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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSun, 16 Jan 2011 23:21:27 +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/2011/Jan/17/t1295220318s11nkrv60cvkusp.htm/, Retrieved Fri, 17 May 2024 13:53:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117490, Retrieved Fri, 17 May 2024 13:53:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W102
Estimated Impact159

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [Opgave 10] [2011-01-16 23:21:27] [781993a4dd4effefeecf3b39fd55765b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96,1
96,5
96,9
97,8
98,9
100,2
101,2
101
101,6
102,4
103,7
103,7
104,6
104,5
104,5
105,6
106,1
107,6
107,7
108,3
108,1
108,1
108
108,2
108,9
109,8
109,9
109,8
110,9
111,1
112,2
112,7
114,6
114,2
114,7
114,7
116
116,3
116,4
116,6
118,1
117,2
108,3
109,5
110,5
110,6
111,2
111,1
111
112,4
112,5
112,4
111,8
111,6
112,9
112,8
113,7
113,8
114
113,8
113,9
114,4
114,4
114,5
113,8
114,3
115
115,4
115,3
114,9
114,3
114,5
115,5
115,8
115,8
116
114,9
114,1
114,1
113,5
115
114,7
115,4
116,1
116,6
117,2
118,2
118
117,7
118,5
117,5
118
117,7
116,3
115
115,7
113,6
114,8
114,9
117,3
117,3
117,7
120
119,6
119,2
117,3
117,5
119
112,5
118,9
118,4
119,4
120,6
118,6
122
122,6
120,6
117,4
116,4
122,2
121
122,4
124,9
126,1
124,5
123,2
126,4
123,9
116
126,6
125,9
126,6
116,7
126,4
129
128,7
128,4
129,2
133,3
128,9
132,7
127,7
131,8
133,9

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' @ www.wessa.org

\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' @ www.wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117490&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' @ www.wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117490&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' @ www.wessa.org






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.488347465828906
beta0.0122883696409223
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.488347465828906 \tabularnewline
beta & 0.0122883696409223 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117490&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]0.488347465828906[/C][/ROW]
[ROW][C]beta[/C][C]0.0122883696409223[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117490&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
alpha0.488347465828906
beta0.0122883696409223
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
396.996.90
497.897.30.499999999999986
598.997.9471742300010.952825769998881
6100.298.82120267913721.37879732086284
7101.299.91152741034151.28847258965851
8101100.9654744044640.0345255955364649
9101.6101.407266749620.192733250380044
10102.4101.9274759932060.47252400679352
11103.7102.5871559574611.11284404253858
12103.7103.5662127591070.133787240893
13104.6104.0679525092190.532047490780513
14104.5104.767374456997-0.267374456997018
15104.5105.074796209891-0.574796209890849
16105.6105.2286399801120.371360019888101
17106.1105.8467652767390.253234723260718
18107.6106.4087240442071.19127595579283
19107.7107.4359217104950.264078289505349
20108.3108.0119094784050.288090521595208
21108.1108.601352388546-0.50135238854638
22108.1108.802264241341-0.702264241341297
23108108.900847016348-0.900847016348393
24108.2108.897046418727-0.697046418727368
25108.9108.988588354992-0.0885883549916713
26109.8109.3767376265420.423262373457661
27109.9110.017387899251-0.117387899250801
28109.8110.393308537283-0.593308537282994
29110.9110.5332540967210.366745903278769
30111.1111.144240649294-0.0442406492942808
31112.2111.554257472550.645742527450125
32112.7112.3051009287780.394899071222127
33114.6112.9358154058631.66418459413738
34114.2114.1963690135290.00363098647093807
35114.7114.6460172645470.0539827354527631
36114.7115.120578614662-0.420578614661636
37116115.3608652422510.639134757748678
38116.3116.122495653720.177504346280202
39116.4116.659755226146-0.259755226145828
40116.6116.98192140487-0.38192140486963
41118.1117.2421361316780.857863868321644
42117.2118.112944890897-0.912944890897322
43108.3118.113505103113-9.81350510311326
44109.5113.708608504292-4.20860850429239
45110.5112.015593120627-1.51559312062675
46110.6111.628609909526-1.02860990952648
47111.2111.473271033395-0.273271033394565
48111.1111.685160085385-0.585160085384587
49111111.741227366953-0.741227366952657
50112.4111.7166314859580.683368514042044
51112.5112.3918342837480.108165716251676
52112.4112.786787354733-0.38678735473303
53111.8112.937710239136-1.13771023913608
54111.6112.715094443392-1.115094443392
55112.9112.496831338930.403168661070282
56112.8113.022427586779-0.222427586779091
57113.7113.2411807057070.458819294293107
58113.8113.7953723845220.00462761547819923
59114114.12778847828-0.127788478280394
60113.8114.394772650303-0.594772650303454
61113.9114.430137058128-0.53013705812792
62114.4114.493884744197-0.093884744196771
