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 computationFri, 07 Jan 2011 10:21:28 +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/07/t1294395895sib1reesfo9zs9c.htm/, Retrieved Sat, 18 May 2024 06:10:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117292, Retrieved Sat, 18 May 2024 06:10:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact221

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [Katrien Monnens] [2011-01-07 10:21:28] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,6
103,5
110,9
115,8
117,8
121,1
128,7
112,7
111,3
116,2
109,8
137,9
98,7
100,5
128,7
98,7
115
121
125,9
117,3
115
113,8
115,7
145,5
101,7
106,9
116,4
114,6
122,5
120,4
116,5
117
114,3
111,5
117,8
141,5
102,6
103,8
119,8
113,6
121,8
123,9
122,7
120
111,6
117,6
121,3
143,7
107,1
107,7
126,4
111,5
127,9
124,9
122
124,9
113,9
120,8
123,3
143,5
107,1
106,5
114,6
122,2
120,2
123,1
127,1
118,5
116,1
120,6
115,7
146,5
108
106,6
122,2
115,8
115,6
124,5
121,7
118,7
113,7
113,4
115,1
143,9
101
103,4
121,5
111,9
117,4
124,3
122
119,7
115
112,2
115,3
142,6
104,1
105,3
124,4
113,9
124,8
131,8
125,6
125
119,7
116,1
120
148,1
109,2
109,4
135,1
114,9
129
138,5
125,6
130,4
120,3
126,2
127,6
150,9
114,6
118,6
131,4
124,5
136,8
136,8
136,6
131
125,8
129,4
124,8
157,1
116,6
114,2
128,4
127,3
133,5
137,2
137,7
131,2
127,7
133,9
124,3
160,6

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'Herman Ole Andreas Wold' @ www.yougetit.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ www.yougetit.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117292&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]'Herman Ole Andreas Wold' @ www.yougetit.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117292&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117292&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'Herman Ole Andreas Wold' @ www.yougetit.org






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.124782904318567
beta0.0179039618345825
gamma0.642046556523616

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.124782904318567 \tabularnewline
beta & 0.0179039618345825 \tabularnewline
gamma & 0.642046556523616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117292&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]0.124782904318567[/C][/ROW]
[ROW][C]beta[/C][C]0.0179039618345825[/C][/ROW]
[ROW][C]gamma[/C][C]0.642046556523616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117292&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117292&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
alpha0.124782904318567
beta0.0179039618345825
gamma0.642046556523616






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
1398.799.5514792359552-0.851479235955182
14100.5101.186626498397-0.686626498396592
15128.7129.071289161111-0.371289161111491
1698.798.894621967739-0.194621967738982
17115115.043003778345-0.0430037783449251
18121120.4421636377860.557836362214118
19125.9128.64681669265-2.74681669265038
20117.3112.6601358580134.63986414198695
21115111.2335427540043.76645724599564
22113.8116.59693691349-2.79693691349004
23115.7110.6356887457865.06431125421422
24145.5139.9028540933385.59714590666169
25101.7100.2725993214411.42740067855866
26106.9102.3396449472424.56035505275817
27116.4131.719787717374-15.319787717374
28114.699.55889567050615.0411043294939
29122.5118.1890157469144.31098425308571
30120.4124.722408429877-4.32240842987672
31116.5130.6914079712-14.1914079712002
32117117.249337309124-0.249337309124485
33114.3114.702800798375-0.402800798375239
34111.5115.871898419633-4.37189841963331
35117.8114.1150001154753.68499988452494
36141.5143.614551806-2.1145518059997
37102.6100.7854123150671.81458768493341
38103.8104.629414267297-0.829414267296826
39119.8121.857412789078-2.05741278907756
40113.6108.2137211712335.38627882876717
41121.8119.4769053621892.32309463781097
42123.9120.8587767340043.04122326599558
43122.7122.0749080449930.625091955007392
44120118.2951457974961.70485420250371
45111.6115.895519604463-4.29551960446308
46117.6114.3409822430723.2590177569281
47121.3118.1405908283973.15940917160302
48143.7144.766002801597-1.0660028015966
49107.1103.5987287872323.50127121276817
50107.7106.239902713521.46009728647982
51126.4123.4807527879332.91924721206662
52111.5114.410825733881-2.91082573388114
53127.9123.1157002337354.78429976626526
54124.9125.273425080016-0.373425080016062
55122124.72602874031-2.7260287403101
56124.9121.1053062272683.79469377273169
57113.9115.502693278039-1.60269327803894
58120.8118.6220610843782.17793891562235
59123.3122.3212723393870.978727660613458
