## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationFri, 15 May 2020 23:22:17 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/May/15/t1589577748oykneo7covvk47l.htm/, Retrieved Wed, 21 Apr 2021 06:56:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319154, Retrieved Wed, 21 Apr 2021 06:56:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact37
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2020-05-15 21:22:17] [d41d8cd98f00b204e9800998ecf8427e] [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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319154&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 1 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319154&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319154&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server Big Analytics Cloud Computing Center

 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.999933893038648 beta FALSE gamma FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.999933893038648 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319154&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]0.999933893038648[/C][/ROW]
[ROW][C]beta[/C][C]FALSE[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319154&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319154&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 Parameter Value alpha 0.999933893038648 beta FALSE gamma FALSE

 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 2 118 112 6 3 132 117.999603358232 14.0003966417681 4 129 131.99907447632 -2.99907447632032 5 121 129.0001982597 -8.00019825970048 6 135 121.000528868797 13.9994711312028 7 148 134.999074537503 13.000925462497 8 148 147.999140548323 0.000859451677087009 9 136 147.999999943184 -11.9999999431843 10 119 136.000793283532 -17.0007932835325 11 104 119.001123870785 -15.0011238707846 12 118 104.000991678716 13.999008321284 13 115 117.999074568098 -2.99907456809794 14 126 115.000198259707 10.9998017402934 15 141 125.999272836531 15.0007271634685 16 135 140.999008347509 -5.99900834750918 17 125 135.000396576213 -10.000396576213 18 149 125.00066109583 23.99933890417 19 170 148.998413476631 21.0015865233694 20 170 169.998611648931 0.00138835106861279 21 158 169.99999990822 -11.9999999082203 22 133 158.00079328353 -25.0007932835302 23 114 133.001652726475 -19.0016527264754 24 140 114.001256141522 25.9987438584776 25 145 139.998281302045 5.00171869795543 26 150 144.999669351575 5.00033064842467 27 178 149.999669443335 28.0003305566649 28 163 177.99814898323 -14.9981489832301 29 172 163.000991482055 8.99900851794482 30 178 171.999405102892 6.0005948971083 31 199 177.999603318905 21.0003966810949 32 199 198.998611727588 0.00138827241178774 33 184 198.999999908226 -14.9999999082255 34 162 184.000991604414 -22.0009916044142 35 146 162.001454418702 -16.0014544187017 36 166 146.001057807529 19.9989421924712 37 171 165.998677930701 5.0013220692986 38 180 170.999669377795 9.00033062220473 39 193 179.999405015491 13.0005949845086 40 181 192.99914057017 -11.9991405701698 41 183 181.000793226722 1.99920677327808 42 218 182.999867838515 35.0001321614849 43 230 217.997686247616 12.0023137523841 44 242 229.999206563509 12.0007934364913 45 209 241.999206664012 -32.9992066640121 46 191 209.00218147728 -18.0021814772796 47 172 191.001190069515 -19.0011900695152 48 194 172.001256110938 21.9987438890624 49 196 193.998545729888 2.00145427011205 50 196 195.99986768994 0.000132310060081409 51 236 195.999999991253 40.0000000087466 52 235 235.997355721545 -0.997355721545347 53 229 235.000065932156 -6.00006593215613 54 243 229.000396646127 13.9996033538733 55 264 242.999074528762 21.0009254712379 56 272 263.998611692632 8.00138830736847 57 237 271.999471052532 -34.9994710525324 58 211 237.00231370868 -26.0023137086802 59 180 211.001718933947 -31.0017189339474 60 201 180.002049429435 20.9979505705646 61 204 200.998611889293 3.00138811070684 62 188 203.999801587352 -15.9998015873522 63 235 188.001057698265 46.9989423017348 64 227 234.996893042738 -7.99689304273767 65 234 227.000528650299 6.99947134970068 66 264 233.999537286218 30.000462713782 67 302 263.998016760571 38.0019832394291 68 293 301.997487804363 -8.99748780436272 69 259 293.000594796579 -34.0005947965785 70 229 259.002247676006 -30.0022476760062 71 203 229.001983357428 -26.0019833574276 72 229 203.001718912109 25.9982810878911 73 242 228.998281332637 13.0017186673631 74 233 241.999140495887 -8.99914049588654 75 267 233.000594905833 33.999405094167 76 269 266.997752402641 2.00224759735852 77 270 268.999867637495 1.00013236250453 78 315 269.999933884289 45.0000661157114 79 364 314.997025182368 49.0029748176315 80 347 363.996760562238 -16.9967605622376 81 312 347.001123604194 -35.0011236041936 82 274 312.002313817925 -38.0023138179254 83 237 274.002512217491 -37.0025122174908 84 278 237.002446123645 40.9975538763549 85 284 277.99728977629 6.00271022370964 86 277 283.999603179067 -6.99960317906726 87 317 277.000462722497 39.9995372775032 88 313 316.997355752135 -3.99735575213509 89 318 313.000264253042 4.99973574695775 