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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 computationSun, 29 Nov 2009 09:16:26 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/29/t12595114570j2h16py10089yj.htm/, Retrieved Fri, 29 Mar 2024 12:57:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61650, Retrieved Fri, 29 Mar 2024 12:57:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
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] [WS8.2] [2009-11-25 18:11:35] [626f1d98f4a7f05bcb9f17666b672c60]
-    D          [(Partial) Autocorrelation Function] [workshop 8] [2009-11-28 08:51:25] [4fe1472705bb0a32f118ba3ca90ffa8e]
- R P               [(Partial) Autocorrelation Function] [Workshop8 Review ...] [2009-11-29 16:16:26] [5ed0eef5d4509bbfdac0ae6d87f3b4bf] [Current]
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Dataseries X:
130
136.7
138.1
139.5
140.4
144.6
151.4
147.9
141.5
143.8
143.6
150.5
150.1
154.9
162.1
176.7
186.6
194.8
196.3
228.8
267.2
237.2
254.7
258.2
257.9
269.6
266.9
269.6
253.9
258.6
274.2
301.5
304.5
285.1
287.7
265.5
264.1
276.1
258.9
239.1
250.1
276.8
297.6
295.4
283
275.8
279.7
254.6
234.6
176.9
148.1
122.7
124.9
121.6
128.4
144.5
151.8
167.1
173.8
203.7
199.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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=61650&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=61650&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61650&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3343552.58990.006016
20.1921261.48820.070968
30.0967370.74930.228295
40.0634550.49150.312427
50.0854740.66210.255228
6-0.085708-0.66390.25465
7-0.177845-1.37760.086725
8-0.346687-2.68540.004677
9-0.161423-1.25040.108009
10-0.056285-0.4360.332206
110.175411.35870.089661
120.1026080.79480.214932
13-0.049971-0.38710.350037
140.1126140.87230.19326
150.0555240.43010.334336
160.024750.19170.424308
170.0548960.42520.336097
18-0.036973-0.28640.387783
19-0.183589-1.42210.080091
20-0.181699-1.40740.082229
210.030890.23930.405856
220.0388060.30060.382385
230.0277040.21460.415406
24-0.097179-0.75270.227273
25-0.016484-0.12770.449413
260.0287060.22240.412396
27-0.043616-0.33780.368328
280.0236430.18310.427652
29-0.155651-1.20570.116339
30-0.134279-1.04010.151228
31-0.157925-1.22330.113003
32-0.086616-0.67090.252423
33-0.107029-0.8290.205182
34-0.112864-0.87420.192737
35-0.013518-0.10470.458477
36-0.007793-0.06040.476032

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.334355 & 2.5899 & 0.006016 \tabularnewline
2 & 0.192126 & 1.4882 & 0.070968 \tabularnewline
3 & 0.096737 & 0.7493 & 0.228295 \tabularnewline
4 & 0.063455 & 0.4915 & 0.312427 \tabularnewline
5 & 0.085474 & 0.6621 & 0.255228 \tabularnewline
6 & -0.085708 & -0.6639 & 0.25465 \tabularnewline
7 & -0.177845 & -1.3776 & 0.086725 \tabularnewline
