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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationWed, 02 Dec 2009 09:07:15 -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/2009/Dec/02/t125977012155k41kcayq9tyrl.htm/, Retrieved Sun, 28 Apr 2024 06:54:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62406, Retrieved Sun, 28 Apr 2024 06:54:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
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]
-   PD      [Structural Time Series Models] [Structural time s...] [2009-12-02 16:07:15] [cf272a759dc2b193d9a85354803ede7b] [Current]
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Dataseries X:
108.5
112.3
116.6
115.5
120.1
132.9
128.1
129.3
132.5
131
124.9
120.8
122
122.1
127.4
135.2
137.3
135
136
138.4
134.7
138.4
133.9
133.6
141.2
151.8
155.4
156.6
161.6
160.7
156
159.5
168.7
169.9
169.9
185.9
190.8
195.8
211.9
227.1
251.3
256.7
251.9
251.2
270.3
267.2
243
229.9
187.2
178.2
175.2
192.4
187
184
194.1
212.7
217.5
200.5
205.9
196.5
206.3




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=62406&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=62406&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62406&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







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1108.5108.5000
2112.3112.1079905670520.2995203334646320.192009432947680.234351320027692
3116.6116.4090300790650.8876148522163260.1909699209353220.406095837952912
4115.5115.3086125150950.5069708313361370.191387484905296-0.196432614080234
5120.1119.9092835733741.405745947684840.1907164266257170.397420833309356
6132.9132.7107102053204.098451498067860.1892897946798541.09442172247909
7128.1127.9098700954531.909807170824710.190129904547208-0.849289434139052
8129.3129.1098199290101.731363425648710.19018007098989-0.067500890602942
9132.5132.3098973170132.105033799615360.1901026829873610.139380176174126
10131130.8097560069551.181722123955560.190243993044685-0.341747259421099
11124.9124.709543947020-0.6900295379207450.190456052980489-0.689855260765352
12120.8120.609470218456-1.56830580031740.190529781543639-0.322941913658471
13122122.651708053429-0.67070203984092-0.6517080534287920.39704415205166
14122.1122.039447201094-0.6563362883318720.06055279890584660.00463021949165108
15127.4127.3463710666690.8850329133205070.05362893333130290.563633530939744
16135.2135.1523313001442.672646662157250.04766869985591810.654463099242995
17137.3137.2519652436142.524692902789280.0480347563857951-0.0542031281548969
18135134.9496777167851.278530899634870.0503222832147156-0.456699678648379
19136135.9495797608491.206601865508260.0504202391509342-0.0263661848720782
20138.4138.3498910885491.514761314550730.05010891145148250.112970838502852
21134.7134.6488819526140.1682743108602720.0511180473862879-0.49365021666894
22138.4138.3493889308551.080164129837500.05061106914502230.334328967300456
23133.9133.848794722423-0.3606089709485420.0512052775769712-0.528244758194134
24133.6133.548799510041-0.3449601437175950.05120048995856390.00573754075203173
25141.2140.4081610175661.487023683511260.791838982434040.736730848354184