60143.5146.752824609563-3.25282460956251
61107.1107.271661800121-0.17166180012147
62106.5108.328972168201-1.82897216820149
63114.6126.121282809906-11.5212828099064
64122.2112.02442892288810.1755710771124
65120.2126.782278955051-6.58227895505138
66123.1124.60708749814-1.50708749814044
67127.1122.5778188633494.52218113665064
68118.5123.511011359969-5.01101135996871
69116.1113.8112946491212.28870535087886
70120.6119.506453023231.09354697676989
71115.7122.365314346164-6.66531434616431
72146.5143.1571715127873.34282848721301
73108106.4533865082661.5466134917344
74106.6106.773525126446-0.173525126445867
75122.2119.1983750247673.001624975233
76115.8119.050635154009-3.25063515400946
77115.6122.663658891911-7.0636588919115
78124.5123.2553017944531.24469820554653
79121.7124.93180113119-3.23180113119049
80118.7119.544149057245-0.844149057245218
81113.7114.470922408307-0.770922408307456
82113.4119.044535840836-5.6445358408355
83115.1116.663351260973-1.56335126097279
84143.9143.3685264176070.531473582393147
85101105.800564697147-4.80056469714739
86103.4104.335173676482-0.935173676482023
87121.5118.0661952417683.4338047582316
88111.9114.494917880283-2.59491788028278
89117.4115.9268992151971.47310078480349
90124.3122.1296343269752.17036567302506
91122121.3831313632540.616868636745693
92119.7117.8322492167031.86775078329663
93115113.1601545903551.8398454096447
94112.2115.283096800987-3.08309680098731
95115.3115.507571867246-0.207571867245747
96142.6143.530933651912-0.93093365191217
97104.1102.8554384261741.24456157382554
98105.3104.3321219534690.967878046530643
99124.4120.8844124638253.51558753617505
100113.9113.8916062790260.00839372097395596
101124.8117.9972240790096.80277592099112
102131.8125.3942684536296.40573154637129
103125.6124.3079995253091.2920004746906
104125121.5228631710563.47713682894432
105119.7116.9624790256642.73752097433604
106116.1116.450764964515-0.350764964514937
107120118.7505597477771.24944025222312
108148.1147.4694024013540.630597598646062
109109.2106.9830316533512.2169683466487
110109.4108.5222928795910.877707120408928
111135.1127.1728998229967.9271001770043
112114.9118.459370337954-3.55937033795384
113129126.2627560827152.73724391728493
114138.5133.1685085086435.33149149135727
115125.6128.97991129073-3.37991129073023
116130.4126.8298265165943.57017348340644
117120.3121.755316356364-1.45531635636365
118126.2118.9567199914377.24328000856278
119127.6123.253367028444.34663297156024
120150.9153.067323096253-2.16732309625272
121114.6111.8528760791322.74712392086769
122118.6112.7694930320745.83050696792559
123131.4136.934545576043-5.53454557604331
124124.5119.6712888306934.82871116930671
125136.8132.545708970724.25429102928027
126136.8141.456402244201-4.65640224420054
127136.6130.8708872399335.72911276006693
128131133.900169820853-2.90016982085334
129125.8124.9608801531770.839119846823039
130129.4127.4342482649911.96575173500862
131124.8129.523521267203-4.72352126720308
132157.1155.1102300520311.98976994796917
133116.6116.240929778260.359070221740154
134114.2118.639110975567-4.43911097556681
135128.4135.305651392091-6.90565139209136
136127.3123.5666822805283.73331771947244
137133.5136.061330560736-2.56133056073585
138137.2139.083020496537-1.88302049653663
139137.7134.6231882461963.07681175380372
140131.2132.437914391599-1.23791439159888
141127.7125.7667408999791.93325910002123
142133.9128.9989944312384.90100556876195
143124.3127.657601896051-3.35760189605145
144160.6157.399412392843.20058760715978

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 98.7 & 99.5514792359552 & -0.851479235955182 \tabularnewline
14 & 100.5 & 101.186626498397 & -0.686626498396592 \tabularnewline
15 & 128.7 & 129.071289161111 & -0.371289161111491 \tabularnewline
16 & 98.7 & 98.894621967739 & -0.194621967738982 \tabularnewline
17 & 115 & 115.043003778345 & -0.0430037783449251 \tabularnewline
18 & 121 & 120.442163637786 & 0.557836362214118 \tabularnewline
19 & 125.9 & 128.64681669265 & -2.74681669265038 \tabularnewline
20 & 117.3 & 112.660135858013 & 4.63986414198695 \tabularnewline
21 & 115 & 111.233542754004 & 3.76645724599564 \tabularnewline
22 & 113.8 & 116.59693691349 & -2.79693691349004 \tabularnewline
23 & 115.7 & 110.635688745786 & 5.06431125421422 \tabularnewline
24 & 145.5 & 139.902854093338 & 5.59714590666169 \tabularnewline
25 & 101.7 & 100.272599321441 & 1.42740067855866 \tabularnewline
26 & 106.9 & 102.339644947242 & 4.56035505275817 \tabularnewline
27 & 116.4 & 131.719787717374 & -15.319787717374 \tabularnewline
28 & 114.6 & 99.558895670506 & 15.0411043294939 \tabularnewline
