FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationTue, 26 Jan 2010 12:49:09 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Jan/26/t12645354414w4uibgy96cs0xi.htm/, Retrieved Fri, 03 May 2024 02:40:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=72653, Retrieved Fri, 03 May 2024 02:40:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W62
Estimated Impact111

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Mean versus Median] [] [2009-10-24 19:18:23] [8c7c3dc396eba234a49aa27457495c03]
- RMPD  [Exponential Smoothing] [] [2010-01-26 19:45:04] [8c7c3dc396eba234a49aa27457495c03]
-   PD      [Exponential Smoothing] [] [2010-01-26 19:49:09] [4ed6a647410123598b51b3bdc215cd7e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8,82000
8,80000
8,82000
8,58000
8,54000
8,42000
8,43000
8,44000
8,09000
7,69000
7,56000
7,54000
7,40000
7,39000
7,37000
7,31000
7,35000
7,26000
7,37000
7,35000
7,33000
7,32000
7,31000
7,33000
7,32000
7,27000
7,48000
7,70000
7,77000
7,80000
7,84000
7,81000
7,78000
7,82000
7,80000
7,81000
7,80000
7,66000
7,41000
7,35000
7,39000
7,32000
7,32000
7,30000
7,29000
7,26000
7,22000
7,21000
7,21000
7,21000
7,20000
7,19000
7,18000
7,12000
7,12000
7,07000
7,08000
7,05000
7,06000
7,07000
7,08000
7,08000
7,09000
7,07000
7,06000
6,99000
6,99000
6,99000
6,98000
6,96000
6,95000
6,91000
6,91000
6,87000
6,91000
6,89000
6,88000
6,90000
6,91000
6,85000
6,86000
6,82000
6,80000
6,83000
6,84000
6,89000
7,14000
7,21000
7,25000
7,31000
7,30000
7,48000
7,49000
7,40000
7,44000
7,42000
7,14000
7,24000
7,33000
7,61000
7,66000
7,69000
7,70000
7,68000
7,71000
7,71000
7,72000
7,68000
7,72000
7,74000
7,76000
7,90000
7,97000
7,96000
7,95000
7,97000
7,93000
7,99000
7,96000
7,92000
7,97000
7,98000
8,00000
8,04000
8,17000
8,29000
8,26000
8,30000
8,32000
8,28000
8,27000
8,32000

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72653&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72653&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.178814212435339
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 1 \tabularnewline
beta & 0.178814212435339 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72653&T=1

[TABLE]
[ROW][C]Estimated Parameters of Exponential Smoothing[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]alpha[/C][C]1[/C][/ROW]
[ROW][C]beta[/C][C]0.178814212435339[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72653&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.178814212435339
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
38.828.780.0399999999999991
48.588.80715256849741-0.227152568497415
58.548.526534460858890.0134655391411140
68.428.48894229063542-0.0689422906354196
78.438.356614429231960.0733855707680409
88.448.379736812272960.0602631877270348
98.098.40051272672522-0.310512726725216
107.697.9949886380447-0.304988638044698
117.567.540452334931010.0195476650689903
127.547.413947735265270.126052264734731
137.47.4164876717095-0.0164876717095011
147.397.273539441677880.116460558322125
157.377.284364244694030.085635755305975
167.317.279677134835370.0303228651646306
177.357.225099294088560.124900705911435
187.267.28743331544874-0.0274333154487358
197.377.192527848752280.17747215124772
207.357.334262391706850.0157376082931533
217.337.31707649973940.0129235002605981
227.327.299387405260410.0206125947395908
237.317.293073230155020.0169267698449813
247.337.286099977173920.0439000228260777
257.327.313949925181460.00605007481853903
267.277.30503176454531-0.0350317645453142
277.487.248767587157920.231232412842078
287.77.50011522894980.199884771050198
297.777.755857466862960.0141425331370382
307.87.8283863527877-0.0283863527877006
317.847.85331046947006-0.0133104694700563
327.817.89093036835462-0.0809303683546245
337.787.84645886827519-0.0664588682751894
347.827.804575078085220.0154249219147822
357.87.84733327334929-0.0473332733492873
367.817.81886941135335-0.00886941135334762
377.87.82728343454743-0.0272834345474333
387.667.8124047686863-0.152404768686303
397.417.64515263000227-0.235152630002272
407.357.35310399766632-0.00310399766631697
417.397.292548958768210.0974510412317864
427.327.34997458995708-0.029974589957078
437.327.274614707260830.0453852927391685
447.37.282730242638130.0172697573618663
457.297.265818320699740.0241816793002556
467.267.26014234863918-0.000142348639184497
477.227.23011689467938-0.0101168946793768
487.217.188307850124990.0216921498750073
497.217.182186714820920.0278132851790787
507.217.187160125505460.0228398744945419
517.27.191244219675320.0087557803246785
527.197.182809877638340.00719012236166439
