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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 computationSat, 05 Dec 2009 02:18:34 -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/05/t1260004796n2u6idevqrpr72u.htm/, Retrieved Tue, 30 Apr 2024 05:19:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64223, Retrieved Tue, 30 Apr 2024 05:19:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
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] [prijsindex van de...] [2009-12-05 09:18:34] [5c2088b06970f9a7d6fea063ee8d5871] [Current]
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Dataseries X:
226.9
235.9
216.2
226.2
198.3
176.7
166.2
157.6
163.4
159.7
191.0
239.4
321.9
362.7
413.6
407.1
383.2
347.7
333.8
312.3
295.4
283.3
287.6
265.7
250.2
234.7
244.0
231.2
223.8
223.5
210.5
201.6
190.7
207.5
198.8
196.6
204.2
227.4
229.7
217.9
221.4
216.3
197.0
193.8
196.8
180.5
174.8
181.6
190.0
190.6
179.0
174.1
161.1
168.6
169.4
152.2
148.3
137.7
145.0
153.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64223&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]3 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=64223&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64223&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1226.9226.9000
2235.9234.9775713535277.990754532539240.04340840932161290.594929822638374
3216.2218.061229343349-13.36102261053300.095609426326639-1.53209263112949
4226.2224.5827651875593.848013610134680.07681249976321411.21454688585603
5198.3200.382580252163-20.45222616221880.0796729048664979-1.70944985815841
6176.7176.857352278825-23.11483587397790.079548580008023-0.187302741310175
7166.2165.234350874981-13.15790015093350.07975667451416560.700421788802957
8157.6157.130620446742-8.778857628075060.07976649095872890.308043882232721
9163.4162.2489486399913.261933311646450.07975569172194920.84700980605779
10159.7160.041836221989-1.476555871682610.0797575873561435-0.333329165429231
11191188.60459409433524.55010661422320.07975647635515271.83084636176085
12239.4237.4475713844745.59793693752290.07975683895449721.48061026230833
13321.9316.22590891136374.02932110603813.144488084990362.12051058399833
14362.7364.7780995413754.403633435663-0.612418506262757-1.33721830318151
15413.6414.54609157191650.3657391950223-0.596714793593977-0.278702153542578
16407.1411.6751399326084.32373016815022-0.500722469679577-3.22948447999051
17383.2386.004893101622-21.6562516035199-0.493514437453643-1.82755058175271
18347.7349.350708232111-34.6516493032746-0.494490899316155-0.914155286411903
19333.8332.891416091497-18.8888811700869-0.4938614906255591.10883123212785
20312.3312.88037156769-19.8611504295694-0.493866633926793-0.0683943103820708
21295.4295.688131170628-17.5487561809360-0.4938699742436590.162665443119619
22283.3283.38918040804-13.0002225676284-0.4938734517143640.319966734825559
23287.6286.8267314543841.24179267764638-0.4938748882434111.00185499427581
24265.7267.759447264724-16.3543989595443-0.493875376889243-1.23780463427042
25250.2245.662989617662-21.29350277330854.97645338314252-0.361247129724354
