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Description of Statistical Computation

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 computationMon, 04 Dec 2017 14:53:33 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/04/t1512395796v025gvc0166wu0o.htm/, Retrieved Tue, 14 May 2024 12:24:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308511, Retrieved Tue, 14 May 2024 12:24:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87

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] [] [2017-12-04 13:53:33] [f1ade19563a25eb31edff11eb9af1158] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
76.7
82.7
95
81.8
91.8
88.2
72.2
75.9
91.2
94.2
90.4
73.1
84.3
82.8
92.8
83.5
90.4
91.8
75.5
75.1
89.1
95.4
85.2
68.4
81
78.8
85.7
88.1
82.7
90.1
79.1
71.5
91
99
84.2
68.1
81.3
84.1
88.1
87.8
83
91.5
81
67.1
90.8
94.7
79.3
75.5
80.2
81.1
96.9
88.4
81.6
98.9
78.5
74.5
97.3
93
86.4
80.6
83.7
86.7
95.8
94.8
88.8
103.3
77.8
81.2
102.8
97.2
94.7
85.2
92.7
91.1
103.5
92.7
102
105.9
84
86.4
105
108.6
103.5
85.2
101.8
99.1
112.8
100.7
107.2
113.8
94.4
90.9
105.5
118.2
108.5
83.9
108.6
109.5
106.5
116.5
104.6
112.3
101.2
86.9
112.8
110.4
91.2
80
86.9
85.1
95.6
92.6
87.7
98.7
85.6
77.4
102
101.9
91.9
81.6
91.6
92.9
109.3
103.8
96.8
117.4
93
89
110
106
100.4
90
97.4
101.6
117
97.7
110.8
103.1
86.1
92.9
112.9
102.4
103.3
89.8
96.5
102.2
112
100
103
112.4
94.5
92.6
104
108.4
100.1
79.1
100.9
97.4
101.7
104.9
103.1
108.2
99.9
85.6
106.8
113
99.3
84.2
104.3
99.1
107.3
111.7
99.9
107.5
101.3
86.1
113
114
96.4
85.5
99.5
102.4
117.9
110.2
99.2
121.8
103.1
89
112.5
114
103.7
89.5
102.1
107.3
118.3
112.3
107.9
121
95.8
96.1
114.9
109.8
106.7
89.5
104
106
124.6
106.8
115.4
120.5
95.5
97.8

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308511&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308511&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308511&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.4884217.11150
20.2578713.75470.000112
30.4021725.85570
40.460146.69970
50.4866637.08590
60.4410036.42110
70.4268226.21460
80.4453516.48440
90.3143274.57674e-06
100.1479412.15410.016182
110.4209856.12960
120.7596811.06110
130.3425844.98811e-06
140.1521192.21490.013916
150.2250243.27640.000614
160.3130094.55754e-06
170.3727875.42790
180.2819044.10462.9e-05
190.2898264.21991.8e-05
200.3414124.9711e-06
210.1688152.4580.007387
220.0529940.77160.220604
230.3250094.73222e-06
240.5771578.40350
250.2536023.69250.000141
260.0709861.03360.151255
270.083611.21740.112406
280.2449013.56580.000224
290.2756724.01384.1e-05
300.1568872.28430.011672
310.2258213.2880.000591
320.2375863.45930.000327
330.0589520.85840.195832
340.0243690.35480.361539
350.2170293.160.000904
360.4563296.64420
370.203512.96320.001696
38-0.026101-0.380.352148
390.0253150.36860.356402
400.2006912.92210.001927
410.1710332.49030.006766
420.0955781.39160.082746
430.185322.69830.003765
440.1282521.86740.031615
450.008410.12250.451326
46-0.008663-0.12610.449873
470.1119921.63060.052227
480.3849745.60530

