Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSun, 05 Dec 2010 20:53:51 +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/05/t12915823023y8gnszin1vl90j.htm/, Retrieved Thu, 31 Oct 2024 23:09:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105498, Retrieved Thu, 31 Oct 2024 23:09:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2010-12-05 20:53:51] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432




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=105498&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=105498&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105498&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.247959489735664
beta0.0345337296488464
gamma1

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.247959489735664 \tabularnewline
beta & 0.0345337296488464 \tabularnewline
gamma & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105498&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]0.247959489735664[/C][/ROW]
[ROW][C]beta[/C][C]0.0345337296488464[/C][/ROW]
[ROW][C]gamma[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105498&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105498&T=1

As an alternative you can also use a QR Code:  

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

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.247959489735664
beta0.0345337296488464
gamma1







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13115110.6431623931624.3568376068376
14126122.0731433875883.92685661241249
15141137.4301320536293.56986794637126
16135132.0625039833172.93749601668324
17125123.063226970461.93677302953955
18149147.499062384581.5009376154205
19170160.1313474162149.86865258378592
20170163.5063250513586.4936749486416
21158153.8500500719094.14994992809099
22133138.731495445679-5.73149544567943
23114123.321997399523-9.32199739952262
24140135.6090430410424.39095695895753
25145136.8604638401148.13953615988575
26150149.1778641595620.822135840438222
27178163.74277873872514.2572212612748
28163160.887368307282.11263169271962
29172151.2616671194820.7383328805198
30178180.52345193849-2.52345193848959
31199198.9079414187770.092058581222858
32199197.6941126433431.30588735665711
33184185.317990243194-1.31799024319412
34162161.6946288593080.305371140692131
35146145.4157878514160.584212148584157
36166170.890657487436-4.89065748743587
37171172.999007307477-1.99900730747703
38180177.5519720772142.44802792278551
39193202.890186966571-9.8901869665709
40181184.973617184963-3.97361718496342
41183187.853581322793-4.85358132279288
42218193.06418760533524.9358123946645
43230220.2479488461149.75205115388616
44242222.44849022136719.5515097786325
45209212.885752351814-3.88575235181398
46191190.0870074036020.912992596398055
47172174.414218571941-2.41421857194123
48194195.248286577793-1.24828657779273
49196200.685635881058-4.68563588105766
50196208.144971709426-12.1449717094265
51236220.68911254080815.310887459192
52235213.78992052088921.2100794791108
53229222.787331740536.21266825947043
54243253.774189579802-10.7741895798021
55264261.0081689744292.99183102557083
56272269.1678075187852.83219248121492
57237237.95618678454-0.956186784540108
58211219.640392882199-8.64039288219888
59180199.162435425533-19.1624354255328
60201216.642919355326-15.6429193553259
61204215.725163322482-11.7251633224818
62188215.568185948207-27.5681859482067
63235244.542771479216-9.54277147921556
64227235.31134790509-8.3113479050898
65234224.8512269469889.14877305301204
66264242.95770303988621.0422969601138
67302267.8723192988734.12768070113
68293283.3377797468779.66222025312288
69259250.734646991188.2653530088202
70229228.7694833009120.230516699088298
71203202.4970072961720.502992703827601
72229227.5877912857491.41220871425051
73242234.0786217080037.92137829199709
74233227.2801229710195.71987702898087
75267278.751213081094-11.7512130810941
76269270.565926630949-1.56592663094943
77270275.634536741208-5.63453674120763
78315299.61859526655415.3814047334461
79364333.52063681328530.4793631867149
80347330.20156310420616.7984368957938
81312298.89764756239913.1023524376009
82274272.7109857773551.28901422264516
83237247.536595543642-10.5365955436417
84278271.109952789846.89004721015976
85284284.437307658674-0.437307658673717
86277274.4220833394962.57791666050417
87317312.4597305930074.54026940699339
88313316.597926811577-3.59792681157705
89318318.709630586872-0.709630586872152
90374360.36858449974713.6314155002531
91413405.8248697102657.17513028973542
92405386.8730198830618.1269801169402
93355353.5646406373531.43535936264743
94306315.946740877587-9.94674087758722
95271279.342602124885-8.34260212488476
96306316.83391044474-10.8339104447398
97315320.372592752745-5.37259275274511
98301311.475546446946-10.4755464469458
99356347.7148140007458.28518599925457
100348346.6559937740841.3440062259159
101355352.2021792990662.79782070093381
102422405.58288763546716.4171123645327
103465446.9653795972118.0346204027904
104467439.1263223763727.8736776236295
105404395.9492591677778.0507408322232
106347351.735857721497-4.73585772149704
107305317.998757011001-12.9987570110005
108336352.790665052808-16.7906650528085
109340359.237140476179-19.2371404761794
110318343.223593245562-25.2235932455621
111362389.947459573451-27.9474595734515
112348374.406789879838-26.4067898798385
113363373.650027638871-10.6500276388712
114435433.3081180437231.69188195627703
115491471.49933393927319.5006660607272
116505470.67927294531334.320727054687
117404413.504477870091-9.50447787009125
118359354.4830410564344.51695894356556
119310316.06644840636-6.0664484063596
120337349.025200176065-12.0252001760649
121360354.1538562087525.84614379124764
122342339.4130666563972.58693334360328
123406390.77767404240915.2223259575908
124396387.2629846674868.73701533251392
125420407.53409746256112.4659025374395
126472482.867470544425-10.8674705444247
127548531.89171001552216.1082899844785
128559541.90102173974217.0989782602576
129463447.87539022493815.1246097750625
130407406.0943450300010.905654969998807
131362359.3809078701012.61909212989883
132405390.64423675049214.3557632495077
133417416.6123145742680.387685425732229
134391398.878286514511-7.87828651451065
135419457.871953443464-38.8719534434641
136461436.32533337469524.6746666253051
137472463.7475613898488.25243861015184
138535520.84739366095514.1526063390452
139622596.93557876272425.0644212372756
140606610.560492406652-4.56049240665152
141508510.143721671589-2.14372167158911
142461453.7040688180887.29593118191156
143390410.234924183382-20.2349241833815
144432444.833325554045-12.8333255540447