26234.7234.427193076188-13.2936863599598-0.3571168669218380.551260925117778
27244242.7711577793745.52803588723932-0.4115241158408961.30551040209414
28231.2232.780646166577-7.89950344738987-0.390740663385054-0.942538568306839
29223.8224.240035215729-8.45485439533761-0.390626284115644-0.0390659884846729
30223.5223.310157458594-1.93473318315231-0.3902625963062620.458655756001019
31210.5211.640936161864-10.3691053738551-0.390512607893125-0.593315702695328
32201.6201.93907417451-9.79099037148415-0.3905103376272740.04066751730194
33190.7191.166200114861-10.6417132872673-0.390509425362144-0.0598441291925417
34207.5205.93193162745311.3717952553939-0.3905219189867781.54854092548502
35198.8200.486885591319-3.19865212204089-0.390520827991362-1.02495856257155
36196.6197.011828170781-3.4381352163987-0.390520832928295-0.016846445534218
37204.2199.1475684990391.365242031199364.625064409943290.34791027983187
38227.4225.95940261820421.9791778628982-0.229252135375411.42832119137737
39229.7231.1712592866627.40577790914908-0.195730249159575-1.01376894182994
40217.9219.536971814108-9.07331031150869-0.175448168684948-1.15724897384732
41221.4220.781681241306-0.135074167471849-0.1769119215929620.6287580510624
42216.3216.775485241292-3.48922695159914-0.177060684725011-0.235947010543113
43197198.330302517460-16.4476313695432-0.177366105196210-0.911558783885738
44193.8193.111716632261-6.71853509201076-0.1773357261480200.684393571719537
45196.8196.2198335032281.79546682166495-0.1773429856355340.598917721873339
46180.5181.918216867296-12.1513634359492-0.177336691821110-0.981090197145223
47174.8174.604421882307-7.95999739613025-0.1773369413637780.294841771161927
48181.6180.6942719549654.21307219792854-0.1773367418274100.856314949706574
49190187.7031965665536.625097421332892.082200222778090.173685235279959
50190.6191.0325596988353.92344228184305-0.209810392122436-0.187799180362499
51179180.302734391925-8.80570224954928-0.185497013266216-0.887167336827954
52174.1174.088706848288-6.56221275658529-0.1877888111503740.157595420752676
53161.1161.733592431604-11.5805985045597-0.187106726213236-0.353017359925939
54168.6167.4529129799983.40889971226353-0.1865549535058891.05443311337359
55169.4169.6778453238932.38306927111239-0.186575020518467-0.072162036180652
56152.2153.794711411814-13.4431706512623-0.186616035250958-1.11329732596853
57148.3147.904392124012-6.89923287119047-0.1866206662207460.460333500986748
58137.7138.109813995339-9.4078172140785-0.186619726653979-0.176466441632588
59145144.0068134749613.85258212674772-0.1866203819028830.932803193713155
60153.4153.1767219930298.45962033149334-0.186620319226440.324082242207736

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 226.9 & 226.9 & 0 & 0 & 0 \tabularnewline
2 & 235.9 & 234.977571353527 & 7.99075453253924 & 0.0434084093216129 & 0.594929822638374 \tabularnewline
3 & 216.2 & 218.061229343349 & -13.3610226105330 & 0.095609426326639 & -1.53209263112949 \tabularnewline
4 & 226.2 & 224.582765187559 & 3.84801361013468 & 0.0768124997632141 & 1.21454688585603 \tabularnewline
