Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationWed, 30 Dec 2015 15:45:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/30/t1451490640v3thg03mewwc06z.htm/, Retrieved Thu, 31 Oct 2024 23:38:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=287215, Retrieved Thu, 31 Oct 2024 23:38:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [vraag 7 voorbeeld...] [2015-12-30 15:45:37] [83287827dabe987cb447ff9f7ffea17c] [Current]
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Dataseries X:
67
72
74
62
56
66
65
59
61
69
74
69
66
68
58
64
66
57
68
62
59
73
61
61
57
58
57
67
81
79
76
78
74
67
84
85
79
82
87
90
87
93
92
82
80
79
77
72
65
73
76
77
76
76
76
75
78
73
80
77
83
84
85
81
84
83
83
88
92
92
89
82
73
81
91
80
81
82
84
87
85
74
81
82
86
85
82
86
88
86
83
81
81
81
82
86
85
87
89
90
90
92
86
86
82
80
79
77
79
76
78
78
77
72
75
79
81
86
88
97
94
96
94
91
92
93
93
87
84
80
78
75
73
81
76
77
71
71
78
67
76
68
82
64
71
81
69
63
70
77
75
76
68




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287215&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287215&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.613329478711963
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.613329478711963 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287215&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]0.613329478711963[/C][/ROW]
[ROW][C]beta[/C][C]FALSE[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287215&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
ParameterValue
alpha0.613329478711963
betaFALSE
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
272675
37470.06664739355983.9333526064402
46272.4790884972581-10.4790884972581
55666.0519546118583-10.0519546118583
66659.88679452973096.11320547026908
76563.63620365407021.36379634592982
85964.4726601559886-5.47266015598859
96161.1161163553484-0.116116355348375
106961.04489877165267.95510122834737
117465.92399686113588.07600313886418
126970.8772476563716-1.87724765637157
136669.7258763298759-3.72587632987594
146867.44068654272790.559313457272111
155867.7837299739132-9.78372997391318
166461.78307996915442.2169200308456
176663.1427823760192.85721762398096
185764.8951981719019-7.89519817190192
196860.05284039280177.94715960719833
206264.9270676519254-2.9270676519254
215963.1318107748153-4.13181077481534
227360.597649426161412.4023505738386
236168.2043766384168-7.20437663841683
246163.785720070332-2.78572007033199
255762.0771558317578-5.07715583175782
265858.9631864921264-0.963186492126397
275758.3724358230081-1.3724358230081
286757.53068047511699.46931952488308
298163.338493283070517.6615067169295
307974.17081599103274.8291840089673
317677.1326969018568-1.13269690185676
327876.43798050150231.5620194984977
337477.3960131062538-3.3960131062538
346775.3131381580962-8.31313815809617
358470.214445465130513.7855545348695
368578.66953244175746.33046755824265
377982.5521948092573-3.55219480925732
388280.37352901861221.62647098138781
398781.37109161776695.62890838223309
409084.82346706155935.17653293844067
418787.9983873102285-0.998387310228452
429387.38604694169345.61395305830661
439290.8292498444581.17075015554198
448291.5473054270585-9.54730542705853
458085.6916615663768-5.69166156637684
467982.200797744866-3.20079774486601
477780.2376541325449-3.23765413254492
487278.2519054111815-6.25190541118151
496574.417427524385-9.41742752438505
507368.64144161004634.35855838995373
517671.31467395529224.68532604470775
527774.18832253588842.81167746411157
537675.91280720925810.0871927907418524
547675.96628511815130.0337148818487094
557675.98696344906040.0130365509396029
567575.9949591500524-0.994959150052381
577875.38472137321112.61527862678895
587376.9887488500661-3.98874885006606
598074.54233159714215.45766840285791
607777.8896805136497-0.889680513649694
618377.34401322799275.65598677200727
628480.81299664646973.18700335353029
638582.76767975194372.23232024805628
648184.1368275660022-3.13682756600224
658482.21291875013681.78708124986323
668383.3089883615313-0.308988361531306
678383.1194766908253-0.119476690825252
688883.04619811432324.95380188567682
699286.08451084250775.91548915749232
709289.71265472379872.28734527620128
718991.1155510096855-2.11555100968553
728289.8180212117265-7.81802121172655
737385.0229983373792-12.0229983373792
748177.64893903455963.35106096544037
759179.704243509625211.2957564903748
768086.632263949524-6.63226394952403
778182.5645009586823-1.56450095868232
788281.60494640124930.395053598750678
798481.84724441903442.15275558096563
808783.16759287730233.83240712269772
818585.5181211400785-0.518121140078478
827485.2003421713245-11.2003421713245
838178.33084214599042.66915785400957
848279.96791534119012.03208465880994
858681.21425276567654.78574723432345
868584.14949262215140.850507377848629
878284.671133868848-2.67113386884796