8 & -0.346687 & -2.6854 & 0.004677 \tabularnewline
9 & -0.161423 & -1.2504 & 0.108009 \tabularnewline
10 & -0.056285 & -0.436 & 0.332206 \tabularnewline
11 & 0.17541 & 1.3587 & 0.089661 \tabularnewline
12 & 0.102608 & 0.7948 & 0.214932 \tabularnewline
13 & -0.049971 & -0.3871 & 0.350037 \tabularnewline
14 & 0.112614 & 0.8723 & 0.19326 \tabularnewline
15 & 0.055524 & 0.4301 & 0.334336 \tabularnewline
16 & 0.02475 & 0.1917 & 0.424308 \tabularnewline
17 & 0.054896 & 0.4252 & 0.336097 \tabularnewline
18 & -0.036973 & -0.2864 & 0.387783 \tabularnewline
19 & -0.183589 & -1.4221 & 0.080091 \tabularnewline
20 & -0.181699 & -1.4074 & 0.082229 \tabularnewline
21 & 0.03089 & 0.2393 & 0.405856 \tabularnewline
22 & 0.038806 & 0.3006 & 0.382385 \tabularnewline
23 & 0.027704 & 0.2146 & 0.415406 \tabularnewline
24 & -0.097179 & -0.7527 & 0.227273 \tabularnewline
25 & -0.016484 & -0.1277 & 0.449413 \tabularnewline
26 & 0.028706 & 0.2224 & 0.412396 \tabularnewline
27 & -0.043616 & -0.3378 & 0.368328 \tabularnewline
28 & 0.023643 & 0.1831 & 0.427652 \tabularnewline
29 & -0.155651 & -1.2057 & 0.116339 \tabularnewline
30 & -0.134279 & -1.0401 & 0.151228 \tabularnewline
31 & -0.157925 & -1.2233 & 0.113003 \tabularnewline
32 & -0.086616 & -0.6709 & 0.252423 \tabularnewline
33 & -0.107029 & -0.829 & 0.205182 \tabularnewline
34 & -0.112864 & -0.8742 & 0.192737 \tabularnewline
35 & -0.013518 & -0.1047 & 0.458477 \tabularnewline
36 & -0.007793 & -0.0604 & 0.476032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61650&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]0.334355[/C][C]2.5899[/C][C]0.006016[/C][/ROW]
[ROW][C]2[/C][C]0.192126[/C][C]1.4882[/C][C]0.070968[/C][/ROW]
[ROW][C]3[/C][C]0.096737[/C][C]0.7493[/C][C]0.228295[/C][/ROW]
[ROW][C]4[/C][C]0.063455[/C][C]0.4915[/C][C]0.312427[/C][/ROW]
[ROW][C]5[/C][C]0.085474[/C][C]0.6621[/C][C]0.255228[/C][/ROW]
[ROW][C]6[/C][C]-0.085708[/C][C]-0.6639[/C][C]0.25465[/C][/ROW]
[ROW][C]7[/C][C]-0.177845[/C][C]-1.3776[/C][C]0.086725[/C][/ROW]
[ROW][C]8[/C][C]-0.346687[/C][C]-2.6854[/C][C]0.004677[/C][/ROW]
[ROW][C]9[/C][C]-0.161423[/C][C]-1.2504[/C][C]0.108009[/C][/ROW]
[ROW][C]10[/C][C]-0.056285[/C][C]-0.436[/C][C]0.332206[/C][/ROW]
[ROW][C]11[/C][C]0.17541[/C][C]1.3587[/C][C]0.089661[/C][/ROW]
[ROW][C]12[/C][C]0.102608[/C][C]0.7948[/C][C]0.214932[/C][/ROW]
[ROW][C]13[/C][C]-0.049971[/C][C]-0.3871[/C][C]0.350037[/C][/ROW]
[ROW][C]14[/C][C]0.112614[/C][C]0.8723[/C][C]0.19326[/C][/ROW]
[ROW][C]15[/C][C]0.055524[/C][C]0.4301[/C][C]0.334336[/C][/ROW]
[ROW][C]16[/C][C]0.02475[/C][C]0.1917[/C][C]0.424308[/C][/ROW]
[ROW][C]17[/C][C]0.054896[/C][C]0.4252[/C][C]0.336097[/C][/ROW]
[ROW][C]18[/C][C]-0.036973[/C][C]-0.2864[/C][C]0.387783[/C][/ROW]
[ROW][C]19[/C][C]-0.183589[/C][C]-1.4221[/C][C]0.080091[/C][/ROW]
[ROW][C]20[/C][C]-0.181699[/C][C]-1.4074[/C][C]0.082229[/C][/ROW]