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 115 & 110.643162393162 & 4.3568376068376 \tabularnewline
14 & 126 & 122.073143387588 & 3.92685661241249 \tabularnewline
15 & 141 & 137.430132053629 & 3.56986794637126 \tabularnewline
16 & 135 & 132.062503983317 & 2.93749601668324 \tabularnewline
17 & 125 & 123.06322697046 & 1.93677302953955 \tabularnewline
18 & 149 & 147.49906238458 & 1.5009376154205 \tabularnewline
19 & 170 & 160.131347416214 & 9.86865258378592 \tabularnewline
20 & 170 & 163.506325051358 & 6.4936749486416 \tabularnewline
21 & 158 & 153.850050071909 & 4.14994992809099 \tabularnewline
22 & 133 & 138.731495445679 & -5.73149544567943 \tabularnewline
23 & 114 & 123.321997399523 & -9.32199739952262 \tabularnewline
24 & 140 & 135.609043041042 & 4.39095695895753 \tabularnewline
25 & 145 & 136.860463840114 & 8.13953615988575 \tabularnewline
26 & 150 & 149.177864159562 & 0.822135840438222 \tabularnewline
27 & 178 & 163.742778738725 & 14.2572212612748 \tabularnewline
28 & 163 & 160.88736830728 & 2.11263169271962 \tabularnewline
29 & 172 & 151.26166711948 & 20.7383328805198 \tabularnewline
30 & 178 & 180.52345193849 & -2.52345193848959 \tabularnewline
31 & 199 & 198.907941418777 & 0.092058581222858 \tabularnewline
32 & 199 & 197.694112643343 & 1.30588735665711 \tabularnewline
33 & 184 & 185.317990243194 & -1.31799024319412 \tabularnewline
34 & 162 & 161.694628859308 & 0.305371140692131 \tabularnewline
35 & 146 & 145.415787851416 & 0.584212148584157 \tabularnewline
36 & 166 & 170.890657487436 & -4.89065748743587 \tabularnewline
37 & 171 & 172.999007307477 & -1.99900730747703 \tabularnewline
38 & 180 & 177.551972077214 & 2.44802792278551 \tabularnewline
39 & 193 & 202.890186966571 & -9.8901869665709 \tabularnewline
40 & 181 & 184.973617184963 & -3.97361718496342 \tabularnewline
41 & 183 & 187.853581322793 & -4.85358132279288 \tabularnewline
42 & 218 & 193.064187605335 & 24.9358123946645 \tabularnewline
43 & 230 & 220.247948846114 & 9.75205115388616 \tabularnewline
44 & 242 & 222.448490221367 & 19.5515097786325 \tabularnewline
45 & 209 & 212.885752351814 & -3.88575235181398 \tabularnewline
46 & 191 & 190.087007403602 & 0.912992596398055 \tabularnewline
47 & 172 & 174.414218571941 & -2.41421857194123 \tabularnewline
48 & 194 & 195.248286577793 & -1.24828657779273 \tabularnewline
49 & 196 & 200.685635881058 & -4.68563588105766 \tabularnewline
50 & 196 & 208.144971709426 & -12.1449717094265 \tabularnewline
51 & 236 & 220.689112540808 & 15.310887459192 \tabularnewline
52 & 235 & 213.789920520889 & 21.2100794791108 \tabularnewline
53 & 229 & 222.78733174053 & 6.21266825947043 \tabularnewline
54 & 243 & 253.774189579802 & -10.7741895798021 \tabularnewline
55 & 264 & 261.008168974429 & 2.99183102557083 \tabularnewline
56 & 272 & 269.167807518785 & 2.83219248121492 \tabularnewline
57 & 237 & 237.95618678454 & -0.956186784540108 \tabularnewline
58 & 211 & 219.640392882199 & -8.64039288219888 \tabularnewline
59 & 180 & 199.162435425533 & -19.1624354255328 \tabularnewline
60 & 201 & 216.642919355326 & -15.6429193553259 \tabularnewline
61 & 204 & 215.725163322482 & -11.7251633224818 \tabularnewline
62 & 188 & 215.568185948207 & -27.5681859482067 \tabularnewline
63 & 235 & 244.542771479216 & -9.54277147921556 \tabularnewline
64 & 227 & 235.31134790509 & -8.3113479050898 \tabularnewline
65 & 234 & 224.851226946988 & 9.14877305301204 \tabularnewline
66 & 264 & 242.957703039886 & 21.0422969601138 \tabularnewline
67 & 302 & 267.87231929887 & 34.12768070113 \tabularnewline
68 & 293 & 283.337779746877 & 9.66222025312288 \tabularnewline
69 & 259 & 250.73464699118 & 8.2653530088202 \tabularnewline
70 & 229 & 228.769483300912 & 0.230516699088298 \tabularnewline
71 & 203 & 202.497007296172 & 0.502992703827601 \tabularnewline
72 & 229 & 227.587791285749 & 1.41220871425051 \tabularnewline
73 & 242 & 234.078621708003 & 7.92137829199709 \tabularnewline
74 & 233 & 227.280122971019 & 5.71987702898087 \tabularnewline
75 & 267 & 278.751213081094 & -11.7512130810941 \tabularnewline
76 & 269 & 270.565926630949 & -1.56592663094943 \tabularnewline
77 & 270 & 275.634536741208 & -5.63453674120763 \tabularnewline
78 & 315 & 299.618595266554 & 15.3814047334461 \tabularnewline
79 & 364 & 333.520636813285 & 30.4793631867149 \tabularnewline
80 & 347 & 330.201563104206 & 16.7984368957938 \tabularnewline
81 & 312 & 298.897647562399 & 13.1023524376009 \tabularnewline
82 & 274 & 272.710985777355 & 1.28901422264516 \tabularnewline
83 & 237 & 247.536595543642 & -10.5365955436417 \tabularnewline
84 & 278 & 271.10995278984 & 6.89004721015976 \tabularnewline
85 & 284 & 284.437307658674 & -0.437307658673717 \tabularnewline
86 & 277 & 274.422083339496 & 2.57791666050417 \tabularnewline
87 & 317 & 312.459730593007 & 4.54026940699339 \tabularnewline
88 & 313 & 316.597926811577 & -3.59792681157705 \tabularnewline
89 & 318 & 318.709630586872 & -0.709630586872152 \tabularnewline
90 & 374 & 360.368584499747 & 13.6314155002531 \tabularnewline
91 & 413 & 405.824869710265 & 7.17513028973542 \tabularnewline
92 & 405 & 386.87301988306 & 18.1269801169402 \tabularnewline
93 & 355 & 353.564640637353 & 1.43535936264743 \tabularnewline
94 & 306 & 315.946740877587 & -9.94674087758722 \tabularnewline
95 & 271 & 279.342602124885 & -8.34260212488476 \tabularnewline
96 & 306 & 316.83391044474 & -10.8339104447398 \tabularnewline
97 & 315 & 320.372592752745 & -5.37259275274511 \tabularnewline
98 & 301 & 311.475546446946 & -10.4755464469458 \tabularnewline
99 & 356 & 347.714814000745 & 8.28518599925457 \tabularnewline
100 & 348 & 346.655993774084 & 1.3440062259159 \tabularnewline
101 & 355 & 352.202179299066 & 2.79782070093381 \tabularnewline
102 & 422 & 405.582887635467 & 16.4171123645327 \tabularnewline
103 & 465 & 446.96537959721 & 18.0346204027904 \tabularnewline
104 & 467 & 439.12632237637 & 27.8736776236295 \tabularnewline
105 & 404 & 395.949259167777 & 8.0507408322232 \tabularnewline
106 & 347 & 351.735857721497 & -4.73585772149704 \tabularnewline
107 & 305 & 317.998757011001 & -12.9987570110005 \tabularnewline
108 & 336 & 352.790665052808 & -16.7906650528085 \tabularnewline
109 & 340 & 359.237140476179 & -19.2371404761794 \tabularnewline
110 & 318 & 343.223593245562 & -25.2235932455621 \tabularnewline
111 & 362 & 389.947459573451 & -27.9474595734515 \tabularnewline
112 & 348 & 374.406789879838 & -26.4067898798385 \tabularnewline
113 & 363 & 373.650027638871 & -10.6500276388712 \tabularnewline
114 & 435 & 433.308118043723 & 1.69188195627703 \tabularnewline
115 & 491 & 471.499333939273 & 19.5006660607272 \tabularnewline
116 & 505 & 470.679272945313 & 34.320727054687 \tabularnewline
117 & 404 & 413.504477870091 & -9.50447787009125 \tabularnewline
118 & 359 & 354.483041056434 & 4.51695894356556 \tabularnewline
119 & 310 & 316.06644840636 & -6.0664484063596 \tabularnewline
120 & 337 & 349.025200176065 & -12.0252001760649 \tabularnewline
121 & 360 & 354.153856208752 & 5.84614379124764 \tabularnewline
122 & 342 & 339.413066656397 & 2.58693334360328 \tabularnewline
