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R Software Module

rwasp_autocorrelation.wasp

Title produced by software

(Partial) Autocorrelation Function

Date of computation

Mon, 08 Dec 2008 14:11:42 -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/08/t1228770788yd3979dbm0mxpma.htm/, Retrieved Thu, 16 May 2024 15:48:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31034, Retrieved Thu, 16 May 2024 15:48:20 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

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] [correlatie zonder...] [2008-12-08 21:11:42] [f24298b2e4c2a19d76cf4460ec5d2246] [Current]
F   P       [(Partial) Autocorrelation Function] [correlatie met aa...] [2008-12-08 21:16:24] [e43247bc0ab243a5af99ac7f55ba0b41] 
F RMP         [Spectral Analysis] [spectum analyse] [2008-12-08 21:19:50] [e43247bc0ab243a5af99ac7f55ba0b41] 
F RMP           [Standard Deviation-Mean Plot] [sd- mean plot] [2008-12-08 21:28:17] [e43247bc0ab243a5af99ac7f55ba0b41] 
F RMP             [ARIMA Backward Selection] [berekening arima ...] [2008-12-08 21:36:28] [e43247bc0ab243a5af99ac7f55ba0b41] 
-   P               [ARIMA Backward Selection] [juiste oplossing] [2008-12-11 14:33:10] [2d4aec5ed1856c4828162be37be304d9] 
-   P         [(Partial) Autocorrelation Function] [stap 2] [2008-12-15 11:10:09] [e43247bc0ab243a5af99ac7f55ba0b41] 
-   P       [(Partial) Autocorrelation Function] [stap 2] [2008-12-15 11:04:14] [e43247bc0ab243a5af99ac7f55ba0b41] 

Feedback Forum

2008-12-11 14:21:05 [Peter Melgers] [reply] 
Situatie 1: lambda=1 / d=0 / D=0 
 
De grafiek vertoont duidelijk een lange termijn trend.  d op 1 zetten 
De grafiek vertoont niet duidelijk seizoenaliteit.  D blijven we af 
 
Situatie 2: lambda=1 / d=1 / D=0 
 
De grafiek vertoont duidelijk seizoenaliteit. Wederkerend (langzaam dalend) patroon bij lag 12, 24, 36,... 
 
We moeten dus ook seizoenaal differentiëren.  D=1 
 
Situatie 3: lambda=1 / d=1 / D=1  (heb je gedaan in volgende berekening)
2008-12-14 10:55:21 [Kevin Engels] [reply] 
• Vervolgens nemen we onder de module ‘Time series Analyses (new)’ de (Partial) Autocorrelation Function waarbij we de time lags op 60 instellen aangezien onze tijdreeks lang genoeg is, d en D laten we op nul en ook de seasonal period laten we op 12 staan. 
 
http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/07/t12286539706aloo6eiuv47chf.htm 
  
We merken een lange termijntrend op, en zien een langzaam dalende autocorrelatie.  
 
•  Spectrale analyse via de module ‘Time series Analyses (new): we laten alles staan behalve de seasonal period die we op 12 zetten. http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/07/t1228655690jdfz8t3tzklgr55.htm 
 
Er is sprake van een lange termijntrend daar het spectrum enkel in het begin heel groot is.  
   
Op het cumulative periodogram zien we dat de plot spectaculair stijgt. Vanuit deze analyse kunnen we afleiden dat er een dalende lange termijn trend is. Dit kunnen we besluiten doordat we zien dat de lage frequentie golven dominant zijn. 70% van de grafiek wordt bepaald door deze lange termijn trend. We zien ook seizonale pieken. We kunnen hierbij opnieuw besluiten dat we d en D instellen op 1. 
2008-12-15 11:05:58 [Lindsay Heyndrickx] [reply] 
Hier had ik meer time lags moeten nemen om een duidelijkere grafiek te krijgen. http://www.freestatistics.org/blog/date/2008/Dec/15/t1229339092dwcf8qx8uqx5nnq.htm,  Als we dit doen zien we hier een duidelijke lange termijn trend die langzaam daalt. Er is ook enige vorm van seizoenaliteit te zien, om de 12lags bereikte de autocorrelatie een relatief hoogtepunt om vervolgens weer aftezwakken. De waarden in de tabel bevestigen dit tevens. Het onderzoek naar seizoenaliteit vereist echter meer onderzoek.

