## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationFri, 13 Dec 2013 08:40:20 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/13/t1386942053e2newv955lg5889.htm/, Retrieved Tue, 29 Sep 2020 14:44:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232296, Retrieved Tue, 29 Sep 2020 14:44:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2013-12-13 13:40:20] [6e7c8e41ae9c2cf944b21192a5249437] [Current]
- R PD    [Exponential Smoothing] [] [2013-12-18 08:43:39] [118d51f3b8c7238175a748a4b2235cf1]
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Dataseries X:
26,73
26,85
27,01
27,09
27,11
27,16
27,13
27,19
27,49
27,63
27,72
27,77
27,81
27,92
28,07
28,14
28,17
28,20
28,21
28,20
28,19
28,24
28,25
28,26
28,33
28,67
28,81
28,99
29,16
29,25
29,25
29,38
29,48
29,65
29,69
29,73
29,81
30,05
30,29
30,37
30,50
30,67
30,76
30,84
30,86
31,09
31,20
31,19
31,18
31,31
31,39
31,39
31,37
31,36
31,37
31,35
31,34
31,47
31,48
31,54
31,55
31,55
31,57
31,66
31,74
31,78
31,80
31,68
31,70
31,70
31,75
31,73
31,82
31,90
31,82
31,51
31,42
30,97
30,99
30,92
30,95
30,82
30,72
30,73


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232296&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232296&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 6 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.999954055201604 beta FALSE gamma FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.999954055201604 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232296&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]0.999954055201604[/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=232296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232296&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 0.999954055201604 beta FALSE gamma FALSE

 Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 2 26.85 26.73 0.120000000000001 3 27.01 26.8499944866242 0.160005513375808 4 27.09 27.0099926485789 0.0800073514210524 5 27.11 27.0899963240784 0.0200036759216324 6 27.16 27.1099990809351 0.0500009190648569 7 27.13 27.1599977027179 -0.0299977027178535 8 27.19 27.1300013782384 0.0599986217615971 9 27.49 27.1899972433754 0.300002756624579 10 27.63 27.4899862164338 0.140013783566172 11 27.72 27.6299935670949 0.0900064329050601 12 27.77 27.7199958646726 0.0500041353274163 13 27.81 27.7699977025701 0.0400022974299148 14 27.92 27.8099981621025 0.110001837897492 15 28.07 27.9199949459877 0.150005054012265 16 28.14 28.069993108048 0.0700068919519659 17 28.17 28.1399967835475 0.0300032164525383 18 28.2 28.1699986215083 0.030001378491729 19 28.21 28.1999986215927 0.0100013784072885 20 28.2 28.2099995404887 -0.00999954048868545 21 28.19 28.2000004594269 -0.0100004594268697 22 28.24 28.1900004594691 0.0499995405309051 23 28.25 28.2399977027812 0.0100022972188114 24 28.26 28.2499995404465 0.010000459553531 25 28.33 28.2599995405309 0.0700004594690959 26 28.67 28.329996783843 0.340003216157001 27 28.81 28.6699843786208 0.140015621379217 28 28.99 28.8099935670105 0.180006432989497 29 29.16 28.9899917296407 0.170008270359276 30 29.25 29.1599921890043 0.0900078109957079 31 29.25 29.2499958646093 4.13539073207403e-06 32 29.38 29.24999999981 