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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

Sun, 27 Nov 2016 13:52:10 +0000

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/2016/Nov/27/t1480254788sb8h7q7roju9joe.htm/, Retrieved Mon, 29 Apr 2024 21:29:58 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 29 Apr 2024 21:29:58 +0200

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No (this computation is public)

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Estimated Impact

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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=&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=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.999955490141846
beta	FALSE
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.999955490141846 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]0.999955490141846[/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=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.999955490141846
beta	FALSE
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
2	76.31	75.99	0.320000000000007
3	76.51	76.3099857568454	0.200014243154598
4	76.75	76.5099910973944	0.240008902605595
5	77.23	76.7499893172378	0.480010682762213
6	77.22	77.2299786347926	-0.00997863479260275
7	77.25	77.2200004441476	0.0299995558523705
8	77.36	77.249998664724	0.110001335275982
9	77.57	77.3599951038562	0.210004896143815
10	77.88	77.5699906527119	0.310009347288144
11	78.29	77.8799862015279	0.410013798472093
12	78.42	78.289981750344	0.130018249656004
13	78.96	78.4199942129061	0.540005787093847
14	79.85	78.959975964419	0.890024035580979
15	80.05	79.8499603851564	0.20003961484359
16	80.16	80.0499910962651	0.110008903734879
17	80.29	80.1599951035193	0.130004896480713
18	80.36	80.2899942135005	0.0700057864994932
19	80.48	80.3599968840524	0.120003115947625
20	80.95	80.4799946586783	0.470005341321666
21	82.3	80.9499790801289	1.35002091987107
22	84.81	82.2999399107604	2.51006008923964
23	85.4	84.8098882775815	0.590111722418527
24	86.13	85.3999737342109	0.73002626578905
25	87.02	86.1299675066345	0.890032493365538
26	87.38	87.01996038478	0.360039615220032
27	87.5	87.3799839746878	0.120016025312196
28	87.91	87.4999946581037	0.410005341896266
29	88.06	87.9099817507204	0.150018249279611
30	88.09	88.059993322709	0.0300066772909986
31	88.16	88.089998664407	0.0700013355929485
32	88.33	88.1599968842505	0.170003115749509
33	88.52	88.3299924331854	0.190007566814558
34	88.96	88.5199915427902	0.440008457209842
35	89.26	88.959980415286	0.300019584714036
36	89.34	89.2599866461709	0.0800133538291448
37	89.09	89.339996438617	-0.249996438616975
38	89.25	89.090011127306	0.159988872693972
39	89.31	89.249992878918	0.0600071210820232
40	89.28	89.3099973290915	-0.0299973290915432
41	89.32	89.2800013351769	0.0399986648231305
42	89.47	89.3199982196651	0.150001780334904
43	89.59	89.469993323442	0.120006676557963
44	89.62	89.5899946585198	0.0300053414801624
45	89.71	89.6199986644665	0.0900013355334721
46	89.9	89.7099959940533	0.19000400594669
47	90.04	89.8999915429487	0.140008457051351
48	90.05	90.0399937682434	0.010006231756563
49	90.18	90.049999554624	0.13000044537597
50	90.5	90.1799942136986	0.32000578630138
51	90.63	90.4999857565878	0.130014243412163
52	90.75	90.6299942130845	0.120005786915527
53	90.76	90.7499946585594	0.0100053414405608
54	90.67	90.7599995546637	-0.0899995546636774
55	90.5	90.6700040058674	-0.170004005867412
56	90.8	90.5000075668542	0.299992433145817
57	91.22	90.7999866473794	0.420013352620643
58	92.19	91.2199813052653	0.970018694734748
