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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

Mon, 18 Jan 2010 06:41:19 -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/18/t1263822185rofp3qw8j1b92uk.htm/, Retrieved Sun, 05 May 2024 03:51:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=72271, Retrieved Sun, 05 May 2024 03:51:55 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W61

Estimated Impact

172

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

-       [Exponential Smoothing] [ Exponential Smoo...] [2010-01-18 13:41:19] [91b501704ec53ded4f914c1fb409b978] [Current]

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Dataseries X:

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Boxplots

1,14
1,15
1,15
1,14
1,14
1,14
1,15
1,14
1,14
1,15
1,15
1,14
1,15
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,18
1,19
1,19
1,19
1,19
1,18
1,19
1,19
1,2
1,21
1,21
1,2
1,21
1,21
1,21
1,21
1,21
1,21
1,2
1,21
1,22
1,22
1,23
1,22
1,23
1,23
1,23
1,23
1,23
1,22
1,22
1,23
1,24
1,24
1,25
1,25
1,25
1,26
1,26
1,26
1,26
1,27
1,27
1,29
1,31
1,32
1,32
1,33
1,33
1,32
1,32
1,31
1,3
1,31
1,29
1,3
1,3
1,32
1,31
1,35
1,35
1,36
1,37
1,37
1,37
1,32
1,32
1,31
1,31
1,34
1,31
1,26
1,27
1,24
1,25
1,27
1,25
1,26
1,27
1,26
1,26
1,28
1,27
1,28
1,27
1,26
1,27
1,27
1,28
1,27
1,26
1,3
1,31
1,28
1,29
1,31
1,29
1,29
1,32
1,3
1,29
1,31
1,29
1,33
1,35
1,32
1,33

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=72271&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=72271&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72271&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	0.812685219892519
beta	0.060457480341854
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.812685219892519 \tabularnewline
beta & 0.060457480341854 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72271&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]0.812685219892519[/C][/ROW]
[ROW][C]beta[/C][C]0.060457480341854[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72271&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72271&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	0.812685219892519
beta	0.060457480341854
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
3	1.15	1.16	-0.01
4	1.14	1.16138181879402	-0.0213818187940171
5	1.14	1.15246325089893	-0.0124632508989266
6	1.14	1.15018031564590	-0.0101803156458962
7	1.15	1.14925249969319	0.000747500306808258
8	1.14	1.15724228510931	-0.0172422851093077
9	1.14	1.14976487432649	-0.0097648743264922
10	1.15	1.14788446816925	0.00211553183075197
11	1.15	1.15576303471770	-0.00576303471769624
12	1.14	1.15695565206573	-0.0169556520657328
13	1.15	1.14821911435376	0.00178088564623979
14	1.17	1.15479698398989	0.0152030160101084
15	1.17	1.17302978886826	-0.00302978886826222
16	1.17	1.17629620038917	-0.00629620038917333
17	1.17	1.17659869695641	-0.00659869695640825
18	1.17	1.17633114591204	-0.00633114591203654
19	1.17	1.17596996208354	-0.00596996208354295
20	1.17	1.17560898545986	-0.00560898545986221
21	1.17	1.17526578347731	-0.00526578347731399
22	1.17	1.17494277345669	-0.00494277345669181
23	1.17	1.17463941610825	-0.0046394161082477
24	1.17	1.17435464482225	-0.00435464482225068
25	1.17	1.17408734661977	-0.00408734661977261
26	1.18	1.17383645452013	0.00616354547986719
27	1.19	1.18221914378864	0.00778085621135882
28	1.19	1.19229849362024	-0.00229849362024481
29	1.19	1.19407357315876	-0.00407357315876156
30	1.19	1.19420592532667	-0.00420592532666531
31	1.18	1.19402406753244	-0.0140240675324419
32	1.19	1.18517410756322	0.00482589243678144
33	1.19	1.19188034155046	-0.00188034155046157
34	1.2	1.19304413166044	0.0069558683395603
35	1.21	1.20173074093636	0.0082692590636435
36	1.21	1.21189101612616	-0.00189101612616405
37	1.2	1.21370127473161	-0.0137012747316145
38	1.21	1.20524032735438	0.00475967264562382
39	1.21	1.21201617557932	-0.00201617557932243
40	1.21	1.21318633154508	-0.00318633154507553
41	1.21	1.21324896534107	-0.00324896534107211
42	1.21	1.21310106648530	-0.00310106648529729
43	1.21	1.21292099845195	-0.0029209984519456
44	1.2	1.21274375192097	-0.0127437519209728
45	1.21	1.20395756132932	0.00604243867067855
46	1.22	1.21073551270882	0.00926448729118379
47	1.22	1.22058716651414	-0.000587166514138149
48	1.23	1.22240367768634	0.0075963223136597
49	1.22	1.23124401862602	-0.0112440186260154
50	1.23	1.22422064169559	0.00577935830440524
51	1.23	1.2313158682269	-0.00131586822689966
52	1.23	1.23258025660149	-0.00258025660148942
53	1.23	1.23269031974045	-0.00269031974044998
54	1.23	1.23257875298045	-0.00257875298044796
55	1.22	1.23243115326320	-0.0124311532631978
56	1.22	1.22366587483675	-0.00366587483675307
57	1.23	1.22184389357137	0.0081561064286313
58	1.24	1.23003019491751	0.00996980508248768
59	1.24	1.24018030779616	-0.000180307796157742
60	1.25	1.24207271491304	0.00792728508695784
61	1.25	1.25094353344598	-0.000943533445983968
62	1.25	1.25255881033374	-0.00255881033374128
63	1.26	1.25273565379473	0.00726434620526839
64	1.26	1.26125254978846	-0.00125254978846168
65	1.26	1.26278634888434	-0.00278634888433982
66	1.26	1.26293675072165	-0.00293675072164734
67	1.27	1.26282063212713	0.00717936787287399
68	1.27	1.27127847676588	-0.00127847676588044
69	1.29	1.27279994080258	0.0172000591974206
70	1.31	1.29018372670259	0.0198162732974145
71	1.32	1.31066730212201	0.00933269787798885
72	1.32	1.32308957326460	-0.00308957326459747
73	1.33	1.32526464855567	0.00473535144432624
74	1.33	1.33403158605679	-0.004031586056785
75	1.32	1.33547567950961	-0.0154756795096069
76	1.32	1.32685896233355	-0.00685896233355199
77	1.31	1.32490792313519	-0.0149079231351921
78	1.3	1.31568314295071	-0.0156831429507140
79	1.31	1.30505779477464	0.00494220522535782
80	1.29	1.31143718709501	-0.0214371870950059
81	1.3	1.29532516598191	0.00467483401808622
82	1.3	1.30066368664458	-0.000663686644579942
83	1.32	1.30163106161860	0.0183689383814016
84	1.31	1.31896848887255	-0.0089684888725523
85	1.35	1.31364854517407	0.0363514548259252
86	1.35	1.34694550230618	0.00305449769381649
87	1.36	1.35333259084153	0.00666740915847086
88	1.37	1.36298342827713	0.00701657172287273
89	1.37	1.37326276948991	-0.00326276948990945
90	1.37	1.37502793269946	-0.00502793269945823
91	1.32	1.37511153693983	-0.0551115369398307
92	1.32	1.3317851465829	-0.0117851465828993
93	1.31	1.32309043486348	-0.0130904348634793
94	1.31	1.31269176361439	-0.00269176361439039
95	1.34	1.31061168464157	0.0293883153584322
96	1.31	1.33604654488336	-0.0260465448833587
97	1.26	1.31515057123636	-0.0551505712363562
98	1.27	1.26789247799249	0.00210752200751174
99	1.24	1.26727073951667	-0.0272707395166698
100	1.25	1.24143383157717	0.00856616842283353
101	1.27	1.24514192974929	0.0248580702507060
102	1.25	1.26331156483824	-0.0133115648382387
103	1.26	1.24980726584827	0.0101927341517285
104	1.27	1.25590536184638	0.0140946381536176
105	1.26	1.26586698801315	-0.00586698801315366
106	1.26	1.25931783348968	0.000682166510322268
107	1.28	1.25812459686964	0.0218754031303605
108	1.27	1.27522959242229	-0.00522959242229382
109	1.28	1.27004981365884	0.00995018634116263
110	1.27	1.27769529825518	-0.00769529825518456
111	1.26	1.27062246599717	-0.0106224659971661
112	1.27	1.26064885521200	0.00935114478799615
113	1.27	1.26736695156818	0.00263304843181733
114	1.28	1.26875471961704	0.0112452803829648
115	1.27	1.27769403452734	-0.00769403452733597
116	1.26	1.27086361790069	-0.0108636179006871
117	1.3	1.26092356665368	0.0390764333463214
118	1.31	1.29348899545533	0.0165110045446704
119	1.28	1.30852706733619	-0.0285270673361875
120	1.29	1.28556174630016	0.00443825369984041
121	1.31	1.28960491871746	0.0203950812825353
122	1.29	1.30761803857114	-0.0176180385711400
123	1.29	1.29387283241798	-0.00387283241798464
124	1.32	1.29110786865923	0.0288921313407686
125	1.3	1.31639006089787	-0.0163900608978749
126	1.29	1.30406679354518	-0.0140667935451844
127	1.31	1.29294046886138	0.0170595311386199
128	1.29	1.3079482324472	-0.0179482324471998
129	1.33	1.29362385526266	0.0363761447373399
130	1.35	1.3252353620033	0.0247646379966995
131	1.32	1.34862712733485	-0.0286271273348464
132	1.33	1.32722166031264	0.0027783396873593

