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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 computationSun, 21 Dec 2008 16:35:20 -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/22/t1229902710xe8hispq6kwf6g5.htm/, Retrieved Mon, 13 May 2024 19:07:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35908, Retrieved Mon, 13 May 2024 19:07:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact227

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [(P)ACF werkloosheid] [2008-12-21 22:54:14] [7a4703cb85a198d9845d72899eff0288]
-   PD    [(Partial) Autocorrelation Function] [(P)ACF Duurzame c...] [2008-12-21 23:35:20] [9f72e095d5529918bf5b0810c01bf6ce] [Current]
-   PD      [(Partial) Autocorrelation Function] [(P)ACF duurzame c...] [2008-12-22 15:15:00] [7a4703cb85a198d9845d72899eff0288]
- RMPD      [Spectral Analysis] [Spectrale analyse...] [2008-12-22 15:20:42] [7a4703cb85a198d9845d72899eff0288]
-             [Spectral Analysis] [Spectrale analyse...] [2008-12-22 16:35:15] [7a4703cb85a198d9845d72899eff0288]
-               [Spectral Analysis] [Spectrale analyse...] [2008-12-22 16:41:38] [7a4703cb85a198d9845d72899eff0288]
- RM D            [ARIMA Backward Selection] [ARIMA backward se...] [2008-12-22 18:39:40] [7a4703cb85a198d9845d72899eff0288]
-   P               [ARIMA Backward Selection] [ARIMA backward se...] [2008-12-23 11:19:18] [7a4703cb85a198d9845d72899eff0288]
-   P               [ARIMA Backward Selection] [ARIMA Backward se...] [2008-12-23 11:24:23] [7a4703cb85a198d9845d72899eff0288]
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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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35908&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]2 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=35908&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35908&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3415332.04920.023892
2-0.200889-1.20530.117969
3-0.083638-0.50180.309423
4-0.037612-0.22570.411365
50.0909910.54590.294234
60.2319361.39160.086289
70.1132240.67930.250633
8-0.091515-0.54910.293167
9-0.123475-0.74080.231795
10-0.125055-0.75030.228967
110.140730.84440.202017
120.5083643.05020.002137
130.1206130.72370.236971
14-0.297574-1.78540.041308
15-0.129196-0.77520.221648
16-0.08973-0.53840.296814
17-0.097215-0.58330.281668
180.0282930.16980.433076
190.0018270.0110.495658
20-0.099791-0.59870.276545
21-0.142497-0.8550.199108
22-0.090619-0.54370.294995
230.0294060.17640.430471
240.1455650.87340.19412
25-0.016315-0.09790.461282
26-0.232866-1.39720.085454
27-0.107365-0.64420.261768
28-0.049751-0.29850.383517
29-0.121145-0.72690.236003
30-0.071631-0.42980.334957
31-0.004936-0.02960.48827
320.0053770.03230.487221
33-0.020993-0.1260.450233
34-0.01623-0.09740.461483
35-0.007821-0.04690.481416
36NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.341533 & 2.0492 & 0.023892 \tabularnewline
2 & -0.200889 & -1.2053 & 0.117969 \tabularnewline
3 & -0.083638 & -0.5018 & 0.309423 \tabularnewline
4 & -0.037612 & -0.2257 & 0.411365 \tabularnewline
5 & 0.090991 & 0.5459 & 0.294234 \tabularnewline
6 & 0.231936 & 1.3916 & 0.086289 \tabularnewline
7 & 0.113224 & 0.6793 & 0.250633 \tabularnewline
8 & -0.091515 & -0.5491 & 0.293167 \tabularnewline
9 & -0.123475 & -0.7408 & 0.231795 \tabularnewline
10 & -0.125055 & -0.7503 & 0.228967 \tabularnewline
11 & 0.14073 & 0.8444 & 0.202017 \tabularnewline
12 & 0.508364 & 3.0502 & 0.002137 \tabularnewline
13 & 0.120613 & 0.7237 & 0.236971 \tabularnewline
14 & -0.297574 & -1.7854 & 0.041308 \tabularnewline
15 & -0.129196 & -0.7752 & 0.221648 \tabularnewline
16 & -0.08973 & -0.5384 & 0.296814 \tabularnewline
17 & -0.097215 & -0.5833 & 0.281668 \tabularnewline
18 & 0.028293 & 0.1698 & 0.433076 \tabularnewline
19 & 0.001827 & 0.011 & 0.495658 \tabularnewline
20 & -0.099791 & -0.5987 & 0.276545 \tabularnewline
21 & -0.142497 & -0.855 & 0.199108 \tabularnewline
22 & -0.090619 & -0.5437 & 0.294995 \tabularnewline
23 & 0.029406 & 0.1764 & 0.430471 \tabularnewline
24 & 0.145565 & 0.8734 & 0.19412 \tabularnewline
25 & -0.016315 & -0.0979 & 0.461282 \tabularnewline
26 & -0.232866 & -1.3972 & 0.085454 \tabularnewline
27 & -0.107365 & -0.6442 & 0.261768 \tabularnewline
28 & -0.049751 & -0.2985 & 0.383517 \tabularnewline
29 & -0.121145 & -0.7269 & 0.236003 \tabularnewline
30 & -0.071631 & -0.4298 & 0.334957 \tabularnewline
31 & -0.004936 & -0.0296 & 0.48827 \tabularnewline
32 & 0.005377 & 0.0323 & 0.487221 \tabularnewline
33 & -0.020993 & -0.126 & 0.450233 \tabularnewline
34 & -0.01623 & -0.0974 & 0.461483 \tabularnewline
35 & -0.007821 & -0.0469 & 0.481416 \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35908&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]0.341533[/C][C]2.0492[/C][C]0.023892[/C][/ROW]
[ROW][C]2[/C][C]-0.200889[/C][C]-1.2053[/C][C]0.117969[/C][/ROW]
[ROW][C]3[/C][C]-0.083638[/C][C]-0.5018[/C][C]0.309423[/C][/ROW]
[ROW][C]4[/C][C]-0.037612[/C][C]-0.2257[/C][C]0.411365[/C][/ROW]
[ROW][C]5[/C][C]0.090991[/C][C]0.5459[/C][C]0.294234[/C][/ROW]
[ROW][C]6[/C][C]0.231936[/C][C]1.3916[/C][C]0.086289[/C][/ROW]
[ROW][C]7[/C][C]0.113224[/C][C]0.6793[/C][C]0.250633[/C][/ROW]
[ROW][C]8[/C][C]-0.091515[/C][C]-0.5491[/C][C]0.293167[/C][/ROW]
[ROW][C]9[/C][C]-0.123475[/C][C]-0.7408[/C][C]0.231795[/C][/ROW]
[ROW][C]10[/C][C]-0.125055[/C][C]-0.7503[/C][C]0.228967[/C][/ROW]
[ROW][C]11[/C][C]0.14073[/C][C]0.8444[/C][C]0.202017[/C][/ROW]
[ROW][C]12[/C][C]0.508364[/C][C]3.0502[/C][C]0.002137[/C][/ROW]
[ROW][C]13[/C][C]0.120613[/C][C]0.7237[/C][C]0.236971[/C][/ROW]
[ROW][C]14[/C][C]-0.297574[/C][C]-1.7854[/C][C]0.041308[/C][/ROW]
[ROW][C]15[/C][C]-0.129196[/C][C]-0.7752[/C][C]0.221648[/C][/ROW]
[ROW][C]16[/C][C]-0.08973[/C][C]-0.5384[/C][C]0.296814[/C][/ROW]
