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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 computationFri, 17 Dec 2010 14:11:00 +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/17/t1292594948ug1eyfby6qqxybz.htm/, Retrieved Sun, 28 Apr 2024 23:19:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111479, Retrieved Sun, 28 Apr 2024 23:19:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Monthly US soldie...] [2010-11-02 12:07:39] [b98453cac15ba1066b407e146608df68]
- RMP   [(Partial) Autocorrelation Function] [Soldiers] [2010-11-29 09:48:36] [b98453cac15ba1066b407e146608df68]
-   PD    [(Partial) Autocorrelation Function] [] [2010-12-03 11:32:19] [8a9a6f7c332640af31ddca253a8ded58]
-   PD      [(Partial) Autocorrelation Function] [] [2010-12-03 14:14:33] [8a9a6f7c332640af31ddca253a8ded58]
-   P           [(Partial) Autocorrelation Function] [] [2010-12-17 14:11:00] [df17410ebb98883e83037e1662207ccb] [Current]
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Dataseries X:
101.76
102.37
102.38
102.86
102.87
102.92
102.95
103.02
104.08
104.16
104.24
104.33
104.73
104.86
105.03
105.62
105.63
105.63
105.94
106.61
107.69
107.78
107.93
108.48
108.14
108.48
108.48
108.89
108.93
109.21
109.47
109.80
111.73
111.85
112.12
112.15
112.17
112.67
112.80
113.44
113.53
114.53
114.51
115.05
116.67
117.07
116.92
117.00
117.02
117.35
117.36
117.82
117.88
118.24
118.50
118.80
119.76
120.09




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111479&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111479&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111479&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.243728-1.6350.054515
20.1387190.93060.178524
30.008370.05610.477735
40.2910491.95240.028563
5-0.103181-0.69220.246197
6-0.173061-1.16090.125897
70.2550011.71060.047021
8-0.285695-1.91650.030833
90.2897631.94380.029095
10-0.250304-1.67910.050033
110.2921781.960.028103
12-0.309274-2.07470.021882
130.1464610.98250.165556
14-0.040484-0.27160.393594
15-0.104534-0.70120.243384
16-0.055723-0.37380.355153
17-0.205059-1.37560.08788
180.2680671.79820.039424
19-0.212837-1.42780.080133
200.0856390.57450.28425
21-0.137971-0.92550.179812
220.3094172.07560.021836
23-0.112567-0.75510.227055
24-0.135622-0.90980.183893
250.0482210.32350.373916
260.0260250.17460.431095
27-0.032728-0.21950.413609
28-0.141388-0.94850.173981
290.1637411.09840.138935
30-0.129348-0.86770.195083
310.0317140.21270.416243
320.0094940.06370.47475
33-0.025533-0.17130.432385
34-0.09876-0.66250.255514
35-0.012853-0.08620.465836
36-0.000706-0.00470.498122
37-0.08916-0.59810.276384
38-0.039962-0.26810.394934
39-0.008345-0.0560.477801
400.0652730.43790.331789
41-0.048213-0.32340.373936
420.0006340.00430.498312
430.0605180.4060.343345
440.0067490.04530.482044
45NANANA
46NANANA
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.243728 & -1.635 & 0.054515 \tabularnewline
2 & 0.138719 & 0.9306 & 0.178524 \tabularnewline