63114.4114.770111740433-0.370111740433018
64114.5114.909222944666-0.409222944665942
65113.8115.02677854692-1.22677854691968
66114.3114.737721051727-0.437721051726569
67115114.8313710232230.168628976776645
68115.4115.2221424360520.177857563947953
69115.3115.618487928244-0.318487928243982
70114.9115.770533112947-0.870533112946703
71114.3115.647764366538-1.34776436653837
72114.5115.283853020714-0.783853020713565
73115.5115.1906224541640.309377545835559
74115.8115.6331248372060.166875162793801
75115.8116.007037959495-0.207037959494542
76116116.197109122485-0.197109122484605
77114.9116.390846157172-1.49084615717156
78114.1115.943843430418-1.84384343041766
79114.1115.313390486147-1.21339048614706
80113.5114.983536090298-1.48353609029806
81115114.5128540818230.48714591817668
82114.7115.007472997958-0.307472997958371
83115.4115.112196636430.287803363570049
84116.1115.509349083830.590650916170347
85116.6116.0579408587810.542059141219426
86117.2116.5860558573220.613944142677866
87118.2117.152959989381.04704001062001
88118117.9376486720110.0623513279886794
89117.7118.241841301805-0.541841301805192
90118.5118.2477264054720.252273594528262
91117.5118.642929398711-1.14292939871055
92118118.349929833222-0.349929833222106
93117.7118.442089669053-0.742089669053144
94116.3118.338285967076-2.03828596707615
95115117.589256345531-2.58925634553084
96115.7116.555623623678-0.85562362367763
97113.6116.363471455839-2.76347145583928
98114.8115.223043057873-0.423043057872988
99114.9115.223018258082-0.3230182580822
100117.3115.2699018851052.03009811489532
101117.3116.478106536630.821893463370031
102117.7117.1012196862650.598780313735062
103120117.6189693718562.38103062814361
104119.6119.0213650328880.578634967111981
105119.2119.5470377255-0.347037725499931
106117.3119.618577933119-2.31857793311916
107117.5118.713407703901-1.21340770390071
108119118.340662902870.659337097130319
109112.5118.886424957645-6.38642495764455
110118.9115.9530820691782.94691793082249
111118.4117.5953379653380.804662034662314
112119.4118.1962573956331.20374260436749
113120.6118.9992904631591.6007095368406
114118.6120.005787174781-1.40578717478114
115122119.5356327156152.46436728438532
116122.6120.9702470326631.62975296733688
117120.6122.007059701097-1.40705970109735
118117.4121.552408841637-4.1524088416368
119116.4119.732155105314-3.33215510531437
120122.2118.292473959093.90752604090986
121121120.4117217950370.588278204962606
122122.4120.9135536159221.48644638407802
123124.9121.8629237470563.03707625294446
124126.1123.5877655219862.51223447801353
125124.5125.071378050633-0.571378050633271
126123.2125.045687378965-1.84568737896456
127126.4124.3866150169162.01338498308382
128123.9125.624193174877-1.72419317487663
129116125.026187637864-9.02618763786352
130126.6120.8081055099325.79189449006797
131125.9123.8611533627082.03884663729221
132126.6125.0936449142371.50635508576342
133116.7126.075135194094-9.37513519409357
134126.4121.6864171396674.71358286033323
135129124.2061750271784.79382497282245
136128.7126.7938866627051.906113337295
137128.4127.9828302140090.417169785991277
138129.2128.4471553887260.752844611273986
139133.3129.0799243298384.22007567016166
140128.9131.431231421509-2.53123142150932
141132.7130.4703648989182.22963510108173
142127.7131.847835505034-4.14783550503442
143131.8130.0859933655581.71400663444214
144133.9131.197052723812.70294727618992

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 96.9 & 96.9 & 0 \tabularnewline
4 & 97.8 & 97.3 & 0.499999999999986 \tabularnewline
5 & 98.9 & 97.947174230001 & 0.952825769998881 \tabularnewline
6 & 100.2 & 98.8212026791372 & 1.37879732086284 \tabularnewline
7 & 101.2 & 99.9115274103415 & 1.28847258965851 \tabularnewline
8 & 101 & 100.965474404464 & 0.0345255955364649 \tabularnewline
9 & 101.6 & 101.40726674962 & 0.192733250380044 \tabularnewline
10 & 102.4 & 101.927475993206 & 0.47252400679352 \tabularnewline
11 & 103.7 & 102.587155957461 & 1.11284404253858 \tabularnewline
12 & 103.7 & 103.566212759107 & 0.133787240893 \tabularnewline
13 & 104.6 & 104.067952509219 & 0.532047490780513 \tabularnewline
14 & 104.5 & 104.767374456997 & -0.267374456997018 \tabularnewline
15 & 104.5 & 105.074796209891 & -0.574796209890849 \tabularnewline
16 & 105.6 & 105.228639980112 & 0.371360019888101 \tabularnewline
17 & 106.1 & 105.846765276739 & 0.253234723260718 \tabularnewline
18 & 107.6 & 106.408724044207 & 1.19127595579283 \tabularnewline
19 & 107.7 & 107.435921710495 & 0.264078289505349 \tabularnewline
20 & 108.3 & 108.011909478405 & 0.288090521595208 \tabularnewline
21 & 108.1 & 108.601352388546 & -0.50135238854638 \tabularnewline
22 & 108.1 & 108.802264241341 & -0.702264241341297 \tabularnewline
23 & 108 & 108.900847016348 & -0.900847016348393 \tabularnewline
24 & 108.2 & 108.897046418727 & -0.697046418727368 \tabularnewline
