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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_notchedbox1.wasp
Title produced by softwareNotched Boxplots
Date of computationTue, 04 Nov 2008 11:45:19 -0700
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/Nov/04/t1225824385nj71w3xjfda9sp3.htm/, Retrieved Sun, 19 May 2024 04:29:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21623, Retrieved Sun, 19 May 2024 04:29:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F    D  [Notched Boxplots] [task 2] [2008-11-04 18:33:41] [b1bd16d1f47bfe13feacf1c27a0abba5]
F R  D      [Notched Boxplots] [task 3] [2008-11-04 18:45:19] [e7b1048c2c3a353441b9143db4404b91] [Current]
Feedback Forum
2008-11-09 19:07:34 [Leen Van Dooren] [reply
Heel goed uitgevoerd en goed uitgelegd
2008-11-10 17:00:13 [Jasmine Hendrikx] [reply
Eigen evaluatie task 3

De student heeft een goede uitleg gegeven over hoe een logtransformatie werkt, namelijk grote schommelingen worden kleiner gemaakt. Hierbij zou aangevuld kunnen worden dat er dus een soort afvlakking optreedt van de gegevens en dat daardoor de kleine schommelingen beter tot uiting komen. Aanvullend zou de student wel nog een vergelijking kunnen maken met de oorspronkelijke grafiek. We zien dus dat de eenheden op de y-as veel kleiner zijn geworden. Ook zien we dat we nu geen outliers meer kunnen bespeuren bij de tijdreeks investeringen. Een opvallende gelijkenis is wel dat de investeringen nog steeds de grootste achteruitgang kennen van alle variabelen sinds maart 2001.

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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21623&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]1 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=21623&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21623&T=0

As an alternative you can also use a 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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Boxplot statistics
Variablelower whiskerlower hingemedianupper hingeupper whisker
X14.454347296253514.566429357671664.622027303054514.663439094112074.74927052996185
X24.197201947661814.389498649512584.469350462845564.544358046591334.69774936728118
X34.567468318804084.572646994282534.577798989191964.605170185988094.61115225766564
X43.346389145167163.756538102587753.99820070166924.276666119016064.98017608661155

\begin{tabular}{lllllllll}
\hline
Boxplot statistics \tabularnewline
Variable & lower whisker & lower hinge & median & upper hinge & upper whisker \tabularnewline
X1 & 4.45434729625351 & 4.56642935767166 & 4.62202730305451 & 4.66343909411207 & 4.74927052996185 \tabularnewline
X2 & 4.19720194766181 & 4.38949864951258 & 4.46935046284556 & 4.54435804659133 & 4.69774936728118 \tabularnewline
X3 & 4.56746831880408 & 4.57264699428253 & 4.57779898919196 & 4.60517018598809 & 4.61115225766564 \tabularnewline
X4 & 3.34638914516716 & 3.75653810258775 & 3.9982007016692 & 4.27666611901606 & 4.98017608661155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21623&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]X1[/C][C]4.45434729625351[/C][C]4.56642935767166[/C][C]4.62202730305451[/C][C]4.66343909411207[/C][C]4.74927052996185[/C][/ROW]
[ROW][C]X2[/C][C]4.19720194766181[/C][C]4.38949864951258[/C][C]4.46935046284556[/C][C]4.54435804659133[/C][C]4.69774936728118[/C][/ROW]
[ROW][C]X3[/C][C]4.56746831880408[/C][C]4.57264699428253[/C][C]4.57779898919196[/C][C]4.60517018598809[/C][C]4.61115225766564[/C][/ROW]
[ROW][C]X4[/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=21623&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21623&T=1

As an alternative you can also use a QR Code:  

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

Boxplot statistics
Variablelower whiskerlower hingemedianupper hingeupper whisker
X14.454347296253514.566429357671664.622027303054514.663439094112074.74927052996185
X24.197201947661814.389498649512584.469350462845564.544358046591334.69774936728118
X34.567468318804084.572646994282534.577798989191964.605170185988094.61115225766564
X43.346389145167163.756538102587753.99820070166924.276666119016064.98017608661155







Boxplot Notches
Variablelower boundmedianupper bound
X14.602402401170954.622027303054514.64165220493808
X24.438022674677764.469350462845564.50067825101336
X34.571219603765484.577798989191964.58437837461843
X43.892979703614323.99820070166924.10342169972408

\begin{tabular}{lllllllll}
\hline
Boxplot Notches \tabularnewline
Variable & lower bound & median & upper bound \tabularnewline
X1 & 4.60240240117095 & 4.62202730305451 & 4.64165220493808 \tabularnewline
X2 & 4.43802267467776 & 4.46935046284556 & 4.50067825101336 \tabularnewline
X3 & 4.57121960376548 & 4.57779898919196 & 4.58437837461843 \tabularnewline
X4 & 3.89297970361432 & 3.9982007016692 & 4.10342169972408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21623&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]X1[/C][C]4.60240240117095[/C][C]4.62202730305451[/C][C]4.64165220493808[/C][/ROW]
[ROW][C]X2[/C][C]4.43802267467776[/C][C]4.46935046284556[/C][C]4.50067825101336[/C][/ROW]
[ROW][C]X3[/C][C]4.57121960376548[/C][C]4.57779898919196[/C][C]4.58437837461843[/C][/ROW]
[ROW][C]X4[/C][C]3.89297970361432[/C][C]3.9982007016692[/C][C]4.10342169972408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21623&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21623&T=2

As an alternative you can also use a QR Code:  

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

Boxplot Notches
Variablelower boundmedianupper bound
X14.602402401170954.622027303054514.64165220493808
X24.438022674677764.469350462845564.50067825101336
X34.571219603765484.577798989191964.58437837461843
X43.892979703614323.99820070166924.10342169972408



Parameters (Session):
par1 = grey ;
Parameters (R input):
par1 = grey ;
R code (references can be found in the software module):
z <- as.data.frame(t(y))
bitmap(file='test1.png')
(r<-boxplot(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')