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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 computationWed, 29 Dec 2010 13:56:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293630877x6d0teh9w00k0yl.htm/, Retrieved Fri, 03 May 2024 07:55:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116845, Retrieved Fri, 03 May 2024 07:55:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2010-12-29 13:56:47] [0956ee981dded61b2e7128dae94e5715] [Current]
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Dataseries X:
3106.54
3125.67
3039.71
3051.67
3112.83
3228.01
3223.98
3328.8
3264.26
3394.14
3549.25
3744.63
3839.25
3912.28
3911.06
3675.8
3703.32
3795.91
3906.01
4070.78
4144.38
4140.3
4388.53
4433.57
4305.23
4471.65
4614.76
4697.86
4639.4
4384.47
4350.83
4325.29
4441.82
4162.5
4127.47
3722.23
3757.12
3719.52
3925.43
3751.41
3168.22
2994.38
3136
2672.2
2100.18
1881.46
1908.64
1900.09
1696.58
1748.74
1953.35
2071.37
2030.98
2169.14
2229.85
2480.93
2525.93
2475.14
2529.66
2453.37
2386.53
2517.3
2457.46
2589.73
2679.07
2506.13
2592.31




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116845&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116845&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116845&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13106.543106.54000
23125.673124.689318688831.251425112482510.9806813111672450.0701190497998039
33039.713038.87740252377-5.770417479535070.8325974762276-0.54236220066074
43051.673050.81063743179-3.802839037381180.8593625682128910.108422757246245
53112.833111.88575314774.928779304922680.9442468523018470.391980985245461
63228.013226.9435349884521.64515204035531.066465011546020.658600675593157
73223.983222.9373340481217.43878496298221.04266595188172-0.152266368909523
83328.83327.6902918936432.49287726274931.109708106361110.515588687799304
93264.263263.2114853220515.21811888333031.04851467794636-0.570545983250874
103394.143393.0323273338636.07466899381471.107672666143560.672615757781974
113549.253548.0922423884258.03377690894571.157757611584190.697169089904114
123744.633743.4252126666783.60592405690581.20478733333120.80356803168967
133839.253844.9430805604986.7855505421687-5.693080560493490.122668586958991
143912.283912.2042046333983.19722699851070.0757953666066259-0.0964490344360934
153911.063910.9350983529367.26528088092890.124901647072729-0.49312600793646
163675.83675.5323763417110.08965773182580.267623658294913-1.76692172616402
173703.323703.0590438771513.38694500916250.2609561228508370.101792253315551
183795.913795.6736062269928.37884307950330.2363937730078780.462508757414791
193906.013905.7941504905243.85355189812150.2158495094776510.477191059563207
204070.784070.5887906179466.75627872405980.1912093820571460.706040926340599
214144.384144.1899210225168.05276791498450.1900789774881160.0399602384641640
224140.34140.1002639224954.38623652509030.199736077512712-0.421175029116889
234388.534388.3512983629791.11529578642690.1787016370249521.13182186307676
244433.574433.3872460096282.3846312895290.182753990375473-0.269024541656719
254305.234342.0555406208249.9482114462747-36.8255406208213-1.10065174031667
264471.654467.2120408642463.90348197596354.437959135758610.397606353006847
274614.764610.3587059305878.92670145347574.401294069424310.462418524703577
284697.864693.460271464579.71801822026724.399728535501390.0243651508779043
294639.44634.9582632756453.52269864063724.44173672435856-0.80674947354213
