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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 computationThu, 03 Dec 2009 10:25:49 -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/03/t12598612074ypicxg40w153t0.htm/, Retrieved Thu, 25 Apr 2024 13:22:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62947, Retrieved Thu, 25 Apr 2024 13:22:10 +0000
QR Codes:

Original text written by user:Uitleg in Word document
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149
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] [Ad hoc forecasting 3] [2009-12-03 17:25:49] [8eb8270f5a1cfdf0409dcfcbf10be18b] [Current]
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Dataseries X:
96.96
93.11
95.62
98.30
96.38
100.82
99.06
94.03
102.07
99.31
98.64
101.82
99.14
97.63
100.06
101.32
101.49
105.43
105.09
99.48
108.53
104.34
106.10
107.35
103.00
104.50
105.17
104.84
106.18
108.86
107.77
102.74
112.63
106.26
108.86
111.38
106.85
107.86
107.94
111.38
111.29
113.72
111.88
109.87
113.72
111.71
114.81
112.05
111.54
110.87
110.87
115.48
111.63
116.24
113.56
106.01
110.45
107.77
108.61
108.19




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=62947&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=62947&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62947&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
196.9696.96000
293.1195.6913669472083-0.000980021030655114-2.58136694720834-2.35439870022924
395.6294.8736856730539-0.07151901415057740.746314326946155-0.843449423446045
498.396.01533591939820.05276497899003412.284664080601811.43549603843488
596.3896.44760317099530.0940450311107307-0.06760317099532060.514396721777169
6100.8298.16785975590320.2792679663291272.652140244096792.268052813189
799.0699.11862196770980.359601884479845-0.05862196770979610.92162536853751
894.0397.59606609729480.123310476354771-3.56606609729481-2.53072956565060
9102.0798.76499679333060.2601861079834933.305003206669411.38499001785332
1099.3199.39728482479820.310521604107985-0.08728482479816680.487976288400459
1198.6499.36433224307270.262934354531628-0.724332243072709-0.447416127904472
12101.82100.3254315959310.3612727113897591.494568404068510.905238791566546
1399.14100.0935228709620.281118249826728-0.953522870961724-0.779133950577501
1497.63100.2196871546770.259560722137456-2.58968715467741-0.207945451559612
15100.06100.4442249408640.254555729207507-0.384224940864347-0.0442832205175691
16101.32100.3300015619040.201763459010150.989998438095807-0.449635778160728
17101.49101.2185719752410.299678803626980.2714280247590830.849420626623094
18105.43102.1251162864160.3861833440900643.304883713584310.767563496073078
19105.09103.3137212793770.5007891071443261.776278720623161.02198740401359
2099.48103.9920853064020.526174494803314-4.512085306401550.225129444599609
21108.53104.8959251177430.580125421411543.634074882257010.475465674998847
22104.34105.1394375280490.5321214313157-0.799437528048958-0.421640736441573
23106.1106.1089224487520.594354402416231-0.008922448751978560.546819405868286
24107.35106.3143047424790.539123195871311.03569525752134-0.48782863608616
