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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 computationThu, 10 Dec 2009 05:56:18 -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/Dec/10/t1260449835xh7vhcyj6nydw5q.htm/, Retrieved Thu, 28 Mar 2024 08:10:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65327, Retrieved Thu, 28 Mar 2024 08:10:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
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]
-   PD        [(Partial) Autocorrelation Function] [W8] [2009-11-25 18:19:32] [315ba876df544ad397193b5931d5f354]
-   P             [(Partial) Autocorrelation Function] [Autocorrelatie] [2009-12-10 12:56:18] [950726a732ba3ca782ecb1a5307d0f6f] [Current]
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Dataseries X:
13132.1
17665.9
16913
17318.8
16224.2
15469.6
16557.5
19414.8
17335
16525.2
18160.4
15553.8
15262.2
18581
17564.1
18948.6
17187.8
17564.8
17668.4
20811.7
17257.8
18984.2
20532.6
17082.3
16894.9
20274.9
20078.6
19900.9
17012.2
19642.9
19024
21691
18835.9
19873.4
21468.2
19406.8
18385.3
20739.3
22268.3
21569
17514.8
21124.7
21251
21393
22145.2
20310.5
23466.9
21264.6
18388.1
22635.4
22014.3
18422.7
16120.2
16037.7
16410.7
17749.8
16349.8
15662.3
17782.3
16398.9




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=65327&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=65327&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65327&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
1-0.327468-2.51530.007318
2-0.249242-1.91450.030207
30.0876820.67350.251631
40.0790310.6070.273074
50.0125830.09670.461665
60.0083930.06450.474407
7-0.109457-0.84080.201941
80.1899151.45880.074967
9-0.00138-0.01060.495791
10-0.242202-1.86040.033908
11-0.175154-1.34540.091825
120.598784.59931.1e-05
13-0.194408-1.49330.070347
14-0.178789-1.37330.087428
15-0.017188-0.1320.447707
160.1588541.22020.113625
17-0.037938-0.29140.385881
18-0.03994-0.30680.380042
190.0038650.02970.488209
200.1146580.88070.191026
21-0.068105-0.52310.301425
22-0.097471-0.74870.228509
23-0.132272-1.0160.156888
240.3460032.65770.005054
25-0.011311-0.08690.465531
26-0.223471-1.71650.045658
27-0.020705-0.1590.437092
280.1740311.33680.093217
29-0.070771-0.54360.294382
30-0.026483-0.20340.419754
310.087770.67420.251416
32-0.032261-0.24780.402574
33-0.029191-0.22420.41168
34-0.004981-0.03830.484806
35-0.121643-0.93440.176964
360.1981471.5220.066676

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.327468 & -2.5153 & 0.007318 \tabularnewline
2 & -0.249242 & -1.9145 & 0.030207 \tabularnewline
3 & 0.087682 & 0.6735 & 0.251631 \tabularnewline
4 & 0.079031 & 0.607 & 0.273074 \tabularnewline
5 & 0.012583 & 0.0967 & 0.461665 \tabularnewline
6 & 0.008393 & 0.0645 & 0.474407 \tabularnewline
7 & -0.109457 & -0.8408 & 0.201941 \tabularnewline
