Free Statistics

of Irreproducible Research!

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 computationTue, 04 Dec 2012 13:00:47 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354644107lv5xdns5c2rowa5.htm/, Retrieved Thu, 28 Mar 2024 16:42:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196443, Retrieved Thu, 28 Mar 2024 16:42:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [workshop 9] [2012-12-04 18:00:47] [468d5fc6f63a6d6dca168804c24af07d] [Current]
Feedback Forum

Post a new message
Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196443&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196443&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196443&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'George Udny Yule' @ yule.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.317498-2.19970.016339
20.237091.64260.0535
30.0738330.51150.305662
4-0.0085-0.05890.476642
50.0875610.60660.273473
6-0.094228-0.65280.258489
7-0.167911-1.16330.125224
8-0.021706-0.15040.440547
9-0.105651-0.7320.233873
10-0.199022-1.37890.087165
110.0744020.51550.304295
12-0.439446-3.04460.001888
130.11640.80640.211982
14-0.10566-0.7320.233854
150.0012040.00830.49669
160.0049190.03410.486477
170.0340480.23590.40726
180.2036361.41080.082372
190.0233930.16210.435966
20-0.004005-0.02770.48899
210.2168361.50230.069788
22-0.089567-0.62050.268921
230.1834171.27080.10497
24-0.067755-0.46940.320447
25-0.012621-0.08740.465342
260.045160.31290.377865
27-0.057715-0.39990.345517
280.0107330.07440.470516
29-0.015257-0.10570.458128
30-0.15548-1.07720.143388
310.0564010.39080.348852
320.054610.37840.353419
33-0.185412-1.28460.102554
340.14471.00250.160562
35-0.123601-0.85630.198035
360.0807060.55910.289331
370.0134140.09290.46317
38-0.025656-0.17770.429834
390.0259390.17970.429069
400.0145260.10060.460129
41-0.011891-0.08240.467343
420.0105090.07280.47113
43-0.008081-0.0560.477792
440.0024660.01710.493221
450.0029640.02050.491849
46-0.002432-0.01680.493314
470.0002240.00160.499384
48NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.317498 & -2.1997 & 0.016339 \tabularnewline
2 & 0.23709 & 1.6426 & 0.0535 \tabularnewline
3 & 0.073833 & 0.5115 & 0.305662 \tabularnewline
4 & -0.0085 & -0.0589 & 0.476642 \tabularnewline
5 & 0.087561 & 0.6066 & 0.273473 \tabularnewline
6 & -0.094228 & -0.6528 & 0.258489 \tabularnewline
7 & -0.167911 & -1.1633 & 0.125224 \tabularnewline
8 & -0.021706 & -0.1504 & 0.440547 \tabularnewline
9 & -0.105651 & -0.732 & 0.233873 \tabularnewline
10 & -0.199022 & -1.3789 & 0.087165 \tabularnewline
11 & 0.074402 & 0.5155 & 0.304295 \tabularnewline
12 & -0.439446 & -3.0446 & 0.001888 \tabularnewline
13 & 0.1164 & 0.8064 & 0.211982 \tabularnewline
14 & -0.10566 & -0.732 & 0.233854 \tabularnewline
15 & 0.001204 & 0.0083 & 0.49669 \tabularnewline
16 & 0.004919 & 0.0341 & 0.486477 \tabularnewline
17 & 0.034048 & 0.2359 & 0.40726 \tabularnewline
18 & 0.203636 & 1.4108 & 0.082372 \tabularnewline
19 & 0.023393 & 0.1621 & 0.435966 \tabularnewline