59	92.51	92.1899568246055	0.320043175394517
60	92.67	92.5099857549237	0.160014245076326
61	93.75	92.6699928777886	1.08000712221136
62	94.1	93.7499519290362	0.350048070963823
63	94.96	94.09998441941	0.860015580589987
64	95.21	94.9599617208285	0.250038279171505
65	95.33	95.2099888708317	0.120011129168333
66	95.43	95.3299946583217	0.10000534167834
67	95.44	95.4299955487764	0.0100044512235655
68	95.64	95.4399995547033	0.200000445296709
69	95.8	95.6399910980086	0.160008901991446
70	95.87	95.7999928780265	0.0700071219735321
71	95.98	95.8699968839929	0.110003116007064
72	96.07	95.9799951037769	0.0900048962230784
73	96.23	96.0699959938948	0.160004006105169
74	96.32	96.2299928782444	0.0900071217556047
75	96.55	96.3199959937958	0.230004006204226
76	96.73	96.5499897625543	0.1800102374457
77	96.61	96.7299919877699	-0.119991987769865
78	96.64	96.6100053408264	0.0299946591736386
79	96.86	96.639998664942	0.220001335058029
80	97.02	96.8599902077718	0.160009792228223
81	97.22	97.0199928779868	0.200007122013162
82	98.1	97.2199910977114	0.880008902288623
83	98.46	98.0999608309286	0.360039169071413
84	98.6	98.4599839747076	0.140016025292354
85	98.78	98.5999937679066	0.180006232093433
86	99.13	98.7799919879482	0.350008012051845
87	99.48	99.129984421193	0.350015578806975
88	99.62	99.4799844208562	0.140015579143764
89	99.68	99.6199937679264	0.0600062320735759
90	99.95	99.6799973291311	0.270002670868877
91	100.12	99.9499879822194	0.170012017780579
92	100.25	100.119992432789	0.130007567210797
93	100.47	100.249994213382	0.220005786618387
94	100.7	100.469990207574	0.230009792426358
95	100.88	100.699989762297	0.180010237703215
96	100.95	100.87999198777	0.0700080122301472
97	100.92	100.949996883953	-0.0299968839533022
98	101.12	100.920001335157	0.199998664842951
99	101.19	101.119991098088	0.0700089019122032
100	101.28	101.189996883914	0.0900031160862937
101	101.28	101.279995993974	4.0060259323127e-06
102	101.3	101.279999999822	0.0200000001782996
103	101.3	101.299999109803	8.90197171088403e-07
104	101.36	101.29999999996	0.0600000000396363
105	101.45	101.359997329409	0.0900026705915025
106	101.58	101.449995993994	0.130004006006104
107	101.73	101.57999421354	0.150005786459872
108	101.84	101.729993323264	0.110006676736276
109	102.01	101.839995103618	0.17000489638157
110	102.14	102.009992433106	0.130007566893823
111	102.16	102.139994213382	0.0200057866183556
112	102.32	102.159999109545	0.160000890454711
113	102.41	102.319992878383	0.0900071216169493
114	102.4	102.409995993796	-0.00999599379576921
115	102.43	102.40000044492	0.0299995550797405
116	102.42	102.429998664724	-0.00999866472406552
117	102.3	102.420000445039	-0.120000445039153
118	102.65	102.300005341203	0.349994658797215
119	102.72	102.649984421787	0.0700155782126046
120	102.86	102.719996883617	0.140003116383468

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 76.31 & 75.99 & 0.320000000000007 \tabularnewline
3 & 76.51 & 76.3099857568454 & 0.200014243154598 \tabularnewline
4 & 76.75 & 76.5099910973944 & 0.240008902605595 \tabularnewline
5 & 77.23 & 76.7499893172378 & 0.480010682762213 \tabularnewline
6 & 77.22 & 77.2299786347926 & -0.00997863479260275 \tabularnewline
7 & 77.25 & 77.2200004441476 & 0.0299995558523705 \tabularnewline
8 & 77.36 & 77.249998664724 & 0.110001335275982 \tabularnewline
9 & 77.57 & 77.3599951038562 & 0.210004896143815 \tabularnewline
10 & 77.88 & 77.5699906527119 & 0.310009347288144 \tabularnewline
11 & 78.29 & 77.8799862015279 & 0.410013798472093 \tabularnewline
12 & 78.42 & 78.289981750344 & 0.130018249656004 \tabularnewline
