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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 computationWed, 16 Mar 2016 22:35:41 +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/2016/Mar/16/t1458167802xirrhhzzu1ef16m.htm/, Retrieved Mon, 06 May 2024 15:38:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294177, Retrieved Mon, 06 May 2024 15:38:06 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

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] [Vers Fruit - Auto...] [2016-03-16 22:34:03] [b1a7cb6d93e9c32863cdeb6d14632a38]
- R PD    [(Partial) Autocorrelation Function] [Vers Fruit - Auto...] [2016-03-16 22:35:41] [9229f16b23a3f07f1433c1291c3e5666] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
82.6
85.99
86.85
86.12
97.19
89.8
90.27
90.68
90.05
90.28
91.52
88.3
85.31
87.86
87.77
88.44
88.73
94.4
94.09
90.32
89.68
94.15
95.2
91.82
90.33
95.14
96.06
97.21
100.33
98.79
102.48
99.29
98.83
97.25
94.55
93.53
93.58
95.79
94.77
94.2
96.23
92.3
88.86
86.44
86.21
88.57
90.69
89
86.88
90.65
90.68
89.64
102.62
101.84
92.51
94.29
94.68
96.94
94.03
89.65
84.9
89.07
89.8
93.22
92.23
98.41
96.63
89.8
90
92.13
93.27
90.81
85.42
88.28
88.73
90.18
92.74
96.13
94.85
94.25
96.94
101.22
98.71
95.51
93.91
98.17
97.59
99.64
107.88
108.49
100.25
99.27
101.73
101.25
97.09
94.74
94.53
93.48
96.05
106.22
98.33
99.86
93.78
88.96
83.77
89.46
86.78
88.4
87.19
92.23
95.99
104.75
105.63
108.71
96.4
93.31
93.77
98.7
95.04
95.61

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294177&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294177&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294177&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.061687-0.67290.251149
2-0.148956-1.62490.053413
3-0.113246-1.23540.109563
40.0568480.62010.268178
5-0.169001-1.84360.033866
60.0451780.49280.31152
7-0.063365-0.69120.245385
80.0096340.10510.458238
9-0.199161-2.17260.015897
100.0078570.08570.465922
110.0679790.74160.229905
120.2616742.85450.002543
130.0841040.91750.180377
14-0.033828-0.3690.356384
15-0.205809-2.24510.013304
16-0.071367-0.77850.218902
17-0.05922-0.6460.259756
180.0831320.90690.183156
19-0.018045-0.19680.422141
20-0.015667-0.17090.432294
21-0.090128-0.98320.163756
22-0.022812-0.24890.401953
230.0610090.66550.2535
240.2470612.69510.004028
250.0471940.51480.303816
26-0.081737-0.89160.187192
27-0.148374-1.61860.054093
280.0510490.55690.289326
29-0.039124-0.42680.335151
300.0126920.13850.445058
310.0662160.72230.235755
320.0006370.00690.497234
33-0.059676-0.6510.258155
34-0.081657-0.89080.187423
350.0835840.91180.181862
360.3045143.32190.000594
37-0.066192-0.72210.235835
