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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationThu, 14 Mar 2013 08:52:49 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Mar/14/t1363265646z2bnj7bjbl6s6uu.htm/, Retrieved Sat, 04 May 2024 08:55:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=207778, Retrieved Sat, 04 May 2024 08:55:29 +0000
QR Codes:

Original text written by user:Degree of non-seasonal differencing: gem farma consumptieprijzen
IsPrivate?No (this computation is public)
User-defined keywordsDegree of non-seasonal differencing: gem farma consumptieprijzen
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2013-03-14 12:34:56] [2a07394484eafc374c02f189b1f9230e]
-   PD    [(Partial) Autocorrelation Function] [] [2013-03-14 12:52:49] [0941a6a4eb2aa1312aa94e558e86fae5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.71
105.82
105.82
105.72
105.76
105.80
105.09
105.06
105.16
105.20
105.21
105.23
105.19
105.16
104.88
104.52
104.09
104.35
104.48
104.47
104.55
104.59
104.59
104.72
104.65
104.72
104.92
105.05
103.74
103.81
103.79
104.28
103.80
103.80
104.02
104.02
104.91
104.97
103.86
104.17
103.21
103.21
101.91
101.84
101.91
101.79
101.79
101.79
102.09
102.18
102.20
101.97
102.05
102.04
101.78
101.79
101.80
101.83
101.83
101.88
101.90
101.91
101.17
101.17
101.23
101.26
101.49
101.51
101.61
101.39
101.43
101.44

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=207778&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=207778&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=207778&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.132941-1.12020.133207
20.083720.70540.241423
3-0.113711-0.95810.170621
40.018490.15580.438315
5-0.004489-0.03780.484966
6-0.237451-2.00080.024619
7-0.108892-0.91750.180982
8-0.104898-0.88390.189871
9-0.031765-0.26770.394869
100.185171.56030.061571
11-0.191727-1.61550.055316
120.1692621.42620.079093
130.0580090.48880.313248
140.1824241.53710.064352
15-0.132324-1.1150.134309
16-0.000971-0.00820.496749
17-0.037493-0.31590.376495
18-0.016001-0.13480.446564
19-0.026229-0.2210.41286
20-0.088411-0.7450.229375
21-0.117705-0.99180.162331
220.1780781.50050.068958
23-0.043664-0.36790.357014
240.0481580.40580.343061
25-0.067461-0.56840.285767
260.0332620.28030.390042
270.042080.35460.361979
28-0.061726-0.52010.302303
290.0060150.05070.479861
30-0.037269-0.3140.377208
31-0.03737-0.31490.376886
320.0655610.55240.291195
33-0.100768-0.84910.199344
340.1361881.14750.127505
350.0222950.18790.425761
360.0481780.4060.342999
37-0.018813-0.15850.437247
38-0.052358-0.44120.330213
390.0015640.01320.494761
40-0.008069-0.0680.472991
410.0031860.02680.489328