8 & 0.189915 & 1.4588 & 0.074967 \tabularnewline
9 & -0.00138 & -0.0106 & 0.495791 \tabularnewline
10 & -0.242202 & -1.8604 & 0.033908 \tabularnewline
11 & -0.175154 & -1.3454 & 0.091825 \tabularnewline
12 & 0.59878 & 4.5993 & 1.1e-05 \tabularnewline
13 & -0.194408 & -1.4933 & 0.070347 \tabularnewline
14 & -0.178789 & -1.3733 & 0.087428 \tabularnewline
15 & -0.017188 & -0.132 & 0.447707 \tabularnewline
16 & 0.158854 & 1.2202 & 0.113625 \tabularnewline
17 & -0.037938 & -0.2914 & 0.385881 \tabularnewline
18 & -0.03994 & -0.3068 & 0.380042 \tabularnewline
19 & 0.003865 & 0.0297 & 0.488209 \tabularnewline
20 & 0.114658 & 0.8807 & 0.191026 \tabularnewline
21 & -0.068105 & -0.5231 & 0.301425 \tabularnewline
22 & -0.097471 & -0.7487 & 0.228509 \tabularnewline
23 & -0.132272 & -1.016 & 0.156888 \tabularnewline
24 & 0.346003 & 2.6577 & 0.005054 \tabularnewline
25 & -0.011311 & -0.0869 & 0.465531 \tabularnewline
26 & -0.223471 & -1.7165 & 0.045658 \tabularnewline
27 & -0.020705 & -0.159 & 0.437092 \tabularnewline
28 & 0.174031 & 1.3368 & 0.093217 \tabularnewline
29 & -0.070771 & -0.5436 & 0.294382 \tabularnewline
30 & -0.026483 & -0.2034 & 0.419754 \tabularnewline
31 & 0.08777 & 0.6742 & 0.251416 \tabularnewline
32 & -0.032261 & -0.2478 & 0.402574 \tabularnewline
33 & -0.029191 & -0.2242 & 0.41168 \tabularnewline
34 & -0.004981 & -0.0383 & 0.484806 \tabularnewline
35 & -0.121643 & -0.9344 & 0.176964 \tabularnewline
36 & 0.198147 & 1.522 & 0.066676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65327&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]-0.327468[/C][C]-2.5153[/C][C]0.007318[/C][/ROW]
[ROW][C]2[/C][C]-0.249242[/C][C]-1.9145[/C][C]0.030207[/C][/ROW]
[ROW][C]3[/C][C]0.087682[/C][C]0.6735[/C][C]0.251631[/C][/ROW]
[ROW][C]4[/C][C]0.079031[/C][C]0.607[/C][C]0.273074[/C][/ROW]
[ROW][C]5[/C][C]0.012583[/C][C]0.0967[/C][C]0.461665[/C][/ROW]
[ROW][C]6[/C][C]0.008393[/C][C]0.0645[/C][C]0.474407[/C][/ROW]
[ROW][C]7[/C][C]-0.109457[/C][C]-0.8408[/C][C]0.201941[/C][/ROW]
[ROW][C]8[/C][C]0.189915[/C][C]1.4588[/C][C]0.074967[/C][/ROW]
[ROW][C]9[/C][C]-0.00138[/C][C]-0.0106[/C][C]0.495791[/C][/ROW]
[ROW][C]10[/C][C]-0.242202[/C][C]-1.8604[/C][C]0.033908[/C][/ROW]
[ROW][C]11[/C][C]-0.175154[/C][C]-1.3454[/C][C]0.091825[/C][/ROW]
[ROW][C]12[/C][C]0.59878[/C][C]4.5993[/C][C]1.1e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.194408[/C][C]-1.4933[/C][C]0.070347[/C][/ROW]
[ROW][C]14[/C][C]-0.178789[/C][C]-1.3733[/C][C]0.087428[/C][/ROW]
[ROW][C]15[/C][C]-0.017188[/C][C]-0.132[/C][C]0.447707[/C][/ROW]
[ROW][C]16[/C][C]0.158854[/C][C]1.2202[/C][C]0.113625[/C][/ROW]
[ROW][C]17[/C][C]-0.037938[/C][C]-0.2914[/C][C]0.385881[/C][/ROW]
[ROW][C]18[/C][C]-0.03994[/C][C]-0.3068[/C][C]0.380042[/C][/ROW]
[ROW][C]19[/C][C]0.003865[/C][C]0.0297[/C][C]0.488209[/C][/ROW]
[ROW][C]20[/C][C]0.114658[/C][C]0.8807[/C][C]0.191026[/C][/ROW]