5 & 198.3 & 200.382580252163 & -20.4522261622188 & 0.0796729048664979 & -1.70944985815841 \tabularnewline
6 & 176.7 & 176.857352278825 & -23.1148358739779 & 0.079548580008023 & -0.187302741310175 \tabularnewline
7 & 166.2 & 165.234350874981 & -13.1579001509335 & 0.0797566745141656 & 0.700421788802957 \tabularnewline
8 & 157.6 & 157.130620446742 & -8.77885762807506 & 0.0797664909587289 & 0.308043882232721 \tabularnewline
9 & 163.4 & 162.248948639991 & 3.26193331164645 & 0.0797556917219492 & 0.84700980605779 \tabularnewline
10 & 159.7 & 160.041836221989 & -1.47655587168261 & 0.0797575873561435 & -0.333329165429231 \tabularnewline
11 & 191 & 188.604594094335 & 24.5501066142232 & 0.0797564763551527 & 1.83084636176085 \tabularnewline
12 & 239.4 & 237.44757138447 & 45.5979369375229 & 0.0797568389544972 & 1.48061026230833 \tabularnewline
13 & 321.9 & 316.225908911363 & 74.0293211060381 & 3.14448808499036 & 2.12051058399833 \tabularnewline
14 & 362.7 & 364.77809954137 & 54.403633435663 & -0.612418506262757 & -1.33721830318151 \tabularnewline
15 & 413.6 & 414.546091571916 & 50.3657391950223 & -0.596714793593977 & -0.278702153542578 \tabularnewline
16 & 407.1 & 411.675139932608 & 4.32373016815022 & -0.500722469679577 & -3.22948447999051 \tabularnewline
17 & 383.2 & 386.004893101622 & -21.6562516035199 & -0.493514437453643 & -1.82755058175271 \tabularnewline
18 & 347.7 & 349.350708232111 & -34.6516493032746 & -0.494490899316155 & -0.914155286411903 \tabularnewline
19 & 333.8 & 332.891416091497 & -18.8888811700869 & -0.493861490625559 & 1.10883123212785 \tabularnewline
20 & 312.3 & 312.88037156769 & -19.8611504295694 & -0.493866633926793 & -0.0683943103820708 \tabularnewline
21 & 295.4 & 295.688131170628 & -17.5487561809360 & -0.493869974243659 & 0.162665443119619 \tabularnewline
22 & 283.3 & 283.38918040804 & -13.0002225676284 & -0.493873451714364 & 0.319966734825559 \tabularnewline
23 & 287.6 & 286.826731454384 & 1.24179267764638 & -0.493874888243411 & 1.00185499427581 \tabularnewline
24 & 265.7 & 267.759447264724 & -16.3543989595443 & -0.493875376889243 & -1.23780463427042 \tabularnewline
25 & 250.2 & 245.662989617662 & -21.2935027733085 & 4.97645338314252 & -0.361247129724354 \tabularnewline
26 & 234.7 & 234.427193076188 & -13.2936863599598 & -0.357116866921838 & 0.551260925117778 \tabularnewline
27 & 244 & 242.771157779374 & 5.52803588723932 & -0.411524115840896 & 1.30551040209414 \tabularnewline
28 & 231.2 & 232.780646166577 & -7.89950344738987 & -0.390740663385054 & -0.942538568306839 \tabularnewline
29 & 223.8 & 224.240035215729 & -8.45485439533761 & -0.390626284115644 & -0.0390659884846729 \tabularnewline
30 & 223.5 & 223.310157458594 & -1.93473318315231 & -0.390262596306262 & 0.458655756001019 \tabularnewline
31 & 210.5 & 211.640936161864 & -10.3691053738551 & -0.390512607893125 & -0.593315702695328 \tabularnewline
32 & 201.6 & 201.93907417451 & -9.79099037148415 & -0.390510337627274 & 0.04066751730194 \tabularnewline
33 & 190.7 & 191.166200114861 & -10.6417132872673 & -0.390509425362144 & -0.0598441291925417 \tabularnewline
