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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 computationMon, 08 Dec 2008 07:08:36 -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/08/t1228745454lgviru23937wtrt.htm/, Retrieved Thu, 16 May 2024 14:05:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30503, Retrieved Thu, 16 May 2024 14:05:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [VRM PAPER] [2008-12-08 12:10:16] [547636b63517c1c2916a747d66b36ebf]
- RMPD  [Spectral Analysis] [SPECTRUM zonder a...] [2008-12-08 12:48:15] [547636b63517c1c2916a747d66b36ebf]
-   P     [Spectral Analysis] [SPECTRUM met aang...] [2008-12-08 12:53:34] [547636b63517c1c2916a747d66b36ebf]
- RMP       [Standard Deviation-Mean Plot] [SDMP PAPER LAMBDA...] [2008-12-08 13:40:16] [547636b63517c1c2916a747d66b36ebf]
- RM          [(Partial) Autocorrelation Function] [PACF zonder aagep...] [2008-12-08 13:59:46] [547636b63517c1c2916a747d66b36ebf]
-                 [(Partial) Autocorrelation Function] [PACF met aangepas...] [2008-12-08 14:08:36] [e11d930c9e2984715c66c796cf63ef19] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30503&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30503&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30503&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.141783-0.98230.165438
2-0.001258-0.00870.496541
30.2476881.7160.046303
40.0479130.33190.370686
50.0862890.59780.276383
60.2548961.7660.04188
7-0.134942-0.93490.177258
8-0.017649-0.12230.451596
90.1465891.01560.157456
10-0.063022-0.43660.332168
11-0.123043-0.85250.199095
120.0463790.32130.37468
13-0.028433-0.1970.422333
14-0.048512-0.33610.369129
150.015980.11070.456153
16-0.237169-1.64320.053443
170.0420160.29110.386117
18-0.028755-0.19920.421465
19-0.018402-0.12750.449542
20-0.070134-0.48590.314623
210.0037170.02580.48978
22-0.315615-2.18660.016837
230.2377581.64720.053021
24-0.131424-0.91050.183547
25-0.131089-0.90820.184153
260.1324170.91740.181757
27-0.116663-0.80830.211462
28-0.128745-0.8920.188429
290.1199450.8310.205043
30-0.091004-0.63050.265681
31-0.047649-0.33010.371373
320.0769030.53280.298316
33-0.014938-0.10350.459
34-0.039612-0.27440.392463
35-0.004697-0.03250.487087
360.0010770.00750.497037

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.141783 & -0.9823 & 0.165438 \tabularnewline
2 & -0.001258 & -0.0087 & 0.496541 \tabularnewline
3 & 0.247688 & 1.716 & 0.046303 \tabularnewline
4 & 0.047913 & 0.3319 & 0.370686 \tabularnewline
5 & 0.086289 & 0.5978 & 0.276383 \tabularnewline
6 & 0.254896 & 1.766 & 0.04188 \tabularnewline
7 & -0.134942 & -0.9349 & 0.177258 \tabularnewline
8 & -0.017649 & -0.1223 & 0.451596 \tabularnewline
9 & 0.146589 & 1.0156 & 0.157456 \tabularnewline
10 & -0.063022 & -0.4366 & 0.332168 \tabularnewline
11 & -0.123043 & -0.8525 & 0.199095 \tabularnewline
