Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Wed, 02 Dec 2009 09:52:51 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/02/t1259772829oj22fhr5cxsuv88.htm/, Retrieved Sat, 27 Apr 2024 14:01:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62431, Retrieved Sat, 27 Apr 2024 14:01:59 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

133

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   [Decomposition by Loess] [] [2009-11-27 15:00:29] [b98453cac15ba1066b407e146608df68]
-   PD      [Decomposition by Loess] [WS9 Berekening3 TVD] [2009-12-02 16:52:51] [37de18e38c1490dd77c2b362ed87f3bb] [Current]
-   P         [Decomposition by Loess] [BDM 9] [2009-12-02 17:27:55] [f5d341d4bbba73282fc6e80153a6d315] 
-   P         [Decomposition by Loess] [TG 9] [2009-12-02 18:04:19] [a21bac9c8d3d56fdec8be4e719e2c7ed] 
-               [Decomposition by Loess] [WorkShop9 (SHW)] [2009-12-04 14:52:37] [37daf76adc256428993ec4063536c760] 
-   PD        [Decomposition by Loess] [blog 5] [2009-12-07 20:41:20] [42ad1186d39724f834063794eac7cea3] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62431&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62431&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62431&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62431&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]601[/C][C]0[/C][C]61[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62431&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62431&T=1

As an alternative you can also use a QR Code:

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

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	101.3	108.507492755687	-5.10946497033895	99.2019722146518	7.20749275568718
2	106.3	117.026239774926	-3.72865644138776	99.3024166664614	10.7262397749263
3	94	78.0649869771466	10.5321519045824	99.402861118271	-15.9350130228534
4	102.8	100.041118746137	6.07265839563559	99.4862228582277	-2.75888125386329
5	102	100.057257037141	4.37315836467499	99.5695845981843	-1.94274296285934
6	105.1	93.6129857691393	17.0097806032509	99.5772336276099	-11.4870142308607
7	92.4	99.2487034307068	-14.0335860877422	99.5848826570354	6.84870343070683
8	81.4	83.4696606310588	-20.2703196270649	99.6006589960061	2.06966063105877
9	105.8	108.150611413957	3.83295325106610	99.616435334977	2.35061141395698
10	120.3	130.106099574637	10.6780437767246	99.8158566486379	9.80609957463747
11	100.7	97.6215798670752	3.76314217062593	100.015277962299	-3.07842013292480
12	88.8	91.0134094270464	-13.1198688646892	99.7064594376428	2.21340942704643
13	94.3	94.3118240573522	-5.10946497033895	99.3976409129867	0.0118240573522002
14	99.9	104.530358502593	-3.72865644138776	98.998297938795	4.63035850259283
15	103.4	97.6688931308146	10.5321519045824	98.5989549646031	-5.73110686918545
16	103.3	102.156157917015	6.07265839563559	98.3711836873494	-1.14384208298495
17	98.8	95.0834292252294	4.37315836467499	98.1434124100956	-3.7165707747706
18	104.2	93.1730705209222	17.0097806032509	98.217148875827	-11.0269294790778
19	91.2	98.142700746184	-14.0335860877422	98.2908853415583	6.94270074618395
20	74.7	70.7750600005025	-20.2703196270649	98.8952596265624	-3.9249399994975
