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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationTue, 20 Dec 2011 12:42:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/20/t1324403008o6azmj6re58zl0a.htm/, Retrieved Mon, 06 May 2024 02:16:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158094, Retrieved Mon, 06 May 2024 02:16:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W102
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [consumptieprijzen...] [2011-12-20 17:42:23] [76c30f62b7052b57088120e90a652e05] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9,26
9,27
9,29
9,27
9,29
9,31
9,33
9,35
9,34
9,35
9,38
9,43
9,47
9,5
9,55
9,58
9,61
9,57
9,61
9,65
9,62
9,63
9,62
9,63
9,65
9,72
9,75
9,77
9,78
9,82
9,84
9,9
9,94
9,96
10,03
10,03
10,12
10,12
10,05
10,14
10,17
10,2
10,2
10,35
10,43
10,52
10,57
10,57
10,57
10,65
10,57
10,61
10,63
10,71
10,72
10,77
10,79
10,82
10,9
10,83
10,92
10,91
10,88
10,87
11
10,99
11,03
11,04
10,99
10,9
11
10,99
10,92
10,98
11,15
11,19
11,33
11,38
11,4
11,45
11,56
11,61
11,82
11,77
11,85
11,82
11,92
11,86
11,87
11,94
11,86
11,92
11,83
11,91
11,93
11,99
11,96
12,12
11,85
12,01
12,1
12,21
12,31
12,31
12,39
12,35
12,41
12,51
12,27
12,51
12,44
12,47
12,51
12,58
12,5
12,52
12,59
12,51
12,67
12,64
12,54
12,6
12,67
12,62
12,72
12,85
12,85
12,82
12,79
12,94
12,71
12,56

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158094&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158094&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158094&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.704886729433071
beta0.0365657311470088
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.704886729433071 \tabularnewline
beta & 0.0365657311470088 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158094&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]0.704886729433071[/C][/ROW]
[ROW][C]beta[/C][C]0.0365657311470088[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158094&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158094&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.704886729433071
beta0.0365657311470088
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
39.299.280.00999999999999979
49.279.29730661428071-0.0273066142807057
59.299.287612471480970.00238752851903357
69.319.298910873710720.0110891262892796
79.339.316628735621850.0133712643781543
89.359.33629988669630.0137001133036971
99.349.35655595530607-0.0165559553060657
109.359.35505819790932-0.00505819790932094
119.389.361534683593020.0184653164069815
129.439.385068520313550.0449314796864542
139.479.428416099657040.0415839003429621
149.59.470475827245090.0295241727549076
159.559.504795789551030.0452042104489703
169.589.551333527245990.028666472754006
179.619.586952902801170.0230470971988321
189.579.61920528708457-0.0492052870845683
199.619.599259673070720.0107403269292767
209.659.621845755552820.0281542444471761
219.629.65743234456673-0.0374323445667297
229.639.64582300995545-0.0158230099554473
239.629.6490379752304-0.0290379752303984
249.639.64218944179251-0.0121894417925059
259.659.646903036796640.00309696320336528
269.729.662471639116350.0575283608836514
279.759.717890987490280.0321090125097214
289.779.756220174645580.0137798253544243
299.789.7819845318596-0.00198453185959835
309.829.79658565216480.0234143478352049
319.849.829693603468570.0103063965314263
329.99.85382757811290.0461724218870998
339.949.904433118327460.0355668816725423
349.969.948479679642890.0115203203571106
3510.039.975873071783990.0541269282160091
3610.0310.0346944016539-0.00469440165393031
3710.1210.0519323599030.0680676400969684
3810.1210.1222137386993-0.00221373869926289
3910.0510.1428976478079-0.092897647807936
4010.1410.09726526393640.0427347360635952
4110.1710.14833982248070.0216601775193173
4210.210.18511748892640.0148825110735658
4310.210.2175012604759-0.0175012604759441
4410.3510.22660705149670.123392948503334
4510.4310.33820771674250.0917922832574778
4610.5210.42989941085350.0901005891464681
4710.5710.52272096776770.0472790322322538
4810.5710.5865767802871-0.0165767802871297
4910.5710.6049942164482-0.0349942164481511
5010.6510.60952748088610.040472519113921
5110.5710.6682994127186-0.0982994127186103
5210.6110.6267192136455-0.0167192136455299
5310.6310.6422128815904-0.0122128815904006
5410.7110.66056821985690.0494317801431361
5510.7210.723650151356-0.00365015135599478
5610.7710.74922125221750.0207787477824688
5710.7910.7925475258586-0.00254752585858675
5810.8210.81936575705170.000634242948338581
5910.910.84844312227370.0515568777262825
6010.8310.9147440399646-0.0847440399646029
