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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationMon, 06 Dec 2010 19:25:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/06/t1291663398wm8qfvv707pmkhw.htm/, Retrieved Mon, 29 Apr 2024 02:39:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105813, Retrieved Mon, 29 Apr 2024 02:39:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:16:10] [b98453cac15ba1066b407e146608df68]
-    D        [(Partial) Autocorrelation Function] [ws8] [2009-11-24 20:12:27] [8b1aef4e7013bd33fbc2a5833375c5f5]
-    D            [(Partial) Autocorrelation Function] [paper timeserie A...] [2010-12-06 19:25:07] [da925928e5a77063c5ecc7b801d712e1] [Current]
-   PD              [(Partial) Autocorrelation Function] [paper timeserie A...] [2010-12-06 19:51:05] [814f53995537cd15c528d8efbf1cf544]
- R  D              [(Partial) Autocorrelation Function] [] [2011-12-10 12:46:37] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
194,9
195,5
196
196,2
196,2
196,2
196,2
197
197,7
198
198,2
198,5
198,6
199,5
200
201,3
202,2
202,9
203,5
203,5
204
204,1
204,3
204,5
204,8
205,1
205,7
206,5
206,9
207,1
207,8
208
208,5
208,6
209
209,1
209,7
209,8
209,9
210
210,8
211,4
211,7
212
212,2
212,4
212,9
213,4
213,7
214
214,3
214,8
215
215,9
216,4
216,9
217,2
217,5
217,9
218,1
218,6
218,9
219,3
220,4
220,9
221
221,8
222
222,2
222,5
222,9
223,1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105813&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105813&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105813&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9581478.13010
20.9161937.77420
30.8744997.42040
40.8321517.0610
50.7883516.68940
60.7430826.30530
70.6979115.9220
80.6532135.54270
90.6099625.17571e-06
100.5687634.82614e-06
110.5272414.47381.4e-05
120.4855864.12035e-05
130.4437023.76490.000169
140.4029423.41910.000519
150.362843.07880.00147
160.3255442.76230.003639
170.2900292.4610.008127
180.2562632.17450.016479
190.2242341.90270.03054
200.1931521.63890.052794
210.1626291.380.085936
220.1324031.12350.132484
230.1022910.8680.194149
240.0720370.61130.271479
250.0421320.35750.36088
260.0130490.11070.45607
27-0.014331-0.12160.451776
28-0.040218-0.34130.36695
29-0.065647-0.5570.289617
30-0.090896-0.77130.221533
31-0.114459-0.97120.167346
32-0.136901-1.16160.124608
33-0.156768-1.33020.093822
34-0.177379-1.50510.068335
35-0.197713-1.67760.048876
36-0.218572-1.85460.033871

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958147 & 8.1301 & 0 \tabularnewline
2 & 0.916193 & 7.7742 & 0 \tabularnewline
3 & 0.874499 & 7.4204 & 0 \tabularnewline
4 & 0.832151 & 7.061 & 0 \tabularnewline
5 & 0.788351 & 6.6894 & 0 \tabularnewline
6 & 0.743082 & 6.3053 & 0 \tabularnewline
7 & 0.697911 & 5.922 & 0 \tabularnewline
8 & 0.653213 & 5.5427 & 0 \tabularnewline
9 & 0.609962 & 5.1757 & 1e-06 \tabularnewline