25 & 108.9 & 108.988588354992 & -0.0885883549916713 \tabularnewline
26 & 109.8 & 109.376737626542 & 0.423262373457661 \tabularnewline
27 & 109.9 & 110.017387899251 & -0.117387899250801 \tabularnewline
28 & 109.8 & 110.393308537283 & -0.593308537282994 \tabularnewline
29 & 110.9 & 110.533254096721 & 0.366745903278769 \tabularnewline
30 & 111.1 & 111.144240649294 & -0.0442406492942808 \tabularnewline
31 & 112.2 & 111.55425747255 & 0.645742527450125 \tabularnewline
32 & 112.7 & 112.305100928778 & 0.394899071222127 \tabularnewline
33 & 114.6 & 112.935815405863 & 1.66418459413738 \tabularnewline
34 & 114.2 & 114.196369013529 & 0.00363098647093807 \tabularnewline
35 & 114.7 & 114.646017264547 & 0.0539827354527631 \tabularnewline
36 & 114.7 & 115.120578614662 & -0.420578614661636 \tabularnewline
37 & 116 & 115.360865242251 & 0.639134757748678 \tabularnewline
38 & 116.3 & 116.12249565372 & 0.177504346280202 \tabularnewline
39 & 116.4 & 116.659755226146 & -0.259755226145828 \tabularnewline
40 & 116.6 & 116.98192140487 & -0.38192140486963 \tabularnewline
41 & 118.1 & 117.242136131678 & 0.857863868321644 \tabularnewline
42 & 117.2 & 118.112944890897 & -0.912944890897322 \tabularnewline
43 & 108.3 & 118.113505103113 & -9.81350510311326 \tabularnewline
44 & 109.5 & 113.708608504292 & -4.20860850429239 \tabularnewline
45 & 110.5 & 112.015593120627 & -1.51559312062675 \tabularnewline
46 & 110.6 & 111.628609909526 & -1.02860990952648 \tabularnewline
47 & 111.2 & 111.473271033395 & -0.273271033394565 \tabularnewline
48 & 111.1 & 111.685160085385 & -0.585160085384587 \tabularnewline
49 & 111 & 111.741227366953 & -0.741227366952657 \tabularnewline
50 & 112.4 & 111.716631485958 & 0.683368514042044 \tabularnewline
51 & 112.5 & 112.391834283748 & 0.108165716251676 \tabularnewline
52 & 112.4 & 112.786787354733 & -0.38678735473303 \tabularnewline
53 & 111.8 & 112.937710239136 & -1.13771023913608 \tabularnewline
54 & 111.6 & 112.715094443392 & -1.115094443392 \tabularnewline
55 & 112.9 & 112.49683133893 & 0.403168661070282 \tabularnewline
56 & 112.8 & 113.022427586779 & -0.222427586779091 \tabularnewline
57 & 113.7 & 113.241180705707 & 0.458819294293107 \tabularnewline
58 & 113.8 & 113.795372384522 & 0.00462761547819923 \tabularnewline
59 & 114 & 114.12778847828 & -0.127788478280394 \tabularnewline
60 & 113.8 & 114.394772650303 & -0.594772650303454 \tabularnewline
61 & 113.9 & 114.430137058128 & -0.53013705812792 \tabularnewline
62 & 114.4 & 114.493884744197 & -0.093884744196771 \tabularnewline
63 & 114.4 & 114.770111740433 & -0.370111740433018 \tabularnewline
64 & 114.5 & 114.909222944666 & -0.409222944665942 \tabularnewline
65 & 113.8 & 115.02677854692 & -1.22677854691968 \tabularnewline
66 & 114.3 & 114.737721051727 & -0.437721051726569 \tabularnewline
67 & 115 & 114.831371023223 & 0.168628976776645 \tabularnewline
68 & 115.4 & 115.222142436052 & 0.177857563947953 \tabularnewline
69 & 115.3 & 115.618487928244 & -0.318487928243982 \tabularnewline
70 & 114.9 & 115.770533112947 & -0.870533112946703 \tabularnewline
71 & 114.3 & 115.647764366538 & -1.34776436653837 \tabularnewline
72 & 114.5 & 115.283853020714 & -0.783853020713565 \tabularnewline
73 & 115.5 & 115.190622454164 & 0.309377545835559 \tabularnewline
74 & 115.8 & 115.633124837206 & 0.166875162793801 \tabularnewline
75 & 115.8 & 116.007037959495 & -0.207037959494542 \tabularnewline
76 & 116 & 116.197109122485 & -0.197109122484605 \tabularnewline
77 & 114.9 & 116.390846157172 & -1.49084615717156 \tabularnewline
78 & 114.1 & 115.943843430418 & -1.84384343041766 \tabularnewline
79 & 114.1 & 115.313390486147 & -1.21339048614706 \tabularnewline
80 & 113.5 & 114.983536090298 & -1.48353609029806 \tabularnewline
81 & 115 & 114.512854081823 & 0.48714591817668 \tabularnewline
82 & 114.7 & 115.007472997958 & -0.307472997958371 \tabularnewline
83 & 115.4 & 115.11219663643 & 0.287803363570049 \tabularnewline
84 & 116.1 & 115.50934908383 & 0.590650916170347 \tabularnewline
85 & 116.6 & 116.057940858781 & 0.542059141219426 \tabularnewline
86 & 117.2 & 116.586055857322 & 0.613944142677866 \tabularnewline
87 & 118.2 & 117.15295998938 & 1.04704001062001 \tabularnewline
88 & 118 & 117.937648672011 & 0.0623513279886794 \tabularnewline
89 & 117.7 & 118.241841301805 & -0.541841301805192 \tabularnewline
90 & 118.5 & 118.247726405472 & 0.252273594528262 \tabularnewline
91 & 117.5 & 118.642929398711 & -1.14292939871055 \tabularnewline
92 & 118 & 118.349929833222 & -0.349929833222106 \tabularnewline
93 & 117.7 & 118.442089669053 & -0.742089669053144 \tabularnewline
94 & 116.3 & 118.338285967076 & -2.03828596707615 \tabularnewline
95 & 115 & 117.589256345531 & -2.58925634553084 \tabularnewline
96 & 115.7 & 116.555623623678 & -0.85562362367763 \tabularnewline
97 & 113.6 & 116.363471455839 & -2.76347145583928 \tabularnewline
