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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 23 Nov 2013 08:22:39 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/23/t1385212985cd34llzs3wokofv.htm/, Retrieved Fri, 01 May 2026 12:09:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=227805, Retrieved Fri, 01 May 2026 12:09:23 +0000

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Estimated Impact

317

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

-       [Classical Decomposition] [Workshop 8 - vraag 1] [2013-11-23 13:22:39] [47fc20d512b54de2decb1f477fea32db] [Current]
- RMP     [Decomposition by Loess] [Workshop 8 - vraag 2] [2013-11-23 14:38:37] [6e6101806504d86600f236fbba4f8aec] 
- RMP     [Structural Time Series Models] [Workshop 8 - vraag 3] [2013-11-23 14:43:16] [6e6101806504d86600f236fbba4f8aec] 
- RMP     [Exponential Smoothing] [Workshop 8 - vraag 4] [2013-11-23 14:47:53] [6e6101806504d86600f236fbba4f8aec] 
- R P       [Exponential Smoothing] [Workshop 8 - vraa...] [2013-11-27 14:59:44] [6e6101806504d86600f236fbba4f8aec] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Sir Maurice George Kendall' @ kendall.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 & 4 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=227805&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=227805&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=227805&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	4 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	9700	NA	NA	155.958	NA
2	9081	NA	NA	-561.217	NA
3	9084	NA	NA	-32.6006	NA
4	9743	NA	NA	133.483	NA
5	8587	NA	NA	-696.592	NA
6	9731	NA	NA	217.674	NA
7	9563	9339.22	9512.54	-173.32	223.778
8	9998	9506.81	9512.17	-5.35475	491.188
9	9437	9454.43	9512.29	-57.8617	-17.43
10	10038	10068.1	9503.67	564.399	-30.0661
11	9918	9866.16	9497.71	368.449	51.8422
12	9252	9585.07	9498.08	86.9828	-333.066
13	9737	9624.04	9468.08	155.958	112.959
14	9035	8851.41	9412.62	-561.217	183.592
15	9133	9324.9	9357.5	-32.6006	-191.899
16	9487	9461.86	9328.38	133.483	25.1422
17	8700	8615.91	9312.5	-696.592	84.0922
18	9627	9520.84	9303.17	217.674	106.159
19	8947	9127.1	9300.42	-173.32	-180.097
20	9283	9273.1	9278.46	-5.35475	9.89641
21	8829	9207.51	9265.37	-57.8617	-378.513
22	9947	9836.48	9272.08	564.399	110.517
23	9628	9637.49	9269.04	368.449	-9.49109
24	9318	9331.23	9244.25	86.9828	-13.2328
25	9605	9403.58	9247.62	155.958	201.417
26	8640	8701.53	9262.75	-561.217	-61.5328
27	9214	9242.19	9274.79	-32.6006	-28.1911
28	9567	9436.27	9302.79	133.483	130.726
29	8547	8607.41	9304	-696.592	-60.4078
30	9185	9527.63	9309.96	217.674	-342.633
31	9470	9143.35	9316.67	-173.32	326.653
32	9123	9308.1	9313.46	-5.35475	-185.104
33	9278	9273.8	9331.67	-57.8617	4.19502
34	10170	9915.15	9350.75	564.399	254.851
35	9434	9743.87	9375.42	368.449	-309.866
36	9655	9502.36	9415.38	86.9828	152.642
37	9429	9580.62	9424.67	155.958	-151.624
38	8739	8850.2	9411.42	-561.217	-111.199
39	9552	9397.82	9430.42	-32.6006	154.184
40	9687	9591.07	9457.58	133.483	95.9339
41	9019	8794.87	9491.46	-696.592	224.134
42	9672	9745.76	9528.08	217.674	-73.7578
43	9206	9372.89	9546.21	-173.32	-166.888
44	9069	9573.48	9578.83	-5.35475	-504.479
45	9788	9560.55	9618.42	-57.8617	227.445
46	10312	10199.8	9635.42	564.399	112.184
47	10105	10005.7	9637.25	368.449	99.3006
