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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationSun, 16 Dec 2012 06:35:45 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t1355657757ucmsj5s8f1fckek.htm/, Retrieved Fri, 26 Apr 2024 18:40:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200253, Retrieved Fri, 26 Apr 2024 18:40:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Blocked Bootstrap Plot - Central Tendency] [Colombia Coffee] [2008-01-07 10:26:26] [74be16979710d4c4e7c6647856088456]
- RM D    [Blocked Bootstrap Plot - Central Tendency] [Paper] [2012-12-09 15:29:42] [9d44b52ac7f20a3e9be7c3c8470fe2cd]
- RMPD        [Histogram] [paper] [2012-12-16 11:35:45] [97e5c69206415429213a02c19f23a896] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
414.89
444.50
481.29
491.09
419.70
432.88
438.01
412.84
402.91
416.20
411.80
393.60
381.70
388.34
370.89
385.96
394.26
381.37
377.40
377.70
347.71
347.70
340.20
341.00
342.00
319.54
302.79
299.10
313.50
326.80
316.00
316.50
317.20
330.40
323.35
325.85
321.50
321.90
347.48
338.89
345.70
340.44
342.40
342.70
348.34
376.66
417.73
423.51
397.56
390.92
408.26
401.12
408.91
438.35
460.23
449.59
450.52
461.15
460.38
465.35
467.57
486.24
476.58
442.07
443.61
451.55
451.07
451.33
437.63
431.28
413.46
406.78
420.17
419.05
404.01
387.51
390.15
384.40
371.05
367.60
375.04
365.14
361.75
366.88
394.26
409.39
410.11
416.81
393.06
374.24
369.05
352.33
362.53
394.73
389.32
380.74
381.73
376.95
383.64
363.83
363.34
358.38
356.95
366.72
367.69
356.31
348.74
358.69
360.17
361.73
354.45
353.91
344.34
338.62
337.24
340.81
352.72
343.06
345.43
344.38
335.02
334.82
329.01
329.31
330.08
342.15
367.18
371.89
392.19
378.84
355.28
364.18
373.83
383.30
386.88
381.91
384.13
377.27
381.43
385.64
385.49
380.36
391.58
389.77
384.39
379.29
378.55
376.64
382.12
391.03
385.22
387.56
386.23
383.67
383.06
383.14
385.31
387.44
399.45
404.76
396.21
392.85
391.93
385.27
383.47
387.35
383.14
381.07
377.85
369.00
355.11
346.58
351.81
344.47
343.84
340.76
324.10
324.01
322.82
324.87
306.04
288.74
289.10
297.49
295.94
308.29
299.10
292.32
292.87
284.11
288.98
295.93
294.12
291.68
287.08
287.33
285.96
282.62
276.44
261.31
256.08
256.69
264.74
310.72
293.18
283.07
284.32
299.86
286.39
279.69
275.19
285.73
281.59
274.47
273.68
270.00
266.01
271.45
265.49
261.87
263.03
260.48
272.36
270.23
267.53
272.39
283.42
283.06
276.16
275.85
281.51
295.50
294.06
302.68
314.49
321.18
313.29
310.26
319.14
316.56
319.07
331.92
356.86
358.97
340.55
328.18
355.68
356.35
351.02
359.77
378.95
378.92
389.91
406.95
413.79
404.88
406.67
403.26
383.78
392.37
398.09
400.51
405.28
420.46
439.38
442.08
424.03
423.35
433.85
429.23
421.87
430.66
424.48
437.93
456.05
469.90
476.67
510.10
549.86
555.00
557.09
610.65
675.39
596.15
633.71
632.59
598.19
585.78
627.83
629.79
631.17
664.75
654.90
679.37
667.31
655.66
665.38
665.41
712.65
754.60
806.25
803.20
889.60
922.30
968.43
909.71
888.66
889.49
939.77
839.03
829.93
806.62
760.86
816.09
858.69
943.00
924.27
890.20
928.65
945.67
934.23
949.38
996.59
1043.16
1127.04
1134.72
1117.96
1095.41
1113.34
1148.69
1205.43
1232.92
1192.97
1215.81
1270.98
1342.02
1369.89
1390.55
1356.40
1372.73
1424.00
1479.76
1512.60
1528.66
1572.21
1757.21
1770.95
1665.21
1738.11
1641.84
1652.21
1742.14
1673.77
1649.69
1591.19
1598.76
1589.90
1630.31
1744.81
1746.58
1721.64

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200253&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200253&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200253&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 time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[200,400[3002140.5927980.5927980.002964
[400,600[500700.1939060.7867040.00097
[600,800[700170.0470910.8337950.000235
[800,1000[900220.0609420.8947370.000305
[1000,1200[110080.0221610.9168980.000111
[1200,1400[130090.0249310.9418280.000125
[1400,1600[150080.0221610.9639890.000111
[1600,1800]1700130.03601110.00018

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[200,400[ & 300 & 214 & 0.592798 & 0.592798 & 0.002964 \tabularnewline
[400,600[ & 500 & 70 & 0.193906 & 0.786704 & 0.00097 \tabularnewline
[600,800[ & 700 & 17 & 0.047091 & 0.833795 & 0.000235 \tabularnewline
[800,1000[ & 900 & 22 & 0.060942 & 0.894737 & 0.000305 \tabularnewline
[1000,1200[ & 1100 & 8 & 0.022161 & 0.916898 & 0.000111 \tabularnewline
[1200,1400[ & 1300 & 9 & 0.024931 & 0.941828 & 0.000125 \tabularnewline
[1400,1600[ & 1500 & 8 & 0.022161 & 0.963989 & 0.000111 \tabularnewline
[1600,1800] & 1700 & 13 & 0.036011 & 1 & 0.00018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200253&T=1