25103105.6096434958730.362575165195605-2.60964349587308-1.57388148443057
26104.5106.076373913790.377400403085289-1.576373913789900.132346390257091
27105.17106.1765106991940.337866657635272-1.00651069919392-0.348798959420721
28104.84105.7070779387820.222975899857415-0.867077938782345-1.00339196360372
29106.18105.9849843180160.2307700905033470.1950156819838150.0682097205664772
30108.86106.2187468545090.2311942687012442.641253145490620.00374328243966299
31107.77106.3691413685730.2197286570435031.4008586314268-0.101655097675369
32102.74106.8986070670060.263739395634661-4.158607067005930.389490496459233
33112.63107.8846217651100.3663870793004194.745378234890340.903999092855174
34106.26107.9799049095400.327900003699129-1.71990490954012-0.337764564139119
35108.86108.2114996086560.3142492773179390.648500391343989-0.119835732872230
36111.38108.8842833100020.3650198638152432.495716689997790.447353213592940
37106.85109.3629244868830.381113107618596-2.512924486882860.142362655773949
38107.86109.4807960574330.343789774283389-1.62079605743325-0.330251126803274
39107.94109.2306719645860.259540914608190-1.29067196458562-0.74260390625776
40111.38110.4100004079800.3899192428766600.9699995920202061.14486603829128
41111.29111.148790615020.4393074163970440.1412093849800300.433707369910372
42113.72111.5176723302110.4293442615518672.20232766978863-0.0877675937289686
43111.88111.5162545806380.3683855002423950.363745419362447-0.538398849073008
44109.87112.7334660427570.48858785721744-2.86346604275751.06139970862604
45113.72111.7537642707420.2806101682103901.96623572925750-1.83217261052912
46111.71112.2287655333100.308132937919581-0.5187655333101430.241957837634612
47114.81113.2904798387130.4147477537301561.519520161287100.93719175067661
48112.05112.2877215505020.214298740856166-0.237721550501600-1.76509080836131
49111.54112.6707489017000.238160946616792-1.130748901700430.210503334246384
50110.87112.7970860233920.222339535949491-1.92708602339242-0.139596124003331
51110.87112.9376187097070.210762397569870-2.06761870970746-0.101994701269503
52115.48113.7191145340120.2915100834386551.760885465988360.710247001111174
53111.63113.1439439274480.168957211384853-1.51394392744759-1.07776134551946
54116.24113.3165247328700.1694694293785592.923475267130250.00450996867662962
55113.56113.4297435252240.1615174905261880.130256474775515-0.0701014886635743
56106.01111.482499538652-0.136677084923912-5.47249953865152-2.62906421090012
57110.45109.846096888250-0.3487823225261110.603903111750462-1.86826176844829
58107.77108.736963978813-0.456301468765184-0.966963978812583-0.946090585318997
59108.61107.368862237890-0.5851938344660151.24113776211045-1.13397580907822
60108.19107.270653538738-0.5163694927536320.9193464612616170.605936834763827

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 96.96 & 96.96 & 0 & 0 & 0 \tabularnewline
2 & 93.11 & 95.6913669472083 & -0.000980021030655114 & -2.58136694720834 & -2.35439870022924 \tabularnewline
3 & 95.62 & 94.8736856730539 & -0.0715190141505774 & 0.746314326946155 & -0.843449423446045 \tabularnewline