0.130000000189998 33 29.48 29.3799940271762 0.100005972823801 34 29.65 29.4799954052457 0.170004594754257 35 29.69 29.6499921891732 0.040007810826836 36 29.73 29.6899981618492 0.0400018381508005 37 29.81 29.7299981621236 0.080001837876388 38 30.05 29.8099963243317 0.240003675668316 39 30.29 30.0499889730795 0.240011026920492 40 30.37 30.2899889727418 0.0800110272582479 41 30.5 30.3699963239095 0.130003676090517 42 30.67 30.4999940270073 0.170005972992691 43 30.76 30.6699921891098 0.0900078108901567 44 30.84 30.7599958646093 0.0800041353907233 45 30.86 30.8399963242261 0.0200036757738715 46 31.09 30.8599990809352 0.23000091906485 47 31.2 31.0899894326541 0.110010567345856 48 31.19 31.1999949455867 -0.00999494558665859 49 31.18 31.1900004592158 -0.0100004592157603 50 31.31 31.1800004594691 0.129999540530918 51 31.39 31.3099940271973 0.0800059728026845 52 31.39 31.3899963241417 3.67585829152972e-06 53 31.37 31.3899999998311 -0.0199999998311142 54 31.36 31.370000918896 -0.0100009188959618 55 31.37 31.3600004594902 0.00999954050979923 56 31.35 31.3699995405731 -0.0199995405731279 57 31.34 31.3500009188749 -0.0100009188748622 58 31.47 31.3400004594902 0.129999540509797 59 31.48 31.4699940271973 0.0100059728026807 60 31.54 31.4799995402776 0.0600004597224029 61 31.55 31.539997243291 0.0100027567090279 62 31.55 31.5499995404254 4.59574639677385e-07 63 31.57 31.5499999999789 0.0200000000211134 64 31.66 31.569999081104 0.0900009188959672 65 31.74 31.6599958649259 0.0800041350740734 66 31.78 31.7399963242261 0.0400036757738604 67 31.8 31.7799981620392 0.0200018379608196 68 31.68 31.7999990810196 -0.119999081019589 69 31.7 31.6800055133336 0.0199944866664161 70 31.7 31.6999990813573 9.18642658831459e-07 71 31.75 31.6999999999578 0.0500000000422069 72 31.73 31.7499977027601 -0.0199977027600795 73 31.82 31.7300009187904 0.0899990812095766 74 31.9 31.8199958650104 0.0800041349896397 75 31.82 31.8999963242261 -0.0799963242261441 76 31.51 31.820003675415 -0.310003675414986 77 31.42 31.5100142430564 -0.0900142430563697 78 30.97 31.4200041356863 -0.450004135686253 79 30.99 30.9700206753493 0.0199793246507092 80 30.92 30.989999082054 -0.069999082053954 81 30.95 30.9200032160937 0.0299967839062845 82 30.82 30.9499986218038 -0.129998621803811 83 30.72 30.8200059727605 -0.100005972760471 84 30.73 30.7200045947543 0.00999540524574627

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 26.85 & 26.73 & 0.120000000000001 \tabularnewline
3 & 27.01 & 26.8499944866242 & 0.160005513375808 \tabularnewline
4 & 27.09 & 27.0099926485789 & 0.0800073514210524 \tabularnewline
5 & 27.11 & 27.0899963240784 & 0.0200036759216324 \tabularnewline
6 & 27.16 & 27.1099990809351 & 0.0500009190648569 \tabularnewline
7 & 27.13 & 27.1599977027179 & -0.0299977027178535 \tabularnewline
8 & 27.19 & 27.1300013782384 & 0.0599986217615971 \tabularnewline
9 & 27.49 & 27.1899972433754 & 0.300002756624579 \tabularnewline
10 & 27.63 & 27.4899862164338 & 0.140013783566172 \tabularnewline
11 & 27.72 & 27.6299935670949 & 0.0900064329050601 \tabularnewline
12 & 27.77 & 27.7199958646726 & 0.0500041353274163 \tabularnewline
13 & 27.81 & 27.7699977025701 & 0.0400022974299148 \tabularnewline