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.488421 & 7.1115 & 0 \tabularnewline
2 & 0.257871 & 3.7547 & 0.000112 \tabularnewline
3 & 0.402172 & 5.8557 & 0 \tabularnewline
4 & 0.46014 & 6.6997 & 0 \tabularnewline
5 & 0.486663 & 7.0859 & 0 \tabularnewline
6 & 0.441003 & 6.4211 & 0 \tabularnewline
7 & 0.426822 & 6.2146 & 0 \tabularnewline
8 & 0.445351 & 6.4844 & 0 \tabularnewline
9 & 0.314327 & 4.5767 & 4e-06 \tabularnewline
10 & 0.147941 & 2.1541 & 0.016182 \tabularnewline
11 & 0.420985 & 6.1296 & 0 \tabularnewline
12 & 0.75968 & 11.0611 & 0 \tabularnewline
13 & 0.342584 & 4.9881 & 1e-06 \tabularnewline
14 & 0.152119 & 2.2149 & 0.013916 \tabularnewline
15 & 0.225024 & 3.2764 & 0.000614 \tabularnewline
16 & 0.313009 & 4.5575 & 4e-06 \tabularnewline
17 & 0.372787 & 5.4279 & 0 \tabularnewline
18 & 0.281904 & 4.1046 & 2.9e-05 \tabularnewline
19 & 0.289826 & 4.2199 & 1.8e-05 \tabularnewline
20 & 0.341412 & 4.971 & 1e-06 \tabularnewline
21 & 0.168815 & 2.458 & 0.007387 \tabularnewline
22 & 0.052994 & 0.7716 & 0.220604 \tabularnewline
23 & 0.325009 & 4.7322 & 2e-06 \tabularnewline
24 & 0.577157 & 8.4035 & 0 \tabularnewline
25 & 0.253602 & 3.6925 & 0.000141 \tabularnewline
26 & 0.070986 & 1.0336 & 0.151255 \tabularnewline
27 & 0.08361 & 1.2174 & 0.112406 \tabularnewline
28 & 0.244901 & 3.5658 & 0.000224 \tabularnewline
29 & 0.275672 & 4.0138 & 4.1e-05 \tabularnewline
30 & 0.156887 & 2.2843 & 0.011672 \tabularnewline
31 & 0.225821 & 3.288 & 0.000591 \tabularnewline
32 & 0.237586 & 3.4593 & 0.000327 \tabularnewline
33 & 0.058952 & 0.8584 & 0.195832 \tabularnewline
34 & 0.024369 & 0.3548 & 0.361539 \tabularnewline
35 & 0.217029 & 3.16 & 0.000904 \tabularnewline
36 & 0.456329 & 6.6442 & 0 \tabularnewline
37 & 0.20351 & 2.9632 & 0.001696 \tabularnewline
38 & -0.026101 & -0.38 & 0.352148 \tabularnewline
39 & 0.025315 & 0.3686 & 0.356402 \tabularnewline
40 & 0.200691 & 2.9221 & 0.001927 \tabularnewline
41 & 0.171033 & 2.4903 & 0.006766 \tabularnewline
42 & 0.095578 & 1.3916 & 0.082746 \tabularnewline
43 & 0.18532 & 2.6983 & 0.003765 \tabularnewline
44 & 0.128252 & 1.8674 & 0.031615 \tabularnewline
45 & 0.00841 & 0.1225 & 0.451326 \tabularnewline
46 & -0.008663 & -0.1261 & 0.449873 \tabularnewline
47 & 0.111992 & 1.6306 & 0.052227 \tabularnewline
48 & 0.384974 & 5.6053 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308511&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]0.488421[/C][C]7.1115[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.257871[/C][C]3.7547[/C][C]0.000112[/C][/ROW]
[ROW][C]3[/C][C]0.402172[/C][C]5.8557[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.46014[/C][C]6.6997[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.486663[/C][C]7.0859[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.441003[/C][C]6.4211[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.426822[/C][C]6.2146[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.445351[/C][C]6.4844[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.314327[/C][C]4.5767[/C][C]4e-06[/C][/ROW]
[ROW][C]10[/C][C]0.147941[/C][C]2.1541[/C][C]0.016182[/C][/ROW]
