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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationTue, 02 Dec 2008 11:15:51 -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/2008/Dec/02/t1228241858yrnyfyjyj5cqse2.htm/, Retrieved Sat, 18 May 2024 03:46:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28198, Retrieved Sat, 18 May 2024 03:46:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnon stationary time series vraag 8 voeding
Estimated Impact191

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Spectral Analysis] [spectral analysis] [2008-12-02 17:36:41] [415d0222c17b651a9576eaac006f530d]
F RMPD      [(Partial) Autocorrelation Function] [autocorrelatie] [2008-12-02 18:15:51] [bb7e3816cefc365f4d7adcd50784b783] [Current]
- RMPD        [Spectral Analysis] [] [2008-12-06 13:43:43] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-06 13:45:34 [Ken Wright] [reply] 
juist, maar om zeker te zijn kan je best verder gebruik maken van de spectraal analyse om te kijken of er een LT trend is en seizoenaliteit.http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/06/t12285710500yhghbxsqcqm3iq.htm
hier bij de spectraal analyse kan je zien bij het cumulative periodogram dat er LT moet gediffertieerd worden (steile helling) en ook seizoenaal(getrapt)

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11,836
11,85
11,897
12,082
11,936
11,928
12,646
12,747
12,447
12,445
12,257
12,878
13,69
13,665
13,78
13,608
13,375
13,376
13,918
14,304
13,877
14,543
14,291
14,788
15,241
15,265
15,322
15,175
14,817
14,579
15,247
15,385
14,891
14,766
14,42
14,85
15,117
15,352
15,099
15,291
15,208
14,995
15,454
15,251
14,975
14,005
13,55
13,422
13,848
13,376
13,038
12,974
12,554
11,971
12,916
12,757
11,924
11,693
11,382
11,821

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28198&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28198&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'George Udny Yule' @ 72.249.76.132






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9130037.07210
20.8120486.29010
30.7316495.66730
40.6651965.15262e-06
50.6180064.78716e-06
60.5564144.313.1e-05
70.4586743.55290.000375
80.3572772.76750.003751
90.2733382.11730.019196
100.1957741.51650.067327
110.1376951.06660.145216
120.0898830.69620.244486
130.0042890.03320.486804
14-0.084943-0.6580.256539
15-0.144606-1.12010.133564
16-0.182046-1.41010.081833
17-0.20988-1.62570.054625
18-0.240767-1.8650.033539
19-0.296188-2.29430.012646
20-0.345951-2.67970.004748
21-0.386613-2.99470.001994
22-0.409321-3.17060.001198
23-0.409425-3.17140.001195
24-0.405914-3.14420.001295
25-0.427026-3.30770.000796
26-0.457815-3.54620.000383
27-0.456707-3.53760.000393
28-0.444966-3.44670.000521
29-0.424513-3.28830.000844
30-0.415616-3.21930.001037
31-0.419297-3.24790.000953
32-0.414145-3.2080.001073
33-0.39198-3.03630.00177
34-0.36468-2.82480.003207
35-0.326032-2.52540.007108
36-0.272652-2.1120.019431

