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 computationSun, 14 Dec 2014 10:08:58 +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/2014/Dec/14/t1418551786ht91opxc08xal1x.htm/, Retrieved Thu, 31 Oct 2024 23:21:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267388, Retrieved Thu, 31 Oct 2024 23:21:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [KUL paper spectra...] [2014-12-13 15:24:43] [bb1b6762b7e5624d262776d3f7139d34]
- R     [Spectral Analysis] [KUL paper spectra...] [2014-12-13 15:25:44] [bb1b6762b7e5624d262776d3f7139d34]
- RMP     [Exponential Smoothing] [KUL paper ES2 man] [2014-12-14 10:08:01] [bb1b6762b7e5624d262776d3f7139d34]
- R           [Exponential Smoothing] [KUL paper ES2 man 2] [2014-12-14 10:08:58] [8568a324fefbb8dbb43f697bfa8d1be6] [Current]
Feedback Forum

Post a new message
Dataseries X:
NA
6
NA
1
1
5.5
NA
6.5
4.5
2
5
0.5
5
NA
NA
NA
5.5
NA
3
NA
0.5
6.5
NA
7.5
5.5
4
7.5
NA
4
NA
NA
NA
3.5
2.5
4.5
4.5
NA
6
2.5
NA
0
5
6.5
5
6
NA
5.5
1
NA
6
5
1
5
6.5
7
4.5
NA
8.5
NA
7.5
3.5
NA
NA
9
NA
3.5
NA
6.5
7.5
NA
NA
NA
NA
7.5
NA
NA
6.5
NA
NA
1.5
NA
NA
NA
0
NA
5.5
5
NA
NA
NA
7
0
4.5
NA
1.5
NA
2.5
5.5
8
1
5
NA
3
3
8
NA
NA
NA
NA
NA
NA
5.5
0.5
7.5
9
9.5
NA
7
8
NA
7
NA
NA
9.5
4
6
8
5.5
9.5
7.5
7
NA
8
7
7
6
10
2.5
NA
8
6
8.5
6
9
NA
NA
5.5
NA
NA
9
NA
8.5
9
NA
9
7.5
10
NA
NA
NA
NA
8.5
NA
10
NA
6.5
NA
8.5
NA
NA
8
NA
7
7.5
7.5
9.5
6
NA
7
NA
NA
NA
10
NA
3.5
NA
NA
NA
NA
6.5
6.5
8.5
4
NA
NA
8.5
NA
NA
NA
NA
10
8
NA
NA
5
NA
4.5
8.5
NA
8.5
7.5
7.5
NA
NA
NA
5.5
8.5
9.5
7
NA
NA
NA
6.5
6.5
NA
NA
NA
10
10
NA
NA
NA
7.5
4.5
4.5
0.5
NA
4.5
5.5
5
NA
NA
8
NA
6.5
8
NA
5.5
NA
5
3.5
NA
9
NA
5
NA
3
NA
NA
0.5
6.5
NA
4.5
8
NA
7.5
NA
NA
9.5
6.5
NA
6
NA
NA
8
NA
NA




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267388&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267388&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267388&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'Gertrude Mary Cox' @ cox.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.493488495312859
beta0.266305351103877
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.493488495312859 \tabularnewline
beta & 0.266305351103877 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267388&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]0.493488495312859[/C][/ROW]
[ROW][C]beta[/C][C]0.266305351103877[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267388&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267388&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
alpha0.493488495312859
beta0.266305351103877
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
31-45
45.5-5.8754643883856311.3754643883856
56.5-3.109762537303169.60976253730317
64.50.04748756266214384.45251243733786
721.244837142103440.755162857896563
850.716829706894344.28317029310566
90.52.49274171150919-1.99274171150919
1050.9096799645386214.09032003546138
115.52.866083449067662.63391655093234
1234.44991426623054-1.44991426623054
130.53.82787581616918-3.32787581616918
146.51.841740075952414.65825992404759
157.54.408852569013563.09114743098644
165.56.60884742715376-1.10884742715376
1746.59046993598847-2.59046993598847
187.55.500492780018351.99950721998165
1946.93838903781492-2.93838903781492
203.55.55333124836955-2.05333124836955
212.54.33519332224501-1.83519332224501
224.52.983525366346961.51647463365304
234.53.485160001043551.01483999895645
2463.872612593964612.12738740603539
252.55.08867286478885-2.58867286478885
2603.63721181509065-3.63721181509065
2751.190311473647273.80968852635273
286.52.919034811916683.58096518808332
2955.00549134252916-0.00549134252915628
3065.321351171649080.678648828350918
315.56.06401340152353-0.56401340152353
3216.11931425055165-5.11931425055165
3363.253853287855192.74614671214481
3454.630801651059970.369198348940028
3514.88327288318253-3.88327288318253
3652.526864094990642.47313590500936
376.53.632286040322492.86771395967751
3875.309298747374281.69070125262572
394.56.62765886196315-2.12765886196315
408.55.782088182656032.71791181734397
417.57.68493512618193-0.184935126181931
423.58.13095657905495-4.63095657905495
4395.774323639658573.22567636034143
443.57.71876262620509-4.21876262620509
456.55.435032626815351.06496737318465
467.55.898719144458251.60128085554175
477.56.837508327018280.662491672981725
486.57.40007959452959-0.900079594529591
491.57.07325269390483-5.57325269390483
5003.70783941512002-3.70783941512002
515.50.7757069633106574.72429303668934
5252.625594971898112.37440502810189
5373.627881331732843.37211866826716
5405.56568710010435-5.56568710010435
554.52.361353591380552.13864640861945
561.53.24007800794597-1.74007800794597
572.51.976017885752690.523982114247314
585.51.89810639659063.6018936034094
5983.812464728917494.18753527108251
6016.56615062249857-5.56615062249857
6153.775008868162371.22499113183763