537.187.174095573705750.00590442629424892
547.127.16515136904344-0.0451513690434391
557.127.097077662547560.0229223374524405
567.077.1011765022663-0.0311765022662946
577.087.045601700567060.0343982994329410
587.057.06175260538927-0.0117526053892751
597.067.029651072512530.0303489274874718
607.077.045077892079460.0249221079205428
617.087.05953431917950.0204656808205019
627.087.073193873777370.00680612622263066
637.097.07441090587760.0155890941223955
647.077.08719845746568-0.0171984574656801
657.067.06412312883885-0.00412312883885324
666.997.05338585480276-0.063385854802763
676.996.972051563096670.0179484369033336
686.996.975260998705980.0147390012940187
696.986.977896541614450.00210345838554549
706.966.96827266986906-0.00827266986905695
716.956.946793398921680.00320660107831650
726.916.9373667847681-0.0273667847680974
736.916.89247321470290.0175267852970977
746.876.89560725301233-0.0256072530123266
756.916.85102831223230.0589716877677056
766.896.90157328813646-0.0115732881364599
776.886.879503819733050.000496180266948976
786.96.869592543816710.0304074561832888
796.916.895029829146290.0149701708537116
806.856.90770670845752-0.057706708457518
816.866.837387928832450.0226120711675497
826.826.8514312885298-0.0314312885298076
836.86.80581092742552-0.00581092742552247
846.836.784771851014410.0452281489855917
856.846.822859286855180.0171407131448245
866.896.835924289976750.0540757100232527
877.146.895593795476440.244406204523563
887.217.189297098452630.0207029015473719
897.257.26299907148795-0.0129990714879478
907.317.300674652757440.00932534724255962
917.37.3623421573803-0.0623421573803036
927.487.341194493606830.138805506393175
937.497.54601489091421-0.0560148909142093
947.47.54599863231073-0.145998632310733
957.447.429892001857450.0101079981425469
967.427.47169945558461-0.0516994555846111
977.147.44245485815091-0.302454858150913
987.247.108371630893410.131628369106585
997.337.231908654049360.0980913459506425
1007.617.339448780822240.270551219177756
1017.667.66782718400294-0.00782718400293536
1027.697.71642757225986-0.0264275722598635
1037.77.74170194673964-0.0417019467396385
1047.687.74424504597637-0.06424504597637
1057.717.71275711867723-0.00275711867723238
1067.717.74226410667237-0.0322641066723728
1077.727.73649482584782-0.0164948258478228
1087.687.74354531655459-0.0635453165545856
1097.727.692182510820920.0278174891790766
1107.747.73715667324040.0028433267595922
1117.767.757665100475620.00233489952437793
1127.97.778082613695190.121917386304812
1137.977.939883175109460.0301168248905412
1147.968.01526849143331-0.0552684914333126
1157.957.99538569966518-0.045385699665176
1167.977.97727009152372-0.00727009152372116
1177.937.99597009583357-0.065970095833574
1187.997.944173705102810.045826294897191
1197.968.01236809793368-0.0523680979336811
1207.927.97300393774493-0.0530039377449327
1217.977.92352608036110.046473919638899
1227.987.98183627770011-0.00183627770011263
12387.991507925149360.0084920748506443
1248.048.013026428825710.0269735711742847
1258.178.057849686711810.112150313288188
1268.298.207903756656820.082096243343182
1278.268.34258373175413-0.0825837317541271
1288.38.297816586800540.00218341319945914
1298.328.33820701211222-0.0182070121122244
1308.288.35495133958058-0.0749513395805774
1318.278.3015489748225-0.0315489748225009
1328.328.285907569736470.0340924302635273

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 8.82 & 8.78 & 0.0399999999999991 \tabularnewline
4 & 8.58 & 8.80715256849741 & -0.227152568497415 \tabularnewline
5 & 8.54 & 8.52653446085889 & 0.0134655391411140 \tabularnewline
6 & 8.42 & 8.48894229063542 & -0.0689422906354196 \tabularnewline
7 & 8.43 & 8.35661442923196 & 0.0733855707680409 \tabularnewline
8 & 8.44 & 8.37973681227296 & 0.0602631877270348 \tabularnewline
9 & 8.09 & 8.40051272672522 & -0.310512726725216 \tabularnewline
10 & 7.69 & 7.9949886380447 & -0.304988638044698 \tabularnewline
11 & 7.56 & 7.54045233493101 & 0.0195476650689903 \tabularnewline
12 & 7.54 & 7.41394773526527 & 0.126052264734731 \tabularnewline
13 & 7.4 & 7.4164876717095 & -0.0164876717095011 \tabularnewline
14 & 7.39 & 7.27353944167788 & 0.116460558322125 \tabularnewline
15 & 7.37 & 7.28436424469403 & 0.085635755305975 \tabularnewline
16 & 7.31 & 7.27967713483537 & 0.0303228651646306 \tabularnewline
17 & 7.35 & 7.22509929408856 & 0.124900705911435 \tabularnewline
18 & 7.26 & 7.28743331544874 & -0.0274333154487358 \tabularnewline
19 & 7.37 & 7.19252784875228 & 0.17747215124772 \tabularnewline
20 & 7.35 & 7.33426239170685 & 0.0157376082931533 \tabularnewline