34 & 207.5 & 205.931931627453 & 11.3717952553939 & -0.390521918986778 & 1.54854092548502 \tabularnewline
35 & 198.8 & 200.486885591319 & -3.19865212204089 & -0.390520827991362 & -1.02495856257155 \tabularnewline
36 & 196.6 & 197.011828170781 & -3.4381352163987 & -0.390520832928295 & -0.016846445534218 \tabularnewline
37 & 204.2 & 199.147568499039 & 1.36524203119936 & 4.62506440994329 & 0.34791027983187 \tabularnewline
38 & 227.4 & 225.959402618204 & 21.9791778628982 & -0.22925213537541 & 1.42832119137737 \tabularnewline
39 & 229.7 & 231.171259286662 & 7.40577790914908 & -0.195730249159575 & -1.01376894182994 \tabularnewline
40 & 217.9 & 219.536971814108 & -9.07331031150869 & -0.175448168684948 & -1.15724897384732 \tabularnewline
41 & 221.4 & 220.781681241306 & -0.135074167471849 & -0.176911921592962 & 0.6287580510624 \tabularnewline
42 & 216.3 & 216.775485241292 & -3.48922695159914 & -0.177060684725011 & -0.235947010543113 \tabularnewline
43 & 197 & 198.330302517460 & -16.4476313695432 & -0.177366105196210 & -0.911558783885738 \tabularnewline
44 & 193.8 & 193.111716632261 & -6.71853509201076 & -0.177335726148020 & 0.684393571719537 \tabularnewline
45 & 196.8 & 196.219833503228 & 1.79546682166495 & -0.177342985635534 & 0.598917721873339 \tabularnewline
46 & 180.5 & 181.918216867296 & -12.1513634359492 & -0.177336691821110 & -0.981090197145223 \tabularnewline
47 & 174.8 & 174.604421882307 & -7.95999739613025 & -0.177336941363778 & 0.294841771161927 \tabularnewline
48 & 181.6 & 180.694271954965 & 4.21307219792854 & -0.177336741827410 & 0.856314949706574 \tabularnewline
49 & 190 & 187.703196566553 & 6.62509742133289 & 2.08220022277809 & 0.173685235279959 \tabularnewline
50 & 190.6 & 191.032559698835 & 3.92344228184305 & -0.209810392122436 & -0.187799180362499 \tabularnewline
51 & 179 & 180.302734391925 & -8.80570224954928 & -0.185497013266216 & -0.887167336827954 \tabularnewline
52 & 174.1 & 174.088706848288 & -6.56221275658529 & -0.187788811150374 & 0.157595420752676 \tabularnewline
53 & 161.1 & 161.733592431604 & -11.5805985045597 & -0.187106726213236 & -0.353017359925939 \tabularnewline
54 & 168.6 & 167.452912979998 & 3.40889971226353 & -0.186554953505889 & 1.05443311337359 \tabularnewline
55 & 169.4 & 169.677845323893 & 2.38306927111239 & -0.186575020518467 & -0.072162036180652 \tabularnewline
56 & 152.2 & 153.794711411814 & -13.4431706512623 & -0.186616035250958 & -1.11329732596853 \tabularnewline
57 & 148.3 & 147.904392124012 & -6.89923287119047 & -0.186620666220746 & 0.460333500986748 \tabularnewline
58 & 137.7 & 138.109813995339 & -9.4078172140785 & -0.186619726653979 & -0.176466441632588 \tabularnewline
59 & 145 & 144.006813474961 & 3.85258212674772 & -0.186620381902883 & 0.932803193713155 \tabularnewline
60 & 153.4 & 153.176721993029 & 8.45962033149334 & -0.18662031922644 & 0.324082242207736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64223&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]226.9[/C][C]226.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]235.9[/C][C]234.977571353527[/C][C]7.99075453253924[/C][C]0.0434084093216129[/C][C]0.594929822638374[/C][/ROW]
