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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 computationFri, 04 Dec 2009 14:58:34 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259964022ndbotwqckbnwmvi.htm/, Retrieved Sun, 28 Apr 2024 15:10:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64180, Retrieved Sun, 28 Apr 2024 15:10:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [] [2009-12-04 21:58:34] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
267413
267366
264777
258863
254844
254868
277267
285351
286602
283042
276687
277915
277128
277103
275037
270150
267140
264993
287259
291186
292300
288186
281477
282656
280190
280408
276836
275216
274352
271311
289802
290726
292300
278506
269826
265861
269034
264176
255198
253353
246057
235372
258556
260993
254663
250643
243422
247105
248541
245039
237080
237085
225554
226839
247934
248333
246969
245098
246263
255765
264319
268347
273046
273963
267430
271993
292710




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64180&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64180&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64180&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1267413267413000
2267366267384.122333201-30.3753802761006-18.1223332005416-0.0118685635287053
3264777265472.599308633-1235.30130680062-695.59930863323-0.381807369849144
4258863260362.338886237-3831.5113822526-1499.33888623652-0.859611134677365
5254844254957.818656406-4881.91848818384-113.818656405891-0.32291221661456
6254868253458.422159559-2664.063489857801409.577840440810.686824351408573
7277267270325.62880365710177.98285510086941.371196342853.99268795809797
8285351285820.19941987313682.0976971436-469.1994198727351.08745140136036
9286602290713.0194257097887.9158831447-4111.01942570904-1.79792559596823
10283042286637.4950748292.82273817782789-3595.49507482945-2.44692621475706
11276687278452.649226503-5392.45604216452-1765.64922650338-1.67422916898651
12277915275928.115308934-3502.788863579351986.884691065640.586387427296748
13277128275910.683400437-1214.396078660091217.316599562920.712845731524051
14277103276148.107282922-256.840859570257954.8927170783250.302021250775385
15275037274331.257826656-1258.14397181627705.74217334406-0.308016135060972
16270150270423.947923455-2934.22822100133-273.947923454906-0.527640203785054
17267140267842.636211314-2708.11388178989-702.6362113136340.0706361080352814
18264993269591.178829119129.630269323205-4598.178829119290.876837291472773
19287259279833.3265576866537.525349954487425.673442313631.98777506224713
20291186289111.7049991028277.311255536782074.295000897960.540335753406478
21292300293779.4394615225982.93201817674-1479.43946152224-0.711963080009041
22288186291657.212801389830.263640439649-3471.21280138862-1.59896466199666
23281477286081.180237180-3240.23570017516-4604.18023717979-1.26318802121446
24282656281914.458199483-3828.20616654204741.541800517047-0.182546715006663
25280190279034.994812125-3225.938855781411155.005187875150.187492564122196
26280408277669.171280809-2043.518146225442738.828719190670.367550879451589
27276836274702.637683679-2625.487591446252133.36231632061-0.180041482231765
28275216274000.71479777-1419.592800714401215.285202229960.375585802506345
29274352275819.193210261620.622797726201-1467.193210261210.636123126815103
30271311279435.2443993272508.34680239798-8124.24439932660.585083779024046
