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Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationMon, 03 May 2010 17:47:07 +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/May/03/t1272908883e02q7tvzcweqo9v.htm/, Retrieved Fri, 29 Mar 2024 10:26:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=75273, Retrieved Fri, 29 Mar 2024 10:26:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W21
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Nieuwe personenwagen] [2010-05-03 17:36:22] [f5ecd041e4b32af12787a4e421b18aaf]
-   PD    [(Partial) Autocorrelation Function] [Personenwagen Aus...] [2010-05-03 17:47:07] [05b8da000f2ebbd12b039a4b088dd3f2] [Current]
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Dataseries X:
40801
49081
52431
59650
75428
78705
68870
70641
80074
76464
69976
92917
92559
73981
71107
96942
86270
69610
57768
80077
71454
70382
69881
84530
79322
80181
82137
88439
91575
82909
73282
94089
108112
95653
85273
105093
102275
99308
79687
93263
114918
103374
65124
104045
101183
95492
85035
90692
107486
98179
82551
106804
110898
89950
65184
95357
98280
92146
77874
100039
104777
102341
71316
88838
85457
70784
70522
88629
88452
98886
79601
108135
113835
101617
68698
79182
86003
84165
68550
90385
100368
99081
81288
103491
111695
82504
62237
78249
92341
84412
75102
90461
106451
98379
72615
98367
116949
95832
68060
83923
87653
78054
57566
78784
88916
84662
63442
77773
88102
87972
61790
95276
104418
95420
82141
104064
96287
78426
59111
76837
76615
65860
57703
68656
77955
65856
60947
69885
80550
73694
67538
76326
84727




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=75273&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=75273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75273&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
1-0.147529-1.6950.046218
2-0.560526-6.440
3-0.067359-0.77390.220188
40.7187988.25840
5-0.151947-1.74570.041592
6-0.509551-5.85430
7-0.130552-1.49990.06801
80.7134198.19660
9-0.11228-1.290.099654
10-0.4956-5.6940
11-0.056412-0.64810.259014
120.7037998.0860
13-0.115689-1.32920.093043
14-0.491062-5.64190
15-0.098082-1.12690.130919
160.6136657.05050
17-0.075953-0.87260.192224
18-0.441046-5.06721e-06
19-0.147302-1.69240.046467
200.6643917.63330
21-0.033936-0.38990.348621
22-0.459268-5.27660
23-0.079554-0.9140.181191
240.6098267.00640
25-0.063607-0.73080.233101
26-0.40413-4.64314e-06
27-0.15703-1.80410.036745
280.5651586.49320
29-0.010837-0.12450.45055
30-0.437754-5.02941e-06
31-0.132728-1.52490.064834
320.597936.86970
33-0.0424-0.48710.313483
34-0.399547-4.59045e-06
35-0.114312-1.31330.095673
360.5499376.31830
37-0.050075-0.57530.283027
38-0.359243-4.12743.2e-05
39-0.152265-1.74940.041274
400.5326916.12020
41-0.036583-0.42030.337474
42-0.358587-4.11993.3e-05
43-0.118799-1.36490.087304
440.5071615.82680
45-0.022322-0.25650.398999
46-0.320971-3.68770.000165
47-0.099893-1.14770.126587
480.4380645.0331e-06

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.147529 & -1.695 & 0.046218 \tabularnewline