3 & 0.00837 & 0.0561 & 0.477735 \tabularnewline
4 & 0.291049 & 1.9524 & 0.028563 \tabularnewline
5 & -0.103181 & -0.6922 & 0.246197 \tabularnewline
6 & -0.173061 & -1.1609 & 0.125897 \tabularnewline
7 & 0.255001 & 1.7106 & 0.047021 \tabularnewline
8 & -0.285695 & -1.9165 & 0.030833 \tabularnewline
9 & 0.289763 & 1.9438 & 0.029095 \tabularnewline
10 & -0.250304 & -1.6791 & 0.050033 \tabularnewline
11 & 0.292178 & 1.96 & 0.028103 \tabularnewline
12 & -0.309274 & -2.0747 & 0.021882 \tabularnewline
13 & 0.146461 & 0.9825 & 0.165556 \tabularnewline
14 & -0.040484 & -0.2716 & 0.393594 \tabularnewline
15 & -0.104534 & -0.7012 & 0.243384 \tabularnewline
16 & -0.055723 & -0.3738 & 0.355153 \tabularnewline
17 & -0.205059 & -1.3756 & 0.08788 \tabularnewline
18 & 0.268067 & 1.7982 & 0.039424 \tabularnewline
19 & -0.212837 & -1.4278 & 0.080133 \tabularnewline
20 & 0.085639 & 0.5745 & 0.28425 \tabularnewline
21 & -0.137971 & -0.9255 & 0.179812 \tabularnewline
22 & 0.309417 & 2.0756 & 0.021836 \tabularnewline
23 & -0.112567 & -0.7551 & 0.227055 \tabularnewline
24 & -0.135622 & -0.9098 & 0.183893 \tabularnewline
25 & 0.048221 & 0.3235 & 0.373916 \tabularnewline
26 & 0.026025 & 0.1746 & 0.431095 \tabularnewline
27 & -0.032728 & -0.2195 & 0.413609 \tabularnewline
28 & -0.141388 & -0.9485 & 0.173981 \tabularnewline
29 & 0.163741 & 1.0984 & 0.138935 \tabularnewline
30 & -0.129348 & -0.8677 & 0.195083 \tabularnewline
31 & 0.031714 & 0.2127 & 0.416243 \tabularnewline
32 & 0.009494 & 0.0637 & 0.47475 \tabularnewline
33 & -0.025533 & -0.1713 & 0.432385 \tabularnewline
34 & -0.09876 & -0.6625 & 0.255514 \tabularnewline
35 & -0.012853 & -0.0862 & 0.465836 \tabularnewline
36 & -0.000706 & -0.0047 & 0.498122 \tabularnewline
37 & -0.08916 & -0.5981 & 0.276384 \tabularnewline
38 & -0.039962 & -0.2681 & 0.394934 \tabularnewline
39 & -0.008345 & -0.056 & 0.477801 \tabularnewline
40 & 0.065273 & 0.4379 & 0.331789 \tabularnewline
41 & -0.048213 & -0.3234 & 0.373936 \tabularnewline
42 & 0.000634 & 0.0043 & 0.498312 \tabularnewline
43 & 0.060518 & 0.406 & 0.343345 \tabularnewline
44 & 0.006749 & 0.0453 & 0.482044 \tabularnewline
45 & NA & NA & NA \tabularnewline
46 & NA & NA & NA \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111479&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]-0.243728[/C][C]-1.635[/C][C]0.054515[/C][/ROW]
[ROW][C]2[/C][C]0.138719[/C][C]0.9306[/C][C]0.178524[/C][/ROW]
[ROW][C]3[/C][C]0.00837[/C][C]0.0561[/C][C]0.477735[/C][/ROW]
[ROW][C]4[/C][C]0.291049[/C][C]1.9524[/C][C]0.028563[/C][/ROW]
[ROW][C]5[/C][C]-0.103181[/C][C]-0.6922[/C][C]0.246197[/C][/ROW]
[ROW][C]6[/C][C]-0.173061[/C][C]-1.1609[/C][C]0.125897[/C][/ROW]
[ROW][C]7[/C][C]0.255001[/C][C]1.7106[/C][C]0.047021[/C][/ROW]
[ROW][C]8[/C][C]-0.285695[/C][C]-1.9165[/C][C]0.030833[/C][/ROW]
[ROW][C]9[/C][C]0.289763[/C][C]1.9438[/C][C]0.029095[/C][/ROW]
[ROW][C]10[/C][C]-0.250304[/C][C]-1.6791[/C][C]0.050033[/C][/ROW]
[ROW][C]11[/C][C]0.292178[/C][C]1.96[/C][C]0.028103[/C][/ROW]
