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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 11:15:23 -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/t13540329656v8u8f9vyy3kk3p.htm/, Retrieved Sat, 04 May 2024 06:25:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=193948, Retrieved Sat, 04 May 2024 06:25:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [workshop 8 struct...] [2012-11-27 16:15:23] [2382f403a285d81cd69bebfa1420b1d7] [Current]
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Dataseries X:
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193948&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]4 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=193948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193948&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
14747000
23542.423120707378-0.536454295839954-0.535153969430316-0.504510309327091
33037.5707063165552-0.910502139727237-0.906019518635422-0.482670369349292
44339.3717383284374-0.724628295971797-0.7237252491434630.311617129891299
58254.88379699708090.2050162437433440.1734835906377831.91231192742919
64049.5357583130189-0.0727582323413861-0.0890898628713239-0.666320516932519
74748.606876527377-0.111537492914559-0.124862519085506-0.104085971655419
81937.9662909580334-0.555338143864358-0.523387591298701-1.29152864606472
95242.8076867733811-0.339417739573498-0.3349209290999690.665862475312188
1013675.90783015797640.9484912998368690.756925663864834.14138810908487
118077.70537813623780.9802782511281730.7830899232683090.105401379117604
124265.36699551002060.4923441249490190.393194181783888-1.65574493166873
135464.20617888627680.653008760160395-6.43844657844433-0.295660017575278
146665.08594862659360.6641348784686420.5947871644768180.0233695673706991
158171.32727835992120.89484733396830.7780755715612610.633567753398398
166368.54412971287170.7614455293288830.680140942699343-0.437806803819148
1713793.53190423746691.561283026561861.221360097828332.95266496222554
187286.38125280021291.291334935613871.0525006592205-1.07498591853264
1910794.19018285012541.485243346160011.165313349104830.809509663512126
205881.85580566590161.084118347934980.946714671433101-1.72236643695966
213665.92032824943170.5969670849709160.696345842904298-2.12480991365475
225261.11215990933230.4433337143546380.621449324166321-0.67528552339475
237967.53268159645170.6127932806768950.700173793658210.746855585659811
247771.04351276586190.6949711827157330.7366835514673710.362039282713133
255469.03850522870010.75407817933564-9.73967339863886-0.396505337567518
268474.93110518315130.9517978318719631.016095789389170.574767114575583
274865.17353048631580.5834368384719470.806803756992623-1.26495239151841
289676.45910239799640.9235986882314640.9725303112343051.29736401104334
298379.05575830039020.9740686216621220.9938026815046220.205427823241077
306674.64298097113970.8167996568849920.935551856212153-0.665688158215429
316169.9518402809230.6590646388758520.883276662637814-0.682838818029787
325364.0008211786690.4717166210552850.826753371172598-0.820728108069637
333051.86029790822450.1158962578293460.727563684034799-1.56694752511959
347459.58536266165230.3303516200483150.7834611367290150.945479366505379
356962.88223603966680.4140589553560120.8040475686566730.368547699959863
365961.47297372926170.3624818351884060.791998425145238-0.226450516503047
374258.43969853955730.351318784731363-10.1600866797838-0.463789062377103
386560.78376007508430.4205713509702760.959086412622520.230244580275622
397064.08082610082510.5138015519486430.9972670078451330.344638621124164
4010077.06972046741050.8997509318202581.124424270334651.52002539466583
416372.16770074411280.7254855643499411.07740547996331-0.712562109766626
4210584.04968098296151.054642150118861.151959455413131.3754197354782
