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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_backtobackhist.wasp
Title produced by softwareBack to Back Histogram
Date of computationMon, 20 Oct 2008 12:26:17 -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/20/t12245272133ia9ni2cjy0vodu.htm/, Retrieved Sun, 19 May 2024 13:57:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17855, Retrieved Sun, 19 May 2024 13:57:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Back to Back Histogram] [Q2 Total / Clothi...] [2007-10-18 10:14:23] [b731da8b544846036771bbf9bf2f34ce]
F    D    [Back to Back Histogram] [b2b histogram] [2008-10-20 18:26:17] [95d95b0e883740fcbc85e18ec42dcafb] [Current]
Feedback Forum
2008-10-28 07:08:36 [An De Koninck] [reply] 
Dit antwoord is erg vaag. Het is correct dat ze niet gelijkaardig zijn, dat zie je al op het eerste zicht. Je had wat gedetailleerder mogen zijn door te zeggen dat de totale productie over het algemeen hoger ligt dan de kledingproductie. Dit is op zich een logisch feit aangezien de totale productie verschillende productgroepen omvat.
Bij de totale productie wordt er rond de 100.000 het meeste geproduceerd, en bij kledingproductie is dit bij 85.000. Dit is een duidelijk verschil.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110.40
96.40
101.90
106.20
81.00
94.70
101.00
109.40
102.30
90.70
96.20
96.10
106.00
103.10
102.00
104.70
86.00
92.10
106.90
112.60
101.70
92.00
97.40
97.00
105.40
102.70
98.10
104.50
87.40
89.90
109.80
111.70
98.60
96.90
95.10
97.00
112.70
102.90
97.40
111.40
87.40
96.80
114.10
110.30
103.90
101.60
94.60
95.90
104.70
102.80
98.10
113.90
80.90
95.70
113.20
105.90
108.80
102.30
99.00
100.70
115.50
Dataseries Y:
Download CSV
Histogram 
109.20
88.60
94.30
98.30
86.40
80.60
104.10
108.20
93.40
71.90
94.10
94.90
96.40
91.10
84.40
86.40
88.00
75.10
109.70
103.00
82.10
68.00
96.40
94.30
90.00
88.00
76.10
82.50
81.40
66.50
97.20
94.10
80.70
70.50
87.80
89.50
99.60
84.20
75.10
92.00
80.80
73.10
99.80
90.00
83.10
72.40
78.80
87.30
91.00
80.10
73.60
86.40
74.50
71.20
92.40
81.50
85.30
69.90
84.20
90.70
100.30

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=17855&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=17855&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17855&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


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = Total product. ; par5 = Prod. Clothing ;
Parameters (R input):
par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = Total product. ; par5 = Prod. Clothing ;
R code (references can be found in the software module):
if (par3 == 'TRUE') par3 <- TRUE
if (par3 == 'FALSE') par3 <- FALSE
library(Hmisc)
z <- data.frame(cbind(x,y))
names(z) <- list(par4,par5)
bitmap(file='plot.png')
out <- histbackback(z, probability=par3, main = main, ylab = ylab)
barplot(-out$left, col=par1, horiz=TRUE, space=0, add=TRUE, axes=FALSE)
barplot(out$right, col=par2, horiz=TRUE, space=0, add=TRUE, axes=FALSE)
dev.off()