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 1.15 & 1.16 & -0.01 \tabularnewline
4 & 1.14 & 1.16138181879402 & -0.0213818187940171 \tabularnewline
5 & 1.14 & 1.15246325089893 & -0.0124632508989266 \tabularnewline
6 & 1.14 & 1.15018031564590 & -0.0101803156458962 \tabularnewline
7 & 1.15 & 1.14925249969319 & 0.000747500306808258 \tabularnewline
8 & 1.14 & 1.15724228510931 & -0.0172422851093077 \tabularnewline
9 & 1.14 & 1.14976487432649 & -0.0097648743264922 \tabularnewline
10 & 1.15 & 1.14788446816925 & 0.00211553183075197 \tabularnewline
11 & 1.15 & 1.15576303471770 & -0.00576303471769624 \tabularnewline
12 & 1.14 & 1.15695565206573 & -0.0169556520657328 \tabularnewline
13 & 1.15 & 1.14821911435376 & 0.00178088564623979 \tabularnewline
14 & 1.17 & 1.15479698398989 & 0.0152030160101084 \tabularnewline
15 & 1.17 & 1.17302978886826 & -0.00302978886826222 \tabularnewline
16 & 1.17 & 1.17629620038917 & -0.00629620038917333 \tabularnewline
17 & 1.17 & 1.17659869695641 & -0.00659869695640825 \tabularnewline
18 & 1.17 & 1.17633114591204 & -0.00633114591203654 \tabularnewline
19 & 1.17 & 1.17596996208354 & -0.00596996208354295 \tabularnewline
20 & 1.17 & 1.17560898545986 & -0.00560898545986221 \tabularnewline
21 & 1.17 & 1.17526578347731 & -0.00526578347731399 \tabularnewline
22 & 1.17 & 1.17494277345669 & -0.00494277345669181 \tabularnewline
23 & 1.17 & 1.17463941610825 & -0.0046394161082477 \tabularnewline
24 & 1.17 & 1.17435464482225 & -0.00435464482225068 \tabularnewline
25 & 1.17 & 1.17408734661977 & -0.00408734661977261 \tabularnewline
26 & 1.18 & 1.17383645452013 & 0.00616354547986719 \tabularnewline
27 & 1.19 & 1.18221914378864 & 0.00778085621135882 \tabularnewline
28 & 1.19 & 1.19229849362024 & -0.00229849362024481 \tabularnewline
29 & 1.19 & 1.19407357315876 & -0.00407357315876156 \tabularnewline
30 & 1.19 & 1.19420592532667 & -0.00420592532666531 \tabularnewline
31 & 1.18 & 1.19402406753244 & -0.0140240675324419 \tabularnewline
32 & 1.19 & 1.18517410756322 & 0.00482589243678144 \tabularnewline
33 & 1.19 & 1.19188034155046 & -0.00188034155046157 \tabularnewline
34 & 1.2 & 1.19304413166044 & 0.0069558683395603 \tabularnewline
35 & 1.21 & 1.20173074093636 & 0.0082692590636435 \tabularnewline
36 & 1.21 & 1.21189101612616 & -0.00189101612616405 \tabularnewline
37 & 1.2 & 1.21370127473161 & -0.0137012747316145 \tabularnewline
38 & 1.21 & 1.20524032735438 & 0.00475967264562382 \tabularnewline
39 & 1.21 & 1.21201617557932 & -0.00201617557932243 \tabularnewline
40 & 1.21 & 1.21318633154508 & -0.00318633154507553 \tabularnewline
41 & 1.21 & 1.21324896534107 & -0.00324896534107211 \tabularnewline
42 & 1.21 & 1.21310106648530 & -0.00310106648529729 \tabularnewline
43 & 1.21 & 1.21292099845195 & -0.0029209984519456 \tabularnewline
44 & 1.2 & 1.21274375192097 & -0.0127437519209728 \tabularnewline
45 & 1.21 & 1.20395756132932 & 0.00604243867067855 \tabularnewline
46 & 1.22 & 1.21073551270882 & 0.00926448729118379 \tabularnewline
47 & 1.22 & 1.22058716651414 & -0.000587166514138149 \tabularnewline
48 & 1.23 & 1.22240367768634 & 0.0075963223136597 \tabularnewline
49 & 1.22 & 1.23124401862602 & -0.0112440186260154 \tabularnewline
50 & 1.23 & 1.22422064169559 & 0.00577935830440524 \tabularnewline
51 & 1.23 & 1.2313158682269 & -0.00131586822689966 \tabularnewline
52 & 1.23 & 1.23258025660149 & -0.00258025660148942 \tabularnewline
53 & 1.23 & 1.23269031974045 & -0.00269031974044998 \tabularnewline
54 & 1.23 & 1.23257875298045 & -0.00257875298044796 \tabularnewline
55 & 1.22 & 1.23243115326320 & -0.0124311532631978 \tabularnewline
56 & 1.22 & 1.22366587483675 & -0.00366587483675307 \tabularnewline
57 & 1.23 & 1.22184389357137 & 0.0081561064286313 \tabularnewline
58 & 1.24 & 1.23003019491751 & 0.00996980508248768 \tabularnewline
59 & 1.24 & 1.24018030779616 & -0.000180307796157742 \tabularnewline
60 & 1.25 & 1.24207271491304 & 0.00792728508695784 \tabularnewline
61 & 1.25 & 1.25094353344598 & -0.000943533445983968 \tabularnewline
62 & 1.25 & 1.25255881033374 & -0.00255881033374128 \tabularnewline
63 & 1.26 & 1.25273565379473 & 0.00726434620526839 \tabularnewline
64 & 1.26 & 1.26125254978846 & -0.00125254978846168 \tabularnewline
65 & 1.26 & 1.26278634888434 & -0.00278634888433982 \tabularnewline
