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

Tue, 24 May 2016 15:28:59 +0100

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/May/24/t1464100155zuz415np99u4d9g.htm/, Retrieved Wed, 08 May 2024 21:41:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295549, Retrieved Wed, 08 May 2024 21:41:09 +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

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Classical Decomposition] [] [2016-05-24 14:28:59] [b787349f7d799cee4daf21043f8c3664] [Current]

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295549&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295549&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	92.88	NA	NA	-0.661589	NA
2	91.69	NA	NA	-0.130755	NA
3	91.66	NA	NA	0.747161	NA
4	90.26	NA	NA	0.498203	NA
5	91.11	NA	NA	0.119036	NA
6	92.33	NA	NA	-0.201589	NA
7	91.82	91.8676	92.2142	-0.346589	-0.0475781
8	92.24	92.2312	92.2346	-0.00335938	0.00877604
9	93.35	92.9591	92.39	0.569141	0.390859
10	93.53	92.8877	92.6446	0.243099	0.642318
11	93.34	92.8959	92.8783	0.0175781	0.444089
12	92.59	92.1647	93.015	-0.850339	0.425339
13	92.42	92.4742	93.1358	-0.661589	-0.0542448
14	92.64	93.2172	93.3479	-0.130755	-0.577161
15	94.44	94.3826	93.6354	0.747161	0.0574219
16	93.59	94.462	93.9637	0.498203	-0.871953
17	93.39	94.4336	94.3146	0.119036	-1.04362
18	93.33	94.4726	94.6742	-0.201589	-1.14258
19	93.72	94.7322	95.0788	-0.346589	-1.01216
20	95.43	95.5737	95.5771	-0.00335938	-0.143724
21	97.06	96.6804	96.1112	0.569141	0.379609
22	97.7	96.871	96.6279	0.243099	0.828984
23	97.59	97.1588	97.1412	0.0175781	0.431172
24	96.97	96.8117	97.6621	-0.850339	0.158255
25	97.75	97.5188	98.1804	-0.661589	0.231172
26	99.27	98.4997	98.6304	-0.130755	0.770339
27	100.63	99.7442	98.9971	0.747161	0.885755
28	99.8	99.7878	99.2896	0.498203	0.0122135
29	99.5	99.6799	99.5608	0.119036	-0.17987
30	99.72	99.6522	99.8537	-0.201589	0.0678385
31	99.77	99.813	100.16	-0.346589	-0.0429948
32	100.18	100.443	100.446	-0.00335938	-0.262891
33	101.11	101.251	100.682	0.569141	-0.141224
34	100.67	101.233	100.99	0.243099	-0.563099
35	101.13	101.419	101.401	0.0175781	-0.288828
36	100.46	100.968	101.818	-0.850339	-0.507995
37	101.6	101.58	102.242	-0.661589	0.0199219
38	102.3	102.528	102.659	-0.130755	-0.227995
39	103.26	103.755	103.008	0.747161	-0.494661
40	104.56	103.809	103.311	0.498203	0.750964
41	104.61	103.727	103.608	0.119036	0.883047
42	104.62	103.715	103.917	-0.201589	0.904505
43	105.03	103.861	104.208	-0.346589	1.16867
44	104.93	104.466	104.47	-0.00335938	0.463776
45	104.73	105.293	104.724	0.569141	-0.563307
