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Author

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R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Sat, 06 Dec 2008 08:41:54 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/06/t1228578151fk5772d78jxdr3g.htm/, Retrieved Sun, 19 May 2024 11:13:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29707, Retrieved Sun, 19 May 2024 11:13:08 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

224

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   [(Partial) Autocorrelation Function] [Identification an...] [2008-12-04 20:22:55] [063e4b67ad7d3a8a83eccec794cd5aa7]
F   PD      [(Partial) Autocorrelation Function] [Eigen tijdreeks t...] [2008-12-06 15:41:54] [6797a1f4a60918966297e9d9220cabc2] [Current]

Feedback Forum

2008-12-15 18:43:33 [Jeroen Michel] [reply] 
Uiteindelijk heeft de student de volgende gegevens ingevoerd: lags = 36, d = 1, D = 1 en Seasonal period = 12  
 
De lange termijntrend is in dit model nu volledig weggewerkt, terwijl er nog wel een beetje sprake is van enige autocorrelatie, waaronder echter geen seizoensinvloeden.  
 
Om de eventueel nog aanwezig resterende autocorrelatie weg te werken, kunnen er ARMA-modellen gebruikt worden.

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Dataseries X:

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Histogram

Boxplots

7,4
7,2
7,1
6,9
6,8
6,8
6,8
6,9
6,7
6,6
6,5
6,4
6,3
6,3
6,3
6,5
6,6
6,5
6,4
6,5
6,7
7,1
7,1
7,2
7,2
7,3
7,3
7,3
7,3
7,4
7,6
7,6
7,6
7,7
7,8
7,9
8,1
8,1
8,1
8,2
8,2
8,2
8,2
8,2
8,2
8,3
8,3
8,4
8,4
8,4
8,3
8
8
8,2
8,6
8,7
8,7
8,5
8,4
8,4
8,4
8,5
8,5
8,5
8,5
8,5
8,4
8,4
8,4
8,5
8,6
8,6
8,6
8,6
8,5
8,4
8,4
8,3
8,2
8,1
8,2
8,1
8
7,9
7,8
7,7
7,7
7,9
7,8
7,6
7,4
7,3
7,1
7,1
7
7
7
6,9
6,8
6,7
6,6
6,6

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=29707&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=29707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29707&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	0.429777	4.3192	1.8e-05
2	0.116824	1.1741	0.121565
3	-0.152504	-1.5326	0.064245
4	-0.070447	-0.708	0.240293
5	0.08264	0.8305	0.204101
6	0.246943	2.4817	0.007362
7	0.288043	2.8948	0.002325
8	0.237646	2.3883	0.009392
9	0.254229	2.555	0.006056
10	0.081693	0.821	0.20679
11	0.034151	0.3432	0.366075
12	-0.136031	-1.3671	0.087315
13	-0.026801	-0.2693	0.394107
14	0.136825	1.3751	0.086075
15	0.217022	2.181	0.01575
16	0.142082	1.4279	0.078201
17	0.074929	0.753	0.226592
18	0.029842	0.2999	0.382433
19	-0.020438	-0.2054	0.418836
20	-0.040782	-0.4099	0.34139
21	-0.130738	-1.3139	0.095928
22	-0.094926	-0.954	0.171183
23	0.000691	0.0069	0.497235
24	0.086449	0.8688	0.193508
25	0.037911	0.381	0.352003
26	0.081673	0.8208	0.206845
27	-0.079332	-0.7973	0.21358
28	-0.120295	-1.2089	0.114752
29	-0.158581	-1.5937	0.057062
30	-0.207048	-2.0808	0.019991
31	-0.036416	-0.366	0.357575
32	0.083885	0.843	0.200602
33	0.247799	2.4903	0.007196
34	0.124049	1.2467	0.1077
35	-0.111259	-1.1181	0.13308
36	-0.327584	-3.2922	0.000685

