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rwasp_autocorrelation.wasp

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(Partial) Autocorrelation Function

Date of computation

Wed, 03 Dec 2008 09:24:10 -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/03/t1228322150q4k4455sndqb6nb.htm/, Retrieved Fri, 17 May 2024 14:07:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28772, Retrieved Fri, 17 May 2024 14:07:31 +0000

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Estimated Impact

317

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] [Taak 10 Stap 4] [2008-12-03 16:24:10] [286e96bd53289970f8e5f25a93fb50b3] [Current]
-   PD      [(Partial) Autocorrelation Function] [Taak 10 Stap 4 Aa...] [2008-12-04 18:42:35] [819b576fab25b35cfda70f80599828ec] 
- RMP         [ARIMA Backward Selection] [Taak 10 deel 2 st...] [2008-12-05 14:47:01] [6fea0e9a9b3b29a63badf2c274e82506] 
- R           [(Partial) Autocorrelation Function] [Step 4] [2008-12-08 17:08:15] [7458e879e85b911182071700fff19fbd] 
F   P         [(Partial) Autocorrelation Function] [Stap 4] [2008-12-08 17:09:00] [74be16979710d4c4e7c6647856088456] 
-   P       [(Partial) Autocorrelation Function] [Identification an...] [2008-12-08 19:12:52] [79c17183721a40a589db5f9f561947d8] 
-   PD        [(Partial) Autocorrelation Function] [ACF/PACF elektric...] [2008-12-19 15:23:40] [44a98561a4b3e6ab8cd5a857b48b0914] 
-   PD        [(Partial) Autocorrelation Function] [(P)ACF elektriciteit] [2008-12-19 15:28:36] [44a98561a4b3e6ab8cd5a857b48b0914] 
- RMPD        [ARIMA Backward Selection] [arima backward el...] [2008-12-19 16:45:52] [44a98561a4b3e6ab8cd5a857b48b0914] 
- RMPD          [(Partial) Autocorrelation Function] [(P)ACF gas] [2008-12-19 17:09:08] [44a98561a4b3e6ab8cd5a857b48b0914] 
- RMPD          [Standard Deviation-Mean Plot] [standdev olie] [2008-12-19 17:13:34] [44a98561a4b3e6ab8cd5a857b48b0914] 
- RMPD          [(Partial) Autocorrelation Function] [(P)ACF olie] [2008-12-19 17:15:28] [44a98561a4b3e6ab8cd5a857b48b0914] 
-   P             [(Partial) Autocorrelation Function] [pacf-acf olie] [2008-12-22 10:01:15] [44a98561a4b3e6ab8cd5a857b48b0914] 

Feedback Forum

2008-12-14 09:55:17 [Kristof Van Esbroeck] [reply] 
Student gebruikt correct de software en trekt een degelijk besluit. 
Ook de verschillende parameters worden duidelijk besproken. 
 
p: Wanneer we de autocorrelatiefunctie in beschouwing nemen merken we, zoals student aangeeft, een duidelijk patroon over de eerste 6 lags. Ze zijn inderdaad alle 6 positief. Om vervolgens de orde te bepalen dienen we naar de Partial Correlation te kijken. Hier kunnen we vaststellen dat zowel lag 1 als lag 2 significant zijn. Lag 3 is een twijfelgeval, student neemt hem in overweging en stelt de p waarde bijgevolg gelijk aan 3. 
 
P: Student stelt geen patroon vast en neemt correct als P waarde 0. 
 
q: Ook q is gelijk aan 0 want we wanneer we de eerste 6 lags van de partial autcorrelation function bekijken kunnen we geen vast patroon vaststellen. 
 
Q: Student merkt op dat enkel lag 12 significant is en trekt correct de conclusie van eerste orde. Q is maw gelijk aan 1.
2008-12-14 11:28:25 [Jeroen Michel] [reply] 
De student heeft een zéér uitgebreide conclusie gemaakt. De conclusie is correct en deze analyse is duidelijk waar te nemen/ af te lezen op de AF-grafiek en PACF-grafiek. 
 
Voorts wordt elke parameter correct geanalyseerd binnen de analyse: 
'Om de waarde van P te bepalen kijken we op de grafiek of de tabel van de autocorrelatie (want AR-model) naar de autocorrelatiewaarden van 12, 24, 36, 48 en 60. We zien dat enkel de lag van 12 significant verschilt van 0, de overige niet. Dit wil zeggen dat er geen saisonaal patroon aanwezig is, de waarde van P is dus 0. 
 
Om q en Q te bepalen moeten we het typische patroon van een MA-model op de grafiek van de partiële autocorrelatie zoeken. In dit geval is er geen sprake van het typische patroon (negatieve, naar 0 convergerende waarden), waardoor we kunnen besluiten dat q = 0.' 
2008-12-14 12:51:01 [Kevin Neelen] [reply] 
De student heeft hier gebruik gemaakt van de juiste methode om deze vraagstelling te kunnen oplossen, namelijk de (Partial) Autocorrelation Function. 
 
De student heeft de volgende gegevens ingevoerd: number of time lags = 60, Lambda = 0,5, d = 1, D = 1 en Seasonal period = 12. Deze waarden werden alle bekomen in voorgaande stappen. Nadien worden voor p, P, q en Q de juiste waarden bepaald. 
 
p = 3 --> bij de partiele correlatie zien we dat de eerste 2 coefficienten significant zijn, de derde is een twijfelgeval waardoor we de waarde van p gelijkstellen aan 3. 
 
