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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, 21 Dec 2011 07:22:34 -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/2011/Dec/21/t13244702025qtuc76zuf0gqtj.htm/, Retrieved Tue, 07 May 2024 13:02:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158580, Retrieved Tue, 07 May 2024 13:02:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [Structural time s...] [2011-12-21 12:22:34] [2fa2d22b72a9c62ab85a23406d5dc0a0] [Current]
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Dataseries X:
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158580&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158580&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158580&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
199119911000
289158991.94533790049-49.5024719631764-76.9453379004859-1.15169174629593
394529409.59913497444-42.120860484340842.40086502556030.953821940062417
491129200.52789118584-42.8909291697164-88.5278911858393-0.34403811927845
584728588.58114297631-45.4198769117254-116.581142976315-1.17209511428282
682308284.74597056205-46.6775741413599-54.745970562045-0.532215490194268
783848395.2925176064-45.9036408378119-11.29251760640160.323806651458902
886258637.1001345797-44.4960155075473-12.1001345797030.592545183836557
982218319.09030314523-45.8264764753524-98.0903031452345-0.563300303317658
1086498629.93846880798-44.100058086436519.06153119202510.734562467492903
1186258673.91014273255-43.6758068166796-48.91014273255470.181379786470392
121044310256.4624777315-35.8795796189493186.5375222685283.34911244559758
131035710079.4578902714-30.2539376913955277.542109728573-0.339958648323967
1485868903.13415020484-52.4781920553042-317.13415020484-2.09169921266493
1588928802.85663324908-53.100457581758289.1433667509232-0.0964619110435536
1683298424.30496850528-54.5293809611259-95.3049685052833-0.670079407352393
1781018202.94268581628-54.942805325949-101.942685816277-0.343371292908953
1879228015.45171321902-55.2964059239315-93.4517132190211-0.272743413716634
1981208132.8116046161-54.7984176245746-12.81160461609540.355249934942122
2078387871.32739240789-55.4033033877712-33.3273924078894-0.42526533057423
2177357849.90925386959-55.3033945653787-114.9092538695890.0699274478197843
2284068274.04374169146-53.8258296242575131.9562583085350.986643096881751
2382098463.96373933143-53.0680518633181-254.963739331430.501718969778341
2494519124.84507114041-53.3208759695002326.1549288595941.46783844809718
25100419372.52877428734-57.7585238973135668.4712257126550.655641529446112
2694119675.15437232312-54.2954684863441-264.1543723231170.704859250052618
271040510162.1949409956-48.5509303301383242.8050590043761.08957524408529
2884678864.85641139516-54.8207226150375-397.856411395155-2.56769334483665
2984648540.57916568073-55.4758292046163-76.5791656807251-0.554578703881003
3081028251.04094203354-55.9667774857442-149.040942033538-0.48157189041654
3176277693.32718213317-57.1119179397546-66.3271821331706-1.03216548293573
3275137530.70030544541-57.3674472678391-17.7003054454145-0.217052586836809
3375107640.22805960025-56.9418441006708-130.228059600250.34335208854999
3482918076.50136643322-55.6132260675563214.498633566781.01496612396894
3580648379.39032768554-54.8756084982583-315.3903276855430.737514421617563
3693838980.49678207546-55.9887155931123402.5032179245421.35020176446479
