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Author's title

Author

*Unverified author*

R Software Module

rwasp_exponentialsmoothing.wasp

Title produced by software

Exponential Smoothing

Date of computation

Wed, 28 Dec 2011 14:34:34 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/28/t1325101124zl64utcs7o4zija.htm/, Retrieved Fri, 03 May 2024 06:56:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160888, Retrieved Fri, 03 May 2024 06:56:50 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W102

Estimated Impact

143

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

-       [Exponential Smoothing] [index uitrusting ...] [2011-12-28 19:34:34] [77544ad9bc6b823fffe5e8df50f1b7b2] [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	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160888&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160888&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160888&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	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	0.887025454798225
beta	0.0292195142885685
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.887025454798225 \tabularnewline
beta & 0.0292195142885685 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160888&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]0.887025454798225[/C][/ROW]
[ROW][C]beta[/C][C]0.0292195142885685[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160888&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160888&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	0.887025454798225
beta	0.0292195142885685
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
3	99.9	99	0.900000000000006
4	100.3	100.021649516974	0.278350483025875
5	100.2	100.499094502327	-0.299094502327307
6	100.2	100.45657902014	-0.256579020140236
7	100.6	100.445125721614	0.154874278385549
8	100.4	100.802656074039	-0.402656074039342
9	100.7	100.65520658953	0.0447934104696088
10	100.9	100.905817163419	-0.00581716341898186
11	99.7	101.11138409811	-1.41138409811008
12	99.7	100.033596490965	-0.333596490965064
13	96.8	99.9031876212681	-3.10318762126813
14	99.2	97.2356510972496	1.96434890275036
15	99.9	99.1140613477702	0.785938652229845
16	99.3	99.9675620238639	-0.667562023863866
17	98.9	99.5144684267888	-0.614468426788747
18	98.9	99.0925441307966	-0.192544130796605
19	98.7	99.0398869793523	-0.339886979352301
20	98.4	98.8477236259737	-0.44772362597368
21	98.6	98.5483021183471	0.0516978816529274
22	98.5	98.6932201297722	-0.193220129772229
23	98.1	98.6158816638821	-0.51588166388207
24	98.3	98.2389633493187	0.0610366506812881
25	98.1	98.3753682406713	-0.275368240671312
26	97.9	98.2062363114855	-0.306236311485534
27	99.1	98.001786446316	1.09821355368402
28	98.5	99.0715833578608	-0.571583357860831
29	98.2	98.6454133481538	-0.445413348153764
30	97.8	98.3196149237812	-0.519614923781234
31	98	97.9145301980864	0.0854698019135611
32	98	98.0483862714321	-0.0483862714321361
33	97.6	98.0622545031413	-0.462254503141295
34	97.6	97.6970301568011	-0.0970301568011251
35	97.6	97.6532542308229	-0.0532542308228727
36	97.5	97.6469283982187	-0.146928398218677
37	96.1	97.553703037901	-1.45370303790101
38	96.1	96.263657574708	-0.163657574708054
39	96.3	96.1136735240641	0.1863264759359
40	96.3	96.2789635291148	0.0210364708852353
41	96.3	96.2981823250426	0.0018176749573513
42	96	96.3004006710948	-0.300400671094792
43	96	96.0267577306315	-0.0267577306315019
44	95.2	95.9951495249025	-0.795149524902541
45	96	95.2613491928471	0.738650807152936
46	96.1	95.907213484095	0.19278651590497
47	95.3	96.0738789823827	-0.773878982382669
48	95.1	95.3630298314195	-0.263029831419473
49	94.8	95.0984995546124	-0.298499554612391
50	94.5	94.7947700837965	-0.294770083796479
51	94.7	94.486708763979	0.213291236020964
52	94.8	94.6348389463037	0.165161053696323
53	94.5	94.7445571507618	-0.244557150761807
54	94.5	94.4845063355656	0.0154936644344446
55	92.8	94.4555287848021	-1.65552878480209
56	92.8	92.9014030411089	-0.101403041108952
57	94.5	92.7231981820788	1.7768018179212
58	94.4	94.2570607966548	0.142939203345207
59	94.2	94.3453504454754	-0.14535044547543
60	94.1	94.1741525787549	-0.0741525787548909
61	92.9	94.0641871120191	-1.16418711201914
62	93.3	92.9571593387801	0.342840661219881
63	93.6	93.1957894610394	0.404210538960555
64	93.6	93.4993327388489	0.100667261151131
65	94	93.5362345422803	0.463765457719731
66	94	93.9072337718555	0.0927662281445407
67	94.2	93.9515515982065	0.248448401793482
68	93.3	94.1404028736551	-0.840402873655094
69	93	93.3416334089507	-0.341633408950713
70	93	92.9764305460789	0.0235694539210982
71	94.7	92.9357828025221	1.76421719747789
72	95.6	94.4848596957651	1.1151403042349
73	95.8	95.4870915740891	0.312908425910933
74	96	95.7858334580039	0.21416654199605
75	95.4	96.002539642874	-0.602539642873964
76	95.3	95.4791897572883	-0.179189757288327
77	94.4	95.3267176752095	-0.926717675209503
78	94.4	94.4871502132987	-0.087150213298699
79	94.3	94.3900416624201	-0.0900416624200773
80	93.9	94.2880345819785	-0.388034581978488
81	94.5	93.9116429404827	0.588357059517335
82	93.6	94.4165848436112	-0.816584843611238
83	93.9	93.6541429003026	0.245857099697389
84	93.9	93.8404862406252	0.0595137593748376
85	93.7	93.8630807993218	-0.163080799321776
86	94.6	93.6840015163305	0.915998483669483
87	94.4	94.4858342886999	-0.0858342886998855
88	94	94.3967911985533	-0.396791198553345
89	91.1	94.0216372000104	-2.92163720001039
90	91.1	91.3311562124217	-0.231156212421737
91	90.7	91.0212091350589	-0.321209135058936
92	90.8	90.6230575791836	0.176942420816417
93	89.8	90.6713652075259	-0.87136520752594
94	90.7	89.7672128469352	0.932787153064766
95	90.3	90.4875659544626	-0.187565954462613
96	89.7	90.209275917885	-0.509275917884992
97	89	89.6324213107729	-0.632421310772855
98	88.4	88.9299422235397	-0.529942223539692
99	89.3	88.3046294126794	0.995370587320608
100	89.3	89.0581063573181	0.24189364268193
101	89.3	89.1494995814556	0.150500418544397
102	89.3	89.1637254274042	0.136274572595809
103	88.4	89.1688646119785	-0.768864611978543
104	89.4	88.351194518429	1.04880548157097
105	91.3	89.1730274818121	2.12697251818794
106	90.9	91.006349888339	-0.106349888339011
107	91	90.8559020469281	0.144097953071906
108	89.3	90.9313426119649	-1.63134261196493
109	88.1	89.3896403255979	-1.2896403255979
110	89	88.1176111830987	0.882388816901297
111	90.1	88.795097331595	1.30490266840496
112	90.6	89.8811850797964	0.718814920203627
113	90.6	90.4660286472874	0.133971352712607
114	90.2	90.5355734135409	-0.335573413540899
115	89.5	90.1799224762299	-0.679922476229876
116	90.5	89.5012026162675	0.998797383732537
117	90.4	90.3374372865647	0.0625627134353408
118	89.7	90.3448295013883	-0.644829501388287
119	90	89.7080338320443	0.291966167955749
120	90.2	89.9097670787405	0.290232921259502
121	89.3	90.1174852798122	-0.817485279812161
122	89.6	89.3214412860272	0.278558713972814
123	89.8	89.5048360252303	0.295163974769679
124	89.4	89.7106102470367	-0.310610247036664
125	89.3	89.3709967771664	-0.0709967771663571
126	89.4	89.2420864277549	0.157913572245107
127	89.5	89.3203182606307	0.179681739369315
128	89.2	89.4225160845579	-0.22251608455791
129	90	89.1621869281288	0.837813071871182
130	88	89.8641115426019	-1.86411154260192
131	88.3	88.1210453597795	0.178954640220468
132	89.1	88.1948691143809	0.905130885619073

