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 computationFri, 16 Jan 2015 08:11:55 +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/16/t1421395922xxmgvdkll1y42xe.htm/, Retrieved Thu, 31 Oct 2024 23:27:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=273241, Retrieved Thu, 31 Oct 2024 23:27:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2015-01-16 08:11:55] [efc983391fe36da4845de855ec76c3b2] [Current]
Feedback Forum

Post a new message
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=273241&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=273241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=273241&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
alpha0.726816553788166
beta0.0787886111272481
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.726816553788166 \tabularnewline
beta & 0.0787886111272481 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=273241&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]0.726816553788166[/C][/ROW]
[ROW][C]beta[/C][C]0.0787886111272481[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=273241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=273241&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
alpha0.726816553788166
beta0.0787886111272481
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
37477-3
46279.6477557381837-17.6477557381837
55670.6386837480392-14.6386837480392
66662.97837281666063.02162718333943
76568.3269012934794-3.32690129347937
85968.8706996216654-9.87069962166544
96164.0931127010467-3.09311270104666
106964.06446146219564.93553853780443
117470.15379980537213.84620019462785
126975.6716411499497-6.67164114994971
136673.1628906532663-7.16289065326627
146869.8869099062462-1.88690990624623
155870.3375456591506-12.3375456591506
166462.48597844021051.51402155978948
176664.78865981488541.21134018511459
185766.9407145902942-9.94071459029421
196860.41801764910197.58198235089809
206267.0652881213165-5.06528812131646
215964.2302500044309-5.23025000443094
227360.97580529022912.024194709771
236170.9507405293717-9.95074052937166
246164.3841012371349-3.3841012371349
255762.396413978611-5.39641397861097
265858.6371195804074-0.637119580407408
275758.3004745672508-1.30047456725079
286757.40722066572569.59277933427439
298164.980693255431116.0193067445689
307978.14241581754460.857584182455383
317680.3334568815634-4.33345688156339
327878.5034085376781-0.503408537678126
337479.4282751090728-5.42827510907281
346776.4628176800113-9.46281768001134
358470.023100929266813.9768990707332
368581.4201435928733.57985640712698
377985.4654435385075-6.46544353850747
388281.83941043274030.160589567259734
398783.03848401437453.96151598562554
409087.2269895219192.77301047808099
418790.7104656280219-3.71046562802194
429389.26916465410053.73083534589949
439293.4499701989452-1.44997019894524
448293.7822481615077-11.7822481615077
458085.9301465907624-5.93014659076243
467981.9918602613577-2.99186026135771
477780.0178405965313-3.01784059653129
487277.8521217541916-5.85212175419164
496573.2912794755224-8.29127947552239
507366.48281797277926.51718202722076
517670.81059698717725.18940301282275
527774.47049470652862.52950529347136
537676.3419865158162-0.341986515816245
547676.106846731765-0.106846731764961
557676.036491871352-0.0364918713519558
567576.0151823859737-1.01518238597369
577875.22441014940492.77558985059511
587377.3477777091485-4.34777770914855
598074.04478889628745.95521110371256
607778.5712072768309-1.57120727683086
618377.53732521220985.46267478779023
628481.92860441373782.07139558626221
638583.97366394545091.02633605454909
648185.3179299072943-4.31792990729433
658482.53062921915581.46937078084417
668384.0337777949763-1.03377779497626
678383.6583974015512-0.658397401551156
688883.51814665252234.48185334747771
699287.37056797354744.62943202645258
709291.59535572987210.404644270127932
718992.7726697088956-3.77266970889563
728290.6978013091687-8.69780130916872
737384.5451872992056-11.5451872992056
748175.66191240358985.33808759641022
759179.35536605953111.644633940469
768088.2993504073032-8.29935040730315
778182.2724555868784-1.27245558687839
788281.27995724395320.72004275604678
798481.77687283258932.22312716741071
808783.49356213430523.50643786569478
818586.3437785925695-1.34377859256949
827485.5918261373761-11.5918261373761
838176.72761870268344.27238129731663
848279.6384371902382.36156280976196
858681.29567574953514.70432425046489
868584.92506460717980.0749353928202225
878285.1940281744556-3.19402817445555
888682.90414930928413.09585069071591
898885.36314200213162.63685799786843
908687.6395305298137-1.63953052981365
918386.713881587714-3.71388158771397
928184.0678850238688-3.06788502386883
938181.7157274290141-0.715727429014095
948181.0321708753184-0.0321708753183714