3797069123.65887542261-57.4099273949267582.3411245773860.420215524995011
3885798990.21992250764-57.8402339228471-411.219922507641-0.152328477542236
3994748993.40695856085-57.3195553463843480.593041439150.123055780039576
4083188732.9961034131-58.3813237948128-414.996103413101-0.416892699012987
4182138326.503740509-59.3194596840775-113.503740508995-0.71641103527579
4280598122.87488962323-59.601785197128-63.8748896232325-0.296912673014368
4391118919.64021815714-57.8909555279393191.3597818428591.76161484341155
4477087999.33414582405-59.7806927316422-291.334145824053-1.77399345601147
4576807874.44779835338-59.9356726819518-194.447798353376-0.133943914188529
4680147848.04638302226-59.8568165215588165.9536169777390.0690026756130236
4780078317.74486881002-59.1837235090388-310.7448688100161.08889584508365
4887188355.00406889497-59.356966576459362.9959311050290.198648462305519
4994868773.31554401697-61.1482682159096712.6844559830330.994542114873641
5091139400.51229893703-58.4858246747535-287.5122989370311.39297539758058
5190258698.80663126749-62.9360811184794326.193368732515-1.3007535453722
5284768770.8372648112-62.2466404037979-294.8372648112020.276675148431817
5379528193.92276185323-63.7566512276659-241.92276185323-1.05891744611829
5477597999.11877486557-64.0174737319384-240.118774865569-0.269633353305909
5578357554.7606627045-64.7317949572543280.239337295498-0.782420193140269
5676007775.55770373016-64.1459514760721-175.5577037301630.587363861556072
5776517827.2828468189-63.8898987977992-176.2828468188970.238392488863024
5883198156.41422087564-63.1059838354875162.5857791243560.808557847109372
5988128948.00059990368-62.4439406519935-136.0005999036771.75704980973029
6086308514.62204911519-61.8860628039259115.377950884815-0.764057251268637

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9911 & 9911 & 0 & 0 & 0 \tabularnewline
2 & 8915 & 8991.94533790049 & -49.5024719631764 & -76.9453379004859 & -1.15169174629593 \tabularnewline
3 & 9452 & 9409.59913497444 & -42.1208604843408 & 42.4008650255603 & 0.953821940062417 \tabularnewline
4 & 9112 & 9200.52789118584 & -42.8909291697164 & -88.5278911858393 & -0.34403811927845 \tabularnewline
5 & 8472 & 8588.58114297631 & -45.4198769117254 & -116.581142976315 & -1.17209511428282 \tabularnewline
6 & 8230 & 8284.74597056205 & -46.6775741413599 & -54.745970562045 & -0.532215490194268 \tabularnewline
7 & 8384 & 8395.2925176064 & -45.9036408378119 & -11.2925176064016 & 0.323806651458902 \tabularnewline
8 & 8625 & 8637.1001345797 & -44.4960155075473 & -12.100134579703 & 0.592545183836557 \tabularnewline
9 & 8221 & 8319.09030314523 & -45.8264764753524 & -98.0903031452345 & -0.563300303317658 \tabularnewline
10 & 8649 & 8629.93846880798 & -44.1000580864365 & 19.0615311920251 & 0.734562467492903 \tabularnewline
11 & 8625 & 8673.91014273255 & -43.6758068166796 & -48.9101427325547 & 0.181379786470392 \tabularnewline
12 & 10443 & 10256.4624777315 & -35.8795796189493 & 186.537522268528 & 3.34911244559758 \tabularnewline
13 & 10357 & 10079.4578902714 & -30.2539376913955 & 277.542109728573 & -0.339958648323967 \tabularnewline
14 & 8586 & 8903.13415020484 & -52.4781920553042 & -317.13415020484 & -2.09169921266493 \tabularnewline
15 & 8892 & 8802.85663324908 & -53.1004575817582 & 89.1433667509232 & -0.0964619110435536 \tabularnewline
