Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Tue, 09 Dec 2008 08:27:54 -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/09/t1228836586r0utvno6a9ncjx5.htm/, Retrieved Sat, 25 May 2024 05:17:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31516, Retrieved Sat, 25 May 2024 05:17:01 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

149

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

-     [(Partial) Autocorrelation Function] [(P)ACF Algemeen i...] [2008-12-03 17:38:11] [74be16979710d4c4e7c6647856088456]
F   P     [(Partial) Autocorrelation Function] [(P)ACF Algemeen i...] [2008-12-09 15:27:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

2008-12-13 11:50:20 [Maarten Van Gucht] [reply] 
zoals de student ook heeft vermeld is, doordat de student de tijdreeks heeft gedifferentieerd met d= 0, D = 1 en lambda =1 kan je nu zien dat de meeste lijnen zich binnen het betrouwbaarheidsinterval bevinden. De seizoenaliteit is weggewerkt en de student heeft een goede conclusie weergegeven, namelijk de tijdreeks is nu stationair. 
2008-12-13 12:31:21 [Maarten Van Gucht] [reply] 
de stappen die je moet overlopen voor het analyseren van een AR proces zijn de volgende: 
- Is er een lange termijn trend? 
We kijken hiervoor enkel naar ongeveer de vijf eerste lijntjes bij de ACF. De eerste drie vertonen gelijkenissen met de ACF van het AR proces. er is namelijk een dalend patroon te herkennen in de eerste 3 lijntjes en ze zijn alle 3 significant positief. 
Welke orde? 
Hiervoor kijken we naar PACF, je ziet dat er 3 streepjes significant zijn.  p moet dus gelijk zijn aan 3 er is hier dus sprake van een AR(3) proces 
 
- Is er een seizoenaal patroon? 
We kijken hiervoor naar lag 12,24,36,48.. je ziet hier een afwisselend patroon. de ene keer negatief, de andere keer postief. en omdat de lag 12 in de PACF buiten het betrouwbaarheidsinterval ligt hebben we hier te maken met een SAR (1) 
P=1 
 
de conclusie is een AR(3) proces en een SAR(1) prcoes. dit heeft de student ook bekomen en heeft dus de opdracht tot een goed einde gebracht. 
 
dezelfde stappen doen we ook met het analyseren van een MA proces. 
-       Is er een niet- seizoenaal patroon/ lange termijn trend? we moeten zien naar de eerste 5 lijntjes van de PACF. maar deze vertonen geen gelijkenis met een MA proces. 
Je ziet geen MA- process.  q = 0. 
-	Seizoenaal patroon?  
Komt overeen met PACF grafiek. je ziet in de PACF een seizoenaal patroon, want op de lag 12, 24,36 zie je een dalend patroon van streepjes die groter zijn dan de andere, wat wijst op seizoenaliteit. Welke orde? Kijken naar ACF en de lijntjes (die op 12,24,26.. ) tellen die buiten het betrouwbaarheidsinterval liggen. dat is er 1, namelijk lag 12 dus Q=1 
er is dus een SMA(1) proces zoals de student ook vermeld in zijn antwoord. 
 
de student heeft deze opdracht goed begrepen en tot een goed einde gebracht. goede antwoorden zijn weergegeven