[ROW][C]11[/C][C]0.420985[/C][C]6.1296[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.75968[/C][C]11.0611[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.342584[/C][C]4.9881[/C][C]1e-06[/C][/ROW]
[ROW][C]14[/C][C]0.152119[/C][C]2.2149[/C][C]0.013916[/C][/ROW]
[ROW][C]15[/C][C]0.225024[/C][C]3.2764[/C][C]0.000614[/C][/ROW]
[ROW][C]16[/C][C]0.313009[/C][C]4.5575[/C][C]4e-06[/C][/ROW]
[ROW][C]17[/C][C]0.372787[/C][C]5.4279[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.281904[/C][C]4.1046[/C][C]2.9e-05[/C][/ROW]
[ROW][C]19[/C][C]0.289826[/C][C]4.2199[/C][C]1.8e-05[/C][/ROW]
[ROW][C]20[/C][C]0.341412[/C][C]4.971[/C][C]1e-06[/C][/ROW]
[ROW][C]21[/C][C]0.168815[/C][C]2.458[/C][C]0.007387[/C][/ROW]
[ROW][C]22[/C][C]0.052994[/C][C]0.7716[/C][C]0.220604[/C][/ROW]
[ROW][C]23[/C][C]0.325009[/C][C]4.7322[/C][C]2e-06[/C][/ROW]
[ROW][C]24[/C][C]0.577157[/C][C]8.4035[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.253602[/C][C]3.6925[/C][C]0.000141[/C][/ROW]
[ROW][C]26[/C][C]0.070986[/C][C]1.0336[/C][C]0.151255[/C][/ROW]
[ROW][C]27[/C][C]0.08361[/C][C]1.2174[/C][C]0.112406[/C][/ROW]
[ROW][C]28[/C][C]0.244901[/C][C]3.5658[/C][C]0.000224[/C][/ROW]
[ROW][C]29[/C][C]0.275672[/C][C]4.0138[/C][C]4.1e-05[/C][/ROW]
[ROW][C]30[/C][C]0.156887[/C][C]2.2843[/C][C]0.011672[/C][/ROW]
[ROW][C]31[/C][C]0.225821[/C][C]3.288[/C][C]0.000591[/C][/ROW]
[ROW][C]32[/C][C]0.237586[/C][C]3.4593[/C][C]0.000327[/C][/ROW]
[ROW][C]33[/C][C]0.058952[/C][C]0.8584[/C][C]0.195832[/C][/ROW]
[ROW][C]34[/C][C]0.024369[/C][C]0.3548[/C][C]0.361539[/C][/ROW]
[ROW][C]35[/C][C]0.217029[/C][C]3.16[/C][C]0.000904[/C][/ROW]
[ROW][C]36[/C][C]0.456329[/C][C]6.6442[/C][C]0[/C][/ROW]
[ROW][C]37[/C][C]0.20351[/C][C]2.9632[/C][C]0.001696[/C][/ROW]
[ROW][C]38[/C][C]-0.026101[/C][C]-0.38[/C][C]0.352148[/C][/ROW]
[ROW][C]39[/C][C]0.025315[/C][C]0.3686[/C][C]0.356402[/C][/ROW]
[ROW][C]40[/C][C]0.200691[/C][C]2.9221[/C][C]0.001927[/C][/ROW]
[ROW][C]41[/C][C]0.171033[/C][C]2.4903[/C][C]0.006766[/C][/ROW]
[ROW][C]42[/C][C]0.095578[/C][C]1.3916[/C][C]0.082746[/C][/ROW]
[ROW][C]43[/C][C]0.18532[/C][C]2.6983[/C][C]0.003765[/C][/ROW]
[ROW][C]44[/C][C]0.128252[/C][C]1.8674[/C][C]0.031615[/C][/ROW]
[ROW][C]45[/C][C]0.00841[/C][C]0.1225[/C][C]0.451326[/C][/ROW]
[ROW][C]46[/C][C]-0.008663[/C][C]-0.1261[/C][C]0.449873[/C][/ROW]
[ROW][C]47[/C][C]0.111992[/C][C]1.6306[/C][C]0.052227[/C][/ROW]
[ROW][C]48[/C][C]0.384974[/C][C]5.6053[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308511&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308511&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
10.4884217.11150
20.2578713.75470.000112
30.4021725.85570
40.460146.69970
50.4866637.08590
60.4410036.42110
70.4268226.21460
80.4453516.48440
90.3143274.57674e-06
100.1479412.15410.016182
110.4209856.12960
120.7596811.06110
130.3425844.98811e-06
140.1521192.21490.013916
150.2250243.27640.000614
160.3130094.55754e-06
170.3727875.42790
180.2819044.10462.9e-05
190.2898264.21991.8e-05
200.3414124.9711e-06
210.1688152.4580.007387
220.0529940.77160.220604
230.3250094.73222e-06