16 & 8329 & 8424.30496850528 & -54.5293809611259 & -95.3049685052833 & -0.670079407352393 \tabularnewline
17 & 8101 & 8202.94268581628 & -54.942805325949 & -101.942685816277 & -0.343371292908953 \tabularnewline
18 & 7922 & 8015.45171321902 & -55.2964059239315 & -93.4517132190211 & -0.272743413716634 \tabularnewline
19 & 8120 & 8132.8116046161 & -54.7984176245746 & -12.8116046160954 & 0.355249934942122 \tabularnewline
20 & 7838 & 7871.32739240789 & -55.4033033877712 & -33.3273924078894 & -0.42526533057423 \tabularnewline
21 & 7735 & 7849.90925386959 & -55.3033945653787 & -114.909253869589 & 0.0699274478197843 \tabularnewline
22 & 8406 & 8274.04374169146 & -53.8258296242575 & 131.956258308535 & 0.986643096881751 \tabularnewline
23 & 8209 & 8463.96373933143 & -53.0680518633181 & -254.96373933143 & 0.501718969778341 \tabularnewline
24 & 9451 & 9124.84507114041 & -53.3208759695002 & 326.154928859594 & 1.46783844809718 \tabularnewline
25 & 10041 & 9372.52877428734 & -57.7585238973135 & 668.471225712655 & 0.655641529446112 \tabularnewline
26 & 9411 & 9675.15437232312 & -54.2954684863441 & -264.154372323117 & 0.704859250052618 \tabularnewline
27 & 10405 & 10162.1949409956 & -48.5509303301383 & 242.805059004376 & 1.08957524408529 \tabularnewline
28 & 8467 & 8864.85641139516 & -54.8207226150375 & -397.856411395155 & -2.56769334483665 \tabularnewline
29 & 8464 & 8540.57916568073 & -55.4758292046163 & -76.5791656807251 & -0.554578703881003 \tabularnewline
30 & 8102 & 8251.04094203354 & -55.9667774857442 & -149.040942033538 & -0.48157189041654 \tabularnewline
31 & 7627 & 7693.32718213317 & -57.1119179397546 & -66.3271821331706 & -1.03216548293573 \tabularnewline
32 & 7513 & 7530.70030544541 & -57.3674472678391 & -17.7003054454145 & -0.217052586836809 \tabularnewline
33 & 7510 & 7640.22805960025 & -56.9418441006708 & -130.22805960025 & 0.34335208854999 \tabularnewline
34 & 8291 & 8076.50136643322 & -55.6132260675563 & 214.49863356678 & 1.01496612396894 \tabularnewline
35 & 8064 & 8379.39032768554 & -54.8756084982583 & -315.390327685543 & 0.737514421617563 \tabularnewline
36 & 9383 & 8980.49678207546 & -55.9887155931123 & 402.503217924542 & 1.35020176446479 \tabularnewline
37 & 9706 & 9123.65887542261 & -57.4099273949267 & 582.341124577386 & 0.420215524995011 \tabularnewline
38 & 8579 & 8990.21992250764 & -57.8402339228471 & -411.219922507641 & -0.152328477542236 \tabularnewline
39 & 9474 & 8993.40695856085 & -57.3195553463843 & 480.59304143915 & 0.123055780039576 \tabularnewline
40 & 8318 & 8732.9961034131 & -58.3813237948128 & -414.996103413101 & -0.416892699012987 \tabularnewline
41 & 8213 & 8326.503740509 & -59.3194596840775 & -113.503740508995 & -0.71641103527579 \tabularnewline
42 & 8059 & 8122.87488962323 & -59.601785197128 & -63.8748896232325 & -0.296912673014368 \tabularnewline
43 & 9111 & 8919.64021815714 & -57.8909555279393 & 191.359781842859 & 1.76161484341155 \tabularnewline
44 & 7708 & 7999.33414582405 & -59.7806927316422 & -291.334145824053 & -1.77399345601147 \tabularnewline
45 & 7680 & 7874.44779835338 & -59.9356726819518 & -194.447798353376 & -0.133943914188529 \tabularnewline
46 & 8014 & 7848.04638302226 & -59.8568165215588 & 165.953616977739 & 0.0690026756130236 \tabularnewline
47 & 8007 & 8317.74486881002 & -59.1837235090388 & -310.744868810016 & 1.08889584508365 \tabularnewline
48 & 8718 & 8355.00406889497 & -59.356966576459 & 362.995931105029 & 0.198648462305519 \tabularnewline