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.429777 & 4.3192 & 1.8e-05 \tabularnewline
2 & 0.116824 & 1.1741 & 0.121565 \tabularnewline
3 & -0.152504 & -1.5326 & 0.064245 \tabularnewline
4 & -0.070447 & -0.708 & 0.240293 \tabularnewline
5 & 0.08264 & 0.8305 & 0.204101 \tabularnewline
6 & 0.246943 & 2.4817 & 0.007362 \tabularnewline
7 & 0.288043 & 2.8948 & 0.002325 \tabularnewline
8 & 0.237646 & 2.3883 & 0.009392 \tabularnewline
9 & 0.254229 & 2.555 & 0.006056 \tabularnewline
10 & 0.081693 & 0.821 & 0.20679 \tabularnewline
11 & 0.034151 & 0.3432 & 0.366075 \tabularnewline
12 & -0.136031 & -1.3671 & 0.087315 \tabularnewline
13 & -0.026801 & -0.2693 & 0.394107 \tabularnewline
14 & 0.136825 & 1.3751 & 0.086075 \tabularnewline
15 & 0.217022 & 2.181 & 0.01575 \tabularnewline
16 & 0.142082 & 1.4279 & 0.078201 \tabularnewline
17 & 0.074929 & 0.753 & 0.226592 \tabularnewline
18 & 0.029842 & 0.2999 & 0.382433 \tabularnewline
19 & -0.020438 & -0.2054 & 0.418836 \tabularnewline
20 & -0.040782 & -0.4099 & 0.34139 \tabularnewline
21 & -0.130738 & -1.3139 & 0.095928 \tabularnewline
22 & -0.094926 & -0.954 & 0.171183 \tabularnewline
23 & 0.000691 & 0.0069 & 0.497235 \tabularnewline
24 & 0.086449 & 0.8688 & 0.193508 \tabularnewline
25 & 0.037911 & 0.381 & 0.352003 \tabularnewline
26 & 0.081673 & 0.8208 & 0.206845 \tabularnewline
27 & -0.079332 & -0.7973 & 0.21358 \tabularnewline
28 & -0.120295 & -1.2089 & 0.114752 \tabularnewline
29 & -0.158581 & -1.5937 & 0.057062 \tabularnewline
30 & -0.207048 & -2.0808 & 0.019991 \tabularnewline
31 & -0.036416 & -0.366 & 0.357575 \tabularnewline
32 & 0.083885 & 0.843 & 0.200602 \tabularnewline
33 & 0.247799 & 2.4903 & 0.007196 \tabularnewline
34 & 0.124049 & 1.2467 & 0.1077 \tabularnewline
35 & -0.111259 & -1.1181 & 0.13308 \tabularnewline
36 & -0.327584 & -3.2922 & 0.000685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29707&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]0.429777[/C][C]4.3192[/C][C]1.8e-05[/C][/ROW]
[ROW][C]2[/C][C]0.116824[/C][C]1.1741[/C][C]0.121565[/C][/ROW]
[ROW][C]3[/C][C]-0.152504[/C][C]-1.5326[/C][C]0.064245[/C][/ROW]
[ROW][C]4[/C][C]-0.070447[/C][C]-0.708[/C][C]0.240293[/C][/ROW]
[ROW][C]5[/C][C]0.08264[/C][C]0.8305[/C][C]0.204101[/C][/ROW]
[ROW][C]6[/C][C]0.246943[/C][C]2.4817[/C][C]0.007362[/C][/ROW]
[ROW][C]7[/C][C]0.288043[/C][C]2.8948[/C][C]0.002325[/C][/ROW]
[ROW][C]8[/C][C]0.237646[/C][C]2.3883[/C][C]0.009392[/C][/ROW]
[ROW][C]9[/C][C]0.254229[/C][C]2.555[/C][C]0.006056[/C][/ROW]
[ROW][C]10[/C][C]0.081693[/C][C]0.821[/C][C]0.20679[/C][/ROW]
[ROW][C]11[/C][C]0.034151[/C][C]0.3432[/C][C]0.366075[/C][/ROW]
[ROW][C]12[/C][C]-0.136031[/C][C]-1.3671[/C][C]0.087315[/C][/ROW]
[ROW][C]13[/C][C]-0.026801[/C][C]-0.2693[/C][C]0.394107[/C][/ROW]
[ROW][C]14[/C][C]0.136825[/C][C]1.3751[/C][C]0.086075[/C][/ROW]
[ROW][C]15[/C][C]0.217022[/C][C]2.181[/C][C]0.01575[/C][/ROW]
[ROW][C]16[/C][C]0.142082[/C][C]1.4279[/C][C]0.078201[/C][/ROW]
[ROW][C]17[/C][C]0.074929[/C][C]0.753[/C][C]0.226592[/C][/ROW]
[ROW][C]18[/C][C]0.029842[/C][C]0.2999[/C][C]0.382433[/C][/ROW]
[ROW][C]19[/C][C]-0.020438[/C][C]-0.2054[/C][C]0.418836[/C][/ROW]
[ROW][C]20[/C][C]-0.040782[/C][C]-0.4099[/C][C]0.34139[/C][/ROW]
[ROW][C]21[/C][C]-0.130738[/C][C]-1.3139[/C][C]0.095928[/C][/ROW]
[ROW][C]22[/C][C]-0.094926[/C][C]-0.954[/C][C]0.171183[/C][/ROW]
[ROW][C]23[/C][C]0.000691[/C][C]0.0069[/C][C]0.497235[/C][/ROW]
[ROW][C]24[/C][C]0.086449[/C][C]0.8688[/C][C]0.193508[/C][/ROW]
[ROW][C]25[/C][C]0.037911[/C][C]0.381[/C][C]0.352003[/C][/ROW]
[ROW][C]26[/C][C]0.081673[/C][C]0.8208[/C][C]0.206845[/C][/ROW]
[ROW][C]27[/C][C]-0.079332[/C][C]-0.7973[/C][C]0.21358[/C][/ROW]
[ROW][C]28[/C][C]-0.120295[/C][C]-1.2089[/C][C]0.114752[/C][/ROW]
[ROW][C]29[/C][C]-0.158581[/C][C]-1.5937[/C][C]0.057062[/C][/ROW]
[ROW][C]30[/C][C]-0.207048[/C][C]-2.0808[/C][C]0.019991[/C][/ROW]
[ROW][C]31[/C][C]-0.036416[/C][C]-0.366[/C][C]0.357575[/C][/ROW]
[ROW][C]32[/C][C]0.083885[/C][C]0.843[/C][C]0.200602[/C][/ROW]
[ROW][C]33[/C][C]0.247799[/C][C]2.4903[/C][C]0.007196[/C][/ROW]
[ROW][C]34[/C][C]0.124049[/C][C]1.2467[/C][C]0.1077[/C][/ROW]
[ROW][C]35[/C][C]-0.111259[/C][C]-1.1181[/C][C]0.13308[/C][/ROW]
[ROW][C]36[/C][C]-0.327584[/C][C]-3.2922[/C][C]0.000685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29707&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	0.429777	4.3192	1.8e-05
2	0.116824	1.1741	0.121565
3	-0.152504	-1.5326	0.064245
4	-0.070447	-0.708	0.240293
5	0.08264	0.8305	0.204101
6	0.246943	2.4817	0.007362
7	0.288043	2.8948	0.002325
8	0.237646	2.3883	0.009392
9	0.254229	2.555	0.006056
10	0.081693	0.821	0.20679
11	0.034151	0.3432	0.366075
12	-0.136031	-1.3671	0.087315
13	-0.026801	-0.2693	0.394107
14	0.136825	1.3751	0.086075
15	0.217022	2.181	0.01575
16	0.142082	1.4279	0.078201
17	0.074929	0.753	0.226592
18	0.029842	0.2999	0.382433
19	-0.020438	-0.2054	0.418836
20	-0.040782	-0.4099	0.34139
21	-0.130738	-1.3139	0.095928
22	-0.094926	-0.954	0.171183
23	0.000691	0.0069	0.497235
24	0.086449	0.8688	0.193508
25	0.037911	0.381	0.352003
26	0.081673	0.8208	0.206845
27	-0.079332	-0.7973	0.21358
28	-0.120295	-1.2089	0.114752
29	-0.158581	-1.5937	0.057062
30	-0.207048	-2.0808	0.019991
31	-0.036416	-0.366	0.357575
32	0.083885	0.843	0.200602
33	0.247799	2.4903	0.007196
34	0.124049	1.2467	0.1077
35	-0.111259	-1.1181	0.13308
36	-0.327584	-3.2922	0.000685