90 374 317.999669482662 56.0003305173378 91 413 373.996297988315 39.0037020116852 92 405 412.997421583779 -7.99742158377853 93 355 405.00052868524 -50.0005286852395 94 306 355.003305383017 -49.0033053830174 95 271 306.003239459615 -35.003239459615 96 306 271.002313957798 34.9976860422018 97 315 305.997686409321 9.00231359067863 98 301 314.999404884403 -13.9994048844034 99 356 301.000925458118 54.9990745418824 100 348 355.996364178305 -7.99636417830487 101 355 348.000528615338 6.99947138466234 102 422 354.999537286216 67.0004627137843 103 465 421.995570803001 43.0044291969992 104 467 464.997157107861 2.00284289213886 105 404 466.999867598142 -62.9998675981423 106 347 404.004164729812 -57.0041647298125 107 305 347.003768372115 -42.0037683721147 108 336 305.002776741492 30.9972232585076 109 340 335.99795086776 4.00204913223996 110 318 339.999735436693 -21.9997354366927 111 362 318.00145433566 43.9985456643398 112 348 361.997091389842 -13.9970913898422 113 363 348.00092530518 14.9990746948205 114 435 362.999008456749 72.0009915432512 115 491 434.995240233235 56.0047597667652 116 505 490.996297695511 14.0037023044894 117 404 504.999074257793 -100.999074257793 118 359 404.006676741899 -45.0066767418986 119 310 359.00297525464 -49.00297525464 120 337 310.003239437791 26.9967605622087 121 360 336.998215326193 23.0017846738072 122 342 359.99847942191 -17.9984794219096 123 406 342.001189824784 63.9988101752164 124 396 405.995769233129 -9.99576923312918 125 420 396.00066078993 23.9993392100696 126 472 419.99841347661 52.0015865233896 127 548 471.996562333129 76.0034376668706 128 559 547.994975643684 11.0050243563164 129 463 558.99927249128 -95.9992724912802 130 407 463.006346220196 -56.0063462201964 131 362 407.003702409365 -45.003702409365 132 405 362.002975058016 42.9970249419841 133 417 404.997157597334 12.0028424026661 134 391 416.999206528561 -25.9992065285612 135 419 391.001718728541 27.9982812714588 136 461 418.998149118702 42.0018508812979 137 472 460.997223385267 11.0027766147329 138 535 471.999272639872 63.0007273601285 139 622 534.995835213351 87.0041647866487 140 606 621.994248419041 -15.994248419041 141 508 606.001057331162 -98.0010573311621 142 461 508.006478552109 -47.0064785521095 143 390 461.003107455461 -71.003107455461 144 432 390.00469379968 41.9953062003196

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 118 & 112 & 6 \tabularnewline
3 & 132 & 117.999603358232 & 14.0003966417681 \tabularnewline
4 & 129 & 131.99907447632 & -2.99907447632032 \tabularnewline
5 & 121 & 129.0001982597 & -8.00019825970048 \tabularnewline
6 & 135 & 121.000528868797 & 13.9994711312028 \tabularnewline
7 & 148 & 134.999074537503 & 13.000925462497 \tabularnewline
8 & 148 & 147.999140548323 & 0.000859451677087009 \tabularnewline
9 & 136 & 147.999999943184 & -11.9999999431843 \tabularnewline
10 & 119 & 136.000793283532 & -17.0007932835325 \tabularnewline
11 & 104 & 119.001123870785 & -15.0011238707846 \tabularnewline
12 & 118 & 104.000991678716 & 13.999008321284 \tabularnewline
13 & 115 & 117.999074568098 & -2.99907456809794 \tabularnewline
14 & 126 & 115.000198259707 & 10.9998017402934 \tabularnewline
15 & 141 & 125.999272836531 & 15.0007271634685 \tabularnewline
16 & 135 & 140.999008347509 & -5.99900834750918 \tabularnewline
17 & 125 & 135.000396576213 & -10.000396576213 \tabularnewline
18 & 149 & 125.00066109583 & 23.99933890417 \tabularnewline
19 & 170 & 148.998413476631 & 21.0015865233694 \tabularnewline
20 & 170 & 169.998611648931 & 0.00138835106861279 \tabularnewline
21 & 158 & 169.99999990822 & -11.9999999082203 \tabularnewline
22 & 133 & 158.00079328353 & -25.0007932835302 \tabularnewline
23 & 114 & 133.001652726475 & -19.0016527264754 \tabularnewline
24 & 140 & 114.001256141522 & 25.9987438584776 \tabularnewline
25 & 145 & 139.998281302045 & 5.00171869795543 \tabularnewline
26 & 150 & 144.999669351575 & 5.00033064842467 \tabularnewline
27 & 178 & 149.999669443335 & 28.0003305566649 \tabularnewline
28 & 163 & 177.99814898323 & -14.9981489832301 \tabularnewline
29 & 172 & 163.000991482055 & 8.99900851794482 \tabularnewline
30 & 178 & 171.999405102892 & 6.0005948971083 \tabularnewline
31 & 199 & 177.999603318905 & 21.0003966810949 \tabularnewline
32 & 199 & 198.998611727588 & 0.00138827241178774 \tabularnewline
33 & 184 & 198.999999908226 & -14.9999999082255 \tabularnewline
34 & 162 & 184.000991604414 & -22.0009916044142 \tabularnewline
35 & 146 & 162.001454418702 & -16.0014544187017 \tabularnewline
36 & 166 & 146.001057807529 & 19.9989421924712 \tabularnewline
37 & 171 & 165.998677930701 & 5.0013220692986 \tabularnewline
38 & 180 & 170.999669377795 & 9.00033062220473 \tabularnewline
39 & 193 & 179.999405015491 & 13.0005949845086 \tabularnewline
40 & 181 & 192.99914057017 & -11.9991405701698 \tabularnewline
41 & 183 & 181.000793226722 & 1.99920677327808 \tabularnewline
42 & 218 & 182.999867838515 & 35.0001321614849 \tabularnewline
43 & 230 & 217.997686247616 & 12.0023137523841 \tabularnewline
44 & 242 & 229.999206563509 & 12.0007934364913 \tabularnewline