42-0.042725-0.360.359955
43-0.016208-0.13660.445877
44-0.010969-0.09240.46331
45-0.002882-0.02430.490348
460.0245120.20650.41848
470.0379220.31950.375128
480.048680.41020.341454

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.132941 & -1.1202 & 0.133207 \tabularnewline
2 & 0.08372 & 0.7054 & 0.241423 \tabularnewline
3 & -0.113711 & -0.9581 & 0.170621 \tabularnewline
4 & 0.01849 & 0.1558 & 0.438315 \tabularnewline
5 & -0.004489 & -0.0378 & 0.484966 \tabularnewline
6 & -0.237451 & -2.0008 & 0.024619 \tabularnewline
7 & -0.108892 & -0.9175 & 0.180982 \tabularnewline
8 & -0.104898 & -0.8839 & 0.189871 \tabularnewline
9 & -0.031765 & -0.2677 & 0.394869 \tabularnewline
10 & 0.18517 & 1.5603 & 0.061571 \tabularnewline
11 & -0.191727 & -1.6155 & 0.055316 \tabularnewline
12 & 0.169262 & 1.4262 & 0.079093 \tabularnewline
13 & 0.058009 & 0.4888 & 0.313248 \tabularnewline
14 & 0.182424 & 1.5371 & 0.064352 \tabularnewline
15 & -0.132324 & -1.115 & 0.134309 \tabularnewline
16 & -0.000971 & -0.0082 & 0.496749 \tabularnewline
17 & -0.037493 & -0.3159 & 0.376495 \tabularnewline
18 & -0.016001 & -0.1348 & 0.446564 \tabularnewline
19 & -0.026229 & -0.221 & 0.41286 \tabularnewline
20 & -0.088411 & -0.745 & 0.229375 \tabularnewline
21 & -0.117705 & -0.9918 & 0.162331 \tabularnewline
22 & 0.178078 & 1.5005 & 0.068958 \tabularnewline
23 & -0.043664 & -0.3679 & 0.357014 \tabularnewline
24 & 0.048158 & 0.4058 & 0.343061 \tabularnewline
25 & -0.067461 & -0.5684 & 0.285767 \tabularnewline
26 & 0.033262 & 0.2803 & 0.390042 \tabularnewline
27 & 0.04208 & 0.3546 & 0.361979 \tabularnewline
28 & -0.061726 & -0.5201 & 0.302303 \tabularnewline
29 & 0.006015 & 0.0507 & 0.479861 \tabularnewline
30 & -0.037269 & -0.314 & 0.377208 \tabularnewline
31 & -0.03737 & -0.3149 & 0.376886 \tabularnewline
32 & 0.065561 & 0.5524 & 0.291195 \tabularnewline
33 & -0.100768 & -0.8491 & 0.199344 \tabularnewline
34 & 0.136188 & 1.1475 & 0.127505 \tabularnewline
35 & 0.022295 & 0.1879 & 0.425761 \tabularnewline
36 & 0.048178 & 0.406 & 0.342999 \tabularnewline
37 & -0.018813 & -0.1585 & 0.437247 \tabularnewline
38 & -0.052358 & -0.4412 & 0.330213 \tabularnewline
39 & 0.001564 & 0.0132 & 0.494761 \tabularnewline
40 & -0.008069 & -0.068 & 0.472991 \tabularnewline
41 & 0.003186 & 0.0268 & 0.489328 \tabularnewline
42 & -0.042725 & -0.36 & 0.359955 \tabularnewline
43 & -0.016208 & -0.1366 & 0.445877 \tabularnewline
44 & -0.010969 & -0.0924 & 0.46331 \tabularnewline
45 & -0.002882 & -0.0243 & 0.490348 \tabularnewline
46 & 0.024512 & 0.2065 & 0.41848 \tabularnewline
47 & 0.037922 & 0.3195 & 0.375128 \tabularnewline