26151.8151.7306511664203.946725630812490.06934883358023980.837608783247037
27155.4155.33038329553.857040997817830.0696167044999834-0.0328123753762335
28156.6156.5288611333093.170329548113920.0711388666909328-0.251484976370749
29161.6161.5296385331263.642994027162780.07036146687420370.173189197355598
30160.7160.6282068063022.469679551064390.0717931936984147-0.430040013420672
31156155.9265307645340.6182266407125350.073469235465853-0.67869832266883
32159.5159.4270304806741.362341701488780.07296951932550080.272798850669425
33168.7168.6280386525963.386053340679040.07196134740405450.741945776264576
34169.9169.8278300608182.821619334945020.0721699391821734-0.206941845093007
35169.9169.8276303392772.09309276093120.0723696607226506-0.267108022297403
36185.9185.8283605498855.683754871782610.07163945011506071.31649613388155
37190.8191.5797244185135.70103908380125-0.7797244185132520.00674254824636057
38195.8195.7453834039165.313802871644020.054616596083769-0.134786750952614
39211.9211.8515554686468.102463971560120.04844453135357281.02083549291497
40227.1227.0545674363219.936344293339010.04543256367897570.67180158286185
41251.3251.25905699883813.62059306221580.04094300116244721.35017346216786
42256.7256.65713773517611.49763556602790.0428622648238606-0.778172340319613
43251.9251.8543152626467.289218675156890.0456847373538225-1.54278319793577
44251.2251.1532889220945.226337319742110.0467110779061335-0.756290805500648
45270.3270.2546110242328.808520170455130.04538897576800561.31334238926104
46267.2267.1537692009725.733789466096520.0462307990276661-1.12731660280892
47243242.952199505244-1.994919499280630.0478004947564810-2.83367806576953
48229.9229.851767521988-4.862164109195090.0482324780116287-1.05126185453105
49187.2190.846821033877-13.6130873322921-3.64682103387687-3.36178761805418
50178.2177.863188963842-13.45355006466300.3368110361578230.0561944119767591
51175.2174.867937333590-10.75164420188820.3320626664103220.989400902380608
52192.4192.077353771140-3.530530771425930.3226462288598262.64577173900073
53187186.676886637842-4.013366926614870.323113362157837-0.176963386407946
54184183.677074463347-3.751678380998180.3229255366532520.0959274445987369
55194.1193.778978905259-0.1749541468331040.3210210947413231.31124736495429
56212.7212.3808937272234.672823836170230.3191062727773861.77731521856663
57217.5217.1809033487304.705660496496990.3190966512703500.0120390754466365
58200.5200.179685200108-0.8986275033943730.320314799892371-2.0547615681818
59205.9205.5799474191360.7276331769556650.3200525808639280.596258950010388
60196.5196.179634654342-1.887238836331220.320365345657877-0.958732070721639
61206.3208.7729035833461.82976239899704-2.472903583346231.41479136733123

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 108.5 & 108.5 & 0 & 0 & 0 \tabularnewline
2 & 112.3 & 112.107990567052 & 0.299520333464632 & 0.19200943294768 & 0.234351320027692 \tabularnewline
3 & 116.6 & 116.409030079065 & 0.887614852216326 & 0.190969920935322 & 0.406095837952912 \tabularnewline
4 & 115.5 & 115.308612515095 & 0.506970831336137 & 0.191387484905296 & -0.196432614080234 \tabularnewline