[ROW][C]17[/C][C]-0.097215[/C][C]-0.5833[/C][C]0.281668[/C][/ROW]
[ROW][C]18[/C][C]0.028293[/C][C]0.1698[/C][C]0.433076[/C][/ROW]
[ROW][C]19[/C][C]0.001827[/C][C]0.011[/C][C]0.495658[/C][/ROW]
[ROW][C]20[/C][C]-0.099791[/C][C]-0.5987[/C][C]0.276545[/C][/ROW]
[ROW][C]21[/C][C]-0.142497[/C][C]-0.855[/C][C]0.199108[/C][/ROW]
[ROW][C]22[/C][C]-0.090619[/C][C]-0.5437[/C][C]0.294995[/C][/ROW]
[ROW][C]23[/C][C]0.029406[/C][C]0.1764[/C][C]0.430471[/C][/ROW]
[ROW][C]24[/C][C]0.145565[/C][C]0.8734[/C][C]0.19412[/C][/ROW]
[ROW][C]25[/C][C]-0.016315[/C][C]-0.0979[/C][C]0.461282[/C][/ROW]
[ROW][C]26[/C][C]-0.232866[/C][C]-1.3972[/C][C]0.085454[/C][/ROW]
[ROW][C]27[/C][C]-0.107365[/C][C]-0.6442[/C][C]0.261768[/C][/ROW]
[ROW][C]28[/C][C]-0.049751[/C][C]-0.2985[/C][C]0.383517[/C][/ROW]
[ROW][C]29[/C][C]-0.121145[/C][C]-0.7269[/C][C]0.236003[/C][/ROW]
[ROW][C]30[/C][C]-0.071631[/C][C]-0.4298[/C][C]0.334957[/C][/ROW]
[ROW][C]31[/C][C]-0.004936[/C][C]-0.0296[/C][C]0.48827[/C][/ROW]
[ROW][C]32[/C][C]0.005377[/C][C]0.0323[/C][C]0.487221[/C][/ROW]
[ROW][C]33[/C][C]-0.020993[/C][C]-0.126[/C][C]0.450233[/C][/ROW]
[ROW][C]34[/C][C]-0.01623[/C][C]-0.0974[/C][C]0.461483[/C][/ROW]
[ROW][C]35[/C][C]-0.007821[/C][C]-0.0469[/C][C]0.481416[/C][/ROW]
[ROW][C]36[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35908&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35908&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
10.3415332.04920.023892
2-0.200889-1.20530.117969
3-0.083638-0.50180.309423
4-0.037612-0.22570.411365
50.0909910.54590.294234
60.2319361.39160.086289
70.1132240.67930.250633
8-0.091515-0.54910.293167
9-0.123475-0.74080.231795
10-0.125055-0.75030.228967
110.140730.84440.202017
120.5083643.05020.002137
130.1206130.72370.236971
14-0.297574-1.78540.041308
15-0.129196-0.77520.221648
16-0.08973-0.53840.296814
17-0.097215-0.58330.281668
180.0282930.16980.433076
190.0018270.0110.495658
20-0.099791-0.59870.276545
21-0.142497-0.8550.199108
22-0.090619-0.54370.294995
230.0294060.17640.430471
240.1455650.87340.19412
25-0.016315-0.09790.461282
26-0.232866-1.39720.085454
27-0.107365-0.64420.261768
28-0.049751-0.29850.383517
29-0.121145-0.72690.236003
30-0.071631-0.42980.334957
31-0.004936-0.02960.48827
320.0053770.03230.487221
33-0.020993-0.1260.450233
34-0.01623-0.09740.461483
35-0.007821-0.04690.481416
36NANANA






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3415332.04920.023892
2-0.359464-2.15680.018888
30.1721291.03280.154299
4-0.188445-1.13070.132835
50.2600111.56010.063746
60.0485120.29110.386333
70.0694210.41650.33975
8-0.105564-0.63340.265243
90.0001097e-040.499741
10-0.167668-1.0060.160563
110.3414782.04890.023909
120.2838051.70280.04861
13-0.253672-1.5220.068368
14-0.025463-0.15280.439713
15-0.008888-0.05330.478883
16-0.181543-1.08930.141639
17-0.091888-0.55130.292408
18-0.146383-0.87830.192803
19-0.002223-0.01330.494717
