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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationTue, 27 Nov 2012 16:20:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/27/t1354051604sxt13rxq5c87u3p.htm/, Retrieved Wed, 11 Feb 2026 15:57:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=194048, Retrieved Wed, 11 Feb 2026 15:57:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact325

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [conusumptieprijzen] [2012-11-20 22:46:29] [3dc52aaca1c2323e282536a0c7c26bc2]
- RMPD    [Classical Decomposition] [WS 8: classical d...] [2012-11-23 15:48:57] [5971e03025aa6333f85f7b726952428d]
- RMP         [Structural Time Series Models] [WS 8 structural t...] [2012-11-27 21:20:25] [292b44c97cfd231f70174a072f53fc18] [Current]
- R             [Structural Time Series Models] [] [2012-12-22 00:11:39] [13a5ed2ca96ae4a72a1110d56328629c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=194048&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=194048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194048&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
197009700000
290819420.72572088135-4.57650590562583-334.77068502008-1.94832687028407
390849148.22896222675-22.2243875445831-61.4635790451628-1.35926283014435
497439343.35355583652-8.81461752562042396.7847884537861.35805752508255
585879095.71396620184-19.3902960412747-504.920651031314-1.70074936554354
697319271.82128915795-13.6798179021033455.8577752173911.45002004532437
795639434.35676669054-10.0348186806924125.5925469877791.31901459662823
899989698.07258538314-5.33029079398532295.1675500901462.05145162030975
994379674.89508662169-5.62166049542184-237.584831043575-0.133629936115216
10100389807.96467915003-3.34401391015703227.6276516358581.03724069661718
1199189889.11518580483-1.9383948565197227.41969198824930.631405014655373
1292529667.90745025653-5.59764314597327-412.107445483491-1.63795975603681
1397379552.08375903964-3.60454864393269186.883929022552-0.867939210236263
1490359409.58737849509-2.67974580145488-372.124540115769-1.07955414058433
1591339311.85260831709-4.16578471700865-177.346353697658-0.67260262783964
1694879154.69504412586-8.33829473764172334.598059687382-1.05056490127207
1787009193.57731081212-6.97830915198858-494.302249056250.333409728930745
1896279253.45730004762-5.3077628287418372.4747553648650.48735258280257
1989479171.22093233486-6.85029544798299-222.960500206711-0.570797213653731
2092839097.5457827484-7.92883151314275186.562423078578-0.499482497733582
2188299105.0035157116-7.72121733031172-276.2599724752820.11528118429673
2299479330.4210576024-5.0818419303781612.6854073312181.74695555872627
2396289424.02459976685-4.23497529720333202.3253542632280.738987074020824
2493189518.24229039398-3.76506232719794-201.8928655683790.738153151287752
2596059459.67413326003-3.82937372764132146.249788945676-0.413062021293639
2686409244.03549954723-4.56264527965705-600.495828111361-1.58338539719983
2792149214.61328985033-4.8113126915217-0.210167812057477-0.181312783575778
2895679224.50668800261-4.57503226375296342.2610226358390.105442009715022
2985479155.48462432579-5.80637815883035-607.469429877878-0.463515977768877
3091858982.93319758843-8.99054792563163204.729986011092-1.21607829142995
3194709195.95738085327-5.1769613828464270.4387454640981.64065882867935
3291239183.67709105827-5.28013567594385-60.5604467156372-0.0529175082976271
3392789382.20732402294-2.87427936202179-107.5756488904381.52370115173463
34101709524.11187032098-1.54142129368911643.488095534361.08336129684396
3594349459.8408060871-1.96807599454922-24.7993398517645-0.469425120516108
3696559554.22704448629-1.4928588578708699.1717364243320.721136120008604
3794299426.43967754841-2.042482255743524.6568154645284-0.943982807976881
3887399354.03443009993-2.44846524725121-613.874673372187-0.522620841809225
