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R Software Module

rwasp_structuraltimeseries.wasp

Title produced by software

Structural Time Series Models

Date of computation

Fri, 04 Dec 2009 02:08:52 -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/t1259917787hzdwioeejadtyrf.htm/, Retrieved Sat, 27 Apr 2024 23:43:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63193, Retrieved Sat, 27 Apr 2024 23:43:51 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

132

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] [decomposition 3] [2009-12-04 09:08:52] [87085ce7f5378f281469a8b1f0969170] [Current]
-             [Structural Time Series Models] [Workshop 9-9] [2009-12-04 22:07:22] [aba88da643e3763d32ff92bd8f92a385] 
-             [Structural Time Series Models] [Workshop 9] [2009-12-05 14:16:23] [b6394cb5c2dcec6d17418d3cdf42d699] 

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Dataseries X:

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5.7
6.1
6
5.9
5.8
5.7
5.6
5.4
5.4
5.5
5.6
5.7
5.9
6.1
6
5.8
5.8
5.7
5.5
5.3
5.2
5.2
5
5.1
5.1
5.2
4.9
4.8
4.5
4.5
4.4
4.4
4.2
4.1
3.9
3.8
3.9
4.2
4.1
3.8
3.6
3.7
3.5
3.4
3.1
3.1
3.1
3.2
3.3
3.5
3.6
3.5
3.3
3.2
3.1
3.2
3
3
3.1
3.4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63193&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]11 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=63193&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63193&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	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	5.7	5.7	0	0	0
2	6.1	6.07884880815518	0.021506092449984	0.0211511918448191	1.52963603821458
3	6	5.98081389252993	0.0196210034839888	0.0191861074700705	-0.801081630437777
4	5.9	5.8806974440651	0.0171237270371641	0.0193025559349021	-0.799935278947233
5	5.8	5.78108815167059	0.0140527026745525	0.0189118483294073	-0.777645075668014
6	5.7	5.68146257095776	0.0104867702924174	0.0185374290422363	-0.75523168838216
7	5.6	5.5818085261228	0.00651170061781887	0.0181914738772051	-0.729844260706367
8	5.4	5.38304916150118	-0.00179307611904878	0.0169508384988162	-1.35693753496063
9	5.4	5.38158935004924	-0.00177825899900189	0.0184106499507612	0.00219809645207262
10	5.5	5.48109825571713	0.00309370584769649	0.018901744282871	0.666675182117168
11	5.6	5.58106636186441	0.00807173407984241	0.0189336381355904	0.636426540786974
12	5.7	5.68085312693168	0.0130549010288591	0.0191468730683169	0.601498343837729
13	5.9	5.9773419778874	0.0209125052151983	-0.0773419778873993	2.21508419190292
14	6.1	6.08917510333549	0.0273773709266605	0.0108248966645065	0.507280713144852
15	6	5.99351037283877	0.0198867847500754	0.0064896271612301	-0.802957580818163
16	5.8	5.79328481101275	0.00616177418513105	0.006715188987245	-1.43433420819565
17	5.8	5.79040339739136	0.00558228993531618	0.0095966026086434	-0.0588758815277227
18	5.7	5.69260724985264	-0.00119305111860298	0.00739275014736436	-0.672481894469816
19	5.5	5.4924861031196	-0.0144831339901178	0.00751389688039589	-1.29307690457191
20	5.3	5.29372235341134	-0.0269980557961081	0.00627764658865619	-1.19708640493505
21	5.2	5.19170791706359	-0.0321644311217447	0.00829208293641082	-0.48702920834903
22	5.2	5.19063437628873	-0.0299974103452812	0.00936562371127295	0.201751549253228
