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Author

*The author of this computation has been verified*

R Software Module

rwasp_exponentialsmoothing.wasp

Title produced by software

Exponential Smoothing

Date of computation

Fri, 16 Dec 2016 06:55:18 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/16/t14818681509hd0egskddors0v.htm/, Retrieved Thu, 02 May 2024 19:16:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300056, Retrieved Thu, 02 May 2024 19:16:14 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

108

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Exponential Smoothing] [] [2016-12-16 05:55:18] [219800a2f11ddd28e3280d87dbde8c8d] [Current]

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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=300056&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=300056&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300056&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	0.0066704308734047
beta	FALSE
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.0066704308734047 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300056&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]0.0066704308734047[/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=300056&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300056&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	0.0066704308734047
beta	FALSE
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
2	101	100	1
3	102	100.006670430873	1.9933295691266
4	103	100.019966797972	2.98003320202783
5	99	100.039844903447	-1.03984490344675
6	101	100.032908689899	0.967091310100756
7	100	100.039359605632	-0.0393596056315459
8	101	100.039097060103	0.960902939897025
9	100	100.04550669674	-0.0455066967396078
10	100	100.045203147465	-0.0452031474647328
11	101	100.044901622994	0.955098377005683
12	100	100.051272540695	-0.051272540695436
13	99	100.050930530757	-1.05093053075703
14	99	100.043920371299	-1.04392037129887
15	102	100.036956972625	1.96304302737522
16	99	100.05005131544	-1.05005131544041
17	101	100.043047020727	0.956952979272756
18	99	100.049430309425	-1.04943030942458
19	99	100.042430157089	-1.0424301570891
20	99	100.035476698786	-1.03547669878589
21	100	100.028569623046	-0.0285696230456125
22	99	100.02837905135	-1.02837905135002
23	99	100.021519319976	-1.02151931997632
24	100	100.014705345967	-0.0147053459665614
25	104	100.014607254973	3.98539274502717
26	100	100.041191541782	-0.0411915417818989
27	102	100.04091677645	1.95908322355012
28	100	100.053984705668	-0.0539847056678155
29	99	100.05362460442	-1.05362460442045
30	99	100.04659647433	-1.04659647433014
31	100	100.039615224896	-0.0396152248957691
32	101	100.039350974277	0.960649025723427
33	100	100.045758917196	-0.0457589171962667
34	102	100.045453685502	1.95454631449773
35	98	100.058491351582	-2.05849135158201
36	100	100.044760327318	-0.0447603273177748
37	100	100.044461756649	-0.0444617566485306
38	101	100.044165177574	0.9558348224257
39	100	100.050541007684	-0.0505410076836768
40	102	100.050203877386	1.94979612261434
41	99	100.063209857639	-1.0632098576388
42	99	100.056117789779	-1.05611778977949
43	100	100.049073029069	-0.0490730290685946
44	99	100.04874569082	-1.04874569082045
45	99	100.041750105186	-1.04175010518605
46	99	100.034801183122	-1.03480118312204
47	100	100.027898613362	-0.0278986133623107
48	101	100.02771251759	0.972287482409584
49	100	100.034198094031	-0.0341980940308986
50	101	100.033969978009	0.96603002199133
51	98	100.040413814492	-2.040413814492
52	101	100.026803375189	0.973196624810711
53	101	100.033295016001	0.966704983998682
54	100	100.039743354772	-0.0397433547720567
55	101	100.039478249471	0.960521750528628
56	99	100.045885343411	-1.04588534341067
57	101	100.038908837526	0.961091162474062
58	101	100.045319729688	0.954680270311741
59	99	100.051687858438	-1.05168785843757
60	98	100.044672647277	-2.04467264727747
61	99	100.031033799725	-1.03103379972507
62	102	100.024156360036	1.97584363996415
63	97	100.037336088453	-3.03733608845289
64	100	100.017075748036	-0.0170757480355661
65	100	100.016961845439	-0.0169618454386864
66	100	100.016848702621	-0.0168487026212034
67	98	100.016736314515	-2.01673631451507
68	101	100.003283814339	0.996716185660787
69	101	100.009932340756	0.990067659243934
70	100	100.016536518637	-0.0165365186370394
71	100	100.016426212933	-0.0164262129325863
72	98	100.016316643015	-2.01631664301472
73	98	100.002866942229	-2.00286694222859
74	101	99.9895069567418	1.01049304325817
75	103	99.9962473807349	3.00375261926506
76	100	100.016283704943	-0.0162837049425661
77	101	100.016175085614	0.983824914385607
78	100	100.022737621697	-0.0227376216973312
79	100	100.022585951964	-0.0225859519635776
80	101	100.022435293932	0.977564706067696
81	101	100.028956071728	0.971043928271598
82	104	100.035433353127	3.96456664687302
83	101	100.061878720888	0.938121279112053
84	98	100.068136394031	-2.06813639403113
85	101	100.054341033178	0.945658966822023
86	100	100.060648985946	-0.0606489859459742
87	99	100.060244431078	-1.06024443107769
88	100	100.053172143891	-0.053172143891274
89	104	100.052817462781	3.94718253721894
90	100	100.07914687104	-0.0791468710402938
91	101	100.078618927308	0.921381072691815
92	100	100.084764936062	-0.0847649360616316
93	104	100.084199517415	3.91580048258486
94	101	100.110319593848	0.88968040615174
95	100	100.116254145497	-0.116254145496924
96	100	100.115478680256	-0.115478680255649
97	99	100.114708387702	-1.11470838770165
98	100	100.107272802457	-0.107272802457487
99	99	100.106557246644	-1.1065572466441
100	100	100.099176033023	-0.0991760330228999
101	100	100.09851448615	-0.0985144861503215
102	99	100.09785735208	-1.09785735208044
103	100	100.090534170505	-0.0905341705045259
104	99	100.089930268578	-1.0899302685785
105	99	100.082659964065	-1.08265996406512
106	102	100.075438155615	1.92456184438458
107	101	100.08827581236	0.911724187640019
108	100	100.094357405529	-0.0943574055292515
109	98	100.093728000978	-2.09372800097827
110	101	100.07976193308	0.920238066919964
111	101	100.085900317493	0.914099682507498
112	99	100.091997756236	-1.09199775623607
113	102	100.084713660689	1.91528633931082
114	99	100.097489445818	-1.09748944581833
115	102	100.090168718336	1.9098312816643
116	101	100.10290811588	0.897091884120087
117	101	100.10889210528	0.891107894719966
118	99	100.114836178893	-1.1148361788925
119	103	100.107399741226	2.89260025877397
120	101	100.126694631297	0.873305368703427
121	101	100.13251995439	0.867480045610122
122	100	100.138306420068	-0.138306420068176
123	101	100.137383856654	0.86261614334623
124	100	100.143137878008	-0.143137878008247
125	99	100.142183086688	-1.14218308668764
126	99	100.134564233363	-1.13456423336311
127	99	100.126996201073	-1.12699620107301
128	100	100.119478650819	-0.119478650819161
129	103	100.118681676738	2.88131832326197
130	101	100.137901311438	0.862098688562369
131	98	100.143651881146	-2.14365188114574
132	100	100.129352799456	-0.129352799455916
133	102	100.128489960549	1.87151003945114
134	100	100.140973738896	-0.140973738895909
135	100	100.140033383316	-0.14003338331564
136	99	100.139099300312	-1.13909930031227
137	101	100.131501017172	0.868498982828413
138	100	100.1372942796	-0.137294279600169
139	100	100.136378467599	-0.136378467598789
140	100	100.135468764458	-0.135468764458054
141	98	100.134565129429	-2.13456512942923
142	98	100.120326660289	-2.1203266602886
143	102	100.106183167872	1.8938168321279

