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*The author of this computation has been verified*

R Software Module

rwasp_structuraltimeseries.wasp

Title produced by software

Structural Time Series Models

Date of computation

Fri, 04 Dec 2009 08:32:40 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259940802qchy5nhwej2s3uh.htm/, Retrieved Sun, 28 Apr 2024 01:59:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63768, Retrieved Sun, 28 Apr 2024 01:59:31 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

144

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [] [2009-12-04 15:32:40] [612b7913d2a3b4fa79d126829bd148db] [Current]
-             [Structural Time Series Models] [] [2009-12-29 11:03:45] [eea7474c6df699240a34279975905c82] 

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Dataseries X:

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8
8,1
7,7
7,5
7,6
7,8
7,8
7,8
7,5
7,5
7,1
7,5
7,5
7,6
7,7
7,7
7,9
8,1
8,2
8,2
8,2
7,9
7,3
6,9
6,6
6,7
6,9
7
7,1
7,2
7,1
6,9
7
6,8
6,4
6,7
6,6
6,4
6,3
6,2
6,5
6,8
6,8
6,4
6,1
5,8
6,1
7,2
7,3
6,9
6,1
5,8
6,2
7,1
7,7
7,9
7,7
7,4
7,5
8
8,1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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=63768&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=63768&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63768&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	8	8	0	0	0
2	8.1	8.0992534482645	0.099315146768711	0.000746551735494343	0.312144661532379
3	7.7	7.71033729004476	-0.378431686426799	-0.0103372900447557	-1.51214927172144
4	7.5	7.49518144618358	-0.219599500213875	0.00481855381641875	0.497206469501047
5	7.6	7.59543379667154	0.0916915119177725	0.00456620332845658	0.974888938016866
6	7.8	7.79997542952185	0.201528226306382	2.45704781487947e-05	0.343976132170661
7	7.8	7.8047055047012	0.00998749078310632	-0.00470550470120262	-0.599848918670261
8	7.8	7.79990695798508	-0.00440352846728825	9.30420149202435e-05	-0.0450684147140476
9	7.5	7.50581770169191	-0.286350290163513	-0.00581770169191252	-0.88297384336456
10	7.5	7.49414521701395	-0.0190105117436494	0.00585478298605043	0.837229093239738
11	7.1	7.10858667133196	-0.37576619516409	-0.00858667133196044	-1.11725325390524
12	7.5	7.484997974067	0.356316841173229	0.0150020259329987	2.2926674822242
13	7.5	7.50864261038258	0.032587076773531	-0.00864261038258466	-1.01407990410654
14	7.6	7.58491489562795	0.0751181182763844	0.0150851043720510	0.133567918038366
15	7.7	7.7084760605823	0.121471978101266	-0.00847606058229988	0.146272311098007
16	7.7	7.70545524508715	0.00261434002097083	-0.00545524508714855	-0.371955138449220
17	7.9	7.89464456833885	0.180749508662983	0.00535543166115181	0.557902812678462
18	8.1	8.09489680165998	0.199372469606793	0.00510319834001557	0.0583215090833516
19	8.2	8.21219543390285	0.121001741196476	-0.0121954339028472	-0.245434002944632
20	8.2	8.1909994410428	-0.0147791354599226	0.009000558957195	-0.425225530166752
21	8.2	8.21852473280732	0.0256162848724262	-0.0185247328073169	0.126506505635344
22	7.9	7.88249894272392	-0.319706625173862	0.0175010572760754	-1.08144919353469