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 99.9 & 99 & 0.900000000000006 \tabularnewline
4 & 100.3 & 100.021649516974 & 0.278350483025875 \tabularnewline
5 & 100.2 & 100.499094502327 & -0.299094502327307 \tabularnewline
6 & 100.2 & 100.45657902014 & -0.256579020140236 \tabularnewline
7 & 100.6 & 100.445125721614 & 0.154874278385549 \tabularnewline
8 & 100.4 & 100.802656074039 & -0.402656074039342 \tabularnewline
9 & 100.7 & 100.65520658953 & 0.0447934104696088 \tabularnewline
10 & 100.9 & 100.905817163419 & -0.00581716341898186 \tabularnewline
11 & 99.7 & 101.11138409811 & -1.41138409811008 \tabularnewline
12 & 99.7 & 100.033596490965 & -0.333596490965064 \tabularnewline
13 & 96.8 & 99.9031876212681 & -3.10318762126813 \tabularnewline
14 & 99.2 & 97.2356510972496 & 1.96434890275036 \tabularnewline
15 & 99.9 & 99.1140613477702 & 0.785938652229845 \tabularnewline
16 & 99.3 & 99.9675620238639 & -0.667562023863866 \tabularnewline
17 & 98.9 & 99.5144684267888 & -0.614468426788747 \tabularnewline
18 & 98.9 & 99.0925441307966 & -0.192544130796605 \tabularnewline
19 & 98.7 & 99.0398869793523 & -0.339886979352301 \tabularnewline
20 & 98.4 & 98.8477236259737 & -0.44772362597368 \tabularnewline
21 & 98.6 & 98.5483021183471 & 0.0516978816529274 \tabularnewline
22 & 98.5 & 98.6932201297722 & -0.193220129772229 \tabularnewline
23 & 98.1 & 98.6158816638821 & -0.51588166388207 \tabularnewline
24 & 98.3 & 98.2389633493187 & 0.0610366506812881 \tabularnewline
25 & 98.1 & 98.3753682406713 & -0.275368240671312 \tabularnewline
26 & 97.9 & 98.2062363114855 & -0.306236311485534 \tabularnewline
27 & 99.1 & 98.001786446316 & 1.09821355368402 \tabularnewline
28 & 98.5 & 99.0715833578608 & -0.571583357860831 \tabularnewline
29 & 98.2 & 98.6454133481538 & -0.445413348153764 \tabularnewline
30 & 97.8 & 98.3196149237812 & -0.519614923781234 \tabularnewline
31 & 98 & 97.9145301980864 & 0.0854698019135611 \tabularnewline
32 & 98 & 98.0483862714321 & -0.0483862714321361 \tabularnewline
33 & 97.6 & 98.0622545031413 & -0.462254503141295 \tabularnewline
34 & 97.6 & 97.6970301568011 & -0.0970301568011251 \tabularnewline
35 & 97.6 & 97.6532542308229 & -0.0532542308228727 \tabularnewline
36 & 97.5 & 97.6469283982187 & -0.146928398218677 \tabularnewline
37 & 96.1 & 97.553703037901 & -1.45370303790101 \tabularnewline
38 & 96.1 & 96.263657574708 & -0.163657574708054 \tabularnewline
39 & 96.3 & 96.1136735240641 & 0.1863264759359 \tabularnewline
40 & 96.3 & 96.2789635291148 & 0.0210364708852353 \tabularnewline
41 & 96.3 & 96.2981823250426 & 0.0018176749573513 \tabularnewline
42 & 96 & 96.3004006710948 & -0.300400671094792 \tabularnewline
43 & 96 & 96.0267577306315 & -0.0267577306315019 \tabularnewline
44 & 95.2 & 95.9951495249025 & -0.795149524902541 \tabularnewline
45 & 96 & 95.2613491928471 & 0.738650807152936 \tabularnewline
46 & 96.1 & 95.907213484095 & 0.19278651590497 \tabularnewline
47 & 95.3 & 96.0738789823827 & -0.773878982382669 \tabularnewline
48 & 95.1 & 95.3630298314195 & -0.263029831419473 \tabularnewline
49 & 94.8 & 95.0984995546124 & -0.298499554612391 \tabularnewline
50 & 94.5 & 94.7947700837965 & -0.294770083796479 \tabularnewline
51 & 94.7 & 94.486708763979 & 0.213291236020964 \tabularnewline
52 & 94.8 & 94.6348389463037 & 0.165161053696323 \tabularnewline
53 & 94.5 & 94.7445571507618 & -0.244557150761807 \tabularnewline
54 & 94.5 & 94.4845063355656 & 0.0154936644344446 \tabularnewline
55 & 92.8 & 94.4555287848021 & -1.65552878480209 \tabularnewline
56 & 92.8 & 92.9014030411089 & -0.101403041108952 \tabularnewline
57 & 94.5 & 92.7231981820788 & 1.7768018179212 \tabularnewline
58 & 94.4 & 94.2570607966548 & 0.142939203345207 \tabularnewline
59 & 94.2 & 94.3453504454754 & -0.14535044547543 \tabularnewline
60 & 94.1 & 94.1741525787549 & -0.0741525787548909 \tabularnewline
61 & 92.9 & 94.0641871120191 & -1.16418711201914 \tabularnewline
62 & 93.3 & 92.9571593387801 & 0.342840661219881 \tabularnewline
63 & 93.6 & 93.1957894610394 & 0.404210538960555 \tabularnewline
64 & 93.6 & 93.4993327388489 & 0.100667261151131 \tabularnewline
65 & 94 & 93.5362345422803 & 0.463765457719731 \tabularnewline
