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Author's title

Author

*Unverified author*

R Software Module

rwasp_exponentialsmoothing.wasp

Title produced by software

Exponential Smoothing

Date of computation

Tue, 19 Jan 2010 13:01:48 -0700

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/2010/Jan/19/t12639313535mrq50po7m3nl91.htm/, Retrieved Thu, 02 May 2024 10:13:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=72294, Retrieved Thu, 02 May 2024 10:13:19 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W62

Estimated Impact

234

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

-       [Exponential Smoothing] [Bruin brood triple] [2010-01-19 20:01:48] [5429fe6e3351c98316e03e842ad8f5e4] [Current]

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Dataseries X:

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Histogram

Boxplots

1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.38
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.43
1.44
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.57
1.58
1.58
1.58
1.58
1.59
1.6
1.6
1.61
1.61
1.61
1.62
1.63
1.63
1.64
1.64
1.64
1.64
1.64
1.65
1.65
1.65
1.65
1.65
1.66
1.66
1.67
1.68
1.68
1.68
1.68
1.69
1.7
1.7
1.71
1.72
1.73
1.74
1.74
1.75
1.75
1.75
1.76
1.79
1.83
1.84
1.85
1.87
1.87
1.87
1.88
1.88
1.88
1.88
1.89
1.89
1.89
1.9
1.89

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	'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72294&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72294&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	'Gwilym Jenkins' @ 72.249.127.135

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	1
beta	0.0330450901927309
gamma	0.00480500770391178

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 1 \tabularnewline
beta & 0.0330450901927309 \tabularnewline
gamma & 0.00480500770391178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72294&T=1

[TABLE]
[ROW][C]Estimated Parameters of Exponential Smoothing[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]alpha[/C][C]1[/C][/ROW]
[ROW][C]beta[/C][C]0.0330450901927309[/C][/ROW]
[ROW][C]gamma[/C][C]0.00480500770391178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72294&T=1

As an alternative you can also use a QR Code:

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

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	1
beta	0.0330450901927309
gamma	0.00480500770391178

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
13	1.38	1.37990651709402	9.34829059822118e-05
14	1.38	1.38008321735619	-8.32173561877703e-05
15	1.38	1.38008046743115	-8.046743114698e-05
16	1.38	1.38007780837763	-7.78083776271643e-05
17	1.38	1.38007523719277	-7.52371927708051e-05
18	1.38	1.37798941763962	0.00201058236038354
19	1.38	1.38222252418172	-0.00222252418172175
20	1.38	1.38006574733635	-6.57473363481209e-05
21	1.38	1.38006357470969	-6.35747096884298e-05
22	1.38	1.38006147387767	-6.14738776727464e-05
23	1.38	1.38005944246784	-5.94424678406202e-05
24	1.43	1.38005747818613	0.0499425218138705
25	1.43	1.43170783332392	-0.00170783332392133
26	1.43	1.43165139781770	-0.00165139781769819
27	1.43	1.43159682722787	-0.00159682722786836
28	1.43	1.4315440599281	-0.00154405992810114
29	1.43	1.43149303632851	-0.00149303632851416
30	1.43	1.42936036547504	0.000639634524955968
31	1.43	1.43354816892228	-0.00354816892227805
32	1.43	1.43134758602689	-0.00134758602688922
33	1.43	1.43130305492509	-0.00130305492508809
34	1.43	1.43125999535756	-0.00125999535756249
35	1.43	1.43121835869733	-0.00121835869732934
36	1.43	1.43117809792429	-0.00117809792428902
37	1.43	1.43113916757213	-0.00113916757212507
38	1.43	1.43110152367696	-0.00110152367695959
39	1.43	1.43106512372771	-0.00106512372770506
40	1.43	1.43102992661806	-0.00102992661805668
41	1.43	1.43099589260007	-0.000995892600071047
42	1.43	1.42887964990595	0.00112035009405376
43	1.44	1.43308333864252	0.00691666135748203
44	1.48	1.44122856700758	0.0387714329924247
45	1.48	1.48250977250771	-0.00250977250771145
46	1.48	1.48242683684883	-0.00242683684883094
47	1.48	1.48234664180628	-0.00234664180627830
48	1.48	1.48226909681614	-0.00226909681613985
49	1.48	1.48219411430719	-0.00219411430719441
50	1.48	1.48212160960202	-0.00212160960202001
51	1.48	1.48205150082137	-0.00205150082136751
52	1.48	1.48198370879169	-0.00198370879169496
53	1.48	1.48191815695576	-0.00191815695575737
54	1.48	1.47977143795282	0.00022856204718269
55	1.48	1.48394565747295	-0.00394565747294751
56	1.48	1.48173193953255	-0.00173193953255102
57	1.48	1.48167470743449	-0.00167470743448961
58	1.48	1.48161936657627	-0.00161936657627049
59	1.48	1.48156585446170	-0.00156585446170254
60	1.48	1.48151411065979	-0.00151411065978690
61	1.48	1.48146407673647	-0.00146407673647242
62	1.48	1.48141569618867	-0.00141569618866666
63	1.48	1.48136891438043	-0.00136891438042652
64	1.48	1.48132367848126	-0.00132367848125936
65	1.48	1.48127993740646	-0.00127993740645982
66	1.48	1.47915430842609	0.00084569157391079
67	1.48	1.48334892104709	-0.00334892104709072
68	1.48	1.48115492231571	-0.00115492231570813
69	1.48	1.48111675780362	-0.00111675780361975
70	1.48	1.48107985444128	-0.00107985444127578
71	1.48	1.48104417055387	-0.00104417055386885
72	1.48	1.48100966584374	-0.00100966584373974
73	1.48	1.48097630134487	-0.000976301344868702
74	1.57	1.48094403937887	0.0890559606211279
75	1.58	1.57388690162980	0.00611309837020224
76	1.58	1.58408890951680	-0.00408890951679819
77	1.58	1.58395379113303	-0.00395379113302563
78	1.58	1.58173980441510	-0.00173980441509825
79	1.59	1.58584897908795	0.00415102091204989
80	1.6	1.59390281661505	0.00609718338495258
81	1.6	1.60410429858992	-0.00410429858992489
82	1.61	1.60396867167284	0.00603132832715714
83	1.61	1.61416797746140	-0.00416797746139586
84	1.61	1.61403024627026	-0.00403024627026261
85	1.62	1.61389706641876	0.00610293358123704
86	1.63	1.62409873840940	0.00590126159060467
87	1.63	1.63429374613091	-0.00429374613090761
88	1.64	1.63415185890275	0.00584814109725285
89	1.64	1.64434511125277	-0.00434511125276571
90	1.64	1.64211819332619	-0.00211819332618735
91	1.64	1.64621486410334	-0.00621486410334415
92	1.64	1.64392616002518	-0.00392616002518054
93	1.65	1.64379641971304	0.00620358028696266
94	1.65	1.65400141758314	-0.00400141758313777
95	1.65	1.65386919037820	-0.00386919037820421
96	1.65	1.65374133263318	-0.00374133263318366
97	1.65	1.65361769995888	-0.00361769995887906
98	1.66	1.65349815273745	0.00650184726255243
99	1.66	1.66371300686666	-0.00371300686665799
100	1.67	1.66359031021986	0.0064096897801369
101	1.68	1.67380211899676	0.00619788100324481
102	1.68	1.68192359520018	-0.00192359520017793
103	1.68	1.68602669648996	-0.00602669648996024
104	1.68	1.68374421042755	-0.00374421042755202
105	1.69	1.68362048265627	0.00637951734372688
106	1.7	1.69383129438228	0.00616870561771732
107	1.7	1.70403513981579	-0.00403513981579251
108	1.71	1.70390179825664	0.00609820174336062
109	1.72	1.71410331388326	0.00589668611673777
110	1.73	1.72429817040783	0.00570182959217203
111	1.74	1.73448658788096	0.00551341211903522
112	1.74	1.74466877908171	-0.00466877908170815
113	1.75	1.74451449885586	0.00548550114413682
114	1.75	1.75261243440259	-0.00261243440259018
115	1.75	1.7566927729388	-0.00669277293880044
116	1.76	1.75438827632007	0.0056117236799349
117	1.79	1.76457371623521	0.0254262837647947
118	1.83	1.79541393007548	0.0345860699245211
119	1.84	1.83655682987555	0.00344317012445328
120	1.85	1.84667060974286	0.00332939025714185
121	1.87	1.85678062974419	0.0132193702558077
122	1.87	1.87721746502659	-0.0072174650265866
123	1.87	1.87697896324382	-0.00697896324382019
124	1.88	1.87674834277398	0.00325165722602350
125	1.88	1.88685579408029	-0.00685579408028603
126	1.88	1.88454591041323	-0.00454591041322705
127	1.88	1.88856235706028	-0.00856235706028041
128	1.89	1.88619607986563	0.00380392013437225
129	1.89	1.89632178074955	-0.00632178074955414
130	1.89	1.89611287693451	-0.00611287693450646
131	1.9	1.89591087636487	0.00408912363513148
132	1.89	1.9060460018242	-0.0160460018242008

