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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 computationFri, 11 Dec 2009 02:15:19 -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/11/t1260522985afz46utfg0pac49.htm/, Retrieved Sun, 28 Apr 2024 22:48:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65907, Retrieved Sun, 28 Apr 2024 22:48:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
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] [WS 9 Structural T...] [2009-12-11 09:15:19] [762da55b2e2304daaed24a7cc507d14d] [Current]
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Dataseries X:
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
127
112.1
114.2
121.1
131.6
125
120.4
117.7
117.5
120.6
127.5
112.3
124.5
115.2
104.7
130.9
129.2
113.5
125.6
107.6
107
121.6
110.7
106.3
118.6
104.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65907&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]7 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=65907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65907&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1108.8108.8000
2128.4117.2111788249770.095590178861555511.16213248645492.91042415177307
3121.1122.6132648553010.446742285937217-1.522562349815271.27584387391735
4119.5122.4317038601640.406649382648722-2.930319386644-0.185107233242648
5128.7124.7642506139340.4970153529150693.930575697468830.652755825876451
6108.7119.4283511275760.314427530908749-10.7114641479625-2.07062832614314
7105.5112.6530973149820.158465266813489-7.13211396268631-2.54476718975896
8119.8113.2725135924590.1667286266414196.526114652858050.165735949731735
9111.3112.7147823712130.154572223289337-1.41262609499108-0.260287107656059
10110.6111.7485066687310.135748087886444-1.14517522864395-0.402205332917689
11120.1114.5739869337620.1815367604274255.518029004516860.964273439457565
1297.5109.0288990214160.0835286660817276-11.5119123385102-2.0520579284585
13107.7109.2404411743630.0814538545698723-1.540832152980860.0481474759572477
14127.3112.8831223778300.056386684847375214.40601520931261.33082013482536
15117.2116.0725194442300.1022301660724111.118893916596141.06917712514347
16119.8119.3240106093060.1871965070958470.4678770818097011.03683012395512
17116.2115.6740371883330.07451101541658370.536029683628525-1.29438045188072
18111115.5746772764580.0699802926197199-4.57420278283701-0.0605916767018478
19112.4117.1098183361920.101020040807521-4.713927074028220.520614000319272
20130.6119.8383966308850.14585184857379310.75413405747830.941596109887867
21109.1117.0491477884650.104399564248782-7.94075484394322-1.05487103715998
22118.8117.3847147607740.1071051860059221.414622638410720.0831074481886406
23123.9116.7020832925570.1001137548262427.20018335269996-0.283758824517858
24101.6115.7701787023400.0948073598168744-14.1672090196103-0.371235482538938
25112.8116.2641288649760.0955408813941065-3.465282957461350.144212800865412
26128116.3537046459130.095518335446298411.6463124649919-0.00213923242881425
27129.6121.1579162263890.1419198929060988.42895016313331.65005392130372
28125.8123.1795475984570.1716936225802222.615345802607060.647216238041346
29119.5122.4185393858580.153851167444917-2.91601813294827-0.321529571579393
30115.7122.044679114830.143587038697963-6.34323500915275-0.18429730523603
31113.6120.9567978717640.121735521118530-7.3533732526361-0.435876159858604
32129.7119.3200454432850.09512664223490810.3849044215012-0.627821539904236