[ROW][C]21[/C][C]-0.068105[/C][C]-0.5231[/C][C]0.301425[/C][/ROW]
[ROW][C]22[/C][C]-0.097471[/C][C]-0.7487[/C][C]0.228509[/C][/ROW]
[ROW][C]23[/C][C]-0.132272[/C][C]-1.016[/C][C]0.156888[/C][/ROW]
[ROW][C]24[/C][C]0.346003[/C][C]2.6577[/C][C]0.005054[/C][/ROW]
[ROW][C]25[/C][C]-0.011311[/C][C]-0.0869[/C][C]0.465531[/C][/ROW]
[ROW][C]26[/C][C]-0.223471[/C][C]-1.7165[/C][C]0.045658[/C][/ROW]
[ROW][C]27[/C][C]-0.020705[/C][C]-0.159[/C][C]0.437092[/C][/ROW]
[ROW][C]28[/C][C]0.174031[/C][C]1.3368[/C][C]0.093217[/C][/ROW]
[ROW][C]29[/C][C]-0.070771[/C][C]-0.5436[/C][C]0.294382[/C][/ROW]
[ROW][C]30[/C][C]-0.026483[/C][C]-0.2034[/C][C]0.419754[/C][/ROW]
[ROW][C]31[/C][C]0.08777[/C][C]0.6742[/C][C]0.251416[/C][/ROW]
[ROW][C]32[/C][C]-0.032261[/C][C]-0.2478[/C][C]0.402574[/C][/ROW]
[ROW][C]33[/C][C]-0.029191[/C][C]-0.2242[/C][C]0.41168[/C][/ROW]
[ROW][C]34[/C][C]-0.004981[/C][C]-0.0383[/C][C]0.484806[/C][/ROW]
[ROW][C]35[/C][C]-0.121643[/C][C]-0.9344[/C][C]0.176964[/C][/ROW]
[ROW][C]36[/C][C]0.198147[/C][C]1.522[/C][C]0.066676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65327&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65327&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
1-0.327468-2.51530.007318
2-0.249242-1.91450.030207
30.0876820.67350.251631
40.0790310.6070.273074
50.0125830.09670.461665
60.0083930.06450.474407
7-0.109457-0.84080.201941
80.1899151.45880.074967
9-0.00138-0.01060.495791
10-0.242202-1.86040.033908
11-0.175154-1.34540.091825
120.598784.59931.1e-05
13-0.194408-1.49330.070347
14-0.178789-1.37330.087428
15-0.017188-0.1320.447707
160.1588541.22020.113625
17-0.037938-0.29140.385881
18-0.03994-0.30680.380042
190.0038650.02970.488209
200.1146580.88070.191026
21-0.068105-0.52310.301425
22-0.097471-0.74870.228509
23-0.132272-1.0160.156888
240.3460032.65770.005054
25-0.011311-0.08690.465531
26-0.223471-1.71650.045658
27-0.020705-0.1590.437092
280.1740311.33680.093217
29-0.070771-0.54360.294382
30-0.026483-0.20340.419754
310.087770.67420.251416
32-0.032261-0.24780.402574
33-0.029191-0.22420.41168
34-0.004981-0.03830.484806
35-0.121643-0.93440.176964
360.1981471.5220.066676







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.327468-2.51530.007318
2-0.399296-3.0670.00163
3-0.209593-1.60990.056377
4-0.091359-0.70170.242798
50.0138130.10610.45793
60.0894460.6870.247372
7-0.044843-0.34440.365867
80.195551.5020.069209
90.1547171.18840.119717
10-0.116329-0.89350.187599
11-0.483647-3.7150.000227
120.2798952.14990.017836
130.0834180.64070.262085
140.0862920.66280.255013
15-0.100422-0.77140.221787
160.1412741.08510.141136
17-0.059584-0.45770.324433
18-0.073777-0.56670.286536
190.120540.92590.179139
20-0.002671-0.02050.49185
21-0.136424-1.04790.14948
220.003680.02830.488773
230.0400210.30740.379808
24-0.11617-0.89230.187924
250.1070080.82190.207209