13 & 78.96 & 78.4199942129061 & 0.540005787093847 \tabularnewline
14 & 79.85 & 78.959975964419 & 0.890024035580979 \tabularnewline
15 & 80.05 & 79.8499603851564 & 0.20003961484359 \tabularnewline
16 & 80.16 & 80.0499910962651 & 0.110008903734879 \tabularnewline
17 & 80.29 & 80.1599951035193 & 0.130004896480713 \tabularnewline
18 & 80.36 & 80.2899942135005 & 0.0700057864994932 \tabularnewline
19 & 80.48 & 80.3599968840524 & 0.120003115947625 \tabularnewline
20 & 80.95 & 80.4799946586783 & 0.470005341321666 \tabularnewline
21 & 82.3 & 80.9499790801289 & 1.35002091987107 \tabularnewline
22 & 84.81 & 82.2999399107604 & 2.51006008923964 \tabularnewline
23 & 85.4 & 84.8098882775815 & 0.590111722418527 \tabularnewline
24 & 86.13 & 85.3999737342109 & 0.73002626578905 \tabularnewline
25 & 87.02 & 86.1299675066345 & 0.890032493365538 \tabularnewline
26 & 87.38 & 87.01996038478 & 0.360039615220032 \tabularnewline
27 & 87.5 & 87.3799839746878 & 0.120016025312196 \tabularnewline
28 & 87.91 & 87.4999946581037 & 0.410005341896266 \tabularnewline
29 & 88.06 & 87.9099817507204 & 0.150018249279611 \tabularnewline
30 & 88.09 & 88.059993322709 & 0.0300066772909986 \tabularnewline
31 & 88.16 & 88.089998664407 & 0.0700013355929485 \tabularnewline
32 & 88.33 & 88.1599968842505 & 0.170003115749509 \tabularnewline
33 & 88.52 & 88.3299924331854 & 0.190007566814558 \tabularnewline
34 & 88.96 & 88.5199915427902 & 0.440008457209842 \tabularnewline
35 & 89.26 & 88.959980415286 & 0.300019584714036 \tabularnewline
36 & 89.34 & 89.2599866461709 & 0.0800133538291448 \tabularnewline
37 & 89.09 & 89.339996438617 & -0.249996438616975 \tabularnewline
38 & 89.25 & 89.090011127306 & 0.159988872693972 \tabularnewline
39 & 89.31 & 89.249992878918 & 0.0600071210820232 \tabularnewline
40 & 89.28 & 89.3099973290915 & -0.0299973290915432 \tabularnewline
41 & 89.32 & 89.2800013351769 & 0.0399986648231305 \tabularnewline
42 & 89.47 & 89.3199982196651 & 0.150001780334904 \tabularnewline
43 & 89.59 & 89.469993323442 & 0.120006676557963 \tabularnewline
44 & 89.62 & 89.5899946585198 & 0.0300053414801624 \tabularnewline
45 & 89.71 & 89.6199986644665 & 0.0900013355334721 \tabularnewline
46 & 89.9 & 89.7099959940533 & 0.19000400594669 \tabularnewline
47 & 90.04 & 89.8999915429487 & 0.140008457051351 \tabularnewline
48 & 90.05 & 90.0399937682434 & 0.010006231756563 \tabularnewline
49 & 90.18 & 90.049999554624 & 0.13000044537597 \tabularnewline
50 & 90.5 & 90.1799942136986 & 0.32000578630138 \tabularnewline
51 & 90.63 & 90.4999857565878 & 0.130014243412163 \tabularnewline
52 & 90.75 & 90.6299942130845 & 0.120005786915527 \tabularnewline
53 & 90.76 & 90.7499946585594 & 0.0100053414405608 \tabularnewline
54 & 90.67 & 90.7599995546637 & -0.0899995546636774 \tabularnewline
55 & 90.5 & 90.6700040058674 & -0.170004005867412 \tabularnewline
56 & 90.8 & 90.5000075668542 & 0.299992433145817 \tabularnewline
57 & 91.22 & 90.7999866473794 & 0.420013352620643 \tabularnewline
58 & 92.19 & 91.2199813052653 & 0.970018694734748 \tabularnewline
59 & 92.51 & 92.1899568246055 & 0.320043175394517 \tabularnewline
60 & 92.67 & 92.5099857549237 & 0.160014245076326 \tabularnewline
61 & 93.75 & 92.6699928777886 & 1.08000712221136 \tabularnewline
62 & 94.1 & 93.7499519290362 & 0.350048070963823 \tabularnewline
63 & 94.96 & 94.09998441941 & 0.860015580589987 \tabularnewline
64 & 95.21 & 94.9599617208285 & 0.250038279171505 \tabularnewline
65 & 95.33 & 95.2099888708317 & 0.120011129168333 \tabularnewline
66 & 95.43 & 95.3299946583217 & 0.10000534167834 \tabularnewline
