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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 computationThu, 30 Dec 2010 14:16:27 +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/2010/Dec/30/t1293718466d05ka1ax4q75sfo.htm/, Retrieved Fri, 03 May 2024 03:50:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117218, Retrieved Fri, 03 May 2024 03:50:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W102
Estimated Impact209

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 triple ...] [2010-12-30 14:16:27] [bf26e49ed6e1a435b77b49c7144b8136] [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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 3 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117218&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117218&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117218&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.474740092278407
beta0.0325150620496534
gamma0.446603668717535

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.474740092278407 \tabularnewline
beta & 0.0325150620496534 \tabularnewline
gamma & 0.446603668717535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117218&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]0.474740092278407[/C][/ROW]
[ROW][C]beta[/C][C]0.0325150620496534[/C][/ROW]
[ROW][C]gamma[/C][C]0.446603668717535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117218&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117218&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
alpha0.474740092278407
beta0.0325150620496534
gamma0.446603668717535






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13104.6100.8663728632483.73362713675211
14104.5102.597193115111.90280688489034
15104.5103.5882217060640.911778293936095
16105.6105.289510354310.310489645690467
17106.1106.201802622241-0.101802622241209
18107.6107.96679177485-0.36679177484973
19107.7108.300318080196-0.600318080196118
20108.3107.7928801203740.507119879625762
21108.1108.656515365481-0.556515365480948
22108.1109.214943165044-1.11494316504383
23108110.007719076527-2.00771907652695
24108.2109.062333575723-0.862333575722971
25108.9110.452409464733-1.55240946473307
26109.8109.1897572487960.610242751203941
27109.9109.2602234254640.639776574535659
28109.8110.612679245938-0.812679245938483
29110.9110.7990533787960.100946621203605
30111.1112.505275379692-1.40527537969247
31112.2112.1821219412320.0178780587679768
32112.7112.1286065561480.571393443851562
33114.6112.6748903919881.92510960801241
34114.2114.22040036075-0.0204003607499317
35114.7115.280218126212-0.580218126212301
36114.7115.260096277888-0.56009627788778
37116116.615325403069-0.615325403068979
38116.3116.302880177104-0.00288017710359156
39116.4116.0777503281740.322249671825645
40116.6116.922390418307-0.322390418306753
41118.1117.5470620781640.552937921835962
42117.2119.112722087452-1.91272208745173
43108.3118.872871602086-10.5728716020862
44109.5113.748227723022-4.24822772302151
45110.5112.076485986073-1.57648598607256
46110.6111.20169625179-0.601696251790017
47111.2111.543684499657-0.343684499657016
48111.1111.333684751072-0.233684751071877
49111112.529067115873-1.52906711587259
50112.4111.6105450165620.789454983438475
51112.5111.5341127297830.965887270216783
52112.4112.2393014652970.160698534702945
53111.8113.012318708271-1.21231870827108
54111.6112.847956574348-1.247956574348
55112.9110.5888523951582.31114760484206
56112.8112.959982607497-0.159982607497184
57113.7113.814506592089-0.114506592089342
58113.8113.843680265151-0.0436802651510817
59114114.500953966612-0.50095396661159
60113.8114.229516245773-0.429516245772732
61113.9115.012452048033-1.11245204803343
62114.4114.826427836268-0.426427836267976
63114.4114.1862147661610.213785233839161
64114.5114.3059168440330.194083155967107
65113.8114.733661853709-0.933661853708713
66114.3114.65849592189-0.358495921889542
67115113.6355562716111.36444372838872
68115.4114.9419460959870.4580539040129
69115.3116.074467869488-0.774467869487779
70114.9115.770681998778-0.870681998777584
71114.3115.879046297837-1.57904629783695
72114.5115.046881460982-0.546881460982434
73115.5115.546410948045-0.0464109480452777
74115.8115.976381221242-0.176381221242366
75115.8115.557890594330.242109405670462
76116115.6396869235770.360313076422969
77114.9115.837633494259-0.937633494259103
78114.1115.851280272102-1.75128027210195
79114.1114.505577159086-0.405577159085951
80113.5114.665995808558-1.16599580855785
81115114.6202706026320.379729397367583
