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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 computationMon, 13 Dec 2010 20:02:45 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292270459qln6ul0dml4uh8z.htm/, Retrieved Mon, 06 May 2024 16:12:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109138, Retrieved Mon, 06 May 2024 16:12:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2010-12-13 08:35:23] [21eff0c210342db4afbdafe426a7c254]
-   PD  [(Partial) Autocorrelation Function] [] [2010-12-13 09:29:04] [21eff0c210342db4afbdafe426a7c254]
-    D    [(Partial) Autocorrelation Function] [] [2010-12-13 09:39:12] [21eff0c210342db4afbdafe426a7c254]
-   PD        [(Partial) Autocorrelation Function] [] [2010-12-13 20:02:45] [81d69fb83507cea26168920232cdff1b] [Current]
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Dataseries X:
31293
30236
30160
32436
30695
27525
26434
25739
25204
24977
24320
22680
22052
21467
21383
21777
21928
21814
22937
23595
20830
19650
19195
19644
18483
18079
19178
18391
18441
18584
20108
20148
19394
17745
17696
17032
16438
15683
15594
15713
15937
16171
15928
16348
15579
15305
15648
14954
15137
15839
16050
15168
17064
16005
14886
14931
14544
13812




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109138&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109138&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109138&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.8327235.64780
20.6624214.49282.4e-05
30.5153563.49530.00053
40.3689412.50230.007979
50.2547311.72770.045379
60.1967111.33420.09436
70.1755761.19080.119917
80.1449350.9830.165375
90.1063450.72130.237198
100.0325330.22070.41317
11-0.002949-0.020.492063
120.001890.01280.494913
130.0249530.16920.433176
140.0207010.14040.444477
150.0491850.33360.370103
160.0383520.26010.397967
17-0.004496-0.03050.487902
18-0.034624-0.23480.40769
19-0.060422-0.40980.341926
20-0.063316-0.42940.334807
21-0.054629-0.37050.356352
22-0.032195-0.21840.414058
23-0.02124-0.14410.443042
24-0.051805-0.35140.363462
25-0.054668-0.37080.356253
26-0.025795-0.17490.430944
27-0.028904-0.1960.422723
28-0.044701-0.30320.38156
29-0.045706-0.310.378984
30-0.054817-0.37180.35588
31-0.104332-0.70760.241377
32-0.164569-1.11620.135075
33-0.225645-1.53040.066383
34-0.281782-1.91110.031115
35-0.326814-2.21660.015819
36-0.358236-2.42970.009537
37-0.369203-2.50410.007943
38-0.331447-2.2480.014707
39-0.298341-2.02340.02443
40-0.28399-1.92610.030141
41-0.221263-1.50070.070136
42-0.160392-1.08780.141168
43-0.108609-0.73660.232546
44-0.07582-0.51420.304775
45-0.034645-0.2350.407636
46NANANA
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.832723 & 5.6478 & 0 \tabularnewline
2 & 0.662421 & 4.4928 & 2.4e-05 \tabularnewline
3 & 0.515356 & 3.4953 & 0.00053 \tabularnewline
4 & 0.368941 & 2.5023 & 0.007979 \tabularnewline
5 & 0.254731 & 1.7277 & 0.045379 \tabularnewline
6 & 0.196711 & 1.3342 & 0.09436 \tabularnewline
