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, 21 Jan 2015 08:21:04 +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/Jan/21/t1421828482hmjfl6m1q4dzoi5.htm/, Retrieved Thu, 31 Oct 2024 22:57:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=275752, Retrieved Thu, 31 Oct 2024 22:57:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [vraag 5.3] [2015-01-21 08:21:04] [3c8f34fed408bc4f957cadcbcbd22146] [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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275752&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275752&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275752&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'Gwilym Jenkins' @ jenkins.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.572095490251119
beta0
gamma0.62569921923147

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.572095490251119 \tabularnewline
beta & 0 \tabularnewline
gamma & 0.62569921923147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275752&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]0.572095490251119[/C][/ROW]
[ROW][C]beta[/C][C]0[/C][/ROW]
[ROW][C]gamma[/C][C]0.62569921923147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275752&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275752&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
alpha0.572095490251119
beta0
gamma0.62569921923147







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
136667.2150106837607-1.21501068376068
146868.161371254937-0.161371254936967
155857.91884752502740.0811524749725976
166463.77340386061130.22659613938869
176666.1695011940266-0.16950119402658
185757.8389930292945-0.838993029294507
196863.04213827151244.95786172848762
206259.97830464496482.02169535503521
215963.8597034775381-4.8597034775381
227369.55428507137693.44571492862308
236175.9170257466958-14.9170257466958
246162.2328586263474-1.23285862634735
255758.3437020956432-1.34370209564317
265859.4985398029035-1.49853980290352
275748.55596116346268.44403883653735
286759.23382808853727.76617191146284
298165.837231810569415.1627681894306
307966.098996394379612.9010036056204
317680.7147788442654-4.7147788442654
327871.33114339864146.66885660135864
337476.0287398184778-2.02873981847779
346785.5665916727225-18.5665916727225
358474.41975968014389.58024031985623
368578.41416030427096.58583969572915
377978.96836864625610.0316313537438901
388280.86857224444551.13142775555447
398774.09260739388112.907392606119
409087.14244711837442.85755288162555
418792.9180131498315-5.91801314983149
429380.513994039760212.4860059602398
439290.17591711200231.82408288799769
448287.580984038159-5.58098403815903
458082.9418116016233-2.94181160162329
467987.5294627279413-8.52946272794134
477789.6608461609643-12.6608461609643
487280.1295021323577-8.1295021323577
496570.510309196894-5.51030919689403
507369.53445255696373.46554744303629
517667.24671820233218.75328179766785
527775.22927062695631.77072937304368
537678.0335040140385-2.0335040140385
547672.77927826184683.22072173815324
557674.28595587409611.71404412590385
567569.64544107369035.35455892630974
577871.96905420574226.03094579425778
587380.1939443559736-7.1939443559736
598081.9832372191393-1.98323721913934
607779.7737249773661-2.7737249773661
618373.91980945225089.08019054774918
628483.69430344406080.30569655593915
638581.01456809265453.98543190734553
648184.3999498786578-3.39994987865782
658483.22751737220020.772482627799803
668380.9853477920542.01465220794604
678381.39864132797651.60135867202347
688877.668369364267210.3316306357328
699283.02043826970948.97956173029057
709289.39139258955782.60860741044216
718998.1837933267032-9.18379332670324
728291.6432305973923-9.64323059739233
737385.0330635118287-12.0330635118287
748180.37948128258980.620518717410178
759178.865064838077612.1349351619224
768084.9353821267249-4.93538212672495
778184.0016608222911-3.00166082229109
788279.93289867902172.06710132097827
798480.26554310401973.73445689598034
808780.09304581906366.90695418093642
818583.12387049256751.87612950743249
827483.7252270358129-9.72522703581288
838182.3042050233882-1.30420502338819
848280.1485097141921.85149028580797
858679.47456789581426.52543210418575
868588.826081317013-3.82608131701302
878287.850649350795-5.85064935079504
888679.06109633309766.93890366690242
898885.43833399048022.56166600951984
908685.90943396983230.0905660301677216
918385.5577284460466-2.55772844604658
928182.6349031959302-1.63490319593022
938179.43201921426721.56798078573283
