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Author's title

consumptieprijsindex van katten- en hondenvoeding (blik, brokken en alu-sch...

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*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 04 May 2012 07:18:28 -0400

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/2012/May/04/t1336130421jf7ucoo27qr0yr8.htm/, Retrieved Fri, 03 May 2024 14:44:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166197, Retrieved Fri, 03 May 2024 14:44:32 +0000

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Estimated Impact

167

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

-       [Classical Decomposition] [consumptieprijsin...] [2012-05-04 11:18:28] [61c74c688bd5b30d4ef8812aa8043069] [Current]

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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	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=166197&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=166197&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166197&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	100.34	NA	NA	0.745486111111116	NA
2	100.21	NA	NA	0.590486111111107	NA
3	100.44	NA	NA	0.796944444444448	NA
4	101.59	NA	NA	0.829861111111106	NA
5	102.44	NA	NA	0.358715277777768	NA
6	103.1	NA	NA	-0.364097222222221	NA
7	103.34	102.279131944444	102.710416666667	-0.431284722222224	1.06086805555557
8	103.44	101.996840277778	103.08625	-1.08940972222222	1.44315972222222
9	103.35	102.445381944444	103.455416666667	-1.01003472222222	0.904618055555545
10	103.67	103.361631944444	103.769166666667	-0.407534722222224	0.308368055555562
11	104.13	103.814965277778	104.001666666667	-0.186701388888886	0.315034722222222
12	104.27	104.337986111111	104.170416666667	0.167569444444445	-0.0679861111110966
13	104.75	105.049652777778	104.304166666667	0.745486111111116	-0.299652777777794
14	104.82	104.984236111111	104.39375	0.590486111111107	-0.164236111111109
15	104.69	105.247361111111	104.450416666667	0.796944444444448	-0.557361111111121
16	104.87	105.341111111111	104.51125	0.829861111111106	-0.471111111111085
17	104.74	104.963298611111	104.604583333333	0.358715277777768	-0.22329861111109
18	104.85	104.412152777778	104.77625	-0.364097222222221	0.437847222222231
19	104.8	104.608715277778	105.04	-0.431284722222224	0.191284722222235
20	104.13	104.279340277778	105.36875	-1.08940972222222	-0.149340277777767
21	104.02	104.704131944444	105.714166666667	-1.01003472222222	-0.684131944444445
22	104.46	105.646215277778	106.05375	-0.407534722222224	-1.18621527777778
23	105.58	106.192465277778	106.379166666667	-0.186701388888886	-0.612465277777773
24	106.94	106.812569444444	106.645	0.167569444444445	0.127430555555549
25	108.41	107.604236111111	106.85875	0.745486111111116	0.805763888888904
26	109.05	107.660069444444	107.069583333333	0.590486111111107	1.38993055555557
27	108.75	108.179444444444	107.3825	0.796944444444448	0.570555555555572
28	108.96	108.732361111111	107.9025	0.829861111111106	0.227638888888904
29	108.46	108.862881944444	108.504166666667	0.358715277777768	-0.402881944444445
30	107.51	108.702569444444	109.066666666667	-0.364097222222221	-1.19256944444443
31	107.27	109.129131944444	109.560416666667	-0.431284722222224	-1.85913194444443
