3
111,4
95,4
141,2
120,4
124,9
106,4
116,5
102,1
94,1
74
87

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19724&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19724&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Descriptive Statistics
# observations97
minimum66.6
Q195.9
median111
mean108.738144329897
Q3120.7
maximum141.2

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 97 \tabularnewline
minimum & 66.6 \tabularnewline
Q1 & 95.9 \tabularnewline
median & 111 \tabularnewline
mean & 108.738144329897 \tabularnewline
Q3 & 120.7 \tabularnewline
maximum & 141.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19724&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]97[/C][/ROW]
[ROW][C]minimum[/C][C]66.6[/C][/ROW]
[ROW][C]Q1[/C][C]95.9[/C][/ROW]
[ROW][C]median[/C][C]111[/C][/ROW]
[ROW][C]mean[/C][C]108.738144329897[/C][/ROW]
[ROW][C]Q3[/C][C]120.7[/C][/ROW]
[ROW][C]maximum[/C][C]141.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19724&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
# observations97
minimum66.6
Q195.9
median111
mean108.738144329897
Q3120.7
maximum141.2


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')