6110.9210.88278399747420.0372160025257831
6210.9110.937751201706-0.0277512017059927
6310.8810.9462086069656-0.0662086069656205
6410.8710.9258513907176-0.0558513907175602
651110.91135528599240.0886447140076108
6610.9911.0009973587321-0.0109973587321228
6711.0311.02011960309730.00988039690271592
6811.0411.0542129646024-0.0142129646023754
6910.9911.0709569004357-0.0809569004357211
7010.911.0385672819186-0.138567281918649
711110.96199734008420.0380026599157919
7210.9911.0108687141724-0.0208687141723871
7310.9211.0177045531036-0.0977045531035738
7410.9810.96786152341550.0121384765844521
7511.1510.99575825325120.154241746748781
7611.1911.12779722697450.0622027730254526
7711.3311.19656240725880.133437592741188
7811.3811.31897938037410.0610206196259213
7911.411.39192337823620.00807662176378265
8011.4511.42775602709420.0222439729058266
8111.5611.47414838546150.0858516145384787
8211.6111.56758972579850.0424102742014618
8311.8211.63150295385990.188497046140117
8411.7711.8032492633648-0.0332492633648318
8511.8511.81783255225580.0321674477441665
8611.8211.8793563189623-0.0593563189622692
8711.9211.87483630585610.0451636941438736
8811.8611.9451551435546-0.085155143554589
8911.8711.9214191138017-0.0514191138017068
9011.9411.92013785156290.019862148437058
9111.8611.9696137460238-0.109613746023781
9211.9211.9249985394274-0.00499853942740103
9311.8311.9539963678103-0.12399636781034
9411.9111.89591823712230.0140817628776713
9511.9311.9355325015757-0.00553250157568108
9611.9911.96117833274740.0288216672525508
9711.9612.0117828314146-0.0517828314146076
9812.1212.00423560176440.115764398235628
9911.8512.1177739833253-0.267773983325339
10012.0111.95405945577260.0559405442274414
10112.112.01996885348550.0800311465144876
10212.2112.10492217573770.105077824262318
10312.3112.21023891800450.0997610819954655
10412.3112.3143792710279-0.00437927102785629
10512.3912.34499959681560.0450004031844315
10612.3512.4115868714899-0.0615868714898973
10712.4112.40145480766720.00854519233282858
10812.5112.4409781546980.0690218453019824
10912.2712.5249097091094-0.254909709109416
11012.5112.37393598863950.136064011360487
11112.4412.5020614641742-0.0620614641741515
11212.4712.4889313057057-0.0189313057057117
11312.5112.50571507488060.0042849251193573
11412.5812.53897409972530.0410259002747466
11512.512.5991887805992-0.0991887805992437
11612.5212.5580114327253-0.0380114327252965
11712.5912.55897745228630.0310225477136719
11812.5112.609404205357-0.0994042053570201
11912.6712.56533275758880.104667242411244
12012.6412.6678063318389-0.0278063318389048
12112.5412.6761843417762-0.136184341776199
12212.612.6046580203779-0.00465802037789054
12312.6712.62572279843290.0442772015670876
12412.6212.6824225965657-0.0624225965657175
12512.7212.66230219934690.0576978006531359
12612.8512.72834021948570.121659780514303
12712.8512.8425999345960.00740006540400451
12812.8212.8765102272719-0.0565102272719287
12912.7912.8639144686885-0.0739144686885407
13012.9412.83714556813930.102854431860669
13112.7112.9376297617485-0.227629761748476
13212.5612.7992929444851-0.239292944485145

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 9.29 & 9.28 & 0.00999999999999979 \tabularnewline
4 & 9.27 & 9.29730661428071 & -0.0273066142807057 \tabularnewline
5 & 9.29 & 9.28761247148097 & 0.00238752851903357 \tabularnewline
6 & 9.31 & 9.29891087371072 & 0.0110891262892796 \tabularnewline
7 & 9.33 & 9.31662873562185 & 0.0133712643781543 \tabularnewline
8 & 9.35 & 9.3362998866963 & 0.0137001133036971 \tabularnewline
9 & 9.34 & 9.35655595530607 & -0.0165559553060657 \tabularnewline
10 & 9.35 & 9.35505819790932 & -0.00505819790932094 \tabularnewline
11 & 9.38 & 9.36153468359302 & 0.0184653164069815 \tabularnewline
12 & 9.43 & 9.38506852031355 & 0.0449314796864542 \tabularnewline
13 & 9.47 & 9.42841609965704 & 0.0415839003429621 \tabularnewline
14 & 9.5 & 9.47047582724509 & 0.0295241727549076 \tabularnewline
15 & 9.55 & 9.50479578955103 & 0.0452042104489703 \tabularnewline
16 & 9.58 & 9.55133352724599 & 0.028666472754006 \tabularnewline
17 & 9.61 & 9.58695290280117 & 0.0230470971988321 \tabularnewline
18 & 9.57 & 9.61920528708457 & -0.0492052870845683 \tabularnewline
19 & 9.61 & 9.59925967307072 & 0.0107403269292767 \tabularnewline
20 & 9.65 & 9.62184575555282 & 0.0281542444471761 \tabularnewline
21 & 9.62 & 9.65743234456673 & -0.0374323445667297 \tabularnewline
22 & 9.63 & 9.64582300995545 & -0.0158230099554473 \tabularnewline
23 & 9.62 & 9.6490379752304 & -0.0290379752303984 \tabularnewline
24 & 9.63 & 9.64218944179251 & -0.0121894417925059 \tabularnewline