38-0.154593-1.68640.047169
39-0.113588-1.23910.108873
400.0464680.50690.306579
41-0.096679-1.05460.146862
420.0348470.38010.352263
430.0256880.28020.389895
440.0028050.03060.487821
45-0.136289-1.48670.069864
460.0213620.2330.408067
470.0365930.39920.345235
480.2293492.50190.006855

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.061687 & -0.6729 & 0.251149 \tabularnewline
2 & -0.148956 & -1.6249 & 0.053413 \tabularnewline
3 & -0.113246 & -1.2354 & 0.109563 \tabularnewline
4 & 0.056848 & 0.6201 & 0.268178 \tabularnewline
5 & -0.169001 & -1.8436 & 0.033866 \tabularnewline
6 & 0.045178 & 0.4928 & 0.31152 \tabularnewline
7 & -0.063365 & -0.6912 & 0.245385 \tabularnewline
8 & 0.009634 & 0.1051 & 0.458238 \tabularnewline
9 & -0.199161 & -2.1726 & 0.015897 \tabularnewline
10 & 0.007857 & 0.0857 & 0.465922 \tabularnewline
11 & 0.067979 & 0.7416 & 0.229905 \tabularnewline
12 & 0.261674 & 2.8545 & 0.002543 \tabularnewline
13 & 0.084104 & 0.9175 & 0.180377 \tabularnewline
14 & -0.033828 & -0.369 & 0.356384 \tabularnewline
15 & -0.205809 & -2.2451 & 0.013304 \tabularnewline
16 & -0.071367 & -0.7785 & 0.218902 \tabularnewline
17 & -0.05922 & -0.646 & 0.259756 \tabularnewline
18 & 0.083132 & 0.9069 & 0.183156 \tabularnewline
19 & -0.018045 & -0.1968 & 0.422141 \tabularnewline
20 & -0.015667 & -0.1709 & 0.432294 \tabularnewline
21 & -0.090128 & -0.9832 & 0.163756 \tabularnewline
22 & -0.022812 & -0.2489 & 0.401953 \tabularnewline
23 & 0.061009 & 0.6655 & 0.2535 \tabularnewline
24 & 0.247061 & 2.6951 & 0.004028 \tabularnewline
25 & 0.047194 & 0.5148 & 0.303816 \tabularnewline
26 & -0.081737 & -0.8916 & 0.187192 \tabularnewline
27 & -0.148374 & -1.6186 & 0.054093 \tabularnewline
28 & 0.051049 & 0.5569 & 0.289326 \tabularnewline
29 & -0.039124 & -0.4268 & 0.335151 \tabularnewline
30 & 0.012692 & 0.1385 & 0.445058 \tabularnewline
31 & 0.066216 & 0.7223 & 0.235755 \tabularnewline
32 & 0.000637 & 0.0069 & 0.497234 \tabularnewline
33 & -0.059676 & -0.651 & 0.258155 \tabularnewline
34 & -0.081657 & -0.8908 & 0.187423 \tabularnewline
35 & 0.083584 & 0.9118 & 0.181862 \tabularnewline
36 & 0.304514 & 3.3219 & 0.000594 \tabularnewline
37 & -0.066192 & -0.7221 & 0.235835 \tabularnewline
38 & -0.154593 & -1.6864 & 0.047169 \tabularnewline
39 & -0.113588 & -1.2391 & 0.108873 \tabularnewline
40 & 0.046468 & 0.5069 & 0.306579 \tabularnewline
41 & -0.096679 & -1.0546 & 0.146862 \tabularnewline
42 & 0.034847 & 0.3801 & 0.352263 \tabularnewline
43 & 0.025688 & 0.2802 & 0.389895 \tabularnewline
44 & 0.002805 & 0.0306 & 0.487821 \tabularnewline
45 & -0.136289 & -1.4867 & 0.069864 \tabularnewline
46 & 0.021362 & 0.233 & 0.408067 \tabularnewline