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.913003 & 7.0721 & 0 \tabularnewline
2 & 0.812048 & 6.2901 & 0 \tabularnewline
3 & 0.731649 & 5.6673 & 0 \tabularnewline
4 & 0.665196 & 5.1526 & 2e-06 \tabularnewline
5 & 0.618006 & 4.7871 & 6e-06 \tabularnewline
6 & 0.556414 & 4.31 & 3.1e-05 \tabularnewline
7 & 0.458674 & 3.5529 & 0.000375 \tabularnewline
8 & 0.357277 & 2.7675 & 0.003751 \tabularnewline
9 & 0.273338 & 2.1173 & 0.019196 \tabularnewline
10 & 0.195774 & 1.5165 & 0.067327 \tabularnewline
11 & 0.137695 & 1.0666 & 0.145216 \tabularnewline
12 & 0.089883 & 0.6962 & 0.244486 \tabularnewline
13 & 0.004289 & 0.0332 & 0.486804 \tabularnewline
14 & -0.084943 & -0.658 & 0.256539 \tabularnewline
15 & -0.144606 & -1.1201 & 0.133564 \tabularnewline
16 & -0.182046 & -1.4101 & 0.081833 \tabularnewline
17 & -0.20988 & -1.6257 & 0.054625 \tabularnewline
18 & -0.240767 & -1.865 & 0.033539 \tabularnewline
19 & -0.296188 & -2.2943 & 0.012646 \tabularnewline
20 & -0.345951 & -2.6797 & 0.004748 \tabularnewline
21 & -0.386613 & -2.9947 & 0.001994 \tabularnewline
22 & -0.409321 & -3.1706 & 0.001198 \tabularnewline
23 & -0.409425 & -3.1714 & 0.001195 \tabularnewline
24 & -0.405914 & -3.1442 & 0.001295 \tabularnewline
25 & -0.427026 & -3.3077 & 0.000796 \tabularnewline
26 & -0.457815 & -3.5462 & 0.000383 \tabularnewline
27 & -0.456707 & -3.5376 & 0.000393 \tabularnewline
28 & -0.444966 & -3.4467 & 0.000521 \tabularnewline
29 & -0.424513 & -3.2883 & 0.000844 \tabularnewline
30 & -0.415616 & -3.2193 & 0.001037 \tabularnewline
31 & -0.419297 & -3.2479 & 0.000953 \tabularnewline
32 & -0.414145 & -3.208 & 0.001073 \tabularnewline
33 & -0.39198 & -3.0363 & 0.00177 \tabularnewline
34 & -0.36468 & -2.8248 & 0.003207 \tabularnewline
35 & -0.326032 & -2.5254 & 0.007108 \tabularnewline
36 & -0.272652 & -2.112 & 0.019431 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28198&T=1