21 & 7.33 & 7.3170764997394 & 0.0129235002605981 \tabularnewline
22 & 7.32 & 7.29938740526041 & 0.0206125947395908 \tabularnewline
23 & 7.31 & 7.29307323015502 & 0.0169267698449813 \tabularnewline
24 & 7.33 & 7.28609997717392 & 0.0439000228260777 \tabularnewline
25 & 7.32 & 7.31394992518146 & 0.00605007481853903 \tabularnewline
26 & 7.27 & 7.30503176454531 & -0.0350317645453142 \tabularnewline
27 & 7.48 & 7.24876758715792 & 0.231232412842078 \tabularnewline
28 & 7.7 & 7.5001152289498 & 0.199884771050198 \tabularnewline
29 & 7.77 & 7.75585746686296 & 0.0141425331370382 \tabularnewline
30 & 7.8 & 7.8283863527877 & -0.0283863527877006 \tabularnewline
31 & 7.84 & 7.85331046947006 & -0.0133104694700563 \tabularnewline
32 & 7.81 & 7.89093036835462 & -0.0809303683546245 \tabularnewline
33 & 7.78 & 7.84645886827519 & -0.0664588682751894 \tabularnewline
34 & 7.82 & 7.80457507808522 & 0.0154249219147822 \tabularnewline
35 & 7.8 & 7.84733327334929 & -0.0473332733492873 \tabularnewline
36 & 7.81 & 7.81886941135335 & -0.00886941135334762 \tabularnewline
37 & 7.8 & 7.82728343454743 & -0.0272834345474333 \tabularnewline
38 & 7.66 & 7.8124047686863 & -0.152404768686303 \tabularnewline
39 & 7.41 & 7.64515263000227 & -0.235152630002272 \tabularnewline
40 & 7.35 & 7.35310399766632 & -0.00310399766631697 \tabularnewline
41 & 7.39 & 7.29254895876821 & 0.0974510412317864 \tabularnewline
42 & 7.32 & 7.34997458995708 & -0.029974589957078 \tabularnewline
43 & 7.32 & 7.27461470726083 & 0.0453852927391685 \tabularnewline
44 & 7.3 & 7.28273024263813 & 0.0172697573618663 \tabularnewline
45 & 7.29 & 7.26581832069974 & 0.0241816793002556 \tabularnewline
46 & 7.26 & 7.26014234863918 & -0.000142348639184497 \tabularnewline
47 & 7.22 & 7.23011689467938 & -0.0101168946793768 \tabularnewline
48 & 7.21 & 7.18830785012499 & 0.0216921498750073 \tabularnewline
49 & 7.21 & 7.18218671482092 & 0.0278132851790787 \tabularnewline
50 & 7.21 & 7.18716012550546 & 0.0228398744945419 \tabularnewline
51 & 7.2 & 7.19124421967532 & 0.0087557803246785 \tabularnewline
52 & 7.19 & 7.18280987763834 & 0.00719012236166439 \tabularnewline
53 & 7.18 & 7.17409557370575 & 0.00590442629424892 \tabularnewline
54 & 7.12 & 7.16515136904344 & -0.0451513690434391 \tabularnewline
55 & 7.12 & 7.09707766254756 & 0.0229223374524405 \tabularnewline
56 & 7.07 & 7.1011765022663 & -0.0311765022662946 \tabularnewline
57 & 7.08 & 7.04560170056706 & 0.0343982994329410 \tabularnewline
58 & 7.05 & 7.06175260538927 & -0.0117526053892751 \tabularnewline
59 & 7.06 & 7.02965107251253 & 0.0303489274874718 \tabularnewline
60 & 7.07 & 7.04507789207946 & 0.0249221079205428 \tabularnewline
61 & 7.08 & 7.0595343191795 & 0.0204656808205019 \tabularnewline
62 & 7.08 & 7.07319387377737 & 0.00680612622263066 \tabularnewline
63 & 7.09 & 7.0744109058776 & 0.0155890941223955 \tabularnewline
64 & 7.07 & 7.08719845746568 & -0.0171984574656801 \tabularnewline
65 & 7.06 & 7.06412312883885 & -0.00412312883885324 \tabularnewline
66 & 6.99 & 7.05338585480276 & -0.063385854802763 \tabularnewline
67 & 6.99 & 6.97205156309667 & 0.0179484369033336 \tabularnewline
68 & 6.99 & 6.97526099870598 & 0.0147390012940187 \tabularnewline
69 & 6.98 & 6.97789654161445 & 0.00210345838554549 \tabularnewline
70 & 6.96 & 6.96827266986906 & -0.00827266986905695 \tabularnewline
71 & 6.95 & 6.94679339892168 & 0.00320660107831650 \tabularnewline
72 & 6.91 & 6.9373667847681 & -0.0273667847680974 \tabularnewline
73 & 6.91 & 6.8924732147029 & 0.0175267852970977 \tabularnewline
74 & 6.87 & 6.89560725301233 & -0.0256072530123266 \tabularnewline
75 & 6.91 & 6.8510283122323 & 0.0589716877677056 \tabularnewline
76 & 6.89 & 6.90157328813646 & -0.0115732881364599 \tabularnewline
77 & 6.88 & 6.87950381973305 & 0.000496180266948976 \tabularnewline
78 & 6.9 & 6.86959254381671 & 0.0304074561832888 \tabularnewline
79 & 6.91 & 6.89502982914629 & 0.0149701708537116 \tabularnewline
80 & 6.85 & 6.90770670845752 & -0.057706708457518 \tabularnewline
81 & 6.86 & 6.83738792883245 & 0.0226120711675497 \tabularnewline
82 & 6.82 & 6.8514312885298 & -0.0314312885298076 \tabularnewline
83 & 6.8 & 6.80581092742552 & -0.00581092742552247 \tabularnewline
84 & 6.83 & 6.78477185101441 & 0.0452281489855917 \tabularnewline
85 & 6.84 & 6.82285928685518 & 0.0171407131448245 \tabularnewline
86 & 6.89 & 6.83592428997675 & 0.0540757100232527 \tabularnewline
87 & 7.14 & 6.89559379547644 & 0.244406204523563 \tabularnewline
88 & 7.21 & 7.18929709845263 & 0.0207029015473719 \tabularnewline
89 & 7.25 & 7.26299907148795 & -0.0129990714879478 \tabularnewline