47 & 0.036593 & 0.3992 & 0.345235 \tabularnewline
48 & 0.229349 & 2.5019 & 0.006855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294177&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]-0.061687[/C][C]-0.6729[/C][C]0.251149[/C][/ROW]
[ROW][C]2[/C][C]-0.148956[/C][C]-1.6249[/C][C]0.053413[/C][/ROW]
[ROW][C]3[/C][C]-0.113246[/C][C]-1.2354[/C][C]0.109563[/C][/ROW]
[ROW][C]4[/C][C]0.056848[/C][C]0.6201[/C][C]0.268178[/C][/ROW]
[ROW][C]5[/C][C]-0.169001[/C][C]-1.8436[/C][C]0.033866[/C][/ROW]
[ROW][C]6[/C][C]0.045178[/C][C]0.4928[/C][C]0.31152[/C][/ROW]
[ROW][C]7[/C][C]-0.063365[/C][C]-0.6912[/C][C]0.245385[/C][/ROW]
[ROW][C]8[/C][C]0.009634[/C][C]0.1051[/C][C]0.458238[/C][/ROW]
[ROW][C]9[/C][C]-0.199161[/C][C]-2.1726[/C][C]0.015897[/C][/ROW]
[ROW][C]10[/C][C]0.007857[/C][C]0.0857[/C][C]0.465922[/C][/ROW]
[ROW][C]11[/C][C]0.067979[/C][C]0.7416[/C][C]0.229905[/C][/ROW]
[ROW][C]12[/C][C]0.261674[/C][C]2.8545[/C][C]0.002543[/C][/ROW]
[ROW][C]13[/C][C]0.084104[/C][C]0.9175[/C][C]0.180377[/C][/ROW]
[ROW][C]14[/C][C]-0.033828[/C][C]-0.369[/C][C]0.356384[/C][/ROW]
[ROW][C]15[/C][C]-0.205809[/C][C]-2.2451[/C][C]0.013304[/C][/ROW]
[ROW][C]16[/C][C]-0.071367[/C][C]-0.7785[/C][C]0.218902[/C][/ROW]
[ROW][C]17[/C][C]-0.05922[/C][C]-0.646[/C][C]0.259756[/C][/ROW]
[ROW][C]18[/C][C]0.083132[/C][C]0.9069[/C][C]0.183156[/C][/ROW]
[ROW][C]19[/C][C]-0.018045[/C][C]-0.1968[/C][C]0.422141[/C][/ROW]
[ROW][C]20[/C][C]-0.015667[/C][C]-0.1709[/C][C]0.432294[/C][/ROW]
[ROW][C]21[/C][C]-0.090128[/C][C]-0.9832[/C][C]0.163756[/C][/ROW]
[ROW][C]22[/C][C]-0.022812[/C][C]-0.2489[/C][C]0.401953[/C][/ROW]
[ROW][C]23[/C][C]0.061009[/C][C]0.6655[/C][C]0.2535[/C][/ROW]
[ROW][C]24[/C][C]0.247061[/C][C]2.6951[/C][C]0.004028[/C][/ROW]
[ROW][C]25[/C][C]0.047194[/C][C]0.5148[/C][C]0.303816[/C][/ROW]
[ROW][C]26[/C][C]-0.081737[/C][C]-0.8916[/C][C]0.187192[/C][/ROW]
[ROW][C]27[/C][C]-0.148374[/C][C]-1.6186[/C][C]0.054093[/C][/ROW]
[ROW][C]28[/C][C]0.051049[/C][C]0.5569[/C][C]0.289326[/C][/ROW]
[ROW][C]29[/C][C]-0.039124[/C][C]-0.4268[/C][C]0.335151[/C][/ROW]
[ROW][C]30[/C][C]0.012692[/C][C]0.1385[/C][C]0.445058[/C][/ROW]
[ROW][C]31[/C][C]0.066216[/C][C]0.7223[/C][C]0.235755[/C][/ROW]
[ROW][C]32[/C][C]0.000637[/C][C]0.0069[/C][C]0.497234[/C][/ROW]
[ROW][C]33[/C][C]-0.059676[/C][C]-0.651[/C][C]0.258155[/C][/ROW]
[ROW][C]34[/C][C]-0.081657[/C][C]-0.8908[/C][C]0.187423[/C][/ROW]
[ROW][C]35[/C][C]0.083584[/C][C]0.9118[/C][C]0.181862[/C][/ROW]
[ROW][C]36[/C][C]0.304514[/C][C]3.3219[/C][C]0.000594[/C][/ROW]
[ROW][C]37[/C][C]-0.066192[/C][C]-0.7221[/C][C]0.235835[/C][/ROW]
[ROW][C]38[/C][C]-0.154593[/C][C]-1.6864[/C][C]0.047169[/C][/ROW]