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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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31034&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31034&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31034&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.958297	18.483	0
2	0.915218	17.6521	0
3	0.883412	17.0386	0
4	0.870088	16.7816	0
5	0.861735	16.6205	0
6	0.832367	16.0541	0
7	0.812153	15.6642	0
8	0.775095	14.9495	0
9	0.745592	14.3805	0
10	0.734652	14.1695	0
11	0.739154	14.2563	0
12	0.743705	14.3441	0
13	0.693188	13.3697	0
14	0.64416	12.4241	0
15	0.611393	11.7921	0
16	0.599793	11.5684	0
17	0.597409	11.5224	0
18	0.577954	11.1472	0
19	0.568953	10.9736	0
20	0.543776	10.488	0
21	0.526232	10.1496	0
22	0.525701	10.1394	0
23	0.537712	10.371	0
24	0.551354	10.6341	0
25	0.512849	9.8915	0

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958297 & 18.483 & 0 \tabularnewline
2 & 0.915218 & 17.6521 & 0 \tabularnewline
3 & 0.883412 & 17.0386 & 0 \tabularnewline
4 & 0.870088 & 16.7816 & 0 \tabularnewline
5 & 0.861735 & 16.6205 & 0 \tabularnewline
6 & 0.832367 & 16.0541 & 0 \tabularnewline
7 & 0.812153 & 15.6642 & 0 \tabularnewline
8 & 0.775095 & 14.9495 & 0 \tabularnewline
9 & 0.745592 & 14.3805 & 0 \tabularnewline
10 & 0.734652 & 14.1695 & 0 \tabularnewline
11 & 0.739154 & 14.2563 & 0 \tabularnewline
12 & 0.743705 & 14.3441 & 0 \tabularnewline
13 & 0.693188 & 13.3697 & 0 \tabularnewline
14 & 0.64416 & 12.4241 & 0 \tabularnewline
15 & 0.611393 & 11.7921 & 0 \tabularnewline
16 & 0.599793 & 11.5684 & 0 \tabularnewline
17 & 0.597409 & 11.5224 & 0 \tabularnewline
18 & 0.577954 & 11.1472 & 0 \tabularnewline
19 & 0.568953 & 10.9736 & 0 \tabularnewline
20 & 0.543776 & 10.488 & 0 \tabularnewline
21 & 0.526232 & 10.1496 & 0 \tabularnewline
22 & 0.525701 & 10.1394 & 0 \tabularnewline
23 & 0.537712 & 10.371 & 0 \tabularnewline
24 & 0.551354 & 10.6341 & 0 \tabularnewline
25 & 0.512849 & 9.8915 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31034&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]0.958297[/C][C]18.483[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.915218[/C][C]17.6521[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.883412[/C][C]17.0386[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.870088[/C][C]16.7816[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.861735[/C][C]16.6205[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.832367[/C][C]16.0541[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.812153[/C][C]15.6642[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.775095[/C][C]14.9495[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.745592[/C][C]14.3805[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.734652[/C][C]14.1695[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.739154[/C][C]14.2563[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.743705[/C][C]14.3441[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.693188[/C][C]13.3697[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.64416[/C][C]12.4241[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.611393[/C][C]11.7921[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.599793[/C][C]11.5684[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.597409[/C][C]11.5224[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.577954[/C][C]11.1472[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.568953[/C][C]10.9736[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.543776[/C][C]10.488[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.526232[/C][C]10.1496[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.525701[/C][C]10.1394[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]0.537712[/C][C]10.371[/C][C]0[/C][/ROW]
[ROW][C]24[/C][C]0.551354[/C][C]10.6341[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.512849[/C][C]9.8915[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31034&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31034&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	0.958297	18.483	0
2	0.915218	17.6521	0
3	0.883412	17.0386	0
4	0.870088	16.7816	0
5	0.861735	16.6205	0
6	0.832367	16.0541	0
7	0.812153	15.6642	0
8	0.775095	14.9495	0
9	0.745592	14.3805	0
10	0.734652	14.1695	0
11	0.739154	14.2563	0
12	0.743705	14.3441	0
13	0.693188	13.3697	0
14	0.64416	12.4241	0
15	0.611393	11.7921	0
16	0.599793	11.5684	0
17	0.597409	11.5224	0
18	0.577954	11.1472	0
19	0.568953	10.9736	0
20	0.543776	10.488	0
21	0.526232	10.1496	0
22	0.525701	10.1394	0
23	0.537712	10.371	0
24	0.551354	10.6341	0
25	0.512849	9.8915	0