958280.84359227940861.1564077205914
968681.58511381659054.41488618340955
978584.94776931001710.0522306899829204
988785.1425655558521.857434444148
998986.75577950924062.24422049075942
1009088.77863095159251.22136904840747
1019090.1279985694688-0.127998569468758
1029290.48929364449341.51070635550658
1038692.1281369840124-6.12813698401243
1048687.8640155843471-1.86401558434713
1058286.592385601138-4.59238560113799
1068083.0747487750433-3.07474877504328
1077980.4840804376933-1.48408043769334
1087778.9645505109054-1.96455051090541
1097976.98330725794442.01669274205562
1107678.011183147178-2.011183147178
1117875.99636202844412.00363797155586
1127877.01431742089550.985682579104477
1137777.3488509647118-0.348850964711843
1147276.6934465332636-4.69344653326357
1157572.61154853243592.3884514675641
1167973.81366558603875.18633441396133
1178177.34632503012983.65367496987017
1188679.97424942947896.02575057052105
1198884.67130144611313.32869855388694
1209787.59870888999299.40129111000711
1219495.4781408113965-1.47814081139654
1229695.36557597981080.634424020189172
1239496.8247884457941-2.82478844579413
1249195.608026894295-4.60802689429504
1259292.8313000726818-0.831300072681799
1269392.75195653620550.248043463794474
1279393.4713019251835-0.471301925183539
1288793.6408261355848-6.64082613558479
1298488.9459519966587-4.94595199665868
1308085.1997111561183-5.19971115611831
1317880.971273190964-2.97127319096396
1327578.1923512644155-3.19235126441555
1337375.0699365644123-2.06993656441226
1348172.64477680690538.35522319309472
1357678.2752564804217-2.27525648042167
1367776.04903529319130.950964706808719
1377176.2221419383054-5.22214193830537
1387171.6094872228529-0.609487222852906
1397870.31448410685687.68551589314316
1406775.4885366133339-8.48853661333393
1417668.42092509762857.57907490237153
1426873.4655343262575-5.46553432625745
1438268.716122534542713.2838774654573
1446478.3547930807135-14.3547930807135
1457167.08319503527363.91680496472644
1468169.315992228077111.6840077719229
1476977.863204146732-8.86320414673199
1486370.9688121036782-7.96881210367816
1497064.26814603928345.73185396071663
1507767.85358472209099.14641527790909
1517574.44455134690350.555448653096505
1527674.8232689089041.17673109109596
1536875.7209301806318-7.7209301806318

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 74 & 77 & -3 \tabularnewline
4 & 62 & 79.6477557381837 & -17.6477557381837 \tabularnewline
5 & 56 & 70.6386837480392 & -14.6386837480392 \tabularnewline
6 & 66 & 62.9783728166606 & 3.02162718333943 \tabularnewline
7 & 65 & 68.3269012934794 & -3.32690129347937 \tabularnewline
8 & 59 & 68.8706996216654 & -9.87069962166544 \tabularnewline
9 & 61 & 64.0931127010467 & -3.09311270104666 \tabularnewline
10 & 69 & 64.0644614621956 & 4.93553853780443 \tabularnewline
11 & 74 & 70.1537998053721 & 3.84620019462785 \tabularnewline
12 & 69 & 75.6716411499497 & -6.67164114994971 \tabularnewline
13 & 66 & 73.1628906532663 & -7.16289065326627 \tabularnewline
14 & 68 & 69.8869099062462 & -1.88690990624623 \tabularnewline
15 & 58 & 70.3375456591506 & -12.3375456591506 \tabularnewline
16 & 64 & 62.4859784402105 & 1.51402155978948 \tabularnewline
17 & 66 & 64.7886598148854 & 1.21134018511459 \tabularnewline
18 & 57 & 66.9407145902942 & -9.94071459029421 \tabularnewline
19 & 68 & 60.4180176491019 & 7.58198235089809 \tabularnewline
20 & 62 & 67.0652881213165 & -5.06528812131646 \tabularnewline
21 & 59 & 64.2302500044309 & -5.23025000443094 \tabularnewline
22 & 73 & 60.975805290229 & 12.024194709771 \tabularnewline
23 & 61 & 70.9507405293717 & -9.95074052937166 \tabularnewline
24 & 61 & 64.3841012371349 & -3.3841012371349 \tabularnewline
25 & 57 & 62.396413978611 & -5.39641397861097 \tabularnewline
26 & 58 & 58.6371195804074 & -0.637119580407408 \tabularnewline
27 & 57 & 58.3004745672508 & -1.30047456725079 \tabularnewline
28 & 67 & 57.4072206657256 & 9.59277933427439 \tabularnewline
29 & 81 & 64.9806932554311 & 16.0193067445689 \tabularnewline
30 & 79 & 78.1424158175446 & 0.857584182455383 \tabularnewline
31 & 76 & 80.3334568815634 & -4.33345688156339 \tabularnewline
32 & 78 & 78.5034085376781 & -0.503408537678126 \tabularnewline
33 & 74 & 79.4282751090728 & -5.42827510907281 \tabularnewline
34 & 67 & 76.4628176800113 & -9.46281768001134 \tabularnewline
35 & 84 & 70.0231009292668 & 13.9768990707332 \tabularnewline
36 & 85 & 81.420143592873 & 3.57985640712698 \tabularnewline
37 & 79 & 85.4654435385075 & -6.46544353850747 \tabularnewline
38 & 82 & 81.8394104327403 & 0.160589567259734 \tabularnewline
39 & 87 & 83.0384840143745 & 3.96151598562554 \tabularnewline
40 & 90 & 87.226989521919 & 2.77301047808099 \tabularnewline
41 & 87 & 90.7104656280219 & -3.71046562802194 \tabularnewline
