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R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Tue, 02 Dec 2008 07:36:46 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228228637yk4ggzx1vtadc3c.htm/, Retrieved Sun, 19 May 2024 10:10:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27866, Retrieved Sun, 19 May 2024 10:10:26 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

192

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    [(Partial) Autocorrelation Function] [] [2008-12-02 14:36:46] [b5935c41f1031f8c061510fc5ad27e97] [Current]

Feedback Forum

2008-12-07 17:06:42 [Samira Zeroual] [reply] 
Wanneer lamda -0.3 is. Bekomen we een perfecte stationaire tijdsreeks.  
 
2008-12-08 18:05:51 [Tinneke De Bock] [reply] 
De student hanteerde de verkeerde calculator. Hij/zij gebruikte de (partiële) autocorrelatie, terwijl hier gevraagd werd naar de juiste lambda transformatie. Om deze te vinden dienen we een standaard deviatie- mean plot te gebruiken. Hierin kiezen we een periode voor 12 maanden. Uit de regressietabel met natuurlijke logaritmes kunnen we dan een waarde voor lambda aflezen gelijk aan 0,31. Deze lambda transformatie is dus het beste om te differentiëren. De mogelijkheid dat we ons hierbij vergissen is zeer klein aangezien de p-waarde een waarde aanneemt die nadert tot 0.
2008-12-08 19:19:33 [Marlies Polfliet] [reply] 
De student gebruikt hier inderdaad de verkeerde software, hij/zij gebruikt hier de (Partial) Autocorrelation Function in plaats van Standard Deviation-Mean Plot (zoals als hint in de opdracht stond aangegeven).  
We zoeken namelijk naar de lambda (transformatie). 
Op de standard deviation mean plot zien we dat de spreiding niet constant is. We moeten dit dus modelleren. Om het model stationair te maken moet er een horizontale trend zijn en een gelijke spreiding. Daarom gaan we proberen de spreiding eruit te halen = variantie stationair maken. 
Eerst kijken we naar de tweede tabel, hier zien we dat de p-waarde kleiner is dan 1%, met andere woorden: er is minder dan 1 % kans dat we ons vergissen bij het verwerpen van de nulhypothese. 
Door uiteindelijk naar de derde tabel te kijken, kunnen we lambda bepalen. We kunnen besluiten dat de optimale lambda om een transformatie uit te voeren hier -0.3 blijkt te zijn.  

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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=27866&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=27866&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27866&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.948047	11.3766	0
2	0.875575	10.5069	0
3	0.806681	9.6802	0
4	0.752625	9.0315	0
5	0.71377	8.5652	0
6	0.681734	8.1808	0
7	0.662904	7.9549	0
8	0.65561	7.8673	0
9	0.670948	8.0514	0
10	0.70272	8.4326	0
11	0.74324	8.9189	0
12	0.760395	9.1247	0
13	0.712661	8.5519	0
14	0.646342	7.7561	0
15	0.585923	7.0311	0
16	0.537955	6.4555	0
17	0.499748	5.997	0
18	0.468734	5.6248	0
19	0.449871	5.3984	0
20	0.441629	5.2995	0
21	0.457224	5.4867	0
22	0.482482	5.7898	0
23	0.517127	6.2055	0
24	0.53219	6.3863	0
25	0.493976	5.9277	0
26	0.437721	5.2527	0
27	0.387603	4.6512	4e-06
28	0.348025	4.1763	2.6e-05
29	0.314984	3.7798	0.000115
30	0.288497	3.462	0.000353
31	0.270802	3.2496	0.000719
32	0.26429	3.1715	0.000927
33	0.276799	3.3216	0.000567
34	0.298521	3.5823	0.000233
35	0.325587	3.907	7.2e-05
36	0.337024	4.0443	4.3e-05