25 & 9.65 & 9.64690303679664 & 0.00309696320336528 \tabularnewline
26 & 9.72 & 9.66247163911635 & 0.0575283608836514 \tabularnewline
27 & 9.75 & 9.71789098749028 & 0.0321090125097214 \tabularnewline
28 & 9.77 & 9.75622017464558 & 0.0137798253544243 \tabularnewline
29 & 9.78 & 9.7819845318596 & -0.00198453185959835 \tabularnewline
30 & 9.82 & 9.7965856521648 & 0.0234143478352049 \tabularnewline
31 & 9.84 & 9.82969360346857 & 0.0103063965314263 \tabularnewline
32 & 9.9 & 9.8538275781129 & 0.0461724218870998 \tabularnewline
33 & 9.94 & 9.90443311832746 & 0.0355668816725423 \tabularnewline
34 & 9.96 & 9.94847967964289 & 0.0115203203571106 \tabularnewline
35 & 10.03 & 9.97587307178399 & 0.0541269282160091 \tabularnewline
36 & 10.03 & 10.0346944016539 & -0.00469440165393031 \tabularnewline
37 & 10.12 & 10.051932359903 & 0.0680676400969684 \tabularnewline
38 & 10.12 & 10.1222137386993 & -0.00221373869926289 \tabularnewline
39 & 10.05 & 10.1428976478079 & -0.092897647807936 \tabularnewline
40 & 10.14 & 10.0972652639364 & 0.0427347360635952 \tabularnewline
41 & 10.17 & 10.1483398224807 & 0.0216601775193173 \tabularnewline
42 & 10.2 & 10.1851174889264 & 0.0148825110735658 \tabularnewline
43 & 10.2 & 10.2175012604759 & -0.0175012604759441 \tabularnewline
44 & 10.35 & 10.2266070514967 & 0.123392948503334 \tabularnewline
45 & 10.43 & 10.3382077167425 & 0.0917922832574778 \tabularnewline
46 & 10.52 & 10.4298994108535 & 0.0901005891464681 \tabularnewline
47 & 10.57 & 10.5227209677677 & 0.0472790322322538 \tabularnewline
48 & 10.57 & 10.5865767802871 & -0.0165767802871297 \tabularnewline
49 & 10.57 & 10.6049942164482 & -0.0349942164481511 \tabularnewline
50 & 10.65 & 10.6095274808861 & 0.040472519113921 \tabularnewline
51 & 10.57 & 10.6682994127186 & -0.0982994127186103 \tabularnewline
52 & 10.61 & 10.6267192136455 & -0.0167192136455299 \tabularnewline
53 & 10.63 & 10.6422128815904 & -0.0122128815904006 \tabularnewline
54 & 10.71 & 10.6605682198569 & 0.0494317801431361 \tabularnewline
55 & 10.72 & 10.723650151356 & -0.00365015135599478 \tabularnewline
56 & 10.77 & 10.7492212522175 & 0.0207787477824688 \tabularnewline
57 & 10.79 & 10.7925475258586 & -0.00254752585858675 \tabularnewline
58 & 10.82 & 10.8193657570517 & 0.000634242948338581 \tabularnewline
59 & 10.9 & 10.8484431222737 & 0.0515568777262825 \tabularnewline
60 & 10.83 & 10.9147440399646 & -0.0847440399646029 \tabularnewline
61 & 10.92 & 10.8827839974742 & 0.0372160025257831 \tabularnewline
62 & 10.91 & 10.937751201706 & -0.0277512017059927 \tabularnewline
63 & 10.88 & 10.9462086069656 & -0.0662086069656205 \tabularnewline
64 & 10.87 & 10.9258513907176 & -0.0558513907175602 \tabularnewline
65 & 11 & 10.9113552859924 & 0.0886447140076108 \tabularnewline
66 & 10.99 & 11.0009973587321 & -0.0109973587321228 \tabularnewline
67 & 11.03 & 11.0201196030973 & 0.00988039690271592 \tabularnewline
68 & 11.04 & 11.0542129646024 & -0.0142129646023754 \tabularnewline
69 & 10.99 & 11.0709569004357 & -0.0809569004357211 \tabularnewline
70 & 10.9 & 11.0385672819186 & -0.138567281918649 \tabularnewline
71 & 11 & 10.9619973400842 & 0.0380026599157919 \tabularnewline
72 & 10.99 & 11.0108687141724 & -0.0208687141723871 \tabularnewline
73 & 10.92 & 11.0177045531036 & -0.0977045531035738 \tabularnewline
74 & 10.98 & 10.9678615234155 & 0.0121384765844521 \tabularnewline
75 & 11.15 & 10.9957582532512 & 0.154241746748781 \tabularnewline
76 & 11.19 & 11.1277972269745 & 0.0622027730254526 \tabularnewline
77 & 11.33 & 11.1965624072588 & 0.133437592741188 \tabularnewline
78 & 11.38 & 11.3189793803741 & 0.0610206196259213 \tabularnewline
79 & 11.4 & 11.3919233782362 & 0.00807662176378265 \tabularnewline
80 & 11.45 & 11.4277560270942 & 0.0222439729058266 \tabularnewline
81 & 11.56 & 11.4741483854615 & 0.0858516145384787 \tabularnewline
82 & 11.61 & 11.5675897257985 & 0.0424102742014618 \tabularnewline
83 & 11.82 & 11.6315029538599 & 0.188497046140117 \tabularnewline
84 & 11.77 & 11.8032492633648 & -0.0332492633648318 \tabularnewline
85 & 11.85 & 11.8178325522558 & 0.0321674477441665 \tabularnewline
86 & 11.82 & 11.8793563189623 & -0.0593563189622692 \tabularnewline
87 & 11.92 & 11.8748363058561 & 0.0451636941438736 \tabularnewline
88 & 11.86 & 11.9451551435546 & -0.085155143554589 \tabularnewline
89 & 11.87 & 11.9214191138017 & -0.0514191138017068 \tabularnewline
90 & 11.94 & 11.9201378515629 & 0.019862148437058 \tabularnewline
91 & 11.86 & 11.9696137460238 & -0.109613746023781 \tabularnewline
92 & 11.92 & 11.9249985394274 & -0.00499853942740103 \tabularnewline