21	108.5	113.667412837367	3.83295325106610	99.4996339115665	5.16741283736737
22	114.5	117.714123159898	10.6780437767246	100.607833063378	3.21412315989781
23	96.9	88.3208256141855	3.76314217062593	101.716032215189	-8.5791743858145
24	89.6	89.2777833800191	-13.1198688646892	103.04208548467	-0.322216619980892
25	97.1	94.9413262161873	-5.10946497033895	104.368138754152	-2.15867378381273
26	100.3	98.4500179812127	-3.72865644138776	105.878638460175	-1.84998201878729
27	122.6	127.278709929219	10.5321519045824	107.389138166198	4.67870992921922
28	115.4	115.486924286510	6.07265839563559	109.240417317855	0.0869242865097704
29	109	102.535145165814	4.37315836467499	111.091696469511	-6.46485483418589
30	129.1	128.178163318819	17.0097806032509	113.012056077930	-0.921836681180608
31	102.8	104.701170401394	-14.0335860877422	114.932415686349	1.90117040139361
32	96.2	96.234577120422	-20.2703196270649	116.435742506643	0.034577120422
33	127.7	133.627977421997	3.83295325106610	117.939069326937	5.92797742199667
34	128.9	128.147926046627	10.6780437767246	118.974030176648	-0.752073953373
35	126.5	129.227866803015	3.76314217062593	120.008991026360	2.72786680301455
36	119.8	132.206139627267	-13.1198688646892	120.513729237422	12.4061396272674
37	113.2	110.490997521855	-5.10946497033895	121.018467448484	-2.70900247814527
38	114.1	110.992781544017	-3.72865644138776	120.935874897371	-3.10721845598346
39	134.1	136.814565749159	10.5321519045824	120.853282346258	2.71456574915946
40	130	133.166001914071	6.07265839563559	120.761339690294	3.16600191407082
41	121.8	118.557444600996	4.37315836467499	120.669397034329	-3.24255539900399
42	132.1	125.827884185815	17.0097806032509	121.362335210934	-6.27211581418509
43	105.3	102.578312700203	-14.0335860877422	122.055273387539	-2.72168729979724
44	103	102.102548456977	-20.2703196270649	124.167771170087	-0.89745154302257
45	117.1	104.086777796298	3.83295325106610	126.280268952635	-13.0132222037016
46	126.3	112.009272260739	10.6780437767246	129.912683962536	-14.2907277392609
47	138.1	138.891758856937	3.76314217062593	133.545098972437	0.79175885693698
48	119.5	114.098265011494	-13.1198688646892	138.021603853195	-5.40173498850555
49	138	138.611356236386	-5.10946497033895	142.498108733952	0.611356236386456
50	135.5	128.066789579828	-3.72865644138776	146.661866861560	-7.43321042017206
51	178.6	195.842223106250	10.5321519045824	150.825624989167	17.2422231062505
52	162.2	165.005057687351	6.07265839563559	153.322283917013	2.80505768735139
53	176.9	193.607898790466	4.37315836467499	155.818942844859	16.7078987904662
54	204.9	234.635859680012	17.0097806032509	158.154359716737	29.7358596800124
55	132.2	117.943809499128	-14.0335860877422	160.489776588615	-14.2561905008724
56	142.5	142.674240643338	-20.2703196270649	162.596078983726	0.174240643338408
57	164.3	160.064665370096	3.83295325106610	164.702381378838	-4.23533462990446
58	174.9	172.605077039867	10.6780437767246	166.516879183408	-2.29492296013302
59	175.4	178.705480841396	3.76314217062593	168.331376987978	3.30548084139571
60	143	129.183759162283	-13.1198688646892	169.936109702407	-13.8162408377173

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 101.3 & 108.507492755687 & -5.10946497033895 & 99.2019722146518 & 7.20749275568718 \tabularnewline
