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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, 04 Dec 2009 08:28:03 -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/04/t1259940561shrjmzvjj60omwl.htm/, Retrieved Sun, 28 Apr 2024 09:14:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63760, Retrieved Sun, 28 Apr 2024 09:14:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
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] [ws 9: structural ...] [2009-12-04 15:28:03] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
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Dataseries X:
79.8
83.4
113.6
112.9
104
109.9
99
106.3
128.9
111.1
102.9
130
87
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137
91
90.5
122.4
123.3
124.3
120
118.1
119
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128
121.6
135.8
143.8
147.5
136.2
156.6
123.3
104.5
139.8
136.5
112.1
118.5
94.4
102.3
111.4
99.2
87.8
115.8




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=63760&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=63760&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63760&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
179.879.8000
283.481.9447540362090.07813984344992381.455245963790950.364584540255019
3113.699.6376134366861.2446255856480613.96238656331402.77888176055049
4112.9108.7827075174181.716328846052334.117292482581881.54102675281805
5104108.3867118840991.61183250653837-4.38671188409927-0.439961061080126
6109.9108.8013080547221.556638997466371.09869194527821-0.250021200364225
799104.0477811879201.25131090063111-5.04778118792031-1.30766756993645
8106.3103.6686208518181.164847146722272.63137914818221-0.335273847813146
9128.9115.2908234955011.7702871120996913.60917650449902.13579185073751
10111.1115.9236898967831.69959716447469-4.82368989678283-0.230976430788645
11102.9109.6265616915191.17445025064903-6.72656169151893-1.61606201215112
12130117.5646397636671.6382935651475812.43536023633301.36119311883401
1387110.8403396998821.21105249121994-23.8403396998815-1.76096187958271
1487.5104.8557751004310.740772562590645-17.3557751004311-1.47589314183236
15117.6104.9729941061630.69215341254857712.6270058938373-0.117046266253148
16103.4101.4934485203910.3513169949131541.90655147960911-0.785454474930295
17110.8106.1669225123630.7020225960987364.63307748763730.84033800167656
18112.6108.0328216212410.7948318084328824.567178378759220.229176248005987
19102.5108.7986503578510.792541117958929-6.29865035785135-0.00570637459432741
20112.4112.0597846064240.9875548204189470.3402153935765050.484216154730760
21135.6116.9784178630421.2997601200996118.62158213695780.769396467918087
22105.1113.7290806305610.936739826577882-8.62908063056121-0.888623307812911
23127.7124.015131426871.682699233592793.684868573129961.82214959996911
24137124.2456624608871.5675291041574812.7543375391134-0.28327821609775
2591118.5923843290100.997288671916355-27.5923843290105-1.42417211827429
2690.5112.4028584273660.419275096908327-21.9028584273658-1.41349454727492
27122.4109.3632854483600.13507477662397213.0367145516403-0.665510284139953
28123.3115.5271301152890.6354456319216687.772869884711091.15099642862555
29124.3118.9613901094760.8679847577313055.338609890524450.539379029035982
30120118.3701776202180.7470578524289451.62982237978228-0.283726687643960
31118.1122.1710167178310.999365314656598-4.071016717830940.594783317277155
32119122.4664416448760.941341335350567-3.46644164487572-0.136788746106619