82114.7114.741557862007-0.0415578620067407
83115.4114.9898685479390.410131452061378
84116.1115.2873779594170.812622040583463
85116.6116.5139092062860.086090793713538
86117.2116.9425289423910.257471057608697
87118.2116.8011075965921.3988924034081
88118117.450591525390.549408474609749
89117.7117.4275397357120.272460264288
90118.5117.8371847617740.662815238226457
91117.5118.0028790488-0.502879048800438
92118117.9868717278550.0131282721451811
93117.7118.929874482049-1.22987448204903
94116.3118.229694870561-1.92969487056135
95115117.699947640108-2.69994764010795
96115.7116.579745238994-0.879745238994346
97113.6116.770636252919-3.17063625291927
98114.8115.581314731849-0.781314731849307
99114.9115.086418005956-0.186418005955801
100117.3114.631465696342.6685343036597
101117.3115.4296404750631.87035952493707
102117.7116.5942684026841.10573159731594
103120116.6084431424673.39155685753283
104119.6118.5341048993921.06589510060813
105119.2119.673339350611-0.473339350611042
106117.3119.167857753131-1.86785775313113
107117.5118.487439828374-0.987439828373653
108119118.6343176598780.365682340122206
109112.5118.925379233257-6.42537923325713
110118.9116.7474743154132.15252568458691
111118.4117.8263086484870.573691351513247
112119.4118.4550339401910.944966059809346
113120.6118.2742181179272.32578188207302
114118.6119.50920915679-0.90920915679034
115122119.1054480264962.89455197350368
116122.6120.2443505924892.35564940751112
117120.6121.649463464064-1.04946346406393
118117.4120.549107749505-3.14910774950516
119116.4119.45294793779-3.05294793778954
120122.2118.8907698902213.30923010977887
121121118.9857253678092.0142746321908
122122.4122.956510778649-0.556510778649113
123124.9122.4668895445222.43311045547841
124126.1124.1821530911541.91784690884626
125124.5124.918839042663-0.418839042662526
126123.2124.181328363591-0.98132836359052
127126.4124.723867593691.67613240630983
128123.9125.227352922982-1.32735292298221
129116124.097796518375-8.09779651837529
130126.6119.0625553452077.53744465479305
131125.9123.1310293488112.76897065118931
132126.6126.98381809783-0.383818097830272
133116.7125.123364145429-8.4233641454286
134126.4123.4764070443052.92359295569538
135129125.3344501344053.66554986559495
136128.7127.5271594292781.17284057072166
137128.4127.363743547231.03625645277029
138129.2127.2092651037671.99073489623281
139133.3129.8562249174053.44377508259512
140128.9130.59166805435-1.69166805435049
141132.7127.7926555907434.90734440925677
142127.7132.891717955207-5.19171795520701
143131.8129.8945351606771.90546483932343
144133.9132.6804531603811.21954683961874

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 104.6 & 100.866372863248 & 3.73362713675211 \tabularnewline
14 & 104.5 & 102.59719311511 & 1.90280688489034 \tabularnewline
15 & 104.5 & 103.588221706064 & 0.911778293936095 \tabularnewline
16 & 105.6 & 105.28951035431 & 0.310489645690467 \tabularnewline
17 & 106.1 & 106.201802622241 & -0.101802622241209 \tabularnewline
18 & 107.6 & 107.96679177485 & -0.36679177484973 \tabularnewline
19 & 107.7 & 108.300318080196 & -0.600318080196118 \tabularnewline
20 & 108.3 & 107.792880120374 & 0.507119879625762 \tabularnewline
21 & 108.1 & 108.656515365481 & -0.556515365480948 \tabularnewline
22 & 108.1 & 109.214943165044 & -1.11494316504383 \tabularnewline
23 & 108 & 110.007719076527 & -2.00771907652695 \tabularnewline
24 & 108.2 & 109.062333575723 & -0.862333575722971 \tabularnewline
25 & 108.9 & 110.452409464733 & -1.55240946473307 \tabularnewline
26 & 109.8 & 109.189757248796 & 0.610242751203941 \tabularnewline
27 & 109.9 & 109.260223425464 & 0.639776574535659 \tabularnewline
28 & 109.8 & 110.612679245938 & -0.812679245938483 \tabularnewline
29 & 110.9 & 110.799053378796 & 0.100946621203605 \tabularnewline
30 & 111.1 & 112.505275379692 & -1.40527537969247 \tabularnewline
31 & 112.2 & 112.182121941232 & 0.0178780587679768 \tabularnewline
32 & 112.7 & 112.128606556148 & 0.571393443851562 \tabularnewline
33 & 114.6 & 112.674890391988 & 1.92510960801241 \tabularnewline
34 & 114.2 & 114.22040036075 & -0.0204003607499317 \tabularnewline
35 & 114.7 & 115.280218126212 & -0.580218126212301 \tabularnewline
36 & 114.7 & 115.260096277888 & -0.56009627788778 \tabularnewline
37 & 116 & 116.615325403069 & -0.615325403068979 \tabularnewline
38 & 116.3 & 116.302880177104 & -0.00288017710359156 \tabularnewline
39 & 116.4 & 116.077750328174 & 0.322249671825645 \tabularnewline
40 & 116.6 & 116.922390418307 & -0.322390418306753 \tabularnewline