304384.474379.9522641173-4.945095334200864.51773588270437-1.80092209209552
314350.834346.30653406083-10.38380322408484.52346593917359-0.167538833016132
324325.294320.76408113751-13.25628151244594.52591886248543-0.0884919552986306
334441.824437.3111053055511.34055544867164.508894694449480.757781890025354
344162.54157.96020455389-43.7435157083244.53979544610951-1.69708175270366
354127.474122.93095535312-42.09221401839724.539044646878430.0508757579892298
363722.233717.66559458979-110.9117688223064.56440541021064-2.12031998516170
373757.123784.04264720296-77.619078703553-26.92264720295621.09406009017536
383719.523716.95981732916-75.65379118084032.560182670841470.0573054390452646
393925.433922.96779140471-22.25900589450372.462208595292271.64384351828422
403751.413748.90499585756-51.0315649457062.50500414243667-0.886045379760065
413168.223165.59337741966-151.9083300858412.6266225803372-3.10702481330344
422994.382991.74931522539-156.0653024922532.63068477460612-0.128050331154495
4331363133.41400249031-99.6452568322552.585997509685831.73808029465606
442672.22669.5696973351-168.6601306163722.63030266489928-2.1261831504745
452100.182097.50992273194-245.1029554916292.67007726806079-2.3550973951135
461881.461878.79203127967-240.1030804488092.667968720325130.154042516060985
471908.641905.98934456817-189.450485546382.650655431830821.56059160643903
481900.091897.44884179425-155.1684684520392.641158205748811.05622867030751
491696.581727.11381190715-158.023333490752-30.5338119071476-0.0923402614704458
501748.741744.47797756568-125.2013203752424.262022434319850.96902374367647
511953.351949.17923184803-62.66590467335594.170768151965651.92555722464145
522071.372067.23974768834-28.41262474928524.13025231165871.05493282760480
532030.982026.84757103887-30.68293541043334.13242896112935-0.0699308060339351
542169.142165.032439237761.318605048332644.107560762241940.985811634930953
552229.852225.7495288917112.57470920124224.100471108285340.346766638977508
562480.932476.8526039709057.77562294411864.077396029096651.39255898252069
572525.932521.8516021887255.35447582979754.07839781127486-0.0745931329995134
582475.142471.0548563462235.23902117832794.08514365377511-0.619747716700826
592529.662525.5758494999638.89292552462964.084150500035210.112576340233638
602453.372449.2810408320717.06495607200474.08895916792645-0.672521988120713
612386.532434.020749383510.9718434668685-47.4907493834995-0.195202294185455
622517.32512.5088587127223.63425370117424.791141287275890.376783040432569
632457.462452.649702271087.808223386946884.81029772892422-0.487358129773467
642589.732584.9428508171131.40142776669034.78714918288580.726674099295957
652679.072674.2915842686742.38323241501074.778415731332750.338280323511151
662506.132501.325278782271.573170911359024.80472121772591-1.25719688562009
672592.312587.5136560563717.60792687736124.796343943633180.493992204352127

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 3106.54 & 3106.54 & 0 & 0 & 0 \tabularnewline
2 & 3125.67 & 3124.68931868883 & 1.25142511248251 & 0.980681311167245 & 0.0701190497998039 \tabularnewline
3 & 3039.71 & 3038.87740252377 & -5.77041747953507 & 0.8325974762276 & -0.54236220066074 \tabularnewline
4 & 3051.67 & 3050.81063743179 & -3.80283903738118 & 0.859362568212891 & 0.108422757246245 \tabularnewline
5 & 3112.83 & 3111.8857531477 & 4.92877930492268 & 0.944246852301847 & 0.391980985245461 \tabularnewline