48 & 0.04868 & 0.4102 & 0.341454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=207778&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]-0.132941[/C][C]-1.1202[/C][C]0.133207[/C][/ROW]
[ROW][C]2[/C][C]0.08372[/C][C]0.7054[/C][C]0.241423[/C][/ROW]
[ROW][C]3[/C][C]-0.113711[/C][C]-0.9581[/C][C]0.170621[/C][/ROW]
[ROW][C]4[/C][C]0.01849[/C][C]0.1558[/C][C]0.438315[/C][/ROW]
[ROW][C]5[/C][C]-0.004489[/C][C]-0.0378[/C][C]0.484966[/C][/ROW]
[ROW][C]6[/C][C]-0.237451[/C][C]-2.0008[/C][C]0.024619[/C][/ROW]
[ROW][C]7[/C][C]-0.108892[/C][C]-0.9175[/C][C]0.180982[/C][/ROW]
[ROW][C]8[/C][C]-0.104898[/C][C]-0.8839[/C][C]0.189871[/C][/ROW]
[ROW][C]9[/C][C]-0.031765[/C][C]-0.2677[/C][C]0.394869[/C][/ROW]
[ROW][C]10[/C][C]0.18517[/C][C]1.5603[/C][C]0.061571[/C][/ROW]
[ROW][C]11[/C][C]-0.191727[/C][C]-1.6155[/C][C]0.055316[/C][/ROW]
[ROW][C]12[/C][C]0.169262[/C][C]1.4262[/C][C]0.079093[/C][/ROW]
[ROW][C]13[/C][C]0.058009[/C][C]0.4888[/C][C]0.313248[/C][/ROW]
[ROW][C]14[/C][C]0.182424[/C][C]1.5371[/C][C]0.064352[/C][/ROW]
[ROW][C]15[/C][C]-0.132324[/C][C]-1.115[/C][C]0.134309[/C][/ROW]
[ROW][C]16[/C][C]-0.000971[/C][C]-0.0082[/C][C]0.496749[/C][/ROW]
[ROW][C]17[/C][C]-0.037493[/C][C]-0.3159[/C][C]0.376495[/C][/ROW]
[ROW][C]18[/C][C]-0.016001[/C][C]-0.1348[/C][C]0.446564[/C][/ROW]
[ROW][C]19[/C][C]-0.026229[/C][C]-0.221[/C][C]0.41286[/C][/ROW]
[ROW][C]20[/C][C]-0.088411[/C][C]-0.745[/C][C]0.229375[/C][/ROW]
[ROW][C]21[/C][C]-0.117705[/C][C]-0.9918[/C][C]0.162331[/C][/ROW]
[ROW][C]22[/C][C]0.178078[/C][C]1.5005[/C][C]0.068958[/C][/ROW]
[ROW][C]23[/C][C]-0.043664[/C][C]-0.3679[/C][C]0.357014[/C][/ROW]
[ROW][C]24[/C][C]0.048158[/C][C]0.4058[/C][C]0.343061[/C][/ROW]
[ROW][C]25[/C][C]-0.067461[/C][C]-0.5684[/C][C]0.285767[/C][/ROW]
[ROW][C]26[/C][C]0.033262[/C][C]0.2803[/C][C]0.390042[/C][/ROW]
[ROW][C]27[/C][C]0.04208[/C][C]0.3546[/C][C]0.361979[/C][/ROW]
[ROW][C]28[/C][C]-0.061726[/C][C]-0.5201[/C][C]0.302303[/C][/ROW]
[ROW][C]29[/C][C]0.006015[/C][C]0.0507[/C][C]0.479861[/C][/ROW]
[ROW][C]30[/C][C]-0.037269[/C][C]-0.314[/C][C]0.377208[/C][/ROW]
[ROW][C]31[/C][C]-0.03737[/C][C]-0.3149[/C][C]0.376886[/C][/ROW]
[ROW][C]32[/C][C]0.065561[/C][C]0.5524[/C][C]0.291195[/C][/ROW]
[ROW][C]33[/C][C]-0.100768[/C][C]-0.8491[/C][C]0.199344[/C][/ROW]
[ROW][C]34[/C][C]0.136188[/C][C]1.1475[/C][C]0.127505[/C][/ROW]
[ROW][C]35[/C][C]0.022295[/C][C]0.1879[/C][C]0.425761[/C][/ROW]
[ROW][C]36[/C][C]0.048178[/C][C]0.406[/C][C]0.342999[/C][/ROW]
[ROW][C]37[/C][C]-0.018813[/C][C]-0.1585[/C][C]0.437247[/C][/ROW]
[ROW][C]38[/C][C]-0.052358[/C][C]-0.4412[/C][C]0.330213[/C][/ROW]
[ROW][C]39[/C][C]0.001564[/C][C]0.0132[/C][C]0.494761[/C][/ROW]