4 & 98.3 & 96.0153359193982 & 0.0527649789900341 & 2.28466408060181 & 1.43549603843488 \tabularnewline
5 & 96.38 & 96.4476031709953 & 0.0940450311107307 & -0.0676031709953206 & 0.514396721777169 \tabularnewline
6 & 100.82 & 98.1678597559032 & 0.279267966329127 & 2.65214024409679 & 2.268052813189 \tabularnewline
7 & 99.06 & 99.1186219677098 & 0.359601884479845 & -0.0586219677097961 & 0.92162536853751 \tabularnewline
8 & 94.03 & 97.5960660972948 & 0.123310476354771 & -3.56606609729481 & -2.53072956565060 \tabularnewline
9 & 102.07 & 98.7649967933306 & 0.260186107983493 & 3.30500320666941 & 1.38499001785332 \tabularnewline
10 & 99.31 & 99.3972848247982 & 0.310521604107985 & -0.0872848247981668 & 0.487976288400459 \tabularnewline
11 & 98.64 & 99.3643322430727 & 0.262934354531628 & -0.724332243072709 & -0.447416127904472 \tabularnewline
12 & 101.82 & 100.325431595931 & 0.361272711389759 & 1.49456840406851 & 0.905238791566546 \tabularnewline
13 & 99.14 & 100.093522870962 & 0.281118249826728 & -0.953522870961724 & -0.779133950577501 \tabularnewline
14 & 97.63 & 100.219687154677 & 0.259560722137456 & -2.58968715467741 & -0.207945451559612 \tabularnewline
15 & 100.06 & 100.444224940864 & 0.254555729207507 & -0.384224940864347 & -0.0442832205175691 \tabularnewline
16 & 101.32 & 100.330001561904 & 0.20176345901015 & 0.989998438095807 & -0.449635778160728 \tabularnewline
17 & 101.49 & 101.218571975241 & 0.29967880362698 & 0.271428024759083 & 0.849420626623094 \tabularnewline
18 & 105.43 & 102.125116286416 & 0.386183344090064 & 3.30488371358431 & 0.767563496073078 \tabularnewline
19 & 105.09 & 103.313721279377 & 0.500789107144326 & 1.77627872062316 & 1.02198740401359 \tabularnewline
20 & 99.48 & 103.992085306402 & 0.526174494803314 & -4.51208530640155 & 0.225129444599609 \tabularnewline
21 & 108.53 & 104.895925117743 & 0.58012542141154 & 3.63407488225701 & 0.475465674998847 \tabularnewline
22 & 104.34 & 105.139437528049 & 0.5321214313157 & -0.799437528048958 & -0.421640736441573 \tabularnewline
23 & 106.1 & 106.108922448752 & 0.594354402416231 & -0.00892244875197856 & 0.546819405868286 \tabularnewline
24 & 107.35 & 106.314304742479 & 0.53912319587131 & 1.03569525752134 & -0.48782863608616 \tabularnewline
25 & 103 & 105.609643495873 & 0.362575165195605 & -2.60964349587308 & -1.57388148443057 \tabularnewline
26 & 104.5 & 106.07637391379 & 0.377400403085289 & -1.57637391378990 & 0.132346390257091 \tabularnewline
27 & 105.17 & 106.176510699194 & 0.337866657635272 & -1.00651069919392 & -0.348798959420721 \tabularnewline
28 & 104.84 & 105.707077938782 & 0.222975899857415 & -0.867077938782345 & -1.00339196360372 \tabularnewline
29 & 106.18 & 105.984984318016 & 0.230770090503347 & 0.195015681983815 & 0.0682097205664772 \tabularnewline
30 & 108.86 & 106.218746854509 & 0.231194268701244 & 2.64125314549062 & 0.00374328243966299 \tabularnewline
31 & 107.77 & 106.369141368573 & 0.219728657043503 & 1.4008586314268 & -0.101655097675369 \tabularnewline
32 & 102.74 & 106.898607067006 & 0.263739395634661 & -4.15860706700593 & 0.389490496459233 \tabularnewline