90 & 7.31 & 7.30067465275744 & 0.00932534724255962 \tabularnewline
91 & 7.3 & 7.3623421573803 & -0.0623421573803036 \tabularnewline
92 & 7.48 & 7.34119449360683 & 0.138805506393175 \tabularnewline
93 & 7.49 & 7.54601489091421 & -0.0560148909142093 \tabularnewline
94 & 7.4 & 7.54599863231073 & -0.145998632310733 \tabularnewline
95 & 7.44 & 7.42989200185745 & 0.0101079981425469 \tabularnewline
96 & 7.42 & 7.47169945558461 & -0.0516994555846111 \tabularnewline
97 & 7.14 & 7.44245485815091 & -0.302454858150913 \tabularnewline
98 & 7.24 & 7.10837163089341 & 0.131628369106585 \tabularnewline
99 & 7.33 & 7.23190865404936 & 0.0980913459506425 \tabularnewline
100 & 7.61 & 7.33944878082224 & 0.270551219177756 \tabularnewline
101 & 7.66 & 7.66782718400294 & -0.00782718400293536 \tabularnewline
102 & 7.69 & 7.71642757225986 & -0.0264275722598635 \tabularnewline
103 & 7.7 & 7.74170194673964 & -0.0417019467396385 \tabularnewline
104 & 7.68 & 7.74424504597637 & -0.06424504597637 \tabularnewline
105 & 7.71 & 7.71275711867723 & -0.00275711867723238 \tabularnewline
106 & 7.71 & 7.74226410667237 & -0.0322641066723728 \tabularnewline
107 & 7.72 & 7.73649482584782 & -0.0164948258478228 \tabularnewline
108 & 7.68 & 7.74354531655459 & -0.0635453165545856 \tabularnewline
109 & 7.72 & 7.69218251082092 & 0.0278174891790766 \tabularnewline
110 & 7.74 & 7.7371566732404 & 0.0028433267595922 \tabularnewline
111 & 7.76 & 7.75766510047562 & 0.00233489952437793 \tabularnewline
112 & 7.9 & 7.77808261369519 & 0.121917386304812 \tabularnewline
113 & 7.97 & 7.93988317510946 & 0.0301168248905412 \tabularnewline
114 & 7.96 & 8.01526849143331 & -0.0552684914333126 \tabularnewline
115 & 7.95 & 7.99538569966518 & -0.045385699665176 \tabularnewline
116 & 7.97 & 7.97727009152372 & -0.00727009152372116 \tabularnewline
117 & 7.93 & 7.99597009583357 & -0.065970095833574 \tabularnewline
118 & 7.99 & 7.94417370510281 & 0.045826294897191 \tabularnewline
119 & 7.96 & 8.01236809793368 & -0.0523680979336811 \tabularnewline
120 & 7.92 & 7.97300393774493 & -0.0530039377449327 \tabularnewline
121 & 7.97 & 7.9235260803611 & 0.046473919638899 \tabularnewline
122 & 7.98 & 7.98183627770011 & -0.00183627770011263 \tabularnewline
123 & 8 & 7.99150792514936 & 0.0084920748506443 \tabularnewline
124 & 8.04 & 8.01302642882571 & 0.0269735711742847 \tabularnewline
125 & 8.17 & 8.05784968671181 & 0.112150313288188 \tabularnewline
126 & 8.29 & 8.20790375665682 & 0.082096243343182 \tabularnewline
127 & 8.26 & 8.34258373175413 & -0.0825837317541271 \tabularnewline
128 & 8.3 & 8.29781658680054 & 0.00218341319945914 \tabularnewline
129 & 8.32 & 8.33820701211222 & -0.0182070121122244 \tabularnewline
130 & 8.28 & 8.35495133958058 & -0.0749513395805774 \tabularnewline
131 & 8.27 & 8.3015489748225 & -0.0315489748225009 \tabularnewline
132 & 8.32 & 8.28590756973647 & 0.0340924302635273 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72653&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]8.82[/C][C]8.78[/C][C]0.0399999999999991[/C][/ROW]
[ROW][C]4[/C][C]8.58[/C][C]8.80715256849741[/C][C]-0.227152568497415[/C][/ROW]
[ROW][C]5[/C][C]8.54[/C][C]8.52653446085889[/C][C]0.0134655391411140[/C][/ROW]
[ROW][C]6[/C][C]8.42[/C][C]8.48894229063542[/C][C]-0.0689422906354196[/C][/ROW]
[ROW][C]7[/C][C]8.43[/C][C]8.35661442923196[/C][C]0.0733855707680409[/C][/ROW]
[ROW][C]8[/C][C]8.44[/C][C]8.37973681227296[/C][C]0.0602631877270348[/C][/ROW]
[ROW][C]9[/C][C]8.09[/C][C]8.40051272672522[/C][C]-0.310512726725216[/C][/ROW]
[ROW][C]10[/C][C]7.69[/C][C]7.9949886380447[/C][C]-0.304988638044698[/C][/ROW]
[ROW][C]11[/C][C]7.56[/C][C]7.54045233493101[/C][C]0.0195476650689903[/C][/ROW]
[ROW][C]12[/C][C]7.54[/C][C]7.41394773526527[/C][C]0.126052264734731[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]7.4164876717095[/C][C]-0.0164876717095011[/C][/ROW]
[ROW][C]14[/C][C]7.39[/C][C]7.27353944167788[/C][C]0.116460558322125[/C][/ROW]
[ROW][C]15[/C][C]7.37[/C][C]7.28436424469403[/C][C]0.085635755305975[/C][/ROW]
[ROW][C]16[/C][C]7.31[/C][C]7.27967713483537[/C][C]0.0303228651646306[/C][/ROW]
[ROW][C]17[/C][C]7.35[/C][C]7.22509929408856[/C][C]0.124900705911435[/C][/ROW]
[ROW][C]18[/C][C]7.26[/C][C]7.28743331544874[/C][C]-0.0274333154487358[/C][/ROW]
[ROW][C]19[/C][C]7.37[/C][C]7.19252784875228[/C][C]0.17747215124772[/C][/ROW]
[ROW][C]20[/C][C]7.35[/C][C]7.33426239170685[/C][C]0.0157376082931533[/C][/ROW]
[ROW][C]21[/C][C]7.33[/C][C]7.3170764997394[/C][C]0.0129235002605981[/C][/ROW]
[ROW][C]22[/C][C]7.32[/C][C]7.29938740526041[/C][C]0.0206125947395908[/C][/ROW]