49 & 9486 & 8773.31554401697 & -61.1482682159096 & 712.684455983033 & 0.994542114873641 \tabularnewline
50 & 9113 & 9400.51229893703 & -58.4858246747535 & -287.512298937031 & 1.39297539758058 \tabularnewline
51 & 9025 & 8698.80663126749 & -62.9360811184794 & 326.193368732515 & -1.3007535453722 \tabularnewline
52 & 8476 & 8770.8372648112 & -62.2466404037979 & -294.837264811202 & 0.276675148431817 \tabularnewline
53 & 7952 & 8193.92276185323 & -63.7566512276659 & -241.92276185323 & -1.05891744611829 \tabularnewline
54 & 7759 & 7999.11877486557 & -64.0174737319384 & -240.118774865569 & -0.269633353305909 \tabularnewline
55 & 7835 & 7554.7606627045 & -64.7317949572543 & 280.239337295498 & -0.782420193140269 \tabularnewline
56 & 7600 & 7775.55770373016 & -64.1459514760721 & -175.557703730163 & 0.587363861556072 \tabularnewline
57 & 7651 & 7827.2828468189 & -63.8898987977992 & -176.282846818897 & 0.238392488863024 \tabularnewline
58 & 8319 & 8156.41422087564 & -63.1059838354875 & 162.585779124356 & 0.808557847109372 \tabularnewline
59 & 8812 & 8948.00059990368 & -62.4439406519935 & -136.000599903677 & 1.75704980973029 \tabularnewline
60 & 8630 & 8514.62204911519 & -61.8860628039259 & 115.377950884815 & -0.764057251268637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158580&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]9911[/C][C]9911[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8915[/C][C]8991.94533790049[/C][C]-49.5024719631764[/C][C]-76.9453379004859[/C][C]-1.15169174629593[/C][/ROW]
[ROW][C]3[/C][C]9452[/C][C]9409.59913497444[/C][C]-42.1208604843408[/C][C]42.4008650255603[/C][C]0.953821940062417[/C][/ROW]
[ROW][C]4[/C][C]9112[/C][C]9200.52789118584[/C][C]-42.8909291697164[/C][C]-88.5278911858393[/C][C]-0.34403811927845[/C][/ROW]
[ROW][C]5[/C][C]8472[/C][C]8588.58114297631[/C][C]-45.4198769117254[/C][C]-116.581142976315[/C][C]-1.17209511428282[/C][/ROW]
[ROW][C]6[/C][C]8230[/C][C]8284.74597056205[/C][C]-46.6775741413599[/C][C]-54.745970562045[/C][C]-0.532215490194268[/C][/ROW]
[ROW][C]7[/C][C]8384[/C][C]8395.2925176064[/C][C]-45.9036408378119[/C][C]-11.2925176064016[/C][C]0.323806651458902[/C][/ROW]
[ROW][C]8[/C][C]8625[/C][C]8637.1001345797[/C][C]-44.4960155075473[/C][C]-12.100134579703[/C][C]0.592545183836557[/C][/ROW]
[ROW][C]9[/C][C]8221[/C][C]8319.09030314523[/C][C]-45.8264764753524[/C][C]-98.0903031452345[/C][C]-0.563300303317658[/C][/ROW]
[ROW][C]10[/C][C]8649[/C][C]8629.93846880798[/C][C]-44.1000580864365[/C][C]19.0615311920251[/C][C]0.734562467492903[/C][/ROW]
[ROW][C]11[/C][C]8625[/C][C]8673.91014273255[/C][C]-43.6758068166796[/C][C]-48.9101427325547[/C][C]0.181379786470392[/C][/ROW]
[ROW][C]12[/C][C]10443[/C][C]10256.4624777315[/C][C]-35.8795796189493[/C][C]186.537522268528[/C][C]3.34911244559758[/C][/ROW]
[ROW][C]13[/C][C]10357[/C][C]10079.4578902714[/C][C]-30.2539376913955[/C][C]277.542109728573[/C][C]-0.339958648323967[/C][/ROW]
[ROW][C]14[/C][C]8586[/C][C]8903.13415020484[/C][C]-52.4781920553042[/C][C]-317.13415020484[/C][C]-2.09169921266493[/C][/ROW]
[ROW][C]15[/C][C]8892[/C][C]8802.85663324908[/C][C]-53.1004575817582[/C][C]89.1433667509232[/C][C]-0.0964619110435536[/C][/ROW]
[ROW][C]16[/C][C]8329[/C][C]8424.30496850528[/C][C]-54.5293809611259[/C][C]-95.3049685052833[/C][C]-0.670079407352393[/C][/ROW]