[ROW][C]40[/C][C]-0.008069[/C][C]-0.068[/C][C]0.472991[/C][/ROW]
[ROW][C]41[/C][C]0.003186[/C][C]0.0268[/C][C]0.489328[/C][/ROW]
[ROW][C]42[/C][C]-0.042725[/C][C]-0.36[/C][C]0.359955[/C][/ROW]
[ROW][C]43[/C][C]-0.016208[/C][C]-0.1366[/C][C]0.445877[/C][/ROW]
[ROW][C]44[/C][C]-0.010969[/C][C]-0.0924[/C][C]0.46331[/C][/ROW]
[ROW][C]45[/C][C]-0.002882[/C][C]-0.0243[/C][C]0.490348[/C][/ROW]
[ROW][C]46[/C][C]0.024512[/C][C]0.2065[/C][C]0.41848[/C][/ROW]
[ROW][C]47[/C][C]0.037922[/C][C]0.3195[/C][C]0.375128[/C][/ROW]
[ROW][C]48[/C][C]0.04868[/C][C]0.4102[/C][C]0.341454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=207778&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=207778&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.132941-1.12020.133207
20.083720.70540.241423
3-0.113711-0.95810.170621
40.018490.15580.438315
5-0.004489-0.03780.484966
6-0.237451-2.00080.024619
7-0.108892-0.91750.180982
8-0.104898-0.88390.189871
9-0.031765-0.26770.394869
100.185171.56030.061571
11-0.191727-1.61550.055316
120.1692621.42620.079093
130.0580090.48880.313248
140.1824241.53710.064352
15-0.132324-1.1150.134309
16-0.000971-0.00820.496749
17-0.037493-0.31590.376495
18-0.016001-0.13480.446564
19-0.026229-0.2210.41286
20-0.088411-0.7450.229375
21-0.117705-0.99180.162331
220.1780781.50050.068958
23-0.043664-0.36790.357014
240.0481580.40580.343061
25-0.067461-0.56840.285767
260.0332620.28030.390042
270.042080.35460.361979
28-0.061726-0.52010.302303
290.0060150.05070.479861
30-0.037269-0.3140.377208
31-0.03737-0.31490.376886
320.0655610.55240.291195
33-0.100768-0.84910.199344
340.1361881.14750.127505
350.0222950.18790.425761
360.0481780.4060.342999
37-0.018813-0.15850.437247
38-0.052358-0.44120.330213
390.0015640.01320.494761
40-0.008069-0.0680.472991
410.0031860.02680.489328
42-0.042725-0.360.359955
43-0.016208-0.13660.445877
44-0.010969-0.09240.46331
45-0.002882-0.02430.490348
460.0245120.20650.41848
470.0379220.31950.375128
480.048680.41020.341454






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.132941-1.12020.133207
20.0672350.56650.286409
3-0.096525-0.81330.209372
4-0.012728-0.10730.457446
50.0109280.09210.463447
6-0.255904-2.15630.017225
7-0.183289-1.54440.063466
8-0.129248-1.08910.139905
9-0.127615-1.07530.14294
100.156261.31670.096092
11-0.194744-1.64090.052616
120.0172040.1450.442577
130.0798740.6730.251556
140.0710080.59830.275763
15-0.154674-1.30330.09834
160.0170040.14330.44324
17-0.066982-0.56440.28713
18-0.026133-0.22020.413174
190.0222140.18720.426027
20-0.067438-0.56820.285831
21-0.109205-0.92020.180297
220.1351341.13870.129336
23-0.072234-0.60870.272347
24-0.065877-0.55510.29029
250.0320030.26970.394102
