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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 computationTue, 01 Dec 2009 13:19:53 -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/01/t1259698850edk46c756iehgps.htm/, Retrieved Fri, 26 Apr 2024 06:20:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62241, Retrieved Fri, 26 Apr 2024 06:20:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
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] [3de techniek] [2009-12-01 20:19:53] [e1f26cfd746b288ac2a466939c6f316e] [Current]
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Dataseries X:
105.7
105.7
111.1
82.4
60
107.3
99.3
113.5
108.9
100.2
103.9
138.7
120.2
100.2
143.2
70.9
85.2
133
136.6
117.9
106.3
122.3
125.5
148.4
126.3
99.6
140.4
80.3
92.6
138.5
110.9
119.6
105
109
129.4
148.6
101.4
134.8
143.7
81.6
90.3
141.5
140.7
140.2
100.2
125.7
119.6
134.7
109
116.3
146.9
97.4
89.4
132.1
139.8
129
112.5
121.9
121.7
123.1




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=62241&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=62241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62241&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
1105.7105.7000
2105.7105.7000
3111.1107.3446418909520.1926458532063762.612083688612880.338190942124921
482.4100.326895275061-0.600075382294971-13.3728498193399-1.42240244421262
56087.5174259941417-1.75053664145693-18.6911621834243-2.69747621349687
6107.389.0505718051163-1.4902476026312015.56053539322020.800690552131691
799.391.5510583669497-1.221516121482694.195902893014521.03906782151807
8113.597.0803743430234-0.8264935982507210.08425501717301.83162986128148
9108.9101.109892214078-0.5732290497640333.079787231349081.35201187170876
10100.2101.863925582374-0.510117976845719-2.978958419564510.375707424520647
11103.9102.401034270145-0.4639029803484010.4466636656543850.299715812680959
12138.7110.632002148510-0.10309458776223819.24495239694232.50755696337587
13120.2114.059543859670-0.1901805608184691.923599800463631.23823994394763
14100.2111.897671017583-0.207307663468807-9.64748941798102-0.605839568422443
15143.2115.869341409268-0.067403366024629223.63028326816221.12517454127873
1670.9107.833054548370-0.40881613499513-30.2736412071263-2.06384449651805
1785.2105.324844806329-0.501441985889379-18.3500702207372-0.552217588272277
18133107.973186321593-0.36976680828296122.27070694976220.854582903422955
19136.6114.966678922867-0.087905667637723314.96599364119662.05648191777593
20117.9116.096642695102-0.04566322306934950.6702329693937470.347822952829285
21106.3114.168671211143-0.104755625208360-6.08193441103327-0.546295481200169
22122.3116.676648421222-0.03095119726685923.105927376598150.76709068478743
23125.5120.6921820958640.06851643078217480.8584643317632871.19918274733023
24148.4124.1427496632150.13239243791482120.90296798566571.01425815229225
25126.3125.0610291254210.1384135708383390.431811122426680.243563898297384
2699.6122.4525348707500.105562095003110-20.1075419925221-0.83038327942993
27140.4119.3445570096270.0414529997028824.1072260256246-0.931303001259527
2880.3116.764696837760-0.0253619980276945-34.0559029742654-0.74137934681779
2992.6115.889376630318-0.0490861674383372-22.5142869220263-0.239630627759551
30138.5116.97627899392-0.017180039799723720.47913333819570.323196170420406
31110.9113.048117077832-0.1225389447257561.50083197840010-1.12744357000475
32119.6113.275214429285-0.1137585418978765.993641895562690.102086436374914
