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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 computationTue, 12 Jan 2010 04:26:17 -0700
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/Jan/12/t1263295628kxlro5owfqurar8.htm/, Retrieved Thu, 31 Oct 2024 23:21:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=71939, Retrieved Thu, 31 Oct 2024 23:21:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2009-12-17 19:09:08] [b98453cac15ba1066b407e146608df68]
- RMP   [(Partial) Autocorrelation Function] [] [2009-12-17 19:26:45] [b98453cac15ba1066b407e146608df68]
-   PD      [(Partial) Autocorrelation Function] [test] [2010-01-12 11:26:17] [66ffaa9e54a90d3ae4874684602d24e9] [Current]
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Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71939&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71939&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'Gwilym Jenkins' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.309815-3.5460.000272
20.0953511.09130.138562
3-0.096891-1.1090.134738
4-0.098995-1.1330.129631
50.0610010.69820.24315
6-0.000288-0.00330.498688
7-0.056108-0.64220.260936
8-0.060966-0.69780.243274
90.1759172.01350.023057
10-0.140279-1.60560.055389
110.0697350.79820.213112
12-0.133673-1.530.064219
130.0871770.99780.160111
140.0024940.02860.488633
150.0653320.74780.227972
16-0.109162-1.24940.106871
17-0.000338-0.00390.498462
180.0440280.50390.307582
19-0.113945-1.30420.097232
20-0.091271-1.04460.149054
210.0419430.48010.315994

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.309815 & -3.546 & 0.000272 \tabularnewline
2 & 0.095351 & 1.0913 & 0.138562 \tabularnewline
3 & -0.096891 & -1.109 & 0.134738 \tabularnewline
4 & -0.098995 & -1.133 & 0.129631 \tabularnewline
5 & 0.061001 & 0.6982 & 0.24315 \tabularnewline
6 & -0.000288 & -0.0033 & 0.498688 \tabularnewline
7 & -0.056108 & -0.6422 & 0.260936 \tabularnewline
8 & -0.060966 & -0.6978 & 0.243274 \tabularnewline
9 & 0.175917 & 2.0135 & 0.023057 \tabularnewline
10 & -0.140279 & -1.6056 & 0.055389 \tabularnewline
11 & 0.069735 & 0.7982 & 0.213112 \tabularnewline
12 & -0.133673 & -1.53 & 0.064219 \tabularnewline
13 & 0.087177 & 0.9978 & 0.160111 \tabularnewline
14 & 0.002494 & 0.0286 & 0.488633 \tabularnewline
15 & 0.065332 & 0.7478 & 0.227972 \tabularnewline
16 & -0.109162 & -1.2494 & 0.106871 \tabularnewline
17 & -0.000338 & -0.0039 & 0.498462 \tabularnewline
18 & 0.044028 & 0.5039 & 0.307582 \tabularnewline
19 & -0.113945 & -1.3042 & 0.097232 \tabularnewline
20 & -0.091271 & -1.0446 & 0.149054 \tabularnewline
21 & 0.041943 & 0.4801 & 0.315994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71939&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]-0.309815[/C][C]-3.546[/C][C]0.000272[/C][/ROW]
[ROW][C]2[/C][C]0.095351[/C][C]1.0913[/C][C]0.138562[/C][/ROW]