33112119.0605220174020.090751907826279-7.05951671069976-0.127128210242285
34116.8117.4033417487490.073897953118858-0.598369862280964-0.627323265370247
35127117.7053972664860.07554040952482069.293952637566780.081901534534699
36112.1121.1835969567740.0937628455099252-9.093301202565821.22155101136178
37114.2121.4283674788840.0944847538632363-7.2287976403530.0541352741272421
38121.1118.4416902384150.07582758169208882.66702805978261-1.09800231279720
39131.6119.7219828980530.08642622582406611.87465127985730.424949908180246
40125120.7093328204430.09708144672887474.288179622876320.315284817311608
41120.4121.5778926865590.107883688253814-1.180013186768150.269681696819412
42117.7122.1825991999980.115228465688199-4.483970188772810.174596772256568
43117.5122.8063489854470.122463256085422-5.307763940030540.180059857507474
44120.6118.2628124414550.0630692138774252.35027621565872-1.66241181166642
45127.5122.7828158265100.1110443003305364.704609932946041.5937213351406
46112.3120.8354354129830.0930351472144077-8.52960853904714-0.737249490652575
47124.5119.0201122650900.07960314273884395.48529884885469-0.683842587799757
48115.2120.2534353672580.086480383281025-5.056707625798130.413279777203966
49104.7117.1110977933440.0678060931119178-12.4019559863291-1.15448580876976
50130.9120.7863519861430.091528417314048910.10348083062571.28474215063655
51129.2120.8158915321270.09102750972886778.38428200121722-0.0219596379699571
52113.5116.8367635878040.0512256512208885-3.32544059702542-1.43578630759011
53125.6119.4995747732660.0802766178358446.093180756125380.920351497895281
54107.6117.2935039284110.0534024354007198-9.68715266390997-0.80765911880492
55107114.3089353413250.0182361598947053-7.30045970956358-1.07760892098400
56121.6116.1016328118770.03726229407765655.493391904002910.631925100307959
57110.7112.3091934994920.00102101549997884-1.59841154879415-1.36763415269111
58106.3111.895588926665-0.00234246877659599-5.59441842845491-0.148292487865067
59118.6112.3193053771740.0006253244132903656.279490137795610.152464752920625
60104.6111.098500861179-0.00702497886673446-6.49504731022547-0.436941679997534

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 108.8 & 108.8 & 0 & 0 & 0 \tabularnewline
2 & 128.4 & 117.211178824977 & 0.0955901788615555 & 11.1621324864549 & 2.91042415177307 \tabularnewline
3 & 121.1 & 122.613264855301 & 0.446742285937217 & -1.52256234981527 & 1.27584387391735 \tabularnewline
4 & 119.5 & 122.431703860164 & 0.406649382648722 & -2.930319386644 & -0.185107233242648 \tabularnewline
5 & 128.7 & 124.764250613934 & 0.497015352915069 & 3.93057569746883 & 0.652755825876451 \tabularnewline
6 & 108.7 & 119.428351127576 & 0.314427530908749 & -10.7114641479625 & -2.07062832614314 \tabularnewline
7 & 105.5 & 112.653097314982 & 0.158465266813489 & -7.13211396268631 & -2.54476718975896 \tabularnewline
8 & 119.8 & 113.272513592459 & 0.166728626641419 & 6.52611465285805 & 0.165735949731735 \tabularnewline
9 & 111.3 & 112.714782371213 & 0.154572223289337 & -1.41262609499108 & -0.260287107656059 \tabularnewline
10 & 110.6 & 111.748506668731 & 0.135748087886444 & -1.14517522864395 & -0.402205332917689 \tabularnewline
11 & 120.1 & 114.573986933762 & 0.181536760427425 & 5.51802900451686 & 0.964273439457565 \tabularnewline
12 & 97.5 & 109.028899021416 & 0.0835286660817276 & -11.5119123385102 & -2.0520579284585 \tabularnewline
13 & 107.7 & 109.240441174363 & 0.0814538545698723 & -1.54083215298086 & 0.0481474759572477 \tabularnewline