10 & 0.568763 & 4.8261 & 4e-06 \tabularnewline
11 & 0.527241 & 4.4738 & 1.4e-05 \tabularnewline
12 & 0.485586 & 4.1203 & 5e-05 \tabularnewline
13 & 0.443702 & 3.7649 & 0.000169 \tabularnewline
14 & 0.402942 & 3.4191 & 0.000519 \tabularnewline
15 & 0.36284 & 3.0788 & 0.00147 \tabularnewline
16 & 0.325544 & 2.7623 & 0.003639 \tabularnewline
17 & 0.290029 & 2.461 & 0.008127 \tabularnewline
18 & 0.256263 & 2.1745 & 0.016479 \tabularnewline
19 & 0.224234 & 1.9027 & 0.03054 \tabularnewline
20 & 0.193152 & 1.6389 & 0.052794 \tabularnewline
21 & 0.162629 & 1.38 & 0.085936 \tabularnewline
22 & 0.132403 & 1.1235 & 0.132484 \tabularnewline
23 & 0.102291 & 0.868 & 0.194149 \tabularnewline
24 & 0.072037 & 0.6113 & 0.271479 \tabularnewline
25 & 0.042132 & 0.3575 & 0.36088 \tabularnewline
26 & 0.013049 & 0.1107 & 0.45607 \tabularnewline
27 & -0.014331 & -0.1216 & 0.451776 \tabularnewline
28 & -0.040218 & -0.3413 & 0.36695 \tabularnewline
29 & -0.065647 & -0.557 & 0.289617 \tabularnewline
30 & -0.090896 & -0.7713 & 0.221533 \tabularnewline
31 & -0.114459 & -0.9712 & 0.167346 \tabularnewline
32 & -0.136901 & -1.1616 & 0.124608 \tabularnewline
33 & -0.156768 & -1.3302 & 0.093822 \tabularnewline
34 & -0.177379 & -1.5051 & 0.068335 \tabularnewline
35 & -0.197713 & -1.6776 & 0.048876 \tabularnewline
36 & -0.218572 & -1.8546 & 0.033871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105813&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]0.958147[/C][C]8.1301[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.916193[/C][C]7.7742[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.874499[/C][C]7.4204[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.832151[/C][C]7.061[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.788351[/C][C]6.6894[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.743082[/C][C]6.3053[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.697911[/C][C]5.922[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.653213[/C][C]5.5427[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.609962[/C][C]5.1757[/C][C]1e-06[/C][/ROW]
[ROW][C]10[/C][C]0.568763[/C][C]4.8261[/C][C]4e-06[/C][/ROW]
[ROW][C]11[/C][C]0.527241[/C][C]4.4738[/C][C]1.4e-05[/C][/ROW]
[ROW][C]12[/C][C]0.485586[/C][C]4.1203[/C][C]5e-05[/C][/ROW]
[ROW][C]13[/C][C]0.443702[/C][C]3.7649[/C][C]0.000169[/C][/ROW]
[ROW][C]14[/C][C]0.402942[/C][C]3.4191[/C][C]0.000519[/C][/ROW]
[ROW][C]15[/C][C]0.36284[/C][C]3.0788[/C][C]0.00147[/C][/ROW]
[ROW][C]16[/C][C]0.325544[/C][C]2.7623[/C][C]0.003639[/C][/ROW]
[ROW][C]17[/C][C]0.290029[/C][C]2.461[/C][C]0.008127[/C][/ROW]
[ROW][C]18[/C][C]0.256263[/C][C]2.1745[/C][C]0.016479[/C][/ROW]
[ROW][C]19[/C][C]0.224234[/C][C]1.9027[/C][C]0.03054[/C][/ROW]
[ROW][C]20[/C][C]0.193152[/C][C]1.6389[/C][C]0.052794[/C][/ROW]
[ROW][C]21[/C][C]0.162629[/C][C]1.38[/C][C]0.085936[/C][/ROW]
