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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationMon, 11 Jul 2016 18:36:24 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Jul/11/t1468258832oajyszvgd75dl94.htm/, Retrieved Tue, 07 May 2024 00:17:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295829, Retrieved Tue, 07 May 2024 00:17:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [reeks A stap 1] [2016-07-11 17:21:44] [74be16979710d4c4e7c6647856088456]
- R PD  [Univariate Data Series] [reeks A stap 2] [2016-07-11 17:30:36] [74be16979710d4c4e7c6647856088456]
- RMP       [Histogram] [Reeks A stap 3] [2016-07-11 17:36:24] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- RMP         [Kernel Density Estimation] [Reeks A stap 6] [2016-07-11 17:51:48] [74be16979710d4c4e7c6647856088456]
- RMP         [Quartiles] [reeks A stap 8] [2016-07-11 17:56:11] [74be16979710d4c4e7c6647856088456]
- RMP         [Notched Boxplots] [reeks A stap 9] [2016-07-11 18:02:33] [74be16979710d4c4e7c6647856088456]
- RMP           [Harrell-Davis Quantiles] [reeks A stap 11] [2016-07-11 18:07:56] [74be16979710d4c4e7c6647856088456]
- RMP           [Harrell-Davis Quantiles] [reeks A stap 12+13] [2016-07-11 18:16:58] [74be16979710d4c4e7c6647856088456]
- RMP           [Central Tendency] [reeks A stap 14] [2016-07-11 18:20:20] [74be16979710d4c4e7c6647856088456]
- RMP           [Mean Plot] [reeks A stap 18] [2016-07-11 18:33:58] [74be16979710d4c4e7c6647856088456]
- RMP           [(Partial) Autocorrelation Function] [reeks A stap 20] [2016-07-11 18:39:14] [74be16979710d4c4e7c6647856088456]
- RMP           [Standard Deviation Plot] [reeks A stap 23&24] [2016-07-11 18:43:19] [74be16979710d4c4e7c6647856088456]
- RMP           [Standard Deviation-Mean Plot] [reeks A stap 26] [2016-07-11 18:48:27] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24514
24442
24364
24222
25689
25618
24514
23780
23851
23851
23922
24072
24514
24735
25105
25397
26722
26573
25468
23851
24143
24442
24364
24735
24442
24955
25176
25247
26872
26573
25468
23851
24143
23922
24293
25105
25027
24884
25247
25468
26722
26793
25468
23559
23409
23851
23481
24663
24663
24222
24806
25176
26430
26793
25247
23409
23409
22818
22376
23338
22968
22084
22676
23189
24735
25326
23702
22526
22526
22084
21792
22376
21643
21493
21864
22376
23922
24222
22305
20909
20246
19584
19213
19947
19506
19584
19947
20246
21714
21935
19584
18480
17375
16635
16122
16855
16492
17076
17297
17518
18480
19064
16122
15388
13543
12367
11997
13030
12439
13179
13179
13251
14134
14725
11854
10821
9126
8022
7437
8905

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 0 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295829&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295829&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295829&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 time0 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[6000,8000[700010.0083330.0083334e-06
[8000,10000[900030.0250.0333331.2e-05
[10000,12000[1100030.0250.0583331.2e-05
[12000,14000[1300070.0583330.1166672.9e-05
[14000,16000[1500030.0250.1416671.2e-05
[16000,18000[1700090.0750.2166673.8e-05
[18000,20000[19000100.0833330.34.2e-05
[20000,22000[2100090.0750.3753.8e-05
[22000,24000[23000280.2333330.6083330.000117
[24000,26000[25000390.3250.9333330.000162
[26000,28000]2700080.06666713.3e-05

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[6000,8000[ & 7000 & 1 & 0.008333 & 0.008333 & 4e-06 \tabularnewline
[8000,10000[ & 9000 & 3 & 0.025 & 0.033333 & 1.2e-05 \tabularnewline
[10000,12000[ & 11000 & 3 & 0.025 & 0.058333 & 1.2e-05 \tabularnewline
[12000,14000[ & 13000 & 7 & 0.058333 & 0.116667 & 2.9e-05 \tabularnewline
[14000,16000[ & 15000 & 3 & 0.025 & 0.141667 & 1.2e-05 \tabularnewline
[16000,18000[ & 17000 & 9 & 0.075 & 0.216667 & 3.8e-05 \tabularnewline
[18000,20000[ & 19000 & 10 & 0.083333 & 0.3 & 4.2e-05 \tabularnewline
[20000,22000[ & 21000 & 9 & 0.075 & 0.375 & 3.8e-05 \tabularnewline
[22000,24000[ & 23000 & 28 & 0.233333 & 0.608333 & 0.000117 \tabularnewline
[24000,26000[ & 25000 & 39 & 0.325 & 0.933333 & 0.000162 \tabularnewline
[26000,28000] & 27000 & 8 & 0.066667 & 1 & 3.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295829&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][6000,8000[[/C][C]7000[/C][C]1[/C][C]0.008333[/C][C]0.008333[/C][C]4e-06[/C][/ROW]
[ROW][C][8000,10000[[/C][C]9000[/C][C]3[/C][C]0.025[/C][C]0.033333[/C][C]1.2e-05[/C][/ROW]
[ROW][C][10000,12000[[/C][C]11000[/C][C]3[/C][C]0.025[/C][C]0.058333[/C][C]1.2e-05[/C][/ROW]
[ROW][C][12000,14000[[/C][C]13000[/C][C]7[/C][C]0.058333[/C][C]0.116667[/C][C]2.9e-05[/C][/ROW]
[ROW][C][14000,16000[[/C][C]15000[/C][C]3[/C][C]0.025[/C][C]0.141667[/C][C]1.2e-05[/C][/ROW]
[ROW][C][16000,18000[[/C][C]17000[/C][C]9[/C][C]0.075[/C][C]0.216667[/C][C]3.8e-05[/C][/ROW]
[ROW][C][18000,20000[[/C][C]19000[/C][C]10[/C][C]0.083333[/C][C]0.3[/C][C]4.2e-05[/C][/ROW]
[ROW][C][20000,22000[[/C][C]21000[/C][C]9[/C][C]0.075[/C][C]0.375[/C][C]3.8e-05[/C][/ROW]
[ROW][C][22000,24000[[/C][C]23000[/C][C]28[/C][C]0.233333[/C][C]0.608333[/C][C]0.000117[/C][/ROW]
[ROW][C][24000,26000[[/C][C]25000[/C][C]39[/C][C]0.325[/C][C]0.933333[/C][C]0.000162[/C][/ROW]
[ROW][C][26000,28000][/C][C]27000[/C][C]8[/C][C]0.066667[/C][C]1[/C][C]3.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295829&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295829&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
[6000,8000[700010.0083330.0083334e-06
[8000,10000[900030.0250.0333331.2e-05
[10000,12000[1100030.0250.0583331.2e-05
[12000,14000[1300070.0583330.1166672.9e-05
[14000,16000[1500030.0250.1416671.2e-05
[16000,18000[1700090.0750.2166673.8e-05
[18000,20000[19000100.0833330.34.2e-05
[20000,22000[2100090.0750.3753.8e-05
[22000,24000[23000280.2333330.6083330.000117
[24000,26000[25000390.3250.9333330.000162
[26000,28000]2700080.06666713.3e-05


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 {
barplot(mytab <- sort(table(x),T),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')
}