438283.5757767600051.010002099309541.14323990423087-0.188779600814963
448182.88821757803310.9606845499152231.13471768769051-0.209813700584229
457580.26542506945650.8567943150656051.11848171642656-0.443017811398387
4610288.21520559687291.062401117341061.14806367540280.876873676008276
47121100.2548986110341.380931275194071.190837576497371.35685396535041
489899.90248257241741.330548466323921.18445824453286-0.214201122578061
497697.34301040547171.27047526380495-14.3644548249269-0.512115122208325
507790.07367543901570.9884748876671561.19072749621659-1.00337159789322
516380.37383786141620.6503318846482391.08632332811487-1.2895433454615
523764.6836548272980.147505170715630.967822554458863-1.99501515439565
533553.7169017394129-0.1886395923334170.906055061070709-1.36421968001592
542342.2030297272841-0.5276302062015530.85606848674527-1.39345073704177
554040.7639349088925-0.5547519053461450.852754631380394-0.112262171198172
562935.8670637169963-0.6835624963607950.839294005936016-0.535016311880205
573735.5352845081213-0.6731399572734690.8402525141469870.0433490954086335
585140.3696788025192-0.5099915382890960.8537780627876940.678638124852691
592032.4012555661145-0.7310409252543690.836938962988917-0.918913562944729
602830.047043947703-0.7791902359548170.833519080359946-0.199951088265664
611326.94672216137-0.830000942269142-9.84824892274962-0.299328411455958
622224.2623452644642-0.8894418499417370.875255740251606-0.220058935879261
632523.6468731427873-0.8808884793371320.8773453917011920.0331894930708584
641318.9124884576243-0.9991962615230240.856205826792138-0.471004564764867
651616.9132372597303-1.029582648685880.852153861388714-0.1227029206697
661314.5360609566958-1.070274405454260.847989095924405-0.165608981947683
671614.0738390280499-1.051979616060680.8494745518027750.0747715180271438
681714.1499933498663-1.018102135579440.8517341971688260.138752512539126
69911.3347929252295-1.072020993877340.84867851603401-0.221032978723444
701712.3863939113676-1.008320089220090.851839822686680.261176424633635
712515.9839210980599-0.870139133091310.857999169927110.566402017143384
721414.4025348544208-0.8914858008955360.857127106405734-0.0874561313946556
73814.8131509555157-0.858354453988709-9.093772499739230.16586572516737
74711.0888606363171-0.9485107533506690.800839309865228-0.342299885264517
75109.79797799806993-0.9591066620829980.798704262242055-0.0415856918264539
7677.88425803990052-0.9883494795497710.794537197302047-0.116771060348618
77107.73015763195991-0.9629547644224090.7971381129598650.102342437320901
7835.11962026097672-1.012916288272920.793371810073669-0.202345054044247

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 47 & 47 & 0 & 0 & 0 \tabularnewline
2 & 35 & 42.423120707378 & -0.536454295839954 & -0.535153969430316 & -0.504510309327091 \tabularnewline
3 & 30 & 37.5707063165552 & -0.910502139727237 & -0.906019518635422 & -0.482670369349292 \tabularnewline
4 & 43 & 39.3717383284374 & -0.724628295971797 & -0.723725249143463 & 0.311617129891299 \tabularnewline
5 & 82 & 54.8837969970809 & 0.205016243743344 & 0.173483590637783 & 1.91231192742919 \tabularnewline
6 & 40 & 49.5357583130189 & -0.0727582323413861 & -0.0890898628713239 & -0.666320516932519 \tabularnewline
7 & 47 & 48.606876527377 & -0.111537492914559 & -0.124862519085506 & -0.104085971655419 \tabularnewline
8 & 19 & 37.9662909580334 & -0.555338143864358 & -0.523387591298701 & -1.29152864606472 \tabularnewline
9 & 52 & 42.8076867733811 & -0.339417739573498 & -0.334920929099969 & 0.665862475312188 \tabularnewline
10 & 136 & 75.9078301579764 & 0.948491299836869 & 0.75692566386483 & 4.14138810908487 \tabularnewline
11 & 80 & 77.7053781362378 & 0.980278251128173 & 0.783089923268309 & 0.105401379117604 \tabularnewline
12 & 42 & 65.3669955100206 & 0.492344124949019 & 0.393194181783888 & -1.65574493166873 \tabularnewline