48	9863	9747.07	9660.08	86.9828	115.934
49	9656	9858.46	9702.5	155.958	-202.458
50	9295	9191.12	9752.33	-561.217	103.884
51	9946	9753.11	9785.71	-32.6006	192.892
52	9701	9910.32	9776.83	133.483	-209.316
53	9049	9080.66	9777.25	-696.592	-31.6578
54	10190	10017.3	9799.67	217.674	172.659
55	9706	9654.1	9827.42	-173.32	51.9034
56	9765	9839.85	9845.21	-5.35475	-74.8536
57	9893	9780.89	9838.75	-57.8617	112.112
58	9994	10414.4	9850	564.399	-420.399
59	10433	10237.7	9869.25	368.449	195.301
60	10073	9968.19	9881.21	86.9828	104.809
61	10112	10045.2	9889.25	155.958	66.7922
62	9266	9353.66	9914.88	-561.217	-87.6578
63	9820	9919.9	9952.5	-32.6006	-99.8994
64	10097	10112.4	9978.92	133.483	-15.3994
65	9115	9304.07	10000.7	-696.592	-189.074
66	10411	10246.3	10028.7	217.674	164.659
67	9678	9900.85	10074.2	-173.32	-222.847
68	10408	10112.1	10117.5	-5.35475	295.855
69	10153	10068.3	10126.2	-57.8617	84.695
70	10368	NA	NA	564.399	NA
71	10581	NA	NA	368.449	NA
72	10597	NA	NA	86.9828	NA
73	10680	NA	NA	155.958	NA
74	9738	NA	NA	-561.217	NA
75	9556	NA	NA	-32.6006	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 9700 & NA & NA & 155.958 & NA \tabularnewline
2 & 9081 & NA & NA & -561.217 & NA \tabularnewline
3 & 9084 & NA & NA & -32.6006 & NA \tabularnewline
4 & 9743 & NA & NA & 133.483 & NA \tabularnewline
5 & 8587 & NA & NA & -696.592 & NA \tabularnewline
6 & 9731 & NA & NA & 217.674 & NA \tabularnewline
7 & 9563 & 9339.22 & 9512.54 & -173.32 & 223.778 \tabularnewline
8 & 9998 & 9506.81 & 9512.17 & -5.35475 & 491.188 \tabularnewline
9 & 9437 & 9454.43 & 9512.29 & -57.8617 & -17.43 \tabularnewline
10 & 10038 & 10068.1 & 9503.67 & 564.399 & -30.0661 \tabularnewline
11 & 9918 & 9866.16 & 9497.71 & 368.449 & 51.8422 \tabularnewline
12 & 9252 & 9585.07 & 9498.08 & 86.9828 & -333.066 \tabularnewline
13 & 9737 & 9624.04 & 9468.08 & 155.958 & 112.959 \tabularnewline
14 & 9035 & 8851.41 & 9412.62 & -561.217 & 183.592 \tabularnewline
15 & 9133 & 9324.9 & 9357.5 & -32.6006 & -191.899 \tabularnewline
16 & 9487 & 9461.86 & 9328.38 & 133.483 & 25.1422 \tabularnewline
17 & 8700 & 8615.91 & 9312.5 & -696.592 & 84.0922 \tabularnewline
18 & 9627 & 9520.84 & 9303.17 & 217.674 & 106.159 \tabularnewline
19 & 8947 & 9127.1 & 9300.42 & -173.32 & -180.097 \tabularnewline
20 & 9283 & 9273.1 & 9278.46 & -5.35475 & 9.89641 \tabularnewline
21 & 8829 & 9207.51 & 9265.37 & -57.8617 & -378.513 \tabularnewline
22 & 9947 & 9836.48 & 9272.08 & 564.399 & 110.517 \tabularnewline
23 & 9628 & 9637.49 & 9269.04 & 368.449 & -9.49109 \tabularnewline
24 & 9318 & 9331.23 & 9244.25 & 86.9828 & -13.2328 \tabularnewline
25 & 9605 & 9403.58 & 9247.62 & 155.958 & 201.417 \tabularnewline
26 & 8640 & 8701.53 & 9262.75 & -561.217 & -61.5328 \tabularnewline
27 & 9214 & 9242.19 & 9274.79 & -32.6006 & -28.1911 \tabularnewline
28 & 9567 & 9436.27 & 9302.79 & 133.483 & 130.726 \tabularnewline
29 & 8547 & 8607.41 & 9304 & -696.592 & -60.4078 \tabularnewline
30 & 9185 & 9527.63 & 9309.96 & 217.674 & -342.633 \tabularnewline
31 & 9470 & 9143.35 & 9316.67 & -173.32 & 326.653 \tabularnewline
32 & 9123 & 9308.1 & 9313.46 & -5.35475 & -185.104 \tabularnewline
33 & 9278 & 9273.8 & 9331.67 & -57.8617 & 4.19502 \tabularnewline
34 & 10170 & 9915.15 & 9350.75 & 564.399 & 254.851 \tabularnewline
35 & 9434 & 9743.87 & 9375.42 & 368.449 & -309.866 \tabularnewline