23	7.3	7.33869729193712	-0.533690185774264	-0.0386972919371166	-0.670133269467272
24	6.9	6.87140224212641	-0.470291519574783	0.028597757873589	0.198546043256021
25	6.6	6.60311922778425	-0.277433260941253	-0.00311922778424517	0.604149594912838
26	6.7	6.6797710450136	0.0608008977403762	0.0202289549863973	1.06092958645746
27	6.9	6.89514738117445	0.206784535759022	0.0048526188255462	0.459307752845182
28	7	7.01767499004384	0.127210418477692	-0.0176749900438427	-0.249046153952195
29	7.1	7.09884793220702	0.0837509293643512	0.00115206779297903	-0.136111223030652
30	7.2	7.19318721825769	0.0937482246273258	0.00681278174231	0.0313085411199649
31	7.1	7.1100720075795	-0.07324315511923	-0.0100720075795037	-0.52296789058124
32	6.9	6.9055879594375	-0.197156910422666	-0.00558795943749705	-0.38806121776312
33	7	6.99642175712162	0.0747549074142069	0.00357824287837512	0.85154738176011
34	6.8	6.78424673668332	-0.196156010514188	0.0157532633166833	-0.848412853688194
35	6.4	6.44173983614233	-0.334337351626327	-0.041739836142333	-0.432743423377657
36	6.7	6.64964151052286	0.177618078097179	0.0503584894771431	1.60329587906902
37	6.6	6.62624203274371	-0.0121423223956272	-0.0262420327437118	-0.594487171643204
38	6.4	6.39927632215368	-0.215023499150526	0.000723677846320193	-0.635800922086482
39	6.3	6.28504196179206	-0.120512429444735	0.0149580382079447	0.296796859166133
40	6.2	6.21239006778479	-0.0755941333569787	-0.0123900677847891	0.140618085687078
41	6.5	6.49161311656742	0.25718047654175	0.0083868834325828	1.04216848423040
42	6.8	6.7862481165473	0.292315279718560	0.0137518834527053	0.110032561872672
43	6.8	6.80639498936865	0.0369978373522829	-0.0063949893686552	-0.799578495942069
44	6.4	6.44677828751598	-0.335055756823133	-0.0467782875159820	-1.16516196744487
45	6.1	6.08224180870059	-0.362710690296217	0.0177581912994072	-0.0866070734016949
46	5.8	5.75679966834911	-0.327750061018043	0.0432003316508939	0.109486350054354
47	6.1	6.14911730473386	0.347731453096235	-0.0491173047338628	2.11541274740739
48	7.2	7.12316501016608	0.93523583883227	0.076834989833919	1.83989654065104
49	7.3	7.34662678923473	0.267642099046376	-0.0466267892347286	-2.09154344595480
50	6.9	6.93040979468902	-0.373894943904836	-0.0304097946890245	-2.00952585364868
51	6.1	6.09048338595636	-0.809120969827583	0.00951661404364303	-1.36527912941267
52	5.8	5.80665831083743	-0.31803189808199	-0.00665831083743158	1.53770947365362
53	6.2	6.17444559647432	0.322660083497355	0.0255544035256774	2.0063694962774
54	7.1	7.06253399377584	0.850975541906616	0.0374660062241608	1.65455577486633
55	7.7	7.69946956489358	0.650976480496868	0.00053043510642342	-0.626336834773619
56	7.9	7.96149209663143	0.287541529634192	-0.0614920966314265	-1.13817113772244
57	7.7	7.68002448200592	-0.244131220656649	0.0199755179940838	-1.66504180735822
58	7.4	7.38064957142924	-0.295750353071808	0.0193504285707568	-0.161655880159988
59	7.5	7.58745451312257	0.173834195968218	-0.0874545131225718	1.47060399206747
60	8	7.89896132520757	0.302465200781383	0.101038674792426	0.402838373145633
61	8.1	8.12341871212448	0.229583418603231	-0.0234187121244858	-0.228334511141484