7 & 0.175576 & 1.1908 & 0.119917 \tabularnewline
8 & 0.144935 & 0.983 & 0.165375 \tabularnewline
9 & 0.106345 & 0.7213 & 0.237198 \tabularnewline
10 & 0.032533 & 0.2207 & 0.41317 \tabularnewline
11 & -0.002949 & -0.02 & 0.492063 \tabularnewline
12 & 0.00189 & 0.0128 & 0.494913 \tabularnewline
13 & 0.024953 & 0.1692 & 0.433176 \tabularnewline
14 & 0.020701 & 0.1404 & 0.444477 \tabularnewline
15 & 0.049185 & 0.3336 & 0.370103 \tabularnewline
16 & 0.038352 & 0.2601 & 0.397967 \tabularnewline
17 & -0.004496 & -0.0305 & 0.487902 \tabularnewline
18 & -0.034624 & -0.2348 & 0.40769 \tabularnewline
19 & -0.060422 & -0.4098 & 0.341926 \tabularnewline
20 & -0.063316 & -0.4294 & 0.334807 \tabularnewline
21 & -0.054629 & -0.3705 & 0.356352 \tabularnewline
22 & -0.032195 & -0.2184 & 0.414058 \tabularnewline
23 & -0.02124 & -0.1441 & 0.443042 \tabularnewline
24 & -0.051805 & -0.3514 & 0.363462 \tabularnewline
25 & -0.054668 & -0.3708 & 0.356253 \tabularnewline
26 & -0.025795 & -0.1749 & 0.430944 \tabularnewline
27 & -0.028904 & -0.196 & 0.422723 \tabularnewline
28 & -0.044701 & -0.3032 & 0.38156 \tabularnewline
29 & -0.045706 & -0.31 & 0.378984 \tabularnewline
30 & -0.054817 & -0.3718 & 0.35588 \tabularnewline
31 & -0.104332 & -0.7076 & 0.241377 \tabularnewline
32 & -0.164569 & -1.1162 & 0.135075 \tabularnewline
33 & -0.225645 & -1.5304 & 0.066383 \tabularnewline
34 & -0.281782 & -1.9111 & 0.031115 \tabularnewline
35 & -0.326814 & -2.2166 & 0.015819 \tabularnewline
36 & -0.358236 & -2.4297 & 0.009537 \tabularnewline
37 & -0.369203 & -2.5041 & 0.007943 \tabularnewline
38 & -0.331447 & -2.248 & 0.014707 \tabularnewline
39 & -0.298341 & -2.0234 & 0.02443 \tabularnewline
40 & -0.28399 & -1.9261 & 0.030141 \tabularnewline
41 & -0.221263 & -1.5007 & 0.070136 \tabularnewline
42 & -0.160392 & -1.0878 & 0.141168 \tabularnewline
43 & -0.108609 & -0.7366 & 0.232546 \tabularnewline
44 & -0.07582 & -0.5142 & 0.304775 \tabularnewline
45 & -0.034645 & -0.235 & 0.407636 \tabularnewline
46 & NA & NA & NA \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109138&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]0.832723[/C][C]5.6478[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.662421[/C][C]4.4928[/C][C]2.4e-05[/C][/ROW]
[ROW][C]3[/C][C]0.515356[/C][C]3.4953[/C][C]0.00053[/C][/ROW]
[ROW][C]4[/C][C]0.368941[/C][C]2.5023[/C][C]0.007979[/C][/ROW]
[ROW][C]5[/C][C]0.254731[/C][C]1.7277[/C][C]0.045379[/C][/ROW]
[ROW][C]6[/C][C]0.196711[/C][C]1.3342[/C][C]0.09436[/C][/ROW]
[ROW][C]7[/C][C]0.175576[/C][C]1.1908[/C][C]0.119917[/C][/ROW]
[ROW][C]8[/C][C]0.144935[/C][C]0.983[/C][C]0.165375[/C][/ROW]
[ROW][C]9[/C][C]0.106345[/C][C]0.7213[/C][C]0.237198[/C][/ROW]
[ROW][C]10[/C][C]0.032533[/C][C]0.2207[/C][C]0.41317[/C][/ROW]
[ROW][C]11[/C][C]-0.002949[/C][C]-0.02[/C][C]0.492063[/C][/ROW]
[ROW][C]12[/C][C]0.00189[/C][C]0.0128[/C][C]0.494913[/C][/ROW]
[ROW][C]13[/C][C]0.024953[/C][C]0.1692[/C][C]0.433176[/C][/ROW]