14 & 127.3 & 112.883122377830 & 0.0563866848473752 & 14.4060152093126 & 1.33082013482536 \tabularnewline
15 & 117.2 & 116.072519444230 & 0.102230166072411 & 1.11889391659614 & 1.06917712514347 \tabularnewline
16 & 119.8 & 119.324010609306 & 0.187196507095847 & 0.467877081809701 & 1.03683012395512 \tabularnewline
17 & 116.2 & 115.674037188333 & 0.0745110154165837 & 0.536029683628525 & -1.29438045188072 \tabularnewline
18 & 111 & 115.574677276458 & 0.0699802926197199 & -4.57420278283701 & -0.0605916767018478 \tabularnewline
19 & 112.4 & 117.109818336192 & 0.101020040807521 & -4.71392707402822 & 0.520614000319272 \tabularnewline
20 & 130.6 & 119.838396630885 & 0.145851848573793 & 10.7541340574783 & 0.941596109887867 \tabularnewline
21 & 109.1 & 117.049147788465 & 0.104399564248782 & -7.94075484394322 & -1.05487103715998 \tabularnewline
22 & 118.8 & 117.384714760774 & 0.107105186005922 & 1.41462263841072 & 0.0831074481886406 \tabularnewline
23 & 123.9 & 116.702083292557 & 0.100113754826242 & 7.20018335269996 & -0.283758824517858 \tabularnewline
24 & 101.6 & 115.770178702340 & 0.0948073598168744 & -14.1672090196103 & -0.371235482538938 \tabularnewline
25 & 112.8 & 116.264128864976 & 0.0955408813941065 & -3.46528295746135 & 0.144212800865412 \tabularnewline
26 & 128 & 116.353704645913 & 0.0955183354462984 & 11.6463124649919 & -0.00213923242881425 \tabularnewline
27 & 129.6 & 121.157916226389 & 0.141919892906098 & 8.4289501631333 & 1.65005392130372 \tabularnewline
28 & 125.8 & 123.179547598457 & 0.171693622580222 & 2.61534580260706 & 0.647216238041346 \tabularnewline
29 & 119.5 & 122.418539385858 & 0.153851167444917 & -2.91601813294827 & -0.321529571579393 \tabularnewline
30 & 115.7 & 122.04467911483 & 0.143587038697963 & -6.34323500915275 & -0.18429730523603 \tabularnewline
31 & 113.6 & 120.956797871764 & 0.121735521118530 & -7.3533732526361 & -0.435876159858604 \tabularnewline
32 & 129.7 & 119.320045443285 & 0.095126642234908 & 10.3849044215012 & -0.627821539904236 \tabularnewline
33 & 112 & 119.060522017402 & 0.090751907826279 & -7.05951671069976 & -0.127128210242285 \tabularnewline
34 & 116.8 & 117.403341748749 & 0.073897953118858 & -0.598369862280964 & -0.627323265370247 \tabularnewline
35 & 127 & 117.705397266486 & 0.0755404095248206 & 9.29395263756678 & 0.081901534534699 \tabularnewline
36 & 112.1 & 121.183596956774 & 0.0937628455099252 & -9.09330120256582 & 1.22155101136178 \tabularnewline
37 & 114.2 & 121.428367478884 & 0.0944847538632363 & -7.228797640353 & 0.0541352741272421 \tabularnewline
38 & 121.1 & 118.441690238415 & 0.0758275816920888 & 2.66702805978261 & -1.09800231279720 \tabularnewline
39 & 131.6 & 119.721982898053 & 0.086426225824066 & 11.8746512798573 & 0.424949908180246 \tabularnewline
40 & 125 & 120.709332820443 & 0.0970814467288747 & 4.28817962287632 & 0.315284817311608 \tabularnewline
41 & 120.4 & 121.577892686559 & 0.107883688253814 & -1.18001318676815 & 0.269681696819412 \tabularnewline
42 & 117.7 & 122.182599199998 & 0.115228465688199 & -4.48397018877281 & 0.174596772256568 \tabularnewline
43 & 117.5 & 122.806348985447 & 0.122463256085422 & -5.30776394003054 & 0.180059857507474 \tabularnewline
44 & 120.6 & 118.262812441455 & 0.063069213877425 & 2.35027621565872 & -1.66241181166642 \tabularnewline
45 & 127.5 & 122.782815826510 & 0.111044300330536 & 4.70460993294604 & 1.5937213351406 \tabularnewline
46 & 112.3 & 120.835435412983 & 0.0930351472144077 & -8.52960853904714 & -0.737249490652575 \tabularnewline