13 & 54 & 64.2061788862768 & 0.653008760160395 & -6.43844657844433 & -0.295660017575278 \tabularnewline
14 & 66 & 65.0859486265936 & 0.664134878468642 & 0.594787164476818 & 0.0233695673706991 \tabularnewline
15 & 81 & 71.3272783599212 & 0.8948473339683 & 0.778075571561261 & 0.633567753398398 \tabularnewline
16 & 63 & 68.5441297128717 & 0.761445529328883 & 0.680140942699343 & -0.437806803819148 \tabularnewline
17 & 137 & 93.5319042374669 & 1.56128302656186 & 1.22136009782833 & 2.95266496222554 \tabularnewline
18 & 72 & 86.3812528002129 & 1.29133493561387 & 1.0525006592205 & -1.07498591853264 \tabularnewline
19 & 107 & 94.1901828501254 & 1.48524334616001 & 1.16531334910483 & 0.809509663512126 \tabularnewline
20 & 58 & 81.8558056659016 & 1.08411834793498 & 0.946714671433101 & -1.72236643695966 \tabularnewline
21 & 36 & 65.9203282494317 & 0.596967084970916 & 0.696345842904298 & -2.12480991365475 \tabularnewline
22 & 52 & 61.1121599093323 & 0.443333714354638 & 0.621449324166321 & -0.67528552339475 \tabularnewline
23 & 79 & 67.5326815964517 & 0.612793280676895 & 0.70017379365821 & 0.746855585659811 \tabularnewline
24 & 77 & 71.0435127658619 & 0.694971182715733 & 0.736683551467371 & 0.362039282713133 \tabularnewline
25 & 54 & 69.0385052287001 & 0.75407817933564 & -9.73967339863886 & -0.396505337567518 \tabularnewline
26 & 84 & 74.9311051831513 & 0.951797831871963 & 1.01609578938917 & 0.574767114575583 \tabularnewline
27 & 48 & 65.1735304863158 & 0.583436838471947 & 0.806803756992623 & -1.26495239151841 \tabularnewline
28 & 96 & 76.4591023979964 & 0.923598688231464 & 0.972530311234305 & 1.29736401104334 \tabularnewline
29 & 83 & 79.0557583003902 & 0.974068621662122 & 0.993802681504622 & 0.205427823241077 \tabularnewline
30 & 66 & 74.6429809711397 & 0.816799656884992 & 0.935551856212153 & -0.665688158215429 \tabularnewline
31 & 61 & 69.951840280923 & 0.659064638875852 & 0.883276662637814 & -0.682838818029787 \tabularnewline
32 & 53 & 64.000821178669 & 0.471716621055285 & 0.826753371172598 & -0.820728108069637 \tabularnewline
33 & 30 & 51.8602979082245 & 0.115896257829346 & 0.727563684034799 & -1.56694752511959 \tabularnewline
34 & 74 & 59.5853626616523 & 0.330351620048315 & 0.783461136729015 & 0.945479366505379 \tabularnewline
35 & 69 & 62.8822360396668 & 0.414058955356012 & 0.804047568656673 & 0.368547699959863 \tabularnewline
36 & 59 & 61.4729737292617 & 0.362481835188406 & 0.791998425145238 & -0.226450516503047 \tabularnewline
37 & 42 & 58.4396985395573 & 0.351318784731363 & -10.1600866797838 & -0.463789062377103 \tabularnewline
38 & 65 & 60.7837600750843 & 0.420571350970276 & 0.95908641262252 & 0.230244580275622 \tabularnewline
39 & 70 & 64.0808261008251 & 0.513801551948643 & 0.997267007845133 & 0.344638621124164 \tabularnewline
40 & 100 & 77.0697204674105 & 0.899750931820258 & 1.12442427033465 & 1.52002539466583 \tabularnewline
41 & 63 & 72.1677007441128 & 0.725485564349941 & 1.07740547996331 & -0.712562109766626 \tabularnewline
42 & 105 & 84.0496809829615 & 1.05464215011886 & 1.15195945541313 & 1.3754197354782 \tabularnewline
43 & 82 & 83.575776760005 & 1.01000209930954 & 1.14323990423087 & -0.188779600814963 \tabularnewline
44 & 81 & 82.8882175780331 & 0.960684549915223 & 1.13471768769051 & -0.209813700584229 \tabularnewline
45 & 75 & 80.2654250694565 & 0.856794315065605 & 1.11848171642656 & -0.443017811398387 \tabularnewline
46 & 102 & 88.2152055968729 & 1.06240111734106 & 1.1480636754028 & 0.876873676008276 \tabularnewline
47 & 121 & 100.254898611034 & 1.38093127519407 & 1.19083757649737 & 1.35685396535041 \tabularnewline
48 & 98 & 99.9024825724174 & 1.33054846632392 & 1.18445824453286 & -0.214201122578061 \tabularnewline