67 & 95.44 & 95.4299955487764 & 0.0100044512235655 \tabularnewline
68 & 95.64 & 95.4399995547033 & 0.200000445296709 \tabularnewline
69 & 95.8 & 95.6399910980086 & 0.160008901991446 \tabularnewline
70 & 95.87 & 95.7999928780265 & 0.0700071219735321 \tabularnewline
71 & 95.98 & 95.8699968839929 & 0.110003116007064 \tabularnewline
72 & 96.07 & 95.9799951037769 & 0.0900048962230784 \tabularnewline
73 & 96.23 & 96.0699959938948 & 0.160004006105169 \tabularnewline
74 & 96.32 & 96.2299928782444 & 0.0900071217556047 \tabularnewline
75 & 96.55 & 96.3199959937958 & 0.230004006204226 \tabularnewline
76 & 96.73 & 96.5499897625543 & 0.1800102374457 \tabularnewline
77 & 96.61 & 96.7299919877699 & -0.119991987769865 \tabularnewline
78 & 96.64 & 96.6100053408264 & 0.0299946591736386 \tabularnewline
79 & 96.86 & 96.639998664942 & 0.220001335058029 \tabularnewline
80 & 97.02 & 96.8599902077718 & 0.160009792228223 \tabularnewline
81 & 97.22 & 97.0199928779868 & 0.200007122013162 \tabularnewline
82 & 98.1 & 97.2199910977114 & 0.880008902288623 \tabularnewline
83 & 98.46 & 98.0999608309286 & 0.360039169071413 \tabularnewline
84 & 98.6 & 98.4599839747076 & 0.140016025292354 \tabularnewline
85 & 98.78 & 98.5999937679066 & 0.180006232093433 \tabularnewline
86 & 99.13 & 98.7799919879482 & 0.350008012051845 \tabularnewline
87 & 99.48 & 99.129984421193 & 0.350015578806975 \tabularnewline
88 & 99.62 & 99.4799844208562 & 0.140015579143764 \tabularnewline
89 & 99.68 & 99.6199937679264 & 0.0600062320735759 \tabularnewline
90 & 99.95 & 99.6799973291311 & 0.270002670868877 \tabularnewline
91 & 100.12 & 99.9499879822194 & 0.170012017780579 \tabularnewline
92 & 100.25 & 100.119992432789 & 0.130007567210797 \tabularnewline
93 & 100.47 & 100.249994213382 & 0.220005786618387 \tabularnewline
94 & 100.7 & 100.469990207574 & 0.230009792426358 \tabularnewline
95 & 100.88 & 100.699989762297 & 0.180010237703215 \tabularnewline
96 & 100.95 & 100.87999198777 & 0.0700080122301472 \tabularnewline
97 & 100.92 & 100.949996883953 & -0.0299968839533022 \tabularnewline
98 & 101.12 & 100.920001335157 & 0.199998664842951 \tabularnewline
99 & 101.19 & 101.119991098088 & 0.0700089019122032 \tabularnewline
100 & 101.28 & 101.189996883914 & 0.0900031160862937 \tabularnewline
101 & 101.28 & 101.279995993974 & 4.0060259323127e-06 \tabularnewline
102 & 101.3 & 101.279999999822 & 0.0200000001782996 \tabularnewline
103 & 101.3 & 101.299999109803 & 8.90197171088403e-07 \tabularnewline
104 & 101.36 & 101.29999999996 & 0.0600000000396363 \tabularnewline
105 & 101.45 & 101.359997329409 & 0.0900026705915025 \tabularnewline
106 & 101.58 & 101.449995993994 & 0.130004006006104 \tabularnewline
107 & 101.73 & 101.57999421354 & 0.150005786459872 \tabularnewline
108 & 101.84 & 101.729993323264 & 0.110006676736276 \tabularnewline
109 & 102.01 & 101.839995103618 & 0.17000489638157 \tabularnewline
110 & 102.14 & 102.009992433106 & 0.130007566893823 \tabularnewline
111 & 102.16 & 102.139994213382 & 0.0200057866183556 \tabularnewline
112 & 102.32 & 102.159999109545 & 0.160000890454711 \tabularnewline
113 & 102.41 & 102.319992878383 & 0.0900071216169493 \tabularnewline
114 & 102.4 & 102.409995993796 & -0.00999599379576921 \tabularnewline
115 & 102.43 & 102.40000044492 & 0.0299995550797405 \tabularnewline
116 & 102.42 & 102.429998664724 & -0.00999866472406552 \tabularnewline
117 & 102.3 & 102.420000445039 & -0.120000445039153 \tabularnewline
118 & 102.65 & 102.300005341203 & 0.349994658797215 \tabularnewline
119 & 102.72 & 102.649984421787 & 0.0700155782126046 \tabularnewline