888683.03284872549762.96715127450243
898884.85269006994773.14730993005232
908686.7830280286917-0.78302802869166
918386.3027738560373-3.30277385603735
928184.2770852886105-3.27708528861046
938182.2671522768524-1.26715227685236
948181.4899704314418-0.489970431441819
958281.18945712214130.810542877858666
968681.68658696289214.31341303710792
978584.33213033241090.66786966758913
988784.74175448748082.25824551251915
998986.12680303047782.87319696952216
1009087.88901943003172.11098056996835
1019089.18374604258140.816253957418581
1029289.68437865678152.31562134321847
1038691.104617488112-5.10461748811201
1048687.9738051051043-1.9738051051043
1058286.7632122489117-4.76321224891167
1068083.8417937632922-3.84179376329223
1077981.4855083971333-2.48550839713334
1087779.9610728275853-2.96107282758534
1097978.14495957381430.855040426185738
1107678.6693810726844-2.66938107268442
1117877.03217097089130.9678290291087
1127877.62576904479680.37423095520316
1137777.8552959214695-0.855295921469477
1147277.3307177198101-5.33071771981014
1157574.06123139955840.938768600441648
1167974.63700585589844.36299414410162
1178177.31295877992363.68704122007642
1188679.57432984942266.42567015057743
1198883.51538277325124.48461722674875
1209786.265930719155810.7340692808442
1219492.84945183563411.15054816436594
1229693.55511694151762.44488305848238
1239495.0546357932883-1.05463579328833
1249194.4077965719598-3.40779657195981
1259292.3176944769233-0.317694476923279
1269392.12284308900230.877156910997741
1279392.66082927997310.3391707200269
1288792.8688526808816-5.86885268088156
1298489.2693123254792-5.26931232547916
1308086.0374877437225-6.03748774372249
1317882.3345185331353-4.33451853313531
1327579.6760305407401-4.67603054074009
1337376.8080831667467-3.80808316674674
1348174.47247350319426.52752649680583
1357678.4759979267586-2.47599792675861
1367776.95739540904780.042604590952152
1377176.9835260606073-5.98352606060726
1387173.3136531409956-2.31365314099557
1397871.89462146610856.10537853389154
1406775.6392300996394-8.63923009963936
1417670.34053560615485.65946439384516
1426873.8116519526208-5.81165195262082
1438270.247194490064511.7528055099355
1446477.4555365668763-13.4555365668763
1457169.20285933852431.79714066147568
1468170.305098683599310.6949013164007
1476976.8645969328632-7.86459693286321
1486372.0410077957505-9.04100779575052
1497066.49589119735213.50410880264795
1507768.64506442263018.35493557736987
1517573.76939270497041.23060729502957
1527674.52416043573011.47583956426995
1536875.4293363463462-7.42933634634623

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 72 & 67 & 5 \tabularnewline
3 & 74 & 70.0666473935598 & 3.9333526064402 \tabularnewline
4 & 62 & 72.4790884972581 & -10.4790884972581 \tabularnewline
5 & 56 & 66.0519546118583 & -10.0519546118583 \tabularnewline
6 & 66 & 59.8867945297309 & 6.11320547026908 \tabularnewline
7 & 65 & 63.6362036540702 & 1.36379634592982 \tabularnewline
8 & 59 & 64.4726601559886 & -5.47266015598859 \tabularnewline
9 & 61 & 61.1161163553484 & -0.116116355348375 \tabularnewline
10 & 69 & 61.0448987716526 & 7.95510122834737 \tabularnewline
11 & 74 & 65.9239968611358 & 8.07600313886418 \tabularnewline
12 & 69 & 70.8772476563716 & -1.87724765637157 \tabularnewline
13 & 66 & 69.7258763298759 & -3.72587632987594 \tabularnewline
14 & 68 & 67.4406865427279 & 0.559313457272111 \tabularnewline
15 & 58 & 67.7837299739132 & -9.78372997391318 \tabularnewline
16 & 64 & 61.7830799691544 & 2.2169200308456 \tabularnewline
17 & 66 & 63.142782376019 & 2.85721762398096 \tabularnewline
18 & 57 & 64.8951981719019 & -7.89519817190192 \tabularnewline
19 & 68 & 60.0528403928017 & 7.94715960719833 \tabularnewline
20 & 62 & 64.9270676519254 & -2.9270676519254 \tabularnewline
21 & 59 & 63.1318107748153 & -4.13181077481534 \tabularnewline
22 & 73 & 60.5976494261614 & 12.4023505738386 \tabularnewline
23 & 61 & 68.2043766384168 & -7.20437663841683 \tabularnewline
24 & 61 & 63.785720070332 & -2.78572007033199 \tabularnewline
25 & 57 & 62.0771558317578 & -5.07715583175782 \tabularnewline
26 & 58 & 58.9631864921264 & -0.963186492126397 \tabularnewline
27 & 57 & 58.3724358230081 & -1.3724358230081 \tabularnewline
28 & 67 & 57.5306804751169 & 9.46931952488308 \tabularnewline
29 & 81 & 63.3384932830705 & 17.6615067169295 \tabularnewline
30 & 79 & 74.1708159910327 & 4.8291840089673 \tabularnewline
31 & 76 & 77.1326969018568 & -1.13269690185676 \tabularnewline
32 & 78 & 76.4379805015023 & 1.5620194984977 \tabularnewline
33 & 74 & 77.3960131062538 & -3.3960131062538 \tabularnewline
34 & 67 & 75.3131381580962 & -8.31313815809617 \tabularnewline
35 & 84 & 70.2144454651305 & 13.7855545348695 \tabularnewline
36 & 85 & 78.6695324417574 & 6.33046755824265 \tabularnewline
37 & 79 & 82.5521948092573 & -3.55219480925732 \tabularnewline
38 & 82 & 80.3735290186122 & 1.62647098138781 \tabularnewline
