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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Fri, 11 Dec 2009 08:57:17 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/11/t12605470928jk1aqiorx36sp1.htm/, Retrieved Sun, 28 Apr 2024 22:23:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66430, Retrieved Sun, 28 Apr 2024 22:23:02 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

141

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]
-    D    [Decomposition by Loess] [Seizoenale decomp...] [2009-12-01 19:51:41] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-    D      [Decomposition by Loess] [seizoenale decomp...] [2009-12-04 19:15:18] [4f1a20f787b3465111b61213cdeef1a9]
-    D          [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 15:57:17] [d1818fb1d9a1b0f34f8553ada228d3d5] [Current]
-    D            [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 16:44:09] [4f1a20f787b3465111b61213cdeef1a9] 

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Dataseries X:

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Histogram

Boxplots

8.3
8.2
8
7.9
7.6
7.6
8.3
8.4
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8
8.2
8.1
8.1
8
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.4
6.1
6.5
7.7
7.9
7.5
6.9
6.6
6.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66430&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]1 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=66430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66430&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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	721	0	73
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 & 721 & 0 & 73 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66430&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]721[/C][C]0[/C][C]73[/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=66430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66430&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	721	0	73
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.3	8.38169615990256	0.277639293792519	7.94066454630492	0.081696159902564
2	8.2	8.18503074300507	0.234155553774526	7.9808137032204	-0.0149692569949327
3	8	7.95503202966448	0.0240051101996253	8.0209628601359	-0.0449679703355192
4	7.9	7.97169041532253	-0.232841275216927	8.0611508598944	0.0716904153225286
5	7.6	7.57168208161353	-0.473020941266434	8.1013388596529	-0.0283179183864704
6	7.6	7.58760053614236	-0.527791762265371	8.14019122612301	-0.0123994638576423
7	8.3	8.18685231873455	0.234104088672330	8.17904359259312	-0.113147681265453
8	8.4	8.23862836146345	0.346806919815766	8.21456471872079	-0.161371638536552
9	8.4	8.30707091802786	0.242843237123693	8.25008584484845	-0.0929290819721427
10	8.4	8.52759194014847	0.00603602267169722	8.26637203717983	0.127591940148468
11	8.4	8.66477968163613	-0.147437911147342	8.28265822951122	0.264779681636126
12	8.6	8.90161391023905	0.0155015625210638	8.28288452723988	0.301613910239054
13	8.9	9.23924988123893	0.277639293792519	8.28311082496855	0.339249881238933
14	8.8	9.05755799665698	0.234155553774526	8.3082864495685	0.257557996656976
15	8.3	8.24253281563193	0.0240051101996253	8.33346207416845	-0.0574671843680754
16	7.5	6.87741119703519	-0.232841275216927	8.35543007818174	-0.622588802964814
17	7.2	6.49562285907141	-0.473020941266434	8.37739808219503	-0.704377140928594
18	7.4	6.94945509767655	-0.527791762265371	8.37833666458882	-0.450544902323447
19	8.8	8.98662066434506	0.234104088672330	8.3792752469826	0.186620664345062
20	9.3	9.86243575244455	0.346806919815766	8.39075732773968	0.562435752444554
21	9.3	9.95491735437955	0.242843237123693	8.40223940849676	0.654917354379553
22	8.7	8.95951958251291	0.00603602267169722	8.43444439481539	0.259519582512914
23	8.2	8.08078853001332	-0.147437911147342	8.46664938113402	-0.119211469986679