23	5	4.99328022398454	-0.0417843741092933	0.00671977601545689	-1.08556691450785
24	5.1	5.09056384911272	-0.0319153865619695	0.009436150887284	0.901520025084876
25	5.1	5.16914873806469	-0.0246871781444536	-0.0691487380646942	0.781885054008576
26	5.2	5.19018446917844	-0.0212497538881903	0.00981553082155947	0.271193288844019
27	4.9	4.89828077600158	-0.0408759769876554	0.00171922399842354	-1.75352693601003
28	4.8	4.79539959618549	-0.0453808676869526	0.00460040381450809	-0.401373510111418
29	4.5	4.49613858087191	-0.0639085674474407	0.00386141912809288	-1.64331607786774
30	4.5	4.49072297243223	-0.0596246536884174	0.00927702756776707	0.378560208904886
31	4.4	4.39349943229872	-0.0623866139156848	0.006500567701279	-0.243303499934338
32	4.4	4.3933599153963	-0.0578022539319941	0.00664008460370301	0.402756441979409
33	4.2	4.19724190768549	-0.0680113421442882	0.00275809231451025	-0.894853206835917
34	4.1	4.08988793987476	-0.0709207211864103	0.0101120601252424	-0.254511840152121
35	3.9	3.89815198280308	-0.0798706248766471	0.00184801719691903	-0.78156431551142
36	3.8	3.79322121014328	-0.0817278823740084	0.0067787898567181	-0.162036599737284
37	3.9	3.9401539810759	-0.0651558348692222	-0.0401539810759024	1.56651754070755
38	4.2	4.18187184769333	-0.0421247978703715	0.0181281523066671	1.87004402768307
39	4.1	4.10109719577813	-0.0450017564418658	-0.00109719577812907	-0.250035773377558
40	3.8	3.79598262698776	-0.0643580364420527	0.00401737301224230	-1.68135936194194
41	3.6	3.59820658684057	-0.0742937758376488	0.00179341315942727	-0.862497209824643
42	3.7	3.68509853743323	-0.0622832402415998	0.0149014625667696	1.04200965505825
43	3.5	3.49879582636976	-0.0715287399007832	0.00120417363024326	-0.801727117250042
44	3.4	3.39102150805701	-0.0742318929348198	0.00897849194299096	-0.234306505665383
45	3.1	3.10135690759451	-0.090304158839998	-0.00135690759450704	-1.39262646837832
46	3.1	3.08457632567703	-0.0848172873631888	0.0154236743229741	0.47527926407406
47	3.1	3.09429375033841	-0.0777600725696301	0.00570624966159481	0.611148465526161
48	3.2	3.19298645835192	-0.0645930659443174	0.00701354164807784	1.14009233794824
49	3.3	3.35273618860181	-0.0479862437124517	-0.0527361886018095	1.51226097657081
50	3.5	3.48072067290545	-0.0348315493139966	0.0192793270945480	1.08868800693393
51	3.6	3.59440210036854	-0.0237389574813225	0.00559789963145971	0.960308896214668
52	3.5	3.49585695527987	-0.0293260501325651	0.00414304472012664	-0.483341446501009
53	3.3	3.30672158577066	-0.0412631631007524	-0.00672158577066073	-1.03268373535159
54	3.2	3.18419733750798	-0.0473336081685154	0.0158026624920215	-0.525128986295893
55	3.1	3.10017755206214	-0.0500743730394271	-0.000177552062141973	-0.237075410837949
56	3.2	3.18270208583513	-0.0401675160373335	0.0172979141648653	0.856887313562906
57	3	3.00727807172696	-0.0502734642826878	-0.00727807172695857	-0.874061653703504
58	3	2.98431228219589	-0.0482330071032828	0.0156877178041127	0.176472542310693
59	3.1	3.09323284994450	-0.0364893619244272	0.00676715005550279	1.01567524940561
60	3.4	3.39004921441626	-0.0115931649931183	0.0099507855837353	2.15284544647217

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 5.7 & 5.7 & 0 & 0 & 0 \tabularnewline
2 & 6.1 & 6.07884880815518 & 0.021506092449984 & 0.0211511918448191 & 1.52963603821458 \tabularnewline