2 & 106.3 & 117.026239774926 & -3.72865644138776 & 99.3024166664614 & 10.7262397749263 \tabularnewline
3 & 94 & 78.0649869771466 & 10.5321519045824 & 99.402861118271 & -15.9350130228534 \tabularnewline
4 & 102.8 & 100.041118746137 & 6.07265839563559 & 99.4862228582277 & -2.75888125386329 \tabularnewline
5 & 102 & 100.057257037141 & 4.37315836467499 & 99.5695845981843 & -1.94274296285934 \tabularnewline
6 & 105.1 & 93.6129857691393 & 17.0097806032509 & 99.5772336276099 & -11.4870142308607 \tabularnewline
7 & 92.4 & 99.2487034307068 & -14.0335860877422 & 99.5848826570354 & 6.84870343070683 \tabularnewline
8 & 81.4 & 83.4696606310588 & -20.2703196270649 & 99.6006589960061 & 2.06966063105877 \tabularnewline
9 & 105.8 & 108.150611413957 & 3.83295325106610 & 99.616435334977 & 2.35061141395698 \tabularnewline
10 & 120.3 & 130.106099574637 & 10.6780437767246 & 99.8158566486379 & 9.80609957463747 \tabularnewline
11 & 100.7 & 97.6215798670752 & 3.76314217062593 & 100.015277962299 & -3.07842013292480 \tabularnewline
12 & 88.8 & 91.0134094270464 & -13.1198688646892 & 99.7064594376428 & 2.21340942704643 \tabularnewline
13 & 94.3 & 94.3118240573522 & -5.10946497033895 & 99.3976409129867 & 0.0118240573522002 \tabularnewline
14 & 99.9 & 104.530358502593 & -3.72865644138776 & 98.998297938795 & 4.63035850259283 \tabularnewline
15 & 103.4 & 97.6688931308146 & 10.5321519045824 & 98.5989549646031 & -5.73110686918545 \tabularnewline
16 & 103.3 & 102.156157917015 & 6.07265839563559 & 98.3711836873494 & -1.14384208298495 \tabularnewline
17 & 98.8 & 95.0834292252294 & 4.37315836467499 & 98.1434124100956 & -3.7165707747706 \tabularnewline
18 & 104.2 & 93.1730705209222 & 17.0097806032509 & 98.217148875827 & -11.0269294790778 \tabularnewline
19 & 91.2 & 98.142700746184 & -14.0335860877422 & 98.2908853415583 & 6.94270074618395 \tabularnewline
20 & 74.7 & 70.7750600005025 & -20.2703196270649 & 98.8952596265624 & -3.9249399994975 \tabularnewline
21 & 108.5 & 113.667412837367 & 3.83295325106610 & 99.4996339115665 & 5.16741283736737 \tabularnewline
22 & 114.5 & 117.714123159898 & 10.6780437767246 & 100.607833063378 & 3.21412315989781 \tabularnewline
23 & 96.9 & 88.3208256141855 & 3.76314217062593 & 101.716032215189 & -8.5791743858145 \tabularnewline
24 & 89.6 & 89.2777833800191 & -13.1198688646892 & 103.04208548467 & -0.322216619980892 \tabularnewline
25 & 97.1 & 94.9413262161873 & -5.10946497033895 & 104.368138754152 & -2.15867378381273 \tabularnewline
26 & 100.3 & 98.4500179812127 & -3.72865644138776 & 105.878638460175 & -1.84998201878729 \tabularnewline
27 & 122.6 & 127.278709929219 & 10.5321519045824 & 107.389138166198 & 4.67870992921922 \tabularnewline
28 & 115.4 & 115.486924286510 & 6.07265839563559 & 109.240417317855 & 0.0869242865097704 \tabularnewline
29 & 109 & 102.535145165814 & 4.37315836467499 & 111.091696469511 & -6.46485483418589 \tabularnewline
30 & 129.1 & 128.178163318819 & 17.0097806032509 & 113.012056077930 & -0.921836681180608 \tabularnewline
31 & 102.8 & 104.701170401394 & -14.0335860877422 & 114.932415686349 & 1.90117040139361 \tabularnewline
32 & 96.2 & 96.234577120422 & -20.2703196270649 & 116.435742506643 & 0.034577120422 \tabularnewline