46	104.33	105.172	104.929	0.243099	-0.842266
47	104.6	105.12	105.103	0.0175781	-0.520495
48	104.41	104.42	105.27	-0.850339	-0.00966146
49	104.63	104.761	105.422	-0.661589	-0.130911
50	105.55	105.449	105.58	-0.130755	0.100755
51	106.12	106.503	105.755	0.747161	-0.382578
52	106.62	106.445	105.947	0.498203	0.174714
53	106.72	106.314	106.195	0.119036	0.40638
54	106.52	106.284	106.485	-0.201589	0.236172
55	106.79	NA	NA	-0.346589	NA
56	106.95	NA	NA	-0.00335938	NA
57	106.92	NA	NA	0.569141	NA
58	106.74	NA	NA	0.243099	NA
59	108.13	NA	NA	0.0175781	NA
60	107.86	NA	NA	-0.850339	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 92.88 & NA & NA & -0.661589 & NA \tabularnewline
2 & 91.69 & NA & NA & -0.130755 & NA \tabularnewline
3 & 91.66 & NA & NA & 0.747161 & NA \tabularnewline
4 & 90.26 & NA & NA & 0.498203 & NA \tabularnewline
5 & 91.11 & NA & NA & 0.119036 & NA \tabularnewline
6 & 92.33 & NA & NA & -0.201589 & NA \tabularnewline
7 & 91.82 & 91.8676 & 92.2142 & -0.346589 & -0.0475781 \tabularnewline
8 & 92.24 & 92.2312 & 92.2346 & -0.00335938 & 0.00877604 \tabularnewline
9 & 93.35 & 92.9591 & 92.39 & 0.569141 & 0.390859 \tabularnewline
10 & 93.53 & 92.8877 & 92.6446 & 0.243099 & 0.642318 \tabularnewline
11 & 93.34 & 92.8959 & 92.8783 & 0.0175781 & 0.444089 \tabularnewline
12 & 92.59 & 92.1647 & 93.015 & -0.850339 & 0.425339 \tabularnewline
13 & 92.42 & 92.4742 & 93.1358 & -0.661589 & -0.0542448 \tabularnewline
14 & 92.64 & 93.2172 & 93.3479 & -0.130755 & -0.577161 \tabularnewline
15 & 94.44 & 94.3826 & 93.6354 & 0.747161 & 0.0574219 \tabularnewline
16 & 93.59 & 94.462 & 93.9637 & 0.498203 & -0.871953 \tabularnewline
17 & 93.39 & 94.4336 & 94.3146 & 0.119036 & -1.04362 \tabularnewline
18 & 93.33 & 94.4726 & 94.6742 & -0.201589 & -1.14258 \tabularnewline
19 & 93.72 & 94.7322 & 95.0788 & -0.346589 & -1.01216 \tabularnewline
20 & 95.43 & 95.5737 & 95.5771 & -0.00335938 & -0.143724 \tabularnewline
21 & 97.06 & 96.6804 & 96.1112 & 0.569141 & 0.379609 \tabularnewline
22 & 97.7 & 96.871 & 96.6279 & 0.243099 & 0.828984 \tabularnewline
23 & 97.59 & 97.1588 & 97.1412 & 0.0175781 & 0.431172 \tabularnewline
24 & 96.97 & 96.8117 & 97.6621 & -0.850339 & 0.158255 \tabularnewline
25 & 97.75 & 97.5188 & 98.1804 & -0.661589 & 0.231172 \tabularnewline
26 & 99.27 & 98.4997 & 98.6304 & -0.130755 & 0.770339 \tabularnewline
27 & 100.63 & 99.7442 & 98.9971 & 0.747161 & 0.885755 \tabularnewline
28 & 99.8 & 99.7878 & 99.2896 & 0.498203 & 0.0122135 \tabularnewline
29 & 99.5 & 99.6799 & 99.5608 & 0.119036 & -0.17987 \tabularnewline
30 & 99.72 & 99.6522 & 99.8537 & -0.201589 & 0.0678385 \tabularnewline