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 1.38 & 1.37990651709402 & 9.34829059822118e-05 \tabularnewline
14 & 1.38 & 1.38008321735619 & -8.32173561877703e-05 \tabularnewline
15 & 1.38 & 1.38008046743115 & -8.046743114698e-05 \tabularnewline
16 & 1.38 & 1.38007780837763 & -7.78083776271643e-05 \tabularnewline
17 & 1.38 & 1.38007523719277 & -7.52371927708051e-05 \tabularnewline
18 & 1.38 & 1.37798941763962 & 0.00201058236038354 \tabularnewline
19 & 1.38 & 1.38222252418172 & -0.00222252418172175 \tabularnewline
20 & 1.38 & 1.38006574733635 & -6.57473363481209e-05 \tabularnewline
21 & 1.38 & 1.38006357470969 & -6.35747096884298e-05 \tabularnewline
22 & 1.38 & 1.38006147387767 & -6.14738776727464e-05 \tabularnewline
23 & 1.38 & 1.38005944246784 & -5.94424678406202e-05 \tabularnewline
24 & 1.43 & 1.38005747818613 & 0.0499425218138705 \tabularnewline
25 & 1.43 & 1.43170783332392 & -0.00170783332392133 \tabularnewline
26 & 1.43 & 1.43165139781770 & -0.00165139781769819 \tabularnewline
27 & 1.43 & 1.43159682722787 & -0.00159682722786836 \tabularnewline
28 & 1.43 & 1.4315440599281 & -0.00154405992810114 \tabularnewline
29 & 1.43 & 1.43149303632851 & -0.00149303632851416 \tabularnewline
30 & 1.43 & 1.42936036547504 & 0.000639634524955968 \tabularnewline
31 & 1.43 & 1.43354816892228 & -0.00354816892227805 \tabularnewline
32 & 1.43 & 1.43134758602689 & -0.00134758602688922 \tabularnewline
33 & 1.43 & 1.43130305492509 & -0.00130305492508809 \tabularnewline
34 & 1.43 & 1.43125999535756 & -0.00125999535756249 \tabularnewline
35 & 1.43 & 1.43121835869733 & -0.00121835869732934 \tabularnewline
36 & 1.43 & 1.43117809792429 & -0.00117809792428902 \tabularnewline
37 & 1.43 & 1.43113916757213 & -0.00113916757212507 \tabularnewline
38 & 1.43 & 1.43110152367696 & -0.00110152367695959 \tabularnewline
39 & 1.43 & 1.43106512372771 & -0.00106512372770506 \tabularnewline
40 & 1.43 & 1.43102992661806 & -0.00102992661805668 \tabularnewline
41 & 1.43 & 1.43099589260007 & -0.000995892600071047 \tabularnewline
42 & 1.43 & 1.42887964990595 & 0.00112035009405376 \tabularnewline
43 & 1.44 & 1.43308333864252 & 0.00691666135748203 \tabularnewline
44 & 1.48 & 1.44122856700758 & 0.0387714329924247 \tabularnewline
45 & 1.48 & 1.48250977250771 & -0.00250977250771145 \tabularnewline
46 & 1.48 & 1.48242683684883 & -0.00242683684883094 \tabularnewline
47 & 1.48 & 1.48234664180628 & -0.00234664180627830 \tabularnewline
48 & 1.48 & 1.48226909681614 & -0.00226909681613985 \tabularnewline
49 & 1.48 & 1.48219411430719 & -0.00219411430719441 \tabularnewline
50 & 1.48 & 1.48212160960202 & -0.00212160960202001 \tabularnewline
51 & 1.48 & 1.48205150082137 & -0.00205150082136751 \tabularnewline
52 & 1.48 & 1.48198370879169 & -0.00198370879169496 \tabularnewline
53 & 1.48 & 1.48191815695576 & -0.00191815695575737 \tabularnewline
54 & 1.48 & 1.47977143795282 & 0.00022856204718269 \tabularnewline
55 & 1.48 & 1.48394565747295 & -0.00394565747294751 \tabularnewline
56 & 1.48 & 1.48173193953255 & -0.00173193953255102 \tabularnewline
57 & 1.48 & 1.48167470743449 & -0.00167470743448961 \tabularnewline
58 & 1.48 & 1.48161936657627 & -0.00161936657627049 \tabularnewline
59 & 1.48 & 1.48156585446170 & -0.00156585446170254 \tabularnewline
60 & 1.48 & 1.48151411065979 & -0.00151411065978690 \tabularnewline
61 & 1.48 & 1.48146407673647 & -0.00146407673647242 \tabularnewline
62 & 1.48 & 1.48141569618867 & -0.00141569618866666 \tabularnewline
63 & 1.48 & 1.48136891438043 & -0.00136891438042652 \tabularnewline
64 & 1.48 & 1.48132367848126 & -0.00132367848125936 \tabularnewline
65 & 1.48 & 1.48127993740646 & -0.00127993740645982 \tabularnewline