42 & 93 & 89.2691646541005 & 3.73083534589949 \tabularnewline
43 & 92 & 93.4499701989452 & -1.44997019894524 \tabularnewline
44 & 82 & 93.7822481615077 & -11.7822481615077 \tabularnewline
45 & 80 & 85.9301465907624 & -5.93014659076243 \tabularnewline
46 & 79 & 81.9918602613577 & -2.99186026135771 \tabularnewline
47 & 77 & 80.0178405965313 & -3.01784059653129 \tabularnewline
48 & 72 & 77.8521217541916 & -5.85212175419164 \tabularnewline
49 & 65 & 73.2912794755224 & -8.29127947552239 \tabularnewline
50 & 73 & 66.4828179727792 & 6.51718202722076 \tabularnewline
51 & 76 & 70.8105969871772 & 5.18940301282275 \tabularnewline
52 & 77 & 74.4704947065286 & 2.52950529347136 \tabularnewline
53 & 76 & 76.3419865158162 & -0.341986515816245 \tabularnewline
54 & 76 & 76.106846731765 & -0.106846731764961 \tabularnewline
55 & 76 & 76.036491871352 & -0.0364918713519558 \tabularnewline
56 & 75 & 76.0151823859737 & -1.01518238597369 \tabularnewline
57 & 78 & 75.2244101494049 & 2.77558985059511 \tabularnewline
58 & 73 & 77.3477777091485 & -4.34777770914855 \tabularnewline
59 & 80 & 74.0447888962874 & 5.95521110371256 \tabularnewline
60 & 77 & 78.5712072768309 & -1.57120727683086 \tabularnewline
61 & 83 & 77.5373252122098 & 5.46267478779023 \tabularnewline
62 & 84 & 81.9286044137378 & 2.07139558626221 \tabularnewline
63 & 85 & 83.9736639454509 & 1.02633605454909 \tabularnewline
64 & 81 & 85.3179299072943 & -4.31792990729433 \tabularnewline
65 & 84 & 82.5306292191558 & 1.46937078084417 \tabularnewline
66 & 83 & 84.0337777949763 & -1.03377779497626 \tabularnewline
67 & 83 & 83.6583974015512 & -0.658397401551156 \tabularnewline
68 & 88 & 83.5181466525223 & 4.48185334747771 \tabularnewline
69 & 92 & 87.3705679735474 & 4.62943202645258 \tabularnewline
70 & 92 & 91.5953557298721 & 0.404644270127932 \tabularnewline
71 & 89 & 92.7726697088956 & -3.77266970889563 \tabularnewline
72 & 82 & 90.6978013091687 & -8.69780130916872 \tabularnewline
73 & 73 & 84.5451872992056 & -11.5451872992056 \tabularnewline
74 & 81 & 75.6619124035898 & 5.33808759641022 \tabularnewline
75 & 91 & 79.355366059531 & 11.644633940469 \tabularnewline
76 & 80 & 88.2993504073032 & -8.29935040730315 \tabularnewline
77 & 81 & 82.2724555868784 & -1.27245558687839 \tabularnewline
78 & 82 & 81.2799572439532 & 0.72004275604678 \tabularnewline
79 & 84 & 81.7768728325893 & 2.22312716741071 \tabularnewline
80 & 87 & 83.4935621343052 & 3.50643786569478 \tabularnewline
81 & 85 & 86.3437785925695 & -1.34377859256949 \tabularnewline
82 & 74 & 85.5918261373761 & -11.5918261373761 \tabularnewline
83 & 81 & 76.7276187026834 & 4.27238129731663 \tabularnewline
84 & 82 & 79.638437190238 & 2.36156280976196 \tabularnewline
85 & 86 & 81.2956757495351 & 4.70432425046489 \tabularnewline
86 & 85 & 84.9250646071798 & 0.0749353928202225 \tabularnewline
87 & 82 & 85.1940281744556 & -3.19402817445555 \tabularnewline
88 & 86 & 82.9041493092841 & 3.09585069071591 \tabularnewline
89 & 88 & 85.3631420021316 & 2.63685799786843 \tabularnewline
90 & 86 & 87.6395305298137 & -1.63953052981365 \tabularnewline
91 & 83 & 86.713881587714 & -3.71388158771397 \tabularnewline
92 & 81 & 84.0678850238688 & -3.06788502386883 \tabularnewline
93 & 81 & 81.7157274290141 & -0.715727429014095 \tabularnewline
94 & 81 & 81.0321708753184 & -0.0321708753183714 \tabularnewline
95 & 82 & 80.8435922794086 & 1.1564077205914 \tabularnewline
96 & 86 & 81.5851138165905 & 4.41488618340955 \tabularnewline
97 & 85 & 84.9477693100171 & 0.0522306899829204 \tabularnewline
98 & 87 & 85.142565555852 & 1.857434444148 \tabularnewline
99 & 89 & 86.7557795092406 & 2.24422049075942 \tabularnewline
100 & 90 & 88.7786309515925 & 1.22136904840747 \tabularnewline
101 & 90 & 90.1279985694688 & -0.127998569468758 \tabularnewline
102 & 92 & 90.4892936444934 & 1.51070635550658 \tabularnewline
103 & 86 & 92.1281369840124 & -6.12813698401243 \tabularnewline
104 & 86 & 87.8640155843471 & -1.86401558434713 \tabularnewline
105 & 82 & 86.592385601138 & -4.59238560113799 \tabularnewline
106 & 80 & 83.0747487750433 & -3.07474877504328 \tabularnewline
107 & 79 & 80.4840804376933 & -1.48408043769334 \tabularnewline
108 & 77 & 78.9645505109054 & -1.96455051090541 \tabularnewline
109 & 79 & 76.9833072579444 & 2.01669274205562 \tabularnewline
110 & 76 & 78.011183147178 & -2.011183147178 \tabularnewline
111 & 78 & 75.9963620284441 & 2.00363797155586 \tabularnewline
112 & 78 & 77.0143174208955 & 0.985682579104477 \tabularnewline
113 & 77 & 77.3488509647118 & -0.348850964711843 \tabularnewline
114 & 72 & 76.6934465332636 & -4.69344653326357 \tabularnewline
115 & 75 & 72.6115485324359 & 2.3884514675641 \tabularnewline
116 & 79 & 73.8136655860387 & 5.18633441396133 \tabularnewline