[ROW][C]22[/C][C]0.132403[/C][C]1.1235[/C][C]0.132484[/C][/ROW]
[ROW][C]23[/C][C]0.102291[/C][C]0.868[/C][C]0.194149[/C][/ROW]
[ROW][C]24[/C][C]0.072037[/C][C]0.6113[/C][C]0.271479[/C][/ROW]
[ROW][C]25[/C][C]0.042132[/C][C]0.3575[/C][C]0.36088[/C][/ROW]
[ROW][C]26[/C][C]0.013049[/C][C]0.1107[/C][C]0.45607[/C][/ROW]
[ROW][C]27[/C][C]-0.014331[/C][C]-0.1216[/C][C]0.451776[/C][/ROW]
[ROW][C]28[/C][C]-0.040218[/C][C]-0.3413[/C][C]0.36695[/C][/ROW]
[ROW][C]29[/C][C]-0.065647[/C][C]-0.557[/C][C]0.289617[/C][/ROW]
[ROW][C]30[/C][C]-0.090896[/C][C]-0.7713[/C][C]0.221533[/C][/ROW]
[ROW][C]31[/C][C]-0.114459[/C][C]-0.9712[/C][C]0.167346[/C][/ROW]
[ROW][C]32[/C][C]-0.136901[/C][C]-1.1616[/C][C]0.124608[/C][/ROW]
[ROW][C]33[/C][C]-0.156768[/C][C]-1.3302[/C][C]0.093822[/C][/ROW]
[ROW][C]34[/C][C]-0.177379[/C][C]-1.5051[/C][C]0.068335[/C][/ROW]
[ROW][C]35[/C][C]-0.197713[/C][C]-1.6776[/C][C]0.048876[/C][/ROW]
[ROW][C]36[/C][C]-0.218572[/C][C]-1.8546[/C][C]0.033871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105813&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105813&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 kACF(k)T-STATP-value
10.9581478.13010
20.9161937.77420
30.8744997.42040
40.8321517.0610
50.7883516.68940
60.7430826.30530
70.6979115.9220
80.6532135.54270
90.6099625.17571e-06
100.5687634.82614e-06
110.5272414.47381.4e-05
120.4855864.12035e-05
130.4437023.76490.000169
140.4029423.41910.000519
150.362843.07880.00147
160.3255442.76230.003639
170.2900292.4610.008127
180.2562632.17450.016479
190.2242341.90270.03054
200.1931521.63890.052794
210.1626291.380.085936
220.1324031.12350.132484
230.1022910.8680.194149
240.0720370.61130.271479
250.0421320.35750.36088
260.0130490.11070.45607
27-0.014331-0.12160.451776
28-0.040218-0.34130.36695
29-0.065647-0.5570.289617
30-0.090896-0.77130.221533
31-0.114459-0.97120.167346
32-0.136901-1.16160.124608
33-0.156768-1.33020.093822
34-0.177379-1.50510.068335
35-0.197713-1.67760.048876
36-0.218572-1.85460.033871







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9581478.13010
2-0.022599-0.19180.424236
3-0.018726-0.15890.437098
4-0.030232-0.25650.399139
5-0.040842-0.34660.364967
6-0.042509-0.36070.359688
7-0.024871-0.2110.416729
8-0.020713-0.17580.430491
9-0.008744-0.07420.47053
10-0.001028-0.00870.496532
11-0.029891-0.25360.40025
12-0.029118-0.24710.402777
13-0.032073-0.27210.393143
14-0.016651-0.14130.444019
15-0.02175-0.18460.427049
160.0056030.04750.481105
17-0.005853-0.04970.480263
18-0.005772-0.0490.480535
19-0.006773-0.05750.477166
20-0.017415-0.14780.441467
21-0.022653-0.19220.424058
22-0.02548-0.21620.414719
23-0.027253-0.23120.408889
24-0.030305-0.25710.398898
25-0.023711-0.20120.420557
26-0.019102-0.16210.435847
27-0.008161-0.06930.472491
28-0.010755-0.09130.46377
29-0.023315-0.19780.421867
30-0.028099-0.23840.406115
31-0.010046-0.08520.466153