66 & 1.48 & 1.47915430842609 & 0.00084569157391079 \tabularnewline
67 & 1.48 & 1.48334892104709 & -0.00334892104709072 \tabularnewline
68 & 1.48 & 1.48115492231571 & -0.00115492231570813 \tabularnewline
69 & 1.48 & 1.48111675780362 & -0.00111675780361975 \tabularnewline
70 & 1.48 & 1.48107985444128 & -0.00107985444127578 \tabularnewline
71 & 1.48 & 1.48104417055387 & -0.00104417055386885 \tabularnewline
72 & 1.48 & 1.48100966584374 & -0.00100966584373974 \tabularnewline
73 & 1.48 & 1.48097630134487 & -0.000976301344868702 \tabularnewline
74 & 1.57 & 1.48094403937887 & 0.0890559606211279 \tabularnewline
75 & 1.58 & 1.57388690162980 & 0.00611309837020224 \tabularnewline
76 & 1.58 & 1.58408890951680 & -0.00408890951679819 \tabularnewline
77 & 1.58 & 1.58395379113303 & -0.00395379113302563 \tabularnewline
78 & 1.58 & 1.58173980441510 & -0.00173980441509825 \tabularnewline
79 & 1.59 & 1.58584897908795 & 0.00415102091204989 \tabularnewline
80 & 1.6 & 1.59390281661505 & 0.00609718338495258 \tabularnewline
81 & 1.6 & 1.60410429858992 & -0.00410429858992489 \tabularnewline
82 & 1.61 & 1.60396867167284 & 0.00603132832715714 \tabularnewline
83 & 1.61 & 1.61416797746140 & -0.00416797746139586 \tabularnewline
84 & 1.61 & 1.61403024627026 & -0.00403024627026261 \tabularnewline
85 & 1.62 & 1.61389706641876 & 0.00610293358123704 \tabularnewline
86 & 1.63 & 1.62409873840940 & 0.00590126159060467 \tabularnewline
87 & 1.63 & 1.63429374613091 & -0.00429374613090761 \tabularnewline
88 & 1.64 & 1.63415185890275 & 0.00584814109725285 \tabularnewline
89 & 1.64 & 1.64434511125277 & -0.00434511125276571 \tabularnewline
90 & 1.64 & 1.64211819332619 & -0.00211819332618735 \tabularnewline
91 & 1.64 & 1.64621486410334 & -0.00621486410334415 \tabularnewline
92 & 1.64 & 1.64392616002518 & -0.00392616002518054 \tabularnewline
93 & 1.65 & 1.64379641971304 & 0.00620358028696266 \tabularnewline
94 & 1.65 & 1.65400141758314 & -0.00400141758313777 \tabularnewline
95 & 1.65 & 1.65386919037820 & -0.00386919037820421 \tabularnewline
96 & 1.65 & 1.65374133263318 & -0.00374133263318366 \tabularnewline
97 & 1.65 & 1.65361769995888 & -0.00361769995887906 \tabularnewline
98 & 1.66 & 1.65349815273745 & 0.00650184726255243 \tabularnewline
99 & 1.66 & 1.66371300686666 & -0.00371300686665799 \tabularnewline
100 & 1.67 & 1.66359031021986 & 0.0064096897801369 \tabularnewline
101 & 1.68 & 1.67380211899676 & 0.00619788100324481 \tabularnewline
102 & 1.68 & 1.68192359520018 & -0.00192359520017793 \tabularnewline
103 & 1.68 & 1.68602669648996 & -0.00602669648996024 \tabularnewline
104 & 1.68 & 1.68374421042755 & -0.00374421042755202 \tabularnewline
105 & 1.69 & 1.68362048265627 & 0.00637951734372688 \tabularnewline
106 & 1.7 & 1.69383129438228 & 0.00616870561771732 \tabularnewline
107 & 1.7 & 1.70403513981579 & -0.00403513981579251 \tabularnewline
108 & 1.71 & 1.70390179825664 & 0.00609820174336062 \tabularnewline
109 & 1.72 & 1.71410331388326 & 0.00589668611673777 \tabularnewline
110 & 1.73 & 1.72429817040783 & 0.00570182959217203 \tabularnewline
111 & 1.74 & 1.73448658788096 & 0.00551341211903522 \tabularnewline
112 & 1.74 & 1.74466877908171 & -0.00466877908170815 \tabularnewline
113 & 1.75 & 1.74451449885586 & 0.00548550114413682 \tabularnewline
114 & 1.75 & 1.75261243440259 & -0.00261243440259018 \tabularnewline
115 & 1.75 & 1.7566927729388 & -0.00669277293880044 \tabularnewline
116 & 1.76 & 1.75438827632007 & 0.0056117236799349 \tabularnewline
117 & 1.79 & 1.76457371623521 & 0.0254262837647947 \tabularnewline
118 & 1.83 & 1.79541393007548 & 0.0345860699245211 \tabularnewline