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8 & 8 & 0 & 0 & 0 \tabularnewline
2 & 8.1 & 8.0992534482645 & 0.099315146768711 & 0.000746551735494343 & 0.312144661532379 \tabularnewline
3 & 7.7 & 7.71033729004476 & -0.378431686426799 & -0.0103372900447557 & -1.51214927172144 \tabularnewline
4 & 7.5 & 7.49518144618358 & -0.219599500213875 & 0.00481855381641875 & 0.497206469501047 \tabularnewline
5 & 7.6 & 7.59543379667154 & 0.0916915119177725 & 0.00456620332845658 & 0.974888938016866 \tabularnewline
6 & 7.8 & 7.79997542952185 & 0.201528226306382 & 2.45704781487947e-05 & 0.343976132170661 \tabularnewline
7 & 7.8 & 7.8047055047012 & 0.00998749078310632 & -0.00470550470120262 & -0.599848918670261 \tabularnewline
8 & 7.8 & 7.79990695798508 & -0.00440352846728825 & 9.30420149202435e-05 & -0.0450684147140476 \tabularnewline
9 & 7.5 & 7.50581770169191 & -0.286350290163513 & -0.00581770169191252 & -0.88297384336456 \tabularnewline
10 & 7.5 & 7.49414521701395 & -0.0190105117436494 & 0.00585478298605043 & 0.837229093239738 \tabularnewline
11 & 7.1 & 7.10858667133196 & -0.37576619516409 & -0.00858667133196044 & -1.11725325390524 \tabularnewline
12 & 7.5 & 7.484997974067 & 0.356316841173229 & 0.0150020259329987 & 2.2926674822242 \tabularnewline
13 & 7.5 & 7.50864261038258 & 0.032587076773531 & -0.00864261038258466 & -1.01407990410654 \tabularnewline
14 & 7.6 & 7.58491489562795 & 0.0751181182763844 & 0.0150851043720510 & 0.133567918038366 \tabularnewline
15 & 7.7 & 7.7084760605823 & 0.121471978101266 & -0.00847606058229988 & 0.146272311098007 \tabularnewline
16 & 7.7 & 7.70545524508715 & 0.00261434002097083 & -0.00545524508714855 & -0.371955138449220 \tabularnewline
17 & 7.9 & 7.89464456833885 & 0.180749508662983 & 0.00535543166115181 & 0.557902812678462 \tabularnewline
18 & 8.1 & 8.09489680165998 & 0.199372469606793 & 0.00510319834001557 & 0.0583215090833516 \tabularnewline
19 & 8.2 & 8.21219543390285 & 0.121001741196476 & -0.0121954339028472 & -0.245434002944632 \tabularnewline
20 & 8.2 & 8.1909994410428 & -0.0147791354599226 & 0.009000558957195 & -0.425225530166752 \tabularnewline
21 & 8.2 & 8.21852473280732 & 0.0256162848724262 & -0.0185247328073169 & 0.126506505635344 \tabularnewline
22 & 7.9 & 7.88249894272392 & -0.319706625173862 & 0.0175010572760754 & -1.08144919353469 \tabularnewline
23 & 7.3 & 7.33869729193712 & -0.533690185774264 & -0.0386972919371166 & -0.670133269467272 \tabularnewline
24 & 6.9 & 6.87140224212641 & -0.470291519574783 & 0.028597757873589 & 0.198546043256021 \tabularnewline
25 & 6.6 & 6.60311922778425 & -0.277433260941253 & -0.00311922778424517 & 0.604149594912838 \tabularnewline
26 & 6.7 & 6.6797710450136 & 0.0608008977403762 & 0.0202289549863973 & 1.06092958645746 \tabularnewline
27 & 6.9 & 6.89514738117445 & 0.206784535759022 & 0.0048526188255462 & 0.459307752845182 \tabularnewline
28 & 7 & 7.01767499004384 & 0.127210418477692 & -0.0176749900438427 & -0.249046153952195 \tabularnewline