P = 0 --> we kijken op de grafiek of de tabel van de autocorrelatie (want AR-model) naar de autocorrelatiewaarden van 12, 24, 36, 48 en 60. We zien dat enkel de lag van 12 significant verschilt van 0, de overige niet. Dit wil zeggen dat er geen saisonaal patroon aanwezig is, de waarde van P is dus 0. 
 
q = 0 --> we moeten het typische patroon van een MA-model op de grafiek van de partiële autocorrelatie proberen te zoeken. In dit geval is er geen sprake van het typische patroon (negatieve, naar 0 convergerende waarden), waardoor we kunnen besluiten dat q = 0. 
 
Q = 1 --> we moeten het typische patroon van een MA-model op de grafiek van de partiele autocorrelatie proberen te zoeken. Bij lags 12, 24, 36, enz. zien we een aflopend patroon indien we de lag van 24 een beetje verlengen. Enkel de lag van twaalf is significant, dus er is sprake van een eerste orde. Q is dus 1.
  2008-12-15 21:19:31 [Nilay Erdogdu] [reply] 
ik ben het volledig eens met de uitleg van Kevin Neelen. De waarden voor p, P, q en Q zijn goed gekozen. Ook de bedenkingen bij het anlyseren van de juiste processen zijn goed gedaan door de student.
2008-12-14 13:07:36 [Matthieu Blondeau] [reply] 
De student heeft de correcte parameters gevonden op de juiste manier.
2008-12-14 13:53:27 [Ilknur Günes] [reply] 
De parameters zijn correct, zeer goede uitleg!
2008-12-14 17:01:00 [Mehmet Yilmaz] [reply] 
De berekening en conclusies zijn correct.
2008-12-15 10:48:14 [Jef Keersmaekers] [reply] 
De berkeningen van de paramaters zijn perfect uitgelegd, ik kan hier niets meer aan toevoegen
2008-12-15 18:05:38 [Steffi Van Isveldt] [reply] 
De parameters werden correct veranderd. De juiste conclusie werd getrokken door de grafieken en tabellen te analyseren. 
2008-12-15 20:19:14 [Michael Van Spaandonck] [reply] 
De waarden van p, P, q en Q moeten bepaald worden, om de vergelijking op te stellen en door middel van de ARIMA backward selection-toepassing de waarden van de verschillende Ф te kunnen berekenen. We beginnen met p en P en moeten hiervoor kijken naar AR-processen. 
 
Wanneer we de autocorrelatie bekijken zien we dat de eerste 5 à 6 coëfficienten alle positief zijn en vrij snel naar 0 convergeren. 
 
Bij de partiële correlatie zien we dat de eerste 2 coëfficienten significant zijn, de derde is een twijfelgeval waardoor we de waarde van p gelijkstellen aan 3. 
 
Om de waarde van P te bepalen kijken we op de grafiek of de tabel van de autocorrelatie (want AR-model) naar de autocorrelatiewaarden van 12, 24, 36, 48 en 60. 
We zien dat enkel de lag van 12 significant verschilt van 0, de overige niet. 
Dit wil zeggen dat er geen saisonaal patroon aanwezig is, de waarde van P is dus 0. 
 
Om q en Q te bepalen moeten we het typische patroon van een MA-model op de grafiek van de partiële autocorrelatie zoeken. In dit geval is er geen sprake van het typische patroon (negatieve, naar 0 convergerende waarden), waardoor we kunnen besluiten dat q = 0. 
 
Bij lags 12, 24, 36, enz. zien we een aflopend patroon indien we de lag van 24 een beetje verlengen. Enkel de lag van twaalf is significant, dus er is sprake van een eerste orde. 
Q is dus 1. 
 
Conclusie 
 
p = 3	λ = 0,5 
P = 0	d = 1 
q = 0	D = 1 
Q = 1 
 
(1 - Ф1B - Ф2B2  - Ф3B3) ▼▼12 Yt0,5 = (1 – Θ1B12) Et 
  2008-12-15 20:22:20 [Michael Van Spaandonck] [reply] 
Gegevensinvoer was de volgende: 
Number of time lags = 60 
Lambda = 0,5 
d = 1 
D = 1 
Seasonal period = 12

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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=28772&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=28772&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28772&T=0

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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