[ROW][C]3[/C][C]216.2[/C][C]218.061229343349[/C][C]-13.3610226105330[/C][C]0.095609426326639[/C][C]-1.53209263112949[/C][/ROW]
[ROW][C]4[/C][C]226.2[/C][C]224.582765187559[/C][C]3.84801361013468[/C][C]0.0768124997632141[/C][C]1.21454688585603[/C][/ROW]
[ROW][C]5[/C][C]198.3[/C][C]200.382580252163[/C][C]-20.4522261622188[/C][C]0.0796729048664979[/C][C]-1.70944985815841[/C][/ROW]
[ROW][C]6[/C][C]176.7[/C][C]176.857352278825[/C][C]-23.1148358739779[/C][C]0.079548580008023[/C][C]-0.187302741310175[/C][/ROW]
[ROW][C]7[/C][C]166.2[/C][C]165.234350874981[/C][C]-13.1579001509335[/C][C]0.0797566745141656[/C][C]0.700421788802957[/C][/ROW]
[ROW][C]8[/C][C]157.6[/C][C]157.130620446742[/C][C]-8.77885762807506[/C][C]0.0797664909587289[/C][C]0.308043882232721[/C][/ROW]
[ROW][C]9[/C][C]163.4[/C][C]162.248948639991[/C][C]3.26193331164645[/C][C]0.0797556917219492[/C][C]0.84700980605779[/C][/ROW]
[ROW][C]10[/C][C]159.7[/C][C]160.041836221989[/C][C]-1.47655587168261[/C][C]0.0797575873561435[/C][C]-0.333329165429231[/C][/ROW]
[ROW][C]11[/C][C]191[/C][C]188.604594094335[/C][C]24.5501066142232[/C][C]0.0797564763551527[/C][C]1.83084636176085[/C][/ROW]
[ROW][C]12[/C][C]239.4[/C][C]237.44757138447[/C][C]45.5979369375229[/C][C]0.0797568389544972[/C][C]1.48061026230833[/C][/ROW]
[ROW][C]13[/C][C]321.9[/C][C]316.225908911363[/C][C]74.0293211060381[/C][C]3.14448808499036[/C][C]2.12051058399833[/C][/ROW]
[ROW][C]14[/C][C]362.7[/C][C]364.77809954137[/C][C]54.403633435663[/C][C]-0.612418506262757[/C][C]-1.33721830318151[/C][/ROW]
[ROW][C]15[/C][C]413.6[/C][C]414.546091571916[/C][C]50.3657391950223[/C][C]-0.596714793593977[/C][C]-0.278702153542578[/C][/ROW]
[ROW][C]16[/C][C]407.1[/C][C]411.675139932608[/C][C]4.32373016815022[/C][C]-0.500722469679577[/C][C]-3.22948447999051[/C][/ROW]
[ROW][C]17[/C][C]383.2[/C][C]386.004893101622[/C][C]-21.6562516035199[/C][C]-0.493514437453643[/C][C]-1.82755058175271[/C][/ROW]
[ROW][C]18[/C][C]347.7[/C][C]349.350708232111[/C][C]-34.6516493032746[/C][C]-0.494490899316155[/C][C]-0.914155286411903[/C][/ROW]
[ROW][C]19[/C][C]333.8[/C][C]332.891416091497[/C][C]-18.8888811700869[/C][C]-0.493861490625559[/C][C]1.10883123212785[/C][/ROW]
[ROW][C]20[/C][C]312.3[/C][C]312.88037156769[/C][C]-19.8611504295694[/C][C]-0.493866633926793[/C][C]-0.0683943103820708[/C][/ROW]
[ROW][C]21[/C][C]295.4[/C][C]295.688131170628[/C][C]-17.5487561809360[/C][C]-0.493869974243659[/C][C]0.162665443119619[/C][/ROW]
[ROW][C]22[/C][C]283.3[/C][C]283.38918040804[/C][C]-13.0002225676284[/C][C]-0.493873451714364[/C][C]0.319966734825559[/C][/ROW]
[ROW][C]23[/C][C]287.6[/C][C]286.826731454384[/C][C]1.24179267764638[/C][C]-0.493874888243411[/C][C]1.00185499427581[/C][/ROW]
[ROW][C]24[/C][C]265.7[/C][C]267.759447264724[/C][C]-16.3543989595443[/C][C]-0.493875376889243[/C][C]-1.23780463427042[/C][/ROW]
[ROW][C]25[/C][C]250.2[/C][C]245.662989617662[/C][C]-21.2935027733085[/C][C]4.97645338314252[/C][C]-0.361247129724354[/C][/ROW]