20 & -0.004005 & -0.0277 & 0.48899 \tabularnewline
21 & 0.216836 & 1.5023 & 0.069788 \tabularnewline
22 & -0.089567 & -0.6205 & 0.268921 \tabularnewline
23 & 0.183417 & 1.2708 & 0.10497 \tabularnewline
24 & -0.067755 & -0.4694 & 0.320447 \tabularnewline
25 & -0.012621 & -0.0874 & 0.465342 \tabularnewline
26 & 0.04516 & 0.3129 & 0.377865 \tabularnewline
27 & -0.057715 & -0.3999 & 0.345517 \tabularnewline
28 & 0.010733 & 0.0744 & 0.470516 \tabularnewline
29 & -0.015257 & -0.1057 & 0.458128 \tabularnewline
30 & -0.15548 & -1.0772 & 0.143388 \tabularnewline
31 & 0.056401 & 0.3908 & 0.348852 \tabularnewline
32 & 0.05461 & 0.3784 & 0.353419 \tabularnewline
33 & -0.185412 & -1.2846 & 0.102554 \tabularnewline
34 & 0.1447 & 1.0025 & 0.160562 \tabularnewline
35 & -0.123601 & -0.8563 & 0.198035 \tabularnewline
36 & 0.080706 & 0.5591 & 0.289331 \tabularnewline
37 & 0.013414 & 0.0929 & 0.46317 \tabularnewline
38 & -0.025656 & -0.1777 & 0.429834 \tabularnewline
39 & 0.025939 & 0.1797 & 0.429069 \tabularnewline
40 & 0.014526 & 0.1006 & 0.460129 \tabularnewline
41 & -0.011891 & -0.0824 & 0.467343 \tabularnewline
42 & 0.010509 & 0.0728 & 0.47113 \tabularnewline
43 & -0.008081 & -0.056 & 0.477792 \tabularnewline
44 & 0.002466 & 0.0171 & 0.493221 \tabularnewline
45 & 0.002964 & 0.0205 & 0.491849 \tabularnewline
46 & -0.002432 & -0.0168 & 0.493314 \tabularnewline
47 & 0.000224 & 0.0016 & 0.499384 \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196443&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]-0.317498[/C][C]-2.1997[/C][C]0.016339[/C][/ROW]
[ROW][C]2[/C][C]0.23709[/C][C]1.6426[/C][C]0.0535[/C][/ROW]
[ROW][C]3[/C][C]0.073833[/C][C]0.5115[/C][C]0.305662[/C][/ROW]
[ROW][C]4[/C][C]-0.0085[/C][C]-0.0589[/C][C]0.476642[/C][/ROW]
[ROW][C]5[/C][C]0.087561[/C][C]0.6066[/C][C]0.273473[/C][/ROW]
[ROW][C]6[/C][C]-0.094228[/C][C]-0.6528[/C][C]0.258489[/C][/ROW]
[ROW][C]7[/C][C]-0.167911[/C][C]-1.1633[/C][C]0.125224[/C][/ROW]
[ROW][C]8[/C][C]-0.021706[/C][C]-0.1504[/C][C]0.440547[/C][/ROW]
[ROW][C]9[/C][C]-0.105651[/C][C]-0.732[/C][C]0.233873[/C][/ROW]
[ROW][C]10[/C][C]-0.199022[/C][C]-1.3789[/C][C]0.087165[/C][/ROW]
[ROW][C]11[/C][C]0.074402[/C][C]0.5155[/C][C]0.304295[/C][/ROW]
[ROW][C]12[/C][C]-0.439446[/C][C]-3.0446[/C][C]0.001888[/C][/ROW]
[ROW][C]13[/C][C]0.1164[/C][C]0.8064[/C][C]0.211982[/C][/ROW]
[ROW][C]14[/C][C]-0.10566[/C][C]-0.732[/C][C]0.233854[/C][/ROW]
[ROW][C]15[/C][C]0.001204[/C][C]0.0083[/C][C]0.49669[/C][/ROW]
[ROW][C]16[/C][C]0.004919[/C][C]0.0341[/C][C]0.486477[/C][/ROW]
[ROW][C]17[/C][C]0.034048[/C][C]0.2359[/C][C]0.40726[/C][/ROW]
[ROW][C]18[/C][C]0.203636[/C][C]1.4108[/C][C]0.082372[/C][/ROW]
[ROW][C]19[/C][C]0.023393[/C][C]0.1621[/C][C]0.435966[/C][/ROW]
[ROW][C]20[/C][C]-0.004005[/C][C]-0.0277[/C][C]0.48899[/C][/ROW]
[ROW][C]21[/C][C]0.216836[/C][C]1.5023[/C][C]0.069788[/C][/ROW]
[ROW][C]22[/C][C]-0.089567[/C][C]-0.6205[/C][C]0.268921[/C][/ROW]
[ROW][C]23[/C][C]0.183417[/C][C]1.2708[/C][C]0.10497[/C][/ROW]
[ROW][C]24[/C][C]-0.067755[/C][C]-0.4694[/C][C]0.320447[/C][/ROW]