6 & 3228.01 & 3226.94353498845 & 21.6451520403553 & 1.06646501154602 & 0.658600675593157 \tabularnewline
7 & 3223.98 & 3222.93733404812 & 17.4387849629822 & 1.04266595188172 & -0.152266368909523 \tabularnewline
8 & 3328.8 & 3327.69029189364 & 32.4928772627493 & 1.10970810636111 & 0.515588687799304 \tabularnewline
9 & 3264.26 & 3263.21148532205 & 15.2181188833303 & 1.04851467794636 & -0.570545983250874 \tabularnewline
10 & 3394.14 & 3393.03232733386 & 36.0746689938147 & 1.10767266614356 & 0.672615757781974 \tabularnewline
11 & 3549.25 & 3548.09224238842 & 58.0337769089457 & 1.15775761158419 & 0.697169089904114 \tabularnewline
12 & 3744.63 & 3743.42521266667 & 83.6059240569058 & 1.2047873333312 & 0.80356803168967 \tabularnewline
13 & 3839.25 & 3844.94308056049 & 86.7855505421687 & -5.69308056049349 & 0.122668586958991 \tabularnewline
14 & 3912.28 & 3912.20420463339 & 83.1972269985107 & 0.0757953666066259 & -0.0964490344360934 \tabularnewline
15 & 3911.06 & 3910.93509835293 & 67.2652808809289 & 0.124901647072729 & -0.49312600793646 \tabularnewline
16 & 3675.8 & 3675.53237634171 & 10.0896577318258 & 0.267623658294913 & -1.76692172616402 \tabularnewline
17 & 3703.32 & 3703.05904387715 & 13.3869450091625 & 0.260956122850837 & 0.101792253315551 \tabularnewline
18 & 3795.91 & 3795.67360622699 & 28.3788430795033 & 0.236393773007878 & 0.462508757414791 \tabularnewline
19 & 3906.01 & 3905.79415049052 & 43.8535518981215 & 0.215849509477651 & 0.477191059563207 \tabularnewline
20 & 4070.78 & 4070.58879061794 & 66.7562787240598 & 0.191209382057146 & 0.706040926340599 \tabularnewline
21 & 4144.38 & 4144.18992102251 & 68.0527679149845 & 0.190078977488116 & 0.0399602384641640 \tabularnewline
22 & 4140.3 & 4140.10026392249 & 54.3862365250903 & 0.199736077512712 & -0.421175029116889 \tabularnewline
23 & 4388.53 & 4388.35129836297 & 91.1152957864269 & 0.178701637024952 & 1.13182186307676 \tabularnewline
24 & 4433.57 & 4433.38724600962 & 82.384631289529 & 0.182753990375473 & -0.269024541656719 \tabularnewline
25 & 4305.23 & 4342.05554062082 & 49.9482114462747 & -36.8255406208213 & -1.10065174031667 \tabularnewline
26 & 4471.65 & 4467.21204086424 & 63.9034819759635 & 4.43795913575861 & 0.397606353006847 \tabularnewline
27 & 4614.76 & 4610.35870593058 & 78.9267014534757 & 4.40129406942431 & 0.462418524703577 \tabularnewline
28 & 4697.86 & 4693.4602714645 & 79.7180182202672 & 4.39972853550139 & 0.0243651508779043 \tabularnewline
29 & 4639.4 & 4634.95826327564 & 53.5226986406372 & 4.44173672435856 & -0.80674947354213 \tabularnewline
30 & 4384.47 & 4379.9522641173 & -4.94509533420086 & 4.51773588270437 & -1.80092209209552 \tabularnewline
31 & 4350.83 & 4346.30653406083 & -10.3838032240848 & 4.52346593917359 & -0.167538833016132 \tabularnewline
32 & 4325.29 & 4320.76408113751 & -13.2562815124459 & 4.52591886248543 & -0.0884919552986306 \tabularnewline
33 & 4441.82 & 4437.31110530555 & 11.3405554486716 & 4.50889469444948 & 0.757781890025354 \tabularnewline
34 & 4162.5 & 4157.96020455389 & -43.743515708324 & 4.53979544610951 & -1.69708175270366 \tabularnewline
35 & 4127.47 & 4122.93095535312 & -42.0922140183972 & 4.53904464687843 & 0.0508757579892298 \tabularnewline
36 & 3722.23 & 3717.66559458979 & -110.911768822306 & 4.56440541021064 & -2.12031998516170 \tabularnewline
37 & 3757.12 & 3784.04264720296 & -77.619078703553 & -26.9226472029562 & 1.09406009017536 \tabularnewline
