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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 computationWed, 03 Dec 2008 14:21:49 -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/2008/Dec/03/t1228339371itius9pb6m3xigw.htm/, Retrieved Fri, 17 May 2024 13:05:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28890, Retrieved Fri, 17 May 2024 13:05:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact210

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [(Partial) Autocorrelation Function] [blok 17 Q6 ACF] [2008-12-02 20:15:24] [6173c35e31b784a490c8cd5476f785d4]
-    D    [(Partial) Autocorrelation Function] [blok 17 Q8 acf x(t)] [2008-12-03 21:00:52] [6173c35e31b784a490c8cd5476f785d4]
-   PD      [(Partial) Autocorrelation Function] [blok 17 Q8 acf x(...] [2008-12-03 21:05:57] [6173c35e31b784a490c8cd5476f785d4]
-   P         [(Partial) Autocorrelation Function] [blok 17 Q8 acf x(...] [2008-12-03 21:08:53] [6173c35e31b784a490c8cd5476f785d4]
-   PD            [(Partial) Autocorrelation Function] [blok 17 Q8 acf y(t)] [2008-12-03 21:21:49] [1237f4df7e9be807e4c0a07b90c45721] [Current]
-   PD              [(Partial) Autocorrelation Function] [blok 17 Q8 acf y(...] [2008-12-03 21:25:22] [6173c35e31b784a490c8cd5476f785d4]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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=28890&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=28890&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28890&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.4306173.65390.000244
20.0614290.52120.301899
30.2125041.80320.037774
40.1544011.31010.097158
50.2984412.53240.006756
60.4682023.97288.3e-05
70.289072.45280.008298
80.1592451.35120.090425
90.1573581.33520.093005
100.0032220.02730.489131
110.2982192.53050.006789
120.6638445.63290
130.2434342.06560.021233
14-0.040888-0.34690.364822
150.0613350.52040.302174
160.0075270.06390.474624
170.1272931.08010.141848
180.2696692.28820.012531
190.1063380.90230.18495
200.0159210.13510.446459
210.0028390.02410.490423
22-0.135885-1.1530.126358
230.1096920.93080.177542
240.3623313.07450.001489
250.024460.20750.418084
26-0.202493-1.71820.045028
27-0.131153-1.11290.134732
28-0.134104-1.13790.129465
29-0.068723-0.58310.280813
300.0372280.31590.376499
31-0.04415-0.37460.35452
32-0.102515-0.86990.193632
33-0.136008-1.15410.126145
34-0.256662-2.17790.016347
35-0.066988-0.56840.285763
360.1424341.20860.115387

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.430617 & 3.6539 & 0.000244 \tabularnewline
2 & 0.061429 & 0.5212 & 0.301899 \tabularnewline
3 & 0.212504 & 1.8032 & 0.037774 \tabularnewline
4 & 0.154401 & 1.3101 & 0.097158 \tabularnewline
5 & 0.298441 & 2.5324 & 0.006756 \tabularnewline
6 & 0.468202 & 3.9728 & 8.3e-05 \tabularnewline
7 & 0.28907 & 2.4528 & 0.008298 \tabularnewline