[ROW][C]21[/C][C]0.03089[/C][C]0.2393[/C][C]0.405856[/C][/ROW]
[ROW][C]22[/C][C]0.038806[/C][C]0.3006[/C][C]0.382385[/C][/ROW]
[ROW][C]23[/C][C]0.027704[/C][C]0.2146[/C][C]0.415406[/C][/ROW]
[ROW][C]24[/C][C]-0.097179[/C][C]-0.7527[/C][C]0.227273[/C][/ROW]
[ROW][C]25[/C][C]-0.016484[/C][C]-0.1277[/C][C]0.449413[/C][/ROW]
[ROW][C]26[/C][C]0.028706[/C][C]0.2224[/C][C]0.412396[/C][/ROW]
[ROW][C]27[/C][C]-0.043616[/C][C]-0.3378[/C][C]0.368328[/C][/ROW]
[ROW][C]28[/C][C]0.023643[/C][C]0.1831[/C][C]0.427652[/C][/ROW]
[ROW][C]29[/C][C]-0.155651[/C][C]-1.2057[/C][C]0.116339[/C][/ROW]
[ROW][C]30[/C][C]-0.134279[/C][C]-1.0401[/C][C]0.151228[/C][/ROW]
[ROW][C]31[/C][C]-0.157925[/C][C]-1.2233[/C][C]0.113003[/C][/ROW]
[ROW][C]32[/C][C]-0.086616[/C][C]-0.6709[/C][C]0.252423[/C][/ROW]
[ROW][C]33[/C][C]-0.107029[/C][C]-0.829[/C][C]0.205182[/C][/ROW]
[ROW][C]34[/C][C]-0.112864[/C][C]-0.8742[/C][C]0.192737[/C][/ROW]
[ROW][C]35[/C][C]-0.013518[/C][C]-0.1047[/C][C]0.458477[/C][/ROW]
[ROW][C]36[/C][C]-0.007793[/C][C]-0.0604[/C][C]0.476032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61650&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61650&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
10.3343552.58990.006016
20.1921261.48820.070968
30.0967370.74930.228295
40.0634550.49150.312427
50.0854740.66210.255228
6-0.085708-0.66390.25465
7-0.177845-1.37760.086725
8-0.346687-2.68540.004677
9-0.161423-1.25040.108009
10-0.056285-0.4360.332206
110.175411.35870.089661
120.1026080.79480.214932
13-0.049971-0.38710.350037
140.1126140.87230.19326
150.0555240.43010.334336
160.024750.19170.424308
170.0548960.42520.336097
18-0.036973-0.28640.387783
19-0.183589-1.42210.080091
20-0.181699-1.40740.082229
210.030890.23930.405856
220.0388060.30060.382385
230.0277040.21460.415406
24-0.097179-0.75270.227273
25-0.016484-0.12770.449413
260.0287060.22240.412396
27-0.043616-0.33780.368328
280.0236430.18310.427652
29-0.155651-1.20570.116339
30-0.134279-1.04010.151228
31-0.157925-1.22330.113003
32-0.086616-0.67090.252423
33-0.107029-0.8290.205182
34-0.112864-0.87420.192737
35-0.013518-0.10470.458477
36-0.007793-0.06040.476032







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3343552.58990.006016
20.0904440.70060.243139
30.0091590.07090.471838
40.0161340.1250.450482
50.0586870.45460.325525
6-0.15522-1.20230.11698
7-0.149398-1.15720.125882
8-0.272488-2.11070.019488
90.0564440.43720.331762
100.0756240.58580.28011
110.3132092.42610.009143
120.0541480.41940.338199
13-0.142674-1.10510.136754
140.0284950.22070.413029
15-0.139702-1.08210.141763
16-0.210109-1.62750.054436
170.0992070.76850.222615
180.0873430.67660.250644
19-0.026243-0.20330.419803
20-0.033694-0.2610.397496
210.1690861.30970.097639
22-0.002775-0.02150.49146
23-0.088089-0.68230.248828
24-0.160846-1.24590.108821
25-0.015395-0.11930.452737
26-0.061174-0.47380.318664
27-0.074033-0.57350.284238