24	8.3	8.10798406189246	0.0155015625210638	8.47651437558648	-0.192015938107545
25	8.5	8.23598133616854	0.277639293792519	8.48637937003894	-0.264018663831461
26	8.6	8.50249417226413	0.234155553774526	8.46335027396135	-0.0975058277358727
27	8.5	8.53567371191662	0.0240051101996253	8.44032117788375	0.0356737119166244
28	8.2	8.2028342323488	-0.232841275216927	8.43000704286812	0.00283423234880686
29	8.1	8.25332803341394	-0.473020941266434	8.41969290785249	0.153328033413944
30	7.9	7.89764343790296	-0.527791762265371	8.43014832436242	-0.00235656209704516
31	8.6	8.52529217045532	0.234104088672330	8.44060374087235	-0.0747078295446766
32	8.7	8.60140842351039	0.346806919815766	8.45178465667384	-0.0985915764896106
33	8.7	8.69419119040097	0.242843237123693	8.46296557247534	-0.00580880959903318
34	8.5	8.5175166649428	0.00603602267169722	8.4764473123855	0.0175166649427965
35	8.4	8.45750885885167	-0.147437911147342	8.48992905229567	0.0575088588516692
36	8.5	8.49277864497527	0.0155015625210638	8.49171979250366	-0.00722135502472732
37	8.7	8.62885017349583	0.277639293792519	8.49351053271165	-0.0711498265041737
38	8.7	8.70149845027473	0.234155553774526	8.46434599595074	0.00149845027472928
39	8.6	8.74081343061054	0.0240051101996253	8.43518145918983	0.140813430610542
40	8.5	8.84718098574828	-0.232841275216927	8.38566028946865	0.347180985748281
41	8.3	8.73688182151897	-0.473020941266434	8.33613911974746	0.436881821518973
42	8	8.25047209161098	-0.527791762265371	8.2773196706544	0.250472091610977
43	8.2	7.94739568976634	0.234104088672330	8.21850022156133	-0.252604310233659
44	8.1	7.69963112989341	0.346806919815766	8.15356195029082	-0.400368870106585
45	8.1	7.868533083856	0.242843237123693	8.08862367902031	-0.231466916144003
46	8	7.95941641054035	0.00603602267169722	8.03454756678795	-0.0405835894596454
47	7.9	7.96696645659176	-0.147437911147342	7.98047145455558	0.066966456591758
48	7.9	7.84836033073857	0.0155015625210638	7.93613810674036	-0.0516396692614256
49	8	7.83055594728234	0.277639293792519	7.89180475892514	-0.169444052717657
50	8	7.9344433202293	0.234155553774526	7.83140112599617	-0.0655566797706921
51	7.9	8.00499739673318	0.0240051101996253	7.7709974930672	0.104997396733181
52	8	8.54246374306716	-0.232841275216927	7.69037753214976	0.542463743067163
53	7.7	8.2632633700341	-0.473020941266434	7.60975757123233	0.563263370034102
54	7.2	7.39969993335546	-0.527791762265371	7.52809182890992	0.199699933355455
55	7.5	7.31946982474017	0.234104088672330	7.4464260865875	-0.180530175259831
56	7.3	6.89817354098995	0.346806919815766	7.35501953919429	-0.401826459010055
57	7	6.49354377107523	0.242843237123693	7.26361299180108	-0.506456228924768
58	7	6.83000651049536	0.00603602267169722	7.16395746683294	-0.169993489504642
59	7	7.08313596928253	-0.147437911147342	7.06430194186481	0.0831359692825284
60	7.2	7.37016762469271	0.0155015625210638	7.01433081278623	0.170167624692708
61	7.3	7.35800102249984	0.277639293792519	6.96435968370764	0.0580010224998393
62	7.1	6.99162072223912	0.234155553774526	6.97422372398636	-0.108379277760884
63	6.8	6.5919071255353	0.0240051101996253	6.98408776426507	-0.208092874464698
64	6.4	6.03966181499422	-0.232841275216927	6.9931794602227	-0.360338185005777
65	6.1	5.6707497850861	-0.473020941266434	7.00227115618033	-0.429250214913901
66	6.5	6.52451746952528	-0.527791762265371	7.00327429274009	0.0245174695252803
67	7.7	8.16161848202782	0.234104088672330	7.00427742929985	0.461618482027822
68	7.9	8.44359314117838	0.346806919815766	7.00959993900586	0.543593141178374
69	7.5	7.74223431416444	0.242843237123693	7.01492244871187	0.242234314164437
70	6.9	6.77170283402314	0.00603602267169722	7.02226114330516	-0.128297165976859
71	6.6	6.31783807324889	-0.147437911147342	7.02959983789845	-0.282161926751111
72	6.9	6.74863785093774	0.0155015625210638	7.0358605865412	-0.151362149062257