8 & 0.159245 & 1.3512 & 0.090425 \tabularnewline
9 & 0.157358 & 1.3352 & 0.093005 \tabularnewline
10 & 0.003222 & 0.0273 & 0.489131 \tabularnewline
11 & 0.298219 & 2.5305 & 0.006789 \tabularnewline
12 & 0.663844 & 5.6329 & 0 \tabularnewline
13 & 0.243434 & 2.0656 & 0.021233 \tabularnewline
14 & -0.040888 & -0.3469 & 0.364822 \tabularnewline
15 & 0.061335 & 0.5204 & 0.302174 \tabularnewline
16 & 0.007527 & 0.0639 & 0.474624 \tabularnewline
17 & 0.127293 & 1.0801 & 0.141848 \tabularnewline
18 & 0.269669 & 2.2882 & 0.012531 \tabularnewline
19 & 0.106338 & 0.9023 & 0.18495 \tabularnewline
20 & 0.015921 & 0.1351 & 0.446459 \tabularnewline
21 & 0.002839 & 0.0241 & 0.490423 \tabularnewline
22 & -0.135885 & -1.153 & 0.126358 \tabularnewline
23 & 0.109692 & 0.9308 & 0.177542 \tabularnewline
24 & 0.362331 & 3.0745 & 0.001489 \tabularnewline
25 & 0.02446 & 0.2075 & 0.418084 \tabularnewline
26 & -0.202493 & -1.7182 & 0.045028 \tabularnewline
27 & -0.131153 & -1.1129 & 0.134732 \tabularnewline
28 & -0.134104 & -1.1379 & 0.129465 \tabularnewline
29 & -0.068723 & -0.5831 & 0.280813 \tabularnewline
30 & 0.037228 & 0.3159 & 0.376499 \tabularnewline
31 & -0.04415 & -0.3746 & 0.35452 \tabularnewline
32 & -0.102515 & -0.8699 & 0.193632 \tabularnewline
33 & -0.136008 & -1.1541 & 0.126145 \tabularnewline
34 & -0.256662 & -2.1779 & 0.016347 \tabularnewline
35 & -0.066988 & -0.5684 & 0.285763 \tabularnewline
36 & 0.142434 & 1.2086 & 0.115387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28890&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]0.430617[/C][C]3.6539[/C][C]0.000244[/C][/ROW]
[ROW][C]2[/C][C]0.061429[/C][C]0.5212[/C][C]0.301899[/C][/ROW]
[ROW][C]3[/C][C]0.212504[/C][C]1.8032[/C][C]0.037774[/C][/ROW]
[ROW][C]4[/C][C]0.154401[/C][C]1.3101[/C][C]0.097158[/C][/ROW]
[ROW][C]5[/C][C]0.298441[/C][C]2.5324[/C][C]0.006756[/C][/ROW]
[ROW][C]6[/C][C]0.468202[/C][C]3.9728[/C][C]8.3e-05[/C][/ROW]
[ROW][C]7[/C][C]0.28907[/C][C]2.4528[/C][C]0.008298[/C][/ROW]
[ROW][C]8[/C][C]0.159245[/C][C]1.3512[/C][C]0.090425[/C][/ROW]
[ROW][C]9[/C][C]0.157358[/C][C]1.3352[/C][C]0.093005[/C][/ROW]
[ROW][C]10[/C][C]0.003222[/C][C]0.0273[/C][C]0.489131[/C][/ROW]
[ROW][C]11[/C][C]0.298219[/C][C]2.5305[/C][C]0.006789[/C][/ROW]
[ROW][C]12[/C][C]0.663844[/C][C]5.6329[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.243434[/C][C]2.0656[/C][C]0.021233[/C][/ROW]
[ROW][C]14[/C][C]-0.040888[/C][C]-0.3469[/C][C]0.364822[/C][/ROW]
[ROW][C]15[/C][C]0.061335[/C][C]0.5204[/C][C]0.302174[/C][/ROW]
[ROW][C]16[/C][C]0.007527[/C][C]0.0639[/C][C]0.474624[/C][/ROW]
[ROW][C]17[/C][C]0.127293[/C][C]1.0801[/C][C]0.141848[/C][/ROW]
[ROW][C]18[/C][C]0.269669[/C][C]2.2882[/C][C]0.012531[/C][/ROW]
[ROW][C]19[/C][C]0.106338[/C][C]0.9023[/C][C]0.18495[/C][/ROW]
[ROW][C]20[/C][C]0.015921[/C][C]0.1351[/C][C]0.446459[/C][/ROW]