260.0212210.1630.435537
270.0004080.00310.498754
28-0.077638-0.59640.276611
290.0794030.60990.272133
30-0.016552-0.12710.449632
310.0888020.68210.248921
32-0.083277-0.63970.262433
33-0.020051-0.1540.439061
34-0.004121-0.03170.487427
350.021090.1620.43593
36-0.055115-0.42330.336791

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.327468 & -2.5153 & 0.007318 \tabularnewline
2 & -0.399296 & -3.067 & 0.00163 \tabularnewline
3 & -0.209593 & -1.6099 & 0.056377 \tabularnewline
4 & -0.091359 & -0.7017 & 0.242798 \tabularnewline
5 & 0.013813 & 0.1061 & 0.45793 \tabularnewline
6 & 0.089446 & 0.687 & 0.247372 \tabularnewline
7 & -0.044843 & -0.3444 & 0.365867 \tabularnewline
8 & 0.19555 & 1.502 & 0.069209 \tabularnewline
9 & 0.154717 & 1.1884 & 0.119717 \tabularnewline
10 & -0.116329 & -0.8935 & 0.187599 \tabularnewline
11 & -0.483647 & -3.715 & 0.000227 \tabularnewline
12 & 0.279895 & 2.1499 & 0.017836 \tabularnewline
13 & 0.083418 & 0.6407 & 0.262085 \tabularnewline
14 & 0.086292 & 0.6628 & 0.255013 \tabularnewline
15 & -0.100422 & -0.7714 & 0.221787 \tabularnewline
16 & 0.141274 & 1.0851 & 0.141136 \tabularnewline
17 & -0.059584 & -0.4577 & 0.324433 \tabularnewline
18 & -0.073777 & -0.5667 & 0.286536 \tabularnewline
19 & 0.12054 & 0.9259 & 0.179139 \tabularnewline
20 & -0.002671 & -0.0205 & 0.49185 \tabularnewline
21 & -0.136424 & -1.0479 & 0.14948 \tabularnewline
22 & 0.00368 & 0.0283 & 0.488773 \tabularnewline
23 & 0.040021 & 0.3074 & 0.379808 \tabularnewline
24 & -0.11617 & -0.8923 & 0.187924 \tabularnewline
25 & 0.107008 & 0.8219 & 0.207209 \tabularnewline
26 & 0.021221 & 0.163 & 0.435537 \tabularnewline
27 & 0.000408 & 0.0031 & 0.498754 \tabularnewline
28 & -0.077638 & -0.5964 & 0.276611 \tabularnewline
29 & 0.079403 & 0.6099 & 0.272133 \tabularnewline
30 & -0.016552 & -0.1271 & 0.449632 \tabularnewline
31 & 0.088802 & 0.6821 & 0.248921 \tabularnewline
32 & -0.083277 & -0.6397 & 0.262433 \tabularnewline
33 & -0.020051 & -0.154 & 0.439061 \tabularnewline
34 & -0.004121 & -0.0317 & 0.487427 \tabularnewline
35 & 0.02109 & 0.162 & 0.43593 \tabularnewline
36 & -0.055115 & -0.4233 & 0.336791 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65327&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]-0.327468[/C][C]-2.5153[/C][C]0.007318[/C][/ROW]
[ROW][C]2[/C][C]-0.399296[/C][C]-3.067[/C][C]0.00163[/C][/ROW]
[ROW][C]3[/C][C]-0.209593[/C][C]-1.6099[/C][C]0.056377[/C][/ROW]
[ROW][C]4[/C][C]-0.091359[/C][C]-0.7017[/C][C]0.242798[/C][/ROW]
[ROW][C]5[/C][C]0.013813[/C][C]0.1061[/C][C]0.45793[/C][/ROW]
[ROW][C]6[/C][C]0.089446[/C][C]0.687[/C][C]0.247372[/C][/ROW]
[ROW][C]7[/C][C]-0.044843[/C][C]-0.3444[/C][C]0.365867[/C][/ROW]
[ROW][C]8[/C][C]0.19555[/C][C]1.502[/C][C]0.069209[/C][/ROW]
[ROW][C]9[/C][C]0.154717[/C][C]1.1884[/C][C]0.119717[/C][/ROW]
[ROW][C]10[/C][C]-0.116329[/C][C]-0.8935[/C][C]0.187599[/C][/ROW]