39 & 87 & 81.3710916177669 & 5.62890838223309 \tabularnewline
40 & 90 & 84.8234670615593 & 5.17653293844067 \tabularnewline
41 & 87 & 87.9983873102285 & -0.998387310228452 \tabularnewline
42 & 93 & 87.3860469416934 & 5.61395305830661 \tabularnewline
43 & 92 & 90.829249844458 & 1.17075015554198 \tabularnewline
44 & 82 & 91.5473054270585 & -9.54730542705853 \tabularnewline
45 & 80 & 85.6916615663768 & -5.69166156637684 \tabularnewline
46 & 79 & 82.200797744866 & -3.20079774486601 \tabularnewline
47 & 77 & 80.2376541325449 & -3.23765413254492 \tabularnewline
48 & 72 & 78.2519054111815 & -6.25190541118151 \tabularnewline
49 & 65 & 74.417427524385 & -9.41742752438505 \tabularnewline
50 & 73 & 68.6414416100463 & 4.35855838995373 \tabularnewline
51 & 76 & 71.3146739552922 & 4.68532604470775 \tabularnewline
52 & 77 & 74.1883225358884 & 2.81167746411157 \tabularnewline
53 & 76 & 75.9128072092581 & 0.0871927907418524 \tabularnewline
54 & 76 & 75.9662851181513 & 0.0337148818487094 \tabularnewline
55 & 76 & 75.9869634490604 & 0.0130365509396029 \tabularnewline
56 & 75 & 75.9949591500524 & -0.994959150052381 \tabularnewline
57 & 78 & 75.3847213732111 & 2.61527862678895 \tabularnewline
58 & 73 & 76.9887488500661 & -3.98874885006606 \tabularnewline
59 & 80 & 74.5423315971421 & 5.45766840285791 \tabularnewline
60 & 77 & 77.8896805136497 & -0.889680513649694 \tabularnewline
61 & 83 & 77.3440132279927 & 5.65598677200727 \tabularnewline
62 & 84 & 80.8129966464697 & 3.18700335353029 \tabularnewline
63 & 85 & 82.7676797519437 & 2.23232024805628 \tabularnewline
64 & 81 & 84.1368275660022 & -3.13682756600224 \tabularnewline
65 & 84 & 82.2129187501368 & 1.78708124986323 \tabularnewline
66 & 83 & 83.3089883615313 & -0.308988361531306 \tabularnewline
67 & 83 & 83.1194766908253 & -0.119476690825252 \tabularnewline
68 & 88 & 83.0461981143232 & 4.95380188567682 \tabularnewline
69 & 92 & 86.0845108425077 & 5.91548915749232 \tabularnewline
70 & 92 & 89.7126547237987 & 2.28734527620128 \tabularnewline
71 & 89 & 91.1155510096855 & -2.11555100968553 \tabularnewline
72 & 82 & 89.8180212117265 & -7.81802121172655 \tabularnewline
73 & 73 & 85.0229983373792 & -12.0229983373792 \tabularnewline
74 & 81 & 77.6489390345596 & 3.35106096544037 \tabularnewline
75 & 91 & 79.7042435096252 & 11.2957564903748 \tabularnewline
76 & 80 & 86.632263949524 & -6.63226394952403 \tabularnewline
77 & 81 & 82.5645009586823 & -1.56450095868232 \tabularnewline
78 & 82 & 81.6049464012493 & 0.395053598750678 \tabularnewline
79 & 84 & 81.8472444190344 & 2.15275558096563 \tabularnewline
80 & 87 & 83.1675928773023 & 3.83240712269772 \tabularnewline
81 & 85 & 85.5181211400785 & -0.518121140078478 \tabularnewline
82 & 74 & 85.2003421713245 & -11.2003421713245 \tabularnewline
83 & 81 & 78.3308421459904 & 2.66915785400957 \tabularnewline
84 & 82 & 79.9679153411901 & 2.03208465880994 \tabularnewline
85 & 86 & 81.2142527656765 & 4.78574723432345 \tabularnewline
86 & 85 & 84.1494926221514 & 0.850507377848629 \tabularnewline
87 & 82 & 84.671133868848 & -2.67113386884796 \tabularnewline
88 & 86 & 83.0328487254976 & 2.96715127450243 \tabularnewline
89 & 88 & 84.8526900699477 & 3.14730993005232 \tabularnewline
90 & 86 & 86.7830280286917 & -0.78302802869166 \tabularnewline
91 & 83 & 86.3027738560373 & -3.30277385603735 \tabularnewline
92 & 81 & 84.2770852886105 & -3.27708528861046 \tabularnewline
93 & 81 & 82.2671522768524 & -1.26715227685236 \tabularnewline
94 & 81 & 81.4899704314418 & -0.489970431441819 \tabularnewline
95 & 82 & 81.1894571221413 & 0.810542877858666 \tabularnewline
96 & 86 & 81.6865869628921 & 4.31341303710792 \tabularnewline
97 & 85 & 84.3321303324109 & 0.66786966758913 \tabularnewline
98 & 87 & 84.7417544874808 & 2.25824551251915 \tabularnewline
99 & 89 & 86.1268030304778 & 2.87319696952216 \tabularnewline
100 & 90 & 87.8890194300317 & 2.11098056996835 \tabularnewline
101 & 90 & 89.1837460425814 & 0.816253957418581 \tabularnewline
102 & 92 & 89.6843786567815 & 2.31562134321847 \tabularnewline
103 & 86 & 91.104617488112 & -5.10461748811201 \tabularnewline
104 & 86 & 87.9738051051043 & -1.9738051051043 \tabularnewline
105 & 82 & 86.7632122489117 & -4.76321224891167 \tabularnewline
106 & 80 & 83.8417937632922 & -3.84179376329223 \tabularnewline
107 & 79 & 81.4855083971333 & -2.48550839713334 \tabularnewline
108 & 77 & 79.9610728275853 & -2.96107282758534 \tabularnewline
109 & 79 & 78.1449595738143 & 0.855040426185738 \tabularnewline
110 & 76 & 78.6693810726844 & -2.66938107268442 \tabularnewline
111 & 78 & 77.0321709708913 & 0.9678290291087 \tabularnewline
112 & 78 & 77.6257690447968 & 0.37423095520316 \tabularnewline
113 & 77 & 77.8552959214695 & -0.855295921469477 \tabularnewline
114 & 72 & 77.3307177198101 & -5.33071771981014 \tabularnewline