31289802283706.5178928233615.318554758376095.482107176560.343006761811845
32290726287664.2578291513830.252965820053061.742170849110.0667088799960358
33292300290980.7052923663507.362554016861319.29470763407-0.100212725080787
34278506283684.568396565-3284.53796030436-5178.56839656472-2.10761969950104
35269826275220.719885419-6539.31094677593-5394.7198854188-1.01010820301569
36265861266141.309029169-8134.9630780944-280.309029168622-0.495554030517184
37269034265228.951525645-3594.223928551633805.04847435541.41136746363201
38264176261316.262102205-3794.395999530892859.73789779521-0.0621118721731808
39255198254440.732648271-5723.06961355785757.267351728663-0.597611648974433
40253353251795.202624142-3801.651170220741557.797375858010.597162007947039
41246057248835.148361518-3274.98099038878-2778.148361518080.163893734322342
42235372245060.110371348-3588.27626577152-9688.1103713482-0.0972272864969765
43258556250197.8425210371868.690572269918358.157478963031.69138684615723
44260993256703.9882294134765.63149807054289.01177058730.898614106163387
45254663252651.530097941-744.8792599893572011.46990205877-1.71012708863621
46250643252537.916555824-350.253528978847-1894.916555824140.122473726968608
47243422249500.056650198-2030.27733056099-6078.05665019831-0.521500576968638
48247105249297.7166209-887.43965285882-2192.716620899870.354913132466749
49248541245541.765564819-2681.882525316862999.23443518059-0.557260860367922
50245039241233.424336006-3698.700729082443805.57566399421-0.315391330523115
51237080237078.606913007-3983.132144797011.39308699292198-0.0881864648943466
52237085234613.134106086-3038.061132028472471.865893913700.293527027191679
53225554229446.719834182-4365.18619200657-3892.71983418243-0.412584975375893
54226839236093.5435714892508.79419983017-9254.543571488922.13409355040976
55247934241002.632055894005.780357785496931.367944110180.46418647867658
56248333242033.5930207242152.655577505246299.4069792757-0.574715002606664
57246969244729.4126420912490.923308343422239.587357908710.104963504920892
58245098246695.1289006082163.78059439296-1597.12890060804-0.101542989311227
59246263251948.2669064088.5055334127-5685.266905999910.597572639694386
60255765256798.2999024564563.14293235591-1033.29990245560.147386786606342
61264319260745.24528514178.958525268083573.75471489991-0.119255771282371
62268347264211.3040757003734.810419908444135.6959242996-0.137752487726044
63273046271491.3802361275939.701333005131554.619763873080.683806941059474
64273963273021.2081421643199.46545399986941.791857836458-0.850851245520718
65267430275571.5887322542795.77529814821-8141.5887322543-0.125438875508055
66271993281004.530486964437.43376612178-9011.53048695990.509718153492717
67292710284960.1252777064137.619061659587749.87472229357-0.0929935029916293

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 267413 & 267413 & 0 & 0 & 0 \tabularnewline
2 & 267366 & 267384.122333201 & -30.3753802761006 & -18.1223332005416 & -0.0118685635287053 \tabularnewline
3 & 264777 & 265472.599308633 & -1235.30130680062 & -695.59930863323 & -0.381807369849144 \tabularnewline
4 & 258863 & 260362.338886237 & -3831.5113822526 & -1499.33888623652 & -0.859611134677365 \tabularnewline
5 & 254844 & 254957.818656406 & -4881.91848818384 & -113.818656405891 & -0.32291221661456 \tabularnewline
6 & 254868 & 253458.422159559 & -2664.06348985780 & 1409.57784044081 & 0.686824351408573 \tabularnewline