[ROW][C]23[/C][C]7.31[/C][C]7.29307323015502[/C][C]0.0169267698449813[/C][/ROW]
[ROW][C]24[/C][C]7.33[/C][C]7.28609997717392[/C][C]0.0439000228260777[/C][/ROW]
[ROW][C]25[/C][C]7.32[/C][C]7.31394992518146[/C][C]0.00605007481853903[/C][/ROW]
[ROW][C]26[/C][C]7.27[/C][C]7.30503176454531[/C][C]-0.0350317645453142[/C][/ROW]
[ROW][C]27[/C][C]7.48[/C][C]7.24876758715792[/C][C]0.231232412842078[/C][/ROW]
[ROW][C]28[/C][C]7.7[/C][C]7.5001152289498[/C][C]0.199884771050198[/C][/ROW]
[ROW][C]29[/C][C]7.77[/C][C]7.75585746686296[/C][C]0.0141425331370382[/C][/ROW]
[ROW][C]30[/C][C]7.8[/C][C]7.8283863527877[/C][C]-0.0283863527877006[/C][/ROW]
[ROW][C]31[/C][C]7.84[/C][C]7.85331046947006[/C][C]-0.0133104694700563[/C][/ROW]
[ROW][C]32[/C][C]7.81[/C][C]7.89093036835462[/C][C]-0.0809303683546245[/C][/ROW]
[ROW][C]33[/C][C]7.78[/C][C]7.84645886827519[/C][C]-0.0664588682751894[/C][/ROW]
[ROW][C]34[/C][C]7.82[/C][C]7.80457507808522[/C][C]0.0154249219147822[/C][/ROW]
[ROW][C]35[/C][C]7.8[/C][C]7.84733327334929[/C][C]-0.0473332733492873[/C][/ROW]
[ROW][C]36[/C][C]7.81[/C][C]7.81886941135335[/C][C]-0.00886941135334762[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]7.82728343454743[/C][C]-0.0272834345474333[/C][/ROW]
[ROW][C]38[/C][C]7.66[/C][C]7.8124047686863[/C][C]-0.152404768686303[/C][/ROW]
[ROW][C]39[/C][C]7.41[/C][C]7.64515263000227[/C][C]-0.235152630002272[/C][/ROW]
[ROW][C]40[/C][C]7.35[/C][C]7.35310399766632[/C][C]-0.00310399766631697[/C][/ROW]
[ROW][C]41[/C][C]7.39[/C][C]7.29254895876821[/C][C]0.0974510412317864[/C][/ROW]
[ROW][C]42[/C][C]7.32[/C][C]7.34997458995708[/C][C]-0.029974589957078[/C][/ROW]
[ROW][C]43[/C][C]7.32[/C][C]7.27461470726083[/C][C]0.0453852927391685[/C][/ROW]
[ROW][C]44[/C][C]7.3[/C][C]7.28273024263813[/C][C]0.0172697573618663[/C][/ROW]
[ROW][C]45[/C][C]7.29[/C][C]7.26581832069974[/C][C]0.0241816793002556[/C][/ROW]
[ROW][C]46[/C][C]7.26[/C][C]7.26014234863918[/C][C]-0.000142348639184497[/C][/ROW]
[ROW][C]47[/C][C]7.22[/C][C]7.23011689467938[/C][C]-0.0101168946793768[/C][/ROW]
[ROW][C]48[/C][C]7.21[/C][C]7.18830785012499[/C][C]0.0216921498750073[/C][/ROW]
[ROW][C]49[/C][C]7.21[/C][C]7.18218671482092[/C][C]0.0278132851790787[/C][/ROW]
[ROW][C]50[/C][C]7.21[/C][C]7.18716012550546[/C][C]0.0228398744945419[/C][/ROW]
[ROW][C]51[/C][C]7.2[/C][C]7.19124421967532[/C][C]0.0087557803246785[/C][/ROW]
[ROW][C]52[/C][C]7.19[/C][C]7.18280987763834[/C][C]0.00719012236166439[/C][/ROW]
[ROW][C]53[/C][C]7.18[/C][C]7.17409557370575[/C][C]0.00590442629424892[/C][/ROW]
[ROW][C]54[/C][C]7.12[/C][C]7.16515136904344[/C][C]-0.0451513690434391[/C][/ROW]
[ROW][C]55[/C][C]7.12[/C][C]7.09707766254756[/C][C]0.0229223374524405[/C][/ROW]
[ROW][C]56[/C][C]7.07[/C][C]7.1011765022663[/C][C]-0.0311765022662946[/C][/ROW]
[ROW][C]57[/C][C]7.08[/C][C]7.04560170056706[/C][C]0.0343982994329410[/C][/ROW]
[ROW][C]58[/C][C]7.05[/C][C]7.06175260538927[/C][C]-0.0117526053892751[/C][/ROW]
[ROW][C]59[/C][C]7.06[/C][C]7.02965107251253[/C][C]0.0303489274874718[/C][/ROW]
[ROW][C]60[/C][C]7.07[/C][C]7.04507789207946[/C][C]0.0249221079205428[/C][/ROW]
[ROW][C]61[/C][C]7.08[/C][C]7.0595343191795[/C][C]0.0204656808205019[/C][/ROW]
[ROW][C]62[/C][C]7.08[/C][C]7.07319387377737[/C][C]0.00680612622263066[/C][/ROW]
[ROW][C]63[/C][C]7.09[/C][C]7.0744109058776[/C][C]0.0155890941223955[/C][/ROW]
[ROW][C]64[/C][C]7.07[/C][C]7.08719845746568[/C][C]-0.0171984574656801[/C][/ROW]
[ROW][C]65[/C][C]7.06[/C][C]7.06412312883885[/C][C]-0.00412312883885324[/C][/ROW]
[ROW][C]66[/C][C]6.99[/C][C]7.05338585480276[/C][C]-0.063385854802763[/C][/ROW]
[ROW][C]67[/C][C]6.99[/C][C]6.97205156309667[/C][C]0.0179484369033336[/C][/ROW]
[ROW][C]68[/C][C]6.99[/C][C]6.97526099870598[/C][C]0.0147390012940187[/C][/ROW]
[ROW][C]69[/C][C]6.98[/C][C]6.97789654161445[/C][C]0.00210345838554549[/C][/ROW]
[ROW][C]70[/C][C]6.96[/C][C]6.96827266986906[/C][C]-0.00827266986905695[/C][/ROW]
[ROW][C]71[/C][C]6.95[/C][C]6.94679339892168[/C][C]0.00320660107831650[/C][/ROW]
[ROW][C]72[/C][C]6.91[/C][C]6.9373667847681[/C][C]-0.0273667847680974[/C][/ROW]
[ROW][C]73[/C][C]6.91[/C][C]6.8924732147029[/C][C]0.0175267852970977[/C][/ROW]
[ROW][C]74[/C][C]6.87[/C][C]6.89560725301233[/C][C]-0.0256072530123266[/C][/ROW]
[ROW][C]75[/C][C]6.91[/C][C]6.8510283122323[/C][C]0.0589716877677056[/C][/ROW]
[ROW][C]76[/C][C]6.89[/C][C]6.90157328813646[/C][C]-0.0115732881364599[/C][/ROW]
[ROW][C]77[/C][C]6.88[/C][C]6.87950381973305[/C][C]0.000496180266948976[/C][/ROW]
[ROW][C]78[/C][C]6.9[/C][C]6.86959254381671[/C][C]0.0304074561832888[/C][/ROW]
[ROW][C]79[/C][C]6.91[/C][C]6.89502982914629[/C][C]0.0149701708537116[/C][/ROW]