31 & 99.77 & 99.813 & 100.16 & -0.346589 & -0.0429948 \tabularnewline
32 & 100.18 & 100.443 & 100.446 & -0.00335938 & -0.262891 \tabularnewline
33 & 101.11 & 101.251 & 100.682 & 0.569141 & -0.141224 \tabularnewline
34 & 100.67 & 101.233 & 100.99 & 0.243099 & -0.563099 \tabularnewline
35 & 101.13 & 101.419 & 101.401 & 0.0175781 & -0.288828 \tabularnewline
36 & 100.46 & 100.968 & 101.818 & -0.850339 & -0.507995 \tabularnewline
37 & 101.6 & 101.58 & 102.242 & -0.661589 & 0.0199219 \tabularnewline
38 & 102.3 & 102.528 & 102.659 & -0.130755 & -0.227995 \tabularnewline
39 & 103.26 & 103.755 & 103.008 & 0.747161 & -0.494661 \tabularnewline
40 & 104.56 & 103.809 & 103.311 & 0.498203 & 0.750964 \tabularnewline
41 & 104.61 & 103.727 & 103.608 & 0.119036 & 0.883047 \tabularnewline
42 & 104.62 & 103.715 & 103.917 & -0.201589 & 0.904505 \tabularnewline
43 & 105.03 & 103.861 & 104.208 & -0.346589 & 1.16867 \tabularnewline
44 & 104.93 & 104.466 & 104.47 & -0.00335938 & 0.463776 \tabularnewline
45 & 104.73 & 105.293 & 104.724 & 0.569141 & -0.563307 \tabularnewline
46 & 104.33 & 105.172 & 104.929 & 0.243099 & -0.842266 \tabularnewline
47 & 104.6 & 105.12 & 105.103 & 0.0175781 & -0.520495 \tabularnewline
48 & 104.41 & 104.42 & 105.27 & -0.850339 & -0.00966146 \tabularnewline
49 & 104.63 & 104.761 & 105.422 & -0.661589 & -0.130911 \tabularnewline
50 & 105.55 & 105.449 & 105.58 & -0.130755 & 0.100755 \tabularnewline
51 & 106.12 & 106.503 & 105.755 & 0.747161 & -0.382578 \tabularnewline
52 & 106.62 & 106.445 & 105.947 & 0.498203 & 0.174714 \tabularnewline
53 & 106.72 & 106.314 & 106.195 & 0.119036 & 0.40638 \tabularnewline
54 & 106.52 & 106.284 & 106.485 & -0.201589 & 0.236172 \tabularnewline
55 & 106.79 & NA & NA & -0.346589 & NA \tabularnewline
56 & 106.95 & NA & NA & -0.00335938 & NA \tabularnewline
57 & 106.92 & NA & NA & 0.569141 & NA \tabularnewline
58 & 106.74 & NA & NA & 0.243099 & NA \tabularnewline
59 & 108.13 & NA & NA & 0.0175781 & NA \tabularnewline
60 & 107.86 & NA & NA & -0.850339 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295549&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]92.88[/C][C]NA[/C][C]NA[/C][C]-0.661589[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]91.69[/C][C]NA[/C][C]NA[/C][C]-0.130755[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.66[/C][C]NA[/C][C]NA[/C][C]0.747161[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]90.26[/C][C]NA[/C][C]NA[/C][C]0.498203[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]91.11[/C][C]NA[/C][C]NA[/C][C]0.119036[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.33[/C][C]NA[/C][C]NA[/C][C]-0.201589[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]91.82[/C][C]91.8676[/C][C]92.2142[/C][C]-0.346589[/C][C]-0.0475781[/C][/ROW]