3995529400.48338530928-2.02148594044035150.7213059879090.359358549534025
4096879364.45881927823-2.42573641654265323.08748370094-0.247868888331268
4190199439.23316278641-1.34211676916011-421.4686771638170.562511911194633
4296729526.30387261511-0.0438604609147463144.2742107031060.648123285400349
4392069354.35504140844-2.4465944170306-145.565818218281-1.27009465068198
4490699291.77971091598-3.19071119967936-221.796133069612-0.446964736972526
4597889527.2849198331-0.71668869142766256.7889155591731.7803555324126
46103129624.440422004310.0993269211548291685.9447506299910.731004447656152
47101059836.14538464051.50094825665009265.3580332771891.58103251411231
4898639824.456330189311.4277012829866238.7616634808706-0.0985099258053247
4996569747.286230953491.00155298250141-89.9896545216511-0.585877635757095
5092959801.810875701471.33980741539451-507.6894910135060.397278009267572
5199469816.196098586941.44365870110054129.5912922197890.0962860833989687
5297019676.985963321130.070266558254931126.2914908472161-1.03368270761176
5390499569.02176377413-1.13320562813747-518.277126906325-0.793405203278772
54101909686.250451246730.25460996684276501.8344411645780.871728956117933
5597069795.07897735561.49863032220728-90.84412735252620.803221347283888
5697659964.848726579753.26699000070155-202.5986915844861.2499298240844
5798939928.378927058582.90252604503863-34.7268604832437-0.295966261330206
5899949736.871588053641.39225477947911260.326960789066-1.44999649381141
59104339841.187742831322.07126977318297590.1166884954330.76798106755937
60100739924.941222406852.55338459304143146.7131005888010.609219810787897
611011210052.81327064413.2839908557190357.12527933179450.93323450938399
6292669964.621827807752.70104439329983-697.121896116203-0.679342920495002
6398209805.670897257431.5138693344125116.9685497523312-1.19660501909699
64100979863.902761777271.99201048491282232.1745910847310.418818861641066
6591159841.32512779411.76344637881469-725.926126884434-0.181327852971248
66104119901.651240607392.33314214817596508.3969426048280.432822110943608
6796789895.089095141982.24753785445854-216.944135860392-0.0659119108685137
681040810131.53077947124.36822757991437272.6403053540151.73986640983884
691015310180.01226955074.72970004680046-27.73555484029140.328371402792495
701036810200.94119570274.84730992876548166.7926681771070.120723392715742
711058110156.27105774794.52693610955514425.543313786251-0.369175546528211
721059710266.99402063335.16344572117128328.2593793733690.791496624619376
731068010388.59496012725.85285140561279289.4919777483630.866895756082703
74973810404.40518438525.91494617666305-666.5684505395320.0740051431809526
75955610104.97253552733.83205984289739-543.978681308488-2.26496731138744

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9700 & 9700 & 0 & 0 & 0 \tabularnewline
2 & 9081 & 9420.72572088135 & -4.57650590562583 & -334.77068502008 & -1.94832687028407 \tabularnewline
3 & 9084 & 9148.22896222675 & -22.2243875445831 & -61.4635790451628 & -1.35926283014435 \tabularnewline
4 & 9743 & 9343.35355583652 & -8.81461752562042 & 396.784788453786 & 1.35805752508255 \tabularnewline
5 & 8587 & 9095.71396620184 & -19.3902960412747 & -504.920651031314 & -1.70074936554354 \tabularnewline
6 & 9731 & 9271.82128915795 & -13.6798179021033 & 455.857775217391 & 1.45002004532437 \tabularnewline
7 & 9563 & 9434.35676669054 & -10.0348186806924 & 125.592546987779 & 1.31901459662823 \tabularnewline
8 & 9998 & 9698.07258538314 & -5.33029079398532 & 295.167550090146 & 2.05145162030975 \tabularnewline
9 & 9437 & 9674.89508662169 & -5.62166049542184 & -237.584831043575 & -0.133629936115216 \tabularnewline
10 & 10038 & 9807.96467915003 & -3.34401391015703 & 227.627651635858 & 1.03724069661718 \tabularnewline
11 & 9918 & 9889.11518580483 & -1.93839485651972 & 27.4196919882493 & 0.631405014655373 \tabularnewline