38 & 3719.52 & 3716.95981732916 & -75.6537911808403 & 2.56018267084147 & 0.0573054390452646 \tabularnewline
39 & 3925.43 & 3922.96779140471 & -22.2590058945037 & 2.46220859529227 & 1.64384351828422 \tabularnewline
40 & 3751.41 & 3748.90499585756 & -51.031564945706 & 2.50500414243667 & -0.886045379760065 \tabularnewline
41 & 3168.22 & 3165.59337741966 & -151.908330085841 & 2.6266225803372 & -3.10702481330344 \tabularnewline
42 & 2994.38 & 2991.74931522539 & -156.065302492253 & 2.63068477460612 & -0.128050331154495 \tabularnewline
43 & 3136 & 3133.41400249031 & -99.645256832255 & 2.58599750968583 & 1.73808029465606 \tabularnewline
44 & 2672.2 & 2669.5696973351 & -168.660130616372 & 2.63030266489928 & -2.1261831504745 \tabularnewline
45 & 2100.18 & 2097.50992273194 & -245.102955491629 & 2.67007726806079 & -2.3550973951135 \tabularnewline
46 & 1881.46 & 1878.79203127967 & -240.103080448809 & 2.66796872032513 & 0.154042516060985 \tabularnewline
47 & 1908.64 & 1905.98934456817 & -189.45048554638 & 2.65065543183082 & 1.56059160643903 \tabularnewline
48 & 1900.09 & 1897.44884179425 & -155.168468452039 & 2.64115820574881 & 1.05622867030751 \tabularnewline
49 & 1696.58 & 1727.11381190715 & -158.023333490752 & -30.5338119071476 & -0.0923402614704458 \tabularnewline
50 & 1748.74 & 1744.47797756568 & -125.201320375242 & 4.26202243431985 & 0.96902374367647 \tabularnewline
51 & 1953.35 & 1949.17923184803 & -62.6659046733559 & 4.17076815196565 & 1.92555722464145 \tabularnewline
52 & 2071.37 & 2067.23974768834 & -28.4126247492852 & 4.1302523116587 & 1.05493282760480 \tabularnewline
53 & 2030.98 & 2026.84757103887 & -30.6829354104333 & 4.13242896112935 & -0.0699308060339351 \tabularnewline
54 & 2169.14 & 2165.03243923776 & 1.31860504833264 & 4.10756076224194 & 0.985811634930953 \tabularnewline
55 & 2229.85 & 2225.74952889171 & 12.5747092012422 & 4.10047110828534 & 0.346766638977508 \tabularnewline
56 & 2480.93 & 2476.85260397090 & 57.7756229441186 & 4.07739602909665 & 1.39255898252069 \tabularnewline
57 & 2525.93 & 2521.85160218872 & 55.3544758297975 & 4.07839781127486 & -0.0745931329995134 \tabularnewline
58 & 2475.14 & 2471.05485634622 & 35.2390211783279 & 4.08514365377511 & -0.619747716700826 \tabularnewline
59 & 2529.66 & 2525.57584949996 & 38.8929255246296 & 4.08415050003521 & 0.112576340233638 \tabularnewline
60 & 2453.37 & 2449.28104083207 & 17.0649560720047 & 4.08895916792645 & -0.672521988120713 \tabularnewline
61 & 2386.53 & 2434.0207493835 & 10.9718434668685 & -47.4907493834995 & -0.195202294185455 \tabularnewline
62 & 2517.3 & 2512.50885871272 & 23.6342537011742 & 4.79114128727589 & 0.376783040432569 \tabularnewline
63 & 2457.46 & 2452.64970227108 & 7.80822338694688 & 4.81029772892422 & -0.487358129773467 \tabularnewline
64 & 2589.73 & 2584.94285081711 & 31.4014277666903 & 4.7871491828858 & 0.726674099295957 \tabularnewline
65 & 2679.07 & 2674.29158426867 & 42.3832324150107 & 4.77841573133275 & 0.338280323511151 \tabularnewline
66 & 2506.13 & 2501.32527878227 & 1.57317091135902 & 4.80472121772591 & -1.25719688562009 \tabularnewline
67 & 2592.31 & 2587.51365605637 & 17.6079268773612 & 4.79634394363318 & 0.493992204352127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116845&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]3106.54[/C][C]3106.54[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]3125.67[/C][C]3124.68931868883[/C][C]1.25142511248251[/C][C]0.980681311167245[/C][C]0.0701190497998039[/C][/ROW]