117 & 81 & 77.3463250301298 & 3.65367496987017 \tabularnewline
118 & 86 & 79.9742494294789 & 6.02575057052105 \tabularnewline
119 & 88 & 84.6713014461131 & 3.32869855388694 \tabularnewline
120 & 97 & 87.5987088899929 & 9.40129111000711 \tabularnewline
121 & 94 & 95.4781408113965 & -1.47814081139654 \tabularnewline
122 & 96 & 95.3655759798108 & 0.634424020189172 \tabularnewline
123 & 94 & 96.8247884457941 & -2.82478844579413 \tabularnewline
124 & 91 & 95.608026894295 & -4.60802689429504 \tabularnewline
125 & 92 & 92.8313000726818 & -0.831300072681799 \tabularnewline
126 & 93 & 92.7519565362055 & 0.248043463794474 \tabularnewline
127 & 93 & 93.4713019251835 & -0.471301925183539 \tabularnewline
128 & 87 & 93.6408261355848 & -6.64082613558479 \tabularnewline
129 & 84 & 88.9459519966587 & -4.94595199665868 \tabularnewline
130 & 80 & 85.1997111561183 & -5.19971115611831 \tabularnewline
131 & 78 & 80.971273190964 & -2.97127319096396 \tabularnewline
132 & 75 & 78.1923512644155 & -3.19235126441555 \tabularnewline
133 & 73 & 75.0699365644123 & -2.06993656441226 \tabularnewline
134 & 81 & 72.6447768069053 & 8.35522319309472 \tabularnewline
135 & 76 & 78.2752564804217 & -2.27525648042167 \tabularnewline
136 & 77 & 76.0490352931913 & 0.950964706808719 \tabularnewline
137 & 71 & 76.2221419383054 & -5.22214193830537 \tabularnewline
138 & 71 & 71.6094872228529 & -0.609487222852906 \tabularnewline
139 & 78 & 70.3144841068568 & 7.68551589314316 \tabularnewline
140 & 67 & 75.4885366133339 & -8.48853661333393 \tabularnewline
141 & 76 & 68.4209250976285 & 7.57907490237153 \tabularnewline
142 & 68 & 73.4655343262575 & -5.46553432625745 \tabularnewline
143 & 82 & 68.7161225345427 & 13.2838774654573 \tabularnewline
144 & 64 & 78.3547930807135 & -14.3547930807135 \tabularnewline
145 & 71 & 67.0831950352736 & 3.91680496472644 \tabularnewline
146 & 81 & 69.3159922280771 & 11.6840077719229 \tabularnewline
147 & 69 & 77.863204146732 & -8.86320414673199 \tabularnewline
148 & 63 & 70.9688121036782 & -7.96881210367816 \tabularnewline
149 & 70 & 64.2681460392834 & 5.73185396071663 \tabularnewline
150 & 77 & 67.8535847220909 & 9.14641527790909 \tabularnewline
151 & 75 & 74.4445513469035 & 0.555448653096505 \tabularnewline
152 & 76 & 74.823268908904 & 1.17673109109596 \tabularnewline
153 & 68 & 75.7209301806318 & -7.7209301806318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=273241&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]74[/C][C]77[/C][C]-3[/C][/ROW]
[ROW][C]4[/C][C]62[/C][C]79.6477557381837[/C][C]-17.6477557381837[/C][/ROW]
[ROW][C]5[/C][C]56[/C][C]70.6386837480392[/C][C]-14.6386837480392[/C][/ROW]
[ROW][C]6[/C][C]66[/C][C]62.9783728166606[/C][C]3.02162718333943[/C][/ROW]
[ROW][C]7[/C][C]65[/C][C]68.3269012934794[/C][C]-3.32690129347937[/C][/ROW]
[ROW][C]8[/C][C]59[/C][C]68.8706996216654[/C][C]-9.87069962166544[/C][/ROW]
[ROW][C]9[/C][C]61[/C][C]64.0931127010467[/C][C]-3.09311270104666[/C][/ROW]
[ROW][C]10[/C][C]69[/C][C]64.0644614621956[/C][C]4.93553853780443[/C][/ROW]
[ROW][C]11[/C][C]74[/C][C]70.1537998053721[/C][C]3.84620019462785[/C][/ROW]
[ROW][C]12[/C][C]69[/C][C]75.6716411499497[/C][C]-6.67164114994971[/C][/ROW]
[ROW][C]13[/C][C]66[/C][C]73.1628906532663[/C][C]-7.16289065326627[/C][/ROW]
[ROW][C]14[/C][C]68[/C][C]69.8869099062462[/C][C]-1.88690990624623[/C][/ROW]
[ROW][C]15[/C][C]58[/C][C]70.3375456591506[/C][C]-12.3375456591506[/C][/ROW]
[ROW][C]16[/C][C]64[/C][C]62.4859784402105[/C][C]1.51402155978948[/C][/ROW]
[ROW][C]17[/C][C]66[/C][C]64.7886598148854[/C][C]1.21134018511459[/C][/ROW]
[ROW][C]18[/C][C]57[/C][C]66.9407145902942[/C][C]-9.94071459029421[/C][/ROW]
[ROW][C]19[/C][C]68[/C][C]60.4180176491019[/C][C]7.58198235089809[/C][/ROW]
[ROW][C]20[/C][C]62[/C][C]67.0652881213165[/C][C]-5.06528812131646[/C][/ROW]
[ROW][C]21[/C][C]59[/C][C]64.2302500044309[/C][C]-5.23025000443094[/C][/ROW]
[ROW][C]22[/C][C]73[/C][C]60.975805290229[/C][C]12.024194709771[/C][/ROW]
[ROW][C]23[/C][C]61[/C][C]70.9507405293717[/C][C]-9.95074052937166[/C][/ROW]
[ROW][C]24[/C][C]61[/C][C]64.3841012371349[/C][C]-3.3841012371349[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]62.396413978611[/C][C]-5.39641397861097[/C][/ROW]
[ROW][C]26[/C][C]58[/C][C]58.6371195804074[/C][C]-0.637119580407408[/C][/ROW]
[ROW][C]27[/C][C]57[/C][C]58.3004745672508[/C][C]-1.30047456725079[/C][/ROW]
[ROW][C]28[/C][C]67[/C][C]57.4072206657256[/C][C]9.59277933427439[/C][/ROW]
[ROW][C]29[/C][C]81[/C][C]64.9806932554311[/C][C]16.0193067445689[/C][/ROW]
[ROW][C]30[/C][C]79[/C][C]78.1424158175446[/C][C]0.857584182455383[/C][/ROW]
[ROW][C]31[/C][C]76[/C][C]80.3334568815634[/C][C]-4.33345688156339[/C][/ROW]
[ROW][C]32[/C][C]78[/C][C]78.5034085376781[/C][C]-0.503408537678126[/C][/ROW]