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & 0.875575 & 10.5069 & 0 \tabularnewline
3 & 0.806681 & 9.6802 & 0 \tabularnewline
4 & 0.752625 & 9.0315 & 0 \tabularnewline
5 & 0.71377 & 8.5652 & 0 \tabularnewline
6 & 0.681734 & 8.1808 & 0 \tabularnewline
7 & 0.662904 & 7.9549 & 0 \tabularnewline
8 & 0.65561 & 7.8673 & 0 \tabularnewline
9 & 0.670948 & 8.0514 & 0 \tabularnewline
10 & 0.70272 & 8.4326 & 0 \tabularnewline
11 & 0.74324 & 8.9189 & 0 \tabularnewline
12 & 0.760395 & 9.1247 & 0 \tabularnewline
13 & 0.712661 & 8.5519 & 0 \tabularnewline
14 & 0.646342 & 7.7561 & 0 \tabularnewline
15 & 0.585923 & 7.0311 & 0 \tabularnewline
16 & 0.537955 & 6.4555 & 0 \tabularnewline
17 & 0.499748 & 5.997 & 0 \tabularnewline
18 & 0.468734 & 5.6248 & 0 \tabularnewline
19 & 0.449871 & 5.3984 & 0 \tabularnewline
20 & 0.441629 & 5.2995 & 0 \tabularnewline
21 & 0.457224 & 5.4867 & 0 \tabularnewline
22 & 0.482482 & 5.7898 & 0 \tabularnewline
23 & 0.517127 & 6.2055 & 0 \tabularnewline
24 & 0.53219 & 6.3863 & 0 \tabularnewline
25 & 0.493976 & 5.9277 & 0 \tabularnewline
26 & 0.437721 & 5.2527 & 0 \tabularnewline
27 & 0.387603 & 4.6512 & 4e-06 \tabularnewline
28 & 0.348025 & 4.1763 & 2.6e-05 \tabularnewline
29 & 0.314984 & 3.7798 & 0.000115 \tabularnewline
30 & 0.288497 & 3.462 & 0.000353 \tabularnewline
31 & 0.270802 & 3.2496 & 0.000719 \tabularnewline
32 & 0.26429 & 3.1715 & 0.000927 \tabularnewline
33 & 0.276799 & 3.3216 & 0.000567 \tabularnewline
34 & 0.298521 & 3.5823 & 0.000233 \tabularnewline
35 & 0.325587 & 3.907 & 7.2e-05 \tabularnewline
36 & 0.337024 & 4.0443 & 4.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27866&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.875575[/C][C]10.5069[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.806681[/C][C]9.6802[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.752625[/C][C]9.0315[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.71377[/C][C]8.5652[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.681734[/C][C]8.1808[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.662904[/C][C]7.9549[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.65561[/C][C]7.8673[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.670948[/C][C]8.0514[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.70272[/C][C]8.4326[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.74324[/C][C]8.9189[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.760395[/C][C]9.1247[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.712661[/C][C]8.5519[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.646342[/C][C]7.7561[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.585923[/C][C]7.0311[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.537955[/C][C]6.4555[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.499748[/C][C]5.997[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.468734[/C][C]5.6248[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.449871[/C][C]5.3984[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.441629[/C][C]5.2995[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.457224[/C][C]5.4867[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.482482[/C][C]5.7898[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]0.517127[/C][C]6.2055[/C][C]0[/C][/ROW]
[ROW][C]24[/C][C]0.53219[/C][C]6.3863[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.493976[/C][C]5.9277[/C][C]0[/C][/ROW]
[ROW][C]26[/C][C]0.437721[/C][C]5.2527[/C][C]0[/C][/ROW]
[ROW][C]27[/C][C]0.387603[/C][C]4.6512[/C][C]4e-06[/C][/ROW]
[ROW][C]28[/C][C]0.348025[/C][C]4.1763[/C][C]2.6e-05[/C][/ROW]
[ROW][C]29[/C][C]0.314984[/C][C]3.7798[/C][C]0.000115[/C][/ROW]
[ROW][C]30[/C][C]0.288497[/C][C]3.462[/C][C]0.000353[/C][/ROW]
[ROW][C]31[/C][C]0.270802[/C][C]3.2496[/C][C]0.000719[/C][/ROW]
[ROW][C]32[/C][C]0.26429[/C][C]3.1715[/C][C]0.000927[/C][/ROW]
[ROW][C]33[/C][C]0.276799[/C][C]3.3216[/C][C]0.000567[/C][/ROW]
[ROW][C]34[/C][C]0.298521[/C][C]3.5823[/C][C]0.000233[/C][/ROW]
[ROW][C]35[/C][C]0.325587[/C][C]3.907[/C][C]7.2e-05[/C][/ROW]
[ROW][C]36[/C][C]0.337024[/C][C]4.0443[/C][C]4.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27866&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27866&T=1