6234.49620409227541-1.49620409227541
6333.67789169225477-0.67789169225477
6483.174319451758554.82568054824145
655.56.02088110710926-0.520881107109259
660.56.16052261613176-5.66052261613176
677.53.019912060048074.48008793995193
6895.472343154792423.52765684520758
699.57.918360280861861.58163971913814
7079.61189726384788-2.61189726384788
7188.8927200387552-0.892720038755195
7278.9046169538521-1.9046169538521
739.58.166852237850881.33314776214912
7449.20208760822927-5.20208760822927
7566.32860829810496-0.328608298104956
7685.816949708311192.18305029168881
775.56.83165920876192-1.33165920876192
789.55.936895181512673.56310481848733
797.57.92589923212054-0.425899232120541
8077.89040458366713-0.890404583667133
8187.508666140345270.491333859654733
8277.8733701435736-0.873370143573598
8377.44983131656136-0.449831316561363
8467.17618781398937-1.17618781398937
85106.389522748820513.61047725117949
862.58.43950578742936-5.93950578742936
8784.996120370349813.00387962965019
8866.36095850237979-0.360958502379786
898.56.017851056872622.48214894312738
9067.40398503280172-1.40398503280172
9196.687846815011992.31215318498801
925.58.10944005170187-2.60944005170187
9396.759354618616122.24064538138388
948.58.097193087680480.402806912319525
9598.581015747385350.418984252614653
9699.12788407362806-0.12788407362806
977.59.38807282304635-1.88807282304635
98108.531500736947911.46849926305209
998.59.52434651594286-1.02434651594286
100109.152383369706870.847616630293128
1016.59.81560511357199-3.31560511357199
1028.57.988592551670320.511407448329683
10388.11737512518709-0.117375125187092
10477.92043545482291-0.92043545482291
1057.57.206232387017140.293767612982863
1067.57.129831100505310.370168899494693
1079.57.139780058447572.36021994155243
10868.44197317489117-2.44197317489117
10977.05341847425976-0.0534184742597636
110106.836567856271223.16343214372878
1113.58.62292991836827-5.12292991836827
1126.55.6468192186830.853180781317002
1136.55.73197424288050.768025757119499
1148.55.876039132827422.62396086717258
11547.28082398212457-3.28082398212457
1168.55.340504057803563.15949594219644
117106.993624541036323.00637545896368
11888.96627556249058-0.966275562490578
11958.85148240122844-3.85148240122844
1204.56.80671632932851-2.30671632932851
1218.55.22112904895893.2788709510411
1228.56.822869549546371.67713045045363
1237.57.85457572173257-0.354575721732569
1247.57.83706041756757-0.337060417567574
1255.57.78389269720376-2.28389269720376
1268.56.469839602031062.03016039796894
1279.57.55152296960611.9484770303939
12878.84896271111936-1.84896271111936
1296.58.02942148766397-1.52942148766397
1306.57.1665757097336-0.666575709733601
131106.641933931869733.35806606813027
132108.544719001219761.45528099878024
1337.59.69975256056594-2.19975256056594
1344.58.76198064712103-4.26198064712103
1354.56.24641925326223-1.74641925326223
1360.54.74274644607835-4.24274644607835
1374.51.449588976007833.05041102399217
1385.52.156401640511423.34359835948858
13953.447309987730841.55269001226916
14084.058478059022293.94152194097771
1416.56.366496606180570.133503393819426
14286.812846643095181.18715335690482
1435.57.93517487909401-2.43517487909401
14456.94989846524068-1.94989846524068
1453.55.94784739955979-2.44784739955979
14694.378371519158934.62162848084107
14756.90496872313016-1.90496872313016
14835.96041691930973-2.96041691930973
1490.54.10595964636306-3.60595964636306
1506.51.459044198604145.04095580139586
1514.53.741757534494890.758242465505105
15284.010648294157983.98935170584202
1537.56.398329414868221.10167058513178
1549.57.505753160091151.99424683990885
1556.59.31573419985589-2.81573419985589
15668.38200501125354-2.38200501125354
15787.349276359197920.650723640802083

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 1 & -4 & 5 \tabularnewline
4 & 5.5 & -5.87546438838563 & 11.3754643883856 \tabularnewline
5 & 6.5 & -3.10976253730316 & 9.60976253730317 \tabularnewline
6 & 4.5 & 0.0474875626621438 & 4.45251243733786 \tabularnewline
7 & 2 & 1.24483714210344 & 0.755162857896563 \tabularnewline
8 & 5 & 0.71682970689434 & 4.28317029310566 \tabularnewline
9 & 0.5 & 2.49274171150919 & -1.99274171150919 \tabularnewline
10 & 5 & 0.909679964538621 & 4.09032003546138 \tabularnewline
11 & 5.5 & 2.86608344906766 & 2.63391655093234 \tabularnewline
12 & 3 & 4.44991426623054 & -1.44991426623054 \tabularnewline
13 & 0.5 & 3.82787581616918 & -3.32787581616918 \tabularnewline
14 & 6.5 & 1.84174007595241 & 4.65825992404759 \tabularnewline
15 & 7.5 & 4.40885256901356 & 3.09114743098644 \tabularnewline
16 & 5.5 & 6.60884742715376 & -1.10884742715376 \tabularnewline
17 & 4 & 6.59046993598847 & -2.59046993598847 \tabularnewline
18 & 7.5 & 5.50049278001835 & 1.99950721998165 \tabularnewline
19 & 4 & 6.93838903781492 & -2.93838903781492 \tabularnewline
20 & 3.5 & 5.55333124836955 & -2.05333124836955 \tabularnewline