29 & 7.1 & 7.09884793220702 & 0.0837509293643512 & 0.00115206779297903 & -0.136111223030652 \tabularnewline
30 & 7.2 & 7.19318721825769 & 0.0937482246273258 & 0.00681278174231 & 0.0313085411199649 \tabularnewline
31 & 7.1 & 7.1100720075795 & -0.07324315511923 & -0.0100720075795037 & -0.52296789058124 \tabularnewline
32 & 6.9 & 6.9055879594375 & -0.197156910422666 & -0.00558795943749705 & -0.38806121776312 \tabularnewline
33 & 7 & 6.99642175712162 & 0.0747549074142069 & 0.00357824287837512 & 0.85154738176011 \tabularnewline
34 & 6.8 & 6.78424673668332 & -0.196156010514188 & 0.0157532633166833 & -0.848412853688194 \tabularnewline
35 & 6.4 & 6.44173983614233 & -0.334337351626327 & -0.041739836142333 & -0.432743423377657 \tabularnewline
36 & 6.7 & 6.64964151052286 & 0.177618078097179 & 0.0503584894771431 & 1.60329587906902 \tabularnewline
37 & 6.6 & 6.62624203274371 & -0.0121423223956272 & -0.0262420327437118 & -0.594487171643204 \tabularnewline
38 & 6.4 & 6.39927632215368 & -0.215023499150526 & 0.000723677846320193 & -0.635800922086482 \tabularnewline
39 & 6.3 & 6.28504196179206 & -0.120512429444735 & 0.0149580382079447 & 0.296796859166133 \tabularnewline
40 & 6.2 & 6.21239006778479 & -0.0755941333569787 & -0.0123900677847891 & 0.140618085687078 \tabularnewline
41 & 6.5 & 6.49161311656742 & 0.25718047654175 & 0.0083868834325828 & 1.04216848423040 \tabularnewline
42 & 6.8 & 6.7862481165473 & 0.292315279718560 & 0.0137518834527053 & 0.110032561872672 \tabularnewline
43 & 6.8 & 6.80639498936865 & 0.0369978373522829 & -0.0063949893686552 & -0.799578495942069 \tabularnewline
44 & 6.4 & 6.44677828751598 & -0.335055756823133 & -0.0467782875159820 & -1.16516196744487 \tabularnewline
45 & 6.1 & 6.08224180870059 & -0.362710690296217 & 0.0177581912994072 & -0.0866070734016949 \tabularnewline
46 & 5.8 & 5.75679966834911 & -0.327750061018043 & 0.0432003316508939 & 0.109486350054354 \tabularnewline
47 & 6.1 & 6.14911730473386 & 0.347731453096235 & -0.0491173047338628 & 2.11541274740739 \tabularnewline
48 & 7.2 & 7.12316501016608 & 0.93523583883227 & 0.076834989833919 & 1.83989654065104 \tabularnewline
49 & 7.3 & 7.34662678923473 & 0.267642099046376 & -0.0466267892347286 & -2.09154344595480 \tabularnewline
50 & 6.9 & 6.93040979468902 & -0.373894943904836 & -0.0304097946890245 & -2.00952585364868 \tabularnewline
51 & 6.1 & 6.09048338595636 & -0.809120969827583 & 0.00951661404364303 & -1.36527912941267 \tabularnewline
52 & 5.8 & 5.80665831083743 & -0.31803189808199 & -0.00665831083743158 & 1.53770947365362 \tabularnewline
53 & 6.2 & 6.17444559647432 & 0.322660083497355 & 0.0255544035256774 & 2.0063694962774 \tabularnewline
54 & 7.1 & 7.06253399377584 & 0.850975541906616 & 0.0374660062241608 & 1.65455577486633 \tabularnewline
55 & 7.7 & 7.69946956489358 & 0.650976480496868 & 0.00053043510642342 & -0.626336834773619 \tabularnewline
56 & 7.9 & 7.96149209663143 & 0.287541529634192 & -0.0614920966314265 & -1.13817113772244 \tabularnewline