36 & 9655 & 9502.36 & 9415.38 & 86.9828 & 152.642 \tabularnewline
37 & 9429 & 9580.62 & 9424.67 & 155.958 & -151.624 \tabularnewline
38 & 8739 & 8850.2 & 9411.42 & -561.217 & -111.199 \tabularnewline
39 & 9552 & 9397.82 & 9430.42 & -32.6006 & 154.184 \tabularnewline
40 & 9687 & 9591.07 & 9457.58 & 133.483 & 95.9339 \tabularnewline
41 & 9019 & 8794.87 & 9491.46 & -696.592 & 224.134 \tabularnewline
42 & 9672 & 9745.76 & 9528.08 & 217.674 & -73.7578 \tabularnewline
43 & 9206 & 9372.89 & 9546.21 & -173.32 & -166.888 \tabularnewline
44 & 9069 & 9573.48 & 9578.83 & -5.35475 & -504.479 \tabularnewline
45 & 9788 & 9560.55 & 9618.42 & -57.8617 & 227.445 \tabularnewline
46 & 10312 & 10199.8 & 9635.42 & 564.399 & 112.184 \tabularnewline
47 & 10105 & 10005.7 & 9637.25 & 368.449 & 99.3006 \tabularnewline
48 & 9863 & 9747.07 & 9660.08 & 86.9828 & 115.934 \tabularnewline
49 & 9656 & 9858.46 & 9702.5 & 155.958 & -202.458 \tabularnewline
50 & 9295 & 9191.12 & 9752.33 & -561.217 & 103.884 \tabularnewline
51 & 9946 & 9753.11 & 9785.71 & -32.6006 & 192.892 \tabularnewline
52 & 9701 & 9910.32 & 9776.83 & 133.483 & -209.316 \tabularnewline
53 & 9049 & 9080.66 & 9777.25 & -696.592 & -31.6578 \tabularnewline
54 & 10190 & 10017.3 & 9799.67 & 217.674 & 172.659 \tabularnewline
55 & 9706 & 9654.1 & 9827.42 & -173.32 & 51.9034 \tabularnewline
56 & 9765 & 9839.85 & 9845.21 & -5.35475 & -74.8536 \tabularnewline
57 & 9893 & 9780.89 & 9838.75 & -57.8617 & 112.112 \tabularnewline
58 & 9994 & 10414.4 & 9850 & 564.399 & -420.399 \tabularnewline
59 & 10433 & 10237.7 & 9869.25 & 368.449 & 195.301 \tabularnewline
60 & 10073 & 9968.19 & 9881.21 & 86.9828 & 104.809 \tabularnewline
61 & 10112 & 10045.2 & 9889.25 & 155.958 & 66.7922 \tabularnewline
62 & 9266 & 9353.66 & 9914.88 & -561.217 & -87.6578 \tabularnewline
63 & 9820 & 9919.9 & 9952.5 & -32.6006 & -99.8994 \tabularnewline
64 & 10097 & 10112.4 & 9978.92 & 133.483 & -15.3994 \tabularnewline
65 & 9115 & 9304.07 & 10000.7 & -696.592 & -189.074 \tabularnewline
66 & 10411 & 10246.3 & 10028.7 & 217.674 & 164.659 \tabularnewline
67 & 9678 & 9900.85 & 10074.2 & -173.32 & -222.847 \tabularnewline
68 & 10408 & 10112.1 & 10117.5 & -5.35475 & 295.855 \tabularnewline
69 & 10153 & 10068.3 & 10126.2 & -57.8617 & 84.695 \tabularnewline
70 & 10368 & NA & NA & 564.399 & NA \tabularnewline
71 & 10581 & NA & NA & 368.449 & NA \tabularnewline
72 & 10597 & NA & NA & 86.9828 & NA \tabularnewline
73 & 10680 & NA & NA & 155.958 & NA \tabularnewline
74 & 9738 & NA & NA & -561.217 & NA \tabularnewline
75 & 9556 & NA & NA & -32.6006 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=227805&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]9700[/C][C]NA[/C][C]NA[/C][C]155.958[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]NA[/C][C]NA[/C][C]-561.217[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]NA[/C][C]NA[/C][C]-32.6006[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]NA[/C][C]NA[/C][C]133.483[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]NA[/C][C]NA[/C][C]-696.592[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]NA[/C][C]NA[/C][C]217.674[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9339.22[/C][C]9512.54[/C][C]-173.32[/C][C]223.778[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9506.81[/C][C]9512