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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_notchedbox1.wasp
Title produced by softwareNotched Boxplots
Date of computationFri, 31 Oct 2008 11:13:42 -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/31/t1225473822ip0yubhtbm63q80.htm/, Retrieved Sun, 19 May 2024 15:51:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20310, Retrieved Sun, 19 May 2024 15:51:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [task2 4tijdsreeksen] [2008-10-31 16:58:48] [44a98561a4b3e6ab8cd5a857b48b0914]
F R  D    [Notched Boxplots] [task3 log] [2008-10-31 17:13:42] [1aceffc2fa350402d9e8f8edd757a2e8] [Current]
Feedback Forum
2008-11-08 17:57:20 [Stéphanie Claes] [reply] 
De R-code werd correct gewijzigd. Door het logaritme toe te passen gaan de grote getallen veel kleiner worden. Zoals de student correct vermeldt worden de grote schommelingen in de dataset afgevlakt. Kleine schommelingen worden dan iets groter. De outliers komen veel dichter naar het gemiddelde in de dataset. Conclusie: je gaat de spreiding verkleinen.
2008-11-09 13:35:11 [Kristof Augustyns] [reply] 
De R-code is inderdaad gewijzigd en er is inderdaad een afzwakking.
Grote getallen worden kleiner --> indrukking.
Outliers gaan dan ook dichter naar het gemiddelde.
Sprijding wordt verkleind, maar de mediaan blijft inderdaad nog altijd het laagst bij de investeringen.
2008-11-09 15:24:56 [Natascha Meeus] [reply] 
Correct. De logaritmen maken grote getallen kleinder zodat grote schommelingen worden afgezwakt en de outliers dichter bij het gemiddelde komen.
2008-11-11 15:01:13 [Jan Mols] [reply] 
De R-code is correct gewijzigd.De log gaat ervoor zorgen dat de grote schommelingen worden afgevlakt en de kleine schommelingen een beetje worden uitvergroot.
Hierdoor wordt de spreiding verkleind en gaan de outliers ook dichter naar het gemiddelde toe komen.
2008-11-11 15:58:00 [Bart Haemels] [reply] 
De grote getallen worden door het gebruik v logaritmes afgezwakt en de kleine getallen worden uitgerokken. Dit heeft als gevolg dat de outliers hier niet meer zozeer parten spelen en ze niet meer zo ver boven de andere getallen gaan uitsteken.

Ik had er wel moeten bij vermelden dat indien je een negatief getallen gebruikt je hier niet het logaritme van kan nemen. Hier moet je mee opletten bij andere tijdreeksen.
2008-11-11 22:24:10 [Liese Tormans] [reply] 
De student heeft de R code juist gewijzigd.
z veranderen in log(z)

De conclusie van de student was ook correct.

