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rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Sat, 06 Dec 2008 11:17:03 -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/06/t1228587531ij1miuhrubvu9jv.htm/, Retrieved Tue, 28 May 2024 01:04:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29790, Retrieved Tue, 28 May 2024 01:04:19 +0000

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User-defined keywords

Estimated Impact

215

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Variance Reduction Matrix] [step 2] [2008-12-06 09:12:19] [9f5bfe3b95f9ec3d2ed4c0a560a9648a]
F RMPD      [(Partial) Autocorrelation Function] [step 2] [2008-12-06 18:17:03] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]
F   P         [(Partial) Autocorrelation Function] [step 3] [2008-12-06 18:22:51] [9f5bfe3b95f9ec3d2ed4c0a560a9648a] 

Feedback Forum

2008-12-13 11:22:48 [Ken Wright] [reply] 
juist, Autocorrelatie is een statistisch hulpmiddel om terugkerende patronen te kunnen identificeren. Deze geeft de bijvoorbeeld de correlatie weer van X op tijdstip t en X 2 perioden vertraagd dus X t-2. Autocorrelatie duidt er dus op dat er kan worden voorspeld op basis van het verleden. Het zal aanduiden of er niet-seizoenaal en/of seizoenaal zal moeten worden gedifferentieerd. De differentiatie zal dan proberen de tijdreeks zodanig te differentiëren zodat er geen autocorrelatie meer aanwezig is.
2008-12-16 19:03:02 [Kevin Vermeiren] [reply] 
De student besluit terecht dat de lange termijn trend en de seizoenaliteit uit de tijdreeks gehaald werden. Verder had nog vermeld kunnen waarom dit zo is. We zien duidelijk geen dalend patroon meer van de seizoenale autocorrelatie coëfficiënten en geen patroon meer. De differentiatie met d=1 en D=1 blijkt voldoende 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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29790&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29790&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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.480171	-4.2679	2.7e-05
2	-0.088768	-0.789	0.21624
3	0.336208	2.9883	0.001869
4	-0.236827	-2.105	0.019236
5	0.012161	0.1081	0.4571
6	0.207696	1.846	0.034316
7	-0.212487	-1.8886	0.031306
8	-0.097451	-0.8662	0.194512
9	0.295778	2.6289	0.005145
10	-0.221283	-1.9668	0.026359
11	0.001136	0.0101	0.495985
12	0.049537	0.4403	0.330464
13	-0.131858	-1.172	0.122363
14	0.038221	0.3397	0.367487
15	0.044485	0.3954	0.346809
16	-0.160522	-1.4268	0.078796
17	0.136362	1.212	0.11456
18	0.049063	0.4361	0.331984
19	-0.165465	-1.4707	0.072674
20	0.188563	1.676	0.048848
21	0.018932	0.1683	0.433399
22	-0.199201	-1.7705	0.040247
23	0.318203	2.8282	0.002965
24	-0.241153	-2.1434	0.017578
25	0.027661	0.2459	0.403215
26	0.218687	1.9437	0.027744
27	-0.15414	-1.37	0.08728
28	-0.085405	-0.7591	0.225028
29	0.178374	1.5854	0.058433
30	-0.104109	-0.9253	0.178803
31	-0.04378	-0.3891	0.349117
32	0.105685	0.9394	0.175207
33	-0.191882	-1.7055	0.046017
34	0.116329	1.034	0.152157
35	0.036381	0.3234	0.373638
36	-0.102881	-0.9144	0.181637