33 & 112.63 & 107.884621765110 & 0.366387079300419 & 4.74537823489034 & 0.903999092855174 \tabularnewline
34 & 106.26 & 107.979904909540 & 0.327900003699129 & -1.71990490954012 & -0.337764564139119 \tabularnewline
35 & 108.86 & 108.211499608656 & 0.314249277317939 & 0.648500391343989 & -0.119835732872230 \tabularnewline
36 & 111.38 & 108.884283310002 & 0.365019863815243 & 2.49571668999779 & 0.447353213592940 \tabularnewline
37 & 106.85 & 109.362924486883 & 0.381113107618596 & -2.51292448688286 & 0.142362655773949 \tabularnewline
38 & 107.86 & 109.480796057433 & 0.343789774283389 & -1.62079605743325 & -0.330251126803274 \tabularnewline
39 & 107.94 & 109.230671964586 & 0.259540914608190 & -1.29067196458562 & -0.74260390625776 \tabularnewline
40 & 111.38 & 110.410000407980 & 0.389919242876660 & 0.969999592020206 & 1.14486603829128 \tabularnewline
41 & 111.29 & 111.14879061502 & 0.439307416397044 & 0.141209384980030 & 0.433707369910372 \tabularnewline
42 & 113.72 & 111.517672330211 & 0.429344261551867 & 2.20232766978863 & -0.0877675937289686 \tabularnewline
43 & 111.88 & 111.516254580638 & 0.368385500242395 & 0.363745419362447 & -0.538398849073008 \tabularnewline
44 & 109.87 & 112.733466042757 & 0.48858785721744 & -2.8634660427575 & 1.06139970862604 \tabularnewline
45 & 113.72 & 111.753764270742 & 0.280610168210390 & 1.96623572925750 & -1.83217261052912 \tabularnewline
46 & 111.71 & 112.228765533310 & 0.308132937919581 & -0.518765533310143 & 0.241957837634612 \tabularnewline
47 & 114.81 & 113.290479838713 & 0.414747753730156 & 1.51952016128710 & 0.93719175067661 \tabularnewline
48 & 112.05 & 112.287721550502 & 0.214298740856166 & -0.237721550501600 & -1.76509080836131 \tabularnewline
49 & 111.54 & 112.670748901700 & 0.238160946616792 & -1.13074890170043 & 0.210503334246384 \tabularnewline
50 & 110.87 & 112.797086023392 & 0.222339535949491 & -1.92708602339242 & -0.139596124003331 \tabularnewline
51 & 110.87 & 112.937618709707 & 0.210762397569870 & -2.06761870970746 & -0.101994701269503 \tabularnewline
52 & 115.48 & 113.719114534012 & 0.291510083438655 & 1.76088546598836 & 0.710247001111174 \tabularnewline
53 & 111.63 & 113.143943927448 & 0.168957211384853 & -1.51394392744759 & -1.07776134551946 \tabularnewline
54 & 116.24 & 113.316524732870 & 0.169469429378559 & 2.92347526713025 & 0.00450996867662962 \tabularnewline
55 & 113.56 & 113.429743525224 & 0.161517490526188 & 0.130256474775515 & -0.0701014886635743 \tabularnewline
56 & 106.01 & 111.482499538652 & -0.136677084923912 & -5.47249953865152 & -2.62906421090012 \tabularnewline
57 & 110.45 & 109.846096888250 & -0.348782322526111 & 0.603903111750462 & -1.86826176844829 \tabularnewline
58 & 107.77 & 108.736963978813 & -0.456301468765184 & -0.966963978812583 & -0.946090585318997 \tabularnewline
59 & 108.61 & 107.368862237890 & -0.585193834466015 & 1.24113776211045 & -1.13397580907822 \tabularnewline
60 & 108.19 & 107.270653538738 & -0.516369492753632 & 0.919346461261617 & 0.605936834763827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62947&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]96.96[/C][C]96.96[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]93.11[/C][C]95.6913669472083[/C][C]-0.000980021030655114[/C][C]-2.58136694720834[/C][C]-2.35439870022924[/C][/ROW]