32	106.72	108.900590277778	109.99	-1.08940972222222	-2.18059027777777
33	108.94	109.512048611111	110.522083333333	-1.01003472222222	-0.5720486111111
34	112.02	110.799548611111	111.207083333333	-0.407534722222224	1.2204513888889
35	112.46	111.764548611111	111.95125	-0.186701388888886	0.695451388888912
36	113.56	112.904236111111	112.736666666667	0.167569444444445	0.655763888888913
37	113.64	114.307152777778	113.561666666667	0.745486111111116	-0.667152777777773
38	114.13	115.014652777778	114.424166666667	0.590486111111107	-0.884652777777774
39	116.44	116.007777777778	115.210833333333	0.796944444444448	0.432222222222222
40	117.71	116.600277777778	115.770416666667	0.829861111111106	1.10972222222222
41	117.57	116.552881944444	116.194166666667	0.358715277777768	1.01711805555556
42	117.25	116.203402777778	116.5675	-0.364097222222221	1.04659722222223
43	117.33	116.528298611111	116.959583333333	-0.431284722222224	0.801701388888887
44	117.36	116.278506944444	117.367916666667	-1.08940972222222	1.08149305555555
45	117.18	116.633715277778	117.64375	-1.01003472222222	0.546284722222225
46	117.21	117.357881944444	117.765416666667	-0.407534722222224	-0.14788194444445
47	117.44	117.643298611111	117.83	-0.186701388888886	-0.203298611111094
48	117.54	118.060486111111	117.892916666667	0.167569444444445	-0.520486111111097
49	119.07	118.714236111111	117.96875	0.745486111111116	0.355763888888887
50	118.5	118.646319444444	118.055833333333	0.590486111111107	-0.14631944444443
51	118.69	118.940694444444	118.14375	0.796944444444448	-0.250694444444434
52	118.38	119.051527777778	118.221666666667	0.829861111111106	-0.671527777777769
53	118.45	118.646215277778	118.2875	0.358715277777768	-0.196215277777753
54	117.88	117.977152777778	118.34125	-0.364097222222221	-0.0971527777777652
55	118.52	NA	NA	-0.431284722222224	NA
56	118.26	NA	NA	-1.08940972222222	NA
57	118.39	NA	NA	-1.01003472222222	NA
58	117.87	NA	NA	-0.407534722222224	NA
59	118.36	NA	NA	-0.186701388888886	NA
60	117.91	NA	NA	0.167569444444445	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 100.34 & NA & NA & 0.745486111111116 & NA \tabularnewline
2 & 100.21 & NA & NA & 0.590486111111107 & NA \tabularnewline
3 & 100.44 & NA & NA & 0.796944444444448 & NA \tabularnewline
4 & 101.59 & NA & NA & 0.829861111111106 & NA \tabularnewline
5 & 102.44 & NA & NA & 0.358715277777768 & NA \tabularnewline
6 & 103.1 & NA & NA & -0.364097222222221 & NA \tabularnewline
7 & 103.34 & 102.279131944444 & 102.710416666667 & -0.431284722222224 & 1.06086805555557 \tabularnewline
8 & 103.44 & 101.996840277778 & 103.08625 & -1.08940972222222 & 1.44315972222222 \tabularnewline
9 & 103.35 & 102.445381944444 & 103.455416666667 & -1.01003472222222 & 0.904618055555545 \tabularnewline
10 & 103.67 & 103.361631944444 & 103.769166666667 & -0.407534722222224 & 0.308368055555562 \tabularnewline
11 & 104.13 & 103.814965277778 & 104.001666666667 & -0.186701388888886 & 0.315034722222222 \tabularnewline
12 & 104.27 & 104.337986111111 & 104.170416666667 & 0.167569444444445 & -0.0679861111110966 \tabularnewline
13 & 104.75 & 105.049652777778 & 104.304166666667 & 0.745486111111116 & -0.299652777777794 \tabularnewline
14 & 104.82 & 104.984236111111 & 104.39375 & 0.590486111111107 & -0.164236111111109 \tabularnewline