14 & 27.92 & 27.8099981621025 & 0.110001837897492 \tabularnewline
15 & 28.07 & 27.9199949459877 & 0.150005054012265 \tabularnewline
16 & 28.14 & 28.069993108048 & 0.0700068919519659 \tabularnewline
17 & 28.17 & 28.1399967835475 & 0.0300032164525383 \tabularnewline
18 & 28.2 & 28.1699986215083 & 0.030001378491729 \tabularnewline
19 & 28.21 & 28.1999986215927 & 0.0100013784072885 \tabularnewline
20 & 28.2 & 28.2099995404887 & -0.00999954048868545 \tabularnewline
21 & 28.19 & 28.2000004594269 & -0.0100004594268697 \tabularnewline
22 & 28.24 & 28.1900004594691 & 0.0499995405309051 \tabularnewline
23 & 28.25 & 28.2399977027812 & 0.0100022972188114 \tabularnewline
24 & 28.26 & 28.2499995404465 & 0.010000459553531 \tabularnewline
25 & 28.33 & 28.2599995405309 & 0.0700004594690959 \tabularnewline
26 & 28.67 & 28.329996783843 & 0.340003216157001 \tabularnewline
27 & 28.81 & 28.6699843786208 & 0.140015621379217 \tabularnewline
28 & 28.99 & 28.8099935670105 & 0.180006432989497 \tabularnewline
29 & 29.16 & 28.9899917296407 & 0.170008270359276 \tabularnewline
30 & 29.25 & 29.1599921890043 & 0.0900078109957079 \tabularnewline
31 & 29.25 & 29.2499958646093 & 4.13539073207403e-06 \tabularnewline
32 & 29.38 & 29.24999999981 & 0.130000000189998 \tabularnewline
33 & 29.48 & 29.3799940271762 & 0.100005972823801 \tabularnewline
34 & 29.65 & 29.4799954052457 & 0.170004594754257 \tabularnewline
35 & 29.69 & 29.6499921891732 & 0.040007810826836 \tabularnewline
36 & 29.73 & 29.6899981618492 & 0.0400018381508005 \tabularnewline
37 & 29.81 & 29.7299981621236 & 0.080001837876388 \tabularnewline
38 & 30.05 & 29.8099963243317 & 0.240003675668316 \tabularnewline
39 & 30.29 & 30.0499889730795 & 0.240011026920492 \tabularnewline
40 & 30.37 & 30.2899889727418 & 0.0800110272582479 \tabularnewline
41 & 30.5 & 30.3699963239095 & 0.130003676090517 \tabularnewline
42 & 30.67 & 30.4999940270073 & 0.170005972992691 \tabularnewline
43 & 30.76 & 30.6699921891098 & 0.0900078108901567 \tabularnewline
44 & 30.84 & 30.7599958646093 & 0.0800041353907233 \tabularnewline
45 & 30.86 & 30.8399963242261 & 0.0200036757738715 \tabularnewline
46 & 31.09 & 30.8599990809352 & 0.23000091906485 \tabularnewline
47 & 31.2 & 31.0899894326541 & 0.110010567345856 \tabularnewline
48 & 31.19 & 31.1999949455867 & -0.00999494558665859 \tabularnewline
49 & 31.18 & 31.1900004592158 & -0.0100004592157603 \tabularnewline
50 & 31.31 & 31.1800004594691 & 0.129999540530918 \tabularnewline
51 & 31.39 & 31.3099940271973 & 0.0800059728026845 \tabularnewline
52 & 31.39 & 31.3899963241417 & 3.67585829152972e-06 \tabularnewline
53 & 31.37 & 31.3899999998311 & -0.0199999998311142 \tabularnewline
54 & 31.36 & 31.370000918896 & -0.0100009188959618 \tabularnewline
55 & 31.37 & 31.3600004594902 & 0.00999954050979923 \tabularnewline
56 & 31.35 & 31.3699995405731 & -0.0199995405731279 \tabularnewline
57 & 31.34 & 31.3500009188749 & -0.0100009188748622 \tabularnewline
58 & 31.47 & 31.3400004594902 & 0.129999540509797 \tabularnewline
59 & 31.48 & 31.4699940271973 & 0.0100059728026807 \tabularnewline
60 & 31.54 & 31.4799995402776 & 0.0600004597224029 \tabularnewline
61 & 31.55 & 31.539997243291 & 0.0100027567090279 \tabularnewline