12 & 0.046379 & 0.3213 & 0.37468 \tabularnewline
13 & -0.028433 & -0.197 & 0.422333 \tabularnewline
14 & -0.048512 & -0.3361 & 0.369129 \tabularnewline
15 & 0.01598 & 0.1107 & 0.456153 \tabularnewline
16 & -0.237169 & -1.6432 & 0.053443 \tabularnewline
17 & 0.042016 & 0.2911 & 0.386117 \tabularnewline
18 & -0.028755 & -0.1992 & 0.421465 \tabularnewline
19 & -0.018402 & -0.1275 & 0.449542 \tabularnewline
20 & -0.070134 & -0.4859 & 0.314623 \tabularnewline
21 & 0.003717 & 0.0258 & 0.48978 \tabularnewline
22 & -0.315615 & -2.1866 & 0.016837 \tabularnewline
23 & 0.237758 & 1.6472 & 0.053021 \tabularnewline
24 & -0.131424 & -0.9105 & 0.183547 \tabularnewline
25 & -0.131089 & -0.9082 & 0.184153 \tabularnewline
26 & 0.132417 & 0.9174 & 0.181757 \tabularnewline
27 & -0.116663 & -0.8083 & 0.211462 \tabularnewline
28 & -0.128745 & -0.892 & 0.188429 \tabularnewline
29 & 0.119945 & 0.831 & 0.205043 \tabularnewline
30 & -0.091004 & -0.6305 & 0.265681 \tabularnewline
31 & -0.047649 & -0.3301 & 0.371373 \tabularnewline
32 & 0.076903 & 0.5328 & 0.298316 \tabularnewline
33 & -0.014938 & -0.1035 & 0.459 \tabularnewline
34 & -0.039612 & -0.2744 & 0.392463 \tabularnewline
35 & -0.004697 & -0.0325 & 0.487087 \tabularnewline
36 & 0.001077 & 0.0075 & 0.497037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30503&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]-0.141783[/C][C]-0.9823[/C][C]0.165438[/C][/ROW]
[ROW][C]2[/C][C]-0.001258[/C][C]-0.0087[/C][C]0.496541[/C][/ROW]
[ROW][C]3[/C][C]0.247688[/C][C]1.716[/C][C]0.046303[/C][/ROW]
[ROW][C]4[/C][C]0.047913[/C][C]0.3319[/C][C]0.370686[/C][/ROW]
[ROW][C]5[/C][C]0.086289[/C][C]0.5978[/C][C]0.276383[/C][/ROW]
[ROW][C]6[/C][C]0.254896[/C][C]1.766[/C][C]0.04188[/C][/ROW]
[ROW][C]7[/C][C]-0.134942[/C][C]-0.9349[/C][C]0.177258[/C][/ROW]
[ROW][C]8[/C][C]-0.017649[/C][C]-0.1223[/C][C]0.451596[/C][/ROW]
[ROW][C]9[/C][C]0.146589[/C][C]1.0156[/C][C]0.157456[/C][/ROW]
[ROW][C]10[/C][C]-0.063022[/C][C]-0.4366[/C][C]0.332168[/C][/ROW]
[ROW][C]11[/C][C]-0.123043[/C][C]-0.8525[/C][C]0.199095[/C][/ROW]
[ROW][C]12[/C][C]0.046379[/C][C]0.3213[/C][C]0.37468[/C][/ROW]
[ROW][C]13[/C][C]-0.028433[/C][C]-0.197[/C][C]0.422333[/C][/ROW]
[ROW][C]14[/C][C]-0.048512[/C][C]-0.3361[/C][C]0.369129[/C][/ROW]
[ROW][C]15[/C][C]0.01598[/C][C]0.1107[/C][C]0.456153[/C][/ROW]
[ROW][C]16[/C][C]-0.237169[/C][C]-1.6432[/C][C]0.053443[/C][/ROW]
[ROW][C]17[/C][C]0.042016[/C][C]0.2911[/C][C]0.386117[/C][/ROW]
[ROW][C]18[/C][C]-0.028755[/C][C]-0.1992[/C][C]0.421465[/C][/ROW]
[ROW][C]19[/C][C]-0.018402[/C][C]-0.1275[/C][C]0.449542[/C][/ROW]
[ROW][C]20[/C][C]-0.070134[/C][C]-0.4859[/C][C]0.314623[/C][/ROW]
[ROW][C]21[/C][C]0.003717[/C][C]0.0258[/C][C]0.48978[/C][/ROW]
[ROW][C]22[/C][C]-0.315615[/C][C]-2.1866[/C][C]0.016837[/C][/ROW]
