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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 computationSat, 04 Dec 2010 16:11:21 +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/04/t1291479035n6o87du3ly9hk25.htm/, Retrieved Sun, 05 May 2024 02:35:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105200, Retrieved Sun, 05 May 2024 02:35:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
-   PD      [(Partial) Autocorrelation Function] [] [2010-12-04 16:11:21] [558c060a42ec367ec2c020fab85c25c7] [Current]
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Dataseries X:
0.4754
0.4531
0.469
0.4716
0.4824
0.527
0.5172
0.515
0.5245
0.53
0.4836
0.4663
0.4592
0.4553
0.4217
0.4366
0.4532
0.4743
0.4776
0.4949
0.5069
0.498
0.5213
0.5394
0.6075
0.5919
0.5758
0.5916
0.6474
0.6704
0.7553
0.7891
0.784
0.7007
0.668
0.6102
0.5238
0.4237
0.3983
0.3879
0.3733
0.394
0.3945
0.4324
0.4233
0.455
0.4344
0.4388
0.4561
0.4512
0.4756
0.4704
0.5107
0.5472
0.5537
0.5539
0.5313
0.5371
0.5459
0.5461




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=105200&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=105200&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105200&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
10.4935083.38330.000726
20.4766863.2680.001014
30.2625651.80010.039136
40.3125892.1430.01866
5-0.004291-0.02940.488328
6-0.01306-0.08950.464518
7-0.074287-0.50930.306468
8-0.193518-1.32670.09551
9-0.284217-1.94850.02867
10-0.373624-2.56140.006846
11-0.32216-2.20860.016057
12-0.546388-3.74580.000245
13-0.359333-2.46350.008738
14-0.352127-2.41410.009862
15-0.100736-0.69060.246603
16-0.238622-1.63590.05427
17-0.02647-0.18150.428391
18-0.04274-0.2930.385402
190.1435930.98440.164974
200.0596330.40880.342264
210.1536581.05340.148766
220.1471541.00880.15911
230.1407590.9650.169744
240.0653650.44810.328062
250.0917630.62910.266167
260.1255750.86090.196832
270.0171850.11780.453357
280.0731240.50130.309245
290.0056380.03870.484665
300.0622880.4270.335655
31-0.076332-0.52330.30161
32-0.010704-0.07340.470906
33-0.060763-0.41660.339445
34-0.014472-0.09920.460693
35-0.040139-0.27520.392194
360.0020290.01390.494481
37-0.004141-0.02840.488736
38-0.0157-0.10760.457373
390.0047780.03280.487005
400.0038770.02660.489454
410.0088010.06030.476072
420.0014850.01020.49596
430.0113690.07790.469101
44-0.014785-0.10140.459849
450.0060190.04130.483631
46-0.000833-0.00570.497734
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.493508 & 3.3833 & 0.000726 \tabularnewline
2 & 0.476686 & 3.268 & 0.001014 \tabularnewline
3 & 0.262565 & 1.8001 & 0.039136 \tabularnewline
4 & 0.312589 & 2.143 & 0.01866 \tabularnewline
5 & -0.004291 & -0.0294 & 0.488328 \tabularnewline
6 & -0.01306 & -0.0895 & 0.464518 \tabularnewline
7 & -0.074287 & -0.5093 & 0.306468 \tabularnewline
8 & -0.193518 & -1.3267 & 0.09551 \tabularnewline
