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R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Tue, 02 Dec 2008 11:10:55 -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/t1228241524tvue5nj5hnzmy05.htm/, Retrieved Tue, 28 May 2024 01:48:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28185, Retrieved Tue, 28 May 2024 01:48:01 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

non stationary time series vraag 8 landbouw d en D =1

Estimated Impact

250

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 18:10:55] [bb7e3816cefc365f4d7adcd50784b783] [Current]
- RMP         [Spectral Analysis] [] [2008-12-06 13:39:53] [74be16979710d4c4e7c6647856088456] 
F RMP         [ARIMA Forecasting] [arima forecasting] [2008-12-15 18:09:31] [415d0222c17b651a9576eaac006f530d] 
F RMP         [ARIMA Forecasting] [arime forecasting] [2008-12-15 18:37:04] [415d0222c17b651a9576eaac006f530d] 

Feedback Forum

2008-12-06 13:41:47 [Ken Wright] [reply] 
juist, maar je moest toch degelijk ook seizoenaal differentieren, dit dit kon je niet afleiden uit de eerste grafiek daarom kan je ook beste gebruik maken van de spectraal analyse waaruit je zal afleiden dat er een groot spectrum is op 6 en 12 (http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/06/t1228570836l6ybqgjvqhamcn0.htm) ok het cumulative periodogram zie je dat het getrapt (seizoenaal)is en een steile helling heeft(Lt)

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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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28185&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28185&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28185&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	'Gwilym Jenkins' @ 72.249.127.135

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.055371	0.3796	0.352974
2	-0.028288	-0.1939	0.423531
3	-0.172359	-1.1816	0.121648
4	0.221414	1.5179	0.067864
5	0.222679	1.5266	0.06678
6	0.03327	0.2281	0.410284
7	-0.074243	-0.509	0.306572
8	-0.08603	-0.5898	0.279078
9	0.233735	1.6024	0.057883
10	0.037843	0.2594	0.398215
11	-0.171442	-1.1753	0.122889
12	-0.431204	-2.9562	0.002429
13	0.055639	0.3814	0.352298
14	0.090479	0.6203	0.269031
15	0.160061	1.0973	0.139045
16	-0.186388	-1.2778	0.103796
17	-0.154587	-1.0598	0.147327
18	-0.030177	-0.2069	0.418498
19	0.040755	0.2794	0.39058
20	-0.072178	-0.4948	0.311514
21	-0.282106	-1.934	0.029572
22	-0.012424	-0.0852	0.466241
23	0.119905	0.822	0.207605
24	0.013853	0.095	0.46237
25	-0.19428	-1.3319	0.094655
26	-0.16993	-1.165	0.124952
27	-0.089692	-0.6149	0.270794
28	0.059907	0.4107	0.34158
29	0.033959	0.2328	0.40846
30	0.037854	0.2595	0.398186
31	-0.127386	-0.8733	0.193465
32	0.108015	0.7405	0.231337
33	0.062874	0.431	0.334203
34	0.001301	0.0089	0.496461
35	-0.029914	-0.2051	0.419199
36	-0.020509	-0.1406	0.444393