[ROW][C]33[/C][C]74[/C][C]79.4282751090728[/C][C]-5.42827510907281[/C][/ROW]
[ROW][C]34[/C][C]67[/C][C]76.4628176800113[/C][C]-9.46281768001134[/C][/ROW]
[ROW][C]35[/C][C]84[/C][C]70.0231009292668[/C][C]13.9768990707332[/C][/ROW]
[ROW][C]36[/C][C]85[/C][C]81.420143592873[/C][C]3.57985640712698[/C][/ROW]
[ROW][C]37[/C][C]79[/C][C]85.4654435385075[/C][C]-6.46544353850747[/C][/ROW]
[ROW][C]38[/C][C]82[/C][C]81.8394104327403[/C][C]0.160589567259734[/C][/ROW]
[ROW][C]39[/C][C]87[/C][C]83.0384840143745[/C][C]3.96151598562554[/C][/ROW]
[ROW][C]40[/C][C]90[/C][C]87.226989521919[/C][C]2.77301047808099[/C][/ROW]
[ROW][C]41[/C][C]87[/C][C]90.7104656280219[/C][C]-3.71046562802194[/C][/ROW]
[ROW][C]42[/C][C]93[/C][C]89.2691646541005[/C][C]3.73083534589949[/C][/ROW]
[ROW][C]43[/C][C]92[/C][C]93.4499701989452[/C][C]-1.44997019894524[/C][/ROW]
[ROW][C]44[/C][C]82[/C][C]93.7822481615077[/C][C]-11.7822481615077[/C][/ROW]
[ROW][C]45[/C][C]80[/C][C]85.9301465907624[/C][C]-5.93014659076243[/C][/ROW]
[ROW][C]46[/C][C]79[/C][C]81.9918602613577[/C][C]-2.99186026135771[/C][/ROW]
[ROW][C]47[/C][C]77[/C][C]80.0178405965313[/C][C]-3.01784059653129[/C][/ROW]
[ROW][C]48[/C][C]72[/C][C]77.8521217541916[/C][C]-5.85212175419164[/C][/ROW]
[ROW][C]49[/C][C]65[/C][C]73.2912794755224[/C][C]-8.29127947552239[/C][/ROW]
[ROW][C]50[/C][C]73[/C][C]66.4828179727792[/C][C]6.51718202722076[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]70.8105969871772[/C][C]5.18940301282275[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]74.4704947065286[/C][C]2.52950529347136[/C][/ROW]
[ROW][C]53[/C][C]76[/C][C]76.3419865158162[/C][C]-0.341986515816245[/C][/ROW]
[ROW][C]54[/C][C]76[/C][C]76.106846731765[/C][C]-0.106846731764961[/C][/ROW]
[ROW][C]55[/C][C]76[/C][C]76.036491871352[/C][C]-0.0364918713519558[/C][/ROW]
[ROW][C]56[/C][C]75[/C][C]76.0151823859737[/C][C]-1.01518238597369[/C][/ROW]
[ROW][C]57[/C][C]78[/C][C]75.2244101494049[/C][C]2.77558985059511[/C][/ROW]
[ROW][C]58[/C][C]73[/C][C]77.3477777091485[/C][C]-4.34777770914855[/C][/ROW]
[ROW][C]59[/C][C]80[/C][C]74.0447888962874[/C][C]5.95521110371256[/C][/ROW]
[ROW][C]60[/C][C]77[/C][C]78.5712072768309[/C][C]-1.57120727683086[/C][/ROW]
[ROW][C]61[/C][C]83[/C][C]77.5373252122098[/C][C]5.46267478779023[/C][/ROW]
[ROW][C]62[/C][C]84[/C][C]81.9286044137378[/C][C]2.07139558626221[/C][/ROW]
[ROW][C]63[/C][C]85[/C][C]83.9736639454509[/C][C]1.02633605454909[/C][/ROW]
[ROW][C]64[/C][C]81[/C][C]85.3179299072943[/C][C]-4.31792990729433[/C][/ROW]
[ROW][C]65[/C][C]84[/C][C]82.5306292191558[/C][C]1.46937078084417[/C][/ROW]
[ROW][C]66[/C][C]83[/C][C]84.0337777949763[/C][C]-1.03377779497626[/C][/ROW]
[ROW][C]67[/C][C]83[/C][C]83.6583974015512[/C][C]-0.658397401551156[/C][/ROW]
[ROW][C]68[/C][C]88[/C][C]83.5181466525223[/C][C]4.48185334747771[/C][/ROW]
[ROW][C]69[/C][C]92[/C][C]87.3705679735474[/C][C]4.62943202645258[/C][/ROW]
[ROW][C]70[/C][C]92[/C][C]91.5953557298721[/C][C]0.404644270127932[/C][/ROW]
[ROW][C]71[/C][C]89[/C][C]92.7726697088956[/C][C]-3.77266970889563[/C][/ROW]
[ROW][C]72[/C][C]82[/C][C]90.6978013091687[/C][C]-8.69780130916872[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]84.5451872992056[/C][C]-11.5451872992056[/C][/ROW]
[ROW][C]74[/C][C]81[/C][C]75.6619124035898[/C][C]5.33808759641022[/C][/ROW]
[ROW][C]75[/C][C]91[/C][C]79.355366059531[/C][C]11.644633940469[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]88.2993504073032[/C][C]-8.29935040730315[/C][/ROW]
[ROW][C]77[/C][C]81[/C][C]82.2724555868784[/C][C]-1.27245558687839[/C][/ROW]
[ROW][C]78[/C][C]82[/C][C]81.2799572439532[/C][C]0.72004275604678[/C][/ROW]
[ROW][C]79[/C][C]84[/C][C]81.7768728325893[/C][C]2.22312716741071[/C][/ROW]
[ROW][C]80[/C][C]87[/C][C]83.4935621343052[/C][C]3.50643786569478[/C][/ROW]
[ROW][C]81[/C][C]85[/C][C]86.3437785925695[/C][C]-1.34377859256949[/C][/ROW]
[ROW][C]82[/C][C]74[/C][C]85.5918261373761[/C][C]-11.5918261373761[/C][/ROW]
[ROW][C]83[/C][C]81[/C][C]76.7276187026834[/C][C]4.27238129731663[/C][/ROW]
[ROW][C]84[/C][C]82[/C][C]79.638437190238[/C][C]2.36156280976196[/C][/ROW]
[ROW][C]85[/C][C]86[/C][C]81.2956757495351[/C][C]4.70432425046489[/C][/ROW]
[ROW][C]86[/C][C]85[/C][C]84.9250646071798[/C][C]0.0749353928202225[/C][/ROW]
[ROW][C]87[/C][C]82[/C][C]85.1940281744556[/C][C]-3.19402817445555[/C][/ROW]
[ROW][C]88[/C][C]86[/C][C]82.9041493092841[/C][C]3.09585069071591[/C][/ROW]
[ROW][C]89[/C][C]88[/C][C]85.3631420021316[/C][C]2.63685799786843[/C][/ROW]
[ROW][C]90[/C][C]86[/C][C]87.6395305298137[/C][C]-1.63953052981365[/C][/ROW]
[ROW][C]91[/C][C]83[/C][C]86.713881587714[/C][C]-3.71388158771397[/C][/ROW]
[ROW][C]92[/C][C]81[/C][C]84.0678850238688[/C][C]-3.06788502386883[/C][/ROW]
[ROW][C]93[/C][C]81[/C][C]81.7157274290141[/C][C]-0.715727429014095[/C][/ROW]