47 & 124.5 & 119.020112265090 & 0.0796031427388439 & 5.48529884885469 & -0.683842587799757 \tabularnewline
48 & 115.2 & 120.253435367258 & 0.086480383281025 & -5.05670762579813 & 0.413279777203966 \tabularnewline
49 & 104.7 & 117.111097793344 & 0.0678060931119178 & -12.4019559863291 & -1.15448580876976 \tabularnewline
50 & 130.9 & 120.786351986143 & 0.0915284173140489 & 10.1034808306257 & 1.28474215063655 \tabularnewline
51 & 129.2 & 120.815891532127 & 0.0910275097288677 & 8.38428200121722 & -0.0219596379699571 \tabularnewline
52 & 113.5 & 116.836763587804 & 0.0512256512208885 & -3.32544059702542 & -1.43578630759011 \tabularnewline
53 & 125.6 & 119.499574773266 & 0.080276617835844 & 6.09318075612538 & 0.920351497895281 \tabularnewline
54 & 107.6 & 117.293503928411 & 0.0534024354007198 & -9.68715266390997 & -0.80765911880492 \tabularnewline
55 & 107 & 114.308935341325 & 0.0182361598947053 & -7.30045970956358 & -1.07760892098400 \tabularnewline
56 & 121.6 & 116.101632811877 & 0.0372622940776565 & 5.49339190400291 & 0.631925100307959 \tabularnewline
57 & 110.7 & 112.309193499492 & 0.00102101549997884 & -1.59841154879415 & -1.36763415269111 \tabularnewline
58 & 106.3 & 111.895588926665 & -0.00234246877659599 & -5.59441842845491 & -0.148292487865067 \tabularnewline
59 & 118.6 & 112.319305377174 & 0.000625324413290365 & 6.27949013779561 & 0.152464752920625 \tabularnewline
60 & 104.6 & 111.098500861179 & -0.00702497886673446 & -6.49504731022547 & -0.436941679997534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65907&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]108.8[/C][C]108.8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]128.4[/C][C]117.211178824977[/C][C]0.0955901788615555[/C][C]11.1621324864549[/C][C]2.91042415177307[/C][/ROW]
[ROW][C]3[/C][C]121.1[/C][C]122.613264855301[/C][C]0.446742285937217[/C][C]-1.52256234981527[/C][C]1.27584387391735[/C][/ROW]
[ROW][C]4[/C][C]119.5[/C][C]122.431703860164[/C][C]0.406649382648722[/C][C]-2.930319386644[/C][C]-0.185107233242648[/C][/ROW]
[ROW][C]5[/C][C]128.7[/C][C]124.764250613934[/C][C]0.497015352915069[/C][C]3.93057569746883[/C][C]0.652755825876451[/C][/ROW]
[ROW][C]6[/C][C]108.7[/C][C]119.428351127576[/C][C]0.314427530908749[/C][C]-10.7114641479625[/C][C]-2.07062832614314[/C][/ROW]
[ROW][C]7[/C][C]105.5[/C][C]112.653097314982[/C][C]0.158465266813489[/C][C]-7.13211396268631[/C][C]-2.54476718975896[/C][/ROW]
[ROW][C]8[/C][C]119.8[/C][C]113.272513592459[/C][C]0.166728626641419[/C][C]6.52611465285805[/C][C]0.165735949731735[/C][/ROW]
[ROW][C]9[/C][C]111.3[/C][C]112.714782371213[/C][C]0.154572223289337[/C][C]-1.41262609499108[/C][C]-0.260287107656059[/C][/ROW]
[ROW][C]10[/C][C]110.6[/C][C]111.748506668731[/C][C]0.135748087886444[/C][C]-1.14517522864395[/C][C]-0.402205332917689[/C][/ROW]
[ROW][C]11[/C][C]120.1[/C][C]114.573986933762[/C][C]0.181536760427425[/C][C]5.51802900451686[/C][C]0.964273439457565[/C][/ROW]
[ROW][C]12[/C][C]97.5[/C][C]109.028899021416[/C][C]0.0835286660817276[/C][C]-11.5119123385102[/C][C]-2.0520579284585[/C][/ROW]
[ROW][C]13[/C][C]107.7[/C][C]109.240441174363[/C][C]0.0814538545698723[/C][C]-1.54083215298086[/C][C]0.0481474759572477[/C][/ROW]
[ROW][C]14[/C][C]127.3[/C][C]112.883122377830[/C][C]0.0563866848473752[/C][C]14.4060152093126[/C][C]1.33082013482536[/C][/ROW]
[ROW][C]15[/C][C]117.2[/C][C]116.072519444230[/C][C]0.102230166072411[/C][C]1.11889391659614[/C][C]1.06917712514347[/C][/ROW]