[ROW][C]3[/C][C]-0.096891[/C][C]-1.109[/C][C]0.134738[/C][/ROW]
[ROW][C]4[/C][C]-0.098995[/C][C]-1.133[/C][C]0.129631[/C][/ROW]
[ROW][C]5[/C][C]0.061001[/C][C]0.6982[/C][C]0.24315[/C][/ROW]
[ROW][C]6[/C][C]-0.000288[/C][C]-0.0033[/C][C]0.498688[/C][/ROW]
[ROW][C]7[/C][C]-0.056108[/C][C]-0.6422[/C][C]0.260936[/C][/ROW]
[ROW][C]8[/C][C]-0.060966[/C][C]-0.6978[/C][C]0.243274[/C][/ROW]
[ROW][C]9[/C][C]0.175917[/C][C]2.0135[/C][C]0.023057[/C][/ROW]
[ROW][C]10[/C][C]-0.140279[/C][C]-1.6056[/C][C]0.055389[/C][/ROW]
[ROW][C]11[/C][C]0.069735[/C][C]0.7982[/C][C]0.213112[/C][/ROW]
[ROW][C]12[/C][C]-0.133673[/C][C]-1.53[/C][C]0.064219[/C][/ROW]
[ROW][C]13[/C][C]0.087177[/C][C]0.9978[/C][C]0.160111[/C][/ROW]
[ROW][C]14[/C][C]0.002494[/C][C]0.0286[/C][C]0.488633[/C][/ROW]
[ROW][C]15[/C][C]0.065332[/C][C]0.7478[/C][C]0.227972[/C][/ROW]
[ROW][C]16[/C][C]-0.109162[/C][C]-1.2494[/C][C]0.106871[/C][/ROW]
[ROW][C]17[/C][C]-0.000338[/C][C]-0.0039[/C][C]0.498462[/C][/ROW]
[ROW][C]18[/C][C]0.044028[/C][C]0.5039[/C][C]0.307582[/C][/ROW]
[ROW][C]19[/C][C]-0.113945[/C][C]-1.3042[/C][C]0.097232[/C][/ROW]
[ROW][C]20[/C][C]-0.091271[/C][C]-1.0446[/C][C]0.149054[/C][/ROW]
[ROW][C]21[/C][C]0.041943[/C][C]0.4801[/C][C]0.315994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71939&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
1-0.309815-3.5460.000272
20.0953511.09130.138562
3-0.096891-1.1090.134738
4-0.098995-1.1330.129631
50.0610010.69820.24315
6-0.000288-0.00330.498688
7-0.056108-0.64220.260936
8-0.060966-0.69780.243274
90.1759172.01350.023057
10-0.140279-1.60560.055389
110.0697350.79820.213112
12-0.133673-1.530.064219
130.0871770.99780.160111
140.0024940.02860.488633
150.0653320.74780.227972
16-0.109162-1.24940.106871
17-0.000338-0.00390.498462
180.0440280.50390.307582
19-0.113945-1.30420.097232
20-0.091271-1.04460.149054
210.0419430.48010.315994







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.309815-3.5460.000272
2-0.000701-0.0080.496806
3-0.074718-0.85520.197005
4-0.166761-1.90870.029247
5-0.015146-0.17340.431319
60.0182880.20930.417265
7-0.088086-1.00820.157611
8-0.133888-1.53240.063916
90.1564051.79010.03787
10-0.05875-0.67240.251251
11-0.052428-0.60010.274749
12-0.115013-1.31640.095172
130.0599560.68620.246893
140.0043180.04940.48033
150.0365870.41880.338041
16-0.086599-0.99120.161715
17-0.027835-0.31860.375275
180.0191170.21880.41357
19-0.113792-1.30240.097531
20-0.256883-2.94020.00194
210.0068430.07830.468846

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.309815 & -3.546 & 0.000272 \tabularnewline
2 & -0.000701 & -0.008 & 0.496806 \tabularnewline
3 & -0.074718 & -0.8552 & 0.197005 \tabularnewline
4 & -0.166761 & -1.9087 & 0.029247 \tabularnewline
5 & -0.015146 & -0.1734 & 0.431319 \tabularnewline
6 & 0.018288 & 0.2093 & 0.417265 \tabularnewline
7 & -0.088086 & -1.0082 & 0.157611 \tabularnewline