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 8.3 & 8.38169615990256 & 0.277639293792519 & 7.94066454630492 & 0.081696159902564 \tabularnewline
2 & 8.2 & 8.18503074300507 & 0.234155553774526 & 7.9808137032204 & -0.0149692569949327 \tabularnewline
3 & 8 & 7.95503202966448 & 0.0240051101996253 & 8.0209628601359 & -0.0449679703355192 \tabularnewline
4 & 7.9 & 7.97169041532253 & -0.232841275216927 & 8.0611508598944 & 0.0716904153225286 \tabularnewline
5 & 7.6 & 7.57168208161353 & -0.473020941266434 & 8.1013388596529 & -0.0283179183864704 \tabularnewline
6 & 7.6 & 7.58760053614236 & -0.527791762265371 & 8.14019122612301 & -0.0123994638576423 \tabularnewline
7 & 8.3 & 8.18685231873455 & 0.234104088672330 & 8.17904359259312 & -0.113147681265453 \tabularnewline
8 & 8.4 & 8.23862836146345 & 0.346806919815766 & 8.21456471872079 & -0.161371638536552 \tabularnewline
9 & 8.4 & 8.30707091802786 & 0.242843237123693 & 8.25008584484845 & -0.0929290819721427 \tabularnewline
10 & 8.4 & 8.52759194014847 & 0.00603602267169722 & 8.26637203717983 & 0.127591940148468 \tabularnewline
11 & 8.4 & 8.66477968163613 & -0.147437911147342 & 8.28265822951122 & 0.264779681636126 \tabularnewline
12 & 8.6 & 8.90161391023905 & 0.0155015625210638 & 8.28288452723988 & 0.301613910239054 \tabularnewline
13 & 8.9 & 9.23924988123893 & 0.277639293792519 & 8.28311082496855 & 0.339249881238933 \tabularnewline
14 & 8.8 & 9.05755799665698 & 0.234155553774526 & 8.3082864495685 & 0.257557996656976 \tabularnewline
15 & 8.3 & 8.24253281563193 & 0.0240051101996253 & 8.33346207416845 & -0.0574671843680754 \tabularnewline
16 & 7.5 & 6.87741119703519 & -0.232841275216927 & 8.35543007818174 & -0.622588802964814 \tabularnewline
17 & 7.2 & 6.49562285907141 & -0.473020941266434 & 8.37739808219503 & -0.704377140928594 \tabularnewline
18 & 7.4 & 6.94945509767655 & -0.527791762265371 & 8.37833666458882 & -0.450544902323447 \tabularnewline
19 & 8.8 & 8.98662066434506 & 0.234104088672330 & 8.3792752469826 & 0.186620664345062 \tabularnewline
20 & 9.3 & 9.86243575244455 & 0.346806919815766 & 8.39075732773968 & 0.562435752444554 \tabularnewline
21 & 9.3 & 9.95491735437955 & 0.242843237123693 & 8.40223940849676 & 0.654917354379553 \tabularnewline
22 & 8.7 & 8.95951958251291 & 0.00603602267169722 & 8.43444439481539 & 0.259519582512914 \tabularnewline
23 & 8.2 & 8.08078853001332 & -0.147437911147342 & 8.46664938113402 & -0.119211469986679 \tabularnewline
24 & 8.3 & 8.10798406189246 & 0.0155015625210638 & 8.47651437558648 & -0.192015938107545 \tabularnewline
25 & 8.5 & 8.23598133616854 & 0.277639293792519 & 8.48637937003894 & -0.264018663831461 \tabularnewline
26 & 8.6 & 8.50249417226413 & 0.234155553774526 & 8.46335027396135 & -0.0975058277358727 \tabularnewline
27 & 8.5 & 8.53567371191662 & 0.0240051101996253 & 8.44032117788375 & 0.0356737119166244 \tabularnewline
28 & 8.2 & 8.2028342323488 & -0.232841275216927 & 8.43000704286812 & 0.00283423234880686 \tabularnewline
29 & 8.1 & 8.25332803341394 & -0.473020941266434 & 8.41969290785249 & 0.153328033413944 \tabularnewline
30 & 7.9 & 7.89764343790296 & -0.527791762265371 & 8.43014832436242 & -0.00235656209704516 \tabularnewline
31 & 8.6 & 8.52529217045532 & 0.234104088672330 & 8.44060374087235 & -0.0747078295446766 \tabularnewline
32 & 8.7 & 8.60140842351039 & 0.346806919815766 & 8.45178465667384 & -0.0985915764896106 \tabularnewline
33 & 8.7 & 8.69419119040097 & 0.242843237123693 & 8.46296557247534 & -0.00580880959903318 \tabularnewline
34 & 8.5 & 8.5175166649428 & 0.00603602267169722 & 8.4764473123855 & 0.0175166649427965 \tabularnewline
35 & 8.4 & 8.45750885885167 & -0.147437911147342 & 8.48992905229567 & 0.0575088588516692 \tabularnewline
36 & 8.5 & 8.49277864497527 & 0.0155015625210638 & 8.49171979250366 & -0.00722135502472732 \tabularnewline