123 & 406 & 390.777674042409 & 15.2223259575908 \tabularnewline
124 & 396 & 387.262984667486 & 8.73701533251392 \tabularnewline
125 & 420 & 407.534097462561 & 12.4659025374395 \tabularnewline
126 & 472 & 482.867470544425 & -10.8674705444247 \tabularnewline
127 & 548 & 531.891710015522 & 16.1082899844785 \tabularnewline
128 & 559 & 541.901021739742 & 17.0989782602576 \tabularnewline
129 & 463 & 447.875390224938 & 15.1246097750625 \tabularnewline
130 & 407 & 406.094345030001 & 0.905654969998807 \tabularnewline
131 & 362 & 359.380907870101 & 2.61909212989883 \tabularnewline
132 & 405 & 390.644236750492 & 14.3557632495077 \tabularnewline
133 & 417 & 416.612314574268 & 0.387685425732229 \tabularnewline
134 & 391 & 398.878286514511 & -7.87828651451065 \tabularnewline
135 & 419 & 457.871953443464 & -38.8719534434641 \tabularnewline
136 & 461 & 436.325333374695 & 24.6746666253051 \tabularnewline
137 & 472 & 463.747561389848 & 8.25243861015184 \tabularnewline
138 & 535 & 520.847393660955 & 14.1526063390452 \tabularnewline
139 & 622 & 596.935578762724 & 25.0644212372756 \tabularnewline
140 & 606 & 610.560492406652 & -4.56049240665152 \tabularnewline
141 & 508 & 510.143721671589 & -2.14372167158911 \tabularnewline
142 & 461 & 453.704068818088 & 7.29593118191156 \tabularnewline
143 & 390 & 410.234924183382 & -20.2349241833815 \tabularnewline
144 & 432 & 444.833325554045 & -12.8333255540447 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105498&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]115[/C][C]110.643162393162[/C][C]4.3568376068376[/C][/ROW]
[ROW][C]14[/C][C]126[/C][C]122.073143387588[/C][C]3.92685661241249[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]137.430132053629[/C][C]3.56986794637126[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]132.062503983317[/C][C]2.93749601668324[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]123.06322697046[/C][C]1.93677302953955[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]147.49906238458[/C][C]1.5009376154205[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]160.131347416214[/C][C]9.86865258378592[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]163.506325051358[/C][C]6.4936749486416[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]153.850050071909[/C][C]4.14994992809099[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]138.731495445679[/C][C]-5.73149544567943[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]123.321997399523[/C][C]-9.32199739952262[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]135.609043041042[/C][C]4.39095695895753[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]136.860463840114[/C][C]8.13953615988575[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]149.177864159562[/C][C]0.822135840438222[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]163.742778738725[/C][C]14.2572212612748[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]160.88736830728[/C][C]2.11263169271962[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]151.26166711948[/C][C]20.7383328805198[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]180.52345193849[/C][C]-2.52345193848959[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]198.907941418777[/C][C]0.092058581222858[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]197.694112643343[/C][C]1.30588735665711[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]185.317990243194[/C][C]-1.31799024319412[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]161.694628859308[/C][C]0.305371140692131[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]145.415787851416[/C][C]0.584212148584157[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]170.890657487436[/C][C]-4.89065748743587[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]172.999007307477[/C][C]-1.99900730747703[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]177.551972077214[/C][C]2.44802792278551[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]202.890186966571[/C][C]-9.8901869665709[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]184.973617184963[/C][C]-3.97361718496342[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]187.853581322793[/C][C]-4.85358132279288[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]193.064187605335[/C][C]24.9358123946645[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]220.247948846114[/C][C]9.75205115388616[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]222.448490221367[/C][C]19.5515097786325[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]212.885752351814[/C][C]-3.88575235181398[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]190.087007403602[/C][C]0.912992596398055[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]174.414218571941[/C][C]-2.41421857194123[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]195.248286577793[/C][C]-1.24828657779273[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]200.685635881058[/C][C]-4.68563588105766[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]208.144971709426[/C][C]-12.1449717094265[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]220.689112540808[/C][C]15.310887459192[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]213.789920520889[/C][C]21.2100794791108[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]222.78733174053[/C][C]6.21266825947043[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]253.774189579802[/C][C]-10.7741895798021[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]261.008168974429[/C][C]2.99183102557083[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]269.167807518785[/C][C]2.83219248121492[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]237.95618678454[/C][C]-0.956186784540108[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]219.640392882199[/C][C]-8.64039288219888[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]199.162435425533[/C][C]-19.1624354255328[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]216.642919355326[/C][C]-15.6429193553259[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]215.725163322482[/C][C]-11.7251633224818[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]215.568185948207[/C][C]-27.5681859482067[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]244.542771479216[/C][C]-9.54277147921556[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]235.31134790509[/C][C]-8.3113479050898[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]224.851226946988[/C][C]9.14877305301204[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]242.957703039886[/C][C]21.0422969601138[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]267.87231929887[/C][C]34.12768070113[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]283.337779746877[/C][C]9.66222025312288[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]250.73464699118[/C][C]8.2653530088202[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]228.769483300912[/C][C]0.230516699088298[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]202.497007296