7 & 277267 & 270325.628803657 & 10177.9828551008 & 6941.37119634285 & 3.99268795809797 \tabularnewline
8 & 285351 & 285820.199419873 & 13682.0976971436 & -469.199419872735 & 1.08745140136036 \tabularnewline
9 & 286602 & 290713.019425709 & 7887.9158831447 & -4111.01942570904 & -1.79792559596823 \tabularnewline
10 & 283042 & 286637.495074829 & 2.82273817782789 & -3595.49507482945 & -2.44692621475706 \tabularnewline
11 & 276687 & 278452.649226503 & -5392.45604216452 & -1765.64922650338 & -1.67422916898651 \tabularnewline
12 & 277915 & 275928.115308934 & -3502.78886357935 & 1986.88469106564 & 0.586387427296748 \tabularnewline
13 & 277128 & 275910.683400437 & -1214.39607866009 & 1217.31659956292 & 0.712845731524051 \tabularnewline
14 & 277103 & 276148.107282922 & -256.840859570257 & 954.892717078325 & 0.302021250775385 \tabularnewline
15 & 275037 & 274331.257826656 & -1258.14397181627 & 705.74217334406 & -0.308016135060972 \tabularnewline
16 & 270150 & 270423.947923455 & -2934.22822100133 & -273.947923454906 & -0.527640203785054 \tabularnewline
17 & 267140 & 267842.636211314 & -2708.11388178989 & -702.636211313634 & 0.0706361080352814 \tabularnewline
18 & 264993 & 269591.178829119 & 129.630269323205 & -4598.17882911929 & 0.876837291472773 \tabularnewline
19 & 287259 & 279833.326557686 & 6537.52534995448 & 7425.67344231363 & 1.98777506224713 \tabularnewline
20 & 291186 & 289111.704999102 & 8277.31125553678 & 2074.29500089796 & 0.540335753406478 \tabularnewline
21 & 292300 & 293779.439461522 & 5982.93201817674 & -1479.43946152224 & -0.711963080009041 \tabularnewline
22 & 288186 & 291657.212801389 & 830.263640439649 & -3471.21280138862 & -1.59896466199666 \tabularnewline
23 & 281477 & 286081.180237180 & -3240.23570017516 & -4604.18023717979 & -1.26318802121446 \tabularnewline
24 & 282656 & 281914.458199483 & -3828.20616654204 & 741.541800517047 & -0.182546715006663 \tabularnewline
25 & 280190 & 279034.994812125 & -3225.93885578141 & 1155.00518787515 & 0.187492564122196 \tabularnewline
26 & 280408 & 277669.171280809 & -2043.51814622544 & 2738.82871919067 & 0.367550879451589 \tabularnewline
27 & 276836 & 274702.637683679 & -2625.48759144625 & 2133.36231632061 & -0.180041482231765 \tabularnewline
28 & 275216 & 274000.71479777 & -1419.59280071440 & 1215.28520222996 & 0.375585802506345 \tabularnewline
29 & 274352 & 275819.193210261 & 620.622797726201 & -1467.19321026121 & 0.636123126815103 \tabularnewline
30 & 271311 & 279435.244399327 & 2508.34680239798 & -8124.2443993266 & 0.585083779024046 \tabularnewline
31 & 289802 & 283706.517892823 & 3615.31855475837 & 6095.48210717656 & 0.343006761811845 \tabularnewline
32 & 290726 & 287664.257829151 & 3830.25296582005 & 3061.74217084911 & 0.0667088799960358 \tabularnewline
33 & 292300 & 290980.705292366 & 3507.36255401686 & 1319.29470763407 & -0.100212725080787 \tabularnewline
34 & 278506 & 283684.568396565 & -3284.53796030436 & -5178.56839656472 & -2.10761969950104 \tabularnewline
35 & 269826 & 275220.719885419 & -6539.31094677593 & -5394.7198854188 & -1.01010820301569 \tabularnewline
36 & 265861 & 266141.309029169 & -8134.9630780944 & -280.309029168622 & -0.495554030517184 \tabularnewline
37 & 269034 & 265228.951525645 & -3594.22392855163 & 3805.0484743554 & 1.41136746363201 \tabularnewline
38 & 264176 & 261316.262102205 & -3794.39599953089 & 2859.73789779521 & -0.0621118721731808 \tabularnewline