49 & 76 & 97.3430104054717 & 1.27047526380495 & -14.3644548249269 & -0.512115122208325 \tabularnewline
50 & 77 & 90.0736754390157 & 0.988474887667156 & 1.19072749621659 & -1.00337159789322 \tabularnewline
51 & 63 & 80.3738378614162 & 0.650331884648239 & 1.08632332811487 & -1.2895433454615 \tabularnewline
52 & 37 & 64.683654827298 & 0.14750517071563 & 0.967822554458863 & -1.99501515439565 \tabularnewline
53 & 35 & 53.7169017394129 & -0.188639592333417 & 0.906055061070709 & -1.36421968001592 \tabularnewline
54 & 23 & 42.2030297272841 & -0.527630206201553 & 0.85606848674527 & -1.39345073704177 \tabularnewline
55 & 40 & 40.7639349088925 & -0.554751905346145 & 0.852754631380394 & -0.112262171198172 \tabularnewline
56 & 29 & 35.8670637169963 & -0.683562496360795 & 0.839294005936016 & -0.535016311880205 \tabularnewline
57 & 37 & 35.5352845081213 & -0.673139957273469 & 0.840252514146987 & 0.0433490954086335 \tabularnewline
58 & 51 & 40.3696788025192 & -0.509991538289096 & 0.853778062787694 & 0.678638124852691 \tabularnewline
59 & 20 & 32.4012555661145 & -0.731040925254369 & 0.836938962988917 & -0.918913562944729 \tabularnewline
60 & 28 & 30.047043947703 & -0.779190235954817 & 0.833519080359946 & -0.199951088265664 \tabularnewline
61 & 13 & 26.94672216137 & -0.830000942269142 & -9.84824892274962 & -0.299328411455958 \tabularnewline
62 & 22 & 24.2623452644642 & -0.889441849941737 & 0.875255740251606 & -0.220058935879261 \tabularnewline
63 & 25 & 23.6468731427873 & -0.880888479337132 & 0.877345391701192 & 0.0331894930708584 \tabularnewline
64 & 13 & 18.9124884576243 & -0.999196261523024 & 0.856205826792138 & -0.471004564764867 \tabularnewline
65 & 16 & 16.9132372597303 & -1.02958264868588 & 0.852153861388714 & -0.1227029206697 \tabularnewline
66 & 13 & 14.5360609566958 & -1.07027440545426 & 0.847989095924405 & -0.165608981947683 \tabularnewline
67 & 16 & 14.0738390280499 & -1.05197961606068 & 0.849474551802775 & 0.0747715180271438 \tabularnewline
68 & 17 & 14.1499933498663 & -1.01810213557944 & 0.851734197168826 & 0.138752512539126 \tabularnewline
69 & 9 & 11.3347929252295 & -1.07202099387734 & 0.84867851603401 & -0.221032978723444 \tabularnewline
70 & 17 & 12.3863939113676 & -1.00832008922009 & 0.85183982268668 & 0.261176424633635 \tabularnewline
71 & 25 & 15.9839210980599 & -0.87013913309131 & 0.85799916992711 & 0.566402017143384 \tabularnewline
72 & 14 & 14.4025348544208 & -0.891485800895536 & 0.857127106405734 & -0.0874561313946556 \tabularnewline
73 & 8 & 14.8131509555157 & -0.858354453988709 & -9.09377249973923 & 0.16586572516737 \tabularnewline
74 & 7 & 11.0888606363171 & -0.948510753350669 & 0.800839309865228 & -0.342299885264517 \tabularnewline
75 & 10 & 9.79797799806993 & -0.959106662082998 & 0.798704262242055 & -0.0415856918264539 \tabularnewline
76 & 7 & 7.88425803990052 & -0.988349479549771 & 0.794537197302047 & -0.116771060348618 \tabularnewline
77 & 10 & 7.73015763195991 & -0.962954764422409 & 0.797138112959865 & 0.102342437320901 \tabularnewline
78 & 3 & 5.11962026097672 & -1.01291628827292 & 0.793371810073669 & -0.202345054044247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193948&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]47[/C][C]47[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]35[/C][C]42.423120707378[/C][C]-0.536454295839954[/C][C]-0.535153969430316[/C][C]-0.504510309327091[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]37.5707063165552[/C][C]-0.910502139727237[/C][C]-0.906019518635422[/C][C]-0.482670369349292[/C][/ROW]
[ROW][C]4[/C][C]43[/C][C]39.3717383284374[/C][C]-0.724628295971797[/C][C]-0.723725249143463[/C][C]0.311617129891299[/C][/ROW]
[ROW][C]5[/C][C]82[/C][C]54.8837969970809[/C][C]0.205016243743344[/C][C]0.173483590637783[/C][C]1.91231192742919[/C][/ROW]