As an alternative you can also use a QR Code:

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

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.948047	11.3766	0
2	0.875575	10.5069	0
3	0.806681	9.6802	0
4	0.752625	9.0315	0
5	0.71377	8.5652	0
6	0.681734	8.1808	0
7	0.662904	7.9549	0
8	0.65561	7.8673	0
9	0.670948	8.0514	0
10	0.70272	8.4326	0
11	0.74324	8.9189	0
12	0.760395	9.1247	0
13	0.712661	8.5519	0
14	0.646342	7.7561	0
15	0.585923	7.0311	0
16	0.537955	6.4555	0
17	0.499748	5.997	0
18	0.468734	5.6248	0
19	0.449871	5.3984	0
20	0.441629	5.2995	0
21	0.457224	5.4867	0
22	0.482482	5.7898	0
23	0.517127	6.2055	0
24	0.53219	6.3863	0
25	0.493976	5.9277	0
26	0.437721	5.2527	0
27	0.387603	4.6512	4e-06
28	0.348025	4.1763	2.6e-05
29	0.314984	3.7798	0.000115
30	0.288497	3.462	0.000353
31	0.270802	3.2496	0.000719
32	0.26429	3.1715	0.000927
33	0.276799	3.3216	0.000567
34	0.298521	3.5823	0.000233
35	0.325587	3.907	7.2e-05
36	0.337024	4.0443	4.3e-05

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.948047	11.3766	0
2	-0.229422	-2.7531	0.003332
3	0.038148	0.4578	0.323903
4	0.093785	1.1254	0.131141
5	0.073607	0.8833	0.189279
6	0.007728	0.0927	0.463123
7	0.125597	1.5072	0.066979
8	0.089951	1.0794	0.141103
9	0.232489	2.7899	0.002994
10	0.166051	1.9926	0.024097
11	0.171274	2.0553	0.020829
12	-0.135431	-1.6252	0.053156
13	-0.539691	-6.4763	0
14	-0.02661	-0.3193	0.374973
15	0.090765	1.0892	0.138947
16	0.024956	0.2995	0.382508
17	0.032516	0.3902	0.348487
18	0.073433	0.8812	0.189841
19	0.048442	0.5813	0.280972
20	-0.045542	-0.5465	0.292784
21	0.045753	0.549	0.291916
22	-0.100179	-1.2021	0.11564
23	0.052435	0.6292	0.265101
24	0.048014	0.5762	0.2827
25	-0.162746	-1.953	0.026382
26	-0.036135	-0.4336	0.332607
27	0.066424	0.7971	0.213357
28	0.006176	0.0741	0.470511
29	0.007537	0.0904	0.464029
30	0.01935	0.2322	0.408354
31	-0.010251	-0.123	0.451132
32	-0.01831	-0.2197	0.413199
33	-0.029001	-0.348	0.364168
34	-0.014805	-0.1777	0.42962
35	-0.047725	-0.5727	0.283872
36	0.046204	0.5544	0.290068