21 & 2.5 & 4.33519332224501 & -1.83519332224501 \tabularnewline
22 & 4.5 & 2.98352536634696 & 1.51647463365304 \tabularnewline
23 & 4.5 & 3.48516000104355 & 1.01483999895645 \tabularnewline
24 & 6 & 3.87261259396461 & 2.12738740603539 \tabularnewline
25 & 2.5 & 5.08867286478885 & -2.58867286478885 \tabularnewline
26 & 0 & 3.63721181509065 & -3.63721181509065 \tabularnewline
27 & 5 & 1.19031147364727 & 3.80968852635273 \tabularnewline
28 & 6.5 & 2.91903481191668 & 3.58096518808332 \tabularnewline
29 & 5 & 5.00549134252916 & -0.00549134252915628 \tabularnewline
30 & 6 & 5.32135117164908 & 0.678648828350918 \tabularnewline
31 & 5.5 & 6.06401340152353 & -0.56401340152353 \tabularnewline
32 & 1 & 6.11931425055165 & -5.11931425055165 \tabularnewline
33 & 6 & 3.25385328785519 & 2.74614671214481 \tabularnewline
34 & 5 & 4.63080165105997 & 0.369198348940028 \tabularnewline
35 & 1 & 4.88327288318253 & -3.88327288318253 \tabularnewline
36 & 5 & 2.52686409499064 & 2.47313590500936 \tabularnewline
37 & 6.5 & 3.63228604032249 & 2.86771395967751 \tabularnewline
38 & 7 & 5.30929874737428 & 1.69070125262572 \tabularnewline
39 & 4.5 & 6.62765886196315 & -2.12765886196315 \tabularnewline
40 & 8.5 & 5.78208818265603 & 2.71791181734397 \tabularnewline
41 & 7.5 & 7.68493512618193 & -0.184935126181931 \tabularnewline
42 & 3.5 & 8.13095657905495 & -4.63095657905495 \tabularnewline
43 & 9 & 5.77432363965857 & 3.22567636034143 \tabularnewline
44 & 3.5 & 7.71876262620509 & -4.21876262620509 \tabularnewline
45 & 6.5 & 5.43503262681535 & 1.06496737318465 \tabularnewline
46 & 7.5 & 5.89871914445825 & 1.60128085554175 \tabularnewline
47 & 7.5 & 6.83750832701828 & 0.662491672981725 \tabularnewline
48 & 6.5 & 7.40007959452959 & -0.900079594529591 \tabularnewline
49 & 1.5 & 7.07325269390483 & -5.57325269390483 \tabularnewline
50 & 0 & 3.70783941512002 & -3.70783941512002 \tabularnewline
51 & 5.5 & 0.775706963310657 & 4.72429303668934 \tabularnewline
52 & 5 & 2.62559497189811 & 2.37440502810189 \tabularnewline
53 & 7 & 3.62788133173284 & 3.37211866826716 \tabularnewline
54 & 0 & 5.56568710010435 & -5.56568710010435 \tabularnewline
55 & 4.5 & 2.36135359138055 & 2.13864640861945 \tabularnewline
56 & 1.5 & 3.24007800794597 & -1.74007800794597 \tabularnewline
57 & 2.5 & 1.97601788575269 & 0.523982114247314 \tabularnewline
58 & 5.5 & 1.8981063965906 & 3.6018936034094 \tabularnewline
59 & 8 & 3.81246472891749 & 4.18753527108251 \tabularnewline
60 & 1 & 6.56615062249857 & -5.56615062249857 \tabularnewline
61 & 5 & 3.77500886816237 & 1.22499113183763 \tabularnewline
62 & 3 & 4.49620409227541 & -1.49620409227541 \tabularnewline
63 & 3 & 3.67789169225477 & -0.67789169225477 \tabularnewline
64 & 8 & 3.17431945175855 & 4.82568054824145 \tabularnewline
65 & 5.5 & 6.02088110710926 & -0.520881107109259 \tabularnewline
66 & 0.5 & 6.16052261613176 & -5.66052261613176 \tabularnewline
67 & 7.5 & 3.01991206004807 & 4.48008793995193 \tabularnewline
68 & 9 & 5.47234315479242 & 3.52765684520758 \tabularnewline
69 & 9.5 & 7.91836028086186 & 1.58163971913814 \tabularnewline
70 & 7 & 9.61189726384788 & -2.61189726384788 \tabularnewline
71 & 8 & 8.8927200387552 & -0.892720038755195 \tabularnewline
72 & 7 & 8.9046169538521 & -1.9046169538521 \tabularnewline
73 & 9.5 & 8.16685223785088 & 1.33314776214912 \tabularnewline
74 & 4 & 9.20208760822927 & -5.20208760822927 \tabularnewline
75 & 6 & 6.32860829810496 & -0.328608298104956 \tabularnewline
76 & 8 & 5.81694970831119 & 2.18305029168881 \tabularnewline
77 & 5.5 & 6.83165920876192 & -1.33165920876192 \tabularnewline
78 & 9.5 & 5.93689518151267 & 3.56310481848733 \tabularnewline
79 & 7.5 & 7.92589923212054 & -0.425899232120541 \tabularnewline
80 & 7 & 7.89040458366713 & -0.890404583667133 \tabularnewline
81 & 8 & 7.50866614034527 & 0.491333859654733 \tabularnewline
82 & 7 & 7.8733701435736 & -0.873370143573598 \tabularnewline
83 & 7 & 7.44983131656136 & -0.449831316561363 \tabularnewline
84 & 6 & 7.17618781398937 & -1.17618781398937 \tabularnewline
85 & 10 & 6.38952274882051 & 3.61047725117949 \tabularnewline
86 & 2.5 & 8.43950578742936 & -5.93950578742936 \tabularnewline
87 & 8 & 4.99612037034981 & 3.00387962965019 \tabularnewline
88 & 6 & 6.36095850237979 & -0.360958502379786 \tabularnewline
89 & 8.5 & 6.01785105687262 & 2.48214894312738 \tabularnewline
90 & 6 & 7.40398503280172 & -1.40398503280172 \tabularnewline
91 & 9 & 6.68784681501199 & 2.31215318498801 \tabularnewline
92 & 5.5 & 8.10944005170187 & -2.60944005170187 \tabularnewline
93 & 9 & 6.75935461861612 & 2.24064538138388 \tabularnewline
94 & 8.5 & 8.09719308768048 & 0.402806912319525 \tabularnewline
95 & 9 & 8.58101574738535 & 0.418984252614653 \tabularnewline
96 & 9 & 9.12788407362806 & -0.12788407362806 \tabularnewline
97 & 7.5 & 9.38807282304635 & -1.88807282304635 \tabularnewline
