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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, 22 Dec 2016 23:45:44 +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/Dec/22/t1482446869wb13sb99kbegtsq.htm/, Retrieved Mon, 29 Apr 2024 02:48:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302725, Retrieved Mon, 29 Apr 2024 02:48:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [ACF1] [2016-12-22 17:08:50] [267314984f6394bb93cd815224aa34ba]
- RM D    [Standard Deviation-Mean Plot] [SMP2] [2016-12-22 22:45:44] [636d0f72197ac5e1dae4a755427db02a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3120
3360
3540
2700
2580
3480
3240
4440
3000
3720
1620
3360
3180
2100
3000
2520
2160
1980
4020
3480
2750
2640
3420
2640
2520
2040
2820
1860
3780
2520
2580
2880
2100
3060
2100
3720
2940
2820
4980
2400
2940
2640
2340
1680
4140
2640
3600
3240
3120
2460
2940

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302725&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302725&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302725&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
13180689.3475175845632820
22824.16666666667619.1410227570182040
32665625.8739344454141920
43030877.1648544134573300

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3180 & 689.347517584563 & 2820 \tabularnewline
2 & 2824.16666666667 & 619.141022757018 & 2040 \tabularnewline
3 & 2665 & 625.873934445414 & 1920 \tabularnewline
4 & 3030 & 877.164854413457 & 3300 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302725&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]3180[/C][C]689.347517584563[/C][C]2820[/C][/ROW]
[ROW][C]2[/C][C]2824.16666666667[/C][C]619.141022757018[/C][C]2040[/C][/ROW]
[ROW][C]3[/C][C]2665[/C][C]625.873934445414[/C][C]1920[/C][/ROW]
[ROW][C]4[/C][C]3030[/C][C]877.164854413457[/C][C]3300[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302725&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302725&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
13180689.3475175845632820
22824.16666666667619.1410227570182040
32665625.8739344454141920
43030877.1648544134573300






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-120.729337166297
beta0.281596524926189
S.D.0.318990456526869
T-STAT0.882774136857195
p-value0.470479650551027

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -120.729337166297 \tabularnewline
beta & 0.281596524926189 \tabularnewline
S.D. & 0.318990456526869 \tabularnewline
T-STAT & 0.882774136857195 \tabularnewline
p-value & 0.470479650551027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302725&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-120.729337166297[/C][/ROW]
[ROW][C]beta[/C][C]0.281596524926189[/C][/ROW]
[ROW][C]S.D.[/C][C]0.318990456526869[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.882774136857195[/C][/ROW]
[ROW][C]p-value[/C][C]0.470479650551027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302725&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302725&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-120.729337166297
beta0.281596524926189
S.D.0.318990456526869
T-STAT0.882774136857195
p-value0.470479650551027






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-2.86104301660762
beta1.178888713876
S.D.1.2146140795409
T-STAT0.970587064429215
p-value0.434138341978818
Lambda-0.178888713875998

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -2.86104301660762 \tabularnewline
beta & 1.178888713876 \tabularnewline
S.D. & 1.2146140795409 \tabularnewline
T-STAT & 0.970587064429215 \tabularnewline
p-value & 0.434138341978818 \tabularnewline
Lambda & -0.178888713875998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302725&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-2.86104301660762[/C][/ROW]
[ROW][C]beta[/C][C]1.178888713876[/C][/ROW]
[ROW][C]S.D.[/C][C]1.2146140795409[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.970587064429215[/C][/ROW]
[ROW][C]p-value[/C][C]0.434138341978818[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.178888713875998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302725&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302725&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-2.86104301660762
beta1.178888713876
S.D.1.2146140795409
T-STAT0.970587064429215
p-value0.434138341978818
Lambda-0.178888713875998


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;
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')