[ROW][C]6[/C][C]40[/C][C]49.5357583130189[/C][C]-0.0727582323413861[/C][C]-0.0890898628713239[/C][C]-0.666320516932519[/C][/ROW]
[ROW][C]7[/C][C]47[/C][C]48.606876527377[/C][C]-0.111537492914559[/C][C]-0.124862519085506[/C][C]-0.104085971655419[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]37.9662909580334[/C][C]-0.555338143864358[/C][C]-0.523387591298701[/C][C]-1.29152864606472[/C][/ROW]
[ROW][C]9[/C][C]52[/C][C]42.8076867733811[/C][C]-0.339417739573498[/C][C]-0.334920929099969[/C][C]0.665862475312188[/C][/ROW]
[ROW][C]10[/C][C]136[/C][C]75.9078301579764[/C][C]0.948491299836869[/C][C]0.75692566386483[/C][C]4.14138810908487[/C][/ROW]
[ROW][C]11[/C][C]80[/C][C]77.7053781362378[/C][C]0.980278251128173[/C][C]0.783089923268309[/C][C]0.105401379117604[/C][/ROW]
[ROW][C]12[/C][C]42[/C][C]65.3669955100206[/C][C]0.492344124949019[/C][C]0.393194181783888[/C][C]-1.65574493166873[/C][/ROW]
[ROW][C]13[/C][C]54[/C][C]64.2061788862768[/C][C]0.653008760160395[/C][C]-6.43844657844433[/C][C]-0.295660017575278[/C][/ROW]
[ROW][C]14[/C][C]66[/C][C]65.0859486265936[/C][C]0.664134878468642[/C][C]0.594787164476818[/C][C]0.0233695673706991[/C][/ROW]
[ROW][C]15[/C][C]81[/C][C]71.3272783599212[/C][C]0.8948473339683[/C][C]0.778075571561261[/C][C]0.633567753398398[/C][/ROW]
[ROW][C]16[/C][C]63[/C][C]68.5441297128717[/C][C]0.761445529328883[/C][C]0.680140942699343[/C][C]-0.437806803819148[/C][/ROW]
[ROW][C]17[/C][C]137[/C][C]93.5319042374669[/C][C]1.56128302656186[/C][C]1.22136009782833[/C][C]2.95266496222554[/C][/ROW]
[ROW][C]18[/C][C]72[/C][C]86.3812528002129[/C][C]1.29133493561387[/C][C]1.0525006592205[/C][C]-1.07498591853264[/C][/ROW]
[ROW][C]19[/C][C]107[/C][C]94.1901828501254[/C][C]1.48524334616001[/C][C]1.16531334910483[/C][C]0.809509663512126[/C][/ROW]
[ROW][C]20[/C][C]58[/C][C]81.8558056659016[/C][C]1.08411834793498[/C][C]0.946714671433101[/C][C]-1.72236643695966[/C][/ROW]
[ROW][C]21[/C][C]36[/C][C]65.9203282494317[/C][C]0.596967084970916[/C][C]0.696345842904298[/C][C]-2.12480991365475[/C][/ROW]
[ROW][C]22[/C][C]52[/C][C]61.1121599093323[/C][C]0.443333714354638[/C][C]0.621449324166321[/C][C]-0.67528552339475[/C][/ROW]
[ROW][C]23[/C][C]79[/C][C]67.5326815964517[/C][C]0.612793280676895[/C][C]0.70017379365821[/C][C]0.746855585659811[/C][/ROW]
[ROW][C]24[/C][C]77[/C][C]71.0435127658619[/C][C]0.694971182715733[/C][C]0.736683551467371[/C][C]0.362039282713133[/C][/ROW]
[ROW][C]25[/C][C]54[/C][C]69.0385052287001[/C][C]0.75407817933564[/C][C]-9.73967339863886[/C][C]-0.396505337567518[/C][/ROW]
[ROW][C]26[/C][C]84[/C][C]74.9311051831513[/C][C]0.951797831871963[/C][C]1.01609578938917[/C][C]0.574767114575583[/C][/ROW]
[ROW][C]27[/C][C]48[/C][C]65.1735304863158[/C][C]0.583436838471947[/C][C]0.806803756992623[/C][C]-1.26495239151841[/C][/ROW]
[ROW][C]28[/C][C]96[/C][C]76.4591023979964[/C][C]0.923598688231464[/C][C]0.972530311234305[/C][C]1.29736401104334[/C][/ROW]
[ROW][C]29[/C][C]83[/C][C]79.0557583003902[/C][C]0.974068621662122[/C][C]0.993802681504622[/C][C]0.205427823241077[/C][/ROW]
[ROW][C]30[/C][C]66[/C][C]74.6429809711397[/C][C]0.816799656884992[/C][C]0.935551856212153[/C][C]-0.665688158215429[/C][/ROW]
[ROW][C]31[/C][C]61[/C][C]69.951840280923[/C][C]0.659064638875852[/C][C]0.883276662637814[/C][C]-0.682838818029787[/C][/ROW]
[ROW][C]32[/C][C]53[/C][C]64.000821178669[/C][C]0.471716621055285[/C][C]0.826753371172598[/C][C]-0.820728108069637[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]51.8602979082245[/C][C]0.115896257829346[/C][C]0.727563684034799[/C][C]-1.56694752511959[/C][/ROW]
[ROW][C]34[/C][C]74[/C][C]59.5853626616523[/C][C]0.330351620048315[/C][C]0.783461136729015[/C][C]0.945479366505379[/C][/ROW]
[ROW][C]35[/C][C]69[/C][C]62.8822360396668[/C][C]0.414058955356012[/C][C]0.804047568656673[/C][C]0.368547699959863[/C][/ROW]
[ROW][C]36[/C][C]59[/C][C]61.4729737292617[/C][C]0.362481835188406[/C][C]0.791998425145238[/C][C]-0.226450516503047[/C][/ROW]