98 & 10 & 8.53150073694791 & 1.46849926305209 \tabularnewline
99 & 8.5 & 9.52434651594286 & -1.02434651594286 \tabularnewline
100 & 10 & 9.15238336970687 & 0.847616630293128 \tabularnewline
101 & 6.5 & 9.81560511357199 & -3.31560511357199 \tabularnewline
102 & 8.5 & 7.98859255167032 & 0.511407448329683 \tabularnewline
103 & 8 & 8.11737512518709 & -0.117375125187092 \tabularnewline
104 & 7 & 7.92043545482291 & -0.92043545482291 \tabularnewline
105 & 7.5 & 7.20623238701714 & 0.293767612982863 \tabularnewline
106 & 7.5 & 7.12983110050531 & 0.370168899494693 \tabularnewline
107 & 9.5 & 7.13978005844757 & 2.36021994155243 \tabularnewline
108 & 6 & 8.44197317489117 & -2.44197317489117 \tabularnewline
109 & 7 & 7.05341847425976 & -0.0534184742597636 \tabularnewline
110 & 10 & 6.83656785627122 & 3.16343214372878 \tabularnewline
111 & 3.5 & 8.62292991836827 & -5.12292991836827 \tabularnewline
112 & 6.5 & 5.646819218683 & 0.853180781317002 \tabularnewline
113 & 6.5 & 5.7319742428805 & 0.768025757119499 \tabularnewline
114 & 8.5 & 5.87603913282742 & 2.62396086717258 \tabularnewline
115 & 4 & 7.28082398212457 & -3.28082398212457 \tabularnewline
116 & 8.5 & 5.34050405780356 & 3.15949594219644 \tabularnewline
117 & 10 & 6.99362454103632 & 3.00637545896368 \tabularnewline
118 & 8 & 8.96627556249058 & -0.966275562490578 \tabularnewline
119 & 5 & 8.85148240122844 & -3.85148240122844 \tabularnewline
120 & 4.5 & 6.80671632932851 & -2.30671632932851 \tabularnewline
121 & 8.5 & 5.2211290489589 & 3.2788709510411 \tabularnewline
122 & 8.5 & 6.82286954954637 & 1.67713045045363 \tabularnewline
123 & 7.5 & 7.85457572173257 & -0.354575721732569 \tabularnewline
124 & 7.5 & 7.83706041756757 & -0.337060417567574 \tabularnewline
125 & 5.5 & 7.78389269720376 & -2.28389269720376 \tabularnewline
126 & 8.5 & 6.46983960203106 & 2.03016039796894 \tabularnewline
127 & 9.5 & 7.5515229696061 & 1.9484770303939 \tabularnewline
128 & 7 & 8.84896271111936 & -1.84896271111936 \tabularnewline
129 & 6.5 & 8.02942148766397 & -1.52942148766397 \tabularnewline
130 & 6.5 & 7.1665757097336 & -0.666575709733601 \tabularnewline
131 & 10 & 6.64193393186973 & 3.35806606813027 \tabularnewline
132 & 10 & 8.54471900121976 & 1.45528099878024 \tabularnewline
133 & 7.5 & 9.69975256056594 & -2.19975256056594 \tabularnewline
134 & 4.5 & 8.76198064712103 & -4.26198064712103 \tabularnewline
135 & 4.5 & 6.24641925326223 & -1.74641925326223 \tabularnewline
136 & 0.5 & 4.74274644607835 & -4.24274644607835 \tabularnewline
137 & 4.5 & 1.44958897600783 & 3.05041102399217 \tabularnewline
138 & 5.5 & 2.15640164051142 & 3.34359835948858 \tabularnewline
139 & 5 & 3.44730998773084 & 1.55269001226916 \tabularnewline
140 & 8 & 4.05847805902229 & 3.94152194097771 \tabularnewline
141 & 6.5 & 6.36649660618057 & 0.133503393819426 \tabularnewline
142 & 8 & 6.81284664309518 & 1.18715335690482 \tabularnewline
143 & 5.5 & 7.93517487909401 & -2.43517487909401 \tabularnewline
144 & 5 & 6.94989846524068 & -1.94989846524068 \tabularnewline
145 & 3.5 & 5.94784739955979 & -2.44784739955979 \tabularnewline
146 & 9 & 4.37837151915893 & 4.62162848084107 \tabularnewline
147 & 5 & 6.90496872313016 & -1.90496872313016 \tabularnewline
148 & 3 & 5.96041691930973 & -2.96041691930973 \tabularnewline
149 & 0.5 & 4.10595964636306 & -3.60595964636306 \tabularnewline
150 & 6.5 & 1.45904419860414 & 5.04095580139586 \tabularnewline
151 & 4.5 & 3.74175753449489 & 0.758242465505105 \tabularnewline
152 & 8 & 4.01064829415798 & 3.98935170584202 \tabularnewline
153 & 7.5 & 6.39832941486822 & 1.10167058513178 \tabularnewline
154 & 9.5 & 7.50575316009115 & 1.99424683990885 \tabularnewline
155 & 6.5 & 9.31573419985589 & -2.81573419985589 \tabularnewline
156 & 6 & 8.38200501125354 & -2.38200501125354 \tabularnewline
157 & 8 & 7.34927635919792 & 0.650723640802083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267388&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]1[/C][C]-4[/C][C]5[/C][/ROW]
[ROW][C]4[/C][C]5.5[/C][C]-5.87546438838563[/C][C]11.3754643883856[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]-3.10976253730316[/C][C]9.60976253730317[/C][/ROW]
[ROW][C]6[/C][C]4.5[/C][C]0.0474875626621438[/C][C]4.45251243733786[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.24483714210344[/C][C]0.755162857896563[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]0.71682970689434[/C][C]4.28317029310566[/C][/ROW]
[ROW][C]9[/C][C]0.5[/C][C]2.49274171150919[/C][C]-1.99274171150919[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]0.909679964538621[/C][C]4.09032003546138[/C][/ROW]
[ROW][C]11[/C][C]5.5[/C][C]2.86608344906766[/C][C]2.63391655093234[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.44991426623054[/C][C]-1.44991426623054[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]3.82787581616918[/C][C]-3.32787581616918[/C][/ROW]