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & -0.229422 & -2.7531 & 0.003332 \tabularnewline
3 & 0.038148 & 0.4578 & 0.323903 \tabularnewline
4 & 0.093785 & 1.1254 & 0.131141 \tabularnewline
5 & 0.073607 & 0.8833 & 0.189279 \tabularnewline
6 & 0.007728 & 0.0927 & 0.463123 \tabularnewline
7 & 0.125597 & 1.5072 & 0.066979 \tabularnewline
8 & 0.089951 & 1.0794 & 0.141103 \tabularnewline
9 & 0.232489 & 2.7899 & 0.002994 \tabularnewline
10 & 0.166051 & 1.9926 & 0.024097 \tabularnewline
11 & 0.171274 & 2.0553 & 0.020829 \tabularnewline
12 & -0.135431 & -1.6252 & 0.053156 \tabularnewline
13 & -0.539691 & -6.4763 & 0 \tabularnewline
14 & -0.02661 & -0.3193 & 0.374973 \tabularnewline
15 & 0.090765 & 1.0892 & 0.138947 \tabularnewline
16 & 0.024956 & 0.2995 & 0.382508 \tabularnewline
17 & 0.032516 & 0.3902 & 0.348487 \tabularnewline
18 & 0.073433 & 0.8812 & 0.189841 \tabularnewline
19 & 0.048442 & 0.5813 & 0.280972 \tabularnewline
20 & -0.045542 & -0.5465 & 0.292784 \tabularnewline
21 & 0.045753 & 0.549 & 0.291916 \tabularnewline
22 & -0.100179 & -1.2021 & 0.11564 \tabularnewline
23 & 0.052435 & 0.6292 & 0.265101 \tabularnewline
24 & 0.048014 & 0.5762 & 0.2827 \tabularnewline
25 & -0.162746 & -1.953 & 0.026382 \tabularnewline
26 & -0.036135 & -0.4336 & 0.332607 \tabularnewline
27 & 0.066424 & 0.7971 & 0.213357 \tabularnewline
28 & 0.006176 & 0.0741 & 0.470511 \tabularnewline
29 & 0.007537 & 0.0904 & 0.464029 \tabularnewline
30 & 0.01935 & 0.2322 & 0.408354 \tabularnewline
31 & -0.010251 & -0.123 & 0.451132 \tabularnewline
32 & -0.01831 & -0.2197 & 0.413199 \tabularnewline
33 & -0.029001 & -0.348 & 0.364168 \tabularnewline
34 & -0.014805 & -0.1777 & 0.42962 \tabularnewline
35 & -0.047725 & -0.5727 & 0.283872 \tabularnewline
36 & 0.046204 & 0.5544 & 0.290068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27866&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.229422[/C][C]-2.7531[/C][C]0.003332[/C][/ROW]
[ROW][C]3[/C][C]0.038148[/C][C]0.4578[/C][C]0.323903[/C][/ROW]
[ROW][C]4[/C][C]0.093785[/C][C]1.1254[/C][C]0.131141[/C][/ROW]
[ROW][C]5[/C][C]0.073607[/C][C]0.8833[/C][C]0.189279[/C][/ROW]
[ROW][C]6[/C][C]0.007728[/C][C]0.0927[/C][C]0.463123[/C][/ROW]
[ROW][C]7[/C][C]0.125597[/C][C]1.5072[/C][C]0.066979[/C][/ROW]
[ROW][C]8[/C][C]0.089951[/C][C]1.0794[/C][C]0.141103[/C][/ROW]
[ROW][C]9[/C][C]0.232489[/C][C]2.7899[/C][C]0.002994[/C][/ROW]
[ROW][C]10[/C][C]0.166051[/C][C]1.9926[/C][C]0.024097[/C][/ROW]
[ROW][C]11[/C][C]0.171274[/C][C]2.0553[/C][C]0.020829[/C][/ROW]
[ROW][C]12[/C][C]-0.135431[/C][C]-1.6252[/C][C]0.053156[/C][/ROW]
[ROW][C]13[/C][C]-0.539691[/C][C]-6.4763[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]-0.02661[/C][C]-0.3193[/C][C]0.374973[/C][/ROW]
[ROW][C]15[/C][C]0.090765[/C][C]1.0892[/C][C]0.138947[/C][/ROW]
[ROW][C]16[/C][C]0.024956[/C][C]0.2995[/C][C]0.382508[/C][/ROW]
[ROW][C]17[/C][C]0.032516[/C][C]0.3902[/C][C]0.348487[/C][/ROW]
[ROW][C]18[/C][C]0.073433[/C][C]0.8812[/C][C]0.189841[/C][/ROW]
[ROW][C]19[/C][C]0.048442[/C][C]0.5813[/C][C]0.280972[/C][/ROW]
[ROW][C]20[/C][C]-0.045542[/C][C]-0.5465[/C][C]0.292784[/C][/ROW]
[ROW][C]21[/C][C]0.045753[/C][C]0.549[/C][C]0.291916[/C][/ROW]
[ROW][C]22[/C][C]-0.100179[/C][C]-1.2021[/C][C]0.11564[/C][/ROW]
[ROW][C]23[/C][C]0.052435[/C][C]0.6292[/C][C]0.265101[/C][/ROW]
[ROW][C]24[/C][C]0.048014[/C][C]0.5762[/C][C]0.2827[/C][/ROW]
[ROW][C]25[/C][C]-0.162746[/C][C]-1.953[/C][C]0.026382[/C][/ROW]
[ROW][C]26[/C][C]-0.036135[/C][C]-0.4336[/C][C]0.332607[/C][/ROW]
[ROW][C]27[/C][C]0.066424[/C][C]0.7971[/C][C]0.213357[/C][/ROW]
[ROW][C]28[/C][C]0.006176[/C][C]0.0741[/C][C]0.470511[/C][/ROW]
[ROW][C]29[/C][C]0.007537[/C][C]0.0904[/C][C]0.464029[/C][/ROW]
[ROW][C]30[/C][C]0.01935[/C][C]0.2322[/C][C]0.408354[/C][/ROW]
[ROW][C]31[/C][C]-0.010251[/C][C]-0.123[/C][C]0.451132[/C][/ROW]
[ROW][C]32[/C][C]-0.01831[/C][C]-0.2197[/C][C]0.413199[/C][/ROW]
[ROW][C]33[/C][C]-0.029001[/C][C]-0.348[/C][C]0.364168[/C][/ROW]
[ROW][C]34[/C][C]-0.014805[/C][C]-0.1777[/C][C]0.42962[/C][/ROW]
[ROW][C]35[/C][C]-0.047725[/C][C]-0.5727[/C][C]0.283872[/C][/ROW]
[ROW][C]36[/C][C]0.046204[/C][C]0.5544[/C][C]0.290068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27866&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27866&T=2