[ROW][C]25[/C][C]-0.012621[/C][C]-0.0874[/C][C]0.465342[/C][/ROW]
[ROW][C]26[/C][C]0.04516[/C][C]0.3129[/C][C]0.377865[/C][/ROW]
[ROW][C]27[/C][C]-0.057715[/C][C]-0.3999[/C][C]0.345517[/C][/ROW]
[ROW][C]28[/C][C]0.010733[/C][C]0.0744[/C][C]0.470516[/C][/ROW]
[ROW][C]29[/C][C]-0.015257[/C][C]-0.1057[/C][C]0.458128[/C][/ROW]
[ROW][C]30[/C][C]-0.15548[/C][C]-1.0772[/C][C]0.143388[/C][/ROW]
[ROW][C]31[/C][C]0.056401[/C][C]0.3908[/C][C]0.348852[/C][/ROW]
[ROW][C]32[/C][C]0.05461[/C][C]0.3784[/C][C]0.353419[/C][/ROW]
[ROW][C]33[/C][C]-0.185412[/C][C]-1.2846[/C][C]0.102554[/C][/ROW]
[ROW][C]34[/C][C]0.1447[/C][C]1.0025[/C][C]0.160562[/C][/ROW]
[ROW][C]35[/C][C]-0.123601[/C][C]-0.8563[/C][C]0.198035[/C][/ROW]
[ROW][C]36[/C][C]0.080706[/C][C]0.5591[/C][C]0.289331[/C][/ROW]
[ROW][C]37[/C][C]0.013414[/C][C]0.0929[/C][C]0.46317[/C][/ROW]
[ROW][C]38[/C][C]-0.025656[/C][C]-0.1777[/C][C]0.429834[/C][/ROW]
[ROW][C]39[/C][C]0.025939[/C][C]0.1797[/C][C]0.429069[/C][/ROW]
[ROW][C]40[/C][C]0.014526[/C][C]0.1006[/C][C]0.460129[/C][/ROW]
[ROW][C]41[/C][C]-0.011891[/C][C]-0.0824[/C][C]0.467343[/C][/ROW]
[ROW][C]42[/C][C]0.010509[/C][C]0.0728[/C][C]0.47113[/C][/ROW]
[ROW][C]43[/C][C]-0.008081[/C][C]-0.056[/C][C]0.477792[/C][/ROW]
[ROW][C]44[/C][C]0.002466[/C][C]0.0171[/C][C]0.493221[/C][/ROW]
[ROW][C]45[/C][C]0.002964[/C][C]0.0205[/C][C]0.491849[/C][/ROW]
[ROW][C]46[/C][C]-0.002432[/C][C]-0.0168[/C][C]0.493314[/C][/ROW]
[ROW][C]47[/C][C]0.000224[/C][C]0.0016[/C][C]0.499384[/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=196443&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196443&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
1-0.317498-2.19970.016339
20.237091.64260.0535
30.0738330.51150.305662
4-0.0085-0.05890.476642
50.0875610.60660.273473
6-0.094228-0.65280.258489
7-0.167911-1.16330.125224
8-0.021706-0.15040.440547
9-0.105651-0.7320.233873
10-0.199022-1.37890.087165
110.0744020.51550.304295
12-0.439446-3.04460.001888
130.11640.80640.211982
14-0.10566-0.7320.233854
150.0012040.00830.49669
160.0049190.03410.486477
170.0340480.23590.40726
180.2036361.41080.082372
190.0233930.16210.435966
20-0.004005-0.02770.48899
210.2168361.50230.069788
22-0.089567-0.62050.268921
230.1834171.27080.10497
24-0.067755-0.46940.320447
25-0.012621-0.08740.465342
260.045160.31290.377865
27-0.057715-0.39990.345517
280.0107330.07440.470516
29-0.015257-0.10570.458128
30-0.15548-1.07720.143388
310.0564010.39080.348852
320.054610.37840.353419
33-0.185412-1.28460.102554
340.14471.00250.160562
35-0.123601-0.85630.198035
360.0807060.55910.289331
370.0134140.09290.46317
38-0.025656-0.17770.429834
390.0259390.17970.429069
400.0145260.10060.460129
41-0.011891-0.08240.467343
420.0105090.07280.47113
43-0.008081-0.0560.477792
440.0024660.01710.493221
450.0029640.02050.491849
46-0.002432-0.01680.493314
470.0002240.00160.499384
48NANANA







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.317498-2.19970.016339
20.1515641.05010.149473
30.2115111.46540.074667
40.0374950.25980.398075
50.034830.24130.405171