172[/C][C]0.502992703827601[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]227.587791285749[/C][C]1.41220871425051[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]234.078621708003[/C][C]7.92137829199709[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]227.280122971019[/C][C]5.71987702898087[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]278.751213081094[/C][C]-11.7512130810941[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]270.565926630949[/C][C]-1.56592663094943[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]275.634536741208[/C][C]-5.63453674120763[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]299.618595266554[/C][C]15.3814047334461[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]333.520636813285[/C][C]30.4793631867149[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]330.201563104206[/C][C]16.7984368957938[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]298.897647562399[/C][C]13.1023524376009[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]272.710985777355[/C][C]1.28901422264516[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]247.536595543642[/C][C]-10.5365955436417[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]271.10995278984[/C][C]6.89004721015976[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]284.437307658674[/C][C]-0.437307658673717[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]274.422083339496[/C][C]2.57791666050417[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]312.459730593007[/C][C]4.54026940699339[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]316.597926811577[/C][C]-3.59792681157705[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]318.709630586872[/C][C]-0.709630586872152[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]360.368584499747[/C][C]13.6314155002531[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]405.824869710265[/C][C]7.17513028973542[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]386.87301988306[/C][C]18.1269801169402[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]353.564640637353[/C][C]1.43535936264743[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]315.946740877587[/C][C]-9.94674087758722[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]279.342602124885[/C][C]-8.34260212488476[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]316.83391044474[/C][C]-10.8339104447398[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]320.372592752745[/C][C]-5.37259275274511[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]311.475546446946[/C][C]-10.4755464469458[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]347.714814000745[/C][C]8.28518599925457[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]346.655993774084[/C][C]1.3440062259159[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]352.202179299066[/C][C]2.79782070093381[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]405.582887635467[/C][C]16.4171123645327[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]446.96537959721[/C][C]18.0346204027904[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]439.12632237637[/C][C]27.8736776236295[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]395.949259167777[/C][C]8.0507408322232[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]351.735857721497[/C][C]-4.73585772149704[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]317.998757011001[/C][C]-12.9987570110005[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]352.790665052808[/C][C]-16.7906650528085[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]359.237140476179[/C][C]-19.2371404761794[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]343.223593245562[/C][C]-25.2235932455621[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]389.947459573451[/C][C]-27.9474595734515[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]374.406789879838[/C][C]-26.4067898798385[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]373.650027638871[/C][C]-10.6500276388712[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]433.308118043723[/C][C]1.69188195627703[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]471.499333939273[/C][C]19.5006660607272[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]470.679272945313[/C][C]34.320727054687[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]413.504477870091[/C][C]-9.50447787009125[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]354.483041056434[/C][C]4.51695894356556[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]316.06644840636[/C][C]-6.0664484063596[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]349.025200176065[/C][C]-12.0252001760649[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]354.153856208752[/C][C]5.84614379124764[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]339.413066656397[/C][C]2.58693334360328[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]390.777674042409[/C][C]15.2223259575908[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]387.262984667486[/C][C]8.73701533251392[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]407.534097462561[/C][C]12.4659025374395[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]482.867470544425[/C][C]-10.8674705444247[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]531.891710015522[/C][C]16.1082899844785[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]541.901021739742[/C][C]17.0989782602576[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]447.875390224938[/C][C]15.1246097750625[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]406.094345030001[/C][C]0.905654969998807[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]359.380907870101[/C][C]2.61909212989883[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]390.644236750492[/C][C]14.3557632495077[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]416.612314574268[/C][C]0.387685425732229[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]398.878286514511[/C][C]-7.87828651451065[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]457.871953443464[/C][C]-38.8719534434641[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]436.325333374695[/C][C]24.6746666253051[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]463.747561389848[/C][C]8.25243861015184[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]520.847393660955[/C][C]14.1526063390452[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]596.935578762724[/C][C]25.0644212372756[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]610.560492406652[/C][C]-4.56049240665152[/C][/ROW]
[ROW][C]141[/C][C]508[/C][C]510.143721671589[/C][C]-2.14372167158911[/C][/ROW]
[ROW][C]142[/C][C]461[/C][C]453.704068818088[/C][C]7.29593118191156[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]410.234924183382[/C][C]-20.2349241833815[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]444.833325554045[/C][C]-12.8333255540447[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105498&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105498&T=2