[ROW][C]37[/C][C]42[/C][C]58.4396985395573[/C][C]0.351318784731363[/C][C]-10.1600866797838[/C][C]-0.463789062377103[/C][/ROW]
[ROW][C]38[/C][C]65[/C][C]60.7837600750843[/C][C]0.420571350970276[/C][C]0.95908641262252[/C][C]0.230244580275622[/C][/ROW]
[ROW][C]39[/C][C]70[/C][C]64.0808261008251[/C][C]0.513801551948643[/C][C]0.997267007845133[/C][C]0.344638621124164[/C][/ROW]
[ROW][C]40[/C][C]100[/C][C]77.0697204674105[/C][C]0.899750931820258[/C][C]1.12442427033465[/C][C]1.52002539466583[/C][/ROW]
[ROW][C]41[/C][C]63[/C][C]72.1677007441128[/C][C]0.725485564349941[/C][C]1.07740547996331[/C][C]-0.712562109766626[/C][/ROW]
[ROW][C]42[/C][C]105[/C][C]84.0496809829615[/C][C]1.05464215011886[/C][C]1.15195945541313[/C][C]1.3754197354782[/C][/ROW]
[ROW][C]43[/C][C]82[/C][C]83.575776760005[/C][C]1.01000209930954[/C][C]1.14323990423087[/C][C]-0.188779600814963[/C][/ROW]
[ROW][C]44[/C][C]81[/C][C]82.8882175780331[/C][C]0.960684549915223[/C][C]1.13471768769051[/C][C]-0.209813700584229[/C][/ROW]
[ROW][C]45[/C][C]75[/C][C]80.2654250694565[/C][C]0.856794315065605[/C][C]1.11848171642656[/C][C]-0.443017811398387[/C][/ROW]
[ROW][C]46[/C][C]102[/C][C]88.2152055968729[/C][C]1.06240111734106[/C][C]1.1480636754028[/C][C]0.876873676008276[/C][/ROW]
[ROW][C]47[/C][C]121[/C][C]100.254898611034[/C][C]1.38093127519407[/C][C]1.19083757649737[/C][C]1.35685396535041[/C][/ROW]
[ROW][C]48[/C][C]98[/C][C]99.9024825724174[/C][C]1.33054846632392[/C][C]1.18445824453286[/C][C]-0.214201122578061[/C][/ROW]
[ROW][C]49[/C][C]76[/C][C]97.3430104054717[/C][C]1.27047526380495[/C][C]-14.3644548249269[/C][C]-0.512115122208325[/C][/ROW]
[ROW][C]50[/C][C]77[/C][C]90.0736754390157[/C][C]0.988474887667156[/C][C]1.19072749621659[/C][C]-1.00337159789322[/C][/ROW]
[ROW][C]51[/C][C]63[/C][C]80.3738378614162[/C][C]0.650331884648239[/C][C]1.08632332811487[/C][C]-1.2895433454615[/C][/ROW]
[ROW][C]52[/C][C]37[/C][C]64.683654827298[/C][C]0.14750517071563[/C][C]0.967822554458863[/C][C]-1.99501515439565[/C][/ROW]
[ROW][C]53[/C][C]35[/C][C]53.7169017394129[/C][C]-0.188639592333417[/C][C]0.906055061070709[/C][C]-1.36421968001592[/C][/ROW]
[ROW][C]54[/C][C]23[/C][C]42.2030297272841[/C][C]-0.527630206201553[/C][C]0.85606848674527[/C][C]-1.39345073704177[/C][/ROW]
[ROW][C]55[/C][C]40[/C][C]40.7639349088925[/C][C]-0.554751905346145[/C][C]0.852754631380394[/C][C]-0.112262171198172[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]35.8670637169963[/C][C]-0.683562496360795[/C][C]0.839294005936016[/C][C]-0.535016311880205[/C][/ROW]
[ROW][C]57[/C][C]37[/C][C]35.5352845081213[/C][C]-0.673139957273469[/C][C]0.840252514146987[/C][C]0.0433490954086335[/C][/ROW]
[ROW][C]58[/C][C]51[/C][C]40.3696788025192[/C][C]-0.509991538289096[/C][C]0.853778062787694[/C][C]0.678638124852691[/C][/ROW]
[ROW][C]59[/C][C]20[/C][C]32.4012555661145[/C][C]-0.731040925254369[/C][C]0.836938962988917[/C][C]-0.918913562944729[/C][/ROW]
[ROW][C]60[/C][C]28[/C][C]30.047043947703[/C][C]-0.779190235954817[/C][C]0.833519080359946[/C][C]-0.199951088265664[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]26.94672216137[/C][C]-0.830000942269142[/C][C]-9.84824892274962[/C][C]-0.299328411455958[/C][/ROW]
[ROW][C]62[/C][C]22[/C][C]24.2623452644642[/C][C]-0.889441849941737[/C][C]0.875255740251606[/C][C]-0.220058935879261[/C][/ROW]