33142.7123.4292474671760.94310760126009519.27075253282360.00415877452811795
34123.6130.1802587356811.42027007832856-6.580258735681081.12279547322485
35129.6128.7203376131311.184055496171510.879662386868657-0.556609252628031
36151.6130.9685527974261.2712313798326420.63144720257430.206305259487293
37110.4133.1455962397811.34551904018070-22.74559623978060.176453761732429
3899.2128.2227246214370.829519243007137-29.0227246214372-1.21953814864134
39130.5124.2975855946720.4367377305105246.20241440532764-0.917659912902119
40136.2126.2549558580460.5625388750190459.945044141954460.292105752547273
41129.7125.7858974458230.4771972701884973.91410255417732-0.198744327970320
42128126.8005762440140.5216449626543751.199423755985950.104084289521931
43121.6126.6204524947680.463635947686511-5.02045249476761-0.136175317736686
44135.8133.2025947993860.9690900366127812.597405200613881.18547529807231
45143.8132.0230705795710.79174762226389711.7769294204291-0.415106356990479
46147.5141.8281574610961.534912381300755.67184253890391.73784006010847
47136.2141.3790566272871.37147631540237-5.17905662728651-0.382647759151948
48156.6138.7028585018771.0381455311686617.8971414981232-0.782735681222912
49123.3139.9731569481001.05727487817780-16.67315694810030.0450108019913095
50104.5136.4862581551440.68242494989097-31.9862581551436-0.880387043208026
51139.8134.9596797967180.5000700050207044.8403202032816-0.426348128139618
52136.5130.6702595530170.1046930183316965.82974044698256-0.921941934001073
53112.1119.613929005214-0.816373771293664-7.51392900521396-2.15064315264510
54118.5116.695800816938-0.9897994813796341.80419918306197-0.406165395511662
5594.4109.501740282498-1.50178539389029-15.1017402824984-1.20089263961595
56102.3103.503923836102-1.87280877275318-1.20392383610232-0.86953378726486
57111.4102.063086133549-1.837171275133939.336913866451170.0833679027876294
5899.296.384506492143-2.153925858418972.81549350785704-0.740254822897015
5987.891.9686151131049-2.3403547644004-4.16861511310487-0.436019763987048
60115.892.744149584744-2.0835824853752123.0558504152560.601698344853956

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 79.8 & 79.8 & 0 & 0 & 0 \tabularnewline
2 & 83.4 & 81.944754036209 & 0.0781398434499238 & 1.45524596379095 & 0.364584540255019 \tabularnewline
3 & 113.6 & 99.637613436686 & 1.24462558564806 & 13.9623865633140 & 2.77888176055049 \tabularnewline
4 & 112.9 & 108.782707517418 & 1.71632884605233 & 4.11729248258188 & 1.54102675281805 \tabularnewline
5 & 104 & 108.386711884099 & 1.61183250653837 & -4.38671188409927 & -0.439961061080126 \tabularnewline
6 & 109.9 & 108.801308054722 & 1.55663899746637 & 1.09869194527821 & -0.250021200364225 \tabularnewline
7 & 99 & 104.047781187920 & 1.25131090063111 & -5.04778118792031 & -1.30766756993645 \tabularnewline
8 & 106.3 & 103.668620851818 & 1.16484714672227 & 2.63137914818221 & -0.335273847813146 \tabularnewline
9 & 128.9 & 115.290823495501 & 1.77028711209969 & 13.6091765044990 & 2.13579185073751 \tabularnewline
10 & 111.1 & 115.923689896783 & 1.69959716447469 & -4.82368989678283 & -0.230976430788645 \tabularnewline
11 & 102.9 & 109.626561691519 & 1.17445025064903 & -6.72656169151893 & -1.61606201215112 \tabularnewline
12 & 130 & 117.564639763667 & 1.63829356514758 & 12.4353602363330 & 1.36119311883401 \tabularnewline
13 & 87 & 110.840339699882 & 1.21105249121994 & -23.8403396998815 & -1.76096187958271 \tabularnewline