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 101 & 100 & 1 \tabularnewline
3 & 102 & 100.006670430873 & 1.9933295691266 \tabularnewline
4 & 103 & 100.019966797972 & 2.98003320202783 \tabularnewline
5 & 99 & 100.039844903447 & -1.03984490344675 \tabularnewline
6 & 101 & 100.032908689899 & 0.967091310100756 \tabularnewline
7 & 100 & 100.039359605632 & -0.0393596056315459 \tabularnewline
8 & 101 & 100.039097060103 & 0.960902939897025 \tabularnewline
9 & 100 & 100.04550669674 & -0.0455066967396078 \tabularnewline
10 & 100 & 100.045203147465 & -0.0452031474647328 \tabularnewline
11 & 101 & 100.044901622994 & 0.955098377005683 \tabularnewline
12 & 100 & 100.051272540695 & -0.051272540695436 \tabularnewline
13 & 99 & 100.050930530757 & -1.05093053075703 \tabularnewline
14 & 99 & 100.043920371299 & -1.04392037129887 \tabularnewline
15 & 102 & 100.036956972625 & 1.96304302737522 \tabularnewline
16 & 99 & 100.05005131544 & -1.05005131544041 \tabularnewline
17 & 101 & 100.043047020727 & 0.956952979272756 \tabularnewline
18 & 99 & 100.049430309425 & -1.04943030942458 \tabularnewline
19 & 99 & 100.042430157089 & -1.0424301570891 \tabularnewline
20 & 99 & 100.035476698786 & -1.03547669878589 \tabularnewline
21 & 100 & 100.028569623046 & -0.0285696230456125 \tabularnewline
22 & 99 & 100.02837905135 & -1.02837905135002 \tabularnewline
23 & 99 & 100.021519319976 & -1.02151931997632 \tabularnewline
24 & 100 & 100.014705345967 & -0.0147053459665614 \tabularnewline
25 & 104 & 100.014607254973 & 3.98539274502717 \tabularnewline
26 & 100 & 100.041191541782 & -0.0411915417818989 \tabularnewline
27 & 102 & 100.04091677645 & 1.95908322355012 \tabularnewline
28 & 100 & 100.053984705668 & -0.0539847056678155 \tabularnewline
29 & 99 & 100.05362460442 & -1.05362460442045 \tabularnewline
30 & 99 & 100.04659647433 & -1.04659647433014 \tabularnewline
31 & 100 & 100.039615224896 & -0.0396152248957691 \tabularnewline
32 & 101 & 100.039350974277 & 0.960649025723427 \tabularnewline
33 & 100 & 100.045758917196 & -0.0457589171962667 \tabularnewline
34 & 102 & 100.045453685502 & 1.95454631449773 \tabularnewline
35 & 98 & 100.058491351582 & -2.05849135158201 \tabularnewline
36 & 100 & 100.044760327318 & -0.0447603273177748 \tabularnewline
37 & 100 & 100.044461756649 & -0.0444617566485306 \tabularnewline
38 & 101 & 100.044165177574 & 0.9558348224257 \tabularnewline
39 & 100 & 100.050541007684 & -0.0505410076836768 \tabularnewline
40 & 102 & 100.050203877386 & 1.94979612261434 \tabularnewline
41 & 99 & 100.063209857639 & -1.0632098576388 \tabularnewline
42 & 99 & 100.056117789779 & -1.05611778977949 \tabularnewline
43 & 100 & 100.049073029069 & -0.0490730290685946 \tabularnewline
44 & 99 & 100.04874569082 & -1.04874569082045 \tabularnewline
45 & 99 & 100.041750105186 & -1.04175010518605 \tabularnewline
46 & 99 & 100.034801183122 & -1.03480118312204 \tabularnewline
47 & 100 & 100.027898613362 & -0.0278986133623107 \tabularnewline
48 & 101 & 100.02771251759 & 0.972287482409584 \tabularnewline
49 & 100 & 100.034198094031 & -0.0341980940308986 \tabularnewline
50 & 101 & 100.033969978009 & 0.96603002199133 \tabularnewline
51 & 98 & 100.040413814492 & -2.040413814492 \tabularnewline
52 & 101 & 100.026803375189 & 0.973196624810711 \tabularnewline
53 & 101 & 100.033295016001 & 0.966704983998682 \tabularnewline
54 & 100 & 100.039743354772 & -0.0397433547720567 \tabularnewline
55 & 101 & 100.039478249471 & 0.960521750528628 \tabularnewline
56 & 99 & 100.045885343411 & -1.04588534341067 \tabularnewline
57 & 101 & 100.038908837526 & 0.961091162474062 \tabularnewline
58 & 101 & 100.045319729688 & 0.954680270311741 \tabularnewline
59 & 99 & 100.051687858438 & -1.05168785843757 \tabularnewline
60 & 98 & 100.044672647277 & -2.04467264727747 \tabularnewline
61 & 99 & 100.031033799725 & -1.03103379972507 \tabularnewline
62 & 102 & 100.024156360036 & 1.97584363996415 \tabularnewline
63 & 97 & 100.037336088453 & -3.03733608845289 \tabularnewline
64 & 100 & 100.017075748036 & -0.0170757480355661 \tabularnewline
65 & 100 & 100.016961845439 & -0.0169618454386864 \tabularnewline
66 & 100 & 100.016848702621 & -0.0168487026212034 \tabularnewline
67 & 98 & 100.016736314515 & -2.01673631451507 \tabularnewline
68 & 101 & 100.003283814339 & 0.996716185660787 \tabularnewline
69 & 101 & 100.009932340756 & 0.990067659243934 \tabularnewline
70 & 100 & 100.016536518637 & -0.0165365186370394 \tabularnewline