115 & 75 & 74.0612313995584 & 0.938768600441648 \tabularnewline
116 & 79 & 74.6370058558984 & 4.36299414410162 \tabularnewline
117 & 81 & 77.3129587799236 & 3.68704122007642 \tabularnewline
118 & 86 & 79.5743298494226 & 6.42567015057743 \tabularnewline
119 & 88 & 83.5153827732512 & 4.48461722674875 \tabularnewline
120 & 97 & 86.2659307191558 & 10.7340692808442 \tabularnewline
121 & 94 & 92.8494518356341 & 1.15054816436594 \tabularnewline
122 & 96 & 93.5551169415176 & 2.44488305848238 \tabularnewline
123 & 94 & 95.0546357932883 & -1.05463579328833 \tabularnewline
124 & 91 & 94.4077965719598 & -3.40779657195981 \tabularnewline
125 & 92 & 92.3176944769233 & -0.317694476923279 \tabularnewline
126 & 93 & 92.1228430890023 & 0.877156910997741 \tabularnewline
127 & 93 & 92.6608292799731 & 0.3391707200269 \tabularnewline
128 & 87 & 92.8688526808816 & -5.86885268088156 \tabularnewline
129 & 84 & 89.2693123254792 & -5.26931232547916 \tabularnewline
130 & 80 & 86.0374877437225 & -6.03748774372249 \tabularnewline
131 & 78 & 82.3345185331353 & -4.33451853313531 \tabularnewline
132 & 75 & 79.6760305407401 & -4.67603054074009 \tabularnewline
133 & 73 & 76.8080831667467 & -3.80808316674674 \tabularnewline
134 & 81 & 74.4724735031942 & 6.52752649680583 \tabularnewline
135 & 76 & 78.4759979267586 & -2.47599792675861 \tabularnewline
136 & 77 & 76.9573954090478 & 0.042604590952152 \tabularnewline
137 & 71 & 76.9835260606073 & -5.98352606060726 \tabularnewline
138 & 71 & 73.3136531409956 & -2.31365314099557 \tabularnewline
139 & 78 & 71.8946214661085 & 6.10537853389154 \tabularnewline
140 & 67 & 75.6392300996394 & -8.63923009963936 \tabularnewline
141 & 76 & 70.3405356061548 & 5.65946439384516 \tabularnewline
142 & 68 & 73.8116519526208 & -5.81165195262082 \tabularnewline
143 & 82 & 70.2471944900645 & 11.7528055099355 \tabularnewline
144 & 64 & 77.4555365668763 & -13.4555365668763 \tabularnewline
145 & 71 & 69.2028593385243 & 1.79714066147568 \tabularnewline
146 & 81 & 70.3050986835993 & 10.6949013164007 \tabularnewline
147 & 69 & 76.8645969328632 & -7.86459693286321 \tabularnewline
148 & 63 & 72.0410077957505 & -9.04100779575052 \tabularnewline
149 & 70 & 66.4958911973521 & 3.50410880264795 \tabularnewline
150 & 77 & 68.6450644226301 & 8.35493557736987 \tabularnewline
151 & 75 & 73.7693927049704 & 1.23060729502957 \tabularnewline
152 & 76 & 74.5241604357301 & 1.47583956426995 \tabularnewline
153 & 68 & 75.4293363463462 & -7.42933634634623 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287215&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]2[/C][C]72[/C][C]67[/C][C]5[/C][/ROW]
[ROW][C]3[/C][C]74[/C][C]70.0666473935598[/C][C]3.9333526064402[/C][/ROW]
[ROW][C]4[/C][C]62[/C][C]72.4790884972581[/C][C]-10.4790884972581[/C][/ROW]
[ROW][C]5[/C][C]56[/C][C]66.0519546118583[/C][C]-10.0519546118583[/C][/ROW]
[ROW][C]6[/C][C]66[/C][C]59.8867945297309[/C][C]6.11320547026908[/C][/ROW]
[ROW][C]7[/C][C]65[/C][C]63.6362036540702[/C][C]1.36379634592982[/C][/ROW]
[ROW][C]8[/C][C]59[/C][C]64.4726601559886[/C][C]-5.47266015598859[/C][/ROW]
[ROW][C]9[/C][C]61[/C][C]61.1161163553484[/C][C]-0.116116355348375[/C][/ROW]
[ROW][C]10[/C][C]69[/C][C]61.0448987716526[/C][C]7.95510122834737[/C][/ROW]
[ROW][C]11[/C][C]74[/C][C]65.9239968611358[/C][C]8.07600313886418[/C][/ROW]
[ROW][C]12[/C][C]69[/C][C]70.8772476563716[/C][C]-1.87724765637157[/C][/ROW]
[ROW][C]13[/C][C]66[/C][C]69.7258763298759[/C][C]-3.72587632987594[/C][/ROW]
[ROW][C]14[/C][C]68[/C][C]67.4406865427279[/C][C]0.559313457272111[/C][/ROW]
[ROW][C]15[/C][C]58[/C][C]67.7837299739132[/C][C]-9.78372997391318[/C][/ROW]
[ROW][C]16[/C][C]64[/C][C]61.7830799691544[/C][C]2.2169200308456[/C][/ROW]
[ROW][C]17[/C][C]66[/C][C]63.142782376019[/C][C]2.85721762398096[/C][/ROW]
[ROW][C]18[/C][C]57[/C][C]64.8951981719019[/C][C]-7.89519817190192[/C][/ROW]
[ROW][C]19[/C][C]68[/C][C]60.0528403928017[/C][C]7.94715960719833[/C][/ROW]
[ROW][C]20[/C][C]62[/C][C]64.9270676519254[/C][C]-2.9270676519254[/C][/ROW]
[ROW][C]21[/C][C]59[/C][C]63.1318107748153[/C][C]-4.13181077481534[/C][/ROW]
[ROW][C]22[/C][C]73[/C][C]60.5976494261614[/C][C]12.4023505738386[/C][/ROW]
[ROW][C]23[/C][C]61[/C][C]68.2043766384168[/C][C]-7.20437663841683[/C][/ROW]
[ROW][C]24[/C][C]61[/C][C]63.785720070332[/C][C]-2.78572007033199[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]62.0771558317578[/C][C]-5.07715583175782[/C][/ROW]
[ROW][C]26[/C][C]58[/C][C]58.9631864921264[/C][C]-0.963186492126397[/C][/ROW]
[ROW][C]27[/C][C]57[/C][C]58.3724358230081[/C][C]-1.3724358230081[/C][/ROW]
[ROW][C]28[/C][C]67[/C][C]57.5306804751169[/C][C]9.46931952488308[/C][/ROW]
[ROW][C]29[/C][C]81[/C][C]63.3384932830705[/C][C]17.6615067169295[/C][/ROW]
[ROW][C]30[/C][C]79[/C][C]74.1708159910327[/C][C]4.8291840089673[/C][/ROW]
[ROW][C]31[/C][C]76[/C][C]77.1326969018568[/C][C]-1.13269690185676[/C][/ROW]