39 & 255198 & 254440.732648271 & -5723.06961355785 & 757.267351728663 & -0.597611648974433 \tabularnewline
40 & 253353 & 251795.202624142 & -3801.65117022074 & 1557.79737585801 & 0.597162007947039 \tabularnewline
41 & 246057 & 248835.148361518 & -3274.98099038878 & -2778.14836151808 & 0.163893734322342 \tabularnewline
42 & 235372 & 245060.110371348 & -3588.27626577152 & -9688.1103713482 & -0.0972272864969765 \tabularnewline
43 & 258556 & 250197.842521037 & 1868.69057226991 & 8358.15747896303 & 1.69138684615723 \tabularnewline
44 & 260993 & 256703.988229413 & 4765.6314980705 & 4289.0117705873 & 0.898614106163387 \tabularnewline
45 & 254663 & 252651.530097941 & -744.879259989357 & 2011.46990205877 & -1.71012708863621 \tabularnewline
46 & 250643 & 252537.916555824 & -350.253528978847 & -1894.91655582414 & 0.122473726968608 \tabularnewline
47 & 243422 & 249500.056650198 & -2030.27733056099 & -6078.05665019831 & -0.521500576968638 \tabularnewline
48 & 247105 & 249297.7166209 & -887.43965285882 & -2192.71662089987 & 0.354913132466749 \tabularnewline
49 & 248541 & 245541.765564819 & -2681.88252531686 & 2999.23443518059 & -0.557260860367922 \tabularnewline
50 & 245039 & 241233.424336006 & -3698.70072908244 & 3805.57566399421 & -0.315391330523115 \tabularnewline
51 & 237080 & 237078.606913007 & -3983.13214479701 & 1.39308699292198 & -0.0881864648943466 \tabularnewline
52 & 237085 & 234613.134106086 & -3038.06113202847 & 2471.86589391370 & 0.293527027191679 \tabularnewline
53 & 225554 & 229446.719834182 & -4365.18619200657 & -3892.71983418243 & -0.412584975375893 \tabularnewline
54 & 226839 & 236093.543571489 & 2508.79419983017 & -9254.54357148892 & 2.13409355040976 \tabularnewline
55 & 247934 & 241002.63205589 & 4005.78035778549 & 6931.36794411018 & 0.46418647867658 \tabularnewline
56 & 248333 & 242033.593020724 & 2152.65557750524 & 6299.4069792757 & -0.574715002606664 \tabularnewline
57 & 246969 & 244729.412642091 & 2490.92330834342 & 2239.58735790871 & 0.104963504920892 \tabularnewline
58 & 245098 & 246695.128900608 & 2163.78059439296 & -1597.12890060804 & -0.101542989311227 \tabularnewline
59 & 246263 & 251948.266906 & 4088.5055334127 & -5685.26690599991 & 0.597572639694386 \tabularnewline
60 & 255765 & 256798.299902456 & 4563.14293235591 & -1033.2999024556 & 0.147386786606342 \tabularnewline
61 & 264319 & 260745.2452851 & 4178.95852526808 & 3573.75471489991 & -0.119255771282371 \tabularnewline
62 & 268347 & 264211.304075700 & 3734.81041990844 & 4135.6959242996 & -0.137752487726044 \tabularnewline
63 & 273046 & 271491.380236127 & 5939.70133300513 & 1554.61976387308 & 0.683806941059474 \tabularnewline
64 & 273963 & 273021.208142164 & 3199.46545399986 & 941.791857836458 & -0.850851245520718 \tabularnewline
65 & 267430 & 275571.588732254 & 2795.77529814821 & -8141.5887322543 & -0.125438875508055 \tabularnewline
66 & 271993 & 281004.53048696 & 4437.43376612178 & -9011.5304869599 & 0.509718153492717 \tabularnewline
67 & 292710 & 284960.125277706 & 4137.61906165958 & 7749.87472229357 & -0.0929935029916293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64180&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]267413[/C][C]267413[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]267366[/C][C]267384.122333201[/C][C]-30.3753802761006[/C][C]-18.1223332005416[/C][C]-0.0118685635287053[/C][/ROW]