5 & 120.1 & 119.909283573374 & 1.40574594768484 & 0.190716426625717 & 0.397420833309356 \tabularnewline
6 & 132.9 & 132.710710205320 & 4.09845149806786 & 0.189289794679854 & 1.09442172247909 \tabularnewline
7 & 128.1 & 127.909870095453 & 1.90980717082471 & 0.190129904547208 & -0.849289434139052 \tabularnewline
8 & 129.3 & 129.109819929010 & 1.73136342564871 & 0.19018007098989 & -0.067500890602942 \tabularnewline
9 & 132.5 & 132.309897317013 & 2.10503379961536 & 0.190102682987361 & 0.139380176174126 \tabularnewline
10 & 131 & 130.809756006955 & 1.18172212395556 & 0.190243993044685 & -0.341747259421099 \tabularnewline
11 & 124.9 & 124.709543947020 & -0.690029537920745 & 0.190456052980489 & -0.689855260765352 \tabularnewline
12 & 120.8 & 120.609470218456 & -1.5683058003174 & 0.190529781543639 & -0.322941913658471 \tabularnewline
13 & 122 & 122.651708053429 & -0.67070203984092 & -0.651708053428792 & 0.39704415205166 \tabularnewline
14 & 122.1 & 122.039447201094 & -0.656336288331872 & 0.0605527989058466 & 0.00463021949165108 \tabularnewline
15 & 127.4 & 127.346371066669 & 0.885032913320507 & 0.0536289333313029 & 0.563633530939744 \tabularnewline
16 & 135.2 & 135.152331300144 & 2.67264666215725 & 0.0476686998559181 & 0.654463099242995 \tabularnewline
17 & 137.3 & 137.251965243614 & 2.52469290278928 & 0.0480347563857951 & -0.0542031281548969 \tabularnewline
18 & 135 & 134.949677716785 & 1.27853089963487 & 0.0503222832147156 & -0.456699678648379 \tabularnewline
19 & 136 & 135.949579760849 & 1.20660186550826 & 0.0504202391509342 & -0.0263661848720782 \tabularnewline
20 & 138.4 & 138.349891088549 & 1.51476131455073 & 0.0501089114514825 & 0.112970838502852 \tabularnewline
21 & 134.7 & 134.648881952614 & 0.168274310860272 & 0.0511180473862879 & -0.49365021666894 \tabularnewline
22 & 138.4 & 138.349388930855 & 1.08016412983750 & 0.0506110691450223 & 0.334328967300456 \tabularnewline
23 & 133.9 & 133.848794722423 & -0.360608970948542 & 0.0512052775769712 & -0.528244758194134 \tabularnewline
24 & 133.6 & 133.548799510041 & -0.344960143717595 & 0.0512004899585639 & 0.00573754075203173 \tabularnewline
25 & 141.2 & 140.408161017566 & 1.48702368351126 & 0.79183898243404 & 0.736730848354184 \tabularnewline
26 & 151.8 & 151.730651166420 & 3.94672563081249 & 0.0693488335802398 & 0.837608783247037 \tabularnewline
27 & 155.4 & 155.3303832955 & 3.85704099781783 & 0.0696167044999834 & -0.0328123753762335 \tabularnewline
28 & 156.6 & 156.528861133309 & 3.17032954811392 & 0.0711388666909328 & -0.251484976370749 \tabularnewline
29 & 161.6 & 161.529638533126 & 3.64299402716278 & 0.0703614668742037 & 0.173189197355598 \tabularnewline
30 & 160.7 & 160.628206806302 & 2.46967955106439 & 0.0717931936984147 & -0.430040013420672 \tabularnewline
31 & 156 & 155.926530764534 & 0.618226640712535 & 0.073469235465853 & -0.67869832266883 \tabularnewline
32 & 159.5 & 159.427030480674 & 1.36234170148878 & 0.0729695193255008 & 0.272798850669425 \tabularnewline
33 & 168.7 & 168.628038652596 & 3.38605334067904 & 0.0719613474040545 & 0.741945776264576 \tabularnewline