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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=31516&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=31516&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31516&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	0.262141	2.0474	0.022467
2	0.362335	2.8299	0.003147
3	0.457275	3.5714	0.00035
4	0.147827	1.1546	0.126385
5	0.253303	1.9784	0.026204
6	0.228481	1.7845	0.039658
7	-0.076432	-0.597	0.276373
8	0.119295	0.9317	0.177576
9	0.002922	0.0228	0.490935
10	-0.13819	-1.0793	0.142352
11	-0.035582	-0.2779	0.391011
12	-0.299	-2.3353	0.011418
13	-0.226853	-1.7718	0.040713
14	-0.201416	-1.5731	0.060434
15	-0.217057	-1.6953	0.047562
16	-0.174561	-1.3634	0.088889
17	-0.045362	-0.3543	0.362173
18	-0.125994	-0.984	0.16449
19	-0.023743	-0.1854	0.426751
20	0.025566	0.1997	0.4212
21	0.088757	0.6932	0.245403
22	-0.028556	-0.223	0.412128
23	0.180313	1.4083	0.082061
24	0.024857	0.1941	0.423356
25	0.11497	0.8979	0.186373
26	0.243868	1.9047	0.03077
27	0.028184	0.2201	0.413256
28	0.129829	1.014	0.157295
29	0.144735	1.1304	0.131362
30	0.053912	0.4211	0.337594
31	0.098836	0.7719	0.221566
32	0.031045	0.2425	0.404616
33	-0.075119	-0.5867	0.279785
34	0.088439	0.6907	0.246178
35	-0.0877	-0.685	0.247982
36	-0.095983	-0.7496	0.228174