.17[/C][C]-5.35475[/C][C]491.188[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9454.43[/C][C]9512.29[/C][C]-57.8617[/C][C]-17.43[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]10068.1[/C][C]9503.67[/C][C]564.399[/C][C]-30.0661[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9866.16[/C][C]9497.71[/C][C]368.449[/C][C]51.8422[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9585.07[/C][C]9498.08[/C][C]86.9828[/C][C]-333.066[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9624.04[/C][C]9468.08[/C][C]155.958[/C][C]112.959[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]8851.41[/C][C]9412.62[/C][C]-561.217[/C][C]183.592[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9324.9[/C][C]9357.5[/C][C]-32.6006[/C][C]-191.899[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9461.86[/C][C]9328.38[/C][C]133.483[/C][C]25.1422[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8615.91[/C][C]9312.5[/C][C]-696.592[/C][C]84.0922[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9520.84[/C][C]9303.17[/C][C]217.674[/C][C]106.159[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9127.1[/C][C]9300.42[/C][C]-173.32[/C][C]-180.097[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9273.1[/C][C]9278.46[/C][C]-5.35475[/C][C]9.89641[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9207.51[/C][C]9265.37[/C][C]-57.8617[/C][C]-378.513[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9836.48[/C][C]9272.08[/C][C]564.399[/C][C]110.517[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9637.49[/C][C]9269.04[/C][C]368.449[/C][C]-9.49109[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9331.23[/C][C]9244.25[/C][C]86.9828[/C][C]-13.2328[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9403.58[/C][C]9247.62[/C][C]155.958[/C][C]201.417[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8701.53[/C][C]9262.75[/C][C]-561.217[/C][C]-61.5328[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9242.19[/C][C]9274.79[/C][C]-32.6006[/C][C]-28.1911[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9436.27[/C][C]9302.79[/C][C]133.483[/C][C]130.726[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8607.41[/C][C]9304[/C][C]-696.592[/C][C]-60.4078[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]9527.63[/C][C]9309.96[/C][C]217.674[/C][C]-342.633[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9143.35[/C][C]9316.67[/C][C]-173.32[/C][C]326.653[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9308.1[/C][C]9313.46[/C][C]-5.35475[/C][C]-185.104[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9273.8[/C][C]9331.67[/C][C]-57.8617[/C][C]4.19502[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]9915.15[/C][C]9350.75[/C][C]564.399[/C][C]254.851[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9743.87[/C][C]9375.42[/C][C]368.449[/C][C]-309.866[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9502.36[/C][C]9415.38[/C][C]86.9828[/C][C]152.642[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9580.62[/C][C]9424.67[/C][C]155.958[/C][C]-151.624[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]8850.2[/C][C]9411.42[/C][C]-561.217[/C][C]-111.199[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9397.82[/C][C]9430.42[/C][C]-32.6006[/C][C]154.184[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9591.07[/C][C]9457