As an alternative you can also use a QR Code:

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

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.948047	11.3766	0
2	-0.229422	-2.7531	0.003332
3	0.038148	0.4578	0.323903
4	0.093785	1.1254	0.131141
5	0.073607	0.8833	0.189279
6	0.007728	0.0927	0.463123
7	0.125597	1.5072	0.066979
8	0.089951	1.0794	0.141103
9	0.232489	2.7899	0.002994
10	0.166051	1.9926	0.024097
11	0.171274	2.0553	0.020829
12	-0.135431	-1.6252	0.053156
13	-0.539691	-6.4763	0
14	-0.02661	-0.3193	0.374973
15	0.090765	1.0892	0.138947
16	0.024956	0.2995	0.382508
17	0.032516	0.3902	0.348487
18	0.073433	0.8812	0.189841
19	0.048442	0.5813	0.280972
20	-0.045542	-0.5465	0.292784
21	0.045753	0.549	0.291916
22	-0.100179	-1.2021	0.11564
23	0.052435	0.6292	0.265101
24	0.048014	0.5762	0.2827
25	-0.162746	-1.953	0.026382
26	-0.036135	-0.4336	0.332607
27	0.066424	0.7971	0.213357
28	0.006176	0.0741	0.470511
29	0.007537	0.0904	0.464029
30	0.01935	0.2322	0.408354
31	-0.010251	-0.123	0.451132
32	-0.01831	-0.2197	0.413199
33	-0.029001	-0.348	0.364168
34	-0.014805	-0.1777	0.42962
35	-0.047725	-0.5727	0.283872
36	0.046204	0.5544	0.290068

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code