120 & 102.86 & 102.719996883617 & 0.140003116383468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]76.31[/C][C]75.99[/C][C]0.320000000000007[/C][/ROW]
[ROW][C]3[/C][C]76.51[/C][C]76.3099857568454[/C][C]0.200014243154598[/C][/ROW]
[ROW][C]4[/C][C]76.75[/C][C]76.5099910973944[/C][C]0.240008902605595[/C][/ROW]
[ROW][C]5[/C][C]77.23[/C][C]76.7499893172378[/C][C]0.480010682762213[/C][/ROW]
[ROW][C]6[/C][C]77.22[/C][C]77.2299786347926[/C][C]-0.00997863479260275[/C][/ROW]
[ROW][C]7[/C][C]77.25[/C][C]77.2200004441476[/C][C]0.0299995558523705[/C][/ROW]
[ROW][C]8[/C][C]77.36[/C][C]77.249998664724[/C][C]0.110001335275982[/C][/ROW]
[ROW][C]9[/C][C]77.57[/C][C]77.3599951038562[/C][C]0.210004896143815[/C][/ROW]
[ROW][C]10[/C][C]77.88[/C][C]77.5699906527119[/C][C]0.310009347288144[/C][/ROW]
[ROW][C]11[/C][C]78.29[/C][C]77.8799862015279[/C][C]0.410013798472093[/C][/ROW]
[ROW][C]12[/C][C]78.42[/C][C]78.289981750344[/C][C]0.130018249656004[/C][/ROW]
[ROW][C]13[/C][C]78.96[/C][C]78.4199942129061[/C][C]0.540005787093847[/C][/ROW]
[ROW][C]14[/C][C]79.85[/C][C]78.959975964419[/C][C]0.890024035580979[/C][/ROW]
[ROW][C]15[/C][C]80.05[/C][C]79.8499603851564[/C][C]0.20003961484359[/C][/ROW]
[ROW][C]16[/C][C]80.16[/C][C]80.0499910962651[/C][C]0.110008903734879[/C][/ROW]
[ROW][C]17[/C][C]80.29[/C][C]80.1599951035193[/C][C]0.130004896480713[/C][/ROW]
[ROW][C]18[/C][C]80.36[/C][C]80.2899942135005[/C][C]0.0700057864994932[/C][/ROW]
[ROW][C]19[/C][C]80.48[/C][C]80.3599968840524[/C][C]0.120003115947625[/C][/ROW]
[ROW][C]20[/C][C]80.95[/C][C]80.4799946586783[/C][C]0.470005341321666[/C][/ROW]
[ROW][C]21[/C][C]82.3[/C][C]80.9499790801289[/C][C]1.35002091987107[/C][/ROW]
[ROW][C]22[/C][C]84.81[/C][C]82.2999399107604[/C][C]2.51006008923964[/C][/ROW]
[ROW][C]23[/C][C]85.4[/C][C]84.8098882775815[/C][C]0.590111722418527[/C][/ROW]
[ROW][C]24[/C][C]86.13[/C][C]85.3999737342109[/C][C]0.73002626578905[/C][/ROW]
[ROW][C]25[/C][C]87.02[/C][C]86.1299675066345[/C][C]0.890032493365538[/C][/ROW]
[ROW][C]26[/C][C]87.38[/C][C]87.01996038478[/C][C]0.360039615220032[/C][/ROW]
[ROW][C]27[/C][C]87.5[/C][C]87.3799839746878[/C][C]0.120016025312196[/C][/ROW]
[ROW][C]28[/C][C]87.91[/C][C]87.4999946581037[/C][C]0.410005341896266[/C][/ROW]
[ROW][C]29[/C][C]88.06[/C][C]87.9099817507204[/C][C]0.150018249279611[/C][/ROW]
[ROW][C]30[/C][C]88.09[/C][C]88.059993322709[/C][C]0.0300066772909986[/C][/ROW]
[ROW][C]31[/C][C]88.16[/C][C]88.089998664407[/C][C]0.0700013355929485[/C][/ROW]
[ROW][C]32[/C][C]88.33[/C][C]88.1599968842505[/C][C]0.170003115749509[/C][/ROW]
[ROW][C]33[/C][C]88.52[/C][C]88.3299924331854[/C][C]0.190007566814558[/C][/ROW]
[ROW][C]34[/C][C]88.96[/C][C]88.5199915427902[/C][C]0.440008457209842[/C][/ROW]
[ROW][C]35[/C][C]89.26[/C][C]88.959980415286[/C][C]0.300019584714036[/C][/ROW]
[ROW][C]36[/C][C]89.34[/C][C]89.2599866461709[/C][C]0.0800133538291448[/C][/ROW]
[ROW][C]37[/C][C]89.09[/C][C]89.339996438617[/C][C]-0.249996438616975[/C][/ROW]
[ROW][C]38[/C][C]89.25[/C][C]89.090011127306[/C][C]0.159988872693972[/C][/ROW]
[ROW][C]39[/C][C]89.31[/C][C]89.249992878918[/C][C]0.0600071210820232[/C][/ROW]
[ROW][C]40[/C][C]89.28[/C][C]89.3099973290915[/C][C]-0.0299973290915432[/C][/ROW]
[ROW][C]41[/C][C]89.32[/C][C]89.2800013351769[/C][C]0.0399986648231305[/C][/ROW]
[ROW][C]42[/C][C]89.47[/C][C]89.3199982196651[/C][C]0.150001780334904[/C][/ROW]
[ROW][C]43[/C][C]89.59[/C][C]89.469993323442[/C][C]0.120006676557963[/C][/ROW]
[ROW][C]44[/C][C]89.62[/C][C]89.5899946585198[/C][C]0.0300053414801624[/C][/ROW]
[ROW][C]45[/C][C]89.71[/C][C]89.6199986644665[/C][C]0.0900013355334721[/C][/ROW]