57 & 7.7 & 7.68002448200592 & -0.244131220656649 & 0.0199755179940838 & -1.66504180735822 \tabularnewline
58 & 7.4 & 7.38064957142924 & -0.295750353071808 & 0.0193504285707568 & -0.161655880159988 \tabularnewline
59 & 7.5 & 7.58745451312257 & 0.173834195968218 & -0.0874545131225718 & 1.47060399206747 \tabularnewline
60 & 8 & 7.89896132520757 & 0.302465200781383 & 0.101038674792426 & 0.402838373145633 \tabularnewline
61 & 8.1 & 8.12341871212448 & 0.229583418603231 & -0.0234187121244858 & -0.228334511141484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63768&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]8[/C][C]8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.1[/C][C]8.0992534482645[/C][C]0.099315146768711[/C][C]0.000746551735494343[/C][C]0.312144661532379[/C][/ROW]
[ROW][C]3[/C][C]7.7[/C][C]7.71033729004476[/C][C]-0.378431686426799[/C][C]-0.0103372900447557[/C][C]-1.51214927172144[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]7.49518144618358[/C][C]-0.219599500213875[/C][C]0.00481855381641875[/C][C]0.497206469501047[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.59543379667154[/C][C]0.0916915119177725[/C][C]0.00456620332845658[/C][C]0.974888938016866[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.79997542952185[/C][C]0.201528226306382[/C][C]2.45704781487947e-05[/C][C]0.343976132170661[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.8047055047012[/C][C]0.00998749078310632[/C][C]-0.00470550470120262[/C][C]-0.599848918670261[/C][/ROW]
[ROW][C]8[/C][C]7.8[/C][C]7.79990695798508[/C][C]-0.00440352846728825[/C][C]9.30420149202435e-05[/C][C]-0.0450684147140476[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.50581770169191[/C][C]-0.286350290163513[/C][C]-0.00581770169191252[/C][C]-0.88297384336456[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]7.49414521701395[/C][C]-0.0190105117436494[/C][C]0.00585478298605043[/C][C]0.837229093239738[/C][/ROW]
[ROW][C]11[/C][C]7.1[/C][C]7.10858667133196[/C][C]-0.37576619516409[/C][C]-0.00858667133196044[/C][C]-1.11725325390524[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.484997974067[/C][C]0.356316841173229[/C][C]0.0150020259329987[/C][C]2.2926674822242[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.50864261038258[/C][C]0.032587076773531[/C][C]-0.00864261038258466[/C][C]-1.01407990410654[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.58491489562795[/C][C]0.0751181182763844[/C][C]0.0150851043720510[/C][C]0.133567918038366[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.7084760605823[/C][C]0.121471978101266[/C][C]-0.00847606058229988[/C][C]0.146272311098007[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]7.70545524508715[/C][C]0.00261434002097083[/C][C]-0.00545524508714855[/C][C]-0.371955138449220[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.89464456833885[/C][C]0.180749508662983[/C][C]0.00535543166115181[/C][C]0.557902812678462[/C][/ROW]