[ROW][C]3[/C][C]95.62[/C][C]94.8736856730539[/C][C]-0.0715190141505774[/C][C]0.746314326946155[/C][C]-0.843449423446045[/C][/ROW]
[ROW][C]4[/C][C]98.3[/C][C]96.0153359193982[/C][C]0.0527649789900341[/C][C]2.28466408060181[/C][C]1.43549603843488[/C][/ROW]
[ROW][C]5[/C][C]96.38[/C][C]96.4476031709953[/C][C]0.0940450311107307[/C][C]-0.0676031709953206[/C][C]0.514396721777169[/C][/ROW]
[ROW][C]6[/C][C]100.82[/C][C]98.1678597559032[/C][C]0.279267966329127[/C][C]2.65214024409679[/C][C]2.268052813189[/C][/ROW]
[ROW][C]7[/C][C]99.06[/C][C]99.1186219677098[/C][C]0.359601884479845[/C][C]-0.0586219677097961[/C][C]0.92162536853751[/C][/ROW]
[ROW][C]8[/C][C]94.03[/C][C]97.5960660972948[/C][C]0.123310476354771[/C][C]-3.56606609729481[/C][C]-2.53072956565060[/C][/ROW]
[ROW][C]9[/C][C]102.07[/C][C]98.7649967933306[/C][C]0.260186107983493[/C][C]3.30500320666941[/C][C]1.38499001785332[/C][/ROW]
[ROW][C]10[/C][C]99.31[/C][C]99.3972848247982[/C][C]0.310521604107985[/C][C]-0.0872848247981668[/C][C]0.487976288400459[/C][/ROW]
[ROW][C]11[/C][C]98.64[/C][C]99.3643322430727[/C][C]0.262934354531628[/C][C]-0.724332243072709[/C][C]-0.447416127904472[/C][/ROW]
[ROW][C]12[/C][C]101.82[/C][C]100.325431595931[/C][C]0.361272711389759[/C][C]1.49456840406851[/C][C]0.905238791566546[/C][/ROW]
[ROW][C]13[/C][C]99.14[/C][C]100.093522870962[/C][C]0.281118249826728[/C][C]-0.953522870961724[/C][C]-0.779133950577501[/C][/ROW]
[ROW][C]14[/C][C]97.63[/C][C]100.219687154677[/C][C]0.259560722137456[/C][C]-2.58968715467741[/C][C]-0.207945451559612[/C][/ROW]
[ROW][C]15[/C][C]100.06[/C][C]100.444224940864[/C][C]0.254555729207507[/C][C]-0.384224940864347[/C][C]-0.0442832205175691[/C][/ROW]
[ROW][C]16[/C][C]101.32[/C][C]100.330001561904[/C][C]0.20176345901015[/C][C]0.989998438095807[/C][C]-0.449635778160728[/C][/ROW]
[ROW][C]17[/C][C]101.49[/C][C]101.218571975241[/C][C]0.29967880362698[/C][C]0.271428024759083[/C][C]0.849420626623094[/C][/ROW]
[ROW][C]18[/C][C]105.43[/C][C]102.125116286416[/C][C]0.386183344090064[/C][C]3.30488371358431[/C][C]0.767563496073078[/C][/ROW]
[ROW][C]19[/C][C]105.09[/C][C]103.313721279377[/C][C]0.500789107144326[/C][C]1.77627872062316[/C][C]1.02198740401359[/C][/ROW]
[ROW][C]20[/C][C]99.48[/C][C]103.992085306402[/C][C]0.526174494803314[/C][C]-4.51208530640155[/C][C]0.225129444599609[/C][/ROW]
[ROW][C]21[/C][C]108.53[/C][C]104.895925117743[/C][C]0.58012542141154[/C][C]3.63407488225701[/C][C]0.475465674998847[/C][/ROW]
[ROW][C]22[/C][C]104.34[/C][C]105.139437528049[/C][C]0.5321214313157[/C][C]-0.799437528048958[/C][C]-0.421640736441573[/C][/ROW]
[ROW][C]23[/C][C]106.1[/C][C]106.108922448752[/C][C]0.594354402416231[/C][C]-0.00892244875197856[/C][C]0.546819405868286[/C][/ROW]
[ROW][C]24[/C][C]107.35[/C][C]106.314304742479[/C][C]0.53912319587131[/C][C]1.03569525752134[/C][C]-0.48782863608616[/C][/ROW]
[ROW][C]25[/C][C]103[/C][C]105.609643495873[/C][C]0.362575165195605[/C][C]-2.60964349587308[/C][C]-1.57388148443057[/C][/ROW]