[ROW][C]12[/C][C]-0.309274[/C][C]-2.0747[/C][C]0.021882[/C][/ROW]
[ROW][C]13[/C][C]0.146461[/C][C]0.9825[/C][C]0.165556[/C][/ROW]
[ROW][C]14[/C][C]-0.040484[/C][C]-0.2716[/C][C]0.393594[/C][/ROW]
[ROW][C]15[/C][C]-0.104534[/C][C]-0.7012[/C][C]0.243384[/C][/ROW]
[ROW][C]16[/C][C]-0.055723[/C][C]-0.3738[/C][C]0.355153[/C][/ROW]
[ROW][C]17[/C][C]-0.205059[/C][C]-1.3756[/C][C]0.08788[/C][/ROW]
[ROW][C]18[/C][C]0.268067[/C][C]1.7982[/C][C]0.039424[/C][/ROW]
[ROW][C]19[/C][C]-0.212837[/C][C]-1.4278[/C][C]0.080133[/C][/ROW]
[ROW][C]20[/C][C]0.085639[/C][C]0.5745[/C][C]0.28425[/C][/ROW]
[ROW][C]21[/C][C]-0.137971[/C][C]-0.9255[/C][C]0.179812[/C][/ROW]
[ROW][C]22[/C][C]0.309417[/C][C]2.0756[/C][C]0.021836[/C][/ROW]
[ROW][C]23[/C][C]-0.112567[/C][C]-0.7551[/C][C]0.227055[/C][/ROW]
[ROW][C]24[/C][C]-0.135622[/C][C]-0.9098[/C][C]0.183893[/C][/ROW]
[ROW][C]25[/C][C]0.048221[/C][C]0.3235[/C][C]0.373916[/C][/ROW]
[ROW][C]26[/C][C]0.026025[/C][C]0.1746[/C][C]0.431095[/C][/ROW]
[ROW][C]27[/C][C]-0.032728[/C][C]-0.2195[/C][C]0.413609[/C][/ROW]
[ROW][C]28[/C][C]-0.141388[/C][C]-0.9485[/C][C]0.173981[/C][/ROW]
[ROW][C]29[/C][C]0.163741[/C][C]1.0984[/C][C]0.138935[/C][/ROW]
[ROW][C]30[/C][C]-0.129348[/C][C]-0.8677[/C][C]0.195083[/C][/ROW]
[ROW][C]31[/C][C]0.031714[/C][C]0.2127[/C][C]0.416243[/C][/ROW]
[ROW][C]32[/C][C]0.009494[/C][C]0.0637[/C][C]0.47475[/C][/ROW]
[ROW][C]33[/C][C]-0.025533[/C][C]-0.1713[/C][C]0.432385[/C][/ROW]
[ROW][C]34[/C][C]-0.09876[/C][C]-0.6625[/C][C]0.255514[/C][/ROW]
[ROW][C]35[/C][C]-0.012853[/C][C]-0.0862[/C][C]0.465836[/C][/ROW]
[ROW][C]36[/C][C]-0.000706[/C][C]-0.0047[/C][C]0.498122[/C][/ROW]
[ROW][C]37[/C][C]-0.08916[/C][C]-0.5981[/C][C]0.276384[/C][/ROW]
[ROW][C]38[/C][C]-0.039962[/C][C]-0.2681[/C][C]0.394934[/C][/ROW]
[ROW][C]39[/C][C]-0.008345[/C][C]-0.056[/C][C]0.477801[/C][/ROW]
[ROW][C]40[/C][C]0.065273[/C][C]0.4379[/C][C]0.331789[/C][/ROW]
[ROW][C]41[/C][C]-0.048213[/C][C]-0.3234[/C][C]0.373936[/C][/ROW]
[ROW][C]42[/C][C]0.000634[/C][C]0.0043[/C][C]0.498312[/C][/ROW]
[ROW][C]43[/C][C]0.060518[/C][C]0.406[/C][C]0.343345[/C][/ROW]
[ROW][C]44[/C][C]0.006749[/C][C]0.0453[/C][C]0.482044[/C][/ROW]
[ROW][C]45[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]46[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]47[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]48[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111479&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111479&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
1-0.243728-1.6350.054515
20.1387190.93060.178524
30.008370.05610.477735
40.2910491.95240.028563
5-0.103181-0.69220.246197
6-0.173061-1.16090.125897
70.2550011.71060.047021
8-0.285695-1.91650.030833
90.2897631.94380.029095
10-0.250304-1.67910.050033
110.2921781.960.028103
12-0.309274-2.07470.021882
130.1464610.98250.165556
14-0.040484-0.27160.393594
15-0.104534-0.70120.243384
16-0.055723-0.37380.355153
17-0.205059-1.37560.08788
180.2680671.79820.039424
19-0.212837-1.42780.080133