12 & 9252 & 9667.90745025653 & -5.59764314597327 & -412.107445483491 & -1.63795975603681 \tabularnewline
13 & 9737 & 9552.08375903964 & -3.60454864393269 & 186.883929022552 & -0.867939210236263 \tabularnewline
14 & 9035 & 9409.58737849509 & -2.67974580145488 & -372.124540115769 & -1.07955414058433 \tabularnewline
15 & 9133 & 9311.85260831709 & -4.16578471700865 & -177.346353697658 & -0.67260262783964 \tabularnewline
16 & 9487 & 9154.69504412586 & -8.33829473764172 & 334.598059687382 & -1.05056490127207 \tabularnewline
17 & 8700 & 9193.57731081212 & -6.97830915198858 & -494.30224905625 & 0.333409728930745 \tabularnewline
18 & 9627 & 9253.45730004762 & -5.3077628287418 & 372.474755364865 & 0.48735258280257 \tabularnewline
19 & 8947 & 9171.22093233486 & -6.85029544798299 & -222.960500206711 & -0.570797213653731 \tabularnewline
20 & 9283 & 9097.5457827484 & -7.92883151314275 & 186.562423078578 & -0.499482497733582 \tabularnewline
21 & 8829 & 9105.0035157116 & -7.72121733031172 & -276.259972475282 & 0.11528118429673 \tabularnewline
22 & 9947 & 9330.4210576024 & -5.0818419303781 & 612.685407331218 & 1.74695555872627 \tabularnewline
23 & 9628 & 9424.02459976685 & -4.23497529720333 & 202.325354263228 & 0.738987074020824 \tabularnewline
24 & 9318 & 9518.24229039398 & -3.76506232719794 & -201.892865568379 & 0.738153151287752 \tabularnewline
25 & 9605 & 9459.67413326003 & -3.82937372764132 & 146.249788945676 & -0.413062021293639 \tabularnewline
26 & 8640 & 9244.03549954723 & -4.56264527965705 & -600.495828111361 & -1.58338539719983 \tabularnewline
27 & 9214 & 9214.61328985033 & -4.8113126915217 & -0.210167812057477 & -0.181312783575778 \tabularnewline
28 & 9567 & 9224.50668800261 & -4.57503226375296 & 342.261022635839 & 0.105442009715022 \tabularnewline
29 & 8547 & 9155.48462432579 & -5.80637815883035 & -607.469429877878 & -0.463515977768877 \tabularnewline
30 & 9185 & 8982.93319758843 & -8.99054792563163 & 204.729986011092 & -1.21607829142995 \tabularnewline
31 & 9470 & 9195.95738085327 & -5.1769613828464 & 270.438745464098 & 1.64065882867935 \tabularnewline
32 & 9123 & 9183.67709105827 & -5.28013567594385 & -60.5604467156372 & -0.0529175082976271 \tabularnewline
33 & 9278 & 9382.20732402294 & -2.87427936202179 & -107.575648890438 & 1.52370115173463 \tabularnewline
34 & 10170 & 9524.11187032098 & -1.54142129368911 & 643.48809553436 & 1.08336129684396 \tabularnewline
35 & 9434 & 9459.8408060871 & -1.96807599454922 & -24.7993398517645 & -0.469425120516108 \tabularnewline
36 & 9655 & 9554.22704448629 & -1.49285885787086 & 99.171736424332 & 0.721136120008604 \tabularnewline
37 & 9429 & 9426.43967754841 & -2.04248225574352 & 4.6568154645284 & -0.943982807976881 \tabularnewline
38 & 8739 & 9354.03443009993 & -2.44846524725121 & -613.874673372187 & -0.522620841809225 \tabularnewline
39 & 9552 & 9400.48338530928 & -2.02148594044035 & 150.721305987909 & 0.359358549534025 \tabularnewline
40 & 9687 & 9364.45881927823 & -2.42573641654265 & 323.08748370094 & -0.247868888331268 \tabularnewline
41 & 9019 & 9439.23316278641 & -1.34211676916011 & -421.468677163817 & 0.562511911194633 \tabularnewline
42 & 9672 & 9526.30387261511 & -0.0438604609147463 & 144.274210703106 & 0.648123285400349 \tabularnewline
43 & 9206 & 9354.35504140844 & -2.4465944170306 & -145.565818218281 & -1.27009465068198 \tabularnewline
44 & 9069 & 9291.77971091598 & -3.19071119967936 & -221.796133069612 & -0.446964736972526 \tabularnewline
45 & 9788 & 9527.2849198331 & -0.71668869142766 & 256.788915559173 & 1.7803555324126 \tabularnewline
46 & 10312 & 9624.44042200431 & 0.0993269211548291 & 685.944750629991 & 0.731004447656152 \tabularnewline
47 & 10105 & 9836.1453846405 & 1.50094825665009 & 265.358033277189 & 1.58103251411231 \tabularnewline