26-0.155432-1.30970.097261
27-0.015627-0.13170.447808
28-0.042456-0.35770.360799
29-0.048032-0.40470.343447
30-0.009487-0.07990.468254
31-0.054939-0.46290.322417
32-0.096702-0.81480.208948
33-0.030261-0.2550.399736
340.0846760.71350.23894
350.013840.11660.453746
36-0.037245-0.31380.377285
370.0029110.02450.49025
38-0.089144-0.75110.227525
39-0.054961-0.46310.322351
400.0491150.41390.340115
41-0.023595-0.19880.421488
42-0.011127-0.09380.462784
430.0431590.36370.358593
44-0.155517-1.31040.097141
45-0.020852-0.17570.430514
460.0068890.0580.476937
47-0.024607-0.20730.418169
480.0201110.16950.432958

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.132941 & -1.1202 & 0.133207 \tabularnewline
2 & 0.067235 & 0.5665 & 0.286409 \tabularnewline
3 & -0.096525 & -0.8133 & 0.209372 \tabularnewline
4 & -0.012728 & -0.1073 & 0.457446 \tabularnewline
5 & 0.010928 & 0.0921 & 0.463447 \tabularnewline
6 & -0.255904 & -2.1563 & 0.017225 \tabularnewline
7 & -0.183289 & -1.5444 & 0.063466 \tabularnewline
8 & -0.129248 & -1.0891 & 0.139905 \tabularnewline
9 & -0.127615 & -1.0753 & 0.14294 \tabularnewline
10 & 0.15626 & 1.3167 & 0.096092 \tabularnewline
11 & -0.194744 & -1.6409 & 0.052616 \tabularnewline
12 & 0.017204 & 0.145 & 0.442577 \tabularnewline
13 & 0.079874 & 0.673 & 0.251556 \tabularnewline
14 & 0.071008 & 0.5983 & 0.275763 \tabularnewline
15 & -0.154674 & -1.3033 & 0.09834 \tabularnewline
16 & 0.017004 & 0.1433 & 0.44324 \tabularnewline
17 & -0.066982 & -0.5644 & 0.28713 \tabularnewline
18 & -0.026133 & -0.2202 & 0.413174 \tabularnewline
19 & 0.022214 & 0.1872 & 0.426027 \tabularnewline
20 & -0.067438 & -0.5682 & 0.285831 \tabularnewline
21 & -0.109205 & -0.9202 & 0.180297 \tabularnewline
22 & 0.135134 & 1.1387 & 0.129336 \tabularnewline
23 & -0.072234 & -0.6087 & 0.272347 \tabularnewline
24 & -0.065877 & -0.5551 & 0.29029 \tabularnewline
25 & 0.032003 & 0.2697 & 0.394102 \tabularnewline
26 & -0.155432 & -1.3097 & 0.097261 \tabularnewline
27 & -0.015627 & -0.1317 & 0.447808 \tabularnewline
28 & -0.042456 & -0.3577 & 0.360799 \tabularnewline
29 & -0.048032 & -0.4047 & 0.343447 \tabularnewline
30 & -0.009487 & -0.0799 & 0.468254 \tabularnewline
31 & -0.054939 & -0.4629 & 0.322417 \tabularnewline
32 & -0.096702 & -0.8148 & 0.208948 \tabularnewline
33 & -0.030261 & -0.255 & 0.399736 \tabularnewline
34 & 0.084676 & 0.7135 & 0.23894 \tabularnewline
35 & 0.01384 & 0.1166 & 0.453746 \tabularnewline
36 & -0.037245 & -0.3138 & 0.377285 \tabularnewline
37 & 0.002911 & 0.0245 & 0.49025 \tabularnewline
38 & -0.089144 & -0.7511 & 0.227525 \tabularnewline
39 & -0.054961 & -0.4631 & 0.322351 \tabularnewline