3 & 6 & 5.98081389252993 & 0.0196210034839888 & 0.0191861074700705 & -0.801081630437777 \tabularnewline
4 & 5.9 & 5.8806974440651 & 0.0171237270371641 & 0.0193025559349021 & -0.799935278947233 \tabularnewline
5 & 5.8 & 5.78108815167059 & 0.0140527026745525 & 0.0189118483294073 & -0.777645075668014 \tabularnewline
6 & 5.7 & 5.68146257095776 & 0.0104867702924174 & 0.0185374290422363 & -0.75523168838216 \tabularnewline
7 & 5.6 & 5.5818085261228 & 0.00651170061781887 & 0.0181914738772051 & -0.729844260706367 \tabularnewline
8 & 5.4 & 5.38304916150118 & -0.00179307611904878 & 0.0169508384988162 & -1.35693753496063 \tabularnewline
9 & 5.4 & 5.38158935004924 & -0.00177825899900189 & 0.0184106499507612 & 0.00219809645207262 \tabularnewline
10 & 5.5 & 5.48109825571713 & 0.00309370584769649 & 0.018901744282871 & 0.666675182117168 \tabularnewline
11 & 5.6 & 5.58106636186441 & 0.00807173407984241 & 0.0189336381355904 & 0.636426540786974 \tabularnewline
12 & 5.7 & 5.68085312693168 & 0.0130549010288591 & 0.0191468730683169 & 0.601498343837729 \tabularnewline
13 & 5.9 & 5.9773419778874 & 0.0209125052151983 & -0.0773419778873993 & 2.21508419190292 \tabularnewline
14 & 6.1 & 6.08917510333549 & 0.0273773709266605 & 0.0108248966645065 & 0.507280713144852 \tabularnewline
15 & 6 & 5.99351037283877 & 0.0198867847500754 & 0.0064896271612301 & -0.802957580818163 \tabularnewline
16 & 5.8 & 5.79328481101275 & 0.00616177418513105 & 0.006715188987245 & -1.43433420819565 \tabularnewline
17 & 5.8 & 5.79040339739136 & 0.00558228993531618 & 0.0095966026086434 & -0.0588758815277227 \tabularnewline
18 & 5.7 & 5.69260724985264 & -0.00119305111860298 & 0.00739275014736436 & -0.672481894469816 \tabularnewline
19 & 5.5 & 5.4924861031196 & -0.0144831339901178 & 0.00751389688039589 & -1.29307690457191 \tabularnewline
20 & 5.3 & 5.29372235341134 & -0.0269980557961081 & 0.00627764658865619 & -1.19708640493505 \tabularnewline
21 & 5.2 & 5.19170791706359 & -0.0321644311217447 & 0.00829208293641082 & -0.48702920834903 \tabularnewline
22 & 5.2 & 5.19063437628873 & -0.0299974103452812 & 0.00936562371127295 & 0.201751549253228 \tabularnewline
23 & 5 & 4.99328022398454 & -0.0417843741092933 & 0.00671977601545689 & -1.08556691450785 \tabularnewline
24 & 5.1 & 5.09056384911272 & -0.0319153865619695 & 0.009436150887284 & 0.901520025084876 \tabularnewline
25 & 5.1 & 5.16914873806469 & -0.0246871781444536 & -0.0691487380646942 & 0.781885054008576 \tabularnewline
26 & 5.2 & 5.19018446917844 & -0.0212497538881903 & 0.00981553082155947 & 0.271193288844019 \tabularnewline
27 & 4.9 & 4.89828077600158 & -0.0408759769876554 & 0.00171922399842354 & -1.75352693601003 \tabularnewline
28 & 4.8 & 4.79539959618549 & -0.0453808676869526 & 0.00460040381450809 & -0.401373510111418 \tabularnewline
29 & 4.5 & 4.49613858087191 & -0.0639085674474407 & 0.00386141912809288 & -1.64331607786774 \tabularnewline
30 & 4.5 & 4.49072297243223 & -0.0596246536884174 & 0.00927702756776707 & 0.378560208904886 \tabularnewline
31 & 4.4 & 4.39349943229872 & -0.0623866139156848 & 0.006500567701279 & -0.243303499934338 \tabularnewline
32 & 4.4 & 4.3933599153963 & -0.0578022539319941 & 0.00664008460370301 & 0.402756441979409 \tabularnewline