As an alternative you can also use a QR Code:  

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

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13115110.6431623931624.3568376068376
14126122.0731433875883.92685661241249
15141137.4301320536293.56986794637126
16135132.0625039833172.93749601668324
17125123.063226970461.93677302953955
18149147.499062384581.5009376154205
19170160.1313474162149.86865258378592
20170163.5063250513586.4936749486416
21158153.8500500719094.14994992809099
22133138.731495445679-5.73149544567943
23114123.321997399523-9.32199739952262
24140135.6090430410424.39095695895753
25145136.8604638401148.13953615988575
26150149.1778641595620.822135840438222
27178163.74277873872514.2572212612748
28163160.887368307282.11263169271962
29172151.2616671194820.7383328805198
30178180.52345193849-2.52345193848959
31199198.9079414187770.092058581222858
32199197.6941126433431.30588735665711
33184185.317990243194-1.31799024319412
34162161.6946288593080.305371140692131
35146145.4157878514160.584212148584157
36166170.890657487436-4.89065748743587
37171172.999007307477-1.99900730747703
38180177.5519720772142.44802792278551
39193202.890186966571-9.8901869665709
40181184.973617184963-3.97361718496342
41183187.853581322793-4.85358132279288
42218193.06418760533524.9358123946645
43230220.2479488461149.75205115388616
44242222.44849022136719.5515097786325
45209212.885752351814-3.88575235181398
46191190.0870074036020.912992596398055
47172174.414218571941-2.41421857194123
48194195.248286577793-1.24828657779273
49196200.685635881058-4.68563588105766
50196208.144971709426-12.1449717094265
51236220.68911254080815.310887459192
52235213.78992052088921.2100794791108
53229222.787331740536.21266825947043
54243253.774189579802-10.7741895798021
55264261.0081689744292.99183102557083
56272269.1678075187852.83219248121492
57237237.95618678454-0.956186784540108
58211219.640392882199-8.64039288219888
59180199.162435425533-19.1624354255328
60201216.642919355326-15.6429193553259
61204215.725163322482-11.7251633224818
62188215.568185948207-27.5681859482067
63235244.542771479216-9.54277147921556
64227235.31134790509-8.3113479050898
65234224.8512269469889.14877305301204
66264242.95770303988621.0422969601138
67302267.8723192988734.12768070113
68293283.3377797468779.66222025312288
69259250.734646991188.2653530088202
70229228.7694833009120.230516699088298
71203202.4970072961720.502992703827601
72229227.5877912857491.41220871425051
73242234.0786217080037.92137829199709
74233227.2801229710195.71987702898087
75267278.751213081094-11.7512130810941
76269270.565926630949-1.56592663094943
77270275.634536741208-5.63453674120763
78315299.61859526655415.3814047334461
79364333.52063681328530.4793631867149
80347330.20156310420616.7984368957938
81312298.89764756239913.1023524376009
82274272.7109857773551.28901422264516
83237247.536595543642-10.5365955436417
84278271.109952789846.89004721015976
85284284.437307658674-0.437307658673717
86277274.4220833394962.57791666050417
87317312.4597305930074.54026940699339
88313316.597926811577-3.59792681157705
89318318.709630586872-0.709630586872152
90374360.36858449974713.6314155002531
91413405.8248697102657.17513028973542
92405386.8730198830618.1269801169402
93355353.5646406373531.43535936264743
94306315.946740877587-9.94674087758722
95271279.342602124885-8.34260212488476
96306316.83391044474-10.8339104447398
97315320.372592752745-5.37259275274511
98301311.475546446946-10.4755464469458
99356347.7148140007458.28518599925457
100348346.6559937740841.3440062259159
101355352.2021792990662.79782070093381
102422405.58288763546716.4171123645327
103465446.9653795972118.0346204027904
104467439.1263223763727.8736776236295
105404395.9492591677778.0507408322232
106347351.735857721497-4.73585772149704
107305317.998757011001-12.9987570110005
108336352.790665052808-16.7906650528085
109340359.237140476179-19.2371404761794
110318343.223593245562-25.2235932455621
111362389.947459573451-27.9474595734515
112348374.406789879838-26.4067898798385
113363373.650027638871-10.6500276388712
114435433.3081180437231.69188195627703
115491471.49933393927319.5006660607272
116505470.67927294531334.320727054687
117404413.504477870091-9.50447787009125
118359354.4830410564344.51695894356556
119310316.06644840636-6.0664484063596
120337349.025200176065-12.0252001760649
121360354.1538562087525.84614379124764
122342339.4130666563972.58693334360328
123406390.77767404240915.2223259575908
124396387.2629846674868.73701533251392
125420407.53409746256112.4659025374395
126472482.867470544425-10.8674705444247
127548531.89171001552216.1082899844785
128559541.90102173974217.0989782602576
129463447.87539022493815.1246097750625
130407406.0943450300010.905654969998807
131362359.3809078701012.61909212989883
132405390.64423675049214.3557632495077
133417416.6123145742680.387685425732229
134391398.878286514511-7.87828651451065
135419457.871953443464-38.8719534434641
136461436.32533337469524.6746666253051
137472463.7475613898488.25243861015184
138535520.84739366095514.1526063390452
139622596.93557876272425.0644212372756
140606610.560492406652-4.56049240665152
141508510.143721671589-2.14372167158911
142461453.7040688180887.29593118191156
143390410.234924183382-20.2349241833815
144432444.833325554045-12.8333255540447