[ROW][C]32[/C][C]78[/C][C]76.4379805015023[/C][C]1.5620194984977[/C][/ROW]
[ROW][C]33[/C][C]74[/C][C]77.3960131062538[/C][C]-3.3960131062538[/C][/ROW]
[ROW][C]34[/C][C]67[/C][C]75.3131381580962[/C][C]-8.31313815809617[/C][/ROW]
[ROW][C]35[/C][C]84[/C][C]70.2144454651305[/C][C]13.7855545348695[/C][/ROW]
[ROW][C]36[/C][C]85[/C][C]78.6695324417574[/C][C]6.33046755824265[/C][/ROW]
[ROW][C]37[/C][C]79[/C][C]82.5521948092573[/C][C]-3.55219480925732[/C][/ROW]
[ROW][C]38[/C][C]82[/C][C]80.3735290186122[/C][C]1.62647098138781[/C][/ROW]
[ROW][C]39[/C][C]87[/C][C]81.3710916177669[/C][C]5.62890838223309[/C][/ROW]
[ROW][C]40[/C][C]90[/C][C]84.8234670615593[/C][C]5.17653293844067[/C][/ROW]
[ROW][C]41[/C][C]87[/C][C]87.9983873102285[/C][C]-0.998387310228452[/C][/ROW]
[ROW][C]42[/C][C]93[/C][C]87.3860469416934[/C][C]5.61395305830661[/C][/ROW]
[ROW][C]43[/C][C]92[/C][C]90.829249844458[/C][C]1.17075015554198[/C][/ROW]
[ROW][C]44[/C][C]82[/C][C]91.5473054270585[/C][C]-9.54730542705853[/C][/ROW]
[ROW][C]45[/C][C]80[/C][C]85.6916615663768[/C][C]-5.69166156637684[/C][/ROW]
[ROW][C]46[/C][C]79[/C][C]82.200797744866[/C][C]-3.20079774486601[/C][/ROW]
[ROW][C]47[/C][C]77[/C][C]80.2376541325449[/C][C]-3.23765413254492[/C][/ROW]
[ROW][C]48[/C][C]72[/C][C]78.2519054111815[/C][C]-6.25190541118151[/C][/ROW]
[ROW][C]49[/C][C]65[/C][C]74.417427524385[/C][C]-9.41742752438505[/C][/ROW]
[ROW][C]50[/C][C]73[/C][C]68.6414416100463[/C][C]4.35855838995373[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]71.3146739552922[/C][C]4.68532604470775[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]74.1883225358884[/C][C]2.81167746411157[/C][/ROW]
[ROW][C]53[/C][C]76[/C][C]75.9128072092581[/C][C]0.0871927907418524[/C][/ROW]
[ROW][C]54[/C][C]76[/C][C]75.9662851181513[/C][C]0.0337148818487094[/C][/ROW]
[ROW][C]55[/C][C]76[/C][C]75.9869634490604[/C][C]0.0130365509396029[/C][/ROW]
[ROW][C]56[/C][C]75[/C][C]75.9949591500524[/C][C]-0.994959150052381[/C][/ROW]
[ROW][C]57[/C][C]78[/C][C]75.3847213732111[/C][C]2.61527862678895[/C][/ROW]
[ROW][C]58[/C][C]73[/C][C]76.9887488500661[/C][C]-3.98874885006606[/C][/ROW]
[ROW][C]59[/C][C]80[/C][C]74.5423315971421[/C][C]5.45766840285791[/C][/ROW]
[ROW][C]60[/C][C]77[/C][C]77.8896805136497[/C][C]-0.889680513649694[/C][/ROW]
[ROW][C]61[/C][C]83[/C][C]77.3440132279927[/C][C]5.65598677200727[/C][/ROW]
[ROW][C]62[/C][C]84[/C][C]80.8129966464697[/C][C]3.18700335353029[/C][/ROW]
[ROW][C]63[/C][C]85[/C][C]82.7676797519437[/C][C]2.23232024805628[/C][/ROW]
[ROW][C]64[/C][C]81[/C][C]84.1368275660022[/C][C]-3.13682756600224[/C][/ROW]
[ROW][C]65[/C][C]84[/C][C]82.2129187501368[/C][C]1.78708124986323[/C][/ROW]
[ROW][C]66[/C][C]83[/C][C]83.3089883615313[/C][C]-0.308988361531306[/C][/ROW]
[ROW][C]67[/C][C]83[/C][C]83.1194766908253[/C][C]-0.119476690825252[/C][/ROW]
[ROW][C]68[/C][C]88[/C][C]83.0461981143232[/C][C]4.95380188567682[/C][/ROW]
[ROW][C]69[/C][C]92[/C][C]86.0845108425077[/C][C]5.91548915749232[/C][/ROW]
[ROW][C]70[/C][C]92[/C][C]89.7126547237987[/C][C]2.28734527620128[/C][/ROW]
[ROW][C]71[/C][C]89[/C][C]91.1155510096855[/C][C]-2.11555100968553[/C][/ROW]
[ROW][C]72[/C][C]82[/C][C]89.8180212117265[/C][C]-7.81802121172655[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]85.0229983373792[/C][C]-12.0229983373792[/C][/ROW]
[ROW][C]74[/C][C]81[/C][C]77.6489390345596[/C][C]3.35106096544037[/C][/ROW]
[ROW][C]75[/C][C]91[/C][C]79.7042435096252[/C][C]11.2957564903748[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]86.632263949524[/C][C]-6.63226394952403[/C][/ROW]
[ROW][C]77[/C][C]81[/C][C]82.5645009586823[/C][C]-1.56450095868232[/C][/ROW]
[ROW][C]78[/C][C]82[/C][C]81.6049464012493[/C][C]0.395053598750678[/C][/ROW]
[ROW][C]79[/C][C]84[/C][C]81.8472444190344[/C][C]2.15275558096563[/C][/ROW]
[ROW][C]80[/C][C]87[/C][C]83.1675928773023[/C][C]3.83240712269772[/C][/ROW]
[ROW][C]81[/C][C]85[/C][C]85.5181211400785[/C][C]-0.518121140078478[/C][/ROW]
[ROW][C]82[/C][C]74[/C][C]85.2003421713245[/C][C]-11.2003421713245[/C][/ROW]
[ROW][C]83[/C][C]81[/C][C]78.3308421459904[/C][C]2.66915785400957[/C][/ROW]
[ROW][C]84[/C][C]82[/C][C]79.9679153411901[/C][C]2.03208465880994[/C][/ROW]
[ROW][C]85[/C][C]86[/C][C]81.2142527656765[/C][C]4.78574723432345[/C][/ROW]
[ROW][C]86[/C][C]85[/C][C]84.1494926221514[/C][C]0.850507377848629[/C][/ROW]
[ROW][C]87[/C][C]82[/C][C]84.671133868848[/C][C]-2.67113386884796[/C][/ROW]
[ROW][C]88[/C][C]86[/C][C]83.0328487254976[/C][C]2.96715127450243[/C][/ROW]
[ROW][C]89[/C][C]88[/C][C]84.8526900699477[/C][C]3.14730993005232[/C][/ROW]
[ROW][C]90[/C][C]86[/C][C]86.7830280286917[/C][C]-0.78302802869166[/C][/ROW]
[ROW][C]91[/C][C]83[/C][C]86.3027738560373[/C][C]-3.30277385603735[/C][/ROW]
[ROW][C]92[/C][C]81[/C][C]84.2770852886105[/C][C]-3.27708528861046[/C][/ROW]
[ROW][C]93[/C][C]81[/C][C]82.2671522768524[/C][C]-1.26715227685236[/C][/ROW]