66 & 94 & 93.9072337718555 & 0.0927662281445407 \tabularnewline
67 & 94.2 & 93.9515515982065 & 0.248448401793482 \tabularnewline
68 & 93.3 & 94.1404028736551 & -0.840402873655094 \tabularnewline
69 & 93 & 93.3416334089507 & -0.341633408950713 \tabularnewline
70 & 93 & 92.9764305460789 & 0.0235694539210982 \tabularnewline
71 & 94.7 & 92.9357828025221 & 1.76421719747789 \tabularnewline
72 & 95.6 & 94.4848596957651 & 1.1151403042349 \tabularnewline
73 & 95.8 & 95.4870915740891 & 0.312908425910933 \tabularnewline
74 & 96 & 95.7858334580039 & 0.21416654199605 \tabularnewline
75 & 95.4 & 96.002539642874 & -0.602539642873964 \tabularnewline
76 & 95.3 & 95.4791897572883 & -0.179189757288327 \tabularnewline
77 & 94.4 & 95.3267176752095 & -0.926717675209503 \tabularnewline
78 & 94.4 & 94.4871502132987 & -0.087150213298699 \tabularnewline
79 & 94.3 & 94.3900416624201 & -0.0900416624200773 \tabularnewline
80 & 93.9 & 94.2880345819785 & -0.388034581978488 \tabularnewline
81 & 94.5 & 93.9116429404827 & 0.588357059517335 \tabularnewline
82 & 93.6 & 94.4165848436112 & -0.816584843611238 \tabularnewline
83 & 93.9 & 93.6541429003026 & 0.245857099697389 \tabularnewline
84 & 93.9 & 93.8404862406252 & 0.0595137593748376 \tabularnewline
85 & 93.7 & 93.8630807993218 & -0.163080799321776 \tabularnewline
86 & 94.6 & 93.6840015163305 & 0.915998483669483 \tabularnewline
87 & 94.4 & 94.4858342886999 & -0.0858342886998855 \tabularnewline
88 & 94 & 94.3967911985533 & -0.396791198553345 \tabularnewline
89 & 91.1 & 94.0216372000104 & -2.92163720001039 \tabularnewline
90 & 91.1 & 91.3311562124217 & -0.231156212421737 \tabularnewline
91 & 90.7 & 91.0212091350589 & -0.321209135058936 \tabularnewline
92 & 90.8 & 90.6230575791836 & 0.176942420816417 \tabularnewline
93 & 89.8 & 90.6713652075259 & -0.87136520752594 \tabularnewline
94 & 90.7 & 89.7672128469352 & 0.932787153064766 \tabularnewline
95 & 90.3 & 90.4875659544626 & -0.187565954462613 \tabularnewline
96 & 89.7 & 90.209275917885 & -0.509275917884992 \tabularnewline
97 & 89 & 89.6324213107729 & -0.632421310772855 \tabularnewline
98 & 88.4 & 88.9299422235397 & -0.529942223539692 \tabularnewline
99 & 89.3 & 88.3046294126794 & 0.995370587320608 \tabularnewline
100 & 89.3 & 89.0581063573181 & 0.24189364268193 \tabularnewline
101 & 89.3 & 89.1494995814556 & 0.150500418544397 \tabularnewline
102 & 89.3 & 89.1637254274042 & 0.136274572595809 \tabularnewline
103 & 88.4 & 89.1688646119785 & -0.768864611978543 \tabularnewline
104 & 89.4 & 88.351194518429 & 1.04880548157097 \tabularnewline
105 & 91.3 & 89.1730274818121 & 2.12697251818794 \tabularnewline
106 & 90.9 & 91.006349888339 & -0.106349888339011 \tabularnewline
107 & 91 & 90.8559020469281 & 0.144097953071906 \tabularnewline
108 & 89.3 & 90.9313426119649 & -1.63134261196493 \tabularnewline
109 & 88.1 & 89.3896403255979 & -1.2896403255979 \tabularnewline
110 & 89 & 88.1176111830987 & 0.882388816901297 \tabularnewline
111 & 90.1 & 88.795097331595 & 1.30490266840496 \tabularnewline
112 & 90.6 & 89.8811850797964 & 0.718814920203627 \tabularnewline
113 & 90.6 & 90.4660286472874 & 0.133971352712607 \tabularnewline
114 & 90.2 & 90.5355734135409 & -0.335573413540899 \tabularnewline
115 & 89.5 & 90.1799224762299 & -0.679922476229876 \tabularnewline
116 & 90.5 & 89.5012026162675 & 0.998797383732537 \tabularnewline
117 & 90.4 & 90.3374372865647 & 0.0625627134353408 \tabularnewline
118 & 89.7 & 90.3448295013883 & -0.644829501388287 \tabularnewline
119 & 90 & 89.7080338320443 & 0.291966167955749 \tabularnewline
120 & 90.2 & 89.9097670787405 & 0.290232921259502 \tabularnewline
121 & 89.3 & 90.1174852798122 & -0.817485279812161 \tabularnewline
122 & 89.6 & 89.3214412860272 & 0.278558713972814 \tabularnewline
123 & 89.8 & 89.5048360252303 & 0.295163974769679 \tabularnewline
124 & 89.4 & 89.7106102470367 & -0.310610247036664 \tabularnewline
125 & 89.3 & 89.3709967771664 & -0.0709967771663571 \tabularnewline
126 & 89.4 & 89.2420864277549 & 0.157913572245107 \tabularnewline
127 & 89.5 & 89.3203182606307 & 0.179681739369315 \tabularnewline