45 & 209 & 241.999206664012 & -32.9992066640121 \tabularnewline
46 & 191 & 209.00218147728 & -18.0021814772796 \tabularnewline
47 & 172 & 191.001190069515 & -19.0011900695152 \tabularnewline
48 & 194 & 172.001256110938 & 21.9987438890624 \tabularnewline
49 & 196 & 193.998545729888 & 2.00145427011205 \tabularnewline
50 & 196 & 195.99986768994 & 0.000132310060081409 \tabularnewline
51 & 236 & 195.999999991253 & 40.0000000087466 \tabularnewline
52 & 235 & 235.997355721545 & -0.997355721545347 \tabularnewline
53 & 229 & 235.000065932156 & -6.00006593215613 \tabularnewline
54 & 243 & 229.000396646127 & 13.9996033538733 \tabularnewline
55 & 264 & 242.999074528762 & 21.0009254712379 \tabularnewline
56 & 272 & 263.998611692632 & 8.00138830736847 \tabularnewline
57 & 237 & 271.999471052532 & -34.9994710525324 \tabularnewline
58 & 211 & 237.00231370868 & -26.0023137086802 \tabularnewline
59 & 180 & 211.001718933947 & -31.0017189339474 \tabularnewline
60 & 201 & 180.002049429435 & 20.9979505705646 \tabularnewline
61 & 204 & 200.998611889293 & 3.00138811070684 \tabularnewline
62 & 188 & 203.999801587352 & -15.9998015873522 \tabularnewline
63 & 235 & 188.001057698265 & 46.9989423017348 \tabularnewline
64 & 227 & 234.996893042738 & -7.99689304273767 \tabularnewline
65 & 234 & 227.000528650299 & 6.99947134970068 \tabularnewline
66 & 264 & 233.999537286218 & 30.000462713782 \tabularnewline
67 & 302 & 263.998016760571 & 38.0019832394291 \tabularnewline
68 & 293 & 301.997487804363 & -8.99748780436272 \tabularnewline
69 & 259 & 293.000594796579 & -34.0005947965785 \tabularnewline
70 & 229 & 259.002247676006 & -30.0022476760062 \tabularnewline
71 & 203 & 229.001983357428 & -26.0019833574276 \tabularnewline
72 & 229 & 203.001718912109 & 25.9982810878911 \tabularnewline
73 & 242 & 228.998281332637 & 13.0017186673631 \tabularnewline
74 & 233 & 241.999140495887 & -8.99914049588654 \tabularnewline
75 & 267 & 233.000594905833 & 33.999405094167 \tabularnewline
76 & 269 & 266.997752402641 & 2.00224759735852 \tabularnewline
77 & 270 & 268.999867637495 & 1.00013236250453 \tabularnewline
78 & 315 & 269.999933884289 & 45.0000661157114 \tabularnewline
79 & 364 & 314.997025182368 & 49.0029748176315 \tabularnewline
80 & 347 & 363.996760562238 & -16.9967605622376 \tabularnewline
81 & 312 & 347.001123604194 & -35.0011236041936 \tabularnewline
82 & 274 & 312.002313817925 & -38.0023138179254 \tabularnewline
83 & 237 & 274.002512217491 & -37.0025122174908 \tabularnewline
84 & 278 & 237.002446123645 & 40.9975538763549 \tabularnewline
85 & 284 & 277.99728977629 & 6.00271022370964 \tabularnewline
86 & 277 & 283.999603179067 & -6.99960317906726 \tabularnewline
87 & 317 & 277.000462722497 & 39.9995372775032 \tabularnewline
88 & 313 & 316.997355752135 & -3.99735575213509 \tabularnewline
89 & 318 & 313.000264253042 & 4.99973574695775 \tabularnewline
90 & 374 & 317.999669482662 & 56.0003305173378 \tabularnewline
91 & 413 & 373.996297988315 & 39.0037020116852 \tabularnewline
92 & 405 & 412.997421583779 & -7.99742158377853 \tabularnewline
93 & 355 & 405.00052868524 & -50.0005286852395 \tabularnewline
94 & 306 & 355.003305383017 & -49.0033053830174 \tabularnewline
95 & 271 & 306.003239459615 & -35.003239459615 \tabularnewline
96 & 306 & 271.002313957798 & 34.9976860422018 \tabularnewline
97 & 315 & 305.997686409321 & 9.00231359067863 \tabularnewline
98 & 301 & 314.999404884403 & -13.9994048844034 \tabularnewline
99 & 356 & 301.000925458118 & 54.9990745418824 \tabularnewline
100 & 348 & 355.996364178305 & -7.99636417830487 \tabularnewline
101 & 355 & 348.000528615338 & 6.99947138466234 \tabularnewline
102 & 422 & 354.999537286216 & 67.0004627137843 \tabularnewline
103 & 465 & 421.995570803001 & 43.0044291969992 \tabularnewline
104 & 467 & 464.997157107861 & 2.00284289213886 \tabularnewline
105 & 404 & 466.999867598142 & -62.9998675981423 \tabularnewline
106 & 347 & 404.004164729812 & -57.0041647298125 \tabularnewline
107 & 305 & 347.003768372115 & -42.0037683721147 \tabularnewline
108 & 336 & 305.002776741492 & 30.9972232585076 \tabularnewline
109 & 340 & 335.99795086776 & 4.00204913223996 \tabularnewline
110 & 318 & 339.999735436693 & -21.9997354366927 \tabularnewline
111 & 362 & 318.00145433566 & 43.9985456643398 \tabularnewline
112 & 348 & 361.997091389842 & -13.9970913898422 \tabularnewline
113 & 363 & 348.00092530518 & 14.9990746948205 \tabularnewline
114 & 435 & 362.999008456749 & 72.0009915432512 \tabularnewline
115 & 491 & 434.995240233235 & 56.0047597667652 \tabularnewline
116 & 505 & 490.996297695511 & 14.0037023044894 \tabularnewline
117 & 404 & 504.999074257793 & -100.999074257793 \tabularnewline
118 & 359 & 404.006676741899 & -45.0066767418986 \tabularnewline
119 & 310 & 359.00297525464 & -49.00297525464 \tabularnewline
120 & 337 & 310.003239437791 & 26.9967605622087 \tabularnewline
121 & 360 & 336.998215326193 & 23.0017846738072 \tabularnewline
122 & 342 & 359.99847942191 & -17.9984794219096 \tabularnewline
123 & 406 & 342.001189824784 & 63.9988101752164 \tabularnewline
124 & 396 & 405.995769233129 & -9.99576923312918 \tabularnewline