93 & 11.83 & 11.9539963678103 & -0.12399636781034 \tabularnewline
94 & 11.91 & 11.8959182371223 & 0.0140817628776713 \tabularnewline
95 & 11.93 & 11.9355325015757 & -0.00553250157568108 \tabularnewline
96 & 11.99 & 11.9611783327474 & 0.0288216672525508 \tabularnewline
97 & 11.96 & 12.0117828314146 & -0.0517828314146076 \tabularnewline
98 & 12.12 & 12.0042356017644 & 0.115764398235628 \tabularnewline
99 & 11.85 & 12.1177739833253 & -0.267773983325339 \tabularnewline
100 & 12.01 & 11.9540594557726 & 0.0559405442274414 \tabularnewline
101 & 12.1 & 12.0199688534855 & 0.0800311465144876 \tabularnewline
102 & 12.21 & 12.1049221757377 & 0.105077824262318 \tabularnewline
103 & 12.31 & 12.2102389180045 & 0.0997610819954655 \tabularnewline
104 & 12.31 & 12.3143792710279 & -0.00437927102785629 \tabularnewline
105 & 12.39 & 12.3449995968156 & 0.0450004031844315 \tabularnewline
106 & 12.35 & 12.4115868714899 & -0.0615868714898973 \tabularnewline
107 & 12.41 & 12.4014548076672 & 0.00854519233282858 \tabularnewline
108 & 12.51 & 12.440978154698 & 0.0690218453019824 \tabularnewline
109 & 12.27 & 12.5249097091094 & -0.254909709109416 \tabularnewline
110 & 12.51 & 12.3739359886395 & 0.136064011360487 \tabularnewline
111 & 12.44 & 12.5020614641742 & -0.0620614641741515 \tabularnewline
112 & 12.47 & 12.4889313057057 & -0.0189313057057117 \tabularnewline
113 & 12.51 & 12.5057150748806 & 0.0042849251193573 \tabularnewline
114 & 12.58 & 12.5389740997253 & 0.0410259002747466 \tabularnewline
115 & 12.5 & 12.5991887805992 & -0.0991887805992437 \tabularnewline
116 & 12.52 & 12.5580114327253 & -0.0380114327252965 \tabularnewline
117 & 12.59 & 12.5589774522863 & 0.0310225477136719 \tabularnewline
118 & 12.51 & 12.609404205357 & -0.0994042053570201 \tabularnewline
119 & 12.67 & 12.5653327575888 & 0.104667242411244 \tabularnewline
120 & 12.64 & 12.6678063318389 & -0.0278063318389048 \tabularnewline
121 & 12.54 & 12.6761843417762 & -0.136184341776199 \tabularnewline
122 & 12.6 & 12.6046580203779 & -0.00465802037789054 \tabularnewline
123 & 12.67 & 12.6257227984329 & 0.0442772015670876 \tabularnewline
124 & 12.62 & 12.6824225965657 & -0.0624225965657175 \tabularnewline
125 & 12.72 & 12.6623021993469 & 0.0576978006531359 \tabularnewline
126 & 12.85 & 12.7283402194857 & 0.121659780514303 \tabularnewline
127 & 12.85 & 12.842599934596 & 0.00740006540400451 \tabularnewline
128 & 12.82 & 12.8765102272719 & -0.0565102272719287 \tabularnewline
129 & 12.79 & 12.8639144686885 & -0.0739144686885407 \tabularnewline
130 & 12.94 & 12.8371455681393 & 0.102854431860669 \tabularnewline
131 & 12.71 & 12.9376297617485 & -0.227629761748476 \tabularnewline
132 & 12.56 & 12.7992929444851 & -0.239292944485145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158094&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]9.29[/C][C]9.28[/C][C]0.00999999999999979[/C][/ROW]
[ROW][C]4[/C][C]9.27[/C][C]9.29730661428071[/C][C]-0.0273066142807057[/C][/ROW]
[ROW][C]5[/C][C]9.29[/C][C]9.28761247148097[/C][C]0.00238752851903357[/C][/ROW]
[ROW][C]6[/C][C]9.31[/C][C]9.29891087371072[/C][C]0.0110891262892796[/C][/ROW]
[ROW][C]7[/C][C]9.33[/C][C]9.31662873562185[/C][C]0.0133712643781543[/C][/ROW]
[ROW][C]8[/C][C]9.35[/C][C]9.3362998866963[/C][C]0.0137001133036971[/C][/ROW]
[ROW][C]9[/C][C]9.34[/C][C]9.35655595530607[/C][C]-0.0165559553060657[/C][/ROW]
[ROW][C]10[/C][C]9.35[/C][C]9.35505819790932[/C][C]-0.00505819790932094[/C][/ROW]
[ROW][C]11[/C][C]9.38[/C][C]9.36153468359302[/C][C]0.0184653164069815[/C][/ROW]
[ROW][C]12[/C][C]9.43[/C][C]9.38506852031355[/C][C]0.0449314796864542[/C][/ROW]
[ROW][C]13[/C][C]9.47[/C][C]9.42841609965704[/C][C]0.0415839003429621[/C][/ROW]
[ROW][C]14[/C][C]9.5[/C][C]9.47047582724509[/C][C]0.0295241727549076[/C][/ROW]
[ROW][C]15[/C][C]9.55[/C][C]9.50479578955103[/C][C]0.0452042104489703[/C][/ROW]
[ROW][C]16[/C][C]9.58[/C][C]9.55133352724599[/C][C]0.028666472754006[/C][/ROW]
[ROW][C]17[/C][C]9.61[/C][C]9.58695290280117[/C][C]0.0230470971988321[/C][/ROW]
[ROW][C]18[/C][C]9.57[/C][C]9.61920528708457[/C][C]-0.0492052870845683[/C][/ROW]
[ROW][C]19[/C][C]9.61[/C][C]9.59925967307072[/C][C]0.0107403269292767[/C][/ROW]
[ROW][C]20[/C][C]9.65[/C][C]9.62184575555282[/C][C]0.0281542444471761[/C][/ROW]
[ROW][C]21[/C][C]9.62[/C][C]9.65743234456673[/C][C]-0.0374323445667297[/C][/ROW]
[ROW][C]22[/C][C]9.63[/C][C]9.64582300995545[/C][C]-0.0158230099554473[/C][/ROW]
[ROW][C]23[/C][C]9.62[/C][C]9.6490379752304[/C][C]-0.0290379752303984[/C][/ROW]
[ROW][C]24[/C][C]9.63[/C][C]9.64218944179251[/C][C]-0.0121894417925059[/C][/ROW]