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.055371 & 0.3796 & 0.352974 \tabularnewline
2 & -0.028288 & -0.1939 & 0.423531 \tabularnewline
3 & -0.172359 & -1.1816 & 0.121648 \tabularnewline
4 & 0.221414 & 1.5179 & 0.067864 \tabularnewline
5 & 0.222679 & 1.5266 & 0.06678 \tabularnewline
6 & 0.03327 & 0.2281 & 0.410284 \tabularnewline
7 & -0.074243 & -0.509 & 0.306572 \tabularnewline
8 & -0.08603 & -0.5898 & 0.279078 \tabularnewline
9 & 0.233735 & 1.6024 & 0.057883 \tabularnewline
10 & 0.037843 & 0.2594 & 0.398215 \tabularnewline
11 & -0.171442 & -1.1753 & 0.122889 \tabularnewline
12 & -0.431204 & -2.9562 & 0.002429 \tabularnewline
13 & 0.055639 & 0.3814 & 0.352298 \tabularnewline
14 & 0.090479 & 0.6203 & 0.269031 \tabularnewline
15 & 0.160061 & 1.0973 & 0.139045 \tabularnewline
16 & -0.186388 & -1.2778 & 0.103796 \tabularnewline
17 & -0.154587 & -1.0598 & 0.147327 \tabularnewline
18 & -0.030177 & -0.2069 & 0.418498 \tabularnewline
19 & 0.040755 & 0.2794 & 0.39058 \tabularnewline
20 & -0.072178 & -0.4948 & 0.311514 \tabularnewline
21 & -0.282106 & -1.934 & 0.029572 \tabularnewline
22 & -0.012424 & -0.0852 & 0.466241 \tabularnewline
23 & 0.119905 & 0.822 & 0.207605 \tabularnewline
24 & 0.013853 & 0.095 & 0.46237 \tabularnewline
25 & -0.19428 & -1.3319 & 0.094655 \tabularnewline
26 & -0.16993 & -1.165 & 0.124952 \tabularnewline
27 & -0.089692 & -0.6149 & 0.270794 \tabularnewline
28 & 0.059907 & 0.4107 & 0.34158 \tabularnewline
29 & 0.033959 & 0.2328 & 0.40846 \tabularnewline
30 & 0.037854 & 0.2595 & 0.398186 \tabularnewline
31 & -0.127386 & -0.8733 & 0.193465 \tabularnewline
32 & 0.108015 & 0.7405 & 0.231337 \tabularnewline
33 & 0.062874 & 0.431 & 0.334203 \tabularnewline
34 & 0.001301 & 0.0089 & 0.496461 \tabularnewline
35 & -0.029914 & -0.2051 & 0.419199 \tabularnewline
36 & -0.020509 & -0.1406 & 0.444393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28185&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]0.055371[/C][C]0.3796[/C][C]0.352974[/C][/ROW]
[ROW][C]2[/C][C]-0.028288[/C][C]-0.1939[/C][C]0.423531[/C][/ROW]
[ROW][C]3[/C][C]-0.172359[/C][C]-1.1816[/C][C]0.121648[/C][/ROW]
[ROW][C]4[/C][C]0.221414[/C][C]1.5179[/C][C]0.067864[/C][/ROW]
[ROW][C]5[/C][C]0.222679[/C][C]1.5266[/C][C]0.06678[/C][/ROW]
[ROW][C]6[/C][C]0.03327[/C][C]0.2281[/C][C]0.410284[/C][/ROW]
[ROW][C]7[/C][C]-0.074243[/C][C]-0.509[/C][C]0.306572[/C][/ROW]
[ROW][C]8[/C][C]-0.08603[/C][C]-0.5898[/C][C]0.279078[/C][/ROW]
[ROW][C]9[/C][C]0.233735[/C][C]1.6024[/C][C]0.057883[/C][/ROW]
[ROW][C]10[/C][C]0.037843[/C][C]0.2594[/C][C]0.398215[/C][/ROW]
[ROW][C]11[/C][C]-0.171442[/C][C]-1.1753[/C][C]0.122889[/C][/ROW]
[ROW][C]12[/C][C]-0.431204[/C][C]-2.9562[/C][C]0.002429[/C][/ROW]
[ROW][C]13[/C][C]0.055639[/C][C]0.3814[/C][C]0.352298[/C][/ROW]
[ROW][C]14[/C][C]0.090479[/C][C]0.6203[/C][C]0.269031[/C][/ROW]
[ROW][C]15[/C][C]0.160061[/C][C]1.0973[/C][C]0.139045[/C][/ROW]
[ROW][C]16[/C][C]-0.186388[/C][C]-1.2778[/C][C]0.103796[/C][/ROW]
[ROW][C]17[/C][C]-0.154587[/C][C]-1.0598[/C][C]0.147327[/C][/ROW]
[ROW][C]18[/C][C]-0.030177[/C][C]-0.2069[/C][C]0.418498[/C][/ROW]
[ROW][C]19[/C][C]0.040755[/C][C]0.2794[/C][C]0.39058[/C][/ROW]
[ROW][C]20[/C][C]-0.072178[/C][C]-0.4948[/C][C]0.311514[/C][/ROW]
[ROW][C]21[/C][C]-0.282106[/C][C]-1.934[/C][C]0.029572[/C][/ROW]
[ROW][C]22[/C][C]-0.012424[/C][C]-0.0852[/C][C]0.466241[/C][/ROW]
[ROW][C]23[/C][C]0.119905[/C][C]0.822[/C][C]0.207605[/C][/ROW]
[ROW][C]24[/C][C]0.013853[/C][C]0.095[/C][C]0.46237[/C][/ROW]
[ROW][C]25[/C][C]-0.19428[/C][C]-1.3319[/C][C]0.094655[/C][/ROW]
[ROW][C]26[/C][C]-0.16993[/C][C]-1.165[/C][C]0.124952[/C][/ROW]
[ROW][C]27[/C][C]-0.089692[/C][C]-0.6149[/C][C]0.270794[/C][/ROW]
[ROW][C]28[/C][C]0.059907[/C][C]0.4107[/C][C]0.34158[/C][/ROW]
[ROW][C]29[/C][C]0.033959[/C][C]0.2328[/C][C]0.40846[/C][/ROW]
[ROW][C]30[/C][C]0.037854[/C][C]0.2595[/C][C]0.398186[/C][/ROW]
[ROW][C]31[/C][C]-0.127386[/C][C]-0.8733[/C][C]0.193465[/C][/ROW]
[ROW][C]32[/C][C]0.108015[/C][C]0.7405[/C][C]0.231337[/C][/ROW]
[ROW][C]33[/C][C]0.062874[/C][C]0.431[/C][C]0.334203[/C][/ROW]
[ROW][C]34[/C][C]0.001301[/C][C]0.0089[/C][C]0.496461[/C][/ROW]
[ROW][C]35[/C][C]-0.029914[/C][C]-0.2051[/C][C]0.419199[/C][/ROW]
[ROW][C]36[/C][C]-0.020509[/C][C]-0.1406[/C][C]0.444393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28185&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28185&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	0.055371	0.3796	0.352974
2	-0.028288	-0.1939	0.423531
3	-0.172359	-1.1816	0.121648
4	0.221414	1.5179	0.067864
5	0.222679	1.5266	0.06678
6	0.03327	0.2281	0.410284
7	-0.074243	-0.509	0.306572
8	-0.08603	-0.5898	0.279078
9	0.233735	1.6024	0.057883
10	0.037843	0.2594	0.398215
11	-0.171442	-1.1753	0.122889
12	-0.431204	-2.9562	0.002429
13	0.055639	0.3814	0.352298
14	0.090479	0.6203	0.269031
15	0.160061	1.0973	0.139045
16	-0.186388	-1.2778	0.103796
17	-0.154587	-1.0598	0.147327
18	-0.030177	-0.2069	0.418498
19	0.040755	0.2794	0.39058
20	-0.072178	-0.4948	0.311514
21	-0.282106	-1.934	0.029572
22	-0.012424	-0.0852	0.466241
23	0.119905	0.822	0.207605
24	0.013853	0.095	0.46237
25	-0.19428	-1.3319	0.094655
26	-0.16993	-1.165	0.124952
27	-0.089692	-0.6149	0.270794
28	0.059907	0.4107	0.34158
29	0.033959	0.2328	0.40846
30	0.037854	0.2595	0.398186
31	-0.127386	-0.8733	0.193465
32	0.108015	0.7405	0.231337
33	0.062874	0.431	0.334203
34	0.001301	0.0089	0.496461
35	-0.029914	-0.2051	0.419199
36	-0.020509	-0.1406	0.444393

