Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationMon, 10 Dec 2012 15:01:31 -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/10/t1355169728ffg1gyq1kst5wpk.htm/, Retrieved Thu, 31 Oct 2024 23:53:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198316, Retrieved Thu, 31 Oct 2024 23:53:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Paper 2012: histo...] [2012-12-10 20:01:31] [7a9100b3135ff0dae36397155af309d9] [Current]
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Dataseries X:
87.28
87.28
87.09
86.92
87.59
90.72
90.69
90.3
89.55
88.94
88.41
87.82
87.07
86.82
86.4
86.02
85.66
85.32
85
84.67
83.94
82.83
81.95
81.19
80.48
78.86
69.47
68.77
70.06
73.95
75.8
77.79
81.57
83.07
84.34
85.1
85.25
84.26
83.63
86.44
85.3
84.1
83.36
82.48
81.58
80.47
79.34
82.13
81.69
80.7
79.88
79.16
78.38
77.42
76.47
75.46
74.48
78.27
80.7
79.91
78.75
77.78
81.14
81.08
80.03
78.91
78.01
76.9
75.97
81.93
80.27
78.67
77.42
76.16
74.7
76.39
76.04
74.65
73.29
71.79
74.39
74.91
74.54
73.08
72.75
71.32
70.38
70.35
70.01
69.36
67.77
69.26
69.8
68.38
67.62
68.39
66.95
65.21
66.64
63.45
60.66
62.34
60.32
58.64
60.46
58.59
61.87
61.85
67.44
77.06
91.74
93.15
94.15
93.11
91.51
89.96
88.16
86.98
88.03
86.24
84.65
83.23
81.7
80.25
78.8
77.51
76.2
75.04
74
75.49
77.14
76.15
76.27
78.19
76.49
77.31
76.65
74.99
73.51
72.07
70.59
71.96
76.29
74.86
74.93
71.9
71.01
77.47
75.78
76.6
76.07
74.57
73.02
72.65
73.16
71.53
69.78
67.98
69.96
72.16
70.47
68.86
67.37
65.87
72.16
71.34
69.93
68.44
67.16
66.01
67.25
70.91
69.75
68.59
67.48
66.31
64.81
66.58
65.97
64.7
64.7
60.94
59.08
58.42
57.77
57.11
53.31
49.96
49.4
48.84
48.3
47.74
47.24
46.76
46.29
48.9
49.23
48.53
48.03
54.34
53.79
53.24
52.96
52.17
51.7
58.55
78.2
77.03
76.19
77.15
75.87
95.47
109.67
112.28
112.01
107.93
105.96
105.06
102.98
102.2
105.23
101.85
99.89
96.23
94.76
91.51
91.63
91.54
85.23
87.83
87.38
84.44
85.19
84.03
86.73
102.52
104.45
106.98
107.02
99.26
94.45
113.44
157.33
147.38
171.89
171.95
132.71
126.02
121.18
115.45
110.48
117.85
117.63
124.65
109.59
111.27
99.78
98.21
99.2
97.97
89.55
87.91
93.34
94.42
93.2
90.29
91.46
89.98
88.35
88.41
82.44
79.89
75.69
75.66
84.5
96.73
87.48
82.39
83.48
79.31
78.16
72.77
72.45
68.46
67.62
68.76
70.07
68.55
65.3
58.96
59.17
62.37
66.28
55.62
55.23
55.85
56.75
50.89
53.88
52.95
55.08
53.61
58.78
61.85
55.91
53.32
46.41
44.57
50
50
53.36
46.23
50.45
49.07
45.85
48.45
49.96
46.53
50.51
47.58
48.05
46.84
47.67
49.16
55.54
55.82
58.22
56.19
57.77
63.19
54.76
55.74
62.54
61.39
69.6
79.23
80
93.68
107.63
100.18
97.3
90.45
80.64
80.58
75.82
85.59
89.35
89.42
104.73
95.32
89.27
90.44
86.97
79.98
81.22
87.35
83.64
82.22
94.4
102.18




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198316&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198316&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198316&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'Gertrude Mary Cox' @ cox.wessa.net







Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[40,50[45250.0694440.0694440.006944
[50,60[55400.1111110.1805560.011111
[60,70[65530.1472220.3277780.014722
[70,80[75930.2583330.5861110.025833
[80,90[85830.2305560.8166670.023056
[90,100[95330.0916670.9083330.009167
[100,110[105170.0472220.9555560.004722
[110,120[11580.0222220.9777780.002222
[120,130[12530.0083330.9861110.000833
[130,140[13510.0027780.9888890.000278
[140,150[14510.0027780.9916670.000278
[150,160[15510.0027780.9944440.000278
[160,170[165000.9944440
[170,180]17520.00555610.000556