948176.75094365846354.24905634153647
958285.5791865174845-3.57918651748452
968682.9668888950633.03311110493701
978584.22034594649810.779654053501886
988787.5132164465206-0.513216446520588
998987.89100266369711.10899733630286
1009086.50730088541733.49269911458272
1019089.74102096088380.258979039116213
1029288.23315299245773.76684700754228
1038689.2755781510526-3.27557815105264
1048686.1891511093707-0.189151109370698
1058284.6709139888065-2.67091398880646
1068080.2826157249487-0.282615724948684
1077984.4223794642721-5.42237946427214
1087782.5259728453525-5.5259728453525
1097978.27947706413760.720522935862363
1107681.192366394358-5.192366394358
1117879.3275627639153-1.32756276391527
1127877.1881271049260.811872895074004
1137778.0223639368263-1.02236393682631
1147276.7206403082642-4.72064030826415
1157571.02187673483553.97812326516453
1167972.91161804841836.08838195158165
1178174.32026328565186.67973671434819
1188675.92087194516510.079128054835
1198884.61242470761063.38757529238939
1209787.72841743241079.27158256758933
1219493.61997013718040.380029862819569
1229694.75495077862771.24504922137227
1239497.6077242506644-3.60772425066438
1249194.7366299594544-3.73662995945442
1259292.4775911362457-0.477591136245735
1269390.49735483826172.50264516173829
1279391.26000307036691.73999692963309
1288792.4343220436096-5.43432204360964
1298487.409209328642-3.40920932864201
1308084.1481288533978-4.14812885339785
1317882.9087389910159-4.90873899101585
1327582.8538289983173-7.85382899831731
1337376.567390945154-3.567390945154
1348175.67566981005785.32433018994224
1357679.562902298986-3.562902298986
1367776.6829372919010.317062708099002
1377177.6155712452101-6.61557124521009
1387172.9217512365581-1.92175123655812
1397870.949031172317.05096882769004
1406773.2408845158788-6.24088451587878
1417668.29654374942367.70345625057637
1426871.1951311795748-3.19513117957479
1438270.297301597237111.7026984027629
1446478.9572030461012-14.9572030461012
1457169.7546055914481.24539440855196
1468173.99692238059157.0030776194085
1476976.4650953633101-7.46509536331014
1486372.3915232615532-9.39152326155325
1497065.91377889416794.08622110583212
1507768.59912713222938.40087286777069
1517574.93428550768480.0657144923152089
1527669.67115194477386.32884805522616
1536875.6513495667451-7.65134956674507

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 66 & 67.2150106837607 & -1.21501068376068 \tabularnewline
14 & 68 & 68.161371254937 & -0.161371254936967 \tabularnewline
15 & 58 & 57.9188475250274 & 0.0811524749725976 \tabularnewline
16 & 64 & 63.7734038606113 & 0.22659613938869 \tabularnewline
17 & 66 & 66.1695011940266 & -0.16950119402658 \tabularnewline
18 & 57 & 57.8389930292945 & -0.838993029294507 \tabularnewline
19 & 68 & 63.0421382715124 & 4.95786172848762 \tabularnewline
20 & 62 & 59.9783046449648 & 2.02169535503521 \tabularnewline
21 & 59 & 63.8597034775381 & -4.8597034775381 \tabularnewline
22 & 73 & 69.5542850713769 & 3.44571492862308 \tabularnewline
23 & 61 & 75.9170257466958 & -14.9170257466958 \tabularnewline
24 & 61 & 62.2328586263474 & -1.23285862634735 \tabularnewline
25 & 57 & 58.3437020956432 & -1.34370209564317 \tabularnewline
26 & 58 & 59.4985398029035 & -1.49853980290352 \tabularnewline
27 & 57 & 48.5559611634626 & 8.44403883653735 \tabularnewline
28 & 67 & 59.2338280885372 & 7.76617191146284 \tabularnewline
29 & 81 & 65.8372318105694 & 15.1627681894306 \tabularnewline
30 & 79 & 66.0989963943796 & 12.9010036056204 \tabularnewline
31 & 76 & 80.7147788442654 & -4.7147788442654 \tabularnewline
32 & 78 & 71.3311433986414 & 6.66885660135864 \tabularnewline
33 & 74 & 76.0287398184778 & -2.02873981847779 \tabularnewline
34 & 67 & 85.5665916727225 & -18.5665916727225 \tabularnewline
35 & 84 & 74.4197596801438 & 9.58024031985623 \tabularnewline
36 & 85 & 78.4141603042709 & 6.58583969572915 \tabularnewline
37 & 79 & 78.9683686462561 & 0.0316313537438901 \tabularnewline
38 & 82 & 80.8685722444455 & 1.13142775555447 \tabularnewline
39 & 87 & 74.092607393881 & 12.907392606119 \tabularnewline
40 & 90 & 87.1424471183744 & 2.85755288162555 \tabularnewline
41 & 87 & 92.9180131498315 & -5.91801314983149 \tabularnewline
42 & 93 & 80.5139940397602 & 12.4860059602398 \tabularnewline
43 & 92 & 90.1759171120023 & 1.82408288799769 \tabularnewline
44 & 82 & 87.580984038159 & -5.58098403815903 \tabularnewline
45 & 80 & 82.9418116016233 & -2.94181160162329 \tabularnewline
46 & 79 & 87.5294627279413 & -8.52946272794134 \tabularnewline