34 & 169.9 & 169.827830060818 & 2.82161933494502 & 0.0721699391821734 & -0.206941845093007 \tabularnewline
35 & 169.9 & 169.827630339277 & 2.0930927609312 & 0.0723696607226506 & -0.267108022297403 \tabularnewline
36 & 185.9 & 185.828360549885 & 5.68375487178261 & 0.0716394501150607 & 1.31649613388155 \tabularnewline
37 & 190.8 & 191.579724418513 & 5.70103908380125 & -0.779724418513252 & 0.00674254824636057 \tabularnewline
38 & 195.8 & 195.745383403916 & 5.31380287164402 & 0.054616596083769 & -0.134786750952614 \tabularnewline
39 & 211.9 & 211.851555468646 & 8.10246397156012 & 0.0484445313535728 & 1.02083549291497 \tabularnewline
40 & 227.1 & 227.054567436321 & 9.93634429333901 & 0.0454325636789757 & 0.67180158286185 \tabularnewline
41 & 251.3 & 251.259056998838 & 13.6205930622158 & 0.0409430011624472 & 1.35017346216786 \tabularnewline
42 & 256.7 & 256.657137735176 & 11.4976355660279 & 0.0428622648238606 & -0.778172340319613 \tabularnewline
43 & 251.9 & 251.854315262646 & 7.28921867515689 & 0.0456847373538225 & -1.54278319793577 \tabularnewline
44 & 251.2 & 251.153288922094 & 5.22633731974211 & 0.0467110779061335 & -0.756290805500648 \tabularnewline
45 & 270.3 & 270.254611024232 & 8.80852017045513 & 0.0453889757680056 & 1.31334238926104 \tabularnewline
46 & 267.2 & 267.153769200972 & 5.73378946609652 & 0.0462307990276661 & -1.12731660280892 \tabularnewline
47 & 243 & 242.952199505244 & -1.99491949928063 & 0.0478004947564810 & -2.83367806576953 \tabularnewline
48 & 229.9 & 229.851767521988 & -4.86216410919509 & 0.0482324780116287 & -1.05126185453105 \tabularnewline
49 & 187.2 & 190.846821033877 & -13.6130873322921 & -3.64682103387687 & -3.36178761805418 \tabularnewline
50 & 178.2 & 177.863188963842 & -13.4535500646630 & 0.336811036157823 & 0.0561944119767591 \tabularnewline
51 & 175.2 & 174.867937333590 & -10.7516442018882 & 0.332062666410322 & 0.989400902380608 \tabularnewline
52 & 192.4 & 192.077353771140 & -3.53053077142593 & 0.322646228859826 & 2.64577173900073 \tabularnewline
53 & 187 & 186.676886637842 & -4.01336692661487 & 0.323113362157837 & -0.176963386407946 \tabularnewline
54 & 184 & 183.677074463347 & -3.75167838099818 & 0.322925536653252 & 0.0959274445987369 \tabularnewline
55 & 194.1 & 193.778978905259 & -0.174954146833104 & 0.321021094741323 & 1.31124736495429 \tabularnewline
56 & 212.7 & 212.380893727223 & 4.67282383617023 & 0.319106272777386 & 1.77731521856663 \tabularnewline
57 & 217.5 & 217.180903348730 & 4.70566049649699 & 0.319096651270350 & 0.0120390754466365 \tabularnewline
58 & 200.5 & 200.179685200108 & -0.898627503394373 & 0.320314799892371 & -2.0547615681818 \tabularnewline
59 & 205.9 & 205.579947419136 & 0.727633176955665 & 0.320052580863928 & 0.596258950010388 \tabularnewline
60 & 196.5 & 196.179634654342 & -1.88723883633122 & 0.320365345657877 & -0.958732070721639 \tabularnewline
61 & 206.3 & 208.772903583346 & 1.82976239899704 & -2.47290358334623 & 1.41479136733123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62406&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]108.5[/C][C]108.5[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]112.3[/C][C]112.107990567052[/C][C]0.299520333464632[/C][C]0.19200943294768[/C][C]0.234351320027692[/C][/ROW]