33105113.683652284626-0.101778182958227-9.184792992963550.15411037847406
34109113.091021042820-0.111845557685268-3.61473435456395-0.146100933780694
35129.4116.490495667621-0.04956307271021199.469644577352821.05258495626533
36148.6119.333678281753-0.0074015781001759226.40636848779090.873169678890295
37101.4115.550594678437-0.0513249479897345-10.3879756007015-1.14689559312488
38134.8121.6839036616590.02627995320298277.009258032136811.86291849004383
39143.7122.3329125634870.035917883917676520.76389462399460.184605385249156
4081.6121.0101492497150.0111010033999638-38.1150948868998-0.397874960559327
4190.3118.927498745418-0.0307478101275528-26.6455086896791-0.610582232646936
42141.5118.219588831063-0.044669812710476423.9223417303094-0.197972368086141
43140.7122.4427731218280.041623218108650614.18564309754251.25537409573191
44140.2126.1169269472100.11154794897346210.58897320348971.07616620875523
45100.2124.0275000265560.0722118925801899-21.6929123064011-0.65647133991494
46125.7125.4342658899890.0938822776085803-1.038160301936760.400356465308575
47119.6123.2059865421250.0602670909621095-1.32286003963186-0.6999493453182
48134.7119.5427457946770.012600880442588118.8361764870504-1.12641386259487
49109119.6008519860440.013132936655692-10.64590835745720.0137845121989755
50116.3117.467922575887-0.01247665031126570.948410174632398-0.647659949334143
51146.9118.325760144995-0.0010484032765950327.72304769520860.260848333349537
5297.4121.8105367675770.0495449346456143-27.7929874386571.03843764354762
5389.4121.8679094389740.0496666138056852-32.47547218042370.00232530902919012
54132.1120.0669579327370.020029235645895613.8206473446498-0.550063539746591
55139.8121.1619625789170.03715597514669117.59665876908450.320454294340977
56129120.4567555477610.02572307434404869.26568050088583-0.222191854850823
57112.5122.9349354337480.0614070846420884-12.83357259625880.737088650664419
58121.9122.8804650980570.0598412153965649-0.866591868259193-0.0349580802515319
59121.7122.2828897221490.05166734382252110.0657419627520186-0.198935307335135
60123.1118.5154190772260.007761492859192438.36315541656304-1.15800237558920

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 105.7 & 105.7 & 0 & 0 & 0 \tabularnewline
2 & 105.7 & 105.7 & 0 & 0 & 0 \tabularnewline
3 & 111.1 & 107.344641890952 & 0.192645853206376 & 2.61208368861288 & 0.338190942124921 \tabularnewline
4 & 82.4 & 100.326895275061 & -0.600075382294971 & -13.3728498193399 & -1.42240244421262 \tabularnewline
5 & 60 & 87.5174259941417 & -1.75053664145693 & -18.6911621834243 & -2.69747621349687 \tabularnewline
6 & 107.3 & 89.0505718051163 & -1.49024760263120 & 15.5605353932202 & 0.800690552131691 \tabularnewline
7 & 99.3 & 91.5510583669497 & -1.22151612148269 & 4.19590289301452 & 1.03906782151807 \tabularnewline
8 & 113.5 & 97.0803743430234 & -0.82649359825072 & 10.0842550171730 & 1.83162986128148 \tabularnewline
9 & 108.9 & 101.109892214078 & -0.573229049764033 & 3.07978723134908 & 1.35201187170876 \tabularnewline
10 & 100.2 & 101.863925582374 & -0.510117976845719 & -2.97895841956451 & 0.375707424520647 \tabularnewline
11 & 103.9 & 102.401034270145 & -0.463902980348401 & 0.446663665654385 & 0.299715812680959 \tabularnewline
12 & 138.7 & 110.632002148510 & -0.103094587762238 & 19.2449523969423 & 2.50755696337587 \tabularnewline
13 & 120.2 & 114.059543859670 & -0.190180560818469 & 1.92359980046363 & 1.23823994394763 \tabularnewline