119 & 1.84 & 1.83655682987555 & 0.00344317012445328 \tabularnewline
120 & 1.85 & 1.84667060974286 & 0.00332939025714185 \tabularnewline
121 & 1.87 & 1.85678062974419 & 0.0132193702558077 \tabularnewline
122 & 1.87 & 1.87721746502659 & -0.0072174650265866 \tabularnewline
123 & 1.87 & 1.87697896324382 & -0.00697896324382019 \tabularnewline
124 & 1.88 & 1.87674834277398 & 0.00325165722602350 \tabularnewline
125 & 1.88 & 1.88685579408029 & -0.00685579408028603 \tabularnewline
126 & 1.88 & 1.88454591041323 & -0.00454591041322705 \tabularnewline
127 & 1.88 & 1.88856235706028 & -0.00856235706028041 \tabularnewline
128 & 1.89 & 1.88619607986563 & 0.00380392013437225 \tabularnewline
129 & 1.89 & 1.89632178074955 & -0.00632178074955414 \tabularnewline
130 & 1.89 & 1.89611287693451 & -0.00611287693450646 \tabularnewline
131 & 1.9 & 1.89591087636487 & 0.00408912363513148 \tabularnewline
132 & 1.89 & 1.9060460018242 & -0.0160460018242008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72294&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]13[/C][C]1.38[/C][C]1.37990651709402[/C][C]9.34829059822118e-05[/C][/ROW]
[ROW][C]14[/C][C]1.38[/C][C]1.38008321735619[/C][C]-8.32173561877703e-05[/C][/ROW]
[ROW][C]15[/C][C]1.38[/C][C]1.38008046743115[/C][C]-8.046743114698e-05[/C][/ROW]
[ROW][C]16[/C][C]1.38[/C][C]1.38007780837763[/C][C]-7.78083776271643e-05[/C][/ROW]
[ROW][C]17[/C][C]1.38[/C][C]1.38007523719277[/C][C]-7.52371927708051e-05[/C][/ROW]
[ROW][C]18[/C][C]1.38[/C][C]1.37798941763962[/C][C]0.00201058236038354[/C][/ROW]
[ROW][C]19[/C][C]1.38[/C][C]1.38222252418172[/C][C]-0.00222252418172175[/C][/ROW]
[ROW][C]20[/C][C]1.38[/C][C]1.38006574733635[/C][C]-6.57473363481209e-05[/C][/ROW]
[ROW][C]21[/C][C]1.38[/C][C]1.38006357470969[/C][C]-6.35747096884298e-05[/C][/ROW]
[ROW][C]22[/C][C]1.38[/C][C]1.38006147387767[/C][C]-6.14738776727464e-05[/C][/ROW]
[ROW][C]23[/C][C]1.38[/C][C]1.38005944246784[/C][C]-5.94424678406202e-05[/C][/ROW]
[ROW][C]24[/C][C]1.43[/C][C]1.38005747818613[/C][C]0.0499425218138705[/C][/ROW]
[ROW][C]25[/C][C]1.43[/C][C]1.43170783332392[/C][C]-0.00170783332392133[/C][/ROW]
[ROW][C]26[/C][C]1.43[/C][C]1.43165139781770[/C][C]-0.00165139781769819[/C][/ROW]
[ROW][C]27[/C][C]1.43[/C][C]1.43159682722787[/C][C]-0.00159682722786836[/C][/ROW]
[ROW][C]28[/C][C]1.43[/C][C]1.4315440599281[/C][C]-0.00154405992810114[/C][/ROW]
[ROW][C]29[/C][C]1.43[/C][C]1.43149303632851[/C][C]-0.00149303632851416[/C][/ROW]
[ROW][C]30[/C][C]1.43[/C][C]1.42936036547504[/C][C]0.000639634524955968[/C][/ROW]
[ROW][C]31[/C][C]1.43[/C][C]1.43354816892228[/C][C]-0.00354816892227805[/C][/ROW]
[ROW][C]32[/C][C]1.43[/C][C]1.43134758602689[/C][C]-0.00134758602688922[/C][/ROW]
[ROW][C]33[/C][C]1.43[/C][C]1.43130305492509[/C][C]-0.00130305492508809[/C][/ROW]
[ROW][C]34[/C][C]1.43[/C][C]1.43125999535756[/C][C]-0.00125999535756249[/C][/ROW]
[ROW][C]35[/C][C]1.43[/C][C]1.43121835869733[/C][C]-0.00121835869732934[/C][/ROW]
[ROW][C]36[/C][C]1.43[/C][C]1.43117809792429[/C][C]-0.00117809792428902[/C][/ROW]
[ROW][C]37[/C][C]1.43[/C][C]1.43113916757213[/C][C]-0.00113916757212507[/C][/ROW]
[ROW][C]38[/C][C]1.43[/C][C]1.43110152367696[/C][C]-0.00110152367695959[/C][/ROW]
[ROW][C]39[/C][C]1.43[/C][C]1.43106512372771[/C][C]-0.00106512372770506[/C][/ROW]
[ROW][C]40[/C][C]1.43[/C][C]1.43102992661806[/C][C]-0.00102992661805668[/C][/ROW]
[ROW][C]41[/C][C]1.43[/C][C]1.43099589260007[/C][C]-0.000995892600071047[/C][/ROW]
[ROW][C]42[/C][C]1.43[/C][C]1.42887964990595[/C][C]0.00112035009405376[/C][/ROW]
[ROW][C]43[/C][C]1.44[/C][C]1.43308333864252[/C][C]0.00691666135748203[/C][/ROW]
[ROW][C]44[/C][C]1.48[/C][C]1.44122856700758[/C][C]0.0387714329924247[/C][/ROW]