[ROW][C]3[/C][C]116.6[/C][C]116.409030079065[/C][C]0.887614852216326[/C][C]0.190969920935322[/C][C]0.406095837952912[/C][/ROW]
[ROW][C]4[/C][C]115.5[/C][C]115.308612515095[/C][C]0.506970831336137[/C][C]0.191387484905296[/C][C]-0.196432614080234[/C][/ROW]
[ROW][C]5[/C][C]120.1[/C][C]119.909283573374[/C][C]1.40574594768484[/C][C]0.190716426625717[/C][C]0.397420833309356[/C][/ROW]
[ROW][C]6[/C][C]132.9[/C][C]132.710710205320[/C][C]4.09845149806786[/C][C]0.189289794679854[/C][C]1.09442172247909[/C][/ROW]
[ROW][C]7[/C][C]128.1[/C][C]127.909870095453[/C][C]1.90980717082471[/C][C]0.190129904547208[/C][C]-0.849289434139052[/C][/ROW]
[ROW][C]8[/C][C]129.3[/C][C]129.109819929010[/C][C]1.73136342564871[/C][C]0.19018007098989[/C][C]-0.067500890602942[/C][/ROW]
[ROW][C]9[/C][C]132.5[/C][C]132.309897317013[/C][C]2.10503379961536[/C][C]0.190102682987361[/C][C]0.139380176174126[/C][/ROW]
[ROW][C]10[/C][C]131[/C][C]130.809756006955[/C][C]1.18172212395556[/C][C]0.190243993044685[/C][C]-0.341747259421099[/C][/ROW]
[ROW][C]11[/C][C]124.9[/C][C]124.709543947020[/C][C]-0.690029537920745[/C][C]0.190456052980489[/C][C]-0.689855260765352[/C][/ROW]
[ROW][C]12[/C][C]120.8[/C][C]120.609470218456[/C][C]-1.5683058003174[/C][C]0.190529781543639[/C][C]-0.322941913658471[/C][/ROW]
[ROW][C]13[/C][C]122[/C][C]122.651708053429[/C][C]-0.67070203984092[/C][C]-0.651708053428792[/C][C]0.39704415205166[/C][/ROW]
[ROW][C]14[/C][C]122.1[/C][C]122.039447201094[/C][C]-0.656336288331872[/C][C]0.0605527989058466[/C][C]0.00463021949165108[/C][/ROW]
[ROW][C]15[/C][C]127.4[/C][C]127.346371066669[/C][C]0.885032913320507[/C][C]0.0536289333313029[/C][C]0.563633530939744[/C][/ROW]
[ROW][C]16[/C][C]135.2[/C][C]135.152331300144[/C][C]2.67264666215725[/C][C]0.0476686998559181[/C][C]0.654463099242995[/C][/ROW]
[ROW][C]17[/C][C]137.3[/C][C]137.251965243614[/C][C]2.52469290278928[/C][C]0.0480347563857951[/C][C]-0.0542031281548969[/C][/ROW]
[ROW][C]18[/C][C]135[/C][C]134.949677716785[/C][C]1.27853089963487[/C][C]0.0503222832147156[/C][C]-0.456699678648379[/C][/ROW]
[ROW][C]19[/C][C]136[/C][C]135.949579760849[/C][C]1.20660186550826[/C][C]0.0504202391509342[/C][C]-0.0263661848720782[/C][/ROW]
[ROW][C]20[/C][C]138.4[/C][C]138.349891088549[/C][C]1.51476131455073[/C][C]0.0501089114514825[/C][C]0.112970838502852[/C][/ROW]
[ROW][C]21[/C][C]134.7[/C][C]134.648881952614[/C][C]0.168274310860272[/C][C]0.0511180473862879[/C][C]-0.49365021666894[/C][/ROW]
[ROW][C]22[/C][C]138.4[/C][C]138.349388930855[/C][C]1.08016412983750[/C][C]0.0506110691450223[/C][C]0.334328967300456[/C][/ROW]
[ROW][C]23[/C][C]133.9[/C][C]133.848794722423[/C][C]-0.360608970948542[/C][C]0.0512052775769712[/C][C]-0.528244758194134[/C][/ROW]
[ROW][C]24[/C][C]133.6[/C][C]133.548799510041[/C][C]-0.344960143717595[/C][C]0.0512004899585639[/C][C]0.00573754075203173[/C][/ROW]
[ROW][C]25[/C][C]141.2[/C][C]140.408161017566[/C][C]1.48702368351126[/C][C]0.79183898243404[/C][C]0.736730848354184[/C][/ROW]
[ROW][C]26[/C][C]151.8[/C][C]151.730651166420[/C][C]3.94672563081249[/C][C]0.0693488335802398[/C][C]0.837608783247037[/C][/ROW]