98 & 114.8 & 115.223043057873 & -0.423043057872988 \tabularnewline
99 & 114.9 & 115.223018258082 & -0.3230182580822 \tabularnewline
100 & 117.3 & 115.269901885105 & 2.03009811489532 \tabularnewline
101 & 117.3 & 116.47810653663 & 0.821893463370031 \tabularnewline
102 & 117.7 & 117.101219686265 & 0.598780313735062 \tabularnewline
103 & 120 & 117.618969371856 & 2.38103062814361 \tabularnewline
104 & 119.6 & 119.021365032888 & 0.578634967111981 \tabularnewline
105 & 119.2 & 119.5470377255 & -0.347037725499931 \tabularnewline
106 & 117.3 & 119.618577933119 & -2.31857793311916 \tabularnewline
107 & 117.5 & 118.713407703901 & -1.21340770390071 \tabularnewline
108 & 119 & 118.34066290287 & 0.659337097130319 \tabularnewline
109 & 112.5 & 118.886424957645 & -6.38642495764455 \tabularnewline
110 & 118.9 & 115.953082069178 & 2.94691793082249 \tabularnewline
111 & 118.4 & 117.595337965338 & 0.804662034662314 \tabularnewline
112 & 119.4 & 118.196257395633 & 1.20374260436749 \tabularnewline
113 & 120.6 & 118.999290463159 & 1.6007095368406 \tabularnewline
114 & 118.6 & 120.005787174781 & -1.40578717478114 \tabularnewline
115 & 122 & 119.535632715615 & 2.46436728438532 \tabularnewline
116 & 122.6 & 120.970247032663 & 1.62975296733688 \tabularnewline
117 & 120.6 & 122.007059701097 & -1.40705970109735 \tabularnewline
118 & 117.4 & 121.552408841637 & -4.1524088416368 \tabularnewline
119 & 116.4 & 119.732155105314 & -3.33215510531437 \tabularnewline
120 & 122.2 & 118.29247395909 & 3.90752604090986 \tabularnewline
121 & 121 & 120.411721795037 & 0.588278204962606 \tabularnewline
122 & 122.4 & 120.913553615922 & 1.48644638407802 \tabularnewline
123 & 124.9 & 121.862923747056 & 3.03707625294446 \tabularnewline
124 & 126.1 & 123.587765521986 & 2.51223447801353 \tabularnewline
125 & 124.5 & 125.071378050633 & -0.571378050633271 \tabularnewline
126 & 123.2 & 125.045687378965 & -1.84568737896456 \tabularnewline
127 & 126.4 & 124.386615016916 & 2.01338498308382 \tabularnewline
128 & 123.9 & 125.624193174877 & -1.72419317487663 \tabularnewline
129 & 116 & 125.026187637864 & -9.02618763786352 \tabularnewline
130 & 126.6 & 120.808105509932 & 5.79189449006797 \tabularnewline
131 & 125.9 & 123.861153362708 & 2.03884663729221 \tabularnewline
132 & 126.6 & 125.093644914237 & 1.50635508576342 \tabularnewline
133 & 116.7 & 126.075135194094 & -9.37513519409357 \tabularnewline
134 & 126.4 & 121.686417139667 & 4.71358286033323 \tabularnewline
135 & 129 & 124.206175027178 & 4.79382497282245 \tabularnewline
136 & 128.7 & 126.793886662705 & 1.906113337295 \tabularnewline
137 & 128.4 & 127.982830214009 & 0.417169785991277 \tabularnewline
138 & 129.2 & 128.447155388726 & 0.752844611273986 \tabularnewline
139 & 133.3 & 129.079924329838 & 4.22007567016166 \tabularnewline
140 & 128.9 & 131.431231421509 & -2.53123142150932 \tabularnewline
141 & 132.7 & 130.470364898918 & 2.22963510108173 \tabularnewline
142 & 127.7 & 131.847835505034 & -4.14783550503442 \tabularnewline
143 & 131.8 & 130.085993365558 & 1.71400663444214 \tabularnewline
144 & 133.9 & 131.19705272381 & 2.70294727618992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117490&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]96.9[/C][C]96.9[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]97.8[/C][C]97.3[/C][C]0.499999999999986[/C][/ROW]
[ROW][C]5[/C][C]98.9[/C][C]97.947174230001[/C][C]0.952825769998881[/C][/ROW]
[ROW][C]6[/C][C]100.2[/C][C]98.8212026791372[/C][C]1.37879732086284[/C][/ROW]
[ROW][C]7[/C][C]101.2[/C][C]99.9115274103415[/C][C]1.28847258965851[/C][/ROW]
[ROW][C]8[/C][C]101[/C][C]100.965474404464[/C][C]0.0345255955364649[/C][/ROW]
[ROW][C]9[/C][C]101.6[/C][C]101.40726674962[/C][C]0.192733250380044[/C][/ROW]
[ROW][C]10[/C][C]102.4[/C][C]101.927475993206[/C][C]0.47252400679352[/C][/ROW]
[ROW][C]11[/C][C]103.7[/C][C]102.587155957461[/C][C]1.11284404253858[/C][/ROW]
[ROW][C]12[/C][C]103.7[/C][C]103.566212759107[/C][C]0.133787240893[/C][/ROW]
[ROW][C]13[/C][C]104.6[/C][C]104.067952509219[/C][C]0.532047490780513[/C][/ROW]
[ROW][C]14[/C][C]104.5[/C][C]104.767374456997[/C][C]-0.267374456997018[/C][/ROW]
[ROW][C]15[/C][C]104.5[/C][C]105.074796209891[/C][C]-0.574796209890849[/C][/ROW]
[ROW][C]16[/C][C]105.6[/C][C]105.228639980112[/C][C]0.371360019888101[/C][/ROW]
[ROW][C]17[/C][C]106.1[/C][C]105.846765276739[/C][C]0.253234723260718[/C][/ROW]
[ROW][C]18[/C][C]107.6[/C][C]106.408724044207[/C][C]1.19127595579283[/C][/ROW]
[ROW][C]19[/C][C]107.7[/C][C]107.435921710495[/C][C]0.264078289505349[/C][/ROW]
[ROW][C]20[/C][C]108.3[/C][C]108.011909478405[/C][C]0.288090521595208[/C][/ROW]
[ROW][C]21[/C][C]108.1[/C][C]108.601352388546[/C][C]-0.50135238854638[/C][/ROW]
[ROW][C]22[/C][C]108.1[/C][C]108.802264241341[/C][C]-0.702264241341297[/C][/ROW]
[ROW][C]23[/C][C]108[/C][C]108.900847016348[/C][C]-0.900847016348393[/C][/ROW]