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.262141 & 2.0474 & 0.022467 \tabularnewline
2 & 0.362335 & 2.8299 & 0.003147 \tabularnewline
3 & 0.457275 & 3.5714 & 0.00035 \tabularnewline
4 & 0.147827 & 1.1546 & 0.126385 \tabularnewline
5 & 0.253303 & 1.9784 & 0.026204 \tabularnewline
6 & 0.228481 & 1.7845 & 0.039658 \tabularnewline
7 & -0.076432 & -0.597 & 0.276373 \tabularnewline
8 & 0.119295 & 0.9317 & 0.177576 \tabularnewline
9 & 0.002922 & 0.0228 & 0.490935 \tabularnewline
10 & -0.13819 & -1.0793 & 0.142352 \tabularnewline
11 & -0.035582 & -0.2779 & 0.391011 \tabularnewline
12 & -0.299 & -2.3353 & 0.011418 \tabularnewline
13 & -0.226853 & -1.7718 & 0.040713 \tabularnewline
14 & -0.201416 & -1.5731 & 0.060434 \tabularnewline
15 & -0.217057 & -1.6953 & 0.047562 \tabularnewline
16 & -0.174561 & -1.3634 & 0.088889 \tabularnewline
17 & -0.045362 & -0.3543 & 0.362173 \tabularnewline
18 & -0.125994 & -0.984 & 0.16449 \tabularnewline
19 & -0.023743 & -0.1854 & 0.426751 \tabularnewline
20 & 0.025566 & 0.1997 & 0.4212 \tabularnewline
21 & 0.088757 & 0.6932 & 0.245403 \tabularnewline
22 & -0.028556 & -0.223 & 0.412128 \tabularnewline
23 & 0.180313 & 1.4083 & 0.082061 \tabularnewline
24 & 0.024857 & 0.1941 & 0.423356 \tabularnewline
25 & 0.11497 & 0.8979 & 0.186373 \tabularnewline
26 & 0.243868 & 1.9047 & 0.03077 \tabularnewline
27 & 0.028184 & 0.2201 & 0.413256 \tabularnewline
28 & 0.129829 & 1.014 & 0.157295 \tabularnewline
29 & 0.144735 & 1.1304 & 0.131362 \tabularnewline
30 & 0.053912 & 0.4211 & 0.337594 \tabularnewline
31 & 0.098836 & 0.7719 & 0.221566 \tabularnewline
32 & 0.031045 & 0.2425 & 0.404616 \tabularnewline
33 & -0.075119 & -0.5867 & 0.279785 \tabularnewline
34 & 0.088439 & 0.6907 & 0.246178 \tabularnewline
35 & -0.0877 & -0.685 & 0.247982 \tabularnewline
36 & -0.095983 & -0.7496 & 0.228174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31516&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]0.262141[/C][C]2.0474[/C][C]0.022467[/C][/ROW]
[ROW][C]2[/C][C]0.362335[/C][C]2.8299[/C][C]0.003147[/C][/ROW]
[ROW][C]3[/C][C]0.457275[/C][C]3.5714[/C][C]0.00035[/C][/ROW]
[ROW][C]4[/C][C]0.147827[/C][C]1.1546[/C][C]0.126385[/C][/ROW]
[ROW][C]5[/C][C]0.253303[/C][C]1.9784[/C][C]0.026204[/C][/ROW]
[ROW][C]6[/C][C]0.228481[/C][C]1.7845[/C][C]0.039658[/C][/ROW]
[ROW][C]7[/C][C]-0.076432[/C][C]-0.597[/C][C]0.276373[/C][/ROW]
[ROW][C]8[/C][C]0.119295[/C][C]0.9317[/C][C]0.177576[/C][/ROW]
[ROW][C]9[/C][C]0.002922[/C][C]0.0228[/C][C]0.490935[/C][/ROW]
[ROW][C]10[/C][C]-0.13819[/C][C]-1.0793[/C][C]0.142352[/C][/ROW]
[ROW][C]11[/C][C]-0.035582[/C][C]-0.2779[/C][C]0.391011[/C][/ROW]
[ROW][C]12[/C][C]-0.299[/C][C]-2.3353[/C][C]0.011418[/C][/ROW]
[ROW][C]13[/C][C]-0.226853[/C][C]-1.7718[/C][C]0.040713[/C][/ROW]
[ROW][C]14[/C][C]-0.201416[/C][C]-1.5731[/C][C]0.060434[/C][/ROW]
[ROW][C]15[/C][C]-0.217057[/C][C]-1.6953[/C][C]0.047562[/C][/ROW]
[ROW][C]16[/C][C]-0.174561[/C][C]-1.3634[/C][C]0.088889[/C][/ROW]
[ROW][C]17[/C][C]-0.045362[/C][C]-0.3543[/C][C]0.362173[/C][/ROW]
[ROW][C]18[/C][C]-0.125994[/C][C]-0.984[/C][C]0.16449[/C][/ROW]
[ROW][C]19[/C][C]-0.023743[/C][C]-0.1854[/C][C]0.426751[/C][/ROW]
[ROW][C]20[/C][C]0.025566[/C][C]0.1997[/C][C]0.4212[/C][/ROW]
[ROW][C]21[/C][C]0.088757[/C][C]0.6932[/C][C]0.245403[/C][/ROW]
[ROW][C]22[/C][C]-0.028556[/C][C]-0.223[/C][C]0.412128[/C][/ROW]
[ROW][C]23[/C][C]0.180313[/C][C]1.4083[/C][C]0.082061[/C][/ROW]
[ROW][C]24[/C][C]0.024857[/C][C]0.1941[/C][C]0.423356[/C][/ROW]
[ROW][C]25[/C][C]0.11497[/C][C]0.8979[/C][C]0.186373[/C][/ROW]
[ROW][C]26[/C][C]0.243868[/C][C]1.9047[/C][C]0.03077[/C][/ROW]
[ROW][C]27[/C][C]0.028184[/C][C]0.2201[/C][C]0.413256[/C][/ROW]
[ROW][C]28[/C][C]0.129829[/C][C]1.014[/C][C]0.157295[/C][/ROW]
[ROW][C]29[/C][C]0.144735[/C][C]1.1304[/C][C]0.131362[/C][/ROW]
[ROW][C]30[/C][C]0.053912[/C][C]0.4211[/C][C]0.337594[/C][/ROW]
[ROW][C]31[/C][C]0.098836[/C][C]0.7719[/C][C]0.221566[/C][/ROW]
[ROW][C]32[/C][C]0.031045[/C][C]0.2425[/C][C]0.404616[/C][/ROW]
[ROW][C]33[/C][C]-0.075119[/C][C]-0.5867[/C][C]0.279785[/C][/ROW]
[ROW][C]34[/C][C]0.088439[/C][C]0.6907[/C][C]0.246178[/C][/ROW]
[ROW][C]35[/C][C]-0.0877[/C][C]-0.685[/C][C]0.247982[/C][/ROW]
[ROW][C]36[/C][C]-0.095983[/C][C]-0.7496[/C][C]0.228174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31516&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31516&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	0.262141	2.0474	0.022467
2	0.362335	2.8299	0.003147
3	0.457275	3.5714	0.00035
4	0.147827	1.1546	0.126385
5	0.253303	1.9784	0.026204
6	0.228481	1.7845	0.039658
7	-0.076432	-0.597	0.276373
8	0.119295	0.9317	0.177576
9	0.002922	0.0228	0.490935
10	-0.13819	-1.0793	0.142352
11	-0.035582	-0.2779	0.391011
12	-0.299	-2.3353	0.011418
13	-0.226853	-1.7718	0.040713
14	-0.201416	-1.5731	0.060434
15	-0.217057	-1.6953	0.047562
16	-0.174561	-1.3634	0.088889
17	-0.045362	-0.3543	0.362173
18	-0.125994	-0.984	0.16449
19	-0.023743	-0.1854	0.426751
20	0.025566	0.1997	0.4212
21	0.088757	0.6932	0.245403
22	-0.028556	-0.223	0.412128
23	0.180313	1.4083	0.082061
24	0.024857	0.1941	0.423356
25	0.11497	0.8979	0.186373
26	0.243868	1.9047	0.03077
27	0.028184	0.2201	0.413256
28	0.129829	1.014	0.157295
29	0.144735	1.1304	0.131362
30	0.053912	0.4211	0.337594
31	0.098836	0.7719	0.221566
32	0.031045	0.2425	0.404616
33	-0.075119	-0.5867	0.279785
34	0.088439	0.6907	0.246178
35	-0.0877	-0.685	0.247982
36	-0.095983	-0.7496	0.228174