[ROW][C]26[/C][C]234.7[/C][C]234.427193076188[/C][C]-13.2936863599598[/C][C]-0.357116866921838[/C][C]0.551260925117778[/C][/ROW]
[ROW][C]27[/C][C]244[/C][C]242.771157779374[/C][C]5.52803588723932[/C][C]-0.411524115840896[/C][C]1.30551040209414[/C][/ROW]
[ROW][C]28[/C][C]231.2[/C][C]232.780646166577[/C][C]-7.89950344738987[/C][C]-0.390740663385054[/C][C]-0.942538568306839[/C][/ROW]
[ROW][C]29[/C][C]223.8[/C][C]224.240035215729[/C][C]-8.45485439533761[/C][C]-0.390626284115644[/C][C]-0.0390659884846729[/C][/ROW]
[ROW][C]30[/C][C]223.5[/C][C]223.310157458594[/C][C]-1.93473318315231[/C][C]-0.390262596306262[/C][C]0.458655756001019[/C][/ROW]
[ROW][C]31[/C][C]210.5[/C][C]211.640936161864[/C][C]-10.3691053738551[/C][C]-0.390512607893125[/C][C]-0.593315702695328[/C][/ROW]
[ROW][C]32[/C][C]201.6[/C][C]201.93907417451[/C][C]-9.79099037148415[/C][C]-0.390510337627274[/C][C]0.04066751730194[/C][/ROW]
[ROW][C]33[/C][C]190.7[/C][C]191.166200114861[/C][C]-10.6417132872673[/C][C]-0.390509425362144[/C][C]-0.0598441291925417[/C][/ROW]
[ROW][C]34[/C][C]207.5[/C][C]205.931931627453[/C][C]11.3717952553939[/C][C]-0.390521918986778[/C][C]1.54854092548502[/C][/ROW]
[ROW][C]35[/C][C]198.8[/C][C]200.486885591319[/C][C]-3.19865212204089[/C][C]-0.390520827991362[/C][C]-1.02495856257155[/C][/ROW]
[ROW][C]36[/C][C]196.6[/C][C]197.011828170781[/C][C]-3.4381352163987[/C][C]-0.390520832928295[/C][C]-0.016846445534218[/C][/ROW]
[ROW][C]37[/C][C]204.2[/C][C]199.147568499039[/C][C]1.36524203119936[/C][C]4.62506440994329[/C][C]0.34791027983187[/C][/ROW]
[ROW][C]38[/C][C]227.4[/C][C]225.959402618204[/C][C]21.9791778628982[/C][C]-0.22925213537541[/C][C]1.42832119137737[/C][/ROW]
[ROW][C]39[/C][C]229.7[/C][C]231.171259286662[/C][C]7.40577790914908[/C][C]-0.195730249159575[/C][C]-1.01376894182994[/C][/ROW]
[ROW][C]40[/C][C]217.9[/C][C]219.536971814108[/C][C]-9.07331031150869[/C][C]-0.175448168684948[/C][C]-1.15724897384732[/C][/ROW]
[ROW][C]41[/C][C]221.4[/C][C]220.781681241306[/C][C]-0.135074167471849[/C][C]-0.176911921592962[/C][C]0.6287580510624[/C][/ROW]
[ROW][C]42[/C][C]216.3[/C][C]216.775485241292[/C][C]-3.48922695159914[/C][C]-0.177060684725011[/C][C]-0.235947010543113[/C][/ROW]
[ROW][C]43[/C][C]197[/C][C]198.330302517460[/C][C]-16.4476313695432[/C][C]-0.177366105196210[/C][C]-0.911558783885738[/C][/ROW]
[ROW][C]44[/C][C]193.8[/C][C]193.111716632261[/C][C]-6.71853509201076[/C][C]-0.177335726148020[/C][C]0.684393571719537[/C][/ROW]
[ROW][C]45[/C][C]196.8[/C][C]196.219833503228[/C][C]1.79546682166495[/C][C]-0.177342985635534[/C][C]0.598917721873339[/C][/ROW]
[ROW][C]46[/C][C]180.5[/C][C]181.918216867296[/C][C]-12.1513634359492[/C][C]-0.177336691821110[/C][C]-0.981090197145223[/C][/ROW]
[ROW][C]47[/C][C]174.8[/C][C]174.604421882307[/C][C]-7.95999739613025[/C][C]-0.177336941363778[/C][C]0.294841771161927[/C][/ROW]
[ROW][C]48[/C][C]181.6[/C][C]180.694271954965[/C][C]4.21307219792854[/C][C]-0.177336741827410[/C][C]0.856314949706574[/C][/ROW]
[ROW][C]49[/C][C]190[/C][C]187.703196566553[/C][C]6.62509742133289[/C][C]2.08220022277809[/C][C]0.173685235279959[/C][/ROW]