14 & 87.5 & 104.855775100431 & 0.740772562590645 & -17.3557751004311 & -1.47589314183236 \tabularnewline
15 & 117.6 & 104.972994106163 & 0.692153412548577 & 12.6270058938373 & -0.117046266253148 \tabularnewline
16 & 103.4 & 101.493448520391 & 0.351316994913154 & 1.90655147960911 & -0.785454474930295 \tabularnewline
17 & 110.8 & 106.166922512363 & 0.702022596098736 & 4.6330774876373 & 0.84033800167656 \tabularnewline
18 & 112.6 & 108.032821621241 & 0.794831808432882 & 4.56717837875922 & 0.229176248005987 \tabularnewline
19 & 102.5 & 108.798650357851 & 0.792541117958929 & -6.29865035785135 & -0.00570637459432741 \tabularnewline
20 & 112.4 & 112.059784606424 & 0.987554820418947 & 0.340215393576505 & 0.484216154730760 \tabularnewline
21 & 135.6 & 116.978417863042 & 1.29976012009961 & 18.6215821369578 & 0.769396467918087 \tabularnewline
22 & 105.1 & 113.729080630561 & 0.936739826577882 & -8.62908063056121 & -0.888623307812911 \tabularnewline
23 & 127.7 & 124.01513142687 & 1.68269923359279 & 3.68486857312996 & 1.82214959996911 \tabularnewline
24 & 137 & 124.245662460887 & 1.56752910415748 & 12.7543375391134 & -0.28327821609775 \tabularnewline
25 & 91 & 118.592384329010 & 0.997288671916355 & -27.5923843290105 & -1.42417211827429 \tabularnewline
26 & 90.5 & 112.402858427366 & 0.419275096908327 & -21.9028584273658 & -1.41349454727492 \tabularnewline
27 & 122.4 & 109.363285448360 & 0.135074776623972 & 13.0367145516403 & -0.665510284139953 \tabularnewline
28 & 123.3 & 115.527130115289 & 0.635445631921668 & 7.77286988471109 & 1.15099642862555 \tabularnewline
29 & 124.3 & 118.961390109476 & 0.867984757731305 & 5.33860989052445 & 0.539379029035982 \tabularnewline
30 & 120 & 118.370177620218 & 0.747057852428945 & 1.62982237978228 & -0.283726687643960 \tabularnewline
31 & 118.1 & 122.171016717831 & 0.999365314656598 & -4.07101671783094 & 0.594783317277155 \tabularnewline
32 & 119 & 122.466441644876 & 0.941341335350567 & -3.46644164487572 & -0.136788746106619 \tabularnewline
33 & 142.7 & 123.429247467176 & 0.943107601260095 & 19.2707525328236 & 0.00415877452811795 \tabularnewline
34 & 123.6 & 130.180258735681 & 1.42027007832856 & -6.58025873568108 & 1.12279547322485 \tabularnewline
35 & 129.6 & 128.720337613131 & 1.18405549617151 & 0.879662386868657 & -0.556609252628031 \tabularnewline
36 & 151.6 & 130.968552797426 & 1.27123137983264 & 20.6314472025743 & 0.206305259487293 \tabularnewline
37 & 110.4 & 133.145596239781 & 1.34551904018070 & -22.7455962397806 & 0.176453761732429 \tabularnewline
38 & 99.2 & 128.222724621437 & 0.829519243007137 & -29.0227246214372 & -1.21953814864134 \tabularnewline
39 & 130.5 & 124.297585594672 & 0.436737730510524 & 6.20241440532764 & -0.917659912902119 \tabularnewline
40 & 136.2 & 126.254955858046 & 0.562538875019045 & 9.94504414195446 & 0.292105752547273 \tabularnewline
41 & 129.7 & 125.785897445823 & 0.477197270188497 & 3.91410255417732 & -0.198744327970320 \tabularnewline
42 & 128 & 126.800576244014 & 0.521644962654375 & 1.19942375598595 & 0.104084289521931 \tabularnewline
43 & 121.6 & 126.620452494768 & 0.463635947686511 & -5.02045249476761 & -0.136175317736686 \tabularnewline
44 & 135.8 & 133.202594799386 & 0.969090036612781 & 2.59740520061388 & 1.18547529807231 \tabularnewline
45 & 143.8 & 132.023070579571 & 0.791747622263897 & 11.7769294204291 & -0.415106356990479 \tabularnewline
46 & 147.5 & 141.828157461096 & 1.53491238130075 & 5.6718425389039 & 1.73784006010847 \tabularnewline