200.0467270.28040.390401
21-0.085707-0.51420.305113
220.0920850.55250.292006
23-0.091909-0.55150.292366
24-0.088852-0.53310.298616
250.0080030.0480.480985
26-0.039784-0.23870.406345
27-0.035859-0.21520.415431
280.0159280.09560.462197
29-0.058971-0.35380.362766
30-0.061325-0.3680.357533
310.0512860.30770.380036
32-0.005987-0.03590.485772
330.0487330.29240.385831
34-0.039037-0.23420.40807
35-0.084648-0.50790.307314
36NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.341533 & 2.0492 & 0.023892 \tabularnewline
2 & -0.359464 & -2.1568 & 0.018888 \tabularnewline
3 & 0.172129 & 1.0328 & 0.154299 \tabularnewline
4 & -0.188445 & -1.1307 & 0.132835 \tabularnewline
5 & 0.260011 & 1.5601 & 0.063746 \tabularnewline
6 & 0.048512 & 0.2911 & 0.386333 \tabularnewline
7 & 0.069421 & 0.4165 & 0.33975 \tabularnewline
8 & -0.105564 & -0.6334 & 0.265243 \tabularnewline
9 & 0.000109 & 7e-04 & 0.499741 \tabularnewline
10 & -0.167668 & -1.006 & 0.160563 \tabularnewline
11 & 0.341478 & 2.0489 & 0.023909 \tabularnewline
12 & 0.283805 & 1.7028 & 0.04861 \tabularnewline
13 & -0.253672 & -1.522 & 0.068368 \tabularnewline
14 & -0.025463 & -0.1528 & 0.439713 \tabularnewline
15 & -0.008888 & -0.0533 & 0.478883 \tabularnewline
16 & -0.181543 & -1.0893 & 0.141639 \tabularnewline
17 & -0.091888 & -0.5513 & 0.292408 \tabularnewline
18 & -0.146383 & -0.8783 & 0.192803 \tabularnewline
19 & -0.002223 & -0.0133 & 0.494717 \tabularnewline
20 & 0.046727 & 0.2804 & 0.390401 \tabularnewline
21 & -0.085707 & -0.5142 & 0.305113 \tabularnewline
22 & 0.092085 & 0.5525 & 0.292006 \tabularnewline
23 & -0.091909 & -0.5515 & 0.292366 \tabularnewline
24 & -0.088852 & -0.5331 & 0.298616 \tabularnewline
25 & 0.008003 & 0.048 & 0.480985 \tabularnewline
26 & -0.039784 & -0.2387 & 0.406345 \tabularnewline
27 & -0.035859 & -0.2152 & 0.415431 \tabularnewline
28 & 0.015928 & 0.0956 & 0.462197 \tabularnewline
29 & -0.058971 & -0.3538 & 0.362766 \tabularnewline
30 & -0.061325 & -0.368 & 0.357533 \tabularnewline
31 & 0.051286 & 0.3077 & 0.380036 \tabularnewline
32 & -0.005987 & -0.0359 & 0.485772 \tabularnewline
33 & 0.048733 & 0.2924 & 0.385831 \tabularnewline
34 & -0.039037 & -0.2342 & 0.40807 \tabularnewline
35 & -0.084648 & -0.5079 & 0.307314 \tabularnewline
36 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35908&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]0.341533[/C][C]2.0492[/C][C]0.023892[/C][/ROW]
[ROW][C]2[/C][C]-0.359464[/C][C]-2.1568[/C][C]0.018888[/C][/ROW]
[ROW][C]3[/C][C]0.172129[/C][C]1.0328[/C][C]0.154299[/C][/ROW]
[ROW][C]4[/C][C]-0.188445[/C][C]-1.1307[/C][C]0.132835[/C][/ROW]
[ROW][C]5[/C][C]0.260011[/C][C]1.5601[/C][C]0.063746[/C][/ROW]
[ROW][C]6[/C][C]0.048512[/C][C]0.2911[/C][C]0.386333[/C][/ROW]
[ROW][C]7[/C][C]0.069421[/C][C]0.4165[/C][C]0.33975[/C][/ROW]
[ROW][C]8[/C][C]-0.105564[/C][C]-0.6334[/C][C]0.265243[/C][/ROW]