33 & 127.7 & 133.627977421997 & 3.83295325106610 & 117.939069326937 & 5.92797742199667 \tabularnewline
34 & 128.9 & 128.147926046627 & 10.6780437767246 & 118.974030176648 & -0.752073953373 \tabularnewline
35 & 126.5 & 129.227866803015 & 3.76314217062593 & 120.008991026360 & 2.72786680301455 \tabularnewline
36 & 119.8 & 132.206139627267 & -13.1198688646892 & 120.513729237422 & 12.4061396272674 \tabularnewline
37 & 113.2 & 110.490997521855 & -5.10946497033895 & 121.018467448484 & -2.70900247814527 \tabularnewline
38 & 114.1 & 110.992781544017 & -3.72865644138776 & 120.935874897371 & -3.10721845598346 \tabularnewline
39 & 134.1 & 136.814565749159 & 10.5321519045824 & 120.853282346258 & 2.71456574915946 \tabularnewline
40 & 130 & 133.166001914071 & 6.07265839563559 & 120.761339690294 & 3.16600191407082 \tabularnewline
41 & 121.8 & 118.557444600996 & 4.37315836467499 & 120.669397034329 & -3.24255539900399 \tabularnewline
42 & 132.1 & 125.827884185815 & 17.0097806032509 & 121.362335210934 & -6.27211581418509 \tabularnewline
43 & 105.3 & 102.578312700203 & -14.0335860877422 & 122.055273387539 & -2.72168729979724 \tabularnewline
44 & 103 & 102.102548456977 & -20.2703196270649 & 124.167771170087 & -0.89745154302257 \tabularnewline
45 & 117.1 & 104.086777796298 & 3.83295325106610 & 126.280268952635 & -13.0132222037016 \tabularnewline
46 & 126.3 & 112.009272260739 & 10.6780437767246 & 129.912683962536 & -14.2907277392609 \tabularnewline
47 & 138.1 & 138.891758856937 & 3.76314217062593 & 133.545098972437 & 0.79175885693698 \tabularnewline
48 & 119.5 & 114.098265011494 & -13.1198688646892 & 138.021603853195 & -5.40173498850555 \tabularnewline
49 & 138 & 138.611356236386 & -5.10946497033895 & 142.498108733952 & 0.611356236386456 \tabularnewline
50 & 135.5 & 128.066789579828 & -3.72865644138776 & 146.661866861560 & -7.43321042017206 \tabularnewline
51 & 178.6 & 195.842223106250 & 10.5321519045824 & 150.825624989167 & 17.2422231062505 \tabularnewline
52 & 162.2 & 165.005057687351 & 6.07265839563559 & 153.322283917013 & 2.80505768735139 \tabularnewline
53 & 176.9 & 193.607898790466 & 4.37315836467499 & 155.818942844859 & 16.7078987904662 \tabularnewline
54 & 204.9 & 234.635859680012 & 17.0097806032509 & 158.154359716737 & 29.7358596800124 \tabularnewline
55 & 132.2 & 117.943809499128 & -14.0335860877422 & 160.489776588615 & -14.2561905008724 \tabularnewline
56 & 142.5 & 142.674240643338 & -20.2703196270649 & 162.596078983726 & 0.174240643338408 \tabularnewline
57 & 164.3 & 160.064665370096 & 3.83295325106610 & 164.702381378838 & -4.23533462990446 \tabularnewline
58 & 174.9 & 172.605077039867 & 10.6780437767246 & 166.516879183408 & -2.29492296013302 \tabularnewline
59 & 175.4 & 178.705480841396 & 3.76314217062593 & 168.331376987978 & 3.30548084139571 \tabularnewline
60 & 143 & 129.183759162283 & -13.1198688646892 & 169.936109702407 & -13.8162408377173 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62431&T=2

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Time Series Components[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Seasonal[/C][C]Trend[/C][C]Remainder[/C][/ROW]