[ROW][C]46[/C][C]89.9[/C][C]89.7099959940533[/C][C]0.19000400594669[/C][/ROW]
[ROW][C]47[/C][C]90.04[/C][C]89.8999915429487[/C][C]0.140008457051351[/C][/ROW]
[ROW][C]48[/C][C]90.05[/C][C]90.0399937682434[/C][C]0.010006231756563[/C][/ROW]
[ROW][C]49[/C][C]90.18[/C][C]90.049999554624[/C][C]0.13000044537597[/C][/ROW]
[ROW][C]50[/C][C]90.5[/C][C]90.1799942136986[/C][C]0.32000578630138[/C][/ROW]
[ROW][C]51[/C][C]90.63[/C][C]90.4999857565878[/C][C]0.130014243412163[/C][/ROW]
[ROW][C]52[/C][C]90.75[/C][C]90.6299942130845[/C][C]0.120005786915527[/C][/ROW]
[ROW][C]53[/C][C]90.76[/C][C]90.7499946585594[/C][C]0.0100053414405608[/C][/ROW]
[ROW][C]54[/C][C]90.67[/C][C]90.7599995546637[/C][C]-0.0899995546636774[/C][/ROW]
[ROW][C]55[/C][C]90.5[/C][C]90.6700040058674[/C][C]-0.170004005867412[/C][/ROW]
[ROW][C]56[/C][C]90.8[/C][C]90.5000075668542[/C][C]0.299992433145817[/C][/ROW]
[ROW][C]57[/C][C]91.22[/C][C]90.7999866473794[/C][C]0.420013352620643[/C][/ROW]
[ROW][C]58[/C][C]92.19[/C][C]91.2199813052653[/C][C]0.970018694734748[/C][/ROW]
[ROW][C]59[/C][C]92.51[/C][C]92.1899568246055[/C][C]0.320043175394517[/C][/ROW]
[ROW][C]60[/C][C]92.67[/C][C]92.5099857549237[/C][C]0.160014245076326[/C][/ROW]
[ROW][C]61[/C][C]93.75[/C][C]92.6699928777886[/C][C]1.08000712221136[/C][/ROW]
[ROW][C]62[/C][C]94.1[/C][C]93.7499519290362[/C][C]0.350048070963823[/C][/ROW]
[ROW][C]63[/C][C]94.96[/C][C]94.09998441941[/C][C]0.860015580589987[/C][/ROW]
[ROW][C]64[/C][C]95.21[/C][C]94.9599617208285[/C][C]0.250038279171505[/C][/ROW]
[ROW][C]65[/C][C]95.33[/C][C]95.2099888708317[/C][C]0.120011129168333[/C][/ROW]
[ROW][C]66[/C][C]95.43[/C][C]95.3299946583217[/C][C]0.10000534167834[/C][/ROW]
[ROW][C]67[/C][C]95.44[/C][C]95.4299955487764[/C][C]0.0100044512235655[/C][/ROW]
[ROW][C]68[/C][C]95.64[/C][C]95.4399995547033[/C][C]0.200000445296709[/C][/ROW]
[ROW][C]69[/C][C]95.8[/C][C]95.6399910980086[/C][C]0.160008901991446[/C][/ROW]
[ROW][C]70[/C][C]95.87[/C][C]95.7999928780265[/C][C]0.0700071219735321[/C][/ROW]
[ROW][C]71[/C][C]95.98[/C][C]95.8699968839929[/C][C]0.110003116007064[/C][/ROW]
[ROW][C]72[/C][C]96.07[/C][C]95.9799951037769[/C][C]0.0900048962230784[/C][/ROW]
[ROW][C]73[/C][C]96.23[/C][C]96.0699959938948[/C][C]0.160004006105169[/C][/ROW]
[ROW][C]74[/C][C]96.32[/C][C]96.2299928782444[/C][C]0.0900071217556047[/C][/ROW]
[ROW][C]75[/C][C]96.55[/C][C]96.3199959937958[/C][C]0.230004006204226[/C][/ROW]
[ROW][C]76[/C][C]96.73[/C][C]96.5499897625543[/C][C]0.1800102374457[/C][/ROW]
[ROW][C]77[/C][C]96.61[/C][C]96.7299919877699[/C][C]-0.119991987769865[/C][/ROW]
[ROW][C]78[/C][C]96.64[/C][C]96.6100053408264[/C][C]0.0299946591736386[/C][/ROW]
[ROW][C]79[/C][C]96.86[/C][C]96.639998664942[/C][C]0.220001335058029[/C][/ROW]
[ROW][C]80[/C][C]97.02[/C][C]96.8599902077718[/C][C]0.160009792228223[/C][/ROW]
[ROW][C]81[/C][C]97.22[/C][C]97.0199928779868[/C][C]0.200007122013162[/C][/ROW]
[ROW][C]82[/C][C]98.1[/C][C]97.2199910977114[/C][C]0.880008902288623[/C][/ROW]
[ROW][C]83[/C][C]98.46[/C][C]98.0999608309286[/C][C]0.360039169071413[/C][/ROW]
[ROW][C]84[/C][C]98.6[/C][C]98.4599839747076[/C][C]0.140016025292354[/C][/ROW]
[ROW][C]85[/C][C]98.78[/C][C]98.5999937679066[/C][C]0.180006232093433[/C][/ROW]
[ROW][C]86[/C][C]99.13[/C][C]98.7799919879482[/C][C]0.350008012051845[/C][/ROW]
[ROW][C]87[/C][C]99.48[/C][C]99.129984421193[/C][C]0.350015578806975[/C][/ROW]
[ROW][C]88[/C][C]99.62[/C][C]99.4799844208562[/C][C]0.140015579143764[/C][/ROW]
[ROW][C]89[/C][C]99.68[/C][C]99.6199937679264[/C][C]0.0600062320735759[/C][/ROW]