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.262141	2.0474	0.022467
2	0.315283	2.4624	0.00832
3	0.369126	2.883	0.002717
4	-0.093905	-0.7334	0.233056
5	0.004184	0.0327	0.48702
6	0.035753	0.2792	0.390502
7	-0.256092	-2.0001	0.024971
8	-0.008058	-0.0629	0.475011
9	-0.010714	-0.0837	0.466792
10	-0.075552	-0.5901	0.278659
11	-0.070153	-0.5479	0.292877
12	-0.268877	-2.1	0.019936
13	-0.06	-0.4686	0.320507
14	-0.061891	-0.4834	0.315278
15	0.160197	1.2512	0.107823
16	0.080433	0.6282	0.266108
17	0.212545	1.66	0.051021
18	0.062186	0.4857	0.314464
19	-0.044669	-0.3489	0.364191
20	0.018131	0.1416	0.443928
21	0.154642	1.2078	0.115895
22	-0.16698	-1.3042	0.09854
23	0.100244	0.7829	0.218347
24	-0.204157	-1.5945	0.057994
25	-0.028158	-0.2199	0.413333
26	0.026238	0.2049	0.419158
27	-0.077964	-0.6089	0.272421
28	0.034443	0.269	0.394415
29	0.033642	0.2628	0.396813
30	0.167816	1.3107	0.097439
31	0.025083	0.1959	0.422669
32	-0.043622	-0.3407	0.367252
33	0.051653	0.4034	0.344024
34	-0.013462	-0.1051	0.458304
35	0.01057	0.0826	0.467239
36	-0.18561	-1.4497	0.076137