29 & 122.5 & 118.189015746914 & 4.31098425308571 \tabularnewline
30 & 120.4 & 124.722408429877 & -4.32240842987672 \tabularnewline
31 & 116.5 & 130.6914079712 & -14.1914079712002 \tabularnewline
32 & 117 & 117.249337309124 & -0.249337309124485 \tabularnewline
33 & 114.3 & 114.702800798375 & -0.402800798375239 \tabularnewline
34 & 111.5 & 115.871898419633 & -4.37189841963331 \tabularnewline
35 & 117.8 & 114.115000115475 & 3.68499988452494 \tabularnewline
36 & 141.5 & 143.614551806 & -2.1145518059997 \tabularnewline
37 & 102.6 & 100.785412315067 & 1.81458768493341 \tabularnewline
38 & 103.8 & 104.629414267297 & -0.829414267296826 \tabularnewline
39 & 119.8 & 121.857412789078 & -2.05741278907756 \tabularnewline
40 & 113.6 & 108.213721171233 & 5.38627882876717 \tabularnewline
41 & 121.8 & 119.476905362189 & 2.32309463781097 \tabularnewline
42 & 123.9 & 120.858776734004 & 3.04122326599558 \tabularnewline
43 & 122.7 & 122.074908044993 & 0.625091955007392 \tabularnewline
44 & 120 & 118.295145797496 & 1.70485420250371 \tabularnewline
45 & 111.6 & 115.895519604463 & -4.29551960446308 \tabularnewline
46 & 117.6 & 114.340982243072 & 3.2590177569281 \tabularnewline
47 & 121.3 & 118.140590828397 & 3.15940917160302 \tabularnewline
48 & 143.7 & 144.766002801597 & -1.0660028015966 \tabularnewline
49 & 107.1 & 103.598728787232 & 3.50127121276817 \tabularnewline
50 & 107.7 & 106.23990271352 & 1.46009728647982 \tabularnewline
51 & 126.4 & 123.480752787933 & 2.91924721206662 \tabularnewline
52 & 111.5 & 114.410825733881 & -2.91082573388114 \tabularnewline
53 & 127.9 & 123.115700233735 & 4.78429976626526 \tabularnewline
54 & 124.9 & 125.273425080016 & -0.373425080016062 \tabularnewline
55 & 122 & 124.72602874031 & -2.7260287403101 \tabularnewline
56 & 124.9 & 121.105306227268 & 3.79469377273169 \tabularnewline
57 & 113.9 & 115.502693278039 & -1.60269327803894 \tabularnewline
58 & 120.8 & 118.622061084378 & 2.17793891562235 \tabularnewline
59 & 123.3 & 122.321272339387 & 0.978727660613458 \tabularnewline
60 & 143.5 & 146.752824609563 & -3.25282460956251 \tabularnewline
61 & 107.1 & 107.271661800121 & -0.17166180012147 \tabularnewline
62 & 106.5 & 108.328972168201 & -1.82897216820149 \tabularnewline
63 & 114.6 & 126.121282809906 & -11.5212828099064 \tabularnewline
64 & 122.2 & 112.024428922888 & 10.1755710771124 \tabularnewline
65 & 120.2 & 126.782278955051 & -6.58227895505138 \tabularnewline
66 & 123.1 & 124.60708749814 & -1.50708749814044 \tabularnewline
67 & 127.1 & 122.577818863349 & 4.52218113665064 \tabularnewline
68 & 118.5 & 123.511011359969 & -5.01101135996871 \tabularnewline
69 & 116.1 & 113.811294649121 & 2.28870535087886 \tabularnewline
70 & 120.6 & 119.50645302323 & 1.09354697676989 \tabularnewline
71 & 115.7 & 122.365314346164 & -6.66531434616431 \tabularnewline
72 & 146.5 & 143.157171512787 & 3.34282848721301 \tabularnewline
73 & 108 & 106.453386508266 & 1.5466134917344 \tabularnewline
74 & 106.6 & 106.773525126446 & -0.173525126445867 \tabularnewline
75 & 122.2 & 119.198375024767 & 3.001624975233 \tabularnewline
76 & 115.8 & 119.050635154009 & -3.25063515400946 \tabularnewline
77 & 115.6 & 122.663658891911 & -7.0636588919115 \tabularnewline
78 & 124.5 & 123.255301794453 & 1.24469820554653 \tabularnewline
79 & 121.7 & 124.93180113119 & -3.23180113119049 \tabularnewline
80 & 118.7 & 119.544149057245 & -0.844149057245218 \tabularnewline
81 & 113.7 & 114.470922408307 & -0.770922408307456 \tabularnewline
82 & 113.4 & 119.044535840836 & -5.6445358408355 \tabularnewline
83 & 115.1 & 116.663351260973 & -1.56335126097279 \tabularnewline
84 & 143.9 & 143.368526417607 & 0.531473582393147 \tabularnewline
85 & 101 & 105.800564697147 & -4.80056469714739 \tabularnewline
86 & 103.4 & 104.335173676482 & -0.935173676482023 \tabularnewline
87 & 121.5 & 118.066195241768 & 3.4338047582316 \tabularnewline
88 & 111.9 & 114.494917880283 & -2.59491788028278 \tabularnewline
89 & 117.4 & 115.926899215197 & 1.47310078480349 \tabularnewline
90 & 124.3 & 122.129634326975 & 2.17036567302506 \tabularnewline
91 & 122 & 121.383131363254 & 0.616868636745693 \tabularnewline
92 & 119.7 & 117.832249216703 & 1.86775078329663 \tabularnewline
93 & 115 & 113.160154590355 & 1.8398454096447 \tabularnewline
94 & 112.2 & 115.283096800987 & -3.08309680098731 \tabularnewline
95 & 115.3 & 115.507571867246 & -0.207571867245747 \tabularnewline
96 & 142.6 & 143.530933651912 & -0.93093365191217 \tabularnewline
97 & 104.1 & 102.855438426174 & 1.24456157382554 \tabularnewline
98 & 105.3 & 104.332121953469 & 0.967878046530643 \tabularnewline