9 & -0.284217 & -1.9485 & 0.02867 \tabularnewline
10 & -0.373624 & -2.5614 & 0.006846 \tabularnewline
11 & -0.32216 & -2.2086 & 0.016057 \tabularnewline
12 & -0.546388 & -3.7458 & 0.000245 \tabularnewline
13 & -0.359333 & -2.4635 & 0.008738 \tabularnewline
14 & -0.352127 & -2.4141 & 0.009862 \tabularnewline
15 & -0.100736 & -0.6906 & 0.246603 \tabularnewline
16 & -0.238622 & -1.6359 & 0.05427 \tabularnewline
17 & -0.02647 & -0.1815 & 0.428391 \tabularnewline
18 & -0.04274 & -0.293 & 0.385402 \tabularnewline
19 & 0.143593 & 0.9844 & 0.164974 \tabularnewline
20 & 0.059633 & 0.4088 & 0.342264 \tabularnewline
21 & 0.153658 & 1.0534 & 0.148766 \tabularnewline
22 & 0.147154 & 1.0088 & 0.15911 \tabularnewline
23 & 0.140759 & 0.965 & 0.169744 \tabularnewline
24 & 0.065365 & 0.4481 & 0.328062 \tabularnewline
25 & 0.091763 & 0.6291 & 0.266167 \tabularnewline
26 & 0.125575 & 0.8609 & 0.196832 \tabularnewline
27 & 0.017185 & 0.1178 & 0.453357 \tabularnewline
28 & 0.073124 & 0.5013 & 0.309245 \tabularnewline
29 & 0.005638 & 0.0387 & 0.484665 \tabularnewline
30 & 0.062288 & 0.427 & 0.335655 \tabularnewline
31 & -0.076332 & -0.5233 & 0.30161 \tabularnewline
32 & -0.010704 & -0.0734 & 0.470906 \tabularnewline
33 & -0.060763 & -0.4166 & 0.339445 \tabularnewline
34 & -0.014472 & -0.0992 & 0.460693 \tabularnewline
35 & -0.040139 & -0.2752 & 0.392194 \tabularnewline
36 & 0.002029 & 0.0139 & 0.494481 \tabularnewline
37 & -0.004141 & -0.0284 & 0.488736 \tabularnewline
38 & -0.0157 & -0.1076 & 0.457373 \tabularnewline
39 & 0.004778 & 0.0328 & 0.487005 \tabularnewline
40 & 0.003877 & 0.0266 & 0.489454 \tabularnewline
41 & 0.008801 & 0.0603 & 0.476072 \tabularnewline
42 & 0.001485 & 0.0102 & 0.49596 \tabularnewline
43 & 0.011369 & 0.0779 & 0.469101 \tabularnewline
44 & -0.014785 & -0.1014 & 0.459849 \tabularnewline
45 & 0.006019 & 0.0413 & 0.483631 \tabularnewline
46 & -0.000833 & -0.0057 & 0.497734 \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105200&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]0.493508[/C][C]3.3833[/C][C]0.000726[/C][/ROW]
[ROW][C]2[/C][C]0.476686[/C][C]3.268[/C][C]0.001014[/C][/ROW]
[ROW][C]3[/C][C]0.262565[/C][C]1.8001[/C][C]0.039136[/C][/ROW]
[ROW][C]4[/C][C]0.312589[/C][C]2.143[/C][C]0.01866[/C][/ROW]
[ROW][C]5[/C][C]-0.004291[/C][C]-0.0294[/C][C]0.488328[/C][/ROW]
[ROW][C]6[/C][C]-0.01306[/C][C]-0.0895[/C][C]0.464518[/C][/ROW]
[ROW][C]7[/C][C]-0.074287[/C][C]-0.5093[/C][C]0.306468[/C][/ROW]
[ROW][C]8[/C][C]-0.193518[/C][C]-1.3267[/C][C]0.09551[/C][/ROW]
[ROW][C]9[/C][C]-0.284217[/C][C]-1.9485[/C][C]0.02867[/C][/ROW]
[ROW][C]10[/C][C]-0.373624[/C][C]-2.5614[/C][C]0.006846[/C][/ROW]
[ROW][C]11[/C][C]-0.32216[/C][C]-2.2086[/C][C]0.016057[/C][/ROW]
[ROW][C]12[/C][C]-0.546388[/C][C]-3.7458[/C][C]0.000245[/C][/ROW]
[ROW][C]13[/C][C]-0.359333[/C][C]-2.4635[/C][C]0.008738[/C][/ROW]
[ROW][C]14[/C][C]-0.352127[/C][C]-2.4141[/C][C]0.009862[/C][/ROW]