62 & 31.55 & 31.5499995404254 & 4.59574639677385e-07 \tabularnewline
63 & 31.57 & 31.5499999999789 & 0.0200000000211134 \tabularnewline
64 & 31.66 & 31.569999081104 & 0.0900009188959672 \tabularnewline
65 & 31.74 & 31.6599958649259 & 0.0800041350740734 \tabularnewline
66 & 31.78 & 31.7399963242261 & 0.0400036757738604 \tabularnewline
67 & 31.8 & 31.7799981620392 & 0.0200018379608196 \tabularnewline
68 & 31.68 & 31.7999990810196 & -0.119999081019589 \tabularnewline
69 & 31.7 & 31.6800055133336 & 0.0199944866664161 \tabularnewline
70 & 31.7 & 31.6999990813573 & 9.18642658831459e-07 \tabularnewline
71 & 31.75 & 31.6999999999578 & 0.0500000000422069 \tabularnewline
72 & 31.73 & 31.7499977027601 & -0.0199977027600795 \tabularnewline
73 & 31.82 & 31.7300009187904 & 0.0899990812095766 \tabularnewline
74 & 31.9 & 31.8199958650104 & 0.0800041349896397 \tabularnewline
75 & 31.82 & 31.8999963242261 & -0.0799963242261441 \tabularnewline
76 & 31.51 & 31.820003675415 & -0.310003675414986 \tabularnewline
77 & 31.42 & 31.5100142430564 & -0.0900142430563697 \tabularnewline
78 & 30.97 & 31.4200041356863 & -0.450004135686253 \tabularnewline
79 & 30.99 & 30.9700206753493 & 0.0199793246507092 \tabularnewline
80 & 30.92 & 30.989999082054 & -0.069999082053954 \tabularnewline
81 & 30.95 & 30.9200032160937 & 0.0299967839062845 \tabularnewline
82 & 30.82 & 30.9499986218038 & -0.129998621803811 \tabularnewline
83 & 30.72 & 30.8200059727605 & -0.100005972760471 \tabularnewline
84 & 30.73 & 30.7200045947543 & 0.00999540524574627 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232296&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]26.85[/C][C]26.73[/C][C]0.120000000000001[/C][/ROW]
[ROW][C]3[/C][C]27.01[/C][C]26.8499944866242[/C][C]0.160005513375808[/C][/ROW]
[ROW][C]4[/C][C]27.09[/C][C]27.0099926485789[/C][C]0.0800073514210524[/C][/ROW]
[ROW][C]5[/C][C]27.11[/C][C]27.0899963240784[/C][C]0.0200036759216324[/C][/ROW]
[ROW][C]6[/C][C]27.16[/C][C]27.1099990809351[/C][C]0.0500009190648569[/C][/ROW]
[ROW][C]7[/C][C]27.13[/C][C]27.1599977027179[/C][C]-0.0299977027178535[/C][/ROW]
[ROW][C]8[/C][C]27.19[/C][C]27.1300013782384[/C][C]0.0599986217615971[/C][/ROW]
[ROW][C]9[/C][C]27.49[/C][C]27.1899972433754[/C][C]0.300002756624579[/C][/ROW]
[ROW][C]10[/C][C]27.63[/C][C]27.4899862164338[/C][C]0.140013783566172[/C][/ROW]
[ROW][C]11[/C][C]27.72[/C][C]27.6299935670949[/C][C]0.0900064329050601[/C][/ROW]
[ROW][C]12[/C][C]27.77[/C][C]27.7199958646726[/C][C]0.0500041353274163[/C][/ROW]
[ROW][C]13[/C][C]27.81[/C][C]27.7699977025701[/C][C]0.0400022974299148[/C][/ROW]
[ROW][C]14[/C][C]27.92[/C][C]27.8099981621025[/C][C]0.110001837897492[/C][/ROW]
[ROW][C]15[/C][C]28.07[/C][C]27.9199949459877[/C][C]0.150005054012265[/C][/ROW]
[ROW][C]16[/C][C]28.14[/C][C]28.069993108048[/C][C]0.0700068919519659[/C][/ROW]
[ROW][C]17[/C][C]28.17[/C][C]28.1399967835475[/C][C]0.0300032164525383[/C][/ROW]
[ROW][C]18[/C][C]28.2[/C][C]28.1699986215083[/C][C]0.030001378491729[/C][/ROW]
[ROW][C]19[/C][C]28.21[/C][C]28.1999986215927[/C][C]0.0100013784072885[/C][/ROW]
[ROW][C]20[/C][C]28.2[/C][C]28.2099995404887[/C][C]-0.00999954048868545[/C][/ROW]