[ROW][C]50[/C][C]190.6[/C][C]191.032559698835[/C][C]3.92344228184305[/C][C]-0.209810392122436[/C][C]-0.187799180362499[/C][/ROW]
[ROW][C]51[/C][C]179[/C][C]180.302734391925[/C][C]-8.80570224954928[/C][C]-0.185497013266216[/C][C]-0.887167336827954[/C][/ROW]
[ROW][C]52[/C][C]174.1[/C][C]174.088706848288[/C][C]-6.56221275658529[/C][C]-0.187788811150374[/C][C]0.157595420752676[/C][/ROW]
[ROW][C]53[/C][C]161.1[/C][C]161.733592431604[/C][C]-11.5805985045597[/C][C]-0.187106726213236[/C][C]-0.353017359925939[/C][/ROW]
[ROW][C]54[/C][C]168.6[/C][C]167.452912979998[/C][C]3.40889971226353[/C][C]-0.186554953505889[/C][C]1.05443311337359[/C][/ROW]
[ROW][C]55[/C][C]169.4[/C][C]169.677845323893[/C][C]2.38306927111239[/C][C]-0.186575020518467[/C][C]-0.072162036180652[/C][/ROW]
[ROW][C]56[/C][C]152.2[/C][C]153.794711411814[/C][C]-13.4431706512623[/C][C]-0.186616035250958[/C][C]-1.11329732596853[/C][/ROW]
[ROW][C]57[/C][C]148.3[/C][C]147.904392124012[/C][C]-6.89923287119047[/C][C]-0.186620666220746[/C][C]0.460333500986748[/C][/ROW]
[ROW][C]58[/C][C]137.7[/C][C]138.109813995339[/C][C]-9.4078172140785[/C][C]-0.186619726653979[/C][C]-0.176466441632588[/C][/ROW]
[ROW][C]59[/C][C]145[/C][C]144.006813474961[/C][C]3.85258212674772[/C][C]-0.186620381902883[/C][C]0.932803193713155[/C][/ROW]
[ROW][C]60[/C][C]153.4[/C][C]153.176721993029[/C][C]8.45962033149334[/C][C]-0.18662031922644[/C][C]0.324082242207736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64223&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64223&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
1226.9226.9000
2235.9234.9775713535277.990754532539240.04340840932161290.594929822638374
3216.2218.061229343349-13.36102261053300.095609426326639-1.53209263112949
4226.2224.5827651875593.848013610134680.07681249976321411.21454688585603
5198.3200.382580252163-20.45222616221880.0796729048664979-1.70944985815841
6176.7176.857352278825-23.11483587397790.079548580008023-0.187302741310175
7166.2165.234350874981-13.15790015093350.07975667451416560.700421788802957
8157.6157.130620446742-8.778857628075060.07976649095872890.308043882232721
9163.4162.2489486399913.261933311646450.07975569172194920.84700980605779
10159.7160.041836221989-1.476555871682610.0797575873561435-0.333329165429231
11191188.60459409433524.55010661422320.07975647635515271.83084636176085
12239.4237.4475713844745.59793693752290.07975683895449721.48061026230833
13321.9316.22590891136374.02932110603813.144488084990362.12051058399833
14362.7364.7780995413754.403633435663-0.612418506262757-1.33721830318151
15413.6414.54609157191650.3657391950223-0.596714793593977-0.278702153542578
16407.1411.6751399326084.32373016815022-0.500722469679577-3.22948447999051
17383.2386.004893101622-21.6562516035199-0.493514437453643-1.82755058175271
18347.7349.350708232111-34.6516493032746-0.494490899316155-0.914155286411903
19333.8332.891416091497-18.8888811700869-0.4938614906255591.10883123212785
20312.3312.88037156769-19.8611504295694-0.493866633926793-0.0683943103820708