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.429777	4.3192	1.8e-05
2	-0.083264	-0.8368	0.202341
3	-0.211338	-2.1239	0.018059
4	0.112389	1.1295	0.130681
5	0.131186	1.3184	0.095176
6	0.14667	1.474	0.071793
7	0.131914	1.3257	0.093962
8	0.104423	1.0494	0.14824
9	0.235803	2.3698	0.009849
10	-0.05647	-0.5675	0.285811
11	0.024928	0.2505	0.401346
12	-0.179058	-1.7995	0.037462
13	0.019362	0.1946	0.423054
14	0.10322	1.0374	0.151025
15	-0.061241	-0.6155	0.269815
16	-0.063679	-0.64	0.261821
17	0.061153	0.6146	0.270106
18	0.047264	0.475	0.317907
19	-0.024482	-0.246	0.403073
20	-0.095048	-0.9552	0.170873
21	-0.120309	-1.2091	0.114726
22	-0.064206	-0.6453	0.260111
23	0.001405	0.0141	0.494382
24	-0.048594	-0.4884	0.313176
25	-0.088613	-0.8906	0.187644
26	0.211968	2.1302	0.01779
27	-0.077009	-0.7739	0.220391
28	-0.051972	-0.5223	0.301297
29	-0.035199	-0.3537	0.362132
30	-0.156116	-1.5689	0.059894
31	0.166507	1.6734	0.048674
32	0.02558	0.2571	0.398822
33	0.164849	1.6567	0.05034
34	0.029596	0.2974	0.38337
35	-0.166305	-1.6713	0.048875
36	-0.007328	-0.0736	0.470718