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.187552	3.5536	0.000215
2	0.317794	6.0213	0
3	0.179563	3.4022	0.000372
4	0.155303	2.9426	0.001733
5	0.127596	2.4176	0.008061
6	0.06371	1.2071	0.114087
7	-0.054701	-1.0364	0.150347
8	-0.011735	-0.2224	0.412083
9	-0.080833	-1.5316	0.063255
10	-0.174708	-3.3102	0.000513
11	-0.055263	-1.0471	0.147883
12	-0.480698	-9.1079	0
13	-0.168514	-3.1929	0.000767
14	-0.172913	-3.2762	0.000577
15	-0.128287	-2.4307	0.007779
16	-0.163167	-3.0916	0.001073
17	-0.121097	-2.2945	0.01117
18	-0.103099	-1.9534	0.025772
19	0.020128	0.3814	0.351578
20	-0.002775	-0.0526	0.479049
21	-0.006426	-0.1217	0.451583
22	-0.006358	-0.1205	0.45209
23	-0.004364	-0.0827	0.467075
24	-0.006343	-0.1202	0.452205
25	0.094911	1.7983	0.036484
26	-0.008505	-0.1611	0.436036
27	0.017661	0.3346	0.369052
28	0.077035	1.4596	0.072638
29	0.068023	1.2888	0.09914
30	0.041632	0.7888	0.215369
31	0.032895	0.6233	0.266752
32	-0.068171	-1.2917	0.098653
33	0.011323	0.2145	0.415122
34	0.002674	0.0507	0.479811
35	-0.0721	-1.3661	0.08638
36	-0.031172	-0.5906	0.277571
37	-0.113841	-2.157	0.015835
38	-0.00152	-0.0288	0.488517
39	-0.046567	-0.8823	0.189098
40	-0.067976	-1.288	0.099295
41	-0.105451	-1.998	0.023234
42	-0.033256	-0.6301	0.264511
43	-0.158298	-2.9993	0.001447
44	0.01837	0.3481	0.364001
45	-0.043872	-0.8313	0.203191
46	0.021162	0.401	0.34434
47	0.042179	0.7992	0.21236
48	0.097675	1.8507	0.032519
49	0.125528	2.3784	0.008955
50	0.090564	1.7159	0.043518
51	0.082527	1.5637	0.059388
52	0.128164	2.4284	0.007829
53	0.160308	3.0374	0.00128
54	0.042213	0.7998	0.212173
55	0.160649	3.0439	0.001254
56	0.042494	0.8052	0.210633
57	0.055698	1.0553	0.145992
58	0.043943	0.8326	0.202814
59	0.043887	0.8315	0.203111
60	-0.02979	-0.5644	0.286401