[ROW][C]21[/C][C]0.002839[/C][C]0.0241[/C][C]0.490423[/C][/ROW]
[ROW][C]22[/C][C]-0.135885[/C][C]-1.153[/C][C]0.126358[/C][/ROW]
[ROW][C]23[/C][C]0.109692[/C][C]0.9308[/C][C]0.177542[/C][/ROW]
[ROW][C]24[/C][C]0.362331[/C][C]3.0745[/C][C]0.001489[/C][/ROW]
[ROW][C]25[/C][C]0.02446[/C][C]0.2075[/C][C]0.418084[/C][/ROW]
[ROW][C]26[/C][C]-0.202493[/C][C]-1.7182[/C][C]0.045028[/C][/ROW]
[ROW][C]27[/C][C]-0.131153[/C][C]-1.1129[/C][C]0.134732[/C][/ROW]
[ROW][C]28[/C][C]-0.134104[/C][C]-1.1379[/C][C]0.129465[/C][/ROW]
[ROW][C]29[/C][C]-0.068723[/C][C]-0.5831[/C][C]0.280813[/C][/ROW]
[ROW][C]30[/C][C]0.037228[/C][C]0.3159[/C][C]0.376499[/C][/ROW]
[ROW][C]31[/C][C]-0.04415[/C][C]-0.3746[/C][C]0.35452[/C][/ROW]
[ROW][C]32[/C][C]-0.102515[/C][C]-0.8699[/C][C]0.193632[/C][/ROW]
[ROW][C]33[/C][C]-0.136008[/C][C]-1.1541[/C][C]0.126145[/C][/ROW]
[ROW][C]34[/C][C]-0.256662[/C][C]-2.1779[/C][C]0.016347[/C][/ROW]
[ROW][C]35[/C][C]-0.066988[/C][C]-0.5684[/C][C]0.285763[/C][/ROW]
[ROW][C]36[/C][C]0.142434[/C][C]1.2086[/C][C]0.115387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28890&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28890&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
10.4306173.65390.000244
20.0614290.52120.301899
30.2125041.80320.037774
40.1544011.31010.097158
50.2984412.53240.006756
60.4682023.97288.3e-05
70.289072.45280.008298
80.1592451.35120.090425
90.1573581.33520.093005
100.0032220.02730.489131
110.2982192.53050.006789
120.6638445.63290
130.2434342.06560.021233
14-0.040888-0.34690.364822
150.0613350.52040.302174
160.0075270.06390.474624
170.1272931.08010.141848
180.2696692.28820.012531
190.1063380.90230.18495
200.0159210.13510.446459
210.0028390.02410.490423
22-0.135885-1.1530.126358
230.1096920.93080.177542
240.3623313.07450.001489
250.024460.20750.418084
26-0.202493-1.71820.045028
27-0.131153-1.11290.134732
28-0.134104-1.13790.129465
29-0.068723-0.58310.280813
300.0372280.31590.376499
31-0.04415-0.37460.35452
32-0.102515-0.86990.193632
33-0.136008-1.15410.126145
34-0.256662-2.17790.016347
35-0.066988-0.56840.285763
360.1424341.20860.115387






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4306173.65390.000244
2-0.152231-1.29170.100293
30.3111482.64020.005077
4-0.106106-0.90030.18547
50.4285683.63650.000258
60.1503631.27590.10305
70.1180611.00180.159901
80.0036530.0310.487679
90.0064770.0550.478161
10-0.252789-2.1450.017664
110.4685593.97598.2e-05
120.26052.21040.015127
13-0.224211-1.90250.030553
14-0.169316-1.43670.077569
15-0.074167-0.62930.265564
16-0.106843-0.90660.183822
17-0.066298-0.56260.287741
18-0.114996-0.97580.166222
19-0.026091-0.22140.412707
20-0.037301-0.31650.376265
210.0375860.31890.375352
22-0.064871-0.55050.291857
230.0663330.56290.28764
24-0.058423-0.49570.310796
25-0.009881-0.08380.466707
26-0.147612-1.25250.107215
27-0.003905-0.03310.486831
28-0.013223-0.11220.455487
