Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Mon, 28 Nov 2016 09:33:57 +0000

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/2016/Nov/28/t1480325772z7p1o24hbouxbvk.htm/, Retrieved Sat, 04 May 2024 08:58:40 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 08:58:40 +0200

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

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	'George Udny Yule' @ yule.wessa.net

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

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	99.78	NA	NA	1.00063	NA
2	99.8	NA	NA	1.00112	NA
3	99.88	NA	NA	1.00041	NA
4	99.74	NA	NA	1.00031	NA
5	100.15	NA	NA	0.998364	NA
6	100.27	NA	NA	0.999164	NA
7	100.26	100.085	100.066	1.00019	1.00175
8	100.36	100.044	100.068	0.999752	1.00316
9	100.37	100.016	100.06	0.999552	1.00354
10	100.54	100.076	100.053	1.00023	1.00463
11	99.8	100.031	100.025	1.00005	0.997695
12	99.82	99.9903	99.9683	1.00022	0.998296
13	99.82	99.9812	99.9179	1.00063	0.998388
14	99.82	99.9796	99.8679	1.00112	0.998404
15	99.67	99.8571	99.8158	1.00041	0.998126
16	99.78	99.7897	99.7587	1.00031	0.999903
17	99.44	99.5672	99.7304	0.998364	0.998722
18	99.61	99.6566	99.74	0.999164	0.999532
19	99.71	99.7732	99.7542	1.00019	0.999367
20	99.71	99.7453	99.77	0.999752	0.999647
21	99.77	99.7482	99.7929	0.999552	1.00022
22	99.77	99.8402	99.8171	1.00023	0.999297
23	99.89	99.8447	99.8396	1.00005	1.00045
24	99.96	99.882	99.86	1.00022	1.00078
25	100.02	99.9391	99.8758	1.00063	1.00081
26	100	100.005	99.8929	1.00112	0.999954
27	100.04	99.9521	99.9108	1.00041	1.00088
28	99.99	99.9647	99.9337	1.00031	1.00025
29	99.77	99.7964	99.96	0.998364	0.999735
30	99.77	99.9031	99.9867	0.999164	0.998668
31	99.93	100.038	100.019	1.00019	0.998918
32	99.9	100.044	100.068	0.999752	0.998565
33	100.01	100.083	100.127	0.999552	0.999274
34	100.08	100.217	100.194	1.00023	0.998634
35	100.21	100.288	100.283	1.00005	0.999217
36	100.28	100.429	100.407	1.00022	0.998519
37	100.48	100.605	100.541	1.00063	0.998758
38	100.72	100.783	100.67	1.00112	0.99938
39	100.74	100.837	100.795	1.00041	0.999041
40	100.88	100.955	100.924	1.00031	0.999253
41	101.03	100.92	101.085	0.998364	1.00109
42	101.47	101.186	101.271	0.999164	1.0028
43	101.46	101.476	101.456	1.00019	0.999846
44	101.46	101.612	101.637	0.999752	0.998505
45	101.45	101.771	101.817	0.999552	0.996845
46	101.74	102.014	101.99	1.00023	0.997318
47	102.41	102.153	102.148	1.00005	1.00251
48	102.54	102.306	102.284	1.00022	1.00228
49	102.67	102.473	102.408	1.00063	1.00192
50	102.87	102.65	102.535	1.00112	1.00214
51	102.9	102.712	102.669	1.00041	1.00183
52	102.88	102.831	102.8	1.00031	1.00047
53	102.82	102.786	102.955	0.998364	1.00033
54	102.94	103.056	103.142	0.999164	0.998876
55	102.97	NA	NA	1.00019	NA
56	103.01	NA	NA	0.999752	NA
57	103.11	NA	NA	0.999552	NA
58	103.21	NA	NA	1.00023	NA
59	104.66	NA	NA	1.00005	NA
60	104.79	NA	NA	1.00022	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 99.78 & NA & NA & 1.00063 & NA \tabularnewline
2 & 99.8 & NA & NA & 1.00112 & NA \tabularnewline
3 & 99.88 & NA & NA & 1.00041 & NA \tabularnewline