47 & 77 & 89.6608461609643 & -12.6608461609643 \tabularnewline
48 & 72 & 80.1295021323577 & -8.1295021323577 \tabularnewline
49 & 65 & 70.510309196894 & -5.51030919689403 \tabularnewline
50 & 73 & 69.5344525569637 & 3.46554744303629 \tabularnewline
51 & 76 & 67.2467182023321 & 8.75328179766785 \tabularnewline
52 & 77 & 75.2292706269563 & 1.77072937304368 \tabularnewline
53 & 76 & 78.0335040140385 & -2.0335040140385 \tabularnewline
54 & 76 & 72.7792782618468 & 3.22072173815324 \tabularnewline
55 & 76 & 74.2859558740961 & 1.71404412590385 \tabularnewline
56 & 75 & 69.6454410736903 & 5.35455892630974 \tabularnewline
57 & 78 & 71.9690542057422 & 6.03094579425778 \tabularnewline
58 & 73 & 80.1939443559736 & -7.1939443559736 \tabularnewline
59 & 80 & 81.9832372191393 & -1.98323721913934 \tabularnewline
60 & 77 & 79.7737249773661 & -2.7737249773661 \tabularnewline
61 & 83 & 73.9198094522508 & 9.08019054774918 \tabularnewline
62 & 84 & 83.6943034440608 & 0.30569655593915 \tabularnewline
63 & 85 & 81.0145680926545 & 3.98543190734553 \tabularnewline
64 & 81 & 84.3999498786578 & -3.39994987865782 \tabularnewline
65 & 84 & 83.2275173722002 & 0.772482627799803 \tabularnewline
66 & 83 & 80.985347792054 & 2.01465220794604 \tabularnewline
67 & 83 & 81.3986413279765 & 1.60135867202347 \tabularnewline
68 & 88 & 77.6683693642672 & 10.3316306357328 \tabularnewline
69 & 92 & 83.0204382697094 & 8.97956173029057 \tabularnewline
70 & 92 & 89.3913925895578 & 2.60860741044216 \tabularnewline
71 & 89 & 98.1837933267032 & -9.18379332670324 \tabularnewline
72 & 82 & 91.6432305973923 & -9.64323059739233 \tabularnewline
73 & 73 & 85.0330635118287 & -12.0330635118287 \tabularnewline
74 & 81 & 80.3794812825898 & 0.620518717410178 \tabularnewline
75 & 91 & 78.8650648380776 & 12.1349351619224 \tabularnewline
76 & 80 & 84.9353821267249 & -4.93538212672495 \tabularnewline
77 & 81 & 84.0016608222911 & -3.00166082229109 \tabularnewline
78 & 82 & 79.9328986790217 & 2.06710132097827 \tabularnewline
79 & 84 & 80.2655431040197 & 3.73445689598034 \tabularnewline
80 & 87 & 80.0930458190636 & 6.90695418093642 \tabularnewline
81 & 85 & 83.1238704925675 & 1.87612950743249 \tabularnewline
82 & 74 & 83.7252270358129 & -9.72522703581288 \tabularnewline
83 & 81 & 82.3042050233882 & -1.30420502338819 \tabularnewline
84 & 82 & 80.148509714192 & 1.85149028580797 \tabularnewline
85 & 86 & 79.4745678958142 & 6.52543210418575 \tabularnewline
86 & 85 & 88.826081317013 & -3.82608131701302 \tabularnewline
87 & 82 & 87.850649350795 & -5.85064935079504 \tabularnewline
88 & 86 & 79.0610963330976 & 6.93890366690242 \tabularnewline
89 & 88 & 85.4383339904802 & 2.56166600951984 \tabularnewline
90 & 86 & 85.9094339698323 & 0.0905660301677216 \tabularnewline
91 & 83 & 85.5577284460466 & -2.55772844604658 \tabularnewline
92 & 81 & 82.6349031959302 & -1.63490319593022 \tabularnewline
93 & 81 & 79.4320192142672 & 1.56798078573283 \tabularnewline
94 & 81 & 76.7509436584635 & 4.24905634153647 \tabularnewline
95 & 82 & 85.5791865174845 & -3.57918651748452 \tabularnewline
96 & 86 & 82.966888895063 & 3.03311110493701 \tabularnewline
97 & 85 & 84.2203459464981 & 0.779654053501886 \tabularnewline
98 & 87 & 87.5132164465206 & -0.513216446520588 \tabularnewline
99 & 89 & 87.8910026636971 & 1.10899733630286 \tabularnewline
100 & 90 & 86.5073008854173 & 3.49269911458272 \tabularnewline
101 & 90 & 89.7410209608838 & 0.258979039116213 \tabularnewline
102 & 92 & 88.2331529924577 & 3.76684700754228 \tabularnewline
103 & 86 & 89.2755781510526 & -3.27557815105264 \tabularnewline
104 & 86 & 86.1891511093707 & -0.189151109370698 \tabularnewline
105 & 82 & 84.6709139888065 & -2.67091398880646 \tabularnewline
106 & 80 & 80.2826157249487 & -0.282615724948684 \tabularnewline
107 & 79 & 84.4223794642721 & -5.42237946427214 \tabularnewline
108 & 77 & 82.5259728453525 & -5.5259728453525 \tabularnewline
109 & 79 & 78.2794770641376 & 0.720522935862363 \tabularnewline
110 & 76 & 81.192366394358 & -5.192366394358 \tabularnewline
111 & 78 & 79.3275627639153 & -1.32756276391527 \tabularnewline
112 & 78 & 77.188127104926 & 0.811872895074004 \tabularnewline
113 & 77 & 78.0223639368263 & -1.02236393682631 \tabularnewline
114 & 72 & 76.7206403082642 & -4.72064030826415 \tabularnewline
115 & 75 & 71.0218767348355 & 3.97812326516453 \tabularnewline
116 & 79 & 72.9116180484183 & 6.08838195158165 \tabularnewline
117 & 81 & 74.3202632856518 & 6.67973671434819 \tabularnewline
118 & 86 & 75.920871945165 & 10.079128054835 \tabularnewline