[ROW][C]27[/C][C]155.4[/C][C]155.3303832955[/C][C]3.85704099781783[/C][C]0.0696167044999834[/C][C]-0.0328123753762335[/C][/ROW]
[ROW][C]28[/C][C]156.6[/C][C]156.528861133309[/C][C]3.17032954811392[/C][C]0.0711388666909328[/C][C]-0.251484976370749[/C][/ROW]
[ROW][C]29[/C][C]161.6[/C][C]161.529638533126[/C][C]3.64299402716278[/C][C]0.0703614668742037[/C][C]0.173189197355598[/C][/ROW]
[ROW][C]30[/C][C]160.7[/C][C]160.628206806302[/C][C]2.46967955106439[/C][C]0.0717931936984147[/C][C]-0.430040013420672[/C][/ROW]
[ROW][C]31[/C][C]156[/C][C]155.926530764534[/C][C]0.618226640712535[/C][C]0.073469235465853[/C][C]-0.67869832266883[/C][/ROW]
[ROW][C]32[/C][C]159.5[/C][C]159.427030480674[/C][C]1.36234170148878[/C][C]0.0729695193255008[/C][C]0.272798850669425[/C][/ROW]
[ROW][C]33[/C][C]168.7[/C][C]168.628038652596[/C][C]3.38605334067904[/C][C]0.0719613474040545[/C][C]0.741945776264576[/C][/ROW]
[ROW][C]34[/C][C]169.9[/C][C]169.827830060818[/C][C]2.82161933494502[/C][C]0.0721699391821734[/C][C]-0.206941845093007[/C][/ROW]
[ROW][C]35[/C][C]169.9[/C][C]169.827630339277[/C][C]2.0930927609312[/C][C]0.0723696607226506[/C][C]-0.267108022297403[/C][/ROW]
[ROW][C]36[/C][C]185.9[/C][C]185.828360549885[/C][C]5.68375487178261[/C][C]0.0716394501150607[/C][C]1.31649613388155[/C][/ROW]
[ROW][C]37[/C][C]190.8[/C][C]191.579724418513[/C][C]5.70103908380125[/C][C]-0.779724418513252[/C][C]0.00674254824636057[/C][/ROW]
[ROW][C]38[/C][C]195.8[/C][C]195.745383403916[/C][C]5.31380287164402[/C][C]0.054616596083769[/C][C]-0.134786750952614[/C][/ROW]
[ROW][C]39[/C][C]211.9[/C][C]211.851555468646[/C][C]8.10246397156012[/C][C]0.0484445313535728[/C][C]1.02083549291497[/C][/ROW]
[ROW][C]40[/C][C]227.1[/C][C]227.054567436321[/C][C]9.93634429333901[/C][C]0.0454325636789757[/C][C]0.67180158286185[/C][/ROW]
[ROW][C]41[/C][C]251.3[/C][C]251.259056998838[/C][C]13.6205930622158[/C][C]0.0409430011624472[/C][C]1.35017346216786[/C][/ROW]
[ROW][C]42[/C][C]256.7[/C][C]256.657137735176[/C][C]11.4976355660279[/C][C]0.0428622648238606[/C][C]-0.778172340319613[/C][/ROW]
[ROW][C]43[/C][C]251.9[/C][C]251.854315262646[/C][C]7.28921867515689[/C][C]0.0456847373538225[/C][C]-1.54278319793577[/C][/ROW]
[ROW][C]44[/C][C]251.2[/C][C]251.153288922094[/C][C]5.22633731974211[/C][C]0.0467110779061335[/C][C]-0.756290805500648[/C][/ROW]
[ROW][C]45[/C][C]270.3[/C][C]270.254611024232[/C][C]8.80852017045513[/C][C]0.0453889757680056[/C][C]1.31334238926104[/C][/ROW]
[ROW][C]46[/C][C]267.2[/C][C]267.153769200972[/C][C]5.73378946609652[/C][C]0.0462307990276661[/C][C]-1.12731660280892[/C][/ROW]
[ROW][C]47[/C][C]243[/C][C]242.952199505244[/C][C]-1.99491949928063[/C][C]0.0478004947564810[/C][C]-2.83367806576953[/C][/ROW]
[ROW][C]48[/C][C]229.9[/C][C]229.851767521988[/C][C]-4.86216410919509[/C][C]0.0482324780116287[/C][C]-1.05126185453105[/C][/ROW]
[ROW][C]49[/C][C]187.2[/C][C]190.846821033877[/C][C]-13.6130873322921[/C][C]-3.64682103387687[/C][C]-3.36178761805418[/C][/ROW]
[ROW][C]50[/C][C]178.2[/C][C]177.863188963842[/C][C]-13.4535500646630[/C][C]0.336811036157823[/C][C]0.0561944119767591[/C][/ROW]