[ROW][C]14[/C][C]0.020701[/C][C]0.1404[/C][C]0.444477[/C][/ROW]
[ROW][C]15[/C][C]0.049185[/C][C]0.3336[/C][C]0.370103[/C][/ROW]
[ROW][C]16[/C][C]0.038352[/C][C]0.2601[/C][C]0.397967[/C][/ROW]
[ROW][C]17[/C][C]-0.004496[/C][C]-0.0305[/C][C]0.487902[/C][/ROW]
[ROW][C]18[/C][C]-0.034624[/C][C]-0.2348[/C][C]0.40769[/C][/ROW]
[ROW][C]19[/C][C]-0.060422[/C][C]-0.4098[/C][C]0.341926[/C][/ROW]
[ROW][C]20[/C][C]-0.063316[/C][C]-0.4294[/C][C]0.334807[/C][/ROW]
[ROW][C]21[/C][C]-0.054629[/C][C]-0.3705[/C][C]0.356352[/C][/ROW]
[ROW][C]22[/C][C]-0.032195[/C][C]-0.2184[/C][C]0.414058[/C][/ROW]
[ROW][C]23[/C][C]-0.02124[/C][C]-0.1441[/C][C]0.443042[/C][/ROW]
[ROW][C]24[/C][C]-0.051805[/C][C]-0.3514[/C][C]0.363462[/C][/ROW]
[ROW][C]25[/C][C]-0.054668[/C][C]-0.3708[/C][C]0.356253[/C][/ROW]
[ROW][C]26[/C][C]-0.025795[/C][C]-0.1749[/C][C]0.430944[/C][/ROW]
[ROW][C]27[/C][C]-0.028904[/C][C]-0.196[/C][C]0.422723[/C][/ROW]
[ROW][C]28[/C][C]-0.044701[/C][C]-0.3032[/C][C]0.38156[/C][/ROW]
[ROW][C]29[/C][C]-0.045706[/C][C]-0.31[/C][C]0.378984[/C][/ROW]
[ROW][C]30[/C][C]-0.054817[/C][C]-0.3718[/C][C]0.35588[/C][/ROW]
[ROW][C]31[/C][C]-0.104332[/C][C]-0.7076[/C][C]0.241377[/C][/ROW]
[ROW][C]32[/C][C]-0.164569[/C][C]-1.1162[/C][C]0.135075[/C][/ROW]
[ROW][C]33[/C][C]-0.225645[/C][C]-1.5304[/C][C]0.066383[/C][/ROW]
[ROW][C]34[/C][C]-0.281782[/C][C]-1.9111[/C][C]0.031115[/C][/ROW]
[ROW][C]35[/C][C]-0.326814[/C][C]-2.2166[/C][C]0.015819[/C][/ROW]
[ROW][C]36[/C][C]-0.358236[/C][C]-2.4297[/C][C]0.009537[/C][/ROW]
[ROW][C]37[/C][C]-0.369203[/C][C]-2.5041[/C][C]0.007943[/C][/ROW]
[ROW][C]38[/C][C]-0.331447[/C][C]-2.248[/C][C]0.014707[/C][/ROW]
[ROW][C]39[/C][C]-0.298341[/C][C]-2.0234[/C][C]0.02443[/C][/ROW]
[ROW][C]40[/C][C]-0.28399[/C][C]-1.9261[/C][C]0.030141[/C][/ROW]
[ROW][C]41[/C][C]-0.221263[/C][C]-1.5007[/C][C]0.070136[/C][/ROW]
[ROW][C]42[/C][C]-0.160392[/C][C]-1.0878[/C][C]0.141168[/C][/ROW]
[ROW][C]43[/C][C]-0.108609[/C][C]-0.7366[/C][C]0.232546[/C][/ROW]
[ROW][C]44[/C][C]-0.07582[/C][C]-0.5142[/C][C]0.304775[/C][/ROW]
[ROW][C]45[/C][C]-0.034645[/C][C]-0.235[/C][C]0.407636[/C][/ROW]
[ROW][C]46[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]47[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]48[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109138&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109138&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
10.8327235.64780
20.6624214.49282.4e-05
30.5153563.49530.00053
40.3689412.50230.007979
50.2547311.72770.045379
60.1967111.33420.09436
70.1755761.19080.119917
80.1449350.9830.165375
90.1063450.72130.237198
100.0325330.22070.41317
11-0.002949-0.020.492063
120.001890.01280.494913
130.0249530.16920.433176
140.0207010.14040.444477
150.0491850.33360.370103
160.0383520.26010.397967
17-0.004496-0.03050.487902
18-0.034624-0.23480.40769
19-0.060422-0.40980.341926
20-0.063316-0.42940.334807
21-0.054629-0.37050.356352