[ROW][C]25[/C][C]9.65[/C][C]9.64690303679664[/C][C]0.00309696320336528[/C][/ROW]
[ROW][C]26[/C][C]9.72[/C][C]9.66247163911635[/C][C]0.0575283608836514[/C][/ROW]
[ROW][C]27[/C][C]9.75[/C][C]9.71789098749028[/C][C]0.0321090125097214[/C][/ROW]
[ROW][C]28[/C][C]9.77[/C][C]9.75622017464558[/C][C]0.0137798253544243[/C][/ROW]
[ROW][C]29[/C][C]9.78[/C][C]9.7819845318596[/C][C]-0.00198453185959835[/C][/ROW]
[ROW][C]30[/C][C]9.82[/C][C]9.7965856521648[/C][C]0.0234143478352049[/C][/ROW]
[ROW][C]31[/C][C]9.84[/C][C]9.82969360346857[/C][C]0.0103063965314263[/C][/ROW]
[ROW][C]32[/C][C]9.9[/C][C]9.8538275781129[/C][C]0.0461724218870998[/C][/ROW]
[ROW][C]33[/C][C]9.94[/C][C]9.90443311832746[/C][C]0.0355668816725423[/C][/ROW]
[ROW][C]34[/C][C]9.96[/C][C]9.94847967964289[/C][C]0.0115203203571106[/C][/ROW]
[ROW][C]35[/C][C]10.03[/C][C]9.97587307178399[/C][C]0.0541269282160091[/C][/ROW]
[ROW][C]36[/C][C]10.03[/C][C]10.0346944016539[/C][C]-0.00469440165393031[/C][/ROW]
[ROW][C]37[/C][C]10.12[/C][C]10.051932359903[/C][C]0.0680676400969684[/C][/ROW]
[ROW][C]38[/C][C]10.12[/C][C]10.1222137386993[/C][C]-0.00221373869926289[/C][/ROW]
[ROW][C]39[/C][C]10.05[/C][C]10.1428976478079[/C][C]-0.092897647807936[/C][/ROW]
[ROW][C]40[/C][C]10.14[/C][C]10.0972652639364[/C][C]0.0427347360635952[/C][/ROW]
[ROW][C]41[/C][C]10.17[/C][C]10.1483398224807[/C][C]0.0216601775193173[/C][/ROW]
[ROW][C]42[/C][C]10.2[/C][C]10.1851174889264[/C][C]0.0148825110735658[/C][/ROW]
[ROW][C]43[/C][C]10.2[/C][C]10.2175012604759[/C][C]-0.0175012604759441[/C][/ROW]
[ROW][C]44[/C][C]10.35[/C][C]10.2266070514967[/C][C]0.123392948503334[/C][/ROW]
[ROW][C]45[/C][C]10.43[/C][C]10.3382077167425[/C][C]0.0917922832574778[/C][/ROW]
[ROW][C]46[/C][C]10.52[/C][C]10.4298994108535[/C][C]0.0901005891464681[/C][/ROW]
[ROW][C]47[/C][C]10.57[/C][C]10.5227209677677[/C][C]0.0472790322322538[/C][/ROW]
[ROW][C]48[/C][C]10.57[/C][C]10.5865767802871[/C][C]-0.0165767802871297[/C][/ROW]
[ROW][C]49[/C][C]10.57[/C][C]10.6049942164482[/C][C]-0.0349942164481511[/C][/ROW]
[ROW][C]50[/C][C]10.65[/C][C]10.6095274808861[/C][C]0.040472519113921[/C][/ROW]
[ROW][C]51[/C][C]10.57[/C][C]10.6682994127186[/C][C]-0.0982994127186103[/C][/ROW]
[ROW][C]52[/C][C]10.61[/C][C]10.6267192136455[/C][C]-0.0167192136455299[/C][/ROW]
[ROW][C]53[/C][C]10.63[/C][C]10.6422128815904[/C][C]-0.0122128815904006[/C][/ROW]
[ROW][C]54[/C][C]10.71[/C][C]10.6605682198569[/C][C]0.0494317801431361[/C][/ROW]
[ROW][C]55[/C][C]10.72[/C][C]10.723650151356[/C][C]-0.00365015135599478[/C][/ROW]
[ROW][C]56[/C][C]10.77[/C][C]10.7492212522175[/C][C]0.0207787477824688[/C][/ROW]
[ROW][C]57[/C][C]10.79[/C][C]10.7925475258586[/C][C]-0.00254752585858675[/C][/ROW]
[ROW][C]58[/C][C]10.82[/C][C]10.8193657570517[/C][C]0.000634242948338581[/C][/ROW]
[ROW][C]59[/C][C]10.9[/C][C]10.8484431222737[/C][C]0.0515568777262825[/C][/ROW]
[ROW][C]60[/C][C]10.83[/C][C]10.9147440399646[/C][C]-0.0847440399646029[/C][/ROW]
[ROW][C]61[/C][C]10.92[/C][C]10.8827839974742[/C][C]0.0372160025257831[/C][/ROW]
[ROW][C]62[/C][C]10.91[/C][C]10.937751201706[/C][C]-0.0277512017059927[/C][/ROW]
[ROW][C]63[/C][C]10.88[/C][C]10.9462086069656[/C][C]-0.0662086069656205[/C][/ROW]
[ROW][C]64[/C][C]10.87[/C][C]10.9258513907176[/C][C]-0.0558513907175602[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]10.9113552859924[/C][C]0.0886447140076108[/C][/ROW]
[ROW][C]66[/C][C]10.99[/C][C]11.0009973587321[/C][C]-0.0109973587321228[/C][/ROW]
[ROW][C]67[/C][C]11.03[/C][C]11.0201196030973[/C][C]0.00988039690271592[/C][/ROW]
[ROW][C]68[/C][C]11.04[/C][C]11.0542129646024[/C][C]-0.0142129646023754[/C][/ROW]
[ROW][C]69[/C][C]10.99[/C][C]11.0709569004357[/C][C]-0.0809569004357211[/C][/ROW]
[ROW][C]70[/C][C]10.9[/C][C]11.0385672819186[/C][C]-0.138567281918649[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.9619973400842[/C][C]0.0380026599157919[/C][/ROW]
[ROW][C]72[/C][C]10.99[/C][C]11.0108687141724[/C][C]-0.0208687141723871[/C][/ROW]
[ROW][C]73[/C][C]10.92[/C][C]11.0177045531036[/C][C]-0.0977045531035738[/C][/ROW]
[ROW][C]74[/C][C]10.98[/C][C]10.9678615234155[/C][C]0.0121384765844521[/C][/ROW]
[ROW][C]75[/C][C]11.15[/C][C]10.9957582532512[/C][C]0.154241746748781[/C][/ROW]
[ROW][C]76[/C][C]11.19[/C][C]11.1277972269745[/C][C]0.0622027730254526[/C][/ROW]
[ROW][C]77[/C][C]11.33[/C][C]11.1965624072588[/C][C]0.133437592741188[/C][/ROW]
[ROW][C]78[/C][C]11.38[/C][C]11.3189793803741[/C][C]0.0610206196259213[/C][/ROW]
[ROW][C]79[/C][C]11.4[/C][C]11.3919233782362[/C][C]0.00807662176378265[/C][/ROW]
[ROW][C]80[/C][C]11.45[/C][C]11.4277560270942[/C][C]0.0222439729058266[/C][/ROW]