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.429777 & 4.3192 & 1.8e-05 \tabularnewline
2 & -0.083264 & -0.8368 & 0.202341 \tabularnewline
3 & -0.211338 & -2.1239 & 0.018059 \tabularnewline
4 & 0.112389 & 1.1295 & 0.130681 \tabularnewline
5 & 0.131186 & 1.3184 & 0.095176 \tabularnewline
6 & 0.14667 & 1.474 & 0.071793 \tabularnewline
7 & 0.131914 & 1.3257 & 0.093962 \tabularnewline
8 & 0.104423 & 1.0494 & 0.14824 \tabularnewline
9 & 0.235803 & 2.3698 & 0.009849 \tabularnewline
10 & -0.05647 & -0.5675 & 0.285811 \tabularnewline
11 & 0.024928 & 0.2505 & 0.401346 \tabularnewline
12 & -0.179058 & -1.7995 & 0.037462 \tabularnewline
13 & 0.019362 & 0.1946 & 0.423054 \tabularnewline
14 & 0.10322 & 1.0374 & 0.151025 \tabularnewline
15 & -0.061241 & -0.6155 & 0.269815 \tabularnewline
16 & -0.063679 & -0.64 & 0.261821 \tabularnewline
17 & 0.061153 & 0.6146 & 0.270106 \tabularnewline
18 & 0.047264 & 0.475 & 0.317907 \tabularnewline
19 & -0.024482 & -0.246 & 0.403073 \tabularnewline
20 & -0.095048 & -0.9552 & 0.170873 \tabularnewline
21 & -0.120309 & -1.2091 & 0.114726 \tabularnewline
22 & -0.064206 & -0.6453 & 0.260111 \tabularnewline
23 & 0.001405 & 0.0141 & 0.494382 \tabularnewline
24 & -0.048594 & -0.4884 & 0.313176 \tabularnewline
25 & -0.088613 & -0.8906 & 0.187644 \tabularnewline
26 & 0.211968 & 2.1302 & 0.01779 \tabularnewline
27 & -0.077009 & -0.7739 & 0.220391 \tabularnewline
28 & -0.051972 & -0.5223 & 0.301297 \tabularnewline
29 & -0.035199 & -0.3537 & 0.362132 \tabularnewline
30 & -0.156116 & -1.5689 & 0.059894 \tabularnewline
31 & 0.166507 & 1.6734 & 0.048674 \tabularnewline
32 & 0.02558 & 0.2571 & 0.398822 \tabularnewline
33 & 0.164849 & 1.6567 & 0.05034 \tabularnewline
34 & 0.029596 & 0.2974 & 0.38337 \tabularnewline
35 & -0.166305 & -1.6713 & 0.048875 \tabularnewline
36 & -0.007328 & -0.0736 & 0.470718 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29707&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]0.429777[/C][C]4.3192[/C][C]1.8e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.083264[/C][C]-0.8368[/C][C]0.202341[/C][/ROW]
[ROW][C]3[/C][C]-0.211338[/C][C]-2.1239[/C][C]0.018059[/C][/ROW]
[ROW][C]4[/C][C]0.112389[/C][C]1.1295[/C][C]0.130681[/C][/ROW]
[ROW][C]5[/C][C]0.131186[/C][C]1.3184[/C][C]0.095176[/C][/ROW]
[ROW][C]6[/C][C]0.14667[/C][C]1.474[/C][C]0.071793[/C][/ROW]
[ROW][C]7[/C][C]0.131914[/C][C]1.3257[/C][C]0.093962[/C][/ROW]
[ROW][C]8[/C][C]0.104423[/C][C]1.0494[/C][C]0.14824[/C][/ROW]
[ROW][C]9[/C][C]0.235803[/C][C]2.3698[/C][C]0.009849[/C][/ROW]
[ROW][C]10[/C][C]-0.05647[/C][C]-0.5675[/C][C]0.285811[/C][/ROW]
[ROW][C]11[/C][C]0.024928[/C][C]0.2505[/C][C]0.401346[/C][/ROW]
[ROW][C]12[/C][C]-0.179058[/C][C]-1.7995[/C][C]0.037462[/C][/ROW]
[ROW][C]13[/C][C]0.019362[/C][C]0.1946[/C][C]0.423054[/C][/ROW]