41 & 118.1 & 117.547062078164 & 0.552937921835962 \tabularnewline
42 & 117.2 & 119.112722087452 & -1.91272208745173 \tabularnewline
43 & 108.3 & 118.872871602086 & -10.5728716020862 \tabularnewline
44 & 109.5 & 113.748227723022 & -4.24822772302151 \tabularnewline
45 & 110.5 & 112.076485986073 & -1.57648598607256 \tabularnewline
46 & 110.6 & 111.20169625179 & -0.601696251790017 \tabularnewline
47 & 111.2 & 111.543684499657 & -0.343684499657016 \tabularnewline
48 & 111.1 & 111.333684751072 & -0.233684751071877 \tabularnewline
49 & 111 & 112.529067115873 & -1.52906711587259 \tabularnewline
50 & 112.4 & 111.610545016562 & 0.789454983438475 \tabularnewline
51 & 112.5 & 111.534112729783 & 0.965887270216783 \tabularnewline
52 & 112.4 & 112.239301465297 & 0.160698534702945 \tabularnewline
53 & 111.8 & 113.012318708271 & -1.21231870827108 \tabularnewline
54 & 111.6 & 112.847956574348 & -1.247956574348 \tabularnewline
55 & 112.9 & 110.588852395158 & 2.31114760484206 \tabularnewline
56 & 112.8 & 112.959982607497 & -0.159982607497184 \tabularnewline
57 & 113.7 & 113.814506592089 & -0.114506592089342 \tabularnewline
58 & 113.8 & 113.843680265151 & -0.0436802651510817 \tabularnewline
59 & 114 & 114.500953966612 & -0.50095396661159 \tabularnewline
60 & 113.8 & 114.229516245773 & -0.429516245772732 \tabularnewline
61 & 113.9 & 115.012452048033 & -1.11245204803343 \tabularnewline
62 & 114.4 & 114.826427836268 & -0.426427836267976 \tabularnewline
63 & 114.4 & 114.186214766161 & 0.213785233839161 \tabularnewline
64 & 114.5 & 114.305916844033 & 0.194083155967107 \tabularnewline
65 & 113.8 & 114.733661853709 & -0.933661853708713 \tabularnewline
66 & 114.3 & 114.65849592189 & -0.358495921889542 \tabularnewline
67 & 115 & 113.635556271611 & 1.36444372838872 \tabularnewline
68 & 115.4 & 114.941946095987 & 0.4580539040129 \tabularnewline
69 & 115.3 & 116.074467869488 & -0.774467869487779 \tabularnewline
70 & 114.9 & 115.770681998778 & -0.870681998777584 \tabularnewline
71 & 114.3 & 115.879046297837 & -1.57904629783695 \tabularnewline
72 & 114.5 & 115.046881460982 & -0.546881460982434 \tabularnewline
73 & 115.5 & 115.546410948045 & -0.0464109480452777 \tabularnewline
74 & 115.8 & 115.976381221242 & -0.176381221242366 \tabularnewline
75 & 115.8 & 115.55789059433 & 0.242109405670462 \tabularnewline
76 & 116 & 115.639686923577 & 0.360313076422969 \tabularnewline
77 & 114.9 & 115.837633494259 & -0.937633494259103 \tabularnewline
78 & 114.1 & 115.851280272102 & -1.75128027210195 \tabularnewline
79 & 114.1 & 114.505577159086 & -0.405577159085951 \tabularnewline
80 & 113.5 & 114.665995808558 & -1.16599580855785 \tabularnewline
81 & 115 & 114.620270602632 & 0.379729397367583 \tabularnewline
82 & 114.7 & 114.741557862007 & -0.0415578620067407 \tabularnewline
83 & 115.4 & 114.989868547939 & 0.410131452061378 \tabularnewline
84 & 116.1 & 115.287377959417 & 0.812622040583463 \tabularnewline
85 & 116.6 & 116.513909206286 & 0.086090793713538 \tabularnewline
86 & 117.2 & 116.942528942391 & 0.257471057608697 \tabularnewline
87 & 118.2 & 116.801107596592 & 1.3988924034081 \tabularnewline
88 & 118 & 117.45059152539 & 0.549408474609749 \tabularnewline
89 & 117.7 & 117.427539735712 & 0.272460264288 \tabularnewline
90 & 118.5 & 117.837184761774 & 0.662815238226457 \tabularnewline
91 & 117.5 & 118.0028790488 & -0.502879048800438 \tabularnewline
92 & 118 & 117.986871727855 & 0.0131282721451811 \tabularnewline
93 & 117.7 & 118.929874482049 & -1.22987448204903 \tabularnewline
94 & 116.3 & 118.229694870561 & -1.92969487056135 \tabularnewline
95 & 115 & 117.699947640108 & -2.69994764010795 \tabularnewline
96 & 115.7 & 116.579745238994 & -0.879745238994346 \tabularnewline
97 & 113.6 & 116.770636252919 & -3.17063625291927 \tabularnewline
98 & 114.8 & 115.581314731849 & -0.781314731849307 \tabularnewline
99 & 114.9 & 115.086418005956 & -0.186418005955801 \tabularnewline
100 & 117.3 & 114.63146569634 & 2.6685343036597 \tabularnewline
101 & 117.3 & 115.429640475063 & 1.87035952493707 \tabularnewline
102 & 117.7 & 116.594268402684 & 1.10573159731594 \tabularnewline
103 & 120 & 116.608443142467 & 3.39155685753283 \tabularnewline
104 & 119.6 & 118.534104899392 & 1.06589510060813 \tabularnewline
105 & 119.2 & 119.673339350611 & -0.473339350611042 \tabularnewline
106 & 117.3 & 119.167857753131 & -1.86785775313113 \tabularnewline
107 & 117.5 & 118.487439828374 & -0.987439828373653 \tabularnewline
108 & 119 & 118.634317659878 & 0.365682340122206 \tabularnewline
