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Description of Statistical Computation

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 computationTue, 24 Nov 2009 12:14:07 -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/24/t1259090110gze2mvb07xehbfj.htm/, Retrieved Wed, 06 Dec 2023 07:48:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59244, Retrieved Wed, 06 Dec 2023 07:48:53 +0000
QR Codes:

Original text written by user:WS 8 Identifying Integration Processes
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

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] [WS 8 Identifying ...] [2009-11-24 19:14:07] [9b6f46453e60f88d91cef176fe926003] [Current]
-   PD            [(Partial) Autocorrelation Function] [WS 8 Identifying ...] [2009-11-24 19:25:42] [101f710c1bf3d900563184d79f7da6e1]
-   P               [(Partial) Autocorrelation Function] [WS 8 Identifying ...] [2009-11-24 19:34:58] [101f710c1bf3d900563184d79f7da6e1]
-   P                 [(Partial) Autocorrelation Function] [WS 9 Estimation o...] [2009-12-02 21:09:56] [101f710c1bf3d900563184d79f7da6e1]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14,5
14,3
15,3
14,4
13,7
14,2
13,5
11,9
14,6
15,6
14,1
14,9
14,2
14,6
17,2
15,4
14,3
17,5
14,5
14,4
16,6
16,7
16,6
16,9
15,7
16,4
18,4
16,9
16,5
18,3
15,1
15,7
18,1
16,8
18,9
19
18,1
17,8
21,5
17,1
18,7
19
16,4
16,9
18,6
19,3
19,4
17,6
18,6
18,1
20,4
18,1
19,6
19,9
19,2
17,8
19,2
22
21,1
19,5
22,2
20,9
22,2
23,5
21,5
24,3
22,8
20,3
23,7
23,3
19,6
18
17,3
16,8
18,2
16,5
16
18,4

Tables (Output of Computation)





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=59244&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=59244&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59244&T=0

As an alternative you can also use a QR Code:  
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
10.7817766.90450
20.7210896.36850
30.773466.8310
40.6563755.7970
50.6183115.46080
60.6131515.41520
70.4734244.18123.8e-05
80.4532144.00277.1e-05
90.4369793.85930.000116
100.3170812.80040.003216
110.3411293.01280.001745
120.4009123.54080.000338
130.2431412.14740.017435
140.2128951.88020.031905
150.2358682.08310.020258
160.1854371.63770.052752
170.1767611.56110.061273
180.1743281.53960.06385
190.1267791.11970.133143
200.1099710.97120.167217
210.0909980.80370.212014
220.0459770.40610.342905
230.0683340.60350.273961
240.1017440.89860.185822
250.034160.30170.381844
26-0.02271-0.20060.42078
270.0022120.01950.49223
28-0.009584-0.08460.466382
29-0.038533-0.34030.367266
30-0.040097-0.35410.362099
31-0.059001-0.52110.301893
32-0.118926-1.05030.148405
33-0.130294-1.15070.126681
34-0.160977-1.42170.079549
35-0.183701-1.62240.054376
36-0.142287-1.25660.106316