200.0856390.57450.28425
21-0.137971-0.92550.179812
220.3094172.07560.021836
23-0.112567-0.75510.227055
24-0.135622-0.90980.183893
250.0482210.32350.373916
260.0260250.17460.431095
27-0.032728-0.21950.413609
28-0.141388-0.94850.173981
290.1637411.09840.138935
30-0.129348-0.86770.195083
310.0317140.21270.416243
320.0094940.06370.47475
33-0.025533-0.17130.432385
34-0.09876-0.66250.255514
35-0.012853-0.08620.465836
36-0.000706-0.00470.498122
37-0.08916-0.59810.276384
38-0.039962-0.26810.394934
39-0.008345-0.0560.477801
400.0652730.43790.331789
41-0.048213-0.32340.373936
420.0006340.00430.498312
430.0605180.4060.343345
440.0067490.04530.482044
45NANANA
46NANANA
47NANANA
48NANANA







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.243728-1.6350.054515
20.0843250.56570.287213
30.0641190.43010.334579
40.3170812.1270.019466
50.0323160.21680.41468
6-0.306899-2.05870.022668
70.1405530.94290.175395
8-0.284631-1.90940.031302
90.3091422.07380.021926
10-0.02482-0.16650.434257
110.1369380.91860.1816
12-0.210219-1.41020.082679
13-0.108258-0.72620.235733
14-0.035796-0.24010.405661
15-0.037297-0.25020.401787
16-0.105714-0.70920.240945
17-0.074201-0.49780.31054
180.0592470.39740.346461
190.2149671.4420.078108
20-0.140106-0.93990.176153
210.0565350.37920.353144
220.0591770.3970.346631
230.0207930.13950.444846
24-0.200883-1.34760.092272
25-0.105042-0.70460.242332
260.1023990.68690.247831
27-0.037339-0.25050.401679
280.0995390.66770.25386
29-0.04036-0.27070.393913
30-0.079138-0.53090.299059
31-0.090501-0.60710.273418
32-0.060934-0.40880.342328
33-0.09203-0.61740.270055
340.0693590.46530.321989
35-0.005407-0.03630.485615
36-0.089661-0.60150.275275
37-0.007434-0.04990.480225
380.0436650.29290.385468
39-0.046695-0.31320.377774
400.0428320.28730.38759
41-0.145712-0.97750.166781
420.0860170.5770.283402
430.0689610.46260.322938
44-0.088976-0.59690.276793
45NANANA
46NANANA
47NANANA
48NANANA

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.243728 & -1.635 & 0.054515 \tabularnewline
2 & 0.084325 & 0.5657 & 0.287213 \tabularnewline
3 & 0.064119 & 0.4301 & 0.334579 \tabularnewline
4 & 0.317081 & 2.127 & 0.019466 \tabularnewline
5 & 0.032316 & 0.2168 & 0.41468 \tabularnewline
6 & -0.306899 & -2.0587 & 0.022668 \tabularnewline
7 & 0.140553 & 0.9429 & 0.175395 \tabularnewline
8 & -0.284631 & -1.9094 & 0.031302 \tabularnewline
9 & 0.309142 & 2.0738 & 0.021926 \tabularnewline
10 & -0.02482 & -0.1665 & 0.434257 \tabularnewline
11 & 0.136938 & 0.9186 & 0.1816 \tabularnewline
12 & -0.210219 & -1.4102 & 0.082679 \tabularnewline
13 & -0.108258 & -0.7262 & 0.235733 \tabularnewline
14 & -0.035796 & -0.2401 & 0.405661 \tabularnewline
15 & -0.037297 & -0.2502 & 0.401787 \tabularnewline
16 & -0.105714 & -0.7092 & 0.240945 \tabularnewline
17 & -0.074201 & -0.4978 & 0.31054 \tabularnewline
18 & 0.059247 & 0.3974 & 0.346461 \tabularnewline
19 & 0.214967 & 1.442 & 0.078108 \tabularnewline
20 & -0.140106 & -0.9399 & 0.176153 \tabularnewline