99 & 124.4 & 120.884412463825 & 3.51558753617505 \tabularnewline
100 & 113.9 & 113.891606279026 & 0.00839372097395596 \tabularnewline
101 & 124.8 & 117.997224079009 & 6.80277592099112 \tabularnewline
102 & 131.8 & 125.394268453629 & 6.40573154637129 \tabularnewline
103 & 125.6 & 124.307999525309 & 1.2920004746906 \tabularnewline
104 & 125 & 121.522863171056 & 3.47713682894432 \tabularnewline
105 & 119.7 & 116.962479025664 & 2.73752097433604 \tabularnewline
106 & 116.1 & 116.450764964515 & -0.350764964514937 \tabularnewline
107 & 120 & 118.750559747777 & 1.24944025222312 \tabularnewline
108 & 148.1 & 147.469402401354 & 0.630597598646062 \tabularnewline
109 & 109.2 & 106.983031653351 & 2.2169683466487 \tabularnewline
110 & 109.4 & 108.522292879591 & 0.877707120408928 \tabularnewline
111 & 135.1 & 127.172899822996 & 7.9271001770043 \tabularnewline
112 & 114.9 & 118.459370337954 & -3.55937033795384 \tabularnewline
113 & 129 & 126.262756082715 & 2.73724391728493 \tabularnewline
114 & 138.5 & 133.168508508643 & 5.33149149135727 \tabularnewline
115 & 125.6 & 128.97991129073 & -3.37991129073023 \tabularnewline
116 & 130.4 & 126.829826516594 & 3.57017348340644 \tabularnewline
117 & 120.3 & 121.755316356364 & -1.45531635636365 \tabularnewline
118 & 126.2 & 118.956719991437 & 7.24328000856278 \tabularnewline
119 & 127.6 & 123.25336702844 & 4.34663297156024 \tabularnewline
120 & 150.9 & 153.067323096253 & -2.16732309625272 \tabularnewline
121 & 114.6 & 111.852876079132 & 2.74712392086769 \tabularnewline
122 & 118.6 & 112.769493032074 & 5.83050696792559 \tabularnewline
123 & 131.4 & 136.934545576043 & -5.53454557604331 \tabularnewline
124 & 124.5 & 119.671288830693 & 4.82871116930671 \tabularnewline
125 & 136.8 & 132.54570897072 & 4.25429102928027 \tabularnewline
126 & 136.8 & 141.456402244201 & -4.65640224420054 \tabularnewline
127 & 136.6 & 130.870887239933 & 5.72911276006693 \tabularnewline
128 & 131 & 133.900169820853 & -2.90016982085334 \tabularnewline
129 & 125.8 & 124.960880153177 & 0.839119846823039 \tabularnewline
130 & 129.4 & 127.434248264991 & 1.96575173500862 \tabularnewline
131 & 124.8 & 129.523521267203 & -4.72352126720308 \tabularnewline
132 & 157.1 & 155.110230052031 & 1.98976994796917 \tabularnewline
133 & 116.6 & 116.24092977826 & 0.359070221740154 \tabularnewline
134 & 114.2 & 118.639110975567 & -4.43911097556681 \tabularnewline
135 & 128.4 & 135.305651392091 & -6.90565139209136 \tabularnewline
136 & 127.3 & 123.566682280528 & 3.73331771947244 \tabularnewline
137 & 133.5 & 136.061330560736 & -2.56133056073585 \tabularnewline
138 & 137.2 & 139.083020496537 & -1.88302049653663 \tabularnewline
139 & 137.7 & 134.623188246196 & 3.07681175380372 \tabularnewline
140 & 131.2 & 132.437914391599 & -1.23791439159888 \tabularnewline
141 & 127.7 & 125.766740899979 & 1.93325910002123 \tabularnewline
142 & 133.9 & 128.998994431238 & 4.90100556876195 \tabularnewline
143 & 124.3 & 127.657601896051 & -3.35760189605145 \tabularnewline
144 & 160.6 & 157.39941239284 & 3.20058760715978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117292&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]98.7[/C][C]99.5514792359552[/C][C]-0.851479235955182[/C][/ROW]
[ROW][C]14[/C][C]100.5[/C][C]101.186626498397[/C][C]-0.686626498396592[/C][/ROW]
[ROW][C]15[/C][C]128.7[/C][C]129.071289161111[/C][C]-0.371289161111491[/C][/ROW]
[ROW][C]16[/C][C]98.7[/C][C]98.894621967739[/C][C]-0.194621967738982[/C][/ROW]
[ROW][C]17[/C][C]115[/C][C]115.043003778345[/C][C]-0.0430037783449251[/C][/ROW]
[ROW][C]18[/C][C]121[/C][C]120.442163637786[/C][C]0.557836362214118[/C][/ROW]
[ROW][C]19[/C][C]125.9[/C][C]128.64681669265[/C][C]-2.74681669265038[/C][/ROW]
[ROW][C]20[/C][C]117.3[/C][C]112.660135858013[/C][C]4.63986414198695[/C][/ROW]
[ROW][C]21[/C][C]115[/C][C]111.233542754004[/C][C]3.76645724599564[/C][/ROW]
[ROW][C]22[/C][C]113.8[/C][C]116.59693691349[/C][C]-2.79693691349004[/C][/ROW]
[ROW][C]23[/C][C]115.7[/C][C]110.635688745786[/C][C]5.06431125421422[/C][/ROW]
[ROW][C]24[/C][C]145.5[/C][C]139.902854093338[/C][C]5.59714590666169[/C][/ROW]
[ROW][C]25[/C][C]101.7[/C][C]100.272599321441[/C][C]1.42740067855866[/C][/ROW]
[ROW][C]26[/C][C]106.9[/C][C]102.339644947242[/C][C]4.56035505275817[/C][/ROW]
[ROW][C]27[/C][C]116.4[/C][C]131.719787717374[/C][C]-15.319787717374[/C][/ROW]
[ROW][C]28[/C][C]114.6[/C][C]99.558895670506[/C][C]15.0411043294939[/C][/ROW]
[ROW][C]29[/C][C]122.5[/C][C]118.189015746914[/C][C]4.31098425308571[/C][/ROW]
[ROW][C]30[/C][C]120.4[/C][C]124.722408429877[/C][C]-4.32240842987672[/C][/ROW]