[ROW][C]1[/C][C]101.3[/C][C]108.507492755687[/C][C]-5.10946497033895[/C][C]99.2019722146518[/C][C]7.20749275568718[/C][/ROW]
[ROW][C]2[/C][C]106.3[/C][C]117.026239774926[/C][C]-3.72865644138776[/C][C]99.3024166664614[/C][C]10.7262397749263[/C][/ROW]
[ROW][C]3[/C][C]94[/C][C]78.0649869771466[/C][C]10.5321519045824[/C][C]99.402861118271[/C][C]-15.9350130228534[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]100.041118746137[/C][C]6.07265839563559[/C][C]99.4862228582277[/C][C]-2.75888125386329[/C][/ROW]
[ROW][C]5[/C][C]102[/C][C]100.057257037141[/C][C]4.37315836467499[/C][C]99.5695845981843[/C][C]-1.94274296285934[/C][/ROW]
[ROW][C]6[/C][C]105.1[/C][C]93.6129857691393[/C][C]17.0097806032509[/C][C]99.5772336276099[/C][C]-11.4870142308607[/C][/ROW]
[ROW][C]7[/C][C]92.4[/C][C]99.2487034307068[/C][C]-14.0335860877422[/C][C]99.5848826570354[/C][C]6.84870343070683[/C][/ROW]
[ROW][C]8[/C][C]81.4[/C][C]83.4696606310588[/C][C]-20.2703196270649[/C][C]99.6006589960061[/C][C]2.06966063105877[/C][/ROW]
[ROW][C]9[/C][C]105.8[/C][C]108.150611413957[/C][C]3.83295325106610[/C][C]99.616435334977[/C][C]2.35061141395698[/C][/ROW]
[ROW][C]10[/C][C]120.3[/C][C]130.106099574637[/C][C]10.6780437767246[/C][C]99.8158566486379[/C][C]9.80609957463747[/C][/ROW]
[ROW][C]11[/C][C]100.7[/C][C]97.6215798670752[/C][C]3.76314217062593[/C][C]100.015277962299[/C][C]-3.07842013292480[/C][/ROW]
[ROW][C]12[/C][C]88.8[/C][C]91.0134094270464[/C][C]-13.1198688646892[/C][C]99.7064594376428[/C][C]2.21340942704643[/C][/ROW]
[ROW][C]13[/C][C]94.3[/C][C]94.3118240573522[/C][C]-5.10946497033895[/C][C]99.3976409129867[/C][C]0.0118240573522002[/C][/ROW]
[ROW][C]14[/C][C]99.9[/C][C]104.530358502593[/C][C]-3.72865644138776[/C][C]98.998297938795[/C][C]4.63035850259283[/C][/ROW]
[ROW][C]15[/C][C]103.4[/C][C]97.6688931308146[/C][C]10.5321519045824[/C][C]98.5989549646031[/C][C]-5.73110686918545[/C][/ROW]
[ROW][C]16[/C][C]103.3[/C][C]102.156157917015[/C][C]6.07265839563559[/C][C]98.3711836873494[/C][C]-1.14384208298495[/C][/ROW]
[ROW][C]17[/C][C]98.8[/C][C]95.0834292252294[/C][C]4.37315836467499[/C][C]98.1434124100956[/C][C]-3.7165707747706[/C][/ROW]
[ROW][C]18[/C][C]104.2[/C][C]93.1730705209222[/C][C]17.0097806032509[/C][C]98.217148875827[/C][C]-11.0269294790778[/C][/ROW]
[ROW][C]19[/C][C]91.2[/C][C]98.142700746184[/C][C]-14.0335860877422[/C][C]98.2908853415583[/C][C]6.94270074618395[/C][/ROW]
[ROW][C]20[/C][C]74.7[/C][C]70.7750600005025[/C][C]-20.2703196270649[/C][C]98.8952596265624[/C][C]-3.9249399994975[/C][/ROW]
[ROW][C]21[/C][C]108.5[/C][C]113.667412837367[/C][C]3.83295325106610[/C][C]99.4996339115665[/C][C]5.16741283736737[/C][/ROW]
[ROW][C]22[/C][C]114.5[/C][C]117.714123159898[/C][C]10.6780437767246[/C][C]100.607833063378[/C][C]3.21412315989781[/C][/ROW]
[ROW][C]23[/C][C]96.9[/C][C]88.3208256141855[/C][C]3.76314217062593[/C][C]101.716032215189[/C][C]-8.5791743858145[/C][/ROW]
[ROW][C]24[/C][C]89.6[/C][C]89.2777833800191[/C][C]-13.1198688646892[/C][C]103.04208548467[/C][C]-0.322216619980892[/C][/ROW]
[ROW][C]25[/C][C]97.1[/C][C]94.9413262161873[/C][C]-5.10946497033895[/C][C]104.368138754152[/C][C]-2.15867378381273[/C][/ROW]
[ROW][C]26[/C][C]100.3[/C][C]98.4500179812127[/C][C]-3.72865644138776[/C][C]105.878638460175[/C][C]-1.84998201878729[/C][/ROW]