66 & 1.26 & 1.26293675072165 & -0.00293675072164734 \tabularnewline
67 & 1.27 & 1.26282063212713 & 0.00717936787287399 \tabularnewline
68 & 1.27 & 1.27127847676588 & -0.00127847676588044 \tabularnewline
69 & 1.29 & 1.27279994080258 & 0.0172000591974206 \tabularnewline
70 & 1.31 & 1.29018372670259 & 0.0198162732974145 \tabularnewline
71 & 1.32 & 1.31066730212201 & 0.00933269787798885 \tabularnewline
72 & 1.32 & 1.32308957326460 & -0.00308957326459747 \tabularnewline
73 & 1.33 & 1.32526464855567 & 0.00473535144432624 \tabularnewline
74 & 1.33 & 1.33403158605679 & -0.004031586056785 \tabularnewline
75 & 1.32 & 1.33547567950961 & -0.0154756795096069 \tabularnewline
76 & 1.32 & 1.32685896233355 & -0.00685896233355199 \tabularnewline
77 & 1.31 & 1.32490792313519 & -0.0149079231351921 \tabularnewline
78 & 1.3 & 1.31568314295071 & -0.0156831429507140 \tabularnewline
79 & 1.31 & 1.30505779477464 & 0.00494220522535782 \tabularnewline
80 & 1.29 & 1.31143718709501 & -0.0214371870950059 \tabularnewline
81 & 1.3 & 1.29532516598191 & 0.00467483401808622 \tabularnewline
82 & 1.3 & 1.30066368664458 & -0.000663686644579942 \tabularnewline
83 & 1.32 & 1.30163106161860 & 0.0183689383814016 \tabularnewline
84 & 1.31 & 1.31896848887255 & -0.0089684888725523 \tabularnewline
85 & 1.35 & 1.31364854517407 & 0.0363514548259252 \tabularnewline
86 & 1.35 & 1.34694550230618 & 0.00305449769381649 \tabularnewline
87 & 1.36 & 1.35333259084153 & 0.00666740915847086 \tabularnewline
88 & 1.37 & 1.36298342827713 & 0.00701657172287273 \tabularnewline
89 & 1.37 & 1.37326276948991 & -0.00326276948990945 \tabularnewline
90 & 1.37 & 1.37502793269946 & -0.00502793269945823 \tabularnewline
91 & 1.32 & 1.37511153693983 & -0.0551115369398307 \tabularnewline
92 & 1.32 & 1.3317851465829 & -0.0117851465828993 \tabularnewline
93 & 1.31 & 1.32309043486348 & -0.0130904348634793 \tabularnewline
94 & 1.31 & 1.31269176361439 & -0.00269176361439039 \tabularnewline
95 & 1.34 & 1.31061168464157 & 0.0293883153584322 \tabularnewline
96 & 1.31 & 1.33604654488336 & -0.0260465448833587 \tabularnewline
97 & 1.26 & 1.31515057123636 & -0.0551505712363562 \tabularnewline
98 & 1.27 & 1.26789247799249 & 0.00210752200751174 \tabularnewline
99 & 1.24 & 1.26727073951667 & -0.0272707395166698 \tabularnewline
100 & 1.25 & 1.24143383157717 & 0.00856616842283353 \tabularnewline
101 & 1.27 & 1.24514192974929 & 0.0248580702507060 \tabularnewline
102 & 1.25 & 1.26331156483824 & -0.0133115648382387 \tabularnewline
103 & 1.26 & 1.24980726584827 & 0.0101927341517285 \tabularnewline
104 & 1.27 & 1.25590536184638 & 0.0140946381536176 \tabularnewline
105 & 1.26 & 1.26586698801315 & -0.00586698801315366 \tabularnewline
106 & 1.26 & 1.25931783348968 & 0.000682166510322268 \tabularnewline
107 & 1.28 & 1.25812459686964 & 0.0218754031303605 \tabularnewline
108 & 1.27 & 1.27522959242229 & -0.00522959242229382 \tabularnewline
109 & 1.28 & 1.27004981365884 & 0.00995018634116263 \tabularnewline
110 & 1.27 & 1.27769529825518 & -0.00769529825518456 \tabularnewline
111 & 1.26 & 1.27062246599717 & -0.0106224659971661 \tabularnewline
112 & 1.27 & 1.26064885521200 & 0.00935114478799615 \tabularnewline
113 & 1.27 & 1.26736695156818 & 0.00263304843181733 \tabularnewline
114 & 1.28 & 1.26875471961704 & 0.0112452803829648 \tabularnewline
115 & 1.27 & 1.27769403452734 & -0.00769403452733597 \tabularnewline
116 & 1.26 & 1.27086361790069 & -0.0108636179006871 \tabularnewline
117 & 1.3 & 1.26092356665368 & 0.0390764333463214 \tabularnewline
118 & 1.31 & 1.29348899545533 & 0.0165110045446704 \tabularnewline
119 & 1.28 & 1.30852706733619 & -0.0285270673361875 \tabularnewline
120 & 1.29 & 1.28556174630016 & 0.00443825369984041 \tabularnewline
121 & 1.31 & 1.28960491871746 & 0.0203950812825353 \tabularnewline
122 & 1.29 & 1.30761803857114 & -0.0176180385711400 \tabularnewline
123 & 1.29 & 1.29387283241798 & -0.00387283241798464 \tabularnewline
124 & 1.32 & 1.29110786865923 & 0.0288921313407686 \tabularnewline
125 & 1.3 & 1.31639006089787 & -0.0163900608978749 \tabularnewline
126 & 1.29 & 1.30406679354518 & -0.0140667935451844 \tabularnewline
127 & 1.31 & 1.29294046886138 & 0.0170595311386199 \tabularnewline
128 & 1.29 & 1.3079482324472 & -0.0179482324471998 \tabularnewline