[ROW][C]31[/C][C]116.5[/C][C]130.6914079712[/C][C]-14.1914079712002[/C][/ROW]
[ROW][C]32[/C][C]117[/C][C]117.249337309124[/C][C]-0.249337309124485[/C][/ROW]
[ROW][C]33[/C][C]114.3[/C][C]114.702800798375[/C][C]-0.402800798375239[/C][/ROW]
[ROW][C]34[/C][C]111.5[/C][C]115.871898419633[/C][C]-4.37189841963331[/C][/ROW]
[ROW][C]35[/C][C]117.8[/C][C]114.115000115475[/C][C]3.68499988452494[/C][/ROW]
[ROW][C]36[/C][C]141.5[/C][C]143.614551806[/C][C]-2.1145518059997[/C][/ROW]
[ROW][C]37[/C][C]102.6[/C][C]100.785412315067[/C][C]1.81458768493341[/C][/ROW]
[ROW][C]38[/C][C]103.8[/C][C]104.629414267297[/C][C]-0.829414267296826[/C][/ROW]
[ROW][C]39[/C][C]119.8[/C][C]121.857412789078[/C][C]-2.05741278907756[/C][/ROW]
[ROW][C]40[/C][C]113.6[/C][C]108.213721171233[/C][C]5.38627882876717[/C][/ROW]
[ROW][C]41[/C][C]121.8[/C][C]119.476905362189[/C][C]2.32309463781097[/C][/ROW]
[ROW][C]42[/C][C]123.9[/C][C]120.858776734004[/C][C]3.04122326599558[/C][/ROW]
[ROW][C]43[/C][C]122.7[/C][C]122.074908044993[/C][C]0.625091955007392[/C][/ROW]
[ROW][C]44[/C][C]120[/C][C]118.295145797496[/C][C]1.70485420250371[/C][/ROW]
[ROW][C]45[/C][C]111.6[/C][C]115.895519604463[/C][C]-4.29551960446308[/C][/ROW]
[ROW][C]46[/C][C]117.6[/C][C]114.340982243072[/C][C]3.2590177569281[/C][/ROW]
[ROW][C]47[/C][C]121.3[/C][C]118.140590828397[/C][C]3.15940917160302[/C][/ROW]
[ROW][C]48[/C][C]143.7[/C][C]144.766002801597[/C][C]-1.0660028015966[/C][/ROW]
[ROW][C]49[/C][C]107.1[/C][C]103.598728787232[/C][C]3.50127121276817[/C][/ROW]
[ROW][C]50[/C][C]107.7[/C][C]106.23990271352[/C][C]1.46009728647982[/C][/ROW]
[ROW][C]51[/C][C]126.4[/C][C]123.480752787933[/C][C]2.91924721206662[/C][/ROW]
[ROW][C]52[/C][C]111.5[/C][C]114.410825733881[/C][C]-2.91082573388114[/C][/ROW]
[ROW][C]53[/C][C]127.9[/C][C]123.115700233735[/C][C]4.78429976626526[/C][/ROW]
[ROW][C]54[/C][C]124.9[/C][C]125.273425080016[/C][C]-0.373425080016062[/C][/ROW]
[ROW][C]55[/C][C]122[/C][C]124.72602874031[/C][C]-2.7260287403101[/C][/ROW]
[ROW][C]56[/C][C]124.9[/C][C]121.105306227268[/C][C]3.79469377273169[/C][/ROW]
[ROW][C]57[/C][C]113.9[/C][C]115.502693278039[/C][C]-1.60269327803894[/C][/ROW]
[ROW][C]58[/C][C]120.8[/C][C]118.622061084378[/C][C]2.17793891562235[/C][/ROW]
[ROW][C]59[/C][C]123.3[/C][C]122.321272339387[/C][C]0.978727660613458[/C][/ROW]
[ROW][C]60[/C][C]143.5[/C][C]146.752824609563[/C][C]-3.25282460956251[/C][/ROW]
[ROW][C]61[/C][C]107.1[/C][C]107.271661800121[/C][C]-0.17166180012147[/C][/ROW]
[ROW][C]62[/C][C]106.5[/C][C]108.328972168201[/C][C]-1.82897216820149[/C][/ROW]
[ROW][C]63[/C][C]114.6[/C][C]126.121282809906[/C][C]-11.5212828099064[/C][/ROW]
[ROW][C]64[/C][C]122.2[/C][C]112.024428922888[/C][C]10.1755710771124[/C][/ROW]
[ROW][C]65[/C][C]120.2[/C][C]126.782278955051[/C][C]-6.58227895505138[/C][/ROW]
[ROW][C]66[/C][C]123.1[/C][C]124.60708749814[/C][C]-1.50708749814044[/C][/ROW]
[ROW][C]67[/C][C]127.1[/C][C]122.577818863349[/C][C]4.52218113665064[/C][/ROW]
[ROW][C]68[/C][C]118.5[/C][C]123.511011359969[/C][C]-5.01101135996871[/C][/ROW]
[ROW][C]69[/C][C]116.1[/C][C]113.811294649121[/C][C]2.28870535087886[/C][/ROW]
[ROW][C]70[/C][C]120.6[/C][C]119.50645302323[/C][C]1.09354697676989[/C][/ROW]
[ROW][C]71[/C][C]115.7[/C][C]122.365314346164[/C][C]-6.66531434616431[/C][/ROW]
[ROW][C]72[/C][C]146.5[/C][C]143.157171512787[/C][C]3.34282848721301[/C][/ROW]
[ROW][C]73[/C][C]108[/C][C]106.453386508266[/C][C]1.5466134917344[/C][/ROW]
[ROW][C]74[/C][C]106.6[/C][C]106.773525126446[/C][C]-0.173525126445867[/C][/ROW]
[ROW][C]75[/C][C]122.2[/C][C]119.198375024767[/C][C]3.001624975233[/C][/ROW]
[ROW][C]76[/C][C]115.8[/C][C]119.050635154009[/C][C]-3.25063515400946[/C][/ROW]
[ROW][C]77[/C][C]115.6[/C][C]122.663658891911[/C][C]-7.0636588919115[/C][/ROW]
[ROW][C]78[/C][C]124.5[/C][C]123.255301794453[/C][C]1.24469820554653[/C][/ROW]
[ROW][C]79[/C][C]121.7[/C][C]124.93180113119[/C][C]-3.23180113119049[/C][/ROW]
[ROW][C]80[/C][C]118.7[/C][C]119.544149057245[/C][C]-0.844149057245218[/C][/ROW]
[ROW][C]81[/C][C]113.7[/C][C]114.470922408307[/C][C]-0.770922408307456[/C][/ROW]
[ROW][C]82[/C][C]113.4[/C][C]119.044535840836[/C][C]-5.6445358408355[/C][/ROW]
[ROW][C]83[/C][C]115.1[/C][C]116.663351260973[/C][C]-1.56335126097279[/C][/ROW]
[ROW][C]84[/C][C]143.9[/C][C]143.368526417607[/C][C]0.531473582393147[/C][/ROW]
[ROW][C]85[/C][C]101[/C][C]105.800564697147[/C][C]-4.80056469714739[/C][/ROW]
[ROW][C]86[/C][C]103.4[/C][C]104.335173676482[/C][C]-0.935173676482023[/C][/ROW]
[ROW][C]87[/C][C]121.5[/C][C]118.066195241768[/C][C]3.4338047582316[/C][/ROW]
[ROW][C]88[/C][C]111.9[/C][C]114.494917880283[/C][C]-2.59491788028278[/C][/ROW]