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.480171 & -4.2679 & 2.7e-05 \tabularnewline
2 & -0.088768 & -0.789 & 0.21624 \tabularnewline
3 & 0.336208 & 2.9883 & 0.001869 \tabularnewline
4 & -0.236827 & -2.105 & 0.019236 \tabularnewline
5 & 0.012161 & 0.1081 & 0.4571 \tabularnewline
6 & 0.207696 & 1.846 & 0.034316 \tabularnewline
7 & -0.212487 & -1.8886 & 0.031306 \tabularnewline
8 & -0.097451 & -0.8662 & 0.194512 \tabularnewline
9 & 0.295778 & 2.6289 & 0.005145 \tabularnewline
10 & -0.221283 & -1.9668 & 0.026359 \tabularnewline
11 & 0.001136 & 0.0101 & 0.495985 \tabularnewline
12 & 0.049537 & 0.4403 & 0.330464 \tabularnewline
13 & -0.131858 & -1.172 & 0.122363 \tabularnewline
14 & 0.038221 & 0.3397 & 0.367487 \tabularnewline
15 & 0.044485 & 0.3954 & 0.346809 \tabularnewline
16 & -0.160522 & -1.4268 & 0.078796 \tabularnewline
17 & 0.136362 & 1.212 & 0.11456 \tabularnewline
18 & 0.049063 & 0.4361 & 0.331984 \tabularnewline
19 & -0.165465 & -1.4707 & 0.072674 \tabularnewline
20 & 0.188563 & 1.676 & 0.048848 \tabularnewline
21 & 0.018932 & 0.1683 & 0.433399 \tabularnewline
22 & -0.199201 & -1.7705 & 0.040247 \tabularnewline
23 & 0.318203 & 2.8282 & 0.002965 \tabularnewline
24 & -0.241153 & -2.1434 & 0.017578 \tabularnewline
25 & 0.027661 & 0.2459 & 0.403215 \tabularnewline
26 & 0.218687 & 1.9437 & 0.027744 \tabularnewline
27 & -0.15414 & -1.37 & 0.08728 \tabularnewline
28 & -0.085405 & -0.7591 & 0.225028 \tabularnewline
29 & 0.178374 & 1.5854 & 0.058433 \tabularnewline
30 & -0.104109 & -0.9253 & 0.178803 \tabularnewline
31 & -0.04378 & -0.3891 & 0.349117 \tabularnewline
32 & 0.105685 & 0.9394 & 0.175207 \tabularnewline
33 & -0.191882 & -1.7055 & 0.046017 \tabularnewline
34 & 0.116329 & 1.034 & 0.152157 \tabularnewline
35 & 0.036381 & 0.3234 & 0.373638 \tabularnewline
36 & -0.102881 & -0.9144 & 0.181637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29790&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]-0.480171[/C][C]-4.2679[/C][C]2.7e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.088768[/C][C]-0.789[/C][C]0.21624[/C][/ROW]
[ROW][C]3[/C][C]0.336208[/C][C]2.9883[/C][C]0.001869[/C][/ROW]
[ROW][C]4[/C][C]-0.236827[/C][C]-2.105[/C][C]0.019236[/C][/ROW]
[ROW][C]5[/C][C]0.012161[/C][C]0.1081[/C][C]0.4571[/C][/ROW]
[ROW][C]6[/C][C]0.207696[/C][C]1.846[/C][C]0.034316[/C][/ROW]
[ROW][C]7[/C][C]-0.212487[/C][C]-1.8886[/C][C]0.031306[/C][/ROW]
[ROW][C]8[/C][C]-0.097451[/C][C]-0.8662[/C][C]0.194512[/C][/ROW]
[ROW][C]9[/C][C]0.295778[/C][C]2.6289[/C][C]0.005145[/C][/ROW]
[ROW][C]10[/C][C]-0.221283[/C][C]-1.9668[/C][C]0.026359[/C][/ROW]
[ROW][C]11[/C][C]0.001136[/C][C]0.0101[/C][C]0.495985[/C][/ROW]
[ROW][C]12[/C][C]0.049537[/C][C]0.4403[/C][C]0.330464[/C][/ROW]
[ROW][C]13[/C][C]-0.131858[/C][C]-1.172[/C][C]0.122363[/C][/ROW]
[ROW][C]14[/C][C]0.038221[/C][C]0.3397[/C][C]0.367487[/C][/ROW]
[ROW][C]15[/C][C]0.044485[/C][C]0.3954[/C][C]0.346809[/C][/ROW]
[ROW][C]16[/C][C]-0.160522[/C][C]-1.4268[/C][C]0.078796[/C][/ROW]
[ROW][C]17[/C][C]0.136362[/C][C]1.212[/C][C]0.11456[/C][/ROW]
[ROW][C]18[/C][C]0.049063[/C][C]0.4361[/C][C]0.331984[/C][/ROW]
[ROW][C]19[/C][C]-0.165465[/C][C]-1.4707[/C][C]0.072674[/C][/ROW]
[ROW][C]20[/C][C]0.188563[/C][C]1.676[/C][C]0.048848[/C][/ROW]
[ROW][C]21[/C][C]0.018932[/C][C]0.1683[/C][C]0.433399[/C][/ROW]
[ROW][C]22[/C][C]-0.199201[/C][C]-1.7705[/C][C]0.040247[/C][/ROW]
[ROW][C]23[/C][C]0.318203[/C][C]2.8282[/C][C]0.002965[/C][/ROW]
[ROW][C]24[/C][C]-0.241153[/C][C]-2.1434[/C][C]0.017578[/C][/ROW]
[ROW][C]25[/C][C]0.027661[/C][C]0.2459[/C][C]0.403215[/C][/ROW]
[ROW][C]26[/C][C]0.218687[/C][C]1.9437[/C][C]0.027744[/C][/ROW]
[ROW][C]27[/C][C]-0.15414[/C][C]-1.37[/C][C]0.08728[/C][/ROW]
[ROW][C]28[/C][C]-0.085405[/C][C]-0.7591[/C][C]0.225028[/C][/ROW]
[ROW][C]29[/C][C]0.178374[/C][C]1.5854[/C][C]0.058433[/C][/ROW]
[ROW][C]30[/C][C]-0.104109[/C][C]-0.9253[/C][C]0.178803[/C][/ROW]
[ROW][C]31[/C][C]-0.04378[/C][C]-0.3891[/C][C]0.349117[/C][/ROW]
[ROW][C]32[/C][C]0.105685[/C][C]0.9394[/C][C]0.175207[/C][/ROW]
[ROW][C]33[/C][C]-0.191882[/C][C]-1.7055[/C][C]0.046017[/C][/ROW]
[ROW][C]34[/C][C]0.116329[/C][C]1.034[/C][C]0.152157[/C][/ROW]
[ROW][C]35[/C][C]0.036381[/C][C]0.3234[/C][C]0.373638[/C][/ROW]
[ROW][C]36[/C][C]-0.102881[/C][C]-0.9144[/C][C]0.181637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29790&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	-0.480171	-4.2679	2.7e-05
2	-0.088768	-0.789	0.21624
3	0.336208	2.9883	0.001869
4	-0.236827	-2.105	0.019236
5	0.012161	0.1081	0.4571
6	0.207696	1.846	0.034316
7	-0.212487	-1.8886	0.031306
8	-0.097451	-0.8662	0.194512
9	0.295778	2.6289	0.005145
10	-0.221283	-1.9668	0.026359
11	0.001136	0.0101	0.495985
12	0.049537	0.4403	0.330464
13	-0.131858	-1.172	0.122363
14	0.038221	0.3397	0.367487
15	0.044485	0.3954	0.346809
16	-0.160522	-1.4268	0.078796
17	0.136362	1.212	0.11456
18	0.049063	0.4361	0.331984
19	-0.165465	-1.4707	0.072674
20	0.188563	1.676	0.048848
21	0.018932	0.1683	0.433399
22	-0.199201	-1.7705	0.040247
23	0.318203	2.8282	0.002965
24	-0.241153	-2.1434	0.017578
25	0.027661	0.2459	0.403215
26	0.218687	1.9437	0.027744
27	-0.15414	-1.37	0.08728
28	-0.085405	-0.7591	0.225028
29	0.178374	1.5854	0.058433
30	-0.104109	-0.9253	0.178803
31	-0.04378	-0.3891	0.349117
32	0.105685	0.9394	0.175207
33	-0.191882	-1.7055	0.046017
34	0.116329	1.034	0.152157
35	0.036381	0.3234	0.373638
36	-0.102881	-0.9144	0.181637