125 & 420 & 396.00066078993 & 23.9993392100696 \tabularnewline
126 & 472 & 419.99841347661 & 52.0015865233896 \tabularnewline
127 & 548 & 471.996562333129 & 76.0034376668706 \tabularnewline
128 & 559 & 547.994975643684 & 11.0050243563164 \tabularnewline
129 & 463 & 558.99927249128 & -95.9992724912802 \tabularnewline
130 & 407 & 463.006346220196 & -56.0063462201964 \tabularnewline
131 & 362 & 407.003702409365 & -45.003702409365 \tabularnewline
132 & 405 & 362.002975058016 & 42.9970249419841 \tabularnewline
133 & 417 & 404.997157597334 & 12.0028424026661 \tabularnewline
134 & 391 & 416.999206528561 & -25.9992065285612 \tabularnewline
135 & 419 & 391.001718728541 & 27.9982812714588 \tabularnewline
136 & 461 & 418.998149118702 & 42.0018508812979 \tabularnewline
137 & 472 & 460.997223385267 & 11.0027766147329 \tabularnewline
138 & 535 & 471.999272639872 & 63.0007273601285 \tabularnewline
139 & 622 & 534.995835213351 & 87.0041647866487 \tabularnewline
140 & 606 & 621.994248419041 & -15.994248419041 \tabularnewline
141 & 508 & 606.001057331162 & -98.0010573311621 \tabularnewline
142 & 461 & 508.006478552109 & -47.0064785521095 \tabularnewline
143 & 390 & 461.003107455461 & -71.003107455461 \tabularnewline
144 & 432 & 390.00469379968 & 41.9953062003196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319154&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]2[/C][C]118[/C][C]112[/C][C]6[/C][/ROW]
[ROW][C]3[/C][C]132[/C][C]117.999603358232[/C][C]14.0003966417681[/C][/ROW]
[ROW][C]4[/C][C]129[/C][C]131.99907447632[/C][C]-2.99907447632032[/C][/ROW]
[ROW][C]5[/C][C]121[/C][C]129.0001982597[/C][C]-8.00019825970048[/C][/ROW]
[ROW][C]6[/C][C]135[/C][C]121.000528868797[/C][C]13.9994711312028[/C][/ROW]
[ROW][C]7[/C][C]148[/C][C]134.999074537503[/C][C]13.000925462497[/C][/ROW]
[ROW][C]8[/C][C]148[/C][C]147.999140548323[/C][C]0.000859451677087009[/C][/ROW]
[ROW][C]9[/C][C]136[/C][C]147.999999943184[/C][C]-11.9999999431843[/C][/ROW]
[ROW][C]10[/C][C]119[/C][C]136.000793283532[/C][C]-17.0007932835325[/C][/ROW]
[ROW][C]11[/C][C]104[/C][C]119.001123870785[/C][C]-15.0011238707846[/C][/ROW]
[ROW][C]12[/C][C]118[/C][C]104.000991678716[/C][C]13.999008321284[/C][/ROW]
[ROW][C]13[/C][C]115[/C][C]117.999074568098[/C][C]-2.99907456809794[/C][/ROW]
[ROW][C]14[/C][C]126[/C][C]115.000198259707[/C][C]10.9998017402934[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]125.999272836531[/C][C]15.0007271634685[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]140.999008347509[/C][C]-5.99900834750918[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]135.000396576213[/C][C]-10.000396576213[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]125.00066109583[/C][C]23.99933890417[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]148.998413476631[/C][C]21.0015865233694[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]169.998611648931[/C][C]0.00138835106861279[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]169.99999990822[/C][C]-11.9999999082203[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]158.00079328353[/C][C]-25.0007932835302[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]133.001652726475[/C][C]-19.0016527264754[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]114.001256141522[/C][C]25.9987438584776[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]139.998281302045[/C][C]5.00171869795543[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]144.999669351575[/C][C]5.00033064842467[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]149.999669443335[/C][C]28.0003305566649[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]177.99814898323[/C][C]-14.9981489832301[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]163.000991482055[/C][C]8.99900851794482[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]171.999405102892[/C][C]6.0005948971083[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]177.999603318905[/C][C]21.0003966810949[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]198.998611727588[/C][C]0.00138827241178774[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]198.999999908226[/C][C]-14.9999999082255[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]184.000991604414[/C][C]-22.0009916044142[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]162.001454418702[/C][C]-16.0014544187017[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]146.001057807529[/C][C]19.9989421924712[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]165.998677930701[/C][C]5.0013220692986[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]170.999669377795[/C][C]9.00033062220473[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]179.999405015491[/C][C]13.0005949845086[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]192.99914057017[/C][C]-11.9991405701698[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]181.000793226722[/C][C]1.99920677327808[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]182.999867838515[/C][C]35.0001321614849[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]217.997686247616[/C][C]12.0023137523841[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