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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 computationWed, 28 May 2008 08:06:29 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/May/28/t1211983633du02jwfknwjmc3x.htm/, Retrieved Mon, 20 May 2024 00:41:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=13441, Retrieved Mon, 20 May 2024 00:41:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Harrell-Davis Quantiles] [Opdracht4-Opgave1...] [2008-03-02 21:35:26] [d7d11ca83a0c912970c6165cecc7cdc7]
- RMPD    [Exponential Smoothing] [Nicky Van Calster...] [2008-05-28 14:06:29] [9e4ffec01482233a36a742caf4f37457] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,70291
0,6885
0,67127
0,66502
0,65825
0,65025
0,65779
0,66014
0,64683
0,64587
0,63702
0,62651
0,61834
0,61466
0,61063
0,59802
0,60151
0,62927
0,62304
0,6071
0,60773
0,58933
0,60039
0,61342
0,6348
0,634
0,62915
0,62168
0,61328
0,6089
0,60857
0,62672
0,62291
0,62393
0,61838
0,62012
0,61659
0,6116
0,61573
0,61407
0,62823
0,64405
0,6387
0,63633
0,63059
0,62994
0,63709
0,64217
0,65711
0,66977
0,68255
0,68902
0,71322
0,70224
0,70045
0,69919
0,69693
0,69763
0,69278
0,70196
0,69215
0,6769
0,67124
0,66532
0,67157
0,66428
0,66576
0,66942
0,6813
0,69144
0,69862
0,695
0,69867
0,68968
0,69233
0,68293
0,68399
0,66895
0,68756
0,68527
0,6776
0,68137
0,67933
0,67922
0,68598
0,68297
0,68935
0,69463
0,6833
0,68666
0,68782
0,67669
0,67511
0,67254
0,67397
0,67286
0,66341
0,668
0,68021
0,67934
0,68136
0,67562
0,6744
0,67766
0,68887
0,69614
0,70896
0,72064
0,74725

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13441&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13441&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.17436144158383
gamma0

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 1 \tabularnewline
beta & 0.17436144158383 \tabularnewline
gamma & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13441&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]0.17436144158383[/C][/ROW]
[ROW][C]gamma[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13441&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
alpha1
beta0.17436144158383
gamma0






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
30.671270.67409-0.00281999999999993
40.665020.6563683007347340.00865169926526632
50.658250.6516268234907750.00662317650922484
60.650250.6460116500947880.00423834990521221
70.657790.6387506548941970.0190393451058027
80.660140.6496103825536570.0105296174463428
90.646830.653796341830928-0.00696634183092759
100.645870.6392716804267210.00659831957327861
110.637020.639462172939549-0.00244217293954907
120.626510.630186352145212-0.00367635214521222
130.618340.619035338085403-0.00069533808540323
140.614660.6107440979344440.00391590206555581
150.610630.6077468802636960.00288311973630440
160.598020.604219585177176-0.00619958517717645
170.601510.5905286165684620.0109813834315378
180.629270.595933346414170.0333366535858300
190.623040.629505973390976-0.00646597339097599
200.60710.622148556949283-0.0150485569492829
210.607730.6035846688658490.00414533113415061
220.589330.604937454778242-0.0156074547782422
230.600390.5838161164636540.0165738835363465
240.613420.5977659626896930.0156540373103066
250.63480.6135254232017250.0212745767982746
260.6340.638614889081359-0.00461488908135854
270.629150.637010230368383-0.00786023036838346
280.621680.630789709270171-0.00910970927017107
290.613280.621731327229414-0.00845132722941444
300.60890.611857741630397-0.00295774163039719
310.608570.6069620255358890.00160797446411154
320.626720.6069123942814810.0198076057185190
330.622910.628516076968886-0.00560607696888615
340.623930.6237285933069610.000201406693038808
350.618380.624783710868304-0.00640371086830405
360.620120.618117150609820.00200284939017947
370.616590.620206370316768-0.00361637031676754
380.61160.616045814775035-0.00444581477503492
390.615730.6102806361018450.00544936389815476
400.614070.615360795046842-0.00129079504684237
410.628230.6134757301616860.0147542698383143
420.644050.6302083059202110.0138416940797891
430.63870.648441763653925-0.00974176365392532
440.636330.641393175699658-0.00506317569965808