[ROW][C]27[/C][C]122.6[/C][C]127.278709929219[/C][C]10.5321519045824[/C][C]107.389138166198[/C][C]4.67870992921922[/C][/ROW]
[ROW][C]28[/C][C]115.4[/C][C]115.486924286510[/C][C]6.07265839563559[/C][C]109.240417317855[/C][C]0.0869242865097704[/C][/ROW]
[ROW][C]29[/C][C]109[/C][C]102.535145165814[/C][C]4.37315836467499[/C][C]111.091696469511[/C][C]-6.46485483418589[/C][/ROW]
[ROW][C]30[/C][C]129.1[/C][C]128.178163318819[/C][C]17.0097806032509[/C][C]113.012056077930[/C][C]-0.921836681180608[/C][/ROW]
[ROW][C]31[/C][C]102.8[/C][C]104.701170401394[/C][C]-14.0335860877422[/C][C]114.932415686349[/C][C]1.90117040139361[/C][/ROW]
[ROW][C]32[/C][C]96.2[/C][C]96.234577120422[/C][C]-20.2703196270649[/C][C]116.435742506643[/C][C]0.034577120422[/C][/ROW]
[ROW][C]33[/C][C]127.7[/C][C]133.627977421997[/C][C]3.83295325106610[/C][C]117.939069326937[/C][C]5.92797742199667[/C][/ROW]
[ROW][C]34[/C][C]128.9[/C][C]128.147926046627[/C][C]10.6780437767246[/C][C]118.974030176648[/C][C]-0.752073953373[/C][/ROW]
[ROW][C]35[/C][C]126.5[/C][C]129.227866803015[/C][C]3.76314217062593[/C][C]120.008991026360[/C][C]2.72786680301455[/C][/ROW]
[ROW][C]36[/C][C]119.8[/C][C]132.206139627267[/C][C]-13.1198688646892[/C][C]120.513729237422[/C][C]12.4061396272674[/C][/ROW]
[ROW][C]37[/C][C]113.2[/C][C]110.490997521855[/C][C]-5.10946497033895[/C][C]121.018467448484[/C][C]-2.70900247814527[/C][/ROW]
[ROW][C]38[/C][C]114.1[/C][C]110.992781544017[/C][C]-3.72865644138776[/C][C]120.935874897371[/C][C]-3.10721845598346[/C][/ROW]
[ROW][C]39[/C][C]134.1[/C][C]136.814565749159[/C][C]10.5321519045824[/C][C]120.853282346258[/C][C]2.71456574915946[/C][/ROW]
[ROW][C]40[/C][C]130[/C][C]133.166001914071[/C][C]6.07265839563559[/C][C]120.761339690294[/C][C]3.16600191407082[/C][/ROW]
[ROW][C]41[/C][C]121.8[/C][C]118.557444600996[/C][C]4.37315836467499[/C][C]120.669397034329[/C][C]-3.24255539900399[/C][/ROW]
[ROW][C]42[/C][C]132.1[/C][C]125.827884185815[/C][C]17.0097806032509[/C][C]121.362335210934[/C][C]-6.27211581418509[/C][/ROW]
[ROW][C]43[/C][C]105.3[/C][C]102.578312700203[/C][C]-14.0335860877422[/C][C]122.055273387539[/C][C]-2.72168729979724[/C][/ROW]
[ROW][C]44[/C][C]103[/C][C]102.102548456977[/C][C]-20.2703196270649[/C][C]124.167771170087[/C][C]-0.89745154302257[/C][/ROW]
[ROW][C]45[/C][C]117.1[/C][C]104.086777796298[/C][C]3.83295325106610[/C][C]126.280268952635[/C][C]-13.0132222037016[/C][/ROW]
[ROW][C]46[/C][C]126.3[/C][C]112.009272260739[/C][C]10.6780437767246[/C][C]129.912683962536[/C][C]-14.2907277392609[/C][/ROW]
[ROW][C]47[/C][C]138.1[/C][C]138.891758856937[/C][C]3.76314217062593[/C][C]133.545098972437[/C][C]0.79175885693698[/C][/ROW]
[ROW][C]48[/C][C]119.5[/C][C]114.098265011494[/C][C]-13.1198688646892[/C][C]138.021603853195[/C][C]-5.40173498850555[/C][/ROW]
[ROW][C]49[/C][C]138[/C][C]138.611356236386[/C][C]-5.10946497033895[/C][C]142.498108733952[/C][C]0.611356236386456[/C][/ROW]
[ROW][C]50[/C][C]135.5[/C][C]128.066789579828[/C][C]-3.72865644138776[/C][C]146.661866861560[/C][C]-7.43321042017206[/C][/ROW]
[ROW][C]51[/C][C]178.6[/C][C]195.842223106250[/C][C]10.5321519045824[/C][C]150.825624989167[/C][C]17.2422231062505[/C][/ROW]