[ROW][C]21[/C][C]28.19[/C][C]28.2000004594269[/C][C]-0.0100004594268697[/C][/ROW]
[ROW][C]22[/C][C]28.24[/C][C]28.1900004594691[/C][C]0.0499995405309051[/C][/ROW]
[ROW][C]23[/C][C]28.25[/C][C]28.2399977027812[/C][C]0.0100022972188114[/C][/ROW]
[ROW][C]24[/C][C]28.26[/C][C]28.2499995404465[/C][C]0.010000459553531[/C][/ROW]
[ROW][C]25[/C][C]28.33[/C][C]28.2599995405309[/C][C]0.0700004594690959[/C][/ROW]
[ROW][C]26[/C][C]28.67[/C][C]28.329996783843[/C][C]0.340003216157001[/C][/ROW]
[ROW][C]27[/C][C]28.81[/C][C]28.6699843786208[/C][C]0.140015621379217[/C][/ROW]
[ROW][C]28[/C][C]28.99[/C][C]28.8099935670105[/C][C]0.180006432989497[/C][/ROW]
[ROW][C]29[/C][C]29.16[/C][C]28.9899917296407[/C][C]0.170008270359276[/C][/ROW]
[ROW][C]30[/C][C]29.25[/C][C]29.1599921890043[/C][C]0.0900078109957079[/C][/ROW]
[ROW][C]31[/C][C]29.25[/C][C]29.2499958646093[/C][C]4.13539073207403e-06[/C][/ROW]
[ROW][C]32[/C][C]29.38[/C][C]29.24999999981[/C][C]0.130000000189998[/C][/ROW]
[ROW][C]33[/C][C]29.48[/C][C]29.3799940271762[/C][C]0.100005972823801[/C][/ROW]
[ROW][C]34[/C][C]29.65[/C][C]29.4799954052457[/C][C]0.170004594754257[/C][/ROW]
[ROW][C]35[/C][C]29.69[/C][C]29.6499921891732[/C][C]0.040007810826836[/C][/ROW]
[ROW][C]36[/C][C]29.73[/C][C]29.6899981618492[/C][C]0.0400018381508005[/C][/ROW]
[ROW][C]37[/C][C]29.81[/C][C]29.7299981621236[/C][C]0.080001837876388[/C][/ROW]
[ROW][C]38[/C][C]30.05[/C][C]29.8099963243317[/C][C]0.240003675668316[/C][/ROW]
[ROW][C]39[/C][C]30.29[/C][C]30.0499889730795[/C][C]0.240011026920492[/C][/ROW]
[ROW][C]40[/C][C]30.37[/C][C]30.2899889727418[/C][C]0.0800110272582479[/C][/ROW]
[ROW][C]41[/C][C]30.5[/C][C]30.3699963239095[/C][C]0.130003676090517[/C][/ROW]
[ROW][C]42[/C][C]30.67[/C][C]30.4999940270073[/C][C]0.170005972992691[/C][/ROW]
[ROW][C]43[/C][C]30.76[/C][C]30.6699921891098[/C][C]0.0900078108901567[/C][/ROW]
[ROW][C]44[/C][C]30.84[/C][C]30.7599958646093[/C][C]0.0800041353907233[/C][/ROW]
[ROW][C]45[/C][C]30.86[/C][C]30.8399963242261[/C][C]0.0200036757738715[/C][/ROW]
[ROW][C]46[/C][C]31.09[/C][C]30.8599990809352[/C][C]0.23000091906485[/C][/ROW]
[ROW][C]47[/C][C]31.2[/C][C]31.0899894326541[/C][C]0.110010567345856[/C][/ROW]
[ROW][C]48[/C][C]31.19[/C][C]31.1999949455867[/C][C]-0.00999494558665859[/C][/ROW]
[ROW][C]49[/C][C]31.18[/C][C]31.1900004592158[/C][C]-0.0100004592157603[/C][/ROW]
[ROW][C]50[/C][C]31.31[/C][C]31.1800004594691[/C][C]0.129999540530918[/C][/ROW]
[ROW][C]51[/C][C]31.39[/C][C]31.3099940271973[/C][C]0.0800059728026845[/C][/ROW]
[ROW][C]52[/C][C]31.39[/C][C]31.3899963241417[/C][C]3.67585829152972e-06[/C][/ROW]
[ROW][C]53[/C][C]31.37[/C][C]31.3899999998311[/C][C]-0.0199999998311142[/C][/ROW]
[ROW][C]54[/C][C]31.36[/C][C]31.370000918896[/C][C]-0.0100009188959618[/C][/ROW]
[ROW][C]55[/C][C]31.37[/C][C]31.3600004594902[/C][C]0.00999954050979923[/C][/ROW]
[ROW][C]56[/C][C]31.35[/C][C]31.3699995405731[/C][C]-0.0199995405731279[/C][/ROW]
[ROW][C]57[/C][C]31.34[/C][C]31.3500009188749[/C][C]-0.0100009188748622[/C][/ROW]
[ROW][C]58[/C][C]31.47[/C][C]31.3400004594902[/C][C]0.129999540509797[/C][/ROW]
[ROW][C]59[/C][C]31.48[/C][C]31.4699940271973[/C][C]0.0100059728026807[/C][/ROW]
[ROW][C]60[/C][C]31.54[/C][C]31.4799995402776[/C][C]0.0600004597224029[/C][/ROW]