[ROW][C]23[/C][C]0.237758[/C][C]1.6472[/C][C]0.053021[/C][/ROW]
[ROW][C]24[/C][C]-0.131424[/C][C]-0.9105[/C][C]0.183547[/C][/ROW]
[ROW][C]25[/C][C]-0.131089[/C][C]-0.9082[/C][C]0.184153[/C][/ROW]
[ROW][C]26[/C][C]0.132417[/C][C]0.9174[/C][C]0.181757[/C][/ROW]
[ROW][C]27[/C][C]-0.116663[/C][C]-0.8083[/C][C]0.211462[/C][/ROW]
[ROW][C]28[/C][C]-0.128745[/C][C]-0.892[/C][C]0.188429[/C][/ROW]
[ROW][C]29[/C][C]0.119945[/C][C]0.831[/C][C]0.205043[/C][/ROW]
[ROW][C]30[/C][C]-0.091004[/C][C]-0.6305[/C][C]0.265681[/C][/ROW]
[ROW][C]31[/C][C]-0.047649[/C][C]-0.3301[/C][C]0.371373[/C][/ROW]
[ROW][C]32[/C][C]0.076903[/C][C]0.5328[/C][C]0.298316[/C][/ROW]
[ROW][C]33[/C][C]-0.014938[/C][C]-0.1035[/C][C]0.459[/C][/ROW]
[ROW][C]34[/C][C]-0.039612[/C][C]-0.2744[/C][C]0.392463[/C][/ROW]
[ROW][C]35[/C][C]-0.004697[/C][C]-0.0325[/C][C]0.487087[/C][/ROW]
[ROW][C]36[/C][C]0.001077[/C][C]0.0075[/C][C]0.497037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30503&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30503&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
1-0.141783-0.98230.165438
2-0.001258-0.00870.496541
30.2476881.7160.046303
40.0479130.33190.370686
50.0862890.59780.276383
60.2548961.7660.04188
7-0.134942-0.93490.177258
8-0.017649-0.12230.451596
90.1465891.01560.157456
10-0.063022-0.43660.332168
11-0.123043-0.85250.199095
120.0463790.32130.37468
13-0.028433-0.1970.422333
14-0.048512-0.33610.369129
150.015980.11070.456153
16-0.237169-1.64320.053443
170.0420160.29110.386117
18-0.028755-0.19920.421465
19-0.018402-0.12750.449542
20-0.070134-0.48590.314623
210.0037170.02580.48978
22-0.315615-2.18660.016837
230.2377581.64720.053021
24-0.131424-0.91050.183547
25-0.131089-0.90820.184153
260.1324170.91740.181757
27-0.116663-0.80830.211462
28-0.128745-0.8920.188429
290.1199450.8310.205043
30-0.091004-0.63050.265681
31-0.047649-0.33010.371373
320.0769030.53280.298316
33-0.014938-0.10350.459
34-0.039612-0.27440.392463
35-0.004697-0.03250.487087
360.0010770.00750.497037






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.141783-0.98230.165438
2-0.021799-0.1510.440294
30.2495471.72890.045126
40.1283140.8890.189223
50.12170.84320.20166
60.2512271.74060.044085
7-0.103635-0.7180.238118
8-0.139368-0.96560.16955
9-0.025174-0.17440.431138
10-0.06326-0.43830.331575
11-0.175984-1.21930.114352
12-0.064065-0.44390.329571
130.0621240.43040.334414
140.0431680.29910.383086
150.0306110.21210.41647
16-0.189339-1.31180.097917
170.0411960.28540.388278
18-0.06943-0.4810.316342
190.0506570.3510.363575
20-0.000309-0.00210.49915
210.0683250.47340.319049
22-0.32924-2.2810.013512
230.1405640.97390.167506
24-0.110143-0.76310.22457
25-0.01741-0.12060.452247
260.1090780.75570.226757
27-0.049777-0.34490.365852
28-0.030007-0.20790.418096
29-0.046868-0.32470.373405
30-0.000261-0.00180.499283
310.0063210.04380.482624