37 & 8.7 & 8.62885017349583 & 0.277639293792519 & 8.49351053271165 & -0.0711498265041737 \tabularnewline
38 & 8.7 & 8.70149845027473 & 0.234155553774526 & 8.46434599595074 & 0.00149845027472928 \tabularnewline
39 & 8.6 & 8.74081343061054 & 0.0240051101996253 & 8.43518145918983 & 0.140813430610542 \tabularnewline
40 & 8.5 & 8.84718098574828 & -0.232841275216927 & 8.38566028946865 & 0.347180985748281 \tabularnewline
41 & 8.3 & 8.73688182151897 & -0.473020941266434 & 8.33613911974746 & 0.436881821518973 \tabularnewline
42 & 8 & 8.25047209161098 & -0.527791762265371 & 8.2773196706544 & 0.250472091610977 \tabularnewline
43 & 8.2 & 7.94739568976634 & 0.234104088672330 & 8.21850022156133 & -0.252604310233659 \tabularnewline
44 & 8.1 & 7.69963112989341 & 0.346806919815766 & 8.15356195029082 & -0.400368870106585 \tabularnewline
45 & 8.1 & 7.868533083856 & 0.242843237123693 & 8.08862367902031 & -0.231466916144003 \tabularnewline
46 & 8 & 7.95941641054035 & 0.00603602267169722 & 8.03454756678795 & -0.0405835894596454 \tabularnewline
47 & 7.9 & 7.96696645659176 & -0.147437911147342 & 7.98047145455558 & 0.066966456591758 \tabularnewline
48 & 7.9 & 7.84836033073857 & 0.0155015625210638 & 7.93613810674036 & -0.0516396692614256 \tabularnewline
49 & 8 & 7.83055594728234 & 0.277639293792519 & 7.89180475892514 & -0.169444052717657 \tabularnewline
50 & 8 & 7.9344433202293 & 0.234155553774526 & 7.83140112599617 & -0.0655566797706921 \tabularnewline
51 & 7.9 & 8.00499739673318 & 0.0240051101996253 & 7.7709974930672 & 0.104997396733181 \tabularnewline
52 & 8 & 8.54246374306716 & -0.232841275216927 & 7.69037753214976 & 0.542463743067163 \tabularnewline
53 & 7.7 & 8.2632633700341 & -0.473020941266434 & 7.60975757123233 & 0.563263370034102 \tabularnewline
54 & 7.2 & 7.39969993335546 & -0.527791762265371 & 7.52809182890992 & 0.199699933355455 \tabularnewline
55 & 7.5 & 7.31946982474017 & 0.234104088672330 & 7.4464260865875 & -0.180530175259831 \tabularnewline
56 & 7.3 & 6.89817354098995 & 0.346806919815766 & 7.35501953919429 & -0.401826459010055 \tabularnewline
57 & 7 & 6.49354377107523 & 0.242843237123693 & 7.26361299180108 & -0.506456228924768 \tabularnewline
58 & 7 & 6.83000651049536 & 0.00603602267169722 & 7.16395746683294 & -0.169993489504642 \tabularnewline
59 & 7 & 7.08313596928253 & -0.147437911147342 & 7.06430194186481 & 0.0831359692825284 \tabularnewline
60 & 7.2 & 7.37016762469271 & 0.0155015625210638 & 7.01433081278623 & 0.170167624692708 \tabularnewline
61 & 7.3 & 7.35800102249984 & 0.277639293792519 & 6.96435968370764 & 0.0580010224998393 \tabularnewline
62 & 7.1 & 6.99162072223912 & 0.234155553774526 & 6.97422372398636 & -0.108379277760884 \tabularnewline
63 & 6.8 & 6.5919071255353 & 0.0240051101996253 & 6.98408776426507 & -0.208092874464698 \tabularnewline
64 & 6.4 & 6.03966181499422 & -0.232841275216927 & 6.9931794602227 & -0.360338185005777 \tabularnewline
65 & 6.1 & 5.6707497850861 & -0.473020941266434 & 7.00227115618033 & -0.429250214913901 \tabularnewline
66 & 6.5 & 6.52451746952528 & -0.527791762265371 & 7.00327429274009 & 0.0245174695252803 \tabularnewline
67 & 7.7 & 8.16161848202782 & 0.234104088672330 & 7.00427742929985 & 0.461618482027822 \tabularnewline
68 & 7.9 & 8.44359314117838 & 0.346806919815766 & 7.00959993900586 & 0.543593141178374 \tabularnewline
69 & 7.5 & 7.74223431416444 & 0.242843237123693 & 7.01492244871187 & 0.242234314164437 \tabularnewline
70 & 6.9 & 6.77170283402314 & 0.00603602267169722 & 7.02226114330516 & -0.128297165976859 \tabularnewline
71 & 6.6 & 6.31783807324889 & -0.147437911147342 & 7.02959983789845 & -0.282161926751111 \tabularnewline
72 & 6.9 & 6.74863785093774 & 0.0155015625210638 & 7.0358605865412 & -0.151362149062257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66430&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]8.3[/C][C]8.38169615990256[/C][C]0.277639293792519[/C][C]7.94066454630492[/C][C]0.081696159902564[/C][/ROW]