47 & 136.2 & 141.379056627287 & 1.37147631540237 & -5.17905662728651 & -0.382647759151948 \tabularnewline
48 & 156.6 & 138.702858501877 & 1.03814553116866 & 17.8971414981232 & -0.782735681222912 \tabularnewline
49 & 123.3 & 139.973156948100 & 1.05727487817780 & -16.6731569481003 & 0.0450108019913095 \tabularnewline
50 & 104.5 & 136.486258155144 & 0.68242494989097 & -31.9862581551436 & -0.880387043208026 \tabularnewline
51 & 139.8 & 134.959679796718 & 0.500070005020704 & 4.8403202032816 & -0.426348128139618 \tabularnewline
52 & 136.5 & 130.670259553017 & 0.104693018331696 & 5.82974044698256 & -0.921941934001073 \tabularnewline
53 & 112.1 & 119.613929005214 & -0.816373771293664 & -7.51392900521396 & -2.15064315264510 \tabularnewline
54 & 118.5 & 116.695800816938 & -0.989799481379634 & 1.80419918306197 & -0.406165395511662 \tabularnewline
55 & 94.4 & 109.501740282498 & -1.50178539389029 & -15.1017402824984 & -1.20089263961595 \tabularnewline
56 & 102.3 & 103.503923836102 & -1.87280877275318 & -1.20392383610232 & -0.86953378726486 \tabularnewline
57 & 111.4 & 102.063086133549 & -1.83717127513393 & 9.33691386645117 & 0.0833679027876294 \tabularnewline
58 & 99.2 & 96.384506492143 & -2.15392585841897 & 2.81549350785704 & -0.740254822897015 \tabularnewline
59 & 87.8 & 91.9686151131049 & -2.3403547644004 & -4.16861511310487 & -0.436019763987048 \tabularnewline
60 & 115.8 & 92.744149584744 & -2.08358248537521 & 23.055850415256 & 0.601698344853956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63760&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]79.8[/C][C]79.8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]83.4[/C][C]81.944754036209[/C][C]0.0781398434499238[/C][C]1.45524596379095[/C][C]0.364584540255019[/C][/ROW]
[ROW][C]3[/C][C]113.6[/C][C]99.637613436686[/C][C]1.24462558564806[/C][C]13.9623865633140[/C][C]2.77888176055049[/C][/ROW]
[ROW][C]4[/C][C]112.9[/C][C]108.782707517418[/C][C]1.71632884605233[/C][C]4.11729248258188[/C][C]1.54102675281805[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]108.386711884099[/C][C]1.61183250653837[/C][C]-4.38671188409927[/C][C]-0.439961061080126[/C][/ROW]
[ROW][C]6[/C][C]109.9[/C][C]108.801308054722[/C][C]1.55663899746637[/C][C]1.09869194527821[/C][C]-0.250021200364225[/C][/ROW]
[ROW][C]7[/C][C]99[/C][C]104.047781187920[/C][C]1.25131090063111[/C][C]-5.04778118792031[/C][C]-1.30766756993645[/C][/ROW]
[ROW][C]8[/C][C]106.3[/C][C]103.668620851818[/C][C]1.16484714672227[/C][C]2.63137914818221[/C][C]-0.335273847813146[/C][/ROW]
[ROW][C]9[/C][C]128.9[/C][C]115.290823495501[/C][C]1.77028711209969[/C][C]13.6091765044990[/C][C]2.13579185073751[/C][/ROW]
[ROW][C]10[/C][C]111.1[/C][C]115.923689896783[/C][C]1.69959716447469[/C][C]-4.82368989678283[/C][C]-0.230976430788645[/C][/ROW]
[ROW][C]11[/C][C]102.9[/C][C]109.626561691519[/C][C]1.17445025064903[/C][C]-6.72656169151893[/C][C]-1.61606201215112[/C][/ROW]
[ROW][C]12[/C][C]130[/C][C]117.564639763667[/C][C]1.63829356514758[/C][C]12.4353602363330[/C][C]1.36119311883401[/C][/ROW]
[ROW][C]13[/C][C]87[/C][C]110.840339699882[/C][C]1.21105249121994[/C][C]-23.8403396998815[/C][C]-1.76096187958271[/C][/ROW]
[ROW][C]14[/C][C]87.5[/C][C]104.855775100431[/C][C]0.740772562590645[/C][C]-17.3557751004311[/C][C]-1.47589314183236[/C][/ROW]
[ROW][C]15[/C][C]117.6[/C][C]104.972994106163[/C][C]0.692153412548577[/C][C]12.6270058938373[/C][C]-0.117046266253148[/C][/ROW]
