FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Moduleexamplesmp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 01 Jul 2010 18:30:57 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Jul/01/t1278009903jwtnlicictbtylo.htm/, Retrieved Fri, 03 May 2024 19:58:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=77904, Retrieved Fri, 03 May 2024 19:58:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsthomas talboom
Estimated Impact214

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Harrell-Davis Quantiles] [percentielen] [2010-07-01 11:45:42] [b6623a0531b43a362887826f077b4445]
- RMPD    [Standard Deviation-Mean Plot] [Standard Deviatio...] [2010-07-01 18:30:57] [58d9ccda37eeb031a0ffa1e9ea016ece] [Current]
Feedback Forum

Post a new message

Dataset

Tables (Output of Computation)





SMP of Airline.ds
L = 1, d = D = 0
SectionMean(k)SE(k)
1126.666713.7201
2139.666719.0708
3170.166718.4383
419722.9664
522528.4669
6238.916734.9245
728442.1405
8328.2547.8618
9368.416757.8909
1038164.5305
11428.333369.8301
12476.166777.7371
Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-11.403254142558
beta0.18861339889948
S.E.0.0065773318024468
T-Stat28.676278552546
The above regression result suggests that a relationship exists between the mean level and the standard error of this time series.
Regression: ln S.E.(k) = -3.7070398932205 + 1.3125925397258 * ln Mean(k)
alpha-3.7070398932205
beta1.3125925397258
S.E.0.054820180413853
T-Stat23.943601239847
Lambda-0.31259253972575
AI Conclusion: A Box-Cox transform is likely to induce stationarity of variance.

\begin{tabular}{lllllllll}
\hline
SMP of Airline.ds \tabularnewline L = 1, d = D = 0 \tabularnewline Section & Mean(k) & SE(k) \tabularnewline 1126.666713.7201 \tabularnewline 2139.666719.0708 \tabularnewline 3170.166718.4383 \tabularnewline 419722.9664 \tabularnewline 522528.4669 \tabularnewline 6238.916734.9245 \tabularnewline 728442.1405 \tabularnewline 8328.2547.8618 \tabularnewline 9368.416757.8909 \tabularnewline 1038164.5305 \tabularnewline 11428.333369.8301 \tabularnewline 12476.166777.7371 \tabularnewline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline alpha-11.403254142558 \tabularnewline beta0.18861339889948 \tabularnewline S.E.0.0065773318024468 \tabularnewline T-Stat28.676278552546 \tabularnewline The above regression result suggests that a relationship exists between the mean level and the standard error of this time series. \tabularnewline
Regression: ln S.E.(k) = -3.7070398932205 + 1.3125925397258 * ln Mean(k) \tabularnewline alpha-3.7070398932205 \tabularnewline beta1.3125925397258 \tabularnewline S.E.0.054820180413853 \tabularnewline T-Stat23.943601239847 \tabularnewline Lambda-0.31259253972575 \tabularnewline AI Conclusion: A Box-Cox transform is likely to induce stationarity of variance. \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=77904&T=0

[TABLE]

[ROW]
SMP of Airline.ds[/C][/ROW] [ROW]L = 1, d = D = 0[/C][/ROW] [ROW][C]Section[/C][C]Mean(k)[/C][C]SE(k)[/C][/ROW] [ROW]1[/C]126.6667[/C]13.7201[/C][/ROW] [ROW]2[/C]139.6667[/C]19.0708[/C][/ROW] [ROW]3[/C]170.1667[/C]18.4383[/C][/ROW] [ROW]4[/C]197[/C]22.9664[/C][/ROW] [ROW]5[/C]225[/C]28.4669[/C][/ROW] [ROW]6[/C]238.9167[/C]34.9245[/C][/ROW] [ROW]7[/C]284[/C]42.1405[/C][/ROW] [ROW]8[/C]328.25[/C]47.8618[/C][/ROW] [ROW]9[/C]368.4167[/C]57.8909[/C][/ROW] [ROW]10[/C]381[/C]64.5305[/C][/ROW] [ROW]11[/C]428.3333[/C]69.8301[/C][/ROW] [ROW]12[/C]476.1667[/C]77.7371[/C][/ROW] [ROW]
Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW] [ROW]alpha[/C]-11.403254142558[/C][/ROW] [ROW]beta[/C]0.18861339889948[/C][/ROW] [ROW]S.E.[/C]0.0065773318024468[/C][/ROW] [ROW]T-Stat[/C]28.676278552546[/C][/ROW] [ROW][C]The above regression result suggests that a relationship exists between the mean level and the standard error of this time series.[/C][/ROW] [ROW]
Regression: ln S.E.(k) = -3.7070398932205 + 1.3125925397258 * ln Mean(k)[/C][/ROW] [ROW]alpha[/C]-3.7070398932205[/C][/ROW] [ROW]beta[/C]1.3125925397258[/C][/ROW] [ROW]S.E.[/C]0.054820180413853[/C][/ROW] [ROW]T-Stat[/C]23.943601239847[/C][/ROW] [ROW]Lambda[/C]-0.31259253972575[/C][/ROW] [ROW][C]AI Conclusion: A Box-Cox transform is likely to induce stationarity of variance.[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=77904&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=77904&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:

SMP of Airline.ds
L = 1, d = D = 0
SectionMean(k)SE(k)
1126.666713.7201
2139.666719.0708
3170.166718.4383
419722.9664
522528.4669
6238.916734.9245
728442.1405
8328.2547.8618
9368.416757.8909
1038164.5305
11428.333369.8301
12476.166777.7371
Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-11.403254142558
beta0.18861339889948
S.E.0.0065773318024468
T-Stat28.676278552546
The above regression result suggests that a relationship exists between the mean level and the standard error of this time series.
Regression: ln S.E.(k) = -3.7070398932205 + 1.3125925397258 * ln Mean(k)
alpha-3.7070398932205
beta1.3125925397258
S.E.0.054820180413853
T-Stat23.943601239847
Lambda-0.31259253972575
AI Conclusion: A Box-Cox transform is likely to induce stationarity of variance.


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.01 ; par2 = 0.99 ; par3 = 0.1 ;
Parameters (R input):
R code (references can be found in the software module):