29-0.123196-1.04540.149678
30-0.041858-0.35520.361746
310.0686310.58230.281075
320.0218330.18530.426775
330.0129660.110.456349
34-0.058522-0.49660.310501
35-0.002338-0.01980.492114
360.0254140.21560.414936

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.430617 & 3.6539 & 0.000244 \tabularnewline
2 & -0.152231 & -1.2917 & 0.100293 \tabularnewline
3 & 0.311148 & 2.6402 & 0.005077 \tabularnewline
4 & -0.106106 & -0.9003 & 0.18547 \tabularnewline
5 & 0.428568 & 3.6365 & 0.000258 \tabularnewline
6 & 0.150363 & 1.2759 & 0.10305 \tabularnewline
7 & 0.118061 & 1.0018 & 0.159901 \tabularnewline
8 & 0.003653 & 0.031 & 0.487679 \tabularnewline
9 & 0.006477 & 0.055 & 0.478161 \tabularnewline
10 & -0.252789 & -2.145 & 0.017664 \tabularnewline
11 & 0.468559 & 3.9759 & 8.2e-05 \tabularnewline
12 & 0.2605 & 2.2104 & 0.015127 \tabularnewline
13 & -0.224211 & -1.9025 & 0.030553 \tabularnewline
14 & -0.169316 & -1.4367 & 0.077569 \tabularnewline
15 & -0.074167 & -0.6293 & 0.265564 \tabularnewline
16 & -0.106843 & -0.9066 & 0.183822 \tabularnewline
17 & -0.066298 & -0.5626 & 0.287741 \tabularnewline
18 & -0.114996 & -0.9758 & 0.166222 \tabularnewline
19 & -0.026091 & -0.2214 & 0.412707 \tabularnewline
20 & -0.037301 & -0.3165 & 0.376265 \tabularnewline
21 & 0.037586 & 0.3189 & 0.375352 \tabularnewline
22 & -0.064871 & -0.5505 & 0.291857 \tabularnewline
23 & 0.066333 & 0.5629 & 0.28764 \tabularnewline
24 & -0.058423 & -0.4957 & 0.310796 \tabularnewline
25 & -0.009881 & -0.0838 & 0.466707 \tabularnewline
26 & -0.147612 & -1.2525 & 0.107215 \tabularnewline
27 & -0.003905 & -0.0331 & 0.486831 \tabularnewline
28 & -0.013223 & -0.1122 & 0.455487 \tabularnewline
29 & -0.123196 & -1.0454 & 0.149678 \tabularnewline
30 & -0.041858 & -0.3552 & 0.361746 \tabularnewline
31 & 0.068631 & 0.5823 & 0.281075 \tabularnewline
32 & 0.021833 & 0.1853 & 0.426775 \tabularnewline
33 & 0.012966 & 0.11 & 0.456349 \tabularnewline
34 & -0.058522 & -0.4966 & 0.310501 \tabularnewline
35 & -0.002338 & -0.0198 & 0.492114 \tabularnewline
36 & 0.025414 & 0.2156 & 0.414936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28890&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]0.430617[/C][C]3.6539[/C][C]0.000244[/C][/ROW]
[ROW][C]2[/C][C]-0.152231[/C][C]-1.2917[/C][C]0.100293[/C][/ROW]
[ROW][C]3[/C][C]0.311148[/C][C]2.6402[/C][C]0.005077[/C][/ROW]
[ROW][C]4[/C][C]-0.106106[/C][C]-0.9003[/C][C]0.18547[/C][/ROW]
[ROW][C]5[/C][C]0.428568[/C][C]3.6365[/C][C]0.000258[/C][/ROW]
[ROW][C]6[/C][C]0.150363[/C][C]1.2759[/C][C]0.10305[/C][/ROW]
[ROW][C]7[/C][C]0.118061[/C][C]1.0018[/C][C]0.159901[/C][/ROW]
[ROW][C]8[/C][C]0.003653[/C][C]0.031[/C][C]0.487679[/C][/ROW]
[ROW][C]9[/C][C]0.006477[/C][C]0.055[/C][C]0.478161[/C][/ROW]
[ROW][C]10[/C][C]-0.252789[/C][C]-2.145[/C][C]0.017664[/C][/ROW]
[ROW][C]11[/C][C]0.468559[/C][C]3.9759[/C][C]8.2e-05[/C][/ROW]