[ROW][C]51[/C][C]175.2[/C][C]174.867937333590[/C][C]-10.7516442018882[/C][C]0.332062666410322[/C][C]0.989400902380608[/C][/ROW]
[ROW][C]52[/C][C]192.4[/C][C]192.077353771140[/C][C]-3.53053077142593[/C][C]0.322646228859826[/C][C]2.64577173900073[/C][/ROW]
[ROW][C]53[/C][C]187[/C][C]186.676886637842[/C][C]-4.01336692661487[/C][C]0.323113362157837[/C][C]-0.176963386407946[/C][/ROW]
[ROW][C]54[/C][C]184[/C][C]183.677074463347[/C][C]-3.75167838099818[/C][C]0.322925536653252[/C][C]0.0959274445987369[/C][/ROW]
[ROW][C]55[/C][C]194.1[/C][C]193.778978905259[/C][C]-0.174954146833104[/C][C]0.321021094741323[/C][C]1.31124736495429[/C][/ROW]
[ROW][C]56[/C][C]212.7[/C][C]212.380893727223[/C][C]4.67282383617023[/C][C]0.319106272777386[/C][C]1.77731521856663[/C][/ROW]
[ROW][C]57[/C][C]217.5[/C][C]217.180903348730[/C][C]4.70566049649699[/C][C]0.319096651270350[/C][C]0.0120390754466365[/C][/ROW]
[ROW][C]58[/C][C]200.5[/C][C]200.179685200108[/C][C]-0.898627503394373[/C][C]0.320314799892371[/C][C]-2.0547615681818[/C][/ROW]
[ROW][C]59[/C][C]205.9[/C][C]205.579947419136[/C][C]0.727633176955665[/C][C]0.320052580863928[/C][C]0.596258950010388[/C][/ROW]
[ROW][C]60[/C][C]196.5[/C][C]196.179634654342[/C][C]-1.88723883633122[/C][C]0.320365345657877[/C][C]-0.958732070721639[/C][/ROW]
[ROW][C]61[/C][C]206.3[/C][C]208.772903583346[/C][C]1.82976239899704[/C][C]-2.47290358334623[/C][C]1.41479136733123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62406&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62406&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
tObservedLevelSlopeSeasonalStand. Residuals
1108.5108.5000
2112.3112.1079905670520.2995203334646320.192009432947680.234351320027692
3116.6116.4090300790650.8876148522163260.1909699209353220.406095837952912
4115.5115.3086125150950.5069708313361370.191387484905296-0.196432614080234
5120.1119.9092835733741.405745947684840.1907164266257170.397420833309356
6132.9132.7107102053204.098451498067860.1892897946798541.09442172247909
7128.1127.9098700954531.909807170824710.190129904547208-0.849289434139052
8129.3129.1098199290101.731363425648710.19018007098989-0.067500890602942
9132.5132.3098973170132.105033799615360.1901026829873610.139380176174126
10131130.8097560069551.181722123955560.190243993044685-0.341747259421099
11124.9124.709543947020-0.6900295379207450.190456052980489-0.689855260765352
12120.8120.609470218456-1.56830580031740.190529781543639-0.322941913658471
13122122.651708053429-0.67070203984092-0.6517080534287920.39704415205166
14122.1122.039447201094-0.6563362883318720.06055279890584660.00463021949165108
15127.4127.3463710666690.8850329133205070.05362893333130290.563633530939744
16135.2135.1523313001442.672646662157250.04766869985591810.654463099242995
17137.3137.2519652436142.524692902789280.0480347563857951-0.0542031281548969
18135134.9496777167851.278530899634870.0503222832147156-0.456699678648379
19136135.9495797608491.206601865508260.0504202391509342-0.0263661848720782
20138.4138.3498910885491.514761314550730.05010891145148250.112970838502852
21134.7134.6488819526140.1682743108602720.0511180473862879-0.49365021666894