[ROW][C]14[/C][C]6.5[/C][C]1.84174007595241[/C][C]4.65825992404759[/C][/ROW]
[ROW][C]15[/C][C]7.5[/C][C]4.40885256901356[/C][C]3.09114743098644[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]6.60884742715376[/C][C]-1.10884742715376[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]6.59046993598847[/C][C]-2.59046993598847[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]5.50049278001835[/C][C]1.99950721998165[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]6.93838903781492[/C][C]-2.93838903781492[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]5.55333124836955[/C][C]-2.05333124836955[/C][/ROW]
[ROW][C]21[/C][C]2.5[/C][C]4.33519332224501[/C][C]-1.83519332224501[/C][/ROW]
[ROW][C]22[/C][C]4.5[/C][C]2.98352536634696[/C][C]1.51647463365304[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]3.48516000104355[/C][C]1.01483999895645[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]3.87261259396461[/C][C]2.12738740603539[/C][/ROW]
[ROW][C]25[/C][C]2.5[/C][C]5.08867286478885[/C][C]-2.58867286478885[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]3.63721181509065[/C][C]-3.63721181509065[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]1.19031147364727[/C][C]3.80968852635273[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]2.91903481191668[/C][C]3.58096518808332[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]5.00549134252916[/C][C]-0.00549134252915628[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]5.32135117164908[/C][C]0.678648828350918[/C][/ROW]
[ROW][C]31[/C][C]5.5[/C][C]6.06401340152353[/C][C]-0.56401340152353[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]6.11931425055165[/C][C]-5.11931425055165[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]3.25385328785519[/C][C]2.74614671214481[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.63080165105997[/C][C]0.369198348940028[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]4.88327288318253[/C][C]-3.88327288318253[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]2.52686409499064[/C][C]2.47313590500936[/C][/ROW]
[ROW][C]37[/C][C]6.5[/C][C]3.63228604032249[/C][C]2.86771395967751[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]5.30929874737428[/C][C]1.69070125262572[/C][/ROW]
[ROW][C]39[/C][C]4.5[/C][C]6.62765886196315[/C][C]-2.12765886196315[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]5.78208818265603[/C][C]2.71791181734397[/C][/ROW]
[ROW][C]41[/C][C]7.5[/C][C]7.68493512618193[/C][C]-0.184935126181931[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]8.13095657905495[/C][C]-4.63095657905495[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]5.77432363965857[/C][C]3.22567636034143[/C][/ROW]
[ROW][C]44[/C][C]3.5[/C][C]7.71876262620509[/C][C]-4.21876262620509[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]5.43503262681535[/C][C]1.06496737318465[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]5.89871914445825[/C][C]1.60128085554175[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]6.83750832701828[/C][C]0.662491672981725[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]7.40007959452959[/C][C]-0.900079594529591[/C][/ROW]
[ROW][C]49[/C][C]1.5[/C][C]7.07325269390483[/C][C]-5.57325269390483[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]3.70783941512002[/C][C]-3.70783941512002[/C][/ROW]
[ROW][C]51[/C][C]5.5[/C][C]0.775706963310657[/C][C]4.72429303668934[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]2.62559497189811[/C][C]2.37440502810189[/C][/ROW]
[ROW][C]53[/C][C]7[/C][C]3.62788133173284[/C][C]3.37211866826716[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]5.56568710010435[/C][C]-5.56568710010435[/C][/ROW]
[ROW][C]55[/C][C]4.5[/C][C]2.36135359138055[/C][C]2.13864640861945[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]3.24007800794597[/C][C]-1.74007800794597[/C][/ROW]
[ROW][C]57[/C][C]2.5[/C][C]1.97601788575269[/C][C]0.523982114247314[/C][/ROW]
[ROW][C]58[/C][C]5.5[/C][C]1.8981063965906[/C][C]3.6018936034094[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]3.81246472891749[/C][C]4.18753527108251[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]6.56615062249857[/C][C]-5.56615062249857[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]3.77500886816237[/C][C]1.22499113183763[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]4.49620409227541[/C][C]-1.49620409227541[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]3.67789169225477[/C][C]-0.67789169225477[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]3.17431945175855[/C][C]4.82568054824145[/C][/ROW]
[ROW][C]65[/C][C]5.5[/C][C]6.02088110710926[/C][C]-0.520881107109259[/C][/ROW]
[ROW][C]66[/C][C]0.5[/C][C]6.16052261613176[/C][C]-5.66052261613176[/C][/ROW]
[ROW][C]67[/C][C]7.5[/C][C]3.01991206004807[/C][C]4.48008793995193[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]5.47234315479242[/C][C]3.52765684520758[/C][/ROW]