[ROW][C]39[/C][C]-0.113588[/C][C]-1.2391[/C][C]0.108873[/C][/ROW]
[ROW][C]40[/C][C]0.046468[/C][C]0.5069[/C][C]0.306579[/C][/ROW]
[ROW][C]41[/C][C]-0.096679[/C][C]-1.0546[/C][C]0.146862[/C][/ROW]
[ROW][C]42[/C][C]0.034847[/C][C]0.3801[/C][C]0.352263[/C][/ROW]
[ROW][C]43[/C][C]0.025688[/C][C]0.2802[/C][C]0.389895[/C][/ROW]
[ROW][C]44[/C][C]0.002805[/C][C]0.0306[/C][C]0.487821[/C][/ROW]
[ROW][C]45[/C][C]-0.136289[/C][C]-1.4867[/C][C]0.069864[/C][/ROW]
[ROW][C]46[/C][C]0.021362[/C][C]0.233[/C][C]0.408067[/C][/ROW]
[ROW][C]47[/C][C]0.036593[/C][C]0.3992[/C][C]0.345235[/C][/ROW]
[ROW][C]48[/C][C]0.229349[/C][C]2.5019[/C][C]0.006855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294177&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294177&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
1-0.061687-0.67290.251149
2-0.148956-1.62490.053413
3-0.113246-1.23540.109563
40.0568480.62010.268178
5-0.169001-1.84360.033866
60.0451780.49280.31152
7-0.063365-0.69120.245385
80.0096340.10510.458238
9-0.199161-2.17260.015897
100.0078570.08570.465922
110.0679790.74160.229905
120.2616742.85450.002543
130.0841040.91750.180377
14-0.033828-0.3690.356384
15-0.205809-2.24510.013304
16-0.071367-0.77850.218902
17-0.05922-0.6460.259756
180.0831320.90690.183156
19-0.018045-0.19680.422141
20-0.015667-0.17090.432294
21-0.090128-0.98320.163756
22-0.022812-0.24890.401953
230.0610090.66550.2535
240.2470612.69510.004028
250.0471940.51480.303816
26-0.081737-0.89160.187192
27-0.148374-1.61860.054093
280.0510490.55690.289326
29-0.039124-0.42680.335151
300.0126920.13850.445058
310.0662160.72230.235755
320.0006370.00690.497234
33-0.059676-0.6510.258155
34-0.081657-0.89080.187423
350.0835840.91180.181862
360.3045143.32190.000594
37-0.066192-0.72210.235835
38-0.154593-1.68640.047169
39-0.113588-1.23910.108873
400.0464680.50690.306579
41-0.096679-1.05460.146862
420.0348470.38010.352263
430.0256880.28020.389895
440.0028050.03060.487821
45-0.136289-1.48670.069864
460.0213620.2330.408067
470.0365930.39920.345235
480.2293492.50190.006855






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.061687-0.67290.251149
2-0.153345-1.67280.048498
3-0.137035-1.49490.068797
40.0143150.15620.438087
5-0.211055-2.30230.011527
60.0090780.0990.460641
7-0.124005-1.35270.089352
8-0.052828-0.57630.282754
9-0.243605-2.65740.004478
10-0.123179-1.34370.090797
11-0.036953-0.40310.343796
120.1664731.8160.035944
130.1510521.64780.051017
140.0085360.09310.462986
15-0.14342-1.56450.060175
16-0.152676-1.66550.049222
17-0.12118-1.32190.094365
18-0.009898-0.1080.4571
19-0.02785-0.30380.380903
20-0.035781-0.39030.348495
21-0.060182-0.65650.256384
22-0.102987-1.12350.131754