[ROW][C]15[/C][C]-0.100736[/C][C]-0.6906[/C][C]0.246603[/C][/ROW]
[ROW][C]16[/C][C]-0.238622[/C][C]-1.6359[/C][C]0.05427[/C][/ROW]
[ROW][C]17[/C][C]-0.02647[/C][C]-0.1815[/C][C]0.428391[/C][/ROW]
[ROW][C]18[/C][C]-0.04274[/C][C]-0.293[/C][C]0.385402[/C][/ROW]
[ROW][C]19[/C][C]0.143593[/C][C]0.9844[/C][C]0.164974[/C][/ROW]
[ROW][C]20[/C][C]0.059633[/C][C]0.4088[/C][C]0.342264[/C][/ROW]
[ROW][C]21[/C][C]0.153658[/C][C]1.0534[/C][C]0.148766[/C][/ROW]
[ROW][C]22[/C][C]0.147154[/C][C]1.0088[/C][C]0.15911[/C][/ROW]
[ROW][C]23[/C][C]0.140759[/C][C]0.965[/C][C]0.169744[/C][/ROW]
[ROW][C]24[/C][C]0.065365[/C][C]0.4481[/C][C]0.328062[/C][/ROW]
[ROW][C]25[/C][C]0.091763[/C][C]0.6291[/C][C]0.266167[/C][/ROW]
[ROW][C]26[/C][C]0.125575[/C][C]0.8609[/C][C]0.196832[/C][/ROW]
[ROW][C]27[/C][C]0.017185[/C][C]0.1178[/C][C]0.453357[/C][/ROW]
[ROW][C]28[/C][C]0.073124[/C][C]0.5013[/C][C]0.309245[/C][/ROW]
[ROW][C]29[/C][C]0.005638[/C][C]0.0387[/C][C]0.484665[/C][/ROW]
[ROW][C]30[/C][C]0.062288[/C][C]0.427[/C][C]0.335655[/C][/ROW]
[ROW][C]31[/C][C]-0.076332[/C][C]-0.5233[/C][C]0.30161[/C][/ROW]
[ROW][C]32[/C][C]-0.010704[/C][C]-0.0734[/C][C]0.470906[/C][/ROW]
[ROW][C]33[/C][C]-0.060763[/C][C]-0.4166[/C][C]0.339445[/C][/ROW]
[ROW][C]34[/C][C]-0.014472[/C][C]-0.0992[/C][C]0.460693[/C][/ROW]
[ROW][C]35[/C][C]-0.040139[/C][C]-0.2752[/C][C]0.392194[/C][/ROW]
[ROW][C]36[/C][C]0.002029[/C][C]0.0139[/C][C]0.494481[/C][/ROW]
[ROW][C]37[/C][C]-0.004141[/C][C]-0.0284[/C][C]0.488736[/C][/ROW]
[ROW][C]38[/C][C]-0.0157[/C][C]-0.1076[/C][C]0.457373[/C][/ROW]
[ROW][C]39[/C][C]0.004778[/C][C]0.0328[/C][C]0.487005[/C][/ROW]
[ROW][C]40[/C][C]0.003877[/C][C]0.0266[/C][C]0.489454[/C][/ROW]
[ROW][C]41[/C][C]0.008801[/C][C]0.0603[/C][C]0.476072[/C][/ROW]
[ROW][C]42[/C][C]0.001485[/C][C]0.0102[/C][C]0.49596[/C][/ROW]
[ROW][C]43[/C][C]0.011369[/C][C]0.0779[/C][C]0.469101[/C][/ROW]
[ROW][C]44[/C][C]-0.014785[/C][C]-0.1014[/C][C]0.459849[/C][/ROW]
[ROW][C]45[/C][C]0.006019[/C][C]0.0413[/C][C]0.483631[/C][/ROW]
[ROW][C]46[/C][C]-0.000833[/C][C]-0.0057[/C][C]0.497734[/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=105200&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105200&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
10.4935083.38330.000726
20.4766863.2680.001014
30.2625651.80010.039136
40.3125892.1430.01866
5-0.004291-0.02940.488328
6-0.01306-0.08950.464518
7-0.074287-0.50930.306468
8-0.193518-1.32670.09551
9-0.284217-1.94850.02867
10-0.373624-2.56140.006846
11-0.32216-2.20860.016057
12-0.546388-3.74580.000245
13-0.359333-2.46350.008738
14-0.352127-2.41410.009862
15-0.100736-0.69060.246603
16-0.238622-1.63590.05427
17-0.02647-0.18150.428391
18-0.04274-0.2930.385402
190.1435930.98440.164974
200.0596330.40880.342264
210.1536581.05340.148766
220.1471541.00880.15911
230.1407590.9650.169744
240.0653650.44810.328062
250.0917630.62910.266167