450.630590.638140353085673-0.00755035308567331
460.629940.631083862637188-0.00114386263718835
470.637090.6302344170987940.00685558290120558
480.642170.6385797664163460.00359023358365396
490.657110.6442857647196150.0128242352803853
500.669770.6614618168703130.00830818312968717
510.682550.6755704436577480.00697955634225245
520.689020.689567409163198-0.000547409163198287
530.713220.6959419621123670.0172780378876332
540.702240.723154585706195-0.0209145857061945
550.700450.708527888392334-0.00807788839233392
560.699190.705329416127293-0.0061394161272933
570.696930.702998938680855-0.00606893868085534
580.697630.699680749783578-0.00205074978357767
590.692780.700023178094985-0.00724317809498531
600.701960.6939102471206950.00804975287930487
610.692150.704493813637124-0.0123438136371243
620.67690.692531528496713-0.0156315284967133
630.671240.674555992653868-0.00331599265386762
640.665320.668317811394458-0.00299781139445776
650.671570.6618751086781240.00969489132187618
660.664280.669815523905005-0.00553552390500467
670.665760.6615603419770060.00419965802299371
680.669420.6637726004040550.0056473995959454
690.68130.6684172891388040.0128827108611964
700.691440.682543537176070.00889646282393064
710.698620.6942347372590470.00438526274095308
720.6950.702179357992283-0.00717935799228342
730.698670.6973075547831020.00136244521689766
740.689680.7012151126952-0.0115351126951998
750.692330.6902138338168330.00211616618316735
760.682930.693232811603161-0.0103028116031607
770.683990.6820363985196670.00195360148033297
780.668950.683437031290058-0.0144870312900581
790.687560.6658710516300540.0216889483699463
800.685270.688262767934275-0.00299276793427461
810.67760.685450944602929-0.00785094460292879
820.681370.6764120425841670.00495795741583283
830.679330.681046519186503-0.00171651918650317
840.679220.6787072244266380.000512775573361934
850.685980.6786866327148190.00729336728518148
860.682970.686718314748663-0.00374831474866311
870.689350.6830547531855760.00629524681442384
880.694630.6905324014952650.00409759850473479
890.68330.696526864677582-0.0132268646775824
900.686660.6828906094847650.00376939051523506
910.687820.6869078458488940.000912154151106126
920.676690.688226890361627-0.0115368903616273
930.675110.675085301526782.4698473220619e-05
940.672540.673509607988175-0.000969607988175025
950.673970.6707705457415860.00319945425841428
960.672860.6727584071983640.000101592801635797
970.663410.671666121065712-0.00825612106571205
980.6680.6607765718948040.00722342810519616
990.680210.6666260592324030.0135839407675969
1000.679340.68120457472703-0.0018645747270305
1010.681360.6800094647896850.00135053521031514
1020.675620.682264946055865-0.00664494605586496
1030.67440.675366323682318-0.000966323682317638
1040.677660.6739778340920320.00368216590796788
1050.688870.6778798618478960.0109901381521037
1060.696140.6910061181793020.00513388182069752
1070.708960.699171269214480.00978873078551967
1080.720640.713698046425520.00694195357448035
1090.747250.7265884554581740.020661544541826

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 0.67127 & 0.67409 & -0.00281999999999993 \tabularnewline
4 & 0.66502 & 0.656368300734734 & 0.00865169926526632 \tabularnewline
5 & 0.65825 & 0.651626823490775 & 0.00662317650922484 \tabularnewline
6 & 0.65025 & 0.646011650094788 & 0.00423834990521221 \tabularnewline
7 & 0.65779 & 0.638750654894197 & 0.0190393451058027 \tabularnewline
8 & 0.66014 & 0.649610382553657 & 0.0105296174463428 \tabularnewline
9 & 0.64683 & 0.653796341830928 & -0.00696634183092759 \tabularnewline
10 & 0.64587 & 0.639271680426721 & 0.00659831957327861 \tabularnewline
11 & 0.63702 & 0.639462172939549 & -0.00244217293954907 \tabularnewline
12 & 0.62651 & 0.630186352145212 & -0.00367635214521222 \tabularnewline
13 & 0.61834 & 0.619035338085403 & -0.00069533808540323 \tabularnewline
14 & 0.61466 & 0.610744097934444 & 0.00391590206555581 \tabularnewline
15 & 0.61063 & 0.607746880263696 & 0.00288311973630440 \tabularnewline
16 & 0.59802 & 0.604219585177176 & -0.00619958517717645 \tabularnewline
17 & 0.60151 & 0.590528616568462 & 0.0109813834315378 \tabularnewline