]229.999206563509[/C][C]12.0007934364913[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]241.999206664012[/C][C]-32.9992066640121[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]209.00218147728[/C][C]-18.0021814772796[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]191.001190069515[/C][C]-19.0011900695152[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]172.001256110938[/C][C]21.9987438890624[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]193.998545729888[/C][C]2.00145427011205[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]195.99986768994[/C][C]0.000132310060081409[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]195.999999991253[/C][C]40.0000000087466[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]235.997355721545[/C][C]-0.997355721545347[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]235.000065932156[/C][C]-6.00006593215613[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]229.000396646127[/C][C]13.9996033538733[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]242.999074528762[/C][C]21.0009254712379[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]263.998611692632[/C][C]8.00138830736847[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]271.999471052532[/C][C]-34.9994710525324[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]237.00231370868[/C][C]-26.0023137086802[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]211.001718933947[/C][C]-31.0017189339474[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]180.002049429435[/C][C]20.9979505705646[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]200.998611889293[/C][C]3.00138811070684[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]203.999801587352[/C][C]-15.9998015873522[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]188.001057698265[/C][C]46.9989423017348[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]234.996893042738[/C][C]-7.99689304273767[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]227.000528650299[/C][C]6.99947134970068[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]233.999537286218[/C][C]30.000462713782[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]263.998016760571[/C][C]38.0019832394291[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]301.997487804363[/C][C]-8.99748780436272[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]293.000594796579[/C][C]-34.0005947965785[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]259.002247676006[/C][C]-30.0022476760062[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]229.001983357428[/C][C]-26.0019833574276[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]203.001718912109[/C][C]25.9982810878911[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]228.998281332637[/C][C]13.0017186673631[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]241.999140495887[/C][C]-8.99914049588654[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]233.000594905833[/C][C]33.999405094167[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]266.997752402641[/C][C]2.00224759735852[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]268.999867637495[/C][C]1.00013236250453[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]269.999933884289[/C][C]45.0000661157114[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]314.997025182368[/C][C]49.0029748176315[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]363.996760562238[/C][C]-16.9967605622376[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]347.001123604194[/C][C]-35.0011236041936[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]312.002313817925[/C][C]-38.0023138179254[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]274.002512217491[/C][C]-37.0025122174908[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]237.002446123645[/C][C]40.9975538763549[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]277.99728977629[/C][C]6.00271022370964[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]283.999603179067[/C][C]-6.99960317906726[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]277.000462722497[/C][C]39.9995372775032[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]316.997355752135[/C][C]-3.99735575213509[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]313.000264253042[/C][C]4.99973574695775[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]317.999669482662[/C][C]56.0003305173378[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]373.996297988315[/C][C]39.0037020116852[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]412.997421583779[/C][C]-7.99742158377853[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]405.00052868524[/C][C]-50.0005286852395[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]355.003305383017[/C][C]-49.0033053830174[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]306.003239459615[/C][C]-35.003239459615[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]271.002313957798[/C][C]34.9976860422018[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]305.997686409321[/C][C]9.00231359067863[