240.5771578.40350
250.2536023.69250.000141
260.0709861.03360.151255
270.083611.21740.112406
280.2449013.56580.000224
290.2756724.01384.1e-05
300.1568872.28430.011672
310.2258213.2880.000591
320.2375863.45930.000327
330.0589520.85840.195832
340.0243690.35480.361539
350.2170293.160.000904
360.4563296.64420
370.203512.96320.001696
38-0.026101-0.380.352148
390.0253150.36860.356402
400.2006912.92210.001927
410.1710332.49030.006766
420.0955781.39160.082746
430.185322.69830.003765
440.1282521.86740.031615
450.008410.12250.451326
46-0.008663-0.12610.449873
470.1119921.63060.052227
480.3849745.60530






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4884217.11150
20.0253670.36940.356117
30.3509125.10930
40.2059422.99860.001518
50.2878884.19172e-05
60.1422062.07060.019806
70.1763772.56810.005457
80.1299871.89260.029884
9-0.118828-1.73020.042528
10-0.325801-4.74372e-06
110.2028592.95370.001747
120.6023468.77030
13-0.2781-4.04923.6e-05
14-0.214754-3.12690.001007
15-0.259679-3.7810.000102
16-0.001783-0.0260.489658
170.029670.4320.333091
180.0176680.25730.398618
190.0683150.99470.160511
200.0660390.96150.168686
210.0155410.22630.410598
220.0549550.80020.212257
23-0.015814-0.23030.409055
240.0691141.00630.157706
25-0.018517-0.26960.393861
26-0.038858-0.56580.286068
27-0.158922-2.31390.010814
280.0776291.13030.129813
29-0.078001-1.13570.128679
30-0.026774-0.38980.348525
310.083681.21840.112214
32-0.028066-0.40860.341607
33-0.005596-0.08150.467571
340.1420552.06830.01991
35-0.090586-1.3190.094303
360.0964291.4040.080887
37-0.046393-0.67550.250049
38-0.057572-0.83830.201416
390.0547280.79690.213215
40-0.01028-0.14970.44058
41-0.106921-1.55680.060506
420.0021640.03150.487445
430.0518610.75510.225513
44-0.084214-1.22620.110748
450.0298490.43460.332142
460.0333920.48620.313668
47-0.062018-0.9030.183775
480.0819951.19390.116932

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.488421 & 7.1115 & 0 \tabularnewline
2 & 0.025367 & 0.3694 & 0.356117 \tabularnewline
3 & 0.350912 & 5.1093 & 0 \tabularnewline
4 & 0.205942 & 2.9986 & 0.001518 \tabularnewline
5 & 0.287888 & 4.1917 & 2e-05 \tabularnewline
6 & 0.142206 & 2.0706 & 0.019806 \tabularnewline
7 & 0.176377 & 2.5681 & 0.005457 \tabularnewline
8 & 0.129987 & 1.8926 & 0.029884 \tabularnewline
9 & -0.118828 & -1.7302 & 0.042528 \tabularnewline
10 & -0.325801 & -4.7437 & 2e-06 \tabularnewline
11 & 0.202859 & 2.9537 & 0.001747 \tabularnewline
12 & 0.602346 & 8.7703 & 0 \tabularnewline
13 & -0.2781 & -4.0492 & 3.6e-05 \tabularnewline
14 & -0.214754 & -3.1269 & 0.001007 \tabularnewline
15 & -0.259679 & -3.781 & 0.000102 \tabularnewline
16 & -0.001783 & -0.026 & 0.489658 \tabularnewline
17 & 0.02967 & 0.432 & 0.333091 \tabularnewline
18 & 0.017668 & 0.2573 & 0.398618 \tabularnewline
19 & 0.068315 & 0.9947 & 0.160511 \tabularnewline
20 & 0.066039 & 0.9615 & 0.168686 \tabularnewline
21 & 0.015541 & 0.2263 & 0.410598 \tabularnewline
22 & 0.054955 & 0.8002 & 0.212257 \tabularnewline
23 & -0.015814 & -0.2303 & 0.409055 \tabularnewline
24 & 0.069114 & 1.0063 & 0.157706 \tabularnewline