33 & 4.2 & 4.19724190768549 & -0.0680113421442882 & 0.00275809231451025 & -0.894853206835917 \tabularnewline
34 & 4.1 & 4.08988793987476 & -0.0709207211864103 & 0.0101120601252424 & -0.254511840152121 \tabularnewline
35 & 3.9 & 3.89815198280308 & -0.0798706248766471 & 0.00184801719691903 & -0.78156431551142 \tabularnewline
36 & 3.8 & 3.79322121014328 & -0.0817278823740084 & 0.0067787898567181 & -0.162036599737284 \tabularnewline
37 & 3.9 & 3.9401539810759 & -0.0651558348692222 & -0.0401539810759024 & 1.56651754070755 \tabularnewline
38 & 4.2 & 4.18187184769333 & -0.0421247978703715 & 0.0181281523066671 & 1.87004402768307 \tabularnewline
39 & 4.1 & 4.10109719577813 & -0.0450017564418658 & -0.00109719577812907 & -0.250035773377558 \tabularnewline
40 & 3.8 & 3.79598262698776 & -0.0643580364420527 & 0.00401737301224230 & -1.68135936194194 \tabularnewline
41 & 3.6 & 3.59820658684057 & -0.0742937758376488 & 0.00179341315942727 & -0.862497209824643 \tabularnewline
42 & 3.7 & 3.68509853743323 & -0.0622832402415998 & 0.0149014625667696 & 1.04200965505825 \tabularnewline
43 & 3.5 & 3.49879582636976 & -0.0715287399007832 & 0.00120417363024326 & -0.801727117250042 \tabularnewline
44 & 3.4 & 3.39102150805701 & -0.0742318929348198 & 0.00897849194299096 & -0.234306505665383 \tabularnewline
45 & 3.1 & 3.10135690759451 & -0.090304158839998 & -0.00135690759450704 & -1.39262646837832 \tabularnewline
46 & 3.1 & 3.08457632567703 & -0.0848172873631888 & 0.0154236743229741 & 0.47527926407406 \tabularnewline
47 & 3.1 & 3.09429375033841 & -0.0777600725696301 & 0.00570624966159481 & 0.611148465526161 \tabularnewline
48 & 3.2 & 3.19298645835192 & -0.0645930659443174 & 0.00701354164807784 & 1.14009233794824 \tabularnewline
49 & 3.3 & 3.35273618860181 & -0.0479862437124517 & -0.0527361886018095 & 1.51226097657081 \tabularnewline
50 & 3.5 & 3.48072067290545 & -0.0348315493139966 & 0.0192793270945480 & 1.08868800693393 \tabularnewline
51 & 3.6 & 3.59440210036854 & -0.0237389574813225 & 0.00559789963145971 & 0.960308896214668 \tabularnewline
52 & 3.5 & 3.49585695527987 & -0.0293260501325651 & 0.00414304472012664 & -0.483341446501009 \tabularnewline
53 & 3.3 & 3.30672158577066 & -0.0412631631007524 & -0.00672158577066073 & -1.03268373535159 \tabularnewline
54 & 3.2 & 3.18419733750798 & -0.0473336081685154 & 0.0158026624920215 & -0.525128986295893 \tabularnewline
55 & 3.1 & 3.10017755206214 & -0.0500743730394271 & -0.000177552062141973 & -0.237075410837949 \tabularnewline
56 & 3.2 & 3.18270208583513 & -0.0401675160373335 & 0.0172979141648653 & 0.856887313562906 \tabularnewline
57 & 3 & 3.00727807172696 & -0.0502734642826878 & -0.00727807172695857 & -0.874061653703504 \tabularnewline
58 & 3 & 2.98431228219589 & -0.0482330071032828 & 0.0156877178041127 & 0.176472542310693 \tabularnewline
59 & 3.1 & 3.09323284994450 & -0.0364893619244272 & 0.00676715005550279 & 1.01567524940561 \tabularnewline
60 & 3.4 & 3.39004921441626 & -0.0115931649931183 & 0.0099507855837353 & 2.15284544647217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63193&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]5.7[/C][C]5.7[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]6.1[/C][C]6.07884880815518[/C][C]0.021506092449984[/C][C]0.0211511918448191[/C][C]1.52963603821458[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.98081389252993[/C][C]0.0196210034839888[/C][C]0.0191861074700705[/C][C]-0.801081630437777[/C][/ROW]