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.187552 & 3.5536 & 0.000215 \tabularnewline
2 & 0.317794 & 6.0213 & 0 \tabularnewline
3 & 0.179563 & 3.4022 & 0.000372 \tabularnewline
4 & 0.155303 & 2.9426 & 0.001733 \tabularnewline
5 & 0.127596 & 2.4176 & 0.008061 \tabularnewline
6 & 0.06371 & 1.2071 & 0.114087 \tabularnewline
7 & -0.054701 & -1.0364 & 0.150347 \tabularnewline
8 & -0.011735 & -0.2224 & 0.412083 \tabularnewline
9 & -0.080833 & -1.5316 & 0.063255 \tabularnewline
10 & -0.174708 & -3.3102 & 0.000513 \tabularnewline
11 & -0.055263 & -1.0471 & 0.147883 \tabularnewline
12 & -0.480698 & -9.1079 & 0 \tabularnewline
13 & -0.168514 & -3.1929 & 0.000767 \tabularnewline
14 & -0.172913 & -3.2762 & 0.000577 \tabularnewline
15 & -0.128287 & -2.4307 & 0.007779 \tabularnewline
16 & -0.163167 & -3.0916 & 0.001073 \tabularnewline
17 & -0.121097 & -2.2945 & 0.01117 \tabularnewline
18 & -0.103099 & -1.9534 & 0.025772 \tabularnewline
19 & 0.020128 & 0.3814 & 0.351578 \tabularnewline
20 & -0.002775 & -0.0526 & 0.479049 \tabularnewline
21 & -0.006426 & -0.1217 & 0.451583 \tabularnewline
22 & -0.006358 & -0.1205 & 0.45209 \tabularnewline
23 & -0.004364 & -0.0827 & 0.467075 \tabularnewline
24 & -0.006343 & -0.1202 & 0.452205 \tabularnewline
25 & 0.094911 & 1.7983 & 0.036484 \tabularnewline
26 & -0.008505 & -0.1611 & 0.436036 \tabularnewline
27 & 0.017661 & 0.3346 & 0.369052 \tabularnewline
28 & 0.077035 & 1.4596 & 0.072638 \tabularnewline
29 & 0.068023 & 1.2888 & 0.09914 \tabularnewline
30 & 0.041632 & 0.7888 & 0.215369 \tabularnewline
31 & 0.032895 & 0.6233 & 0.266752 \tabularnewline
32 & -0.068171 & -1.2917 & 0.098653 \tabularnewline
33 & 0.011323 & 0.2145 & 0.415122 \tabularnewline
34 & 0.002674 & 0.0507 & 0.479811 \tabularnewline
35 & -0.0721 & -1.3661 & 0.08638 \tabularnewline
36 & -0.031172 & -0.5906 & 0.277571 \tabularnewline
37 & -0.113841 & -2.157 & 0.015835 \tabularnewline
38 & -0.00152 & -0.0288 & 0.488517 \tabularnewline
39 & -0.046567 & -0.8823 & 0.189098 \tabularnewline
40 & -0.067976 & -1.288 & 0.099295 \tabularnewline
41 & -0.105451 & -1.998 & 0.023234 \tabularnewline
42 & -0.033256 & -0.6301 & 0.264511 \tabularnewline
43 & -0.158298 & -2.9993 & 0.001447 \tabularnewline
44 & 0.01837 & 0.3481 & 0.364001 \tabularnewline
45 & -0.043872 & -0.8313 & 0.203191 \tabularnewline
46 & 0.021162 & 0.401 & 0.34434 \tabularnewline
47 & 0.042179 & 0.7992 & 0.21236 \tabularnewline
48 & 0.097675 & 1.8507 & 0.032519 \tabularnewline
49 & 0.125528 & 2.3784 & 0.008955 \tabularnewline
50 & 0.090564 & 1.7159 & 0.043518 \tabularnewline
51 & 0.082527 & 1.5637 & 0.059388 \tabularnewline
52 & 0.128164 & 2.4284 & 0.007829 \tabularnewline
53 & 0.160308 & 3.0374 & 0.00128 \tabularnewline
54 & 0.042213 & 0.7998 & 0.212173 \tabularnewline
55 & 0.160649 & 3.0439 & 0.001254 \tabularnewline
56 & 0.042494 & 0.8052 & 0.210633 \tabularnewline
57 & 0.055698 & 1.0553 & 0.145992 \tabularnewline
58 & 0.043943 & 0.8326 & 0.202814 \tabularnewline
59 & 0.043887 & 0.8315 & 0.203111 \tabularnewline
60 & -0.02979 & -0.5644 & 0.286401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28772&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]0.187552[/C][C]3.5536[/C][C]0.000215[/C][/ROW]
[ROW][C]2[/C][C]0.317794[/C][C]6.0213[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.179563[/C][C]3.4022[/C][C]0.000372[/C][/ROW]
[ROW][C]4[/C][C]0.155303[/C][C]2.9426[/C][C]0.001733[/C][/ROW]
[ROW][C]5[/C][C]0.127596[/C][C]2.4176[/C][C]0.008061[/C][/ROW]
[ROW][C]6[/C][C]0.06371[/C][C]1.2071[/C][C]0.114087[/C][/ROW]
[ROW][C]7[/C][C]-0.054701[/C][C]-1.0364[/C][C]0.150347[/C][/ROW]
[ROW][C]8[/C][C]-0.011735[/C][C]-0.2224[/C][C]0.412083[/C][/ROW]
[ROW][C]9[/C][C]-0.080833[/C][C]-1.5316[/C][C]0.063255[/C][/ROW]
[ROW][C]10[/C][C]-0.174708[/C][C]-3.3102[/C][C]0.000513[/C][/ROW]
[ROW][C]11[/C][C]-0.055263[/C][C]-1.0471[/C][C]0.147883[/C][/ROW]
[ROW][C]12[/C][C]-0.480698[/C][C]-9.1079[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.168514[/C][C]-3.1929[/C][C]0.000767[/C][/ROW]
[ROW][C]14[/C][C]-0.172913[/C][C]-3.2762[/C][C]0.000577[/C][/ROW]
[ROW][C]15[/C][C]-0.128287[/C][C]-2.4307[/C][C]0.007779[/C][/ROW]
[ROW][C]16[/C][C]-0.163167[/C][C]-3.0916[/C][C]0.001073[/C][/ROW]
[ROW][C]17[/C][C]-0.121097[/C][C]-2.2945[/C][C]0.01117[/C][/ROW]
[ROW][C]18[/C][C]-0.103099[/C][C]-1.9534[/C][C]0.025772[/C][/ROW]
[ROW][C]19[/C][C]0.020128[/C][C]0.3814[/C][C]0.351578[/C][/ROW]
[ROW][C]20[/C][C]-0.002775[/C][C]-0.0526[/C][C]0.479049[/C][/ROW]