22-0.032195-0.21840.414058
23-0.02124-0.14410.443042
24-0.051805-0.35140.363462
25-0.054668-0.37080.356253
26-0.025795-0.17490.430944
27-0.028904-0.1960.422723
28-0.044701-0.30320.38156
29-0.045706-0.310.378984
30-0.054817-0.37180.35588
31-0.104332-0.70760.241377
32-0.164569-1.11620.135075
33-0.225645-1.53040.066383
34-0.281782-1.91110.031115
35-0.326814-2.21660.015819
36-0.358236-2.42970.009537
37-0.369203-2.50410.007943
38-0.331447-2.2480.014707
39-0.298341-2.02340.02443
40-0.28399-1.92610.030141
41-0.221263-1.50070.070136
42-0.160392-1.08780.141168
43-0.108609-0.73660.232546
44-0.07582-0.51420.304775
45-0.034645-0.2350.407636
46NANANA
47NANANA
48NANANA







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.8327235.64780
2-0.101141-0.6860.248086
3-0.025791-0.17490.430953
4-0.097148-0.65890.256627
50.0042460.02880.488576
60.0882620.59860.27618
70.0655560.44460.329339
8-0.058088-0.3940.347712
9-0.055356-0.37540.354529
10-0.148332-1.0060.159831
110.1014940.68840.247341
120.1095020.74270.230727
130.063480.43050.334405
14-0.128265-0.86990.194426
150.0710020.48160.3162
16-0.11985-0.81290.210243
17-0.019267-0.13070.448302
180.0271730.18430.427294
19-0.007229-0.0490.480555
200.0085470.0580.477013
21-0.006236-0.04230.483222
220.0153870.10440.458668
230.0124840.08470.466446
24-0.156494-1.06140.147026
250.1280270.86830.194863
260.1078820.73170.234035
27-0.10526-0.71390.239446
28-0.118073-0.80080.213678
290.0186990.12680.449818
30-0.021828-0.1480.441478
31-0.100166-0.67940.250157
32-0.054158-0.36730.357532
33-0.087733-0.5950.277369
34-0.119396-0.80980.211117
35-0.084945-0.57610.28367
360.005730.03890.484583
370.0203480.1380.44542
380.0095610.06480.474288
39-0.037943-0.25730.39903
40-0.02716-0.18420.427331
410.0917610.62240.268391
420.0031760.02150.491453
430.0566840.38450.351208
44-0.04414-0.29940.383003
450.0080620.05470.478316
46NANANA
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.832723 & 5.6478 & 0 \tabularnewline
2 & -0.101141 & -0.686 & 0.248086 \tabularnewline
3 & -0.025791 & -0.1749 & 0.430953 \tabularnewline
4 & -0.097148 & -0.6589 & 0.256627 \tabularnewline
5 & 0.004246 & 0.0288 & 0.488576 \tabularnewline
6 & 0.088262 & 0.5986 & 0.27618 \tabularnewline
7 & 0.065556 & 0.4446 & 0.329339 \tabularnewline
8 & -0.058088 & -0.394 & 0.347712 \tabularnewline
9 & -0.055356 & -0.3754 & 0.354529 \tabularnewline
10 & -0.148332 & -1.006 & 0.159831 \tabularnewline
11 & 0.101494 & 0.6884 & 0.247341 \tabularnewline
12 & 0.109502 & 0.7427 & 0.230727 \tabularnewline
13 & 0.06348 & 0.4305 & 0.334405 \tabularnewline
14 & -0.128265 & -0.8699 & 0.194426 \tabularnewline
15 & 0.071002 & 0.4816 & 0.3162 \tabularnewline
16 & -0.11985 & -0.8129 & 0.210243 \tabularnewline
17 & -0.019267 & -0.1307 & 0.448302 \tabularnewline
18 & 0.027173 & 0.1843 & 0.427294 \tabularnewline
19 & -0.007229 & -0.049 & 0.480555 \tabularnewline
20 & 0.008547 & 0.058 & 0.477013 \tabularnewline