[ROW][C]3[/C][C]3039.71[/C][C]3038.87740252377[/C][C]-5.77041747953507[/C][C]0.8325974762276[/C][C]-0.54236220066074[/C][/ROW]
[ROW][C]4[/C][C]3051.67[/C][C]3050.81063743179[/C][C]-3.80283903738118[/C][C]0.859362568212891[/C][C]0.108422757246245[/C][/ROW]
[ROW][C]5[/C][C]3112.83[/C][C]3111.8857531477[/C][C]4.92877930492268[/C][C]0.944246852301847[/C][C]0.391980985245461[/C][/ROW]
[ROW][C]6[/C][C]3228.01[/C][C]3226.94353498845[/C][C]21.6451520403553[/C][C]1.06646501154602[/C][C]0.658600675593157[/C][/ROW]
[ROW][C]7[/C][C]3223.98[/C][C]3222.93733404812[/C][C]17.4387849629822[/C][C]1.04266595188172[/C][C]-0.152266368909523[/C][/ROW]
[ROW][C]8[/C][C]3328.8[/C][C]3327.69029189364[/C][C]32.4928772627493[/C][C]1.10970810636111[/C][C]0.515588687799304[/C][/ROW]
[ROW][C]9[/C][C]3264.26[/C][C]3263.21148532205[/C][C]15.2181188833303[/C][C]1.04851467794636[/C][C]-0.570545983250874[/C][/ROW]
[ROW][C]10[/C][C]3394.14[/C][C]3393.03232733386[/C][C]36.0746689938147[/C][C]1.10767266614356[/C][C]0.672615757781974[/C][/ROW]
[ROW][C]11[/C][C]3549.25[/C][C]3548.09224238842[/C][C]58.0337769089457[/C][C]1.15775761158419[/C][C]0.697169089904114[/C][/ROW]
[ROW][C]12[/C][C]3744.63[/C][C]3743.42521266667[/C][C]83.6059240569058[/C][C]1.2047873333312[/C][C]0.80356803168967[/C][/ROW]
[ROW][C]13[/C][C]3839.25[/C][C]3844.94308056049[/C][C]86.7855505421687[/C][C]-5.69308056049349[/C][C]0.122668586958991[/C][/ROW]
[ROW][C]14[/C][C]3912.28[/C][C]3912.20420463339[/C][C]83.1972269985107[/C][C]0.0757953666066259[/C][C]-0.0964490344360934[/C][/ROW]
[ROW][C]15[/C][C]3911.06[/C][C]3910.93509835293[/C][C]67.2652808809289[/C][C]0.124901647072729[/C][C]-0.49312600793646[/C][/ROW]
[ROW][C]16[/C][C]3675.8[/C][C]3675.53237634171[/C][C]10.0896577318258[/C][C]0.267623658294913[/C][C]-1.76692172616402[/C][/ROW]
[ROW][C]17[/C][C]3703.32[/C][C]3703.05904387715[/C][C]13.3869450091625[/C][C]0.260956122850837[/C][C]0.101792253315551[/C][/ROW]
[ROW][C]18[/C][C]3795.91[/C][C]3795.67360622699[/C][C]28.3788430795033[/C][C]0.236393773007878[/C][C]0.462508757414791[/C][/ROW]
[ROW][C]19[/C][C]3906.01[/C][C]3905.79415049052[/C][C]43.8535518981215[/C][C]0.215849509477651[/C][C]0.477191059563207[/C][/ROW]
[ROW][C]20[/C][C]4070.78[/C][C]4070.58879061794[/C][C]66.7562787240598[/C][C]0.191209382057146[/C][C]0.706040926340599[/C][/ROW]
[ROW][C]21[/C][C]4144.38[/C][C]4144.18992102251[/C][C]68.0527679149845[/C][C]0.190078977488116[/C][C]0.0399602384641640[/C][/ROW]
[ROW][C]22[/C][C]4140.3[/C][C]4140.10026392249[/C][C]54.3862365250903[/C][C]0.199736077512712[/C][C]-0.421175029116889[/C][/ROW]
[ROW][C]23[/C][C]4388.53[/C][C]4388.35129836297[/C][C]91.1152957864269[/C][C]0.178701637024952[/C][C]1.13182186307676[/C][/ROW]
[ROW][C]24[/C][C]4433.57[/C][C]4433.38724600962[/C][C]82.384631289529[/C][C]0.182753990375473[/C][C]-0.269024541656719[/C][/ROW]
[ROW][C]25[/C][C]4305.23[/C][C]4342.05554062082[/C][C]49.9482114462747[/C][C]-36.8255406208213[/C][C]-1.10065174031667[/C][/ROW]
[ROW][C]26[/C][C]4471.65[/C][C]4467.21204086424[/C][C]63.9034819759635[/C][C]4.43795913575861[/C][C]0.397606353006847[/C][/ROW]
[ROW][C]27[/C][C]4614.76[/C][C]4610.35870593058[/C][C]78.9267014534757[/C][C]4.40129406942431[/C][C]0.462418524703577[/C][/ROW]
[ROW][C]28[/C][C]4697.86[/C][C]4693.4602714645[/C][C]79.7180182202672[/C][C]4.39972853550139[/C][C]0.0243651508779043[/C][/ROW]