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]314.999404884403[/C][C]-13.9994048844034[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]301.000925458118[/C][C]54.9990745418824[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]355.996364178305[/C][C]-7.99636417830487[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]348.000528615338[/C][C]6.99947138466234[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]354.999537286216[/C][C]67.0004627137843[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]421.995570803001[/C][C]43.0044291969992[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]464.997157107861[/C][C]2.00284289213886[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]466.999867598142[/C][C]-62.9998675981423[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]404.004164729812[/C][C]-57.0041647298125[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]347.003768372115[/C][C]-42.0037683721147[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]305.002776741492[/C][C]30.9972232585076[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]335.99795086776[/C][C]4.00204913223996[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]339.999735436693[/C][C]-21.9997354366927[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]318.00145433566[/C][C]43.9985456643398[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]361.997091389842[/C][C]-13.9970913898422[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]348.00092530518[/C][C]14.9990746948205[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]362.999008456749[/C][C]72.0009915432512[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]434.995240233235[/C][C]56.0047597667652[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]490.996297695511[/C][C]14.0037023044894[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]504.999074257793[/C][C]-100.999074257793[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]404.006676741899[/C][C]-45.0066767418986[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]359.00297525464[/C][C]-49.00297525464[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]310.003239437791[/C][C]26.9967605622087[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]336.998215326193[/C][C]23.0017846738072[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]359.99847942191[/C][C]-17.9984794219096[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]342.001189824784[/C][C]63.9988101752164[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]405.995769233129[/C][C]-9.99576923312918[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]396.00066078993[/C][C]23.9993392100696[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]419.99841347661[/C][C]52.0015865233896[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]471.996562333129[/C][C]76.0034376668706[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]547.994975643684[/C][C]11.0050243563164[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]558.99927249128[/C][C]-95.9992724912802[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]463.006346220196[/C][C]-56.0063462201964[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]407.003702409365[/C][C]-45.003702409365[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]362.002975058016[/C][C]42.9970249419841[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]404.997157597334[/C][C]12.0028424026661[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]416.999206528561[/C][C]-25.9992065285612[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]391.001718728541[/C][C]27.9982812714588[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]418.998149118702[/C][C]42.0018508812979[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]460.997223385267[/C][C]11.0027766147329[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]471.999272639872[/C][C]63.0007273601285[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]534.995835213351[/C][C]87.0041647866487[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]621.994248419041[/C][C]-15.994248419041[/C][/ROW]
[ROW][C]141[/C][C]508[/C][C]606.001057331162[/C][C]-98.0010573311621[/C][/ROW]
[ROW][C]142[/C][C]461[/C][C]508.006478552109[/C][C]-47.0064785521095[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]461.003107455461[/C][C]-71.003107455461[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]390.00469379968[/C][C]41.9953062003196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319154&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319154&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 t Observed Fitted Residuals 2 118 112 6 3 132 117.999603358232 14.0003966417681 4 129 131.99907447632 -2.99907447632032 5 121 129.0001982597 -8.00019825970048 6 135 121.000528868797 13.9994711312028 7 148 134.999074537503 13.000925462497 8 148 147.999140548323 0.000859451677087009 9 136 147.999999943184 -11.9999999431843 10 119 136.000793283532 -17.0007932835325 11 104 119.001123870785 -15.0011238707846 12 118 104.000991678716 13.999008321284 13 115 117.999074568098 -2.99907456809794 14 126 115.000198259707 10.9998017402934 