109 & 112.5 & 118.925379233257 & -6.42537923325713 \tabularnewline
110 & 118.9 & 116.747474315413 & 2.15252568458691 \tabularnewline
111 & 118.4 & 117.826308648487 & 0.573691351513247 \tabularnewline
112 & 119.4 & 118.455033940191 & 0.944966059809346 \tabularnewline
113 & 120.6 & 118.274218117927 & 2.32578188207302 \tabularnewline
114 & 118.6 & 119.50920915679 & -0.90920915679034 \tabularnewline
115 & 122 & 119.105448026496 & 2.89455197350368 \tabularnewline
116 & 122.6 & 120.244350592489 & 2.35564940751112 \tabularnewline
117 & 120.6 & 121.649463464064 & -1.04946346406393 \tabularnewline
118 & 117.4 & 120.549107749505 & -3.14910774950516 \tabularnewline
119 & 116.4 & 119.45294793779 & -3.05294793778954 \tabularnewline
120 & 122.2 & 118.890769890221 & 3.30923010977887 \tabularnewline
121 & 121 & 118.985725367809 & 2.0142746321908 \tabularnewline
122 & 122.4 & 122.956510778649 & -0.556510778649113 \tabularnewline
123 & 124.9 & 122.466889544522 & 2.43311045547841 \tabularnewline
124 & 126.1 & 124.182153091154 & 1.91784690884626 \tabularnewline
125 & 124.5 & 124.918839042663 & -0.418839042662526 \tabularnewline
126 & 123.2 & 124.181328363591 & -0.98132836359052 \tabularnewline
127 & 126.4 & 124.72386759369 & 1.67613240630983 \tabularnewline
128 & 123.9 & 125.227352922982 & -1.32735292298221 \tabularnewline
129 & 116 & 124.097796518375 & -8.09779651837529 \tabularnewline
130 & 126.6 & 119.062555345207 & 7.53744465479305 \tabularnewline
131 & 125.9 & 123.131029348811 & 2.76897065118931 \tabularnewline
132 & 126.6 & 126.98381809783 & -0.383818097830272 \tabularnewline
133 & 116.7 & 125.123364145429 & -8.4233641454286 \tabularnewline
134 & 126.4 & 123.476407044305 & 2.92359295569538 \tabularnewline
135 & 129 & 125.334450134405 & 3.66554986559495 \tabularnewline
136 & 128.7 & 127.527159429278 & 1.17284057072166 \tabularnewline
137 & 128.4 & 127.36374354723 & 1.03625645277029 \tabularnewline
138 & 129.2 & 127.209265103767 & 1.99073489623281 \tabularnewline
139 & 133.3 & 129.856224917405 & 3.44377508259512 \tabularnewline
140 & 128.9 & 130.59166805435 & -1.69166805435049 \tabularnewline
141 & 132.7 & 127.792655590743 & 4.90734440925677 \tabularnewline
142 & 127.7 & 132.891717955207 & -5.19171795520701 \tabularnewline
143 & 131.8 & 129.894535160677 & 1.90546483932343 \tabularnewline
144 & 133.9 & 132.680453160381 & 1.21954683961874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117218&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]13[/C][C]104.6[/C][C]100.866372863248[/C][C]3.73362713675211[/C][/ROW]
[ROW][C]14[/C][C]104.5[/C][C]102.59719311511[/C][C]1.90280688489034[/C][/ROW]
[ROW][C]15[/C][C]104.5[/C][C]103.588221706064[/C][C]0.911778293936095[/C][/ROW]
[ROW][C]16[/C][C]105.6[/C][C]105.28951035431[/C][C]0.310489645690467[/C][/ROW]
[ROW][C]17[/C][C]106.1[/C][C]106.201802622241[/C][C]-0.101802622241209[/C][/ROW]
[ROW][C]18[/C][C]107.6[/C][C]107.96679177485[/C][C]-0.36679177484973[/C][/ROW]
[ROW][C]19[/C][C]107.7[/C][C]108.300318080196[/C][C]-0.600318080196118[/C][/ROW]
[ROW][C]20[/C][C]108.3[/C][C]107.792880120374[/C][C]0.507119879625762[/C][/ROW]
[ROW][C]21[/C][C]108.1[/C][C]108.656515365481[/C][C]-0.556515365480948[/C][/ROW]
[ROW][C]22[/C][C]108.1[/C][C]109.214943165044[/C][C]-1.11494316504383[/C][/ROW]
[ROW][C]23[/C][C]108[/C][C]110.007719076527[/C][C]-2.00771907652695[/C][/ROW]
[ROW][C]24[/C][C]108.2[/C][C]109.062333575723[/C][C]-0.862333575722971[/C][/ROW]
[ROW][C]25[/C][C]108.9[/C][C]110.452409464733[/C][C]-1.55240946473307[/C][/ROW]
[ROW][C]26[/C][C]109.8[/C][C]109.189757248796[/C][C]0.610242751203941[/C][/ROW]
[ROW][C]27[/C][C]109.9[/C][C]109.260223425464[/C][C]0.639776574535659[/C][/ROW]
[ROW][C]28[/C][C]109.8[/C][C]110.612679245938[/C][C]-0.812679245938483[/C][/ROW]
[ROW][C]29[/C][C]110.9[/C][C]110.799053378796[/C][C]0.100946621203605[/C][/ROW]
[ROW][C]30[/C][C]111.1[/C][C]112.505275379692[/C][C]-1.40527537969247[/C][/ROW]
[ROW][C]31[/C][C]112.2[/C][C]112.182121941232[/C][C]0.0178780587679768[/C][/ROW]
[ROW][C]32[/C][C]112.7[/C][C]112.128606556148[/C][C]0.571393443851562[/C][/ROW]
[ROW][C]33[/C][C]114.6[/C][C]112.674890391988[/C][C]1.92510960801241[/C][/ROW]
[ROW][C]34[/C][C]114.2[/C][C]114.22040036075[/C][C]-0.0204003607499317[/C][/ROW]
[ROW][C]35[/C][C]114.7[/C][C]115.280218126212[/C][C]-0.580218126212301[/C][/ROW]
[ROW][C]36[/C][C]114.7[/C][C]115.260096277888[/C][C]-0.56009627788778[/C][/ROW]
[ROW][C]37[/C][C]116[/C][C]116.615325403069[/C][C]-0.615325403068979[/C][/ROW]