[ROW][C]8[/C][C]92.24[/C][C]92.2312[/C][C]92.2346[/C][C]-0.00335938[/C][C]0.00877604[/C][/ROW]
[ROW][C]9[/C][C]93.35[/C][C]92.9591[/C][C]92.39[/C][C]0.569141[/C][C]0.390859[/C][/ROW]
[ROW][C]10[/C][C]93.53[/C][C]92.8877[/C][C]92.6446[/C][C]0.243099[/C][C]0.642318[/C][/ROW]
[ROW][C]11[/C][C]93.34[/C][C]92.8959[/C][C]92.8783[/C][C]0.0175781[/C][C]0.444089[/C][/ROW]
[ROW][C]12[/C][C]92.59[/C][C]92.1647[/C][C]93.015[/C][C]-0.850339[/C][C]0.425339[/C][/ROW]
[ROW][C]13[/C][C]92.42[/C][C]92.4742[/C][C]93.1358[/C][C]-0.661589[/C][C]-0.0542448[/C][/ROW]
[ROW][C]14[/C][C]92.64[/C][C]93.2172[/C][C]93.3479[/C][C]-0.130755[/C][C]-0.577161[/C][/ROW]
[ROW][C]15[/C][C]94.44[/C][C]94.3826[/C][C]93.6354[/C][C]0.747161[/C][C]0.0574219[/C][/ROW]
[ROW][C]16[/C][C]93.59[/C][C]94.462[/C][C]93.9637[/C][C]0.498203[/C][C]-0.871953[/C][/ROW]
[ROW][C]17[/C][C]93.39[/C][C]94.4336[/C][C]94.3146[/C][C]0.119036[/C][C]-1.04362[/C][/ROW]
[ROW][C]18[/C][C]93.33[/C][C]94.4726[/C][C]94.6742[/C][C]-0.201589[/C][C]-1.14258[/C][/ROW]
[ROW][C]19[/C][C]93.72[/C][C]94.7322[/C][C]95.0788[/C][C]-0.346589[/C][C]-1.01216[/C][/ROW]
[ROW][C]20[/C][C]95.43[/C][C]95.5737[/C][C]95.5771[/C][C]-0.00335938[/C][C]-0.143724[/C][/ROW]
[ROW][C]21[/C][C]97.06[/C][C]96.6804[/C][C]96.1112[/C][C]0.569141[/C][C]0.379609[/C][/ROW]
[ROW][C]22[/C][C]97.7[/C][C]96.871[/C][C]96.6279[/C][C]0.243099[/C][C]0.828984[/C][/ROW]
[ROW][C]23[/C][C]97.59[/C][C]97.1588[/C][C]97.1412[/C][C]0.0175781[/C][C]0.431172[/C][/ROW]
[ROW][C]24[/C][C]96.97[/C][C]96.8117[/C][C]97.6621[/C][C]-0.850339[/C][C]0.158255[/C][/ROW]
[ROW][C]25[/C][C]97.75[/C][C]97.5188[/C][C]98.1804[/C][C]-0.661589[/C][C]0.231172[/C][/ROW]
[ROW][C]26[/C][C]99.27[/C][C]98.4997[/C][C]98.6304[/C][C]-0.130755[/C][C]0.770339[/C][/ROW]
[ROW][C]27[/C][C]100.63[/C][C]99.7442[/C][C]98.9971[/C][C]0.747161[/C][C]0.885755[/C][/ROW]
[ROW][C]28[/C][C]99.8[/C][C]99.7878[/C][C]99.2896[/C][C]0.498203[/C][C]0.0122135[/C][/ROW]
[ROW][C]29[/C][C]99.5[/C][C]99.6799[/C][C]99.5608[/C][C]0.119036[/C][C]-0.17987[/C][/ROW]
[ROW][C]30[/C][C]99.72[/C][C]99.6522[/C][C]99.8537[/C][C]-0.201589[/C][C]0.0678385[/C][/ROW]
[ROW][C]31[/C][C]99.77[/C][C]99.813[/C][C]100.16[/C][C]-0.346589[/C][C]-0.0429948[/C][/ROW]
[ROW][C]32[/C][C]100.18[/C][C]100.443[/C][C]100.446[/C][C]-0.00335938[/C][C]-0.262891[/C][/ROW]
[ROW][C]33[/C][C]101.11[/C][C]101.251[/C][C]100.682[/C][C]0.569141[/C][C]-0.141224[/C][/ROW]