48 & 9863 & 9824.45633018931 & 1.42770128298662 & 38.7616634808706 & -0.0985099258053247 \tabularnewline
49 & 9656 & 9747.28623095349 & 1.00155298250141 & -89.9896545216511 & -0.585877635757095 \tabularnewline
50 & 9295 & 9801.81087570147 & 1.33980741539451 & -507.689491013506 & 0.397278009267572 \tabularnewline
51 & 9946 & 9816.19609858694 & 1.44365870110054 & 129.591292219789 & 0.0962860833989687 \tabularnewline
52 & 9701 & 9676.98596332113 & 0.0702665582549311 & 26.2914908472161 & -1.03368270761176 \tabularnewline
53 & 9049 & 9569.02176377413 & -1.13320562813747 & -518.277126906325 & -0.793405203278772 \tabularnewline
54 & 10190 & 9686.25045124673 & 0.25460996684276 & 501.834441164578 & 0.871728956117933 \tabularnewline
55 & 9706 & 9795.0789773556 & 1.49863032220728 & -90.8441273525262 & 0.803221347283888 \tabularnewline
56 & 9765 & 9964.84872657975 & 3.26699000070155 & -202.598691584486 & 1.2499298240844 \tabularnewline
57 & 9893 & 9928.37892705858 & 2.90252604503863 & -34.7268604832437 & -0.295966261330206 \tabularnewline
58 & 9994 & 9736.87158805364 & 1.39225477947911 & 260.326960789066 & -1.44999649381141 \tabularnewline
59 & 10433 & 9841.18774283132 & 2.07126977318297 & 590.116688495433 & 0.76798106755937 \tabularnewline
60 & 10073 & 9924.94122240685 & 2.55338459304143 & 146.713100588801 & 0.609219810787897 \tabularnewline
61 & 10112 & 10052.8132706441 & 3.28399085571903 & 57.1252793317945 & 0.93323450938399 \tabularnewline
62 & 9266 & 9964.62182780775 & 2.70104439329983 & -697.121896116203 & -0.679342920495002 \tabularnewline
63 & 9820 & 9805.67089725743 & 1.51386933441251 & 16.9685497523312 & -1.19660501909699 \tabularnewline
64 & 10097 & 9863.90276177727 & 1.99201048491282 & 232.174591084731 & 0.418818861641066 \tabularnewline
65 & 9115 & 9841.3251277941 & 1.76344637881469 & -725.926126884434 & -0.181327852971248 \tabularnewline
66 & 10411 & 9901.65124060739 & 2.33314214817596 & 508.396942604828 & 0.432822110943608 \tabularnewline
67 & 9678 & 9895.08909514198 & 2.24753785445854 & -216.944135860392 & -0.0659119108685137 \tabularnewline
68 & 10408 & 10131.5307794712 & 4.36822757991437 & 272.640305354015 & 1.73986640983884 \tabularnewline
69 & 10153 & 10180.0122695507 & 4.72970004680046 & -27.7355548402914 & 0.328371402792495 \tabularnewline
70 & 10368 & 10200.9411957027 & 4.84730992876548 & 166.792668177107 & 0.120723392715742 \tabularnewline
71 & 10581 & 10156.2710577479 & 4.52693610955514 & 425.543313786251 & -0.369175546528211 \tabularnewline
72 & 10597 & 10266.9940206333 & 5.16344572117128 & 328.259379373369 & 0.791496624619376 \tabularnewline
73 & 10680 & 10388.5949601272 & 5.85285140561279 & 289.491977748363 & 0.866895756082703 \tabularnewline
74 & 9738 & 10404.4051843852 & 5.91494617666305 & -666.568450539532 & 0.0740051431809526 \tabularnewline
75 & 9556 & 10104.9725355273 & 3.83205984289739 & -543.978681308488 & -2.26496731138744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=194048&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]9700[/C][C]9700[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9420.72572088135[/C][C]-4.57650590562583[/C][C]-334.77068502008[/C][C]-1.94832687028407[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]9148.22896222675[/C][C]-22.2243875445831[/C][C]-61.4635790451628[/C][C]-1.35926283014435[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9343.35355583652[/C][C]-8.81461752562042[/C][C]396.784788453786[/C][C]1.35805752508255[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]9095.71396620184[/C][C]-19.3902960412747[/C][C]-504.920651031314[/C][C]-1.70074936554354[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9271.82128915795[/C][C]-13.6798179021033[/C][C]455.857775217391[/C][C]1.45002004532437[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9434.35676669054[/C][C]-10.0348186806924[/C][C]125.592546987779[/C][C]1.31901459662823