[ROW][C]38[/C][C]116.3[/C][C]116.302880177104[/C][C]-0.00288017710359156[/C][/ROW]
[ROW][C]39[/C][C]116.4[/C][C]116.077750328174[/C][C]0.322249671825645[/C][/ROW]
[ROW][C]40[/C][C]116.6[/C][C]116.922390418307[/C][C]-0.322390418306753[/C][/ROW]
[ROW][C]41[/C][C]118.1[/C][C]117.547062078164[/C][C]0.552937921835962[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]119.112722087452[/C][C]-1.91272208745173[/C][/ROW]
[ROW][C]43[/C][C]108.3[/C][C]118.872871602086[/C][C]-10.5728716020862[/C][/ROW]
[ROW][C]44[/C][C]109.5[/C][C]113.748227723022[/C][C]-4.24822772302151[/C][/ROW]
[ROW][C]45[/C][C]110.5[/C][C]112.076485986073[/C][C]-1.57648598607256[/C][/ROW]
[ROW][C]46[/C][C]110.6[/C][C]111.20169625179[/C][C]-0.601696251790017[/C][/ROW]
[ROW][C]47[/C][C]111.2[/C][C]111.543684499657[/C][C]-0.343684499657016[/C][/ROW]
[ROW][C]48[/C][C]111.1[/C][C]111.333684751072[/C][C]-0.233684751071877[/C][/ROW]
[ROW][C]49[/C][C]111[/C][C]112.529067115873[/C][C]-1.52906711587259[/C][/ROW]
[ROW][C]50[/C][C]112.4[/C][C]111.610545016562[/C][C]0.789454983438475[/C][/ROW]
[ROW][C]51[/C][C]112.5[/C][C]111.534112729783[/C][C]0.965887270216783[/C][/ROW]
[ROW][C]52[/C][C]112.4[/C][C]112.239301465297[/C][C]0.160698534702945[/C][/ROW]
[ROW][C]53[/C][C]111.8[/C][C]113.012318708271[/C][C]-1.21231870827108[/C][/ROW]
[ROW][C]54[/C][C]111.6[/C][C]112.847956574348[/C][C]-1.247956574348[/C][/ROW]
[ROW][C]55[/C][C]112.9[/C][C]110.588852395158[/C][C]2.31114760484206[/C][/ROW]
[ROW][C]56[/C][C]112.8[/C][C]112.959982607497[/C][C]-0.159982607497184[/C][/ROW]
[ROW][C]57[/C][C]113.7[/C][C]113.814506592089[/C][C]-0.114506592089342[/C][/ROW]
[ROW][C]58[/C][C]113.8[/C][C]113.843680265151[/C][C]-0.0436802651510817[/C][/ROW]
[ROW][C]59[/C][C]114[/C][C]114.500953966612[/C][C]-0.50095396661159[/C][/ROW]
[ROW][C]60[/C][C]113.8[/C][C]114.229516245773[/C][C]-0.429516245772732[/C][/ROW]
[ROW][C]61[/C][C]113.9[/C][C]115.012452048033[/C][C]-1.11245204803343[/C][/ROW]
[ROW][C]62[/C][C]114.4[/C][C]114.826427836268[/C][C]-0.426427836267976[/C][/ROW]
[ROW][C]63[/C][C]114.4[/C][C]114.186214766161[/C][C]0.213785233839161[/C][/ROW]
[ROW][C]64[/C][C]114.5[/C][C]114.305916844033[/C][C]0.194083155967107[/C][/ROW]
[ROW][C]65[/C][C]113.8[/C][C]114.733661853709[/C][C]-0.933661853708713[/C][/ROW]
[ROW][C]66[/C][C]114.3[/C][C]114.65849592189[/C][C]-0.358495921889542[/C][/ROW]
[ROW][C]67[/C][C]115[/C][C]113.635556271611[/C][C]1.36444372838872[/C][/ROW]
[ROW][C]68[/C][C]115.4[/C][C]114.941946095987[/C][C]0.4580539040129[/C][/ROW]
[ROW][C]69[/C][C]115.3[/C][C]116.074467869488[/C][C]-0.774467869487779[/C][/ROW]
[ROW][C]70[/C][C]114.9[/C][C]115.770681998778[/C][C]-0.870681998777584[/C][/ROW]
[ROW][C]71[/C][C]114.3[/C][C]115.879046297837[/C][C]-1.57904629783695[/C][/ROW]
[ROW][C]72[/C][C]114.5[/C][C]115.046881460982[/C][C]-0.546881460982434[/C][/ROW]
[ROW][C]73[/C][C]115.5[/C][C]115.546410948045[/C][C]-0.0464109480452777[/C][/ROW]
[ROW][C]74[/C][C]115.8[/C][C]115.976381221242[/C][C]-0.176381221242366[/C][/ROW]
[ROW][C]75[/C][C]115.8[/C][C]115.55789059433[/C][C]0.242109405670462[/C][/ROW]
[ROW][C]76[/C][C]116[/C][C]115.639686923577[/C][C]0.360313076422969[/C][/ROW]
[ROW][C]77[/C][C]114.9[/C][C]115.837633494259[/C][C]-0.937633494259103[/C][/ROW]
[ROW][C]78[/C][C]114.1[/C][C]115.851280272102[/C][C]-1.75128027210195[/C][/ROW]
[ROW][C]79[/C][C]114.1[/C][C]114.505577159086[/C][C]-0.405577159085951[/C][/ROW]
[ROW][C]80[/C][C]113.5[/C][C]114.665995808558[/C][C]-1.16599580855785[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]114.620270602632[/C][C]0.379729397367583[/C][/ROW]
[ROW][C]82[/C][C]114.7[/C][C]114.741557862007[/C][C]-0.0415578620067407[/C][/ROW]
[ROW][C]83[/C][C]115.4[/C][C]114.989868547939[/C][C]0.410131452061378[/C][/ROW]
[ROW][C]84[/C][C]116.1[/C][C]115.287377959417[/C][C]0.812622040583463[/C][/ROW]
[ROW][C]85[/C][C]116.6[/C][C]116.513909206286[/C][C]0.086090793713538[/C][/ROW]
[ROW][C]86[/C][C]117.2[/C][C]116.942528942391[/C][C]0.257471057608697[/C][/ROW]
[ROW][C]87[/C][C]118.2[/C][C]116.801107596592[/C][C]1.3988924034081[/C][/ROW]
[ROW][C]88[/C][C]118[/C][C]117.45059152539[/C][C]0.549408474609749[/C][/ROW]
[ROW][C]89[/C][C]117.7[/C][C]117.427539735712[/C][C]0.272460264288[/C][/ROW]
[ROW][C]90[/C][C]118.5[/C][C]117.837184761774[/C][C]0.662815238226457[/C][/ROW]
[ROW][C]91[/C][C]117.5[/C][C]118.0028790488[/C][C]-0.502879048800438[/C][/ROW]
[ROW][C]92[/C][C]118[/C][C]117.986871727855[/C][C]0.0131282721451811[/C][/ROW]
[ROW][C]93[/C][C]117.7[/C][C]118.929874482049[/C][C]-1.22987448204903[/C][/ROW]