[ROW][C]45[/C][C]1.48[/C][C]1.48250977250771[/C][C]-0.00250977250771145[/C][/ROW]
[ROW][C]46[/C][C]1.48[/C][C]1.48242683684883[/C][C]-0.00242683684883094[/C][/ROW]
[ROW][C]47[/C][C]1.48[/C][C]1.48234664180628[/C][C]-0.00234664180627830[/C][/ROW]
[ROW][C]48[/C][C]1.48[/C][C]1.48226909681614[/C][C]-0.00226909681613985[/C][/ROW]
[ROW][C]49[/C][C]1.48[/C][C]1.48219411430719[/C][C]-0.00219411430719441[/C][/ROW]
[ROW][C]50[/C][C]1.48[/C][C]1.48212160960202[/C][C]-0.00212160960202001[/C][/ROW]
[ROW][C]51[/C][C]1.48[/C][C]1.48205150082137[/C][C]-0.00205150082136751[/C][/ROW]
[ROW][C]52[/C][C]1.48[/C][C]1.48198370879169[/C][C]-0.00198370879169496[/C][/ROW]
[ROW][C]53[/C][C]1.48[/C][C]1.48191815695576[/C][C]-0.00191815695575737[/C][/ROW]
[ROW][C]54[/C][C]1.48[/C][C]1.47977143795282[/C][C]0.00022856204718269[/C][/ROW]
[ROW][C]55[/C][C]1.48[/C][C]1.48394565747295[/C][C]-0.00394565747294751[/C][/ROW]
[ROW][C]56[/C][C]1.48[/C][C]1.48173193953255[/C][C]-0.00173193953255102[/C][/ROW]
[ROW][C]57[/C][C]1.48[/C][C]1.48167470743449[/C][C]-0.00167470743448961[/C][/ROW]
[ROW][C]58[/C][C]1.48[/C][C]1.48161936657627[/C][C]-0.00161936657627049[/C][/ROW]
[ROW][C]59[/C][C]1.48[/C][C]1.48156585446170[/C][C]-0.00156585446170254[/C][/ROW]
[ROW][C]60[/C][C]1.48[/C][C]1.48151411065979[/C][C]-0.00151411065978690[/C][/ROW]
[ROW][C]61[/C][C]1.48[/C][C]1.48146407673647[/C][C]-0.00146407673647242[/C][/ROW]
[ROW][C]62[/C][C]1.48[/C][C]1.48141569618867[/C][C]-0.00141569618866666[/C][/ROW]
[ROW][C]63[/C][C]1.48[/C][C]1.48136891438043[/C][C]-0.00136891438042652[/C][/ROW]
[ROW][C]64[/C][C]1.48[/C][C]1.48132367848126[/C][C]-0.00132367848125936[/C][/ROW]
[ROW][C]65[/C][C]1.48[/C][C]1.48127993740646[/C][C]-0.00127993740645982[/C][/ROW]
[ROW][C]66[/C][C]1.48[/C][C]1.47915430842609[/C][C]0.00084569157391079[/C][/ROW]
[ROW][C]67[/C][C]1.48[/C][C]1.48334892104709[/C][C]-0.00334892104709072[/C][/ROW]
[ROW][C]68[/C][C]1.48[/C][C]1.48115492231571[/C][C]-0.00115492231570813[/C][/ROW]
[ROW][C]69[/C][C]1.48[/C][C]1.48111675780362[/C][C]-0.00111675780361975[/C][/ROW]
[ROW][C]70[/C][C]1.48[/C][C]1.48107985444128[/C][C]-0.00107985444127578[/C][/ROW]
[ROW][C]71[/C][C]1.48[/C][C]1.48104417055387[/C][C]-0.00104417055386885[/C][/ROW]
[ROW][C]72[/C][C]1.48[/C][C]1.48100966584374[/C][C]-0.00100966584373974[/C][/ROW]
[ROW][C]73[/C][C]1.48[/C][C]1.48097630134487[/C][C]-0.000976301344868702[/C][/ROW]
[ROW][C]74[/C][C]1.57[/C][C]1.48094403937887[/C][C]0.0890559606211279[/C][/ROW]
[ROW][C]75[/C][C]1.58[/C][C]1.57388690162980[/C][C]0.00611309837020224[/C][/ROW]
[ROW][C]76[/C][C]1.58[/C][C]1.58408890951680[/C][C]-0.00408890951679819[/C][/ROW]
[ROW][C]77[/C][C]1.58[/C][C]1.58395379113303[/C][C]-0.00395379113302563[/C][/ROW]
[ROW][C]78[/C][C]1.58[/C][C]1.58173980441510[/C][C]-0.00173980441509825[/C][/ROW]
[ROW][C]79[/C][C]1.59[/C][C]1.58584897908795[/C][C]0.00415102091204989[/C][/ROW]
[ROW][C]80[/C][C]1.6[/C][C]1.59390281661505[/C][C]0.00609718338495258[/C][/ROW]
[ROW][C]81[/C][C]1.6[/C][C]1.60410429858992[/C][C]-0.00410429858992489[/C][/ROW]
[ROW][C]82[/C][C]1.61[/C][C]1.60396867167284[/C][C]0.00603132832715714[/C][/ROW]
[ROW][C]83[/C][C]1.61[/C][C]1.61416797746140[/C][C]-0.00416797746139586[/C][/ROW]
[ROW][C]84[/C][C]1.61[/C][C]1.61403024627026[/C][C]-0.00403024627026261[/C][/ROW]
[ROW][C]85[/C][C]1.62[/C][C]1.61389706641876[/C][C]0.00610293358123704[/C][/ROW]
[ROW][C]86[/C][C]1.63[/C][C]1.62409873840940[/C][C]0.00590126159060467[/C][/ROW]
[ROW][C]87[/C][C]1.63[/C][C]1.63429374613091[/C][C]-0.00429374613090761[/C][/ROW]
[ROW][C]88[/C][C]1.64[/C][C]1.63415185890275[/C][C]0.00584814109725285[/C][/ROW]