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.480171	-4.2679	2.7e-05
2	-0.415022	-3.6888	0.000206
3	0.120288	1.0691	0.14413
4	0.004778	0.0425	0.483116
5	-0.019606	-0.1743	0.431052
6	0.140288	1.2469	0.108058
7	0.009832	0.0874	0.465292
8	-0.262606	-2.3341	0.011067
9	0.019155	0.1703	0.432623
10	-0.00048	-0.0043	0.498304
11	-0.00118	-0.0105	0.49583
12	-0.173728	-1.5441	0.063277
13	-0.161043	-1.4314	0.078132
14	-0.13097	-1.1641	0.123947
15	-0.109964	-0.9774	0.165682
16	-0.208131	-1.8499	0.034033
17	0.037227	0.3309	0.370805
18	0.128078	1.1384	0.129202
19	-0.035766	-0.3179	0.375703
20	0.016893	0.1502	0.440513
21	0.183437	1.6304	0.053496
22	-0.060902	-0.5413	0.294908
23	0.140162	1.2458	0.108263
24	-0.165055	-1.467	0.073167
25	0.016778	0.1491	0.440919
26	0.036743	0.3266	0.372425
27	0.075792	0.6737	0.251249
28	-0.128972	-1.1463	0.127561
29	-0.031941	-0.2839	0.388614
30	-0.029729	-0.2642	0.396142
31	0.070639	0.6279	0.265954
32	-0.09266	-0.8236	0.206328
33	-0.020823	-0.1851	0.426822
34	0.097487	0.8665	0.194426
35	0.076177	0.6771	0.250169
36	-0.054237	-0.4821	0.315546