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.958297	18.483	0
2	-0.038138	-0.7356	0.231227
3	0.116008	2.2375	0.012923
4	0.207345	3.9991	3.8e-05
5	0.071295	1.3751	0.084967
6	-0.216629	-4.1782	1.8e-05
7	0.177843	3.4301	0.000336
8	-0.280159	-5.4035	0
9	0.051585	0.9949	0.160206
10	0.221443	4.271	1.2e-05
11	0.189049	3.6462	0.000152
12	-0.045031	-0.8685	0.192833
13	-0.550158	-10.6111	0
14	0.048685	0.939	0.17417
15	0.139152	2.6839	0.003802
16	0.058966	1.1373	0.128074
17	0.145015	2.797	0.002713
18	0.056415	1.0881	0.13863
19	0.089168	1.7198	0.04315
20	-0.085304	-1.6453	0.050378
21	0.010706	0.2065	0.418258
22	0.020453	0.3945	0.346725
23	-0.016396	-0.3162	0.376
24	0.056489	1.0895	0.138315
25	-0.235006	-4.5326	4e-06

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958297 & 18.483 & 0 \tabularnewline
2 & -0.038138 & -0.7356 & 0.231227 \tabularnewline
3 & 0.116008 & 2.2375 & 0.012923 \tabularnewline
4 & 0.207345 & 3.9991 & 3.8e-05 \tabularnewline
5 & 0.071295 & 1.3751 & 0.084967 \tabularnewline
6 & -0.216629 & -4.1782 & 1.8e-05 \tabularnewline
7 & 0.177843 & 3.4301 & 0.000336 \tabularnewline
8 & -0.280159 & -5.4035 & 0 \tabularnewline
9 & 0.051585 & 0.9949 & 0.160206 \tabularnewline
10 & 0.221443 & 4.271 & 1.2e-05 \tabularnewline
11 & 0.189049 & 3.6462 & 0.000152 \tabularnewline
12 & -0.045031 & -0.8685 & 0.192833 \tabularnewline
13 & -0.550158 & -10.6111 & 0 \tabularnewline
14 & 0.048685 & 0.939 & 0.17417 \tabularnewline
15 & 0.139152 & 2.6839 & 0.003802 \tabularnewline
16 & 0.058966 & 1.1373 & 0.128074 \tabularnewline
17 & 0.145015 & 2.797 & 0.002713 \tabularnewline
18 & 0.056415 & 1.0881 & 0.13863 \tabularnewline
19 & 0.089168 & 1.7198 & 0.04315 \tabularnewline
20 & -0.085304 & -1.6453 & 0.050378 \tabularnewline
21 & 0.010706 & 0.2065 & 0.418258 \tabularnewline
22 & 0.020453 & 0.3945 & 0.346725 \tabularnewline
23 & -0.016396 & -0.3162 & 0.376 \tabularnewline
24 & 0.056489 & 1.0895 & 0.138315 \tabularnewline
25 & -0.235006 & -4.5326 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31034&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]0.958297[/C][C]18.483[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.038138[/C][C]-0.7356[/C][C]0.231227[/C][/ROW]
[ROW][C]3[/C][C]0.116008[/C][C]2.2375[/C][C]0.012923[/C][/ROW]
[ROW][C]4[/C][C]0.207345[/C][C]3.9991[/C][C]3.8e-05[/C][/ROW]