[ROW][C]81[/C][C]11.56[/C][C]11.4741483854615[/C][C]0.0858516145384787[/C][/ROW]
[ROW][C]82[/C][C]11.61[/C][C]11.5675897257985[/C][C]0.0424102742014618[/C][/ROW]
[ROW][C]83[/C][C]11.82[/C][C]11.6315029538599[/C][C]0.188497046140117[/C][/ROW]
[ROW][C]84[/C][C]11.77[/C][C]11.8032492633648[/C][C]-0.0332492633648318[/C][/ROW]
[ROW][C]85[/C][C]11.85[/C][C]11.8178325522558[/C][C]0.0321674477441665[/C][/ROW]
[ROW][C]86[/C][C]11.82[/C][C]11.8793563189623[/C][C]-0.0593563189622692[/C][/ROW]
[ROW][C]87[/C][C]11.92[/C][C]11.8748363058561[/C][C]0.0451636941438736[/C][/ROW]
[ROW][C]88[/C][C]11.86[/C][C]11.9451551435546[/C][C]-0.085155143554589[/C][/ROW]
[ROW][C]89[/C][C]11.87[/C][C]11.9214191138017[/C][C]-0.0514191138017068[/C][/ROW]
[ROW][C]90[/C][C]11.94[/C][C]11.9201378515629[/C][C]0.019862148437058[/C][/ROW]
[ROW][C]91[/C][C]11.86[/C][C]11.9696137460238[/C][C]-0.109613746023781[/C][/ROW]
[ROW][C]92[/C][C]11.92[/C][C]11.9249985394274[/C][C]-0.00499853942740103[/C][/ROW]
[ROW][C]93[/C][C]11.83[/C][C]11.9539963678103[/C][C]-0.12399636781034[/C][/ROW]
[ROW][C]94[/C][C]11.91[/C][C]11.8959182371223[/C][C]0.0140817628776713[/C][/ROW]
[ROW][C]95[/C][C]11.93[/C][C]11.9355325015757[/C][C]-0.00553250157568108[/C][/ROW]
[ROW][C]96[/C][C]11.99[/C][C]11.9611783327474[/C][C]0.0288216672525508[/C][/ROW]
[ROW][C]97[/C][C]11.96[/C][C]12.0117828314146[/C][C]-0.0517828314146076[/C][/ROW]
[ROW][C]98[/C][C]12.12[/C][C]12.0042356017644[/C][C]0.115764398235628[/C][/ROW]
[ROW][C]99[/C][C]11.85[/C][C]12.1177739833253[/C][C]-0.267773983325339[/C][/ROW]
[ROW][C]100[/C][C]12.01[/C][C]11.9540594557726[/C][C]0.0559405442274414[/C][/ROW]
[ROW][C]101[/C][C]12.1[/C][C]12.0199688534855[/C][C]0.0800311465144876[/C][/ROW]
[ROW][C]102[/C][C]12.21[/C][C]12.1049221757377[/C][C]0.105077824262318[/C][/ROW]
[ROW][C]103[/C][C]12.31[/C][C]12.2102389180045[/C][C]0.0997610819954655[/C][/ROW]
[ROW][C]104[/C][C]12.31[/C][C]12.3143792710279[/C][C]-0.00437927102785629[/C][/ROW]
[ROW][C]105[/C][C]12.39[/C][C]12.3449995968156[/C][C]0.0450004031844315[/C][/ROW]
[ROW][C]106[/C][C]12.35[/C][C]12.4115868714899[/C][C]-0.0615868714898973[/C][/ROW]
[ROW][C]107[/C][C]12.41[/C][C]12.4014548076672[/C][C]0.00854519233282858[/C][/ROW]
[ROW][C]108[/C][C]12.51[/C][C]12.440978154698[/C][C]0.0690218453019824[/C][/ROW]
[ROW][C]109[/C][C]12.27[/C][C]12.5249097091094[/C][C]-0.254909709109416[/C][/ROW]
[ROW][C]110[/C][C]12.51[/C][C]12.3739359886395[/C][C]0.136064011360487[/C][/ROW]
[ROW][C]111[/C][C]12.44[/C][C]12.5020614641742[/C][C]-0.0620614641741515[/C][/ROW]
[ROW][C]112[/C][C]12.47[/C][C]12.4889313057057[/C][C]-0.0189313057057117[/C][/ROW]
[ROW][C]113[/C][C]12.51[/C][C]12.5057150748806[/C][C]0.0042849251193573[/C][/ROW]
[ROW][C]114[/C][C]12.58[/C][C]12.5389740997253[/C][C]0.0410259002747466[/C][/ROW]
[ROW][C]115[/C][C]12.5[/C][C]12.5991887805992[/C][C]-0.0991887805992437[/C][/ROW]
[ROW][C]116[/C][C]12.52[/C][C]12.5580114327253[/C][C]-0.0380114327252965[/C][/ROW]
[ROW][C]117[/C][C]12.59[/C][C]12.5589774522863[/C][C]0.0310225477136719[/C][/ROW]
[ROW][C]118[/C][C]12.51[/C][C]12.609404205357[/C][C]-0.0994042053570201[/C][/ROW]
[ROW][C]119[/C][C]12.67[/C][C]12.5653327575888[/C][C]0.104667242411244[/C][/ROW]
[ROW][C]120[/C][C]12.64[/C][C]12.6678063318389[/C][C]-0.0278063318389048[/C][/ROW]
[ROW][C]121[/C][C]12.54[/C][C]12.6761843417762[/C][C]-0.136184341776199[/C][/ROW]
[ROW][C]122[/C][C]12.6[/C][C]12.6046580203779[/C][C]-0.00465802037789054[/C][/ROW]
[ROW][C]123[/C][C]12.67[/C][C]12.6257227984329[/C][C]0.0442772015670876[/C][/ROW]
[ROW][C]124[/C][C]12.62[/C][C]12.6824225965657[/C][C]-0.0624225965657175[/C][/ROW]
[ROW][C]125[/C][C]12.72[/C][C]12.6623021993469[/C][C]0.0576978006531359[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]12.7283402194857[/C][C]0.121659780514303[/C][/ROW]
[ROW][C]127[/C][C]12.85[/C][C]12.842599934596[/C][C]0.00740006540400451[/C][/ROW]
[ROW][C]128[/C][C]12.82[/C][C]12.8765102272719[/C][C]-0.0565102272719287[/C][/ROW]
[ROW][C]129[/C][C]12.79[/C][C]12.8639144686885[/C][C]-0.0739144686885407[/C][/ROW]
[ROW][C]130[/C][C]12.94[/C][C]12.8371455681393[/C][C]0.102854431860669[/C][/ROW]
[ROW][C]131[/C][C]12.71[/C][C]12.9376297617485[/C][C]-0.227629761748476[/C][/ROW]
[ROW][C]132[/C][C]12.56[/C][C]12.7992929444851[/C][C]-0.239292944485145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158094&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158094&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
39.299.280.00999999999999979
49.279.29730661428071-0.0273066142807057
59.299.287612471480970.00238752851903357
69.319.298910873710720.0110891262892796