[ROW][C]94[/C][C]116.3[/C][C]118.229694870561[/C][C]-1.92969487056135[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]117.699947640108[/C][C]-2.69994764010795[/C][/ROW]
[ROW][C]96[/C][C]115.7[/C][C]116.579745238994[/C][C]-0.879745238994346[/C][/ROW]
[ROW][C]97[/C][C]113.6[/C][C]116.770636252919[/C][C]-3.17063625291927[/C][/ROW]
[ROW][C]98[/C][C]114.8[/C][C]115.581314731849[/C][C]-0.781314731849307[/C][/ROW]
[ROW][C]99[/C][C]114.9[/C][C]115.086418005956[/C][C]-0.186418005955801[/C][/ROW]
[ROW][C]100[/C][C]117.3[/C][C]114.63146569634[/C][C]2.6685343036597[/C][/ROW]
[ROW][C]101[/C][C]117.3[/C][C]115.429640475063[/C][C]1.87035952493707[/C][/ROW]
[ROW][C]102[/C][C]117.7[/C][C]116.594268402684[/C][C]1.10573159731594[/C][/ROW]
[ROW][C]103[/C][C]120[/C][C]116.608443142467[/C][C]3.39155685753283[/C][/ROW]
[ROW][C]104[/C][C]119.6[/C][C]118.534104899392[/C][C]1.06589510060813[/C][/ROW]
[ROW][C]105[/C][C]119.2[/C][C]119.673339350611[/C][C]-0.473339350611042[/C][/ROW]
[ROW][C]106[/C][C]117.3[/C][C]119.167857753131[/C][C]-1.86785775313113[/C][/ROW]
[ROW][C]107[/C][C]117.5[/C][C]118.487439828374[/C][C]-0.987439828373653[/C][/ROW]
[ROW][C]108[/C][C]119[/C][C]118.634317659878[/C][C]0.365682340122206[/C][/ROW]
[ROW][C]109[/C][C]112.5[/C][C]118.925379233257[/C][C]-6.42537923325713[/C][/ROW]
[ROW][C]110[/C][C]118.9[/C][C]116.747474315413[/C][C]2.15252568458691[/C][/ROW]
[ROW][C]111[/C][C]118.4[/C][C]117.826308648487[/C][C]0.573691351513247[/C][/ROW]
[ROW][C]112[/C][C]119.4[/C][C]118.455033940191[/C][C]0.944966059809346[/C][/ROW]
[ROW][C]113[/C][C]120.6[/C][C]118.274218117927[/C][C]2.32578188207302[/C][/ROW]
[ROW][C]114[/C][C]118.6[/C][C]119.50920915679[/C][C]-0.90920915679034[/C][/ROW]
[ROW][C]115[/C][C]122[/C][C]119.105448026496[/C][C]2.89455197350368[/C][/ROW]
[ROW][C]116[/C][C]122.6[/C][C]120.244350592489[/C][C]2.35564940751112[/C][/ROW]
[ROW][C]117[/C][C]120.6[/C][C]121.649463464064[/C][C]-1.04946346406393[/C][/ROW]
[ROW][C]118[/C][C]117.4[/C][C]120.549107749505[/C][C]-3.14910774950516[/C][/ROW]
[ROW][C]119[/C][C]116.4[/C][C]119.45294793779[/C][C]-3.05294793778954[/C][/ROW]
[ROW][C]120[/C][C]122.2[/C][C]118.890769890221[/C][C]3.30923010977887[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]118.985725367809[/C][C]2.0142746321908[/C][/ROW]
[ROW][C]122[/C][C]122.4[/C][C]122.956510778649[/C][C]-0.556510778649113[/C][/ROW]
[ROW][C]123[/C][C]124.9[/C][C]122.466889544522[/C][C]2.43311045547841[/C][/ROW]
[ROW][C]124[/C][C]126.1[/C][C]124.182153091154[/C][C]1.91784690884626[/C][/ROW]
[ROW][C]125[/C][C]124.5[/C][C]124.918839042663[/C][C]-0.418839042662526[/C][/ROW]
[ROW][C]126[/C][C]123.2[/C][C]124.181328363591[/C][C]-0.98132836359052[/C][/ROW]
[ROW][C]127[/C][C]126.4[/C][C]124.72386759369[/C][C]1.67613240630983[/C][/ROW]
[ROW][C]128[/C][C]123.9[/C][C]125.227352922982[/C][C]-1.32735292298221[/C][/ROW]
[ROW][C]129[/C][C]116[/C][C]124.097796518375[/C][C]-8.09779651837529[/C][/ROW]
[ROW][C]130[/C][C]126.6[/C][C]119.062555345207[/C][C]7.53744465479305[/C][/ROW]
[ROW][C]131[/C][C]125.9[/C][C]123.131029348811[/C][C]2.76897065118931[/C][/ROW]
[ROW][C]132[/C][C]126.6[/C][C]126.98381809783[/C][C]-0.383818097830272[/C][/ROW]
[ROW][C]133[/C][C]116.7[/C][C]125.123364145429[/C][C]-8.4233641454286[/C][/ROW]
[ROW][C]134[/C][C]126.4[/C][C]123.476407044305[/C][C]2.92359295569538[/C][/ROW]
[ROW][C]135[/C][C]129[/C][C]125.334450134405[/C][C]3.66554986559495[/C][/ROW]
[ROW][C]136[/C][C]128.7[/C][C]127.527159429278[/C][C]1.17284057072166[/C][/ROW]
[ROW][C]137[/C][C]128.4[/C][C]127.36374354723[/C][C]1.03625645277029[/C][/ROW]
[ROW][C]138[/C][C]129.2[/C][C]127.209265103767[/C][C]1.99073489623281[/C][/ROW]
[ROW][C]139[/C][C]133.3[/C][C]129.856224917405[/C][C]3.44377508259512[/C][/ROW]
[ROW][C]140[/C][C]128.9[/C][C]130.59166805435[/C][C]-1.69166805435049[/C][/ROW]
[ROW][C]141[/C][C]132.7[/C][C]127.792655590743[/C][C]4.90734440925677[/C][/ROW]
[ROW][C]142[/C][C]127.7[/C][C]132.891717955207[/C][C]-5.19171795520701[/C][/ROW]
[ROW][C]143[/C][C]131.8[/C][C]129.894535160677[/C][C]1.90546483932343[/C][/ROW]
[ROW][C]144[/C][C]133.9[/C][C]132.680453160381[/C][C]1.21954683961874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117218&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117218&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
13104.6100.8663728632483.73362713675211
14104.5102.597193115111.90280688489034
15104.5103.5882217060640.911778293936095
16105.6105.289510354310.310489645690467