15 & 104.69 & 105.247361111111 & 104.450416666667 & 0.796944444444448 & -0.557361111111121 \tabularnewline
16 & 104.87 & 105.341111111111 & 104.51125 & 0.829861111111106 & -0.471111111111085 \tabularnewline
17 & 104.74 & 104.963298611111 & 104.604583333333 & 0.358715277777768 & -0.22329861111109 \tabularnewline
18 & 104.85 & 104.412152777778 & 104.77625 & -0.364097222222221 & 0.437847222222231 \tabularnewline
19 & 104.8 & 104.608715277778 & 105.04 & -0.431284722222224 & 0.191284722222235 \tabularnewline
20 & 104.13 & 104.279340277778 & 105.36875 & -1.08940972222222 & -0.149340277777767 \tabularnewline
21 & 104.02 & 104.704131944444 & 105.714166666667 & -1.01003472222222 & -0.684131944444445 \tabularnewline
22 & 104.46 & 105.646215277778 & 106.05375 & -0.407534722222224 & -1.18621527777778 \tabularnewline
23 & 105.58 & 106.192465277778 & 106.379166666667 & -0.186701388888886 & -0.612465277777773 \tabularnewline
24 & 106.94 & 106.812569444444 & 106.645 & 0.167569444444445 & 0.127430555555549 \tabularnewline
25 & 108.41 & 107.604236111111 & 106.85875 & 0.745486111111116 & 0.805763888888904 \tabularnewline
26 & 109.05 & 107.660069444444 & 107.069583333333 & 0.590486111111107 & 1.38993055555557 \tabularnewline
27 & 108.75 & 108.179444444444 & 107.3825 & 0.796944444444448 & 0.570555555555572 \tabularnewline
28 & 108.96 & 108.732361111111 & 107.9025 & 0.829861111111106 & 0.227638888888904 \tabularnewline
29 & 108.46 & 108.862881944444 & 108.504166666667 & 0.358715277777768 & -0.402881944444445 \tabularnewline
30 & 107.51 & 108.702569444444 & 109.066666666667 & -0.364097222222221 & -1.19256944444443 \tabularnewline
31 & 107.27 & 109.129131944444 & 109.560416666667 & -0.431284722222224 & -1.85913194444443 \tabularnewline
32 & 106.72 & 108.900590277778 & 109.99 & -1.08940972222222 & -2.18059027777777 \tabularnewline
33 & 108.94 & 109.512048611111 & 110.522083333333 & -1.01003472222222 & -0.5720486111111 \tabularnewline
34 & 112.02 & 110.799548611111 & 111.207083333333 & -0.407534722222224 & 1.2204513888889 \tabularnewline
35 & 112.46 & 111.764548611111 & 111.95125 & -0.186701388888886 & 0.695451388888912 \tabularnewline
36 & 113.56 & 112.904236111111 & 112.736666666667 & 0.167569444444445 & 0.655763888888913 \tabularnewline
37 & 113.64 & 114.307152777778 & 113.561666666667 & 0.745486111111116 & -0.667152777777773 \tabularnewline
38 & 114.13 & 115.014652777778 & 114.424166666667 & 0.590486111111107 & -0.884652777777774 \tabularnewline
39 & 116.44 & 116.007777777778 & 115.210833333333 & 0.796944444444448 & 0.432222222222222 \tabularnewline
40 & 117.71 & 116.600277777778 & 115.770416666667 & 0.829861111111106 & 1.10972222222222 \tabularnewline
41 & 117.57 & 116.552881944444 & 116.194166666667 & 0.358715277777768 & 1.01711805555556 \tabularnewline
42 & 117.25 & 116.203402777778 & 116.5675 & -0.364097222222221 & 1.04659722222223 \tabularnewline
43 & 117.33 & 116.528298611111 & 116.959583333333 & -0.431284722222224 & 0.801701388888887 \tabularnewline
44 & 117.36 & 116.278506944444 & 117.367916666667 & -1.08940972222222 & 1.08149305555555 \tabularnewline
45 & 117.18 & 116.633715277778 & 117.64375 & -1.01003472222222 & 0.546284722222225 \tabularnewline
46 & 117.21 & 117.357881944444 & 117.765416666667 & -0.407534722222224 & -0.14788194444445 \tabularnewline