260.1255750.86090.196832
270.0171850.11780.453357
280.0731240.50130.309245
290.0056380.03870.484665
300.0622880.4270.335655
31-0.076332-0.52330.30161
32-0.010704-0.07340.470906
33-0.060763-0.41660.339445
34-0.014472-0.09920.460693
35-0.040139-0.27520.392194
360.0020290.01390.494481
37-0.004141-0.02840.488736
38-0.0157-0.10760.457373
390.0047780.03280.487005
400.0038770.02660.489454
410.0088010.06030.476072
420.0014850.01020.49596
430.0113690.07790.469101
44-0.014785-0.10140.459849
450.0060190.04130.483631
46-0.000833-0.00570.497734
47NANANA
48NANANA







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4935083.38330.000726
20.3081982.11290.019974
3-0.076364-0.52350.301534
40.1397460.95810.171469
5-0.307891-2.11080.020069
6-0.080282-0.55040.292331
70.0719750.49340.312
8-0.257883-1.7680.04178
9-0.062153-0.42610.33599
10-0.197578-1.35450.091023
11-0.051724-0.35460.362237
12-0.322819-2.21310.01589
130.0706580.48440.315175
140.039440.27040.394024
150.1410230.96680.169294
16-0.111456-0.76410.224312
17-0.075519-0.51770.303537
18-0.037677-0.25830.398653
190.0990510.67910.250216
20-0.077429-0.53080.29902
21-0.111126-0.76180.22498
22-0.098885-0.67790.250572
23-0.099569-0.68260.249103
24-0.239134-1.63940.053902
250.0669180.45880.324258
260.0253850.1740.431295
270.0278240.19080.424771
280.0205010.14050.444413
29-0.092013-0.63080.265611
30-0.028311-0.19410.423471
310.0621950.42640.335887
32-0.088752-0.60850.272909
33-0.017787-0.12190.451734
34-0.130002-0.89120.188668
350.019170.13140.448
36-0.168208-1.15320.127335
370.0112880.07740.469323
38-0.015824-0.10850.457037
390.0541520.37120.35606
400.0018420.01260.49499
41-0.091585-0.62790.266562
420.0338650.23220.408707
43-0.018551-0.12720.44967
44-0.059313-0.40660.343063
45-0.026687-0.1830.42781
46-0.041826-0.28670.387785
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.493508 & 3.3833 & 0.000726 \tabularnewline
2 & 0.308198 & 2.1129 & 0.019974 \tabularnewline
3 & -0.076364 & -0.5235 & 0.301534 \tabularnewline
4 & 0.139746 & 0.9581 & 0.171469 \tabularnewline
5 & -0.307891 & -2.1108 & 0.020069 \tabularnewline
6 & -0.080282 & -0.5504 & 0.292331 \tabularnewline
7 & 0.071975 & 0.4934 & 0.312 \tabularnewline
8 & -0.257883 & -1.768 & 0.04178 \tabularnewline
9 & -0.062153 & -0.4261 & 0.33599 \tabularnewline
10 & -0.197578 & -1.3545 & 0.091023 \tabularnewline
11 & -0.051724 & -0.3546 & 0.362237 \tabularnewline
12 & -0.322819 & -2.2131 & 0.01589 \tabularnewline
13 & 0.070658 & 0.4844 & 0.315175 \tabularnewline
14 & 0.03944 & 0.2704 & 0.394024 \tabularnewline
15 & 0.141023 & 0.9668 & 0.169294 \tabularnewline
16 & -0.111456 & -0.7641 & 0.224312 \tabularnewline
17 & -0.075519 & -0.5177 & 0.303537 \tabularnewline
18 & -0.037677 & -0.2583 & 0.398653 \tabularnewline
19 & 0.099051 & 0.6791 & 0.250216 \tabularnewline
20 & -0.077429 & -0.5308 & 0.29902 \tabularnewline
21 & -0.111126 & -0.7618 & 0.22498 \tabularnewline