[ROW][C]24[/C][C]108.2[/C][C]108.897046418727[/C][C]-0.697046418727368[/C][/ROW]
[ROW][C]25[/C][C]108.9[/C][C]108.988588354992[/C][C]-0.0885883549916713[/C][/ROW]
[ROW][C]26[/C][C]109.8[/C][C]109.376737626542[/C][C]0.423262373457661[/C][/ROW]
[ROW][C]27[/C][C]109.9[/C][C]110.017387899251[/C][C]-0.117387899250801[/C][/ROW]
[ROW][C]28[/C][C]109.8[/C][C]110.393308537283[/C][C]-0.593308537282994[/C][/ROW]
[ROW][C]29[/C][C]110.9[/C][C]110.533254096721[/C][C]0.366745903278769[/C][/ROW]
[ROW][C]30[/C][C]111.1[/C][C]111.144240649294[/C][C]-0.0442406492942808[/C][/ROW]
[ROW][C]31[/C][C]112.2[/C][C]111.55425747255[/C][C]0.645742527450125[/C][/ROW]
[ROW][C]32[/C][C]112.7[/C][C]112.305100928778[/C][C]0.394899071222127[/C][/ROW]
[ROW][C]33[/C][C]114.6[/C][C]112.935815405863[/C][C]1.66418459413738[/C][/ROW]
[ROW][C]34[/C][C]114.2[/C][C]114.196369013529[/C][C]0.00363098647093807[/C][/ROW]
[ROW][C]35[/C][C]114.7[/C][C]114.646017264547[/C][C]0.0539827354527631[/C][/ROW]
[ROW][C]36[/C][C]114.7[/C][C]115.120578614662[/C][C]-0.420578614661636[/C][/ROW]
[ROW][C]37[/C][C]116[/C][C]115.360865242251[/C][C]0.639134757748678[/C][/ROW]
[ROW][C]38[/C][C]116.3[/C][C]116.12249565372[/C][C]0.177504346280202[/C][/ROW]
[ROW][C]39[/C][C]116.4[/C][C]116.659755226146[/C][C]-0.259755226145828[/C][/ROW]
[ROW][C]40[/C][C]116.6[/C][C]116.98192140487[/C][C]-0.38192140486963[/C][/ROW]
[ROW][C]41[/C][C]118.1[/C][C]117.242136131678[/C][C]0.857863868321644[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]118.112944890897[/C][C]-0.912944890897322[/C][/ROW]
[ROW][C]43[/C][C]108.3[/C][C]118.113505103113[/C][C]-9.81350510311326[/C][/ROW]
[ROW][C]44[/C][C]109.5[/C][C]113.708608504292[/C][C]-4.20860850429239[/C][/ROW]
[ROW][C]45[/C][C]110.5[/C][C]112.015593120627[/C][C]-1.51559312062675[/C][/ROW]
[ROW][C]46[/C][C]110.6[/C][C]111.628609909526[/C][C]-1.02860990952648[/C][/ROW]
[ROW][C]47[/C][C]111.2[/C][C]111.473271033395[/C][C]-0.273271033394565[/C][/ROW]
[ROW][C]48[/C][C]111.1[/C][C]111.685160085385[/C][C]-0.585160085384587[/C][/ROW]
[ROW][C]49[/C][C]111[/C][C]111.741227366953[/C][C]-0.741227366952657[/C][/ROW]
[ROW][C]50[/C][C]112.4[/C][C]111.716631485958[/C][C]0.683368514042044[/C][/ROW]
[ROW][C]51[/C][C]112.5[/C][C]112.391834283748[/C][C]0.108165716251676[/C][/ROW]
[ROW][C]52[/C][C]112.4[/C][C]112.786787354733[/C][C]-0.38678735473303[/C][/ROW]
[ROW][C]53[/C][C]111.8[/C][C]112.937710239136[/C][C]-1.13771023913608[/C][/ROW]
[ROW][C]54[/C][C]111.6[/C][C]112.715094443392[/C][C]-1.115094443392[/C][/ROW]
[ROW][C]55[/C][C]112.9[/C][C]112.49683133893[/C][C]0.403168661070282[/C][/ROW]
[ROW][C]56[/C][C]112.8[/C][C]113.022427586779[/C][C]-0.222427586779091[/C][/ROW]
[ROW][C]57[/C][C]113.7[/C][C]113.241180705707[/C][C]0.458819294293107[/C][/ROW]
[ROW][C]58[/C][C]113.8[/C][C]113.795372384522[/C][C]0.00462761547819923[/C][/ROW]
[ROW][C]59[/C][C]114[/C][C]114.12778847828[/C][C]-0.127788478280394[/C][/ROW]
[ROW][C]60[/C][C]113.8[/C][C]114.394772650303[/C][C]-0.594772650303454[/C][/ROW]
[ROW][C]61[/C][C]113.9[/C][C]114.430137058128[/C][C]-0.53013705812792[/C][/ROW]
[ROW][C]62[/C][C]114.4[/C][C]114.493884744197[/C][C]-0.093884744196771[/C][/ROW]
[ROW][C]63[/C][C]114.4[/C][C]114.770111740433[/C][C]-0.370111740433018[/C][/ROW]
[ROW][C]64[/C][C]114.5[/C][C]114.909222944666[/C][C]-0.409222944665942[/C][/ROW]
[ROW][C]65[/C][C]113.8[/C][C]115.02677854692[/C][C]-1.22677854691968[/C][/ROW]
[ROW][C]66[/C][C]114.3[/C][C]114.737721051727[/C][C]-0.437721051726569[/C][/ROW]
[ROW][C]67[/C][C]115[/C][C]114.831371023223[/C][C]0.168628976776645[/C][/ROW]
[ROW][C]68[/C][C]115.4[/C][C]115.222142436052[/C][C]0.177857563947953[/C][/ROW]
[ROW][C]69[/C][C]115.3[/C][C]115.618487928244[/C][C]-0.318487928243982[/C][/ROW]
[ROW][C]70[/C][C]114.9[/C][C]115.770533112947[/C][C]-0.870533112946703[/C][/ROW]
[ROW][C]71[/C][C]114.3[/C][C]115.647764366538[/C][C]-1.34776436653837[/C][/ROW]
[ROW][C]72[/C][C]114.5[/C][C]115.283853020714[/C][C]-0.783853020713565[/C][/ROW]
[ROW][C]73[/C][C]115.5[/C][C]115.190622454164[/C][C]0.309377545835559[/C][/ROW]
[ROW][C]74[/C][C]115.8[/C][C]115.633124837206[/C][C]0.166875162793801[/C][/ROW]
[ROW][C]75[/C][C]115.8[/C][C]116.007037959495[/C][C]-0.207037959494542[/C][/ROW]
[ROW][C]76[/C][C]116[/C][C]116.197109122485[/C][C]-0.197109122484605[/C][/ROW]
[ROW][C]77[/C][C]114.9[/C][C]116.390846157172[/C][C]-1.49084615717156[/C][/ROW]
[ROW][C]78[/C][C]114.1[/C][C]115.943843430418[/C][C]-1.84384343041766[/C][/ROW]
[ROW][C]79[/C][C]114.1[/C][C]115.313390486147[/C][C]-1.21339048614706[/C][/ROW]
[ROW][C]80[/C][C]113.5[/C][C]114.983536090298[/C][C]-1.48353609029806[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]114.512854081823[/C][C]0.48714591817668[/C][/ROW]
[ROW][C]82[/C][C]114.7[/C][C]115.007472997958[/C][C]-0.307472997958371[/C][/ROW]
[ROW][C]83[/C][C]115.4[/C][C]115.11219663643[/C][C]0.287803363570049[/C][/ROW]