[ROW][C]89[/C][C]117.4[/C][C]115.926899215197[/C][C]1.47310078480349[/C][/ROW]
[ROW][C]90[/C][C]124.3[/C][C]122.129634326975[/C][C]2.17036567302506[/C][/ROW]
[ROW][C]91[/C][C]122[/C][C]121.383131363254[/C][C]0.616868636745693[/C][/ROW]
[ROW][C]92[/C][C]119.7[/C][C]117.832249216703[/C][C]1.86775078329663[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]113.160154590355[/C][C]1.8398454096447[/C][/ROW]
[ROW][C]94[/C][C]112.2[/C][C]115.283096800987[/C][C]-3.08309680098731[/C][/ROW]
[ROW][C]95[/C][C]115.3[/C][C]115.507571867246[/C][C]-0.207571867245747[/C][/ROW]
[ROW][C]96[/C][C]142.6[/C][C]143.530933651912[/C][C]-0.93093365191217[/C][/ROW]
[ROW][C]97[/C][C]104.1[/C][C]102.855438426174[/C][C]1.24456157382554[/C][/ROW]
[ROW][C]98[/C][C]105.3[/C][C]104.332121953469[/C][C]0.967878046530643[/C][/ROW]
[ROW][C]99[/C][C]124.4[/C][C]120.884412463825[/C][C]3.51558753617505[/C][/ROW]
[ROW][C]100[/C][C]113.9[/C][C]113.891606279026[/C][C]0.00839372097395596[/C][/ROW]
[ROW][C]101[/C][C]124.8[/C][C]117.997224079009[/C][C]6.80277592099112[/C][/ROW]
[ROW][C]102[/C][C]131.8[/C][C]125.394268453629[/C][C]6.40573154637129[/C][/ROW]
[ROW][C]103[/C][C]125.6[/C][C]124.307999525309[/C][C]1.2920004746906[/C][/ROW]
[ROW][C]104[/C][C]125[/C][C]121.522863171056[/C][C]3.47713682894432[/C][/ROW]
[ROW][C]105[/C][C]119.7[/C][C]116.962479025664[/C][C]2.73752097433604[/C][/ROW]
[ROW][C]106[/C][C]116.1[/C][C]116.450764964515[/C][C]-0.350764964514937[/C][/ROW]
[ROW][C]107[/C][C]120[/C][C]118.750559747777[/C][C]1.24944025222312[/C][/ROW]
[ROW][C]108[/C][C]148.1[/C][C]147.469402401354[/C][C]0.630597598646062[/C][/ROW]
[ROW][C]109[/C][C]109.2[/C][C]106.983031653351[/C][C]2.2169683466487[/C][/ROW]
[ROW][C]110[/C][C]109.4[/C][C]108.522292879591[/C][C]0.877707120408928[/C][/ROW]
[ROW][C]111[/C][C]135.1[/C][C]127.172899822996[/C][C]7.9271001770043[/C][/ROW]
[ROW][C]112[/C][C]114.9[/C][C]118.459370337954[/C][C]-3.55937033795384[/C][/ROW]
[ROW][C]113[/C][C]129[/C][C]126.262756082715[/C][C]2.73724391728493[/C][/ROW]
[ROW][C]114[/C][C]138.5[/C][C]133.168508508643[/C][C]5.33149149135727[/C][/ROW]
[ROW][C]115[/C][C]125.6[/C][C]128.97991129073[/C][C]-3.37991129073023[/C][/ROW]
[ROW][C]116[/C][C]130.4[/C][C]126.829826516594[/C][C]3.57017348340644[/C][/ROW]
[ROW][C]117[/C][C]120.3[/C][C]121.755316356364[/C][C]-1.45531635636365[/C][/ROW]
[ROW][C]118[/C][C]126.2[/C][C]118.956719991437[/C][C]7.24328000856278[/C][/ROW]
[ROW][C]119[/C][C]127.6[/C][C]123.25336702844[/C][C]4.34663297156024[/C][/ROW]
[ROW][C]120[/C][C]150.9[/C][C]153.067323096253[/C][C]-2.16732309625272[/C][/ROW]
[ROW][C]121[/C][C]114.6[/C][C]111.852876079132[/C][C]2.74712392086769[/C][/ROW]
[ROW][C]122[/C][C]118.6[/C][C]112.769493032074[/C][C]5.83050696792559[/C][/ROW]
[ROW][C]123[/C][C]131.4[/C][C]136.934545576043[/C][C]-5.53454557604331[/C][/ROW]
[ROW][C]124[/C][C]124.5[/C][C]119.671288830693[/C][C]4.82871116930671[/C][/ROW]
[ROW][C]125[/C][C]136.8[/C][C]132.54570897072[/C][C]4.25429102928027[/C][/ROW]
[ROW][C]126[/C][C]136.8[/C][C]141.456402244201[/C][C]-4.65640224420054[/C][/ROW]
[ROW][C]127[/C][C]136.6[/C][C]130.870887239933[/C][C]5.72911276006693[/C][/ROW]
[ROW][C]128[/C][C]131[/C][C]133.900169820853[/C][C]-2.90016982085334[/C][/ROW]
[ROW][C]129[/C][C]125.8[/C][C]124.960880153177[/C][C]0.839119846823039[/C][/ROW]
[ROW][C]130[/C][C]129.4[/C][C]127.434248264991[/C][C]1.96575173500862[/C][/ROW]
[ROW][C]131[/C][C]124.8[/C][C]129.523521267203[/C][C]-4.72352126720308[/C][/ROW]
[ROW][C]132[/C][C]157.1[/C][C]155.110230052031[/C][C]1.98976994796917[/C][/ROW]
[ROW][C]133[/C][C]116.6[/C][C]116.24092977826[/C][C]0.359070221740154[/C][/ROW]
[ROW][C]134[/C][C]114.2[/C][C]118.639110975567[/C][C]-4.43911097556681[/C][/ROW]
[ROW][C]135[/C][C]128.4[/C][C]135.305651392091[/C][C]-6.90565139209136[/C][/ROW]
[ROW][C]136[/C][C]127.3[/C][C]123.566682280528[/C][C]3.73331771947244[/C][/ROW]
[ROW][C]137[/C][C]133.5[/C][C]136.061330560736[/C][C]-2.56133056073585[/C][/ROW]
[ROW][C]138[/C][C]137.2[/C][C]139.083020496537[/C][C]-1.88302049653663[/C][/ROW]
[ROW][C]139[/C][C]137.7[/C][C]134.623188246196[/C][C]3.07681175380372[/C][/ROW]
[ROW][C]140[/C][C]131.2[/C][C]132.437914391599[/C][C]-1.23791439159888[/C][/ROW]
[ROW][C]141[/C][C]127.7[/C][C]125.766740899979[/C][C]1.93325910002123[/C][/ROW]
[ROW][C]142[/C][C]133.9[/C][C]128.998994431238[/C][C]4.90100556876195[/C][/ROW]
[ROW][C]143[/C][C]124.3[/C][C]127.657601896051[/C][C]-3.35760189605145[/C][/ROW]
[ROW][C]144[/C][C]160.6[/C][C]157.39941239284[/C][C]3.20058760715978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117292&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117292&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