[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9698.07258538314[/C][C]-5.33029079398532[/C][C]295.167550090146[/C][C]2.05145162030975[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9674.89508662169[/C][C]-5.62166049542184[/C][C]-237.584831043575[/C][C]-0.133629936115216[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9807.96467915003[/C][C]-3.34401391015703[/C][C]227.627651635858[/C][C]1.03724069661718[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9889.11518580483[/C][C]-1.93839485651972[/C][C]27.4196919882493[/C][C]0.631405014655373[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9667.90745025653[/C][C]-5.59764314597327[/C][C]-412.107445483491[/C][C]-1.63795975603681[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9552.08375903964[/C][C]-3.60454864393269[/C][C]186.883929022552[/C][C]-0.867939210236263[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9409.58737849509[/C][C]-2.67974580145488[/C][C]-372.124540115769[/C][C]-1.07955414058433[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9311.85260831709[/C][C]-4.16578471700865[/C][C]-177.346353697658[/C][C]-0.67260262783964[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9154.69504412586[/C][C]-8.33829473764172[/C][C]334.598059687382[/C][C]-1.05056490127207[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]9193.57731081212[/C][C]-6.97830915198858[/C][C]-494.30224905625[/C][C]0.333409728930745[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9253.45730004762[/C][C]-5.3077628287418[/C][C]372.474755364865[/C][C]0.48735258280257[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9171.22093233486[/C][C]-6.85029544798299[/C][C]-222.960500206711[/C][C]-0.570797213653731[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9097.5457827484[/C][C]-7.92883151314275[/C][C]186.562423078578[/C][C]-0.499482497733582[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9105.0035157116[/C][C]-7.72121733031172[/C][C]-276.259972475282[/C][C]0.11528118429673[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9330.4210576024[/C][C]-5.0818419303781[/C][C]612.685407331218[/C][C]1.74695555872627[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9424.02459976685[/C][C]-4.23497529720333[/C][C]202.325354263228[/C][C]0.738987074020824[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9518.24229039398[/C][C]-3.76506232719794[/C][C]-201.892865568379[/C][C]0.738153151287752[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9459.67413326003[/C][C]-3.82937372764132[/C][C]146.249788945676[/C][C]-0.413062021293639[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]9244.03549954723[/C][C]-4.56264527965705[/C][C]-600.495828111361[/C][C]-1.58338539719983[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9214.61328985033[/C][C]-4.8113126915217[/C][C]-0.210167812057477[/C][C]-0.181312783575778[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9224.50668800261[/C][C]-4.57503226375296[/C][C]342.261022635839[/C][C]0.105442009715022[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]9155.48462432579[/C][C]-5.80637815883035[/C][C]-607.469429877878[/C][C]-0.463515977768877[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8982.93319758843[/C][C]-8.99054792563163[/C][C]204.729986011092[/C][C]-1.21607829142995[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9195.95738085327[/C][C]-5.1769613828464[/C][C]270.438745464098[/C][C]1.64065882867935[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9183.67709105827[/C][C]-5.28013567594385[/C][C]-60.5604467156372[/C][C]-0.0529175082976271[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9382.20732402294[/C][C]-2.87427936202179[/C][C]-107.575648890438[/C][C]1.52370115173463[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]9524.11187032098[/C][C]-1.54142129368911[/C][C]643.48809553436[/C][C]1.08336129684396[