[ROW][C]94[/C][C]81[/C][C]81.4899704314418[/C][C]-0.489970431441819[/C][/ROW]
[ROW][C]95[/C][C]82[/C][C]81.1894571221413[/C][C]0.810542877858666[/C][/ROW]
[ROW][C]96[/C][C]86[/C][C]81.6865869628921[/C][C]4.31341303710792[/C][/ROW]
[ROW][C]97[/C][C]85[/C][C]84.3321303324109[/C][C]0.66786966758913[/C][/ROW]
[ROW][C]98[/C][C]87[/C][C]84.7417544874808[/C][C]2.25824551251915[/C][/ROW]
[ROW][C]99[/C][C]89[/C][C]86.1268030304778[/C][C]2.87319696952216[/C][/ROW]
[ROW][C]100[/C][C]90[/C][C]87.8890194300317[/C][C]2.11098056996835[/C][/ROW]
[ROW][C]101[/C][C]90[/C][C]89.1837460425814[/C][C]0.816253957418581[/C][/ROW]
[ROW][C]102[/C][C]92[/C][C]89.6843786567815[/C][C]2.31562134321847[/C][/ROW]
[ROW][C]103[/C][C]86[/C][C]91.104617488112[/C][C]-5.10461748811201[/C][/ROW]
[ROW][C]104[/C][C]86[/C][C]87.9738051051043[/C][C]-1.9738051051043[/C][/ROW]
[ROW][C]105[/C][C]82[/C][C]86.7632122489117[/C][C]-4.76321224891167[/C][/ROW]
[ROW][C]106[/C][C]80[/C][C]83.8417937632922[/C][C]-3.84179376329223[/C][/ROW]
[ROW][C]107[/C][C]79[/C][C]81.4855083971333[/C][C]-2.48550839713334[/C][/ROW]
[ROW][C]108[/C][C]77[/C][C]79.9610728275853[/C][C]-2.96107282758534[/C][/ROW]
[ROW][C]109[/C][C]79[/C][C]78.1449595738143[/C][C]0.855040426185738[/C][/ROW]
[ROW][C]110[/C][C]76[/C][C]78.6693810726844[/C][C]-2.66938107268442[/C][/ROW]
[ROW][C]111[/C][C]78[/C][C]77.0321709708913[/C][C]0.9678290291087[/C][/ROW]
[ROW][C]112[/C][C]78[/C][C]77.6257690447968[/C][C]0.37423095520316[/C][/ROW]
[ROW][C]113[/C][C]77[/C][C]77.8552959214695[/C][C]-0.855295921469477[/C][/ROW]
[ROW][C]114[/C][C]72[/C][C]77.3307177198101[/C][C]-5.33071771981014[/C][/ROW]
[ROW][C]115[/C][C]75[/C][C]74.0612313995584[/C][C]0.938768600441648[/C][/ROW]
[ROW][C]116[/C][C]79[/C][C]74.6370058558984[/C][C]4.36299414410162[/C][/ROW]
[ROW][C]117[/C][C]81[/C][C]77.3129587799236[/C][C]3.68704122007642[/C][/ROW]
[ROW][C]118[/C][C]86[/C][C]79.5743298494226[/C][C]6.42567015057743[/C][/ROW]
[ROW][C]119[/C][C]88[/C][C]83.5153827732512[/C][C]4.48461722674875[/C][/ROW]
[ROW][C]120[/C][C]97[/C][C]86.2659307191558[/C][C]10.7340692808442[/C][/ROW]
[ROW][C]121[/C][C]94[/C][C]92.8494518356341[/C][C]1.15054816436594[/C][/ROW]
[ROW][C]122[/C][C]96[/C][C]93.5551169415176[/C][C]2.44488305848238[/C][/ROW]
[ROW][C]123[/C][C]94[/C][C]95.0546357932883[/C][C]-1.05463579328833[/C][/ROW]
[ROW][C]124[/C][C]91[/C][C]94.4077965719598[/C][C]-3.40779657195981[/C][/ROW]
[ROW][C]125[/C][C]92[/C][C]92.3176944769233[/C][C]-0.317694476923279[/C][/ROW]
[ROW][C]126[/C][C]93[/C][C]92.1228430890023[/C][C]0.877156910997741[/C][/ROW]
[ROW][C]127[/C][C]93[/C][C]92.6608292799731[/C][C]0.3391707200269[/C][/ROW]
[ROW][C]128[/C][C]87[/C][C]92.8688526808816[/C][C]-5.86885268088156[/C][/ROW]
[ROW][C]129[/C][C]84[/C][C]89.2693123254792[/C][C]-5.26931232547916[/C][/ROW]
[ROW][C]130[/C][C]80[/C][C]86.0374877437225[/C][C]-6.03748774372249[/C][/ROW]
[ROW][C]131[/C][C]78[/C][C]82.3345185331353[/C][C]-4.33451853313531[/C][/ROW]
[ROW][C]132[/C][C]75[/C][C]79.6760305407401[/C][C]-4.67603054074009[/C][/ROW]
[ROW][C]133[/C][C]73[/C][C]76.8080831667467[/C][C]-3.80808316674674[/C][/ROW]
[ROW][C]134[/C][C]81[/C][C]74.4724735031942[/C][C]6.52752649680583[/C][/ROW]
[ROW][C]135[/C][C]76[/C][C]78.4759979267586[/C][C]-2.47599792675861[/C][/ROW]
[ROW][C]136[/C][C]77[/C][C]76.9573954090478[/C][C]0.042604590952152[/C][/ROW]
[ROW][C]137[/C][C]71[/C][C]76.9835260606073[/C][C]-5.98352606060726[/C][/ROW]
[ROW][C]138[/C][C]71[/C][C]73.3136531409956[/C][C]-2.31365314099557[/C][/ROW]
[ROW][C]139[/C][C]78[/C][C]71.8946214661085[/C][C]6.10537853389154[/C][/ROW]
[ROW][C]140[/C][C]67[/C][C]75.6392300996394[/C][C]-8.63923009963936[/C][/ROW]
[ROW][C]141[/C][C]76[/C][C]70.3405356061548[/C][C]5.65946439384516[/C][/ROW]
[ROW][C]142[/C][C]68[/C][C]73.8116519526208[/C][C]-5.81165195262082[/C][/ROW]
[ROW][C]143[/C][C]82[/C][C]70.2471944900645[/C][C]11.7528055099355[/C][/ROW]
[ROW][C]144[/C][C]64[/C][C]77.4555365668763[/C][C]-13.4555365668763[/C][/ROW]
[ROW][C]145[/C][C]71[/C][C]69.2028593385243[/C][C]1.79714066147568[/C][/ROW]
[ROW][C]146[/C][C]81[/C][C]70.3050986835993[/C][C]10.6949013164007[/C][/ROW]
[ROW][C]147[/C][C]69[/C][C]76.8645969328632[/C][C]-7.86459693286321[/C][/ROW]
[ROW][C]148[/C][C]63[/C][C]72.0410077957505[/C][C]-9.04100779575052[/C][/ROW]
[ROW][C]149[/C][C]70[/C][C]66.4958911973521[/C][C]3.50410880264795[/C][/ROW]
[ROW][C]150[/C][C]77[/C][C]68.6450644226301[/C][C]8.35493557736987[/C][/ROW]
[ROW][C]151[/C][C]75[/C][C]73.7693927049704[/C][C]1.23060729502957[/C][/ROW]
[ROW][C]152[/C][C]76[/C][C]74.5241604357301[/C][C]1.47583956426995[/C][/ROW]
[ROW][C]153[/C][C]68[/C][C]75.4293363463462[/C][C]-7.42933634634623[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287215&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287215&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