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.781776 & 6.9045 & 0 \tabularnewline
2 & 0.721089 & 6.3685 & 0 \tabularnewline
3 & 0.77346 & 6.831 & 0 \tabularnewline
4 & 0.656375 & 5.797 & 0 \tabularnewline
5 & 0.618311 & 5.4608 & 0 \tabularnewline
6 & 0.613151 & 5.4152 & 0 \tabularnewline
7 & 0.473424 & 4.1812 & 3.8e-05 \tabularnewline
8 & 0.453214 & 4.0027 & 7.1e-05 \tabularnewline
9 & 0.436979 & 3.8593 & 0.000116 \tabularnewline
10 & 0.317081 & 2.8004 & 0.003216 \tabularnewline
11 & 0.341129 & 3.0128 & 0.001745 \tabularnewline
12 & 0.400912 & 3.5408 & 0.000338 \tabularnewline
13 & 0.243141 & 2.1474 & 0.017435 \tabularnewline
14 & 0.212895 & 1.8802 & 0.031905 \tabularnewline
15 & 0.235868 & 2.0831 & 0.020258 \tabularnewline
16 & 0.185437 & 1.6377 & 0.052752 \tabularnewline
17 & 0.176761 & 1.5611 & 0.061273 \tabularnewline
18 & 0.174328 & 1.5396 & 0.06385 \tabularnewline
19 & 0.126779 & 1.1197 & 0.133143 \tabularnewline
20 & 0.109971 & 0.9712 & 0.167217 \tabularnewline
21 & 0.090998 & 0.8037 & 0.212014 \tabularnewline
22 & 0.045977 & 0.4061 & 0.342905 \tabularnewline
23 & 0.068334 & 0.6035 & 0.273961 \tabularnewline
24 & 0.101744 & 0.8986 & 0.185822 \tabularnewline
25 & 0.03416 & 0.3017 & 0.381844 \tabularnewline
26 & -0.02271 & -0.2006 & 0.42078 \tabularnewline
27 & 0.002212 & 0.0195 & 0.49223 \tabularnewline
28 & -0.009584 & -0.0846 & 0.466382 \tabularnewline
29 & -0.038533 & -0.3403 & 0.367266 \tabularnewline
30 & -0.040097 & -0.3541 & 0.362099 \tabularnewline
31 & -0.059001 & -0.5211 & 0.301893 \tabularnewline
32 & -0.118926 & -1.0503 & 0.148405 \tabularnewline
33 & -0.130294 & -1.1507 & 0.126681 \tabularnewline
34 & -0.160977 & -1.4217 & 0.079549 \tabularnewline
35 & -0.183701 & -1.6224 & 0.054376 \tabularnewline
36 & -0.142287 & -1.2566 & 0.106316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59244&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]0.781776[/C][C]6.9045[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.721089[/C][C]6.3685[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.77346[/C][C]6.831[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.656375[/C][C]5.797[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.618311[/C][C]5.4608[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.613151[/C][C]5.4152[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.473424[/C][C]4.1812[/C][C]3.8e-05[/C][/ROW]
[ROW][C]8[/C][C]0.453214[/C][C]4.0027[/C][C]7.1e-05[/C][/ROW]
[ROW][C]9[/C][C]0.436979[/C][C]3.8593[/C][C]0.000116[/C][/ROW]
[ROW][C]10[/C][C]0.317081[/C][C]2.8004[/C][C]0.003216[/C][/ROW]
[ROW][C]11[/C][C]0.341129[/C][C]3.0128[/C][C]0.001745[/C][/ROW]