21 & -0.006236 & -0.0423 & 0.483222 \tabularnewline
22 & 0.015387 & 0.1044 & 0.458668 \tabularnewline
23 & 0.012484 & 0.0847 & 0.466446 \tabularnewline
24 & -0.156494 & -1.0614 & 0.147026 \tabularnewline
25 & 0.128027 & 0.8683 & 0.194863 \tabularnewline
26 & 0.107882 & 0.7317 & 0.234035 \tabularnewline
27 & -0.10526 & -0.7139 & 0.239446 \tabularnewline
28 & -0.118073 & -0.8008 & 0.213678 \tabularnewline
29 & 0.018699 & 0.1268 & 0.449818 \tabularnewline
30 & -0.021828 & -0.148 & 0.441478 \tabularnewline
31 & -0.100166 & -0.6794 & 0.250157 \tabularnewline
32 & -0.054158 & -0.3673 & 0.357532 \tabularnewline
33 & -0.087733 & -0.595 & 0.277369 \tabularnewline
34 & -0.119396 & -0.8098 & 0.211117 \tabularnewline
35 & -0.084945 & -0.5761 & 0.28367 \tabularnewline
36 & 0.00573 & 0.0389 & 0.484583 \tabularnewline
37 & 0.020348 & 0.138 & 0.44542 \tabularnewline
38 & 0.009561 & 0.0648 & 0.474288 \tabularnewline
39 & -0.037943 & -0.2573 & 0.39903 \tabularnewline
40 & -0.02716 & -0.1842 & 0.427331 \tabularnewline
41 & 0.091761 & 0.6224 & 0.268391 \tabularnewline
42 & 0.003176 & 0.0215 & 0.491453 \tabularnewline
43 & 0.056684 & 0.3845 & 0.351208 \tabularnewline
44 & -0.04414 & -0.2994 & 0.383003 \tabularnewline
45 & 0.008062 & 0.0547 & 0.478316 \tabularnewline
46 & NA & NA & NA \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109138&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]0.832723[/C][C]5.6478[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.101141[/C][C]-0.686[/C][C]0.248086[/C][/ROW]
[ROW][C]3[/C][C]-0.025791[/C][C]-0.1749[/C][C]0.430953[/C][/ROW]
[ROW][C]4[/C][C]-0.097148[/C][C]-0.6589[/C][C]0.256627[/C][/ROW]
[ROW][C]5[/C][C]0.004246[/C][C]0.0288[/C][C]0.488576[/C][/ROW]
[ROW][C]6[/C][C]0.088262[/C][C]0.5986[/C][C]0.27618[/C][/ROW]
[ROW][C]7[/C][C]0.065556[/C][C]0.4446[/C][C]0.329339[/C][/ROW]
[ROW][C]8[/C][C]-0.058088[/C][C]-0.394[/C][C]0.347712[/C][/ROW]
[ROW][C]9[/C][C]-0.055356[/C][C]-0.3754[/C][C]0.354529[/C][/ROW]
[ROW][C]10[/C][C]-0.148332[/C][C]-1.006[/C][C]0.159831[/C][/ROW]
[ROW][C]11[/C][C]0.101494[/C][C]0.6884[/C][C]0.247341[/C][/ROW]
[ROW][C]12[/C][C]0.109502[/C][C]0.7427[/C][C]0.230727[/C][/ROW]
[ROW][C]13[/C][C]0.06348[/C][C]0.4305[/C][C]0.334405[/C][/ROW]
[ROW][C]14[/C][C]-0.128265[/C][C]-0.8699[/C][C]0.194426[/C][/ROW]
[ROW][C]15[/C][C]0.071002[/C][C]0.4816[/C][C]0.3162[/C][/ROW]
[ROW][C]16[/C][C]-0.11985[/C][C]-0.8129[/C][C]0.210243[/C][/ROW]
[ROW][C]17[/C][C]-0.019267[/C][C]-0.1307[/C][C]0.448302[/C][/ROW]
[ROW][C]18[/C][C]0.027173[/C][C]0.1843[/C][C]0.427294[/C][/ROW]
[ROW][C]19[/C][C]-0.007229[/C][C]-0.049[/C][C]0.480555[/C][/ROW]
[ROW][C]20[/C][C]0.008547[/C][C]0.058[/C][C]0.477013[/C][/ROW]
[ROW][C]21[/C][C]-0.006236[/C][C]-0.0423[/C][C]0.483222[/C][/ROW]
[ROW][C]22[/C][C]0.015387[/C][C]0.1044[/C][C]0.458668[/C][/ROW]
[ROW][C]23[/C][C]0.012484[/C][C]0.0847[/C][C]0.466446[/C][/ROW]
[ROW][C]24[/C][C]-0.156494[/C][C]-1.0614[/C][C]0.147026[/C][/ROW]