[ROW][C]17[/C][C]8101[/C][C]8202.94268581628[/C][C]-54.942805325949[/C][C]-101.942685816277[/C][C]-0.343371292908953[/C][/ROW]
[ROW][C]18[/C][C]7922[/C][C]8015.45171321902[/C][C]-55.2964059239315[/C][C]-93.4517132190211[/C][C]-0.272743413716634[/C][/ROW]
[ROW][C]19[/C][C]8120[/C][C]8132.8116046161[/C][C]-54.7984176245746[/C][C]-12.8116046160954[/C][C]0.355249934942122[/C][/ROW]
[ROW][C]20[/C][C]7838[/C][C]7871.32739240789[/C][C]-55.4033033877712[/C][C]-33.3273924078894[/C][C]-0.42526533057423[/C][/ROW]
[ROW][C]21[/C][C]7735[/C][C]7849.90925386959[/C][C]-55.3033945653787[/C][C]-114.909253869589[/C][C]0.0699274478197843[/C][/ROW]
[ROW][C]22[/C][C]8406[/C][C]8274.04374169146[/C][C]-53.8258296242575[/C][C]131.956258308535[/C][C]0.986643096881751[/C][/ROW]
[ROW][C]23[/C][C]8209[/C][C]8463.96373933143[/C][C]-53.0680518633181[/C][C]-254.96373933143[/C][C]0.501718969778341[/C][/ROW]
[ROW][C]24[/C][C]9451[/C][C]9124.84507114041[/C][C]-53.3208759695002[/C][C]326.154928859594[/C][C]1.46783844809718[/C][/ROW]
[ROW][C]25[/C][C]10041[/C][C]9372.52877428734[/C][C]-57.7585238973135[/C][C]668.471225712655[/C][C]0.655641529446112[/C][/ROW]
[ROW][C]26[/C][C]9411[/C][C]9675.15437232312[/C][C]-54.2954684863441[/C][C]-264.154372323117[/C][C]0.704859250052618[/C][/ROW]
[ROW][C]27[/C][C]10405[/C][C]10162.1949409956[/C][C]-48.5509303301383[/C][C]242.805059004376[/C][C]1.08957524408529[/C][/ROW]
[ROW][C]28[/C][C]8467[/C][C]8864.85641139516[/C][C]-54.8207226150375[/C][C]-397.856411395155[/C][C]-2.56769334483665[/C][/ROW]
[ROW][C]29[/C][C]8464[/C][C]8540.57916568073[/C][C]-55.4758292046163[/C][C]-76.5791656807251[/C][C]-0.554578703881003[/C][/ROW]
[ROW][C]30[/C][C]8102[/C][C]8251.04094203354[/C][C]-55.9667774857442[/C][C]-149.040942033538[/C][C]-0.48157189041654[/C][/ROW]
[ROW][C]31[/C][C]7627[/C][C]7693.32718213317[/C][C]-57.1119179397546[/C][C]-66.3271821331706[/C][C]-1.03216548293573[/C][/ROW]
[ROW][C]32[/C][C]7513[/C][C]7530.70030544541[/C][C]-57.3674472678391[/C][C]-17.7003054454145[/C][C]-0.217052586836809[/C][/ROW]
[ROW][C]33[/C][C]7510[/C][C]7640.22805960025[/C][C]-56.9418441006708[/C][C]-130.22805960025[/C][C]0.34335208854999[/C][/ROW]
[ROW][C]34[/C][C]8291[/C][C]8076.50136643322[/C][C]-55.6132260675563[/C][C]214.49863356678[/C][C]1.01496612396894[/C][/ROW]
[ROW][C]35[/C][C]8064[/C][C]8379.39032768554[/C][C]-54.8756084982583[/C][C]-315.390327685543[/C][C]0.737514421617563[/C][/ROW]
[ROW][C]36[/C][C]9383[/C][C]8980.49678207546[/C][C]-55.9887155931123[/C][C]402.503217924542[/C][C]1.35020176446479[/C][/ROW]
[ROW][C]37[/C][C]9706[/C][C]9123.65887542261[/C][C]-57.4099273949267[/C][C]582.341124577386[/C][C]0.420215524995011[/C][/ROW]
[ROW][C]38[/C][C]8579[/C][C]8990.21992250764[/C][C]-57.8402339228471[/C][C]-411.219922507641[/C][C]-0.152328477542236[/C][/ROW]
[ROW][C]39[/C][C]9474[/C][C]8993.40695856085[/C][C]-57.3195553463843[/C][C]480.59304143915[/C][C]0.123055780039576[/C][/ROW]
[ROW][C]40[/C][C]8318[/C][C]8732.9961034131[/C][C]-58.3813237948128[/C][C]-414.996103413101[/C][C]-0.416892699012987[/C][/ROW]
[ROW][C]41[/C][C]8213[/C][C]8326.503740509[/C][C]-59.3194596840775[/C][C]-113.503740508995[/C][C]-0.71641103527579[/C][/ROW]
[ROW][C]42[/C][C]8059[/C][C]8122.87488962323[/C][C]-59.601785197128[/C][C]-63.8748896232325[/C][C]-0.296912673014368[/C][/ROW]
[ROW][C]43[/C][C]9111[/C][C]8919.64021815714[/C][C]-57.8909555279393[/C][C]191.359781842859[/C][C]1.76161484341155[/C][/ROW]