[ROW][C]63[/C][C]25[/C][C]23.6468731427873[/C][C]-0.880888479337132[/C][C]0.877345391701192[/C][C]0.0331894930708584[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]18.9124884576243[/C][C]-0.999196261523024[/C][C]0.856205826792138[/C][C]-0.471004564764867[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]16.9132372597303[/C][C]-1.02958264868588[/C][C]0.852153861388714[/C][C]-0.1227029206697[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]14.5360609566958[/C][C]-1.07027440545426[/C][C]0.847989095924405[/C][C]-0.165608981947683[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]14.0738390280499[/C][C]-1.05197961606068[/C][C]0.849474551802775[/C][C]0.0747715180271438[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]14.1499933498663[/C][C]-1.01810213557944[/C][C]0.851734197168826[/C][C]0.138752512539126[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]11.3347929252295[/C][C]-1.07202099387734[/C][C]0.84867851603401[/C][C]-0.221032978723444[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]12.3863939113676[/C][C]-1.00832008922009[/C][C]0.85183982268668[/C][C]0.261176424633635[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]15.9839210980599[/C][C]-0.87013913309131[/C][C]0.85799916992711[/C][C]0.566402017143384[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.4025348544208[/C][C]-0.891485800895536[/C][C]0.857127106405734[/C][C]-0.0874561313946556[/C][/ROW]
[ROW][C]73[/C][C]8[/C][C]14.8131509555157[/C][C]-0.858354453988709[/C][C]-9.09377249973923[/C][C]0.16586572516737[/C][/ROW]
[ROW][C]74[/C][C]7[/C][C]11.0888606363171[/C][C]-0.948510753350669[/C][C]0.800839309865228[/C][C]-0.342299885264517[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.79797799806993[/C][C]-0.959106662082998[/C][C]0.798704262242055[/C][C]-0.0415856918264539[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]7.88425803990052[/C][C]-0.988349479549771[/C][C]0.794537197302047[/C][C]-0.116771060348618[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]7.73015763195991[/C][C]-0.962954764422409[/C][C]0.797138112959865[/C][C]0.102342437320901[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]5.11962026097672[/C][C]-1.01291628827292[/C][C]0.793371810073669[/C][C]-0.202345054044247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=193948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193948&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
14747000
23542.423120707378-0.536454295839954-0.535153969430316-0.504510309327091
33037.5707063165552-0.910502139727237-0.906019518635422-0.482670369349292
44339.3717383284374-0.724628295971797-0.7237252491434630.311617129891299
58254.88379699708090.2050162437433440.1734835906377831.91231192742919
64049.5357583130189-0.0727582323413861-0.0890898628713239-0.666320516932519
74748.606876527377-0.111537492914559-0.124862519085506-0.104085971655419
81937.9662909580334-0.555338143864358-0.523387591298701-1.29152864606472
95242.8076867733811-0.339417739573498-0.3349209290999690.665862475312188
1013675.90783015797640.9484912998368690.756925663864834.14138810908487
118077.70537813623780.9802782511281730.7830899232683090.105401379117604
124265.36699551002060.4923441249490190.393194181783888-1.65574493166873
135464.20617888627680.653008760160395-6.43844657844433-0.295660017575278
146665.08594862659360.6641348784686420.5947871644768180.0233695673706991
158171.32727835992120.89484733396830.7780755715612610.633567753398398
166368.54412971287170.7614455293288830.680140942699343-0.437806803819148
1713793.53190423746691.561283026561861.221360097828332.95266496222554
187286.38125280021291.291334935613871.0525006592205-1.07498591853264
1910794.19018285012541.485243346160011.165313349104830.809509663512126
205881.85580566590161.084118347934980.946714671433101-1.72236643695966
213665.92032824943170.5969670849709160.696345842904298-2.12480991365475
225261.11215990933230.4433337143546380.621449324166321-0.67528552339475
237967.53268159645170.6127932806768950.700173793658210.746855585659811
247771.04351276586190.6949711827157330.7366835514673710.362039282713133
255469.03850522870010.75407817933564-9.73967339863886-0.396505337567518
268474.93110518315130.9517978318719631.016095789389170.574767114575583