[ROW][C]69[/C][C]9.5[/C][C]7.91836028086186[/C][C]1.58163971913814[/C][/ROW]
[ROW][C]70[/C][C]7[/C][C]9.61189726384788[/C][C]-2.61189726384788[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.8927200387552[/C][C]-0.892720038755195[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]8.9046169538521[/C][C]-1.9046169538521[/C][/ROW]
[ROW][C]73[/C][C]9.5[/C][C]8.16685223785088[/C][C]1.33314776214912[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]9.20208760822927[/C][C]-5.20208760822927[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]6.32860829810496[/C][C]-0.328608298104956[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]5.81694970831119[/C][C]2.18305029168881[/C][/ROW]
[ROW][C]77[/C][C]5.5[/C][C]6.83165920876192[/C][C]-1.33165920876192[/C][/ROW]
[ROW][C]78[/C][C]9.5[/C][C]5.93689518151267[/C][C]3.56310481848733[/C][/ROW]
[ROW][C]79[/C][C]7.5[/C][C]7.92589923212054[/C][C]-0.425899232120541[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]7.89040458366713[/C][C]-0.890404583667133[/C][/ROW]
[ROW][C]81[/C][C]8[/C][C]7.50866614034527[/C][C]0.491333859654733[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]7.8733701435736[/C][C]-0.873370143573598[/C][/ROW]
[ROW][C]83[/C][C]7[/C][C]7.44983131656136[/C][C]-0.449831316561363[/C][/ROW]
[ROW][C]84[/C][C]6[/C][C]7.17618781398937[/C][C]-1.17618781398937[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]6.38952274882051[/C][C]3.61047725117949[/C][/ROW]
[ROW][C]86[/C][C]2.5[/C][C]8.43950578742936[/C][C]-5.93950578742936[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]4.99612037034981[/C][C]3.00387962965019[/C][/ROW]
[ROW][C]88[/C][C]6[/C][C]6.36095850237979[/C][C]-0.360958502379786[/C][/ROW]
[ROW][C]89[/C][C]8.5[/C][C]6.01785105687262[/C][C]2.48214894312738[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]7.40398503280172[/C][C]-1.40398503280172[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]6.68784681501199[/C][C]2.31215318498801[/C][/ROW]
[ROW][C]92[/C][C]5.5[/C][C]8.10944005170187[/C][C]-2.60944005170187[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]6.75935461861612[/C][C]2.24064538138388[/C][/ROW]
[ROW][C]94[/C][C]8.5[/C][C]8.09719308768048[/C][C]0.402806912319525[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]8.58101574738535[/C][C]0.418984252614653[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]9.12788407362806[/C][C]-0.12788407362806[/C][/ROW]
[ROW][C]97[/C][C]7.5[/C][C]9.38807282304635[/C][C]-1.88807282304635[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]8.53150073694791[/C][C]1.46849926305209[/C][/ROW]
[ROW][C]99[/C][C]8.5[/C][C]9.52434651594286[/C][C]-1.02434651594286[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]9.15238336970687[/C][C]0.847616630293128[/C][/ROW]
[ROW][C]101[/C][C]6.5[/C][C]9.81560511357199[/C][C]-3.31560511357199[/C][/ROW]
[ROW][C]102[/C][C]8.5[/C][C]7.98859255167032[/C][C]0.511407448329683[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]8.11737512518709[/C][C]-0.117375125187092[/C][/ROW]
[ROW][C]104[/C][C]7[/C][C]7.92043545482291[/C][C]-0.92043545482291[/C][/ROW]
[ROW][C]105[/C][C]7.5[/C][C]7.20623238701714[/C][C]0.293767612982863[/C][/ROW]
[ROW][C]106[/C][C]7.5[/C][C]7.12983110050531[/C][C]0.370168899494693[/C][/ROW]
[ROW][C]107[/C][C]9.5[/C][C]7.13978005844757[/C][C]2.36021994155243[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]8.44197317489117[/C][C]-2.44197317489117[/C][/ROW]
[ROW][C]109[/C][C]7[/C][C]7.05341847425976[/C][C]-0.0534184742597636[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]6.83656785627122[/C][C]3.16343214372878[/C][/ROW]
[ROW][C]111[/C][C]3.5[/C][C]8.62292991836827[/C][C]-5.12292991836827[/C][/ROW]
[ROW][C]112[/C][C]6.5[/C][C]5.646819218683[/C][C]0.853180781317002[/C][/ROW]
[ROW][C]113[/C][C]6.5[/C][C]5.7319742428805[/C][C]0.768025757119499[/C][/ROW]
[ROW][C]114[/C][C]8.5[/C][C]5.87603913282742[/C][C]2.62396086717258[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]7.28082398212457[/C][C]-3.28082398212457[/C][/ROW]
[ROW][C]116[/C][C]8.5[/C][C]5.34050405780356[/C][C]3.15949594219644[/C][/ROW]
[ROW][C]117[/C][C]10[/C][C]6.99362454103632[/C][C]3.00637545896368[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]8.96627556249058[/C][C]-0.966275562490578[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]8.85148240122844[/C][C]-3.85148240122844[/C][/ROW]
[ROW][C]120[/C][C]4.5[/C][C]6.80671632932851[/C][C]-2.30671632932851[/C][/ROW]
[ROW][C]121[/C][C]8.5[/C][C]5.2211290489589[/C][C]3.2788709510411[/C][/ROW]
[ROW][C]122[/C][C]8.5[/C][C]6.82286954954637[/C][C]1.67713045045363[/C][/ROW]
[ROW][C]123[/C][C]7.5[/C][C]7.85457572173257[/C][C]-0.354575721732569[/C][/ROW]
[ROW][C]124[/C][C]7.5[/C][C]7.83706041756757[/C][C]-0.337060417567574[/C][/ROW]