[ROW][C]44[/C][C]7708[/C][C]7999.33414582405[/C][C]-59.7806927316422[/C][C]-291.334145824053[/C][C]-1.77399345601147[/C][/ROW]
[ROW][C]45[/C][C]7680[/C][C]7874.44779835338[/C][C]-59.9356726819518[/C][C]-194.447798353376[/C][C]-0.133943914188529[/C][/ROW]
[ROW][C]46[/C][C]8014[/C][C]7848.04638302226[/C][C]-59.8568165215588[/C][C]165.953616977739[/C][C]0.0690026756130236[/C][/ROW]
[ROW][C]47[/C][C]8007[/C][C]8317.74486881002[/C][C]-59.1837235090388[/C][C]-310.744868810016[/C][C]1.08889584508365[/C][/ROW]
[ROW][C]48[/C][C]8718[/C][C]8355.00406889497[/C][C]-59.356966576459[/C][C]362.995931105029[/C][C]0.198648462305519[/C][/ROW]
[ROW][C]49[/C][C]9486[/C][C]8773.31554401697[/C][C]-61.1482682159096[/C][C]712.684455983033[/C][C]0.994542114873641[/C][/ROW]
[ROW][C]50[/C][C]9113[/C][C]9400.51229893703[/C][C]-58.4858246747535[/C][C]-287.512298937031[/C][C]1.39297539758058[/C][/ROW]
[ROW][C]51[/C][C]9025[/C][C]8698.80663126749[/C][C]-62.9360811184794[/C][C]326.193368732515[/C][C]-1.3007535453722[/C][/ROW]
[ROW][C]52[/C][C]8476[/C][C]8770.8372648112[/C][C]-62.2466404037979[/C][C]-294.837264811202[/C][C]0.276675148431817[/C][/ROW]
[ROW][C]53[/C][C]7952[/C][C]8193.92276185323[/C][C]-63.7566512276659[/C][C]-241.92276185323[/C][C]-1.05891744611829[/C][/ROW]
[ROW][C]54[/C][C]7759[/C][C]7999.11877486557[/C][C]-64.0174737319384[/C][C]-240.118774865569[/C][C]-0.269633353305909[/C][/ROW]
[ROW][C]55[/C][C]7835[/C][C]7554.7606627045[/C][C]-64.7317949572543[/C][C]280.239337295498[/C][C]-0.782420193140269[/C][/ROW]
[ROW][C]56[/C][C]7600[/C][C]7775.55770373016[/C][C]-64.1459514760721[/C][C]-175.557703730163[/C][C]0.587363861556072[/C][/ROW]
[ROW][C]57[/C][C]7651[/C][C]7827.2828468189[/C][C]-63.8898987977992[/C][C]-176.282846818897[/C][C]0.238392488863024[/C][/ROW]
[ROW][C]58[/C][C]8319[/C][C]8156.41422087564[/C][C]-63.1059838354875[/C][C]162.585779124356[/C][C]0.808557847109372[/C][/ROW]
[ROW][C]59[/C][C]8812[/C][C]8948.00059990368[/C][C]-62.4439406519935[/C][C]-136.000599903677[/C][C]1.75704980973029[/C][/ROW]
[ROW][C]60[/C][C]8630[/C][C]8514.62204911519[/C][C]-61.8860628039259[/C][C]115.377950884815[/C][C]-0.764057251268637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158580&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158580&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
199119911000
289158991.94533790049-49.5024719631764-76.9453379004859-1.15169174629593
394529409.59913497444-42.120860484340842.40086502556030.953821940062417
491129200.52789118584-42.8909291697164-88.5278911858393-0.34403811927845
584728588.58114297631-45.4198769117254-116.581142976315-1.17209511428282
682308284.74597056205-46.6775741413599-54.745970562045-0.532215490194268
783848395.2925176064-45.9036408378119-11.29251760640160.323806651458902
886258637.1001345797-44.4960155075473-12.1001345797030.592545183836557
982218319.09030314523-45.8264764753524-98.0903031452345-0.563300303317658
1086498629.93846880798-44.100058086436519.06153119202510.734562467492903
1186258673.91014273255-43.6758068166796-48.91014273255470.181379786470392
121044310256.4624777315-35.8795796189493186.5375222685283.34911244559758
131035710079.4578902714-30.2539376913955277.542109728573-0.339958648323967
1485868903.13415020484-52.4781920553042-317.13415020484-2.09169921266493
1588928802.85663324908-53.100457581758289.1433667509232-0.0964619110435536