[ROW][C]12[/C][C]0.400912[/C][C]3.5408[/C][C]0.000338[/C][/ROW]
[ROW][C]13[/C][C]0.243141[/C][C]2.1474[/C][C]0.017435[/C][/ROW]
[ROW][C]14[/C][C]0.212895[/C][C]1.8802[/C][C]0.031905[/C][/ROW]
[ROW][C]15[/C][C]0.235868[/C][C]2.0831[/C][C]0.020258[/C][/ROW]
[ROW][C]16[/C][C]0.185437[/C][C]1.6377[/C][C]0.052752[/C][/ROW]
[ROW][C]17[/C][C]0.176761[/C][C]1.5611[/C][C]0.061273[/C][/ROW]
[ROW][C]18[/C][C]0.174328[/C][C]1.5396[/C][C]0.06385[/C][/ROW]
[ROW][C]19[/C][C]0.126779[/C][C]1.1197[/C][C]0.133143[/C][/ROW]
[ROW][C]20[/C][C]0.109971[/C][C]0.9712[/C][C]0.167217[/C][/ROW]
[ROW][C]21[/C][C]0.090998[/C][C]0.8037[/C][C]0.212014[/C][/ROW]
[ROW][C]22[/C][C]0.045977[/C][C]0.4061[/C][C]0.342905[/C][/ROW]
[ROW][C]23[/C][C]0.068334[/C][C]0.6035[/C][C]0.273961[/C][/ROW]
[ROW][C]24[/C][C]0.101744[/C][C]0.8986[/C][C]0.185822[/C][/ROW]
[ROW][C]25[/C][C]0.03416[/C][C]0.3017[/C][C]0.381844[/C][/ROW]
[ROW][C]26[/C][C]-0.02271[/C][C]-0.2006[/C][C]0.42078[/C][/ROW]
[ROW][C]27[/C][C]0.002212[/C][C]0.0195[/C][C]0.49223[/C][/ROW]
[ROW][C]28[/C][C]-0.009584[/C][C]-0.0846[/C][C]0.466382[/C][/ROW]
[ROW][C]29[/C][C]-0.038533[/C][C]-0.3403[/C][C]0.367266[/C][/ROW]
[ROW][C]30[/C][C]-0.040097[/C][C]-0.3541[/C][C]0.362099[/C][/ROW]
[ROW][C]31[/C][C]-0.059001[/C][C]-0.5211[/C][C]0.301893[/C][/ROW]
[ROW][C]32[/C][C]-0.118926[/C][C]-1.0503[/C][C]0.148405[/C][/ROW]
[ROW][C]33[/C][C]-0.130294[/C][C]-1.1507[/C][C]0.126681[/C][/ROW]
[ROW][C]34[/C][C]-0.160977[/C][C]-1.4217[/C][C]0.079549[/C][/ROW]
[ROW][C]35[/C][C]-0.183701[/C][C]-1.6224[/C][C]0.054376[/C][/ROW]
[ROW][C]36[/C][C]-0.142287[/C][C]-1.2566[/C][C]0.106316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59244&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59244&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.7817766.90450
20.7210896.36850
30.773466.8310
40.6563755.7970
50.6183115.46080
60.6131515.41520
70.4734244.18123.8e-05
80.4532144.00277.1e-05
90.4369793.85930.000116
100.3170812.80040.003216
110.3411293.01280.001745
120.4009123.54080.000338
130.2431412.14740.017435
140.2128951.88020.031905
150.2358682.08310.020258
160.1854371.63770.052752
170.1767611.56110.061273
180.1743281.53960.06385
190.1267791.11970.133143
200.1099710.97120.167217
210.0909980.80370.212014
220.0459770.40610.342905
230.0683340.60350.273961
240.1017440.89860.185822
250.034160.30170.381844
26-0.02271-0.20060.42078
270.0022120.01950.49223
28-0.009584-0.08460.466382
29-0.038533-0.34030.367266
30-0.040097-0.35410.362099
31-0.059001-0.52110.301893
32-0.118926-1.05030.148405
33-0.130294-1.15070.126681
34-0.160977-1.42170.079549
35-0.183701-1.62240.054376
36-0.142287-1.25660.106316