32-0.016381-0.1390.44492
330.0028670.02430.490329
34-0.035857-0.30430.380904
35-0.024736-0.20990.417174
36-0.03654-0.31010.378709

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958147 & 8.1301 & 0 \tabularnewline
2 & -0.022599 & -0.1918 & 0.424236 \tabularnewline
3 & -0.018726 & -0.1589 & 0.437098 \tabularnewline
4 & -0.030232 & -0.2565 & 0.399139 \tabularnewline
5 & -0.040842 & -0.3466 & 0.364967 \tabularnewline
6 & -0.042509 & -0.3607 & 0.359688 \tabularnewline
7 & -0.024871 & -0.211 & 0.416729 \tabularnewline
8 & -0.020713 & -0.1758 & 0.430491 \tabularnewline
9 & -0.008744 & -0.0742 & 0.47053 \tabularnewline
10 & -0.001028 & -0.0087 & 0.496532 \tabularnewline
11 & -0.029891 & -0.2536 & 0.40025 \tabularnewline
12 & -0.029118 & -0.2471 & 0.402777 \tabularnewline
13 & -0.032073 & -0.2721 & 0.393143 \tabularnewline
14 & -0.016651 & -0.1413 & 0.444019 \tabularnewline
15 & -0.02175 & -0.1846 & 0.427049 \tabularnewline
16 & 0.005603 & 0.0475 & 0.481105 \tabularnewline
17 & -0.005853 & -0.0497 & 0.480263 \tabularnewline
18 & -0.005772 & -0.049 & 0.480535 \tabularnewline
19 & -0.006773 & -0.0575 & 0.477166 \tabularnewline
20 & -0.017415 & -0.1478 & 0.441467 \tabularnewline
21 & -0.022653 & -0.1922 & 0.424058 \tabularnewline
22 & -0.02548 & -0.2162 & 0.414719 \tabularnewline
23 & -0.027253 & -0.2312 & 0.408889 \tabularnewline
24 & -0.030305 & -0.2571 & 0.398898 \tabularnewline
25 & -0.023711 & -0.2012 & 0.420557 \tabularnewline
26 & -0.019102 & -0.1621 & 0.435847 \tabularnewline
27 & -0.008161 & -0.0693 & 0.472491 \tabularnewline
28 & -0.010755 & -0.0913 & 0.46377 \tabularnewline
29 & -0.023315 & -0.1978 & 0.421867 \tabularnewline
30 & -0.028099 & -0.2384 & 0.406115 \tabularnewline
31 & -0.010046 & -0.0852 & 0.466153 \tabularnewline
32 & -0.016381 & -0.139 & 0.44492 \tabularnewline
33 & 0.002867 & 0.0243 & 0.490329 \tabularnewline
34 & -0.035857 & -0.3043 & 0.380904 \tabularnewline
35 & -0.024736 & -0.2099 & 0.417174 \tabularnewline
36 & -0.03654 & -0.3101 & 0.378709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105813&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]0.958147[/C][C]8.1301[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.022599[/C][C]-0.1918[/C][C]0.424236[/C][/ROW]
[ROW][C]3[/C][C]-0.018726[/C][C]-0.1589[/C][C]0.437098[/C][/ROW]
[ROW][C]4[/C][C]-0.030232[/C][C]-0.2565[/C][C]0.399139[/C][/ROW]
[ROW][C]5[/C][C]-0.040842[/C][C]-0.3466[/C][C]0.364967[/C][/ROW]
[ROW][C]6[/C][C]-0.042509[/C][C]-0.3607[/C][C]0.359688[/C][/ROW]
[ROW][C]7[/C][C]-0.024871[/C][C]-0.211[/C][C]0.416729[/C][/ROW]
[ROW][C]8[/C][C]-0.020713[/C][C]-0.1758[/C][C]0.430491[/C][/ROW]
[ROW][C]9[/C][C]-0.008744[/C][C]-0.0742[/C][C]0.47053[/C][/ROW]
[ROW][C]10[/C][C]-0.001028[/C][C]-0.0087[/C][C]0.496532[/C][/ROW]
[ROW][C]11[/C][C]-0.029891[/C][C]-0.2536[/C][C]0.40025[/C][/ROW]