[TABLE]
[ROW][C]Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]ACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.913003[/C][C]7.0721[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.812048[/C][C]6.2901[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.731649[/C][C]5.6673[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.665196[/C][C]5.1526[/C][C]2e-06[/C][/ROW]
[ROW][C]5[/C][C]0.618006[/C][C]4.7871[/C][C]6e-06[/C][/ROW]
[ROW][C]6[/C][C]0.556414[/C][C]4.31[/C][C]3.1e-05[/C][/ROW]
[ROW][C]7[/C][C]0.458674[/C][C]3.5529[/C][C]0.000375[/C][/ROW]
[ROW][C]8[/C][C]0.357277[/C][C]2.7675[/C][C]0.003751[/C][/ROW]
[ROW][C]9[/C][C]0.273338[/C][C]2.1173[/C][C]0.019196[/C][/ROW]
[ROW][C]10[/C][C]0.195774[/C][C]1.5165[/C][C]0.067327[/C][/ROW]
[ROW][C]11[/C][C]0.137695[/C][C]1.0666[/C][C]0.145216[/C][/ROW]
[ROW][C]12[/C][C]0.089883[/C][C]0.6962[/C][C]0.244486[/C][/ROW]
[ROW][C]13[/C][C]0.004289[/C][C]0.0332[/C][C]0.486804[/C][/ROW]
[ROW][C]14[/C][C]-0.084943[/C][C]-0.658[/C][C]0.256539[/C][/ROW]
[ROW][C]15[/C][C]-0.144606[/C][C]-1.1201[/C][C]0.133564[/C][/ROW]
[ROW][C]16[/C][C]-0.182046[/C][C]-1.4101[/C][C]0.081833[/C][/ROW]
[ROW][C]17[/C][C]-0.20988[/C][C]-1.6257[/C][C]0.054625[/C][/ROW]
[ROW][C]18[/C][C]-0.240767[/C][C]-1.865[/C][C]0.033539[/C][/ROW]
[ROW][C]19[/C][C]-0.296188[/C][C]-2.2943[/C][C]0.012646[/C][/ROW]
[ROW][C]20[/C][C]-0.345951[/C][C]-2.6797[/C][C]0.004748[/C][/ROW]
[ROW][C]21[/C][C]-0.386613[/C][C]-2.9947[/C][C]0.001994[/C][/ROW]
[ROW][C]22[/C][C]-0.409321[/C][C]-3.1706[/C][C]0.001198[/C][/ROW]
[ROW][C]23[/C][C]-0.409425[/C][C]-3.1714[/C][C]0.001195[/C][/ROW]
[ROW][C]24[/C][C]-0.405914[/C][C]-3.1442[/C][C]0.001295[/C][/ROW]
[ROW][C]25[/C][C]-0.427026[/C][C]-3.3077[/C][C]0.000796[/C][/ROW]
[ROW][C]26[/C][C]-0.457815[/C][C]-3.5462[/C][C]0.000383[/C][/ROW]
[ROW][C]27[/C][C]-0.456707[/C][C]-3.5376[/C][C]0.000393[/C][/ROW]
[ROW][C]28[/C][C]-0.444966[/C][C]-3.4467[/C][C]0.000521[/C][/ROW]
[ROW][C]29[/C][C]-0.424513[/C][C]-3.2883[/C][C]0.000844[/C][/ROW]
[ROW][C]30[/C][C]-0.415616[/C][C]-3.2193[/C][C]0.001037[/C][/ROW]
[ROW][C]31[/C][C]-0.419297[/C][C]-3.2479[/C][C]0.000953[/C][/ROW]
[ROW][C]32[/C][C]-0.414145[/C][C]-3.208[/C][C]0.001073[/C][/ROW]
[ROW][C]33[/C][C]-0.39198[/C][C]-3.0363[/C][C]0.00177[/C][/ROW]
[ROW][C]34[/C][C]-0.36468[/C][C]-2.8248[/C][C]0.003207[/C][/ROW]
[ROW][C]35[/C][C]-0.326032[/C][C]-2.5254[/C][C]0.007108[/C][/ROW]
[ROW][C]36[/C][C]-0.272652[/C][C]-2.112[/C][C]0.019431[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28198&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:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9130037.07210
20.8120486.29010
30.7316495.66730
40.6651965.15262e-06
50.6180064.78716e-06
60.5564144.313.1e-05
70.4586743.55290.000375
80.3572772.76750.003751
90.2733382.11730.019196
100.1957741.51650.067327
110.1376951.06660.145216
120.0898830.69620.244486
130.0042890.03320.486804
14-0.084943-0.6580.256539
15-0.144606-1.12010.133564
16-0.182046-1.41010.081833
17-0.20988-1.62570.054625
18-0.240767-1.8650.033539
19-0.296188-2.29430.012646
20-0.345951-2.67970.004748
21-0.386613-2.99470.001994
22-0.409321-3.17060.001198
23-0.409425-3.17140.001195
24-0.405914-3.14420.001295
25-0.427026-3.30770.000796
26-0.457815-3.54620.000383
27-0.456707-3.53760.000393
28-0.444966-3.44670.000521
29-0.424513-3.28830.000844
30-0.415616-3.21930.001037
31-0.419297-3.24790.000953
32-0.414145-3.2080.001073
33-0.39198-3.03630.00177
34-0.36468-2.82480.003207
35-0.326032-2.52540.007108
36-0.272652-2.1120.019431






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9130037.07210
2-0.129345-1.00190.160208
30.076030.58890.279062
40.0177050.13710.445688
50.0789490.61150.271578
6-0.124761-0.96640.168864
7-0.219835-1.70280.046887
8-0.062822-0.48660.314153
90.0025390.01970.492187
10-0.079948-0.61930.269041
110.0369070.28590.387976
120.0317410.24590.403314
13-0.231815-1.79560.038794
14-0.056125-0.43470.332656
150.0749260.58040.281917
160.0317130.24560.403398
17-0.072012-0.55780.289527
18-0.045507-0.35250.362851
19-0.095354-0.73860.231513
20-0.011551-0.08950.4645
21-0.127159-0.9850.164296
220.0229690.17790.429694
230.0464680.35990.360077
24-0.037956-0.2940.384885
25-0.124253-0.96250.169841
26-0.054323-0.42080.337709
270.1080410.83690.202989
28-0.103101-0.79860.213832
29-0.046878-0.36310.358896
30-0.082689-0.64050.26214
310.0115520.08950.464498
32-0.040572-0.31430.377205
33-0.004642-0.0360.485717
34-0.035994-0.27880.390674
350.0252890.19590.42268
360.0666980.51660.303654