129 & 1.33 & 1.29362385526266 & 0.0363761447373399 \tabularnewline
130 & 1.35 & 1.3252353620033 & 0.0247646379966995 \tabularnewline
131 & 1.32 & 1.34862712733485 & -0.0286271273348464 \tabularnewline
132 & 1.33 & 1.32722166031264 & 0.0027783396873593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72271&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]3[/C][C]1.15[/C][C]1.16[/C][C]-0.01[/C][/ROW]
[ROW][C]4[/C][C]1.14[/C][C]1.16138181879402[/C][C]-0.0213818187940171[/C][/ROW]
[ROW][C]5[/C][C]1.14[/C][C]1.15246325089893[/C][C]-0.0124632508989266[/C][/ROW]
[ROW][C]6[/C][C]1.14[/C][C]1.15018031564590[/C][C]-0.0101803156458962[/C][/ROW]
[ROW][C]7[/C][C]1.15[/C][C]1.14925249969319[/C][C]0.000747500306808258[/C][/ROW]
[ROW][C]8[/C][C]1.14[/C][C]1.15724228510931[/C][C]-0.0172422851093077[/C][/ROW]
[ROW][C]9[/C][C]1.14[/C][C]1.14976487432649[/C][C]-0.0097648743264922[/C][/ROW]
[ROW][C]10[/C][C]1.15[/C][C]1.14788446816925[/C][C]0.00211553183075197[/C][/ROW]
[ROW][C]11[/C][C]1.15[/C][C]1.15576303471770[/C][C]-0.00576303471769624[/C][/ROW]
[ROW][C]12[/C][C]1.14[/C][C]1.15695565206573[/C][C]-0.0169556520657328[/C][/ROW]
[ROW][C]13[/C][C]1.15[/C][C]1.14821911435376[/C][C]0.00178088564623979[/C][/ROW]
[ROW][C]14[/C][C]1.17[/C][C]1.15479698398989[/C][C]0.0152030160101084[/C][/ROW]
[ROW][C]15[/C][C]1.17[/C][C]1.17302978886826[/C][C]-0.00302978886826222[/C][/ROW]
[ROW][C]16[/C][C]1.17[/C][C]1.17629620038917[/C][C]-0.00629620038917333[/C][/ROW]
[ROW][C]17[/C][C]1.17[/C][C]1.17659869695641[/C][C]-0.00659869695640825[/C][/ROW]
[ROW][C]18[/C][C]1.17[/C][C]1.17633114591204[/C][C]-0.00633114591203654[/C][/ROW]
[ROW][C]19[/C][C]1.17[/C][C]1.17596996208354[/C][C]-0.00596996208354295[/C][/ROW]
[ROW][C]20[/C][C]1.17[/C][C]1.17560898545986[/C][C]-0.00560898545986221[/C][/ROW]
[ROW][C]21[/C][C]1.17[/C][C]1.17526578347731[/C][C]-0.00526578347731399[/C][/ROW]
[ROW][C]22[/C][C]1.17[/C][C]1.17494277345669[/C][C]-0.00494277345669181[/C][/ROW]
[ROW][C]23[/C][C]1.17[/C][C]1.17463941610825[/C][C]-0.0046394161082477[/C][/ROW]
[ROW][C]24[/C][C]1.17[/C][C]1.17435464482225[/C][C]-0.00435464482225068[/C][/ROW]
[ROW][C]25[/C][C]1.17[/C][C]1.17408734661977[/C][C]-0.00408734661977261[/C][/ROW]
[ROW][C]26[/C][C]1.18[/C][C]1.17383645452013[/C][C]0.00616354547986719[/C][/ROW]
[ROW][C]27[/C][C]1.19[/C][C]1.18221914378864[/C][C]0.00778085621135882[/C][/ROW]
[ROW][C]28[/C][C]1.19[/C][C]1.19229849362024[/C][C]-0.00229849362024481[/C][/ROW]
[ROW][C]29[/C][C]1.19[/C][C]1.19407357315876[/C][C]-0.00407357315876156[/C][/ROW]
[ROW][C]30[/C][C]1.19[/C][C]1.19420592532667[/C][C]-0.00420592532666531[/C][/ROW]
[ROW][C]31[/C][C]1.18[/C][C]1.19402406753244[/C][C]-0.0140240675324419[/C][/ROW]
[ROW][C]32[/C][C]1.19[/C][C]1.18517410756322[/C][C]0.00482589243678144[/C][/ROW]
[ROW][C]33[/C][C]1.19[/C][C]1.19188034155046[/C][C]-0.00188034155046157[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]1.19304413166044[/C][C]0.0069558683395603[/C][/ROW]
[ROW][C]35[/C][C]1.21[/C][C]1.20173074093636[/C][C]0.0082692590636435[/C][/ROW]
[ROW][C]36[/C][C]1.21[/C][C]1.21189101612616[/C][C]-0.00189101612616405[/C][/ROW]
[ROW][C]37[/C][C]1.2[/C][C]1.21370127473161[/C][C]-0.0137012747316145[/C][/ROW]
[ROW][C]38[/C][C]1.21[/C][C]1.20524032735438[/C][C]0.00475967264562382[/C][/ROW]
[ROW][C]39[/C][C]1.21[/C][C]1.21201617557932[/C][C]-0.00201617557932243[/C][/ROW]
[ROW][C]40[/C][C]1.21[/C][C]1.21318633154508[/C][C]-0.00318633154507553[/C][/ROW]
[ROW][C]41[/C][C]1.21[/C][C]1.21324896534107[/C][C]-0.00324896534107211[/C][/ROW]
[ROW][C]42[/C][C]1.21[/C][C]1.21310106648530[/C][C]-0.00310106648529729[/C][/ROW]
[ROW][C]43[/C][C]1.21[/C][C]1.21292099845195[/C][C]-0.0029209984519456[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]1.21274375192097[/C][C]-0.0127437519209728[/C][/ROW]
[ROW][C]45[/C][C]1.21[/C][C]1.20395756132932[/C][C]0.00604243867067855[/C][/ROW]
[ROW][C]46[/C][C]1.22[/C][C]1.21073551270882[/C][C]0.00926448729118379[/C][/ROW]
[ROW][C]47[/C][C]1.22[/C][C]1.22058716651414[/C][C]-0.000587166514138149[/C][/ROW]
[ROW][C]48[/C][C]1.23[/C][C]1.22240367768634[/C][C]0.0075963223136597[/C][/ROW]
[ROW][C]49[/C][C]1.22[/C][C]1.23124401862602[/C][C]-0.0112440186260154[/C][/ROW]
[ROW][C]50[/C][C]1.23[/C][C]1.22422064169559[/C][C]0.00577935830440524[/C][/ROW]