[ROW][C]125[/C][C]5.5[/C][C]7.78389269720376[/C][C]-2.28389269720376[/C][/ROW]
[ROW][C]126[/C][C]8.5[/C][C]6.46983960203106[/C][C]2.03016039796894[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]7.5515229696061[/C][C]1.9484770303939[/C][/ROW]
[ROW][C]128[/C][C]7[/C][C]8.84896271111936[/C][C]-1.84896271111936[/C][/ROW]
[ROW][C]129[/C][C]6.5[/C][C]8.02942148766397[/C][C]-1.52942148766397[/C][/ROW]
[ROW][C]130[/C][C]6.5[/C][C]7.1665757097336[/C][C]-0.666575709733601[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]6.64193393186973[/C][C]3.35806606813027[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]8.54471900121976[/C][C]1.45528099878024[/C][/ROW]
[ROW][C]133[/C][C]7.5[/C][C]9.69975256056594[/C][C]-2.19975256056594[/C][/ROW]
[ROW][C]134[/C][C]4.5[/C][C]8.76198064712103[/C][C]-4.26198064712103[/C][/ROW]
[ROW][C]135[/C][C]4.5[/C][C]6.24641925326223[/C][C]-1.74641925326223[/C][/ROW]
[ROW][C]136[/C][C]0.5[/C][C]4.74274644607835[/C][C]-4.24274644607835[/C][/ROW]
[ROW][C]137[/C][C]4.5[/C][C]1.44958897600783[/C][C]3.05041102399217[/C][/ROW]
[ROW][C]138[/C][C]5.5[/C][C]2.15640164051142[/C][C]3.34359835948858[/C][/ROW]
[ROW][C]139[/C][C]5[/C][C]3.44730998773084[/C][C]1.55269001226916[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]4.05847805902229[/C][C]3.94152194097771[/C][/ROW]
[ROW][C]141[/C][C]6.5[/C][C]6.36649660618057[/C][C]0.133503393819426[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]6.81284664309518[/C][C]1.18715335690482[/C][/ROW]
[ROW][C]143[/C][C]5.5[/C][C]7.93517487909401[/C][C]-2.43517487909401[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]6.94989846524068[/C][C]-1.94989846524068[/C][/ROW]
[ROW][C]145[/C][C]3.5[/C][C]5.94784739955979[/C][C]-2.44784739955979[/C][/ROW]
[ROW][C]146[/C][C]9[/C][C]4.37837151915893[/C][C]4.62162848084107[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]6.90496872313016[/C][C]-1.90496872313016[/C][/ROW]
[ROW][C]148[/C][C]3[/C][C]5.96041691930973[/C][C]-2.96041691930973[/C][/ROW]
[ROW][C]149[/C][C]0.5[/C][C]4.10595964636306[/C][C]-3.60595964636306[/C][/ROW]
[ROW][C]150[/C][C]6.5[/C][C]1.45904419860414[/C][C]5.04095580139586[/C][/ROW]
[ROW][C]151[/C][C]4.5[/C][C]3.74175753449489[/C][C]0.758242465505105[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]4.01064829415798[/C][C]3.98935170584202[/C][/ROW]
[ROW][C]153[/C][C]7.5[/C][C]6.39832941486822[/C][C]1.10167058513178[/C][/ROW]
[ROW][C]154[/C][C]9.5[/C][C]7.50575316009115[/C][C]1.99424683990885[/C][/ROW]
[ROW][C]155[/C][C]6.5[/C][C]9.31573419985589[/C][C]-2.81573419985589[/C][/ROW]
[ROW][C]156[/C][C]6[/C][C]8.38200501125354[/C][C]-2.38200501125354[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]7.34927635919792[/C][C]0.650723640802083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267388&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267388&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
31-45
45.5-5.8754643883856311.3754643883856
56.5-3.109762537303169.60976253730317
64.50.04748756266214384.45251243733786
721.244837142103440.755162857896563
850.716829706894344.28317029310566
90.52.49274171150919-1.99274171150919
1050.9096799645386214.09032003546138
115.52.866083449067662.63391655093234
1234.44991426623054-1.44991426623054
130.53.82787581616918-3.32787581616918
146.51.841740075952414.65825992404759
157.54.408852569013563.09114743098644
165.56.60884742715376-1.10884742715376
1746.59046993598847-2.59046993598847
187.55.500492780018351.99950721998165
1946.93838903781492-2.93838903781492
203.55.55333124836955-2.05333124836955
212.54.33519332224501-1.83519332224501
224.52.983525366346961.51647463365304
234.53.485160001043551.01483999895645
2463.872612593964612.12738740603539
252.55.08867286478885-2.58867286478885
2603.63721181509065-3.63721181509065
2751.190311473647273.80968852635273
286.52.919034811916683.58096518808332
2955.00549134252916-0.00549134252915628
3065.321351171649080.678648828350918
315.56.06401340152353-0.56401340152353
3216.11931425055165-5.11931425055165
3363.253853287855192.74614671214481
3454.630801651059970.369198348940028
3514.88327288318253-3.88327288318253
3652.526864094990642.47313590500936
376.53.632286040322492.86771395967751
3875.309298747374281.69070125262572
394.56.62765886196315-2.12765886196315
408.55.782088182656032.71791181734397
417.57.68493512618193-0.184935126181931
423.58.13095657905495-4.63095657905495
4395.774323639658573.22567636034143
443.57.71876262620509-4.21876262620509
456.55.435032626815351.06496737318465
467.55.898719144458251.60128085554175
477.56.837508327018280.662491672981725
486.57.40007959452959-0.900079594529591
491.57.07325269390483-5.57325269390483
5003.70783941512002-3.70783941512002
515.50.7757069633106574.72429303668934
5252.625594971898112.37440502810189
5373.627881331732843.37211866826716
5405.56568710010435-5.56568710010435
554.52.361353591380552.13864640861945