40 & 0.049115 & 0.4139 & 0.340115 \tabularnewline
41 & -0.023595 & -0.1988 & 0.421488 \tabularnewline
42 & -0.011127 & -0.0938 & 0.462784 \tabularnewline
43 & 0.043159 & 0.3637 & 0.358593 \tabularnewline
44 & -0.155517 & -1.3104 & 0.097141 \tabularnewline
45 & -0.020852 & -0.1757 & 0.430514 \tabularnewline
46 & 0.006889 & 0.058 & 0.476937 \tabularnewline
47 & -0.024607 & -0.2073 & 0.418169 \tabularnewline
48 & 0.020111 & 0.1695 & 0.432958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=207778&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]-0.132941[/C][C]-1.1202[/C][C]0.133207[/C][/ROW]
[ROW][C]2[/C][C]0.067235[/C][C]0.5665[/C][C]0.286409[/C][/ROW]
[ROW][C]3[/C][C]-0.096525[/C][C]-0.8133[/C][C]0.209372[/C][/ROW]
[ROW][C]4[/C][C]-0.012728[/C][C]-0.1073[/C][C]0.457446[/C][/ROW]
[ROW][C]5[/C][C]0.010928[/C][C]0.0921[/C][C]0.463447[/C][/ROW]
[ROW][C]6[/C][C]-0.255904[/C][C]-2.1563[/C][C]0.017225[/C][/ROW]
[ROW][C]7[/C][C]-0.183289[/C][C]-1.5444[/C][C]0.063466[/C][/ROW]
[ROW][C]8[/C][C]-0.129248[/C][C]-1.0891[/C][C]0.139905[/C][/ROW]
[ROW][C]9[/C][C]-0.127615[/C][C]-1.0753[/C][C]0.14294[/C][/ROW]
[ROW][C]10[/C][C]0.15626[/C][C]1.3167[/C][C]0.096092[/C][/ROW]
[ROW][C]11[/C][C]-0.194744[/C][C]-1.6409[/C][C]0.052616[/C][/ROW]
[ROW][C]12[/C][C]0.017204[/C][C]0.145[/C][C]0.442577[/C][/ROW]
[ROW][C]13[/C][C]0.079874[/C][C]0.673[/C][C]0.251556[/C][/ROW]
[ROW][C]14[/C][C]0.071008[/C][C]0.5983[/C][C]0.275763[/C][/ROW]
[ROW][C]15[/C][C]-0.154674[/C][C]-1.3033[/C][C]0.09834[/C][/ROW]
[ROW][C]16[/C][C]0.017004[/C][C]0.1433[/C][C]0.44324[/C][/ROW]
[ROW][C]17[/C][C]-0.066982[/C][C]-0.5644[/C][C]0.28713[/C][/ROW]
[ROW][C]18[/C][C]-0.026133[/C][C]-0.2202[/C][C]0.413174[/C][/ROW]
[ROW][C]19[/C][C]0.022214[/C][C]0.1872[/C][C]0.426027[/C][/ROW]
[ROW][C]20[/C][C]-0.067438[/C][C]-0.5682[/C][C]0.285831[/C][/ROW]
[ROW][C]21[/C][C]-0.109205[/C][C]-0.9202[/C][C]0.180297[/C][/ROW]
[ROW][C]22[/C][C]0.135134[/C][C]1.1387[/C][C]0.129336[/C][/ROW]
[ROW][C]23[/C][C]-0.072234[/C][C]-0.6087[/C][C]0.272347[/C][/ROW]
[ROW][C]24[/C][C]-0.065877[/C][C]-0.5551[/C][C]0.29029[/C][/ROW]
[ROW][C]25[/C][C]0.032003[/C][C]0.2697[/C][C]0.394102[/C][/ROW]
[ROW][C]26[/C][C]-0.155432[/C][C]-1.3097[/C][C]0.097261[/C][/ROW]
[ROW][C]27[/C][C]-0.015627[/C][C]-0.1317[/C][C]0.447808[/C][/ROW]
[ROW][C]28[/C][C]-0.042456[/C][C]-0.3577[/C][C]0.360799[/C][/ROW]
[ROW][C]29[/C][C]-0.048032[/C][C]-0.4047[/C][C]0.343447[/C][/ROW]
[ROW][C]30[/C][C]-0.009487[/C][C]-0.0799[/C][C]0.468254[/C][/ROW]
[ROW][C]31[/C][C]-0.054939[/C][C]-0.4629[/C][C]0.322417[/C][/ROW]
[ROW][C]32[/C][C]-0.096702[/C][C]-0.8148[/C][C]0.208948[/C][/ROW]