[ROW][C]4[/C][C]5.9[/C][C]5.8806974440651[/C][C]0.0171237270371641[/C][C]0.0193025559349021[/C][C]-0.799935278947233[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]5.78108815167059[/C][C]0.0140527026745525[/C][C]0.0189118483294073[/C][C]-0.777645075668014[/C][/ROW]
[ROW][C]6[/C][C]5.7[/C][C]5.68146257095776[/C][C]0.0104867702924174[/C][C]0.0185374290422363[/C][C]-0.75523168838216[/C][/ROW]
[ROW][C]7[/C][C]5.6[/C][C]5.5818085261228[/C][C]0.00651170061781887[/C][C]0.0181914738772051[/C][C]-0.729844260706367[/C][/ROW]
[ROW][C]8[/C][C]5.4[/C][C]5.38304916150118[/C][C]-0.00179307611904878[/C][C]0.0169508384988162[/C][C]-1.35693753496063[/C][/ROW]
[ROW][C]9[/C][C]5.4[/C][C]5.38158935004924[/C][C]-0.00177825899900189[/C][C]0.0184106499507612[/C][C]0.00219809645207262[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]5.48109825571713[/C][C]0.00309370584769649[/C][C]0.018901744282871[/C][C]0.666675182117168[/C][/ROW]
[ROW][C]11[/C][C]5.6[/C][C]5.58106636186441[/C][C]0.00807173407984241[/C][C]0.0189336381355904[/C][C]0.636426540786974[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]5.68085312693168[/C][C]0.0130549010288591[/C][C]0.0191468730683169[/C][C]0.601498343837729[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]5.9773419778874[/C][C]0.0209125052151983[/C][C]-0.0773419778873993[/C][C]2.21508419190292[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]6.08917510333549[/C][C]0.0273773709266605[/C][C]0.0108248966645065[/C][C]0.507280713144852[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.99351037283877[/C][C]0.0198867847500754[/C][C]0.0064896271612301[/C][C]-0.802957580818163[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.79328481101275[/C][C]0.00616177418513105[/C][C]0.006715188987245[/C][C]-1.43433420819565[/C][/ROW]
[ROW][C]17[/C][C]5.8[/C][C]5.79040339739136[/C][C]0.00558228993531618[/C][C]0.0095966026086434[/C][C]-0.0588758815277227[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]5.69260724985264[/C][C]-0.00119305111860298[/C][C]0.00739275014736436[/C][C]-0.672481894469816[/C][/ROW]
[ROW][C]19[/C][C]5.5[/C][C]5.4924861031196[/C][C]-0.0144831339901178[/C][C]0.00751389688039589[/C][C]-1.29307690457191[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.29372235341134[/C][C]-0.0269980557961081[/C][C]0.00627764658865619[/C][C]-1.19708640493505[/C][/ROW]
[ROW][C]21[/C][C]5.2[/C][C]5.19170791706359[/C][C]-0.0321644311217447[/C][C]0.00829208293641082[/C][C]-0.48702920834903[/C][/ROW]
[ROW][C]22[/C][C]5.2[/C][C]5.19063437628873[/C][C]-0.0299974103452812[/C][C]0.00936562371127295[/C][C]0.201751549253228[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.99328022398454[/C][C]-0.0417843741092933[/C][C]0.00671977601545689[/C][C]-1.08556691450785[/C][/ROW]
[ROW][C]24[/C][C]5.1[/C][C]5.09056384911272[/C][C]-0.0319153865619695[/C][C]0.009436150887284[/C][C]0.901520025084876[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]5.16914873806469[/C][C]-0.0246871781444536[/C][C]-0.0691487380646942[/C][C]0.781885054008576[/C][/ROW]
[ROW][C]26[/C][C]5.2[/C][C]5.19018446917844[/C][C]-0.0212497538881903[/C][C]0.00981553082155947[/C][C]0.271193288844019[/C][/ROW]