[ROW][C]84[/C][C]116.1[/C][C]115.50934908383[/C][C]0.590650916170347[/C][/ROW]
[ROW][C]85[/C][C]116.6[/C][C]116.057940858781[/C][C]0.542059141219426[/C][/ROW]
[ROW][C]86[/C][C]117.2[/C][C]116.586055857322[/C][C]0.613944142677866[/C][/ROW]
[ROW][C]87[/C][C]118.2[/C][C]117.15295998938[/C][C]1.04704001062001[/C][/ROW]
[ROW][C]88[/C][C]118[/C][C]117.937648672011[/C][C]0.0623513279886794[/C][/ROW]
[ROW][C]89[/C][C]117.7[/C][C]118.241841301805[/C][C]-0.541841301805192[/C][/ROW]
[ROW][C]90[/C][C]118.5[/C][C]118.247726405472[/C][C]0.252273594528262[/C][/ROW]
[ROW][C]91[/C][C]117.5[/C][C]118.642929398711[/C][C]-1.14292939871055[/C][/ROW]
[ROW][C]92[/C][C]118[/C][C]118.349929833222[/C][C]-0.349929833222106[/C][/ROW]
[ROW][C]93[/C][C]117.7[/C][C]118.442089669053[/C][C]-0.742089669053144[/C][/ROW]
[ROW][C]94[/C][C]116.3[/C][C]118.338285967076[/C][C]-2.03828596707615[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]117.589256345531[/C][C]-2.58925634553084[/C][/ROW]
[ROW][C]96[/C][C]115.7[/C][C]116.555623623678[/C][C]-0.85562362367763[/C][/ROW]
[ROW][C]97[/C][C]113.6[/C][C]116.363471455839[/C][C]-2.76347145583928[/C][/ROW]
[ROW][C]98[/C][C]114.8[/C][C]115.223043057873[/C][C]-0.423043057872988[/C][/ROW]
[ROW][C]99[/C][C]114.9[/C][C]115.223018258082[/C][C]-0.3230182580822[/C][/ROW]
[ROW][C]100[/C][C]117.3[/C][C]115.269901885105[/C][C]2.03009811489532[/C][/ROW]
[ROW][C]101[/C][C]117.3[/C][C]116.47810653663[/C][C]0.821893463370031[/C][/ROW]
[ROW][C]102[/C][C]117.7[/C][C]117.101219686265[/C][C]0.598780313735062[/C][/ROW]
[ROW][C]103[/C][C]120[/C][C]117.618969371856[/C][C]2.38103062814361[/C][/ROW]
[ROW][C]104[/C][C]119.6[/C][C]119.021365032888[/C][C]0.578634967111981[/C][/ROW]
[ROW][C]105[/C][C]119.2[/C][C]119.5470377255[/C][C]-0.347037725499931[/C][/ROW]
[ROW][C]106[/C][C]117.3[/C][C]119.618577933119[/C][C]-2.31857793311916[/C][/ROW]
[ROW][C]107[/C][C]117.5[/C][C]118.713407703901[/C][C]-1.21340770390071[/C][/ROW]
[ROW][C]108[/C][C]119[/C][C]118.34066290287[/C][C]0.659337097130319[/C][/ROW]
[ROW][C]109[/C][C]112.5[/C][C]118.886424957645[/C][C]-6.38642495764455[/C][/ROW]
[ROW][C]110[/C][C]118.9[/C][C]115.953082069178[/C][C]2.94691793082249[/C][/ROW]
[ROW][C]111[/C][C]118.4[/C][C]117.595337965338[/C][C]0.804662034662314[/C][/ROW]
[ROW][C]112[/C][C]119.4[/C][C]118.196257395633[/C][C]1.20374260436749[/C][/ROW]
[ROW][C]113[/C][C]120.6[/C][C]118.999290463159[/C][C]1.6007095368406[/C][/ROW]
[ROW][C]114[/C][C]118.6[/C][C]120.005787174781[/C][C]-1.40578717478114[/C][/ROW]
[ROW][C]115[/C][C]122[/C][C]119.535632715615[/C][C]2.46436728438532[/C][/ROW]
[ROW][C]116[/C][C]122.6[/C][C]120.970247032663[/C][C]1.62975296733688[/C][/ROW]
[ROW][C]117[/C][C]120.6[/C][C]122.007059701097[/C][C]-1.40705970109735[/C][/ROW]
[ROW][C]118[/C][C]117.4[/C][C]121.552408841637[/C][C]-4.1524088416368[/C][/ROW]
[ROW][C]119[/C][C]116.4[/C][C]119.732155105314[/C][C]-3.33215510531437[/C][/ROW]
[ROW][C]120[/C][C]122.2[/C][C]118.29247395909[/C][C]3.90752604090986[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]120.411721795037[/C][C]0.588278204962606[/C][/ROW]
[ROW][C]122[/C][C]122.4[/C][C]120.913553615922[/C][C]1.48644638407802[/C][/ROW]
[ROW][C]123[/C][C]124.9[/C][C]121.862923747056[/C][C]3.03707625294446[/C][/ROW]
[ROW][C]124[/C][C]126.1[/C][C]123.587765521986[/C][C]2.51223447801353[/C][/ROW]
[ROW][C]125[/C][C]124.5[/C][C]125.071378050633[/C][C]-0.571378050633271[/C][/ROW]
[ROW][C]126[/C][C]123.2[/C][C]125.045687378965[/C][C]-1.84568737896456[/C][/ROW]
[ROW][C]127[/C][C]126.4[/C][C]124.386615016916[/C][C]2.01338498308382[/C][/ROW]
[ROW][C]128[/C][C]123.9[/C][C]125.624193174877[/C][C]-1.72419317487663[/C][/ROW]
[ROW][C]129[/C][C]116[/C][C]125.026187637864[/C][C]-9.02618763786352[/C][/ROW]
[ROW][C]130[/C][C]126.6[/C][C]120.808105509932[/C][C]5.79189449006797[/C][/ROW]
[ROW][C]131[/C][C]125.9[/C][C]123.861153362708[/C][C]2.03884663729221[/C][/ROW]
[ROW][C]132[/C][C]126.6[/C][C]125.093644914237[/C][C]1.50635508576342[/C][/ROW]
[ROW][C]133[/C][C]116.7[/C][C]126.075135194094[/C][C]-9.37513519409357[/C][/ROW]
[ROW][C]134[/C][C]126.4[/C][C]121.686417139667[/C][C]4.71358286033323[/C][/ROW]
[ROW][C]135[/C][C]129[/C][C]124.206175027178[/C][C]4.79382497282245[/C][/ROW]
[ROW][C]136[/C][C]128.7[/C][C]126.793886662705[/C][C]1.906113337295[/C][/ROW]
[ROW][C]137[/C][C]128.4[/C][C]127.982830214009[/C][C]0.417169785991277[/C][/ROW]
[ROW][C]138[/C][C]129.2[/C][C]128.447155388726[/C][C]0.752844611273986[/C][/ROW]
[ROW][C]139[/C][C]133.3[/C][C]129.079924329838[/C][C]4.22007567016166[/C][/ROW]
[ROW][C]140[/C][C]128.9[/C][C]131.431231421509[/C][C]-2.53123142150932[/C][/ROW]
[ROW][C]141[/C][C]132.7[/C][C]130.470364898918[/C][C]2.22963510108173[/C][/ROW]
[ROW][C]142[/C][C]127.7[/C][C]131.847835505034[/C][C]-4.14783550503442[/C][/ROW]
[ROW][C]143[/C][C]131.8[/C][C]130.085993365558[/C][C]1.71400663444214[/C][/ROW]