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.7817766.90450
20.2826842.49660.007324
30.4139473.65590.000232
4-0.158283-1.39790.08305
50.0461580.40770.342322
6-0.025769-0.22760.410282
7-0.244856-2.16250.016822
80.0397510.35110.36324
9-0.024204-0.21380.415643
10-0.080532-0.71120.239529
110.1760561.55490.062011
120.3066282.70810.004157
13-0.279457-2.46810.007886
14-0.164259-1.45070.075438
15-0.063886-0.56420.287109
160.1623631.4340.077791
17-0.040377-0.35660.361177
180.0426080.37630.353857
190.1366461.20680.115573
20-0.153317-1.35410.089813
21-0.104312-0.92130.179879
220.0003650.00320.498717
230.0487870.43090.333873
240.0376440.33250.370217
250.0785980.69420.244822
26-0.154248-1.36230.088515
27-0.055575-0.49080.312464
28-0.051885-0.45820.324027
290.0306980.27110.393508
30-0.034829-0.30760.379603
310.0350790.30980.378765
32-0.090556-0.79980.213136
33-0.056538-0.49930.309475
34-0.074771-0.66040.255484
35-0.058361-0.51540.303855
360.1146521.01260.157197

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.781776 & 6.9045 & 0 \tabularnewline
2 & 0.282684 & 2.4966 & 0.007324 \tabularnewline
3 & 0.413947 & 3.6559 & 0.000232 \tabularnewline
4 & -0.158283 & -1.3979 & 0.08305 \tabularnewline
5 & 0.046158 & 0.4077 & 0.342322 \tabularnewline
6 & -0.025769 & -0.2276 & 0.410282 \tabularnewline
7 & -0.244856 & -2.1625 & 0.016822 \tabularnewline
8 & 0.039751 & 0.3511 & 0.36324 \tabularnewline
9 & -0.024204 & -0.2138 & 0.415643 \tabularnewline
10 & -0.080532 & -0.7112 & 0.239529 \tabularnewline
11 & 0.176056 & 1.5549 & 0.062011 \tabularnewline
12 & 0.306628 & 2.7081 & 0.004157 \tabularnewline
13 & -0.279457 & -2.4681 & 0.007886 \tabularnewline
14 & -0.164259 & -1.4507 & 0.075438 \tabularnewline
15 & -0.063886 & -0.5642 & 0.287109 \tabularnewline
16 & 0.162363 & 1.434 & 0.077791 \tabularnewline
17 & -0.040377 & -0.3566 & 0.361177 \tabularnewline
18 & 0.042608 & 0.3763 & 0.353857 \tabularnewline
19 & 0.136646 & 1.2068 & 0.115573 \tabularnewline
20 & -0.153317 & -1.3541 & 0.089813 \tabularnewline
21 & -0.104312 & -0.9213 & 0.179879 \tabularnewline
22 & 0.000365 & 0.0032 & 0.498717 \tabularnewline
23 & 0.048787 & 0.4309 & 0.333873 \tabularnewline
24 & 0.037644 & 0.3325 & 0.370217 \tabularnewline
25 & 0.078598 & 0.6942 & 0.244822 \tabularnewline
26 & -0.154248 & -1.3623 & 0.088515 \tabularnewline
27 & -0.055575 & -0.4908 & 0.312464 \tabularnewline
28 & -0.051885 & -0.4582 & 0.324027 \tabularnewline
29 & 0.030698 & 0.2711 & 0.393508 \tabularnewline
30 & -0.034829 & -0.3076 & 0.379603 \tabularnewline
31 & 0.035079 & 0.3098 & 0.378765 \tabularnewline
32 & -0.090556 & -0.7998 & 0.213136 \tabularnewline
33 & -0.056538 & -0.4993 & 0.309475 \tabularnewline
34 & -0.074771 & -0.6604 & 0.255484 \tabularnewline
35 & -0.058361 & -0.5154 & 0.303855 \tabularnewline
36 & 0.114652 & 1.0126 & 0.157197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59244&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]0.781776[/C][C]6.9045[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.282684[/C][C]2.4966[/C][C]0.007324[/C][/ROW]
[ROW][C]3[/C][C]0.413947[/C][C]3.6559[/C][C]0.000232[/C][/ROW]
[ROW][C]4[/C][C]-0.158283[/C][C]-1.3979[/C][C]0.08305[/C][/ROW]
[ROW][C]5[/C][C]0.046158[/C][C]0.4077[/C][C]0.342322[/C][/ROW]