[ROW][C]89[/C][C]1.64[/C][C]1.64434511125277[/C][C]-0.00434511125276571[/C][/ROW]
[ROW][C]90[/C][C]1.64[/C][C]1.64211819332619[/C][C]-0.00211819332618735[/C][/ROW]
[ROW][C]91[/C][C]1.64[/C][C]1.64621486410334[/C][C]-0.00621486410334415[/C][/ROW]
[ROW][C]92[/C][C]1.64[/C][C]1.64392616002518[/C][C]-0.00392616002518054[/C][/ROW]
[ROW][C]93[/C][C]1.65[/C][C]1.64379641971304[/C][C]0.00620358028696266[/C][/ROW]
[ROW][C]94[/C][C]1.65[/C][C]1.65400141758314[/C][C]-0.00400141758313777[/C][/ROW]
[ROW][C]95[/C][C]1.65[/C][C]1.65386919037820[/C][C]-0.00386919037820421[/C][/ROW]
[ROW][C]96[/C][C]1.65[/C][C]1.65374133263318[/C][C]-0.00374133263318366[/C][/ROW]
[ROW][C]97[/C][C]1.65[/C][C]1.65361769995888[/C][C]-0.00361769995887906[/C][/ROW]
[ROW][C]98[/C][C]1.66[/C][C]1.65349815273745[/C][C]0.00650184726255243[/C][/ROW]
[ROW][C]99[/C][C]1.66[/C][C]1.66371300686666[/C][C]-0.00371300686665799[/C][/ROW]
[ROW][C]100[/C][C]1.67[/C][C]1.66359031021986[/C][C]0.0064096897801369[/C][/ROW]
[ROW][C]101[/C][C]1.68[/C][C]1.67380211899676[/C][C]0.00619788100324481[/C][/ROW]
[ROW][C]102[/C][C]1.68[/C][C]1.68192359520018[/C][C]-0.00192359520017793[/C][/ROW]
[ROW][C]103[/C][C]1.68[/C][C]1.68602669648996[/C][C]-0.00602669648996024[/C][/ROW]
[ROW][C]104[/C][C]1.68[/C][C]1.68374421042755[/C][C]-0.00374421042755202[/C][/ROW]
[ROW][C]105[/C][C]1.69[/C][C]1.68362048265627[/C][C]0.00637951734372688[/C][/ROW]
[ROW][C]106[/C][C]1.7[/C][C]1.69383129438228[/C][C]0.00616870561771732[/C][/ROW]
[ROW][C]107[/C][C]1.7[/C][C]1.70403513981579[/C][C]-0.00403513981579251[/C][/ROW]
[ROW][C]108[/C][C]1.71[/C][C]1.70390179825664[/C][C]0.00609820174336062[/C][/ROW]
[ROW][C]109[/C][C]1.72[/C][C]1.71410331388326[/C][C]0.00589668611673777[/C][/ROW]
[ROW][C]110[/C][C]1.73[/C][C]1.72429817040783[/C][C]0.00570182959217203[/C][/ROW]
[ROW][C]111[/C][C]1.74[/C][C]1.73448658788096[/C][C]0.00551341211903522[/C][/ROW]
[ROW][C]112[/C][C]1.74[/C][C]1.74466877908171[/C][C]-0.00466877908170815[/C][/ROW]
[ROW][C]113[/C][C]1.75[/C][C]1.74451449885586[/C][C]0.00548550114413682[/C][/ROW]
[ROW][C]114[/C][C]1.75[/C][C]1.75261243440259[/C][C]-0.00261243440259018[/C][/ROW]
[ROW][C]115[/C][C]1.75[/C][C]1.7566927729388[/C][C]-0.00669277293880044[/C][/ROW]
[ROW][C]116[/C][C]1.76[/C][C]1.75438827632007[/C][C]0.0056117236799349[/C][/ROW]
[ROW][C]117[/C][C]1.79[/C][C]1.76457371623521[/C][C]0.0254262837647947[/C][/ROW]
[ROW][C]118[/C][C]1.83[/C][C]1.79541393007548[/C][C]0.0345860699245211[/C][/ROW]
[ROW][C]119[/C][C]1.84[/C][C]1.83655682987555[/C][C]0.00344317012445328[/C][/ROW]
[ROW][C]120[/C][C]1.85[/C][C]1.84667060974286[/C][C]0.00332939025714185[/C][/ROW]
[ROW][C]121[/C][C]1.87[/C][C]1.85678062974419[/C][C]0.0132193702558077[/C][/ROW]
[ROW][C]122[/C][C]1.87[/C][C]1.87721746502659[/C][C]-0.0072174650265866[/C][/ROW]
[ROW][C]123[/C][C]1.87[/C][C]1.87697896324382[/C][C]-0.00697896324382019[/C][/ROW]
[ROW][C]124[/C][C]1.88[/C][C]1.87674834277398[/C][C]0.00325165722602350[/C][/ROW]
[ROW][C]125[/C][C]1.88[/C][C]1.88685579408029[/C][C]-0.00685579408028603[/C][/ROW]
[ROW][C]126[/C][C]1.88[/C][C]1.88454591041323[/C][C]-0.00454591041322705[/C][/ROW]
[ROW][C]127[/C][C]1.88[/C][C]1.88856235706028[/C][C]-0.00856235706028041[/C][/ROW]
[ROW][C]128[/C][C]1.89[/C][C]1.88619607986563[/C][C]0.00380392013437225[/C][/ROW]
[ROW][C]129[/C][C]1.89[/C][C]1.89632178074955[/C][C]-0.00632178074955414[/C][/ROW]
[ROW][C]130[/C][C]1.89[/C][C]1.89611287693451[/C][C]-0.00611287693450646[/C][/ROW]
[ROW][C]131[/C][C]1.9[/C][C]1.89591087636487[/C][C]0.00408912363513148[/C][/ROW]
[ROW][C]132[/C][C]1.89[/C][C]1.9060460018242[/C][C]-0.0160460018242008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72294&T=2