128 & 89.2 & 89.4225160845579 & -0.22251608455791 \tabularnewline
129 & 90 & 89.1621869281288 & 0.837813071871182 \tabularnewline
130 & 88 & 89.8641115426019 & -1.86411154260192 \tabularnewline
131 & 88.3 & 88.1210453597795 & 0.178954640220468 \tabularnewline
132 & 89.1 & 88.1948691143809 & 0.905130885619073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160888&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]3[/C][C]99.9[/C][C]99[/C][C]0.900000000000006[/C][/ROW]
[ROW][C]4[/C][C]100.3[/C][C]100.021649516974[/C][C]0.278350483025875[/C][/ROW]
[ROW][C]5[/C][C]100.2[/C][C]100.499094502327[/C][C]-0.299094502327307[/C][/ROW]
[ROW][C]6[/C][C]100.2[/C][C]100.45657902014[/C][C]-0.256579020140236[/C][/ROW]
[ROW][C]7[/C][C]100.6[/C][C]100.445125721614[/C][C]0.154874278385549[/C][/ROW]
[ROW][C]8[/C][C]100.4[/C][C]100.802656074039[/C][C]-0.402656074039342[/C][/ROW]
[ROW][C]9[/C][C]100.7[/C][C]100.65520658953[/C][C]0.0447934104696088[/C][/ROW]
[ROW][C]10[/C][C]100.9[/C][C]100.905817163419[/C][C]-0.00581716341898186[/C][/ROW]
[ROW][C]11[/C][C]99.7[/C][C]101.11138409811[/C][C]-1.41138409811008[/C][/ROW]
[ROW][C]12[/C][C]99.7[/C][C]100.033596490965[/C][C]-0.333596490965064[/C][/ROW]
[ROW][C]13[/C][C]96.8[/C][C]99.9031876212681[/C][C]-3.10318762126813[/C][/ROW]
[ROW][C]14[/C][C]99.2[/C][C]97.2356510972496[/C][C]1.96434890275036[/C][/ROW]
[ROW][C]15[/C][C]99.9[/C][C]99.1140613477702[/C][C]0.785938652229845[/C][/ROW]
[ROW][C]16[/C][C]99.3[/C][C]99.9675620238639[/C][C]-0.667562023863866[/C][/ROW]
[ROW][C]17[/C][C]98.9[/C][C]99.5144684267888[/C][C]-0.614468426788747[/C][/ROW]
[ROW][C]18[/C][C]98.9[/C][C]99.0925441307966[/C][C]-0.192544130796605[/C][/ROW]
[ROW][C]19[/C][C]98.7[/C][C]99.0398869793523[/C][C]-0.339886979352301[/C][/ROW]
[ROW][C]20[/C][C]98.4[/C][C]98.8477236259737[/C][C]-0.44772362597368[/C][/ROW]
[ROW][C]21[/C][C]98.6[/C][C]98.5483021183471[/C][C]0.0516978816529274[/C][/ROW]
[ROW][C]22[/C][C]98.5[/C][C]98.6932201297722[/C][C]-0.193220129772229[/C][/ROW]
[ROW][C]23[/C][C]98.1[/C][C]98.6158816638821[/C][C]-0.51588166388207[/C][/ROW]
[ROW][C]24[/C][C]98.3[/C][C]98.2389633493187[/C][C]0.0610366506812881[/C][/ROW]
[ROW][C]25[/C][C]98.1[/C][C]98.3753682406713[/C][C]-0.275368240671312[/C][/ROW]
[ROW][C]26[/C][C]97.9[/C][C]98.2062363114855[/C][C]-0.306236311485534[/C][/ROW]
[ROW][C]27[/C][C]99.1[/C][C]98.001786446316[/C][C]1.09821355368402[/C][/ROW]
[ROW][C]28[/C][C]98.5[/C][C]99.0715833578608[/C][C]-0.571583357860831[/C][/ROW]
[ROW][C]29[/C][C]98.2[/C][C]98.6454133481538[/C][C]-0.445413348153764[/C][/ROW]
[ROW][C]30[/C][C]97.8[/C][C]98.3196149237812[/C][C]-0.519614923781234[/C][/ROW]
[ROW][C]31[/C][C]98[/C][C]97.9145301980864[/C][C]0.0854698019135611[/C][/ROW]
[ROW][C]32[/C][C]98[/C][C]98.0483862714321[/C][C]-0.0483862714321361[/C][/ROW]
[ROW][C]33[/C][C]97.6[/C][C]98.0622545031413[/C][C]-0.462254503141295[/C][/ROW]
[ROW][C]34[/C][C]97.6[/C][C]97.6970301568011[/C][C]-0.0970301568011251[/C][/ROW]
[ROW][C]35[/C][C]97.6[/C][C]97.6532542308229[/C][C]-0.0532542308228727[/C][/ROW]
[ROW][C]36[/C][C]97.5[/C][C]97.6469283982187[/C][C]-0.146928398218677[/C][/ROW]
[ROW][C]37[/C][C]96.1[/C][C]97.553703037901[/C][C]-1.45370303790101[/C][/ROW]
[ROW][C]38[/C][C]96.1[/C][C]96.263657574708[/C][C]-0.163657574708054[/C][/ROW]
[ROW][C]39[/C][C]96.3[/C][C]96.1136735240641[/C][C]0.1863264759359[/C][/ROW]
[ROW][C]40[/C][C]96.3[/C][C]96.2789635291148[/C][C]0.0210364708852353[/C][/ROW]
[ROW][C]41[/C][C]96.3[/C][C]96.2981823250426[/C][C]0.0018176749573513[/C][/ROW]
[ROW][C]42[/C][C]96[/C][C]96.3004006710948[/C][C]-0.300400671094792[/C][/ROW]
[ROW][C]43[/C][C]96[/C][C]96.0267577306315[/C][C]-0.0267577306315019[/C][/ROW]
[ROW][C]44[/C][C]95.2[/C][C]95.9951495249025[/C][C]-0.795149524902541[/C][/ROW]
[ROW][C]45[/C][C]96[/C][C]95.2613491928471[/C][C]0.738650807152936[/C][/ROW]
[ROW][C]46[/C][C]96.1[/C][C]95.907213484095[/C][C]0.19278651590497[/C][/ROW]
[ROW][C]47[/C][C]95.3[/C][C]96.0738789823827[/C][C]-0.773878982382669[/C][/ROW]
[ROW][C]48[/C][C]95.1[/C][C]95.3630298314195[/C][C]-0.263029831419473[/C][/ROW]
[ROW][C]49[/C][C]94.8[/C][C]95.0984995546124[/C][C]-0.298499554612391[/C][/ROW]