[ROW][C]90[/C][C]99.95[/C][C]99.6799973291311[/C][C]0.270002670868877[/C][/ROW]
[ROW][C]91[/C][C]100.12[/C][C]99.9499879822194[/C][C]0.170012017780579[/C][/ROW]
[ROW][C]92[/C][C]100.25[/C][C]100.119992432789[/C][C]0.130007567210797[/C][/ROW]
[ROW][C]93[/C][C]100.47[/C][C]100.249994213382[/C][C]0.220005786618387[/C][/ROW]
[ROW][C]94[/C][C]100.7[/C][C]100.469990207574[/C][C]0.230009792426358[/C][/ROW]
[ROW][C]95[/C][C]100.88[/C][C]100.699989762297[/C][C]0.180010237703215[/C][/ROW]
[ROW][C]96[/C][C]100.95[/C][C]100.87999198777[/C][C]0.0700080122301472[/C][/ROW]
[ROW][C]97[/C][C]100.92[/C][C]100.949996883953[/C][C]-0.0299968839533022[/C][/ROW]
[ROW][C]98[/C][C]101.12[/C][C]100.920001335157[/C][C]0.199998664842951[/C][/ROW]
[ROW][C]99[/C][C]101.19[/C][C]101.119991098088[/C][C]0.0700089019122032[/C][/ROW]
[ROW][C]100[/C][C]101.28[/C][C]101.189996883914[/C][C]0.0900031160862937[/C][/ROW]
[ROW][C]101[/C][C]101.28[/C][C]101.279995993974[/C][C]4.0060259323127e-06[/C][/ROW]
[ROW][C]102[/C][C]101.3[/C][C]101.279999999822[/C][C]0.0200000001782996[/C][/ROW]
[ROW][C]103[/C][C]101.3[/C][C]101.299999109803[/C][C]8.90197171088403e-07[/C][/ROW]
[ROW][C]104[/C][C]101.36[/C][C]101.29999999996[/C][C]0.0600000000396363[/C][/ROW]
[ROW][C]105[/C][C]101.45[/C][C]101.359997329409[/C][C]0.0900026705915025[/C][/ROW]
[ROW][C]106[/C][C]101.58[/C][C]101.449995993994[/C][C]0.130004006006104[/C][/ROW]
[ROW][C]107[/C][C]101.73[/C][C]101.57999421354[/C][C]0.150005786459872[/C][/ROW]
[ROW][C]108[/C][C]101.84[/C][C]101.729993323264[/C][C]0.110006676736276[/C][/ROW]
[ROW][C]109[/C][C]102.01[/C][C]101.839995103618[/C][C]0.17000489638157[/C][/ROW]
[ROW][C]110[/C][C]102.14[/C][C]102.009992433106[/C][C]0.130007566893823[/C][/ROW]
[ROW][C]111[/C][C]102.16[/C][C]102.139994213382[/C][C]0.0200057866183556[/C][/ROW]
[ROW][C]112[/C][C]102.32[/C][C]102.159999109545[/C][C]0.160000890454711[/C][/ROW]
[ROW][C]113[/C][C]102.41[/C][C]102.319992878383[/C][C]0.0900071216169493[/C][/ROW]
[ROW][C]114[/C][C]102.4[/C][C]102.409995993796[/C][C]-0.00999599379576921[/C][/ROW]
[ROW][C]115[/C][C]102.43[/C][C]102.40000044492[/C][C]0.0299995550797405[/C][/ROW]
[ROW][C]116[/C][C]102.42[/C][C]102.429998664724[/C][C]-0.00999866472406552[/C][/ROW]
[ROW][C]117[/C][C]102.3[/C][C]102.420000445039[/C][C]-0.120000445039153[/C][/ROW]
[ROW][C]118[/C][C]102.65[/C][C]102.300005341203[/C][C]0.349994658797215[/C][/ROW]
[ROW][C]119[/C][C]102.72[/C][C]102.649984421787[/C][C]0.0700155782126046[/C][/ROW]
[ROW][C]120[/C][C]102.86[/C][C]102.719996883617[/C][C]0.140003116383468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	76.31	75.99	0.320000000000007
3	76.51	76.3099857568454	0.200014243154598
4	76.75	76.5099910973944	0.240008902605595
5	77.23	76.7499893172378	0.480010682762213
6	77.22	77.2299786347926	-0.00997863479260275
7	77.25	77.2200004441476	0.0299995558523705
8	77.36	77.249998664724	0.110001335275982
9	77.57	77.3599951038562	0.210004896143815
10	77.88	77.5699906527119	0.310009347288144
11	78.29	77.8799862015279	0.410013798472093
12	78.42	78.289981750344	0.130018249656004
13	78.96	78.4199942129061	0.540005787093847
14	79.85	78.959975964419	0.890024035580979
15	80.05	79.8499603851564	0.20003961484359
16	80.16	80.0499910962651	0.110008903734879
17	80.29	80.1599951035193	0.130004896480713
18	80.36	80.2899942135005	0.0700057864994932
19	80.48	80.3599968840524	0.120003115947625
20	80.95	80.4799946586783	0.470005341321666
21	82.3	80.9499790801289	1.35002091987107
22	84.81	82.2999399107604	2.51006008923964
23	85.4	84.8098882775815	0.590111722418527
24	86.13	85.3999737342109	0.73002626578905