6-0.097613-0.67630.251053
7-0.300549-2.08230.021336
8-0.180209-1.24850.108947
9-0.047568-0.32960.371584
10-0.180872-1.25310.108116
110.0528320.3660.357975
12-0.367502-2.54610.007078
13-0.229612-1.59080.05911
14-0.090358-0.6260.267135
150.0309860.21470.415465
16-0.035251-0.24420.404048
17-0.017644-0.12220.451608
180.1525731.05710.147889
19-0.086426-0.59880.27607
20-0.308332-2.13620.018896
210.0266080.18430.427259
22-0.208952-1.44770.077106
230.0575790.39890.34586
24-0.164161-1.13730.130521
25-0.1566-1.0850.14168
26-0.145022-1.00470.16003
27-0.080718-0.55920.289302
280.0289240.20040.42101
290.0549550.38070.352539
30-0.008544-0.05920.476521
31-0.050272-0.34830.364571
32-0.079047-0.54770.293234
33-0.069694-0.48290.315697
34-0.113372-0.78550.218021
350.009820.0680.473021
36-0.130569-0.90460.185095
37-0.07486-0.51860.303195
38-0.033763-0.23390.408022
39-0.094531-0.65490.25782
400.0021220.01470.494165
410.036360.25190.401094
42-0.068021-0.47130.319795
43-0.010315-0.07150.471664
440.021420.14840.441324
45-0.045552-0.31560.376841
46-0.095524-0.66180.255628
47-0.000613-0.00420.498316
48NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.317498 & -2.1997 & 0.016339 \tabularnewline
2 & 0.151564 & 1.0501 & 0.149473 \tabularnewline
3 & 0.211511 & 1.4654 & 0.074667 \tabularnewline
4 & 0.037495 & 0.2598 & 0.398075 \tabularnewline
5 & 0.03483 & 0.2413 & 0.405171 \tabularnewline
6 & -0.097613 & -0.6763 & 0.251053 \tabularnewline
7 & -0.300549 & -2.0823 & 0.021336 \tabularnewline
8 & -0.180209 & -1.2485 & 0.108947 \tabularnewline
9 & -0.047568 & -0.3296 & 0.371584 \tabularnewline
10 & -0.180872 & -1.2531 & 0.108116 \tabularnewline
11 & 0.052832 & 0.366 & 0.357975 \tabularnewline
12 & -0.367502 & -2.5461 & 0.007078 \tabularnewline
13 & -0.229612 & -1.5908 & 0.05911 \tabularnewline
14 & -0.090358 & -0.626 & 0.267135 \tabularnewline
15 & 0.030986 & 0.2147 & 0.415465 \tabularnewline
16 & -0.035251 & -0.2442 & 0.404048 \tabularnewline
17 & -0.017644 & -0.1222 & 0.451608 \tabularnewline
18 & 0.152573 & 1.0571 & 0.147889 \tabularnewline
19 & -0.086426 & -0.5988 & 0.27607 \tabularnewline
20 & -0.308332 & -2.1362 & 0.018896 \tabularnewline
21 & 0.026608 & 0.1843 & 0.427259 \tabularnewline
22 & -0.208952 & -1.4477 & 0.077106 \tabularnewline
23 & 0.057579 & 0.3989 & 0.34586 \tabularnewline
24 & -0.164161 & -1.1373 & 0.130521 \tabularnewline
25 & -0.1566 & -1.085 & 0.14168 \tabularnewline
26 & -0.145022 & -1.0047 & 0.16003 \tabularnewline
27 & -0.080718 & -0.5592 & 0.289302 \tabularnewline
28 & 0.028924 & 0.2004 & 0.42101 \tabularnewline
29 & 0.054955 & 0.3807 & 0.352539 \tabularnewline
30 & -0.008544 & -0.0592 & 0.476521 \tabularnewline
31 & -0.050272 & -0.3483 & 0.364571 \tabularnewline
32 & -0.079047 & -0.5477 & 0.293234 \tabularnewline
33 & -0.069694 & -0.4829 & 0.315697 \tabularnewline
34 & -0.113372 & -0.7855 & 0.218021 \tabularnewline
35 & 0.00982 & 0.068 & 0.473021 \tabularnewline
36 & -0.130569 & -0.9046 & 0.185095 \tabularnewline
37 & -0.07486 & -0.5186 & 0.303195 \tabularnewline