De log gaat ervoor zorgen dat de grote schommelingen worden afgevlakt en de kleine schommelingen een beetje worden uitvergroot.
Hierdoor wordt de spreiding verkleind en gaan de outliers ook dichter naar het gemiddelde toe komen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110.40	109.20	99.90	72.50
96.40	88.60	99.80	59.40
101.90	94.30	99.80	85.70
106.20	98.30	100.30	88.20
81.00	86.40	99.90	62.80
94.70	80.60	99.90	87.00
101.00	104.10	100.00	79.20
109.40	108.20	100.10	112.00
102.30	93.40	100.10	79.20
90.70	71.90	100.20	132.10
96.20	94.10	100.30	40.10
96.10	94.90	100.60	69.00
106.00	96.40	100.00	59.40
103.10	91.10	100.10	73.80
102.00	84.40	100.20	57.40
104.70	86.40	100.00	81.10
86.00	88.00	100.10	46.60
92.10	75.10	100.10	41.40
106.90	109.70	100.10	71.20
112.60	103.00	100.50	67.90
101.70	82.10	100.50	72.00
92.00	68.00	100.50	145.50
97.40	96.40	96.30	39.70
97.00	94.30	96.30	51.90
105.40	90.00	96.80	73.70
102.70	88.00	96.80	70.90
98.10	76.10	96.90	60.80
104.50	82.50	96.80	61.00
87.40	81.40	96.80	54.50
89.90	66.50	96.80	39.10
109.80	97.20	96.80	66.60
111.70	94.10	97.00	58.50
98.60	80.70	97.00	59.80
96.90	70.50	97.00	80.90
95.10	87.80	96.80	37.30
97.00	89.50	96.90	44.60
112.70	99.60	97.20	48.70
102.90	84.20	97.30	54.00
97.40	75.10	97.30	49.50
111.40	92.00	97.20	61.60
87.40	80.80	97.30	35.00
96.80	73.10	97.30	35.70
114.10	99.80	97.30	51.30
110.30	90.00	97.30	49.00
103.90	83.10	97.30	41.50
101.60	72.40	97.30	72.50
94.60	78.80	98.10	42.10
95.90	87.30	96.80	44.10
104.70	91.00	96.80	45.10
102.80	80.10	96.80	50.30
98.10	73.60	96.80	40.90
113.90	86.40	96.80	47.20
80.90	74.50	96.80	36.90
95.70	71.20	96.80	40.90
113.20	92.40	96.80	38.30
105.90	81.50	96.80	46.30
108.80	85.30	96.80	28.40
102.30	69.90	96.80	78.40
99.00	84.20	96.90	36.80
100.70	90.70	97.10	50.70
115.50	100.30	97.10	42.80

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20310&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]2 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=20310&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20310&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Boxplot statistics
Variablelower whiskerlower hingemedianupper hingeupper whisker
tot_productie4.454347296253514.566429357671664.622027303054514.663439094112074.74927052996185
kleding4.197201947661814.389498649512584.469350462845564.544358046591334.69774936728118
kleding_-_bont4.567468318804084.572646994282534.577798989191964.605170185988094.61115225766564
investeringen3.346389145167163.756538102587753.99820070166924.276666119016064.98017608661155

\begin{tabular}{lllllllll}
\hline
Boxplot statistics \tabularnewline
Variable & lower whisker & lower hinge & median & upper hinge & upper whisker \tabularnewline
tot_productie & 4.45434729625351 & 4.56642935767166 & 4.62202730305451 & 4.66343909411207 & 4.74927052996185 \tabularnewline
kleding & 4.19720194766181 & 4.38949864951258 & 4.46935046284556 & 4.54435804659133 & 4.69774936728118 \tabularnewline
kleding_-_bont & 4.56746831880408 & 4.57264699428253 & 4.57779898919196 & 4.60517018598809 & 4.61115225766564 \tabularnewline
investeringen & 3.34638914516716 & 3.75653810258775 & 3.9982007016692 & 4.27666611901606 & 4.98017608661155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20310&T=1

[TABLE]
[ROW][C]Boxplot statistics[/C][/ROW]
[ROW][C]Variable[/C][C]lower whisker[/C][C]lower hinge[/C][C]median[/C][C]upper hinge[/C][C]upper whisker[/C][/ROW]
[ROW][C]tot_productie[/C][C]4.45434729625351[/C][C]4.56642935767166[/C][C]4.62202730305451[/C][C]4.66343909411207[/C][C]4.74927052996185[/C][/ROW]
[ROW][C]kleding[/C][C]4.19720194766181[/C][C]4.38949864951258[/C][C]4.46935046284556[/C][C]4.54435804659133[/C][C]4.69774936728118[/C][/ROW]
[ROW][C]kleding_-_bont[/C][C]4.56746831880408[/C][C]4.57264699428253[/C][C]4.57779898919196[/C][C]4.60517018598809[/C][C]4.61115225766564[/C][/ROW]
[ROW][C]investeringen[/C][C]3.34638914516716[/C][C]3.75653810258775[/C][C]3.9982007016692[/C][C]4.27666611901606[/C][C]4.98017608661155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20310&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20310&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:

Boxplot statistics
Variablelower whiskerlower hingemedianupper hingeupper whisker
tot_productie4.454347296253514.566429357671664.622027303054514.663439094112074.74927052996185
kleding4.197201947661814.389498649512584.469350462845564.544358046591334.69774936728118
kleding_-_bont4.567468318804084.572646994282534.577798989191964.605170185988094.61115225766564
investeringen3.346389145167163.756538102587753.99820070166924.276666119016064.98017608661155






Boxplot Notches
Variablelower boundmedianupper bound
tot_productie4.602402401170954.622027303054514.64165220493808
kleding4.438022674677764.469350462845564.50067825101336
kleding_-_bont4.571219603765484.577798989191964.58437837461843
investeringen3.892979703614323.99820070166924.10342169972408

\begin{tabular}{lllllllll}
\hline
Boxplot Notches \tabularnewline
Variable & lower bound & median & upper bound \tabularnewline
tot_productie & 4.60240240117095 & 4.62202730305451 & 4.64165220493808 \tabularnewline
kleding & 4.43802267467776 & 4.46935046284556 & 4.50067825101336 \tabularnewline
kleding_-_bont & 4.57121960376548 & 4.57779898919196 & 4.58437837461843 \tabularnewline
investeringen & 3.89297970361432 & 3.9982007016692 & 4.10342169972408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20310&T=2

[TABLE]
[ROW][C]Boxplot Notches[/C][/ROW]
[ROW][C]Variable[/C][C]lower bound[/C][C]median[/C][C]upper bound[/C][/ROW]
[ROW][C]tot_productie[/C][C]4.60240240117095[/C][C]4.62202730305451[/C][C]4.64165220493808[/C][/ROW]
[ROW][C]kleding[/C][C]4.43802267467776[/C][C]4.46935046284556[/C][C]4.50067825101336[/C][/ROW]
[ROW][C]kleding_-_bont[/C][C]4.57121960376548[/C][C]4.57779898919196[/C][C]4.58437837461843[/C][/ROW]
[ROW][C]investeringen[/C][C]3.89297970361432[/C][C]3.9982007016692[/C][C]4.10342169972408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20310&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20310&T=2

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


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

Boxplot Notches
Variablelower boundmedianupper bound
tot_productie4.602402401170954.622027303054514.64165220493808
kleding4.438022674677764.469350462845564.50067825101336
kleding_-_bont4.571219603765484.577798989191964.58437837461843
investeringen3.892979703614323.99820070166924.10342169972408


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = red ;
Parameters (R input):
par1 = red ;
R code (references can be found in the software module):
z <- as.data.frame(t(y))
bitmap(file='test1.png')
(r<-boxplot(z<-log(z) ,xlab=xlab,ylab=ylab,main=main,notch=TRUE,col=par1))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('overview.htm','Boxplot statistics','Boxplot overview'),6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',1,TRUE)
a<-table.element(a,hyperlink('lower_whisker.htm','lower whisker','definition of lower whisker'),1,TRUE)
a<-table.element(a,hyperlink('lower_hinge.htm','lower hinge','definition of lower hinge'),1,TRUE)
a<-table.element(a,hyperlink('central_tendency.htm','median','definitions about measures of central tendency'),1,TRUE)
a<-table.element(a,hyperlink('upper_hinge.htm','upper hinge','definition of upper hinge'),1,TRUE)
a<-table.element(a,hyperlink('upper_whisker.htm','upper whisker','definition of upper whisker'),1,TRUE)
a<-table.row.end(a)
for (i in 1:length(y[,1]))
{
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE)
for (j in 1:5)
{
a<-table.element(a,r$stats[j,i])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Boxplot Notches',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',1,TRUE)
a<-table.element(a,'lower bound',1,TRUE)
a<-table.element(a,'median',1,TRUE)
a<-table.element(a,'upper bound',1,TRUE)
a<-table.row.end(a)
for (i in 1:length(y[,1]))
{
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE)
a<-table.element(a,r$conf[1,i])
a<-table.element(a,r$stats[3,i])
a<-table.element(a,r$conf[2,i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')