[ROW][C]18[/C][C]8.1[/C][C]8.09489680165998[/C][C]0.199372469606793[/C][C]0.00510319834001557[/C][C]0.0583215090833516[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.21219543390285[/C][C]0.121001741196476[/C][C]-0.0121954339028472[/C][C]-0.245434002944632[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.1909994410428[/C][C]-0.0147791354599226[/C][C]0.009000558957195[/C][C]-0.425225530166752[/C][/ROW]
[ROW][C]21[/C][C]8.2[/C][C]8.21852473280732[/C][C]0.0256162848724262[/C][C]-0.0185247328073169[/C][C]0.126506505635344[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.88249894272392[/C][C]-0.319706625173862[/C][C]0.0175010572760754[/C][C]-1.08144919353469[/C][/ROW]
[ROW][C]23[/C][C]7.3[/C][C]7.33869729193712[/C][C]-0.533690185774264[/C][C]-0.0386972919371166[/C][C]-0.670133269467272[/C][/ROW]
[ROW][C]24[/C][C]6.9[/C][C]6.87140224212641[/C][C]-0.470291519574783[/C][C]0.028597757873589[/C][C]0.198546043256021[/C][/ROW]
[ROW][C]25[/C][C]6.6[/C][C]6.60311922778425[/C][C]-0.277433260941253[/C][C]-0.00311922778424517[/C][C]0.604149594912838[/C][/ROW]
[ROW][C]26[/C][C]6.7[/C][C]6.6797710450136[/C][C]0.0608008977403762[/C][C]0.0202289549863973[/C][C]1.06092958645746[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]6.89514738117445[/C][C]0.206784535759022[/C][C]0.0048526188255462[/C][C]0.459307752845182[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]7.01767499004384[/C][C]0.127210418477692[/C][C]-0.0176749900438427[/C][C]-0.249046153952195[/C][/ROW]
[ROW][C]29[/C][C]7.1[/C][C]7.09884793220702[/C][C]0.0837509293643512[/C][C]0.00115206779297903[/C][C]-0.136111223030652[/C][/ROW]
[ROW][C]30[/C][C]7.2[/C][C]7.19318721825769[/C][C]0.0937482246273258[/C][C]0.00681278174231[/C][C]0.0313085411199649[/C][/ROW]
[ROW][C]31[/C][C]7.1[/C][C]7.1100720075795[/C][C]-0.07324315511923[/C][C]-0.0100720075795037[/C][C]-0.52296789058124[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]6.9055879594375[/C][C]-0.197156910422666[/C][C]-0.00558795943749705[/C][C]-0.38806121776312[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]6.99642175712162[/C][C]0.0747549074142069[/C][C]0.00357824287837512[/C][C]0.85154738176011[/C][/ROW]
[ROW][C]34[/C][C]6.8[/C][C]6.78424673668332[/C][C]-0.196156010514188[/C][C]0.0157532633166833[/C][C]-0.848412853688194[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]6.44173983614233[/C][C]-0.334337351626327[/C][C]-0.041739836142333[/C][C]-0.432743423377657[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]6.64964151052286[/C][C]0.177618078097179[/C][C]0.0503584894771431[/C][C]1.60329587906902[/C][/ROW]
[ROW][C]37[/C][C]6.6[/C][C]6.62624203274371[/C][C]-0.0121423223956272[/C][C]-0.0262420327437118[/C][C]-0.594487171643204[/C][/ROW]
[ROW][C]38[/C][C]6.4[/C][C]6.39927632215368[/C][C]-0.215023499150526[/C][C]0.000723677846320193[/C][C]-0.635800922086482[/C][/ROW]
[ROW][C]39[/C][C]6.3[/C][C]6.28504196179206[/C][C]-0.120512429444735[/C][C]0.0149580382079447[/C][C]0.296796859166133[/C][/ROW]
[ROW][C]40[/C][C]6.2[/C][C]6.21239006778479[/C][C]-0.0755941333569787[/C][C]-0.0123900677847891[/C][C]0.140618085687078[/C][/ROW]
