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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 26 Nov 2009 03:28:44 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/26/t1259231371dwyqf62h6qzkgdm.htm/, Retrieved Sun, 28 Apr 2024 22:05:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59790, Retrieved Sun, 28 Apr 2024 22:05:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-11-22 12:50:05] [b98453cac15ba1066b407e146608df68]
-   PD          [Standard Deviation-Mean Plot] [Removing heterosk...] [2009-11-26 10:28:44] [371dc2189c569d90e2c1567f632c3ec0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1919
1911
1870
2263
1802
1863
1989
2197
2409
2502
2593
2598
2053
2213
2238
2359
2151
2474
3079
2312
2565
1972
2484
2202
2151
1976
2012
2114
1772
1957
2070
1990
2182
2008
1916
2397
2114
1778
1641
2186
1773
1785
2217
2153
1895
2475
1793
2308
2051
1898
2142
1874
1560
1808
1575
1525
1997
1753
1623
2251
1890

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59790&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59790&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'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12159.66666666667304.522079609504796
22341.83333333333291.6463715882751107
32045.41666666667156.219809204463625
42009.83333333333264.095655306018834
51838.08333333333240.700288300365726

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2159.66666666667 & 304.522079609504 & 796 \tabularnewline
2 & 2341.83333333333 & 291.646371588275 & 1107 \tabularnewline
3 & 2045.41666666667 & 156.219809204463 & 625 \tabularnewline
4 & 2009.83333333333 & 264.095655306018 & 834 \tabularnewline
5 & 1838.08333333333 & 240.700288300365 & 726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59790&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]2159.66666666667[/C][C]304.522079609504[/C][C]796[/C][/ROW]
[ROW][C]2[/C][C]2341.83333333333[/C][C]291.646371588275[/C][C]1107[/C][/ROW]
[ROW][C]3[/C][C]2045.41666666667[/C][C]156.219809204463[/C][C]625[/C][/ROW]
[ROW][C]4[/C][C]2009.83333333333[/C][C]264.095655306018[/C][C]834[/C][/ROW]
[ROW][C]5[/C][C]1838.08333333333[/C][C]240.700288300365[/C][C]726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59790&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:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12159.66666666667304.522079609504796
22341.83333333333291.6463715882751107
32045.41666666667156.219809204463625
42009.83333333333264.095655306018834
51838.08333333333240.700288300365726






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-42.9498397648746
beta0.141602405313505
S.D.0.161943379307256
T-STAT0.874394531713715
p-value0.446262664687178

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -42.9498397648746 \tabularnewline
beta & 0.141602405313505 \tabularnewline
S.D. & 0.161943379307256 \tabularnewline
T-STAT & 0.874394531713715 \tabularnewline
p-value & 0.446262664687178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59790&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-42.9498397648746[/C][/ROW]
[ROW][C]beta[/C][C]0.141602405313505[/C][/ROW]
[ROW][C]S.D.[/C][C]0.161943379307256[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.874394531713715[/C][/ROW]
[ROW][C]p-value[/C][C]0.446262664687178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59790&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-42.9498397648746
beta0.141602405313505
S.D.0.161943379307256
T-STAT0.874394531713715
p-value0.446262664687178






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.04875874449315
beta1.11961556011730
S.D.1.59648862952852
T-STAT0.701298800009587
p-value0.533621887973714
Lambda-0.119615560117302

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.04875874449315 \tabularnewline
beta & 1.11961556011730 \tabularnewline
S.D. & 1.59648862952852 \tabularnewline
T-STAT & 0.701298800009587 \tabularnewline
p-value & 0.533621887973714 \tabularnewline
Lambda & -0.119615560117302 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59790&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.04875874449315[/C][/ROW]
[ROW][C]beta[/C][C]1.11961556011730[/C][/ROW]
[ROW][C]S.D.[/C][C]1.59648862952852[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.701298800009587[/C][/ROW]
[ROW][C]p-value[/C][C]0.533621887973714[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.119615560117302[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59790&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59790&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.04875874449315
beta1.11961556011730
S.D.1.59648862952852
T-STAT0.701298800009587
p-value0.533621887973714
Lambda-0.119615560117302


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')