119 & 88 & 84.6124247076106 & 3.38757529238939 \tabularnewline
120 & 97 & 87.7284174324107 & 9.27158256758933 \tabularnewline
121 & 94 & 93.6199701371804 & 0.380029862819569 \tabularnewline
122 & 96 & 94.7549507786277 & 1.24504922137227 \tabularnewline
123 & 94 & 97.6077242506644 & -3.60772425066438 \tabularnewline
124 & 91 & 94.7366299594544 & -3.73662995945442 \tabularnewline
125 & 92 & 92.4775911362457 & -0.477591136245735 \tabularnewline
126 & 93 & 90.4973548382617 & 2.50264516173829 \tabularnewline
127 & 93 & 91.2600030703669 & 1.73999692963309 \tabularnewline
128 & 87 & 92.4343220436096 & -5.43432204360964 \tabularnewline
129 & 84 & 87.409209328642 & -3.40920932864201 \tabularnewline
130 & 80 & 84.1481288533978 & -4.14812885339785 \tabularnewline
131 & 78 & 82.9087389910159 & -4.90873899101585 \tabularnewline
132 & 75 & 82.8538289983173 & -7.85382899831731 \tabularnewline
133 & 73 & 76.567390945154 & -3.567390945154 \tabularnewline
134 & 81 & 75.6756698100578 & 5.32433018994224 \tabularnewline
135 & 76 & 79.562902298986 & -3.562902298986 \tabularnewline
136 & 77 & 76.682937291901 & 0.317062708099002 \tabularnewline
137 & 71 & 77.6155712452101 & -6.61557124521009 \tabularnewline
138 & 71 & 72.9217512365581 & -1.92175123655812 \tabularnewline
139 & 78 & 70.94903117231 & 7.05096882769004 \tabularnewline
140 & 67 & 73.2408845158788 & -6.24088451587878 \tabularnewline
141 & 76 & 68.2965437494236 & 7.70345625057637 \tabularnewline
142 & 68 & 71.1951311795748 & -3.19513117957479 \tabularnewline
143 & 82 & 70.2973015972371 & 11.7026984027629 \tabularnewline
144 & 64 & 78.9572030461012 & -14.9572030461012 \tabularnewline
145 & 71 & 69.754605591448 & 1.24539440855196 \tabularnewline
146 & 81 & 73.9969223805915 & 7.0030776194085 \tabularnewline
147 & 69 & 76.4650953633101 & -7.46509536331014 \tabularnewline
148 & 63 & 72.3915232615532 & -9.39152326155325 \tabularnewline
149 & 70 & 65.9137788941679 & 4.08622110583212 \tabularnewline
150 & 77 & 68.5991271322293 & 8.40087286777069 \tabularnewline
151 & 75 & 74.9342855076848 & 0.0657144923152089 \tabularnewline
152 & 76 & 69.6711519447738 & 6.32884805522616 \tabularnewline
153 & 68 & 75.6513495667451 & -7.65134956674507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275752&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]13[/C][C]66[/C][C]67.2150106837607[/C][C]-1.21501068376068[/C][/ROW]
[ROW][C]14[/C][C]68[/C][C]68.161371254937[/C][C]-0.161371254936967[/C][/ROW]
[ROW][C]15[/C][C]58[/C][C]57.9188475250274[/C][C]0.0811524749725976[/C][/ROW]
[ROW][C]16[/C][C]64[/C][C]63.7734038606113[/C][C]0.22659613938869[/C][/ROW]
[ROW][C]17[/C][C]66[/C][C]66.1695011940266[/C][C]-0.16950119402658[/C][/ROW]
[ROW][C]18[/C][C]57[/C][C]57.8389930292945[/C][C]-0.838993029294507[/C][/ROW]
[ROW][C]19[/C][C]68[/C][C]63.0421382715124[/C][C]4.95786172848762[/C][/ROW]
[ROW][C]20[/C][C]62[/C][C]59.9783046449648[/C][C]2.02169535503521[/C][/ROW]
[ROW][C]21[/C][C]59[/C][C]63.8597034775381[/C][C]-4.8597034775381[/C][/ROW]
[ROW][C]22[/C][C]73[/C][C]69.5542850713769[/C][C]3.44571492862308[/C][/ROW]
[ROW][C]23[/C][C]61[/C][C]75.9170257466958[/C][C]-14.9170257466958[/C][/ROW]
[ROW][C]24[/C][C]61[/C][C]62.2328586263474[/C][C]-1.23285862634735[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]58.3437020956432[/C][C]-1.34370209564317[/C][/ROW]
[ROW][C]26[/C][C]58[/C][C]59.4985398029035[/C][C]-1.49853980290352[/C][/ROW]
[ROW][C]27[/C][C]57[/C][C]48.5559611634626[/C][C]8.44403883653735[/C][/ROW]
[ROW][C]28[/C][C]67[/C][C]59.2338280885372[/C][C]7.76617191146284[/C][/ROW]
[ROW][C]29[/C][C]81[/C][C]65.8372318105694[/C][C]15.1627681894306[/C][/ROW]
[ROW][C]30[/C][C]79[/C][C]66.0989963943796[/C][C]12.9010036056204[/C][/ROW]
[ROW][C]31[/C][C]76[/C][C]80.7147788442654[/C][C]-4.7147788442654[/C][/ROW]
[ROW][C]32[/C][C]78[/C][C]71.3311433986414[/C][C]6.66885660135864[/C][/ROW]
[ROW][C]33[/C][C]74[/C][C]76.0287398184778[/C][C]-2.02873981847779[/C][/ROW]
[ROW][C]34[/C][C]67[/C][C]85.5665916727225[/C][C]-18.5665916727225[/C][/ROW]
[ROW][C]35[/C][C]84[/C][C]74.4197596801438[/C][C]9.58024031985623[/C][/ROW]
[ROW][C]36[/C][C]85[/C][C]78.4141603042709[/C][C]6.58583969572915[/C][/ROW]
[ROW][C]37[/C][C]79[/C][C]78.9683686462561[/C][C]0.0316313537438901[/C][/ROW]
[ROW][C]38[/C][C]82[/C][C]80.8685722444455[/C][C]1.13142775555447[/C][/ROW]
[ROW][C]39[/C][C]87[/C][C]74.092607393881[/C][C]12.907392606119[/C][/ROW]
[ROW][C]40[/C][C]90[/C][C]87.1424471183744[/C][C]2.85755288162555[/C][/ROW]
[ROW][C]41[/C][C]87[/C][C]92.9180131498315[/C][C]-5.91801314983149[/C][/ROW]