14 & 100.2 & 111.897671017583 & -0.207307663468807 & -9.64748941798102 & -0.605839568422443 \tabularnewline
15 & 143.2 & 115.869341409268 & -0.0674033660246292 & 23.6302832681622 & 1.12517454127873 \tabularnewline
16 & 70.9 & 107.833054548370 & -0.40881613499513 & -30.2736412071263 & -2.06384449651805 \tabularnewline
17 & 85.2 & 105.324844806329 & -0.501441985889379 & -18.3500702207372 & -0.552217588272277 \tabularnewline
18 & 133 & 107.973186321593 & -0.369766808282961 & 22.2707069497622 & 0.854582903422955 \tabularnewline
19 & 136.6 & 114.966678922867 & -0.0879056676377233 & 14.9659936411966 & 2.05648191777593 \tabularnewline
20 & 117.9 & 116.096642695102 & -0.0456632230693495 & 0.670232969393747 & 0.347822952829285 \tabularnewline
21 & 106.3 & 114.168671211143 & -0.104755625208360 & -6.08193441103327 & -0.546295481200169 \tabularnewline
22 & 122.3 & 116.676648421222 & -0.0309511972668592 & 3.10592737659815 & 0.76709068478743 \tabularnewline
23 & 125.5 & 120.692182095864 & 0.0685164307821748 & 0.858464331763287 & 1.19918274733023 \tabularnewline
24 & 148.4 & 124.142749663215 & 0.132392437914821 & 20.9029679856657 & 1.01425815229225 \tabularnewline
25 & 126.3 & 125.061029125421 & 0.138413570838339 & 0.43181112242668 & 0.243563898297384 \tabularnewline
26 & 99.6 & 122.452534870750 & 0.105562095003110 & -20.1075419925221 & -0.83038327942993 \tabularnewline
27 & 140.4 & 119.344557009627 & 0.04145299970288 & 24.1072260256246 & -0.931303001259527 \tabularnewline
28 & 80.3 & 116.764696837760 & -0.0253619980276945 & -34.0559029742654 & -0.74137934681779 \tabularnewline
29 & 92.6 & 115.889376630318 & -0.0490861674383372 & -22.5142869220263 & -0.239630627759551 \tabularnewline
30 & 138.5 & 116.97627899392 & -0.0171800397997237 & 20.4791333381957 & 0.323196170420406 \tabularnewline
31 & 110.9 & 113.048117077832 & -0.122538944725756 & 1.50083197840010 & -1.12744357000475 \tabularnewline
32 & 119.6 & 113.275214429285 & -0.113758541897876 & 5.99364189556269 & 0.102086436374914 \tabularnewline
33 & 105 & 113.683652284626 & -0.101778182958227 & -9.18479299296355 & 0.15411037847406 \tabularnewline
34 & 109 & 113.091021042820 & -0.111845557685268 & -3.61473435456395 & -0.146100933780694 \tabularnewline
35 & 129.4 & 116.490495667621 & -0.0495630727102119 & 9.46964457735282 & 1.05258495626533 \tabularnewline
36 & 148.6 & 119.333678281753 & -0.00740157810017592 & 26.4063684877909 & 0.873169678890295 \tabularnewline
37 & 101.4 & 115.550594678437 & -0.0513249479897345 & -10.3879756007015 & -1.14689559312488 \tabularnewline
38 & 134.8 & 121.683903661659 & 0.0262799532029827 & 7.00925803213681 & 1.86291849004383 \tabularnewline
39 & 143.7 & 122.332912563487 & 0.0359178839176765 & 20.7638946239946 & 0.184605385249156 \tabularnewline
40 & 81.6 & 121.010149249715 & 0.0111010033999638 & -38.1150948868998 & -0.397874960559327 \tabularnewline
41 & 90.3 & 118.927498745418 & -0.0307478101275528 & -26.6455086896791 & -0.610582232646936 \tabularnewline
42 & 141.5 & 118.219588831063 & -0.0446698127104764 & 23.9223417303094 & -0.197972368086141 \tabularnewline
43 & 140.7 & 122.442773121828 & 0.0416232181086506 & 14.1856430975425 & 1.25537409573191 \tabularnewline
44 & 140.2 & 126.116926947210 & 0.111547948973462 & 10.5889732034897 & 1.07616620875523 \tabularnewline
45 & 100.2 & 124.027500026556 & 0.0722118925801899 & -21.6929123064011 & -0.65647133991494 \tabularnewline
46 & 125.7 & 125.434265889989 & 0.0938822776085803 & -1.03816030193676 & 0.400356465308575 \tabularnewline