[ROW][C]21[/C][C]-0.006426[/C][C]-0.1217[/C][C]0.451583[/C][/ROW]
[ROW][C]22[/C][C]-0.006358[/C][C]-0.1205[/C][C]0.45209[/C][/ROW]
[ROW][C]23[/C][C]-0.004364[/C][C]-0.0827[/C][C]0.467075[/C][/ROW]
[ROW][C]24[/C][C]-0.006343[/C][C]-0.1202[/C][C]0.452205[/C][/ROW]
[ROW][C]25[/C][C]0.094911[/C][C]1.7983[/C][C]0.036484[/C][/ROW]
[ROW][C]26[/C][C]-0.008505[/C][C]-0.1611[/C][C]0.436036[/C][/ROW]
[ROW][C]27[/C][C]0.017661[/C][C]0.3346[/C][C]0.369052[/C][/ROW]
[ROW][C]28[/C][C]0.077035[/C][C]1.4596[/C][C]0.072638[/C][/ROW]
[ROW][C]29[/C][C]0.068023[/C][C]1.2888[/C][C]0.09914[/C][/ROW]
[ROW][C]30[/C][C]0.041632[/C][C]0.7888[/C][C]0.215369[/C][/ROW]
[ROW][C]31[/C][C]0.032895[/C][C]0.6233[/C][C]0.266752[/C][/ROW]
[ROW][C]32[/C][C]-0.068171[/C][C]-1.2917[/C][C]0.098653[/C][/ROW]
[ROW][C]33[/C][C]0.011323[/C][C]0.2145[/C][C]0.415122[/C][/ROW]
[ROW][C]34[/C][C]0.002674[/C][C]0.0507[/C][C]0.479811[/C][/ROW]
[ROW][C]35[/C][C]-0.0721[/C][C]-1.3661[/C][C]0.08638[/C][/ROW]
[ROW][C]36[/C][C]-0.031172[/C][C]-0.5906[/C][C]0.277571[/C][/ROW]
[ROW][C]37[/C][C]-0.113841[/C][C]-2.157[/C][C]0.015835[/C][/ROW]
[ROW][C]38[/C][C]-0.00152[/C][C]-0.0288[/C][C]0.488517[/C][/ROW]
[ROW][C]39[/C][C]-0.046567[/C][C]-0.8823[/C][C]0.189098[/C][/ROW]
[ROW][C]40[/C][C]-0.067976[/C][C]-1.288[/C][C]0.099295[/C][/ROW]
[ROW][C]41[/C][C]-0.105451[/C][C]-1.998[/C][C]0.023234[/C][/ROW]
[ROW][C]42[/C][C]-0.033256[/C][C]-0.6301[/C][C]0.264511[/C][/ROW]
[ROW][C]43[/C][C]-0.158298[/C][C]-2.9993[/C][C]0.001447[/C][/ROW]
[ROW][C]44[/C][C]0.01837[/C][C]0.3481[/C][C]0.364001[/C][/ROW]
[ROW][C]45[/C][C]-0.043872[/C][C]-0.8313[/C][C]0.203191[/C][/ROW]
[ROW][C]46[/C][C]0.021162[/C][C]0.401[/C][C]0.34434[/C][/ROW]
[ROW][C]47[/C][C]0.042179[/C][C]0.7992[/C][C]0.21236[/C][/ROW]
[ROW][C]48[/C][C]0.097675[/C][C]1.8507[/C][C]0.032519[/C][/ROW]
[ROW][C]49[/C][C]0.125528[/C][C]2.3784[/C][C]0.008955[/C][/ROW]
[ROW][C]50[/C][C]0.090564[/C][C]1.7159[/C][C]0.043518[/C][/ROW]
[ROW][C]51[/C][C]0.082527[/C][C]1.5637[/C][C]0.059388[/C][/ROW]
[ROW][C]52[/C][C]0.128164[/C][C]2.4284[/C][C]0.007829[/C][/ROW]
[ROW][C]53[/C][C]0.160308[/C][C]3.0374[/C][C]0.00128[/C][/ROW]
[ROW][C]54[/C][C]0.042213[/C][C]0.7998[/C][C]0.212173[/C][/ROW]
[ROW][C]55[/C][C]0.160649[/C][C]3.0439[/C][C]0.001254[/C][/ROW]
[ROW][C]56[/C][C]0.042494[/C][C]0.8052[/C][C]0.210633[/C][/ROW]
[ROW][C]57[/C][C]0.055698[/C][C]1.0553[/C][C]0.145992[/C][/ROW]
[ROW][C]58[/C][C]0.043943[/C][C]0.8326[/C][C]0.202814[/C][/ROW]
[ROW][C]59[/C][C]0.043887[/C][C]0.8315[/C][C]0.203111[/C][/ROW]
[ROW][C]60[/C][C]-0.02979[/C][C]-0.5644[/C][C]0.286401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28772&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28772&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	0.187552	3.5536	0.000215
2	0.317794	6.0213	0
3	0.179563	3.4022	0.000372
4	0.155303	2.9426	0.001733
5	0.127596	2.4176	0.008061
6	0.06371	1.2071	0.114087
7	-0.054701	-1.0364	0.150347
8	-0.011735	-0.2224	0.412083
9	-0.080833	-1.5316	0.063255
10	-0.174708	-3.3102	0.000513
11	-0.055263	-1.0471	0.147883
12	-0.480698	-9.1079	0
13	-0.168514	-3.1929	0.000767
14	-0.172913	-3.2762	0.000577
15	-0.128287	-2.4307	0.007779
16	-0.163167	-3.0916	0.001073
17	-0.121097	-2.2945	0.01117
18	-0.103099	-1.9534	0.025772
19	0.020128	0.3814	0.351578
20	-0.002775	-0.0526	0.479049
21	-0.006426	-0.1217	0.451583
22	-0.006358	-0.1205	0.45209
23	-0.004364	-0.0827	0.467075
24	-0.006343	-0.1202	0.452205
25	0.094911	1.7983	0.036484
26	-0.008505	-0.1611	0.436036
27	0.017661	0.3346	0.369052
28	0.077035	1.4596	0.072638
29	0.068023	1.2888	0.09914
30	0.041632	0.7888	0.215369
31	0.032895	0.6233	0.266752
32	-0.068171	-1.2917	0.098653
33	0.011323	0.2145	0.415122
34	0.002674	0.0507	0.479811
35	-0.0721	-1.3661	0.08638
36	-0.031172	-0.5906	0.277571
37	-0.113841	-2.157	0.015835
38	-0.00152	-0.0288	0.488517
39	-0.046567	-0.8823	0.189098
40	-0.067976	-1.288	0.099295
41	-0.105451	-1.998	0.023234
42	-0.033256	-0.6301	0.264511
43	-0.158298	-2.9993	0.001447
44	0.01837	0.3481	0.364001
45	-0.043872	-0.8313	0.203191
46	0.021162	0.401	0.34434
47	0.042179	0.7992	0.21236
48	0.097675	1.8507	0.032519
49	0.125528	2.3784	0.008955
50	0.090564	1.7159	0.043518
51	0.082527	1.5637	0.059388
52	0.128164	2.4284	0.007829
53	0.160308	3.0374	0.00128
54	0.042213	0.7998	0.212173
55	0.160649	3.0439	0.001254
56	0.042494	0.8052	0.210633
57	0.055698	1.0553	0.145992
58	0.043943	0.8326	0.202814
59	0.043887	0.8315	0.203111
60	-0.02979	-0.5644	0.286401