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.262141 & 2.0474 & 0.022467 \tabularnewline
2 & 0.315283 & 2.4624 & 0.00832 \tabularnewline
3 & 0.369126 & 2.883 & 0.002717 \tabularnewline
4 & -0.093905 & -0.7334 & 0.233056 \tabularnewline
5 & 0.004184 & 0.0327 & 0.48702 \tabularnewline
6 & 0.035753 & 0.2792 & 0.390502 \tabularnewline
7 & -0.256092 & -2.0001 & 0.024971 \tabularnewline
8 & -0.008058 & -0.0629 & 0.475011 \tabularnewline
9 & -0.010714 & -0.0837 & 0.466792 \tabularnewline
10 & -0.075552 & -0.5901 & 0.278659 \tabularnewline
11 & -0.070153 & -0.5479 & 0.292877 \tabularnewline
12 & -0.268877 & -2.1 & 0.019936 \tabularnewline
13 & -0.06 & -0.4686 & 0.320507 \tabularnewline
14 & -0.061891 & -0.4834 & 0.315278 \tabularnewline
15 & 0.160197 & 1.2512 & 0.107823 \tabularnewline
16 & 0.080433 & 0.6282 & 0.266108 \tabularnewline
17 & 0.212545 & 1.66 & 0.051021 \tabularnewline
18 & 0.062186 & 0.4857 & 0.314464 \tabularnewline
19 & -0.044669 & -0.3489 & 0.364191 \tabularnewline
20 & 0.018131 & 0.1416 & 0.443928 \tabularnewline
21 & 0.154642 & 1.2078 & 0.115895 \tabularnewline
22 & -0.16698 & -1.3042 & 0.09854 \tabularnewline
23 & 0.100244 & 0.7829 & 0.218347 \tabularnewline
24 & -0.204157 & -1.5945 & 0.057994 \tabularnewline
25 & -0.028158 & -0.2199 & 0.413333 \tabularnewline
26 & 0.026238 & 0.2049 & 0.419158 \tabularnewline
27 & -0.077964 & -0.6089 & 0.272421 \tabularnewline
28 & 0.034443 & 0.269 & 0.394415 \tabularnewline
29 & 0.033642 & 0.2628 & 0.396813 \tabularnewline
30 & 0.167816 & 1.3107 & 0.097439 \tabularnewline
31 & 0.025083 & 0.1959 & 0.422669 \tabularnewline
32 & -0.043622 & -0.3407 & 0.367252 \tabularnewline
33 & 0.051653 & 0.4034 & 0.344024 \tabularnewline
34 & -0.013462 & -0.1051 & 0.458304 \tabularnewline
35 & 0.01057 & 0.0826 & 0.467239 \tabularnewline
36 & -0.18561 & -1.4497 & 0.076137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31516&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]0.262141[/C][C]2.0474[/C][C]0.022467[/C][/ROW]
[ROW][C]2[/C][C]0.315283[/C][C]2.4624[/C][C]0.00832[/C][/ROW]
[ROW][C]3[/C][C]0.369126[/C][C]2.883[/C][C]0.002717[/C][/ROW]
[ROW][C]4[/C][C]-0.093905[/C][C]-0.7334[/C][C]0.233056[/C][/ROW]
[ROW][C]5[/C][C]0.004184[/C][C]0.0327[/C][C]0.48702[/C][/ROW]
[ROW][C]6[/C][C]0.035753[/C][C]0.2792[/C][C]0.390502[/C][/ROW]
[ROW][C]7[/C][C]-0.256092[/C][C]-2.0001[/C][C]0.024971[/C][/ROW]
[ROW][C]8[/C][C]-0.008058[/C][C]-0.0629[/C][C]0.475011[/C][/ROW]
[ROW][C]9[/C][C]-0.010714[/C][C]-0.0837[/C][C]0.466792[/C][/ROW]
[ROW][C]10[/C][C]-0.075552[/C][C]-0.5901[/C][C]0.278659[/C][/ROW]
[ROW][C]11[/C][C]-0.070153[/C][C]-0.5479[/C][C]0.292877[/C][/ROW]
[ROW][C]12[/C][C]-0.268877[/C][C]-2.1[/C][C]0.019936[/C][/ROW]
[ROW][C]13[/C][C]-0.06[/C][C]-0.4686[/C][C]0.320507[/C][/ROW]
[ROW][C]14[/C][C]-0.061891[/C][C]-0.4834[/C][C]0.315278[/C][/ROW]