[ROW][C]34[/C][C]100.67[/C][C]101.233[/C][C]100.99[/C][C]0.243099[/C][C]-0.563099[/C][/ROW]
[ROW][C]35[/C][C]101.13[/C][C]101.419[/C][C]101.401[/C][C]0.0175781[/C][C]-0.288828[/C][/ROW]
[ROW][C]36[/C][C]100.46[/C][C]100.968[/C][C]101.818[/C][C]-0.850339[/C][C]-0.507995[/C][/ROW]
[ROW][C]37[/C][C]101.6[/C][C]101.58[/C][C]102.242[/C][C]-0.661589[/C][C]0.0199219[/C][/ROW]
[ROW][C]38[/C][C]102.3[/C][C]102.528[/C][C]102.659[/C][C]-0.130755[/C][C]-0.227995[/C][/ROW]
[ROW][C]39[/C][C]103.26[/C][C]103.755[/C][C]103.008[/C][C]0.747161[/C][C]-0.494661[/C][/ROW]
[ROW][C]40[/C][C]104.56[/C][C]103.809[/C][C]103.311[/C][C]0.498203[/C][C]0.750964[/C][/ROW]
[ROW][C]41[/C][C]104.61[/C][C]103.727[/C][C]103.608[/C][C]0.119036[/C][C]0.883047[/C][/ROW]
[ROW][C]42[/C][C]104.62[/C][C]103.715[/C][C]103.917[/C][C]-0.201589[/C][C]0.904505[/C][/ROW]
[ROW][C]43[/C][C]105.03[/C][C]103.861[/C][C]104.208[/C][C]-0.346589[/C][C]1.16867[/C][/ROW]
[ROW][C]44[/C][C]104.93[/C][C]104.466[/C][C]104.47[/C][C]-0.00335938[/C][C]0.463776[/C][/ROW]
[ROW][C]45[/C][C]104.73[/C][C]105.293[/C][C]104.724[/C][C]0.569141[/C][C]-0.563307[/C][/ROW]
[ROW][C]46[/C][C]104.33[/C][C]105.172[/C][C]104.929[/C][C]0.243099[/C][C]-0.842266[/C][/ROW]
[ROW][C]47[/C][C]104.6[/C][C]105.12[/C][C]105.103[/C][C]0.0175781[/C][C]-0.520495[/C][/ROW]
[ROW][C]48[/C][C]104.41[/C][C]104.42[/C][C]105.27[/C][C]-0.850339[/C][C]-0.00966146[/C][/ROW]
[ROW][C]49[/C][C]104.63[/C][C]104.761[/C][C]105.422[/C][C]-0.661589[/C][C]-0.130911[/C][/ROW]
[ROW][C]50[/C][C]105.55[/C][C]105.449[/C][C]105.58[/C][C]-0.130755[/C][C]0.100755[/C][/ROW]
[ROW][C]51[/C][C]106.12[/C][C]106.503[/C][C]105.755[/C][C]0.747161[/C][C]-0.382578[/C][/ROW]
[ROW][C]52[/C][C]106.62[/C][C]106.445[/C][C]105.947[/C][C]0.498203[/C][C]0.174714[/C][/ROW]
[ROW][C]53[/C][C]106.72[/C][C]106.314[/C][C]106.195[/C][C]0.119036[/C][C]0.40638[/C][/ROW]
[ROW][C]54[/C][C]106.52[/C][C]106.284[/C][C]106.485[/C][C]-0.201589[/C][C]0.236172[/C][/ROW]
[ROW][C]55[/C][C]106.79[/C][C]NA[/C][C]NA[/C][C]-0.346589[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]106.95[/C][C]NA[/C][C]NA[/C][C]-0.00335938[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]106.92[/C][C]NA[/C][C]NA[/C][C]0.569141[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]106.74[/C][C]NA[/C][C]NA[/C][C]0.243099[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]108.13[/C][C]NA[/C][C]NA[/C][C]0.0175781[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]107.86[/C][C]NA[/C][C]NA[/C][C]-0.850339[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295549&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	92.88	NA	NA	-0.661589	NA