18 & 0.62927 & 0.59593334641417 & 0.0333366535858300 \tabularnewline
19 & 0.62304 & 0.629505973390976 & -0.00646597339097599 \tabularnewline
20 & 0.6071 & 0.622148556949283 & -0.0150485569492829 \tabularnewline
21 & 0.60773 & 0.603584668865849 & 0.00414533113415061 \tabularnewline
22 & 0.58933 & 0.604937454778242 & -0.0156074547782422 \tabularnewline
23 & 0.60039 & 0.583816116463654 & 0.0165738835363465 \tabularnewline
24 & 0.61342 & 0.597765962689693 & 0.0156540373103066 \tabularnewline
25 & 0.6348 & 0.613525423201725 & 0.0212745767982746 \tabularnewline
26 & 0.634 & 0.638614889081359 & -0.00461488908135854 \tabularnewline
27 & 0.62915 & 0.637010230368383 & -0.00786023036838346 \tabularnewline
28 & 0.62168 & 0.630789709270171 & -0.00910970927017107 \tabularnewline
29 & 0.61328 & 0.621731327229414 & -0.00845132722941444 \tabularnewline
30 & 0.6089 & 0.611857741630397 & -0.00295774163039719 \tabularnewline
31 & 0.60857 & 0.606962025535889 & 0.00160797446411154 \tabularnewline
32 & 0.62672 & 0.606912394281481 & 0.0198076057185190 \tabularnewline
33 & 0.62291 & 0.628516076968886 & -0.00560607696888615 \tabularnewline
34 & 0.62393 & 0.623728593306961 & 0.000201406693038808 \tabularnewline
35 & 0.61838 & 0.624783710868304 & -0.00640371086830405 \tabularnewline
36 & 0.62012 & 0.61811715060982 & 0.00200284939017947 \tabularnewline
37 & 0.61659 & 0.620206370316768 & -0.00361637031676754 \tabularnewline
38 & 0.6116 & 0.616045814775035 & -0.00444581477503492 \tabularnewline
39 & 0.61573 & 0.610280636101845 & 0.00544936389815476 \tabularnewline
40 & 0.61407 & 0.615360795046842 & -0.00129079504684237 \tabularnewline
41 & 0.62823 & 0.613475730161686 & 0.0147542698383143 \tabularnewline
42 & 0.64405 & 0.630208305920211 & 0.0138416940797891 \tabularnewline
43 & 0.6387 & 0.648441763653925 & -0.00974176365392532 \tabularnewline
44 & 0.63633 & 0.641393175699658 & -0.00506317569965808 \tabularnewline
45 & 0.63059 & 0.638140353085673 & -0.00755035308567331 \tabularnewline
46 & 0.62994 & 0.631083862637188 & -0.00114386263718835 \tabularnewline
47 & 0.63709 & 0.630234417098794 & 0.00685558290120558 \tabularnewline
48 & 0.64217 & 0.638579766416346 & 0.00359023358365396 \tabularnewline
49 & 0.65711 & 0.644285764719615 & 0.0128242352803853 \tabularnewline
50 & 0.66977 & 0.661461816870313 & 0.00830818312968717 \tabularnewline
51 & 0.68255 & 0.675570443657748 & 0.00697955634225245 \tabularnewline
52 & 0.68902 & 0.689567409163198 & -0.000547409163198287 \tabularnewline
53 & 0.71322 & 0.695941962112367 & 0.0172780378876332 \tabularnewline
54 & 0.70224 & 0.723154585706195 & -0.0209145857061945 \tabularnewline
55 & 0.70045 & 0.708527888392334 & -0.00807788839233392 \tabularnewline
56 & 0.69919 & 0.705329416127293 & -0.0061394161272933 \tabularnewline
57 & 0.69693 & 0.702998938680855 & -0.00606893868085534 \tabularnewline
58 & 0.69763 & 0.699680749783578 & -0.00205074978357767 \tabularnewline
59 & 0.69278 & 0.700023178094985 & -0.00724317809498531 \tabularnewline
60 & 0.70196 & 0.693910247120695 & 0.00804975287930487 \tabularnewline
61 & 0.69215 & 0.704493813637124 & -0.0123438136371243 \tabularnewline
62 & 0.6769 & 0.692531528496713 & -0.0156315284967133 \tabularnewline
63 & 0.67124 & 0.674555992653868 & -0.00331599265386762 \tabularnewline
64 & 0.66532 & 0.668317811394458 & -0.00299781139445776 \tabularnewline
65 & 0.67157 & 0.661875108678124 & 0.00969489132187618 \tabularnewline
66 & 0.66428 & 0.669815523905005 & -0.00553552390500467 \tabularnewline
67 & 0.66576 & 0.661560341977006 & 0.00419965802299371 \tabularnewline
68 & 0.66942 & 0.663772600404055 & 0.0056473995959454 \tabularnewline
69 & 0.6813 & 0.668417289138804 & 0.0128827108611964 \tabularnewline
70 & 0.69144 & 0.68254353717607 & 0.00889646282393064 \tabularnewline
71 & 0.69862 & 0.694234737259047 & 0.00438526274095308 \tabularnewline
72 & 0.695 & 0.702179357992283 & -0.00717935799228342 \tabularnewline
73 & 0.69867 & 0.697307554783102 & 0.00136244521689766 \tabularnewline
74 & 0.68968 & 0.7012151126952 & -0.0115351126951998 \tabularnewline
75 & 0.69233 & 0.690213833816833 & 0.00211616618316735 \tabularnewline