As an alternative you can also use a QR Code:

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

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
13	1.38	1.37990651709402	9.34829059822118e-05
14	1.38	1.38008321735619	-8.32173561877703e-05
15	1.38	1.38008046743115	-8.046743114698e-05
16	1.38	1.38007780837763	-7.78083776271643e-05
17	1.38	1.38007523719277	-7.52371927708051e-05
18	1.38	1.37798941763962	0.00201058236038354
19	1.38	1.38222252418172	-0.00222252418172175
20	1.38	1.38006574733635	-6.57473363481209e-05
21	1.38	1.38006357470969	-6.35747096884298e-05
22	1.38	1.38006147387767	-6.14738776727464e-05
23	1.38	1.38005944246784	-5.94424678406202e-05
24	1.43	1.38005747818613	0.0499425218138705
25	1.43	1.43170783332392	-0.00170783332392133
26	1.43	1.43165139781770	-0.00165139781769819
27	1.43	1.43159682722787	-0.00159682722786836
28	1.43	1.4315440599281	-0.00154405992810114
29	1.43	1.43149303632851	-0.00149303632851416
30	1.43	1.42936036547504	0.000639634524955968
31	1.43	1.43354816892228	-0.00354816892227805
32	1.43	1.43134758602689	-0.00134758602688922
33	1.43	1.43130305492509	-0.00130305492508809
34	1.43	1.43125999535756	-0.00125999535756249
35	1.43	1.43121835869733	-0.00121835869732934
36	1.43	1.43117809792429	-0.00117809792428902
37	1.43	1.43113916757213	-0.00113916757212507
38	1.43	1.43110152367696	-0.00110152367695959
39	1.43	1.43106512372771	-0.00106512372770506
40	1.43	1.43102992661806	-0.00102992661805668
41	1.43	1.43099589260007	-0.000995892600071047
42	1.43	1.42887964990595	0.00112035009405376
43	1.44	1.43308333864252	0.00691666135748203
44	1.48	1.44122856700758	0.0387714329924247
45	1.48	1.48250977250771	-0.00250977250771145
46	1.48	1.48242683684883	-0.00242683684883094
47	1.48	1.48234664180628	-0.00234664180627830
48	1.48	1.48226909681614	-0.00226909681613985
49	1.48	1.48219411430719	-0.00219411430719441
50	1.48	1.48212160960202	-0.00212160960202001
51	1.48	1.48205150082137	-0.00205150082136751
52	1.48	1.48198370879169	-0.00198370879169496
53	1.48	1.48191815695576	-0.00191815695575737
54	1.48	1.47977143795282	0.00022856204718269
55	1.48	1.48394565747295	-0.00394565747294751
56	1.48	1.48173193953255	-0.00173193953255102
57	1.48	1.48167470743449	-0.00167470743448961
58	1.48	1.48161936657627	-0.00161936657627049
59	1.48	1.48156585446170	-0.00156585446170254
60	1.48	1.48151411065979	-0.00151411065978690
61	1.48	1.48146407673647	-0.00146407673647242
62	1.48	1.48141569618867	-0.00141569618866666
63	1.48	1.48136891438043	-0.00136891438042652
64	1.48	1.48132367848126	-0.00132367848125936
65	1.48	1.48127993740646	-0.00127993740645982
66	1.48	1.47915430842609	0.00084569157391079
67	1.48	1.48334892104709	-0.00334892104709072
68	1.48	1.48115492231571	-0.00115492231570813
69	1.48	1.48111675780362	-0.00111675780361975
70	1.48	1.48107985444128	-0.00107985444127578
71	1.48	1.48104417055387	-0.00104417055386885
72	1.48	1.48100966584374	-0.00100966584373974
73	1.48	1.48097630134487	-0.000976301344868702
74	1.57	1.48094403937887	0.0890559606211279
75	1.58	1.57388690162980	0.00611309837020224
76	1.58	1.58408890951680	-0.00408890951679819
77	1.58	1.58395379113303	-0.00395379113302563
78	1.58	1.58173980441510	-0.00173980441509825
79	1.59	1.58584897908795	0.00415102091204989
80	1.6	1.59390281661505	0.00609718338495258
81	1.6	1.60410429858992	-0.00410429858992489
82	1.61	1.60396867167284	0.00603132832715714
83	1.61	1.61416797746140	-0.00416797746139586
84	1.61	1.61403024627026	-0.00403024627026261
85	1.62	1.61389706641876	0.00610293358123704
86	1.63	1.62409873840940	0.00590126159060467
87	1.63	1.63429374613091	-0.00429374613090761
88	1.64	1.63415185890275	0.00584814109725285
89	1.64	1.64434511125277	-0.00434511125276571
90	1.64	1.64211819332619	-0.00211819332618735
91	1.64	1.64621486410334	-0.00621486410334415
92	1.64	1.64392616002518	-0.00392616002518054
93	1.65	1.64379641971304	0.00620358028696266
94	1.65	1.65400141758314	-0.00400141758313777
95	1.65	1.65386919037820	-0.00386919037820421
96	1.65	1.65374133263318	-0.00374133263318366
97	1.65	1.65361769995888	-0.00361769995887906
98	1.66	1.65349815273745	0.00650184726255243
99	1.66	1.66371300686666	-0.00371300686665799
100	1.67	1.66359031021986	0.0064096897801369
101	1.68	1.67380211899676	0.00619788100324481
102	1.68	1.68192359520018	-0.00192359520017793
103	1.68	1.68602669648996	-0.00602669648996024
104	1.68	1.68374421042755	-0.00374421042755202
105	1.69	1.68362048265627	0.00637951734372688
106	1.7	1.69383129438228	0.00616870561771732
107	1.7	1.70403513981579	-0.00403513981579251
108	1.71	1.70390179825664	0.00609820174336062
109	1.72	1.71410331388326	0.00589668611673777
110	1.73	1.72429817040783	0.00570182959217203
111	1.74	1.73448658788096	0.00551341211903522
112	1.74	1.74466877908171	-0.00466877908170815
113	1.75	1.74451449885586	0.00548550114413682
114	1.75	1.75261243440259	-0.00261243440259018
115	1.75	1.7566927729388	-0.00669277293880044
116	1.76	1.75438827632007	0.0056117236799349
117	1.79	1.76457371623521	0.0254262837647947
118	1.83	1.79541393007548	0.0345860699245211
119	1.84	1.83655682987555	0.00344317012445328
120	1.85	1.84667060974286	0.00332939025714185
121	1.87	1.85678062974419	0.0132193702558077
122	1.87	1.87721746502659	-0.0072174650265866
123	1.87	1.87697896324382	-0.00697896324382019
124	1.88	1.87674834277398	0.00325165722602350
125	1.88	1.88685579408029	-0.00685579408028603
126	1.88	1.88454591041323	-0.00454591041322705
127	1.88	1.88856235706028	-0.00856235706028041
128	1.89	1.88619607986563	0.00380392013437225
129	1.89	1.89632178074955	-0.00632178074955414
130	1.89	1.89611287693451	-0.00611287693450646
131	1.9	1.89591087636487	0.00408912363513148
132	1.89	1.9060460018242	-0.0160460018242008