[ROW][C]16[/C][C]103.4[/C][C]101.493448520391[/C][C]0.351316994913154[/C][C]1.90655147960911[/C][C]-0.785454474930295[/C][/ROW]
[ROW][C]17[/C][C]110.8[/C][C]106.166922512363[/C][C]0.702022596098736[/C][C]4.6330774876373[/C][C]0.84033800167656[/C][/ROW]
[ROW][C]18[/C][C]112.6[/C][C]108.032821621241[/C][C]0.794831808432882[/C][C]4.56717837875922[/C][C]0.229176248005987[/C][/ROW]
[ROW][C]19[/C][C]102.5[/C][C]108.798650357851[/C][C]0.792541117958929[/C][C]-6.29865035785135[/C][C]-0.00570637459432741[/C][/ROW]
[ROW][C]20[/C][C]112.4[/C][C]112.059784606424[/C][C]0.987554820418947[/C][C]0.340215393576505[/C][C]0.484216154730760[/C][/ROW]
[ROW][C]21[/C][C]135.6[/C][C]116.978417863042[/C][C]1.29976012009961[/C][C]18.6215821369578[/C][C]0.769396467918087[/C][/ROW]
[ROW][C]22[/C][C]105.1[/C][C]113.729080630561[/C][C]0.936739826577882[/C][C]-8.62908063056121[/C][C]-0.888623307812911[/C][/ROW]
[ROW][C]23[/C][C]127.7[/C][C]124.01513142687[/C][C]1.68269923359279[/C][C]3.68486857312996[/C][C]1.82214959996911[/C][/ROW]
[ROW][C]24[/C][C]137[/C][C]124.245662460887[/C][C]1.56752910415748[/C][C]12.7543375391134[/C][C]-0.28327821609775[/C][/ROW]
[ROW][C]25[/C][C]91[/C][C]118.592384329010[/C][C]0.997288671916355[/C][C]-27.5923843290105[/C][C]-1.42417211827429[/C][/ROW]
[ROW][C]26[/C][C]90.5[/C][C]112.402858427366[/C][C]0.419275096908327[/C][C]-21.9028584273658[/C][C]-1.41349454727492[/C][/ROW]
[ROW][C]27[/C][C]122.4[/C][C]109.363285448360[/C][C]0.135074776623972[/C][C]13.0367145516403[/C][C]-0.665510284139953[/C][/ROW]
[ROW][C]28[/C][C]123.3[/C][C]115.527130115289[/C][C]0.635445631921668[/C][C]7.77286988471109[/C][C]1.15099642862555[/C][/ROW]
[ROW][C]29[/C][C]124.3[/C][C]118.961390109476[/C][C]0.867984757731305[/C][C]5.33860989052445[/C][C]0.539379029035982[/C][/ROW]
[ROW][C]30[/C][C]120[/C][C]118.370177620218[/C][C]0.747057852428945[/C][C]1.62982237978228[/C][C]-0.283726687643960[/C][/ROW]
[ROW][C]31[/C][C]118.1[/C][C]122.171016717831[/C][C]0.999365314656598[/C][C]-4.07101671783094[/C][C]0.594783317277155[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]122.466441644876[/C][C]0.941341335350567[/C][C]-3.46644164487572[/C][C]-0.136788746106619[/C][/ROW]
[ROW][C]33[/C][C]142.7[/C][C]123.429247467176[/C][C]0.943107601260095[/C][C]19.2707525328236[/C][C]0.00415877452811795[/C][/ROW]
[ROW][C]34[/C][C]123.6[/C][C]130.180258735681[/C][C]1.42027007832856[/C][C]-6.58025873568108[/C][C]1.12279547322485[/C][/ROW]
[ROW][C]35[/C][C]129.6[/C][C]128.720337613131[/C][C]1.18405549617151[/C][C]0.879662386868657[/C][C]-0.556609252628031[/C][/ROW]
[ROW][C]36[/C][C]151.6[/C][C]130.968552797426[/C][C]1.27123137983264[/C][C]20.6314472025743[/C][C]0.206305259487293[/C][/ROW]
[ROW][C]37[/C][C]110.4[/C][C]133.145596239781[/C][C]1.34551904018070[/C][C]-22.7455962397806[/C][C]0.176453761732429[/C][/ROW]
[ROW][C]38[/C][C]99.2[/C][C]128.222724621437[/C][C]0.829519243007137[/C][C]-29.0227246214372[/C][C]-1.21953814864134[/C][/ROW]
[ROW][C]39[/C][C]130.5[/C][C]124.297585594672[/C][C]0.436737730510524[/C][C]6.20241440532764[/C][C]-0.917659912902119[/C][/ROW]
[ROW][C]40[/C][C]136.2[/C][C]126.254955858046[/C][C]0.562538875019045[/C][C]9.94504414195446[/C][C]0.292105752547273[/C][/ROW]
[ROW][C]41[/C][C]129.7[/C][C]125.785897445823[/C][C]0.477197270188497[/C][C]3.91410255417732[/C][C]-0.198744327970320[/C][/ROW]
[ROW][C]42[/C][C]128[/C][C]126.800576244014[/C][C]0.521644962654375[/C][C]1.19942375598595[/C][C]0.104084289521931[/C][/ROW]