71 & 100 & 100.016426212933 & -0.0164262129325863 \tabularnewline
72 & 98 & 100.016316643015 & -2.01631664301472 \tabularnewline
73 & 98 & 100.002866942229 & -2.00286694222859 \tabularnewline
74 & 101 & 99.9895069567418 & 1.01049304325817 \tabularnewline
75 & 103 & 99.9962473807349 & 3.00375261926506 \tabularnewline
76 & 100 & 100.016283704943 & -0.0162837049425661 \tabularnewline
77 & 101 & 100.016175085614 & 0.983824914385607 \tabularnewline
78 & 100 & 100.022737621697 & -0.0227376216973312 \tabularnewline
79 & 100 & 100.022585951964 & -0.0225859519635776 \tabularnewline
80 & 101 & 100.022435293932 & 0.977564706067696 \tabularnewline
81 & 101 & 100.028956071728 & 0.971043928271598 \tabularnewline
82 & 104 & 100.035433353127 & 3.96456664687302 \tabularnewline
83 & 101 & 100.061878720888 & 0.938121279112053 \tabularnewline
84 & 98 & 100.068136394031 & -2.06813639403113 \tabularnewline
85 & 101 & 100.054341033178 & 0.945658966822023 \tabularnewline
86 & 100 & 100.060648985946 & -0.0606489859459742 \tabularnewline
87 & 99 & 100.060244431078 & -1.06024443107769 \tabularnewline
88 & 100 & 100.053172143891 & -0.053172143891274 \tabularnewline
89 & 104 & 100.052817462781 & 3.94718253721894 \tabularnewline
90 & 100 & 100.07914687104 & -0.0791468710402938 \tabularnewline
91 & 101 & 100.078618927308 & 0.921381072691815 \tabularnewline
92 & 100 & 100.084764936062 & -0.0847649360616316 \tabularnewline
93 & 104 & 100.084199517415 & 3.91580048258486 \tabularnewline
94 & 101 & 100.110319593848 & 0.88968040615174 \tabularnewline
95 & 100 & 100.116254145497 & -0.116254145496924 \tabularnewline
96 & 100 & 100.115478680256 & -0.115478680255649 \tabularnewline
97 & 99 & 100.114708387702 & -1.11470838770165 \tabularnewline
98 & 100 & 100.107272802457 & -0.107272802457487 \tabularnewline
99 & 99 & 100.106557246644 & -1.1065572466441 \tabularnewline
100 & 100 & 100.099176033023 & -0.0991760330228999 \tabularnewline
101 & 100 & 100.09851448615 & -0.0985144861503215 \tabularnewline
102 & 99 & 100.09785735208 & -1.09785735208044 \tabularnewline
103 & 100 & 100.090534170505 & -0.0905341705045259 \tabularnewline
104 & 99 & 100.089930268578 & -1.0899302685785 \tabularnewline
105 & 99 & 100.082659964065 & -1.08265996406512 \tabularnewline
106 & 102 & 100.075438155615 & 1.92456184438458 \tabularnewline
107 & 101 & 100.08827581236 & 0.911724187640019 \tabularnewline
108 & 100 & 100.094357405529 & -0.0943574055292515 \tabularnewline
109 & 98 & 100.093728000978 & -2.09372800097827 \tabularnewline
110 & 101 & 100.07976193308 & 0.920238066919964 \tabularnewline
111 & 101 & 100.085900317493 & 0.914099682507498 \tabularnewline
112 & 99 & 100.091997756236 & -1.09199775623607 \tabularnewline
113 & 102 & 100.084713660689 & 1.91528633931082 \tabularnewline
114 & 99 & 100.097489445818 & -1.09748944581833 \tabularnewline
115 & 102 & 100.090168718336 & 1.9098312816643 \tabularnewline
116 & 101 & 100.10290811588 & 0.897091884120087 \tabularnewline
117 & 101 & 100.10889210528 & 0.891107894719966 \tabularnewline
118 & 99 & 100.114836178893 & -1.1148361788925 \tabularnewline
119 & 103 & 100.107399741226 & 2.89260025877397 \tabularnewline
120 & 101 & 100.126694631297 & 0.873305368703427 \tabularnewline
121 & 101 & 100.13251995439 & 0.867480045610122 \tabularnewline
122 & 100 & 100.138306420068 & -0.138306420068176 \tabularnewline
123 & 101 & 100.137383856654 & 0.86261614334623 \tabularnewline
124 & 100 & 100.143137878008 & -0.143137878008247 \tabularnewline
125 & 99 & 100.142183086688 & -1.14218308668764 \tabularnewline
126 & 99 & 100.134564233363 & -1.13456423336311 \tabularnewline
127 & 99 & 100.126996201073 & -1.12699620107301 \tabularnewline
128 & 100 & 100.119478650819 & -0.119478650819161 \tabularnewline
129 & 103 & 100.118681676738 & 2.88131832326197 \tabularnewline
130 & 101 & 100.137901311438 & 0.862098688562369 \tabularnewline
131 & 98 & 100.143651881146 & -2.14365188114574 \tabularnewline
132 & 100 & 100.129352799456 & -0.129352799455916 \tabularnewline
133 & 102 & 100.128489960549 & 1.87151003945114 \tabularnewline
134 & 100 & 100.140973738896 & -0.140973738895909 \tabularnewline
135 & 100 & 100.140033383316 & -0.14003338331564 \tabularnewline
136 & 99 & 100.139099300312 & -1.13909930031227 \tabularnewline
137 & 101 & 100.131501017172 & 0.868498982828413 \tabularnewline
138 & 100 & 100.1372942796 & -0.137294279600169 \tabularnewline