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.913003 & 7.0721 & 0 \tabularnewline
2 & -0.129345 & -1.0019 & 0.160208 \tabularnewline
3 & 0.07603 & 0.5889 & 0.279062 \tabularnewline
4 & 0.017705 & 0.1371 & 0.445688 \tabularnewline
5 & 0.078949 & 0.6115 & 0.271578 \tabularnewline
6 & -0.124761 & -0.9664 & 0.168864 \tabularnewline
7 & -0.219835 & -1.7028 & 0.046887 \tabularnewline
8 & -0.062822 & -0.4866 & 0.314153 \tabularnewline
9 & 0.002539 & 0.0197 & 0.492187 \tabularnewline
10 & -0.079948 & -0.6193 & 0.269041 \tabularnewline
11 & 0.036907 & 0.2859 & 0.387976 \tabularnewline
12 & 0.031741 & 0.2459 & 0.403314 \tabularnewline
13 & -0.231815 & -1.7956 & 0.038794 \tabularnewline
14 & -0.056125 & -0.4347 & 0.332656 \tabularnewline
15 & 0.074926 & 0.5804 & 0.281917 \tabularnewline
16 & 0.031713 & 0.2456 & 0.403398 \tabularnewline
17 & -0.072012 & -0.5578 & 0.289527 \tabularnewline
18 & -0.045507 & -0.3525 & 0.362851 \tabularnewline
19 & -0.095354 & -0.7386 & 0.231513 \tabularnewline
20 & -0.011551 & -0.0895 & 0.4645 \tabularnewline
21 & -0.127159 & -0.985 & 0.164296 \tabularnewline
22 & 0.022969 & 0.1779 & 0.429694 \tabularnewline
23 & 0.046468 & 0.3599 & 0.360077 \tabularnewline
24 & -0.037956 & -0.294 & 0.384885 \tabularnewline
25 & -0.124253 & -0.9625 & 0.169841 \tabularnewline
26 & -0.054323 & -0.4208 & 0.337709 \tabularnewline
27 & 0.108041 & 0.8369 & 0.202989 \tabularnewline
28 & -0.103101 & -0.7986 & 0.213832 \tabularnewline
29 & -0.046878 & -0.3631 & 0.358896 \tabularnewline
30 & -0.082689 & -0.6405 & 0.26214 \tabularnewline
31 & 0.011552 & 0.0895 & 0.464498 \tabularnewline
32 & -0.040572 & -0.3143 & 0.377205 \tabularnewline
33 & -0.004642 & -0.036 & 0.485717 \tabularnewline
34 & -0.035994 & -0.2788 & 0.390674 \tabularnewline
35 & 0.025289 & 0.1959 & 0.42268 \tabularnewline
36 & 0.066698 & 0.5166 & 0.303654 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28198&T=2