22 & -0.098885 & -0.6779 & 0.250572 \tabularnewline
23 & -0.099569 & -0.6826 & 0.249103 \tabularnewline
24 & -0.239134 & -1.6394 & 0.053902 \tabularnewline
25 & 0.066918 & 0.4588 & 0.324258 \tabularnewline
26 & 0.025385 & 0.174 & 0.431295 \tabularnewline
27 & 0.027824 & 0.1908 & 0.424771 \tabularnewline
28 & 0.020501 & 0.1405 & 0.444413 \tabularnewline
29 & -0.092013 & -0.6308 & 0.265611 \tabularnewline
30 & -0.028311 & -0.1941 & 0.423471 \tabularnewline
31 & 0.062195 & 0.4264 & 0.335887 \tabularnewline
32 & -0.088752 & -0.6085 & 0.272909 \tabularnewline
33 & -0.017787 & -0.1219 & 0.451734 \tabularnewline
34 & -0.130002 & -0.8912 & 0.188668 \tabularnewline
35 & 0.01917 & 0.1314 & 0.448 \tabularnewline
36 & -0.168208 & -1.1532 & 0.127335 \tabularnewline
37 & 0.011288 & 0.0774 & 0.469323 \tabularnewline
38 & -0.015824 & -0.1085 & 0.457037 \tabularnewline
39 & 0.054152 & 0.3712 & 0.35606 \tabularnewline
40 & 0.001842 & 0.0126 & 0.49499 \tabularnewline
41 & -0.091585 & -0.6279 & 0.266562 \tabularnewline
42 & 0.033865 & 0.2322 & 0.408707 \tabularnewline
43 & -0.018551 & -0.1272 & 0.44967 \tabularnewline
44 & -0.059313 & -0.4066 & 0.343063 \tabularnewline
45 & -0.026687 & -0.183 & 0.42781 \tabularnewline
46 & -0.041826 & -0.2867 & 0.387785 \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105200&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]0.493508[/C][C]3.3833[/C][C]0.000726[/C][/ROW]
[ROW][C]2[/C][C]0.308198[/C][C]2.1129[/C][C]0.019974[/C][/ROW]
[ROW][C]3[/C][C]-0.076364[/C][C]-0.5235[/C][C]0.301534[/C][/ROW]
[ROW][C]4[/C][C]0.139746[/C][C]0.9581[/C][C]0.171469[/C][/ROW]
[ROW][C]5[/C][C]-0.307891[/C][C]-2.1108[/C][C]0.020069[/C][/ROW]
[ROW][C]6[/C][C]-0.080282[/C][C]-0.5504[/C][C]0.292331[/C][/ROW]
[ROW][C]7[/C][C]0.071975[/C][C]0.4934[/C][C]0.312[/C][/ROW]
[ROW][C]8[/C][C]-0.257883[/C][C]-1.768[/C][C]0.04178[/C][/ROW]
[ROW][C]9[/C][C]-0.062153[/C][C]-0.4261[/C][C]0.33599[/C][/ROW]
[ROW][C]10[/C][C]-0.197578[/C][C]-1.3545[/C][C]0.091023[/C][/ROW]
[ROW][C]11[/C][C]-0.051724[/C][C]-0.3546[/C][C]0.362237[/C][/ROW]
[ROW][C]12[/C][C]-0.322819[/C][C]-2.2131[/C][C]0.01589[/C][/ROW]
[ROW][C]13[/C][C]0.070658[/C][C]0.4844[/C][C]0.315175[/C][/ROW]
[ROW][C]14[/C][C]0.03944[/C][C]0.2704[/C][C]0.394024[/C][/ROW]
[ROW][C]15[/C][C]0.141023[/C][C]0.9668[/C][C]0.169294[/C][/ROW]
[ROW][C]16[/C][C]-0.111456[/C][C]-0.7641[/C][C]0.224312[/C][/ROW]
[ROW][C]17[/C][C]-0.075519[/C][C]-0.5177[/C][C]0.303537[/C][/ROW]
[ROW][C]18[/C][C]-0.037677[/C][C]-0.2583[/C][C]0.398653[/C][/ROW]
[ROW][C]19[/C][C]0.099051[/C][C]0.6791[/C][C]0.250216[/C][/ROW]
[ROW][C]20[/C][C]-0.077429[/C][C]-0.5308[/C][C]0.29902[/C][/ROW]
[ROW][C]21[/C][C]-0.111126[/C][C]-0.7618[/C][C]0.22498[/C][/ROW]
[ROW][C]22[/C][C]-0.098885[/C][C]-0.6779[/C][C]0.250572[/C][/ROW]
[ROW][C]23[/C][C]-0.099569[/C][C]-0.6826[/C][C]0.249103[/C][/ROW]