23-0.050837-0.55460.290115
240.0775010.84540.199784
250.0011690.01280.494921
26-0.060469-0.65960.255381
27-0.080946-0.8830.189503
280.0809910.88350.189372
290.0129170.14090.444089
30-0.026988-0.29440.384481
310.0296790.32380.373344
32-0.039093-0.42650.335274
330.073640.80330.211697
34-0.075739-0.82620.205167
350.0262840.28670.38741
360.2376232.59220.005366
37-0.044909-0.48990.312555
38-0.041151-0.44890.327157
39-0.089139-0.97240.166413
400.0642690.70110.242308
41-0.061992-0.67630.250097
42-0.035791-0.39040.348459
43-0.028341-0.30920.378868
44-0.050523-0.55110.291287
45-0.050153-0.54710.292666
46-0.058577-0.6390.262024
47-0.08799-0.95990.169536
480.0679970.74180.229849

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.061687 & -0.6729 & 0.251149 \tabularnewline
2 & -0.153345 & -1.6728 & 0.048498 \tabularnewline
3 & -0.137035 & -1.4949 & 0.068797 \tabularnewline
4 & 0.014315 & 0.1562 & 0.438087 \tabularnewline
5 & -0.211055 & -2.3023 & 0.011527 \tabularnewline
6 & 0.009078 & 0.099 & 0.460641 \tabularnewline
7 & -0.124005 & -1.3527 & 0.089352 \tabularnewline
8 & -0.052828 & -0.5763 & 0.282754 \tabularnewline
9 & -0.243605 & -2.6574 & 0.004478 \tabularnewline
10 & -0.123179 & -1.3437 & 0.090797 \tabularnewline
11 & -0.036953 & -0.4031 & 0.343796 \tabularnewline
12 & 0.166473 & 1.816 & 0.035944 \tabularnewline
13 & 0.151052 & 1.6478 & 0.051017 \tabularnewline
14 & 0.008536 & 0.0931 & 0.462986 \tabularnewline
15 & -0.14342 & -1.5645 & 0.060175 \tabularnewline
16 & -0.152676 & -1.6655 & 0.049222 \tabularnewline
17 & -0.12118 & -1.3219 & 0.094365 \tabularnewline
18 & -0.009898 & -0.108 & 0.4571 \tabularnewline
19 & -0.02785 & -0.3038 & 0.380903 \tabularnewline
20 & -0.035781 & -0.3903 & 0.348495 \tabularnewline
21 & -0.060182 & -0.6565 & 0.256384 \tabularnewline
22 & -0.102987 & -1.1235 & 0.131754 \tabularnewline
23 & -0.050837 & -0.5546 & 0.290115 \tabularnewline
24 & 0.077501 & 0.8454 & 0.199784 \tabularnewline
25 & 0.001169 & 0.0128 & 0.494921 \tabularnewline
26 & -0.060469 & -0.6596 & 0.255381 \tabularnewline
27 & -0.080946 & -0.883 & 0.189503 \tabularnewline
28 & 0.080991 & 0.8835 & 0.189372 \tabularnewline
29 & 0.012917 & 0.1409 & 0.444089 \tabularnewline
30 & -0.026988 & -0.2944 & 0.384481 \tabularnewline
31 & 0.029679 & 0.3238 & 0.373344 \tabularnewline
32 & -0.039093 & -0.4265 & 0.335274 \tabularnewline
33 & 0.07364 & 0.8033 & 0.211697 \tabularnewline
34 & -0.075739 & -0.8262 & 0.205167 \tabularnewline
35 & 0.026284 & 0.2867 & 0.38741 \tabularnewline
36 & 0.237623 & 2.5922 & 0.005366 \tabularnewline
37 & -0.044909 & -0.4899 & 0.312555 \tabularnewline
38 & -0.041151 & -0.4489 & 0.327157 \tabularnewline