[ROW][C]27[/C][C]4.9[/C][C]4.89828077600158[/C][C]-0.0408759769876554[/C][C]0.00171922399842354[/C][C]-1.75352693601003[/C][/ROW]
[ROW][C]28[/C][C]4.8[/C][C]4.79539959618549[/C][C]-0.0453808676869526[/C][C]0.00460040381450809[/C][C]-0.401373510111418[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.49613858087191[/C][C]-0.0639085674474407[/C][C]0.00386141912809288[/C][C]-1.64331607786774[/C][/ROW]
[ROW][C]30[/C][C]4.5[/C][C]4.49072297243223[/C][C]-0.0596246536884174[/C][C]0.00927702756776707[/C][C]0.378560208904886[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]4.39349943229872[/C][C]-0.0623866139156848[/C][C]0.006500567701279[/C][C]-0.243303499934338[/C][/ROW]
[ROW][C]32[/C][C]4.4[/C][C]4.3933599153963[/C][C]-0.0578022539319941[/C][C]0.00664008460370301[/C][C]0.402756441979409[/C][/ROW]
[ROW][C]33[/C][C]4.2[/C][C]4.19724190768549[/C][C]-0.0680113421442882[/C][C]0.00275809231451025[/C][C]-0.894853206835917[/C][/ROW]
[ROW][C]34[/C][C]4.1[/C][C]4.08988793987476[/C][C]-0.0709207211864103[/C][C]0.0101120601252424[/C][C]-0.254511840152121[/C][/ROW]
[ROW][C]35[/C][C]3.9[/C][C]3.89815198280308[/C][C]-0.0798706248766471[/C][C]0.00184801719691903[/C][C]-0.78156431551142[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]3.79322121014328[/C][C]-0.0817278823740084[/C][C]0.0067787898567181[/C][C]-0.162036599737284[/C][/ROW]
[ROW][C]37[/C][C]3.9[/C][C]3.9401539810759[/C][C]-0.0651558348692222[/C][C]-0.0401539810759024[/C][C]1.56651754070755[/C][/ROW]
[ROW][C]38[/C][C]4.2[/C][C]4.18187184769333[/C][C]-0.0421247978703715[/C][C]0.0181281523066671[/C][C]1.87004402768307[/C][/ROW]
[ROW][C]39[/C][C]4.1[/C][C]4.10109719577813[/C][C]-0.0450017564418658[/C][C]-0.00109719577812907[/C][C]-0.250035773377558[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.79598262698776[/C][C]-0.0643580364420527[/C][C]0.00401737301224230[/C][C]-1.68135936194194[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]3.59820658684057[/C][C]-0.0742937758376488[/C][C]0.00179341315942727[/C][C]-0.862497209824643[/C][/ROW]
[ROW][C]42[/C][C]3.7[/C][C]3.68509853743323[/C][C]-0.0622832402415998[/C][C]0.0149014625667696[/C][C]1.04200965505825[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]3.49879582636976[/C][C]-0.0715287399007832[/C][C]0.00120417363024326[/C][C]-0.801727117250042[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]3.39102150805701[/C][C]-0.0742318929348198[/C][C]0.00897849194299096[/C][C]-0.234306505665383[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]3.10135690759451[/C][C]-0.090304158839998[/C][C]-0.00135690759450704[/C][C]-1.39262646837832[/C][/ROW]
[ROW][C]46[/C][C]3.1[/C][C]3.08457632567703[/C][C]-0.0848172873631888[/C][C]0.0154236743229741[/C][C]0.47527926407406[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]3.09429375033841[/C][C]-0.0777600725696301[/C][C]0.00570624966159481[/C][C]0.611148465526161[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.19298645835192[/C][C]-0.0645930659443174[/C][C]0.00701354164807784[/C][C]1.14009233794824[/C][/ROW]
[ROW][C]49[/C][C]3.3[/C][C]3.35273618860181[/C][C]-0.0479862437124517[/C][C]-0.0527361886018095[/C][C]1.51226097657081[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]3.48072067290545[/C][C]-0.0348315493139966[/C][C]0.0192793270945480[/C][C]1.08868800693393[/C][/ROW]