[ROW][C]2[/C][C]8.2[/C][C]8.18503074300507[/C][C]0.234155553774526[/C][C]7.9808137032204[/C][C]-0.0149692569949327[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]7.95503202966448[/C][C]0.0240051101996253[/C][C]8.0209628601359[/C][C]-0.0449679703355192[/C][/ROW]
[ROW][C]4[/C][C]7.9[/C][C]7.97169041532253[/C][C]-0.232841275216927[/C][C]8.0611508598944[/C][C]0.0716904153225286[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.57168208161353[/C][C]-0.473020941266434[/C][C]8.1013388596529[/C][C]-0.0283179183864704[/C][/ROW]
[ROW][C]6[/C][C]7.6[/C][C]7.58760053614236[/C][C]-0.527791762265371[/C][C]8.14019122612301[/C][C]-0.0123994638576423[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.18685231873455[/C][C]0.234104088672330[/C][C]8.17904359259312[/C][C]-0.113147681265453[/C][/ROW]
[ROW][C]8[/C][C]8.4[/C][C]8.23862836146345[/C][C]0.346806919815766[/C][C]8.21456471872079[/C][C]-0.161371638536552[/C][/ROW]
[ROW][C]9[/C][C]8.4[/C][C]8.30707091802786[/C][C]0.242843237123693[/C][C]8.25008584484845[/C][C]-0.0929290819721427[/C][/ROW]
[ROW][C]10[/C][C]8.4[/C][C]8.52759194014847[/C][C]0.00603602267169722[/C][C]8.26637203717983[/C][C]0.127591940148468[/C][/ROW]
[ROW][C]11[/C][C]8.4[/C][C]8.66477968163613[/C][C]-0.147437911147342[/C][C]8.28265822951122[/C][C]0.264779681636126[/C][/ROW]
[ROW][C]12[/C][C]8.6[/C][C]8.90161391023905[/C][C]0.0155015625210638[/C][C]8.28288452723988[/C][C]0.301613910239054[/C][/ROW]
[ROW][C]13[/C][C]8.9[/C][C]9.23924988123893[/C][C]0.277639293792519[/C][C]8.28311082496855[/C][C]0.339249881238933[/C][/ROW]
[ROW][C]14[/C][C]8.8[/C][C]9.05755799665698[/C][C]0.234155553774526[/C][C]8.3082864495685[/C][C]0.257557996656976[/C][/ROW]
[ROW][C]15[/C][C]8.3[/C][C]8.24253281563193[/C][C]0.0240051101996253[/C][C]8.33346207416845[/C][C]-0.0574671843680754[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]6.87741119703519[/C][C]-0.232841275216927[/C][C]8.35543007818174[/C][C]-0.622588802964814[/C][/ROW]
[ROW][C]17[/C][C]7.2[/C][C]6.49562285907141[/C][C]-0.473020941266434[/C][C]8.37739808219503[/C][C]-0.704377140928594[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]6.94945509767655[/C][C]-0.527791762265371[/C][C]8.37833666458882[/C][C]-0.450544902323447[/C][/ROW]
[ROW][C]19[/C][C]8.8[/C][C]8.98662066434506[/C][C]0.234104088672330[/C][C]8.3792752469826[/C][C]0.186620664345062[/C][/ROW]
[ROW][C]20[/C][C]9.3[/C][C]9.86243575244455[/C][C]0.346806919815766[/C][C]8.39075732773968[/C][C]0.562435752444554[/C][/ROW]
[ROW][C]21[/C][C]9.3[/C][C]9.95491735437955[/C][C]0.242843237123693[/C][C]8.40223940849676[/C][C]0.654917354379553[/C][/ROW]
[ROW][C]22[/C][C]8.7[/C][C]8.95951958251291[/C][C]0.00603602267169722[/C][C]8.43444439481539[/C][C]0.259519582512914[/C][/ROW]
[ROW][C]23[/C][C]8.2[/C][C]8.08078853001332[/C][C]-0.147437911147342[/C][C]8.46664938113402[/C][C]-0.119211469986679[/C][/ROW]
[ROW][C]24[/C][C]8.3[/C][C]8.10798406189246[/C][C]0.0155015625210638[/C][C]8.47651437558648[/C][C]-0.192015938107545[/C][/ROW]
[ROW][C]25[/C][C]8.5[/C][C]8.23598133616854[/C][C]0.277639293792519[/C][C]8.48637937003894[/C][C]-0.264018663831461[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]8.50249417226413[/C][C]0.234155553774526[/C][C]8.46335027396135[/C][C]-0.0975058277358727[/C][/ROW]
[ROW][C]27[/C][C]8.5[/C][C]8.53567371191662[/C][C]0.0240051101996253[/C][C]8.44032117788375[/C][C]0.0356737119166244[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]8.2028342323488[/C][C]-0.232841275216927[/C][C]8.43000704286812[/C][C]0.00283423234880686[/C][/ROW]
[ROW][C]29[/C][C]8.1[/C][C]8.25332803341394[/C][C]-0.473020941266434[/C][C]8.41969290785249[/C][C]0.153328033413944[/C][/ROW]