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.187552	3.5536	0.000215
2	0.292922	5.5501	0
3	0.093512	1.7718	0.038638
4	0.033995	0.6441	0.259959
5	0.032214	0.6104	0.271005
6	-0.024285	-0.4601	0.322846
7	-0.139405	-2.6414	0.004309
8	-0.030697	-0.5816	0.280592
9	-0.045877	-0.8693	0.192645
10	-0.156693	-2.9689	0.001595
11	0.037591	0.7122	0.238389
12	-0.427945	-8.1084	0
13	-0.036401	-0.6897	0.245414
14	0.123319	2.3366	0.010006
15	0.047112	0.8926	0.186322
16	-0.060609	-1.1484	0.125789
17	-0.017722	-0.3358	0.368615
18	0.007818	0.1481	0.44116
19	0.023368	0.4428	0.329106
20	0.047983	0.9091	0.181941
21	-0.03962	-0.7507	0.226664
22	-0.157258	-2.9796	0.001541
23	0.010195	0.1932	0.42347
24	-0.263321	-4.9892	0
25	0.056986	1.0797	0.140495
26	0.017452	0.3307	0.370544
27	-0.01152	-0.2183	0.413674
28	0.033761	0.6397	0.261394
29	0.035746	0.6773	0.249331
30	-0.027638	-0.5237	0.300414
31	0.037568	0.7118	0.238523
32	-0.076586	-1.4511	0.073812
33	-0.04242	-0.8037	0.211038
34	-0.086262	-1.6344	0.051522
35	-0.078156	-1.4808	0.069763
36	-0.219062	-4.1506	2.1e-05
37	-0.029885	-0.5662	0.285794
38	0.062408	1.1825	0.118901
39	-0.055153	-1.045	0.148363
40	-0.019687	-0.373	0.354676
41	-0.030763	-0.5829	0.280174
42	-0.01269	-0.2404	0.405061
43	-0.11499	-2.1787	0.015
44	-0.017441	-0.3305	0.37062
45	-0.006089	-0.1154	0.454112
46	0.002743	0.052	0.479293
47	-0.026375	-0.4997	0.308785
48	-0.056646	-1.0733	0.141932
49	0.037272	0.7062	0.240259
50	0.038077	0.7214	0.235552
51	-0.031982	-0.606	0.272461
52	0.055379	1.0493	0.147378
53	0.0148	0.2804	0.389656
54	-0.095807	-1.8153	0.035157
55	-0.024197	-0.4585	0.323449
56	-0.032014	-0.6066	0.272256
57	-0.058488	-1.1082	0.134259
58	0.043184	0.8182	0.206889
59	0.009712	0.184	0.427053
60	-0.049846	-0.9444	0.172791