[TABLE]
[ROW][C]Frequency Table (Histogram)[/C][/ROW]
[ROW][C]Bins[/C][C]Midpoint[/C][C]Abs. Frequency[/C][C]Rel. Frequency[/C][C]Cumul. Rel. Freq.[/C][C]Density[/C][/ROW]
[ROW][C][200,400[[/C][C]300[/C][C]214[/C][C]0.592798[/C][C]0.592798[/C][C]0.002964[/C][/ROW]
[ROW][C][400,600[[/C][C]500[/C][C]70[/C][C]0.193906[/C][C]0.786704[/C][C]0.00097[/C][/ROW]
[ROW][C][600,800[[/C][C]700[/C][C]17[/C][C]0.047091[/C][C]0.833795[/C][C]0.000235[/C][/ROW]
[ROW][C][800,1000[[/C][C]900[/C][C]22[/C][C]0.060942[/C][C]0.894737[/C][C]0.000305[/C][/ROW]
[ROW][C][1000,1200[[/C][C]1100[/C][C]8[/C][C]0.022161[/C][C]0.916898[/C][C]0.000111[/C][/ROW]
[ROW][C][1200,1400[[/C][C]1300[/C][C]9[/C][C]0.024931[/C][C]0.941828[/C][C]0.000125[/C][/ROW]
[ROW][C][1400,1600[[/C][C]1500[/C][C]8[/C][C]0.022161[/C][C]0.963989[/C][C]0.000111[/C][/ROW]
[ROW][C][1600,1800][/C][C]1700[/C][C]13[/C][C]0.036011[/C][C]1[/C][C]0.00018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200253&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200253&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:

Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[200,400[3002140.5927980.5927980.002964
[400,600[500700.1939060.7867040.00097
[600,800[700170.0470910.8337950.000235
[800,1000[900220.0609420.8947370.000305
[1000,1200[110080.0221610.9168980.000111
[1200,1400[130090.0249310.9418280.000125
[1400,1600[150080.0221610.9639890.000111
[1600,1800]1700130.03601110.00018


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par3 == 'TRUE') par3 <- TRUE
if (par3 == 'FALSE') par3 <- FALSE
if (par4 == 'Unknown') par1 <- as.numeric(par1)
if (par4 == 'Interval/Ratio') par1 <- as.numeric(par1)
if (par4 == '3-point Likert') par1 <- c(1:3 - 0.5, 3.5)
if (par4 == '4-point Likert') par1 <- c(1:4 - 0.5, 4.5)
if (par4 == '5-point Likert') par1 <- c(1:5 - 0.5, 5.5)
if (par4 == '6-point Likert') par1 <- c(1:6 - 0.5, 6.5)
if (par4 == '7-point Likert') par1 <- c(1:7 - 0.5, 7.5)
if (par4 == '8-point Likert') par1 <- c(1:8 - 0.5, 8.5)
if (par4 == '9-point Likert') par1 <- c(1:9 - 0.5, 9.5)
if (par4 == '10-point Likert') par1 <- c(1:10 - 0.5, 10.5)
bitmap(file='test1.png')
if(is.numeric(x[1])) {
if (is.na(par1)) {
myhist<-hist(x,col=par2,main=main,xlab=xlab,right=par3)
} else {
if (par1 < 0) par1 <- 3
if (par1 > 50) par1 <- 50
myhist<-hist(x,breaks=par1,col=par2,main=main,xlab=xlab,right=par3)
}
} else {
plot(mytab <- table(x),col=par2,main='Frequency Plot',xlab=xlab,ylab='Absolute Frequency')
}
dev.off()
if(is.numeric(x[1])) {
myhist
n <- length(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('histogram.htm','Frequency Table (Histogram)',''),6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bins',header=TRUE)
a<-table.element(a,'Midpoint',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.element(a,'Cumul. Rel. Freq.',header=TRUE)
a<-table.element(a,'Density',header=TRUE)
a<-table.row.end(a)
crf <- 0
if (par3 == FALSE) mybracket <- '[' else mybracket <- ']'
mynumrows <- (length(myhist$breaks)-1)
for (i in 1:mynumrows) {
a<-table.row.start(a)
if (i == 1)
dum <- paste('[',myhist$breaks[i],sep='')
else
dum <- paste(mybracket,myhist$breaks[i],sep='')
dum <- paste(dum,myhist$breaks[i+1],sep=',')
if (i==mynumrows)
dum <- paste(dum,']',sep='')
else
dum <- paste(dum,mybracket,sep='')
a<-table.element(a,dum,header=TRUE)
a<-table.element(a,myhist$mids[i])
a<-table.element(a,myhist$counts[i])
rf <- myhist$counts[i]/n
crf <- crf + rf
a<-table.element(a,round(rf,6))
a<-table.element(a,round(crf,6))
a<-table.element(a,round(myhist$density[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
} else {
mytab
reltab <- mytab / sum(mytab)
n <- length(mytab)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Frequency Table (Categorical Data)',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Category',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,labels(mytab)$x[i],header=TRUE)
a<-table.element(a,mytab[i])
a<-table.element(a,round(reltab[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
}