[ROW][C]30[/C][C]7.9[/C][C]7.89764343790296[/C][C]-0.527791762265371[/C][C]8.43014832436242[/C][C]-0.00235656209704516[/C][/ROW]
[ROW][C]31[/C][C]8.6[/C][C]8.52529217045532[/C][C]0.234104088672330[/C][C]8.44060374087235[/C][C]-0.0747078295446766[/C][/ROW]
[ROW][C]32[/C][C]8.7[/C][C]8.60140842351039[/C][C]0.346806919815766[/C][C]8.45178465667384[/C][C]-0.0985915764896106[/C][/ROW]
[ROW][C]33[/C][C]8.7[/C][C]8.69419119040097[/C][C]0.242843237123693[/C][C]8.46296557247534[/C][C]-0.00580880959903318[/C][/ROW]
[ROW][C]34[/C][C]8.5[/C][C]8.5175166649428[/C][C]0.00603602267169722[/C][C]8.4764473123855[/C][C]0.0175166649427965[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.45750885885167[/C][C]-0.147437911147342[/C][C]8.48992905229567[/C][C]0.0575088588516692[/C][/ROW]
[ROW][C]36[/C][C]8.5[/C][C]8.49277864497527[/C][C]0.0155015625210638[/C][C]8.49171979250366[/C][C]-0.00722135502472732[/C][/ROW]
[ROW][C]37[/C][C]8.7[/C][C]8.62885017349583[/C][C]0.277639293792519[/C][C]8.49351053271165[/C][C]-0.0711498265041737[/C][/ROW]
[ROW][C]38[/C][C]8.7[/C][C]8.70149845027473[/C][C]0.234155553774526[/C][C]8.46434599595074[/C][C]0.00149845027472928[/C][/ROW]
[ROW][C]39[/C][C]8.6[/C][C]8.74081343061054[/C][C]0.0240051101996253[/C][C]8.43518145918983[/C][C]0.140813430610542[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]8.84718098574828[/C][C]-0.232841275216927[/C][C]8.38566028946865[/C][C]0.347180985748281[/C][/ROW]
[ROW][C]41[/C][C]8.3[/C][C]8.73688182151897[/C][C]-0.473020941266434[/C][C]8.33613911974746[/C][C]0.436881821518973[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]8.25047209161098[/C][C]-0.527791762265371[/C][C]8.2773196706544[/C][C]0.250472091610977[/C][/ROW]
[ROW][C]43[/C][C]8.2[/C][C]7.94739568976634[/C][C]0.234104088672330[/C][C]8.21850022156133[/C][C]-0.252604310233659[/C][/ROW]
[ROW][C]44[/C][C]8.1[/C][C]7.69963112989341[/C][C]0.346806919815766[/C][C]8.15356195029082[/C][C]-0.400368870106585[/C][/ROW]
[ROW][C]45[/C][C]8.1[/C][C]7.868533083856[/C][C]0.242843237123693[/C][C]8.08862367902031[/C][C]-0.231466916144003[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]7.95941641054035[/C][C]0.00603602267169722[/C][C]8.03454756678795[/C][C]-0.0405835894596454[/C][/ROW]
[ROW][C]47[/C][C]7.9[/C][C]7.96696645659176[/C][C]-0.147437911147342[/C][C]7.98047145455558[/C][C]0.066966456591758[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]7.84836033073857[/C][C]0.0155015625210638[/C][C]7.93613810674036[/C][C]-0.0516396692614256[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.83055594728234[/C][C]0.277639293792519[/C][C]7.89180475892514[/C][C]-0.169444052717657[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]7.9344433202293[/C][C]0.234155553774526[/C][C]7.83140112599617[/C][C]-0.0655566797706921[/C][/ROW]
[ROW][C]51[/C][C]7.9[/C][C]8.00499739673318[/C][C]0.0240051101996253[/C][C]7.7709974930672[/C][C]0.104997396733181[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]8.54246374306716[/C][C]-0.232841275216927[/C][C]7.69037753214976[/C][C]0.542463743067163[/C][/ROW]
[ROW][C]53[/C][C]7.7[/C][C]8.2632633700341[/C][C]-0.473020941266434[/C][C]7.60975757123233[/C][C]0.563263370034102[/C][/ROW]
[ROW][C]54[/C][C]7.2[/C][C]7.39969993335546[/C][C]-0.527791762265371[/C][C]7.52809182890992[/C][C]0.199699933355455[/C][/ROW]
[ROW][C]55[/C][C]7.5[/C][C]7.31946982474017[/C][C]0.234104088672330[/C][C]7.4464260865875[/C][C]-0.180530175259831[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]6.89817354098995[/C][C]0.346806919815766[/C][C]7.35501953919429[/C][C]-0.401826459010055[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]6.49354377107523[/C][C]0.242843237123693[/C][C]7.26361299180108[/C][C]-0.506456228924768[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]6.83000651049536[/C][C]0.00603602267169722[/C][C]7.16395746683294[/C][C]-0.169993489504642[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]7.08313596928253[/C][C]-0.147437911147342[/C][C]7.06430194186481[/C][C]0.0831359692825284[/C][/ROW]