[ROW][C]50[/C][C]94.5[/C][C]94.7947700837965[/C][C]-0.294770083796479[/C][/ROW]
[ROW][C]51[/C][C]94.7[/C][C]94.486708763979[/C][C]0.213291236020964[/C][/ROW]
[ROW][C]52[/C][C]94.8[/C][C]94.6348389463037[/C][C]0.165161053696323[/C][/ROW]
[ROW][C]53[/C][C]94.5[/C][C]94.7445571507618[/C][C]-0.244557150761807[/C][/ROW]
[ROW][C]54[/C][C]94.5[/C][C]94.4845063355656[/C][C]0.0154936644344446[/C][/ROW]
[ROW][C]55[/C][C]92.8[/C][C]94.4555287848021[/C][C]-1.65552878480209[/C][/ROW]
[ROW][C]56[/C][C]92.8[/C][C]92.9014030411089[/C][C]-0.101403041108952[/C][/ROW]
[ROW][C]57[/C][C]94.5[/C][C]92.7231981820788[/C][C]1.7768018179212[/C][/ROW]
[ROW][C]58[/C][C]94.4[/C][C]94.2570607966548[/C][C]0.142939203345207[/C][/ROW]
[ROW][C]59[/C][C]94.2[/C][C]94.3453504454754[/C][C]-0.14535044547543[/C][/ROW]
[ROW][C]60[/C][C]94.1[/C][C]94.1741525787549[/C][C]-0.0741525787548909[/C][/ROW]
[ROW][C]61[/C][C]92.9[/C][C]94.0641871120191[/C][C]-1.16418711201914[/C][/ROW]
[ROW][C]62[/C][C]93.3[/C][C]92.9571593387801[/C][C]0.342840661219881[/C][/ROW]
[ROW][C]63[/C][C]93.6[/C][C]93.1957894610394[/C][C]0.404210538960555[/C][/ROW]
[ROW][C]64[/C][C]93.6[/C][C]93.4993327388489[/C][C]0.100667261151131[/C][/ROW]
[ROW][C]65[/C][C]94[/C][C]93.5362345422803[/C][C]0.463765457719731[/C][/ROW]
[ROW][C]66[/C][C]94[/C][C]93.9072337718555[/C][C]0.0927662281445407[/C][/ROW]
[ROW][C]67[/C][C]94.2[/C][C]93.9515515982065[/C][C]0.248448401793482[/C][/ROW]
[ROW][C]68[/C][C]93.3[/C][C]94.1404028736551[/C][C]-0.840402873655094[/C][/ROW]
[ROW][C]69[/C][C]93[/C][C]93.3416334089507[/C][C]-0.341633408950713[/C][/ROW]
[ROW][C]70[/C][C]93[/C][C]92.9764305460789[/C][C]0.0235694539210982[/C][/ROW]
[ROW][C]71[/C][C]94.7[/C][C]92.9357828025221[/C][C]1.76421719747789[/C][/ROW]
[ROW][C]72[/C][C]95.6[/C][C]94.4848596957651[/C][C]1.1151403042349[/C][/ROW]
[ROW][C]73[/C][C]95.8[/C][C]95.4870915740891[/C][C]0.312908425910933[/C][/ROW]
[ROW][C]74[/C][C]96[/C][C]95.7858334580039[/C][C]0.21416654199605[/C][/ROW]
[ROW][C]75[/C][C]95.4[/C][C]96.002539642874[/C][C]-0.602539642873964[/C][/ROW]
[ROW][C]76[/C][C]95.3[/C][C]95.4791897572883[/C][C]-0.179189757288327[/C][/ROW]
[ROW][C]77[/C][C]94.4[/C][C]95.3267176752095[/C][C]-0.926717675209503[/C][/ROW]
[ROW][C]78[/C][C]94.4[/C][C]94.4871502132987[/C][C]-0.087150213298699[/C][/ROW]
[ROW][C]79[/C][C]94.3[/C][C]94.3900416624201[/C][C]-0.0900416624200773[/C][/ROW]
[ROW][C]80[/C][C]93.9[/C][C]94.2880345819785[/C][C]-0.388034581978488[/C][/ROW]
[ROW][C]81[/C][C]94.5[/C][C]93.9116429404827[/C][C]0.588357059517335[/C][/ROW]
[ROW][C]82[/C][C]93.6[/C][C]94.4165848436112[/C][C]-0.816584843611238[/C][/ROW]
[ROW][C]83[/C][C]93.9[/C][C]93.6541429003026[/C][C]0.245857099697389[/C][/ROW]
[ROW][C]84[/C][C]93.9[/C][C]93.8404862406252[/C][C]0.0595137593748376[/C][/ROW]
[ROW][C]85[/C][C]93.7[/C][C]93.8630807993218[/C][C]-0.163080799321776[/C][/ROW]
[ROW][C]86[/C][C]94.6[/C][C]93.6840015163305[/C][C]0.915998483669483[/C][/ROW]
[ROW][C]87[/C][C]94.4[/C][C]94.4858342886999[/C][C]-0.0858342886998855[/C][/ROW]
[ROW][C]88[/C][C]94[/C][C]94.3967911985533[/C][C]-0.396791198553345[/C][/ROW]
[ROW][C]89[/C][C]91.1[/C][C]94.0216372000104[/C][C]-2.92163720001039[/C][/ROW]
[ROW][C]90[/C][C]91.1[/C][C]91.3311562124217[/C][C]-0.231156212421737[/C][/ROW]
[ROW][C]91[/C][C]90.7[/C][C]91.0212091350589[/C][C]-0.321209135058936[/C][/ROW]
[ROW][C]92[/C][C]90.8[/C][C]90.6230575791836[/C][C]0.176942420816417[/C][/ROW]
[ROW][C]93[/C][C]89.8[/C][C]90.6713652075259[/C][C]-0.87136520752594[/C][/ROW]
[ROW][C]94[/C][C]90.7[/C][C]89.7672128469352[/C][C]0.932787153064766[/C][/ROW]
[ROW][C]95[/C][C]90.3[/C][C]90.4875659544626[/C][C]-0.187565954462613[/C][/ROW]
[ROW][C]96[/C][C]89.7[/C][C]90.209275917885[/C][C]-0.509275917884992[/C][/ROW]
[ROW][C]97[/C][C]89[/C][C]89.6324213107729[/C][C]-0.632421310772855[/C][/ROW]
[ROW][C]98[/C][C]88.4[/C][C]88.9299422235397[/C][C]-0.529942223539692[/C][/ROW]
[ROW][C]99[/C][C]89.3[/C][C]88.3046294126794[/C][C]0.995370587320608[/C][/ROW]
[ROW][C]100[/C][C]89.3[/C][C]89.0581063573181[/C][C]0.24189364268193[/C][/ROW]
[ROW][C]101[/C][C]89.3[/C][C]89.1494995814556[/C][C]0.150500418544397[/C][/ROW]