[ROW][C]80[/C][C]6.85[/C][C]6.90770670845752[/C][C]-0.057706708457518[/C][/ROW]
[ROW][C]81[/C][C]6.86[/C][C]6.83738792883245[/C][C]0.0226120711675497[/C][/ROW]
[ROW][C]82[/C][C]6.82[/C][C]6.8514312885298[/C][C]-0.0314312885298076[/C][/ROW]
[ROW][C]83[/C][C]6.8[/C][C]6.80581092742552[/C][C]-0.00581092742552247[/C][/ROW]
[ROW][C]84[/C][C]6.83[/C][C]6.78477185101441[/C][C]0.0452281489855917[/C][/ROW]
[ROW][C]85[/C][C]6.84[/C][C]6.82285928685518[/C][C]0.0171407131448245[/C][/ROW]
[ROW][C]86[/C][C]6.89[/C][C]6.83592428997675[/C][C]0.0540757100232527[/C][/ROW]
[ROW][C]87[/C][C]7.14[/C][C]6.89559379547644[/C][C]0.244406204523563[/C][/ROW]
[ROW][C]88[/C][C]7.21[/C][C]7.18929709845263[/C][C]0.0207029015473719[/C][/ROW]
[ROW][C]89[/C][C]7.25[/C][C]7.26299907148795[/C][C]-0.0129990714879478[/C][/ROW]
[ROW][C]90[/C][C]7.31[/C][C]7.30067465275744[/C][C]0.00932534724255962[/C][/ROW]
[ROW][C]91[/C][C]7.3[/C][C]7.3623421573803[/C][C]-0.0623421573803036[/C][/ROW]
[ROW][C]92[/C][C]7.48[/C][C]7.34119449360683[/C][C]0.138805506393175[/C][/ROW]
[ROW][C]93[/C][C]7.49[/C][C]7.54601489091421[/C][C]-0.0560148909142093[/C][/ROW]
[ROW][C]94[/C][C]7.4[/C][C]7.54599863231073[/C][C]-0.145998632310733[/C][/ROW]
[ROW][C]95[/C][C]7.44[/C][C]7.42989200185745[/C][C]0.0101079981425469[/C][/ROW]
[ROW][C]96[/C][C]7.42[/C][C]7.47169945558461[/C][C]-0.0516994555846111[/C][/ROW]
[ROW][C]97[/C][C]7.14[/C][C]7.44245485815091[/C][C]-0.302454858150913[/C][/ROW]
[ROW][C]98[/C][C]7.24[/C][C]7.10837163089341[/C][C]0.131628369106585[/C][/ROW]
[ROW][C]99[/C][C]7.33[/C][C]7.23190865404936[/C][C]0.0980913459506425[/C][/ROW]
[ROW][C]100[/C][C]7.61[/C][C]7.33944878082224[/C][C]0.270551219177756[/C][/ROW]
[ROW][C]101[/C][C]7.66[/C][C]7.66782718400294[/C][C]-0.00782718400293536[/C][/ROW]
[ROW][C]102[/C][C]7.69[/C][C]7.71642757225986[/C][C]-0.0264275722598635[/C][/ROW]
[ROW][C]103[/C][C]7.7[/C][C]7.74170194673964[/C][C]-0.0417019467396385[/C][/ROW]
[ROW][C]104[/C][C]7.68[/C][C]7.74424504597637[/C][C]-0.06424504597637[/C][/ROW]
[ROW][C]105[/C][C]7.71[/C][C]7.71275711867723[/C][C]-0.00275711867723238[/C][/ROW]
[ROW][C]106[/C][C]7.71[/C][C]7.74226410667237[/C][C]-0.0322641066723728[/C][/ROW]
[ROW][C]107[/C][C]7.72[/C][C]7.73649482584782[/C][C]-0.0164948258478228[/C][/ROW]
[ROW][C]108[/C][C]7.68[/C][C]7.74354531655459[/C][C]-0.0635453165545856[/C][/ROW]
[ROW][C]109[/C][C]7.72[/C][C]7.69218251082092[/C][C]0.0278174891790766[/C][/ROW]
[ROW][C]110[/C][C]7.74[/C][C]7.7371566732404[/C][C]0.0028433267595922[/C][/ROW]
[ROW][C]111[/C][C]7.76[/C][C]7.75766510047562[/C][C]0.00233489952437793[/C][/ROW]
[ROW][C]112[/C][C]7.9[/C][C]7.77808261369519[/C][C]0.121917386304812[/C][/ROW]
[ROW][C]113[/C][C]7.97[/C][C]7.93988317510946[/C][C]0.0301168248905412[/C][/ROW]
[ROW][C]114[/C][C]7.96[/C][C]8.01526849143331[/C][C]-0.0552684914333126[/C][/ROW]
[ROW][C]115[/C][C]7.95[/C][C]7.99538569966518[/C][C]-0.045385699665176[/C][/ROW]
[ROW][C]116[/C][C]7.97[/C][C]7.97727009152372[/C][C]-0.00727009152372116[/C][/ROW]
[ROW][C]117[/C][C]7.93[/C][C]7.99597009583357[/C][C]-0.065970095833574[/C][/ROW]
[ROW][C]118[/C][C]7.99[/C][C]7.94417370510281[/C][C]0.045826294897191[/C][/ROW]
[ROW][C]119[/C][C]7.96[/C][C]8.01236809793368[/C][C]-0.0523680979336811[/C][/ROW]
[ROW][C]120[/C][C]7.92[/C][C]7.97300393774493[/C][C]-0.0530039377449327[/C][/ROW]
[ROW][C]121[/C][C]7.97[/C][C]7.9235260803611[/C][C]0.046473919638899[/C][/ROW]
[ROW][C]122[/C][C]7.98[/C][C]7.98183627770011[/C][C]-0.00183627770011263[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]7.99150792514936[/C][C]0.0084920748506443[/C][/ROW]
[ROW][C]124[/C][C]8.04[/C][C]8.01302642882571[/C][C]0.0269735711742847[/C][/ROW]
[ROW][C]125[/C][C]8.17[/C][C]8.05784968671181[/C][C]0.112150313288188[/C][/ROW]
[ROW][C]126[/C][C]8.29[/C][C]8.20790375665682[/C][C]0.082096243343182[/C][/ROW]
[ROW][C]127[/C][C]8.26[/C][C]8.34258373175413[/C][C]-0.0825837317541271[/C][/ROW]
[ROW][C]128[/C][C]8.3[/C][C]8.29781658680054[/C][C]0.00218341319945914[/C][/ROW]
[ROW][C]129[/C][C]8.32[/C][C]8.33820701211222[/C][C]-0.0182070121122244[/C][/ROW]
[ROW][C]130[/C][C]8.28[/C][C]8.35495133958058[/C][C]-0.0749513395805774[/C][/ROW]
[ROW][C]131[/C][C]8.27[/C][C]8.3015489748225[/C][C]-0.0315489748225009[/C][/ROW]
[ROW][C]132[/C][C]8.32[/C][C]8.28590756973647[/C][C]0.0340924302635273[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72653&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72653&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
38.828.780.0399999999999991
48.588.80715256849741-0.227152568497415
58.548.526534460858890.0134655391411140