[ROW][C]60[/C][C]7.2[/C][C]7.37016762469271[/C][C]0.0155015625210638[/C][C]7.01433081278623[/C][C]0.170167624692708[/C][/ROW]
[ROW][C]61[/C][C]7.3[/C][C]7.35800102249984[/C][C]0.277639293792519[/C][C]6.96435968370764[/C][C]0.0580010224998393[/C][/ROW]
[ROW][C]62[/C][C]7.1[/C][C]6.99162072223912[/C][C]0.234155553774526[/C][C]6.97422372398636[/C][C]-0.108379277760884[/C][/ROW]
[ROW][C]63[/C][C]6.8[/C][C]6.5919071255353[/C][C]0.0240051101996253[/C][C]6.98408776426507[/C][C]-0.208092874464698[/C][/ROW]
[ROW][C]64[/C][C]6.4[/C][C]6.03966181499422[/C][C]-0.232841275216927[/C][C]6.9931794602227[/C][C]-0.360338185005777[/C][/ROW]
[ROW][C]65[/C][C]6.1[/C][C]5.6707497850861[/C][C]-0.473020941266434[/C][C]7.00227115618033[/C][C]-0.429250214913901[/C][/ROW]
[ROW][C]66[/C][C]6.5[/C][C]6.52451746952528[/C][C]-0.527791762265371[/C][C]7.00327429274009[/C][C]0.0245174695252803[/C][/ROW]
[ROW][C]67[/C][C]7.7[/C][C]8.16161848202782[/C][C]0.234104088672330[/C][C]7.00427742929985[/C][C]0.461618482027822[/C][/ROW]
[ROW][C]68[/C][C]7.9[/C][C]8.44359314117838[/C][C]0.346806919815766[/C][C]7.00959993900586[/C][C]0.543593141178374[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]7.74223431416444[/C][C]0.242843237123693[/C][C]7.01492244871187[/C][C]0.242234314164437[/C][/ROW]
[ROW][C]70[/C][C]6.9[/C][C]6.77170283402314[/C][C]0.00603602267169722[/C][C]7.02226114330516[/C][C]-0.128297165976859[/C][/ROW]
[ROW][C]71[/C][C]6.6[/C][C]6.31783807324889[/C][C]-0.147437911147342[/C][C]7.02959983789845[/C][C]-0.282161926751111[/C][/ROW]
[ROW][C]72[/C][C]6.9[/C][C]6.74863785093774[/C][C]0.0155015625210638[/C][C]7.0358605865412[/C][C]-0.151362149062257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66430&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	8.3	8.38169615990256	0.277639293792519	7.94066454630492	0.081696159902564
2	8.2	8.18503074300507	0.234155553774526	7.9808137032204	-0.0149692569949327
3	8	7.95503202966448	0.0240051101996253	8.0209628601359	-0.0449679703355192
4	7.9	7.97169041532253	-0.232841275216927	8.0611508598944	0.0716904153225286
5	7.6	7.57168208161353	-0.473020941266434	8.1013388596529	-0.0283179183864704
6	7.6	7.58760053614236	-0.527791762265371	8.14019122612301	-0.0123994638576423
7	8.3	8.18685231873455	0.234104088672330	8.17904359259312	-0.113147681265453
8	8.4	8.23862836146345	0.346806919815766	8.21456471872079	-0.161371638536552
9	8.4	8.30707091802786	0.242843237123693	8.25008584484845	-0.0929290819721427
10	8.4	8.52759194014847	0.00603602267169722	8.26637203717983	0.127591940148468
11	8.4	8.66477968163613	-0.147437911147342	8.28265822951122	0.264779681636126
12	8.6	8.90161391023905	0.0155015625210638	8.28288452723988	0.301613910239054
13	8.9	9.23924988123893	0.277639293792519	8.28311082496855	0.339249881238933
14	8.8	9.05755799665698	0.234155553774526	8.3082864495685	0.257557996656976
15	8.3	8.24253281563193	0.0240051101996253	8.33346207416845	-0.0574671843680754
16	7.5	6.87741119703519	-0.232841275216927	8.35543007818174	-0.622588802964814
17	7.2	6.49562285907141	-0.473020941266434	8.37739808219503	-0.704377140928594
18	7.4	6.94945509767655	-0.527791762265371	8.37833666458882	-0.450544902323447
19	8.8	8.98662066434506	0.234104088672330	8.3792752469826	0.186620664345062
20	9.3	9.86243575244455	0.346806919815766	8.39075732773968	0.562435752444554
21	9.3	9.95491735437955	0.242843237123693	8.40223940849676	0.654917354379553
22	8.7	8.95951958251291	0.00603602267169722	8.43444439481539	0.259519582512914
23	8.2	8.08078853001332	-0.147437911147342	8.46664938113402	-0.119211469986679
24	8.3	8.10798406189246	0.0155015625210638	8.47651437558648	-0.192015938107545
25	8.5	8.23598133616854	0.277639293792519	8.48637937003894	-0.264018663831461