8 & -0.133888 & -1.5324 & 0.063916 \tabularnewline
9 & 0.156405 & 1.7901 & 0.03787 \tabularnewline
10 & -0.05875 & -0.6724 & 0.251251 \tabularnewline
11 & -0.052428 & -0.6001 & 0.274749 \tabularnewline
12 & -0.115013 & -1.3164 & 0.095172 \tabularnewline
13 & 0.059956 & 0.6862 & 0.246893 \tabularnewline
14 & 0.004318 & 0.0494 & 0.48033 \tabularnewline
15 & 0.036587 & 0.4188 & 0.338041 \tabularnewline
16 & -0.086599 & -0.9912 & 0.161715 \tabularnewline
17 & -0.027835 & -0.3186 & 0.375275 \tabularnewline
18 & 0.019117 & 0.2188 & 0.41357 \tabularnewline
19 & -0.113792 & -1.3024 & 0.097531 \tabularnewline
20 & -0.256883 & -2.9402 & 0.00194 \tabularnewline
21 & 0.006843 & 0.0783 & 0.468846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71939&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]-0.309815[/C][C]-3.546[/C][C]0.000272[/C][/ROW]
[ROW][C]2[/C][C]-0.000701[/C][C]-0.008[/C][C]0.496806[/C][/ROW]
[ROW][C]3[/C][C]-0.074718[/C][C]-0.8552[/C][C]0.197005[/C][/ROW]
[ROW][C]4[/C][C]-0.166761[/C][C]-1.9087[/C][C]0.029247[/C][/ROW]
[ROW][C]5[/C][C]-0.015146[/C][C]-0.1734[/C][C]0.431319[/C][/ROW]
[ROW][C]6[/C][C]0.018288[/C][C]0.2093[/C][C]0.417265[/C][/ROW]
[ROW][C]7[/C][C]-0.088086[/C][C]-1.0082[/C][C]0.157611[/C][/ROW]
[ROW][C]8[/C][C]-0.133888[/C][C]-1.5324[/C][C]0.063916[/C][/ROW]
[ROW][C]9[/C][C]0.156405[/C][C]1.7901[/C][C]0.03787[/C][/ROW]
[ROW][C]10[/C][C]-0.05875[/C][C]-0.6724[/C][C]0.251251[/C][/ROW]
[ROW][C]11[/C][C]-0.052428[/C][C]-0.6001[/C][C]0.274749[/C][/ROW]
[ROW][C]12[/C][C]-0.115013[/C][C]-1.3164[/C][C]0.095172[/C][/ROW]
[ROW][C]13[/C][C]0.059956[/C][C]0.6862[/C][C]0.246893[/C][/ROW]
[ROW][C]14[/C][C]0.004318[/C][C]0.0494[/C][C]0.48033[/C][/ROW]
[ROW][C]15[/C][C]0.036587[/C][C]0.4188[/C][C]0.338041[/C][/ROW]
[ROW][C]16[/C][C]-0.086599[/C][C]-0.9912[/C][C]0.161715[/C][/ROW]
[ROW][C]17[/C][C]-0.027835[/C][C]-0.3186[/C][C]0.375275[/C][/ROW]
[ROW][C]18[/C][C]0.019117[/C][C]0.2188[/C][C]0.41357[/C][/ROW]
[ROW][C]19[/C][C]-0.113792[/C][C]-1.3024[/C][C]0.097531[/C][/ROW]
[ROW][C]20[/C][C]-0.256883[/C][C]-2.9402[/C][C]0.00194[/C][/ROW]
[ROW][C]21[/C][C]0.006843[/C][C]0.0783[/C][C]0.468846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71939&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
1-0.309815-3.5460.000272
2-0.000701-0.0080.496806
3-0.074718-0.85520.197005
4-0.166761-1.90870.029247
5-0.015146-0.17340.431319
60.0182880.20930.417265
7-0.088086-1.00820.157611
8-0.133888-1.53240.063916
90.1564051.79010.03787
10-0.05875-0.67240.251251
11-0.052428-0.60010.274749
12-0.115013-1.31640.095172
130.0599560.68620.246893
140.0043180.04940.48033
150.0365870.41880.338041
16-0.086599-0.99120.161715
17-0.027835-0.31860.375275
180.0191170.21880.41357
19-0.113792-1.30240.097531
20-0.256883-2.94020.00194
210.0068430.07830.468846



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')