21 & 0.056535 & 0.3792 & 0.353144 \tabularnewline
22 & 0.059177 & 0.397 & 0.346631 \tabularnewline
23 & 0.020793 & 0.1395 & 0.444846 \tabularnewline
24 & -0.200883 & -1.3476 & 0.092272 \tabularnewline
25 & -0.105042 & -0.7046 & 0.242332 \tabularnewline
26 & 0.102399 & 0.6869 & 0.247831 \tabularnewline
27 & -0.037339 & -0.2505 & 0.401679 \tabularnewline
28 & 0.099539 & 0.6677 & 0.25386 \tabularnewline
29 & -0.04036 & -0.2707 & 0.393913 \tabularnewline
30 & -0.079138 & -0.5309 & 0.299059 \tabularnewline
31 & -0.090501 & -0.6071 & 0.273418 \tabularnewline
32 & -0.060934 & -0.4088 & 0.342328 \tabularnewline
33 & -0.09203 & -0.6174 & 0.270055 \tabularnewline
34 & 0.069359 & 0.4653 & 0.321989 \tabularnewline
35 & -0.005407 & -0.0363 & 0.485615 \tabularnewline
36 & -0.089661 & -0.6015 & 0.275275 \tabularnewline
37 & -0.007434 & -0.0499 & 0.480225 \tabularnewline
38 & 0.043665 & 0.2929 & 0.385468 \tabularnewline
39 & -0.046695 & -0.3132 & 0.377774 \tabularnewline
40 & 0.042832 & 0.2873 & 0.38759 \tabularnewline
41 & -0.145712 & -0.9775 & 0.166781 \tabularnewline
42 & 0.086017 & 0.577 & 0.283402 \tabularnewline
43 & 0.068961 & 0.4626 & 0.322938 \tabularnewline
44 & -0.088976 & -0.5969 & 0.276793 \tabularnewline
45 & NA & NA & NA \tabularnewline
46 & NA & NA & NA \tabularnewline
47 & NA & NA & NA \tabularnewline
48 & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111479&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]-0.243728[/C][C]-1.635[/C][C]0.054515[/C][/ROW]
[ROW][C]2[/C][C]0.084325[/C][C]0.5657[/C][C]0.287213[/C][/ROW]
[ROW][C]3[/C][C]0.064119[/C][C]0.4301[/C][C]0.334579[/C][/ROW]
[ROW][C]4[/C][C]0.317081[/C][C]2.127[/C][C]0.019466[/C][/ROW]
[ROW][C]5[/C][C]0.032316[/C][C]0.2168[/C][C]0.41468[/C][/ROW]
[ROW][C]6[/C][C]-0.306899[/C][C]-2.0587[/C][C]0.022668[/C][/ROW]
[ROW][C]7[/C][C]0.140553[/C][C]0.9429[/C][C]0.175395[/C][/ROW]
[ROW][C]8[/C][C]-0.284631[/C][C]-1.9094[/C][C]0.031302[/C][/ROW]
[ROW][C]9[/C][C]0.309142[/C][C]2.0738[/C][C]0.021926[/C][/ROW]
[ROW][C]10[/C][C]-0.02482[/C][C]-0.1665[/C][C]0.434257[/C][/ROW]
[ROW][C]11[/C][C]0.136938[/C][C]0.9186[/C][C]0.1816[/C][/ROW]
[ROW][C]12[/C][C]-0.210219[/C][C]-1.4102[/C][C]0.082679[/C][/ROW]
[ROW][C]13[/C][C]-0.108258[/C][C]-0.7262[/C][C]0.235733[/C][/ROW]
[ROW][C]14[/C][C]-0.035796[/C][C]-0.2401[/C][C]0.405661[/C][/ROW]
[ROW][C]15[/C][C]-0.037297[/C][C]-0.2502[/C][C]0.401787[/C][/ROW]
[ROW][C]16[/C][C]-0.105714[/C][C]-0.7092[/C][C]0.240945[/C][/ROW]
[ROW][C]17[/C][C]-0.074201[/C][C]-0.4978[/C][C]0.31054[/C][/ROW]
[ROW][C]18[/C][C]0.059247[/C][C]0.3974[/C][C]0.346461[/C][/ROW]
[ROW][C]19[/C][C]0.214967[/C][C]1.442[/C][C]0.078108[/C][/ROW]
[ROW][C]20[/C][C]-0.140106[/C][C]-0.9399[/C][C]0.176153[/C][/ROW]
[ROW][C]21[/C][C]0.056535[/C][C]0.3792[/C][C]0.353144[/C][/ROW]
[ROW][C]22[/C][C]0.059177[/C][C]0.397[/C][C]0.346631[/C][/ROW]
[ROW][C]23[/C][C]0.020793[/C][C]0.1395[/C][C]0.444846[/C][/ROW]
[ROW][C]24[/C][C]-0.200883[/C][C]-1.3476[/C][C]0.092272[/C][/ROW]