[ROW][C]61[/C][C]31.55[/C][C]31.539997243291[/C][C]0.0100027567090279[/C][/ROW]
[ROW][C]62[/C][C]31.55[/C][C]31.5499995404254[/C][C]4.59574639677385e-07[/C][/ROW]
[ROW][C]63[/C][C]31.57[/C][C]31.5499999999789[/C][C]0.0200000000211134[/C][/ROW]
[ROW][C]64[/C][C]31.66[/C][C]31.569999081104[/C][C]0.0900009188959672[/C][/ROW]
[ROW][C]65[/C][C]31.74[/C][C]31.6599958649259[/C][C]0.0800041350740734[/C][/ROW]
[ROW][C]66[/C][C]31.78[/C][C]31.7399963242261[/C][C]0.0400036757738604[/C][/ROW]
[ROW][C]67[/C][C]31.8[/C][C]31.7799981620392[/C][C]0.0200018379608196[/C][/ROW]
[ROW][C]68[/C][C]31.68[/C][C]31.7999990810196[/C][C]-0.119999081019589[/C][/ROW]
[ROW][C]69[/C][C]31.7[/C][C]31.6800055133336[/C][C]0.0199944866664161[/C][/ROW]
[ROW][C]70[/C][C]31.7[/C][C]31.6999990813573[/C][C]9.18642658831459e-07[/C][/ROW]
[ROW][C]71[/C][C]31.75[/C][C]31.6999999999578[/C][C]0.0500000000422069[/C][/ROW]
[ROW][C]72[/C][C]31.73[/C][C]31.7499977027601[/C][C]-0.0199977027600795[/C][/ROW]
[ROW][C]73[/C][C]31.82[/C][C]31.7300009187904[/C][C]0.0899990812095766[/C][/ROW]
[ROW][C]74[/C][C]31.9[/C][C]31.8199958650104[/C][C]0.0800041349896397[/C][/ROW]
[ROW][C]75[/C][C]31.82[/C][C]31.8999963242261[/C][C]-0.0799963242261441[/C][/ROW]
[ROW][C]76[/C][C]31.51[/C][C]31.820003675415[/C][C]-0.310003675414986[/C][/ROW]
[ROW][C]77[/C][C]31.42[/C][C]31.5100142430564[/C][C]-0.0900142430563697[/C][/ROW]
[ROW][C]78[/C][C]30.97[/C][C]31.4200041356863[/C][C]-0.450004135686253[/C][/ROW]
[ROW][C]79[/C][C]30.99[/C][C]30.9700206753493[/C][C]0.0199793246507092[/C][/ROW]
[ROW][C]80[/C][C]30.92[/C][C]30.989999082054[/C][C]-0.069999082053954[/C][/ROW]
[ROW][C]81[/C][C]30.95[/C][C]30.9200032160937[/C][C]0.0299967839062845[/C][/ROW]
[ROW][C]82[/C][C]30.82[/C][C]30.9499986218038[/C][C]-0.129998621803811[/C][/ROW]
[ROW][C]83[/C][C]30.72[/C][C]30.8200059727605[/C][C]-0.100005972760471[/C][/ROW]
[ROW][C]84[/C][C]30.73[/C][C]30.7200045947543[/C][C]0.00999540524574627[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232296&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232296&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 26.85 26.73 0.120000000000001 3 27.01 26.8499944866242 0.160005513375808 4 27.09 27.0099926485789 0.0800073514210524 5 27.11 27.0899963240784 0.0200036759216324 6 27.16 27.1099990809351 0.0500009190648569 7 27.13 27.1599977027179 -0.0299977027178535 8 27.19 27.1300013782384 0.0599986217615971 9 27.49 27.1899972433754 0.300002756624579 10 27.63 27.4899862164338 0.140013783566172 11 27.72 27.6299935670949 0.0900064329050601 12 27.77 27.7199958646726 0.0500041353274163 13 27.81 27.7699977025701 0.0400022974299148 14 27.92 27.8099981621025 0.110001837897492 15 28.07 27.9199949459877 0.150005054012265 16 28.14 28.069993108048 0.0700068919519659 17 28.17 28.1399967835475 0.0300032164525383 18 28.2 28.1699986215083 0.030001378491729 19 28.21 28.1999986215927 0.0100013784072885 20 28.2 28.2099995404887 -0.00999954048868545 21 28.19 28.2000004594269 -0.0100004594268697 22 28.24 28.1900004594691 0.0499995405309051 23 28.25 28.2399977027812 0.0100022972188114 24 28.26 28.2499995404465 0.010000459553531 25 28.33 28.2599995405309 