[ROW][C]6[/C][C]-0.025769[/C][C]-0.2276[/C][C]0.410282[/C][/ROW]
[ROW][C]7[/C][C]-0.244856[/C][C]-2.1625[/C][C]0.016822[/C][/ROW]
[ROW][C]8[/C][C]0.039751[/C][C]0.3511[/C][C]0.36324[/C][/ROW]
[ROW][C]9[/C][C]-0.024204[/C][C]-0.2138[/C][C]0.415643[/C][/ROW]
[ROW][C]10[/C][C]-0.080532[/C][C]-0.7112[/C][C]0.239529[/C][/ROW]
[ROW][C]11[/C][C]0.176056[/C][C]1.5549[/C][C]0.062011[/C][/ROW]
[ROW][C]12[/C][C]0.306628[/C][C]2.7081[/C][C]0.004157[/C][/ROW]
[ROW][C]13[/C][C]-0.279457[/C][C]-2.4681[/C][C]0.007886[/C][/ROW]
[ROW][C]14[/C][C]-0.164259[/C][C]-1.4507[/C][C]0.075438[/C][/ROW]
[ROW][C]15[/C][C]-0.063886[/C][C]-0.5642[/C][C]0.287109[/C][/ROW]
[ROW][C]16[/C][C]0.162363[/C][C]1.434[/C][C]0.077791[/C][/ROW]
[ROW][C]17[/C][C]-0.040377[/C][C]-0.3566[/C][C]0.361177[/C][/ROW]
[ROW][C]18[/C][C]0.042608[/C][C]0.3763[/C][C]0.353857[/C][/ROW]
[ROW][C]19[/C][C]0.136646[/C][C]1.2068[/C][C]0.115573[/C][/ROW]
[ROW][C]20[/C][C]-0.153317[/C][C]-1.3541[/C][C]0.089813[/C][/ROW]
[ROW][C]21[/C][C]-0.104312[/C][C]-0.9213[/C][C]0.179879[/C][/ROW]
[ROW][C]22[/C][C]0.000365[/C][C]0.0032[/C][C]0.498717[/C][/ROW]
[ROW][C]23[/C][C]0.048787[/C][C]0.4309[/C][C]0.333873[/C][/ROW]
[ROW][C]24[/C][C]0.037644[/C][C]0.3325[/C][C]0.370217[/C][/ROW]
[ROW][C]25[/C][C]0.078598[/C][C]0.6942[/C][C]0.244822[/C][/ROW]
[ROW][C]26[/C][C]-0.154248[/C][C]-1.3623[/C][C]0.088515[/C][/ROW]
[ROW][C]27[/C][C]-0.055575[/C][C]-0.4908[/C][C]0.312464[/C][/ROW]
[ROW][C]28[/C][C]-0.051885[/C][C]-0.4582[/C][C]0.324027[/C][/ROW]
[ROW][C]29[/C][C]0.030698[/C][C]0.2711[/C][C]0.393508[/C][/ROW]
[ROW][C]30[/C][C]-0.034829[/C][C]-0.3076[/C][C]0.379603[/C][/ROW]
[ROW][C]31[/C][C]0.035079[/C][C]0.3098[/C][C]0.378765[/C][/ROW]
[ROW][C]32[/C][C]-0.090556[/C][C]-0.7998[/C][C]0.213136[/C][/ROW]
[ROW][C]33[/C][C]-0.056538[/C][C]-0.4993[/C][C]0.309475[/C][/ROW]
[ROW][C]34[/C][C]-0.074771[/C][C]-0.6604[/C][C]0.255484[/C][/ROW]
[ROW][C]35[/C][C]-0.058361[/C][C]-0.5154[/C][C]0.303855[/C][/ROW]
[ROW][C]36[/C][C]0.114652[/C][C]1.0126[/C][C]0.157197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59244&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59244&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.7817766.90450
20.2826842.49660.007324
30.4139473.65590.000232
4-0.158283-1.39790.08305
50.0461580.40770.342322
6-0.025769-0.22760.410282
7-0.244856-2.16250.016822
80.0397510.35110.36324
9-0.024204-0.21380.415643
10-0.080532-0.71120.239529
110.1760561.55490.062011
120.3066282.70810.004157
13-0.279457-2.46810.007886
14-0.164259-1.45070.075438
15-0.063886-0.56420.287109
160.1623631.4340.077791
17-0.040377-0.35660.361177
180.0426080.37630.353857
190.1366461.20680.115573
20-0.153317-1.35410.089813
21-0.104312-0.92130.179879
220.0003650.00320.498717
230.0487870.43090.333873
240.0376440.33250.370217
250.0785980.69420.244822
26-0.154248-1.36230.088515
27-0.055575-0.49080.312464
28-0.051885-0.45820.324027
290.0306980.27110.393508
30-0.034829-0.30760.379603
310.0350790.30980.378765
32-0.090556-0.79980.213136
33-0.056538-0.49930.309475
34-0.074771-0.66040.255484
35-0.058361-0.51540.303855
360.1146521.01260.157197


Figures (Output of Computation)

Input Parameters & R Code

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')