139 & 100 & 100.136378467599 & -0.136378467598789 \tabularnewline
140 & 100 & 100.135468764458 & -0.135468764458054 \tabularnewline
141 & 98 & 100.134565129429 & -2.13456512942923 \tabularnewline
142 & 98 & 100.120326660289 & -2.1203266602886 \tabularnewline
143 & 102 & 100.106183167872 & 1.8938168321279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300056&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]101[/C][C]100[/C][C]1[/C][/ROW]
[ROW][C]3[/C][C]102[/C][C]100.006670430873[/C][C]1.9933295691266[/C][/ROW]
[ROW][C]4[/C][C]103[/C][C]100.019966797972[/C][C]2.98003320202783[/C][/ROW]
[ROW][C]5[/C][C]99[/C][C]100.039844903447[/C][C]-1.03984490344675[/C][/ROW]
[ROW][C]6[/C][C]101[/C][C]100.032908689899[/C][C]0.967091310100756[/C][/ROW]
[ROW][C]7[/C][C]100[/C][C]100.039359605632[/C][C]-0.0393596056315459[/C][/ROW]
[ROW][C]8[/C][C]101[/C][C]100.039097060103[/C][C]0.960902939897025[/C][/ROW]
[ROW][C]9[/C][C]100[/C][C]100.04550669674[/C][C]-0.0455066967396078[/C][/ROW]
[ROW][C]10[/C][C]100[/C][C]100.045203147465[/C][C]-0.0452031474647328[/C][/ROW]
[ROW][C]11[/C][C]101[/C][C]100.044901622994[/C][C]0.955098377005683[/C][/ROW]
[ROW][C]12[/C][C]100[/C][C]100.051272540695[/C][C]-0.051272540695436[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]100.050930530757[/C][C]-1.05093053075703[/C][/ROW]
[ROW][C]14[/C][C]99[/C][C]100.043920371299[/C][C]-1.04392037129887[/C][/ROW]
[ROW][C]15[/C][C]102[/C][C]100.036956972625[/C][C]1.96304302737522[/C][/ROW]
[ROW][C]16[/C][C]99[/C][C]100.05005131544[/C][C]-1.05005131544041[/C][/ROW]
[ROW][C]17[/C][C]101[/C][C]100.043047020727[/C][C]0.956952979272756[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]100.049430309425[/C][C]-1.04943030942458[/C][/ROW]
[ROW][C]19[/C][C]99[/C][C]100.042430157089[/C][C]-1.0424301570891[/C][/ROW]
[ROW][C]20[/C][C]99[/C][C]100.035476698786[/C][C]-1.03547669878589[/C][/ROW]
[ROW][C]21[/C][C]100[/C][C]100.028569623046[/C][C]-0.0285696230456125[/C][/ROW]
[ROW][C]22[/C][C]99[/C][C]100.02837905135[/C][C]-1.02837905135002[/C][/ROW]
[ROW][C]23[/C][C]99[/C][C]100.021519319976[/C][C]-1.02151931997632[/C][/ROW]
[ROW][C]24[/C][C]100[/C][C]100.014705345967[/C][C]-0.0147053459665614[/C][/ROW]
[ROW][C]25[/C][C]104[/C][C]100.014607254973[/C][C]3.98539274502717[/C][/ROW]
[ROW][C]26[/C][C]100[/C][C]100.041191541782[/C][C]-0.0411915417818989[/C][/ROW]
[ROW][C]27[/C][C]102[/C][C]100.04091677645[/C][C]1.95908322355012[/C][/ROW]
[ROW][C]28[/C][C]100[/C][C]100.053984705668[/C][C]-0.0539847056678155[/C][/ROW]
[ROW][C]29[/C][C]99[/C][C]100.05362460442[/C][C]-1.05362460442045[/C][/ROW]
[ROW][C]30[/C][C]99[/C][C]100.04659647433[/C][C]-1.04659647433014[/C][/ROW]
[ROW][C]31[/C][C]100[/C][C]100.039615224896[/C][C]-0.0396152248957691[/C][/ROW]
[ROW][C]32[/C][C]101[/C][C]100.039350974277[/C][C]0.960649025723427[/C][/ROW]
[ROW][C]33[/C][C]100[/C][C]100.045758917196[/C][C]-0.0457589171962667[/C][/ROW]
[ROW][C]34[/C][C]102[/C][C]100.045453685502[/C][C]1.95454631449773[/C][/ROW]
[ROW][C]35[/C][C]98[/C][C]100.058491351582[/C][C]-2.05849135158201[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]100.044760327318[/C][C]-0.0447603273177748[/C][/ROW]
[ROW][C]37[/C][C]100[/C][C]100.044461756649[/C][C]-0.0444617566485306[/C][/ROW]
[ROW][C]38[/C][C]101[/C][C]100.044165177574[/C][C]0.9558348224257[/C][/ROW]
[ROW][C]39[/C][C]100[/C][C]100.050541007684[/C][C]-0.0505410076836768[/C][/ROW]
[ROW][C]40[/C][C]102[/C][C]100.050203877386[/C][C]1.94979612261434[/C][/ROW]
[ROW][C]41[/C][C]99[/C][C]100.063209857639[/C][C]-1.0632098576388[/C][/ROW]
[ROW][C]42[/C][C]99[/C][C]100.056117789779[/C][C]-1.05611778977949[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]100.049073029069[/C][C]-0.0490730290685946[/C][/ROW]
[ROW][C]44[/C][C]99[/C][C]100.04874569082[/C][C]-1.04874569082045[/C][/ROW]
[ROW][C]45[/C][C]99[/C][C]100.041750105186[/C][C]-1.04175010518605[/C][/ROW]
[ROW][C]46[/C][C]99[/C][C]100.034801183122[/C][C]-1.03480118312204[/C][/ROW]
[ROW][C]47[/C][C]100[/C][C]100.027898613362[/C][C]-0.0278986133623107[/C][/ROW]
[ROW][C]48[/C][C]101[/C][C]100.02771251759[/C][C]0.972287482409584[/C][/ROW]
[ROW][C]49[/C][C]100[/C][C]100.034198094031[/C][C]-0.0341980940308986[/C][/ROW]
[ROW][C]50[/C][C]101[/C][C]100.033969978009[/C][C]0.96603002199133[/C][/ROW]
[ROW][C]51[/C][C]98[/C][C]100.040413814492[/C][C]-2.040413814492[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]100.026803375189[/C][C]0.973196624810711[/C][/ROW]