tObservedFittedResiduals
272675
37470.06664739355983.9333526064402
46272.4790884972581-10.4790884972581
55666.0519546118583-10.0519546118583
66659.88679452973096.11320547026908
76563.63620365407021.36379634592982
85964.4726601559886-5.47266015598859
96161.1161163553484-0.116116355348375
106961.04489877165267.95510122834737
117465.92399686113588.07600313886418
126970.8772476563716-1.87724765637157
136669.7258763298759-3.72587632987594
146867.44068654272790.559313457272111
155867.7837299739132-9.78372997391318
166461.78307996915442.2169200308456
176663.1427823760192.85721762398096
185764.8951981719019-7.89519817190192
196860.05284039280177.94715960719833
206264.9270676519254-2.9270676519254
215963.1318107748153-4.13181077481534
227360.597649426161412.4023505738386
236168.2043766384168-7.20437663841683
246163.785720070332-2.78572007033199
255762.0771558317578-5.07715583175782
265858.9631864921264-0.963186492126397
275758.3724358230081-1.3724358230081
286757.53068047511699.46931952488308
298163.338493283070517.6615067169295
307974.17081599103274.8291840089673
317677.1326969018568-1.13269690185676
327876.43798050150231.5620194984977
337477.3960131062538-3.3960131062538
346775.3131381580962-8.31313815809617
358470.214445465130513.7855545348695
368578.66953244175746.33046755824265
377982.5521948092573-3.55219480925732
388280.37352901861221.62647098138781
398781.37109161776695.62890838223309
409084.82346706155935.17653293844067
418787.9983873102285-0.998387310228452
429387.38604694169345.61395305830661
439290.8292498444581.17075015554198
448291.5473054270585-9.54730542705853
458085.6916615663768-5.69166156637684
467982.200797744866-3.20079774486601
477780.2376541325449-3.23765413254492
487278.2519054111815-6.25190541118151
496574.417427524385-9.41742752438505
507368.64144161004634.35855838995373
517671.31467395529224.68532604470775
527774.18832253588842.81167746411157
537675.91280720925810.0871927907418524
547675.96628511815130.0337148818487094
557675.98696344906040.0130365509396029
567575.9949591500524-0.994959150052381
577875.38472137321112.61527862678895
587376.9887488500661-3.98874885006606
598074.54233159714215.45766840285791
607777.8896805136497-0.889680513649694
618377.34401322799275.65598677200727
628480.81299664646973.18700335353029
638582.76767975194372.23232024805628
648184.1368275660022-3.13682756600224
658482.21291875013681.78708124986323
668383.3089883615313-0.308988361531306
678383.1194766908253-0.119476690825252
688883.04619811432324.95380188567682
699286.08451084250775.91548915749232
709289.71265472379872.28734527620128
718991.1155510096855-2.11555100968553
728289.8180212117265-7.81802121172655
737385.0229983373792-12.0229983373792
748177.64893903455963.35106096544037
759179.704243509625211.2957564903748
768086.632263949524-6.63226394952403
778182.5645009586823-1.56450095868232
788281.60494640124930.395053598750678
798481.84724441903442.15275558096563
808783.16759287730233.83240712269772
818585.5181211400785-0.518121140078478
827485.2003421713245-11.2003421713245
838178.33084214599042.66915785400957
848279.96791534119012.03208465880994
858681.21425276567654.78574723432345
868584.14949262215140.850507377848629
878284.671133868848-2.67113386884796
888683.03284872549762.96715127450243
898884.85269006994773.14730993005232
908686.7830280286917-0.78302802869166
918386.3027738560373-3.30277385603735
928184.2770852886105-3.27708528861046
938182.2671522768524-1.26715227685236
948181.4899704314418-0.489970431441819
958281.18945712214130.810542877858666
968681.68658696289214.31341303710792
978584.33213033241090.66786966758913
988784.74175448748082.25824551251915
998986.12680303047782.87319696952216
1009087.88901943003172.11098056996835
1019089.18374604258140.816253957418581
1029289.68437865678152.31562134321847
1038691.104617488112-5.10461748811201
1048687.9738051051043-1.9738051051043
1058286.7632122489117-4.76321224891167
1068083.8417937632922-3.84179376329223
1077981.4855083971333-2.48550839713334
1087779.9610728275853-2.96107282758534
1097978.14495957381430.855040426185738
1107678.6693810726844-2.66938107268442
1117877.03217097089130.9678290291087
1127877.62576904479680.37423095520316
1137777.8552959214695-0.855295921469477
1147277.3307177198101-5.33071771981014
1157574.06123139955840.938768600441648
1167974.63700585589844.36299414410162
1178177.31295877992363.68704122007642
1188679.57432984942266.42567015057743
1198883.51538277325124.48461722674875
1209786.265930719155810.7340692808442
1219492.84945183563411.15054816436594
1229693.55511694151762.44488305848238
1239495.0546357932883-1.05463579328833
1249194.4077965719598-3.40779657195981
1259292.3176944769233-0.317694476923279
1269392.12284308900230.877156910997741
1279392.66082927997310.3391707200269
1288792.8688526808816-5.86885268088156