68.428.48894229063542-0.0689422906354196
78.438.356614429231960.0733855707680409
88.448.379736812272960.0602631877270348
98.098.40051272672522-0.310512726725216
107.697.9949886380447-0.304988638044698
117.567.540452334931010.0195476650689903
127.547.413947735265270.126052264734731
137.47.4164876717095-0.0164876717095011
147.397.273539441677880.116460558322125
157.377.284364244694030.085635755305975
167.317.279677134835370.0303228651646306
177.357.225099294088560.124900705911435
187.267.28743331544874-0.0274333154487358
197.377.192527848752280.17747215124772
207.357.334262391706850.0157376082931533
217.337.31707649973940.0129235002605981
227.327.299387405260410.0206125947395908
237.317.293073230155020.0169267698449813
247.337.286099977173920.0439000228260777
257.327.313949925181460.00605007481853903
267.277.30503176454531-0.0350317645453142
277.487.248767587157920.231232412842078
287.77.50011522894980.199884771050198
297.777.755857466862960.0141425331370382
307.87.8283863527877-0.0283863527877006
317.847.85331046947006-0.0133104694700563
327.817.89093036835462-0.0809303683546245
337.787.84645886827519-0.0664588682751894
347.827.804575078085220.0154249219147822
357.87.84733327334929-0.0473332733492873
367.817.81886941135335-0.00886941135334762
377.87.82728343454743-0.0272834345474333
387.667.8124047686863-0.152404768686303
397.417.64515263000227-0.235152630002272
407.357.35310399766632-0.00310399766631697
417.397.292548958768210.0974510412317864
427.327.34997458995708-0.029974589957078
437.327.274614707260830.0453852927391685
447.37.282730242638130.0172697573618663
457.297.265818320699740.0241816793002556
467.267.26014234863918-0.000142348639184497
477.227.23011689467938-0.0101168946793768
487.217.188307850124990.0216921498750073
497.217.182186714820920.0278132851790787
507.217.187160125505460.0228398744945419
517.27.191244219675320.0087557803246785
527.197.182809877638340.00719012236166439
537.187.174095573705750.00590442629424892
547.127.16515136904344-0.0451513690434391
557.127.097077662547560.0229223374524405
567.077.1011765022663-0.0311765022662946
577.087.045601700567060.0343982994329410
587.057.06175260538927-0.0117526053892751
597.067.029651072512530.0303489274874718
607.077.045077892079460.0249221079205428
617.087.05953431917950.0204656808205019
627.087.073193873777370.00680612622263066
637.097.07441090587760.0155890941223955
647.077.08719845746568-0.0171984574656801
657.067.06412312883885-0.00412312883885324
666.997.05338585480276-0.063385854802763
676.996.972051563096670.0179484369033336
686.996.975260998705980.0147390012940187
696.986.977896541614450.00210345838554549
706.966.96827266986906-0.00827266986905695
716.956.946793398921680.00320660107831650
726.916.9373667847681-0.0273667847680974
736.916.89247321470290.0175267852970977
746.876.89560725301233-0.0256072530123266
756.916.85102831223230.0589716877677056
766.896.90157328813646-0.0115732881364599
776.886.879503819733050.000496180266948976
786.96.869592543816710.0304074561832888
796.916.895029829146290.0149701708537116
806.856.90770670845752-0.057706708457518
816.866.837387928832450.0226120711675497
826.826.8514312885298-0.0314312885298076
836.86.80581092742552-0.00581092742552247
846.836.784771851014410.0452281489855917
856.846.822859286855180.0171407131448245
866.896.835924289976750.0540757100232527
877.146.895593795476440.244406204523563
887.217.189297098452630.0207029015473719
897.257.26299907148795-0.0129990714879478
907.317.300674652757440.00932534724255962
917.37.3623421573803-0.0623421573803036
927.487.341194493606830.138805506393175
937.497.54601489091421-0.0560148909142093
947.47.54599863231073-0.145998632310733
957.447.429892001857450.0101079981425469
967.427.47169945558461-0.0516994555846111
977.147.44245485815091-0.302454858150913
987.247.108371630893410.131628369106585
997.337.231908654049360.0980913459506425
1007.617.339448780822240.270551219177756
1017.667.66782718400294-0.00782718400293536
1027.697.71642757225986-0.0264275722598635
1037.77.74170194673964-0.0417019467396385
1047.687.74424504597637-0.06424504597637
1057.717.71275711867723-0.00275711867723238
1067.717.74226410667237-0.0322641066723728
1077.727.73649482584782-0.0164948258478228
1087.687.74354531655459-0.0635453165545856
1097.727.692182510820920.0278174891790766
1107.747.73715667324040.0028433267595922
1117.767.757665100475620.00233489952437793
1127.97.778082613695190.121917386304812
1137.977.939883175109460.0301168248905412
1147.968.01526849143331-0.0552684914333126
1157.957.99538569966518-0.045385699665176