25 & -0.018517 & -0.2696 & 0.393861 \tabularnewline
26 & -0.038858 & -0.5658 & 0.286068 \tabularnewline
27 & -0.158922 & -2.3139 & 0.010814 \tabularnewline
28 & 0.077629 & 1.1303 & 0.129813 \tabularnewline
29 & -0.078001 & -1.1357 & 0.128679 \tabularnewline
30 & -0.026774 & -0.3898 & 0.348525 \tabularnewline
31 & 0.08368 & 1.2184 & 0.112214 \tabularnewline
32 & -0.028066 & -0.4086 & 0.341607 \tabularnewline
33 & -0.005596 & -0.0815 & 0.467571 \tabularnewline
34 & 0.142055 & 2.0683 & 0.01991 \tabularnewline
35 & -0.090586 & -1.319 & 0.094303 \tabularnewline
36 & 0.096429 & 1.404 & 0.080887 \tabularnewline
37 & -0.046393 & -0.6755 & 0.250049 \tabularnewline
38 & -0.057572 & -0.8383 & 0.201416 \tabularnewline
39 & 0.054728 & 0.7969 & 0.213215 \tabularnewline
40 & -0.01028 & -0.1497 & 0.44058 \tabularnewline
41 & -0.106921 & -1.5568 & 0.060506 \tabularnewline
42 & 0.002164 & 0.0315 & 0.487445 \tabularnewline
43 & 0.051861 & 0.7551 & 0.225513 \tabularnewline
44 & -0.084214 & -1.2262 & 0.110748 \tabularnewline
45 & 0.029849 & 0.4346 & 0.332142 \tabularnewline
46 & 0.033392 & 0.4862 & 0.313668 \tabularnewline
47 & -0.062018 & -0.903 & 0.183775 \tabularnewline
48 & 0.081995 & 1.1939 & 0.116932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308511&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]0.488421[/C][C]7.1115[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.025367[/C][C]0.3694[/C][C]0.356117[/C][/ROW]
[ROW][C]3[/C][C]0.350912[/C][C]5.1093[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.205942[/C][C]2.9986[/C][C]0.001518[/C][/ROW]
[ROW][C]5[/C][C]0.287888[/C][C]4.1917[/C][C]2e-05[/C][/ROW]
[ROW][C]6[/C][C]0.142206[/C][C]2.0706[/C][C]0.019806[/C][/ROW]
[ROW][C]7[/C][C]0.176377[/C][C]2.5681[/C][C]0.005457[/C][/ROW]
[ROW][C]8[/C][C]0.129987[/C][C]1.8926[/C][C]0.029884[/C][/ROW]
[ROW][C]9[/C][C]-0.118828[/C][C]-1.7302[/C][C]0.042528[/C][/ROW]
[ROW][C]10[/C][C]-0.325801[/C][C]-4.7437[/C][C]2e-06[/C][/ROW]
[ROW][C]11[/C][C]0.202859[/C][C]2.9537[/C][C]0.001747[/C][/ROW]
[ROW][C]12[/C][C]0.602346[/C][C]8.7703[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.2781[/C][C]-4.0492[/C][C]3.6e-05[/C][/ROW]
[ROW][C]14[/C][C]-0.214754[/C][C]-3.1269[/C][C]0.001007[/C][/ROW]
[ROW][C]15[/C][C]-0.259679[/C][C]-3.781[/C][C]0.000102[/C][/ROW]
[ROW][C]16[/C][C]-0.001783[/C][C]-0.026[/C][C]0.489658[/C][/ROW]
[ROW][C]17[/C][C]0.02967[/C][C]0.432[/C][C]0.333091[/C][/ROW]
[ROW][C]18[/C][C]0.017668[/C][C]0.2573[/C][C]0.398618[/C][/ROW]
[ROW][C]19[/C][C]0.068315[/C][C]0.9947[/C][C]0.160511[/C][/ROW]
[ROW][C]20[/C][C]0.066039[/C][C]0.9615[/C][C]0.168686[/C][/ROW]
[ROW][C]21[/C][C]0.015541[/C][C]0.2263[/C][C]0.410598[/C][/ROW]
[ROW][C]22[/C][C]0.054955[/C][C]0.8002[/C][C]0.212257[/C][/ROW]
[ROW][C]23[/C][C]-0.015814[/C][C]-0.2303[/C][C]0.409055[/C][/ROW]
[ROW][C]24[/C][C]0.069114[/C][C]1.0063[/C][C]0.157706[/C][/ROW]
[ROW][C]25[/C][C]-0.018517[/C][C]-0.2696[/C][C]0.393861[/C][/ROW]
[ROW][C]26[/C][C]-0.038858[/C][C]-0.5658[/C][C]0.286068[/C][/ROW]
[ROW][C]27[/C][C]-0.158922[/C][C]-2.3139[/C][C]0.010814[/C][/ROW]