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.055371	0.3796	0.352974
2	-0.031451	-0.2156	0.41511
3	-0.169689	-1.1633	0.125284
4	0.247209	1.6948	0.048367
5	0.198482	1.3607	0.090045
6	-0.018895	-0.1295	0.448742
7	0.006139	0.0421	0.483303
8	-0.068473	-0.4694	0.320466
9	0.173033	1.1863	0.120743
10	-0.04709	-0.3228	0.374126
11	-0.227697	-1.561	0.062615
12	-0.369892	-2.5359	0.007301
13	0.043047	0.2951	0.384602
14	-0.012468	-0.0855	0.466124
15	0.144655	0.9917	0.16321
16	0.049358	0.3384	0.368292
17	0.020518	0.1407	0.444368
18	-0.011131	-0.0763	0.469748
19	-0.076377	-0.5236	0.301505
20	-0.153626	-1.0532	0.148816
21	-0.192981	-1.323	0.096116
22	-0.052193	-0.3578	0.361041
23	0.018928	0.1298	0.448655
24	-0.21555	-1.4777	0.073074
25	-0.051711	-0.3545	0.362269
26	0.043065	0.2952	0.384555
27	0.010191	0.0699	0.472298
28	-0.024575	-0.1685	0.433466
29	0.037242	0.2553	0.399795
30	0.170808	1.171	0.123751
31	-0.120145	-0.8237	0.207143
32	-0.036095	-0.2475	0.402818
33	-0.087433	-0.5994	0.275889
34	-0.026736	-0.1833	0.427678
35	0.039626	0.2717	0.393536
36	-0.148882	-1.0207	0.156315