[ROW][C]12[/C][C]0.2605[/C][C]2.2104[/C][C]0.015127[/C][/ROW]
[ROW][C]13[/C][C]-0.224211[/C][C]-1.9025[/C][C]0.030553[/C][/ROW]
[ROW][C]14[/C][C]-0.169316[/C][C]-1.4367[/C][C]0.077569[/C][/ROW]
[ROW][C]15[/C][C]-0.074167[/C][C]-0.6293[/C][C]0.265564[/C][/ROW]
[ROW][C]16[/C][C]-0.106843[/C][C]-0.9066[/C][C]0.183822[/C][/ROW]
[ROW][C]17[/C][C]-0.066298[/C][C]-0.5626[/C][C]0.287741[/C][/ROW]
[ROW][C]18[/C][C]-0.114996[/C][C]-0.9758[/C][C]0.166222[/C][/ROW]
[ROW][C]19[/C][C]-0.026091[/C][C]-0.2214[/C][C]0.412707[/C][/ROW]
[ROW][C]20[/C][C]-0.037301[/C][C]-0.3165[/C][C]0.376265[/C][/ROW]
[ROW][C]21[/C][C]0.037586[/C][C]0.3189[/C][C]0.375352[/C][/ROW]
[ROW][C]22[/C][C]-0.064871[/C][C]-0.5505[/C][C]0.291857[/C][/ROW]
[ROW][C]23[/C][C]0.066333[/C][C]0.5629[/C][C]0.28764[/C][/ROW]
[ROW][C]24[/C][C]-0.058423[/C][C]-0.4957[/C][C]0.310796[/C][/ROW]
[ROW][C]25[/C][C]-0.009881[/C][C]-0.0838[/C][C]0.466707[/C][/ROW]
[ROW][C]26[/C][C]-0.147612[/C][C]-1.2525[/C][C]0.107215[/C][/ROW]
[ROW][C]27[/C][C]-0.003905[/C][C]-0.0331[/C][C]0.486831[/C][/ROW]
[ROW][C]28[/C][C]-0.013223[/C][C]-0.1122[/C][C]0.455487[/C][/ROW]
[ROW][C]29[/C][C]-0.123196[/C][C]-1.0454[/C][C]0.149678[/C][/ROW]
[ROW][C]30[/C][C]-0.041858[/C][C]-0.3552[/C][C]0.361746[/C][/ROW]
[ROW][C]31[/C][C]0.068631[/C][C]0.5823[/C][C]0.281075[/C][/ROW]
[ROW][C]32[/C][C]0.021833[/C][C]0.1853[/C][C]0.426775[/C][/ROW]
[ROW][C]33[/C][C]0.012966[/C][C]0.11[/C][C]0.456349[/C][/ROW]
[ROW][C]34[/C][C]-0.058522[/C][C]-0.4966[/C][C]0.310501[/C][/ROW]
[ROW][C]35[/C][C]-0.002338[/C][C]-0.0198[/C][C]0.492114[/C][/ROW]
[ROW][C]36[/C][C]0.025414[/C][C]0.2156[/C][C]0.414936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28890&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28890&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
10.4306173.65390.000244
2-0.152231-1.29170.100293
30.3111482.64020.005077
4-0.106106-0.90030.18547
50.4285683.63650.000258
60.1503631.27590.10305
70.1180611.00180.159901
80.0036530.0310.487679
90.0064770.0550.478161
10-0.252789-2.1450.017664
110.4685593.97598.2e-05
120.26052.21040.015127
13-0.224211-1.90250.030553
14-0.169316-1.43670.077569
15-0.074167-0.62930.265564
16-0.106843-0.90660.183822
17-0.066298-0.56260.287741
18-0.114996-0.97580.166222
19-0.026091-0.22140.412707
20-0.037301-0.31650.376265
210.0375860.31890.375352
22-0.064871-0.55050.291857
230.0663330.56290.28764
24-0.058423-0.49570.310796
25-0.009881-0.08380.466707
26-0.147612-1.25250.107215
27-0.003905-0.03310.486831
28-0.013223-0.11220.455487
29-0.123196-1.04540.149678
30-0.041858-0.35520.361746
310.0686310.58230.281075
320.0218330.18530.426775
330.0129660.110.456349
34-0.058522-0.49660.310501
35-0.002338-0.01980.492114
360.0254140.21560.414936


Figures (Output of Computation)

Input Parameters & R Code

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