0.0700004594690959 26 28.67 28.329996783843 0.340003216157001 27 28.81 28.6699843786208 0.140015621379217 28 28.99 28.8099935670105 0.180006432989497 29 29.16 28.9899917296407 0.170008270359276 30 29.25 29.1599921890043 0.0900078109957079 31 29.25 29.2499958646093 4.13539073207403e-06 32 29.38 29.24999999981 0.130000000189998 33 29.48 29.3799940271762 0.100005972823801 34 29.65 29.4799954052457 0.170004594754257 35 29.69 29.6499921891732 0.040007810826836 36 29.73 29.6899981618492 0.0400018381508005 37 29.81 29.7299981621236 0.080001837876388 38 30.05 29.8099963243317 0.240003675668316 39 30.29 30.0499889730795 0.240011026920492 40 30.37 30.2899889727418 0.0800110272582479 41 30.5 30.3699963239095 0.130003676090517 42 30.67 30.4999940270073 0.170005972992691 43 30.76 30.6699921891098 0.0900078108901567 44 30.84 30.7599958646093 0.0800041353907233 45 30.86 30.8399963242261 0.0200036757738715 46 31.09 30.8599990809352 0.23000091906485 47 31.2 31.0899894326541 0.110010567345856 48 31.19 31.1999949455867 -0.00999494558665859 49 31.18 31.1900004592158 -0.0100004592157603 50 31.31 31.1800004594691 0.129999540530918 51 31.39 31.3099940271973 0.0800059728026845 52 31.39 31.3899963241417 3.67585829152972e-06 53 31.37 31.3899999998311 -0.0199999998311142 54 31.36 31.370000918896 -0.0100009188959618 55 31.37 31.3600004594902 0.00999954050979923 56 31.35 31.3699995405731 -0.0199995405731279 57 31.34 31.3500009188749 -0.0100009188748622 58 31.47 31.3400004594902 0.129999540509797 59 31.48 31.4699940271973 0.0100059728026807 60 31.54 31.4799995402776 0.0600004597224029 61 31.55 31.539997243291 0.0100027567090279 62 31.55 31.5499995404254 4.59574639677385e-07 63 31.57 31.5499999999789 0.0200000000211134 64 31.66 31.569999081104 0.0900009188959672 65 31.74 31.6599958649259 0.0800041350740734 66 31.78 31.7399963242261 0.0400036757738604 67 31.8 31.7799981620392 0.0200018379608196 68 31.68 31.7999990810196 -0.119999081019589 69 31.7 31.6800055133336 0.0199944866664161 70 31.7 31.6999990813573 9.18642658831459e-07 71 31.75 31.6999999999578 0.0500000000422069 72 31.73 31.7499977027601 -0.0199977027600795 73 31.82 31.7300009187904 0.0899990812095766 74 31.9 31.8199958650104 0.0800041349896397 75 31.82 31.8999963242261 -0.0799963242261441 76 31.51 31.820003675415 -0.310003675414986 77 31.42 31.5100142430564 -0.0900142430563697 78 30.97 31.4200041356863 -0.450004135686253 79 30.99 30.9700206753493 0.0199793246507092 80 30.92 30.989999082054 -0.069999082053954 81 30.95 30.9200032160937 0.0299967839062845 82 30.82 30.9499986218038 -0.129998621803811 83 30.72 30.8200059727605 -0.100005972760471 84 30.73 30.7200045947543 0.00999540524574627

 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 85 30.7299995407631 30.5146352660396 30.9453638154866 86 30.7299995407631 30.4254354592466 31.0345636222797 87 30.7299995407631 30.356989100329 31.1030099811973 88 30.7299995407631 30.2992858335332 31.1607132479931 89 30.7299995407631 30.2484480829535 31.2115509985728 90 30.7299995407631 30.2024871566038 31.2575119249225 91 30.7299995407631 30.1602216679352 31.2997774135911 92 30.7299995407631 30.120881872913 31.3391172086133 93 30.7299995407631 30.0839331028405 31.3760659786858 94 30.7299995407631 30.0489860672305 31.4110130142957 95 30.7299995407631 30.0157468823461 31.4442521991801 96 30.7299995407631 29.9839872292168 31.4760118523094