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.055371 & 0.3796 & 0.352974 \tabularnewline
2 & -0.031451 & -0.2156 & 0.41511 \tabularnewline
3 & -0.169689 & -1.1633 & 0.125284 \tabularnewline
4 & 0.247209 & 1.6948 & 0.048367 \tabularnewline
5 & 0.198482 & 1.3607 & 0.090045 \tabularnewline
6 & -0.018895 & -0.1295 & 0.448742 \tabularnewline
7 & 0.006139 & 0.0421 & 0.483303 \tabularnewline
8 & -0.068473 & -0.4694 & 0.320466 \tabularnewline
9 & 0.173033 & 1.1863 & 0.120743 \tabularnewline
10 & -0.04709 & -0.3228 & 0.374126 \tabularnewline
11 & -0.227697 & -1.561 & 0.062615 \tabularnewline
12 & -0.369892 & -2.5359 & 0.007301 \tabularnewline
13 & 0.043047 & 0.2951 & 0.384602 \tabularnewline
14 & -0.012468 & -0.0855 & 0.466124 \tabularnewline
15 & 0.144655 & 0.9917 & 0.16321 \tabularnewline
16 & 0.049358 & 0.3384 & 0.368292 \tabularnewline
17 & 0.020518 & 0.1407 & 0.444368 \tabularnewline
18 & -0.011131 & -0.0763 & 0.469748 \tabularnewline
19 & -0.076377 & -0.5236 & 0.301505 \tabularnewline
20 & -0.153626 & -1.0532 & 0.148816 \tabularnewline
21 & -0.192981 & -1.323 & 0.096116 \tabularnewline
22 & -0.052193 & -0.3578 & 0.361041 \tabularnewline
23 & 0.018928 & 0.1298 & 0.448655 \tabularnewline
24 & -0.21555 & -1.4777 & 0.073074 \tabularnewline
25 & -0.051711 & -0.3545 & 0.362269 \tabularnewline
26 & 0.043065 & 0.2952 & 0.384555 \tabularnewline
27 & 0.010191 & 0.0699 & 0.472298 \tabularnewline
28 & -0.024575 & -0.1685 & 0.433466 \tabularnewline
29 & 0.037242 & 0.2553 & 0.399795 \tabularnewline
30 & 0.170808 & 1.171 & 0.123751 \tabularnewline
31 & -0.120145 & -0.8237 & 0.207143 \tabularnewline
32 & -0.036095 & -0.2475 & 0.402818 \tabularnewline
33 & -0.087433 & -0.5994 & 0.275889 \tabularnewline
34 & -0.026736 & -0.1833 & 0.427678 \tabularnewline
35 & 0.039626 & 0.2717 & 0.393536 \tabularnewline
36 & -0.148882 & -1.0207 & 0.156315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28185&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]0.055371[/C][C]0.3796[/C][C]0.352974[/C][/ROW]
[ROW][C]2[/C][C]-0.031451[/C][C]-0.2156[/C][C]0.41511[/C][/ROW]
[ROW][C]3[/C][C]-0.169689[/C][C]-1.1633[/C][C]0.125284[/C][/ROW]
[ROW][C]4[/C][C]0.247209[/C][C]1.6948[/C][C]0.048367[/C][/ROW]
[ROW][C]5[/C][C]0.198482[/C][C]1.3607[/C][C]0.090045[/C][/ROW]
[ROW][C]6[/C][C]-0.018895[/C][C]-0.1295[/C][C]0.448742[/C][/ROW]
[ROW][C]7[/C][C]0.006139[/C][C]0.0421[/C][C]0.483303[/C][/ROW]
[ROW][C]8[/C][C]-0.068473[/C][C]-0.4694[/C][C]0.320466[/C][/ROW]
[ROW][C]9[/C][C]0.173033[/C][C]1.1863[/C][C]0.120743[/C][/ROW]
[ROW][C]10[/C][C]-0.04709[/C][C]-0.3228[/C][C]0.374126[/C][/ROW]
[ROW][C]11[/C][C]-0.227697[/C][C]-1.561[/C][C]0.062615[/C][/ROW]
[ROW][C]12[/C][C]-0.369892[/C][C]-2.5359[/C][C]0.007301[/C][/ROW]
[ROW][C]13[/C][C]0.043047[/C][C]0.2951[/C][C]0.384602[/C][/ROW]