47 & 117.44 & 117.643298611111 & 117.83 & -0.186701388888886 & -0.203298611111094 \tabularnewline
48 & 117.54 & 118.060486111111 & 117.892916666667 & 0.167569444444445 & -0.520486111111097 \tabularnewline
49 & 119.07 & 118.714236111111 & 117.96875 & 0.745486111111116 & 0.355763888888887 \tabularnewline
50 & 118.5 & 118.646319444444 & 118.055833333333 & 0.590486111111107 & -0.14631944444443 \tabularnewline
51 & 118.69 & 118.940694444444 & 118.14375 & 0.796944444444448 & -0.250694444444434 \tabularnewline
52 & 118.38 & 119.051527777778 & 118.221666666667 & 0.829861111111106 & -0.671527777777769 \tabularnewline
53 & 118.45 & 118.646215277778 & 118.2875 & 0.358715277777768 & -0.196215277777753 \tabularnewline
54 & 117.88 & 117.977152777778 & 118.34125 & -0.364097222222221 & -0.0971527777777652 \tabularnewline
55 & 118.52 & NA & NA & -0.431284722222224 & NA \tabularnewline
56 & 118.26 & NA & NA & -1.08940972222222 & NA \tabularnewline
57 & 118.39 & NA & NA & -1.01003472222222 & NA \tabularnewline
58 & 117.87 & NA & NA & -0.407534722222224 & NA \tabularnewline
59 & 118.36 & NA & NA & -0.186701388888886 & NA \tabularnewline
60 & 117.91 & NA & NA & 0.167569444444445 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166197&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]100.34[/C][C]NA[/C][C]NA[/C][C]0.745486111111116[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.21[/C][C]NA[/C][C]NA[/C][C]0.590486111111107[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.44[/C][C]NA[/C][C]NA[/C][C]0.796944444444448[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.59[/C][C]NA[/C][C]NA[/C][C]0.829861111111106[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.44[/C][C]NA[/C][C]NA[/C][C]0.358715277777768[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]103.1[/C][C]NA[/C][C]NA[/C][C]-0.364097222222221[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]103.34[/C][C]102.279131944444[/C][C]102.710416666667[/C][C]-0.431284722222224[/C][C]1.06086805555557[/C][/ROW]
[ROW][C]8[/C][C]103.44[/C][C]101.996840277778[/C][C]103.08625[/C][C]-1.08940972222222[/C][C]1.44315972222222[/C][/ROW]
[ROW][C]9[/C][C]103.35[/C][C]102.445381944444[/C][C]103.455416666667[/C][C]-1.01003472222222[/C][C]0.904618055555545[/C][/ROW]
[ROW][C]10[/C][C]103.67[/C][C]103.361631944444[/C][C]103.769166666667[/C][C]-0.407534722222224[/C][C]0.308368055555562[/C][/ROW]
[ROW][C]11[/C][C]104.13[/C][C]103.814965277778[/C][C]104.001666666667[/C][C]-0.186701388888886[/C][C]0.315034722222222[/C][/ROW]
[ROW][C]12[/C][C]104.27[/C][C]104.337986111111[/C][C]104.170416666667[/C][C]0.167569444444445[/C][C]-0.0679861111110966[/C][/ROW]
[ROW][C]13[/C][C]104.75[/C][C]105.049652777778[/C][C]104.304166666667[/C][C]0.745486111111116[/C][C]-0.299652777777794[/C][/ROW]
[ROW][C]14[/C][C]104.82[/C][C]104.984236111111[/C][C]104.39375[/C][C]0.590486111111107[/C][C]-0.164236111111109[/C][/ROW]
[ROW][C]15[/C][C]104.69[/C][C]105.247361111111[/C][C]104.450416666667[/C][C]0.796944444444448[/C][C]-0.557361111111121[/C][/ROW]
[ROW][C]16[/C][C]104.87[/C][C]105.341111111111[/C][C]104.51125[/C][C]0.829861111111106[/C][C]-0.471111111111085[/C][/ROW]
[ROW][C]17[/C][C]104.74[/C][C]104.963298611111[/C][C]104.604583333333[/C][C]0.358715277777768[/C][C]-0.22329861111109[/C][/ROW]