1398.799.5514792359552-0.851479235955182
14100.5101.186626498397-0.686626498396592
15128.7129.071289161111-0.371289161111491
1698.798.894621967739-0.194621967738982
17115115.043003778345-0.0430037783449251
18121120.4421636377860.557836362214118
19125.9128.64681669265-2.74681669265038
20117.3112.6601358580134.63986414198695
21115111.2335427540043.76645724599564
22113.8116.59693691349-2.79693691349004
23115.7110.6356887457865.06431125421422
24145.5139.9028540933385.59714590666169
25101.7100.2725993214411.42740067855866
26106.9102.3396449472424.56035505275817
27116.4131.719787717374-15.319787717374
28114.699.55889567050615.0411043294939
29122.5118.1890157469144.31098425308571
30120.4124.722408429877-4.32240842987672
31116.5130.6914079712-14.1914079712002
32117117.249337309124-0.249337309124485
33114.3114.702800798375-0.402800798375239
34111.5115.871898419633-4.37189841963331
35117.8114.1150001154753.68499988452494
36141.5143.614551806-2.1145518059997
37102.6100.7854123150671.81458768493341
38103.8104.629414267297-0.829414267296826
39119.8121.857412789078-2.05741278907756
40113.6108.2137211712335.38627882876717
41121.8119.4769053621892.32309463781097
42123.9120.8587767340043.04122326599558
43122.7122.0749080449930.625091955007392
44120118.2951457974961.70485420250371
45111.6115.895519604463-4.29551960446308
46117.6114.3409822430723.2590177569281
47121.3118.1405908283973.15940917160302
48143.7144.766002801597-1.0660028015966
49107.1103.5987287872323.50127121276817
50107.7106.239902713521.46009728647982
51126.4123.4807527879332.91924721206662
52111.5114.410825733881-2.91082573388114
53127.9123.1157002337354.78429976626526
54124.9125.273425080016-0.373425080016062
55122124.72602874031-2.7260287403101
56124.9121.1053062272683.79469377273169
57113.9115.502693278039-1.60269327803894
58120.8118.6220610843782.17793891562235
59123.3122.3212723393870.978727660613458
60143.5146.752824609563-3.25282460956251
61107.1107.271661800121-0.17166180012147
62106.5108.328972168201-1.82897216820149
63114.6126.121282809906-11.5212828099064
64122.2112.02442892288810.1755710771124
65120.2126.782278955051-6.58227895505138
66123.1124.60708749814-1.50708749814044
67127.1122.5778188633494.52218113665064
68118.5123.511011359969-5.01101135996871
69116.1113.8112946491212.28870535087886
70120.6119.506453023231.09354697676989
71115.7122.365314346164-6.66531434616431
72146.5143.1571715127873.34282848721301
73108106.4533865082661.5466134917344
74106.6106.773525126446-0.173525126445867
75122.2119.1983750247673.001624975233
76115.8119.050635154009-3.25063515400946
77115.6122.663658891911-7.0636588919115
78124.5123.2553017944531.24469820554653
79121.7124.93180113119-3.23180113119049
80118.7119.544149057245-0.844149057245218
81113.7114.470922408307-0.770922408307456
82113.4119.044535840836-5.6445358408355
83115.1116.663351260973-1.56335126097279
84143.9143.3685264176070.531473582393147
85101105.800564697147-4.80056469714739
86103.4104.335173676482-0.935173676482023
87121.5118.0661952417683.4338047582316
88111.9114.494917880283-2.59491788028278
89117.4115.9268992151971.47310078480349
90124.3122.1296343269752.17036567302506
91122121.3831313632540.616868636745693
92119.7117.8322492167031.86775078329663
93115113.1601545903551.8398454096447
94112.2115.283096800987-3.08309680098731
95115.3115.507571867246-0.207571867245747
96142.6143.530933651912-0.93093365191217
97104.1102.8554384261741.24456157382554
98105.3104.3321219534690.967878046530643
99124.4120.8844124638253.51558753617505
100113.9113.8916062790260.00839372097395596
101124.8117.9972240790096.80277592099112
102131.8125.3942684536296.40573154637129
103125.6124.3079995253091.2920004746906
104125121.5228631710563.47713682894432
105119.7116.9624790256642.73752097433604
106116.1116.450764964515-0.350764964514937
107120118.7505597477771.24944025222312
108148.1147.4694024013540.630597598646062
109109.2106.9830316533512.2169683466487
110109.4108.5222928795910.877707120408928
111135.1127.1728998229967.9271001770043
112114.9118.459370337954-3.55937033795384
113129126.2627560827152.73724391728493
114138.5133.1685085086435.33149149135727
115125.6128.97991129073-3.37991129073023
116130.4126.8298265165943.57017348340644
117120.3121.755316356364-1.45531635636365
118126.2118.9567199914377.24328000856278
119127.6123.253367028444.34663297156024
120150.9153.067323096253-2.16732309625272
121114.6111.8528760791322.74712392086769
122118.6112.7694930320745.83050696792559
123131.4136.934545576043-5.53454557604331