[ROW][C]42[/C][C]93[/C][C]80.5139940397602[/C][C]12.4860059602398[/C][/ROW]
[ROW][C]43[/C][C]92[/C][C]90.1759171120023[/C][C]1.82408288799769[/C][/ROW]
[ROW][C]44[/C][C]82[/C][C]87.580984038159[/C][C]-5.58098403815903[/C][/ROW]
[ROW][C]45[/C][C]80[/C][C]82.9418116016233[/C][C]-2.94181160162329[/C][/ROW]
[ROW][C]46[/C][C]79[/C][C]87.5294627279413[/C][C]-8.52946272794134[/C][/ROW]
[ROW][C]47[/C][C]77[/C][C]89.6608461609643[/C][C]-12.6608461609643[/C][/ROW]
[ROW][C]48[/C][C]72[/C][C]80.1295021323577[/C][C]-8.1295021323577[/C][/ROW]
[ROW][C]49[/C][C]65[/C][C]70.510309196894[/C][C]-5.51030919689403[/C][/ROW]
[ROW][C]50[/C][C]73[/C][C]69.5344525569637[/C][C]3.46554744303629[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]67.2467182023321[/C][C]8.75328179766785[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]75.2292706269563[/C][C]1.77072937304368[/C][/ROW]
[ROW][C]53[/C][C]76[/C][C]78.0335040140385[/C][C]-2.0335040140385[/C][/ROW]
[ROW][C]54[/C][C]76[/C][C]72.7792782618468[/C][C]3.22072173815324[/C][/ROW]
[ROW][C]55[/C][C]76[/C][C]74.2859558740961[/C][C]1.71404412590385[/C][/ROW]
[ROW][C]56[/C][C]75[/C][C]69.6454410736903[/C][C]5.35455892630974[/C][/ROW]
[ROW][C]57[/C][C]78[/C][C]71.9690542057422[/C][C]6.03094579425778[/C][/ROW]
[ROW][C]58[/C][C]73[/C][C]80.1939443559736[/C][C]-7.1939443559736[/C][/ROW]
[ROW][C]59[/C][C]80[/C][C]81.9832372191393[/C][C]-1.98323721913934[/C][/ROW]
[ROW][C]60[/C][C]77[/C][C]79.7737249773661[/C][C]-2.7737249773661[/C][/ROW]
[ROW][C]61[/C][C]83[/C][C]73.9198094522508[/C][C]9.08019054774918[/C][/ROW]
[ROW][C]62[/C][C]84[/C][C]83.6943034440608[/C][C]0.30569655593915[/C][/ROW]
[ROW][C]63[/C][C]85[/C][C]81.0145680926545[/C][C]3.98543190734553[/C][/ROW]
[ROW][C]64[/C][C]81[/C][C]84.3999498786578[/C][C]-3.39994987865782[/C][/ROW]
[ROW][C]65[/C][C]84[/C][C]83.2275173722002[/C][C]0.772482627799803[/C][/ROW]
[ROW][C]66[/C][C]83[/C][C]80.985347792054[/C][C]2.01465220794604[/C][/ROW]
[ROW][C]67[/C][C]83[/C][C]81.3986413279765[/C][C]1.60135867202347[/C][/ROW]
[ROW][C]68[/C][C]88[/C][C]77.6683693642672[/C][C]10.3316306357328[/C][/ROW]
[ROW][C]69[/C][C]92[/C][C]83.0204382697094[/C][C]8.97956173029057[/C][/ROW]
[ROW][C]70[/C][C]92[/C][C]89.3913925895578[/C][C]2.60860741044216[/C][/ROW]
[ROW][C]71[/C][C]89[/C][C]98.1837933267032[/C][C]-9.18379332670324[/C][/ROW]
[ROW][C]72[/C][C]82[/C][C]91.6432305973923[/C][C]-9.64323059739233[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]85.0330635118287[/C][C]-12.0330635118287[/C][/ROW]
[ROW][C]74[/C][C]81[/C][C]80.3794812825898[/C][C]0.620518717410178[/C][/ROW]
[ROW][C]75[/C][C]91[/C][C]78.8650648380776[/C][C]12.1349351619224[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]84.9353821267249[/C][C]-4.93538212672495[/C][/ROW]
[ROW][C]77[/C][C]81[/C][C]84.0016608222911[/C][C]-3.00166082229109[/C][/ROW]
[ROW][C]78[/C][C]82[/C][C]79.9328986790217[/C][C]2.06710132097827[/C][/ROW]
[ROW][C]79[/C][C]84[/C][C]80.2655431040197[/C][C]3.73445689598034[/C][/ROW]
[ROW][C]80[/C][C]87[/C][C]80.0930458190636[/C][C]6.90695418093642[/C][/ROW]
[ROW][C]81[/C][C]85[/C][C]83.1238704925675[/C][C]1.87612950743249[/C][/ROW]
[ROW][C]82[/C][C]74[/C][C]83.7252270358129[/C][C]-9.72522703581288[/C][/ROW]
[ROW][C]83[/C][C]81[/C][C]82.3042050233882[/C][C]-1.30420502338819[/C][/ROW]
[ROW][C]84[/C][C]82[/C][C]80.148509714192[/C][C]1.85149028580797[/C][/ROW]
[ROW][C]85[/C][C]86[/C][C]79.4745678958142[/C][C]6.52543210418575[/C][/ROW]
[ROW][C]86[/C][C]85[/C][C]88.826081317013[/C][C]-3.82608131701302[/C][/ROW]
[ROW][C]87[/C][C]82[/C][C]87.850649350795[/C][C]-5.85064935079504[/C][/ROW]
[ROW][C]88[/C][C]86[/C][C]79.0610963330976[/C][C]6.93890366690242[/C][/ROW]
[ROW][C]89[/C][C]88[/C][C]85.4383339904802[/C][C]2.56166600951984[/C][/ROW]
[ROW][C]90[/C][C]86[/C][C]85.9094339698323[/C][C]0.0905660301677216[/C][/ROW]
[ROW][C]91[/C][C]83[/C][C]85.5577284460466[/C][C]-2.55772844604658[/C][/ROW]
[ROW][C]92[/C][C]81[/C][C]82.6349031959302[/C][C]-1.63490319593022[/C][/ROW]
[ROW][C]93[/C][C]81[/C][C]79.4320192142672[/C][C]1.56798078573283[/C][/ROW]
[ROW][C]94[/C][C]81[/C][C]76.7509436584635[/C][C]4.24905634153647[/C][/ROW]
[ROW][C]95[/C][C]82[/C][C]85.5791865174845[/C][C]-3.57918651748452[/C][/ROW]
[ROW][C]96[/C][C]86[/C][C]82.966888895063[/C][C]3.03311110493701[/C][/ROW]
[ROW][C]97[/C][C]85[/C][C]84.2203459464981[/C][C]0.779654053501886[/C][/ROW]
[ROW][C]98[/C][C]87[/C][C]87.5132164465206[/C][C]-0.513216446520588[/C][/ROW]