79.339.316628735621850.0133712643781543
89.359.33629988669630.0137001133036971
99.349.35655595530607-0.0165559553060657
109.359.35505819790932-0.00505819790932094
119.389.361534683593020.0184653164069815
129.439.385068520313550.0449314796864542
139.479.428416099657040.0415839003429621
149.59.470475827245090.0295241727549076
159.559.504795789551030.0452042104489703
169.589.551333527245990.028666472754006
179.619.586952902801170.0230470971988321
189.579.61920528708457-0.0492052870845683
199.619.599259673070720.0107403269292767
209.659.621845755552820.0281542444471761
219.629.65743234456673-0.0374323445667297
229.639.64582300995545-0.0158230099554473
239.629.6490379752304-0.0290379752303984
249.639.64218944179251-0.0121894417925059
259.659.646903036796640.00309696320336528
269.729.662471639116350.0575283608836514
279.759.717890987490280.0321090125097214
289.779.756220174645580.0137798253544243
299.789.7819845318596-0.00198453185959835
309.829.79658565216480.0234143478352049
319.849.829693603468570.0103063965314263
329.99.85382757811290.0461724218870998
339.949.904433118327460.0355668816725423
349.969.948479679642890.0115203203571106
3510.039.975873071783990.0541269282160091
3610.0310.0346944016539-0.00469440165393031
3710.1210.0519323599030.0680676400969684
3810.1210.1222137386993-0.00221373869926289
3910.0510.1428976478079-0.092897647807936
4010.1410.09726526393640.0427347360635952
4110.1710.14833982248070.0216601775193173
4210.210.18511748892640.0148825110735658
4310.210.2175012604759-0.0175012604759441
4410.3510.22660705149670.123392948503334
4510.4310.33820771674250.0917922832574778
4610.5210.42989941085350.0901005891464681
4710.5710.52272096776770.0472790322322538
4810.5710.5865767802871-0.0165767802871297
4910.5710.6049942164482-0.0349942164481511
5010.6510.60952748088610.040472519113921
5110.5710.6682994127186-0.0982994127186103
5210.6110.6267192136455-0.0167192136455299
5310.6310.6422128815904-0.0122128815904006
5410.7110.66056821985690.0494317801431361
5510.7210.723650151356-0.00365015135599478
5610.7710.74922125221750.0207787477824688
5710.7910.7925475258586-0.00254752585858675
5810.8210.81936575705170.000634242948338581
5910.910.84844312227370.0515568777262825
6010.8310.9147440399646-0.0847440399646029
6110.9210.88278399747420.0372160025257831
6210.9110.937751201706-0.0277512017059927
6310.8810.9462086069656-0.0662086069656205
6410.8710.9258513907176-0.0558513907175602
651110.91135528599240.0886447140076108
6610.9911.0009973587321-0.0109973587321228
6711.0311.02011960309730.00988039690271592
6811.0411.0542129646024-0.0142129646023754
6910.9911.0709569004357-0.0809569004357211
7010.911.0385672819186-0.138567281918649
711110.96199734008420.0380026599157919
7210.9911.0108687141724-0.0208687141723871
7310.9211.0177045531036-0.0977045531035738
7410.9810.96786152341550.0121384765844521
7511.1510.99575825325120.154241746748781
7611.1911.12779722697450.0622027730254526
7711.3311.19656240725880.133437592741188
7811.3811.31897938037410.0610206196259213
7911.411.39192337823620.00807662176378265
8011.4511.42775602709420.0222439729058266
8111.5611.47414838546150.0858516145384787
8211.6111.56758972579850.0424102742014618
8311.8211.63150295385990.188497046140117
8411.7711.8032492633648-0.0332492633648318
8511.8511.81783255225580.0321674477441665
8611.8211.8793563189623-0.0593563189622692
8711.9211.87483630585610.0451636941438736
8811.8611.9451551435546-0.085155143554589
8911.8711.9214191138017-0.0514191138017068
9011.9411.92013785156290.019862148437058
9111.8611.9696137460238-0.109613746023781
9211.9211.9249985394274-0.00499853942740103
9311.8311.9539963678103-0.12399636781034
9411.9111.89591823712230.0140817628776713
9511.9311.9355325015757-0.00553250157568108
9611.9911.96117833274740.0288216672525508
9711.9612.0117828314146-0.0517828314146076
9812.1212.00423560176440.115764398235628
9911.8512.1177739833253-0.267773983325339
10012.0111.95405945577260.0559405442274414
10112.112.01996885348550.0800311465144876
10212.2112.10492217573770.105077824262318
10312.3112.21023891800450.0997610819954655
10412.3112.3143792710279-0.00437927102785629
10512.3912.34499959681560.0450004031844315
10612.3512.4115868714899-0.0615868714898973
10712.4112.40145480766720.00854519233282858
10812.5112.4409781546980.0690218453019824
10912.2712.5249097091094-0.254909709109416
11012.5112.37393598863950.136064011360487
11112.4412.5020614641742-0.0620614641741515
11212.4712.4889313057057-0.0189313057057117
11312.5112.50571507488060.0042849251193573
11412.5812.53897409972530.0410259002747466
11512.512.5991887805992-0.0991887805992437