[ROW][C]18[/C][C]104.85[/C][C]104.412152777778[/C][C]104.77625[/C][C]-0.364097222222221[/C][C]0.437847222222231[/C][/ROW]
[ROW][C]19[/C][C]104.8[/C][C]104.608715277778[/C][C]105.04[/C][C]-0.431284722222224[/C][C]0.191284722222235[/C][/ROW]
[ROW][C]20[/C][C]104.13[/C][C]104.279340277778[/C][C]105.36875[/C][C]-1.08940972222222[/C][C]-0.149340277777767[/C][/ROW]
[ROW][C]21[/C][C]104.02[/C][C]104.704131944444[/C][C]105.714166666667[/C][C]-1.01003472222222[/C][C]-0.684131944444445[/C][/ROW]
[ROW][C]22[/C][C]104.46[/C][C]105.646215277778[/C][C]106.05375[/C][C]-0.407534722222224[/C][C]-1.18621527777778[/C][/ROW]
[ROW][C]23[/C][C]105.58[/C][C]106.192465277778[/C][C]106.379166666667[/C][C]-0.186701388888886[/C][C]-0.612465277777773[/C][/ROW]
[ROW][C]24[/C][C]106.94[/C][C]106.812569444444[/C][C]106.645[/C][C]0.167569444444445[/C][C]0.127430555555549[/C][/ROW]
[ROW][C]25[/C][C]108.41[/C][C]107.604236111111[/C][C]106.85875[/C][C]0.745486111111116[/C][C]0.805763888888904[/C][/ROW]
[ROW][C]26[/C][C]109.05[/C][C]107.660069444444[/C][C]107.069583333333[/C][C]0.590486111111107[/C][C]1.38993055555557[/C][/ROW]
[ROW][C]27[/C][C]108.75[/C][C]108.179444444444[/C][C]107.3825[/C][C]0.796944444444448[/C][C]0.570555555555572[/C][/ROW]
[ROW][C]28[/C][C]108.96[/C][C]108.732361111111[/C][C]107.9025[/C][C]0.829861111111106[/C][C]0.227638888888904[/C][/ROW]
[ROW][C]29[/C][C]108.46[/C][C]108.862881944444[/C][C]108.504166666667[/C][C]0.358715277777768[/C][C]-0.402881944444445[/C][/ROW]
[ROW][C]30[/C][C]107.51[/C][C]108.702569444444[/C][C]109.066666666667[/C][C]-0.364097222222221[/C][C]-1.19256944444443[/C][/ROW]
[ROW][C]31[/C][C]107.27[/C][C]109.129131944444[/C][C]109.560416666667[/C][C]-0.431284722222224[/C][C]-1.85913194444443[/C][/ROW]
[ROW][C]32[/C][C]106.72[/C][C]108.900590277778[/C][C]109.99[/C][C]-1.08940972222222[/C][C]-2.18059027777777[/C][/ROW]
[ROW][C]33[/C][C]108.94[/C][C]109.512048611111[/C][C]110.522083333333[/C][C]-1.01003472222222[/C][C]-0.5720486111111[/C][/ROW]
[ROW][C]34[/C][C]112.02[/C][C]110.799548611111[/C][C]111.207083333333[/C][C]-0.407534722222224[/C][C]1.2204513888889[/C][/ROW]
[ROW][C]35[/C][C]112.46[/C][C]111.764548611111[/C][C]111.95125[/C][C]-0.186701388888886[/C][C]0.695451388888912[/C][/ROW]
[ROW][C]36[/C][C]113.56[/C][C]112.904236111111[/C][C]112.736666666667[/C][C]0.167569444444445[/C][C]0.655763888888913[/C][/ROW]
[ROW][C]37[/C][C]113.64[/C][C]114.307152777778[/C][C]113.561666666667[/C][C]0.745486111111116[/C][C]-0.667152777777773[/C][/ROW]
[ROW][C]38[/C][C]114.13[/C][C]115.014652777778[/C][C]114.424166666667[/C][C]0.590486111111107[/C][C]-0.884652777777774[/C][/ROW]
[ROW][C]39[/C][C]116.44[/C][C]116.007777777778[/C][C]115.210833333333[/C][C]0.796944444444448[/C][C]0.432222222222222[/C][/ROW]
[ROW][C]40[/C][C]117.71[/C][C]116.600277777778[/C][C]115.770416666667[/C][C]0.829861111111106[/C][C]1.10972222222222[/C][/ROW]
[ROW][C]41[/C][C]117.57[/C][C]116.552881944444[/C][C]116.194166666667[/C][C]0.358715277777768[/C][C]1.01711805555556[/C][/ROW]
[ROW][C]42[/C][C]117.25[/C][C]116.203402777778[/C][C]116.5675[/C][C]-0.364097222222221[/C][C]1.04659722222223[/C][/ROW]
[ROW][C]43[/C][C]117.33[/C][C]116.528298611111[/C][C]116.959583333333[/C][C]-0.431284722222224[/C][C]0.801701388888887[/C][/ROW]