2 & -0.560526 & -6.44 & 0 \tabularnewline
3 & -0.067359 & -0.7739 & 0.220188 \tabularnewline
4 & 0.718798 & 8.2584 & 0 \tabularnewline
5 & -0.151947 & -1.7457 & 0.041592 \tabularnewline
6 & -0.509551 & -5.8543 & 0 \tabularnewline
7 & -0.130552 & -1.4999 & 0.06801 \tabularnewline
8 & 0.713419 & 8.1966 & 0 \tabularnewline
9 & -0.11228 & -1.29 & 0.099654 \tabularnewline
10 & -0.4956 & -5.694 & 0 \tabularnewline
11 & -0.056412 & -0.6481 & 0.259014 \tabularnewline
12 & 0.703799 & 8.086 & 0 \tabularnewline
13 & -0.115689 & -1.3292 & 0.093043 \tabularnewline
14 & -0.491062 & -5.6419 & 0 \tabularnewline
15 & -0.098082 & -1.1269 & 0.130919 \tabularnewline
16 & 0.613665 & 7.0505 & 0 \tabularnewline
17 & -0.075953 & -0.8726 & 0.192224 \tabularnewline
18 & -0.441046 & -5.0672 & 1e-06 \tabularnewline
19 & -0.147302 & -1.6924 & 0.046467 \tabularnewline
20 & 0.664391 & 7.6333 & 0 \tabularnewline
21 & -0.033936 & -0.3899 & 0.348621 \tabularnewline
22 & -0.459268 & -5.2766 & 0 \tabularnewline
23 & -0.079554 & -0.914 & 0.181191 \tabularnewline
24 & 0.609826 & 7.0064 & 0 \tabularnewline
25 & -0.063607 & -0.7308 & 0.233101 \tabularnewline
26 & -0.40413 & -4.6431 & 4e-06 \tabularnewline
27 & -0.15703 & -1.8041 & 0.036745 \tabularnewline
28 & 0.565158 & 6.4932 & 0 \tabularnewline
29 & -0.010837 & -0.1245 & 0.45055 \tabularnewline
30 & -0.437754 & -5.0294 & 1e-06 \tabularnewline
31 & -0.132728 & -1.5249 & 0.064834 \tabularnewline
32 & 0.59793 & 6.8697 & 0 \tabularnewline
33 & -0.0424 & -0.4871 & 0.313483 \tabularnewline
34 & -0.399547 & -4.5904 & 5e-06 \tabularnewline
35 & -0.114312 & -1.3133 & 0.095673 \tabularnewline
36 & 0.549937 & 6.3183 & 0 \tabularnewline
37 & -0.050075 & -0.5753 & 0.283027 \tabularnewline
38 & -0.359243 & -4.1274 & 3.2e-05 \tabularnewline
39 & -0.152265 & -1.7494 & 0.041274 \tabularnewline
40 & 0.532691 & 6.1202 & 0 \tabularnewline
41 & -0.036583 & -0.4203 & 0.337474 \tabularnewline
42 & -0.358587 & -4.1199 & 3.3e-05 \tabularnewline
43 & -0.118799 & -1.3649 & 0.087304 \tabularnewline
44 & 0.507161 & 5.8268 & 0 \tabularnewline
45 & -0.022322 & -0.2565 & 0.398999 \tabularnewline
46 & -0.320971 & -3.6877 & 0.000165 \tabularnewline
47 & -0.099893 & -1.1477 & 0.126587 \tabularnewline
48 & 0.438064 & 5.033 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75273&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]-0.147529[/C][C]-1.695[/C][C]0.046218[/C][/ROW]
[ROW][C]2[/C][C]-0.560526[/C][C]-6.44[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]-0.067359[/C][C]-0.7739[/C][C]0.220188[/C][/ROW]
[ROW][C]4[/C][C]0.718798[/C][C]8.2584[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]-0.151947[/C][C]-1.7457[/C][C]0.041592[/C][/ROW]
[ROW][C]6[/C][C]-0.509551[/C][C]-5.8543[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]-0.130552[/C][C]-1.4999[/C][C]0.06801[/C][/ROW]
[ROW][C]8[/C][C]0.713419[/C][C]8.1966[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]-0.11228[/C][C]-1.29[/C][C]0.099654[/C][/ROW]