1683298424.30496850528-54.5293809611259-95.3049685052833-0.670079407352393
1781018202.94268581628-54.942805325949-101.942685816277-0.343371292908953
1879228015.45171321902-55.2964059239315-93.4517132190211-0.272743413716634
1981208132.8116046161-54.7984176245746-12.81160461609540.355249934942122
2078387871.32739240789-55.4033033877712-33.3273924078894-0.42526533057423
2177357849.90925386959-55.3033945653787-114.9092538695890.0699274478197843
2284068274.04374169146-53.8258296242575131.9562583085350.986643096881751
2382098463.96373933143-53.0680518633181-254.963739331430.501718969778341
2494519124.84507114041-53.3208759695002326.1549288595941.46783844809718
25100419372.52877428734-57.7585238973135668.4712257126550.655641529446112
2694119675.15437232312-54.2954684863441-264.1543723231170.704859250052618
271040510162.1949409956-48.5509303301383242.8050590043761.08957524408529
2884678864.85641139516-54.8207226150375-397.856411395155-2.56769334483665
2984648540.57916568073-55.4758292046163-76.5791656807251-0.554578703881003
3081028251.04094203354-55.9667774857442-149.040942033538-0.48157189041654
3176277693.32718213317-57.1119179397546-66.3271821331706-1.03216548293573
3275137530.70030544541-57.3674472678391-17.7003054454145-0.217052586836809
3375107640.22805960025-56.9418441006708-130.228059600250.34335208854999
3482918076.50136643322-55.6132260675563214.498633566781.01496612396894
3580648379.39032768554-54.8756084982583-315.3903276855430.737514421617563
3693838980.49678207546-55.9887155931123402.5032179245421.35020176446479
3797069123.65887542261-57.4099273949267582.3411245773860.420215524995011
3885798990.21992250764-57.8402339228471-411.219922507641-0.152328477542236
3994748993.40695856085-57.3195553463843480.593041439150.123055780039576
4083188732.9961034131-58.3813237948128-414.996103413101-0.416892699012987
4182138326.503740509-59.3194596840775-113.503740508995-0.71641103527579
4280598122.87488962323-59.601785197128-63.8748896232325-0.296912673014368
4391118919.64021815714-57.8909555279393191.3597818428591.76161484341155
4477087999.33414582405-59.7806927316422-291.334145824053-1.77399345601147
4576807874.44779835338-59.9356726819518-194.447798353376-0.133943914188529
4680147848.04638302226-59.8568165215588165.9536169777390.0690026756130236
4780078317.74486881002-59.1837235090388-310.7448688100161.08889584508365
4887188355.00406889497-59.356966576459362.9959311050290.198648462305519
4994868773.31554401697-61.1482682159096712.6844559830330.994542114873641
5091139400.51229893703-58.4858246747535-287.5122989370311.39297539758058
5190258698.80663126749-62.9360811184794326.193368732515-1.3007535453722
5284768770.8372648112-62.2466404037979-294.8372648112020.276675148431817
5379528193.92276185323-63.7566512276659-241.92276185323-1.05891744611829
5477597999.11877486557-64.0174737319384-240.118774865569-0.269633353305909
5578357554.7606627045-64.7317949572543280.239337295498-0.782420193140269
5676007775.55770373016-64.1459514760721-175.5577037301630.587363861556072
5776517827.2828468189-63.8898987977992-176.2828468188970.238392488863024
5883198156.41422087564-63.1059838354875162.5857791243560.808557847109372
5988128948.00059990368-62.4439406519935-136.0005999036771.75704980973029
6086308514.62204911519-61.8860628039259115.377950884815-0.764057251268637



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')