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Author

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R Software Module

rwasp_notchedbox1.wasp

Title produced by software

Notched Boxplots

Date of computation

Mon, 03 Nov 2008 14:35:09 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/03/t1225748291e7uk171j2h6s5n4.htm/, Retrieved Sun, 19 May 2024 10:11:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21323, Retrieved Sun, 19 May 2024 10:11:36 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

189

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] [2008-10-30 15:19:51] [cb714085b233acee8e8acd879ea442b6]
F R  D    [Notched Boxplots] [WS3A T2] [2008-11-03 20:59:28] [2bdcdcd86f586cbd821f1fe8acabf575]
F R PD        [Notched Boxplots] [WS3A T3] [2008-11-03 21:35:09] [cf57b030c45fee9c58a27190db97b24d] [Current]
-   P           [Notched Boxplots] [notched boxplot p...] [2008-11-11 12:12:57] [c29178f7f550574a75dc881e636e0923] 

Feedback Forum

2008-11-09 17:01:30 [Kevin Engels] [reply] 
De conclusie van de student is  correct. Het gebruik van het logaritme zorgt ervoor dat grote schommelingen worden afgevlakt en kleine schommelingen groter worden, waardoor je de spreiding gaat verkleinen. De outliers zullen maw niet zo ver boven de andere getallen gaan uitsteken.
2008-11-09 17:05:02 [Kevin Engels] [reply] 
de conclusie va nde student is juist. We gaan hier de staarten van de grafiek afknippen, waardoor je de outliers gaat wegtrimmen en de spreiding kleiner wordt.
2008-11-10 11:31:25 [Pieter Broos] [reply] 
antwoord op task 3 is correct, enkel is er niets gezegd over de functie van de logaritme. Omdat we z vervangen door log(z) in de R code gaan de grote schommelingen afgevlakt worden (logaritme grote getallen worden kleinere getallen) en kleinere schommelingen krijgen relatief een groter belang.
2008-11-11 13:14:37 [Bernard Femont] [reply] 
Als de outliers eruit gelaten worden door invoeren van een functie die grote schommelingen afvlakt. (z wordt vervangen door log z), ziet men veel duidelijker dat serie4 (investeringen) de grootste relatieve daling kent sinds maart 2001. 
Dit komt door het feit dat de outliers de andere 3 series niet meer kunnen beïnvloeden. En de mediaan hoger is. 
2008-11-11 13:14:41 [Bernard Femont] [reply] 
Als de outliers eruit gelaten worden door invoeren van een functie die grote schommelingen afvlakt. (z wordt vervangen door log z), ziet men veel duidelijker dat serie4 (investeringen) de grootste relatieve daling kent sinds maart 2001. 
Dit komt door het feit dat de outliers de andere 3 series niet meer kunnen beïnvloeden. En de mediaan hoger is. 
2008-11-12 12:01:12 [Mehmet Yilmaz] [reply] 
De conclusie is correct.  
 
Iets meer uitleg over de logaritmen: 
Het gebruik van het logaritme zorgt ervoor dat grote schommelingen worden afgevlakt en kleine schommelingen groter worden, waardoor je de spreiding gaat verkleinen. De outliers zullen maw niet zo ver boven de andere getallen gaan uitsteken.

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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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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=21323&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=21323&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21323&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Boxplot statistics
Variable	lower whisker	lower hinge	median	upper hinge	upper whisker
Xt	4.45434729625351	4.56642935767166	4.62202730305451	4.66343909411207	4.74927052996185
Xk	4.19720194766181	4.38949864951258	4.46935046284556	4.54435804659133	4.69774936728118
Xa	4.56746831880408	4.57264699428253	4.57779898919196	4.60517018598809	4.61115225766564
Xi	3.34638914516716	3.75653810258775	3.9982007016692	4.27666611901606	4.98017608661155

\begin{tabular}{lllllllll}
\hline
Boxplot statistics \tabularnewline
Variable & lower whisker & lower hinge & median & upper hinge & upper whisker \tabularnewline
Xt & 4.45434729625351 & 4.56642935767166 & 4.62202730305451 & 4.66343909411207 & 4.74927052996185 \tabularnewline
Xk & 4.19720194766181 & 4.38949864951258 & 4.46935046284556 & 4.54435804659133 & 4.69774936728118 \tabularnewline
Xa & 4.56746831880408 & 4.57264699428253 & 4.57779898919196 & 4.60517018598809 & 4.61115225766564 \tabularnewline
Xi & 3.34638914516716 & 3.75653810258775 & 3.9982007016692 & 4.27666611901606 & 4.98017608661155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21323&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]Xt[/C][C]4.45434729625351[/C][C]4.56642935767166[/C][C]4.62202730305451[/C][C]4.66343909411207[/C][C]4.74927052996185[/C][/ROW]
[ROW][C]Xk[/C][C]4.19720194766181[/C][C]4.38949864951258[/C][C]4.46935046284556[/C][C]4.54435804659133[/C][C]4.69774936728118[/C][/ROW]
[ROW][C]Xa[/C][C]4.56746831880408[/C][C]4.57264699428253[/C][C]4.57779898919196[/C][C]4.60517018598809[/C][C]4.61115225766564[/C][/ROW]
[ROW][C]Xi[/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=21323&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21323&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
Variable	lower whisker	lower hinge	median	upper hinge	upper whisker
Xt	4.45434729625351	4.56642935767166	4.62202730305451	4.66343909411207	4.74927052996185
Xk	4.19720194766181	4.38949864951258	4.46935046284556	4.54435804659133	4.69774936728118
Xa	4.56746831880408	4.57264699428253	4.57779898919196	4.60517018598809	4.61115225766564
Xi	3.34638914516716	3.75653810258775	3.9982007016692	4.27666611901606	4.98017608661155

Boxplot Notches
Variable	lower bound	median	upper bound
Xt	4.60240240117095	4.62202730305451	4.64165220493808
Xk	4.43802267467776	4.46935046284556	4.50067825101336
Xa	4.57121960376548	4.57779898919196	4.58437837461843
Xi	3.89297970361432	3.9982007016692	4.10342169972408

\begin{tabular}{lllllllll}
\hline
Boxplot Notches \tabularnewline
Variable & lower bound & median & upper bound \tabularnewline
Xt & 4.60240240117095 & 4.62202730305451 & 4.64165220493808 \tabularnewline
Xk & 4.43802267467776 & 4.46935046284556 & 4.50067825101336 \tabularnewline
Xa & 4.57121960376548 & 4.57779898919196 & 4.58437837461843 \tabularnewline
Xi & 3.89297970361432 & 3.9982007016692 & 4.10342169972408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21323&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]Xt[/C][C]4.60240240117095[/C][C]4.62202730305451[/C][C]4.64165220493808[/C][/ROW]
[ROW][C]Xk[/C][C]4.43802267467776[/C][C]4.46935046284556[/C][C]4.50067825101336[/C][/ROW]
[ROW][C]Xa[/C][C]4.57121960376548[/C][C]4.57779898919196[/C][C]4.58437837461843[/C][/ROW]
[ROW][C]Xi[/C][C]3.89297970361432[/C][C]3.9982007016692[/C][C]4.10342169972408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21323&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21323&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
Variable	lower bound	median	upper bound
Xt	4.60240240117095	4.62202730305451	4.64165220493808
Xk	4.43802267467776	4.46935046284556	4.50067825101336
Xa	4.57121960376548	4.57779898919196	4.58437837461843
Xi	3.89297970361432	3.9982007016692	4.10342169972408

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 36 ;

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')
z<- log(z)
(r<-boxplot(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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code