[ROW][C]94[/C][C]81[/C][C]81.0321708753184[/C][C]-0.0321708753183714[/C][/ROW]
[ROW][C]95[/C][C]82[/C][C]80.8435922794086[/C][C]1.1564077205914[/C][/ROW]
[ROW][C]96[/C][C]86[/C][C]81.5851138165905[/C][C]4.41488618340955[/C][/ROW]
[ROW][C]97[/C][C]85[/C][C]84.9477693100171[/C][C]0.0522306899829204[/C][/ROW]
[ROW][C]98[/C][C]87[/C][C]85.142565555852[/C][C]1.857434444148[/C][/ROW]
[ROW][C]99[/C][C]89[/C][C]86.7557795092406[/C][C]2.24422049075942[/C][/ROW]
[ROW][C]100[/C][C]90[/C][C]88.7786309515925[/C][C]1.22136904840747[/C][/ROW]
[ROW][C]101[/C][C]90[/C][C]90.1279985694688[/C][C]-0.127998569468758[/C][/ROW]
[ROW][C]102[/C][C]92[/C][C]90.4892936444934[/C][C]1.51070635550658[/C][/ROW]
[ROW][C]103[/C][C]86[/C][C]92.1281369840124[/C][C]-6.12813698401243[/C][/ROW]
[ROW][C]104[/C][C]86[/C][C]87.8640155843471[/C][C]-1.86401558434713[/C][/ROW]
[ROW][C]105[/C][C]82[/C][C]86.592385601138[/C][C]-4.59238560113799[/C][/ROW]
[ROW][C]106[/C][C]80[/C][C]83.0747487750433[/C][C]-3.07474877504328[/C][/ROW]
[ROW][C]107[/C][C]79[/C][C]80.4840804376933[/C][C]-1.48408043769334[/C][/ROW]
[ROW][C]108[/C][C]77[/C][C]78.9645505109054[/C][C]-1.96455051090541[/C][/ROW]
[ROW][C]109[/C][C]79[/C][C]76.9833072579444[/C][C]2.01669274205562[/C][/ROW]
[ROW][C]110[/C][C]76[/C][C]78.011183147178[/C][C]-2.011183147178[/C][/ROW]
[ROW][C]111[/C][C]78[/C][C]75.9963620284441[/C][C]2.00363797155586[/C][/ROW]
[ROW][C]112[/C][C]78[/C][C]77.0143174208955[/C][C]0.985682579104477[/C][/ROW]
[ROW][C]113[/C][C]77[/C][C]77.3488509647118[/C][C]-0.348850964711843[/C][/ROW]
[ROW][C]114[/C][C]72[/C][C]76.6934465332636[/C][C]-4.69344653326357[/C][/ROW]
[ROW][C]115[/C][C]75[/C][C]72.6115485324359[/C][C]2.3884514675641[/C][/ROW]
[ROW][C]116[/C][C]79[/C][C]73.8136655860387[/C][C]5.18633441396133[/C][/ROW]
[ROW][C]117[/C][C]81[/C][C]77.3463250301298[/C][C]3.65367496987017[/C][/ROW]
[ROW][C]118[/C][C]86[/C][C]79.9742494294789[/C][C]6.02575057052105[/C][/ROW]
[ROW][C]119[/C][C]88[/C][C]84.6713014461131[/C][C]3.32869855388694[/C][/ROW]
[ROW][C]120[/C][C]97[/C][C]87.5987088899929[/C][C]9.40129111000711[/C][/ROW]
[ROW][C]121[/C][C]94[/C][C]95.4781408113965[/C][C]-1.47814081139654[/C][/ROW]
[ROW][C]122[/C][C]96[/C][C]95.3655759798108[/C][C]0.634424020189172[/C][/ROW]
[ROW][C]123[/C][C]94[/C][C]96.8247884457941[/C][C]-2.82478844579413[/C][/ROW]
[ROW][C]124[/C][C]91[/C][C]95.608026894295[/C][C]-4.60802689429504[/C][/ROW]
[ROW][C]125[/C][C]92[/C][C]92.8313000726818[/C][C]-0.831300072681799[/C][/ROW]
[ROW][C]126[/C][C]93[/C][C]92.7519565362055[/C][C]0.248043463794474[/C][/ROW]
[ROW][C]127[/C][C]93[/C][C]93.4713019251835[/C][C]-0.471301925183539[/C][/ROW]
[ROW][C]128[/C][C]87[/C][C]93.6408261355848[/C][C]-6.64082613558479[/C][/ROW]
[ROW][C]129[/C][C]84[/C][C]88.9459519966587[/C][C]-4.94595199665868[/C][/ROW]
[ROW][C]130[/C][C]80[/C][C]85.1997111561183[/C][C]-5.19971115611831[/C][/ROW]
[ROW][C]131[/C][C]78[/C][C]80.971273190964[/C][C]-2.97127319096396[/C][/ROW]
[ROW][C]132[/C][C]75[/C][C]78.1923512644155[/C][C]-3.19235126441555[/C][/ROW]
[ROW][C]133[/C][C]73[/C][C]75.0699365644123[/C][C]-2.06993656441226[/C][/ROW]
[ROW][C]134[/C][C]81[/C][C]72.6447768069053[/C][C]8.35522319309472[/C][/ROW]
[ROW][C]135[/C][C]76[/C][C]78.2752564804217[/C][C]-2.27525648042167[/C][/ROW]
[ROW][C]136[/C][C]77[/C][C]76.0490352931913[/C][C]0.950964706808719[/C][/ROW]
[ROW][C]137[/C][C]71[/C][C]76.2221419383054[/C][C]-5.22214193830537[/C][/ROW]
[ROW][C]138[/C][C]71[/C][C]71.6094872228529[/C][C]-0.609487222852906[/C][/ROW]
[ROW][C]139[/C][C]78[/C][C]70.3144841068568[/C][C]7.68551589314316[/C][/ROW]
[ROW][C]140[/C][C]67[/C][C]75.4885366133339[/C][C]-8.48853661333393[/C][/ROW]
[ROW][C]141[/C][C]76[/C][C]68.4209250976285[/C][C]7.57907490237153[/C][/ROW]
[ROW][C]142[/C][C]68[/C][C]73.4655343262575[/C][C]-5.46553432625745[/C][/ROW]
[ROW][C]143[/C][C]82[/C][C]68.7161225345427[/C][C]13.2838774654573[/C][/ROW]
[ROW][C]144[/C][C]64[/C][C]78.3547930807135[/C][C]-14.3547930807135[/C][/ROW]
[ROW][C]145[/C][C]71[/C][C]67.0831950352736[/C][C]3.91680496472644[/C][/ROW]
[ROW][C]146[/C][C]81[/C][C]69.3159922280771[/C][C]11.6840077719229[/C][/ROW]
[ROW][C]147[/C][C]69[/C][C]77.863204146732[/C][C]-8.86320414673199[/C][/ROW]
[ROW][C]148[/C][C]63[/C][C]70.9688121036782[/C][C]-7.96881210367816[/C][/ROW]
[ROW][C]149[/C][C]70[/C][C]64.2681460392834[/C][C]5.73185396071663[/C][/ROW]
[ROW][C]150[/C][C]77[/C][C]67.8535847220909[/C][C]9.14641527790909[/C][/ROW]
[ROW][C]151[/C][C]75[/C][C]74.4445513469035[/C][C]0.555448653096505[/C][/ROW]
[ROW][C]152[/C][C]76[/C][C]74.823268908904[/C][C]1.17673109109596[/C][/ROW]
[ROW][C]153[/C][C]68[/C][C]75.7209301806318[/C][C]-7.7209301806318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=273241&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=273241&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