280.0414690.32120.374581
29-0.084291-0.65290.258153
300.0962890.74590.229334
31-0.032099-0.24860.402244
32-0.177963-1.37850.086585
33-0.157437-1.21950.113714
34-0.019174-0.14850.441216
350.0232540.18010.428832
360.0318980.24710.402844

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.334355 & 2.5899 & 0.006016 \tabularnewline
2 & 0.090444 & 0.7006 & 0.243139 \tabularnewline
3 & 0.009159 & 0.0709 & 0.471838 \tabularnewline
4 & 0.016134 & 0.125 & 0.450482 \tabularnewline
5 & 0.058687 & 0.4546 & 0.325525 \tabularnewline
6 & -0.15522 & -1.2023 & 0.11698 \tabularnewline
7 & -0.149398 & -1.1572 & 0.125882 \tabularnewline
8 & -0.272488 & -2.1107 & 0.019488 \tabularnewline
9 & 0.056444 & 0.4372 & 0.331762 \tabularnewline
10 & 0.075624 & 0.5858 & 0.28011 \tabularnewline
11 & 0.313209 & 2.4261 & 0.009143 \tabularnewline
12 & 0.054148 & 0.4194 & 0.338199 \tabularnewline
13 & -0.142674 & -1.1051 & 0.136754 \tabularnewline
14 & 0.028495 & 0.2207 & 0.413029 \tabularnewline
15 & -0.139702 & -1.0821 & 0.141763 \tabularnewline
16 & -0.210109 & -1.6275 & 0.054436 \tabularnewline
17 & 0.099207 & 0.7685 & 0.222615 \tabularnewline
18 & 0.087343 & 0.6766 & 0.250644 \tabularnewline
19 & -0.026243 & -0.2033 & 0.419803 \tabularnewline
20 & -0.033694 & -0.261 & 0.397496 \tabularnewline
21 & 0.169086 & 1.3097 & 0.097639 \tabularnewline
22 & -0.002775 & -0.0215 & 0.49146 \tabularnewline
23 & -0.088089 & -0.6823 & 0.248828 \tabularnewline
24 & -0.160846 & -1.2459 & 0.108821 \tabularnewline
25 & -0.015395 & -0.1193 & 0.452737 \tabularnewline
26 & -0.061174 & -0.4738 & 0.318664 \tabularnewline
27 & -0.074033 & -0.5735 & 0.284238 \tabularnewline
28 & 0.041469 & 0.3212 & 0.374581 \tabularnewline
29 & -0.084291 & -0.6529 & 0.258153 \tabularnewline
30 & 0.096289 & 0.7459 & 0.229334 \tabularnewline
31 & -0.032099 & -0.2486 & 0.402244 \tabularnewline
32 & -0.177963 & -1.3785 & 0.086585 \tabularnewline
33 & -0.157437 & -1.2195 & 0.113714 \tabularnewline
34 & -0.019174 & -0.1485 & 0.441216 \tabularnewline
35 & 0.023254 & 0.1801 & 0.428832 \tabularnewline
36 & 0.031898 & 0.2471 & 0.402844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61650&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]0.334355[/C][C]2.5899[/C][C]0.006016[/C][/ROW]
[ROW][C]2[/C][C]0.090444[/C][C]0.7006[/C][C]0.243139[/C][/ROW]
[ROW][C]3[/C][C]0.009159[/C][C]0.0709[/C][C]0.471838[/C][/ROW]
[ROW][C]4[/C][C]0.016134[/C][C]0.125[/C][C]0.450482[/C][/ROW]
[ROW][C]5[/C][C]0.058687[/C][C]0.4546[/C][C]0.325525[/C][/ROW]
[ROW][C]6[/C][C]-0.15522[/C][C]-1.2023[/C][C]0.11698[/C][/ROW]
[ROW][C]7[/C][C]-0.149398[/C][C]-1.1572[/C][C]0.125882[/C][/ROW]
[ROW][C]8[/C][C]-0.272488[/C][C]-2.1107[/C][C]0.019488[/C][/ROW]
[ROW][C]9[/C][C]0.056444[/C][C]0.4372[/C][C]0.331762[/C][/ROW]
[ROW][C]10[/C][C]0.075624[/C][C]0.5858[/C][C]0.28011[/C][/ROW]
[ROW][C]11[/C][C]0.313209[/C][C]2.4261[/C][C]0.009143[/C][/ROW]