[ROW][C]11[/C][C]-0.483647[/C][C]-3.715[/C][C]0.000227[/C][/ROW]
[ROW][C]12[/C][C]0.279895[/C][C]2.1499[/C][C]0.017836[/C][/ROW]
[ROW][C]13[/C][C]0.083418[/C][C]0.6407[/C][C]0.262085[/C][/ROW]
[ROW][C]14[/C][C]0.086292[/C][C]0.6628[/C][C]0.255013[/C][/ROW]
[ROW][C]15[/C][C]-0.100422[/C][C]-0.7714[/C][C]0.221787[/C][/ROW]
[ROW][C]16[/C][C]0.141274[/C][C]1.0851[/C][C]0.141136[/C][/ROW]
[ROW][C]17[/C][C]-0.059584[/C][C]-0.4577[/C][C]0.324433[/C][/ROW]
[ROW][C]18[/C][C]-0.073777[/C][C]-0.5667[/C][C]0.286536[/C][/ROW]
[ROW][C]19[/C][C]0.12054[/C][C]0.9259[/C][C]0.179139[/C][/ROW]
[ROW][C]20[/C][C]-0.002671[/C][C]-0.0205[/C][C]0.49185[/C][/ROW]
[ROW][C]21[/C][C]-0.136424[/C][C]-1.0479[/C][C]0.14948[/C][/ROW]
[ROW][C]22[/C][C]0.00368[/C][C]0.0283[/C][C]0.488773[/C][/ROW]
[ROW][C]23[/C][C]0.040021[/C][C]0.3074[/C][C]0.379808[/C][/ROW]
[ROW][C]24[/C][C]-0.11617[/C][C]-0.8923[/C][C]0.187924[/C][/ROW]
[ROW][C]25[/C][C]0.107008[/C][C]0.8219[/C][C]0.207209[/C][/ROW]
[ROW][C]26[/C][C]0.021221[/C][C]0.163[/C][C]0.435537[/C][/ROW]
[ROW][C]27[/C][C]0.000408[/C][C]0.0031[/C][C]0.498754[/C][/ROW]
[ROW][C]28[/C][C]-0.077638[/C][C]-0.5964[/C][C]0.276611[/C][/ROW]
[ROW][C]29[/C][C]0.079403[/C][C]0.6099[/C][C]0.272133[/C][/ROW]
[ROW][C]30[/C][C]-0.016552[/C][C]-0.1271[/C][C]0.449632[/C][/ROW]
[ROW][C]31[/C][C]0.088802[/C][C]0.6821[/C][C]0.248921[/C][/ROW]
[ROW][C]32[/C][C]-0.083277[/C][C]-0.6397[/C][C]0.262433[/C][/ROW]
[ROW][C]33[/C][C]-0.020051[/C][C]-0.154[/C][C]0.439061[/C][/ROW]
[ROW][C]34[/C][C]-0.004121[/C][C]-0.0317[/C][C]0.487427[/C][/ROW]
[ROW][C]35[/C][C]0.02109[/C][C]0.162[/C][C]0.43593[/C][/ROW]
[ROW][C]36[/C][C]-0.055115[/C][C]-0.4233[/C][C]0.336791[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65327&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65327&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
1-0.327468-2.51530.007318
2-0.399296-3.0670.00163
3-0.209593-1.60990.056377
4-0.091359-0.70170.242798
50.0138130.10610.45793
60.0894460.6870.247372
7-0.044843-0.34440.365867
80.195551.5020.069209
90.1547171.18840.119717
10-0.116329-0.89350.187599
11-0.483647-3.7150.000227
120.2798952.14990.017836
130.0834180.64070.262085
140.0862920.66280.255013
15-0.100422-0.77140.221787
160.1412741.08510.141136
17-0.059584-0.45770.324433
18-0.073777-0.56670.286536
190.120540.92590.179139
20-0.002671-0.02050.49185
21-0.136424-1.04790.14948
220.003680.02830.488773
230.0400210.30740.379808
24-0.11617-0.89230.187924
250.1070080.82190.207209
260.0212210.1630.435537
270.0004080.00310.498754
28-0.077638-0.59640.276611
290.0794030.60990.272133
30-0.016552-0.12710.449632
310.0888020.68210.248921
32-0.083277-0.63970.262433
33-0.020051-0.1540.439061
34-0.004121-0.03170.487427
350.021090.1620.43593
36-0.055115-0.42330.336791



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