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[40,50[ & 45 & 25 & 0.069444 & 0.069444 & 0.006944 \tabularnewline
[50,60[ & 55 & 40 & 0.111111 & 0.180556 & 0.011111 \tabularnewline
[60,70[ & 65 & 53 & 0.147222 & 0.327778 & 0.014722 \tabularnewline
[70,80[ & 75 & 93 & 0.258333 & 0.586111 & 0.025833 \tabularnewline
[80,90[ & 85 & 83 & 0.230556 & 0.816667 & 0.023056 \tabularnewline
[90,100[ & 95 & 33 & 0.091667 & 0.908333 & 0.009167 \tabularnewline
[100,110[ & 105 & 17 & 0.047222 & 0.955556 & 0.004722 \tabularnewline
[110,120[ & 115 & 8 & 0.022222 & 0.977778 & 0.002222 \tabularnewline
[120,130[ & 125 & 3 & 0.008333 & 0.986111 & 0.000833 \tabularnewline
[130,140[ & 135 & 1 & 0.002778 & 0.988889 & 0.000278 \tabularnewline
[140,150[ & 145 & 1 & 0.002778 & 0.991667 & 0.000278 \tabularnewline
[150,160[ & 155 & 1 & 0.002778 & 0.994444 & 0.000278 \tabularnewline
[160,170[ & 165 & 0 & 0 & 0.994444 & 0 \tabularnewline
[170,180] & 175 & 2 & 0.005556 & 1 & 0.000556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198316&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][40,50[[/C][C]45[/C][C]25[/C][C]0.069444[/C][C]0.069444[/C][C]0.006944[/C][/ROW]
[ROW][C][50,60[[/C][C]55[/C][C]40[/C][C]0.111111[/C][C]0.180556[/C][C]0.011111[/C][/ROW]
[ROW][C][60,70[[/C][C]65[/C][C]53[/C][C]0.147222[/C][C]0.327778[/C][C]0.014722[/C][/ROW]
[ROW][C][70,80[[/C][C]75[/C][C]93[/C][C]0.258333[/C][C]0.586111[/C][C]0.025833[/C][/ROW]
[ROW][C][80,90[[/C][C]85[/C][C]83[/C][C]0.230556[/C][C]0.816667[/C][C]0.023056[/C][/ROW]
[ROW][C][90,100[[/C][C]95[/C][C]33[/C][C]0.091667[/C][C]0.908333[/C][C]0.009167[/C][/ROW]
[ROW][C][100,110[[/C][C]105[/C][C]17[/C][C]0.047222[/C][C]0.955556[/C][C]0.004722[/C][/ROW]
[ROW][C][110,120[[/C][C]115[/C][C]8[/C][C]0.022222[/C][C]0.977778[/C][C]0.002222[/C][/ROW]
[ROW][C][120,130[[/C][C]125[/C][C]3[/C][C]0.008333[/C][C]0.986111[/C][C]0.000833[/C][/ROW]
[ROW][C][130,140[[/C][C]135[/C][C]1[/C][C]0.002778[/C][C]0.988889[/C][C]0.000278[/C][/ROW]
[ROW][C][140,150[[/C][C]145[/C][C]1[/C][C]0.002778[/C][C]0.991667[/C][C]0.000278[/C][/ROW]
[ROW][C][150,160[[/C][C]155[/C][C]1[/C][C]0.002778[/C][C]0.994444[/C][C]0.000278[/C][/ROW]
[ROW][C][160,170[[/C][C]165[/C][C]0[/C][C]0[/C][C]0.994444[/C][C]0[/C][/ROW]
[ROW][C][170,180][/C][C]175[/C][C]2[/C][C]0.005556[/C][C]1[/C][C]0.000556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198316&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198316&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
[40,50[45250.0694440.0694440.006944
[50,60[55400.1111110.1805560.011111
[60,70[65530.1472220.3277780.014722
[70,80[75930.2583330.5861110.025833
[80,90[85830.2305560.8166670.023056
[90,100[95330.0916670.9083330.009167
[100,110[105170.0472220.9555560.004722
[110,120[11580.0222220.9777780.002222
[120,130[12530.0083330.9861110.000833
[130,140[13510.0027780.9888890.000278
[140,150[14510.0027780.9916670.000278
[150,160[15510.0027780.9944440.000278
[160,170[165000.9944440
[170,180]17520.00555610.000556



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')
}