[ROW][C]51[/C][C]1.23[/C][C]1.2313158682269[/C][C]-0.00131586822689966[/C][/ROW]
[ROW][C]52[/C][C]1.23[/C][C]1.23258025660149[/C][C]-0.00258025660148942[/C][/ROW]
[ROW][C]53[/C][C]1.23[/C][C]1.23269031974045[/C][C]-0.00269031974044998[/C][/ROW]
[ROW][C]54[/C][C]1.23[/C][C]1.23257875298045[/C][C]-0.00257875298044796[/C][/ROW]
[ROW][C]55[/C][C]1.22[/C][C]1.23243115326320[/C][C]-0.0124311532631978[/C][/ROW]
[ROW][C]56[/C][C]1.22[/C][C]1.22366587483675[/C][C]-0.00366587483675307[/C][/ROW]
[ROW][C]57[/C][C]1.23[/C][C]1.22184389357137[/C][C]0.0081561064286313[/C][/ROW]
[ROW][C]58[/C][C]1.24[/C][C]1.23003019491751[/C][C]0.00996980508248768[/C][/ROW]
[ROW][C]59[/C][C]1.24[/C][C]1.24018030779616[/C][C]-0.000180307796157742[/C][/ROW]
[ROW][C]60[/C][C]1.25[/C][C]1.24207271491304[/C][C]0.00792728508695784[/C][/ROW]
[ROW][C]61[/C][C]1.25[/C][C]1.25094353344598[/C][C]-0.000943533445983968[/C][/ROW]
[ROW][C]62[/C][C]1.25[/C][C]1.25255881033374[/C][C]-0.00255881033374128[/C][/ROW]
[ROW][C]63[/C][C]1.26[/C][C]1.25273565379473[/C][C]0.00726434620526839[/C][/ROW]
[ROW][C]64[/C][C]1.26[/C][C]1.26125254978846[/C][C]-0.00125254978846168[/C][/ROW]
[ROW][C]65[/C][C]1.26[/C][C]1.26278634888434[/C][C]-0.00278634888433982[/C][/ROW]
[ROW][C]66[/C][C]1.26[/C][C]1.26293675072165[/C][C]-0.00293675072164734[/C][/ROW]
[ROW][C]67[/C][C]1.27[/C][C]1.26282063212713[/C][C]0.00717936787287399[/C][/ROW]
[ROW][C]68[/C][C]1.27[/C][C]1.27127847676588[/C][C]-0.00127847676588044[/C][/ROW]
[ROW][C]69[/C][C]1.29[/C][C]1.27279994080258[/C][C]0.0172000591974206[/C][/ROW]
[ROW][C]70[/C][C]1.31[/C][C]1.29018372670259[/C][C]0.0198162732974145[/C][/ROW]
[ROW][C]71[/C][C]1.32[/C][C]1.31066730212201[/C][C]0.00933269787798885[/C][/ROW]
[ROW][C]72[/C][C]1.32[/C][C]1.32308957326460[/C][C]-0.00308957326459747[/C][/ROW]
[ROW][C]73[/C][C]1.33[/C][C]1.32526464855567[/C][C]0.00473535144432624[/C][/ROW]
[ROW][C]74[/C][C]1.33[/C][C]1.33403158605679[/C][C]-0.004031586056785[/C][/ROW]
[ROW][C]75[/C][C]1.32[/C][C]1.33547567950961[/C][C]-0.0154756795096069[/C][/ROW]
[ROW][C]76[/C][C]1.32[/C][C]1.32685896233355[/C][C]-0.00685896233355199[/C][/ROW]
[ROW][C]77[/C][C]1.31[/C][C]1.32490792313519[/C][C]-0.0149079231351921[/C][/ROW]
[ROW][C]78[/C][C]1.3[/C][C]1.31568314295071[/C][C]-0.0156831429507140[/C][/ROW]
[ROW][C]79[/C][C]1.31[/C][C]1.30505779477464[/C][C]0.00494220522535782[/C][/ROW]
[ROW][C]80[/C][C]1.29[/C][C]1.31143718709501[/C][C]-0.0214371870950059[/C][/ROW]
[ROW][C]81[/C][C]1.3[/C][C]1.29532516598191[/C][C]0.00467483401808622[/C][/ROW]
[ROW][C]82[/C][C]1.3[/C][C]1.30066368664458[/C][C]-0.000663686644579942[/C][/ROW]
[ROW][C]83[/C][C]1.32[/C][C]1.30163106161860[/C][C]0.0183689383814016[/C][/ROW]
[ROW][C]84[/C][C]1.31[/C][C]1.31896848887255[/C][C]-0.0089684888725523[/C][/ROW]
[ROW][C]85[/C][C]1.35[/C][C]1.31364854517407[/C][C]0.0363514548259252[/C][/ROW]
[ROW][C]86[/C][C]1.35[/C][C]1.34694550230618[/C][C]0.00305449769381649[/C][/ROW]
[ROW][C]87[/C][C]1.36[/C][C]1.35333259084153[/C][C]0.00666740915847086[/C][/ROW]
[ROW][C]88[/C][C]1.37[/C][C]1.36298342827713[/C][C]0.00701657172287273[/C][/ROW]
[ROW][C]89[/C][C]1.37[/C][C]1.37326276948991[/C][C]-0.00326276948990945[/C][/ROW]
[ROW][C]90[/C][C]1.37[/C][C]1.37502793269946[/C][C]-0.00502793269945823[/C][/ROW]
[ROW][C]91[/C][C]1.32[/C][C]1.37511153693983[/C][C]-0.0551115369398307[/C][/ROW]
[ROW][C]92[/C][C]1.32[/C][C]1.3317851465829[/C][C]-0.0117851465828993[/C][/ROW]
[ROW][C]93[/C][C]1.31[/C][C]1.32309043486348[/C][C]-0.0130904348634793[/C][/ROW]
[ROW][C]94[/C][C]1.31[/C][C]1.31269176361439[/C][C]-0.00269176361439039[/C][/ROW]
[ROW][C]95[/C][C]1.34[/C][C]1.31061168464157[/C][C]0.0293883153584322[/C][/ROW]
[ROW][C]96[/C][C]1.31[/C][C]1.33604654488336[/C][C]-0.0260465448833587[/C][/ROW]
[ROW][C]97[/C][C]1.26[/C][C]1.31515057123636[/C][C]-0.0551505712363562[/C][/ROW]
[ROW][C]98[/C][C]1.27[/C][C]1.26789247799249[/C][C]0.00210752200751174[/C][/ROW]
[ROW][C]99[/C][C]1.24[/C][C]1.26727073951667[/C][C]-0.0272707395166698[/C][/ROW]
[ROW][C]100[/C][C]1.25[/C][C]1.24143383157717[/C][C]0.00856616842283353[/C][/ROW]
[ROW][C]101[/C][C]1.27[/C][C]1.24514192974929[/C][C]0.0248580702507060[/C][/ROW]
[ROW][C]102[/C][C]1.25[/C][C]1.26331156483824[/C][C]-0.0133115648382387[/C][/ROW]