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145453.497721751321428.415266965019478.580176537622
146429.390569251949403.496001603239455.285136900659
147467.036052088741440.301472551842493.770631625639
148503.25740665386475.65579207998530.85902122774
149512.339520167483.844695250901540.8343450831
150571.887965749639542.4745714567601.301360042577
151652.609534991378622.252994696776682.96607528598
152637.462256917306606.138741269286668.785772565325
153539.754768946301507.441160256023572.068377636578
154490.724986086209457.398842867372524.051129305046
155424.459265263961390.098787394923458.819743132999
156469.531518789793434.115513646614504.947523932972
157491.029240541114446.09196301134535.966518070888
158466.922088041742421.090140639751512.754035443733
159504.567570878534457.816174748357551.318967008711
160540.788925443653493.093767563127588.48408332418
161549.871038956794501.208272912143598.533805001444
162609.419484539432559.765728963449659.073240115416
163690.141053781172639.473388773077740.808718789266
164674.993775707099623.289737529118726.69781388508
165577.286287736094524.52386205752630.048713414667
166528.256504876002474.414118810952582.098890941052
167461.990784053754407.04729721448516.934270893028
168507.063037579586450.997732343634563.128342815539
169528.560759330907464.861422671682592.260095990132
170504.453606831535439.708653578642569.198560084428
171542.099089668327476.286957564125607.911221772529
172578.320444233447511.419913345831645.220975121063
173587.402557746587519.3927486333655.412366859874
174646.951003329225577.811374309983716.090632348467
175727.672572570965657.382916219568797.962228922362
176712.525294496892641.065733457534783.98485553625
177614.817806525887542.168788749912687.466824301861
178565.788023665795491.930317157262639.645730174328
179499.522302843547424.436989994278574.607615692816
180544.59455636938468.263027956773620.926084781987
181566.0922781207483.006199790292649.178356451109
182541.985125621329457.700011407708626.270239834949
183579.63060845812494.127415792212665.133801124028
184615.85196302324529.111919746892702.592006299588
185624.93407653638536.938677829201712.92947524356
186684.482522119018595.213527079886773.751517158151
187765.204091360758674.643519290785855.764663430731
188750.056813286686658.186939716822841.92668685655
189652.34932531568559.152677818696745.545972812664
190603.319542455588508.778896278832697.860188632345
191537.053821633341441.152195185495632.955448081186
192582.126075159173484.846725381216679.40542493713