38 & -0.033763 & -0.2339 & 0.408022 \tabularnewline
39 & -0.094531 & -0.6549 & 0.25782 \tabularnewline
40 & 0.002122 & 0.0147 & 0.494165 \tabularnewline
41 & 0.03636 & 0.2519 & 0.401094 \tabularnewline
42 & -0.068021 & -0.4713 & 0.319795 \tabularnewline
43 & -0.010315 & -0.0715 & 0.471664 \tabularnewline
44 & 0.02142 & 0.1484 & 0.441324 \tabularnewline
45 & -0.045552 & -0.3156 & 0.376841 \tabularnewline
46 & -0.095524 & -0.6618 & 0.255628 \tabularnewline
47 & -0.000613 & -0.0042 & 0.498316 \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196443&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]-0.317498[/C][C]-2.1997[/C][C]0.016339[/C][/ROW]
[ROW][C]2[/C][C]0.151564[/C][C]1.0501[/C][C]0.149473[/C][/ROW]
[ROW][C]3[/C][C]0.211511[/C][C]1.4654[/C][C]0.074667[/C][/ROW]
[ROW][C]4[/C][C]0.037495[/C][C]0.2598[/C][C]0.398075[/C][/ROW]
[ROW][C]5[/C][C]0.03483[/C][C]0.2413[/C][C]0.405171[/C][/ROW]
[ROW][C]6[/C][C]-0.097613[/C][C]-0.6763[/C][C]0.251053[/C][/ROW]
[ROW][C]7[/C][C]-0.300549[/C][C]-2.0823[/C][C]0.021336[/C][/ROW]
[ROW][C]8[/C][C]-0.180209[/C][C]-1.2485[/C][C]0.108947[/C][/ROW]
[ROW][C]9[/C][C]-0.047568[/C][C]-0.3296[/C][C]0.371584[/C][/ROW]
[ROW][C]10[/C][C]-0.180872[/C][C]-1.2531[/C][C]0.108116[/C][/ROW]
[ROW][C]11[/C][C]0.052832[/C][C]0.366[/C][C]0.357975[/C][/ROW]
[ROW][C]12[/C][C]-0.367502[/C][C]-2.5461[/C][C]0.007078[/C][/ROW]
[ROW][C]13[/C][C]-0.229612[/C][C]-1.5908[/C][C]0.05911[/C][/ROW]
[ROW][C]14[/C][C]-0.090358[/C][C]-0.626[/C][C]0.267135[/C][/ROW]
[ROW][C]15[/C][C]0.030986[/C][C]0.2147[/C][C]0.415465[/C][/ROW]
[ROW][C]16[/C][C]-0.035251[/C][C]-0.2442[/C][C]0.404048[/C][/ROW]
[ROW][C]17[/C][C]-0.017644[/C][C]-0.1222[/C][C]0.451608[/C][/ROW]
[ROW][C]18[/C][C]0.152573[/C][C]1.0571[/C][C]0.147889[/C][/ROW]
[ROW][C]19[/C][C]-0.086426[/C][C]-0.5988[/C][C]0.27607[/C][/ROW]
[ROW][C]20[/C][C]-0.308332[/C][C]-2.1362[/C][C]0.018896[/C][/ROW]
[ROW][C]21[/C][C]0.026608[/C][C]0.1843[/C][C]0.427259[/C][/ROW]
[ROW][C]22[/C][C]-0.208952[/C][C]-1.4477[/C][C]0.077106[/C][/ROW]
[ROW][C]23[/C][C]0.057579[/C][C]0.3989[/C][C]0.34586[/C][/ROW]
[ROW][C]24[/C][C]-0.164161[/C][C]-1.1373[/C][C]0.130521[/C][/ROW]
[ROW][C]25[/C][C]-0.1566[/C][C]-1.085[/C][C]0.14168[/C][/ROW]
[ROW][C]26[/C][C]-0.145022[/C][C]-1.0047[/C][C]0.16003[/C][/ROW]
[ROW][C]27[/C][C]-0.080718[/C][C]-0.5592[/C][C]0.289302[/C][/ROW]
[ROW][C]28[/C][C]0.028924[/C][C]0.2004[/C][C]0.42101[/C][/ROW]
[ROW][C]29[/C][C]0.054955[/C][C]0.3807[/C][C]0.352539[/C][/ROW]
[ROW][C]30[/C][C]-0.008544[/C][C]-0.0592[/C][C]0.476521[/C][/ROW]
[ROW][C]31[/C][C]-0.050272[/C][C]-0.3483[/C][C]0.364571[/C][/ROW]
[ROW][C]32[/C][C]-0.079047[/C][C]-0.5477[/C][C]0.293234[/C][/ROW]
[ROW][C]33[/C][C]-0.069694[/C][C]-0.4829[/C][C]0.315697[/C][/ROW]
[ROW][C]34[/C][C]-0.113372[/C][C]-0.7855[/C][C]0.218021[/C][/ROW]
[ROW][C]35[/C][C]0.00982[/C][C]0.068[/C][C]0.473021[/C][/ROW]
[ROW][C]36[/C][C]-0.130569[/C][C]-0.9046[/C][C]0.185095[/C][/ROW]
[ROW][C]37[/C][C]-0.07486[/C][C]-0.5186[/C][C]0.303195[/C][/ROW]
[ROW][C]38[/C][C]-0.033763[/C][C]-0.2339[/C][C]0.408022[/C][/ROW]