[ROW][C]14[/C][C]0.10322[/C][C]1.0374[/C][C]0.151025[/C][/ROW]
[ROW][C]15[/C][C]-0.061241[/C][C]-0.6155[/C][C]0.269815[/C][/ROW]
[ROW][C]16[/C][C]-0.063679[/C][C]-0.64[/C][C]0.261821[/C][/ROW]
[ROW][C]17[/C][C]0.061153[/C][C]0.6146[/C][C]0.270106[/C][/ROW]
[ROW][C]18[/C][C]0.047264[/C][C]0.475[/C][C]0.317907[/C][/ROW]
[ROW][C]19[/C][C]-0.024482[/C][C]-0.246[/C][C]0.403073[/C][/ROW]
[ROW][C]20[/C][C]-0.095048[/C][C]-0.9552[/C][C]0.170873[/C][/ROW]
[ROW][C]21[/C][C]-0.120309[/C][C]-1.2091[/C][C]0.114726[/C][/ROW]
[ROW][C]22[/C][C]-0.064206[/C][C]-0.6453[/C][C]0.260111[/C][/ROW]
[ROW][C]23[/C][C]0.001405[/C][C]0.0141[/C][C]0.494382[/C][/ROW]
[ROW][C]24[/C][C]-0.048594[/C][C]-0.4884[/C][C]0.313176[/C][/ROW]
[ROW][C]25[/C][C]-0.088613[/C][C]-0.8906[/C][C]0.187644[/C][/ROW]
[ROW][C]26[/C][C]0.211968[/C][C]2.1302[/C][C]0.01779[/C][/ROW]
[ROW][C]27[/C][C]-0.077009[/C][C]-0.7739[/C][C]0.220391[/C][/ROW]
[ROW][C]28[/C][C]-0.051972[/C][C]-0.5223[/C][C]0.301297[/C][/ROW]
[ROW][C]29[/C][C]-0.035199[/C][C]-0.3537[/C][C]0.362132[/C][/ROW]
[ROW][C]30[/C][C]-0.156116[/C][C]-1.5689[/C][C]0.059894[/C][/ROW]
[ROW][C]31[/C][C]0.166507[/C][C]1.6734[/C][C]0.048674[/C][/ROW]
[ROW][C]32[/C][C]0.02558[/C][C]0.2571[/C][C]0.398822[/C][/ROW]
[ROW][C]33[/C][C]0.164849[/C][C]1.6567[/C][C]0.05034[/C][/ROW]
[ROW][C]34[/C][C]0.029596[/C][C]0.2974[/C][C]0.38337[/C][/ROW]
[ROW][C]35[/C][C]-0.166305[/C][C]-1.6713[/C][C]0.048875[/C][/ROW]
[ROW][C]36[/C][C]-0.007328[/C][C]-0.0736[/C][C]0.470718[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29707&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29707&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	0.429777	4.3192	1.8e-05
2	-0.083264	-0.8368	0.202341
3	-0.211338	-2.1239	0.018059
4	0.112389	1.1295	0.130681
5	0.131186	1.3184	0.095176
6	0.14667	1.474	0.071793
7	0.131914	1.3257	0.093962
8	0.104423	1.0494	0.14824
9	0.235803	2.3698	0.009849
10	-0.05647	-0.5675	0.285811
11	0.024928	0.2505	0.401346
12	-0.179058	-1.7995	0.037462
13	0.019362	0.1946	0.423054
14	0.10322	1.0374	0.151025
15	-0.061241	-0.6155	0.269815
16	-0.063679	-0.64	0.261821
17	0.061153	0.6146	0.270106
18	0.047264	0.475	0.317907
19	-0.024482	-0.246	0.403073
20	-0.095048	-0.9552	0.170873
21	-0.120309	-1.2091	0.114726
22	-0.064206	-0.6453	0.260111
23	0.001405	0.0141	0.494382
24	-0.048594	-0.4884	0.313176
25	-0.088613	-0.8906	0.187644
26	0.211968	2.1302	0.01779
27	-0.077009	-0.7739	0.220391
28	-0.051972	-0.5223	0.301297
29	-0.035199	-0.3537	0.362132
30	-0.156116	-1.5689	0.059894
31	0.166507	1.6734	0.048674
32	0.02558	0.2571	0.398822
33	0.164849	1.6567	0.05034
34	0.029596	0.2974	0.38337
35	-0.166305	-1.6713	0.048875
36	-0.007328	-0.0736	0.470718

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;

Parameters (R input):

par1 = 36 ; par2 = 2.0 ; par3 = 1 ; 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