76 & 0.68293 & 0.693232811603161 & -0.0103028116031607 \tabularnewline
77 & 0.68399 & 0.682036398519667 & 0.00195360148033297 \tabularnewline
78 & 0.66895 & 0.683437031290058 & -0.0144870312900581 \tabularnewline
79 & 0.68756 & 0.665871051630054 & 0.0216889483699463 \tabularnewline
80 & 0.68527 & 0.688262767934275 & -0.00299276793427461 \tabularnewline
81 & 0.6776 & 0.685450944602929 & -0.00785094460292879 \tabularnewline
82 & 0.68137 & 0.676412042584167 & 0.00495795741583283 \tabularnewline
83 & 0.67933 & 0.681046519186503 & -0.00171651918650317 \tabularnewline
84 & 0.67922 & 0.678707224426638 & 0.000512775573361934 \tabularnewline
85 & 0.68598 & 0.678686632714819 & 0.00729336728518148 \tabularnewline
86 & 0.68297 & 0.686718314748663 & -0.00374831474866311 \tabularnewline
87 & 0.68935 & 0.683054753185576 & 0.00629524681442384 \tabularnewline
88 & 0.69463 & 0.690532401495265 & 0.00409759850473479 \tabularnewline
89 & 0.6833 & 0.696526864677582 & -0.0132268646775824 \tabularnewline
90 & 0.68666 & 0.682890609484765 & 0.00376939051523506 \tabularnewline
91 & 0.68782 & 0.686907845848894 & 0.000912154151106126 \tabularnewline
92 & 0.67669 & 0.688226890361627 & -0.0115368903616273 \tabularnewline
93 & 0.67511 & 0.67508530152678 & 2.4698473220619e-05 \tabularnewline
94 & 0.67254 & 0.673509607988175 & -0.000969607988175025 \tabularnewline
95 & 0.67397 & 0.670770545741586 & 0.00319945425841428 \tabularnewline
96 & 0.67286 & 0.672758407198364 & 0.000101592801635797 \tabularnewline
97 & 0.66341 & 0.671666121065712 & -0.00825612106571205 \tabularnewline
98 & 0.668 & 0.660776571894804 & 0.00722342810519616 \tabularnewline
99 & 0.68021 & 0.666626059232403 & 0.0135839407675969 \tabularnewline
100 & 0.67934 & 0.68120457472703 & -0.0018645747270305 \tabularnewline
101 & 0.68136 & 0.680009464789685 & 0.00135053521031514 \tabularnewline
102 & 0.67562 & 0.682264946055865 & -0.00664494605586496 \tabularnewline
103 & 0.6744 & 0.675366323682318 & -0.000966323682317638 \tabularnewline
104 & 0.67766 & 0.673977834092032 & 0.00368216590796788 \tabularnewline
105 & 0.68887 & 0.677879861847896 & 0.0109901381521037 \tabularnewline
106 & 0.69614 & 0.691006118179302 & 0.00513388182069752 \tabularnewline
107 & 0.70896 & 0.69917126921448 & 0.00978873078551967 \tabularnewline
108 & 0.72064 & 0.71369804642552 & 0.00694195357448035 \tabularnewline
109 & 0.74725 & 0.726588455458174 & 0.020661544541826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13441&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]0.67127[/C][C]0.67409[/C][C]-0.00281999999999993[/C][/ROW]
[ROW][C]4[/C][C]0.66502[/C][C]0.656368300734734[/C][C]0.00865169926526632[/C][/ROW]
[ROW][C]5[/C][C]0.65825[/C][C]0.651626823490775[/C][C]0.00662317650922484[/C][/ROW]
[ROW][C]6[/C][C]0.65025[/C][C]0.646011650094788[/C][C]0.00423834990521221[/C][/ROW]
[ROW][C]7[/C][C]0.65779[/C][C]0.638750654894197[/C][C]0.0190393451058027[/C][/ROW]
[ROW][C]8[/C][C]0.66014[/C][C]0.649610382553657[/C][C]0.0105296174463428[/C][/ROW]
[ROW][C]9[/C][C]0.64683[/C][C]0.653796341830928[/C][C]-0.00696634183092759[/C][/ROW]
[ROW][C]10[/C][C]0.64587[/C][C]0.639271680426721[/C][C]0.00659831957327861[/C][/ROW]
[ROW][C]11[/C][C]0.63702[/C][C]0.639462172939549[/C][C]-0.00244217293954907[/C][/ROW]
[ROW][C]12[/C][C]0.62651[/C][C]0.630186352145212[/C][C]-0.00367635214521222[/C][/ROW]
[ROW][C]13[/C][C]0.61834[/C][C]0.619035338085403[/C][C]-0.00069533808540323[/C][/ROW]
[ROW][C]14[/C][C]0.61466[/C][C]0.610744097934444[/C][C]0.00391590206555581[/C][/ROW]
[ROW][C]15[/C][C]0.61063[/C][C]0.607746880263696[/C][C]0.00288311973630440[/C][/ROW]
[ROW][C]16[/C][C]0.59802[/C][C]0.604219585177176[/C][C]-0.00619958517717645[/C][/ROW]
[ROW][C]17[/C][C]0.60151[/C][C]0.590528616568462[/C][C]0.0109813834315378[/C][/ROW]
[ROW][C]18[/C][C]0.62927[/C][C]0.59593334641417[/C][C]0.0333366535858300[/C][/ROW]
[ROW][C]19[/C][C]0.62304[/C][C]0.629505973390976[/C][C]-0.00646597339097599[/C][/ROW]
[ROW][C]20[/C][C]0.6071[/C][C]0.622148556949283[/C][C]-0.0150485569492829[/C][/ROW]
[ROW][C]21[/C][C]0.60773[/C][C]0.603584668865849[/C][C]0.00414533113415061[/C][/ROW]