1298489.2693123254792-5.26931232547916
1308086.0374877437225-6.03748774372249
1317882.3345185331353-4.33451853313531
1327579.6760305407401-4.67603054074009
1337376.8080831667467-3.80808316674674
1348174.47247350319426.52752649680583
1357678.4759979267586-2.47599792675861
1367776.95739540904780.042604590952152
1377176.9835260606073-5.98352606060726
1387173.3136531409956-2.31365314099557
1397871.89462146610856.10537853389154
1406775.6392300996394-8.63923009963936
1417670.34053560615485.65946439384516
1426873.8116519526208-5.81165195262082
1438270.247194490064511.7528055099355
1446477.4555365668763-13.4555365668763
1457169.20285933852431.79714066147568
1468170.305098683599310.6949013164007
1476976.8645969328632-7.86459693286321
1486372.0410077957505-9.04100779575052
1497066.49589119735213.50410880264795
1507768.64506442263018.35493557736987
1517573.76939270497041.23060729502957
1527674.52416043573011.47583956426995
1536875.4293363463462-7.42933634634623







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
15470.872705357865960.198007196919681.5474035188121
15570.872705357865958.35017399786383.3952367178687
15670.872705357865956.74194449734685.0034662183857
15770.872705357865955.298912841842586.4464978738893
15870.872705357865953.978693869557687.7667168461741
15970.872705357865952.754420808769188.9909899069626
16070.872705357865951.607793060194190.1376176555376
16170.872705357865950.525679612761791.21973110297
16270.872705357865949.498279809868692.2471309058631
16370.872705357865948.518048609397993.2273621063338
16470.872705357865947.579030482982694.1663802327491
16570.872705357865946.676426718130595.0689839976012

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
154 & 70.8727053578659 & 60.1980071969196 & 81.5474035188121 \tabularnewline
155 & 70.8727053578659 & 58.350173997863 & 83.3952367178687 \tabularnewline
156 & 70.8727053578659 & 56.741944497346 & 85.0034662183857 \tabularnewline
157 & 70.8727053578659 & 55.2989128418425 & 86.4464978738893 \tabularnewline
158 & 70.8727053578659 & 53.9786938695576 & 87.7667168461741 \tabularnewline
159 & 70.8727053578659 & 52.7544208087691 & 88.9909899069626 \tabularnewline
160 & 70.8727053578659 & 51.6077930601941 & 90.1376176555376 \tabularnewline
161 & 70.8727053578659 & 50.5256796127617 & 91.21973110297 \tabularnewline
162 & 70.8727053578659 & 49.4982798098686 & 92.2471309058631 \tabularnewline
163 & 70.8727053578659 & 48.5180486093979 & 93.2273621063338 \tabularnewline
164 & 70.8727053578659 & 47.5790304829826 & 94.1663802327491 \tabularnewline
165 & 70.8727053578659 & 46.6764267181305 & 95.0689839976012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287215&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]154[/C][C]70.8727053578659[/C][C]60.1980071969196[/C][C]81.5474035188121[/C][/ROW]
[ROW][C]155[/C][C]70.8727053578659[/C][C]58.350173997863[/C][C]83.3952367178687[/C][/ROW]
[ROW][C]156[/C][C]70.8727053578659[/C][C]56.741944497346[/C][C]85.0034662183857[/C][/ROW]
[ROW][C]157[/C][C]70.8727053578659[/C][C]55.2989128418425[/C][C]86.4464978738893[/C][/ROW]
[ROW][C]158[/C][C]70.8727053578659[/C][C]53.9786938695576[/C][C]87.7667168461741[/C][/ROW]
[ROW][C]159[/C][C]70.8727053578659[/C][C]52.7544208087691[/C][C]88.9909899069626[/C][/ROW]
[ROW][C]160[/C][C]70.8727053578659[/C][C]51.6077930601941[/C][C]90.1376176555376[/C][/ROW]
[ROW][C]161[/C][C]70.8727053578659[/C][C]50.5256796127617[/C][C]91.21973110297[/C][/ROW]
[ROW][C]162[/C][C]70.8727053578659[/C][C]49.4982798098686[/C][C]92.2471309058631[/C][/ROW]
[ROW][C]163[/C][C]70.8727053578659[/C][C]48.5180486093979[/C][C]93.2273621063338[/C][/ROW]
[ROW][C]164[/C][C]70.8727053578659[/C][C]47.5790304829826[/C][C]94.1663802327491[/C][/ROW]
[ROW][C]165[/C][C]70.8727053578659[/C][C]46.6764267181305[/C][C]95.0689839976012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287215&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287215&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
tForecast95% Lower Bound95% Upper Bound
15470.872705357865960.198007196919681.5474035188121
15570.872705357865958.35017399786383.3952367178687
15670.872705357865956.74194449734685.0034662183857
15770.872705357865955.298912841842586.4464978738893
15870.872705357865953.978693869557687.7667168461741
15970.872705357865952.754420808769188.9909899069626
16070.872705357865951.607793060194190.1376176555376
16170.872705357865950.525679612761791.21973110297
16270.872705357865949.498279809868692.2471309058631
16370.872705357865948.518048609397993.2273621063338
16470.872705357865947.579030482982694.1663802327491
16570.872705357865946.676426718130595.0689839976012



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = Exact Pearson Chi-Squared by Simulation ;
Parameters (R input):
par1 = 12 ; par2 = Single ; 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')