17106.1106.201802622241-0.101802622241209
18107.6107.96679177485-0.36679177484973
19107.7108.300318080196-0.600318080196118
20108.3107.7928801203740.507119879625762
21108.1108.656515365481-0.556515365480948
22108.1109.214943165044-1.11494316504383
23108110.007719076527-2.00771907652695
24108.2109.062333575723-0.862333575722971
25108.9110.452409464733-1.55240946473307
26109.8109.1897572487960.610242751203941
27109.9109.2602234254640.639776574535659
28109.8110.612679245938-0.812679245938483
29110.9110.7990533787960.100946621203605
30111.1112.505275379692-1.40527537969247
31112.2112.1821219412320.0178780587679768
32112.7112.1286065561480.571393443851562
33114.6112.6748903919881.92510960801241
34114.2114.22040036075-0.0204003607499317
35114.7115.280218126212-0.580218126212301
36114.7115.260096277888-0.56009627788778
37116116.615325403069-0.615325403068979
38116.3116.302880177104-0.00288017710359156
39116.4116.0777503281740.322249671825645
40116.6116.922390418307-0.322390418306753
41118.1117.5470620781640.552937921835962
42117.2119.112722087452-1.91272208745173
43108.3118.872871602086-10.5728716020862
44109.5113.748227723022-4.24822772302151
45110.5112.076485986073-1.57648598607256
46110.6111.20169625179-0.601696251790017
47111.2111.543684499657-0.343684499657016
48111.1111.333684751072-0.233684751071877
49111112.529067115873-1.52906711587259
50112.4111.6105450165620.789454983438475
51112.5111.5341127297830.965887270216783
52112.4112.2393014652970.160698534702945
53111.8113.012318708271-1.21231870827108
54111.6112.847956574348-1.247956574348
55112.9110.5888523951582.31114760484206
56112.8112.959982607497-0.159982607497184
57113.7113.814506592089-0.114506592089342
58113.8113.843680265151-0.0436802651510817
59114114.500953966612-0.50095396661159
60113.8114.229516245773-0.429516245772732
61113.9115.012452048033-1.11245204803343
62114.4114.826427836268-0.426427836267976
63114.4114.1862147661610.213785233839161
64114.5114.3059168440330.194083155967107
65113.8114.733661853709-0.933661853708713
66114.3114.65849592189-0.358495921889542
67115113.6355562716111.36444372838872
68115.4114.9419460959870.4580539040129
69115.3116.074467869488-0.774467869487779
70114.9115.770681998778-0.870681998777584
71114.3115.879046297837-1.57904629783695
72114.5115.046881460982-0.546881460982434
73115.5115.546410948045-0.0464109480452777
74115.8115.976381221242-0.176381221242366
75115.8115.557890594330.242109405670462
76116115.6396869235770.360313076422969
77114.9115.837633494259-0.937633494259103
78114.1115.851280272102-1.75128027210195
79114.1114.505577159086-0.405577159085951
80113.5114.665995808558-1.16599580855785
81115114.6202706026320.379729397367583
82114.7114.741557862007-0.0415578620067407
83115.4114.9898685479390.410131452061378
84116.1115.2873779594170.812622040583463
85116.6116.5139092062860.086090793713538
86117.2116.9425289423910.257471057608697
87118.2116.8011075965921.3988924034081
88118117.450591525390.549408474609749
89117.7117.4275397357120.272460264288
90118.5117.8371847617740.662815238226457
91117.5118.0028790488-0.502879048800438
92118117.9868717278550.0131282721451811
93117.7118.929874482049-1.22987448204903
94116.3118.229694870561-1.92969487056135
95115117.699947640108-2.69994764010795
96115.7116.579745238994-0.879745238994346
97113.6116.770636252919-3.17063625291927
98114.8115.581314731849-0.781314731849307
99114.9115.086418005956-0.186418005955801
100117.3114.631465696342.6685343036597
101117.3115.4296404750631.87035952493707
102117.7116.5942684026841.10573159731594
103120116.6084431424673.39155685753283
104119.6118.5341048993921.06589510060813
105119.2119.673339350611-0.473339350611042
106117.3119.167857753131-1.86785775313113
107117.5118.487439828374-0.987439828373653
108119118.6343176598780.365682340122206
109112.5118.925379233257-6.42537923325713
110118.9116.7474743154132.15252568458691
111118.4117.8263086484870.573691351513247
112119.4118.4550339401910.944966059809346
113120.6118.2742181179272.32578188207302
114118.6119.50920915679-0.90920915679034
115122119.1054480264962.89455197350368
116122.6120.2443505924892.35564940751112
117120.6121.649463464064-1.04946346406393
118117.4120.549107749505-3.14910774950516
119116.4119.45294793779-3.05294793778954
120122.2118.8907698902213.30923010977887
121121118.9857253678092.0142746321908
122122.4122.956510778649-0.556510778649113
123124.9122.4668895445222.43311045547841
124126.1124.1821530911541.91784690884626
125124.5124.918839042663-0.418839042662526