[ROW][C]29[/C][C]4639.4[/C][C]4634.95826327564[/C][C]53.5226986406372[/C][C]4.44173672435856[/C][C]-0.80674947354213[/C][/ROW]
[ROW][C]30[/C][C]4384.47[/C][C]4379.9522641173[/C][C]-4.94509533420086[/C][C]4.51773588270437[/C][C]-1.80092209209552[/C][/ROW]
[ROW][C]31[/C][C]4350.83[/C][C]4346.30653406083[/C][C]-10.3838032240848[/C][C]4.52346593917359[/C][C]-0.167538833016132[/C][/ROW]
[ROW][C]32[/C][C]4325.29[/C][C]4320.76408113751[/C][C]-13.2562815124459[/C][C]4.52591886248543[/C][C]-0.0884919552986306[/C][/ROW]
[ROW][C]33[/C][C]4441.82[/C][C]4437.31110530555[/C][C]11.3405554486716[/C][C]4.50889469444948[/C][C]0.757781890025354[/C][/ROW]
[ROW][C]34[/C][C]4162.5[/C][C]4157.96020455389[/C][C]-43.743515708324[/C][C]4.53979544610951[/C][C]-1.69708175270366[/C][/ROW]
[ROW][C]35[/C][C]4127.47[/C][C]4122.93095535312[/C][C]-42.0922140183972[/C][C]4.53904464687843[/C][C]0.0508757579892298[/C][/ROW]
[ROW][C]36[/C][C]3722.23[/C][C]3717.66559458979[/C][C]-110.911768822306[/C][C]4.56440541021064[/C][C]-2.12031998516170[/C][/ROW]
[ROW][C]37[/C][C]3757.12[/C][C]3784.04264720296[/C][C]-77.619078703553[/C][C]-26.9226472029562[/C][C]1.09406009017536[/C][/ROW]
[ROW][C]38[/C][C]3719.52[/C][C]3716.95981732916[/C][C]-75.6537911808403[/C][C]2.56018267084147[/C][C]0.0573054390452646[/C][/ROW]
[ROW][C]39[/C][C]3925.43[/C][C]3922.96779140471[/C][C]-22.2590058945037[/C][C]2.46220859529227[/C][C]1.64384351828422[/C][/ROW]
[ROW][C]40[/C][C]3751.41[/C][C]3748.90499585756[/C][C]-51.031564945706[/C][C]2.50500414243667[/C][C]-0.886045379760065[/C][/ROW]
[ROW][C]41[/C][C]3168.22[/C][C]3165.59337741966[/C][C]-151.908330085841[/C][C]2.6266225803372[/C][C]-3.10702481330344[/C][/ROW]
[ROW][C]42[/C][C]2994.38[/C][C]2991.74931522539[/C][C]-156.065302492253[/C][C]2.63068477460612[/C][C]-0.128050331154495[/C][/ROW]
[ROW][C]43[/C][C]3136[/C][C]3133.41400249031[/C][C]-99.645256832255[/C][C]2.58599750968583[/C][C]1.73808029465606[/C][/ROW]
[ROW][C]44[/C][C]2672.2[/C][C]2669.5696973351[/C][C]-168.660130616372[/C][C]2.63030266489928[/C][C]-2.1261831504745[/C][/ROW]
[ROW][C]45[/C][C]2100.18[/C][C]2097.50992273194[/C][C]-245.102955491629[/C][C]2.67007726806079[/C][C]-2.3550973951135[/C][/ROW]
[ROW][C]46[/C][C]1881.46[/C][C]1878.79203127967[/C][C]-240.103080448809[/C][C]2.66796872032513[/C][C]0.154042516060985[/C][/ROW]
[ROW][C]47[/C][C]1908.64[/C][C]1905.98934456817[/C][C]-189.45048554638[/C][C]2.65065543183082[/C][C]1.56059160643903[/C][/ROW]
[ROW][C]48[/C][C]1900.09[/C][C]1897.44884179425[/C][C]-155.168468452039[/C][C]2.64115820574881[/C][C]1.05622867030751[/C][/ROW]
[ROW][C]49[/C][C]1696.58[/C][C]1727.11381190715[/C][C]-158.023333490752[/C][C]-30.5338119071476[/C][C]-0.0923402614704458[/C][/ROW]
[ROW][C]50[/C][C]1748.74[/C][C]1744.47797756568[/C][C]-125.201320375242[/C][C]4.26202243431985[/C][C]0.96902374367647[/C][/ROW]
[ROW][C]51[/C][C]1953.35[/C][C]1949.17923184803[/C][C]-62.6659046733559[/C][C]4.17076815196565[/C][C]1.92555722464145[/C][/ROW]
[ROW][C]52[/C][C]2071.37[/C][C]2067.23974768834[/C][C]-28.4126247492852[/C][C]4.1302523116587[/C][C]1.05493282760480[/C][/ROW]
[ROW][C]53[/C][C]2030.98[/C][C]2026.84757103887[/C][C]-30.6829354104333[/C][C]4.13242896112935[/C][C]-0.0699308060339351[/C][/ROW]
[ROW][C]54[/C][C]2169.14[/C][C]2165.03243923776[/C][C]1.31860504833264[/C][C]4.10756076224194[/C][C]0.985811634930953[/C][/ROW]
[ROW][C]55[/C][C]2229.85[/C][C]2225.74952889171[/C][C]12.5747092012422[/C][C]4.10047110828534[/C][C]0.346766638977508[/C][/ROW]