[ROW][C]15[/C][C]0.160197[/C][C]1.2512[/C][C]0.107823[/C][/ROW]
[ROW][C]16[/C][C]0.080433[/C][C]0.6282[/C][C]0.266108[/C][/ROW]
[ROW][C]17[/C][C]0.212545[/C][C]1.66[/C][C]0.051021[/C][/ROW]
[ROW][C]18[/C][C]0.062186[/C][C]0.4857[/C][C]0.314464[/C][/ROW]
[ROW][C]19[/C][C]-0.044669[/C][C]-0.3489[/C][C]0.364191[/C][/ROW]
[ROW][C]20[/C][C]0.018131[/C][C]0.1416[/C][C]0.443928[/C][/ROW]
[ROW][C]21[/C][C]0.154642[/C][C]1.2078[/C][C]0.115895[/C][/ROW]
[ROW][C]22[/C][C]-0.16698[/C][C]-1.3042[/C][C]0.09854[/C][/ROW]
[ROW][C]23[/C][C]0.100244[/C][C]0.7829[/C][C]0.218347[/C][/ROW]
[ROW][C]24[/C][C]-0.204157[/C][C]-1.5945[/C][C]0.057994[/C][/ROW]
[ROW][C]25[/C][C]-0.028158[/C][C]-0.2199[/C][C]0.413333[/C][/ROW]
[ROW][C]26[/C][C]0.026238[/C][C]0.2049[/C][C]0.419158[/C][/ROW]
[ROW][C]27[/C][C]-0.077964[/C][C]-0.6089[/C][C]0.272421[/C][/ROW]
[ROW][C]28[/C][C]0.034443[/C][C]0.269[/C][C]0.394415[/C][/ROW]
[ROW][C]29[/C][C]0.033642[/C][C]0.2628[/C][C]0.396813[/C][/ROW]
[ROW][C]30[/C][C]0.167816[/C][C]1.3107[/C][C]0.097439[/C][/ROW]
[ROW][C]31[/C][C]0.025083[/C][C]0.1959[/C][C]0.422669[/C][/ROW]
[ROW][C]32[/C][C]-0.043622[/C][C]-0.3407[/C][C]0.367252[/C][/ROW]
[ROW][C]33[/C][C]0.051653[/C][C]0.4034[/C][C]0.344024[/C][/ROW]
[ROW][C]34[/C][C]-0.013462[/C][C]-0.1051[/C][C]0.458304[/C][/ROW]
[ROW][C]35[/C][C]0.01057[/C][C]0.0826[/C][C]0.467239[/C][/ROW]
[ROW][C]36[/C][C]-0.18561[/C][C]-1.4497[/C][C]0.076137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31516&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31516&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	0.262141	2.0474	0.022467
2	0.315283	2.4624	0.00832
3	0.369126	2.883	0.002717
4	-0.093905	-0.7334	0.233056
5	0.004184	0.0327	0.48702
6	0.035753	0.2792	0.390502
7	-0.256092	-2.0001	0.024971
8	-0.008058	-0.0629	0.475011
9	-0.010714	-0.0837	0.466792
10	-0.075552	-0.5901	0.278659
11	-0.070153	-0.5479	0.292877
12	-0.268877	-2.1	0.019936
13	-0.06	-0.4686	0.320507
14	-0.061891	-0.4834	0.315278
15	0.160197	1.2512	0.107823
16	0.080433	0.6282	0.266108
17	0.212545	1.66	0.051021
18	0.062186	0.4857	0.314464
19	-0.044669	-0.3489	0.364191
20	0.018131	0.1416	0.443928
21	0.154642	1.2078	0.115895
22	-0.16698	-1.3042	0.09854
23	0.100244	0.7829	0.218347
24	-0.204157	-1.5945	0.057994
25	-0.028158	-0.2199	0.413333
26	0.026238	0.2049	0.419158
27	-0.077964	-0.6089	0.272421
28	0.034443	0.269	0.394415
29	0.033642	0.2628	0.396813
30	0.167816	1.3107	0.097439
31	0.025083	0.1959	0.422669
32	-0.043622	-0.3407	0.367252
33	0.051653	0.4034	0.344024
34	-0.013462	-0.1051	0.458304
35	0.01057	0.0826	0.467239
36	-0.18561	-1.4497	0.076137

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 = 1 ; par5 = 12 ;

Parameters (R input):

par1 = 36 ; par2 = 1 ; par3 = 0 ; 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