[ROW][C]33[/C][C]-0.030261[/C][C]-0.255[/C][C]0.399736[/C][/ROW]
[ROW][C]34[/C][C]0.084676[/C][C]0.7135[/C][C]0.23894[/C][/ROW]
[ROW][C]35[/C][C]0.01384[/C][C]0.1166[/C][C]0.453746[/C][/ROW]
[ROW][C]36[/C][C]-0.037245[/C][C]-0.3138[/C][C]0.377285[/C][/ROW]
[ROW][C]37[/C][C]0.002911[/C][C]0.0245[/C][C]0.49025[/C][/ROW]
[ROW][C]38[/C][C]-0.089144[/C][C]-0.7511[/C][C]0.227525[/C][/ROW]
[ROW][C]39[/C][C]-0.054961[/C][C]-0.4631[/C][C]0.322351[/C][/ROW]
[ROW][C]40[/C][C]0.049115[/C][C]0.4139[/C][C]0.340115[/C][/ROW]
[ROW][C]41[/C][C]-0.023595[/C][C]-0.1988[/C][C]0.421488[/C][/ROW]
[ROW][C]42[/C][C]-0.011127[/C][C]-0.0938[/C][C]0.462784[/C][/ROW]
[ROW][C]43[/C][C]0.043159[/C][C]0.3637[/C][C]0.358593[/C][/ROW]
[ROW][C]44[/C][C]-0.155517[/C][C]-1.3104[/C][C]0.097141[/C][/ROW]
[ROW][C]45[/C][C]-0.020852[/C][C]-0.1757[/C][C]0.430514[/C][/ROW]
[ROW][C]46[/C][C]0.006889[/C][C]0.058[/C][C]0.476937[/C][/ROW]
[ROW][C]47[/C][C]-0.024607[/C][C]-0.2073[/C][C]0.418169[/C][/ROW]
[ROW][C]48[/C][C]0.020111[/C][C]0.1695[/C][C]0.432958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=207778&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=207778&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.132941-1.12020.133207
20.0672350.56650.286409
3-0.096525-0.81330.209372
4-0.012728-0.10730.457446
50.0109280.09210.463447
6-0.255904-2.15630.017225
7-0.183289-1.54440.063466
8-0.129248-1.08910.139905
9-0.127615-1.07530.14294
100.156261.31670.096092
11-0.194744-1.64090.052616
120.0172040.1450.442577
130.0798740.6730.251556
140.0710080.59830.275763
15-0.154674-1.30330.09834
160.0170040.14330.44324
17-0.066982-0.56440.28713
18-0.026133-0.22020.413174
190.0222140.18720.426027
20-0.067438-0.56820.285831
21-0.109205-0.92020.180297
220.1351341.13870.129336
23-0.072234-0.60870.272347
24-0.065877-0.55510.29029
250.0320030.26970.394102
26-0.155432-1.30970.097261
27-0.015627-0.13170.447808
28-0.042456-0.35770.360799
29-0.048032-0.40470.343447
30-0.009487-0.07990.468254
31-0.054939-0.46290.322417
32-0.096702-0.81480.208948
33-0.030261-0.2550.399736
340.0846760.71350.23894
350.013840.11660.453746
36-0.037245-0.31380.377285
370.0029110.02450.49025
38-0.089144-0.75110.227525
39-0.054961-0.46310.322351
400.0491150.41390.340115
41-0.023595-0.19880.421488
42-0.011127-0.09380.462784
430.0431590.36370.358593
44-0.155517-1.31040.097141
45-0.020852-0.17570.430514
460.0068890.0580.476937
47-0.024607-0.20730.418169
480.0201110.16950.432958


Figures (Output of Computation)

Input Parameters & R Code

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 ; par8 = ;
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 (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')