[ROW][C]41[/C][C]6.5[/C][C]6.49161311656742[/C][C]0.25718047654175[/C][C]0.0083868834325828[/C][C]1.04216848423040[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]6.7862481165473[/C][C]0.292315279718560[/C][C]0.0137518834527053[/C][C]0.110032561872672[/C][/ROW]
[ROW][C]43[/C][C]6.8[/C][C]6.80639498936865[/C][C]0.0369978373522829[/C][C]-0.0063949893686552[/C][C]-0.799578495942069[/C][/ROW]
[ROW][C]44[/C][C]6.4[/C][C]6.44677828751598[/C][C]-0.335055756823133[/C][C]-0.0467782875159820[/C][C]-1.16516196744487[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]6.08224180870059[/C][C]-0.362710690296217[/C][C]0.0177581912994072[/C][C]-0.0866070734016949[/C][/ROW]
[ROW][C]46[/C][C]5.8[/C][C]5.75679966834911[/C][C]-0.327750061018043[/C][C]0.0432003316508939[/C][C]0.109486350054354[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]6.14911730473386[/C][C]0.347731453096235[/C][C]-0.0491173047338628[/C][C]2.11541274740739[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.12316501016608[/C][C]0.93523583883227[/C][C]0.076834989833919[/C][C]1.83989654065104[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.34662678923473[/C][C]0.267642099046376[/C][C]-0.0466267892347286[/C][C]-2.09154344595480[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]6.93040979468902[/C][C]-0.373894943904836[/C][C]-0.0304097946890245[/C][C]-2.00952585364868[/C][/ROW]
[ROW][C]51[/C][C]6.1[/C][C]6.09048338595636[/C][C]-0.809120969827583[/C][C]0.00951661404364303[/C][C]-1.36527912941267[/C][/ROW]
[ROW][C]52[/C][C]5.8[/C][C]5.80665831083743[/C][C]-0.31803189808199[/C][C]-0.00665831083743158[/C][C]1.53770947365362[/C][/ROW]
[ROW][C]53[/C][C]6.2[/C][C]6.17444559647432[/C][C]0.322660083497355[/C][C]0.0255544035256774[/C][C]2.0063694962774[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.06253399377584[/C][C]0.850975541906616[/C][C]0.0374660062241608[/C][C]1.65455577486633[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.69946956489358[/C][C]0.650976480496868[/C][C]0.00053043510642342[/C][C]-0.626336834773619[/C][/ROW]
[ROW][C]56[/C][C]7.9[/C][C]7.96149209663143[/C][C]0.287541529634192[/C][C]-0.0614920966314265[/C][C]-1.13817113772244[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.68002448200592[/C][C]-0.244131220656649[/C][C]0.0199755179940838[/C][C]-1.66504180735822[/C][/ROW]
[ROW][C]58[/C][C]7.4[/C][C]7.38064957142924[/C][C]-0.295750353071808[/C][C]0.0193504285707568[/C][C]-0.161655880159988[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.58745451312257[/C][C]0.173834195968218[/C][C]-0.0874545131225718[/C][C]1.47060399206747[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.89896132520757[/C][C]0.302465200781383[/C][C]0.101038674792426[/C][C]0.402838373145633[/C][/ROW]
[ROW][C]61[/C][C]8.1[/C][C]8.12341871212448[/C][C]0.229583418603231[/C][C]-0.0234187121244858[/C][C]-0.228334511141484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63768&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63768&T=1