[ROW][C]16[/C][C]119.8[/C][C]119.324010609306[/C][C]0.187196507095847[/C][C]0.467877081809701[/C][C]1.03683012395512[/C][/ROW]
[ROW][C]17[/C][C]116.2[/C][C]115.674037188333[/C][C]0.0745110154165837[/C][C]0.536029683628525[/C][C]-1.29438045188072[/C][/ROW]
[ROW][C]18[/C][C]111[/C][C]115.574677276458[/C][C]0.0699802926197199[/C][C]-4.57420278283701[/C][C]-0.0605916767018478[/C][/ROW]
[ROW][C]19[/C][C]112.4[/C][C]117.109818336192[/C][C]0.101020040807521[/C][C]-4.71392707402822[/C][C]0.520614000319272[/C][/ROW]
[ROW][C]20[/C][C]130.6[/C][C]119.838396630885[/C][C]0.145851848573793[/C][C]10.7541340574783[/C][C]0.941596109887867[/C][/ROW]
[ROW][C]21[/C][C]109.1[/C][C]117.049147788465[/C][C]0.104399564248782[/C][C]-7.94075484394322[/C][C]-1.05487103715998[/C][/ROW]
[ROW][C]22[/C][C]118.8[/C][C]117.384714760774[/C][C]0.107105186005922[/C][C]1.41462263841072[/C][C]0.0831074481886406[/C][/ROW]
[ROW][C]23[/C][C]123.9[/C][C]116.702083292557[/C][C]0.100113754826242[/C][C]7.20018335269996[/C][C]-0.283758824517858[/C][/ROW]
[ROW][C]24[/C][C]101.6[/C][C]115.770178702340[/C][C]0.0948073598168744[/C][C]-14.1672090196103[/C][C]-0.371235482538938[/C][/ROW]
[ROW][C]25[/C][C]112.8[/C][C]116.264128864976[/C][C]0.0955408813941065[/C][C]-3.46528295746135[/C][C]0.144212800865412[/C][/ROW]
[ROW][C]26[/C][C]128[/C][C]116.353704645913[/C][C]0.0955183354462984[/C][C]11.6463124649919[/C][C]-0.00213923242881425[/C][/ROW]
[ROW][C]27[/C][C]129.6[/C][C]121.157916226389[/C][C]0.141919892906098[/C][C]8.4289501631333[/C][C]1.65005392130372[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]123.179547598457[/C][C]0.171693622580222[/C][C]2.61534580260706[/C][C]0.647216238041346[/C][/ROW]
[ROW][C]29[/C][C]119.5[/C][C]122.418539385858[/C][C]0.153851167444917[/C][C]-2.91601813294827[/C][C]-0.321529571579393[/C][/ROW]
[ROW][C]30[/C][C]115.7[/C][C]122.04467911483[/C][C]0.143587038697963[/C][C]-6.34323500915275[/C][C]-0.18429730523603[/C][/ROW]
[ROW][C]31[/C][C]113.6[/C][C]120.956797871764[/C][C]0.121735521118530[/C][C]-7.3533732526361[/C][C]-0.435876159858604[/C][/ROW]
[ROW][C]32[/C][C]129.7[/C][C]119.320045443285[/C][C]0.095126642234908[/C][C]10.3849044215012[/C][C]-0.627821539904236[/C][/ROW]
[ROW][C]33[/C][C]112[/C][C]119.060522017402[/C][C]0.090751907826279[/C][C]-7.05951671069976[/C][C]-0.127128210242285[/C][/ROW]
[ROW][C]34[/C][C]116.8[/C][C]117.403341748749[/C][C]0.073897953118858[/C][C]-0.598369862280964[/C][C]-0.627323265370247[/C][/ROW]
[ROW][C]35[/C][C]127[/C][C]117.705397266486[/C][C]0.0755404095248206[/C][C]9.29395263756678[/C][C]0.081901534534699[/C][/ROW]
[ROW][C]36[/C][C]112.1[/C][C]121.183596956774[/C][C]0.0937628455099252[/C][C]-9.09330120256582[/C][C]1.22155101136178[/C][/ROW]
[ROW][C]37[/C][C]114.2[/C][C]121.428367478884[/C][C]0.0944847538632363[/C][C]-7.228797640353[/C][C]0.0541352741272421[/C][/ROW]
[ROW][C]38[/C][C]121.1[/C][C]118.441690238415[/C][C]0.0758275816920888[/C][C]2.66702805978261[/C][C]-1.09800231279720[/C][/ROW]
[ROW][C]39[/C][C]131.6[/C][C]119.721982898053[/C][C]0.086426225824066[/C][C]11.8746512798573[/C][C]0.424949908180246[/C][/ROW]
[ROW][C]40[/C][C]125[/C][C]120.709332820443[/C][C]0.0970814467288747[/C][C]4.28817962287632[/C][C]0.315284817311608[/C][/ROW]
[ROW][C]41[/C][C]120.4[/C][C]121.577892686559[/C][C]0.107883688253814[/C][C]-1.18001318676815[/C][C]0.269681696819412[/C][/ROW]
[ROW][C]42[/C][C]117.7[/C][C]122.182599199998[/C][C]0.115228465688199[/C][C]-4.48397018877281[/C][C]0.174596772256568[/C][/ROW]