[ROW][C]12[/C][C]-0.029118[/C][C]-0.2471[/C][C]0.402777[/C][/ROW]
[ROW][C]13[/C][C]-0.032073[/C][C]-0.2721[/C][C]0.393143[/C][/ROW]
[ROW][C]14[/C][C]-0.016651[/C][C]-0.1413[/C][C]0.444019[/C][/ROW]
[ROW][C]15[/C][C]-0.02175[/C][C]-0.1846[/C][C]0.427049[/C][/ROW]
[ROW][C]16[/C][C]0.005603[/C][C]0.0475[/C][C]0.481105[/C][/ROW]
[ROW][C]17[/C][C]-0.005853[/C][C]-0.0497[/C][C]0.480263[/C][/ROW]
[ROW][C]18[/C][C]-0.005772[/C][C]-0.049[/C][C]0.480535[/C][/ROW]
[ROW][C]19[/C][C]-0.006773[/C][C]-0.0575[/C][C]0.477166[/C][/ROW]
[ROW][C]20[/C][C]-0.017415[/C][C]-0.1478[/C][C]0.441467[/C][/ROW]
[ROW][C]21[/C][C]-0.022653[/C][C]-0.1922[/C][C]0.424058[/C][/ROW]
[ROW][C]22[/C][C]-0.02548[/C][C]-0.2162[/C][C]0.414719[/C][/ROW]
[ROW][C]23[/C][C]-0.027253[/C][C]-0.2312[/C][C]0.408889[/C][/ROW]
[ROW][C]24[/C][C]-0.030305[/C][C]-0.2571[/C][C]0.398898[/C][/ROW]
[ROW][C]25[/C][C]-0.023711[/C][C]-0.2012[/C][C]0.420557[/C][/ROW]
[ROW][C]26[/C][C]-0.019102[/C][C]-0.1621[/C][C]0.435847[/C][/ROW]
[ROW][C]27[/C][C]-0.008161[/C][C]-0.0693[/C][C]0.472491[/C][/ROW]
[ROW][C]28[/C][C]-0.010755[/C][C]-0.0913[/C][C]0.46377[/C][/ROW]
[ROW][C]29[/C][C]-0.023315[/C][C]-0.1978[/C][C]0.421867[/C][/ROW]
[ROW][C]30[/C][C]-0.028099[/C][C]-0.2384[/C][C]0.406115[/C][/ROW]
[ROW][C]31[/C][C]-0.010046[/C][C]-0.0852[/C][C]0.466153[/C][/ROW]
[ROW][C]32[/C][C]-0.016381[/C][C]-0.139[/C][C]0.44492[/C][/ROW]
[ROW][C]33[/C][C]0.002867[/C][C]0.0243[/C][C]0.490329[/C][/ROW]
[ROW][C]34[/C][C]-0.035857[/C][C]-0.3043[/C][C]0.380904[/C][/ROW]
[ROW][C]35[/C][C]-0.024736[/C][C]-0.2099[/C][C]0.417174[/C][/ROW]
[ROW][C]36[/C][C]-0.03654[/C][C]-0.3101[/C][C]0.378709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105813&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105813&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 kPACF(k)T-STATP-value
10.9581478.13010
2-0.022599-0.19180.424236
3-0.018726-0.15890.437098
4-0.030232-0.25650.399139
5-0.040842-0.34660.364967
6-0.042509-0.36070.359688
7-0.024871-0.2110.416729
8-0.020713-0.17580.430491
9-0.008744-0.07420.47053
10-0.001028-0.00870.496532
11-0.029891-0.25360.40025
12-0.029118-0.24710.402777
13-0.032073-0.27210.393143
14-0.016651-0.14130.444019
15-0.02175-0.18460.427049
160.0056030.04750.481105
17-0.005853-0.04970.480263
18-0.005772-0.0490.480535
19-0.006773-0.05750.477166
20-0.017415-0.14780.441467
21-0.022653-0.19220.424058
22-0.02548-0.21620.414719
23-0.027253-0.23120.408889
24-0.030305-0.25710.398898
25-0.023711-0.20120.420557
26-0.019102-0.16210.435847
27-0.008161-0.06930.472491
28-0.010755-0.09130.46377
29-0.023315-0.19780.421867
30-0.028099-0.23840.406115
31-0.010046-0.08520.466153
32-0.016381-0.1390.44492
330.0028670.02430.490329
34-0.035857-0.30430.380904
35-0.024736-0.20990.417174
36-0.03654-0.31010.378709



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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')