4 & 99.74 & NA & NA & 1.00031 & NA \tabularnewline
5 & 100.15 & NA & NA & 0.998364 & NA \tabularnewline
6 & 100.27 & NA & NA & 0.999164 & NA \tabularnewline
7 & 100.26 & 100.085 & 100.066 & 1.00019 & 1.00175 \tabularnewline
8 & 100.36 & 100.044 & 100.068 & 0.999752 & 1.00316 \tabularnewline
9 & 100.37 & 100.016 & 100.06 & 0.999552 & 1.00354 \tabularnewline
10 & 100.54 & 100.076 & 100.053 & 1.00023 & 1.00463 \tabularnewline
11 & 99.8 & 100.031 & 100.025 & 1.00005 & 0.997695 \tabularnewline
12 & 99.82 & 99.9903 & 99.9683 & 1.00022 & 0.998296 \tabularnewline
13 & 99.82 & 99.9812 & 99.9179 & 1.00063 & 0.998388 \tabularnewline
14 & 99.82 & 99.9796 & 99.8679 & 1.00112 & 0.998404 \tabularnewline
15 & 99.67 & 99.8571 & 99.8158 & 1.00041 & 0.998126 \tabularnewline
16 & 99.78 & 99.7897 & 99.7587 & 1.00031 & 0.999903 \tabularnewline
17 & 99.44 & 99.5672 & 99.7304 & 0.998364 & 0.998722 \tabularnewline
18 & 99.61 & 99.6566 & 99.74 & 0.999164 & 0.999532 \tabularnewline
19 & 99.71 & 99.7732 & 99.7542 & 1.00019 & 0.999367 \tabularnewline
20 & 99.71 & 99.7453 & 99.77 & 0.999752 & 0.999647 \tabularnewline
21 & 99.77 & 99.7482 & 99.7929 & 0.999552 & 1.00022 \tabularnewline
22 & 99.77 & 99.8402 & 99.8171 & 1.00023 & 0.999297 \tabularnewline
23 & 99.89 & 99.8447 & 99.8396 & 1.00005 & 1.00045 \tabularnewline
24 & 99.96 & 99.882 & 99.86 & 1.00022 & 1.00078 \tabularnewline
25 & 100.02 & 99.9391 & 99.8758 & 1.00063 & 1.00081 \tabularnewline
26 & 100 & 100.005 & 99.8929 & 1.00112 & 0.999954 \tabularnewline
27 & 100.04 & 99.9521 & 99.9108 & 1.00041 & 1.00088 \tabularnewline
28 & 99.99 & 99.9647 & 99.9337 & 1.00031 & 1.00025 \tabularnewline
29 & 99.77 & 99.7964 & 99.96 & 0.998364 & 0.999735 \tabularnewline
30 & 99.77 & 99.9031 & 99.9867 & 0.999164 & 0.998668 \tabularnewline
31 & 99.93 & 100.038 & 100.019 & 1.00019 & 0.998918 \tabularnewline
32 & 99.9 & 100.044 & 100.068 & 0.999752 & 0.998565 \tabularnewline
33 & 100.01 & 100.083 & 100.127 & 0.999552 & 0.999274 \tabularnewline
34 & 100.08 & 100.217 & 100.194 & 1.00023 & 0.998634 \tabularnewline
35 & 100.21 & 100.288 & 100.283 & 1.00005 & 0.999217 \tabularnewline
36 & 100.28 & 100.429 & 100.407 & 1.00022 & 0.998519 \tabularnewline
37 & 100.48 & 100.605 & 100.541 & 1.00063 & 0.998758 \tabularnewline
38 & 100.72 & 100.783 & 100.67 & 1.00112 & 0.99938 \tabularnewline
39 & 100.74 & 100.837 & 100.795 & 1.00041 & 0.999041 \tabularnewline
40 & 100.88 & 100.955 & 100.924 & 1.00031 & 0.999253 \tabularnewline
41 & 101.03 & 100.92 & 101.085 & 0.998364 & 1.00109 \tabularnewline
42 & 101.47 & 101.186 & 101.271 & 0.999164 & 1.0028 \tabularnewline
43 & 101.46 & 101.476 & 101.456 & 1.00019 & 0.999846 \tabularnewline
44 & 101.46 & 101.612 & 101.637 & 0.999752 & 0.998505 \tabularnewline
45 & 101.45 & 101.771 & 101.817 & 0.999552 & 0.996845 \tabularnewline
46 & 101.74 & 102.014 & 101.99 & 1.00023 & 0.997318 \tabularnewline
47 & 102.41 & 102.153 & 102.148 & 1.00005 & 1.00251 \tabularnewline
48 & 102.54 & 102.306 & 102.284 & 1.00022 & 1.00228 \tabularnewline
49 & 102.67 & 102.473 & 102.408 & 1.00063 & 1.00192 \tabularnewline