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9459.8408060871[/C][C]-1.96807599454922[/C][C]-24.7993398517645[/C][C]-0.469425120516108[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9554.22704448629[/C][C]-1.49285885787086[/C][C]99.171736424332[/C][C]0.721136120008604[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9426.43967754841[/C][C]-2.04248225574352[/C][C]4.6568154645284[/C][C]-0.943982807976881[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]9354.03443009993[/C][C]-2.44846524725121[/C][C]-613.874673372187[/C][C]-0.522620841809225[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9400.48338530928[/C][C]-2.02148594044035[/C][C]150.721305987909[/C][C]0.359358549534025[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9364.45881927823[/C][C]-2.42573641654265[/C][C]323.08748370094[/C][C]-0.247868888331268[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9439.23316278641[/C][C]-1.34211676916011[/C][C]-421.468677163817[/C][C]0.562511911194633[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9526.30387261511[/C][C]-0.0438604609147463[/C][C]144.274210703106[/C][C]0.648123285400349[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9354.35504140844[/C][C]-2.4465944170306[/C][C]-145.565818218281[/C][C]-1.27009465068198[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9291.77971091598[/C][C]-3.19071119967936[/C][C]-221.796133069612[/C][C]-0.446964736972526[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9527.2849198331[/C][C]-0.71668869142766[/C][C]256.788915559173[/C][C]1.7803555324126[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]9624.44042200431[/C][C]0.0993269211548291[/C][C]685.944750629991[/C][C]0.731004447656152[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]9836.1453846405[/C][C]1.50094825665009[/C][C]265.358033277189[/C][C]1.58103251411231[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9824.45633018931[/C][C]1.42770128298662[/C][C]38.7616634808706[/C][C]-0.0985099258053247[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9747.28623095349[/C][C]1.00155298250141[/C][C]-89.9896545216511[/C][C]-0.585877635757095[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9801.81087570147[/C][C]1.33980741539451[/C][C]-507.689491013506[/C][C]0.397278009267572[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9816.19609858694[/C][C]1.44365870110054[/C][C]129.591292219789[/C][C]0.0962860833989687[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9676.98596332113[/C][C]0.0702665582549311[/C][C]26.2914908472161[/C][C]-1.03368270761176[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9569.02176377413[/C][C]-1.13320562813747[/C][C]-518.277126906325[/C][C]-0.793405203278772[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]9686.25045124673[/C][C]0.25460996684276[/C][C]501.834441164578[/C][C]0.871728956117933[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9795.0789773556[/C][C]1.49863032220728[/C][C]-90.8441273525262[/C][C]0.803221347283888[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9964.84872657975[/C][C]3.26699000070155[/C][C]-202.598691584486[/C][C]1.2499298240844[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9928.37892705858[/C][C]2.90252604503863[/C][C]-34.7268604832437[/C][C]-0.295966261330206[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9736.87158805364[/C][C]1.39225477947911[/C][C]260.326960789066[/C][C]-1.44999649381141[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]9841.18774283132[/C][C]2.07126977318297[/C][C]590.116688495433[/C][C]0.76798106755937[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9924.94122240685[/C][C]2.55338459304143[/C][C]146.713100588801[/C][C]0.609219810787897[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10052.8132706441[/C][C]3.28399085571903[/C][C]57.1252793317945[/C][C]0.93323450938399[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