124124.5119.6712888306934.82871116930671
125136.8132.545708970724.25429102928027
126136.8141.456402244201-4.65640224420054
127136.6130.8708872399335.72911276006693
128131133.900169820853-2.90016982085334
129125.8124.9608801531770.839119846823039
130129.4127.4342482649911.96575173500862
131124.8129.523521267203-4.72352126720308
132157.1155.1102300520311.98976994796917
133116.6116.240929778260.359070221740154
134114.2118.639110975567-4.43911097556681
135128.4135.305651392091-6.90565139209136
136127.3123.5666822805283.73331771947244
137133.5136.061330560736-2.56133056073585
138137.2139.083020496537-1.88302049653663
139137.7134.6231882461963.07681175380372
140131.2132.437914391599-1.23791439159888
141127.7125.7667408999791.93325910002123
142133.9128.9989944312384.90100556876195
143124.3127.657601896051-3.35760189605145
144160.6157.399412392843.20058760715978






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145117.418455390456111.749787679765123.087123101147
146117.052279094637111.288247667941122.816310521332
147133.062275809172127.145566274249138.978985344096
148128.075932643284122.08422648088134.067638805688
149136.701686126751130.55228060113142.851091652373
150140.501162596636134.217157901978146.785167291293
151139.046247670726132.671093882388145.421401459063
152133.976352361688127.549644560669140.403060162706
153129.164861206277122.688499854547135.641222558007
154133.893570864278127.246643057547140.540498671008
155127.21420442533120.555704891543133.872703959117
156161.563355429668108.982952144954214.143758714383

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 117.418455390456 & 111.749787679765 & 123.087123101147 \tabularnewline
146 & 117.052279094637 & 111.288247667941 & 122.816310521332 \tabularnewline
147 & 133.062275809172 & 127.145566274249 & 138.978985344096 \tabularnewline
148 & 128.075932643284 & 122.08422648088 & 134.067638805688 \tabularnewline
149 & 136.701686126751 & 130.55228060113 & 142.851091652373 \tabularnewline
150 & 140.501162596636 & 134.217157901978 & 146.785167291293 \tabularnewline
151 & 139.046247670726 & 132.671093882388 & 145.421401459063 \tabularnewline
152 & 133.976352361688 & 127.549644560669 & 140.403060162706 \tabularnewline
153 & 129.164861206277 & 122.688499854547 & 135.641222558007 \tabularnewline
154 & 133.893570864278 & 127.246643057547 & 140.540498671008 \tabularnewline
155 & 127.21420442533 & 120.555704891543 & 133.872703959117 \tabularnewline
156 & 161.563355429668 & 108.982952144954 & 214.143758714383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117292&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]117.418455390456[/C][C]111.749787679765[/C][C]123.087123101147[/C][/ROW]
[ROW][C]146[/C][C]117.052279094637[/C][C]111.288247667941[/C][C]122.816310521332[/C][/ROW]
[ROW][C]147[/C][C]133.062275809172[/C][C]127.145566274249[/C][C]138.978985344096[/C][/ROW]
[ROW][C]148[/C][C]128.075932643284[/C][C]122.08422648088[/C][C]134.067638805688[/C][/ROW]
[ROW][C]149[/C][C]136.701686126751[/C][C]130.55228060113[/C][C]142.851091652373[/C][/ROW]
[ROW][C]150[/C][C]140.501162596636[/C][C]134.217157901978[/C][C]146.785167291293[/C][/ROW]
[ROW][C]151[/C][C]139.046247670726[/C][C]132.671093882388[/C][C]145.421401459063[/C][/ROW]
[ROW][C]152[/C][C]133.976352361688[/C][C]127.549644560669[/C][C]140.403060162706[/C][/ROW]
[ROW][C]153[/C][C]129.164861206277[/C][C]122.688499854547[/C][C]135.641222558007[/C][/ROW]
[ROW][C]154[/C][C]133.893570864278[/C][C]127.246643057547[/C][C]140.540498671008[/C][/ROW]
[ROW][C]155[/C][C]127.21420442533[/C][C]120.555704891543[/C][C]133.872703959117[/C][/ROW]
[ROW][C]156[/C][C]161.563355429668[/C][C]108.982952144954[/C][C]214.143758714383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117292&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117292&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
145117.418455390456111.749787679765123.087123101147
146117.052279094637111.288247667941122.816310521332
147133.062275809172127.145566274249138.978985344096
148128.075932643284122.08422648088134.067638805688
149136.701686126751130.55228060113142.851091652373
150140.501162596636134.217157901978146.785167291293
151139.046247670726132.671093882388145.421401459063
152133.976352361688127.549644560669140.403060162706
153129.164861206277122.688499854547135.641222558007
154133.893570864278127.246643057547140.540498671008
155127.21420442533120.555704891543133.872703959117
156161.563355429668108.982952144954214.143758714383


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
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')