[ROW][C]53[/C][C]101[/C][C]100.033295016001[/C][C]0.966704983998682[/C][/ROW]
[ROW][C]54[/C][C]100[/C][C]100.039743354772[/C][C]-0.0397433547720567[/C][/ROW]
[ROW][C]55[/C][C]101[/C][C]100.039478249471[/C][C]0.960521750528628[/C][/ROW]
[ROW][C]56[/C][C]99[/C][C]100.045885343411[/C][C]-1.04588534341067[/C][/ROW]
[ROW][C]57[/C][C]101[/C][C]100.038908837526[/C][C]0.961091162474062[/C][/ROW]
[ROW][C]58[/C][C]101[/C][C]100.045319729688[/C][C]0.954680270311741[/C][/ROW]
[ROW][C]59[/C][C]99[/C][C]100.051687858438[/C][C]-1.05168785843757[/C][/ROW]
[ROW][C]60[/C][C]98[/C][C]100.044672647277[/C][C]-2.04467264727747[/C][/ROW]
[ROW][C]61[/C][C]99[/C][C]100.031033799725[/C][C]-1.03103379972507[/C][/ROW]
[ROW][C]62[/C][C]102[/C][C]100.024156360036[/C][C]1.97584363996415[/C][/ROW]
[ROW][C]63[/C][C]97[/C][C]100.037336088453[/C][C]-3.03733608845289[/C][/ROW]
[ROW][C]64[/C][C]100[/C][C]100.017075748036[/C][C]-0.0170757480355661[/C][/ROW]
[ROW][C]65[/C][C]100[/C][C]100.016961845439[/C][C]-0.0169618454386864[/C][/ROW]
[ROW][C]66[/C][C]100[/C][C]100.016848702621[/C][C]-0.0168487026212034[/C][/ROW]
[ROW][C]67[/C][C]98[/C][C]100.016736314515[/C][C]-2.01673631451507[/C][/ROW]
[ROW][C]68[/C][C]101[/C][C]100.003283814339[/C][C]0.996716185660787[/C][/ROW]
[ROW][C]69[/C][C]101[/C][C]100.009932340756[/C][C]0.990067659243934[/C][/ROW]
[ROW][C]70[/C][C]100[/C][C]100.016536518637[/C][C]-0.0165365186370394[/C][/ROW]
[ROW][C]71[/C][C]100[/C][C]100.016426212933[/C][C]-0.0164262129325863[/C][/ROW]
[ROW][C]72[/C][C]98[/C][C]100.016316643015[/C][C]-2.01631664301472[/C][/ROW]
[ROW][C]73[/C][C]98[/C][C]100.002866942229[/C][C]-2.00286694222859[/C][/ROW]
[ROW][C]74[/C][C]101[/C][C]99.9895069567418[/C][C]1.01049304325817[/C][/ROW]
[ROW][C]75[/C][C]103[/C][C]99.9962473807349[/C][C]3.00375261926506[/C][/ROW]
[ROW][C]76[/C][C]100[/C][C]100.016283704943[/C][C]-0.0162837049425661[/C][/ROW]
[ROW][C]77[/C][C]101[/C][C]100.016175085614[/C][C]0.983824914385607[/C][/ROW]
[ROW][C]78[/C][C]100[/C][C]100.022737621697[/C][C]-0.0227376216973312[/C][/ROW]
[ROW][C]79[/C][C]100[/C][C]100.022585951964[/C][C]-0.0225859519635776[/C][/ROW]
[ROW][C]80[/C][C]101[/C][C]100.022435293932[/C][C]0.977564706067696[/C][/ROW]
[ROW][C]81[/C][C]101[/C][C]100.028956071728[/C][C]0.971043928271598[/C][/ROW]
[ROW][C]82[/C][C]104[/C][C]100.035433353127[/C][C]3.96456664687302[/C][/ROW]
[ROW][C]83[/C][C]101[/C][C]100.061878720888[/C][C]0.938121279112053[/C][/ROW]
[ROW][C]84[/C][C]98[/C][C]100.068136394031[/C][C]-2.06813639403113[/C][/ROW]
[ROW][C]85[/C][C]101[/C][C]100.054341033178[/C][C]0.945658966822023[/C][/ROW]
[ROW][C]86[/C][C]100[/C][C]100.060648985946[/C][C]-0.0606489859459742[/C][/ROW]
[ROW][C]87[/C][C]99[/C][C]100.060244431078[/C][C]-1.06024443107769[/C][/ROW]
[ROW][C]88[/C][C]100[/C][C]100.053172143891[/C][C]-0.053172143891274[/C][/ROW]
[ROW][C]89[/C][C]104[/C][C]100.052817462781[/C][C]3.94718253721894[/C][/ROW]
[ROW][C]90[/C][C]100[/C][C]100.07914687104[/C][C]-0.0791468710402938[/C][/ROW]
[ROW][C]91[/C][C]101[/C][C]100.078618927308[/C][C]0.921381072691815[/C][/ROW]
[ROW][C]92[/C][C]100[/C][C]100.084764936062[/C][C]-0.0847649360616316[/C][/ROW]
[ROW][C]93[/C][C]104[/C][C]100.084199517415[/C][C]3.91580048258486[/C][/ROW]
[ROW][C]94[/C][C]101[/C][C]100.110319593848[/C][C]0.88968040615174[/C][/ROW]
[ROW][C]95[/C][C]100[/C][C]100.116254145497[/C][C]-0.116254145496924[/C][/ROW]
[ROW][C]96[/C][C]100[/C][C]100.115478680256[/C][C]-0.115478680255649[/C][/ROW]
[ROW][C]97[/C][C]99[/C][C]100.114708387702[/C][C]-1.11470838770165[/C][/ROW]
[ROW][C]98[/C][C]100[/C][C]100.107272802457[/C][C]-0.107272802457487[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]100.106557246644[/C][C]-1.1065572466441[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]100.099176033023[/C][C]-0.0991760330228999[/C][/ROW]
[ROW][C]101[/C][C]100[/C][C]100.09851448615[/C][C]-0.0985144861503215[/C][/ROW]
[ROW][C]102[/C][C]99[/C][C]100.09785735208[/C][C]-1.09785735208044[/C][/ROW]
[ROW][C]103[/C][C]100[/C][C]100.090534170505[/C][C]-0.0905341705045259[/C][/ROW]
[ROW][C]104[/C][C]99[/C][C]100.089930268578[/C][C]-1.0899302685785[/C][/ROW]
[ROW][C]105[/C][C]99[/C][C]100.082659964065[/C][C]-1.08265996406512[/C][/ROW]
[ROW][C]106[/C][C]102[/C][C]100.075438155615[/C][C]1.92456184438458[/C][/ROW]
[ROW][C]107[/C][C]101[/C][C]100.08827581236[/C][C]0.911724187640019[/C][/ROW]
[ROW][C]108[/C][C]100[/C][C]100.094357405529[/C][C]-0.0943574055292515[/C][/ROW]