9964.62182780775[/C][C]2.70104439329983[/C][C]-697.121896116203[/C][C]-0.679342920495002[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9805.67089725743[/C][C]1.51386933441251[/C][C]16.9685497523312[/C][C]-1.19660501909699[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]9863.90276177727[/C][C]1.99201048491282[/C][C]232.174591084731[/C][C]0.418818861641066[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9841.3251277941[/C][C]1.76344637881469[/C][C]-725.926126884434[/C][C]-0.181327852971248[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]9901.65124060739[/C][C]2.33314214817596[/C][C]508.396942604828[/C][C]0.432822110943608[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9895.08909514198[/C][C]2.24753785445854[/C][C]-216.944135860392[/C][C]-0.0659119108685137[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10131.5307794712[/C][C]4.36822757991437[/C][C]272.640305354015[/C][C]1.73986640983884[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10180.0122695507[/C][C]4.72970004680046[/C][C]-27.7355548402914[/C][C]0.328371402792495[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10200.9411957027[/C][C]4.84730992876548[/C][C]166.792668177107[/C][C]0.120723392715742[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10156.2710577479[/C][C]4.52693610955514[/C][C]425.543313786251[/C][C]-0.369175546528211[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10266.9940206333[/C][C]5.16344572117128[/C][C]328.259379373369[/C][C]0.791496624619376[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10388.5949601272[/C][C]5.85285140561279[/C][C]289.491977748363[/C][C]0.866895756082703[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]10404.4051843852[/C][C]5.91494617666305[/C][C]-666.568450539532[/C][C]0.0740051431809526[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]10104.9725355273[/C][C]3.83205984289739[/C][C]-543.978681308488[/C][C]-2.26496731138744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=194048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194048&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
197009700000
290819420.72572088135-4.57650590562583-334.77068502008-1.94832687028407
390849148.22896222675-22.2243875445831-61.4635790451628-1.35926283014435
497439343.35355583652-8.81461752562042396.7847884537861.35805752508255
585879095.71396620184-19.3902960412747-504.920651031314-1.70074936554354
697319271.82128915795-13.6798179021033455.8577752173911.45002004532437
795639434.35676669054-10.0348186806924125.5925469877791.31901459662823
899989698.07258538314-5.33029079398532295.1675500901462.05145162030975
994379674.89508662169-5.62166049542184-237.584831043575-0.133629936115216
10100389807.96467915003-3.34401391015703227.6276516358581.03724069661718
1199189889.11518580483-1.9383948565197227.41969198824930.631405014655373
1292529667.90745025653-5.59764314597327-412.107445483491-1.63795975603681
1397379552.08375903964-3.60454864393269186.883929022552-0.867939210236263
1490359409.58737849509-2.67974580145488-372.124540115769-1.07955414058433
1591339311.85260831709-4.16578471700865-177.346353697658-0.67260262783964
1694879154.69504412586-8.33829473764172334.598059687382-1.05056490127207
1787009193.57731081212-6.97830915198858-494.302249056250.333409728930745
1896279253.45730004762-5.3077628287418372.4747553648650.48735258280257
1989479171.22093233486-6.85029544798299-222.960500206711-0.570797213653731
2092839097.5457827484-7.92883151314275186.562423078578-0.499482497733582
2188299105.0035157116-7.72121733031172-276.2599724752820.11528118429673
2299479330.4210576024-5.0818419303781612.6854073312181.74695555872627
2396289424.02459976685-4.23497529720333202.3253542632280.738987074020824
2493189518.24229039398-3.76506232719794-201.8928655683790.738153151287752