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.187552 & 3.5536 & 0.000215 \tabularnewline
2 & 0.292922 & 5.5501 & 0 \tabularnewline
3 & 0.093512 & 1.7718 & 0.038638 \tabularnewline
4 & 0.033995 & 0.6441 & 0.259959 \tabularnewline
5 & 0.032214 & 0.6104 & 0.271005 \tabularnewline
6 & -0.024285 & -0.4601 & 0.322846 \tabularnewline
7 & -0.139405 & -2.6414 & 0.004309 \tabularnewline
8 & -0.030697 & -0.5816 & 0.280592 \tabularnewline
9 & -0.045877 & -0.8693 & 0.192645 \tabularnewline
10 & -0.156693 & -2.9689 & 0.001595 \tabularnewline
11 & 0.037591 & 0.7122 & 0.238389 \tabularnewline
12 & -0.427945 & -8.1084 & 0 \tabularnewline
13 & -0.036401 & -0.6897 & 0.245414 \tabularnewline
14 & 0.123319 & 2.3366 & 0.010006 \tabularnewline
15 & 0.047112 & 0.8926 & 0.186322 \tabularnewline
16 & -0.060609 & -1.1484 & 0.125789 \tabularnewline
17 & -0.017722 & -0.3358 & 0.368615 \tabularnewline
18 & 0.007818 & 0.1481 & 0.44116 \tabularnewline
19 & 0.023368 & 0.4428 & 0.329106 \tabularnewline
20 & 0.047983 & 0.9091 & 0.181941 \tabularnewline
21 & -0.03962 & -0.7507 & 0.226664 \tabularnewline
22 & -0.157258 & -2.9796 & 0.001541 \tabularnewline
23 & 0.010195 & 0.1932 & 0.42347 \tabularnewline
24 & -0.263321 & -4.9892 & 0 \tabularnewline
25 & 0.056986 & 1.0797 & 0.140495 \tabularnewline
26 & 0.017452 & 0.3307 & 0.370544 \tabularnewline
27 & -0.01152 & -0.2183 & 0.413674 \tabularnewline
28 & 0.033761 & 0.6397 & 0.261394 \tabularnewline
29 & 0.035746 & 0.6773 & 0.249331 \tabularnewline
30 & -0.027638 & -0.5237 & 0.300414 \tabularnewline
31 & 0.037568 & 0.7118 & 0.238523 \tabularnewline
32 & -0.076586 & -1.4511 & 0.073812 \tabularnewline
33 & -0.04242 & -0.8037 & 0.211038 \tabularnewline
34 & -0.086262 & -1.6344 & 0.051522 \tabularnewline
35 & -0.078156 & -1.4808 & 0.069763 \tabularnewline
36 & -0.219062 & -4.1506 & 2.1e-05 \tabularnewline
37 & -0.029885 & -0.5662 & 0.285794 \tabularnewline
38 & 0.062408 & 1.1825 & 0.118901 \tabularnewline
39 & -0.055153 & -1.045 & 0.148363 \tabularnewline
40 & -0.019687 & -0.373 & 0.354676 \tabularnewline
41 & -0.030763 & -0.5829 & 0.280174 \tabularnewline
42 & -0.01269 & -0.2404 & 0.405061 \tabularnewline
43 & -0.11499 & -2.1787 & 0.015 \tabularnewline
44 & -0.017441 & -0.3305 & 0.37062 \tabularnewline
45 & -0.006089 & -0.1154 & 0.454112 \tabularnewline
46 & 0.002743 & 0.052 & 0.479293 \tabularnewline
47 & -0.026375 & -0.4997 & 0.308785 \tabularnewline
48 & -0.056646 & -1.0733 & 0.141932 \tabularnewline
49 & 0.037272 & 0.7062 & 0.240259 \tabularnewline
50 & 0.038077 & 0.7214 & 0.235552 \tabularnewline
51 & -0.031982 & -0.606 & 0.272461 \tabularnewline
52 & 0.055379 & 1.0493 & 0.147378 \tabularnewline
53 & 0.0148 & 0.2804 & 0.389656 \tabularnewline
54 & -0.095807 & -1.8153 & 0.035157 \tabularnewline
55 & -0.024197 & -0.4585 & 0.323449 \tabularnewline
56 & -0.032014 & -0.6066 & 0.272256 \tabularnewline
57 & -0.058488 & -1.1082 & 0.134259 \tabularnewline
58 & 0.043184 & 0.8182 & 0.206889 \tabularnewline
59 & 0.009712 & 0.184 & 0.427053 \tabularnewline
60 & -0.049846 & -0.9444 & 0.172791 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28772&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]0.187552[/C][C]3.5536[/C][C]0.000215[/C][/ROW]
[ROW][C]2[/C][C]0.292922[/C][C]5.5501[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.093512[/C][C]1.7718[/C][C]0.038638[/C][/ROW]
[ROW][C]4[/C][C]0.033995[/C][C]0.6441[/C][C]0.259959[/C][/ROW]
[ROW][C]5[/C][C]0.032214[/C][C]0.6104[/C][C]0.271005[/C][/ROW]
[ROW][C]6[/C][C]-0.024285[/C][C]-0.4601[/C][C]0.322846[/C][/ROW]
[ROW][C]7[/C][C]-0.139405[/C][C]-2.6414[/C][C]0.004309[/C][/ROW]
[ROW][C]8[/C][C]-0.030697[/C][C]-0.5816[/C][C]0.280592[/C][/ROW]
[ROW][C]9[/C][C]-0.045877[/C][C]-0.8693[/C][C]0.192645[/C][/ROW]
[ROW][C]10[/C][C]-0.156693[/C][C]-2.9689[/C][C]0.001595[/C][/ROW]
[ROW][C]11[/C][C]0.037591[/C][C]0.7122[/C][C]0.238389[/C][/ROW]
[ROW][C]12[/C][C]-0.427945[/C][C]-8.1084[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.036401[/C][C]-0.6897[/C][C]0.245414[/C][/ROW]
[ROW][C]14[/C][C]0.123319[/C][C]2.3366[/C][C]0.010006[/C][/ROW]
[ROW][C]15[/C][C]0.047112[/C][C]0.8926[/C][C]0.186322[/C][/ROW]
[ROW][C]16[/C][C]-0.060609[/C][C]-1.1484[/C][C]0.125789[/C][/ROW]
[ROW][C]17[/C][C]-0.017722[/C][C]-0.3358[/C][C]0.368615[/C][/ROW]
[ROW][C]18[/C][C]0.007818[/C][C]0.1481[/C][C]0.44116[/C][/ROW]
[ROW][C]19[/C][C]0.023368[/C][C]0.4428[/C][C]0.329106[/C][/ROW]
[ROW][C]20[/C][C]0.047983[/C][C]0.9091[/C][C]0.181941[/C][/ROW]
[ROW][C]21[/C][C]-0.03962[/C][C]-0.7507[/C][C]0.226664[/C][/ROW]