[ROW][C]52[/C][C]162.2[/C][C]165.005057687351[/C][C]6.07265839563559[/C][C]153.322283917013[/C][C]2.80505768735139[/C][/ROW]
[ROW][C]53[/C][C]176.9[/C][C]193.607898790466[/C][C]4.37315836467499[/C][C]155.818942844859[/C][C]16.7078987904662[/C][/ROW]
[ROW][C]54[/C][C]204.9[/C][C]234.635859680012[/C][C]17.0097806032509[/C][C]158.154359716737[/C][C]29.7358596800124[/C][/ROW]
[ROW][C]55[/C][C]132.2[/C][C]117.943809499128[/C][C]-14.0335860877422[/C][C]160.489776588615[/C][C]-14.2561905008724[/C][/ROW]
[ROW][C]56[/C][C]142.5[/C][C]142.674240643338[/C][C]-20.2703196270649[/C][C]162.596078983726[/C][C]0.174240643338408[/C][/ROW]
[ROW][C]57[/C][C]164.3[/C][C]160.064665370096[/C][C]3.83295325106610[/C][C]164.702381378838[/C][C]-4.23533462990446[/C][/ROW]
[ROW][C]58[/C][C]174.9[/C][C]172.605077039867[/C][C]10.6780437767246[/C][C]166.516879183408[/C][C]-2.29492296013302[/C][/ROW]
[ROW][C]59[/C][C]175.4[/C][C]178.705480841396[/C][C]3.76314217062593[/C][C]168.331376987978[/C][C]3.30548084139571[/C][/ROW]
[ROW][C]60[/C][C]143[/C][C]129.183759162283[/C][C]-13.1198688646892[/C][C]169.936109702407[/C][C]-13.8162408377173[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62431&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62431&T=2

As an alternative you can also use a QR Code:

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

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	101.3	108.507492755687	-5.10946497033895	99.2019722146518	7.20749275568718
2	106.3	117.026239774926	-3.72865644138776	99.3024166664614	10.7262397749263
3	94	78.0649869771466	10.5321519045824	99.402861118271	-15.9350130228534
4	102.8	100.041118746137	6.07265839563559	99.4862228582277	-2.75888125386329
5	102	100.057257037141	4.37315836467499	99.5695845981843	-1.94274296285934
6	105.1	93.6129857691393	17.0097806032509	99.5772336276099	-11.4870142308607
7	92.4	99.2487034307068	-14.0335860877422	99.5848826570354	6.84870343070683
8	81.4	83.4696606310588	-20.2703196270649	99.6006589960061	2.06966063105877
9	105.8	108.150611413957	3.83295325106610	99.616435334977	2.35061141395698
10	120.3	130.106099574637	10.6780437767246	99.8158566486379	9.80609957463747
11	100.7	97.6215798670752	3.76314217062593	100.015277962299	-3.07842013292480
12	88.8	91.0134094270464	-13.1198688646892	99.7064594376428	2.21340942704643
13	94.3	94.3118240573522	-5.10946497033895	99.3976409129867	0.0118240573522002
14	99.9	104.530358502593	-3.72865644138776	98.998297938795	4.63035850259283
15	103.4	97.6688931308146	10.5321519045824	98.5989549646031	-5.73110686918545
16	103.3	102.156157917015	6.07265839563559	98.3711836873494	-1.14384208298495
17	98.8	95.0834292252294	4.37315836467499	98.1434124100956	-3.7165707747706
18	104.2	93.1730705209222	17.0097806032509	98.217148875827	-11.0269294790778
19	91.2	98.142700746184	-14.0335860877422	98.2908853415583	6.94270074618395
20	74.7	70.7750600005025	-20.2703196270649	98.8952596265624	-3.9249399994975
21	108.5	113.667412837367	3.83295325106610	99.4996339115665	5.16741283736737
22	114.5	117.714123159898	10.6780437767246	100.607833063378	3.21412315989781
23	96.9	88.3208256141855	3.76314217062593	101.716032215189	-8.5791743858145
24	89.6	89.2777833800191	-13.1198688646892	103.04208548467	-0.322216619980892