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 453.497721751321 & 428.415266965019 & 478.580176537622 \tabularnewline
146 & 429.390569251949 & 403.496001603239 & 455.285136900659 \tabularnewline
147 & 467.036052088741 & 440.301472551842 & 493.770631625639 \tabularnewline
148 & 503.25740665386 & 475.65579207998 & 530.85902122774 \tabularnewline
149 & 512.339520167 & 483.844695250901 & 540.8343450831 \tabularnewline
150 & 571.887965749639 & 542.4745714567 & 601.301360042577 \tabularnewline
151 & 652.609534991378 & 622.252994696776 & 682.96607528598 \tabularnewline
152 & 637.462256917306 & 606.138741269286 & 668.785772565325 \tabularnewline
153 & 539.754768946301 & 507.441160256023 & 572.068377636578 \tabularnewline
154 & 490.724986086209 & 457.398842867372 & 524.051129305046 \tabularnewline
155 & 424.459265263961 & 390.098787394923 & 458.819743132999 \tabularnewline
156 & 469.531518789793 & 434.115513646614 & 504.947523932972 \tabularnewline
157 & 491.029240541114 & 446.09196301134 & 535.966518070888 \tabularnewline
158 & 466.922088041742 & 421.090140639751 & 512.754035443733 \tabularnewline
159 & 504.567570878534 & 457.816174748357 & 551.318967008711 \tabularnewline
160 & 540.788925443653 & 493.093767563127 & 588.48408332418 \tabularnewline
161 & 549.871038956794 & 501.208272912143 & 598.533805001444 \tabularnewline
162 & 609.419484539432 & 559.765728963449 & 659.073240115416 \tabularnewline
163 & 690.141053781172 & 639.473388773077 & 740.808718789266 \tabularnewline
164 & 674.993775707099 & 623.289737529118 & 726.69781388508 \tabularnewline
165 & 577.286287736094 & 524.52386205752 & 630.048713414667 \tabularnewline
166 & 528.256504876002 & 474.414118810952 & 582.098890941052 \tabularnewline
167 & 461.990784053754 & 407.04729721448 & 516.934270893028 \tabularnewline
168 & 507.063037579586 & 450.997732343634 & 563.128342815539 \tabularnewline
169 & 528.560759330907 & 464.861422671682 & 592.260095990132 \tabularnewline
170 & 504.453606831535 & 439.708653578642 & 569.198560084428 \tabularnewline
171 & 542.099089668327 & 476.286957564125 & 607.911221772529 \tabularnewline
172 & 578.320444233447 & 511.419913345831 & 645.220975121063 \tabularnewline
173 & 587.402557746587 & 519.3927486333 & 655.412366859874 \tabularnewline
174 & 646.951003329225 & 577.811374309983 & 716.090632348467 \tabularnewline
175 & 727.672572570965 & 657.382916219568 & 797.962228922362 \tabularnewline
176 & 712.525294496892 & 641.065733457534 & 783.98485553625 \tabularnewline
177 & 614.817806525887 & 542.168788749912 & 687.466824301861 \tabularnewline
178 & 565.788023665795 & 491.930317157262 & 639.645730174328 \tabularnewline
179 & 499.522302843547 & 424.436989994278 & 574.607615692816 \tabularnewline
180 & 544.59455636938 & 468.263027956773 & 620.926084781987 \tabularnewline
181 & 566.0922781207 & 483.006199790292 & 649.178356451109 \tabularnewline
182 & 541.985125621329 & 457.700011407708 & 626.270239834949 \tabularnewline
183 & 579.63060845812 & 494.127415792212 & 665.133801124028 \tabularnewline
184 & 615.85196302324 & 529.111919746892 & 702.592006299588 \tabularnewline
185 & 624.93407653638 & 536.938677829201 & 712.92947524356 \tabularnewline
186 & 684.482522119018 & 595.213527079886 & 773.751517158151 \tabularnewline
187 & 765.204091360758 & 674.643519290785 & 855.764663430731 \tabularnewline
188 & 750.056813286686 & 658.186939716822 & 841.92668685655 \tabularnewline
189 & 652.34932531568 & 559.152677818696 & 745.545972812664 \tabularnewline
190 & 603.319542455588 & 508.778896278832 & 697.860188632345 \tabularnewline
191 & 537.053821633341 & 441.152195185495 & 632.955448081186 \tabularnewline
192 & 582.126075159173 & 484.846725381216 & 679.40542493713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105498&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]453.497721751321[/C][C]428.415266965019[/C][C]478.580176537622[/C][/ROW]
[ROW][C]146[/C][C]429.390569251949[/C][C]403.496001603239[/C][C]455.285136900659[/C][/ROW]
[ROW][C]147[/C][C]467.036052088741[/C][C]440.301472551842[/C][C]493.770631625639[/C][/ROW]
[ROW][C]148[/C][C]503.25740665386[/C][C]475.65579207998[/C][C]530.85902122774[/C][/ROW]
[ROW][C]149[/C][C]512.339520167[/C][C]483.844695250901[/C][C]540.8343450831[/C][/ROW]
[ROW][C]150[/C][C]571.887965749639[/C][C]542.4745714567[/C][C]601.301360042577[/C][/ROW]
[ROW][C]151[/C][C]652.609534991378[/C][C]622.252994696776[/C][C]682.96607528598[/C][/ROW]
[ROW][C]152[/C][C]637.462256917306[/C][C]606.138741269286[/C][C]668.785772565325[/C][/ROW]
[ROW][C]153[/C][C]539.754768946301[/C][C]507.441160256023[/C][C]572.068377636578[/C][/ROW]
[ROW][C]154[/C][C]490.724986086209[/C][C]457.398842867372[/C][C]524.051129305046[/C][/ROW]
[ROW][C]155[/C][C]424.459265263961[/C][C]390.098787394923[/C][C]458.819743132999[/C][/ROW]
[ROW][C]156[/C][C]469.531518789793[/C][C]434.115513646614[/C][C]504.947523932972[/C][/ROW]
[ROW][C]157[/C][C]491.029240541114[/C][C]446.09196301134[/C][C]535.966518070888[/C][/ROW]
[ROW][C]158[/C][C]466.922088041742[/C][C]421.090140639751[/C][C]512.754035443733[/C][/ROW]
[ROW][C]159[/C][C]504.567570878534[/C][C]457.816174748357[/C][C]551.318967008711[/C][/ROW]
[ROW][C]160[/C][C]540.788925443653[/C][C]493.093767563127[/C][C]588.48408332418[/C][/ROW]
[ROW][C]161[/C][C]549.871038956794[/C][C]501.208272912143[/C][C]598.533805001444[/C][/ROW]
[ROW][C]162[/C][C]609.419484539432[/C][C]559.765728963449[/C][C]659.073240115416[/C][/ROW]
[ROW][C]163[/C][C]690.141053781172[/C][C]639.473388773077[/C][C]740.808718789266[/C][/ROW]
[ROW][C]164[/C][C]674.993775707099[/C][C]623.289737529118[/C][C]726.69781388508[/C][/ROW]
[ROW][C]165[/C][C]577.286287736094[/C][C]524.52386205752[/C][C]630.048713414667[/C][/ROW]
[ROW][C]166[/C][C]528.256504876002[/C][C]474.414118810952[/C][C]582.098890941052[/C][/ROW]
[ROW][C]167[/C][C]461.990784053754[/C][C]407.04729721448[/C][C]516.934270893028[/C][/ROW]
[ROW][C]168[/C][C]507.063037579586[/C][C]450.997732343634[/C][C]563.128342815539[/C][/ROW]
[ROW][C]169[/C][C]528.560759330907[/C][C]464.861422671682[/C][C]592.260095990132[/C][/ROW]
[ROW][C]170[/C][C]504.453606831535[/C][C]439.708653578642[/C][C]569.198560084428[/C][/ROW]
[ROW][C]171[/C][C]542.099089668327[/C][C]476.286957564125[/C][C]607.911221772529[/C][/ROW]
[ROW][C]172[/C][C]578.320444233447[/C][C]511.419913345831[/C][C]645.220975121063[/C][/ROW]
[ROW][C]173[/C][C]587.402557746587[/C][C]519.3927486333[/C][C]655.412366859874[/C][/ROW]
[ROW][C]174[/C][C]646.951003329225[/C][C]577.811374309983[/C][C]716.090632348467[/C][/ROW]
[ROW][C]175[/C][C]727.672572570965[/C][C]657.382916219568[/C][C]797.962228922362[/C][/ROW]
[ROW][C]176[/C][C]712.525294496892[/C][C]641.065733457534[/C][C]783.98485553625[/C][/ROW]
[ROW][C]177[/C][C]614.817806525887[/C][C]542.168788749912[/C][C]687.466824301861[/C][/ROW]
[ROW][C]178[/C][C]565.788023665795[/C][C]491.930317157262[/C][C]639.645730174328[/C][/ROW]
[ROW][C]179[/C][C]499.522302843547[/C][C]424.436989994278[/C][C]574.607615692816[/C][/ROW]
[ROW][C]180[/C][C]544.59455636938[/C][C]468.263027956773[/C][C]620.926084781987[/C][/ROW]
[ROW][C]181[/C][C]566.0922781207[/C][C]483.006199790292[/C][C]649.178356451109[/C][/ROW]
[ROW][C]182[/C][C]541.985125621329[/C][C]457.700011407708[/C][C]626.270239834949[/C][/ROW]
[ROW][C]183[/C][C]579.63060845812[/C][C]494.127415792212[/C][C]665.133801124028[/C][/ROW]
[ROW][C]184[/C][C]615.85196302324[/C][C]529.111919746892[/C][C]702.592006299588[/C][/ROW]
[ROW][C]185[/C][C]624.93407653638[/C][C]536.938677829201[/C][C]712.92947524356[/C][/ROW]
[ROW][C]186[/C][C]684.482522119018[/C][C]595.213527079886[/C][C]773.751517158151[/C][/ROW]
[ROW][C]187[/C][C]765.204091360758[/C][C]674.643519290785[/C][C]855.764663430731[/C][/ROW]
[ROW][C]188[/C][C]750.056813286686[/C][C]658.186939716822[/C][C]841.92668685655[/C][/ROW]
[ROW][C]189[/C][C]652.34932531568[/C][C]559.152677818696[/C][C]745.545972812664[/C][/ROW]
[ROW][C]190[/C][C]603.319542455588[/C][C]508.778896278832[/C][C]697.860188632345[/C][/ROW]
[ROW][C]191[/C][C]537.053821633341[/C][C]441.152195185495[/C][C]632.955448081186[/C][/ROW]
[ROW][C]192[/C][C]582.126075159173[/C][C]484.846725381216[/C][C]679.40542493713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105498&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105498&T=3