[ROW][C]22[/C][C]0.58933[/C][C]0.604937454778242[/C][C]-0.0156074547782422[/C][/ROW]
[ROW][C]23[/C][C]0.60039[/C][C]0.583816116463654[/C][C]0.0165738835363465[/C][/ROW]
[ROW][C]24[/C][C]0.61342[/C][C]0.597765962689693[/C][C]0.0156540373103066[/C][/ROW]
[ROW][C]25[/C][C]0.6348[/C][C]0.613525423201725[/C][C]0.0212745767982746[/C][/ROW]
[ROW][C]26[/C][C]0.634[/C][C]0.638614889081359[/C][C]-0.00461488908135854[/C][/ROW]
[ROW][C]27[/C][C]0.62915[/C][C]0.637010230368383[/C][C]-0.00786023036838346[/C][/ROW]
[ROW][C]28[/C][C]0.62168[/C][C]0.630789709270171[/C][C]-0.00910970927017107[/C][/ROW]
[ROW][C]29[/C][C]0.61328[/C][C]0.621731327229414[/C][C]-0.00845132722941444[/C][/ROW]
[ROW][C]30[/C][C]0.6089[/C][C]0.611857741630397[/C][C]-0.00295774163039719[/C][/ROW]
[ROW][C]31[/C][C]0.60857[/C][C]0.606962025535889[/C][C]0.00160797446411154[/C][/ROW]
[ROW][C]32[/C][C]0.62672[/C][C]0.606912394281481[/C][C]0.0198076057185190[/C][/ROW]
[ROW][C]33[/C][C]0.62291[/C][C]0.628516076968886[/C][C]-0.00560607696888615[/C][/ROW]
[ROW][C]34[/C][C]0.62393[/C][C]0.623728593306961[/C][C]0.000201406693038808[/C][/ROW]
[ROW][C]35[/C][C]0.61838[/C][C]0.624783710868304[/C][C]-0.00640371086830405[/C][/ROW]
[ROW][C]36[/C][C]0.62012[/C][C]0.61811715060982[/C][C]0.00200284939017947[/C][/ROW]
[ROW][C]37[/C][C]0.61659[/C][C]0.620206370316768[/C][C]-0.00361637031676754[/C][/ROW]
[ROW][C]38[/C][C]0.6116[/C][C]0.616045814775035[/C][C]-0.00444581477503492[/C][/ROW]
[ROW][C]39[/C][C]0.61573[/C][C]0.610280636101845[/C][C]0.00544936389815476[/C][/ROW]
[ROW][C]40[/C][C]0.61407[/C][C]0.615360795046842[/C][C]-0.00129079504684237[/C][/ROW]
[ROW][C]41[/C][C]0.62823[/C][C]0.613475730161686[/C][C]0.0147542698383143[/C][/ROW]
[ROW][C]42[/C][C]0.64405[/C][C]0.630208305920211[/C][C]0.0138416940797891[/C][/ROW]
[ROW][C]43[/C][C]0.6387[/C][C]0.648441763653925[/C][C]-0.00974176365392532[/C][/ROW]
[ROW][C]44[/C][C]0.63633[/C][C]0.641393175699658[/C][C]-0.00506317569965808[/C][/ROW]
[ROW][C]45[/C][C]0.63059[/C][C]0.638140353085673[/C][C]-0.00755035308567331[/C][/ROW]
[ROW][C]46[/C][C]0.62994[/C][C]0.631083862637188[/C][C]-0.00114386263718835[/C][/ROW]
[ROW][C]47[/C][C]0.63709[/C][C]0.630234417098794[/C][C]0.00685558290120558[/C][/ROW]
[ROW][C]48[/C][C]0.64217[/C][C]0.638579766416346[/C][C]0.00359023358365396[/C][/ROW]
[ROW][C]49[/C][C]0.65711[/C][C]0.644285764719615[/C][C]0.0128242352803853[/C][/ROW]
[ROW][C]50[/C][C]0.66977[/C][C]0.661461816870313[/C][C]0.00830818312968717[/C][/ROW]
[ROW][C]51[/C][C]0.68255[/C][C]0.675570443657748[/C][C]0.00697955634225245[/C][/ROW]
[ROW][C]52[/C][C]0.68902[/C][C]0.689567409163198[/C][C]-0.000547409163198287[/C][/ROW]
[ROW][C]53[/C][C]0.71322[/C][C]0.695941962112367[/C][C]0.0172780378876332[/C][/ROW]
[ROW][C]54[/C][C]0.70224[/C][C]0.723154585706195[/C][C]-0.0209145857061945[/C][/ROW]
[ROW][C]55[/C][C]0.70045[/C][C]0.708527888392334[/C][C]-0.00807788839233392[/C][/ROW]
[ROW][C]56[/C][C]0.69919[/C][C]0.705329416127293[/C][C]-0.0061394161272933[/C][/ROW]
[ROW][C]57[/C][C]0.69693[/C][C]0.702998938680855[/C][C]-0.00606893868085534[/C][/ROW]
[ROW][C]58[/C][C]0.69763[/C][C]0.699680749783578[/C][C]-0.00205074978357767[/C][/ROW]
[ROW][C]59[/C][C]0.69278[/C][C]0.700023178094985[/C][C]-0.00724317809498531[/C][/ROW]
[ROW][C]60[/C][C]0.70196[/C][C]0.693910247120695[/C][C]0.00804975287930487[/C][/ROW]
[ROW][C]61[/C][C]0.69215[/C][C]0.704493813637124[/C][C]-0.0123438136371243[/C][/ROW]
[ROW][C]62[/C][C]0.6769[/C][C]0.692531528496713[/C][C]-0.0156315284967133[/C][/ROW]
[ROW][C]63[/C][C]0.67124[/C][C]0.674555992653868[/C][C]-0.00331599265386762[/C][/ROW]
[ROW][C]64[/C][C]0.66532[/C][C]0.668317811394458[/C][C]-0.00299781139445776[/C][/ROW]
[ROW][C]65[/C][C]0.67157[/C][C]0.661875108678124[/C][C]0.00969489132187618[/C][/ROW]
[ROW][C]66[/C][C]0.66428[/C][C]0.669815523905005[/C][C]-0.00553552390500467[/C][/ROW]
[ROW][C]67[/C][C]0.66576[/C][C]0.661560341977006[/C][C]0.00419965802299371[/C][/ROW]
[ROW][C]68[/C][C]0.66942[/C][C]0.663772600404055[/C][C]0.0056473995959454[/C][/ROW]
[ROW][C]69[/C][C]0.6813[/C][C]0.668417289138804[/C][C]0.0128827108611964[/C][/ROW]
[ROW][C]70[/C][C]0.69144[/C][C]0.68254353717607[/C][C]0.00889646282393064[/C][/ROW]