50 & 102.87 & 102.65 & 102.535 & 1.00112 & 1.00214 \tabularnewline
51 & 102.9 & 102.712 & 102.669 & 1.00041 & 1.00183 \tabularnewline
52 & 102.88 & 102.831 & 102.8 & 1.00031 & 1.00047 \tabularnewline
53 & 102.82 & 102.786 & 102.955 & 0.998364 & 1.00033 \tabularnewline
54 & 102.94 & 103.056 & 103.142 & 0.999164 & 0.998876 \tabularnewline
55 & 102.97 & NA & NA & 1.00019 & NA \tabularnewline
56 & 103.01 & NA & NA & 0.999752 & NA \tabularnewline
57 & 103.11 & NA & NA & 0.999552 & NA \tabularnewline
58 & 103.21 & NA & NA & 1.00023 & NA \tabularnewline
59 & 104.66 & NA & NA & 1.00005 & NA \tabularnewline
60 & 104.79 & NA & NA & 1.00022 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]99.78[/C][C]NA[/C][C]NA[/C][C]1.00063[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]99.8[/C][C]NA[/C][C]NA[/C][C]1.00112[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]99.88[/C][C]NA[/C][C]NA[/C][C]1.00041[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]99.74[/C][C]NA[/C][C]NA[/C][C]1.00031[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.15[/C][C]NA[/C][C]NA[/C][C]0.998364[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.27[/C][C]NA[/C][C]NA[/C][C]0.999164[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.26[/C][C]100.085[/C][C]100.066[/C][C]1.00019[/C][C]1.00175[/C][/ROW]
[ROW][C]8[/C][C]100.36[/C][C]100.044[/C][C]100.068[/C][C]0.999752[/C][C]1.00316[/C][/ROW]
[ROW][C]9[/C][C]100.37[/C][C]100.016[/C][C]100.06[/C][C]0.999552[/C][C]1.00354[/C][/ROW]
[ROW][C]10[/C][C]100.54[/C][C]100.076[/C][C]100.053[/C][C]1.00023[/C][C]1.00463[/C][/ROW]
[ROW][C]11[/C][C]99.8[/C][C]100.031[/C][C]100.025[/C][C]1.00005[/C][C]0.997695[/C][/ROW]
[ROW][C]12[/C][C]99.82[/C][C]99.9903[/C][C]99.9683[/C][C]1.00022[/C][C]0.998296[/C][/ROW]
[ROW][C]13[/C][C]99.82[/C][C]99.9812[/C][C]99.9179[/C][C]1.00063[/C][C]0.998388[/C][/ROW]
[ROW][C]14[/C][C]99.82[/C][C]99.9796[/C][C]99.8679[/C][C]1.00112[/C][C]0.998404[/C][/ROW]
[ROW][C]15[/C][C]99.67[/C][C]99.8571[/C][C]99.8158[/C][C]1.00041[/C][C]0.998126[/C][/ROW]
[ROW][C]16[/C][C]99.78[/C][C]99.7897[/C][C]99.7587[/C][C]1.00031[/C][C]0.999903[/C][/ROW]
[ROW][C]17[/C][C]99.44[/C][C]99.5672[/C][C]99.7304[/C][C]0.998364[/C][C]0.998722[/C][/ROW]
[ROW][C]18[/C][C]99.61[/C][C]99.6566[/C][C]99.74[/C][C]0.999164[/C][C]0.999532[/C][/ROW]
[ROW][C]19[/C][C]99.71[/C][C]99.7732[/C][C]99.7542[/C][C]1.00019[/C][C]0.999367[/C][/ROW]
[ROW][C]20[/C][C]99.71[/C][C]99.7453[/C][C]99.77[/C][C]0.999752[/C][C]0.999647[/C][/ROW]
[ROW][C]21[/C][C]99.77[/C][C]99.7482[/C][C]99.7929[/C][C]0.999552[/C][C]1.00022[/C][/ROW]
[ROW][C]22[/C][C]99.77[/C][C]99.8402[/C][C]99.8171[/C][C]1.00023[/C][C]0.999297[/C][/ROW]
[ROW][C]23[/C][C]99.89[/C][C]99.8447[/C][C]99.8396[/C][C]1.00005[/C][C]1.00045[/C][/ROW]
[ROW][C]24[/C][C]99.96[/C][C]99.882[/C][C]99.86[/C][C]1.00022[/C][C]1.00078[/C][/ROW]
[ROW][C]25[/C][C]100.02[/C][C]99.9391[/C][C]99.8758[/C][C]1.00063[/C][C]1.00081[/C][/ROW]
[ROW][C]26[/C][C]100[/C][C]100.005[/C][C]99.8929[/C][C]1.00112[/C][C]0.999954[/C][/ROW]