[ROW][C]3[/C][C]264777[/C][C]265472.599308633[/C][C]-1235.30130680062[/C][C]-695.59930863323[/C][C]-0.381807369849144[/C][/ROW]
[ROW][C]4[/C][C]258863[/C][C]260362.338886237[/C][C]-3831.5113822526[/C][C]-1499.33888623652[/C][C]-0.859611134677365[/C][/ROW]
[ROW][C]5[/C][C]254844[/C][C]254957.818656406[/C][C]-4881.91848818384[/C][C]-113.818656405891[/C][C]-0.32291221661456[/C][/ROW]
[ROW][C]6[/C][C]254868[/C][C]253458.422159559[/C][C]-2664.06348985780[/C][C]1409.57784044081[/C][C]0.686824351408573[/C][/ROW]
[ROW][C]7[/C][C]277267[/C][C]270325.628803657[/C][C]10177.9828551008[/C][C]6941.37119634285[/C][C]3.99268795809797[/C][/ROW]
[ROW][C]8[/C][C]285351[/C][C]285820.199419873[/C][C]13682.0976971436[/C][C]-469.199419872735[/C][C]1.08745140136036[/C][/ROW]
[ROW][C]9[/C][C]286602[/C][C]290713.019425709[/C][C]7887.9158831447[/C][C]-4111.01942570904[/C][C]-1.79792559596823[/C][/ROW]
[ROW][C]10[/C][C]283042[/C][C]286637.495074829[/C][C]2.82273817782789[/C][C]-3595.49507482945[/C][C]-2.44692621475706[/C][/ROW]
[ROW][C]11[/C][C]276687[/C][C]278452.649226503[/C][C]-5392.45604216452[/C][C]-1765.64922650338[/C][C]-1.67422916898651[/C][/ROW]
[ROW][C]12[/C][C]277915[/C][C]275928.115308934[/C][C]-3502.78886357935[/C][C]1986.88469106564[/C][C]0.586387427296748[/C][/ROW]
[ROW][C]13[/C][C]277128[/C][C]275910.683400437[/C][C]-1214.39607866009[/C][C]1217.31659956292[/C][C]0.712845731524051[/C][/ROW]
[ROW][C]14[/C][C]277103[/C][C]276148.107282922[/C][C]-256.840859570257[/C][C]954.892717078325[/C][C]0.302021250775385[/C][/ROW]
[ROW][C]15[/C][C]275037[/C][C]274331.257826656[/C][C]-1258.14397181627[/C][C]705.74217334406[/C][C]-0.308016135060972[/C][/ROW]
[ROW][C]16[/C][C]270150[/C][C]270423.947923455[/C][C]-2934.22822100133[/C][C]-273.947923454906[/C][C]-0.527640203785054[/C][/ROW]
[ROW][C]17[/C][C]267140[/C][C]267842.636211314[/C][C]-2708.11388178989[/C][C]-702.636211313634[/C][C]0.0706361080352814[/C][/ROW]
[ROW][C]18[/C][C]264993[/C][C]269591.178829119[/C][C]129.630269323205[/C][C]-4598.17882911929[/C][C]0.876837291472773[/C][/ROW]
[ROW][C]19[/C][C]287259[/C][C]279833.326557686[/C][C]6537.52534995448[/C][C]7425.67344231363[/C][C]1.98777506224713[/C][/ROW]
[ROW][C]20[/C][C]291186[/C][C]289111.704999102[/C][C]8277.31125553678[/C][C]2074.29500089796[/C][C]0.540335753406478[/C][/ROW]
[ROW][C]21[/C][C]292300[/C][C]293779.439461522[/C][C]5982.93201817674[/C][C]-1479.43946152224[/C][C]-0.711963080009041[/C][/ROW]
[ROW][C]22[/C][C]288186[/C][C]291657.212801389[/C][C]830.263640439649[/C][C]-3471.21280138862[/C][C]-1.59896466199666[/C][/ROW]
[ROW][C]23[/C][C]281477[/C][C]286081.180237180[/C][C]-3240.23570017516[/C][C]-4604.18023717979[/C][C]-1.26318802121446[/C][/ROW]
[ROW][C]24[/C][C]282656[/C][C]281914.458199483[/C][C]-3828.20616654204[/C][C]741.541800517047[/C][C]-0.182546715006663[/C][/ROW]
[ROW][C]25[/C][C]280190[/C][C]279034.994812125[/C][C]-3225.93885578141[/C][C]1155.00518787515[/C][C]0.187492564122196[/C][/ROW]
[ROW][C]26[/C][C]280408[/C][C]277669.171280809[/C][C]-2043.51814622544[/C][C]2738.82871919067[/C][C]0.367550879451589[/C][/ROW]
[ROW][C]27[/C][C]276836[/C][C]274702.637683679[/C][C]-2625.48759144625[/C][C]2133.36231632061[/C][C]-0.180041482231765[/C][/ROW]
[ROW][C]28[/C][C]275216[/C][C]274000.71479777[/C][C]-1419.59280071440[/C][C]1215.28520222996[/C][C]0.375585802506345[/C][/ROW]