[ROW][C]56[/C][C]2480.93[/C][C]2476.85260397090[/C][C]57.7756229441186[/C][C]4.07739602909665[/C][C]1.39255898252069[/C][/ROW]
[ROW][C]57[/C][C]2525.93[/C][C]2521.85160218872[/C][C]55.3544758297975[/C][C]4.07839781127486[/C][C]-0.0745931329995134[/C][/ROW]
[ROW][C]58[/C][C]2475.14[/C][C]2471.05485634622[/C][C]35.2390211783279[/C][C]4.08514365377511[/C][C]-0.619747716700826[/C][/ROW]
[ROW][C]59[/C][C]2529.66[/C][C]2525.57584949996[/C][C]38.8929255246296[/C][C]4.08415050003521[/C][C]0.112576340233638[/C][/ROW]
[ROW][C]60[/C][C]2453.37[/C][C]2449.28104083207[/C][C]17.0649560720047[/C][C]4.08895916792645[/C][C]-0.672521988120713[/C][/ROW]
[ROW][C]61[/C][C]2386.53[/C][C]2434.0207493835[/C][C]10.9718434668685[/C][C]-47.4907493834995[/C][C]-0.195202294185455[/C][/ROW]
[ROW][C]62[/C][C]2517.3[/C][C]2512.50885871272[/C][C]23.6342537011742[/C][C]4.79114128727589[/C][C]0.376783040432569[/C][/ROW]
[ROW][C]63[/C][C]2457.46[/C][C]2452.64970227108[/C][C]7.80822338694688[/C][C]4.81029772892422[/C][C]-0.487358129773467[/C][/ROW]
[ROW][C]64[/C][C]2589.73[/C][C]2584.94285081711[/C][C]31.4014277666903[/C][C]4.7871491828858[/C][C]0.726674099295957[/C][/ROW]
[ROW][C]65[/C][C]2679.07[/C][C]2674.29158426867[/C][C]42.3832324150107[/C][C]4.77841573133275[/C][C]0.338280323511151[/C][/ROW]
[ROW][C]66[/C][C]2506.13[/C][C]2501.32527878227[/C][C]1.57317091135902[/C][C]4.80472121772591[/C][C]-1.25719688562009[/C][/ROW]
[ROW][C]67[/C][C]2592.31[/C][C]2587.51365605637[/C][C]17.6079268773612[/C][C]4.79634394363318[/C][C]0.493992204352127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116845&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116845&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
13106.543106.54000
23125.673124.689318688831.251425112482510.9806813111672450.0701190497998039
33039.713038.87740252377-5.770417479535070.8325974762276-0.54236220066074
43051.673050.81063743179-3.802839037381180.8593625682128910.108422757246245
53112.833111.88575314774.928779304922680.9442468523018470.391980985245461
63228.013226.9435349884521.64515204035531.066465011546020.658600675593157
73223.983222.9373340481217.43878496298221.04266595188172-0.152266368909523
83328.83327.6902918936432.49287726274931.109708106361110.515588687799304
93264.263263.2114853220515.21811888333031.04851467794636-0.570545983250874
103394.143393.0323273338636.07466899381471.107672666143560.672615757781974
113549.253548.0922423884258.03377690894571.157757611584190.697169089904114
123744.633743.4252126666783.60592405690581.20478733333120.80356803168967
133839.253844.9430805604986.7855505421687-5.693080560493490.122668586958991
143912.283912.2042046333983.19722699851070.0757953666066259-0.0964490344360934
153911.063910.9350983529367.26528088092890.124901647072729-0.49312600793646
163675.83675.5323763417110.08965773182580.267623658294913-1.76692172616402
173703.323703.0590438771513.38694500916250.2609561228508370.101792253315551
183795.913795.6736062269928.37884307950330.2363937730078780.462508757414791
193906.013905.7941504905243.85355189812150.2158495094776510.477191059563207
204070.784070.5887906179466.75627872405980.1912093820571460.706040926340599
214144.384144.1899210225168.05276791498450.1900789774881160.0399602384641640
224140.34140.1002639224954.38623652509030.199736077512712-0.421175029116889
234388.534388.3512983629791.11529578642690.1787016370249521.13182186307676