[ROW][C]99[/C][C]89[/C][C]87.8910026636971[/C][C]1.10899733630286[/C][/ROW]
[ROW][C]100[/C][C]90[/C][C]86.5073008854173[/C][C]3.49269911458272[/C][/ROW]
[ROW][C]101[/C][C]90[/C][C]89.7410209608838[/C][C]0.258979039116213[/C][/ROW]
[ROW][C]102[/C][C]92[/C][C]88.2331529924577[/C][C]3.76684700754228[/C][/ROW]
[ROW][C]103[/C][C]86[/C][C]89.2755781510526[/C][C]-3.27557815105264[/C][/ROW]
[ROW][C]104[/C][C]86[/C][C]86.1891511093707[/C][C]-0.189151109370698[/C][/ROW]
[ROW][C]105[/C][C]82[/C][C]84.6709139888065[/C][C]-2.67091398880646[/C][/ROW]
[ROW][C]106[/C][C]80[/C][C]80.2826157249487[/C][C]-0.282615724948684[/C][/ROW]
[ROW][C]107[/C][C]79[/C][C]84.4223794642721[/C][C]-5.42237946427214[/C][/ROW]
[ROW][C]108[/C][C]77[/C][C]82.5259728453525[/C][C]-5.5259728453525[/C][/ROW]
[ROW][C]109[/C][C]79[/C][C]78.2794770641376[/C][C]0.720522935862363[/C][/ROW]
[ROW][C]110[/C][C]76[/C][C]81.192366394358[/C][C]-5.192366394358[/C][/ROW]
[ROW][C]111[/C][C]78[/C][C]79.3275627639153[/C][C]-1.32756276391527[/C][/ROW]
[ROW][C]112[/C][C]78[/C][C]77.188127104926[/C][C]0.811872895074004[/C][/ROW]
[ROW][C]113[/C][C]77[/C][C]78.0223639368263[/C][C]-1.02236393682631[/C][/ROW]
[ROW][C]114[/C][C]72[/C][C]76.7206403082642[/C][C]-4.72064030826415[/C][/ROW]
[ROW][C]115[/C][C]75[/C][C]71.0218767348355[/C][C]3.97812326516453[/C][/ROW]
[ROW][C]116[/C][C]79[/C][C]72.9116180484183[/C][C]6.08838195158165[/C][/ROW]
[ROW][C]117[/C][C]81[/C][C]74.3202632856518[/C][C]6.67973671434819[/C][/ROW]
[ROW][C]118[/C][C]86[/C][C]75.920871945165[/C][C]10.079128054835[/C][/ROW]
[ROW][C]119[/C][C]88[/C][C]84.6124247076106[/C][C]3.38757529238939[/C][/ROW]
[ROW][C]120[/C][C]97[/C][C]87.7284174324107[/C][C]9.27158256758933[/C][/ROW]
[ROW][C]121[/C][C]94[/C][C]93.6199701371804[/C][C]0.380029862819569[/C][/ROW]
[ROW][C]122[/C][C]96[/C][C]94.7549507786277[/C][C]1.24504922137227[/C][/ROW]
[ROW][C]123[/C][C]94[/C][C]97.6077242506644[/C][C]-3.60772425066438[/C][/ROW]
[ROW][C]124[/C][C]91[/C][C]94.7366299594544[/C][C]-3.73662995945442[/C][/ROW]
[ROW][C]125[/C][C]92[/C][C]92.4775911362457[/C][C]-0.477591136245735[/C][/ROW]
[ROW][C]126[/C][C]93[/C][C]90.4973548382617[/C][C]2.50264516173829[/C][/ROW]
[ROW][C]127[/C][C]93[/C][C]91.2600030703669[/C][C]1.73999692963309[/C][/ROW]
[ROW][C]128[/C][C]87[/C][C]92.4343220436096[/C][C]-5.43432204360964[/C][/ROW]
[ROW][C]129[/C][C]84[/C][C]87.409209328642[/C][C]-3.40920932864201[/C][/ROW]
[ROW][C]130[/C][C]80[/C][C]84.1481288533978[/C][C]-4.14812885339785[/C][/ROW]
[ROW][C]131[/C][C]78[/C][C]82.9087389910159[/C][C]-4.90873899101585[/C][/ROW]
[ROW][C]132[/C][C]75[/C][C]82.8538289983173[/C][C]-7.85382899831731[/C][/ROW]
[ROW][C]133[/C][C]73[/C][C]76.567390945154[/C][C]-3.567390945154[/C][/ROW]
[ROW][C]134[/C][C]81[/C][C]75.6756698100578[/C][C]5.32433018994224[/C][/ROW]
[ROW][C]135[/C][C]76[/C][C]79.562902298986[/C][C]-3.562902298986[/C][/ROW]
[ROW][C]136[/C][C]77[/C][C]76.682937291901[/C][C]0.317062708099002[/C][/ROW]
[ROW][C]137[/C][C]71[/C][C]77.6155712452101[/C][C]-6.61557124521009[/C][/ROW]
[ROW][C]138[/C][C]71[/C][C]72.9217512365581[/C][C]-1.92175123655812[/C][/ROW]
[ROW][C]139[/C][C]78[/C][C]70.94903117231[/C][C]7.05096882769004[/C][/ROW]
[ROW][C]140[/C][C]67[/C][C]73.2408845158788[/C][C]-6.24088451587878[/C][/ROW]
[ROW][C]141[/C][C]76[/C][C]68.2965437494236[/C][C]7.70345625057637[/C][/ROW]
[ROW][C]142[/C][C]68[/C][C]71.1951311795748[/C][C]-3.19513117957479[/C][/ROW]
[ROW][C]143[/C][C]82[/C][C]70.2973015972371[/C][C]11.7026984027629[/C][/ROW]
[ROW][C]144[/C][C]64[/C][C]78.9572030461012[/C][C]-14.9572030461012[/C][/ROW]
[ROW][C]145[/C][C]71[/C][C]69.754605591448[/C][C]1.24539440855196[/C][/ROW]
[ROW][C]146[/C][C]81[/C][C]73.9969223805915[/C][C]7.0030776194085[/C][/ROW]
[ROW][C]147[/C][C]69[/C][C]76.4650953633101[/C][C]-7.46509536331014[/C][/ROW]
[ROW][C]148[/C][C]63[/C][C]72.3915232615532[/C][C]-9.39152326155325[/C][/ROW]
[ROW][C]149[/C][C]70[/C][C]65.9137788941679[/C][C]4.08622110583212[/C][/ROW]
[ROW][C]150[/C][C]77[/C][C]68.5991271322293[/C][C]8.40087286777069[/C][/ROW]
[ROW][C]151[/C][C]75[/C][C]74.9342855076848[/C][C]0.0657144923152089[/C][/ROW]
[ROW][C]152[/C][C]76[/C][C]69.6711519447738[/C][C]6.32884805522616[/C][/ROW]
[ROW][C]153[/C][C]68[/C][C]75.6513495667451[/C][C]-7.65134956674507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275752&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275752&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
136667.2150106837607-1.21501068376068
146868.161371254937-0.161371254936967
155857.91884752502740.0811524749725976