[ROW][C]29[/C][C]274352[/C][C]275819.193210261[/C][C]620.622797726201[/C][C]-1467.19321026121[/C][C]0.636123126815103[/C][/ROW]
[ROW][C]30[/C][C]271311[/C][C]279435.244399327[/C][C]2508.34680239798[/C][C]-8124.2443993266[/C][C]0.585083779024046[/C][/ROW]
[ROW][C]31[/C][C]289802[/C][C]283706.517892823[/C][C]3615.31855475837[/C][C]6095.48210717656[/C][C]0.343006761811845[/C][/ROW]
[ROW][C]32[/C][C]290726[/C][C]287664.257829151[/C][C]3830.25296582005[/C][C]3061.74217084911[/C][C]0.0667088799960358[/C][/ROW]
[ROW][C]33[/C][C]292300[/C][C]290980.705292366[/C][C]3507.36255401686[/C][C]1319.29470763407[/C][C]-0.100212725080787[/C][/ROW]
[ROW][C]34[/C][C]278506[/C][C]283684.568396565[/C][C]-3284.53796030436[/C][C]-5178.56839656472[/C][C]-2.10761969950104[/C][/ROW]
[ROW][C]35[/C][C]269826[/C][C]275220.719885419[/C][C]-6539.31094677593[/C][C]-5394.7198854188[/C][C]-1.01010820301569[/C][/ROW]
[ROW][C]36[/C][C]265861[/C][C]266141.309029169[/C][C]-8134.9630780944[/C][C]-280.309029168622[/C][C]-0.495554030517184[/C][/ROW]
[ROW][C]37[/C][C]269034[/C][C]265228.951525645[/C][C]-3594.22392855163[/C][C]3805.0484743554[/C][C]1.41136746363201[/C][/ROW]
[ROW][C]38[/C][C]264176[/C][C]261316.262102205[/C][C]-3794.39599953089[/C][C]2859.73789779521[/C][C]-0.0621118721731808[/C][/ROW]
[ROW][C]39[/C][C]255198[/C][C]254440.732648271[/C][C]-5723.06961355785[/C][C]757.267351728663[/C][C]-0.597611648974433[/C][/ROW]
[ROW][C]40[/C][C]253353[/C][C]251795.202624142[/C][C]-3801.65117022074[/C][C]1557.79737585801[/C][C]0.597162007947039[/C][/ROW]
[ROW][C]41[/C][C]246057[/C][C]248835.148361518[/C][C]-3274.98099038878[/C][C]-2778.14836151808[/C][C]0.163893734322342[/C][/ROW]
[ROW][C]42[/C][C]235372[/C][C]245060.110371348[/C][C]-3588.27626577152[/C][C]-9688.1103713482[/C][C]-0.0972272864969765[/C][/ROW]
[ROW][C]43[/C][C]258556[/C][C]250197.842521037[/C][C]1868.69057226991[/C][C]8358.15747896303[/C][C]1.69138684615723[/C][/ROW]
[ROW][C]44[/C][C]260993[/C][C]256703.988229413[/C][C]4765.6314980705[/C][C]4289.0117705873[/C][C]0.898614106163387[/C][/ROW]
[ROW][C]45[/C][C]254663[/C][C]252651.530097941[/C][C]-744.879259989357[/C][C]2011.46990205877[/C][C]-1.71012708863621[/C][/ROW]
[ROW][C]46[/C][C]250643[/C][C]252537.916555824[/C][C]-350.253528978847[/C][C]-1894.91655582414[/C][C]0.122473726968608[/C][/ROW]
[ROW][C]47[/C][C]243422[/C][C]249500.056650198[/C][C]-2030.27733056099[/C][C]-6078.05665019831[/C][C]-0.521500576968638[/C][/ROW]
[ROW][C]48[/C][C]247105[/C][C]249297.7166209[/C][C]-887.43965285882[/C][C]-2192.71662089987[/C][C]0.354913132466749[/C][/ROW]
[ROW][C]49[/C][C]248541[/C][C]245541.765564819[/C][C]-2681.88252531686[/C][C]2999.23443518059[/C][C]-0.557260860367922[/C][/ROW]
[ROW][C]50[/C][C]245039[/C][C]241233.424336006[/C][C]-3698.70072908244[/C][C]3805.57566399421[/C][C]-0.315391330523115[/C][/ROW]
[ROW][C]51[/C][C]237080[/C][C]237078.606913007[/C][C]-3983.13214479701[/C][C]1.39308699292198[/C][C]-0.0881864648943466[/C][/ROW]
[ROW][C]52[/C][C]237085[/C][C]234613.134106086[/C][C]-3038.06113202847[/C][C]2471.86589391370[/C][C]0.293527027191679[/C][/ROW]
[ROW][C]53[/C][C]225554[/C][C]229446.719834182[/C][C]-4365.18619200657[/C][C]-3892.71983418243[/C][C]-0.412584975375893[/C][/ROW]
[ROW][C]54[/C][C]226839[/C][C]236093.543571489[/C][C]2508.79419983017[/C][C]-9254.54357148892[/C][C]2.13409355040976[/C][/ROW]
[ROW][C]55[/C][C]247934[/C][C]241002.63205589[/C][C]4005.78035778549[/C][C]6931.36794411018[/C][C]0.46418647867658[/C][/ROW]