[ROW][C]103[/C][C]1.26[/C][C]1.24980726584827[/C][C]0.0101927341517285[/C][/ROW]
[ROW][C]104[/C][C]1.27[/C][C]1.25590536184638[/C][C]0.0140946381536176[/C][/ROW]
[ROW][C]105[/C][C]1.26[/C][C]1.26586698801315[/C][C]-0.00586698801315366[/C][/ROW]
[ROW][C]106[/C][C]1.26[/C][C]1.25931783348968[/C][C]0.000682166510322268[/C][/ROW]
[ROW][C]107[/C][C]1.28[/C][C]1.25812459686964[/C][C]0.0218754031303605[/C][/ROW]
[ROW][C]108[/C][C]1.27[/C][C]1.27522959242229[/C][C]-0.00522959242229382[/C][/ROW]
[ROW][C]109[/C][C]1.28[/C][C]1.27004981365884[/C][C]0.00995018634116263[/C][/ROW]
[ROW][C]110[/C][C]1.27[/C][C]1.27769529825518[/C][C]-0.00769529825518456[/C][/ROW]
[ROW][C]111[/C][C]1.26[/C][C]1.27062246599717[/C][C]-0.0106224659971661[/C][/ROW]
[ROW][C]112[/C][C]1.27[/C][C]1.26064885521200[/C][C]0.00935114478799615[/C][/ROW]
[ROW][C]113[/C][C]1.27[/C][C]1.26736695156818[/C][C]0.00263304843181733[/C][/ROW]
[ROW][C]114[/C][C]1.28[/C][C]1.26875471961704[/C][C]0.0112452803829648[/C][/ROW]
[ROW][C]115[/C][C]1.27[/C][C]1.27769403452734[/C][C]-0.00769403452733597[/C][/ROW]
[ROW][C]116[/C][C]1.26[/C][C]1.27086361790069[/C][C]-0.0108636179006871[/C][/ROW]
[ROW][C]117[/C][C]1.3[/C][C]1.26092356665368[/C][C]0.0390764333463214[/C][/ROW]
[ROW][C]118[/C][C]1.31[/C][C]1.29348899545533[/C][C]0.0165110045446704[/C][/ROW]
[ROW][C]119[/C][C]1.28[/C][C]1.30852706733619[/C][C]-0.0285270673361875[/C][/ROW]
[ROW][C]120[/C][C]1.29[/C][C]1.28556174630016[/C][C]0.00443825369984041[/C][/ROW]
[ROW][C]121[/C][C]1.31[/C][C]1.28960491871746[/C][C]0.0203950812825353[/C][/ROW]
[ROW][C]122[/C][C]1.29[/C][C]1.30761803857114[/C][C]-0.0176180385711400[/C][/ROW]
[ROW][C]123[/C][C]1.29[/C][C]1.29387283241798[/C][C]-0.00387283241798464[/C][/ROW]
[ROW][C]124[/C][C]1.32[/C][C]1.29110786865923[/C][C]0.0288921313407686[/C][/ROW]
[ROW][C]125[/C][C]1.3[/C][C]1.31639006089787[/C][C]-0.0163900608978749[/C][/ROW]
[ROW][C]126[/C][C]1.29[/C][C]1.30406679354518[/C][C]-0.0140667935451844[/C][/ROW]
[ROW][C]127[/C][C]1.31[/C][C]1.29294046886138[/C][C]0.0170595311386199[/C][/ROW]
[ROW][C]128[/C][C]1.29[/C][C]1.3079482324472[/C][C]-0.0179482324471998[/C][/ROW]
[ROW][C]129[/C][C]1.33[/C][C]1.29362385526266[/C][C]0.0363761447373399[/C][/ROW]
[ROW][C]130[/C][C]1.35[/C][C]1.3252353620033[/C][C]0.0247646379966995[/C][/ROW]
[ROW][C]131[/C][C]1.32[/C][C]1.34862712733485[/C][C]-0.0286271273348464[/C][/ROW]
[ROW][C]132[/C][C]1.33[/C][C]1.32722166031264[/C][C]0.0027783396873593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72271&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72271&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
3	1.15	1.16	-0.01
4	1.14	1.16138181879402	-0.0213818187940171
5	1.14	1.15246325089893	-0.0124632508989266
6	1.14	1.15018031564590	-0.0101803156458962
7	1.15	1.14925249969319	0.000747500306808258
8	1.14	1.15724228510931	-0.0172422851093077
9	1.14	1.14976487432649	-0.0097648743264922
10	1.15	1.14788446816925	0.00211553183075197
11	1.15	1.15576303471770	-0.00576303471769624
12	1.14	1.15695565206573	-0.0169556520657328
13	1.15	1.14821911435376	0.00178088564623979
14	1.17	1.15479698398989	0.0152030160101084
15	1.17	1.17302978886826	-0.00302978886826222
16	1.17	1.17629620038917	-0.00629620038917333
17	1.17	1.17659869695641	-0.00659869695640825
18	1.17	1.17633114591204	-0.00633114591203654
19	1.17	1.17596996208354	-0.00596996208354295
20	1.17	1.17560898545986	-0.00560898545986221
21	1.17	1.17526578347731	-0.00526578347731399
22	1.17	1.17494277345669	-0.00494277345669181
23	1.17	1.17463941610825	-0.0046394161082477
24	1.17	1.17435464482225	-0.00435464482225068
25	1.17	1.17408734661977	-0.00408734661977261
26	1.18	1.17383645452013	0.00616354547986719
27	1.19	1.18221914378864	0.00778085621135882
28	1.19	1.19229849362024	-0.00229849362024481
29	1.19	1.19407357315876	-0.00407357315876156
30	1.19	1.19420592532667	-0.00420592532666531
31	1.18	1.19402406753244	-0.0140240675324419
32	1.19	1.18517410756322	0.00482589243678144
33	1.19	1.19188034155046	-0.00188034155046157
34	1.2	1.19304413166044	0.0069558683395603
35	1.21	1.20173074093636	0.0082692590636435
36	1.21	1.21189101612616	-0.00189101612616405
37	1.2	1.21370127473161	-0.0137012747316145
38	1.21	1.20524032735438	0.00475967264562382