39 & -0.089139 & -0.9724 & 0.166413 \tabularnewline
40 & 0.064269 & 0.7011 & 0.242308 \tabularnewline
41 & -0.061992 & -0.6763 & 0.250097 \tabularnewline
42 & -0.035791 & -0.3904 & 0.348459 \tabularnewline
43 & -0.028341 & -0.3092 & 0.378868 \tabularnewline
44 & -0.050523 & -0.5511 & 0.291287 \tabularnewline
45 & -0.050153 & -0.5471 & 0.292666 \tabularnewline
46 & -0.058577 & -0.639 & 0.262024 \tabularnewline
47 & -0.08799 & -0.9599 & 0.169536 \tabularnewline
48 & 0.067997 & 0.7418 & 0.229849 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294177&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]-0.061687[/C][C]-0.6729[/C][C]0.251149[/C][/ROW]
[ROW][C]2[/C][C]-0.153345[/C][C]-1.6728[/C][C]0.048498[/C][/ROW]
[ROW][C]3[/C][C]-0.137035[/C][C]-1.4949[/C][C]0.068797[/C][/ROW]
[ROW][C]4[/C][C]0.014315[/C][C]0.1562[/C][C]0.438087[/C][/ROW]
[ROW][C]5[/C][C]-0.211055[/C][C]-2.3023[/C][C]0.011527[/C][/ROW]
[ROW][C]6[/C][C]0.009078[/C][C]0.099[/C][C]0.460641[/C][/ROW]
[ROW][C]7[/C][C]-0.124005[/C][C]-1.3527[/C][C]0.089352[/C][/ROW]
[ROW][C]8[/C][C]-0.052828[/C][C]-0.5763[/C][C]0.282754[/C][/ROW]
[ROW][C]9[/C][C]-0.243605[/C][C]-2.6574[/C][C]0.004478[/C][/ROW]
[ROW][C]10[/C][C]-0.123179[/C][C]-1.3437[/C][C]0.090797[/C][/ROW]
[ROW][C]11[/C][C]-0.036953[/C][C]-0.4031[/C][C]0.343796[/C][/ROW]
[ROW][C]12[/C][C]0.166473[/C][C]1.816[/C][C]0.035944[/C][/ROW]
[ROW][C]13[/C][C]0.151052[/C][C]1.6478[/C][C]0.051017[/C][/ROW]
[ROW][C]14[/C][C]0.008536[/C][C]0.0931[/C][C]0.462986[/C][/ROW]
[ROW][C]15[/C][C]-0.14342[/C][C]-1.5645[/C][C]0.060175[/C][/ROW]
[ROW][C]16[/C][C]-0.152676[/C][C]-1.6655[/C][C]0.049222[/C][/ROW]
[ROW][C]17[/C][C]-0.12118[/C][C]-1.3219[/C][C]0.094365[/C][/ROW]
[ROW][C]18[/C][C]-0.009898[/C][C]-0.108[/C][C]0.4571[/C][/ROW]
[ROW][C]19[/C][C]-0.02785[/C][C]-0.3038[/C][C]0.380903[/C][/ROW]
[ROW][C]20[/C][C]-0.035781[/C][C]-0.3903[/C][C]0.348495[/C][/ROW]
[ROW][C]21[/C][C]-0.060182[/C][C]-0.6565[/C][C]0.256384[/C][/ROW]
[ROW][C]22[/C][C]-0.102987[/C][C]-1.1235[/C][C]0.131754[/C][/ROW]
[ROW][C]23[/C][C]-0.050837[/C][C]-0.5546[/C][C]0.290115[/C][/ROW]
[ROW][C]24[/C][C]0.077501[/C][C]0.8454[/C][C]0.199784[/C][/ROW]
[ROW][C]25[/C][C]0.001169[/C][C]0.0128[/C][C]0.494921[/C][/ROW]
[ROW][C]26[/C][C]-0.060469[/C][C]-0.6596[/C][C]0.255381[/C][/ROW]
[ROW][C]27[/C][C]-0.080946[/C][C]-0.883[/C][C]0.189503[/C][/ROW]
[ROW][C]28[/C][C]0.080991[/C][C]0.8835[/C][C]0.189372[/C][/ROW]
[ROW][C]29[/C][C]0.012917[/C][C]0.1409[/C][C]0.444089[/C][/ROW]
[ROW][C]30[/C][C]-0.026988[/C][C]-0.2944[/C][C]0.384481[/C][/ROW]
[ROW][C]31[/C][C]0.029679[/C][C]0.3238[/C][C]0.373344[/C][/ROW]
[ROW][C]32[/C][C]-0.039093[/C][C]-0.4265[/C][C]0.335274[/C][/ROW]