[ROW][C]102[/C][C]89.3[/C][C]89.1637254274042[/C][C]0.136274572595809[/C][/ROW]
[ROW][C]103[/C][C]88.4[/C][C]89.1688646119785[/C][C]-0.768864611978543[/C][/ROW]
[ROW][C]104[/C][C]89.4[/C][C]88.351194518429[/C][C]1.04880548157097[/C][/ROW]
[ROW][C]105[/C][C]91.3[/C][C]89.1730274818121[/C][C]2.12697251818794[/C][/ROW]
[ROW][C]106[/C][C]90.9[/C][C]91.006349888339[/C][C]-0.106349888339011[/C][/ROW]
[ROW][C]107[/C][C]91[/C][C]90.8559020469281[/C][C]0.144097953071906[/C][/ROW]
[ROW][C]108[/C][C]89.3[/C][C]90.9313426119649[/C][C]-1.63134261196493[/C][/ROW]
[ROW][C]109[/C][C]88.1[/C][C]89.3896403255979[/C][C]-1.2896403255979[/C][/ROW]
[ROW][C]110[/C][C]89[/C][C]88.1176111830987[/C][C]0.882388816901297[/C][/ROW]
[ROW][C]111[/C][C]90.1[/C][C]88.795097331595[/C][C]1.30490266840496[/C][/ROW]
[ROW][C]112[/C][C]90.6[/C][C]89.8811850797964[/C][C]0.718814920203627[/C][/ROW]
[ROW][C]113[/C][C]90.6[/C][C]90.4660286472874[/C][C]0.133971352712607[/C][/ROW]
[ROW][C]114[/C][C]90.2[/C][C]90.5355734135409[/C][C]-0.335573413540899[/C][/ROW]
[ROW][C]115[/C][C]89.5[/C][C]90.1799224762299[/C][C]-0.679922476229876[/C][/ROW]
[ROW][C]116[/C][C]90.5[/C][C]89.5012026162675[/C][C]0.998797383732537[/C][/ROW]
[ROW][C]117[/C][C]90.4[/C][C]90.3374372865647[/C][C]0.0625627134353408[/C][/ROW]
[ROW][C]118[/C][C]89.7[/C][C]90.3448295013883[/C][C]-0.644829501388287[/C][/ROW]
[ROW][C]119[/C][C]90[/C][C]89.7080338320443[/C][C]0.291966167955749[/C][/ROW]
[ROW][C]120[/C][C]90.2[/C][C]89.9097670787405[/C][C]0.290232921259502[/C][/ROW]
[ROW][C]121[/C][C]89.3[/C][C]90.1174852798122[/C][C]-0.817485279812161[/C][/ROW]
[ROW][C]122[/C][C]89.6[/C][C]89.3214412860272[/C][C]0.278558713972814[/C][/ROW]
[ROW][C]123[/C][C]89.8[/C][C]89.5048360252303[/C][C]0.295163974769679[/C][/ROW]
[ROW][C]124[/C][C]89.4[/C][C]89.7106102470367[/C][C]-0.310610247036664[/C][/ROW]
[ROW][C]125[/C][C]89.3[/C][C]89.3709967771664[/C][C]-0.0709967771663571[/C][/ROW]
[ROW][C]126[/C][C]89.4[/C][C]89.2420864277549[/C][C]0.157913572245107[/C][/ROW]
[ROW][C]127[/C][C]89.5[/C][C]89.3203182606307[/C][C]0.179681739369315[/C][/ROW]
[ROW][C]128[/C][C]89.2[/C][C]89.4225160845579[/C][C]-0.22251608455791[/C][/ROW]
[ROW][C]129[/C][C]90[/C][C]89.1621869281288[/C][C]0.837813071871182[/C][/ROW]
[ROW][C]130[/C][C]88[/C][C]89.8641115426019[/C][C]-1.86411154260192[/C][/ROW]
[ROW][C]131[/C][C]88.3[/C][C]88.1210453597795[/C][C]0.178954640220468[/C][/ROW]
[ROW][C]132[/C][C]89.1[/C][C]88.1948691143809[/C][C]0.905130885619073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160888&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160888&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
3	99.9	99	0.900000000000006
4	100.3	100.021649516974	0.278350483025875
5	100.2	100.499094502327	-0.299094502327307
6	100.2	100.45657902014	-0.256579020140236
7	100.6	100.445125721614	0.154874278385549
8	100.4	100.802656074039	-0.402656074039342
9	100.7	100.65520658953	0.0447934104696088
10	100.9	100.905817163419	-0.00581716341898186
11	99.7	101.11138409811	-1.41138409811008
12	99.7	100.033596490965	-0.333596490965064
13	96.8	99.9031876212681	-3.10318762126813
14	99.2	97.2356510972496	1.96434890275036
15	99.9	99.1140613477702	0.785938652229845
16	99.3	99.9675620238639	-0.667562023863866
17	98.9	99.5144684267888	-0.614468426788747
18	98.9	99.0925441307966	-0.192544130796605
19	98.7	99.0398869793523	-0.339886979352301
20	98.4	98.8477236259737	-0.44772362597368
21	98.6	98.5483021183471	0.0516978816529274
22	98.5	98.6932201297722	-0.193220129772229
23	98.1	98.6158816638821	-0.51588166388207
24	98.3	98.2389633493187	0.0610366506812881
25	98.1	98.3753682406713	-0.275368240671312
26	97.9	98.2062363114855	-0.306236311485534
27	99.1	98.001786446316	1.09821355368402
28	98.5	99.0715833578608	-0.571583357860831
29	98.2	98.6454133481538	-0.445413348153764
30	97.8	98.3196149237812	-0.519614923781234
31	98	97.9145301980864	0.0854698019135611
32	98	98.0483862714321	-0.0483862714321361
33	97.6	98.0622545031413	-0.462254503141295
34	97.6	97.6970301568011	-0.0970301568011251
35	97.6	97.6532542308229	-0.0532542308228727
36	97.5	97.6469283982187	-0.146928398218677
37	96.1	97.553703037901	-1.45370303790101