2596059459.67413326003-3.82937372764132146.249788945676-0.413062021293639
2686409244.03549954723-4.56264527965705-600.495828111361-1.58338539719983
2792149214.61328985033-4.8113126915217-0.210167812057477-0.181312783575778
2895679224.50668800261-4.57503226375296342.2610226358390.105442009715022
2985479155.48462432579-5.80637815883035-607.469429877878-0.463515977768877
3091858982.93319758843-8.99054792563163204.729986011092-1.21607829142995
3194709195.95738085327-5.1769613828464270.4387454640981.64065882867935
3291239183.67709105827-5.28013567594385-60.5604467156372-0.0529175082976271
3392789382.20732402294-2.87427936202179-107.5756488904381.52370115173463
34101709524.11187032098-1.54142129368911643.488095534361.08336129684396
3594349459.8408060871-1.96807599454922-24.7993398517645-0.469425120516108
3696559554.22704448629-1.4928588578708699.1717364243320.721136120008604
3794299426.43967754841-2.042482255743524.6568154645284-0.943982807976881
3887399354.03443009993-2.44846524725121-613.874673372187-0.522620841809225
3995529400.48338530928-2.02148594044035150.7213059879090.359358549534025
4096879364.45881927823-2.42573641654265323.08748370094-0.247868888331268
4190199439.23316278641-1.34211676916011-421.4686771638170.562511911194633
4296729526.30387261511-0.0438604609147463144.2742107031060.648123285400349
4392069354.35504140844-2.4465944170306-145.565818218281-1.27009465068198
4490699291.77971091598-3.19071119967936-221.796133069612-0.446964736972526
4597889527.2849198331-0.71668869142766256.7889155591731.7803555324126
46103129624.440422004310.0993269211548291685.9447506299910.731004447656152
47101059836.14538464051.50094825665009265.3580332771891.58103251411231
4898639824.456330189311.4277012829866238.7616634808706-0.0985099258053247
4996569747.286230953491.00155298250141-89.9896545216511-0.585877635757095
5092959801.810875701471.33980741539451-507.6894910135060.397278009267572
5199469816.196098586941.44365870110054129.5912922197890.0962860833989687
5297019676.985963321130.070266558254931126.2914908472161-1.03368270761176
5390499569.02176377413-1.13320562813747-518.277126906325-0.793405203278772
54101909686.250451246730.25460996684276501.8344411645780.871728956117933
5597069795.07897735561.49863032220728-90.84412735252620.803221347283888
5697659964.848726579753.26699000070155-202.5986915844861.2499298240844
5798939928.378927058582.90252604503863-34.7268604832437-0.295966261330206
5899949736.871588053641.39225477947911260.326960789066-1.44999649381141
59104339841.187742831322.07126977318297590.1166884954330.76798106755937
60100739924.941222406852.55338459304143146.7131005888010.609219810787897
611011210052.81327064413.2839908557190357.12527933179450.93323450938399
6292669964.621827807752.70104439329983-697.121896116203-0.679342920495002
6398209805.670897257431.5138693344125116.9685497523312-1.19660501909699
64100979863.902761777271.99201048491282232.1745910847310.418818861641066
6591159841.32512779411.76344637881469-725.926126884434-0.181327852971248
66104119901.651240607392.33314214817596508.3969426048280.432822110943608
6796789895.089095141982.24753785445854-216.944135860392-0.0659119108685137
681040810131.53077947124.36822757991437272.6403053540151.73986640983884
691015310180.01226955074.72970004680046-27.73555484029140.328371402792495
701036810200.94119570274.84730992876548166.7926681771070.120723392715742
711058110156.27105774794.52693610955514425.543313786251-0.369175546528211
721059710266.99402063335.16344572117128328.2593793733690.791496624619376
731068010388.59496012725.85285140561279289.4919777483630.866895756082703
74973810404.40518438525.91494617666305-666.5684505395320.0740051431809526
75955610104.97253552733.83205984289739-543.978681308488-2.26496731138744


Figures (Output of Computation)

Input Parameters & R Code

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')