[ROW][C]28[/C][C]0.077629[/C][C]1.1303[/C][C]0.129813[/C][/ROW]
[ROW][C]29[/C][C]-0.078001[/C][C]-1.1357[/C][C]0.128679[/C][/ROW]
[ROW][C]30[/C][C]-0.026774[/C][C]-0.3898[/C][C]0.348525[/C][/ROW]
[ROW][C]31[/C][C]0.08368[/C][C]1.2184[/C][C]0.112214[/C][/ROW]
[ROW][C]32[/C][C]-0.028066[/C][C]-0.4086[/C][C]0.341607[/C][/ROW]
[ROW][C]33[/C][C]-0.005596[/C][C]-0.0815[/C][C]0.467571[/C][/ROW]
[ROW][C]34[/C][C]0.142055[/C][C]2.0683[/C][C]0.01991[/C][/ROW]
[ROW][C]35[/C][C]-0.090586[/C][C]-1.319[/C][C]0.094303[/C][/ROW]
[ROW][C]36[/C][C]0.096429[/C][C]1.404[/C][C]0.080887[/C][/ROW]
[ROW][C]37[/C][C]-0.046393[/C][C]-0.6755[/C][C]0.250049[/C][/ROW]
[ROW][C]38[/C][C]-0.057572[/C][C]-0.8383[/C][C]0.201416[/C][/ROW]
[ROW][C]39[/C][C]0.054728[/C][C]0.7969[/C][C]0.213215[/C][/ROW]
[ROW][C]40[/C][C]-0.01028[/C][C]-0.1497[/C][C]0.44058[/C][/ROW]
[ROW][C]41[/C][C]-0.106921[/C][C]-1.5568[/C][C]0.060506[/C][/ROW]
[ROW][C]42[/C][C]0.002164[/C][C]0.0315[/C][C]0.487445[/C][/ROW]
[ROW][C]43[/C][C]0.051861[/C][C]0.7551[/C][C]0.225513[/C][/ROW]
[ROW][C]44[/C][C]-0.084214[/C][C]-1.2262[/C][C]0.110748[/C][/ROW]
[ROW][C]45[/C][C]0.029849[/C][C]0.4346[/C][C]0.332142[/C][/ROW]
[ROW][C]46[/C][C]0.033392[/C][C]0.4862[/C][C]0.313668[/C][/ROW]
[ROW][C]47[/C][C]-0.062018[/C][C]-0.903[/C][C]0.183775[/C][/ROW]
[ROW][C]48[/C][C]0.081995[/C][C]1.1939[/C][C]0.116932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308511&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308511&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
10.4884217.11150
20.0253670.36940.356117
30.3509125.10930
40.2059422.99860.001518
50.2878884.19172e-05
60.1422062.07060.019806
70.1763772.56810.005457
80.1299871.89260.029884
9-0.118828-1.73020.042528
10-0.325801-4.74372e-06
110.2028592.95370.001747
120.6023468.77030
13-0.2781-4.04923.6e-05
14-0.214754-3.12690.001007
15-0.259679-3.7810.000102
16-0.001783-0.0260.489658
170.029670.4320.333091
180.0176680.25730.398618
190.0683150.99470.160511
200.0660390.96150.168686
210.0155410.22630.410598
220.0549550.80020.212257
23-0.015814-0.23030.409055
240.0691141.00630.157706
25-0.018517-0.26960.393861
26-0.038858-0.56580.286068
27-0.158922-2.31390.010814
280.0776291.13030.129813
29-0.078001-1.13570.128679
30-0.026774-0.38980.348525
310.083681.21840.112214
32-0.028066-0.40860.341607
33-0.005596-0.08150.467571
340.1420552.06830.01991
35-0.090586-1.3190.094303
360.0964291.4040.080887
37-0.046393-0.67550.250049
38-0.057572-0.83830.201416
390.0547280.79690.213215
40-0.01028-0.14970.44058
41-0.106921-1.55680.060506
420.0021640.03150.487445
430.0518610.75510.225513
44-0.084214-1.22620.110748
450.0298490.43460.332142
460.0333920.48620.313668
47-0.062018-0.9030.183775
480.0819951.19390.116932


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 48 ; par2 = 1 ; par3 = 0 ; 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)
x <- na.omit(x)
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,'ACF(k)',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,'PACF(k)',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')