[ROW][C]56[/C][C]248333[/C][C]242033.593020724[/C][C]2152.65557750524[/C][C]6299.4069792757[/C][C]-0.574715002606664[/C][/ROW]
[ROW][C]57[/C][C]246969[/C][C]244729.412642091[/C][C]2490.92330834342[/C][C]2239.58735790871[/C][C]0.104963504920892[/C][/ROW]
[ROW][C]58[/C][C]245098[/C][C]246695.128900608[/C][C]2163.78059439296[/C][C]-1597.12890060804[/C][C]-0.101542989311227[/C][/ROW]
[ROW][C]59[/C][C]246263[/C][C]251948.266906[/C][C]4088.5055334127[/C][C]-5685.26690599991[/C][C]0.597572639694386[/C][/ROW]
[ROW][C]60[/C][C]255765[/C][C]256798.299902456[/C][C]4563.14293235591[/C][C]-1033.2999024556[/C][C]0.147386786606342[/C][/ROW]
[ROW][C]61[/C][C]264319[/C][C]260745.2452851[/C][C]4178.95852526808[/C][C]3573.75471489991[/C][C]-0.119255771282371[/C][/ROW]
[ROW][C]62[/C][C]268347[/C][C]264211.304075700[/C][C]3734.81041990844[/C][C]4135.6959242996[/C][C]-0.137752487726044[/C][/ROW]
[ROW][C]63[/C][C]273046[/C][C]271491.380236127[/C][C]5939.70133300513[/C][C]1554.61976387308[/C][C]0.683806941059474[/C][/ROW]
[ROW][C]64[/C][C]273963[/C][C]273021.208142164[/C][C]3199.46545399986[/C][C]941.791857836458[/C][C]-0.850851245520718[/C][/ROW]
[ROW][C]65[/C][C]267430[/C][C]275571.588732254[/C][C]2795.77529814821[/C][C]-8141.5887322543[/C][C]-0.125438875508055[/C][/ROW]
[ROW][C]66[/C][C]271993[/C][C]281004.53048696[/C][C]4437.43376612178[/C][C]-9011.5304869599[/C][C]0.509718153492717[/C][/ROW]
[ROW][C]67[/C][C]292710[/C][C]284960.125277706[/C][C]4137.61906165958[/C][C]7749.87472229357[/C][C]-0.0929935029916293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64180&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64180&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
1267413267413000
2267366267384.122333201-30.3753802761006-18.1223332005416-0.0118685635287053
3264777265472.599308633-1235.30130680062-695.59930863323-0.381807369849144
4258863260362.338886237-3831.5113822526-1499.33888623652-0.859611134677365
5254844254957.818656406-4881.91848818384-113.818656405891-0.32291221661456
6254868253458.422159559-2664.063489857801409.577840440810.686824351408573
7277267270325.62880365710177.98285510086941.371196342853.99268795809797
8285351285820.19941987313682.0976971436-469.1994198727351.08745140136036
9286602290713.0194257097887.9158831447-4111.01942570904-1.79792559596823
10283042286637.4950748292.82273817782789-3595.49507482945-2.44692621475706
11276687278452.649226503-5392.45604216452-1765.64922650338-1.67422916898651
12277915275928.115308934-3502.788863579351986.884691065640.586387427296748
13277128275910.683400437-1214.396078660091217.316599562920.712845731524051
14277103276148.107282922-256.840859570257954.8927170783250.302021250775385
15275037274331.257826656-1258.14397181627705.74217334406-0.308016135060972
16270150270423.947923455-2934.22822100133-273.947923454906-0.527640203785054
17267140267842.636211314-2708.11388178989-702.6362113136340.0706361080352814
18264993269591.178829119129.630269323205-4598.178829119290.876837291472773
19287259279833.3265576866537.525349954487425.673442313631.98777506224713
20291186289111.7049991028277.311255536782074.295000897960.540335753406478
21292300293779.4394615225982.93201817674-1479.43946152224-0.711963080009041
22288186291657.212801389830.263640439649-3471.21280138862-1.59896466199666
23281477286081.180237180-3240.23570017516-4604.18023717979-1.26318802121446