.58[/C][C]133.483[/C][C]95.9339[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]8794.87[/C][C]9491.46[/C][C]-696.592[/C][C]224.134[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9745.76[/C][C]9528.08[/C][C]217.674[/C][C]-73.7578[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9372.89[/C][C]9546.21[/C][C]-173.32[/C][C]-166.888[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9573.48[/C][C]9578.83[/C][C]-5.35475[/C][C]-504.479[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9560.55[/C][C]9618.42[/C][C]-57.8617[/C][C]227.445[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10199.8[/C][C]9635.42[/C][C]564.399[/C][C]112.184[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10005.7[/C][C]9637.25[/C][C]368.449[/C][C]99.3006[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9747.07[/C][C]9660.08[/C][C]86.9828[/C][C]115.934[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9858.46[/C][C]9702.5[/C][C]155.958[/C][C]-202.458[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9191.12[/C][C]9752.33[/C][C]-561.217[/C][C]103.884[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9753.11[/C][C]9785.71[/C][C]-32.6006[/C][C]192.892[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9910.32[/C][C]9776.83[/C][C]133.483[/C][C]-209.316[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9080.66[/C][C]9777.25[/C][C]-696.592[/C][C]-31.6578[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10017.3[/C][C]9799.67[/C][C]217.674[/C][C]172.659[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9654.1[/C][C]9827.42[/C][C]-173.32[/C][C]51.9034[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9839.85[/C][C]9845.21[/C][C]-5.35475[/C][C]-74.8536[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9780.89[/C][C]9838.75[/C][C]-57.8617[/C][C]112.112[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]10414.4[/C][C]9850[/C][C]564.399[/C][C]-420.399[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10237.7[/C][C]9869.25[/C][C]368.449[/C][C]195.301[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9968.19[/C][C]9881.21[/C][C]86.9828[/C][C]104.809[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10045.2[/C][C]9889.25[/C][C]155.958[/C][C]66.7922[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9353.66[/C][C]9914.88[/C][C]-561.217[/C][C]-87.6578[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9919.9[/C][C]9952.5[/C][C]-32.6006[/C][C]-99.8994[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10112.4[/C][C]9978.92[/C][C]133.483[/C][C]-15.3994[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9304.07[/C][C]10000.7[/C][C]-696.592[/C][C]-189.074[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10246.3[/C][C]10028.7[/C][C]217.674[/C][C]164.659[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9900.85[/C][C]10074.2[/C][C]-173.32[/C][C]-222.847[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10112.1[/C][C]10117.5[/C][C]-5.35475[/C][C]295.855[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10068.3[/C][C]10126.2[/C][C]-57.8617[/C][C]84.695[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]NA[/C][C]NA[/C][C]564.399[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]NA[/C][C]NA[/C][C]368.449[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]NA[/C][C]NA[/C][C]86.9828[/C][C]NA[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]NA[/C][C]NA[/C][C]155.958[/C][C]NA[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]NA[/C][C]NA[/C][C]-561.217[/C][C]NA[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]NA[/C][C]NA[/C][C]-32.6006[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=227805&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=227805&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	9700	NA	NA	155.958	NA