39	1.21	1.21201617557932	-0.00201617557932243
40	1.21	1.21318633154508	-0.00318633154507553
41	1.21	1.21324896534107	-0.00324896534107211
42	1.21	1.21310106648530	-0.00310106648529729
43	1.21	1.21292099845195	-0.0029209984519456
44	1.2	1.21274375192097	-0.0127437519209728
45	1.21	1.20395756132932	0.00604243867067855
46	1.22	1.21073551270882	0.00926448729118379
47	1.22	1.22058716651414	-0.000587166514138149
48	1.23	1.22240367768634	0.0075963223136597
49	1.22	1.23124401862602	-0.0112440186260154
50	1.23	1.22422064169559	0.00577935830440524
51	1.23	1.2313158682269	-0.00131586822689966
52	1.23	1.23258025660149	-0.00258025660148942
53	1.23	1.23269031974045	-0.00269031974044998
54	1.23	1.23257875298045	-0.00257875298044796
55	1.22	1.23243115326320	-0.0124311532631978
56	1.22	1.22366587483675	-0.00366587483675307
57	1.23	1.22184389357137	0.0081561064286313
58	1.24	1.23003019491751	0.00996980508248768
59	1.24	1.24018030779616	-0.000180307796157742
60	1.25	1.24207271491304	0.00792728508695784
61	1.25	1.25094353344598	-0.000943533445983968
62	1.25	1.25255881033374	-0.00255881033374128
63	1.26	1.25273565379473	0.00726434620526839
64	1.26	1.26125254978846	-0.00125254978846168
65	1.26	1.26278634888434	-0.00278634888433982
66	1.26	1.26293675072165	-0.00293675072164734
67	1.27	1.26282063212713	0.00717936787287399
68	1.27	1.27127847676588	-0.00127847676588044
69	1.29	1.27279994080258	0.0172000591974206
70	1.31	1.29018372670259	0.0198162732974145
71	1.32	1.31066730212201	0.00933269787798885
72	1.32	1.32308957326460	-0.00308957326459747
73	1.33	1.32526464855567	0.00473535144432624
74	1.33	1.33403158605679	-0.004031586056785
75	1.32	1.33547567950961	-0.0154756795096069
76	1.32	1.32685896233355	-0.00685896233355199
77	1.31	1.32490792313519	-0.0149079231351921
78	1.3	1.31568314295071	-0.0156831429507140
79	1.31	1.30505779477464	0.00494220522535782
80	1.29	1.31143718709501	-0.0214371870950059
81	1.3	1.29532516598191	0.00467483401808622
82	1.3	1.30066368664458	-0.000663686644579942
83	1.32	1.30163106161860	0.0183689383814016
84	1.31	1.31896848887255	-0.0089684888725523
85	1.35	1.31364854517407	0.0363514548259252
86	1.35	1.34694550230618	0.00305449769381649
87	1.36	1.35333259084153	0.00666740915847086
88	1.37	1.36298342827713	0.00701657172287273
89	1.37	1.37326276948991	-0.00326276948990945
90	1.37	1.37502793269946	-0.00502793269945823
91	1.32	1.37511153693983	-0.0551115369398307
92	1.32	1.3317851465829	-0.0117851465828993
93	1.31	1.32309043486348	-0.0130904348634793
94	1.31	1.31269176361439	-0.00269176361439039
95	1.34	1.31061168464157	0.0293883153584322
96	1.31	1.33604654488336	-0.0260465448833587
97	1.26	1.31515057123636	-0.0551505712363562
98	1.27	1.26789247799249	0.00210752200751174
99	1.24	1.26727073951667	-0.0272707395166698
100	1.25	1.24143383157717	0.00856616842283353
101	1.27	1.24514192974929	0.0248580702507060
102	1.25	1.26331156483824	-0.0133115648382387
103	1.26	1.24980726584827	0.0101927341517285
104	1.27	1.25590536184638	0.0140946381536176
105	1.26	1.26586698801315	-0.00586698801315366
106	1.26	1.25931783348968	0.000682166510322268
107	1.28	1.25812459686964	0.0218754031303605
108	1.27	1.27522959242229	-0.00522959242229382
109	1.28	1.27004981365884	0.00995018634116263
110	1.27	1.27769529825518	-0.00769529825518456
111	1.26	1.27062246599717	-0.0106224659971661
112	1.27	1.26064885521200	0.00935114478799615
113	1.27	1.26736695156818	0.00263304843181733
114	1.28	1.26875471961704	0.0112452803829648
115	1.27	1.27769403452734	-0.00769403452733597
116	1.26	1.27086361790069	-0.0108636179006871
117	1.3	1.26092356665368	0.0390764333463214
118	1.31	1.29348899545533	0.0165110045446704
119	1.28	1.30852706733619	-0.0285270673361875
120	1.29	1.28556174630016	0.00443825369984041
121	1.31	1.28960491871746	0.0203950812825353
122	1.29	1.30761803857114	-0.0176180385711400
123	1.29	1.29387283241798	-0.00387283241798464
124	1.32	1.29110786865923	0.0288921313407686
125	1.3	1.31639006089787	-0.0163900608978749
126	1.29	1.30406679354518	-0.0140667935451844
127	1.31	1.29294046886138	0.0170595311386199
128	1.29	1.3079482324472	-0.0179482324471998
129	1.33	1.29362385526266	0.0363761447373399
130	1.35	1.3252353620033	0.0247646379966995
131	1.32	1.34862712733485	-0.0286271273348464
132	1.33	1.32722166031264	0.0027783396873593