[ROW][C]109[/C][C]98[/C][C]100.093728000978[/C][C]-2.09372800097827[/C][/ROW]
[ROW][C]110[/C][C]101[/C][C]100.07976193308[/C][C]0.920238066919964[/C][/ROW]
[ROW][C]111[/C][C]101[/C][C]100.085900317493[/C][C]0.914099682507498[/C][/ROW]
[ROW][C]112[/C][C]99[/C][C]100.091997756236[/C][C]-1.09199775623607[/C][/ROW]
[ROW][C]113[/C][C]102[/C][C]100.084713660689[/C][C]1.91528633931082[/C][/ROW]
[ROW][C]114[/C][C]99[/C][C]100.097489445818[/C][C]-1.09748944581833[/C][/ROW]
[ROW][C]115[/C][C]102[/C][C]100.090168718336[/C][C]1.9098312816643[/C][/ROW]
[ROW][C]116[/C][C]101[/C][C]100.10290811588[/C][C]0.897091884120087[/C][/ROW]
[ROW][C]117[/C][C]101[/C][C]100.10889210528[/C][C]0.891107894719966[/C][/ROW]
[ROW][C]118[/C][C]99[/C][C]100.114836178893[/C][C]-1.1148361788925[/C][/ROW]
[ROW][C]119[/C][C]103[/C][C]100.107399741226[/C][C]2.89260025877397[/C][/ROW]
[ROW][C]120[/C][C]101[/C][C]100.126694631297[/C][C]0.873305368703427[/C][/ROW]
[ROW][C]121[/C][C]101[/C][C]100.13251995439[/C][C]0.867480045610122[/C][/ROW]
[ROW][C]122[/C][C]100[/C][C]100.138306420068[/C][C]-0.138306420068176[/C][/ROW]
[ROW][C]123[/C][C]101[/C][C]100.137383856654[/C][C]0.86261614334623[/C][/ROW]
[ROW][C]124[/C][C]100[/C][C]100.143137878008[/C][C]-0.143137878008247[/C][/ROW]
[ROW][C]125[/C][C]99[/C][C]100.142183086688[/C][C]-1.14218308668764[/C][/ROW]
[ROW][C]126[/C][C]99[/C][C]100.134564233363[/C][C]-1.13456423336311[/C][/ROW]
[ROW][C]127[/C][C]99[/C][C]100.126996201073[/C][C]-1.12699620107301[/C][/ROW]
[ROW][C]128[/C][C]100[/C][C]100.119478650819[/C][C]-0.119478650819161[/C][/ROW]
[ROW][C]129[/C][C]103[/C][C]100.118681676738[/C][C]2.88131832326197[/C][/ROW]
[ROW][C]130[/C][C]101[/C][C]100.137901311438[/C][C]0.862098688562369[/C][/ROW]
[ROW][C]131[/C][C]98[/C][C]100.143651881146[/C][C]-2.14365188114574[/C][/ROW]
[ROW][C]132[/C][C]100[/C][C]100.129352799456[/C][C]-0.129352799455916[/C][/ROW]
[ROW][C]133[/C][C]102[/C][C]100.128489960549[/C][C]1.87151003945114[/C][/ROW]
[ROW][C]134[/C][C]100[/C][C]100.140973738896[/C][C]-0.140973738895909[/C][/ROW]
[ROW][C]135[/C][C]100[/C][C]100.140033383316[/C][C]-0.14003338331564[/C][/ROW]
[ROW][C]136[/C][C]99[/C][C]100.139099300312[/C][C]-1.13909930031227[/C][/ROW]
[ROW][C]137[/C][C]101[/C][C]100.131501017172[/C][C]0.868498982828413[/C][/ROW]
[ROW][C]138[/C][C]100[/C][C]100.1372942796[/C][C]-0.137294279600169[/C][/ROW]
[ROW][C]139[/C][C]100[/C][C]100.136378467599[/C][C]-0.136378467598789[/C][/ROW]
[ROW][C]140[/C][C]100[/C][C]100.135468764458[/C][C]-0.135468764458054[/C][/ROW]
[ROW][C]141[/C][C]98[/C][C]100.134565129429[/C][C]-2.13456512942923[/C][/ROW]
[ROW][C]142[/C][C]98[/C][C]100.120326660289[/C][C]-2.1203266602886[/C][/ROW]
[ROW][C]143[/C][C]102[/C][C]100.106183167872[/C][C]1.8938168321279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300056&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300056&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	101	100	1
3	102	100.006670430873	1.9933295691266
4	103	100.019966797972	2.98003320202783
5	99	100.039844903447	-1.03984490344675
6	101	100.032908689899	0.967091310100756
7	100	100.039359605632	-0.0393596056315459
8	101	100.039097060103	0.960902939897025
9	100	100.04550669674	-0.0455066967396078
10	100	100.045203147465	-0.0452031474647328
11	101	100.044901622994	0.955098377005683
12	100	100.051272540695	-0.051272540695436
13	99	100.050930530757	-1.05093053075703
14	99	100.043920371299	-1.04392037129887
15	102	100.036956972625	1.96304302737522
16	99	100.05005131544	-1.05005131544041
17	101	100.043047020727	0.956952979272756
18	99	100.049430309425	-1.04943030942458
19	99	100.042430157089	-1.0424301570891
20	99	100.035476698786	-1.03547669878589
21	100	100.028569623046	-0.0285696230456125
22	99	100.02837905135	-1.02837905135002
23	99	100.021519319976	-1.02151931997632
24	100	100.014705345967	-0.0147053459665614
25	104	100.014607254973	3.98539274502717
26	100	100.041191541782	-0.0411915417818989
27	102	100.04091677645	1.95908322355012
28	100	100.053984705668	-0.0539847056678155
29	99	100.05362460442	-1.05362460442045
30	99	100.04659647433	-1.04659647433014
31	100	100.039615224896	-0.0396152248957691
32	101	100.039350974277	0.960649025723427
33	100	100.045758917196	-0.0457589171962667
34	102	100.045453685502	1.95454631449773
35	98	100.058491351582	-2.05849135158201
36	100	100.044760327318	-0.0447603273177748
37	100	100.044461756649	-0.0444617566485306
38	101	100.044165177574	0.9558348224257