[ROW][C]43[/C][C]121.6[/C][C]126.620452494768[/C][C]0.463635947686511[/C][C]-5.02045249476761[/C][C]-0.136175317736686[/C][/ROW]
[ROW][C]44[/C][C]135.8[/C][C]133.202594799386[/C][C]0.969090036612781[/C][C]2.59740520061388[/C][C]1.18547529807231[/C][/ROW]
[ROW][C]45[/C][C]143.8[/C][C]132.023070579571[/C][C]0.791747622263897[/C][C]11.7769294204291[/C][C]-0.415106356990479[/C][/ROW]
[ROW][C]46[/C][C]147.5[/C][C]141.828157461096[/C][C]1.53491238130075[/C][C]5.6718425389039[/C][C]1.73784006010847[/C][/ROW]
[ROW][C]47[/C][C]136.2[/C][C]141.379056627287[/C][C]1.37147631540237[/C][C]-5.17905662728651[/C][C]-0.382647759151948[/C][/ROW]
[ROW][C]48[/C][C]156.6[/C][C]138.702858501877[/C][C]1.03814553116866[/C][C]17.8971414981232[/C][C]-0.782735681222912[/C][/ROW]
[ROW][C]49[/C][C]123.3[/C][C]139.973156948100[/C][C]1.05727487817780[/C][C]-16.6731569481003[/C][C]0.0450108019913095[/C][/ROW]
[ROW][C]50[/C][C]104.5[/C][C]136.486258155144[/C][C]0.68242494989097[/C][C]-31.9862581551436[/C][C]-0.880387043208026[/C][/ROW]
[ROW][C]51[/C][C]139.8[/C][C]134.959679796718[/C][C]0.500070005020704[/C][C]4.8403202032816[/C][C]-0.426348128139618[/C][/ROW]
[ROW][C]52[/C][C]136.5[/C][C]130.670259553017[/C][C]0.104693018331696[/C][C]5.82974044698256[/C][C]-0.921941934001073[/C][/ROW]
[ROW][C]53[/C][C]112.1[/C][C]119.613929005214[/C][C]-0.816373771293664[/C][C]-7.51392900521396[/C][C]-2.15064315264510[/C][/ROW]
[ROW][C]54[/C][C]118.5[/C][C]116.695800816938[/C][C]-0.989799481379634[/C][C]1.80419918306197[/C][C]-0.406165395511662[/C][/ROW]
[ROW][C]55[/C][C]94.4[/C][C]109.501740282498[/C][C]-1.50178539389029[/C][C]-15.1017402824984[/C][C]-1.20089263961595[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]103.503923836102[/C][C]-1.87280877275318[/C][C]-1.20392383610232[/C][C]-0.86953378726486[/C][/ROW]
[ROW][C]57[/C][C]111.4[/C][C]102.063086133549[/C][C]-1.83717127513393[/C][C]9.33691386645117[/C][C]0.0833679027876294[/C][/ROW]
[ROW][C]58[/C][C]99.2[/C][C]96.384506492143[/C][C]-2.15392585841897[/C][C]2.81549350785704[/C][C]-0.740254822897015[/C][/ROW]
[ROW][C]59[/C][C]87.8[/C][C]91.9686151131049[/C][C]-2.3403547644004[/C][C]-4.16861511310487[/C][C]-0.436019763987048[/C][/ROW]
[ROW][C]60[/C][C]115.8[/C][C]92.744149584744[/C][C]-2.08358248537521[/C][C]23.055850415256[/C][C]0.601698344853956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63760&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63760&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
179.879.8000
283.481.9447540362090.07813984344992381.455245963790950.364584540255019
3113.699.6376134366861.2446255856480613.96238656331402.77888176055049
4112.9108.7827075174181.716328846052334.117292482581881.54102675281805
5104108.3867118840991.61183250653837-4.38671188409927-0.439961061080126
6109.9108.8013080547221.556638997466371.09869194527821-0.250021200364225
799104.0477811879201.25131090063111-5.04778118792031-1.30766756993645
8106.3103.6686208518181.164847146722272.63137914818221-0.335273847813146
9128.9115.2908234955011.7702871120996913.60917650449902.13579185073751
10111.1115.9236898967831.69959716447469-4.82368989678283-0.230976430788645
11102.9109.6265616915191.17445025064903-6.72656169151893-1.61606201215112
12130117.5646397636671.6382935651475812.43536023633301.36119311883401
1387110.8403396998821.21105249121994-23.8403396998815-1.76096187958271
1487.5104.8557751004310.740772562590645-17.3557751004311-1.47589314183236