32-0.064613-0.44770.328209
330.0455390.31550.376875
340.0687010.4760.318126
35-0.074238-0.51430.304688
36-0.087385-0.60540.273876

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.141783 & -0.9823 & 0.165438 \tabularnewline
2 & -0.021799 & -0.151 & 0.440294 \tabularnewline
3 & 0.249547 & 1.7289 & 0.045126 \tabularnewline
4 & 0.128314 & 0.889 & 0.189223 \tabularnewline
5 & 0.1217 & 0.8432 & 0.20166 \tabularnewline
6 & 0.251227 & 1.7406 & 0.044085 \tabularnewline
7 & -0.103635 & -0.718 & 0.238118 \tabularnewline
8 & -0.139368 & -0.9656 & 0.16955 \tabularnewline
9 & -0.025174 & -0.1744 & 0.431138 \tabularnewline
10 & -0.06326 & -0.4383 & 0.331575 \tabularnewline
11 & -0.175984 & -1.2193 & 0.114352 \tabularnewline
12 & -0.064065 & -0.4439 & 0.329571 \tabularnewline
13 & 0.062124 & 0.4304 & 0.334414 \tabularnewline
14 & 0.043168 & 0.2991 & 0.383086 \tabularnewline
15 & 0.030611 & 0.2121 & 0.41647 \tabularnewline
16 & -0.189339 & -1.3118 & 0.097917 \tabularnewline
17 & 0.041196 & 0.2854 & 0.388278 \tabularnewline
18 & -0.06943 & -0.481 & 0.316342 \tabularnewline
19 & 0.050657 & 0.351 & 0.363575 \tabularnewline
20 & -0.000309 & -0.0021 & 0.49915 \tabularnewline
21 & 0.068325 & 0.4734 & 0.319049 \tabularnewline
22 & -0.32924 & -2.281 & 0.013512 \tabularnewline
23 & 0.140564 & 0.9739 & 0.167506 \tabularnewline
24 & -0.110143 & -0.7631 & 0.22457 \tabularnewline
25 & -0.01741 & -0.1206 & 0.452247 \tabularnewline
26 & 0.109078 & 0.7557 & 0.226757 \tabularnewline
27 & -0.049777 & -0.3449 & 0.365852 \tabularnewline
28 & -0.030007 & -0.2079 & 0.418096 \tabularnewline
29 & -0.046868 & -0.3247 & 0.373405 \tabularnewline
30 & -0.000261 & -0.0018 & 0.499283 \tabularnewline
31 & 0.006321 & 0.0438 & 0.482624 \tabularnewline
32 & -0.064613 & -0.4477 & 0.328209 \tabularnewline
33 & 0.045539 & 0.3155 & 0.376875 \tabularnewline
34 & 0.068701 & 0.476 & 0.318126 \tabularnewline
35 & -0.074238 & -0.5143 & 0.304688 \tabularnewline
36 & -0.087385 & -0.6054 & 0.273876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30503&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]-0.141783[/C][C]-0.9823[/C][C]0.165438[/C][/ROW]
[ROW][C]2[/C][C]-0.021799[/C][C]-0.151[/C][C]0.440294[/C][/ROW]
[ROW][C]3[/C][C]0.249547[/C][C]1.7289[/C][C]0.045126[/C][/ROW]
[ROW][C]4[/C][C]0.128314[/C][C]0.889[/C][C]0.189223[/C][/ROW]
[ROW][C]5[/C][C]0.1217[/C][C]0.8432[/C][C]0.20166[/C][/ROW]
[ROW][C]6[/C][C]0.251227[/C][C]1.7406[/C][C]0.044085[/C][/ROW]
[ROW][C]7[/C][C]-0.103635[/C][C]-0.718[/C][C]0.238118[/C][/ROW]
[ROW][C]8[/C][C]-0.139368[/C][C]-0.9656[/C][C]0.16955[/C][/ROW]
[ROW][C]9[/C][C]-0.025174[/C][C]-0.1744[/C][C]0.431138[/C][/ROW]
[ROW][C]10[/C][C]-0.06326[/C][C]-0.4383[/C][C]0.331575[/C][/ROW]
[ROW][C]11[/C][C]-0.175984[/C][C]-1.2193[/C][C]0.114352[/C][/ROW]
[ROW][C]12[/C][C]-0.064065[/C][C]-0.4439[/C][C]0.329571[/C][/ROW]