[ROW][C]71[/C][C]0.69862[/C][C]0.694234737259047[/C][C]0.00438526274095308[/C][/ROW]
[ROW][C]72[/C][C]0.695[/C][C]0.702179357992283[/C][C]-0.00717935799228342[/C][/ROW]
[ROW][C]73[/C][C]0.69867[/C][C]0.697307554783102[/C][C]0.00136244521689766[/C][/ROW]
[ROW][C]74[/C][C]0.68968[/C][C]0.7012151126952[/C][C]-0.0115351126951998[/C][/ROW]
[ROW][C]75[/C][C]0.69233[/C][C]0.690213833816833[/C][C]0.00211616618316735[/C][/ROW]
[ROW][C]76[/C][C]0.68293[/C][C]0.693232811603161[/C][C]-0.0103028116031607[/C][/ROW]
[ROW][C]77[/C][C]0.68399[/C][C]0.682036398519667[/C][C]0.00195360148033297[/C][/ROW]
[ROW][C]78[/C][C]0.66895[/C][C]0.683437031290058[/C][C]-0.0144870312900581[/C][/ROW]
[ROW][C]79[/C][C]0.68756[/C][C]0.665871051630054[/C][C]0.0216889483699463[/C][/ROW]
[ROW][C]80[/C][C]0.68527[/C][C]0.688262767934275[/C][C]-0.00299276793427461[/C][/ROW]
[ROW][C]81[/C][C]0.6776[/C][C]0.685450944602929[/C][C]-0.00785094460292879[/C][/ROW]
[ROW][C]82[/C][C]0.68137[/C][C]0.676412042584167[/C][C]0.00495795741583283[/C][/ROW]
[ROW][C]83[/C][C]0.67933[/C][C]0.681046519186503[/C][C]-0.00171651918650317[/C][/ROW]
[ROW][C]84[/C][C]0.67922[/C][C]0.678707224426638[/C][C]0.000512775573361934[/C][/ROW]
[ROW][C]85[/C][C]0.68598[/C][C]0.678686632714819[/C][C]0.00729336728518148[/C][/ROW]
[ROW][C]86[/C][C]0.68297[/C][C]0.686718314748663[/C][C]-0.00374831474866311[/C][/ROW]
[ROW][C]87[/C][C]0.68935[/C][C]0.683054753185576[/C][C]0.00629524681442384[/C][/ROW]
[ROW][C]88[/C][C]0.69463[/C][C]0.690532401495265[/C][C]0.00409759850473479[/C][/ROW]
[ROW][C]89[/C][C]0.6833[/C][C]0.696526864677582[/C][C]-0.0132268646775824[/C][/ROW]
[ROW][C]90[/C][C]0.68666[/C][C]0.682890609484765[/C][C]0.00376939051523506[/C][/ROW]
[ROW][C]91[/C][C]0.68782[/C][C]0.686907845848894[/C][C]0.000912154151106126[/C][/ROW]
[ROW][C]92[/C][C]0.67669[/C][C]0.688226890361627[/C][C]-0.0115368903616273[/C][/ROW]
[ROW][C]93[/C][C]0.67511[/C][C]0.67508530152678[/C][C]2.4698473220619e-05[/C][/ROW]
[ROW][C]94[/C][C]0.67254[/C][C]0.673509607988175[/C][C]-0.000969607988175025[/C][/ROW]
[ROW][C]95[/C][C]0.67397[/C][C]0.670770545741586[/C][C]0.00319945425841428[/C][/ROW]
[ROW][C]96[/C][C]0.67286[/C][C]0.672758407198364[/C][C]0.000101592801635797[/C][/ROW]
[ROW][C]97[/C][C]0.66341[/C][C]0.671666121065712[/C][C]-0.00825612106571205[/C][/ROW]
[ROW][C]98[/C][C]0.668[/C][C]0.660776571894804[/C][C]0.00722342810519616[/C][/ROW]
[ROW][C]99[/C][C]0.68021[/C][C]0.666626059232403[/C][C]0.0135839407675969[/C][/ROW]
[ROW][C]100[/C][C]0.67934[/C][C]0.68120457472703[/C][C]-0.0018645747270305[/C][/ROW]
[ROW][C]101[/C][C]0.68136[/C][C]0.680009464789685[/C][C]0.00135053521031514[/C][/ROW]
[ROW][C]102[/C][C]0.67562[/C][C]0.682264946055865[/C][C]-0.00664494605586496[/C][/ROW]
[ROW][C]103[/C][C]0.6744[/C][C]0.675366323682318[/C][C]-0.000966323682317638[/C][/ROW]
[ROW][C]104[/C][C]0.67766[/C][C]0.673977834092032[/C][C]0.00368216590796788[/C][/ROW]
[ROW][C]105[/C][C]0.68887[/C][C]0.677879861847896[/C][C]0.0109901381521037[/C][/ROW]
[ROW][C]106[/C][C]0.69614[/C][C]0.691006118179302[/C][C]0.00513388182069752[/C][/ROW]
[ROW][C]107[/C][C]0.70896[/C][C]0.69917126921448[/C][C]0.00978873078551967[/C][/ROW]
[ROW][C]108[/C][C]0.72064[/C][C]0.71369804642552[/C][C]0.00694195357448035[/C][/ROW]
[ROW][C]109[/C][C]0.74725[/C][C]0.726588455458174[/C][C]0.020661544541826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13441&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
30.671270.67409-0.00281999999999993
40.665020.6563683007347340.00865169926526632
50.658250.6516268234907750.00662317650922484
60.650250.6460116500947880.00423834990521221
70.657790.6387506548941970.0190393451058027
80.660140.6496103825536570.0105296174463428
90.646830.653796341830928-0.00696634183092759
100.645870.6392716804267210.00659831957327861
110.637020.639462172939549-0.00244217293954907
120.626510.630186352145212-0.00367635214521222
130.618340.619035338085403-0.00069533808540323
140.614660.6107440979344440.00391590206555581
150.610630.6077468802636960.00288311973630440
160.598020.604219585177176-0.00619958517717645
170.601510.5905286165684620.0109813834315378