[ROW][C]9[/C][C]0.000109[/C][C]7e-04[/C][C]0.499741[/C][/ROW]
[ROW][C]10[/C][C]-0.167668[/C][C]-1.006[/C][C]0.160563[/C][/ROW]
[ROW][C]11[/C][C]0.341478[/C][C]2.0489[/C][C]0.023909[/C][/ROW]
[ROW][C]12[/C][C]0.283805[/C][C]1.7028[/C][C]0.04861[/C][/ROW]
[ROW][C]13[/C][C]-0.253672[/C][C]-1.522[/C][C]0.068368[/C][/ROW]
[ROW][C]14[/C][C]-0.025463[/C][C]-0.1528[/C][C]0.439713[/C][/ROW]
[ROW][C]15[/C][C]-0.008888[/C][C]-0.0533[/C][C]0.478883[/C][/ROW]
[ROW][C]16[/C][C]-0.181543[/C][C]-1.0893[/C][C]0.141639[/C][/ROW]
[ROW][C]17[/C][C]-0.091888[/C][C]-0.5513[/C][C]0.292408[/C][/ROW]
[ROW][C]18[/C][C]-0.146383[/C][C]-0.8783[/C][C]0.192803[/C][/ROW]
[ROW][C]19[/C][C]-0.002223[/C][C]-0.0133[/C][C]0.494717[/C][/ROW]
[ROW][C]20[/C][C]0.046727[/C][C]0.2804[/C][C]0.390401[/C][/ROW]
[ROW][C]21[/C][C]-0.085707[/C][C]-0.5142[/C][C]0.305113[/C][/ROW]
[ROW][C]22[/C][C]0.092085[/C][C]0.5525[/C][C]0.292006[/C][/ROW]
[ROW][C]23[/C][C]-0.091909[/C][C]-0.5515[/C][C]0.292366[/C][/ROW]
[ROW][C]24[/C][C]-0.088852[/C][C]-0.5331[/C][C]0.298616[/C][/ROW]
[ROW][C]25[/C][C]0.008003[/C][C]0.048[/C][C]0.480985[/C][/ROW]
[ROW][C]26[/C][C]-0.039784[/C][C]-0.2387[/C][C]0.406345[/C][/ROW]
[ROW][C]27[/C][C]-0.035859[/C][C]-0.2152[/C][C]0.415431[/C][/ROW]
[ROW][C]28[/C][C]0.015928[/C][C]0.0956[/C][C]0.462197[/C][/ROW]
[ROW][C]29[/C][C]-0.058971[/C][C]-0.3538[/C][C]0.362766[/C][/ROW]
[ROW][C]30[/C][C]-0.061325[/C][C]-0.368[/C][C]0.357533[/C][/ROW]
[ROW][C]31[/C][C]0.051286[/C][C]0.3077[/C][C]0.380036[/C][/ROW]
[ROW][C]32[/C][C]-0.005987[/C][C]-0.0359[/C][C]0.485772[/C][/ROW]
[ROW][C]33[/C][C]0.048733[/C][C]0.2924[/C][C]0.385831[/C][/ROW]
[ROW][C]34[/C][C]-0.039037[/C][C]-0.2342[/C][C]0.40807[/C][/ROW]
[ROW][C]35[/C][C]-0.084648[/C][C]-0.5079[/C][C]0.307314[/C][/ROW]
[ROW][C]36[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35908&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35908&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
10.3415332.04920.023892
2-0.359464-2.15680.018888
30.1721291.03280.154299
4-0.188445-1.13070.132835
50.2600111.56010.063746
60.0485120.29110.386333
70.0694210.41650.33975
8-0.105564-0.63340.265243
90.0001097e-040.499741
10-0.167668-1.0060.160563
110.3414782.04890.023909
120.2838051.70280.04861
13-0.253672-1.5220.068368
14-0.025463-0.15280.439713
15-0.008888-0.05330.478883
16-0.181543-1.08930.141639
17-0.091888-0.55130.292408
18-0.146383-0.87830.192803
19-0.002223-0.01330.494717
200.0467270.28040.390401
21-0.085707-0.51420.305113
220.0920850.55250.292006
23-0.091909-0.55150.292366
24-0.088852-0.53310.298616
250.0080030.0480.480985
26-0.039784-0.23870.406345
27-0.035859-0.21520.415431
280.0159280.09560.462197
29-0.058971-0.35380.362766
30-0.061325-0.3680.357533
310.0512860.30770.380036
32-0.005987-0.03590.485772
330.0487330.29240.385831
34-0.039037-0.23420.40807
35-0.084648-0.50790.307314
36NANANA


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')