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
85 & 30.7299995407631 & 30.5146352660396 & 30.9453638154866 \tabularnewline
86 & 30.7299995407631 & 30.4254354592466 & 31.0345636222797 \tabularnewline
87 & 30.7299995407631 & 30.356989100329 & 31.1030099811973 \tabularnewline
88 & 30.7299995407631 & 30.2992858335332 & 31.1607132479931 \tabularnewline
89 & 30.7299995407631 & 30.2484480829535 & 31.2115509985728 \tabularnewline
90 & 30.7299995407631 & 30.2024871566038 & 31.2575119249225 \tabularnewline
91 & 30.7299995407631 & 30.1602216679352 & 31.2997774135911 \tabularnewline
92 & 30.7299995407631 & 30.120881872913 & 31.3391172086133 \tabularnewline
93 & 30.7299995407631 & 30.0839331028405 & 31.3760659786858 \tabularnewline
94 & 30.7299995407631 & 30.0489860672305 & 31.4110130142957 \tabularnewline
95 & 30.7299995407631 & 30.0157468823461 & 31.4442521991801 \tabularnewline
96 & 30.7299995407631 & 29.9839872292168 & 31.4760118523094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232296&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]85[/C][C]30.7299995407631[/C][C]30.5146352660396[/C][C]30.9453638154866[/C][/ROW]
[ROW][C]86[/C][C]30.7299995407631[/C][C]30.4254354592466[/C][C]31.0345636222797[/C][/ROW]
[ROW][C]87[/C][C]30.7299995407631[/C][C]30.356989100329[/C][C]31.1030099811973[/C][/ROW]
[ROW][C]88[/C][C]30.7299995407631[/C][C]30.2992858335332[/C][C]31.1607132479931[/C][/ROW]
[ROW][C]89[/C][C]30.7299995407631[/C][C]30.2484480829535[/C][C]31.2115509985728[/C][/ROW]
[ROW][C]90[/C][C]30.7299995407631[/C][C]30.2024871566038[/C][C]31.2575119249225[/C][/ROW]
[ROW][C]91[/C][C]30.7299995407631[/C][C]30.1602216679352[/C][C]31.2997774135911[/C][/ROW]
[ROW][C]92[/C][C]30.7299995407631[/C][C]30.120881872913[/C][C]31.3391172086133[/C][/ROW]
[ROW][C]93[/C][C]30.7299995407631[/C][C]30.0839331028405[/C][C]31.3760659786858[/C][/ROW]
[ROW][C]94[/C][C]30.7299995407631[/C][C]30.0489860672305[/C][C]31.4110130142957[/C][/ROW]
[ROW][C]95[/C][C]30.7299995407631[/C][C]30.0157468823461[/C][C]31.4442521991801[/C][/ROW]
[ROW][C]96[/C][C]30.7299995407631[/C][C]29.9839872292168[/C][C]31.4760118523094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232296&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232296&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 85 30.7299995407631 30.5146352660396 30.9453638154866 86 30.7299995407631 30.4254354592466 31.0345636222797 87 30.7299995407631 30.356989100329 31.1030099811973 88 30.7299995407631 30.2992858335332 31.1607132479931 89 30.7299995407631 30.2484480829535 31.2115509985728 90 30.7299995407631 30.2024871566038 31.2575119249225 91 30.7299995407631 30.1602216679352 31.2997774135911 92 30.7299995407631 30.120881872913 31.3391172086133 93 30.7299995407631 30.0839331028405 31.3760659786858 94 30.7299995407631 30.0489860672305 31.4110130142957 95 30.7299995407631 30.0157468823461 31.4442521991801 96 30.7299995407631 29.9839872292168 31.4760118523094

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