180.629270.595933346414170.0333366535858300
190.623040.629505973390976-0.00646597339097599
200.60710.622148556949283-0.0150485569492829
210.607730.6035846688658490.00414533113415061
220.589330.604937454778242-0.0156074547782422
230.600390.5838161164636540.0165738835363465
240.613420.5977659626896930.0156540373103066
250.63480.6135254232017250.0212745767982746
260.6340.638614889081359-0.00461488908135854
270.629150.637010230368383-0.00786023036838346
280.621680.630789709270171-0.00910970927017107
290.613280.621731327229414-0.00845132722941444
300.60890.611857741630397-0.00295774163039719
310.608570.6069620255358890.00160797446411154
320.626720.6069123942814810.0198076057185190
330.622910.628516076968886-0.00560607696888615
340.623930.6237285933069610.000201406693038808
350.618380.624783710868304-0.00640371086830405
360.620120.618117150609820.00200284939017947
370.616590.620206370316768-0.00361637031676754
380.61160.616045814775035-0.00444581477503492
390.615730.6102806361018450.00544936389815476
400.614070.615360795046842-0.00129079504684237
410.628230.6134757301616860.0147542698383143
420.644050.6302083059202110.0138416940797891
430.63870.648441763653925-0.00974176365392532
440.636330.641393175699658-0.00506317569965808
450.630590.638140353085673-0.00755035308567331
460.629940.631083862637188-0.00114386263718835
470.637090.6302344170987940.00685558290120558
480.642170.6385797664163460.00359023358365396
490.657110.6442857647196150.0128242352803853
500.669770.6614618168703130.00830818312968717
510.682550.6755704436577480.00697955634225245
520.689020.689567409163198-0.000547409163198287
530.713220.6959419621123670.0172780378876332
540.702240.723154585706195-0.0209145857061945
550.700450.708527888392334-0.00807788839233392
560.699190.705329416127293-0.0061394161272933
570.696930.702998938680855-0.00606893868085534
580.697630.699680749783578-0.00205074978357767
590.692780.700023178094985-0.00724317809498531
600.701960.6939102471206950.00804975287930487
610.692150.704493813637124-0.0123438136371243
620.67690.692531528496713-0.0156315284967133
630.671240.674555992653868-0.00331599265386762
640.665320.668317811394458-0.00299781139445776
650.671570.6618751086781240.00969489132187618
660.664280.669815523905005-0.00553552390500467
670.665760.6615603419770060.00419965802299371
680.669420.6637726004040550.0056473995959454
690.68130.6684172891388040.0128827108611964
700.691440.682543537176070.00889646282393064
710.698620.6942347372590470.00438526274095308
720.6950.702179357992283-0.00717935799228342
730.698670.6973075547831020.00136244521689766
740.689680.7012151126952-0.0115351126951998
750.692330.6902138338168330.00211616618316735
760.682930.693232811603161-0.0103028116031607
770.683990.6820363985196670.00195360148033297
780.668950.683437031290058-0.0144870312900581
790.687560.6658710516300540.0216889483699463
800.685270.688262767934275-0.00299276793427461
810.67760.685450944602929-0.00785094460292879
820.681370.6764120425841670.00495795741583283
830.679330.681046519186503-0.00171651918650317
840.679220.6787072244266380.000512775573361934
850.685980.6786866327148190.00729336728518148
860.682970.686718314748663-0.00374831474866311
870.689350.6830547531855760.00629524681442384
880.694630.6905324014952650.00409759850473479
890.68330.696526864677582-0.0132268646775824
900.686660.6828906094847650.00376939051523506
910.687820.6869078458488940.000912154151106126
920.676690.688226890361627-0.0115368903616273
930.675110.675085301526782.4698473220619e-05
940.672540.673509607988175-0.000969607988175025
950.673970.6707705457415860.00319945425841428
960.672860.6727584071983640.000101592801635797
970.663410.671666121065712-0.00825612106571205
980.6680.6607765718948040.00722342810519616
990.680210.6666260592324030.0135839407675969
1000.679340.68120457472703-0.0018645747270305
1010.681360.6800094647896850.00135053521031514
1020.675620.682264946055865-0.00664494605586496
1030.67440.675366323682318-0.000966323682317638
1040.677660.6739778340920320.00368216590796788
1050.688870.6778798618478960.0109901381521037
1060.696140.6910061181793020.00513388182069752