15117.6104.9729941061630.69215341254857712.6270058938373-0.117046266253148
16103.4101.4934485203910.3513169949131541.90655147960911-0.785454474930295
17110.8106.1669225123630.7020225960987364.63307748763730.84033800167656
18112.6108.0328216212410.7948318084328824.567178378759220.229176248005987
19102.5108.7986503578510.792541117958929-6.29865035785135-0.00570637459432741
20112.4112.0597846064240.9875548204189470.3402153935765050.484216154730760
21135.6116.9784178630421.2997601200996118.62158213695780.769396467918087
22105.1113.7290806305610.936739826577882-8.62908063056121-0.888623307812911
23127.7124.015131426871.682699233592793.684868573129961.82214959996911
24137124.2456624608871.5675291041574812.7543375391134-0.28327821609775
2591118.5923843290100.997288671916355-27.5923843290105-1.42417211827429
2690.5112.4028584273660.419275096908327-21.9028584273658-1.41349454727492
27122.4109.3632854483600.13507477662397213.0367145516403-0.665510284139953
28123.3115.5271301152890.6354456319216687.772869884711091.15099642862555
29124.3118.9613901094760.8679847577313055.338609890524450.539379029035982
30120118.3701776202180.7470578524289451.62982237978228-0.283726687643960
31118.1122.1710167178310.999365314656598-4.071016717830940.594783317277155
32119122.4664416448760.941341335350567-3.46644164487572-0.136788746106619
33142.7123.4292474671760.94310760126009519.27075253282360.00415877452811795
34123.6130.1802587356811.42027007832856-6.580258735681081.12279547322485
35129.6128.7203376131311.184055496171510.879662386868657-0.556609252628031
36151.6130.9685527974261.2712313798326420.63144720257430.206305259487293
37110.4133.1455962397811.34551904018070-22.74559623978060.176453761732429
3899.2128.2227246214370.829519243007137-29.0227246214372-1.21953814864134
39130.5124.2975855946720.4367377305105246.20241440532764-0.917659912902119
40136.2126.2549558580460.5625388750190459.945044141954460.292105752547273
41129.7125.7858974458230.4771972701884973.91410255417732-0.198744327970320
42128126.8005762440140.5216449626543751.199423755985950.104084289521931
43121.6126.6204524947680.463635947686511-5.02045249476761-0.136175317736686
44135.8133.2025947993860.9690900366127812.597405200613881.18547529807231
45143.8132.0230705795710.79174762226389711.7769294204291-0.415106356990479
46147.5141.8281574610961.534912381300755.67184253890391.73784006010847
47136.2141.3790566272871.37147631540237-5.17905662728651-0.382647759151948
48156.6138.7028585018771.0381455311686617.8971414981232-0.782735681222912
49123.3139.9731569481001.05727487817780-16.67315694810030.0450108019913095
50104.5136.4862581551440.68242494989097-31.9862581551436-0.880387043208026
51139.8134.9596797967180.5000700050207044.8403202032816-0.426348128139618
52136.5130.6702595530170.1046930183316965.82974044698256-0.921941934001073
53112.1119.613929005214-0.816373771293664-7.51392900521396-2.15064315264510
54118.5116.695800816938-0.9897994813796341.80419918306197-0.406165395511662
5594.4109.501740282498-1.50178539389029-15.1017402824984-1.20089263961595
56102.3103.503923836102-1.87280877275318-1.20392383610232-0.86953378726486
57111.4102.063086133549-1.837171275133939.336913866451170.0833679027876294
5899.296.384506492143-2.153925858418972.81549350785704-0.740254822897015
5987.891.9686151131049-2.3403547644004-4.16861511310487-0.436019763987048
60115.892.744149584744-2.0835824853752123.0558504152560.601698344853956



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')