26	8.6	8.50249417226413	0.234155553774526	8.46335027396135	-0.0975058277358727
27	8.5	8.53567371191662	0.0240051101996253	8.44032117788375	0.0356737119166244
28	8.2	8.2028342323488	-0.232841275216927	8.43000704286812	0.00283423234880686
29	8.1	8.25332803341394	-0.473020941266434	8.41969290785249	0.153328033413944
30	7.9	7.89764343790296	-0.527791762265371	8.43014832436242	-0.00235656209704516
31	8.6	8.52529217045532	0.234104088672330	8.44060374087235	-0.0747078295446766
32	8.7	8.60140842351039	0.346806919815766	8.45178465667384	-0.0985915764896106
33	8.7	8.69419119040097	0.242843237123693	8.46296557247534	-0.00580880959903318
34	8.5	8.5175166649428	0.00603602267169722	8.4764473123855	0.0175166649427965
35	8.4	8.45750885885167	-0.147437911147342	8.48992905229567	0.0575088588516692
36	8.5	8.49277864497527	0.0155015625210638	8.49171979250366	-0.00722135502472732
37	8.7	8.62885017349583	0.277639293792519	8.49351053271165	-0.0711498265041737
38	8.7	8.70149845027473	0.234155553774526	8.46434599595074	0.00149845027472928
39	8.6	8.74081343061054	0.0240051101996253	8.43518145918983	0.140813430610542
40	8.5	8.84718098574828	-0.232841275216927	8.38566028946865	0.347180985748281
41	8.3	8.73688182151897	-0.473020941266434	8.33613911974746	0.436881821518973
42	8	8.25047209161098	-0.527791762265371	8.2773196706544	0.250472091610977
43	8.2	7.94739568976634	0.234104088672330	8.21850022156133	-0.252604310233659
44	8.1	7.69963112989341	0.346806919815766	8.15356195029082	-0.400368870106585
45	8.1	7.868533083856	0.242843237123693	8.08862367902031	-0.231466916144003
46	8	7.95941641054035	0.00603602267169722	8.03454756678795	-0.0405835894596454
47	7.9	7.96696645659176	-0.147437911147342	7.98047145455558	0.066966456591758
48	7.9	7.84836033073857	0.0155015625210638	7.93613810674036	-0.0516396692614256
49	8	7.83055594728234	0.277639293792519	7.89180475892514	-0.169444052717657
50	8	7.9344433202293	0.234155553774526	7.83140112599617	-0.0655566797706921
51	7.9	8.00499739673318	0.0240051101996253	7.7709974930672	0.104997396733181
52	8	8.54246374306716	-0.232841275216927	7.69037753214976	0.542463743067163
53	7.7	8.2632633700341	-0.473020941266434	7.60975757123233	0.563263370034102
54	7.2	7.39969993335546	-0.527791762265371	7.52809182890992	0.199699933355455
55	7.5	7.31946982474017	0.234104088672330	7.4464260865875	-0.180530175259831
56	7.3	6.89817354098995	0.346806919815766	7.35501953919429	-0.401826459010055
57	7	6.49354377107523	0.242843237123693	7.26361299180108	-0.506456228924768
58	7	6.83000651049536	0.00603602267169722	7.16395746683294	-0.169993489504642
59	7	7.08313596928253	-0.147437911147342	7.06430194186481	0.0831359692825284
60	7.2	7.37016762469271	0.0155015625210638	7.01433081278623	0.170167624692708
61	7.3	7.35800102249984	0.277639293792519	6.96435968370764	0.0580010224998393
62	7.1	6.99162072223912	0.234155553774526	6.97422372398636	-0.108379277760884
63	6.8	6.5919071255353	0.0240051101996253	6.98408776426507	-0.208092874464698
64	6.4	6.03966181499422	-0.232841275216927	6.9931794602227	-0.360338185005777
65	6.1	5.6707497850861	-0.473020941266434	7.00227115618033	-0.429250214913901
66	6.5	6.52451746952528	-0.527791762265371	7.00327429274009	0.0245174695252803
67	7.7	8.16161848202782	0.234104088672330	7.00427742929985	0.461618482027822
68	7.9	8.44359314117838	0.346806919815766	7.00959993900586	0.543593141178374
69	7.5	7.74223431416444	0.242843237123693	7.01492244871187	0.242234314164437
70	6.9	6.77170283402314	0.00603602267169722	7.02226114330516	-0.128297165976859
71	6.6	6.31783807324889	-0.147437911147342	7.02959983789845	-0.282161926751111
72	6.9	6.74863785093774	0.0155015625210638	7.0358605865412	-0.151362149062257

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 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

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