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.480171 & -4.2679 & 2.7e-05 \tabularnewline
2 & -0.415022 & -3.6888 & 0.000206 \tabularnewline
3 & 0.120288 & 1.0691 & 0.14413 \tabularnewline
4 & 0.004778 & 0.0425 & 0.483116 \tabularnewline
5 & -0.019606 & -0.1743 & 0.431052 \tabularnewline
6 & 0.140288 & 1.2469 & 0.108058 \tabularnewline
7 & 0.009832 & 0.0874 & 0.465292 \tabularnewline
8 & -0.262606 & -2.3341 & 0.011067 \tabularnewline
9 & 0.019155 & 0.1703 & 0.432623 \tabularnewline
10 & -0.00048 & -0.0043 & 0.498304 \tabularnewline
11 & -0.00118 & -0.0105 & 0.49583 \tabularnewline
12 & -0.173728 & -1.5441 & 0.063277 \tabularnewline
13 & -0.161043 & -1.4314 & 0.078132 \tabularnewline
14 & -0.13097 & -1.1641 & 0.123947 \tabularnewline
15 & -0.109964 & -0.9774 & 0.165682 \tabularnewline
16 & -0.208131 & -1.8499 & 0.034033 \tabularnewline
17 & 0.037227 & 0.3309 & 0.370805 \tabularnewline
18 & 0.128078 & 1.1384 & 0.129202 \tabularnewline
19 & -0.035766 & -0.3179 & 0.375703 \tabularnewline
20 & 0.016893 & 0.1502 & 0.440513 \tabularnewline
21 & 0.183437 & 1.6304 & 0.053496 \tabularnewline
22 & -0.060902 & -0.5413 & 0.294908 \tabularnewline
23 & 0.140162 & 1.2458 & 0.108263 \tabularnewline
24 & -0.165055 & -1.467 & 0.073167 \tabularnewline
25 & 0.016778 & 0.1491 & 0.440919 \tabularnewline
26 & 0.036743 & 0.3266 & 0.372425 \tabularnewline
27 & 0.075792 & 0.6737 & 0.251249 \tabularnewline
28 & -0.128972 & -1.1463 & 0.127561 \tabularnewline
29 & -0.031941 & -0.2839 & 0.388614 \tabularnewline
30 & -0.029729 & -0.2642 & 0.396142 \tabularnewline
31 & 0.070639 & 0.6279 & 0.265954 \tabularnewline
32 & -0.09266 & -0.8236 & 0.206328 \tabularnewline
33 & -0.020823 & -0.1851 & 0.426822 \tabularnewline
34 & 0.097487 & 0.8665 & 0.194426 \tabularnewline
35 & 0.076177 & 0.6771 & 0.250169 \tabularnewline
36 & -0.054237 & -0.4821 & 0.315546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29790&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]-0.480171[/C][C]-4.2679[/C][C]2.7e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.415022[/C][C]-3.6888[/C][C]0.000206[/C][/ROW]
[ROW][C]3[/C][C]0.120288[/C][C]1.0691[/C][C]0.14413[/C][/ROW]
[ROW][C]4[/C][C]0.004778[/C][C]0.0425[/C][C]0.483116[/C][/ROW]
[ROW][C]5[/C][C]-0.019606[/C][C]-0.1743[/C][C]0.431052[/C][/ROW]
[ROW][C]6[/C][C]0.140288[/C][C]1.2469[/C][C]0.108058[/C][/ROW]
[ROW][C]7[/C][C]0.009832[/C][C]0.0874[/C][C]0.465292[/C][/ROW]
[ROW][C]8[/C][C]-0.262606[/C][C]-2.3341[/C][C]0.011067[/C][/ROW]
[ROW][C]9[/C][C]0.019155[/C][C]0.1703[/C][C]0.432623[/C][/ROW]
[ROW][C]10[/C][C]-0.00048[/C][C]-0.0043[/C][C]0.498304[/C][/ROW]
[ROW][C]11[/C][C]-0.00118[/C][C]-0.0105[/C][C]0.49583[/C][/ROW]
[ROW][C]12[/C][C]-0.173728[/C][C]-1.5441[/C][C]0.063277[/C][/ROW]
[ROW][C]13[/C][C]-0.161043[/C][C]-1.4314[/C][C]0.078132[/C][/ROW]