47 & 119.6 & 123.205986542125 & 0.0602670909621095 & -1.32286003963186 & -0.6999493453182 \tabularnewline
48 & 134.7 & 119.542745794677 & 0.0126008804425881 & 18.8361764870504 & -1.12641386259487 \tabularnewline
49 & 109 & 119.600851986044 & 0.013132936655692 & -10.6459083574572 & 0.0137845121989755 \tabularnewline
50 & 116.3 & 117.467922575887 & -0.0124766503112657 & 0.948410174632398 & -0.647659949334143 \tabularnewline
51 & 146.9 & 118.325760144995 & -0.00104840327659503 & 27.7230476952086 & 0.260848333349537 \tabularnewline
52 & 97.4 & 121.810536767577 & 0.0495449346456143 & -27.792987438657 & 1.03843764354762 \tabularnewline
53 & 89.4 & 121.867909438974 & 0.0496666138056852 & -32.4754721804237 & 0.00232530902919012 \tabularnewline
54 & 132.1 & 120.066957932737 & 0.0200292356458956 & 13.8206473446498 & -0.550063539746591 \tabularnewline
55 & 139.8 & 121.161962578917 & 0.037155975146691 & 17.5966587690845 & 0.320454294340977 \tabularnewline
56 & 129 & 120.456755547761 & 0.0257230743440486 & 9.26568050088583 & -0.222191854850823 \tabularnewline
57 & 112.5 & 122.934935433748 & 0.0614070846420884 & -12.8335725962588 & 0.737088650664419 \tabularnewline
58 & 121.9 & 122.880465098057 & 0.0598412153965649 & -0.866591868259193 & -0.0349580802515319 \tabularnewline
59 & 121.7 & 122.282889722149 & 0.0516673438225211 & 0.0657419627520186 & -0.198935307335135 \tabularnewline
60 & 123.1 & 118.515419077226 & 0.00776149285919243 & 8.36315541656304 & -1.15800237558920 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62241&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]105.7[/C][C]105.7[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]105.7[/C][C]105.7[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]107.344641890952[/C][C]0.192645853206376[/C][C]2.61208368861288[/C][C]0.338190942124921[/C][/ROW]
[ROW][C]4[/C][C]82.4[/C][C]100.326895275061[/C][C]-0.600075382294971[/C][C]-13.3728498193399[/C][C]-1.42240244421262[/C][/ROW]
[ROW][C]5[/C][C]60[/C][C]87.5174259941417[/C][C]-1.75053664145693[/C][C]-18.6911621834243[/C][C]-2.69747621349687[/C][/ROW]
[ROW][C]6[/C][C]107.3[/C][C]89.0505718051163[/C][C]-1.49024760263120[/C][C]15.5605353932202[/C][C]0.800690552131691[/C][/ROW]
[ROW][C]7[/C][C]99.3[/C][C]91.5510583669497[/C][C]-1.22151612148269[/C][C]4.19590289301452[/C][C]1.03906782151807[/C][/ROW]
[ROW][C]8[/C][C]113.5[/C][C]97.0803743430234[/C][C]-0.82649359825072[/C][C]10.0842550171730[/C][C]1.83162986128148[/C][/ROW]
[ROW][C]9[/C][C]108.9[/C][C]101.109892214078[/C][C]-0.573229049764033[/C][C]3.07978723134908[/C][C]1.35201187170876[/C][/ROW]
[ROW][C]10[/C][C]100.2[/C][C]101.863925582374[/C][C]-0.510117976845719[/C][C]-2.97895841956451[/C][C]0.375707424520647[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]102.401034270145[/C][C]-0.463902980348401[/C][C]0.446663665654385[/C][C]0.299715812680959[/C][/ROW]
[ROW][C]12[/C][C]138.7[/C][C]110.632002148510[/C][C]-0.103094587762238[/C][C]19.2449523969423[/C][C]2.50755696337587[/C][/ROW]
[ROW][C]13[/C][C]120.2[/C][C]114.059543859670[/C][C]-0.190180560818469[/C][C]1.92359980046363[/C][C]1.23823994394763[/C][/ROW]
[ROW][C]14[/C][C]100.2[/C][C]111.897671017583[/C][C]-0.207307663468807[/C][C]-9.64748941798102[/C][C]-0.605839568422443[/C][/ROW]
[ROW][C]15[/C][C]143.2[/C][C]115.869341409268[/C][C]-0.0674033660246292[/C][C]23.6302832681622[/C][C]1.12517454127873[/C][/ROW]