21295.4295.688131170628-17.5487561809360-0.4938699742436590.162665443119619
22283.3283.38918040804-13.0002225676284-0.4938734517143640.319966734825559
23287.6286.8267314543841.24179267764638-0.4938748882434111.00185499427581
24265.7267.759447264724-16.3543989595443-0.493875376889243-1.23780463427042
25250.2245.662989617662-21.29350277330854.97645338314252-0.361247129724354
26234.7234.427193076188-13.2936863599598-0.3571168669218380.551260925117778
27244242.7711577793745.52803588723932-0.4115241158408961.30551040209414
28231.2232.780646166577-7.89950344738987-0.390740663385054-0.942538568306839
29223.8224.240035215729-8.45485439533761-0.390626284115644-0.0390659884846729
30223.5223.310157458594-1.93473318315231-0.3902625963062620.458655756001019
31210.5211.640936161864-10.3691053738551-0.390512607893125-0.593315702695328
32201.6201.93907417451-9.79099037148415-0.3905103376272740.04066751730194
33190.7191.166200114861-10.6417132872673-0.390509425362144-0.0598441291925417
34207.5205.93193162745311.3717952553939-0.3905219189867781.54854092548502
35198.8200.486885591319-3.19865212204089-0.390520827991362-1.02495856257155
36196.6197.011828170781-3.4381352163987-0.390520832928295-0.016846445534218
37204.2199.1475684990391.365242031199364.625064409943290.34791027983187
38227.4225.95940261820421.9791778628982-0.229252135375411.42832119137737
39229.7231.1712592866627.40577790914908-0.195730249159575-1.01376894182994
40217.9219.536971814108-9.07331031150869-0.175448168684948-1.15724897384732
41221.4220.781681241306-0.135074167471849-0.1769119215929620.6287580510624
42216.3216.775485241292-3.48922695159914-0.177060684725011-0.235947010543113
43197198.330302517460-16.4476313695432-0.177366105196210-0.911558783885738
44193.8193.111716632261-6.71853509201076-0.1773357261480200.684393571719537
45196.8196.2198335032281.79546682166495-0.1773429856355340.598917721873339
46180.5181.918216867296-12.1513634359492-0.177336691821110-0.981090197145223
47174.8174.604421882307-7.95999739613025-0.1773369413637780.294841771161927
48181.6180.6942719549654.21307219792854-0.1773367418274100.856314949706574
49190187.7031965665536.625097421332892.082200222778090.173685235279959
50190.6191.0325596988353.92344228184305-0.209810392122436-0.187799180362499
51179180.302734391925-8.80570224954928-0.185497013266216-0.887167336827954
52174.1174.088706848288-6.56221275658529-0.1877888111503740.157595420752676
53161.1161.733592431604-11.5805985045597-0.187106726213236-0.353017359925939
54168.6167.4529129799983.40889971226353-0.1865549535058891.05443311337359
55169.4169.6778453238932.38306927111239-0.186575020518467-0.072162036180652
56152.2153.794711411814-13.4431706512623-0.186616035250958-1.11329732596853
57148.3147.904392124012-6.89923287119047-0.1866206662207460.460333500986748
58137.7138.109813995339-9.4078172140785-0.186619726653979-0.176466441632588
59145144.0068134749613.85258212674772-0.1866203819028830.932803193713155
60153.4153.1767219930298.45962033149334-0.186620319226440.324082242207736



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')