[ROW][C]14[/C][C]-0.13097[/C][C]-1.1641[/C][C]0.123947[/C][/ROW]
[ROW][C]15[/C][C]-0.109964[/C][C]-0.9774[/C][C]0.165682[/C][/ROW]
[ROW][C]16[/C][C]-0.208131[/C][C]-1.8499[/C][C]0.034033[/C][/ROW]
[ROW][C]17[/C][C]0.037227[/C][C]0.3309[/C][C]0.370805[/C][/ROW]
[ROW][C]18[/C][C]0.128078[/C][C]1.1384[/C][C]0.129202[/C][/ROW]
[ROW][C]19[/C][C]-0.035766[/C][C]-0.3179[/C][C]0.375703[/C][/ROW]
[ROW][C]20[/C][C]0.016893[/C][C]0.1502[/C][C]0.440513[/C][/ROW]
[ROW][C]21[/C][C]0.183437[/C][C]1.6304[/C][C]0.053496[/C][/ROW]
[ROW][C]22[/C][C]-0.060902[/C][C]-0.5413[/C][C]0.294908[/C][/ROW]
[ROW][C]23[/C][C]0.140162[/C][C]1.2458[/C][C]0.108263[/C][/ROW]
[ROW][C]24[/C][C]-0.165055[/C][C]-1.467[/C][C]0.073167[/C][/ROW]
[ROW][C]25[/C][C]0.016778[/C][C]0.1491[/C][C]0.440919[/C][/ROW]
[ROW][C]26[/C][C]0.036743[/C][C]0.3266[/C][C]0.372425[/C][/ROW]
[ROW][C]27[/C][C]0.075792[/C][C]0.6737[/C][C]0.251249[/C][/ROW]
[ROW][C]28[/C][C]-0.128972[/C][C]-1.1463[/C][C]0.127561[/C][/ROW]
[ROW][C]29[/C][C]-0.031941[/C][C]-0.2839[/C][C]0.388614[/C][/ROW]
[ROW][C]30[/C][C]-0.029729[/C][C]-0.2642[/C][C]0.396142[/C][/ROW]
[ROW][C]31[/C][C]0.070639[/C][C]0.6279[/C][C]0.265954[/C][/ROW]
[ROW][C]32[/C][C]-0.09266[/C][C]-0.8236[/C][C]0.206328[/C][/ROW]
[ROW][C]33[/C][C]-0.020823[/C][C]-0.1851[/C][C]0.426822[/C][/ROW]
[ROW][C]34[/C][C]0.097487[/C][C]0.8665[/C][C]0.194426[/C][/ROW]
[ROW][C]35[/C][C]0.076177[/C][C]0.6771[/C][C]0.250169[/C][/ROW]
[ROW][C]36[/C][C]-0.054237[/C][C]-0.4821[/C][C]0.315546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29790&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	-0.480171	-4.2679	2.7e-05
2	-0.415022	-3.6888	0.000206
3	0.120288	1.0691	0.14413
4	0.004778	0.0425	0.483116
5	-0.019606	-0.1743	0.431052
6	0.140288	1.2469	0.108058
7	0.009832	0.0874	0.465292
8	-0.262606	-2.3341	0.011067
9	0.019155	0.1703	0.432623
10	-0.00048	-0.0043	0.498304
11	-0.00118	-0.0105	0.49583
12	-0.173728	-1.5441	0.063277
13	-0.161043	-1.4314	0.078132
14	-0.13097	-1.1641	0.123947
15	-0.109964	-0.9774	0.165682
16	-0.208131	-1.8499	0.034033
17	0.037227	0.3309	0.370805
18	0.128078	1.1384	0.129202
19	-0.035766	-0.3179	0.375703
20	0.016893	0.1502	0.440513
21	0.183437	1.6304	0.053496
22	-0.060902	-0.5413	0.294908
23	0.140162	1.2458	0.108263
24	-0.165055	-1.467	0.073167
25	0.016778	0.1491	0.440919
26	0.036743	0.3266	0.372425
27	0.075792	0.6737	0.251249
28	-0.128972	-1.1463	0.127561
29	-0.031941	-0.2839	0.388614
30	-0.029729	-0.2642	0.396142
31	0.070639	0.6279	0.265954
32	-0.09266	-0.8236	0.206328
33	-0.020823	-0.1851	0.426822
34	0.097487	0.8665	0.194426
35	0.076177	0.6771	0.250169
36	-0.054237	-0.4821	0.315546

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ;

Parameters (R input):

par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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