[ROW][C]144[/C][C]133.9[/C][C]131.19705272381[/C][C]2.70294727618992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117490&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117490&T=2

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
396.996.90
497.897.30.499999999999986
598.997.9471742300010.952825769998881
6100.298.82120267913721.37879732086284
7101.299.91152741034151.28847258965851
8101100.9654744044640.0345255955364649
9101.6101.407266749620.192733250380044
10102.4101.9274759932060.47252400679352
11103.7102.5871559574611.11284404253858
12103.7103.5662127591070.133787240893
13104.6104.0679525092190.532047490780513
14104.5104.767374456997-0.267374456997018
15104.5105.074796209891-0.574796209890849
16105.6105.2286399801120.371360019888101
17106.1105.8467652767390.253234723260718
18107.6106.4087240442071.19127595579283
19107.7107.4359217104950.264078289505349
20108.3108.0119094784050.288090521595208
21108.1108.601352388546-0.50135238854638
22108.1108.802264241341-0.702264241341297
23108108.900847016348-0.900847016348393
24108.2108.897046418727-0.697046418727368
25108.9108.988588354992-0.0885883549916713
26109.8109.3767376265420.423262373457661
27109.9110.017387899251-0.117387899250801
28109.8110.393308537283-0.593308537282994
29110.9110.5332540967210.366745903278769
30111.1111.144240649294-0.0442406492942808
31112.2111.554257472550.645742527450125
32112.7112.3051009287780.394899071222127
33114.6112.9358154058631.66418459413738
34114.2114.1963690135290.00363098647093807
35114.7114.6460172645470.0539827354527631
36114.7115.120578614662-0.420578614661636
37116115.3608652422510.639134757748678
38116.3116.122495653720.177504346280202
39116.4116.659755226146-0.259755226145828
40116.6116.98192140487-0.38192140486963
41118.1117.2421361316780.857863868321644
42117.2118.112944890897-0.912944890897322
43108.3118.113505103113-9.81350510311326
44109.5113.708608504292-4.20860850429239
45110.5112.015593120627-1.51559312062675
46110.6111.628609909526-1.02860990952648
47111.2111.473271033395-0.273271033394565
48111.1111.685160085385-0.585160085384587
49111111.741227366953-0.741227366952657
50112.4111.7166314859580.683368514042044
51112.5112.3918342837480.108165716251676
52112.4112.786787354733-0.38678735473303
53111.8112.937710239136-1.13771023913608
54111.6112.715094443392-1.115094443392
55112.9112.496831338930.403168661070282
56112.8113.022427586779-0.222427586779091
57113.7113.2411807057070.458819294293107
58113.8113.7953723845220.00462761547819923
59114114.12778847828-0.127788478280394
60113.8114.394772650303-0.594772650303454
61113.9114.430137058128-0.53013705812792
62114.4114.493884744197-0.093884744196771
63114.4114.770111740433-0.370111740433018
64114.5114.909222944666-0.409222944665942
65113.8115.02677854692-1.22677854691968
66114.3114.737721051727-0.437721051726569
67115114.8313710232230.168628976776645
68115.4115.2221424360520.177857563947953
69115.3115.618487928244-0.318487928243982
70114.9115.770533112947-0.870533112946703
71114.3115.647764366538-1.34776436653837
72114.5115.283853020714-0.783853020713565
73115.5115.1906224541640.309377545835559
74115.8115.6331248372060.166875162793801
75115.8116.007037959495-0.207037959494542
76116116.197109122485-0.197109122484605
77114.9116.390846157172-1.49084615717156
78114.1115.943843430418-1.84384343041766
79114.1115.313390486147-1.21339048614706
80113.5114.983536090298-1.48353609029806
81115114.5128540818230.48714591817668
82114.7115.007472997958-0.307472997958371
83115.4115.112196636430.287803363570049
84116.1115.509349083830.590650916170347
85116.6116.0579408587810.542059141219426
86117.2116.5860558573220.613944142677866
87118.2117.152959989381.04704001062001
88118117.9376486720110.0623513279886794
89117.7118.241841301805-0.541841301805192
90118.5118.2477264054720.252273594528262
91117.5118.642929398711-1.14292939871055
92118118.349929833222-0.349929833222106
93117.7118.442089669053-0.742089669053144
94116.3118.338285967076-2.03828596707615
95115117.589256345531-2.58925634553084
96115.7116.555623623678-0.85562362367763
97113.6116.363471455839-2.76347145583928
98114.8115.223043057873-0.423043057872988
99114.9115.223018258082-0.3230182580822
100117.3115.2699018851052.03009811489532
101117.3116.478106536630.821893463370031
102117.7117.1012196862650.598780313735062
103120117.6189693718562.38103062814361
104119.6119.0213650328880.578634967111981
105119.2119.5470377255-0.347037725499931
106117.3119.618577933119-2.31857793311916
107117.5118.713407703901-1.21340770390071
108119118.340662902870.659337097130319
109112.5118.886424957645-6.38642495764455
110118.9115.9530820691782.94691793082249
111118.4117.5953379653380.804662034662314
112119.4118.1962573956331.20374260436749
113120.6118.9992904631591.6007095368406
114118.6120.005787174781-1.40578717478114
115122119.5356327156152.46436728438532
116122.6120.9702470326631.62975296733688