37477-3
46279.6477557381837-17.6477557381837
55670.6386837480392-14.6386837480392
66662.97837281666063.02162718333943
76568.3269012934794-3.32690129347937
85968.8706996216654-9.87069962166544
96164.0931127010467-3.09311270104666
106964.06446146219564.93553853780443
117470.15379980537213.84620019462785
126975.6716411499497-6.67164114994971
136673.1628906532663-7.16289065326627
146869.8869099062462-1.88690990624623
155870.3375456591506-12.3375456591506
166462.48597844021051.51402155978948
176664.78865981488541.21134018511459
185766.9407145902942-9.94071459029421
196860.41801764910197.58198235089809
206267.0652881213165-5.06528812131646
215964.2302500044309-5.23025000443094
227360.97580529022912.024194709771
236170.9507405293717-9.95074052937166
246164.3841012371349-3.3841012371349
255762.396413978611-5.39641397861097
265858.6371195804074-0.637119580407408
275758.3004745672508-1.30047456725079
286757.40722066572569.59277933427439
298164.980693255431116.0193067445689
307978.14241581754460.857584182455383
317680.3334568815634-4.33345688156339
327878.5034085376781-0.503408537678126
337479.4282751090728-5.42827510907281
346776.4628176800113-9.46281768001134
358470.023100929266813.9768990707332
368581.4201435928733.57985640712698
377985.4654435385075-6.46544353850747
388281.83941043274030.160589567259734
398783.03848401437453.96151598562554
409087.2269895219192.77301047808099
418790.7104656280219-3.71046562802194
429389.26916465410053.73083534589949
439293.4499701989452-1.44997019894524
448293.7822481615077-11.7822481615077
458085.9301465907624-5.93014659076243
467981.9918602613577-2.99186026135771
477780.0178405965313-3.01784059653129
487277.8521217541916-5.85212175419164
496573.2912794755224-8.29127947552239
507366.48281797277926.51718202722076
517670.81059698717725.18940301282275
527774.47049470652862.52950529347136
537676.3419865158162-0.341986515816245
547676.106846731765-0.106846731764961
557676.036491871352-0.0364918713519558
567576.0151823859737-1.01518238597369
577875.22441014940492.77558985059511
587377.3477777091485-4.34777770914855
598074.04478889628745.95521110371256
607778.5712072768309-1.57120727683086
618377.53732521220985.46267478779023
628481.92860441373782.07139558626221
638583.97366394545091.02633605454909
648185.3179299072943-4.31792990729433
658482.53062921915581.46937078084417
668384.0337777949763-1.03377779497626
678383.6583974015512-0.658397401551156
688883.51814665252234.48185334747771
699287.37056797354744.62943202645258
709291.59535572987210.404644270127932
718992.7726697088956-3.77266970889563
728290.6978013091687-8.69780130916872
737384.5451872992056-11.5451872992056
748175.66191240358985.33808759641022
759179.35536605953111.644633940469
768088.2993504073032-8.29935040730315
778182.2724555868784-1.27245558687839
788281.27995724395320.72004275604678
798481.77687283258932.22312716741071
808783.49356213430523.50643786569478
818586.3437785925695-1.34377859256949
827485.5918261373761-11.5918261373761
838176.72761870268344.27238129731663
848279.6384371902382.36156280976196
858681.29567574953514.70432425046489
868584.92506460717980.0749353928202225
878285.1940281744556-3.19402817445555
888682.90414930928413.09585069071591
898885.36314200213162.63685799786843
908687.6395305298137-1.63953052981365
918386.713881587714-3.71388158771397
928184.0678850238688-3.06788502386883
938181.7157274290141-0.715727429014095
948181.0321708753184-0.0321708753183714
958280.84359227940861.1564077205914
968681.58511381659054.41488618340955
978584.94776931001710.0522306899829204
988785.1425655558521.857434444148
998986.75577950924062.24422049075942
1009088.77863095159251.22136904840747
1019090.1279985694688-0.127998569468758
1029290.48929364449341.51070635550658
1038692.1281369840124-6.12813698401243
1048687.8640155843471-1.86401558434713
1058286.592385601138-4.59238560113799
1068083.0747487750433-3.07474877504328
1077980.4840804376933-1.48408043769334
1087778.9645505109054-1.96455051090541
1097976.98330725794442.01669274205562
1107678.011183147178-2.011183147178
1117875.99636202844412.00363797155586
1127877.01431742089550.985682579104477
1137777.3488509647118-0.348850964711843
1147276.6934465332636-4.69344653326357
1157572.61154853243592.3884514675641
1167973.81366558603875.18633441396133
1178177.34632503012983.65367496987017
1188679.97424942947896.02575057052105
1198884.67130144611313.32869855388694
1209787.59870888999299.40129111000711
1219495.4781408113965-1.47814081139654
1229695.36557597981080.634424020189172
1239496.8247884457941-2.82478844579413
1249195.608026894295-4.60802689429504
1259292.8313000726818-0.831300072681799