[ROW][C]13[/C][C]0.062124[/C][C]0.4304[/C][C]0.334414[/C][/ROW]
[ROW][C]14[/C][C]0.043168[/C][C]0.2991[/C][C]0.383086[/C][/ROW]
[ROW][C]15[/C][C]0.030611[/C][C]0.2121[/C][C]0.41647[/C][/ROW]
[ROW][C]16[/C][C]-0.189339[/C][C]-1.3118[/C][C]0.097917[/C][/ROW]
[ROW][C]17[/C][C]0.041196[/C][C]0.2854[/C][C]0.388278[/C][/ROW]
[ROW][C]18[/C][C]-0.06943[/C][C]-0.481[/C][C]0.316342[/C][/ROW]
[ROW][C]19[/C][C]0.050657[/C][C]0.351[/C][C]0.363575[/C][/ROW]
[ROW][C]20[/C][C]-0.000309[/C][C]-0.0021[/C][C]0.49915[/C][/ROW]
[ROW][C]21[/C][C]0.068325[/C][C]0.4734[/C][C]0.319049[/C][/ROW]
[ROW][C]22[/C][C]-0.32924[/C][C]-2.281[/C][C]0.013512[/C][/ROW]
[ROW][C]23[/C][C]0.140564[/C][C]0.9739[/C][C]0.167506[/C][/ROW]
[ROW][C]24[/C][C]-0.110143[/C][C]-0.7631[/C][C]0.22457[/C][/ROW]
[ROW][C]25[/C][C]-0.01741[/C][C]-0.1206[/C][C]0.452247[/C][/ROW]
[ROW][C]26[/C][C]0.109078[/C][C]0.7557[/C][C]0.226757[/C][/ROW]
[ROW][C]27[/C][C]-0.049777[/C][C]-0.3449[/C][C]0.365852[/C][/ROW]
[ROW][C]28[/C][C]-0.030007[/C][C]-0.2079[/C][C]0.418096[/C][/ROW]
[ROW][C]29[/C][C]-0.046868[/C][C]-0.3247[/C][C]0.373405[/C][/ROW]
[ROW][C]30[/C][C]-0.000261[/C][C]-0.0018[/C][C]0.499283[/C][/ROW]
[ROW][C]31[/C][C]0.006321[/C][C]0.0438[/C][C]0.482624[/C][/ROW]
[ROW][C]32[/C][C]-0.064613[/C][C]-0.4477[/C][C]0.328209[/C][/ROW]
[ROW][C]33[/C][C]0.045539[/C][C]0.3155[/C][C]0.376875[/C][/ROW]
[ROW][C]34[/C][C]0.068701[/C][C]0.476[/C][C]0.318126[/C][/ROW]
[ROW][C]35[/C][C]-0.074238[/C][C]-0.5143[/C][C]0.304688[/C][/ROW]
[ROW][C]36[/C][C]-0.087385[/C][C]-0.6054[/C][C]0.273876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30503&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30503&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
1-0.141783-0.98230.165438
2-0.021799-0.1510.440294
30.2495471.72890.045126
40.1283140.8890.189223
50.12170.84320.20166
60.2512271.74060.044085
7-0.103635-0.7180.238118
8-0.139368-0.96560.16955
9-0.025174-0.17440.431138
10-0.06326-0.43830.331575
11-0.175984-1.21930.114352
12-0.064065-0.44390.329571
130.0621240.43040.334414
140.0431680.29910.383086
150.0306110.21210.41647
16-0.189339-1.31180.097917
170.0411960.28540.388278
18-0.06943-0.4810.316342
190.0506570.3510.363575
20-0.000309-0.00210.49915
210.0683250.47340.319049
22-0.32924-2.2810.013512
230.1405640.97390.167506
24-0.110143-0.76310.22457
25-0.01741-0.12060.452247
260.1090780.75570.226757
27-0.049777-0.34490.365852
28-0.030007-0.20790.418096
29-0.046868-0.32470.373405
30-0.000261-0.00180.499283
310.0063210.04380.482624
32-0.064613-0.44770.328209
330.0455390.31550.376875
340.0687010.4760.318126
35-0.074238-0.51430.304688
36-0.087385-0.60540.273876


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 36 ; par2 = -0.6 ; par3 = 0 ; par4 = 1 ; 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')