As an alternative you can also use a QR Code:

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

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	8	8	0	0	0
2	8.1	8.0992534482645	0.099315146768711	0.000746551735494343	0.312144661532379
3	7.7	7.71033729004476	-0.378431686426799	-0.0103372900447557	-1.51214927172144
4	7.5	7.49518144618358	-0.219599500213875	0.00481855381641875	0.497206469501047
5	7.6	7.59543379667154	0.0916915119177725	0.00456620332845658	0.974888938016866
6	7.8	7.79997542952185	0.201528226306382	2.45704781487947e-05	0.343976132170661
7	7.8	7.8047055047012	0.00998749078310632	-0.00470550470120262	-0.599848918670261
8	7.8	7.79990695798508	-0.00440352846728825	9.30420149202435e-05	-0.0450684147140476
9	7.5	7.50581770169191	-0.286350290163513	-0.00581770169191252	-0.88297384336456
10	7.5	7.49414521701395	-0.0190105117436494	0.00585478298605043	0.837229093239738
11	7.1	7.10858667133196	-0.37576619516409	-0.00858667133196044	-1.11725325390524
12	7.5	7.484997974067	0.356316841173229	0.0150020259329987	2.2926674822242
13	7.5	7.50864261038258	0.032587076773531	-0.00864261038258466	-1.01407990410654
14	7.6	7.58491489562795	0.0751181182763844	0.0150851043720510	0.133567918038366
15	7.7	7.7084760605823	0.121471978101266	-0.00847606058229988	0.146272311098007
16	7.7	7.70545524508715	0.00261434002097083	-0.00545524508714855	-0.371955138449220
17	7.9	7.89464456833885	0.180749508662983	0.00535543166115181	0.557902812678462
18	8.1	8.09489680165998	0.199372469606793	0.00510319834001557	0.0583215090833516
19	8.2	8.21219543390285	0.121001741196476	-0.0121954339028472	-0.245434002944632
20	8.2	8.1909994410428	-0.0147791354599226	0.009000558957195	-0.425225530166752
21	8.2	8.21852473280732	0.0256162848724262	-0.0185247328073169	0.126506505635344
22	7.9	7.88249894272392	-0.319706625173862	0.0175010572760754	-1.08144919353469
23	7.3	7.33869729193712	-0.533690185774264	-0.0386972919371166	-0.670133269467272
24	6.9	6.87140224212641	-0.470291519574783	0.028597757873589	0.198546043256021
25	6.6	6.60311922778425	-0.277433260941253	-0.00311922778424517	0.604149594912838
26	6.7	6.6797710450136	0.0608008977403762	0.0202289549863973	1.06092958645746
27	6.9	6.89514738117445	0.206784535759022	0.0048526188255462	0.459307752845182
28	7	7.01767499004384	0.127210418477692	-0.0176749900438427	-0.249046153952195
29	7.1	7.09884793220702	0.0837509293643512	0.00115206779297903	-0.136111223030652
30	7.2	7.19318721825769	0.0937482246273258	0.00681278174231	0.0313085411199649
31	7.1	7.1100720075795	-0.07324315511923	-0.0100720075795037	-0.52296789058124
32	6.9	6.9055879594375	-0.197156910422666	-0.00558795943749705	-0.38806121776312
33	7	6.99642175712162	0.0747549074142069	0.00357824287837512	0.85154738176011
34	6.8	6.78424673668332	-0.196156010514188	0.0157532633166833	-0.848412853688194
35	6.4	6.44173983614233	-0.334337351626327	-0.041739836142333	-0.432743423377657
36	6.7	6.64964151052286	0.177618078097179	0.0503584894771431	1.60329587906902
37	6.6	6.62624203274371	-0.0121423223956272	-0.0262420327437118	-0.594487171643204
38	6.4	6.39927632215368	-0.215023499150526	0.000723677846320193	-0.635800922086482
39	6.3	6.28504196179206	-0.120512429444735	0.0149580382079447	0.296796859166133
40	6.2	6.21239006778479	-0.0755941333569787	-0.0123900677847891	0.140618085687078
41	6.5	6.49161311656742	0.25718047654175	0.0083868834325828	1.04216848423040
42	6.8	6.7862481165473	0.292315279718560	0.0137518834527053	0.110032561872672
43	6.8	6.80639498936865	0.0369978373522829	-0.0063949893686552	-0.799578495942069
44	6.4	6.44677828751598	-0.335055756823133	-0.0467782875159820	-1.16516196744487
45	6.1	6.08224180870059	-0.362710690296217	0.0177581912994072	-0.0866070734016949
46	5.8	5.75679966834911	-0.327750061018043	0.0432003316508939	0.109486350054354
47	6.1	6.14911730473386	0.347731453096235	-0.0491173047338628	2.11541274740739
48	7.2	7.12316501016608	0.93523583883227	0.076834989833919	1.83989654065104
49	7.3	7.34662678923473	0.267642099046376	-0.0466267892347286	-2.09154344595480
50	6.9	6.93040979468902	-0.373894943904836	-0.0304097946890245	-2.00952585364868
51	6.1	6.09048338595636	-0.809120969827583	0.00951661404364303	-1.36527912941267
52	5.8	5.80665831083743	-0.31803189808199	-0.00665831083743158	1.53770947365362
53	6.2	6.17444559647432	0.322660083497355	0.0255544035256774	2.0063694962774
54	7.1	7.06253399377584	0.850975541906616	0.0374660062241608	1.65455577486633
55	7.7	7.69946956489358	0.650976480496868	0.00053043510642342	-0.626336834773619
56	7.9	7.96149209663143	0.287541529634192	-0.0614920966314265	-1.13817113772244
57	7.7	7.68002448200592	-0.244131220656649	0.0199755179940838	-1.66504180735822
58	7.4	7.38064957142924	-0.295750353071808	0.0193504285707568	-0.161655880159988
59	7.5	7.58745451312257	0.173834195968218	-0.0874545131225718	1.47060399206747
60	8	7.89896132520757	0.302465200781383	0.101038674792426	0.402838373145633
61	8.1	8.12341871212448	0.229583418603231	-0.0234187121244858	-0.228334511141484

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code