39	100	100.050541007684	-0.0505410076836768
40	102	100.050203877386	1.94979612261434
41	99	100.063209857639	-1.0632098576388
42	99	100.056117789779	-1.05611778977949
43	100	100.049073029069	-0.0490730290685946
44	99	100.04874569082	-1.04874569082045
45	99	100.041750105186	-1.04175010518605
46	99	100.034801183122	-1.03480118312204
47	100	100.027898613362	-0.0278986133623107
48	101	100.02771251759	0.972287482409584
49	100	100.034198094031	-0.0341980940308986
50	101	100.033969978009	0.96603002199133
51	98	100.040413814492	-2.040413814492
52	101	100.026803375189	0.973196624810711
53	101	100.033295016001	0.966704983998682
54	100	100.039743354772	-0.0397433547720567
55	101	100.039478249471	0.960521750528628
56	99	100.045885343411	-1.04588534341067
57	101	100.038908837526	0.961091162474062
58	101	100.045319729688	0.954680270311741
59	99	100.051687858438	-1.05168785843757
60	98	100.044672647277	-2.04467264727747
61	99	100.031033799725	-1.03103379972507
62	102	100.024156360036	1.97584363996415
63	97	100.037336088453	-3.03733608845289
64	100	100.017075748036	-0.0170757480355661
65	100	100.016961845439	-0.0169618454386864
66	100	100.016848702621	-0.0168487026212034
67	98	100.016736314515	-2.01673631451507
68	101	100.003283814339	0.996716185660787
69	101	100.009932340756	0.990067659243934
70	100	100.016536518637	-0.0165365186370394
71	100	100.016426212933	-0.0164262129325863
72	98	100.016316643015	-2.01631664301472
73	98	100.002866942229	-2.00286694222859
74	101	99.9895069567418	1.01049304325817
75	103	99.9962473807349	3.00375261926506
76	100	100.016283704943	-0.0162837049425661
77	101	100.016175085614	0.983824914385607
78	100	100.022737621697	-0.0227376216973312
79	100	100.022585951964	-0.0225859519635776
80	101	100.022435293932	0.977564706067696
81	101	100.028956071728	0.971043928271598
82	104	100.035433353127	3.96456664687302
83	101	100.061878720888	0.938121279112053
84	98	100.068136394031	-2.06813639403113
85	101	100.054341033178	0.945658966822023
86	100	100.060648985946	-0.0606489859459742
87	99	100.060244431078	-1.06024443107769
88	100	100.053172143891	-0.053172143891274
89	104	100.052817462781	3.94718253721894
90	100	100.07914687104	-0.0791468710402938
91	101	100.078618927308	0.921381072691815
92	100	100.084764936062	-0.0847649360616316
93	104	100.084199517415	3.91580048258486
94	101	100.110319593848	0.88968040615174
95	100	100.116254145497	-0.116254145496924
96	100	100.115478680256	-0.115478680255649
97	99	100.114708387702	-1.11470838770165
98	100	100.107272802457	-0.107272802457487
99	99	100.106557246644	-1.1065572466441
100	100	100.099176033023	-0.0991760330228999
101	100	100.09851448615	-0.0985144861503215
102	99	100.09785735208	-1.09785735208044
103	100	100.090534170505	-0.0905341705045259
104	99	100.089930268578	-1.0899302685785
105	99	100.082659964065	-1.08265996406512
106	102	100.075438155615	1.92456184438458
107	101	100.08827581236	0.911724187640019
108	100	100.094357405529	-0.0943574055292515
109	98	100.093728000978	-2.09372800097827
110	101	100.07976193308	0.920238066919964
111	101	100.085900317493	0.914099682507498
112	99	100.091997756236	-1.09199775623607
113	102	100.084713660689	1.91528633931082
114	99	100.097489445818	-1.09748944581833
115	102	100.090168718336	1.9098312816643
116	101	100.10290811588	0.897091884120087
117	101	100.10889210528	0.891107894719966
118	99	100.114836178893	-1.1148361788925
119	103	100.107399741226	2.89260025877397
120	101	100.126694631297	0.873305368703427
121	101	100.13251995439	0.867480045610122
122	100	100.138306420068	-0.138306420068176
123	101	100.137383856654	0.86261614334623
124	100	100.143137878008	-0.143137878008247
125	99	100.142183086688	-1.14218308668764
126	99	100.134564233363	-1.13456423336311
127	99	100.126996201073	-1.12699620107301
128	100	100.119478650819	-0.119478650819161
129	103	100.118681676738	2.88131832326197
130	101	100.137901311438	0.862098688562369
131	98	100.143651881146	-2.14365188114574
132	100	100.129352799456	-0.129352799455916
133	102	100.128489960549	1.87151003945114
134	100	100.140973738896	-0.140973738895909
135	100	100.140033383316	-0.14003338331564
136	99	100.139099300312	-1.13909930031227
137	101	100.131501017172	0.868498982828413
138	100	100.1372942796	-0.137294279600169
139	100	100.136378467599	-0.136378467598789
140	100	100.135468764458	-0.135468764458054
141	98	100.134565129429	-2.13456512942923
142	98	100.120326660289	-2.1203266602886
143	102	100.106183167872	1.8938168321279