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
133	1.89551576024669	1.87305534603914	1.91797617445424
134	1.90103152049337	1.86873861399600	1.93332442699074
135	1.90654728074006	1.86634545799856	1.94674910348156
136	1.91206304098675	1.86488639239692	1.95923968957657
137	1.91757880123344	1.86398512238925	1.97117248007763
138	1.92101122814679	1.86136873480422	1.98065372148936
139	1.92861032172681	1.86317660172650	1.99404404172712
140	1.93412608197350	1.86308793549109	2.00516422845591
141	1.93964184222019	1.86313780062387	2.0161458838165
142	1.94515760246687	1.86329171318116	2.02702349175259
143	1.95067336271356	1.86352414389728	2.03782258152984
144	1.95618912296025	1.86381565460201	2.04856259131848

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 1.89551576024669 & 1.87305534603914 & 1.91797617445424 \tabularnewline
134 & 1.90103152049337 & 1.86873861399600 & 1.93332442699074 \tabularnewline
135 & 1.90654728074006 & 1.86634545799856 & 1.94674910348156 \tabularnewline
136 & 1.91206304098675 & 1.86488639239692 & 1.95923968957657 \tabularnewline
137 & 1.91757880123344 & 1.86398512238925 & 1.97117248007763 \tabularnewline
138 & 1.92101122814679 & 1.86136873480422 & 1.98065372148936 \tabularnewline
139 & 1.92861032172681 & 1.86317660172650 & 1.99404404172712 \tabularnewline
140 & 1.93412608197350 & 1.86308793549109 & 2.00516422845591 \tabularnewline
141 & 1.93964184222019 & 1.86313780062387 & 2.0161458838165 \tabularnewline
142 & 1.94515760246687 & 1.86329171318116 & 2.02702349175259 \tabularnewline
143 & 1.95067336271356 & 1.86352414389728 & 2.03782258152984 \tabularnewline
144 & 1.95618912296025 & 1.86381565460201 & 2.04856259131848 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72294&T=3

[TABLE]
[ROW][C]Extrapolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Forecast[/C][C]95% Lower Bound[/C][C]95% Upper Bound[/C][/ROW]
[ROW][C]133[/C][C]1.89551576024669[/C][C]1.87305534603914[/C][C]1.91797617445424[/C][/ROW]
[ROW][C]134[/C][C]1.90103152049337[/C][C]1.86873861399600[/C][C]1.93332442699074[/C][/ROW]
[ROW][C]135[/C][C]1.90654728074006[/C][C]1.86634545799856[/C][C]1.94674910348156[/C][/ROW]
[ROW][C]136[/C][C]1.91206304098675[/C][C]1.86488639239692[/C][C]1.95923968957657[/C][/ROW]
[ROW][C]137[/C][C]1.91757880123344[/C][C]1.86398512238925[/C][C]1.97117248007763[/C][/ROW]
[ROW][C]138[/C][C]1.92101122814679[/C][C]1.86136873480422[/C][C]1.98065372148936[/C][/ROW]
[ROW][C]139[/C][C]1.92861032172681[/C][C]1.86317660172650[/C][C]1.99404404172712[/C][/ROW]
[ROW][C]140[/C][C]1.93412608197350[/C][C]1.86308793549109[/C][C]2.00516422845591[/C][/ROW]
[ROW][C]141[/C][C]1.93964184222019[/C][C]1.86313780062387[/C][C]2.0161458838165[/C][/ROW]
[ROW][C]142[/C][C]1.94515760246687[/C][C]1.86329171318116[/C][C]2.02702349175259[/C][/ROW]
[ROW][C]143[/C][C]1.95067336271356[/C][C]1.86352414389728[/C][C]2.03782258152984[/C][/ROW]
[ROW][C]144[/C][C]1.95618912296025[/C][C]1.86381565460201[/C][C]2.04856259131848[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72294&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72294&T=3

As an alternative you can also use a QR Code:

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

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
133	1.89551576024669	1.87305534603914	1.91797617445424
134	1.90103152049337	1.86873861399600	1.93332442699074
135	1.90654728074006	1.86634545799856	1.94674910348156
136	1.91206304098675	1.86488639239692	1.95923968957657
137	1.91757880123344	1.86398512238925	1.97117248007763
138	1.92101122814679	1.86136873480422	1.98065372148936
139	1.92861032172681	1.86317660172650	1.99404404172712
140	1.93412608197350	1.86308793549109	2.00516422845591
141	1.93964184222019	1.86313780062387	2.0161458838165
142	1.94515760246687	1.86329171318116	2.02702349175259
143	1.95067336271356	1.86352414389728	2.03782258152984
144	1.95618912296025	1.86381565460201	2.04856259131848

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ;

Parameters (R input):

par1 = 12 ; par2 = Triple ; par3 = additive ;

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

par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
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,'Interpolation Forecasts of Exponential Smoothing',4,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,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code