1167.977.97727009152372-0.00727009152372116
1177.937.99597009583357-0.065970095833574
1187.997.944173705102810.045826294897191
1197.968.01236809793368-0.0523680979336811
1207.927.97300393774493-0.0530039377449327
1217.977.92352608036110.046473919638899
1227.987.98183627770011-0.00183627770011263
12387.991507925149360.0084920748506443
1248.048.013026428825710.0269735711742847
1258.178.057849686711810.112150313288188
1268.298.207903756656820.082096243343182
1278.268.34258373175413-0.0825837317541271
1288.38.297816586800540.00218341319945914
1298.328.33820701211222-0.0182070121122244
1308.288.35495133958058-0.0749513395805774
1318.278.3015489748225-0.0315489748225009
1328.328.285907569736470.0340924302635273






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1338.342003780804058.172619361401558.51138820020656
1348.36400756160818.102167356512658.62584776670356
1358.386011342412168.037525472293258.73449721253107
1368.408015123216217.973075846064978.84295440036746
1378.430018904020277.906965962453558.95307184558698
1388.452022684824327.838415494873789.06562987477485
1398.474026465628377.767064547481969.18098838377478
1408.496030246432437.692747446043559.2993130468213
1418.518034027236487.615397410484629.42067064398834
1428.540037808040537.535001124889419.54507449119165
1438.562041588844587.451575181246729.67250799644244
1448.584045369648647.365153104950429.80293763434685

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 8.34200378080405 & 8.17261936140155 & 8.51138820020656 \tabularnewline
134 & 8.3640075616081 & 8.10216735651265 & 8.62584776670356 \tabularnewline
135 & 8.38601134241216 & 8.03752547229325 & 8.73449721253107 \tabularnewline
136 & 8.40801512321621 & 7.97307584606497 & 8.84295440036746 \tabularnewline
137 & 8.43001890402027 & 7.90696596245355 & 8.95307184558698 \tabularnewline
138 & 8.45202268482432 & 7.83841549487378 & 9.06562987477485 \tabularnewline
139 & 8.47402646562837 & 7.76706454748196 & 9.18098838377478 \tabularnewline
140 & 8.49603024643243 & 7.69274744604355 & 9.2993130468213 \tabularnewline
141 & 8.51803402723648 & 7.61539741048462 & 9.42067064398834 \tabularnewline
142 & 8.54003780804053 & 7.53500112488941 & 9.54507449119165 \tabularnewline
143 & 8.56204158884458 & 7.45157518124672 & 9.67250799644244 \tabularnewline
144 & 8.58404536964864 & 7.36515310495042 & 9.80293763434685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=72653&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]133[/C][C]8.34200378080405[/C][C]8.17261936140155[/C][C]8.51138820020656[/C][/ROW]
[ROW][C]134[/C][C]8.3640075616081[/C][C]8.10216735651265[/C][C]8.62584776670356[/C][/ROW]
[ROW][C]135[/C][C]8.38601134241216[/C][C]8.03752547229325[/C][C]8.73449721253107[/C][/ROW]
[ROW][C]136[/C][C]8.40801512321621[/C][C]7.97307584606497[/C][C]8.84295440036746[/C][/ROW]
[ROW][C]137[/C][C]8.43001890402027[/C][C]7.90696596245355[/C][C]8.95307184558698[/C][/ROW]
[ROW][C]138[/C][C]8.45202268482432[/C][C]7.83841549487378[/C][C]9.06562987477485[/C][/ROW]
[ROW][C]139[/C][C]8.47402646562837[/C][C]7.76706454748196[/C][C]9.18098838377478[/C][/ROW]
[ROW][C]140[/C][C]8.49603024643243[/C][C]7.69274744604355[/C][C]9.2993130468213[/C][/ROW]
[ROW][C]141[/C][C]8.51803402723648[/C][C]7.61539741048462[/C][C]9.42067064398834[/C][/ROW]
[ROW][C]142[/C][C]8.54003780804053[/C][C]7.53500112488941[/C][C]9.54507449119165[/C][/ROW]
[ROW][C]143[/C][C]8.56204158884458[/C][C]7.45157518124672[/C][C]9.67250799644244[/C][/ROW]
[ROW][C]144[/C][C]8.58404536964864[/C][C]7.36515310495042[/C][C]9.80293763434685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=72653&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=72653&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1338.342003780804058.172619361401558.51138820020656
1348.36400756160818.102167356512658.62584776670356
1358.386011342412168.037525472293258.73449721253107
1368.408015123216217.973075846064978.84295440036746
1378.430018904020277.906965962453558.95307184558698
1388.452022684824327.838415494873789.06562987477485
1398.474026465628377.767064547481969.18098838377478
1408.496030246432437.692747446043559.2993130468213
1418.518034027236487.615397410484629.42067064398834
1428.540037808040537.535001124889419.54507449119165
1438.562041588844587.451575181246729.67250799644244
1448.584045369648647.365153104950429.80293763434685


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = additive ;
Parameters (R input):
par1 = 12 ; par2 = Double ; par3 = additive ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=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')