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
144	100.118815742138	97.4595918314116	102.778039652864
145	100.118815742138	97.4595326714537	102.778098812822
146	100.118815742138	97.4594735128119	102.778157971464
147	100.118815742138	97.459414355486	102.778217128789
148	100.118815742138	97.4593551994761	102.778276284799
149	100.118815742138	97.459296044782	102.778335439493
150	100.118815742138	97.4592368914036	102.778394592872
151	100.118815742138	97.4591777393408	102.778453744935
152	100.118815742138	97.4591185885936	102.778512895682
153	100.118815742138	97.4590594391618	102.778572045114
154	100.118815742138	97.4590002910455	102.77863119323
155	100.118815742138	97.4589411442444	102.778690340031
156	100.118815742138	97.4588819987585	102.778749485517
157	100.118815742138	97.4588228545877	102.778808629688
158	100.118815742138	97.458763711732	102.778867772543
159	100.118815742138	97.4587045701911	102.778926914084
160	100.118815742138	97.4586454299652	102.77898605431
161	100.118815742138	97.458586291054	102.779045193221

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
144 & 100.118815742138 & 97.4595918314116 & 102.778039652864 \tabularnewline
145 & 100.118815742138 & 97.4595326714537 & 102.778098812822 \tabularnewline
146 & 100.118815742138 & 97.4594735128119 & 102.778157971464 \tabularnewline
147 & 100.118815742138 & 97.459414355486 & 102.778217128789 \tabularnewline
148 & 100.118815742138 & 97.4593551994761 & 102.778276284799 \tabularnewline
149 & 100.118815742138 & 97.459296044782 & 102.778335439493 \tabularnewline
150 & 100.118815742138 & 97.4592368914036 & 102.778394592872 \tabularnewline
151 & 100.118815742138 & 97.4591777393408 & 102.778453744935 \tabularnewline
152 & 100.118815742138 & 97.4591185885936 & 102.778512895682 \tabularnewline
153 & 100.118815742138 & 97.4590594391618 & 102.778572045114 \tabularnewline
154 & 100.118815742138 & 97.4590002910455 & 102.77863119323 \tabularnewline
155 & 100.118815742138 & 97.4589411442444 & 102.778690340031 \tabularnewline
156 & 100.118815742138 & 97.4588819987585 & 102.778749485517 \tabularnewline
157 & 100.118815742138 & 97.4588228545877 & 102.778808629688 \tabularnewline
158 & 100.118815742138 & 97.458763711732 & 102.778867772543 \tabularnewline
159 & 100.118815742138 & 97.4587045701911 & 102.778926914084 \tabularnewline
160 & 100.118815742138 & 97.4586454299652 & 102.77898605431 \tabularnewline
161 & 100.118815742138 & 97.458586291054 & 102.779045193221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300056&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]144[/C][C]100.118815742138[/C][C]97.4595918314116[/C][C]102.778039652864[/C][/ROW]
[ROW][C]145[/C][C]100.118815742138[/C][C]97.4595326714537[/C][C]102.778098812822[/C][/ROW]
[ROW][C]146[/C][C]100.118815742138[/C][C]97.4594735128119[/C][C]102.778157971464[/C][/ROW]
[ROW][C]147[/C][C]100.118815742138[/C][C]97.459414355486[/C][C]102.778217128789[/C][/ROW]
[ROW][C]148[/C][C]100.118815742138[/C][C]97.4593551994761[/C][C]102.778276284799[/C][/ROW]
[ROW][C]149[/C][C]100.118815742138[/C][C]97.459296044782[/C][C]102.778335439493[/C][/ROW]
[ROW][C]150[/C][C]100.118815742138[/C][C]97.4592368914036[/C][C]102.778394592872[/C][/ROW]
[ROW][C]151[/C][C]100.118815742138[/C][C]97.4591777393408[/C][C]102.778453744935[/C][/ROW]
[ROW][C]152[/C][C]100.118815742138[/C][C]97.4591185885936[/C][C]102.778512895682[/C][/ROW]
[ROW][C]153[/C][C]100.118815742138[/C][C]97.4590594391618[/C][C]102.778572045114[/C][/ROW]
[ROW][C]154[/C][C]100.118815742138[/C][C]97.4590002910455[/C][C]102.77863119323[/C][/ROW]
[ROW][C]155[/C][C]100.118815742138[/C][C]97.4589411442444[/C][C]102.778690340031[/C][/ROW]
[ROW][C]156[/C][C]100.118815742138[/C][C]97.4588819987585[/C][C]102.778749485517[/C][/ROW]
[ROW][C]157[/C][C]100.118815742138[/C][C]97.4588228545877[/C][C]102.778808629688[/C][/ROW]
[ROW][C]158[/C][C]100.118815742138[/C][C]97.458763711732[/C][C]102.778867772543[/C][/ROW]
[ROW][C]159[/C][C]100.118815742138[/C][C]97.4587045701911[/C][C]102.778926914084[/C][/ROW]
[ROW][C]160[/C][C]100.118815742138[/C][C]97.4586454299652[/C][C]102.77898605431[/C][/ROW]
[ROW][C]161[/C][C]100.118815742138[/C][C]97.458586291054[/C][C]102.779045193221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300056&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300056&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
144	100.118815742138	97.4595918314116	102.778039652864
145	100.118815742138	97.4595326714537	102.778098812822
146	100.118815742138	97.4594735128119	102.778157971464
147	100.118815742138	97.459414355486	102.778217128789
148	100.118815742138	97.4593551994761	102.778276284799
149	100.118815742138	97.459296044782	102.778335439493
150	100.118815742138	97.4592368914036	102.778394592872
151	100.118815742138	97.4591777393408	102.778453744935
152	100.118815742138	97.4591185885936	102.778512895682
153	100.118815742138	97.4590594391618	102.778572045114
154	100.118815742138	97.4590002910455	102.77863119323
155	100.118815742138	97.4589411442444	102.778690340031
156	100.118815742138	97.4588819987585	102.778749485517
157	100.118815742138	97.4588228545877	102.778808629688
158	100.118815742138	97.458763711732	102.778867772543
159	100.118815742138	97.4587045701911	102.778926914084
160	100.118815742138	97.4586454299652	102.77898605431
161	100.118815742138	97.458586291054	102.779045193221

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = Single ; par3 = additive ; par4 = 18 ;

Parameters (R input):

par1 = 12 ; par2 = Single ; par3 = additive ; par4 = 18 ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code