2	9081	NA	NA	-561.217	NA
3	9084	NA	NA	-32.6006	NA
4	9743	NA	NA	133.483	NA
5	8587	NA	NA	-696.592	NA
6	9731	NA	NA	217.674	NA
7	9563	9339.22	9512.54	-173.32	223.778
8	9998	9506.81	9512.17	-5.35475	491.188
9	9437	9454.43	9512.29	-57.8617	-17.43
10	10038	10068.1	9503.67	564.399	-30.0661
11	9918	9866.16	9497.71	368.449	51.8422
12	9252	9585.07	9498.08	86.9828	-333.066
13	9737	9624.04	9468.08	155.958	112.959
14	9035	8851.41	9412.62	-561.217	183.592
15	9133	9324.9	9357.5	-32.6006	-191.899
16	9487	9461.86	9328.38	133.483	25.1422
17	8700	8615.91	9312.5	-696.592	84.0922
18	9627	9520.84	9303.17	217.674	106.159
19	8947	9127.1	9300.42	-173.32	-180.097
20	9283	9273.1	9278.46	-5.35475	9.89641
21	8829	9207.51	9265.37	-57.8617	-378.513
22	9947	9836.48	9272.08	564.399	110.517
23	9628	9637.49	9269.04	368.449	-9.49109
24	9318	9331.23	9244.25	86.9828	-13.2328
25	9605	9403.58	9247.62	155.958	201.417
26	8640	8701.53	9262.75	-561.217	-61.5328
27	9214	9242.19	9274.79	-32.6006	-28.1911
28	9567	9436.27	9302.79	133.483	130.726
29	8547	8607.41	9304	-696.592	-60.4078
30	9185	9527.63	9309.96	217.674	-342.633
31	9470	9143.35	9316.67	-173.32	326.653
32	9123	9308.1	9313.46	-5.35475	-185.104
33	9278	9273.8	9331.67	-57.8617	4.19502
34	10170	9915.15	9350.75	564.399	254.851
35	9434	9743.87	9375.42	368.449	-309.866
36	9655	9502.36	9415.38	86.9828	152.642
37	9429	9580.62	9424.67	155.958	-151.624
38	8739	8850.2	9411.42	-561.217	-111.199
39	9552	9397.82	9430.42	-32.6006	154.184
40	9687	9591.07	9457.58	133.483	95.9339
41	9019	8794.87	9491.46	-696.592	224.134
42	9672	9745.76	9528.08	217.674	-73.7578
43	9206	9372.89	9546.21	-173.32	-166.888
44	9069	9573.48	9578.83	-5.35475	-504.479
45	9788	9560.55	9618.42	-57.8617	227.445
46	10312	10199.8	9635.42	564.399	112.184
47	10105	10005.7	9637.25	368.449	99.3006
48	9863	9747.07	9660.08	86.9828	115.934
49	9656	9858.46	9702.5	155.958	-202.458
50	9295	9191.12	9752.33	-561.217	103.884
51	9946	9753.11	9785.71	-32.6006	192.892
52	9701	9910.32	9776.83	133.483	-209.316
53	9049	9080.66	9777.25	-696.592	-31.6578
54	10190	10017.3	9799.67	217.674	172.659
55	9706	9654.1	9827.42	-173.32	51.9034
56	9765	9839.85	9845.21	-5.35475	-74.8536
57	9893	9780.89	9838.75	-57.8617	112.112
58	9994	10414.4	9850	564.399	-420.399
59	10433	10237.7	9869.25	368.449	195.301
60	10073	9968.19	9881.21	86.9828	104.809
61	10112	10045.2	9889.25	155.958	66.7922
62	9266	9353.66	9914.88	-561.217	-87.6578
63	9820	9919.9	9952.5	-32.6006	-99.8994
64	10097	10112.4	9978.92	133.483	-15.3994
65	9115	9304.07	10000.7	-696.592	-189.074
66	10411	10246.3	10028.7	217.674	164.659
67	9678	9900.85	10074.2	-173.32	-222.847
68	10408	10112.1	10117.5	-5.35475	295.855
69	10153	10068.3	10126.2	-57.8617	84.695
70	10368	NA	NA	564.399	NA
71	10581	NA	NA	368.449	NA
72	10597	NA	NA	86.9828	NA
73	10680	NA	NA	155.958	NA
74	9738	NA	NA	-561.217	NA
75	9556	NA	NA	-32.6006	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