11612.5212.5580114327253-0.0380114327252965
11712.5912.55897745228630.0310225477136719
11812.5112.609404205357-0.0994042053570201
11912.6712.56533275758880.104667242411244
12012.6412.6678063318389-0.0278063318389048
12112.5412.6761843417762-0.136184341776199
12212.612.6046580203779-0.00465802037789054
12312.6712.62572279843290.0442772015670876
12412.6212.6824225965657-0.0624225965657175
12512.7212.66230219934690.0576978006531359
12612.8512.72834021948570.121659780514303
12712.8512.8425999345960.00740006540400451
12812.8212.8765102272719-0.0565102272719287
12912.7912.8639144686885-0.0739144686885407
13012.9412.83714556813930.102854431860669
13112.7112.9376297617485-0.227629761748476
13212.5612.7992929444851-0.239292944485145






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
13312.646567200957612.499871425545112.7932629763701
13412.662515878444612.480834115722112.8441976411671
13512.678464555931612.465575588284112.891353523579
13612.694413233418512.452569085335812.9362573815013
13712.710361910905512.441034055547812.9796897662633
13812.726310588392512.430511652976913.0221095238082
13912.742259265879512.420707195377513.0638113363815
14012.758207943366512.411419452002113.1049964347309
14112.774156620853512.402504668340113.1458085733669
14212.790105298340512.393856544708813.1863540519721
14312.806053975827412.385394311605113.2267136400498
14412.822002653314412.377055239149613.2669500674793

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 12.6465672009576 & 12.4998714255451 & 12.7932629763701 \tabularnewline
134 & 12.6625158784446 & 12.4808341157221 & 12.8441976411671 \tabularnewline
135 & 12.6784645559316 & 12.4655755882841 & 12.891353523579 \tabularnewline
136 & 12.6944132334185 & 12.4525690853358 & 12.9362573815013 \tabularnewline
137 & 12.7103619109055 & 12.4410340555478 & 12.9796897662633 \tabularnewline
138 & 12.7263105883925 & 12.4305116529769 & 13.0221095238082 \tabularnewline
139 & 12.7422592658795 & 12.4207071953775 & 13.0638113363815 \tabularnewline
140 & 12.7582079433665 & 12.4114194520021 & 13.1049964347309 \tabularnewline
141 & 12.7741566208535 & 12.4025046683401 & 13.1458085733669 \tabularnewline
142 & 12.7901052983405 & 12.3938565447088 & 13.1863540519721 \tabularnewline
143 & 12.8060539758274 & 12.3853943116051 & 13.2267136400498 \tabularnewline
144 & 12.8220026533144 & 12.3770552391496 & 13.2669500674793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158094&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]12.6465672009576[/C][C]12.4998714255451[/C][C]12.7932629763701[/C][/ROW]
[ROW][C]134[/C][C]12.6625158784446[/C][C]12.4808341157221[/C][C]12.8441976411671[/C][/ROW]
[ROW][C]135[/C][C]12.6784645559316[/C][C]12.4655755882841[/C][C]12.891353523579[/C][/ROW]
[ROW][C]136[/C][C]12.6944132334185[/C][C]12.4525690853358[/C][C]12.9362573815013[/C][/ROW]
[ROW][C]137[/C][C]12.7103619109055[/C][C]12.4410340555478[/C][C]12.9796897662633[/C][/ROW]
[ROW][C]138[/C][C]12.7263105883925[/C][C]12.4305116529769[/C][C]13.0221095238082[/C][/ROW]
[ROW][C]139[/C][C]12.7422592658795[/C][C]12.4207071953775[/C][C]13.0638113363815[/C][/ROW]
[ROW][C]140[/C][C]12.7582079433665[/C][C]12.4114194520021[/C][C]13.1049964347309[/C][/ROW]
[ROW][C]141[/C][C]12.7741566208535[/C][C]12.4025046683401[/C][C]13.1458085733669[/C][/ROW]
[ROW][C]142[/C][C]12.7901052983405[/C][C]12.3938565447088[/C][C]13.1863540519721[/C][/ROW]
[ROW][C]143[/C][C]12.8060539758274[/C][C]12.3853943116051[/C][C]13.2267136400498[/C][/ROW]
[ROW][C]144[/C][C]12.8220026533144[/C][C]12.3770552391496[/C][C]13.2669500674793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158094&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158094&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
13312.646567200957612.499871425545112.7932629763701
13412.662515878444612.480834115722112.8441976411671
13512.678464555931612.465575588284112.891353523579
13612.694413233418512.452569085335812.9362573815013
13712.710361910905512.441034055547812.9796897662633
13812.726310588392512.430511652976913.0221095238082
13912.742259265879512.420707195377513.0638113363815
14012.758207943366512.411419452002113.1049964347309
14112.774156620853512.402504668340113.1458085733669
14212.790105298340512.393856544708813.1863540519721
14312.806053975827412.385394311605113.2267136400498
14412.822002653314412.377055239149613.2669500674793


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = multiplicative ;
Parameters (R input):
par1 = 12 ; par2 = Double ; par3 = multiplicative ;
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')