[ROW][C]22[/C][C]-0.157258[/C][C]-2.9796[/C][C]0.001541[/C][/ROW]
[ROW][C]23[/C][C]0.010195[/C][C]0.1932[/C][C]0.42347[/C][/ROW]
[ROW][C]24[/C][C]-0.263321[/C][C]-4.9892[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.056986[/C][C]1.0797[/C][C]0.140495[/C][/ROW]
[ROW][C]26[/C][C]0.017452[/C][C]0.3307[/C][C]0.370544[/C][/ROW]
[ROW][C]27[/C][C]-0.01152[/C][C]-0.2183[/C][C]0.413674[/C][/ROW]
[ROW][C]28[/C][C]0.033761[/C][C]0.6397[/C][C]0.261394[/C][/ROW]
[ROW][C]29[/C][C]0.035746[/C][C]0.6773[/C][C]0.249331[/C][/ROW]
[ROW][C]30[/C][C]-0.027638[/C][C]-0.5237[/C][C]0.300414[/C][/ROW]
[ROW][C]31[/C][C]0.037568[/C][C]0.7118[/C][C]0.238523[/C][/ROW]
[ROW][C]32[/C][C]-0.076586[/C][C]-1.4511[/C][C]0.073812[/C][/ROW]
[ROW][C]33[/C][C]-0.04242[/C][C]-0.8037[/C][C]0.211038[/C][/ROW]
[ROW][C]34[/C][C]-0.086262[/C][C]-1.6344[/C][C]0.051522[/C][/ROW]
[ROW][C]35[/C][C]-0.078156[/C][C]-1.4808[/C][C]0.069763[/C][/ROW]
[ROW][C]36[/C][C]-0.219062[/C][C]-4.1506[/C][C]2.1e-05[/C][/ROW]
[ROW][C]37[/C][C]-0.029885[/C][C]-0.5662[/C][C]0.285794[/C][/ROW]
[ROW][C]38[/C][C]0.062408[/C][C]1.1825[/C][C]0.118901[/C][/ROW]
[ROW][C]39[/C][C]-0.055153[/C][C]-1.045[/C][C]0.148363[/C][/ROW]
[ROW][C]40[/C][C]-0.019687[/C][C]-0.373[/C][C]0.354676[/C][/ROW]
[ROW][C]41[/C][C]-0.030763[/C][C]-0.5829[/C][C]0.280174[/C][/ROW]
[ROW][C]42[/C][C]-0.01269[/C][C]-0.2404[/C][C]0.405061[/C][/ROW]
[ROW][C]43[/C][C]-0.11499[/C][C]-2.1787[/C][C]0.015[/C][/ROW]
[ROW][C]44[/C][C]-0.017441[/C][C]-0.3305[/C][C]0.37062[/C][/ROW]
[ROW][C]45[/C][C]-0.006089[/C][C]-0.1154[/C][C]0.454112[/C][/ROW]
[ROW][C]46[/C][C]0.002743[/C][C]0.052[/C][C]0.479293[/C][/ROW]
[ROW][C]47[/C][C]-0.026375[/C][C]-0.4997[/C][C]0.308785[/C][/ROW]
[ROW][C]48[/C][C]-0.056646[/C][C]-1.0733[/C][C]0.141932[/C][/ROW]
[ROW][C]49[/C][C]0.037272[/C][C]0.7062[/C][C]0.240259[/C][/ROW]
[ROW][C]50[/C][C]0.038077[/C][C]0.7214[/C][C]0.235552[/C][/ROW]
[ROW][C]51[/C][C]-0.031982[/C][C]-0.606[/C][C]0.272461[/C][/ROW]
[ROW][C]52[/C][C]0.055379[/C][C]1.0493[/C][C]0.147378[/C][/ROW]
[ROW][C]53[/C][C]0.0148[/C][C]0.2804[/C][C]0.389656[/C][/ROW]
[ROW][C]54[/C][C]-0.095807[/C][C]-1.8153[/C][C]0.035157[/C][/ROW]
[ROW][C]55[/C][C]-0.024197[/C][C]-0.4585[/C][C]0.323449[/C][/ROW]
[ROW][C]56[/C][C]-0.032014[/C][C]-0.6066[/C][C]0.272256[/C][/ROW]
[ROW][C]57[/C][C]-0.058488[/C][C]-1.1082[/C][C]0.134259[/C][/ROW]
[ROW][C]58[/C][C]0.043184[/C][C]0.8182[/C][C]0.206889[/C][/ROW]
[ROW][C]59[/C][C]0.009712[/C][C]0.184[/C][C]0.427053[/C][/ROW]
[ROW][C]60[/C][C]-0.049846[/C][C]-0.9444[/C][C]0.172791[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28772&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28772&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	0.187552	3.5536	0.000215
2	0.292922	5.5501	0
3	0.093512	1.7718	0.038638
4	0.033995	0.6441	0.259959
5	0.032214	0.6104	0.271005
6	-0.024285	-0.4601	0.322846
7	-0.139405	-2.6414	0.004309
8	-0.030697	-0.5816	0.280592
9	-0.045877	-0.8693	0.192645
10	-0.156693	-2.9689	0.001595
11	0.037591	0.7122	0.238389
12	-0.427945	-8.1084	0
13	-0.036401	-0.6897	0.245414
14	0.123319	2.3366	0.010006
15	0.047112	0.8926	0.186322
16	-0.060609	-1.1484	0.125789
17	-0.017722	-0.3358	0.368615
18	0.007818	0.1481	0.44116
19	0.023368	0.4428	0.329106
20	0.047983	0.9091	0.181941
21	-0.03962	-0.7507	0.226664
22	-0.157258	-2.9796	0.001541
23	0.010195	0.1932	0.42347
24	-0.263321	-4.9892	0
25	0.056986	1.0797	0.140495
26	0.017452	0.3307	0.370544
27	-0.01152	-0.2183	0.413674
28	0.033761	0.6397	0.261394
29	0.035746	0.6773	0.249331
30	-0.027638	-0.5237	0.300414
31	0.037568	0.7118	0.238523
32	-0.076586	-1.4511	0.073812
33	-0.04242	-0.8037	0.211038
34	-0.086262	-1.6344	0.051522
35	-0.078156	-1.4808	0.069763
36	-0.219062	-4.1506	2.1e-05
37	-0.029885	-0.5662	0.285794
38	0.062408	1.1825	0.118901
39	-0.055153	-1.045	0.148363
40	-0.019687	-0.373	0.354676
41	-0.030763	-0.5829	0.280174
42	-0.01269	-0.2404	0.405061
43	-0.11499	-2.1787	0.015
44	-0.017441	-0.3305	0.37062
45	-0.006089	-0.1154	0.454112
46	0.002743	0.052	0.479293
47	-0.026375	-0.4997	0.308785
48	-0.056646	-1.0733	0.141932
49	0.037272	0.7062	0.240259
50	0.038077	0.7214	0.235552
51	-0.031982	-0.606	0.272461
52	0.055379	1.0493	0.147378
53	0.0148	0.2804	0.389656
54	-0.095807	-1.8153	0.035157
55	-0.024197	-0.4585	0.323449
56	-0.032014	-0.6066	0.272256
57	-0.058488	-1.1082	0.134259
58	0.043184	0.8182	0.206889
59	0.009712	0.184	0.427053
60	-0.049846	-0.9444	0.172791

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 60 ; par2 = 0.5 ; 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