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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 17:01:21 -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/21/t1224543731jf64e57vj5pvkkf.htm/, Retrieved Mon, 13 May 2024 22:27:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18290, Retrieved Mon, 13 May 2024 22:27:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178
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] [Task 1 - Q2 - Bac...] [2008-10-19 10:43:59] [33f4701c7363e8b81858dafbf0350eed]
F           [Back to Back Histogram] [investigating ass Q2] [2008-10-20 23:01:21] [77ff908fa699164c8765f2430a4f222b] [Current]
Feedback Forum
2008-10-26 22:06:50 [Evelien Blockx] [reply
Je hebt volgens mij gelijk. De verdeling is niet gelijkaardig. De totale productie is helemaal niet symmetrisch, als je het histogram zou kantelen, neigt het meer naar rechts. De kledingproductie is meer symmetrisch (hoewel ook niet helemaal symmetrisch) dan de totale productie.

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Dataseries X:
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:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18290&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18290&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18290&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'George Udny Yule' @ 72.249.76.132



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()