[ROW][C]10[/C][C]-0.4956[/C][C]-5.694[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]-0.056412[/C][C]-0.6481[/C][C]0.259014[/C][/ROW]
[ROW][C]12[/C][C]0.703799[/C][C]8.086[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.115689[/C][C]-1.3292[/C][C]0.093043[/C][/ROW]
[ROW][C]14[/C][C]-0.491062[/C][C]-5.6419[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]-0.098082[/C][C]-1.1269[/C][C]0.130919[/C][/ROW]
[ROW][C]16[/C][C]0.613665[/C][C]7.0505[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]-0.075953[/C][C]-0.8726[/C][C]0.192224[/C][/ROW]
[ROW][C]18[/C][C]-0.441046[/C][C]-5.0672[/C][C]1e-06[/C][/ROW]
[ROW][C]19[/C][C]-0.147302[/C][C]-1.6924[/C][C]0.046467[/C][/ROW]
[ROW][C]20[/C][C]0.664391[/C][C]7.6333[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]-0.033936[/C][C]-0.3899[/C][C]0.348621[/C][/ROW]
[ROW][C]22[/C][C]-0.459268[/C][C]-5.2766[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]-0.079554[/C][C]-0.914[/C][C]0.181191[/C][/ROW]
[ROW][C]24[/C][C]0.609826[/C][C]7.0064[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]-0.063607[/C][C]-0.7308[/C][C]0.233101[/C][/ROW]
[ROW][C]26[/C][C]-0.40413[/C][C]-4.6431[/C][C]4e-06[/C][/ROW]
[ROW][C]27[/C][C]-0.15703[/C][C]-1.8041[/C][C]0.036745[/C][/ROW]
[ROW][C]28[/C][C]0.565158[/C][C]6.4932[/C][C]0[/C][/ROW]
[ROW][C]29[/C][C]-0.010837[/C][C]-0.1245[/C][C]0.45055[/C][/ROW]
[ROW][C]30[/C][C]-0.437754[/C][C]-5.0294[/C][C]1e-06[/C][/ROW]
[ROW][C]31[/C][C]-0.132728[/C][C]-1.5249[/C][C]0.064834[/C][/ROW]
[ROW][C]32[/C][C]0.59793[/C][C]6.8697[/C][C]0[/C][/ROW]
[ROW][C]33[/C][C]-0.0424[/C][C]-0.4871[/C][C]0.313483[/C][/ROW]
[ROW][C]34[/C][C]-0.399547[/C][C]-4.5904[/C][C]5e-06[/C][/ROW]
[ROW][C]35[/C][C]-0.114312[/C][C]-1.3133[/C][C]0.095673[/C][/ROW]
[ROW][C]36[/C][C]0.549937[/C][C]6.3183[/C][C]0[/C][/ROW]
[ROW][C]37[/C][C]-0.050075[/C][C]-0.5753[/C][C]0.283027[/C][/ROW]
[ROW][C]38[/C][C]-0.359243[/C][C]-4.1274[/C][C]3.2e-05[/C][/ROW]
[ROW][C]39[/C][C]-0.152265[/C][C]-1.7494[/C][C]0.041274[/C][/ROW]
[ROW][C]40[/C][C]0.532691[/C][C]6.1202[/C][C]0[/C][/ROW]
[ROW][C]41[/C][C]-0.036583[/C][C]-0.4203[/C][C]0.337474[/C][/ROW]
[ROW][C]42[/C][C]-0.358587[/C][C]-4.1199[/C][C]3.3e-05[/C][/ROW]
[ROW][C]43[/C][C]-0.118799[/C][C]-1.3649[/C][C]0.087304[/C][/ROW]
[ROW][C]44[/C][C]0.507161[/C][C]5.8268[/C][C]0[/C][/ROW]
[ROW][C]45[/C][C]-0.022322[/C][C]-0.2565[/C][C]0.398999[/C][/ROW]
[ROW][C]46[/C][C]-0.320971[/C][C]-3.6877[/C][C]0.000165[/C][/ROW]
[ROW][C]47[/C][C]-0.099893[/C][C]-1.1477[/C][C]0.126587[/C][/ROW]
[ROW][C]48[/C][C]0.438064[/C][C]5.033[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75273&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
1-0.147529-1.6950.046218
2-0.560526-6.440
3-0.067359-0.77390.220188
40.7187988.25840
5-0.151947-1.74570.041592
6-0.509551-5.85430
7-0.130552-1.49990.06801
80.7134198.19660
9-0.11228-1.290.099654
10-0.4956-5.6940
11-0.056412-0.64810.259014
120.7037998.0860
13-0.115689-1.32920.093043
14-0.491062-5.64190
15-0.098082-1.12690.130919
160.6136657.05050
17-0.075953-0.87260.192224