126123.2124.181328363591-0.98132836359052
127126.4124.723867593691.67613240630983
128123.9125.227352922982-1.32735292298221
129116124.097796518375-8.09779651837529
130126.6119.0625553452077.53744465479305
131125.9123.1310293488112.76897065118931
132126.6126.98381809783-0.383818097830272
133116.7125.123364145429-8.4233641454286
134126.4123.4764070443052.92359295569538
135129125.3344501344053.66554986559495
136128.7127.5271594292781.17284057072166
137128.4127.363743547231.03625645277029
138129.2127.2092651037671.99073489623281
139133.3129.8562249174053.44377508259512
140128.9130.59166805435-1.69166805435049
141132.7127.7926555907434.90734440925677
142127.7132.891717955207-5.19171795520701
143131.8129.8945351606771.90546483932343
144133.9132.6804531603811.21954683961874






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145129.802650577125125.352785565395134.252515588855
146135.053840765468130.098135759507140.009545771429
147135.890293728639130.447767175451141.332820281826
148135.893796322636129.977971400085141.809621245186
149135.259165491255128.87981472758141.638516254929
150134.938262920505128.102480112309141.774045728701
151137.051894143682129.764792375331144.338995912032
152134.965487249412127.230674435004142.70030006382
153134.561442255238126.381357033234142.741527477243
154134.929821018225126.305977128069143.55366490838
155136.110471645627127.043640156897145.177303134356
156137.84970956157128.340057621961147.359361501179

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 129.802650577125 & 125.352785565395 & 134.252515588855 \tabularnewline
146 & 135.053840765468 & 130.098135759507 & 140.009545771429 \tabularnewline
147 & 135.890293728639 & 130.447767175451 & 141.332820281826 \tabularnewline
148 & 135.893796322636 & 129.977971400085 & 141.809621245186 \tabularnewline
149 & 135.259165491255 & 128.87981472758 & 141.638516254929 \tabularnewline
150 & 134.938262920505 & 128.102480112309 & 141.774045728701 \tabularnewline
151 & 137.051894143682 & 129.764792375331 & 144.338995912032 \tabularnewline
152 & 134.965487249412 & 127.230674435004 & 142.70030006382 \tabularnewline
153 & 134.561442255238 & 126.381357033234 & 142.741527477243 \tabularnewline
154 & 134.929821018225 & 126.305977128069 & 143.55366490838 \tabularnewline
155 & 136.110471645627 & 127.043640156897 & 145.177303134356 \tabularnewline
156 & 137.84970956157 & 128.340057621961 & 147.359361501179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117218&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]129.802650577125[/C][C]125.352785565395[/C][C]134.252515588855[/C][/ROW]
[ROW][C]146[/C][C]135.053840765468[/C][C]130.098135759507[/C][C]140.009545771429[/C][/ROW]
[ROW][C]147[/C][C]135.890293728639[/C][C]130.447767175451[/C][C]141.332820281826[/C][/ROW]
[ROW][C]148[/C][C]135.893796322636[/C][C]129.977971400085[/C][C]141.809621245186[/C][/ROW]
[ROW][C]149[/C][C]135.259165491255[/C][C]128.87981472758[/C][C]141.638516254929[/C][/ROW]
[ROW][C]150[/C][C]134.938262920505[/C][C]128.102480112309[/C][C]141.774045728701[/C][/ROW]
[ROW][C]151[/C][C]137.051894143682[/C][C]129.764792375331[/C][C]144.338995912032[/C][/ROW]
[ROW][C]152[/C][C]134.965487249412[/C][C]127.230674435004[/C][C]142.70030006382[/C][/ROW]
[ROW][C]153[/C][C]134.561442255238[/C][C]126.381357033234[/C][C]142.741527477243[/C][/ROW]
[ROW][C]154[/C][C]134.929821018225[/C][C]126.305977128069[/C][C]143.55366490838[/C][/ROW]
[ROW][C]155[/C][C]136.110471645627[/C][C]127.043640156897[/C][C]145.177303134356[/C][/ROW]
[ROW][C]156[/C][C]137.84970956157[/C][C]128.340057621961[/C][C]147.359361501179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117218&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117218&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
145129.802650577125125.352785565395134.252515588855
146135.053840765468130.098135759507140.009545771429
147135.890293728639130.447767175451141.332820281826
148135.893796322636129.977971400085141.809621245186
149135.259165491255128.87981472758141.638516254929
150134.938262920505128.102480112309141.774045728701
151137.051894143682129.764792375331144.338995912032
152134.965487249412127.230674435004142.70030006382
153134.561442255238126.381357033234142.741527477243
154134.929821018225126.305977128069143.55366490838
155136.110471645627127.043640156897145.177303134356
156137.84970956157128.340057621961147.359361501179


Figures (Output of Computation)

Input Parameters & R Code

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