117120.6122.007059701097-1.40705970109735
118117.4121.552408841637-4.1524088416368
119116.4119.732155105314-3.33215510531437
120122.2118.292473959093.90752604090986
121121120.4117217950370.588278204962606
122122.4120.9135536159221.48644638407802
123124.9121.8629237470563.03707625294446
124126.1123.5877655219862.51223447801353
125124.5125.071378050633-0.571378050633271
126123.2125.045687378965-1.84568737896456
127126.4124.3866150169162.01338498308382
128123.9125.624193174877-1.72419317487663
129116125.026187637864-9.02618763786352
130126.6120.8081055099325.79189449006797
131125.9123.8611533627082.03884663729221
132126.6125.0936449142371.50635508576342
133116.7126.075135194094-9.37513519409357
134126.4121.6864171396674.71358286033323
135129124.2061750271784.79382497282245
136128.7126.7938866627051.906113337295
137128.4127.9828302140090.417169785991277
138129.2128.4471553887260.752844611273986
139133.3129.0799243298384.22007567016166
140128.9131.431231421509-2.53123142150932
141132.7130.4703648989182.22963510108173
142127.7131.847835505034-4.14783550503442
143131.8130.0859933655581.71400663444214
144133.9131.197052723812.70294727618992






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145132.80727910917128.599004755087137.015553463254
146133.097528041934128.403122266893137.791933816975
147133.387776974698128.242776519163138.532777430232
148133.678025907461128.109219853087139.246831961835
149133.968274840225127.996638997015139.939910683435
150134.258523772988127.900945105182140.616102440795
151134.548772705752127.819137351337141.278408060167
152134.839021638516127.748939367211141.929103909821
153135.129270571279127.688578005351142.569963137208
154135.419519504043127.636641806457143.202397201629
155135.709768436807127.591986700982143.827550172632
156136.00001736957127.553671027083144.446363712058

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 132.80727910917 & 128.599004755087 & 137.015553463254 \tabularnewline
146 & 133.097528041934 & 128.403122266893 & 137.791933816975 \tabularnewline
147 & 133.387776974698 & 128.242776519163 & 138.532777430232 \tabularnewline
148 & 133.678025907461 & 128.109219853087 & 139.246831961835 \tabularnewline
149 & 133.968274840225 & 127.996638997015 & 139.939910683435 \tabularnewline
150 & 134.258523772988 & 127.900945105182 & 140.616102440795 \tabularnewline
151 & 134.548772705752 & 127.819137351337 & 141.278408060167 \tabularnewline
152 & 134.839021638516 & 127.748939367211 & 141.929103909821 \tabularnewline
153 & 135.129270571279 & 127.688578005351 & 142.569963137208 \tabularnewline
154 & 135.419519504043 & 127.636641806457 & 143.202397201629 \tabularnewline
155 & 135.709768436807 & 127.591986700982 & 143.827550172632 \tabularnewline
156 & 136.00001736957 & 127.553671027083 & 144.446363712058 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117490&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]145[/C][C]132.80727910917[/C][C]128.599004755087[/C][C]137.015553463254[/C][/ROW]
[ROW][C]146[/C][C]133.097528041934[/C][C]128.403122266893[/C][C]137.791933816975[/C][/ROW]
[ROW][C]147[/C][C]133.387776974698[/C][C]128.242776519163[/C][C]138.532777430232[/C][/ROW]
[ROW][C]148[/C][C]133.678025907461[/C][C]128.109219853087[/C][C]139.246831961835[/C][/ROW]
[ROW][C]149[/C][C]133.968274840225[/C][C]127.996638997015[/C][C]139.939910683435[/C][/ROW]
[ROW][C]150[/C][C]134.258523772988[/C][C]127.900945105182[/C][C]140.616102440795[/C][/ROW]
[ROW][C]151[/C][C]134.548772705752[/C][C]127.819137351337[/C][C]141.278408060167[/C][/ROW]
[ROW][C]152[/C][C]134.839021638516[/C][C]127.748939367211[/C][C]141.929103909821[/C][/ROW]
[ROW][C]153[/C][C]135.129270571279[/C][C]127.688578005351[/C][C]142.569963137208[/C][/ROW]
[ROW][C]154[/C][C]135.419519504043[/C][C]127.636641806457[/C][C]143.202397201629[/C][/ROW]
[ROW][C]155[/C][C]135.709768436807[/C][C]127.591986700982[/C][C]143.827550172632[/C][/ROW]
[ROW][C]156[/C][C]136.00001736957[/C][C]127.553671027083[/C][C]144.446363712058[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117490&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117490&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
145132.80727910917128.599004755087137.015553463254
146133.097528041934128.403122266893137.791933816975
147133.387776974698128.242776519163138.532777430232
148133.678025907461128.109219853087139.246831961835
149133.968274840225127.996638997015139.939910683435
150134.258523772988127.900945105182140.616102440795
151134.548772705752127.819137351337141.278408060167
152134.839021638516127.748939367211141.929103909821
153135.129270571279127.688578005351142.569963137208
154135.419519504043127.636641806457143.202397201629
155135.709768436807127.591986700982143.827550172632
156136.00001736957127.553671027083144.446363712058


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')