[ROW][C]44[/C][C]117.36[/C][C]116.278506944444[/C][C]117.367916666667[/C][C]-1.08940972222222[/C][C]1.08149305555555[/C][/ROW]
[ROW][C]45[/C][C]117.18[/C][C]116.633715277778[/C][C]117.64375[/C][C]-1.01003472222222[/C][C]0.546284722222225[/C][/ROW]
[ROW][C]46[/C][C]117.21[/C][C]117.357881944444[/C][C]117.765416666667[/C][C]-0.407534722222224[/C][C]-0.14788194444445[/C][/ROW]
[ROW][C]47[/C][C]117.44[/C][C]117.643298611111[/C][C]117.83[/C][C]-0.186701388888886[/C][C]-0.203298611111094[/C][/ROW]
[ROW][C]48[/C][C]117.54[/C][C]118.060486111111[/C][C]117.892916666667[/C][C]0.167569444444445[/C][C]-0.520486111111097[/C][/ROW]
[ROW][C]49[/C][C]119.07[/C][C]118.714236111111[/C][C]117.96875[/C][C]0.745486111111116[/C][C]0.355763888888887[/C][/ROW]
[ROW][C]50[/C][C]118.5[/C][C]118.646319444444[/C][C]118.055833333333[/C][C]0.590486111111107[/C][C]-0.14631944444443[/C][/ROW]
[ROW][C]51[/C][C]118.69[/C][C]118.940694444444[/C][C]118.14375[/C][C]0.796944444444448[/C][C]-0.250694444444434[/C][/ROW]
[ROW][C]52[/C][C]118.38[/C][C]119.051527777778[/C][C]118.221666666667[/C][C]0.829861111111106[/C][C]-0.671527777777769[/C][/ROW]
[ROW][C]53[/C][C]118.45[/C][C]118.646215277778[/C][C]118.2875[/C][C]0.358715277777768[/C][C]-0.196215277777753[/C][/ROW]
[ROW][C]54[/C][C]117.88[/C][C]117.977152777778[/C][C]118.34125[/C][C]-0.364097222222221[/C][C]-0.0971527777777652[/C][/ROW]
[ROW][C]55[/C][C]118.52[/C][C]NA[/C][C]NA[/C][C]-0.431284722222224[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]118.26[/C][C]NA[/C][C]NA[/C][C]-1.08940972222222[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]118.39[/C][C]NA[/C][C]NA[/C][C]-1.01003472222222[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]117.87[/C][C]NA[/C][C]NA[/C][C]-0.407534722222224[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]118.36[/C][C]NA[/C][C]NA[/C][C]-0.186701388888886[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]117.91[/C][C]NA[/C][C]NA[/C][C]0.167569444444445[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166197&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166197&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	100.34	NA	NA	0.745486111111116	NA
2	100.21	NA	NA	0.590486111111107	NA
3	100.44	NA	NA	0.796944444444448	NA
4	101.59	NA	NA	0.829861111111106	NA
5	102.44	NA	NA	0.358715277777768	NA
6	103.1	NA	NA	-0.364097222222221	NA
7	103.34	102.279131944444	102.710416666667	-0.431284722222224	1.06086805555557
8	103.44	101.996840277778	103.08625	-1.08940972222222	1.44315972222222
9	103.35	102.445381944444	103.455416666667	-1.01003472222222	0.904618055555545
10	103.67	103.361631944444	103.769166666667	-0.407534722222224	0.308368055555562
11	104.13	103.814965277778	104.001666666667	-0.186701388888886	0.315034722222222
12	104.27	104.337986111111	104.170416666667	0.167569444444445	-0.0679861111110966
13	104.75	105.049652777778	104.304166666667	0.745486111111116	-0.299652777777794
14	104.82	104.984236111111	104.39375	0.590486111111107	-0.164236111111109
15	104.69	105.247361111111	104.450416666667	0.796944444444448	-0.557361111111121
16	104.87	105.341111111111	104.51125	0.829861111111106	-0.471111111111085
17	104.74	104.963298611111	104.604583333333	0.358715277777768	-0.22329861111109