38	96.1	96.263657574708	-0.163657574708054
39	96.3	96.1136735240641	0.1863264759359
40	96.3	96.2789635291148	0.0210364708852353
41	96.3	96.2981823250426	0.0018176749573513
42	96	96.3004006710948	-0.300400671094792
43	96	96.0267577306315	-0.0267577306315019
44	95.2	95.9951495249025	-0.795149524902541
45	96	95.2613491928471	0.738650807152936
46	96.1	95.907213484095	0.19278651590497
47	95.3	96.0738789823827	-0.773878982382669
48	95.1	95.3630298314195	-0.263029831419473
49	94.8	95.0984995546124	-0.298499554612391
50	94.5	94.7947700837965	-0.294770083796479
51	94.7	94.486708763979	0.213291236020964
52	94.8	94.6348389463037	0.165161053696323
53	94.5	94.7445571507618	-0.244557150761807
54	94.5	94.4845063355656	0.0154936644344446
55	92.8	94.4555287848021	-1.65552878480209
56	92.8	92.9014030411089	-0.101403041108952
57	94.5	92.7231981820788	1.7768018179212
58	94.4	94.2570607966548	0.142939203345207
59	94.2	94.3453504454754	-0.14535044547543
60	94.1	94.1741525787549	-0.0741525787548909
61	92.9	94.0641871120191	-1.16418711201914
62	93.3	92.9571593387801	0.342840661219881
63	93.6	93.1957894610394	0.404210538960555
64	93.6	93.4993327388489	0.100667261151131
65	94	93.5362345422803	0.463765457719731
66	94	93.9072337718555	0.0927662281445407
67	94.2	93.9515515982065	0.248448401793482
68	93.3	94.1404028736551	-0.840402873655094
69	93	93.3416334089507	-0.341633408950713
70	93	92.9764305460789	0.0235694539210982
71	94.7	92.9357828025221	1.76421719747789
72	95.6	94.4848596957651	1.1151403042349
73	95.8	95.4870915740891	0.312908425910933
74	96	95.7858334580039	0.21416654199605
75	95.4	96.002539642874	-0.602539642873964
76	95.3	95.4791897572883	-0.179189757288327
77	94.4	95.3267176752095	-0.926717675209503
78	94.4	94.4871502132987	-0.087150213298699
79	94.3	94.3900416624201	-0.0900416624200773
80	93.9	94.2880345819785	-0.388034581978488
81	94.5	93.9116429404827	0.588357059517335
82	93.6	94.4165848436112	-0.816584843611238
83	93.9	93.6541429003026	0.245857099697389
84	93.9	93.8404862406252	0.0595137593748376
85	93.7	93.8630807993218	-0.163080799321776
86	94.6	93.6840015163305	0.915998483669483
87	94.4	94.4858342886999	-0.0858342886998855
88	94	94.3967911985533	-0.396791198553345
89	91.1	94.0216372000104	-2.92163720001039
90	91.1	91.3311562124217	-0.231156212421737
91	90.7	91.0212091350589	-0.321209135058936
92	90.8	90.6230575791836	0.176942420816417
93	89.8	90.6713652075259	-0.87136520752594
94	90.7	89.7672128469352	0.932787153064766
95	90.3	90.4875659544626	-0.187565954462613
96	89.7	90.209275917885	-0.509275917884992
97	89	89.6324213107729	-0.632421310772855
98	88.4	88.9299422235397	-0.529942223539692
99	89.3	88.3046294126794	0.995370587320608
100	89.3	89.0581063573181	0.24189364268193
101	89.3	89.1494995814556	0.150500418544397
102	89.3	89.1637254274042	0.136274572595809
103	88.4	89.1688646119785	-0.768864611978543
104	89.4	88.351194518429	1.04880548157097
105	91.3	89.1730274818121	2.12697251818794
106	90.9	91.006349888339	-0.106349888339011
107	91	90.8559020469281	0.144097953071906
108	89.3	90.9313426119649	-1.63134261196493
109	88.1	89.3896403255979	-1.2896403255979
110	89	88.1176111830987	0.882388816901297
111	90.1	88.795097331595	1.30490266840496
112	90.6	89.8811850797964	0.718814920203627
113	90.6	90.4660286472874	0.133971352712607
114	90.2	90.5355734135409	-0.335573413540899
115	89.5	90.1799224762299	-0.679922476229876
116	90.5	89.5012026162675	0.998797383732537
117	90.4	90.3374372865647	0.0625627134353408
118	89.7	90.3448295013883	-0.644829501388287
119	90	89.7080338320443	0.291966167955749
120	90.2	89.9097670787405	0.290232921259502
121	89.3	90.1174852798122	-0.817485279812161
122	89.6	89.3214412860272	0.278558713972814
123	89.8	89.5048360252303	0.295163974769679
124	89.4	89.7106102470367	-0.310610247036664
125	89.3	89.3709967771664	-0.0709967771663571
126	89.4	89.2420864277549	0.157913572245107
127	89.5	89.3203182606307	0.179681739369315
128	89.2	89.4225160845579	-0.22251608455791
129	90	89.1621869281288	0.837813071871182
130	88	89.8641115426019	-1.86411154260192
131	88.3	88.1210453597795	0.178954640220468
132	89.1	88.1948691143809	0.905130885619073