[ROW][C]12[/C][C]0.054148[/C][C]0.4194[/C][C]0.338199[/C][/ROW]
[ROW][C]13[/C][C]-0.142674[/C][C]-1.1051[/C][C]0.136754[/C][/ROW]
[ROW][C]14[/C][C]0.028495[/C][C]0.2207[/C][C]0.413029[/C][/ROW]
[ROW][C]15[/C][C]-0.139702[/C][C]-1.0821[/C][C]0.141763[/C][/ROW]
[ROW][C]16[/C][C]-0.210109[/C][C]-1.6275[/C][C]0.054436[/C][/ROW]
[ROW][C]17[/C][C]0.099207[/C][C]0.7685[/C][C]0.222615[/C][/ROW]
[ROW][C]18[/C][C]0.087343[/C][C]0.6766[/C][C]0.250644[/C][/ROW]
[ROW][C]19[/C][C]-0.026243[/C][C]-0.2033[/C][C]0.419803[/C][/ROW]
[ROW][C]20[/C][C]-0.033694[/C][C]-0.261[/C][C]0.397496[/C][/ROW]
[ROW][C]21[/C][C]0.169086[/C][C]1.3097[/C][C]0.097639[/C][/ROW]
[ROW][C]22[/C][C]-0.002775[/C][C]-0.0215[/C][C]0.49146[/C][/ROW]
[ROW][C]23[/C][C]-0.088089[/C][C]-0.6823[/C][C]0.248828[/C][/ROW]
[ROW][C]24[/C][C]-0.160846[/C][C]-1.2459[/C][C]0.108821[/C][/ROW]
[ROW][C]25[/C][C]-0.015395[/C][C]-0.1193[/C][C]0.452737[/C][/ROW]
[ROW][C]26[/C][C]-0.061174[/C][C]-0.4738[/C][C]0.318664[/C][/ROW]
[ROW][C]27[/C][C]-0.074033[/C][C]-0.5735[/C][C]0.284238[/C][/ROW]
[ROW][C]28[/C][C]0.041469[/C][C]0.3212[/C][C]0.374581[/C][/ROW]
[ROW][C]29[/C][C]-0.084291[/C][C]-0.6529[/C][C]0.258153[/C][/ROW]
[ROW][C]30[/C][C]0.096289[/C][C]0.7459[/C][C]0.229334[/C][/ROW]
[ROW][C]31[/C][C]-0.032099[/C][C]-0.2486[/C][C]0.402244[/C][/ROW]
[ROW][C]32[/C][C]-0.177963[/C][C]-1.3785[/C][C]0.086585[/C][/ROW]
[ROW][C]33[/C][C]-0.157437[/C][C]-1.2195[/C][C]0.113714[/C][/ROW]
[ROW][C]34[/C][C]-0.019174[/C][C]-0.1485[/C][C]0.441216[/C][/ROW]
[ROW][C]35[/C][C]0.023254[/C][C]0.1801[/C][C]0.428832[/C][/ROW]
[ROW][C]36[/C][C]0.031898[/C][C]0.2471[/C][C]0.402844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61650&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61650&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
10.3343552.58990.006016
20.0904440.70060.243139
30.0091590.07090.471838
40.0161340.1250.450482
50.0586870.45460.325525
6-0.15522-1.20230.11698
7-0.149398-1.15720.125882
8-0.272488-2.11070.019488
90.0564440.43720.331762
100.0756240.58580.28011
110.3132092.42610.009143
120.0541480.41940.338199
13-0.142674-1.10510.136754
140.0284950.22070.413029
15-0.139702-1.08210.141763
16-0.210109-1.62750.054436
170.0992070.76850.222615
180.0873430.67660.250644
19-0.026243-0.20330.419803
20-0.033694-0.2610.397496
210.1690861.30970.097639
22-0.002775-0.02150.49146
23-0.088089-0.68230.248828
24-0.160846-1.24590.108821
25-0.015395-0.11930.452737
26-0.061174-0.47380.318664
27-0.074033-0.57350.284238
280.0414690.32120.374581
29-0.084291-0.65290.258153
300.0962890.74590.229334
31-0.032099-0.24860.402244
32-0.177963-1.37850.086585
33-0.157437-1.21950.113714
34-0.019174-0.14850.441216
350.0232540.18010.428832
360.0318980.24710.402844



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 1 ; 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')