[ROW][C]25[/C][C]0.128027[/C][C]0.8683[/C][C]0.194863[/C][/ROW]
[ROW][C]26[/C][C]0.107882[/C][C]0.7317[/C][C]0.234035[/C][/ROW]
[ROW][C]27[/C][C]-0.10526[/C][C]-0.7139[/C][C]0.239446[/C][/ROW]
[ROW][C]28[/C][C]-0.118073[/C][C]-0.8008[/C][C]0.213678[/C][/ROW]
[ROW][C]29[/C][C]0.018699[/C][C]0.1268[/C][C]0.449818[/C][/ROW]
[ROW][C]30[/C][C]-0.021828[/C][C]-0.148[/C][C]0.441478[/C][/ROW]
[ROW][C]31[/C][C]-0.100166[/C][C]-0.6794[/C][C]0.250157[/C][/ROW]
[ROW][C]32[/C][C]-0.054158[/C][C]-0.3673[/C][C]0.357532[/C][/ROW]
[ROW][C]33[/C][C]-0.087733[/C][C]-0.595[/C][C]0.277369[/C][/ROW]
[ROW][C]34[/C][C]-0.119396[/C][C]-0.8098[/C][C]0.211117[/C][/ROW]
[ROW][C]35[/C][C]-0.084945[/C][C]-0.5761[/C][C]0.28367[/C][/ROW]
[ROW][C]36[/C][C]0.00573[/C][C]0.0389[/C][C]0.484583[/C][/ROW]
[ROW][C]37[/C][C]0.020348[/C][C]0.138[/C][C]0.44542[/C][/ROW]
[ROW][C]38[/C][C]0.009561[/C][C]0.0648[/C][C]0.474288[/C][/ROW]
[ROW][C]39[/C][C]-0.037943[/C][C]-0.2573[/C][C]0.39903[/C][/ROW]
[ROW][C]40[/C][C]-0.02716[/C][C]-0.1842[/C][C]0.427331[/C][/ROW]
[ROW][C]41[/C][C]0.091761[/C][C]0.6224[/C][C]0.268391[/C][/ROW]
[ROW][C]42[/C][C]0.003176[/C][C]0.0215[/C][C]0.491453[/C][/ROW]
[ROW][C]43[/C][C]0.056684[/C][C]0.3845[/C][C]0.351208[/C][/ROW]
[ROW][C]44[/C][C]-0.04414[/C][C]-0.2994[/C][C]0.383003[/C][/ROW]
[ROW][C]45[/C][C]0.008062[/C][C]0.0547[/C][C]0.478316[/C][/ROW]
[ROW][C]46[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]47[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]48[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109138&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109138&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
10.8327235.64780
2-0.101141-0.6860.248086
3-0.025791-0.17490.430953
4-0.097148-0.65890.256627
50.0042460.02880.488576
60.0882620.59860.27618
70.0655560.44460.329339
8-0.058088-0.3940.347712
9-0.055356-0.37540.354529
10-0.148332-1.0060.159831
110.1014940.68840.247341
120.1095020.74270.230727
130.063480.43050.334405
14-0.128265-0.86990.194426
150.0710020.48160.3162
16-0.11985-0.81290.210243
17-0.019267-0.13070.448302
180.0271730.18430.427294
19-0.007229-0.0490.480555
200.0085470.0580.477013
21-0.006236-0.04230.483222
220.0153870.10440.458668
230.0124840.08470.466446
24-0.156494-1.06140.147026
250.1280270.86830.194863
260.1078820.73170.234035
27-0.10526-0.71390.239446
28-0.118073-0.80080.213678
290.0186990.12680.449818
30-0.021828-0.1480.441478
31-0.100166-0.67940.250157
32-0.054158-0.36730.357532
33-0.087733-0.5950.277369
34-0.119396-0.80980.211117
35-0.084945-0.57610.28367
360.005730.03890.484583
370.0203480.1380.44542
380.0095610.06480.474288
39-0.037943-0.25730.39903
40-0.02716-0.18420.427331
410.0917610.62240.268391
420.0031760.02150.491453
430.0566840.38450.351208
44-0.04414-0.29940.383003
450.0080620.05470.478316
46NANANA
47NANANA
48NANANA



Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; 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')