[TABLE]
[ROW][C]Partial Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]PACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.913003[/C][C]7.0721[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.129345[/C][C]-1.0019[/C][C]0.160208[/C][/ROW]
[ROW][C]3[/C][C]0.07603[/C][C]0.5889[/C][C]0.279062[/C][/ROW]
[ROW][C]4[/C][C]0.017705[/C][C]0.1371[/C][C]0.445688[/C][/ROW]
[ROW][C]5[/C][C]0.078949[/C][C]0.6115[/C][C]0.271578[/C][/ROW]
[ROW][C]6[/C][C]-0.124761[/C][C]-0.9664[/C][C]0.168864[/C][/ROW]
[ROW][C]7[/C][C]-0.219835[/C][C]-1.7028[/C][C]0.046887[/C][/ROW]
[ROW][C]8[/C][C]-0.062822[/C][C]-0.4866[/C][C]0.314153[/C][/ROW]
[ROW][C]9[/C][C]0.002539[/C][C]0.0197[/C][C]0.492187[/C][/ROW]
[ROW][C]10[/C][C]-0.079948[/C][C]-0.6193[/C][C]0.269041[/C][/ROW]
[ROW][C]11[/C][C]0.036907[/C][C]0.2859[/C][C]0.387976[/C][/ROW]
[ROW][C]12[/C][C]0.031741[/C][C]0.2459[/C][C]0.403314[/C][/ROW]
[ROW][C]13[/C][C]-0.231815[/C][C]-1.7956[/C][C]0.038794[/C][/ROW]
[ROW][C]14[/C][C]-0.056125[/C][C]-0.4347[/C][C]0.332656[/C][/ROW]
[ROW][C]15[/C][C]0.074926[/C][C]0.5804[/C][C]0.281917[/C][/ROW]
[ROW][C]16[/C][C]0.031713[/C][C]0.2456[/C][C]0.403398[/C][/ROW]
[ROW][C]17[/C][C]-0.072012[/C][C]-0.5578[/C][C]0.289527[/C][/ROW]
[ROW][C]18[/C][C]-0.045507[/C][C]-0.3525[/C][C]0.362851[/C][/ROW]
[ROW][C]19[/C][C]-0.095354[/C][C]-0.7386[/C][C]0.231513[/C][/ROW]
[ROW][C]20[/C][C]-0.011551[/C][C]-0.0895[/C][C]0.4645[/C][/ROW]
[ROW][C]21[/C][C]-0.127159[/C][C]-0.985[/C][C]0.164296[/C][/ROW]
[ROW][C]22[/C][C]0.022969[/C][C]0.1779[/C][C]0.429694[/C][/ROW]
[ROW][C]23[/C][C]0.046468[/C][C]0.3599[/C][C]0.360077[/C][/ROW]
[ROW][C]24[/C][C]-0.037956[/C][C]-0.294[/C][C]0.384885[/C][/ROW]
[ROW][C]25[/C][C]-0.124253[/C][C]-0.9625[/C][C]0.169841[/C][/ROW]
[ROW][C]26[/C][C]-0.054323[/C][C]-0.4208[/C][C]0.337709[/C][/ROW]
[ROW][C]27[/C][C]0.108041[/C][C]0.8369[/C][C]0.202989[/C][/ROW]
[ROW][C]28[/C][C]-0.103101[/C][C]-0.7986[/C][C]0.213832[/C][/ROW]
[ROW][C]29[/C][C]-0.046878[/C][C]-0.3631[/C][C]0.358896[/C][/ROW]
[ROW][C]30[/C][C]-0.082689[/C][C]-0.6405[/C][C]0.26214[/C][/ROW]
[ROW][C]31[/C][C]0.011552[/C][C]0.0895[/C][C]0.464498[/C][/ROW]
[ROW][C]32[/C][C]-0.040572[/C][C]-0.3143[/C][C]0.377205[/C][/ROW]
[ROW][C]33[/C][C]-0.004642[/C][C]-0.036[/C][C]0.485717[/C][/ROW]
[ROW][C]34[/C][C]-0.035994[/C][C]-0.2788[/C][C]0.390674[/C][/ROW]
[ROW][C]35[/C][C]0.025289[/C][C]0.1959[/C][C]0.42268[/C][/ROW]
[ROW][C]36[/C][C]0.066698[/C][C]0.5166[/C][C]0.303654[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28198&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28198&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:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9130037.07210
2-0.129345-1.00190.160208
30.076030.58890.279062
40.0177050.13710.445688
50.0789490.61150.271578
6-0.124761-0.96640.168864
7-0.219835-1.70280.046887
8-0.062822-0.48660.314153
90.0025390.01970.492187
10-0.079948-0.61930.269041
110.0369070.28590.387976
120.0317410.24590.403314
13-0.231815-1.79560.038794
14-0.056125-0.43470.332656
150.0749260.58040.281917
160.0317130.24560.403398
17-0.072012-0.55780.289527
18-0.045507-0.35250.362851
19-0.095354-0.73860.231513
20-0.011551-0.08950.4645
21-0.127159-0.9850.164296
220.0229690.17790.429694
230.0464680.35990.360077
24-0.037956-0.2940.384885
25-0.124253-0.96250.169841
26-0.054323-0.42080.337709
270.1080410.83690.202989
28-0.103101-0.79860.213832
29-0.046878-0.36310.358896
30-0.082689-0.64050.26214
310.0115520.08950.464498
32-0.040572-0.31430.377205
33-0.004642-0.0360.485717
34-0.035994-0.27880.390674
350.0252890.19590.42268
360.0666980.51660.303654


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
R code (references can be found in the software module):
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
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,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')