15 141 125.999272836531 15.0007271634685 16 135 140.999008347509 -5.99900834750918 17 125 135.000396576213 -10.000396576213 18 149 125.00066109583 23.99933890417 19 170 148.998413476631 21.0015865233694 20 170 169.998611648931 0.00138835106861279 21 158 169.99999990822 -11.9999999082203 22 133 158.00079328353 -25.0007932835302 23 114 133.001652726475 -19.0016527264754 24 140 114.001256141522 25.9987438584776 25 145 139.998281302045 5.00171869795543 26 150 144.999669351575 5.00033064842467 27 178 149.999669443335 28.0003305566649 28 163 177.99814898323 -14.9981489832301 29 172 163.000991482055 8.99900851794482 30 178 171.999405102892 6.0005948971083 31 199 177.999603318905 21.0003966810949 32 199 198.998611727588 0.00138827241178774 33 184 198.999999908226 -14.9999999082255 34 162 184.000991604414 -22.0009916044142 35 146 162.001454418702 -16.0014544187017 36 166 146.001057807529 19.9989421924712 37 171 165.998677930701 5.0013220692986 38 180 170.999669377795 9.00033062220473 39 193 179.999405015491 13.0005949845086 40 181 192.99914057017 -11.9991405701698 41 183 181.000793226722 1.99920677327808 42 218 182.999867838515 35.0001321614849 43 230 217.997686247616 12.0023137523841 44 242 229.999206563509 12.0007934364913 45 209 241.999206664012 -32.9992066640121 46 191 209.00218147728 -18.0021814772796 47 172 191.001190069515 -19.0011900695152 48 194 172.001256110938 21.9987438890624 49 196 193.998545729888 2.00145427011205 50 196 195.99986768994 0.000132310060081409 51 236 195.999999991253 40.0000000087466 52 235 235.997355721545 -0.997355721545347 53 229 235.000065932156 -6.00006593215613 54 243 229.000396646127 13.9996033538733 55 264 242.999074528762 21.0009254712379 56 272 263.998611692632 8.00138830736847 57 237 271.999471052532 -34.9994710525324 58 211 237.00231370868 -26.0023137086802 59 180 211.001718933947 -31.0017189339474 60 201 180.002049429435 20.9979505705646 61 204 200.998611889293 3.00138811070684 62 188 203.999801587352 -15.9998015873522 63 235 188.001057698265 46.9989423017348 64 227 234.996893042738 -7.99689304273767 65 234 227.000528650299 6.99947134970068 66 264 233.999537286218 30.000462713782 67 302 263.998016760571 38.0019832394291 68 293 301.997487804363 -8.99748780436272 69 259 293.000594796579 -34.0005947965785 70 229 259.002247676006 -30.0022476760062 71 203 229.001983357428 -26.0019833574276 72 229 203.001718912109 25.9982810878911 73 242 228.998281332637 13.0017186673631 74 233 241.999140495887 -8.99914049588654 75 267 233.000594905833 33.999405094167 76 269 266.997752402641 2.00224759735852 77 270 268.999867637495 1.00013236250453 78 315 269.999933884289 45.0000661157114 79 364 314.997025182368 49.0029748176315 80 347 363.996760562238 -16.9967605622376 81 312 347.001123604194 -35.0011236041936 82 274 312.002313817925 -38.0023138179254 83 237 274.002512217491 -37.0025122174908 84 278 237.002446123645 40.9975538763549 85 284 277.99728977629 6.00271022370964 86 277 283.999603179067 -6.99960317906726 87 317 277.000462722497 39.9995372775032 88 313 316.997355752135 -3.99735575213509 89 318 313.000264253042 4.99973574695775 90 374 317.999669482662 56.0003305173378 91 413 373.996297988315 39.0037020116852 92 405 412.997421583779 -7.99742158377853 93 355 405.00052868524 -50.0005286852395 94 306 355.003305383017 -49.0033053830174 95 271 306.003239459615 -35.003239459615 96 306 271.002313957798 34.9976860422018 97 315 305.997686409321 9.00231359067863 98 301 314.999404884403 -13.9994048844034 99 356 301.000925458118 54.9990745418824 100 348 355.996364178305 -7.99636417830487 101 355 348.000528615338 6.99947138466234 102 422 354.999537286216 67.0004627137843 103 465 421.995570803001 43.0044291969992 104 467 464.997157107861 2.00284289213886 105 404 466.999867598142 -62.9998675981423 106 347 404.004164729812 -57.0041647298125 107 305 347.003768372115 -42.0037683721147 108 336 305.002776741492 30.9972232585076 109 340 335.99795086776 4.00204913223996 110 318 339.999735436693 -21.9997354366927 111 362 318.00145433566 43.9985456643398 112 348 361.997091389842 -13.9970913898422 113 363 348.00092530518 14.9990746948205 114 435 362.999008456749 72.0009915432512 115 491 434.995240233235 56.0047597667652 116 505 490.996297695511 14.0037023044894 117 404 504.999074257793 -100.999074257793 118 359 404.006676741899 -45.0066767418986 119 310 359.00297525464 -49.00297525464 120 337 310.003239437791 26.9967605622087 121 360 336.998215326193 23.0017846738072 122 342 359.99847942191 -17.9984794219096 123 406 342.001189824784 63.9988101752164 124 396 405.995769233129 -9.99576923312918 125 420 396.00066078993 23.9993392100696 126 472 419.99841347661 52.0015865233896 127 548 471.996562333129 76.0034376668706 128 559 547.994975643684 11.0050243563164 129 463 558.99927249128 -95.9992724912802 130 407 463.006346220196 -56.0063462201964 131 362 407.003702409365 -45.003702409365 132 405 362.002975058016 42.9970249419841 133 417 404.997157597334 12.0028424026661 134 391 416.999206528561 -25.9992065285612 135 419 391.001718728541 27.9982812714588 136 461 418.998149118702 42.0018508812979 137 472 460.997223385267 11.0027766147329 138 535 471.999272639872 63.0007273601285 139 622 534.995835213351 87.0041647866487 140 606 621.994248419041 -15.994248419041 141 508 606.001057331162 -98.0010573311621 142 461 508.006478552109 -47.0064785521095 143 390 461.003107455461 -71.003107455461 144 432 390.00469379968 41.9953062003196