18-0.441046-5.06721e-06
19-0.147302-1.69240.046467
200.6643917.63330
21-0.033936-0.38990.348621
22-0.459268-5.27660
23-0.079554-0.9140.181191
240.6098267.00640
25-0.063607-0.73080.233101
26-0.40413-4.64314e-06
27-0.15703-1.80410.036745
280.5651586.49320
29-0.010837-0.12450.45055
30-0.437754-5.02941e-06
31-0.132728-1.52490.064834
320.597936.86970
33-0.0424-0.48710.313483
34-0.399547-4.59045e-06
35-0.114312-1.31330.095673
360.5499376.31830
37-0.050075-0.57530.283027
38-0.359243-4.12743.2e-05
39-0.152265-1.74940.041274
400.5326916.12020
41-0.036583-0.42030.337474
42-0.358587-4.11993.3e-05
43-0.118799-1.36490.087304
440.5071615.82680
45-0.022322-0.25650.398999
46-0.320971-3.68770.000165
47-0.099893-1.14770.126587
480.4380645.0331e-06







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.147529-1.6950.046218
2-0.595246-6.83890
3-0.454527-5.22210
40.4470925.13670
5-0.056271-0.64650.259536
6-0.118835-1.36530.087238
7-0.429259-4.93181e-06
80.1594271.83170.034627
9-0.04463-0.51280.304488
10-0.058471-0.67180.251449
11-0.037161-0.42690.335056
120.1612681.85280.03307
130.0679650.78090.218142
14-0.047373-0.54430.293584
15-0.164308-1.88780.030627
16-0.157399-1.80840.036412
170.0091740.10540.458108
180.0928621.06690.143982
19-0.164586-1.8910.030411
200.1174011.34880.08985
210.0291110.33450.369283
220.0074540.08560.465943
230.0789090.90660.183137
24-0.001084-0.01250.495041
250.0509320.58520.279719
260.1392311.59960.056034
27-0.014702-0.16890.433063
280.0014610.01680.493317
290.0041210.04730.481155
30-0.050399-0.5790.281776
310.0074180.08520.466105
320.0680430.78180.217879
33-0.060208-0.69170.245159
340.0266950.30670.379777
350.0006920.0080.496833
360.0540740.62130.267748
37-0.052826-0.60690.272471
380.0649690.74640.228365
390.0079030.09080.463893
400.0535630.61540.269676
41-0.058427-0.67130.251608
420.0005240.0060.497602
43-0.002257-0.02590.489676
44-0.070008-0.80430.211326
450.106551.22420.111534
46-0.028299-0.32510.372797
470.14861.70730.04506
48-0.027278-0.31340.377234

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.147529 & -1.695 & 0.046218 \tabularnewline
2 & -0.595246 & -6.8389 & 0 \tabularnewline
3 & -0.454527 & -5.2221 & 0 \tabularnewline
4 & 0.447092 & 5.1367 & 0 \tabularnewline
5 & -0.056271 & -0.6465 & 0.259536 \tabularnewline
6 & -0.118835 & -1.3653 & 0.087238 \tabularnewline
7 & -0.429259 & -4.9318 & 1e-06 \tabularnewline
8 & 0.159427 & 1.8317 & 0.034627 \tabularnewline
9 & -0.04463 & -0.5128 & 0.304488 \tabularnewline
10 & -0.058471 & -0.6718 & 0.251449 \tabularnewline
11 & -0.037161 & -0.4269 & 0.335056 \tabularnewline
12 & 0.161268 & 1.8528 & 0.03307 \tabularnewline
13 & 0.067965 & 0.7809 & 0.218142 \tabularnewline
14 & -0.047373 & -0.5443 & 0.293584 \tabularnewline
15 & -0.164308 & -1.8878 & 0.030627 \tabularnewline
16 & -0.157399 & -1.8084 & 0.036412 \tabularnewline
17 & 0.009174 & 0.1054 & 0.458108 \tabularnewline
18 & 0.092862 & 1.0669 & 0.143982 \tabularnewline
19 & -0.164586 & -1.891 & 0.030411 \tabularnewline