[ROW][C]26[/C][C]104.5[/C][C]106.07637391379[/C][C]0.377400403085289[/C][C]-1.57637391378990[/C][C]0.132346390257091[/C][/ROW]
[ROW][C]27[/C][C]105.17[/C][C]106.176510699194[/C][C]0.337866657635272[/C][C]-1.00651069919392[/C][C]-0.348798959420721[/C][/ROW]
[ROW][C]28[/C][C]104.84[/C][C]105.707077938782[/C][C]0.222975899857415[/C][C]-0.867077938782345[/C][C]-1.00339196360372[/C][/ROW]
[ROW][C]29[/C][C]106.18[/C][C]105.984984318016[/C][C]0.230770090503347[/C][C]0.195015681983815[/C][C]0.0682097205664772[/C][/ROW]
[ROW][C]30[/C][C]108.86[/C][C]106.218746854509[/C][C]0.231194268701244[/C][C]2.64125314549062[/C][C]0.00374328243966299[/C][/ROW]
[ROW][C]31[/C][C]107.77[/C][C]106.369141368573[/C][C]0.219728657043503[/C][C]1.4008586314268[/C][C]-0.101655097675369[/C][/ROW]
[ROW][C]32[/C][C]102.74[/C][C]106.898607067006[/C][C]0.263739395634661[/C][C]-4.15860706700593[/C][C]0.389490496459233[/C][/ROW]
[ROW][C]33[/C][C]112.63[/C][C]107.884621765110[/C][C]0.366387079300419[/C][C]4.74537823489034[/C][C]0.903999092855174[/C][/ROW]
[ROW][C]34[/C][C]106.26[/C][C]107.979904909540[/C][C]0.327900003699129[/C][C]-1.71990490954012[/C][C]-0.337764564139119[/C][/ROW]
[ROW][C]35[/C][C]108.86[/C][C]108.211499608656[/C][C]0.314249277317939[/C][C]0.648500391343989[/C][C]-0.119835732872230[/C][/ROW]
[ROW][C]36[/C][C]111.38[/C][C]108.884283310002[/C][C]0.365019863815243[/C][C]2.49571668999779[/C][C]0.447353213592940[/C][/ROW]
[ROW][C]37[/C][C]106.85[/C][C]109.362924486883[/C][C]0.381113107618596[/C][C]-2.51292448688286[/C][C]0.142362655773949[/C][/ROW]
[ROW][C]38[/C][C]107.86[/C][C]109.480796057433[/C][C]0.343789774283389[/C][C]-1.62079605743325[/C][C]-0.330251126803274[/C][/ROW]
[ROW][C]39[/C][C]107.94[/C][C]109.230671964586[/C][C]0.259540914608190[/C][C]-1.29067196458562[/C][C]-0.74260390625776[/C][/ROW]
[ROW][C]40[/C][C]111.38[/C][C]110.410000407980[/C][C]0.389919242876660[/C][C]0.969999592020206[/C][C]1.14486603829128[/C][/ROW]
[ROW][C]41[/C][C]111.29[/C][C]111.14879061502[/C][C]0.439307416397044[/C][C]0.141209384980030[/C][C]0.433707369910372[/C][/ROW]
[ROW][C]42[/C][C]113.72[/C][C]111.517672330211[/C][C]0.429344261551867[/C][C]2.20232766978863[/C][C]-0.0877675937289686[/C][/ROW]
[ROW][C]43[/C][C]111.88[/C][C]111.516254580638[/C][C]0.368385500242395[/C][C]0.363745419362447[/C][C]-0.538398849073008[/C][/ROW]
[ROW][C]44[/C][C]109.87[/C][C]112.733466042757[/C][C]0.48858785721744[/C][C]-2.8634660427575[/C][C]1.06139970862604[/C][/ROW]
[ROW][C]45[/C][C]113.72[/C][C]111.753764270742[/C][C]0.280610168210390[/C][C]1.96623572925750[/C][C]-1.83217261052912[/C][/ROW]
[ROW][C]46[/C][C]111.71[/C][C]112.228765533310[/C][C]0.308132937919581[/C][C]-0.518765533310143[/C][C]0.241957837634612[/C][/ROW]
[ROW][C]47[/C][C]114.81[/C][C]113.290479838713[/C][C]0.414747753730156[/C][C]1.51952016128710[/C][C]0.93719175067661[/C][/ROW]
[ROW][C]48[/C][C]112.05[/C][C]112.287721550502[/C][C]0.214298740856166[/C][C]-0.237721550501600[/C][C]-1.76509080836131[/C][/ROW]
[ROW][C]49[/C][C]111.54[/C][C]112.670748901700[/C][C]0.238160946616792[/C][C]-1.13074890170043[/C][C]0.210503334246384[/C][/ROW]