[ROW][C]16[/C][C]70.9[/C][C]107.833054548370[/C][C]-0.40881613499513[/C][C]-30.2736412071263[/C][C]-2.06384449651805[/C][/ROW]
[ROW][C]17[/C][C]85.2[/C][C]105.324844806329[/C][C]-0.501441985889379[/C][C]-18.3500702207372[/C][C]-0.552217588272277[/C][/ROW]
[ROW][C]18[/C][C]133[/C][C]107.973186321593[/C][C]-0.369766808282961[/C][C]22.2707069497622[/C][C]0.854582903422955[/C][/ROW]
[ROW][C]19[/C][C]136.6[/C][C]114.966678922867[/C][C]-0.0879056676377233[/C][C]14.9659936411966[/C][C]2.05648191777593[/C][/ROW]
[ROW][C]20[/C][C]117.9[/C][C]116.096642695102[/C][C]-0.0456632230693495[/C][C]0.670232969393747[/C][C]0.347822952829285[/C][/ROW]
[ROW][C]21[/C][C]106.3[/C][C]114.168671211143[/C][C]-0.104755625208360[/C][C]-6.08193441103327[/C][C]-0.546295481200169[/C][/ROW]
[ROW][C]22[/C][C]122.3[/C][C]116.676648421222[/C][C]-0.0309511972668592[/C][C]3.10592737659815[/C][C]0.76709068478743[/C][/ROW]
[ROW][C]23[/C][C]125.5[/C][C]120.692182095864[/C][C]0.0685164307821748[/C][C]0.858464331763287[/C][C]1.19918274733023[/C][/ROW]
[ROW][C]24[/C][C]148.4[/C][C]124.142749663215[/C][C]0.132392437914821[/C][C]20.9029679856657[/C][C]1.01425815229225[/C][/ROW]
[ROW][C]25[/C][C]126.3[/C][C]125.061029125421[/C][C]0.138413570838339[/C][C]0.43181112242668[/C][C]0.243563898297384[/C][/ROW]
[ROW][C]26[/C][C]99.6[/C][C]122.452534870750[/C][C]0.105562095003110[/C][C]-20.1075419925221[/C][C]-0.83038327942993[/C][/ROW]
[ROW][C]27[/C][C]140.4[/C][C]119.344557009627[/C][C]0.04145299970288[/C][C]24.1072260256246[/C][C]-0.931303001259527[/C][/ROW]
[ROW][C]28[/C][C]80.3[/C][C]116.764696837760[/C][C]-0.0253619980276945[/C][C]-34.0559029742654[/C][C]-0.74137934681779[/C][/ROW]
[ROW][C]29[/C][C]92.6[/C][C]115.889376630318[/C][C]-0.0490861674383372[/C][C]-22.5142869220263[/C][C]-0.239630627759551[/C][/ROW]
[ROW][C]30[/C][C]138.5[/C][C]116.97627899392[/C][C]-0.0171800397997237[/C][C]20.4791333381957[/C][C]0.323196170420406[/C][/ROW]
[ROW][C]31[/C][C]110.9[/C][C]113.048117077832[/C][C]-0.122538944725756[/C][C]1.50083197840010[/C][C]-1.12744357000475[/C][/ROW]
[ROW][C]32[/C][C]119.6[/C][C]113.275214429285[/C][C]-0.113758541897876[/C][C]5.99364189556269[/C][C]0.102086436374914[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]113.683652284626[/C][C]-0.101778182958227[/C][C]-9.18479299296355[/C][C]0.15411037847406[/C][/ROW]
[ROW][C]34[/C][C]109[/C][C]113.091021042820[/C][C]-0.111845557685268[/C][C]-3.61473435456395[/C][C]-0.146100933780694[/C][/ROW]
[ROW][C]35[/C][C]129.4[/C][C]116.490495667621[/C][C]-0.0495630727102119[/C][C]9.46964457735282[/C][C]1.05258495626533[/C][/ROW]
[ROW][C]36[/C][C]148.6[/C][C]119.333678281753[/C][C]-0.00740157810017592[/C][C]26.4063684877909[/C][C]0.873169678890295[/C][/ROW]
[ROW][C]37[/C][C]101.4[/C][C]115.550594678437[/C][C]-0.0513249479897345[/C][C]-10.3879756007015[/C][C]-1.14689559312488[/C][/ROW]
[ROW][C]38[/C][C]134.8[/C][C]121.683903661659[/C][C]0.0262799532029827[/C][C]7.00925803213681[/C][C]1.86291849004383[/C][/ROW]
[ROW][C]39[/C][C]143.7[/C][C]122.332912563487[/C][C]0.0359178839176765[/C][C]20.7638946239946[/C][C]0.184605385249156[/C][/ROW]
[ROW][C]40[/C][C]81.6[/C][C]121.010149249715[/C][C]0.0111010033999638[/C][C]-38.1150948868998[/C][C]-0.397874960559327[/C][/ROW]
[ROW][C]41[/C][C]90.3[/C][C]118.927498745418[/C][C]-0.0307478101275528[/C][C]-26.6455086896791[/C][C]-0.610582232646936[/C][/ROW]
[ROW][C]42[/C][C]141.5[/C][C]118.219588831063[/C][C]-0.0446698127104764[/C][C]23.9223417303094[/C][C]-0.197972368086141[/C][/ROW]