20 & 0.117401 & 1.3488 & 0.08985 \tabularnewline
21 & 0.029111 & 0.3345 & 0.369283 \tabularnewline
22 & 0.007454 & 0.0856 & 0.465943 \tabularnewline
23 & 0.078909 & 0.9066 & 0.183137 \tabularnewline
24 & -0.001084 & -0.0125 & 0.495041 \tabularnewline
25 & 0.050932 & 0.5852 & 0.279719 \tabularnewline
26 & 0.139231 & 1.5996 & 0.056034 \tabularnewline
27 & -0.014702 & -0.1689 & 0.433063 \tabularnewline
28 & 0.001461 & 0.0168 & 0.493317 \tabularnewline
29 & 0.004121 & 0.0473 & 0.481155 \tabularnewline
30 & -0.050399 & -0.579 & 0.281776 \tabularnewline
31 & 0.007418 & 0.0852 & 0.466105 \tabularnewline
32 & 0.068043 & 0.7818 & 0.217879 \tabularnewline
33 & -0.060208 & -0.6917 & 0.245159 \tabularnewline
34 & 0.026695 & 0.3067 & 0.379777 \tabularnewline
35 & 0.000692 & 0.008 & 0.496833 \tabularnewline
36 & 0.054074 & 0.6213 & 0.267748 \tabularnewline
37 & -0.052826 & -0.6069 & 0.272471 \tabularnewline
38 & 0.064969 & 0.7464 & 0.228365 \tabularnewline
39 & 0.007903 & 0.0908 & 0.463893 \tabularnewline
40 & 0.053563 & 0.6154 & 0.269676 \tabularnewline
41 & -0.058427 & -0.6713 & 0.251608 \tabularnewline
42 & 0.000524 & 0.006 & 0.497602 \tabularnewline
43 & -0.002257 & -0.0259 & 0.489676 \tabularnewline
44 & -0.070008 & -0.8043 & 0.211326 \tabularnewline
45 & 0.10655 & 1.2242 & 0.111534 \tabularnewline
46 & -0.028299 & -0.3251 & 0.372797 \tabularnewline
47 & 0.1486 & 1.7073 & 0.04506 \tabularnewline
48 & -0.027278 & -0.3134 & 0.377234 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75273&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]-0.147529[/C][C]-1.695[/C][C]0.046218[/C][/ROW]
[ROW][C]2[/C][C]-0.595246[/C][C]-6.8389[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]-0.454527[/C][C]-5.2221[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.447092[/C][C]5.1367[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]-0.056271[/C][C]-0.6465[/C][C]0.259536[/C][/ROW]
[ROW][C]6[/C][C]-0.118835[/C][C]-1.3653[/C][C]0.087238[/C][/ROW]
[ROW][C]7[/C][C]-0.429259[/C][C]-4.9318[/C][C]1e-06[/C][/ROW]
[ROW][C]8[/C][C]0.159427[/C][C]1.8317[/C][C]0.034627[/C][/ROW]
[ROW][C]9[/C][C]-0.04463[/C][C]-0.5128[/C][C]0.304488[/C][/ROW]
[ROW][C]10[/C][C]-0.058471[/C][C]-0.6718[/C][C]0.251449[/C][/ROW]
[ROW][C]11[/C][C]-0.037161[/C][C]-0.4269[/C][C]0.335056[/C][/ROW]
[ROW][C]12[/C][C]0.161268[/C][C]1.8528[/C][C]0.03307[/C][/ROW]
[ROW][C]13[/C][C]0.067965[/C][C]0.7809[/C][C]0.218142[/C][/ROW]
[ROW][C]14[/C][C]-0.047373[/C][C]-0.5443[/C][C]0.293584[/C][/ROW]
[ROW][C]15[/C][C]-0.164308[/C][C]-1.8878[/C][C]0.030627[/C][/ROW]
[ROW][C]16[/C][C]-0.157399[/C][C]-1.8084[/C][C]0.036412[/C][/ROW]
[ROW][C]17[/C][C]0.009174[/C][C]0.1054[/C][C]0.458108[/C][/ROW]
[ROW][C]18[/C][C]0.092862[/C][C]1.0669[/C][C]0.143982[/C][/ROW]
[ROW][C]19[/C][C]-0.164586[/C][C]-1.891[/C][C]0.030411[/C][/ROW]
[ROW][C]20[/C][C]0.117401[/C][C]1.3488[/C][C]0.08985[/C][/ROW]
[ROW][C]21[/C][C]0.029111[/C][C]0.3345[/C][C]0.369283[/C][/ROW]
[ROW][C]22[/C][C]0.007454[/C][C]0.0856[/C][C]0.465943[/C][/ROW]