166463.77340386061130.22659613938869
176666.1695011940266-0.16950119402658
185757.8389930292945-0.838993029294507
196863.04213827151244.95786172848762
206259.97830464496482.02169535503521
215963.8597034775381-4.8597034775381
227369.55428507137693.44571492862308
236175.9170257466958-14.9170257466958
246162.2328586263474-1.23285862634735
255758.3437020956432-1.34370209564317
265859.4985398029035-1.49853980290352
275748.55596116346268.44403883653735
286759.23382808853727.76617191146284
298165.837231810569415.1627681894306
307966.098996394379612.9010036056204
317680.7147788442654-4.7147788442654
327871.33114339864146.66885660135864
337476.0287398184778-2.02873981847779
346785.5665916727225-18.5665916727225
358474.41975968014389.58024031985623
368578.41416030427096.58583969572915
377978.96836864625610.0316313537438901
388280.86857224444551.13142775555447
398774.09260739388112.907392606119
409087.14244711837442.85755288162555
418792.9180131498315-5.91801314983149
429380.513994039760212.4860059602398
439290.17591711200231.82408288799769
448287.580984038159-5.58098403815903
458082.9418116016233-2.94181160162329
467987.5294627279413-8.52946272794134
477789.6608461609643-12.6608461609643
487280.1295021323577-8.1295021323577
496570.510309196894-5.51030919689403
507369.53445255696373.46554744303629
517667.24671820233218.75328179766785
527775.22927062695631.77072937304368
537678.0335040140385-2.0335040140385
547672.77927826184683.22072173815324
557674.28595587409611.71404412590385
567569.64544107369035.35455892630974
577871.96905420574226.03094579425778
587380.1939443559736-7.1939443559736
598081.9832372191393-1.98323721913934
607779.7737249773661-2.7737249773661
618373.91980945225089.08019054774918
628483.69430344406080.30569655593915
638581.01456809265453.98543190734553
648184.3999498786578-3.39994987865782
658483.22751737220020.772482627799803
668380.9853477920542.01465220794604
678381.39864132797651.60135867202347
688877.668369364267210.3316306357328
699283.02043826970948.97956173029057
709289.39139258955782.60860741044216
718998.1837933267032-9.18379332670324
728291.6432305973923-9.64323059739233
737385.0330635118287-12.0330635118287
748180.37948128258980.620518717410178
759178.865064838077612.1349351619224
768084.9353821267249-4.93538212672495
778184.0016608222911-3.00166082229109
788279.93289867902172.06710132097827
798480.26554310401973.73445689598034
808780.09304581906366.90695418093642
818583.12387049256751.87612950743249
827483.7252270358129-9.72522703581288
838182.3042050233882-1.30420502338819
848280.1485097141921.85149028580797
858679.47456789581426.52543210418575
868588.826081317013-3.82608131701302
878287.850649350795-5.85064935079504
888679.06109633309766.93890366690242
898885.43833399048022.56166600951984
908685.90943396983230.0905660301677216
918385.5577284460466-2.55772844604658
928182.6349031959302-1.63490319593022
938179.43201921426721.56798078573283
948176.75094365846354.24905634153647
958285.5791865174845-3.57918651748452
968682.9668888950633.03311110493701
978584.22034594649810.779654053501886
988787.5132164465206-0.513216446520588
998987.89100266369711.10899733630286
1009086.50730088541733.49269911458272
1019089.74102096088380.258979039116213
1029288.23315299245773.76684700754228
1038689.2755781510526-3.27557815105264
1048686.1891511093707-0.189151109370698
1058284.6709139888065-2.67091398880646
1068080.2826157249487-0.282615724948684
1077984.4223794642721-5.42237946427214
1087782.5259728453525-5.5259728453525
1097978.27947706413760.720522935862363
1107681.192366394358-5.192366394358
1117879.3275627639153-1.32756276391527
1127877.1881271049260.811872895074004
1137778.0223639368263-1.02236393682631
1147276.7206403082642-4.72064030826415
1157571.02187673483553.97812326516453
1167972.91161804841836.08838195158165
1178174.32026328565186.67973671434819
1188675.92087194516510.079128054835
1198884.61242470761063.38757529238939
1209787.72841743241079.27158256758933
1219493.61997013718040.380029862819569
1229694.75495077862771.24504922137227
1239497.6077242506644-3.60772425066438
1249194.7366299594544-3.73662995945442
1259292.4775911362457-0.477591136245735
1269390.49735483826172.50264516173829
1279391.26000307036691.73999692963309
1288792.4343220436096-5.43432204360964
1298487.409209328642-3.40920932864201
1308084.1481288533978-4.14812885339785
1317882.9087389910159-4.90873899101585
1327582.8538289983173-7.85382899831731
1337376.567390945154-3.567390945154
1348175.67566981005785.32433018994224