1070.708960.699171269214480.00978873078551967
1080.720640.713698046425520.00694195357448035
1090.747250.7265884554581740.020661544541826






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1100.7568010321498350.7382090361800960.775393028119574
1110.766352064299670.737675004087190.79502912451215
1120.7759030964495060.7378091074800940.813997085418918
1130.7854541285993410.7379881982452360.832920058953446
1140.7950051607491760.7380053447889930.85200497670936
1150.8045561928990120.7377724897097510.871339896088272
1160.8141072250488470.73724836666780.890966083429893
1170.8236582571986820.7364134297868430.910903084610521
1180.8332092893485170.7352592612839470.931159317413088
1190.8427603214983530.7337835076086490.951737135388057
1200.8523113536481880.7319872438282850.97263546346809
1210.8618623857980230.729873515419570.993851256176476

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
110 & 0.756801032149835 & 0.738209036180096 & 0.775393028119574 \tabularnewline
111 & 0.76635206429967 & 0.73767500408719 & 0.79502912451215 \tabularnewline
112 & 0.775903096449506 & 0.737809107480094 & 0.813997085418918 \tabularnewline
113 & 0.785454128599341 & 0.737988198245236 & 0.832920058953446 \tabularnewline
114 & 0.795005160749176 & 0.738005344788993 & 0.85200497670936 \tabularnewline
115 & 0.804556192899012 & 0.737772489709751 & 0.871339896088272 \tabularnewline
116 & 0.814107225048847 & 0.7372483666678 & 0.890966083429893 \tabularnewline
117 & 0.823658257198682 & 0.736413429786843 & 0.910903084610521 \tabularnewline
118 & 0.833209289348517 & 0.735259261283947 & 0.931159317413088 \tabularnewline
119 & 0.842760321498353 & 0.733783507608649 & 0.951737135388057 \tabularnewline
120 & 0.852311353648188 & 0.731987243828285 & 0.97263546346809 \tabularnewline
121 & 0.861862385798023 & 0.72987351541957 & 0.993851256176476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13441&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]110[/C][C]0.756801032149835[/C][C]0.738209036180096[/C][C]0.775393028119574[/C][/ROW]
[ROW][C]111[/C][C]0.76635206429967[/C][C]0.73767500408719[/C][C]0.79502912451215[/C][/ROW]
[ROW][C]112[/C][C]0.775903096449506[/C][C]0.737809107480094[/C][C]0.813997085418918[/C][/ROW]
[ROW][C]113[/C][C]0.785454128599341[/C][C]0.737988198245236[/C][C]0.832920058953446[/C][/ROW]
[ROW][C]114[/C][C]0.795005160749176[/C][C]0.738005344788993[/C][C]0.85200497670936[/C][/ROW]
[ROW][C]115[/C][C]0.804556192899012[/C][C]0.737772489709751[/C][C]0.871339896088272[/C][/ROW]
[ROW][C]116[/C][C]0.814107225048847[/C][C]0.7372483666678[/C][C]0.890966083429893[/C][/ROW]
[ROW][C]117[/C][C]0.823658257198682[/C][C]0.736413429786843[/C][C]0.910903084610521[/C][/ROW]
[ROW][C]118[/C][C]0.833209289348517[/C][C]0.735259261283947[/C][C]0.931159317413088[/C][/ROW]
[ROW][C]119[/C][C]0.842760321498353[/C][C]0.733783507608649[/C][C]0.951737135388057[/C][/ROW]
[ROW][C]120[/C][C]0.852311353648188[/C][C]0.731987243828285[/C][C]0.97263546346809[/C][/ROW]
[ROW][C]121[/C][C]0.861862385798023[/C][C]0.72987351541957[/C][C]0.993851256176476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13441&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13441&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
1100.7568010321498350.7382090361800960.775393028119574
1110.766352064299670.737675004087190.79502912451215
1120.7759030964495060.7378091074800940.813997085418918
1130.7854541285993410.7379881982452360.832920058953446
1140.7950051607491760.7380053447889930.85200497670936
1150.8045561928990120.7377724897097510.871339896088272
1160.8141072250488470.73724836666780.890966083429893
1170.8236582571986820.7364134297868430.910903084610521
1180.8332092893485170.7352592612839470.931159317413088
1190.8427603214983530.7337835076086490.951737135388057
1200.8523113536481880.7319872438282850.97263546346809
1210.8618623857980230.729873515419570.993851256176476


Figures (Output of Computation)

Input Parameters & R Code

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=0, beta=0)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=0)
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')