[ROW][C]43[/C][C]117.5[/C][C]122.806348985447[/C][C]0.122463256085422[/C][C]-5.30776394003054[/C][C]0.180059857507474[/C][/ROW]
[ROW][C]44[/C][C]120.6[/C][C]118.262812441455[/C][C]0.063069213877425[/C][C]2.35027621565872[/C][C]-1.66241181166642[/C][/ROW]
[ROW][C]45[/C][C]127.5[/C][C]122.782815826510[/C][C]0.111044300330536[/C][C]4.70460993294604[/C][C]1.5937213351406[/C][/ROW]
[ROW][C]46[/C][C]112.3[/C][C]120.835435412983[/C][C]0.0930351472144077[/C][C]-8.52960853904714[/C][C]-0.737249490652575[/C][/ROW]
[ROW][C]47[/C][C]124.5[/C][C]119.020112265090[/C][C]0.0796031427388439[/C][C]5.48529884885469[/C][C]-0.683842587799757[/C][/ROW]
[ROW][C]48[/C][C]115.2[/C][C]120.253435367258[/C][C]0.086480383281025[/C][C]-5.05670762579813[/C][C]0.413279777203966[/C][/ROW]
[ROW][C]49[/C][C]104.7[/C][C]117.111097793344[/C][C]0.0678060931119178[/C][C]-12.4019559863291[/C][C]-1.15448580876976[/C][/ROW]
[ROW][C]50[/C][C]130.9[/C][C]120.786351986143[/C][C]0.0915284173140489[/C][C]10.1034808306257[/C][C]1.28474215063655[/C][/ROW]
[ROW][C]51[/C][C]129.2[/C][C]120.815891532127[/C][C]0.0910275097288677[/C][C]8.38428200121722[/C][C]-0.0219596379699571[/C][/ROW]
[ROW][C]52[/C][C]113.5[/C][C]116.836763587804[/C][C]0.0512256512208885[/C][C]-3.32544059702542[/C][C]-1.43578630759011[/C][/ROW]
[ROW][C]53[/C][C]125.6[/C][C]119.499574773266[/C][C]0.080276617835844[/C][C]6.09318075612538[/C][C]0.920351497895281[/C][/ROW]
[ROW][C]54[/C][C]107.6[/C][C]117.293503928411[/C][C]0.0534024354007198[/C][C]-9.68715266390997[/C][C]-0.80765911880492[/C][/ROW]
[ROW][C]55[/C][C]107[/C][C]114.308935341325[/C][C]0.0182361598947053[/C][C]-7.30045970956358[/C][C]-1.07760892098400[/C][/ROW]
[ROW][C]56[/C][C]121.6[/C][C]116.101632811877[/C][C]0.0372622940776565[/C][C]5.49339190400291[/C][C]0.631925100307959[/C][/ROW]
[ROW][C]57[/C][C]110.7[/C][C]112.309193499492[/C][C]0.00102101549997884[/C][C]-1.59841154879415[/C][C]-1.36763415269111[/C][/ROW]
[ROW][C]58[/C][C]106.3[/C][C]111.895588926665[/C][C]-0.00234246877659599[/C][C]-5.59441842845491[/C][C]-0.148292487865067[/C][/ROW]
[ROW][C]59[/C][C]118.6[/C][C]112.319305377174[/C][C]0.000625324413290365[/C][C]6.27949013779561[/C][C]0.152464752920625[/C][/ROW]
[ROW][C]60[/C][C]104.6[/C][C]111.098500861179[/C][C]-0.00702497886673446[/C][C]-6.49504731022547[/C][C]-0.436941679997534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65907&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
1108.8108.8000
2128.4117.2111788249770.095590178861555511.16213248645492.91042415177307
3121.1122.6132648553010.446742285937217-1.522562349815271.27584387391735
4119.5122.4317038601640.406649382648722-2.930319386644-0.185107233242648
5128.7124.7642506139340.4970153529150693.930575697468830.652755825876451
6108.7119.4283511275760.314427530908749-10.7114641479625-2.07062832614314
7105.5112.6530973149820.158465266813489-7.13211396268631-2.54476718975896
8119.8113.2725135924590.1667286266414196.526114652858050.165735949731735
9111.3112.7147823712130.154572223289337-1.41262609499108-0.260287107656059
10110.6111.7485066687310.135748087886444-1.14517522864395-0.402205332917689
11120.1114.5739869337620.1815367604274255.518029004516860.964273439457565
1297.5109.0288990214160.0835286660817276-11.5119123385102-2.0520579284585
13107.7109.2404411743630.0814538545698723-1.540832152980860.0481474759572477
14127.3112.8831223778300.056386684847375214.40601520931261.33082013482536
15117.2116.0725194442300.1022301660724111.118893916596141.06917712514347