22138.4138.3493889308551.080164129837500.05061106914502230.334328967300456
23133.9133.848794722423-0.3606089709485420.0512052775769712-0.528244758194134
24133.6133.548799510041-0.3449601437175950.05120048995856390.00573754075203173
25141.2140.4081610175661.487023683511260.791838982434040.736730848354184
26151.8151.7306511664203.946725630812490.06934883358023980.837608783247037
27155.4155.33038329553.857040997817830.0696167044999834-0.0328123753762335
28156.6156.5288611333093.170329548113920.0711388666909328-0.251484976370749
29161.6161.5296385331263.642994027162780.07036146687420370.173189197355598
30160.7160.6282068063022.469679551064390.0717931936984147-0.430040013420672
31156155.9265307645340.6182266407125350.073469235465853-0.67869832266883
32159.5159.4270304806741.362341701488780.07296951932550080.272798850669425
33168.7168.6280386525963.386053340679040.07196134740405450.741945776264576
34169.9169.8278300608182.821619334945020.0721699391821734-0.206941845093007
35169.9169.8276303392772.09309276093120.0723696607226506-0.267108022297403
36185.9185.8283605498855.683754871782610.07163945011506071.31649613388155
37190.8191.5797244185135.70103908380125-0.7797244185132520.00674254824636057
38195.8195.7453834039165.313802871644020.054616596083769-0.134786750952614
39211.9211.8515554686468.102463971560120.04844453135357281.02083549291497
40227.1227.0545674363219.936344293339010.04543256367897570.67180158286185
41251.3251.25905699883813.62059306221580.04094300116244721.35017346216786
42256.7256.65713773517611.49763556602790.0428622648238606-0.778172340319613
43251.9251.8543152626467.289218675156890.0456847373538225-1.54278319793577
44251.2251.1532889220945.226337319742110.0467110779061335-0.756290805500648
45270.3270.2546110242328.808520170455130.04538897576800561.31334238926104
46267.2267.1537692009725.733789466096520.0462307990276661-1.12731660280892
47243242.952199505244-1.994919499280630.0478004947564810-2.83367806576953
48229.9229.851767521988-4.862164109195090.0482324780116287-1.05126185453105
49187.2190.846821033877-13.6130873322921-3.64682103387687-3.36178761805418
50178.2177.863188963842-13.45355006466300.3368110361578230.0561944119767591
51175.2174.867937333590-10.75164420188820.3320626664103220.989400902380608
52192.4192.077353771140-3.530530771425930.3226462288598262.64577173900073
53187186.676886637842-4.013366926614870.323113362157837-0.176963386407946
54184183.677074463347-3.751678380998180.3229255366532520.0959274445987369
55194.1193.778978905259-0.1749541468331040.3210210947413231.31124736495429
56212.7212.3808937272234.672823836170230.3191062727773861.77731521856663
57217.5217.1809033487304.705660496496990.3190966512703500.0120390754466365
58200.5200.179685200108-0.8986275033943730.320314799892371-2.0547615681818
59205.9205.5799474191360.7276331769556650.3200525808639280.596258950010388
60196.5196.179634654342-1.887238836331220.320365345657877-0.958732070721639
61206.3208.7729035833461.82976239899704-2.472903583346231.41479136733123



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
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')