 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 145 431.997223817916 365.838723296861 498.155724338972 146 431.997223817916 338.43806762164 525.556380014192 147 431.997223817916 317.412389633265 546.582058002567 148 431.997223817916 299.686783027748 564.307664608084 149 431.997223817916 284.070142933268 579.924304702565 150 431.997223817916 269.951582790402 594.04286484543 151 431.997223817916 256.968202530066 607.026245105766 152 431.997223817916 244.883550319832 619.110897316 153 431.997223817916 233.533384978413 630.461062657419 154 431.997223817916 222.798122854559 641.196324781273 155 431.997223817916 212.587487568948 651.406960066884 156 431.997223817916 202.831343116171 661.163104519661

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 431.997223817916 & 365.838723296861 & 498.155724338972 \tabularnewline
146 & 431.997223817916 & 338.43806762164 & 525.556380014192 \tabularnewline
147 & 431.997223817916 & 317.412389633265 & 546.582058002567 \tabularnewline
148 & 431.997223817916 & 299.686783027748 & 564.307664608084 \tabularnewline
149 & 431.997223817916 & 284.070142933268 & 579.924304702565 \tabularnewline
150 & 431.997223817916 & 269.951582790402 & 594.04286484543 \tabularnewline
151 & 431.997223817916 & 256.968202530066 & 607.026245105766 \tabularnewline
152 & 431.997223817916 & 244.883550319832 & 619.110897316 \tabularnewline
153 & 431.997223817916 & 233.533384978413 & 630.461062657419 \tabularnewline
154 & 431.997223817916 & 222.798122854559 & 641.196324781273 \tabularnewline
155 & 431.997223817916 & 212.587487568948 & 651.406960066884 \tabularnewline
156 & 431.997223817916 & 202.831343116171 & 661.163104519661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319154&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]431.997223817916[/C][C]365.838723296861[/C][C]498.155724338972[/C][/ROW]
[ROW][C]146[/C][C]431.997223817916[/C][C]338.43806762164[/C][C]525.556380014192[/C][/ROW]
[ROW][C]147[/C][C]431.997223817916[/C][C]317.412389633265[/C][C]546.582058002567[/C][/ROW]
[ROW][C]148[/C][C]431.997223817916[/C][C]299.686783027748[/C][C]564.307664608084[/C][/ROW]
[ROW][C]149[/C][C]431.997223817916[/C][C]284.070142933268[/C][C]579.924304702565[/C][/ROW]
[ROW][C]150[/C][C]431.997223817916[/C][C]269.951582790402[/C][C]594.04286484543[/C][/ROW]
[ROW][C]151[/C][C]431.997223817916[/C][C]256.968202530066[/C][C]607.026245105766[/C][/ROW]
[ROW][C]152[/C][C]431.997223817916[/C][C]244.883550319832[/C][C]619.110897316[/C][/ROW]
[ROW][C]153[/C][C]431.997223817916[/C][C]233.533384978413[/C][C]630.461062657419[/C][/ROW]
[ROW][C]154[/C][C]431.997223817916[/C][C]222.798122854559[/C][C]641.196324781273[/C][/ROW]
[ROW][C]155[/C][C]431.997223817916[/C][C]212.587487568948[/C][C]651.406960066884[/C][/ROW]
[ROW][C]156[/C][C]431.997223817916[/C][C]202.831343116171[/C][C]661.163104519661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319154&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319154&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 t Forecast 95% Lower Bound 95% Upper Bound 145 431.997223817916 365.838723296861 498.155724338972 146 431.997223817916 338.43806762164 525.556380014192 147 431.997223817916 317.412389633265 546.582058002567 148 431.997223817916 299.686783027748 564.307664608084 149 431.997223817916 284.070142933268 579.924304702565 150 431.997223817916 269.951582790402 594.04286484543 151 431.997223817916 256.968202530066 607.026245105766 152 431.997223817916 244.883550319832 619.110897316 153 431.997223817916 233.533384978413 630.461062657419 154 431.997223817916 222.798122854559 641.196324781273 155 431.997223817916 212.587487568948 651.406960066884 156 431.997223817916 202.831343116171 661.163104519661

par1 <- as.numeric(par1)par4 <- as.numeric(par4)if (par2 == 'Single') K <- 1if (par2 == 'Double') K <- 2if (par2 == 'Triple') K <- par1nx <- length(x)nxmK <- nx - Kx <- 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)fitmyresid <- x - fit$fitted[,'xhat']bitmap(file='test1.png')op <- par(mfrow=c(2,1))plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')par(op)dev.off()bitmap(file='test2.png')p <- predict(fit, par4, prediction.interval=TRUE)np <- length(p[,1])plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')dev.off()bitmap(file='test3.png')op <- par(mfrow = c(2,2))acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')spectrum(myresid,main='Residals Periodogram')cpgram(myresid,main='Residal Cumulative Periodogram')qqnorm(myresid,main='Residual Normal QQ Plot')qqline(myresid)par(op)dev.off()load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'Value',header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'alpha',header=TRUE)a<-table.element(a,fit$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')