16119.8119.3240106093060.1871965070958470.4678770818097011.03683012395512
17116.2115.6740371883330.07451101541658370.536029683628525-1.29438045188072
18111115.5746772764580.0699802926197199-4.57420278283701-0.0605916767018478
19112.4117.1098183361920.101020040807521-4.713927074028220.520614000319272
20130.6119.8383966308850.14585184857379310.75413405747830.941596109887867
21109.1117.0491477884650.104399564248782-7.94075484394322-1.05487103715998
22118.8117.3847147607740.1071051860059221.414622638410720.0831074481886406
23123.9116.7020832925570.1001137548262427.20018335269996-0.283758824517858
24101.6115.7701787023400.0948073598168744-14.1672090196103-0.371235482538938
25112.8116.2641288649760.0955408813941065-3.465282957461350.144212800865412
26128116.3537046459130.095518335446298411.6463124649919-0.00213923242881425
27129.6121.1579162263890.1419198929060988.42895016313331.65005392130372
28125.8123.1795475984570.1716936225802222.615345802607060.647216238041346
29119.5122.4185393858580.153851167444917-2.91601813294827-0.321529571579393
30115.7122.044679114830.143587038697963-6.34323500915275-0.18429730523603
31113.6120.9567978717640.121735521118530-7.3533732526361-0.435876159858604
32129.7119.3200454432850.09512664223490810.3849044215012-0.627821539904236
33112119.0605220174020.090751907826279-7.05951671069976-0.127128210242285
34116.8117.4033417487490.073897953118858-0.598369862280964-0.627323265370247
35127117.7053972664860.07554040952482069.293952637566780.081901534534699
36112.1121.1835969567740.0937628455099252-9.093301202565821.22155101136178
37114.2121.4283674788840.0944847538632363-7.2287976403530.0541352741272421
38121.1118.4416902384150.07582758169208882.66702805978261-1.09800231279720
39131.6119.7219828980530.08642622582406611.87465127985730.424949908180246
40125120.7093328204430.09708144672887474.288179622876320.315284817311608
41120.4121.5778926865590.107883688253814-1.180013186768150.269681696819412
42117.7122.1825991999980.115228465688199-4.483970188772810.174596772256568
43117.5122.8063489854470.122463256085422-5.307763940030540.180059857507474
44120.6118.2628124414550.0630692138774252.35027621565872-1.66241181166642
45127.5122.7828158265100.1110443003305364.704609932946041.5937213351406
46112.3120.8354354129830.0930351472144077-8.52960853904714-0.737249490652575
47124.5119.0201122650900.07960314273884395.48529884885469-0.683842587799757
48115.2120.2534353672580.086480383281025-5.056707625798130.413279777203966
49104.7117.1110977933440.0678060931119178-12.4019559863291-1.15448580876976
50130.9120.7863519861430.091528417314048910.10348083062571.28474215063655
51129.2120.8158915321270.09102750972886778.38428200121722-0.0219596379699571
52113.5116.8367635878040.0512256512208885-3.32544059702542-1.43578630759011
53125.6119.4995747732660.0802766178358446.093180756125380.920351497895281
54107.6117.2935039284110.0534024354007198-9.68715266390997-0.80765911880492
55107114.3089353413250.0182361598947053-7.30045970956358-1.07760892098400
56121.6116.1016328118770.03726229407765655.493391904002910.631925100307959
57110.7112.3091934994920.00102101549997884-1.59841154879415-1.36763415269111
58106.3111.895588926665-0.00234246877659599-5.59441842845491-0.148292487865067
59118.6112.3193053771740.0006253244132903656.279490137795610.152464752920625
60104.6111.098500861179-0.00702497886673446-6.49504731022547-0.436941679997534



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')