[ROW][C]33[/C][C]0.07364[/C][C]0.8033[/C][C]0.211697[/C][/ROW]
[ROW][C]34[/C][C]-0.075739[/C][C]-0.8262[/C][C]0.205167[/C][/ROW]
[ROW][C]35[/C][C]0.026284[/C][C]0.2867[/C][C]0.38741[/C][/ROW]
[ROW][C]36[/C][C]0.237623[/C][C]2.5922[/C][C]0.005366[/C][/ROW]
[ROW][C]37[/C][C]-0.044909[/C][C]-0.4899[/C][C]0.312555[/C][/ROW]
[ROW][C]38[/C][C]-0.041151[/C][C]-0.4489[/C][C]0.327157[/C][/ROW]
[ROW][C]39[/C][C]-0.089139[/C][C]-0.9724[/C][C]0.166413[/C][/ROW]
[ROW][C]40[/C][C]0.064269[/C][C]0.7011[/C][C]0.242308[/C][/ROW]
[ROW][C]41[/C][C]-0.061992[/C][C]-0.6763[/C][C]0.250097[/C][/ROW]
[ROW][C]42[/C][C]-0.035791[/C][C]-0.3904[/C][C]0.348459[/C][/ROW]
[ROW][C]43[/C][C]-0.028341[/C][C]-0.3092[/C][C]0.378868[/C][/ROW]
[ROW][C]44[/C][C]-0.050523[/C][C]-0.5511[/C][C]0.291287[/C][/ROW]
[ROW][C]45[/C][C]-0.050153[/C][C]-0.5471[/C][C]0.292666[/C][/ROW]
[ROW][C]46[/C][C]-0.058577[/C][C]-0.639[/C][C]0.262024[/C][/ROW]
[ROW][C]47[/C][C]-0.08799[/C][C]-0.9599[/C][C]0.169536[/C][/ROW]
[ROW][C]48[/C][C]0.067997[/C][C]0.7418[/C][C]0.229849[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294177&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294177&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
1-0.061687-0.67290.251149
2-0.153345-1.67280.048498
3-0.137035-1.49490.068797
40.0143150.15620.438087
5-0.211055-2.30230.011527
60.0090780.0990.460641
7-0.124005-1.35270.089352
8-0.052828-0.57630.282754
9-0.243605-2.65740.004478
10-0.123179-1.34370.090797
11-0.036953-0.40310.343796
120.1664731.8160.035944
130.1510521.64780.051017
140.0085360.09310.462986
15-0.14342-1.56450.060175
16-0.152676-1.66550.049222
17-0.12118-1.32190.094365
18-0.009898-0.1080.4571
19-0.02785-0.30380.380903
20-0.035781-0.39030.348495
21-0.060182-0.65650.256384
22-0.102987-1.12350.131754
23-0.050837-0.55460.290115
240.0775010.84540.199784
250.0011690.01280.494921
26-0.060469-0.65960.255381
27-0.080946-0.8830.189503
280.0809910.88350.189372
290.0129170.14090.444089
30-0.026988-0.29440.384481
310.0296790.32380.373344
32-0.039093-0.42650.335274
330.073640.80330.211697
34-0.075739-0.82620.205167
350.0262840.28670.38741
360.2376232.59220.005366
37-0.044909-0.48990.312555
38-0.041151-0.44890.327157
39-0.089139-0.97240.166413
400.0642690.70110.242308
41-0.061992-0.67630.250097
42-0.035791-0.39040.348459
43-0.028341-0.30920.378868
44-0.050523-0.55110.291287
45-0.050153-0.54710.292666
46-0.058577-0.6390.262024
47-0.08799-0.95990.169536
480.0679970.74180.229849


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)
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,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')