[ROW][C]50[/C][C]110.87[/C][C]112.797086023392[/C][C]0.222339535949491[/C][C]-1.92708602339242[/C][C]-0.139596124003331[/C][/ROW]
[ROW][C]51[/C][C]110.87[/C][C]112.937618709707[/C][C]0.210762397569870[/C][C]-2.06761870970746[/C][C]-0.101994701269503[/C][/ROW]
[ROW][C]52[/C][C]115.48[/C][C]113.719114534012[/C][C]0.291510083438655[/C][C]1.76088546598836[/C][C]0.710247001111174[/C][/ROW]
[ROW][C]53[/C][C]111.63[/C][C]113.143943927448[/C][C]0.168957211384853[/C][C]-1.51394392744759[/C][C]-1.07776134551946[/C][/ROW]
[ROW][C]54[/C][C]116.24[/C][C]113.316524732870[/C][C]0.169469429378559[/C][C]2.92347526713025[/C][C]0.00450996867662962[/C][/ROW]
[ROW][C]55[/C][C]113.56[/C][C]113.429743525224[/C][C]0.161517490526188[/C][C]0.130256474775515[/C][C]-0.0701014886635743[/C][/ROW]
[ROW][C]56[/C][C]106.01[/C][C]111.482499538652[/C][C]-0.136677084923912[/C][C]-5.47249953865152[/C][C]-2.62906421090012[/C][/ROW]
[ROW][C]57[/C][C]110.45[/C][C]109.846096888250[/C][C]-0.348782322526111[/C][C]0.603903111750462[/C][C]-1.86826176844829[/C][/ROW]
[ROW][C]58[/C][C]107.77[/C][C]108.736963978813[/C][C]-0.456301468765184[/C][C]-0.966963978812583[/C][C]-0.946090585318997[/C][/ROW]
[ROW][C]59[/C][C]108.61[/C][C]107.368862237890[/C][C]-0.585193834466015[/C][C]1.24113776211045[/C][C]-1.13397580907822[/C][/ROW]
[ROW][C]60[/C][C]108.19[/C][C]107.270653538738[/C][C]-0.516369492753632[/C][C]0.919346461261617[/C][C]0.605936834763827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62947&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62947&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
196.9696.96000
293.1195.6913669472083-0.000980021030655114-2.58136694720834-2.35439870022924
395.6294.8736856730539-0.07151901415057740.746314326946155-0.843449423446045
498.396.01533591939820.05276497899003412.284664080601811.43549603843488
596.3896.44760317099530.0940450311107307-0.06760317099532060.514396721777169
6100.8298.16785975590320.2792679663291272.652140244096792.268052813189
799.0699.11862196770980.359601884479845-0.05862196770979610.92162536853751
894.0397.59606609729480.123310476354771-3.56606609729481-2.53072956565060
9102.0798.76499679333060.2601861079834933.305003206669411.38499001785332
1099.3199.39728482479820.310521604107985-0.08728482479816680.487976288400459
1198.6499.36433224307270.262934354531628-0.724332243072709-0.447416127904472
12101.82100.3254315959310.3612727113897591.494568404068510.905238791566546
1399.14100.0935228709620.281118249826728-0.953522870961724-0.779133950577501
1497.63100.2196871546770.259560722137456-2.58968715467741-0.207945451559612
15100.06100.4442249408640.254555729207507-0.384224940864347-0.0442832205175691
16101.32100.3300015619040.201763459010150.989998438095807-0.449635778160728
17101.49101.2185719752410.299678803626980.2714280247590830.849420626623094
18105.43102.1251162864160.3861833440900643.304883713584310.767563496073078
19105.09103.3137212793770.5007891071443261.776278720623161.02198740401359
2099.48103.9920853064020.526174494803314-4.512085306401550.225129444599609
