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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationThu, 01 Oct 2015 20:40:25 +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/2015/Oct/01/t14437284798kciuzt595qeskx.htm/, Retrieved Wed, 15 May 2024 20:41:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280911, Retrieved Wed, 15 May 2024 20:41:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2015-09-23 09:15:26] [bd9c1a0059305f52e49de3f4dc24b4f4]
-   PD  [Univariate Data Series] [] [2015-10-01 19:15:18] [bd9c1a0059305f52e49de3f4dc24b4f4]
- RMP       [Histogram] [] [2015-10-01 19:40:25] [1d0d2a0cfdb7bd945f85de3fbad0315e] [Current]
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Dataseries X:
173019
173690
172439
171914
171968
169500
173898
172308
171568
164939
161275
160770
162466
160185
154836
154103
150495
142707
149962
149967
144572
143819
141070
144119
145330
143279
139063
139202
133632
134476
141859
140693
138047
138346
140167
146796
152228
155410
159032
160312
157687
160141
167421
167628
164403
163405
163229
171154
173323
172381
168983
165380
161641
161933
172018
168455
164332
161193
157645
161694
163411
161834
159511
156359
154223
151497
160607
159672
155601
154668
153960
157307
165218
165616
162212
159787
157454
156485
165887
166836
163541
163973
164805
167521
174347
173374
172198
171055
168385
167281
177670
177280
174846
174476
174595
178392
185345
183293
181081
177795
173552
170734
179293
178659
175894
174815
173506
175376




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280911&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280911&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'Herman Ole Andreas Wold' @ wold.wessa.net







Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[130000,135000[13250020.0185190.0185194e-06
[135000,140000[13750040.0370370.0555567e-06
[140000,145000[14250090.0833330.1388891.7e-05
[145000,150000[14750040.0370370.1759267e-06
[150000,155000[15250080.0740740.251.5e-05
[155000,160000[157500120.1111110.3611112.2e-05
[160000,165000[162500220.2037040.5648154.1e-05
[165000,170000[167500130.120370.6851852.4e-05
[170000,175000[172500230.2129630.8981484.3e-05
[175000,180000[17750080.0740740.9722221.5e-05
[180000,185000[18250020.0185190.9907414e-06
[185000,190000]18750010.00925912e-06

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[130000,135000[ & 132500 & 2 & 0.018519 & 0.018519 & 4e-06 \tabularnewline
[135000,140000[ & 137500 & 4 & 0.037037 & 0.055556 & 7e-06 \tabularnewline
[140000,145000[ & 142500 & 9 & 0.083333 & 0.138889 & 1.7e-05 \tabularnewline
[145000,150000[ & 147500 & 4 & 0.037037 & 0.175926 & 7e-06 \tabularnewline
[150000,155000[ & 152500 & 8 & 0.074074 & 0.25 & 1.5e-05 \tabularnewline
[155000,160000[ & 157500 & 12 & 0.111111 & 0.361111 & 2.2e-05 \tabularnewline
[160000,165000[ & 162500 & 22 & 0.203704 & 0.564815 & 4.1e-05 \tabularnewline
[165000,170000[ & 167500 & 13 & 0.12037 & 0.685185 & 2.4e-05 \tabularnewline
[170000,175000[ & 172500 & 23 & 0.212963 & 0.898148 & 4.3e-05 \tabularnewline
[175000,180000[ & 177500 & 8 & 0.074074 & 0.972222 & 1.5e-05 \tabularnewline
[180000,185000[ & 182500 & 2 & 0.018519 & 0.990741 & 4e-06 \tabularnewline
[185000,190000] & 187500 & 1 & 0.009259 & 1 & 2e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280911&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][130000,135000[[/C][C]132500[/C][C]2[/C][C]0.018519[/C][C]0.018519[/C][C]4e-06[/C][/ROW]
[ROW][C][135000,140000[[/C][C]137500[/C][C]4[/C][C]0.037037[/C][C]0.055556[/C][C]7e-06[/C][/ROW]
[ROW][C][140000,145000[[/C][C]142500[/C][C]9[/C][C]0.083333[/C][C]0.138889[/C][C]1.7e-05[/C][/ROW]
[ROW][C][145000,150000[[/C][C]147500[/C][C]4[/C][C]0.037037[/C][C]0.175926[/C][C]7e-06[/C][/ROW]
[ROW][C][150000,155000[[/C][C]152500[/C][C]8[/C][C]0.074074[/C][C]0.25[/C][C]1.5e-05[/C][/ROW]
[ROW][C][155000,160000[[/C][C]157500[/C][C]12[/C][C]0.111111[/C][C]0.361111[/C][C]2.2e-05[/C][/ROW]
[ROW][C][160000,165000[[/C][C]162500[/C][C]22[/C][C]0.203704[/C][C]0.564815[/C][C]4.1e-05[/C][/ROW]
[ROW][C][165000,170000[[/C][C]167500[/C][C]13[/C][C]0.12037[/C][C]0.685185[/C][C]2.4e-05[/C][/ROW]
[ROW][C][170000,175000[[/C][C]172500[/C][C]23[/C][C]0.212963[/C][C]0.898148[/C][C]4.3e-05[/C][/ROW]
[ROW][C][175000,180000[[/C][C]177500[/C][C]8[/C][C]0.074074[/C][C]0.972222[/C][C]1.5e-05[/C][/ROW]
[ROW][C][180000,185000[[/C][C]182500[/C][C]2[/C][C]0.018519[/C][C]0.990741[/C][C]4e-06[/C][/ROW]
[ROW][C][185000,190000][/C][C]187500[/C][C]1[/C][C]0.009259[/C][C]1[/C][C]2e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280911&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[130000,135000[13250020.0185190.0185194e-06
[135000,140000[13750040.0370370.0555567e-06
[140000,145000[14250090.0833330.1388891.7e-05
[145000,150000[14750040.0370370.1759267e-06
[150000,155000[15250080.0740740.251.5e-05
[155000,160000[157500120.1111110.3611112.2e-05
[160000,165000[162500220.2037040.5648154.1e-05
[165000,170000[167500130.120370.6851852.4e-05
[170000,175000[172500230.2129630.8981484.3e-05
[175000,180000[17750080.0740740.9722221.5e-05
[180000,185000[18250020.0185190.9907414e-06
[185000,190000]18750010.00925912e-06



Parameters (Session):
par1 = 12 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = 12 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
R code (references can be found in the software module):
par4 <- 'Unknown'
par3 <- 'FALSE'
par2 <- 'grey'
par1 <- '12'
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')
}