18	104.85	104.412152777778	104.77625	-0.364097222222221	0.437847222222231
19	104.8	104.608715277778	105.04	-0.431284722222224	0.191284722222235
20	104.13	104.279340277778	105.36875	-1.08940972222222	-0.149340277777767
21	104.02	104.704131944444	105.714166666667	-1.01003472222222	-0.684131944444445
22	104.46	105.646215277778	106.05375	-0.407534722222224	-1.18621527777778
23	105.58	106.192465277778	106.379166666667	-0.186701388888886	-0.612465277777773
24	106.94	106.812569444444	106.645	0.167569444444445	0.127430555555549
25	108.41	107.604236111111	106.85875	0.745486111111116	0.805763888888904
26	109.05	107.660069444444	107.069583333333	0.590486111111107	1.38993055555557
27	108.75	108.179444444444	107.3825	0.796944444444448	0.570555555555572
28	108.96	108.732361111111	107.9025	0.829861111111106	0.227638888888904
29	108.46	108.862881944444	108.504166666667	0.358715277777768	-0.402881944444445
30	107.51	108.702569444444	109.066666666667	-0.364097222222221	-1.19256944444443
31	107.27	109.129131944444	109.560416666667	-0.431284722222224	-1.85913194444443
32	106.72	108.900590277778	109.99	-1.08940972222222	-2.18059027777777
33	108.94	109.512048611111	110.522083333333	-1.01003472222222	-0.5720486111111
34	112.02	110.799548611111	111.207083333333	-0.407534722222224	1.2204513888889
35	112.46	111.764548611111	111.95125	-0.186701388888886	0.695451388888912
36	113.56	112.904236111111	112.736666666667	0.167569444444445	0.655763888888913
37	113.64	114.307152777778	113.561666666667	0.745486111111116	-0.667152777777773
38	114.13	115.014652777778	114.424166666667	0.590486111111107	-0.884652777777774
39	116.44	116.007777777778	115.210833333333	0.796944444444448	0.432222222222222
40	117.71	116.600277777778	115.770416666667	0.829861111111106	1.10972222222222
41	117.57	116.552881944444	116.194166666667	0.358715277777768	1.01711805555556
42	117.25	116.203402777778	116.5675	-0.364097222222221	1.04659722222223
43	117.33	116.528298611111	116.959583333333	-0.431284722222224	0.801701388888887
44	117.36	116.278506944444	117.367916666667	-1.08940972222222	1.08149305555555
45	117.18	116.633715277778	117.64375	-1.01003472222222	0.546284722222225
46	117.21	117.357881944444	117.765416666667	-0.407534722222224	-0.14788194444445
47	117.44	117.643298611111	117.83	-0.186701388888886	-0.203298611111094
48	117.54	118.060486111111	117.892916666667	0.167569444444445	-0.520486111111097
49	119.07	118.714236111111	117.96875	0.745486111111116	0.355763888888887
50	118.5	118.646319444444	118.055833333333	0.590486111111107	-0.14631944444443
51	118.69	118.940694444444	118.14375	0.796944444444448	-0.250694444444434
52	118.38	119.051527777778	118.221666666667	0.829861111111106	-0.671527777777769
53	118.45	118.646215277778	118.2875	0.358715277777768	-0.196215277777753
54	117.88	117.977152777778	118.34125	-0.364097222222221	-0.0971527777777652
55	118.52	NA	NA	-0.431284722222224	NA
56	118.26	NA	NA	-1.08940972222222	NA
57	118.39	NA	NA	-1.01003472222222	NA
58	117.87	NA	NA	-0.407534722222224	NA
59	118.36	NA	NA	-0.186701388888886	NA
60	117.91	NA	NA	0.167569444444445	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

consumptieprijsindex van katten- en hondenvoeding (blik, brokken en alu-sch...

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code