561.53.24007800794597-1.74007800794597
572.51.976017885752690.523982114247314
585.51.89810639659063.6018936034094
5983.812464728917494.18753527108251
6016.56615062249857-5.56615062249857
6153.775008868162371.22499113183763
6234.49620409227541-1.49620409227541
6333.67789169225477-0.67789169225477
6483.174319451758554.82568054824145
655.56.02088110710926-0.520881107109259
660.56.16052261613176-5.66052261613176
677.53.019912060048074.48008793995193
6895.472343154792423.52765684520758
699.57.918360280861861.58163971913814
7079.61189726384788-2.61189726384788
7188.8927200387552-0.892720038755195
7278.9046169538521-1.9046169538521
739.58.166852237850881.33314776214912
7449.20208760822927-5.20208760822927
7566.32860829810496-0.328608298104956
7685.816949708311192.18305029168881
775.56.83165920876192-1.33165920876192
789.55.936895181512673.56310481848733
797.57.92589923212054-0.425899232120541
8077.89040458366713-0.890404583667133
8187.508666140345270.491333859654733
8277.8733701435736-0.873370143573598
8377.44983131656136-0.449831316561363
8467.17618781398937-1.17618781398937
85106.389522748820513.61047725117949
862.58.43950578742936-5.93950578742936
8784.996120370349813.00387962965019
8866.36095850237979-0.360958502379786
898.56.017851056872622.48214894312738
9067.40398503280172-1.40398503280172
9196.687846815011992.31215318498801
925.58.10944005170187-2.60944005170187
9396.759354618616122.24064538138388
948.58.097193087680480.402806912319525
9598.581015747385350.418984252614653
9699.12788407362806-0.12788407362806
977.59.38807282304635-1.88807282304635
98108.531500736947911.46849926305209
998.59.52434651594286-1.02434651594286
100109.152383369706870.847616630293128
1016.59.81560511357199-3.31560511357199
1028.57.988592551670320.511407448329683
10388.11737512518709-0.117375125187092
10477.92043545482291-0.92043545482291
1057.57.206232387017140.293767612982863
1067.57.129831100505310.370168899494693
1079.57.139780058447572.36021994155243
10868.44197317489117-2.44197317489117
10977.05341847425976-0.0534184742597636
110106.836567856271223.16343214372878
1113.58.62292991836827-5.12292991836827
1126.55.6468192186830.853180781317002
1136.55.73197424288050.768025757119499
1148.55.876039132827422.62396086717258
11547.28082398212457-3.28082398212457
1168.55.340504057803563.15949594219644
117106.993624541036323.00637545896368
11888.96627556249058-0.966275562490578
11958.85148240122844-3.85148240122844
1204.56.80671632932851-2.30671632932851
1218.55.22112904895893.2788709510411
1228.56.822869549546371.67713045045363
1237.57.85457572173257-0.354575721732569
1247.57.83706041756757-0.337060417567574
1255.57.78389269720376-2.28389269720376
1268.56.469839602031062.03016039796894
1279.57.55152296960611.9484770303939
12878.84896271111936-1.84896271111936
1296.58.02942148766397-1.52942148766397
1306.57.1665757097336-0.666575709733601
131106.641933931869733.35806606813027
132108.544719001219761.45528099878024
1337.59.69975256056594-2.19975256056594
1344.58.76198064712103-4.26198064712103
1354.56.24641925326223-1.74641925326223
1360.54.74274644607835-4.24274644607835
1374.51.449588976007833.05041102399217
1385.52.156401640511423.34359835948858
13953.447309987730841.55269001226916
14084.058478059022293.94152194097771
1416.56.366496606180570.133503393819426
14286.812846643095181.18715335690482
1435.57.93517487909401-2.43517487909401
14456.94989846524068-1.94989846524068
1453.55.94784739955979-2.44784739955979
14694.378371519158934.62162848084107
14756.90496872313016-1.90496872313016
14835.96041691930973-2.96041691930973
1490.54.10595964636306-3.60595964636306
1506.51.459044198604145.04095580139586
1514.53.741757534494890.758242465505105
15284.010648294157983.98935170584202
1537.56.398329414868221.10167058513178
1549.57.505753160091151.99424683990885
1556.59.31573419985589-2.81573419985589
15668.38200501125354-2.38200501125354
15787.349276359197920.650723640802083







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1587.898681613774592.0522518295411613.745111398008

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
158 & 7.89868161377459 & 2.05225182954116 & 13.745111398008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267388&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]158[/C][C]7.89868161377459[/C][C]2.05225182954116[/C][C]13.745111398008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267388&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267388&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
1587.898681613774592.0522518295411613.745111398008



Parameters (Session):
par1 = 1 ; par2 = Double ; par3 = additive ;
Parameters (R input):
par1 = 1 ; par2 = Double ; par3 = additive ;
R code (references can be found in the software module):
x<-na.omit(x)
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')