[ROW][C]23[/C][C]0.078909[/C][C]0.9066[/C][C]0.183137[/C][/ROW]
[ROW][C]24[/C][C]-0.001084[/C][C]-0.0125[/C][C]0.495041[/C][/ROW]
[ROW][C]25[/C][C]0.050932[/C][C]0.5852[/C][C]0.279719[/C][/ROW]
[ROW][C]26[/C][C]0.139231[/C][C]1.5996[/C][C]0.056034[/C][/ROW]
[ROW][C]27[/C][C]-0.014702[/C][C]-0.1689[/C][C]0.433063[/C][/ROW]
[ROW][C]28[/C][C]0.001461[/C][C]0.0168[/C][C]0.493317[/C][/ROW]
[ROW][C]29[/C][C]0.004121[/C][C]0.0473[/C][C]0.481155[/C][/ROW]
[ROW][C]30[/C][C]-0.050399[/C][C]-0.579[/C][C]0.281776[/C][/ROW]
[ROW][C]31[/C][C]0.007418[/C][C]0.0852[/C][C]0.466105[/C][/ROW]
[ROW][C]32[/C][C]0.068043[/C][C]0.7818[/C][C]0.217879[/C][/ROW]
[ROW][C]33[/C][C]-0.060208[/C][C]-0.6917[/C][C]0.245159[/C][/ROW]
[ROW][C]34[/C][C]0.026695[/C][C]0.3067[/C][C]0.379777[/C][/ROW]
[ROW][C]35[/C][C]0.000692[/C][C]0.008[/C][C]0.496833[/C][/ROW]
[ROW][C]36[/C][C]0.054074[/C][C]0.6213[/C][C]0.267748[/C][/ROW]
[ROW][C]37[/C][C]-0.052826[/C][C]-0.6069[/C][C]0.272471[/C][/ROW]
[ROW][C]38[/C][C]0.064969[/C][C]0.7464[/C][C]0.228365[/C][/ROW]
[ROW][C]39[/C][C]0.007903[/C][C]0.0908[/C][C]0.463893[/C][/ROW]
[ROW][C]40[/C][C]0.053563[/C][C]0.6154[/C][C]0.269676[/C][/ROW]
[ROW][C]41[/C][C]-0.058427[/C][C]-0.6713[/C][C]0.251608[/C][/ROW]
[ROW][C]42[/C][C]0.000524[/C][C]0.006[/C][C]0.497602[/C][/ROW]
[ROW][C]43[/C][C]-0.002257[/C][C]-0.0259[/C][C]0.489676[/C][/ROW]
[ROW][C]44[/C][C]-0.070008[/C][C]-0.8043[/C][C]0.211326[/C][/ROW]
[ROW][C]45[/C][C]0.10655[/C][C]1.2242[/C][C]0.111534[/C][/ROW]
[ROW][C]46[/C][C]-0.028299[/C][C]-0.3251[/C][C]0.372797[/C][/ROW]
[ROW][C]47[/C][C]0.1486[/C][C]1.7073[/C][C]0.04506[/C][/ROW]
[ROW][C]48[/C][C]-0.027278[/C][C]-0.3134[/C][C]0.377234[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75273&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
1-0.147529-1.6950.046218
2-0.595246-6.83890
3-0.454527-5.22210
40.4470925.13670
5-0.056271-0.64650.259536
6-0.118835-1.36530.087238
7-0.429259-4.93181e-06
80.1594271.83170.034627
9-0.04463-0.51280.304488
10-0.058471-0.67180.251449
11-0.037161-0.42690.335056
120.1612681.85280.03307
130.0679650.78090.218142
14-0.047373-0.54430.293584
15-0.164308-1.88780.030627
16-0.157399-1.80840.036412
170.0091740.10540.458108
180.0928621.06690.143982
19-0.164586-1.8910.030411
200.1174011.34880.08985
210.0291110.33450.369283
220.0074540.08560.465943
230.0789090.90660.183137
24-0.001084-0.01250.495041
250.0509320.58520.279719
260.1392311.59960.056034
27-0.014702-0.16890.433063
280.0014610.01680.493317
290.0041210.04730.481155
30-0.050399-0.5790.281776
310.0074180.08520.466105
320.0680430.78180.217879
33-0.060208-0.69170.245159
340.0266950.30670.379777
350.0006920.0080.496833
360.0540740.62130.267748
37-0.052826-0.60690.272471
380.0649690.74640.228365
390.0079030.09080.463893
400.0535630.61540.269676
41-0.058427-0.67130.251608
420.0005240.0060.497602
43-0.002257-0.02590.489676
44-0.070008-0.80430.211326
450.106551.22420.111534
46-0.028299-0.32510.372797
470.14861.70730.04506
48-0.027278-0.31340.377234



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