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
133	88.9362892755939	87.4318457157594	90.4407328354284
134	88.8748353013387	86.8377350682585	90.911935534419
135	88.8133813270835	86.3345033638578	91.2922592903093
136	88.7519273528283	85.8793732453792	91.6244814602775
137	88.6904733785732	85.4542700976853	91.926676659461
138	88.629019404318	85.0496045685902	92.2084342400457
139	88.5675654300628	84.6595986111321	92.4755322489934
140	88.5061114558076	84.280471287173	92.7317516244421
141	88.4446574815524	83.909600482835	92.9797144802698
142	88.3832035072972	83.5450874500886	93.2213195645058
143	88.321749533042	83.185510566285	93.4579884997991
144	88.2602955587868	82.8297767909249	93.6908143266488

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 88.9362892755939 & 87.4318457157594 & 90.4407328354284 \tabularnewline
134 & 88.8748353013387 & 86.8377350682585 & 90.911935534419 \tabularnewline
135 & 88.8133813270835 & 86.3345033638578 & 91.2922592903093 \tabularnewline
136 & 88.7519273528283 & 85.8793732453792 & 91.6244814602775 \tabularnewline
137 & 88.6904733785732 & 85.4542700976853 & 91.926676659461 \tabularnewline
138 & 88.629019404318 & 85.0496045685902 & 92.2084342400457 \tabularnewline
139 & 88.5675654300628 & 84.6595986111321 & 92.4755322489934 \tabularnewline
140 & 88.5061114558076 & 84.280471287173 & 92.7317516244421 \tabularnewline
141 & 88.4446574815524 & 83.909600482835 & 92.9797144802698 \tabularnewline
142 & 88.3832035072972 & 83.5450874500886 & 93.2213195645058 \tabularnewline
143 & 88.321749533042 & 83.185510566285 & 93.4579884997991 \tabularnewline
144 & 88.2602955587868 & 82.8297767909249 & 93.6908143266488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160888&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]133[/C][C]88.9362892755939[/C][C]87.4318457157594[/C][C]90.4407328354284[/C][/ROW]
[ROW][C]134[/C][C]88.8748353013387[/C][C]86.8377350682585[/C][C]90.911935534419[/C][/ROW]
[ROW][C]135[/C][C]88.8133813270835[/C][C]86.3345033638578[/C][C]91.2922592903093[/C][/ROW]
[ROW][C]136[/C][C]88.7519273528283[/C][C]85.8793732453792[/C][C]91.6244814602775[/C][/ROW]
[ROW][C]137[/C][C]88.6904733785732[/C][C]85.4542700976853[/C][C]91.926676659461[/C][/ROW]
[ROW][C]138[/C][C]88.629019404318[/C][C]85.0496045685902[/C][C]92.2084342400457[/C][/ROW]
[ROW][C]139[/C][C]88.5675654300628[/C][C]84.6595986111321[/C][C]92.4755322489934[/C][/ROW]
[ROW][C]140[/C][C]88.5061114558076[/C][C]84.280471287173[/C][C]92.7317516244421[/C][/ROW]
[ROW][C]141[/C][C]88.4446574815524[/C][C]83.909600482835[/C][C]92.9797144802698[/C][/ROW]
[ROW][C]142[/C][C]88.3832035072972[/C][C]83.5450874500886[/C][C]93.2213195645058[/C][/ROW]
[ROW][C]143[/C][C]88.321749533042[/C][C]83.185510566285[/C][C]93.4579884997991[/C][/ROW]
[ROW][C]144[/C][C]88.2602955587868[/C][C]82.8297767909249[/C][C]93.6908143266488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160888&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160888&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
133	88.9362892755939	87.4318457157594	90.4407328354284
134	88.8748353013387	86.8377350682585	90.911935534419
135	88.8133813270835	86.3345033638578	91.2922592903093
136	88.7519273528283	85.8793732453792	91.6244814602775
137	88.6904733785732	85.4542700976853	91.926676659461
138	88.629019404318	85.0496045685902	92.2084342400457
139	88.5675654300628	84.6595986111321	92.4755322489934
140	88.5061114558076	84.280471287173	92.7317516244421
141	88.4446574815524	83.909600482835	92.9797144802698
142	88.3832035072972	83.5450874500886	93.2213195645058
143	88.321749533042	83.185510566285	93.4579884997991
144	88.2602955587868	82.8297767909249	93.6908143266488

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 = Double ; par3 = additive ;

Parameters (R input):

par1 = 12 ; par2 = Double ; par3 = additive ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code