[ROW][C]39[/C][C]-0.094531[/C][C]-0.6549[/C][C]0.25782[/C][/ROW]
[ROW][C]40[/C][C]0.002122[/C][C]0.0147[/C][C]0.494165[/C][/ROW]
[ROW][C]41[/C][C]0.03636[/C][C]0.2519[/C][C]0.401094[/C][/ROW]
[ROW][C]42[/C][C]-0.068021[/C][C]-0.4713[/C][C]0.319795[/C][/ROW]
[ROW][C]43[/C][C]-0.010315[/C][C]-0.0715[/C][C]0.471664[/C][/ROW]
[ROW][C]44[/C][C]0.02142[/C][C]0.1484[/C][C]0.441324[/C][/ROW]
[ROW][C]45[/C][C]-0.045552[/C][C]-0.3156[/C][C]0.376841[/C][/ROW]
[ROW][C]46[/C][C]-0.095524[/C][C]-0.6618[/C][C]0.255628[/C][/ROW]
[ROW][C]47[/C][C]-0.000613[/C][C]-0.0042[/C][C]0.498316[/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=196443&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196443&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
1-0.317498-2.19970.016339
20.1515641.05010.149473
30.2115111.46540.074667
40.0374950.25980.398075
50.034830.24130.405171
6-0.097613-0.67630.251053
7-0.300549-2.08230.021336
8-0.180209-1.24850.108947
9-0.047568-0.32960.371584
10-0.180872-1.25310.108116
110.0528320.3660.357975
12-0.367502-2.54610.007078
13-0.229612-1.59080.05911
14-0.090358-0.6260.267135
150.0309860.21470.415465
16-0.035251-0.24420.404048
17-0.017644-0.12220.451608
180.1525731.05710.147889
19-0.086426-0.59880.27607
20-0.308332-2.13620.018896
210.0266080.18430.427259
22-0.208952-1.44770.077106
230.0575790.39890.34586
24-0.164161-1.13730.130521
25-0.1566-1.0850.14168
26-0.145022-1.00470.16003
27-0.080718-0.55920.289302
280.0289240.20040.42101
290.0549550.38070.352539
30-0.008544-0.05920.476521
31-0.050272-0.34830.364571
32-0.079047-0.54770.293234
33-0.069694-0.48290.315697
34-0.113372-0.78550.218021
350.009820.0680.473021
36-0.130569-0.90460.185095
37-0.07486-0.51860.303195
38-0.033763-0.23390.408022
39-0.094531-0.65490.25782
400.0021220.01470.494165
410.036360.25190.401094
42-0.068021-0.47130.319795
43-0.010315-0.07150.471664
440.021420.14840.441324
45-0.045552-0.31560.376841
46-0.095524-0.66180.255628
47-0.000613-0.00420.498316
48NANANA



Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 2 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 2 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ; par8 = ;
R code (references can be found in the software module):
par8 <- ''
par7 <- '0.95'
par6 <- 'White Noise'
par5 <- '12'
par4 <- '2'
par3 <- '0'
par2 <- '1'
par1 <- 'Default'
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 (par8 != '') par8 <- as.numeric(par8)
ox <- x
if (par8 == '') {
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
} else {
x <- log(x,base=par8)
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='picts.png')
op <- par(mfrow=c(2,1))
plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
if (par8=='') {
mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
} else {
mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
}
plot(x,type='l', main=mytitle,xlab='time',ylab='value')
par(op)
dev.off()
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
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')