[ROW][C]14[/C][C]-0.012468[/C][C]-0.0855[/C][C]0.466124[/C][/ROW]
[ROW][C]15[/C][C]0.144655[/C][C]0.9917[/C][C]0.16321[/C][/ROW]
[ROW][C]16[/C][C]0.049358[/C][C]0.3384[/C][C]0.368292[/C][/ROW]
[ROW][C]17[/C][C]0.020518[/C][C]0.1407[/C][C]0.444368[/C][/ROW]
[ROW][C]18[/C][C]-0.011131[/C][C]-0.0763[/C][C]0.469748[/C][/ROW]
[ROW][C]19[/C][C]-0.076377[/C][C]-0.5236[/C][C]0.301505[/C][/ROW]
[ROW][C]20[/C][C]-0.153626[/C][C]-1.0532[/C][C]0.148816[/C][/ROW]
[ROW][C]21[/C][C]-0.192981[/C][C]-1.323[/C][C]0.096116[/C][/ROW]
[ROW][C]22[/C][C]-0.052193[/C][C]-0.3578[/C][C]0.361041[/C][/ROW]
[ROW][C]23[/C][C]0.018928[/C][C]0.1298[/C][C]0.448655[/C][/ROW]
[ROW][C]24[/C][C]-0.21555[/C][C]-1.4777[/C][C]0.073074[/C][/ROW]
[ROW][C]25[/C][C]-0.051711[/C][C]-0.3545[/C][C]0.362269[/C][/ROW]
[ROW][C]26[/C][C]0.043065[/C][C]0.2952[/C][C]0.384555[/C][/ROW]
[ROW][C]27[/C][C]0.010191[/C][C]0.0699[/C][C]0.472298[/C][/ROW]
[ROW][C]28[/C][C]-0.024575[/C][C]-0.1685[/C][C]0.433466[/C][/ROW]
[ROW][C]29[/C][C]0.037242[/C][C]0.2553[/C][C]0.399795[/C][/ROW]
[ROW][C]30[/C][C]0.170808[/C][C]1.171[/C][C]0.123751[/C][/ROW]
[ROW][C]31[/C][C]-0.120145[/C][C]-0.8237[/C][C]0.207143[/C][/ROW]
[ROW][C]32[/C][C]-0.036095[/C][C]-0.2475[/C][C]0.402818[/C][/ROW]
[ROW][C]33[/C][C]-0.087433[/C][C]-0.5994[/C][C]0.275889[/C][/ROW]
[ROW][C]34[/C][C]-0.026736[/C][C]-0.1833[/C][C]0.427678[/C][/ROW]
[ROW][C]35[/C][C]0.039626[/C][C]0.2717[/C][C]0.393536[/C][/ROW]
[ROW][C]36[/C][C]-0.148882[/C][C]-1.0207[/C][C]0.156315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28185&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28185&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	0.055371	0.3796	0.352974
2	-0.031451	-0.2156	0.41511
3	-0.169689	-1.1633	0.125284
4	0.247209	1.6948	0.048367
5	0.198482	1.3607	0.090045
6	-0.018895	-0.1295	0.448742
7	0.006139	0.0421	0.483303
8	-0.068473	-0.4694	0.320466
9	0.173033	1.1863	0.120743
10	-0.04709	-0.3228	0.374126
11	-0.227697	-1.561	0.062615
12	-0.369892	-2.5359	0.007301
13	0.043047	0.2951	0.384602
14	-0.012468	-0.0855	0.466124
15	0.144655	0.9917	0.16321
16	0.049358	0.3384	0.368292
17	0.020518	0.1407	0.444368
18	-0.011131	-0.0763	0.469748
19	-0.076377	-0.5236	0.301505
20	-0.153626	-1.0532	0.148816
21	-0.192981	-1.323	0.096116
22	-0.052193	-0.3578	0.361041
23	0.018928	0.1298	0.448655
24	-0.21555	-1.4777	0.073074
25	-0.051711	-0.3545	0.362269
26	0.043065	0.2952	0.384555
27	0.010191	0.0699	0.472298
28	-0.024575	-0.1685	0.433466
29	0.037242	0.2553	0.399795
30	0.170808	1.171	0.123751
31	-0.120145	-0.8237	0.207143
32	-0.036095	-0.2475	0.402818
33	-0.087433	-0.5994	0.275889
34	-0.026736	-0.1833	0.427678
35	0.039626	0.2717	0.393536
36	-0.148882	-1.0207	0.156315

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 = 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