[ROW][C]5[/C][C]0.071295[/C][C]1.3751[/C][C]0.084967[/C][/ROW]
[ROW][C]6[/C][C]-0.216629[/C][C]-4.1782[/C][C]1.8e-05[/C][/ROW]
[ROW][C]7[/C][C]0.177843[/C][C]3.4301[/C][C]0.000336[/C][/ROW]
[ROW][C]8[/C][C]-0.280159[/C][C]-5.4035[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.051585[/C][C]0.9949[/C][C]0.160206[/C][/ROW]
[ROW][C]10[/C][C]0.221443[/C][C]4.271[/C][C]1.2e-05[/C][/ROW]
[ROW][C]11[/C][C]0.189049[/C][C]3.6462[/C][C]0.000152[/C][/ROW]
[ROW][C]12[/C][C]-0.045031[/C][C]-0.8685[/C][C]0.192833[/C][/ROW]
[ROW][C]13[/C][C]-0.550158[/C][C]-10.6111[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.048685[/C][C]0.939[/C][C]0.17417[/C][/ROW]
[ROW][C]15[/C][C]0.139152[/C][C]2.6839[/C][C]0.003802[/C][/ROW]
[ROW][C]16[/C][C]0.058966[/C][C]1.1373[/C][C]0.128074[/C][/ROW]
[ROW][C]17[/C][C]0.145015[/C][C]2.797[/C][C]0.002713[/C][/ROW]
[ROW][C]18[/C][C]0.056415[/C][C]1.0881[/C][C]0.13863[/C][/ROW]
[ROW][C]19[/C][C]0.089168[/C][C]1.7198[/C][C]0.04315[/C][/ROW]
[ROW][C]20[/C][C]-0.085304[/C][C]-1.6453[/C][C]0.050378[/C][/ROW]
[ROW][C]21[/C][C]0.010706[/C][C]0.2065[/C][C]0.418258[/C][/ROW]
[ROW][C]22[/C][C]0.020453[/C][C]0.3945[/C][C]0.346725[/C][/ROW]
[ROW][C]23[/C][C]-0.016396[/C][C]-0.3162[/C][C]0.376[/C][/ROW]
[ROW][C]24[/C][C]0.056489[/C][C]1.0895[/C][C]0.138315[/C][/ROW]
[ROW][C]25[/C][C]-0.235006[/C][C]-4.5326[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31034&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31034&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	0.958297	18.483	0
2	-0.038138	-0.7356	0.231227
3	0.116008	2.2375	0.012923
4	0.207345	3.9991	3.8e-05
5	0.071295	1.3751	0.084967
6	-0.216629	-4.1782	1.8e-05
7	0.177843	3.4301	0.000336
8	-0.280159	-5.4035	0
9	0.051585	0.9949	0.160206
10	0.221443	4.271	1.2e-05
11	0.189049	3.6462	0.000152
12	-0.045031	-0.8685	0.192833
13	-0.550158	-10.6111	0
14	0.048685	0.939	0.17417
15	0.139152	2.6839	0.003802
16	0.058966	1.1373	0.128074
17	0.145015	2.797	0.002713
18	0.056415	1.0881	0.13863
19	0.089168	1.7198	0.04315
20	-0.085304	-1.6453	0.050378
21	0.010706	0.2065	0.418258
22	0.020453	0.3945	0.346725
23	-0.016396	-0.3162	0.376
24	0.056489	1.0895	0.138315
25	-0.235006	-4.5326	4e-06

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 0 ; 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