1357679.562902298986-3.562902298986
1367776.6829372919010.317062708099002
1377177.6155712452101-6.61557124521009
1387172.9217512365581-1.92175123655812
1397870.949031172317.05096882769004
1406773.2408845158788-6.24088451587878
1417668.29654374942367.70345625057637
1426871.1951311795748-3.19513117957479
1438270.297301597237111.7026984027629
1446478.9572030461012-14.9572030461012
1457169.7546055914481.24539440855196
1468173.99692238059157.0030776194085
1476976.4650953633101-7.46509536331014
1486372.3915232615532-9.39152326155325
1497065.91377889416794.08622110583212
1507768.59912713222938.40087286777069
1517574.93428550768480.0657144923152089
1527669.67115194477386.32884805522616
1536875.6513495667451-7.65134956674507







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
15466.8475392934554.945675323855578.7494032630445
15571.766367556196758.05444268203885.4782924303554
15666.593298870409251.283844224393481.9027535164249
15770.285725452475953.53037050660187.0410803983508
15875.357117042590757.271088258855593.4431458263258
15969.945159245617850.619866105022589.270452386213
16069.626553659231449.136812988013190.1162943304498
16172.130182172377150.538702589504193.72166175525
16273.633024525215950.993357823131796.2726912273001
16372.930430135820149.28900407508396.5718561965572
16469.306589967357244.704160331462793.9090196032518
16567.923030790413942.39575005405393.4503115267748

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
154 & 66.84753929345 & 54.9456753238555 & 78.7494032630445 \tabularnewline
155 & 71.7663675561967 & 58.054442682038 & 85.4782924303554 \tabularnewline
156 & 66.5932988704092 & 51.2838442243934 & 81.9027535164249 \tabularnewline
157 & 70.2857254524759 & 53.530370506601 & 87.0410803983508 \tabularnewline
158 & 75.3571170425907 & 57.2710882588555 & 93.4431458263258 \tabularnewline
159 & 69.9451592456178 & 50.6198661050225 & 89.270452386213 \tabularnewline
160 & 69.6265536592314 & 49.1368129880131 & 90.1162943304498 \tabularnewline
161 & 72.1301821723771 & 50.5387025895041 & 93.72166175525 \tabularnewline
162 & 73.6330245252159 & 50.9933578231317 & 96.2726912273001 \tabularnewline
163 & 72.9304301358201 & 49.289004075083 & 96.5718561965572 \tabularnewline
164 & 69.3065899673572 & 44.7041603314627 & 93.9090196032518 \tabularnewline
165 & 67.9230307904139 & 42.395750054053 & 93.4503115267748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275752&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]66.84753929345[/C][C]54.9456753238555[/C][C]78.7494032630445[/C][/ROW]
[ROW][C]155[/C][C]71.7663675561967[/C][C]58.054442682038[/C][C]85.4782924303554[/C][/ROW]
[ROW][C]156[/C][C]66.5932988704092[/C][C]51.2838442243934[/C][C]81.9027535164249[/C][/ROW]
[ROW][C]157[/C][C]70.2857254524759[/C][C]53.530370506601[/C][C]87.0410803983508[/C][/ROW]
[ROW][C]158[/C][C]75.3571170425907[/C][C]57.2710882588555[/C][C]93.4431458263258[/C][/ROW]
[ROW][C]159[/C][C]69.9451592456178[/C][C]50.6198661050225[/C][C]89.270452386213[/C][/ROW]
[ROW][C]160[/C][C]69.6265536592314[/C][C]49.1368129880131[/C][C]90.1162943304498[/C][/ROW]
[ROW][C]161[/C][C]72.1301821723771[/C][C]50.5387025895041[/C][C]93.72166175525[/C][/ROW]
[ROW][C]162[/C][C]73.6330245252159[/C][C]50.9933578231317[/C][C]96.2726912273001[/C][/ROW]
[ROW][C]163[/C][C]72.9304301358201[/C][C]49.289004075083[/C][C]96.5718561965572[/C][/ROW]
[ROW][C]164[/C][C]69.3065899673572[/C][C]44.7041603314627[/C][C]93.9090196032518[/C][/ROW]
[ROW][C]165[/C][C]67.9230307904139[/C][C]42.395750054053[/C][C]93.4503115267748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275752&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275752&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
15466.8475392934554.945675323855578.7494032630445
15571.766367556196758.05444268203885.4782924303554
15666.593298870409251.283844224393481.9027535164249
15770.285725452475953.53037050660187.0410803983508
15875.357117042590757.271088258855593.4431458263258
15969.945159245617850.619866105022589.270452386213
16069.626553659231449.136812988013190.1162943304498
16172.130182172377150.538702589504193.72166175525
16273.633024525215950.993357823131796.2726912273001
16372.930430135820149.28900407508396.5718561965572
16469.306589967357244.704160331462793.9090196032518
16567.923030790413942.39575005405393.4503115267748



Parameters (Session):
par1 = 12 ; par2 = Single ; par3 = additive ;
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = additive ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')