244433.574433.3872460096282.3846312895290.182753990375473-0.269024541656719
254305.234342.0555406208249.9482114462747-36.8255406208213-1.10065174031667
264471.654467.2120408642463.90348197596354.437959135758610.397606353006847
274614.764610.3587059305878.92670145347574.401294069424310.462418524703577
284697.864693.460271464579.71801822026724.399728535501390.0243651508779043
294639.44634.9582632756453.52269864063724.44173672435856-0.80674947354213
304384.474379.9522641173-4.945095334200864.51773588270437-1.80092209209552
314350.834346.30653406083-10.38380322408484.52346593917359-0.167538833016132
324325.294320.76408113751-13.25628151244594.52591886248543-0.0884919552986306
334441.824437.3111053055511.34055544867164.508894694449480.757781890025354
344162.54157.96020455389-43.7435157083244.53979544610951-1.69708175270366
354127.474122.93095535312-42.09221401839724.539044646878430.0508757579892298
363722.233717.66559458979-110.9117688223064.56440541021064-2.12031998516170
373757.123784.04264720296-77.619078703553-26.92264720295621.09406009017536
383719.523716.95981732916-75.65379118084032.560182670841470.0573054390452646
393925.433922.96779140471-22.25900589450372.462208595292271.64384351828422
403751.413748.90499585756-51.0315649457062.50500414243667-0.886045379760065
413168.223165.59337741966-151.9083300858412.6266225803372-3.10702481330344
422994.382991.74931522539-156.0653024922532.63068477460612-0.128050331154495
4331363133.41400249031-99.6452568322552.585997509685831.73808029465606
442672.22669.5696973351-168.6601306163722.63030266489928-2.1261831504745
452100.182097.50992273194-245.1029554916292.67007726806079-2.3550973951135
461881.461878.79203127967-240.1030804488092.667968720325130.154042516060985
471908.641905.98934456817-189.450485546382.650655431830821.56059160643903
481900.091897.44884179425-155.1684684520392.641158205748811.05622867030751
491696.581727.11381190715-158.023333490752-30.5338119071476-0.0923402614704458
501748.741744.47797756568-125.2013203752424.262022434319850.96902374367647
511953.351949.17923184803-62.66590467335594.170768151965651.92555722464145
522071.372067.23974768834-28.41262474928524.13025231165871.05493282760480
532030.982026.84757103887-30.68293541043334.13242896112935-0.0699308060339351
542169.142165.032439237761.318605048332644.107560762241940.985811634930953
552229.852225.7495288917112.57470920124224.100471108285340.346766638977508
562480.932476.8526039709057.77562294411864.077396029096651.39255898252069
572525.932521.8516021887255.35447582979754.07839781127486-0.0745931329995134
582475.142471.0548563462235.23902117832794.08514365377511-0.619747716700826
592529.662525.5758494999638.89292552462964.084150500035210.112576340233638
602453.372449.2810408320717.06495607200474.08895916792645-0.672521988120713
612386.532434.020749383510.9718434668685-47.4907493834995-0.195202294185455
622517.32512.5088587127223.63425370117424.791141287275890.376783040432569
632457.462452.649702271087.808223386946884.81029772892422-0.487358129773467
642589.732584.9428508171131.40142776669034.78714918288580.726674099295957
652679.072674.2915842686742.38323241501074.778415731332750.338280323511151
662506.132501.325278782271.573170911359024.80472121772591-1.25719688562009
672592.312587.5136560563717.60792687736124.796343943633180.493992204352127



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')