1269392.75195653620550.248043463794474
1279393.4713019251835-0.471301925183539
1288793.6408261355848-6.64082613558479
1298488.9459519966587-4.94595199665868
1308085.1997111561183-5.19971115611831
1317880.971273190964-2.97127319096396
1327578.1923512644155-3.19235126441555
1337375.0699365644123-2.06993656441226
1348172.64477680690538.35522319309472
1357678.2752564804217-2.27525648042167
1367776.04903529319130.950964706808719
1377176.2221419383054-5.22214193830537
1387171.6094872228529-0.609487222852906
1397870.31448410685687.68551589314316
1406775.4885366133339-8.48853661333393
1417668.42092509762857.57907490237153
1426873.4655343262575-5.46553432625745
1438268.716122534542713.2838774654573
1446478.3547930807135-14.3547930807135
1457167.08319503527363.91680496472644
1468169.315992228077111.6840077719229
1476977.863204146732-8.86320414673199
1486370.9688121036782-7.96881210367816
1497064.26814603928345.73185396071663
1507767.85358472209099.14641527790909
1517574.44455134690350.555448653096505
1527674.8232689089041.17673109109596
1536875.7209301806318-7.7209301806318







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
15469.709485911568758.296868746847481.12210307629
15569.309741508431454.8072565364883.8122264803829
15668.909997105294251.516901769023386.3030924415651
15768.510252702156948.31879040023488.7017150040798
15868.110508299019745.160509209728691.0605073883107
15967.710763895882442.012594221783993.408933569981
16067.311019492745238.857025337485495.7650136480049
16166.911275089607935.68214975985398.1404004193629
16266.511530686470732.4801390705316100.54292230241
16366.111786283333429.2455950364493102.977977530218
16465.712041880196225.9747315992836105.449352161109
16565.312297477058922.6648689081153107.959726046003

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
154 & 69.7094859115687 & 58.2968687468474 & 81.12210307629 \tabularnewline
155 & 69.3097415084314 & 54.80725653648 & 83.8122264803829 \tabularnewline
156 & 68.9099971052942 & 51.5169017690233 & 86.3030924415651 \tabularnewline
157 & 68.5102527021569 & 48.318790400234 & 88.7017150040798 \tabularnewline
158 & 68.1105082990197 & 45.1605092097286 & 91.0605073883107 \tabularnewline
159 & 67.7107638958824 & 42.0125942217839 & 93.408933569981 \tabularnewline
160 & 67.3110194927452 & 38.8570253374854 & 95.7650136480049 \tabularnewline
161 & 66.9112750896079 & 35.682149759853 & 98.1404004193629 \tabularnewline
162 & 66.5115306864707 & 32.4801390705316 & 100.54292230241 \tabularnewline
163 & 66.1117862833334 & 29.2455950364493 & 102.977977530218 \tabularnewline
164 & 65.7120418801962 & 25.9747315992836 & 105.449352161109 \tabularnewline
165 & 65.3122974770589 & 22.6648689081153 & 107.959726046003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=273241&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]69.7094859115687[/C][C]58.2968687468474[/C][C]81.12210307629[/C][/ROW]
[ROW][C]155[/C][C]69.3097415084314[/C][C]54.80725653648[/C][C]83.8122264803829[/C][/ROW]
[ROW][C]156[/C][C]68.9099971052942[/C][C]51.5169017690233[/C][C]86.3030924415651[/C][/ROW]
[ROW][C]157[/C][C]68.5102527021569[/C][C]48.318790400234[/C][C]88.7017150040798[/C][/ROW]
[ROW][C]158[/C][C]68.1105082990197[/C][C]45.1605092097286[/C][C]91.0605073883107[/C][/ROW]
[ROW][C]159[/C][C]67.7107638958824[/C][C]42.0125942217839[/C][C]93.408933569981[/C][/ROW]
[ROW][C]160[/C][C]67.3110194927452[/C][C]38.8570253374854[/C][C]95.7650136480049[/C][/ROW]
[ROW][C]161[/C][C]66.9112750896079[/C][C]35.682149759853[/C][C]98.1404004193629[/C][/ROW]
[ROW][C]162[/C][C]66.5115306864707[/C][C]32.4801390705316[/C][C]100.54292230241[/C][/ROW]
[ROW][C]163[/C][C]66.1117862833334[/C][C]29.2455950364493[/C][C]102.977977530218[/C][/ROW]
[ROW][C]164[/C][C]65.7120418801962[/C][C]25.9747315992836[/C][C]105.449352161109[/C][/ROW]
[ROW][C]165[/C][C]65.3122974770589[/C][C]22.6648689081153[/C][C]107.959726046003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=273241&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=273241&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
15469.709485911568758.296868746847481.12210307629
15569.309741508431454.8072565364883.8122264803829
15668.909997105294251.516901769023386.3030924415651
15768.510252702156948.31879040023488.7017150040798
15868.110508299019745.160509209728691.0605073883107
15967.710763895882442.012594221783993.408933569981
16067.311019492745238.857025337485495.7650136480049
16166.911275089607935.68214975985398.1404004193629
16266.511530686470732.4801390705316100.54292230241
16366.111786283333429.2455950364493102.977977530218
16465.712041880196225.9747315992836105.449352161109
16565.312297477058922.6648689081153107.959726046003



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