[ROW][C]51[/C][C]3.6[/C][C]3.59440210036854[/C][C]-0.0237389574813225[/C][C]0.00559789963145971[/C][C]0.960308896214668[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]3.49585695527987[/C][C]-0.0293260501325651[/C][C]0.00414304472012664[/C][C]-0.483341446501009[/C][/ROW]
[ROW][C]53[/C][C]3.3[/C][C]3.30672158577066[/C][C]-0.0412631631007524[/C][C]-0.00672158577066073[/C][C]-1.03268373535159[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]3.18419733750798[/C][C]-0.0473336081685154[/C][C]0.0158026624920215[/C][C]-0.525128986295893[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]3.10017755206214[/C][C]-0.0500743730394271[/C][C]-0.000177552062141973[/C][C]-0.237075410837949[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]3.18270208583513[/C][C]-0.0401675160373335[/C][C]0.0172979141648653[/C][C]0.856887313562906[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]3.00727807172696[/C][C]-0.0502734642826878[/C][C]-0.00727807172695857[/C][C]-0.874061653703504[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.98431228219589[/C][C]-0.0482330071032828[/C][C]0.0156877178041127[/C][C]0.176472542310693[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]3.09323284994450[/C][C]-0.0364893619244272[/C][C]0.00676715005550279[/C][C]1.01567524940561[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]3.39004921441626[/C][C]-0.0115931649931183[/C][C]0.0099507855837353[/C][C]2.15284544647217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63193&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63193&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	5.7	5.7	0	0	0
2	6.1	6.07884880815518	0.021506092449984	0.0211511918448191	1.52963603821458
3	6	5.98081389252993	0.0196210034839888	0.0191861074700705	-0.801081630437777
4	5.9	5.8806974440651	0.0171237270371641	0.0193025559349021	-0.799935278947233
5	5.8	5.78108815167059	0.0140527026745525	0.0189118483294073	-0.777645075668014
6	5.7	5.68146257095776	0.0104867702924174	0.0185374290422363	-0.75523168838216
7	5.6	5.5818085261228	0.00651170061781887	0.0181914738772051	-0.729844260706367
8	5.4	5.38304916150118	-0.00179307611904878	0.0169508384988162	-1.35693753496063
9	5.4	5.38158935004924	-0.00177825899900189	0.0184106499507612	0.00219809645207262
10	5.5	5.48109825571713	0.00309370584769649	0.018901744282871	0.666675182117168
11	5.6	5.58106636186441	0.00807173407984241	0.0189336381355904	0.636426540786974
12	5.7	5.68085312693168	0.0130549010288591	0.0191468730683169	0.601498343837729
13	5.9	5.9773419778874	0.0209125052151983	-0.0773419778873993	2.21508419190292
14	6.1	6.08917510333549	0.0273773709266605	0.0108248966645065	0.507280713144852
15	6	5.99351037283877	0.0198867847500754	0.0064896271612301	-0.802957580818163
16	5.8	5.79328481101275	0.00616177418513105	0.006715188987245	-1.43433420819565
17	5.8	5.79040339739136	0.00558228993531618	0.0095966026086434	-0.0588758815277227
18	5.7	5.69260724985264	-0.00119305111860298	0.00739275014736436	-0.672481894469816
19	5.5	5.4924861031196	-0.0144831339901178	0.00751389688039589	-1.29307690457191
20	5.3	5.29372235341134	-0.0269980557961081	0.00627764658865619	-1.19708640493505
21	5.2	5.19170791706359	-0.0321644311217447	0.00829208293641082	-0.48702920834903