As an alternative you can also use a QR Code:  

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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145453.497721751321428.415266965019478.580176537622
146429.390569251949403.496001603239455.285136900659
147467.036052088741440.301472551842493.770631625639
148503.25740665386475.65579207998530.85902122774
149512.339520167483.844695250901540.8343450831
150571.887965749639542.4745714567601.301360042577
151652.609534991378622.252994696776682.96607528598
152637.462256917306606.138741269286668.785772565325
153539.754768946301507.441160256023572.068377636578
154490.724986086209457.398842867372524.051129305046
155424.459265263961390.098787394923458.819743132999
156469.531518789793434.115513646614504.947523932972
157491.029240541114446.09196301134535.966518070888
158466.922088041742421.090140639751512.754035443733
159504.567570878534457.816174748357551.318967008711
160540.788925443653493.093767563127588.48408332418
161549.871038956794501.208272912143598.533805001444
162609.419484539432559.765728963449659.073240115416
163690.141053781172639.473388773077740.808718789266
164674.993775707099623.289737529118726.69781388508
165577.286287736094524.52386205752630.048713414667
166528.256504876002474.414118810952582.098890941052
167461.990784053754407.04729721448516.934270893028
168507.063037579586450.997732343634563.128342815539
169528.560759330907464.861422671682592.260095990132
170504.453606831535439.708653578642569.198560084428
171542.099089668327476.286957564125607.911221772529
172578.320444233447511.419913345831645.220975121063
173587.402557746587519.3927486333655.412366859874
174646.951003329225577.811374309983716.090632348467
175727.672572570965657.382916219568797.962228922362
176712.525294496892641.065733457534783.98485553625
177614.817806525887542.168788749912687.466824301861
178565.788023665795491.930317157262639.645730174328
179499.522302843547424.436989994278574.607615692816
180544.59455636938468.263027956773620.926084781987
181566.0922781207483.006199790292649.178356451109
182541.985125621329457.700011407708626.270239834949
183579.63060845812494.127415792212665.133801124028
184615.85196302324529.111919746892702.592006299588
185624.93407653638536.938677829201712.92947524356
186684.482522119018595.213527079886773.751517158151
187765.204091360758674.643519290785855.764663430731
188750.056813286686658.186939716822841.92668685655
189652.34932531568559.152677818696745.545972812664
190603.319542455588508.778896278832697.860188632345
191537.053821633341441.152195185495632.955448081186
192582.126075159173484.846725381216679.40542493713



Parameters (Session):
par1 = 3 ; par2 = equal ; par3 = 2 ; par4 = no ;
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$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, 48, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$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')