[ROW][C]43[/C][C]140.7[/C][C]122.442773121828[/C][C]0.0416232181086506[/C][C]14.1856430975425[/C][C]1.25537409573191[/C][/ROW]
[ROW][C]44[/C][C]140.2[/C][C]126.116926947210[/C][C]0.111547948973462[/C][C]10.5889732034897[/C][C]1.07616620875523[/C][/ROW]
[ROW][C]45[/C][C]100.2[/C][C]124.027500026556[/C][C]0.0722118925801899[/C][C]-21.6929123064011[/C][C]-0.65647133991494[/C][/ROW]
[ROW][C]46[/C][C]125.7[/C][C]125.434265889989[/C][C]0.0938822776085803[/C][C]-1.03816030193676[/C][C]0.400356465308575[/C][/ROW]
[ROW][C]47[/C][C]119.6[/C][C]123.205986542125[/C][C]0.0602670909621095[/C][C]-1.32286003963186[/C][C]-0.6999493453182[/C][/ROW]
[ROW][C]48[/C][C]134.7[/C][C]119.542745794677[/C][C]0.0126008804425881[/C][C]18.8361764870504[/C][C]-1.12641386259487[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]119.600851986044[/C][C]0.013132936655692[/C][C]-10.6459083574572[/C][C]0.0137845121989755[/C][/ROW]
[ROW][C]50[/C][C]116.3[/C][C]117.467922575887[/C][C]-0.0124766503112657[/C][C]0.948410174632398[/C][C]-0.647659949334143[/C][/ROW]
[ROW][C]51[/C][C]146.9[/C][C]118.325760144995[/C][C]-0.00104840327659503[/C][C]27.7230476952086[/C][C]0.260848333349537[/C][/ROW]
[ROW][C]52[/C][C]97.4[/C][C]121.810536767577[/C][C]0.0495449346456143[/C][C]-27.792987438657[/C][C]1.03843764354762[/C][/ROW]
[ROW][C]53[/C][C]89.4[/C][C]121.867909438974[/C][C]0.0496666138056852[/C][C]-32.4754721804237[/C][C]0.00232530902919012[/C][/ROW]
[ROW][C]54[/C][C]132.1[/C][C]120.066957932737[/C][C]0.0200292356458956[/C][C]13.8206473446498[/C][C]-0.550063539746591[/C][/ROW]
[ROW][C]55[/C][C]139.8[/C][C]121.161962578917[/C][C]0.037155975146691[/C][C]17.5966587690845[/C][C]0.320454294340977[/C][/ROW]
[ROW][C]56[/C][C]129[/C][C]120.456755547761[/C][C]0.0257230743440486[/C][C]9.26568050088583[/C][C]-0.222191854850823[/C][/ROW]
[ROW][C]57[/C][C]112.5[/C][C]122.934935433748[/C][C]0.0614070846420884[/C][C]-12.8335725962588[/C][C]0.737088650664419[/C][/ROW]
[ROW][C]58[/C][C]121.9[/C][C]122.880465098057[/C][C]0.0598412153965649[/C][C]-0.866591868259193[/C][C]-0.0349580802515319[/C][/ROW]
[ROW][C]59[/C][C]121.7[/C][C]122.282889722149[/C][C]0.0516673438225211[/C][C]0.0657419627520186[/C][C]-0.198935307335135[/C][/ROW]
[ROW][C]60[/C][C]123.1[/C][C]118.515419077226[/C][C]0.00776149285919243[/C][C]8.36315541656304[/C][C]-1.15800237558920[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62241&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
1105.7105.7000
2105.7105.7000
3111.1107.3446418909520.1926458532063762.612083688612880.338190942124921
482.4100.326895275061-0.600075382294971-13.3728498193399-1.42240244421262
56087.5174259941417-1.75053664145693-18.6911621834243-2.69747621349687
6107.389.0505718051163-1.4902476026312015.56053539322020.800690552131691
799.391.5510583669497-1.221516121482694.195902893014521.03906782151807
8113.597.0803743430234-0.8264935982507210.08425501717301.83162986128148
9108.9101.109892214078-0.5732290497640333.079787231349081.35201187170876
10100.2101.863925582374-0.510117976845719-2.978958419564510.375707424520647
11103.9102.401034270145-0.4639029803484010.4466636656543850.299715812680959
12138.7110.632002148510-0.10309458776223819.24495239694232.50755696337587
13120.2114.059543859670-0.1901805608184691.923599800463631.23823994394763
14100.2111.897671017583-0.207307663468807-9.64748941798102-0.605839568422443
15143.2115.869341409268-0.067403366024629223.63028326816221.12517454127873