24282656281914.458199483-3828.20616654204741.541800517047-0.182546715006663
25280190279034.994812125-3225.938855781411155.005187875150.187492564122196
26280408277669.171280809-2043.518146225442738.828719190670.367550879451589
27276836274702.637683679-2625.487591446252133.36231632061-0.180041482231765
28275216274000.71479777-1419.592800714401215.285202229960.375585802506345
29274352275819.193210261620.622797726201-1467.193210261210.636123126815103
30271311279435.2443993272508.34680239798-8124.24439932660.585083779024046
31289802283706.5178928233615.318554758376095.482107176560.343006761811845
32290726287664.2578291513830.252965820053061.742170849110.0667088799960358
33292300290980.7052923663507.362554016861319.29470763407-0.100212725080787
34278506283684.568396565-3284.53796030436-5178.56839656472-2.10761969950104
35269826275220.719885419-6539.31094677593-5394.7198854188-1.01010820301569
36265861266141.309029169-8134.9630780944-280.309029168622-0.495554030517184
37269034265228.951525645-3594.223928551633805.04847435541.41136746363201
38264176261316.262102205-3794.395999530892859.73789779521-0.0621118721731808
39255198254440.732648271-5723.06961355785757.267351728663-0.597611648974433
40253353251795.202624142-3801.651170220741557.797375858010.597162007947039
41246057248835.148361518-3274.98099038878-2778.148361518080.163893734322342
42235372245060.110371348-3588.27626577152-9688.1103713482-0.0972272864969765
43258556250197.8425210371868.690572269918358.157478963031.69138684615723
44260993256703.9882294134765.63149807054289.01177058730.898614106163387
45254663252651.530097941-744.8792599893572011.46990205877-1.71012708863621
46250643252537.916555824-350.253528978847-1894.916555824140.122473726968608
47243422249500.056650198-2030.27733056099-6078.05665019831-0.521500576968638
48247105249297.7166209-887.43965285882-2192.716620899870.354913132466749
49248541245541.765564819-2681.882525316862999.23443518059-0.557260860367922
50245039241233.424336006-3698.700729082443805.57566399421-0.315391330523115
51237080237078.606913007-3983.132144797011.39308699292198-0.0881864648943466
52237085234613.134106086-3038.061132028472471.865893913700.293527027191679
53225554229446.719834182-4365.18619200657-3892.71983418243-0.412584975375893
54226839236093.5435714892508.79419983017-9254.543571488922.13409355040976
55247934241002.632055894005.780357785496931.367944110180.46418647867658
56248333242033.5930207242152.655577505246299.4069792757-0.574715002606664
57246969244729.4126420912490.923308343422239.587357908710.104963504920892
58245098246695.1289006082163.78059439296-1597.12890060804-0.101542989311227
59246263251948.2669064088.5055334127-5685.266905999910.597572639694386
60255765256798.2999024564563.14293235591-1033.29990245560.147386786606342
61264319260745.24528514178.958525268083573.75471489991-0.119255771282371
62268347264211.3040757003734.810419908444135.6959242996-0.137752487726044
63273046271491.3802361275939.701333005131554.619763873080.683806941059474
64273963273021.2081421643199.46545399986941.791857836458-0.850851245520718
65267430275571.5887322542795.77529814821-8141.5887322543-0.125438875508055
66271993281004.530486964437.43376612178-9011.53048695990.509718153492717
67292710284960.1252777064137.619061659587749.87472229357-0.0929935029916293



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')