21108.53104.8959251177430.580125421411543.634074882257010.475465674998847
22104.34105.1394375280490.5321214313157-0.799437528048958-0.421640736441573
23106.1106.1089224487520.594354402416231-0.008922448751978560.546819405868286
24107.35106.3143047424790.539123195871311.03569525752134-0.48782863608616
25103105.6096434958730.362575165195605-2.60964349587308-1.57388148443057
26104.5106.076373913790.377400403085289-1.576373913789900.132346390257091
27105.17106.1765106991940.337866657635272-1.00651069919392-0.348798959420721
28104.84105.7070779387820.222975899857415-0.867077938782345-1.00339196360372
29106.18105.9849843180160.2307700905033470.1950156819838150.0682097205664772
30108.86106.2187468545090.2311942687012442.641253145490620.00374328243966299
31107.77106.3691413685730.2197286570435031.4008586314268-0.101655097675369
32102.74106.8986070670060.263739395634661-4.158607067005930.389490496459233
33112.63107.8846217651100.3663870793004194.745378234890340.903999092855174
34106.26107.9799049095400.327900003699129-1.71990490954012-0.337764564139119
35108.86108.2114996086560.3142492773179390.648500391343989-0.119835732872230
36111.38108.8842833100020.3650198638152432.495716689997790.447353213592940
37106.85109.3629244868830.381113107618596-2.512924486882860.142362655773949
38107.86109.4807960574330.343789774283389-1.62079605743325-0.330251126803274
39107.94109.2306719645860.259540914608190-1.29067196458562-0.74260390625776
40111.38110.4100004079800.3899192428766600.9699995920202061.14486603829128
41111.29111.148790615020.4393074163970440.1412093849800300.433707369910372
42113.72111.5176723302110.4293442615518672.20232766978863-0.0877675937289686
43111.88111.5162545806380.3683855002423950.363745419362447-0.538398849073008
44109.87112.7334660427570.48858785721744-2.86346604275751.06139970862604
45113.72111.7537642707420.2806101682103901.96623572925750-1.83217261052912
46111.71112.2287655333100.308132937919581-0.5187655333101430.241957837634612
47114.81113.2904798387130.4147477537301561.519520161287100.93719175067661
48112.05112.2877215505020.214298740856166-0.237721550501600-1.76509080836131
49111.54112.6707489017000.238160946616792-1.130748901700430.210503334246384
50110.87112.7970860233920.222339535949491-1.92708602339242-0.139596124003331
51110.87112.9376187097070.210762397569870-2.06761870970746-0.101994701269503
52115.48113.7191145340120.2915100834386551.760885465988360.710247001111174
53111.63113.1439439274480.168957211384853-1.51394392744759-1.07776134551946
54116.24113.3165247328700.1694694293785592.923475267130250.00450996867662962
55113.56113.4297435252240.1615174905261880.130256474775515-0.0701014886635743
56106.01111.482499538652-0.136677084923912-5.47249953865152-2.62906421090012
57110.45109.846096888250-0.3487823225261110.603903111750462-1.86826176844829
58107.77108.736963978813-0.456301468765184-0.966963978812583-0.946090585318997
59108.61107.368862237890-0.5851938344660151.24113776211045-1.13397580907822
60108.19107.270653538738-0.5163694927536320.9193464612616170.605936834763827



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')