1670.9107.833054548370-0.40881613499513-30.2736412071263-2.06384449651805
1785.2105.324844806329-0.501441985889379-18.3500702207372-0.552217588272277
18133107.973186321593-0.36976680828296122.27070694976220.854582903422955
19136.6114.966678922867-0.087905667637723314.96599364119662.05648191777593
20117.9116.096642695102-0.04566322306934950.6702329693937470.347822952829285
21106.3114.168671211143-0.104755625208360-6.08193441103327-0.546295481200169
22122.3116.676648421222-0.03095119726685923.105927376598150.76709068478743
23125.5120.6921820958640.06851643078217480.8584643317632871.19918274733023
24148.4124.1427496632150.13239243791482120.90296798566571.01425815229225
25126.3125.0610291254210.1384135708383390.431811122426680.243563898297384
2699.6122.4525348707500.105562095003110-20.1075419925221-0.83038327942993
27140.4119.3445570096270.0414529997028824.1072260256246-0.931303001259527
2880.3116.764696837760-0.0253619980276945-34.0559029742654-0.74137934681779
2992.6115.889376630318-0.0490861674383372-22.5142869220263-0.239630627759551
30138.5116.97627899392-0.017180039799723720.47913333819570.323196170420406
31110.9113.048117077832-0.1225389447257561.50083197840010-1.12744357000475
32119.6113.275214429285-0.1137585418978765.993641895562690.102086436374914
33105113.683652284626-0.101778182958227-9.184792992963550.15411037847406
34109113.091021042820-0.111845557685268-3.61473435456395-0.146100933780694
35129.4116.490495667621-0.04956307271021199.469644577352821.05258495626533
36148.6119.333678281753-0.0074015781001759226.40636848779090.873169678890295
37101.4115.550594678437-0.0513249479897345-10.3879756007015-1.14689559312488
38134.8121.6839036616590.02627995320298277.009258032136811.86291849004383
39143.7122.3329125634870.035917883917676520.76389462399460.184605385249156
4081.6121.0101492497150.0111010033999638-38.1150948868998-0.397874960559327
4190.3118.927498745418-0.0307478101275528-26.6455086896791-0.610582232646936
42141.5118.219588831063-0.044669812710476423.9223417303094-0.197972368086141
43140.7122.4427731218280.041623218108650614.18564309754251.25537409573191
44140.2126.1169269472100.11154794897346210.58897320348971.07616620875523
45100.2124.0275000265560.0722118925801899-21.6929123064011-0.65647133991494
46125.7125.4342658899890.0938822776085803-1.038160301936760.400356465308575
47119.6123.2059865421250.0602670909621095-1.32286003963186-0.6999493453182
48134.7119.5427457946770.012600880442588118.8361764870504-1.12641386259487
49109119.6008519860440.013132936655692-10.64590835745720.0137845121989755
50116.3117.467922575887-0.01247665031126570.948410174632398-0.647659949334143
51146.9118.325760144995-0.0010484032765950327.72304769520860.260848333349537
5297.4121.8105367675770.0495449346456143-27.7929874386571.03843764354762
5389.4121.8679094389740.0496666138056852-32.47547218042370.00232530902919012
54132.1120.0669579327370.020029235645895613.8206473446498-0.550063539746591
55139.8121.1619625789170.03715597514669117.59665876908450.320454294340977
56129120.4567555477610.02572307434404869.26568050088583-0.222191854850823
57112.5122.9349354337480.0614070846420884-12.83357259625880.737088650664419
58121.9122.8804650980570.0598412153965649-0.866591868259193-0.0349580802515319
59121.7122.2828897221490.05166734382252110.0657419627520186-0.198935307335135
60123.1118.5154190772260.007761492859192438.36315541656304-1.15800237558920



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')