25	97.1	94.9413262161873	-5.10946497033895	104.368138754152	-2.15867378381273
26	100.3	98.4500179812127	-3.72865644138776	105.878638460175	-1.84998201878729
27	122.6	127.278709929219	10.5321519045824	107.389138166198	4.67870992921922
28	115.4	115.486924286510	6.07265839563559	109.240417317855	0.0869242865097704
29	109	102.535145165814	4.37315836467499	111.091696469511	-6.46485483418589
30	129.1	128.178163318819	17.0097806032509	113.012056077930	-0.921836681180608
31	102.8	104.701170401394	-14.0335860877422	114.932415686349	1.90117040139361
32	96.2	96.234577120422	-20.2703196270649	116.435742506643	0.034577120422
33	127.7	133.627977421997	3.83295325106610	117.939069326937	5.92797742199667
34	128.9	128.147926046627	10.6780437767246	118.974030176648	-0.752073953373
35	126.5	129.227866803015	3.76314217062593	120.008991026360	2.72786680301455
36	119.8	132.206139627267	-13.1198688646892	120.513729237422	12.4061396272674
37	113.2	110.490997521855	-5.10946497033895	121.018467448484	-2.70900247814527
38	114.1	110.992781544017	-3.72865644138776	120.935874897371	-3.10721845598346
39	134.1	136.814565749159	10.5321519045824	120.853282346258	2.71456574915946
40	130	133.166001914071	6.07265839563559	120.761339690294	3.16600191407082
41	121.8	118.557444600996	4.37315836467499	120.669397034329	-3.24255539900399
42	132.1	125.827884185815	17.0097806032509	121.362335210934	-6.27211581418509
43	105.3	102.578312700203	-14.0335860877422	122.055273387539	-2.72168729979724
44	103	102.102548456977	-20.2703196270649	124.167771170087	-0.89745154302257
45	117.1	104.086777796298	3.83295325106610	126.280268952635	-13.0132222037016
46	126.3	112.009272260739	10.6780437767246	129.912683962536	-14.2907277392609
47	138.1	138.891758856937	3.76314217062593	133.545098972437	0.79175885693698
48	119.5	114.098265011494	-13.1198688646892	138.021603853195	-5.40173498850555
49	138	138.611356236386	-5.10946497033895	142.498108733952	0.611356236386456
50	135.5	128.066789579828	-3.72865644138776	146.661866861560	-7.43321042017206
51	178.6	195.842223106250	10.5321519045824	150.825624989167	17.2422231062505
52	162.2	165.005057687351	6.07265839563559	153.322283917013	2.80505768735139
53	176.9	193.607898790466	4.37315836467499	155.818942844859	16.7078987904662
54	204.9	234.635859680012	17.0097806032509	158.154359716737	29.7358596800124
55	132.2	117.943809499128	-14.0335860877422	160.489776588615	-14.2561905008724
56	142.5	142.674240643338	-20.2703196270649	162.596078983726	0.174240643338408
57	164.3	160.064665370096	3.83295325106610	164.702381378838	-4.23533462990446
58	174.9	172.605077039867	10.6780437767246	166.516879183408	-2.29492296013302
59	175.4	178.705480841396	3.76314217062593	168.331376987978	3.30548084139571
60	143	129.183759162283	-13.1198688646892	169.936109702407	-13.8162408377173

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = ; par5 = 1 ; par6 = ; par7 = 1 ; par8 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',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,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',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,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code