[ROW][C]24[/C][C]-0.239134[/C][C]-1.6394[/C][C]0.053902[/C][/ROW]
[ROW][C]25[/C][C]0.066918[/C][C]0.4588[/C][C]0.324258[/C][/ROW]
[ROW][C]26[/C][C]0.025385[/C][C]0.174[/C][C]0.431295[/C][/ROW]
[ROW][C]27[/C][C]0.027824[/C][C]0.1908[/C][C]0.424771[/C][/ROW]
[ROW][C]28[/C][C]0.020501[/C][C]0.1405[/C][C]0.444413[/C][/ROW]
[ROW][C]29[/C][C]-0.092013[/C][C]-0.6308[/C][C]0.265611[/C][/ROW]
[ROW][C]30[/C][C]-0.028311[/C][C]-0.1941[/C][C]0.423471[/C][/ROW]
[ROW][C]31[/C][C]0.062195[/C][C]0.4264[/C][C]0.335887[/C][/ROW]
[ROW][C]32[/C][C]-0.088752[/C][C]-0.6085[/C][C]0.272909[/C][/ROW]
[ROW][C]33[/C][C]-0.017787[/C][C]-0.1219[/C][C]0.451734[/C][/ROW]
[ROW][C]34[/C][C]-0.130002[/C][C]-0.8912[/C][C]0.188668[/C][/ROW]
[ROW][C]35[/C][C]0.01917[/C][C]0.1314[/C][C]0.448[/C][/ROW]
[ROW][C]36[/C][C]-0.168208[/C][C]-1.1532[/C][C]0.127335[/C][/ROW]
[ROW][C]37[/C][C]0.011288[/C][C]0.0774[/C][C]0.469323[/C][/ROW]
[ROW][C]38[/C][C]-0.015824[/C][C]-0.1085[/C][C]0.457037[/C][/ROW]
[ROW][C]39[/C][C]0.054152[/C][C]0.3712[/C][C]0.35606[/C][/ROW]
[ROW][C]40[/C][C]0.001842[/C][C]0.0126[/C][C]0.49499[/C][/ROW]
[ROW][C]41[/C][C]-0.091585[/C][C]-0.6279[/C][C]0.266562[/C][/ROW]
[ROW][C]42[/C][C]0.033865[/C][C]0.2322[/C][C]0.408707[/C][/ROW]
[ROW][C]43[/C][C]-0.018551[/C][C]-0.1272[/C][C]0.44967[/C][/ROW]
[ROW][C]44[/C][C]-0.059313[/C][C]-0.4066[/C][C]0.343063[/C][/ROW]
[ROW][C]45[/C][C]-0.026687[/C][C]-0.183[/C][C]0.42781[/C][/ROW]
[ROW][C]46[/C][C]-0.041826[/C][C]-0.2867[/C][C]0.387785[/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=105200&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105200&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
10.4935083.38330.000726
20.3081982.11290.019974
3-0.076364-0.52350.301534
40.1397460.95810.171469
5-0.307891-2.11080.020069
6-0.080282-0.55040.292331
70.0719750.49340.312
8-0.257883-1.7680.04178
9-0.062153-0.42610.33599
10-0.197578-1.35450.091023
11-0.051724-0.35460.362237
12-0.322819-2.21310.01589
130.0706580.48440.315175
140.039440.27040.394024
150.1410230.96680.169294
16-0.111456-0.76410.224312
17-0.075519-0.51770.303537
18-0.037677-0.25830.398653
190.0990510.67910.250216
20-0.077429-0.53080.29902
21-0.111126-0.76180.22498
22-0.098885-0.67790.250572
23-0.099569-0.68260.249103
24-0.239134-1.63940.053902
250.0669180.45880.324258
260.0253850.1740.431295
270.0278240.19080.424771
280.0205010.14050.444413
29-0.092013-0.63080.265611
30-0.028311-0.19410.423471
310.0621950.42640.335887
32-0.088752-0.60850.272909
33-0.017787-0.12190.451734
34-0.130002-0.89120.188668
350.019170.13140.448
36-0.168208-1.15320.127335
370.0112880.07740.469323
38-0.015824-0.10850.457037
390.0541520.37120.35606
400.0018420.01260.49499
41-0.091585-0.62790.266562
420.0338650.23220.408707
43-0.018551-0.12720.44967
44-0.059313-0.40660.343063
45-0.026687-0.1830.42781
46-0.041826-0.28670.387785
47NANANA
48NANANA



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