[ROW][C]27[/C][C]100.04[/C][C]99.9521[/C][C]99.9108[/C][C]1.00041[/C][C]1.00088[/C][/ROW]
[ROW][C]28[/C][C]99.99[/C][C]99.9647[/C][C]99.9337[/C][C]1.00031[/C][C]1.00025[/C][/ROW]
[ROW][C]29[/C][C]99.77[/C][C]99.7964[/C][C]99.96[/C][C]0.998364[/C][C]0.999735[/C][/ROW]
[ROW][C]30[/C][C]99.77[/C][C]99.9031[/C][C]99.9867[/C][C]0.999164[/C][C]0.998668[/C][/ROW]
[ROW][C]31[/C][C]99.93[/C][C]100.038[/C][C]100.019[/C][C]1.00019[/C][C]0.998918[/C][/ROW]
[ROW][C]32[/C][C]99.9[/C][C]100.044[/C][C]100.068[/C][C]0.999752[/C][C]0.998565[/C][/ROW]
[ROW][C]33[/C][C]100.01[/C][C]100.083[/C][C]100.127[/C][C]0.999552[/C][C]0.999274[/C][/ROW]
[ROW][C]34[/C][C]100.08[/C][C]100.217[/C][C]100.194[/C][C]1.00023[/C][C]0.998634[/C][/ROW]
[ROW][C]35[/C][C]100.21[/C][C]100.288[/C][C]100.283[/C][C]1.00005[/C][C]0.999217[/C][/ROW]
[ROW][C]36[/C][C]100.28[/C][C]100.429[/C][C]100.407[/C][C]1.00022[/C][C]0.998519[/C][/ROW]
[ROW][C]37[/C][C]100.48[/C][C]100.605[/C][C]100.541[/C][C]1.00063[/C][C]0.998758[/C][/ROW]
[ROW][C]38[/C][C]100.72[/C][C]100.783[/C][C]100.67[/C][C]1.00112[/C][C]0.99938[/C][/ROW]
[ROW][C]39[/C][C]100.74[/C][C]100.837[/C][C]100.795[/C][C]1.00041[/C][C]0.999041[/C][/ROW]
[ROW][C]40[/C][C]100.88[/C][C]100.955[/C][C]100.924[/C][C]1.00031[/C][C]0.999253[/C][/ROW]
[ROW][C]41[/C][C]101.03[/C][C]100.92[/C][C]101.085[/C][C]0.998364[/C][C]1.00109[/C][/ROW]
[ROW][C]42[/C][C]101.47[/C][C]101.186[/C][C]101.271[/C][C]0.999164[/C][C]1.0028[/C][/ROW]
[ROW][C]43[/C][C]101.46[/C][C]101.476[/C][C]101.456[/C][C]1.00019[/C][C]0.999846[/C][/ROW]
[ROW][C]44[/C][C]101.46[/C][C]101.612[/C][C]101.637[/C][C]0.999752[/C][C]0.998505[/C][/ROW]
[ROW][C]45[/C][C]101.45[/C][C]101.771[/C][C]101.817[/C][C]0.999552[/C][C]0.996845[/C][/ROW]
[ROW][C]46[/C][C]101.74[/C][C]102.014[/C][C]101.99[/C][C]1.00023[/C][C]0.997318[/C][/ROW]
[ROW][C]47[/C][C]102.41[/C][C]102.153[/C][C]102.148[/C][C]1.00005[/C][C]1.00251[/C][/ROW]
[ROW][C]48[/C][C]102.54[/C][C]102.306[/C][C]102.284[/C][C]1.00022[/C][C]1.00228[/C][/ROW]
[ROW][C]49[/C][C]102.67[/C][C]102.473[/C][C]102.408[/C][C]1.00063[/C][C]1.00192[/C][/ROW]
[ROW][C]50[/C][C]102.87[/C][C]102.65[/C][C]102.535[/C][C]1.00112[/C][C]1.00214[/C][/ROW]
[ROW][C]51[/C][C]102.9[/C][C]102.712[/C][C]102.669[/C][C]1.00041[/C][C]1.00183[/C][/ROW]
[ROW][C]52[/C][C]102.88[/C][C]102.831[/C][C]102.8[/C][C]1.00031[/C][C]1.00047[/C][/ROW]
[ROW][C]53[/C][C]102.82[/C][C]102.786[/C][C]102.955[/C][C]0.998364[/C][C]1.00033[/C][/ROW]
[ROW][C]54[/C][C]102.94[/C][C]103.056[/C][C]103.142[/C][C]0.999164[/C][C]0.998876[/C][/ROW]
[ROW][C]55[/C][C]102.97[/C][C]NA[/C][C]NA[/C][C]1.00019[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]103.01[/C][C]NA[/C][C]NA[/C][C]0.999752[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]103.11[/C][C]NA[/C][C]NA[/C][C]0.999552[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]103.21[/C][C]NA[/C][C]NA[/C][C]1.00023[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]104.66[/C][C]NA[/C][C]NA[/C][C]1.00005[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.79[/C][C]NA[/C][C]NA[/C][C]1.00022[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:

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

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	99.78	NA	NA	1.00063	NA
2	99.8	NA	NA	1.00112	NA
3	99.88	NA	NA	1.00041	NA
4	99.74	NA	NA	1.00031	NA
5	100.15	NA	NA	0.998364	NA
6	100.27	NA	NA	0.999164	NA
7	100.26	100.085	100.066	1.00019	1.00175
8	100.36	100.044	100.068	0.999752	1.00316
9	100.37	100.016	100.06	0.999552	1.00354
10	100.54	100.076	100.053	1.00023	1.00463
11	99.8	100.031	100.025	1.00005	0.997695
12	99.82	99.9903	99.9683	1.00022	0.998296
13	99.82	99.9812	99.9179	1.00063	0.998388
14	99.82	99.9796	99.8679	1.00112	0.998404
15	99.67	99.8571	99.8158	1.00041	0.998126
16	99.78	99.7897	99.7587	1.00031	0.999903
17	99.44	99.5672	99.7304	0.998364	0.998722
18	99.61	99.6566	99.74	0.999164	0.999532
19	99.71	99.7732	99.7542	1.00019	0.999367
20	99.71	99.7453	99.77	0.999752	0.999647
21	99.77	99.7482	99.7929	0.999552	1.00022
22	99.77	99.8402	99.8171	1.00023	0.999297
23	99.89	99.8447	99.8396	1.00005	1.00045
24	99.96	99.882	99.86	1.00022	1.00078
25	100.02	99.9391	99.8758	1.00063	1.00081
26	100	100.005	99.8929	1.00112	0.999954
27	100.04	99.9521	99.9108	1.00041	1.00088
28	99.99	99.9647	99.9337	1.00031	1.00025
29	99.77	99.7964	99.96	0.998364	0.999735
30	99.77	99.9031	99.9867	0.999164	0.998668
31	99.93	100.038	100.019	1.00019	0.998918
32	99.9	100.044	100.068	0.999752	0.998565
33	100.01	100.083	100.127	0.999552	0.999274
34	100.08	100.217	100.194	1.00023	0.998634
35	100.21	100.288	100.283	1.00005	0.999217
36	100.28	100.429	100.407	1.00022	0.998519
37	100.48	100.605	100.541	1.00063	0.998758
38	100.72	100.783	100.67	1.00112	0.99938
39	100.74	100.837	100.795	1.00041	0.999041
40	100.88	100.955	100.924	1.00031	0.999253
41	101.03	100.92	101.085	0.998364	1.00109
42	101.47	101.186	101.271	0.999164	1.0028
43	101.46	101.476	101.456	1.00019	0.999846
44	101.46	101.612	101.637	0.999752	0.998505
45	101.45	101.771	101.817	0.999552	0.996845
46	101.74	102.014	101.99	1.00023	0.997318
47	102.41	102.153	102.148	1.00005	1.00251
48	102.54	102.306	102.284	1.00022	1.00228
49	102.67	102.473	102.408	1.00063	1.00192
50	102.87	102.65	102.535	1.00112	1.00214
51	102.9	102.712	102.669	1.00041	1.00183
52	102.88	102.831	102.8	1.00031	1.00047
53	102.82	102.786	102.955	0.998364	1.00033
54	102.94	103.056	103.142	0.999164	0.998876
55	102.97	NA	NA	1.00019	NA
56	103.01	NA	NA	0.999752	NA
57	103.11	NA	NA	0.999552	NA
58	103.21	NA	NA	1.00023	NA
59	104.66	NA	NA	1.00005	NA
60	104.79	NA	NA	1.00022	NA

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 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

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

par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code