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
133	1.33147546005119	1.30325428527781	1.35969663482457
134	1.33347134418998	1.29621586095356	1.3707268274264
135	1.33546722832877	1.29020269643896	1.38073176021859
136	1.33746311246756	1.28470896185157	1.39021726308356
137	1.33945899660635	1.27950748315788	1.39941051005483
138	1.34145488074514	1.27447522880968	1.40843453268061
139	1.34345076488393	1.26953787638222	1.41736365338565
140	1.34544664902273	1.26464718641235	1.42624611163311
141	1.34744253316152	1.25977022715031	1.43511483917272
142	1.34943841730031	1.25488366813451	1.44399316646611
143	1.3514343014391	1.24997051254310	1.45289809033510
144	1.35343018557789	1.24501811015802	1.46184226099776

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 1.33147546005119 & 1.30325428527781 & 1.35969663482457 \tabularnewline
134 & 1.33347134418998 & 1.29621586095356 & 1.3707268274264 \tabularnewline
135 & 1.33546722832877 & 1.29020269643896 & 1.38073176021859 \tabularnewline
136 & 1.33746311246756 & 1.28470896185157 & 1.39021726308356 \tabularnewline
137 & 1.33945899660635 & 1.27950748315788 & 1.39941051005483 \tabularnewline
138 & 1.34145488074514 & 1.27447522880968 & 1.40843453268061 \tabularnewline
139 & 1.34345076488393 & 1.26953787638222 & 1.41736365338565 \tabularnewline
140 & 1.34544664902273 & 1.26464718641235 & 1.42624611163311 \tabularnewline
141 & 1.34744253316152 & 1.25977022715031 & 1.43511483917272 \tabularnewline
142 & 1.34943841730031 & 1.25488366813451 & 1.44399316646611 \tabularnewline
143 & 1.3514343014391 & 1.24997051254310 & 1.45289809033510 \tabularnewline
144 & 1.35343018557789 & 1.24501811015802 & 1.46184226099776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72271&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]1.33147546005119[/C][C]1.30325428527781[/C][C]1.35969663482457[/C][/ROW]
[ROW][C]134[/C][C]1.33347134418998[/C][C]1.29621586095356[/C][C]1.3707268274264[/C][/ROW]
[ROW][C]135[/C][C]1.33546722832877[/C][C]1.29020269643896[/C][C]1.38073176021859[/C][/ROW]
[ROW][C]136[/C][C]1.33746311246756[/C][C]1.28470896185157[/C][C]1.39021726308356[/C][/ROW]
[ROW][C]137[/C][C]1.33945899660635[/C][C]1.27950748315788[/C][C]1.39941051005483[/C][/ROW]
[ROW][C]138[/C][C]1.34145488074514[/C][C]1.27447522880968[/C][C]1.40843453268061[/C][/ROW]
[ROW][C]139[/C][C]1.34345076488393[/C][C]1.26953787638222[/C][C]1.41736365338565[/C][/ROW]
[ROW][C]140[/C][C]1.34544664902273[/C][C]1.26464718641235[/C][C]1.42624611163311[/C][/ROW]
[ROW][C]141[/C][C]1.34744253316152[/C][C]1.25977022715031[/C][C]1.43511483917272[/C][/ROW]
[ROW][C]142[/C][C]1.34943841730031[/C][C]1.25488366813451[/C][C]1.44399316646611[/C][/ROW]
[ROW][C]143[/C][C]1.3514343014391[/C][C]1.24997051254310[/C][C]1.45289809033510[/C][/ROW]
[ROW][C]144[/C][C]1.35343018557789[/C][C]1.24501811015802[/C][C]1.46184226099776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72271&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72271&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	1.33147546005119	1.30325428527781	1.35969663482457
134	1.33347134418998	1.29621586095356	1.3707268274264
135	1.33546722832877	1.29020269643896	1.38073176021859
136	1.33746311246756	1.28470896185157	1.39021726308356
137	1.33945899660635	1.27950748315788	1.39941051005483
138	1.34145488074514	1.27447522880968	1.40843453268061
139	1.34345076488393	1.26953787638222	1.41736365338565
140	1.34544664902273	1.26464718641235	1.42624611163311
141	1.34744253316152	1.25977022715031	1.43511483917272
142	1.34943841730031	1.25488366813451	1.44399316646611
143	1.3514343014391	1.24997051254310	1.45289809033510
144	1.35343018557789	1.24501811015802	1.46184226099776

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 = Double ; par3 = additive ;

Parameters (R input):

par1 = 12 ; par2 = Double ; 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