2	91.69	NA	NA	-0.130755	NA
3	91.66	NA	NA	0.747161	NA
4	90.26	NA	NA	0.498203	NA
5	91.11	NA	NA	0.119036	NA
6	92.33	NA	NA	-0.201589	NA
7	91.82	91.8676	92.2142	-0.346589	-0.0475781
8	92.24	92.2312	92.2346	-0.00335938	0.00877604
9	93.35	92.9591	92.39	0.569141	0.390859
10	93.53	92.8877	92.6446	0.243099	0.642318
11	93.34	92.8959	92.8783	0.0175781	0.444089
12	92.59	92.1647	93.015	-0.850339	0.425339
13	92.42	92.4742	93.1358	-0.661589	-0.0542448
14	92.64	93.2172	93.3479	-0.130755	-0.577161
15	94.44	94.3826	93.6354	0.747161	0.0574219
16	93.59	94.462	93.9637	0.498203	-0.871953
17	93.39	94.4336	94.3146	0.119036	-1.04362
18	93.33	94.4726	94.6742	-0.201589	-1.14258
19	93.72	94.7322	95.0788	-0.346589	-1.01216
20	95.43	95.5737	95.5771	-0.00335938	-0.143724
21	97.06	96.6804	96.1112	0.569141	0.379609
22	97.7	96.871	96.6279	0.243099	0.828984
23	97.59	97.1588	97.1412	0.0175781	0.431172
24	96.97	96.8117	97.6621	-0.850339	0.158255
25	97.75	97.5188	98.1804	-0.661589	0.231172
26	99.27	98.4997	98.6304	-0.130755	0.770339
27	100.63	99.7442	98.9971	0.747161	0.885755
28	99.8	99.7878	99.2896	0.498203	0.0122135
29	99.5	99.6799	99.5608	0.119036	-0.17987
30	99.72	99.6522	99.8537	-0.201589	0.0678385
31	99.77	99.813	100.16	-0.346589	-0.0429948
32	100.18	100.443	100.446	-0.00335938	-0.262891
33	101.11	101.251	100.682	0.569141	-0.141224
34	100.67	101.233	100.99	0.243099	-0.563099
35	101.13	101.419	101.401	0.0175781	-0.288828
36	100.46	100.968	101.818	-0.850339	-0.507995
37	101.6	101.58	102.242	-0.661589	0.0199219
38	102.3	102.528	102.659	-0.130755	-0.227995
39	103.26	103.755	103.008	0.747161	-0.494661
40	104.56	103.809	103.311	0.498203	0.750964
41	104.61	103.727	103.608	0.119036	0.883047
42	104.62	103.715	103.917	-0.201589	0.904505
43	105.03	103.861	104.208	-0.346589	1.16867
44	104.93	104.466	104.47	-0.00335938	0.463776
45	104.73	105.293	104.724	0.569141	-0.563307
46	104.33	105.172	104.929	0.243099	-0.842266
47	104.6	105.12	105.103	0.0175781	-0.520495
48	104.41	104.42	105.27	-0.850339	-0.00966146
49	104.63	104.761	105.422	-0.661589	-0.130911
50	105.55	105.449	105.58	-0.130755	0.100755
51	106.12	106.503	105.755	0.747161	-0.382578
52	106.62	106.445	105.947	0.498203	0.174714
53	106.72	106.314	106.195	0.119036	0.40638
54	106.52	106.284	106.485	-0.201589	0.236172
55	106.79	NA	NA	-0.346589	NA
56	106.95	NA	NA	-0.00335938	NA
57	106.92	NA	NA	0.569141	NA
58	106.74	NA	NA	0.243099	NA
59	108.13	NA	NA	0.0175781	NA
60	107.86	NA	NA	-0.850339	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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