[ROW][C]25[/C][C]-0.105042[/C][C]-0.7046[/C][C]0.242332[/C][/ROW]
[ROW][C]26[/C][C]0.102399[/C][C]0.6869[/C][C]0.247831[/C][/ROW]
[ROW][C]27[/C][C]-0.037339[/C][C]-0.2505[/C][C]0.401679[/C][/ROW]
[ROW][C]28[/C][C]0.099539[/C][C]0.6677[/C][C]0.25386[/C][/ROW]
[ROW][C]29[/C][C]-0.04036[/C][C]-0.2707[/C][C]0.393913[/C][/ROW]
[ROW][C]30[/C][C]-0.079138[/C][C]-0.5309[/C][C]0.299059[/C][/ROW]
[ROW][C]31[/C][C]-0.090501[/C][C]-0.6071[/C][C]0.273418[/C][/ROW]
[ROW][C]32[/C][C]-0.060934[/C][C]-0.4088[/C][C]0.342328[/C][/ROW]
[ROW][C]33[/C][C]-0.09203[/C][C]-0.6174[/C][C]0.270055[/C][/ROW]
[ROW][C]34[/C][C]0.069359[/C][C]0.4653[/C][C]0.321989[/C][/ROW]
[ROW][C]35[/C][C]-0.005407[/C][C]-0.0363[/C][C]0.485615[/C][/ROW]
[ROW][C]36[/C][C]-0.089661[/C][C]-0.6015[/C][C]0.275275[/C][/ROW]
[ROW][C]37[/C][C]-0.007434[/C][C]-0.0499[/C][C]0.480225[/C][/ROW]
[ROW][C]38[/C][C]0.043665[/C][C]0.2929[/C][C]0.385468[/C][/ROW]
[ROW][C]39[/C][C]-0.046695[/C][C]-0.3132[/C][C]0.377774[/C][/ROW]
[ROW][C]40[/C][C]0.042832[/C][C]0.2873[/C][C]0.38759[/C][/ROW]
[ROW][C]41[/C][C]-0.145712[/C][C]-0.9775[/C][C]0.166781[/C][/ROW]
[ROW][C]42[/C][C]0.086017[/C][C]0.577[/C][C]0.283402[/C][/ROW]
[ROW][C]43[/C][C]0.068961[/C][C]0.4626[/C][C]0.322938[/C][/ROW]
[ROW][C]44[/C][C]-0.088976[/C][C]-0.5969[/C][C]0.276793[/C][/ROW]
[ROW][C]45[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]46[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]47[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]48[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111479&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111479&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
1-0.243728-1.6350.054515
20.0843250.56570.287213
30.0641190.43010.334579
40.3170812.1270.019466
50.0323160.21680.41468
6-0.306899-2.05870.022668
70.1405530.94290.175395
8-0.284631-1.90940.031302
90.3091422.07380.021926
10-0.02482-0.16650.434257
110.1369380.91860.1816
12-0.210219-1.41020.082679
13-0.108258-0.72620.235733
14-0.035796-0.24010.405661
15-0.037297-0.25020.401787
16-0.105714-0.70920.240945
17-0.074201-0.49780.31054
180.0592470.39740.346461
190.2149671.4420.078108
20-0.140106-0.93990.176153
210.0565350.37920.353144
220.0591770.3970.346631
230.0207930.13950.444846
24-0.200883-1.34760.092272
25-0.105042-0.70460.242332
260.1023990.68690.247831
27-0.037339-0.25050.401679
280.0995390.66770.25386
29-0.04036-0.27070.393913
30-0.079138-0.53090.299059
31-0.090501-0.60710.273418
32-0.060934-0.40880.342328
33-0.09203-0.61740.270055
340.0693590.46530.321989
35-0.005407-0.03630.485615
36-0.089661-0.60150.275275
37-0.007434-0.04990.480225
380.0436650.29290.385468
39-0.046695-0.31320.377774
400.0428320.28730.38759
41-0.145712-0.97750.166781
420.0860170.5770.283402
430.0689610.46260.322938
44-0.088976-0.59690.276793
45NANANA
46NANANA
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 ; 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 (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')