274865.17353048631580.5834368384719470.806803756992623-1.26495239151841
289676.45910239799640.9235986882314640.9725303112343051.29736401104334
298379.05575830039020.9740686216621220.9938026815046220.205427823241077
306674.64298097113970.8167996568849920.935551856212153-0.665688158215429
316169.9518402809230.6590646388758520.883276662637814-0.682838818029787
325364.0008211786690.4717166210552850.826753371172598-0.820728108069637
333051.86029790822450.1158962578293460.727563684034799-1.56694752511959
347459.58536266165230.3303516200483150.7834611367290150.945479366505379
356962.88223603966680.4140589553560120.8040475686566730.368547699959863
365961.47297372926170.3624818351884060.791998425145238-0.226450516503047
374258.43969853955730.351318784731363-10.1600866797838-0.463789062377103
386560.78376007508430.4205713509702760.959086412622520.230244580275622
397064.08082610082510.5138015519486430.9972670078451330.344638621124164
4010077.06972046741050.8997509318202581.124424270334651.52002539466583
416372.16770074411280.7254855643499411.07740547996331-0.712562109766626
4210584.04968098296151.054642150118861.151959455413131.3754197354782
438283.5757767600051.010002099309541.14323990423087-0.188779600814963
448182.88821757803310.9606845499152231.13471768769051-0.209813700584229
457580.26542506945650.8567943150656051.11848171642656-0.443017811398387
4610288.21520559687291.062401117341061.14806367540280.876873676008276
47121100.2548986110341.380931275194071.190837576497371.35685396535041
489899.90248257241741.330548466323921.18445824453286-0.214201122578061
497697.34301040547171.27047526380495-14.3644548249269-0.512115122208325
507790.07367543901570.9884748876671561.19072749621659-1.00337159789322
516380.37383786141620.6503318846482391.08632332811487-1.2895433454615
523764.6836548272980.147505170715630.967822554458863-1.99501515439565
533553.7169017394129-0.1886395923334170.906055061070709-1.36421968001592
542342.2030297272841-0.5276302062015530.85606848674527-1.39345073704177
554040.7639349088925-0.5547519053461450.852754631380394-0.112262171198172
562935.8670637169963-0.6835624963607950.839294005936016-0.535016311880205
573735.5352845081213-0.6731399572734690.8402525141469870.0433490954086335
585140.3696788025192-0.5099915382890960.8537780627876940.678638124852691
592032.4012555661145-0.7310409252543690.836938962988917-0.918913562944729
602830.047043947703-0.7791902359548170.833519080359946-0.199951088265664
611326.94672216137-0.830000942269142-9.84824892274962-0.299328411455958
622224.2623452644642-0.8894418499417370.875255740251606-0.220058935879261
632523.6468731427873-0.8808884793371320.8773453917011920.0331894930708584
641318.9124884576243-0.9991962615230240.856205826792138-0.471004564764867
651616.9132372597303-1.029582648685880.852153861388714-0.1227029206697
661314.5360609566958-1.070274405454260.847989095924405-0.165608981947683
671614.0738390280499-1.051979616060680.8494745518027750.0747715180271438
681714.1499933498663-1.018102135579440.8517341971688260.138752512539126
69911.3347929252295-1.072020993877340.84867851603401-0.221032978723444
701712.3863939113676-1.008320089220090.851839822686680.261176424633635
712515.9839210980599-0.870139133091310.857999169927110.566402017143384
721414.4025348544208-0.8914858008955360.857127106405734-0.0874561313946556
73814.8131509555157-0.858354453988709-9.093772499739230.16586572516737
74711.0888606363171-0.9485107533506690.800839309865228-0.342299885264517
75109.79797799806993-0.9591066620829980.798704262242055-0.0415856918264539
7677.88425803990052-0.9883494795497710.794537197302047-0.116771060348618
77107.73015763195991-0.9629547644224090.7971381129598650.102342437320901
7835.11962026097672-1.012916288272920.793371810073669-0.202345054044247



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')