22	5.2	5.19063437628873	-0.0299974103452812	0.00936562371127295	0.201751549253228
23	5	4.99328022398454	-0.0417843741092933	0.00671977601545689	-1.08556691450785
24	5.1	5.09056384911272	-0.0319153865619695	0.009436150887284	0.901520025084876
25	5.1	5.16914873806469	-0.0246871781444536	-0.0691487380646942	0.781885054008576
26	5.2	5.19018446917844	-0.0212497538881903	0.00981553082155947	0.271193288844019
27	4.9	4.89828077600158	-0.0408759769876554	0.00171922399842354	-1.75352693601003
28	4.8	4.79539959618549	-0.0453808676869526	0.00460040381450809	-0.401373510111418
29	4.5	4.49613858087191	-0.0639085674474407	0.00386141912809288	-1.64331607786774
30	4.5	4.49072297243223	-0.0596246536884174	0.00927702756776707	0.378560208904886
31	4.4	4.39349943229872	-0.0623866139156848	0.006500567701279	-0.243303499934338
32	4.4	4.3933599153963	-0.0578022539319941	0.00664008460370301	0.402756441979409
33	4.2	4.19724190768549	-0.0680113421442882	0.00275809231451025	-0.894853206835917
34	4.1	4.08988793987476	-0.0709207211864103	0.0101120601252424	-0.254511840152121
35	3.9	3.89815198280308	-0.0798706248766471	0.00184801719691903	-0.78156431551142
36	3.8	3.79322121014328	-0.0817278823740084	0.0067787898567181	-0.162036599737284
37	3.9	3.9401539810759	-0.0651558348692222	-0.0401539810759024	1.56651754070755
38	4.2	4.18187184769333	-0.0421247978703715	0.0181281523066671	1.87004402768307
39	4.1	4.10109719577813	-0.0450017564418658	-0.00109719577812907	-0.250035773377558
40	3.8	3.79598262698776	-0.0643580364420527	0.00401737301224230	-1.68135936194194
41	3.6	3.59820658684057	-0.0742937758376488	0.00179341315942727	-0.862497209824643
42	3.7	3.68509853743323	-0.0622832402415998	0.0149014625667696	1.04200965505825
43	3.5	3.49879582636976	-0.0715287399007832	0.00120417363024326	-0.801727117250042
44	3.4	3.39102150805701	-0.0742318929348198	0.00897849194299096	-0.234306505665383
45	3.1	3.10135690759451	-0.090304158839998	-0.00135690759450704	-1.39262646837832
46	3.1	3.08457632567703	-0.0848172873631888	0.0154236743229741	0.47527926407406
47	3.1	3.09429375033841	-0.0777600725696301	0.00570624966159481	0.611148465526161
48	3.2	3.19298645835192	-0.0645930659443174	0.00701354164807784	1.14009233794824
49	3.3	3.35273618860181	-0.0479862437124517	-0.0527361886018095	1.51226097657081
50	3.5	3.48072067290545	-0.0348315493139966	0.0192793270945480	1.08868800693393
51	3.6	3.59440210036854	-0.0237389574813225	0.00559789963145971	0.960308896214668
52	3.5	3.49585695527987	-0.0293260501325651	0.00414304472012664	-0.483341446501009
53	3.3	3.30672158577066	-0.0412631631007524	-0.00672158577066073	-1.03268373535159
54	3.2	3.18419733750798	-0.0473336081685154	0.0158026624920215	-0.525128986295893
55	3.1	3.10017755206214	-0.0500743730394271	-0.000177552062141973	-0.237075410837949
56	3.2	3.18270208583513	-0.0401675160373335	0.0172979141648653	0.856887313562906
57	3	3.00727807172696	-0.0502734642826878	-0.00727807172695857	-0.874061653703504
58	3	2.98431228219589	-0.0482330071032828	0.0156877178041127	0.176472542310693
59	3.1	3.09323284994450	-0.0364893619244272	0.00676715005550279	1.01567524940561
60	3.4	3.39004921441626	-0.0115931649931183	0.0099507855837353	2.15284544647217

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