25	87.02	86.1299675066345	0.890032493365538
26	87.38	87.01996038478	0.360039615220032
27	87.5	87.3799839746878	0.120016025312196
28	87.91	87.4999946581037	0.410005341896266
29	88.06	87.9099817507204	0.150018249279611
30	88.09	88.059993322709	0.0300066772909986
31	88.16	88.089998664407	0.0700013355929485
32	88.33	88.1599968842505	0.170003115749509
33	88.52	88.3299924331854	0.190007566814558
34	88.96	88.5199915427902	0.440008457209842
35	89.26	88.959980415286	0.300019584714036
36	89.34	89.2599866461709	0.0800133538291448
37	89.09	89.339996438617	-0.249996438616975
38	89.25	89.090011127306	0.159988872693972
39	89.31	89.249992878918	0.0600071210820232
40	89.28	89.3099973290915	-0.0299973290915432
41	89.32	89.2800013351769	0.0399986648231305
42	89.47	89.3199982196651	0.150001780334904
43	89.59	89.469993323442	0.120006676557963
44	89.62	89.5899946585198	0.0300053414801624
45	89.71	89.6199986644665	0.0900013355334721
46	89.9	89.7099959940533	0.19000400594669
47	90.04	89.8999915429487	0.140008457051351
48	90.05	90.0399937682434	0.010006231756563
49	90.18	90.049999554624	0.13000044537597
50	90.5	90.1799942136986	0.32000578630138
51	90.63	90.4999857565878	0.130014243412163
52	90.75	90.6299942130845	0.120005786915527
53	90.76	90.7499946585594	0.0100053414405608
54	90.67	90.7599995546637	-0.0899995546636774
55	90.5	90.6700040058674	-0.170004005867412
56	90.8	90.5000075668542	0.299992433145817
57	91.22	90.7999866473794	0.420013352620643
58	92.19	91.2199813052653	0.970018694734748
59	92.51	92.1899568246055	0.320043175394517
60	92.67	92.5099857549237	0.160014245076326
61	93.75	92.6699928777886	1.08000712221136
62	94.1	93.7499519290362	0.350048070963823
63	94.96	94.09998441941	0.860015580589987
64	95.21	94.9599617208285	0.250038279171505
65	95.33	95.2099888708317	0.120011129168333
66	95.43	95.3299946583217	0.10000534167834
67	95.44	95.4299955487764	0.0100044512235655
68	95.64	95.4399995547033	0.200000445296709
69	95.8	95.6399910980086	0.160008901991446
70	95.87	95.7999928780265	0.0700071219735321
71	95.98	95.8699968839929	0.110003116007064
72	96.07	95.9799951037769	0.0900048962230784
73	96.23	96.0699959938948	0.160004006105169
74	96.32	96.2299928782444	0.0900071217556047
75	96.55	96.3199959937958	0.230004006204226
76	96.73	96.5499897625543	0.1800102374457
77	96.61	96.7299919877699	-0.119991987769865
78	96.64	96.6100053408264	0.0299946591736386
79	96.86	96.639998664942	0.220001335058029
80	97.02	96.8599902077718	0.160009792228223
81	97.22	97.0199928779868	0.200007122013162
82	98.1	97.2199910977114	0.880008902288623
83	98.46	98.0999608309286	0.360039169071413
84	98.6	98.4599839747076	0.140016025292354
85	98.78	98.5999937679066	0.180006232093433
86	99.13	98.7799919879482	0.350008012051845
87	99.48	99.129984421193	0.350015578806975
88	99.62	99.4799844208562	0.140015579143764
89	99.68	99.6199937679264	0.0600062320735759
90	99.95	99.6799973291311	0.270002670868877
91	100.12	99.9499879822194	0.170012017780579
92	100.25	100.119992432789	0.130007567210797
93	100.47	100.249994213382	0.220005786618387
94	100.7	100.469990207574	0.230009792426358
95	100.88	100.699989762297	0.180010237703215
96	100.95	100.87999198777	0.0700080122301472
97	100.92	100.949996883953	-0.0299968839533022
98	101.12	100.920001335157	0.199998664842951
99	101.19	101.119991098088	0.0700089019122032
100	101.28	101.189996883914	0.0900031160862937
101	101.28	101.279995993974	4.0060259323127e-06
102	101.3	101.279999999822	0.0200000001782996
103	101.3	101.299999109803	8.90197171088403e-07
104	101.36	101.29999999996	0.0600000000396363
105	101.45	101.359997329409	0.0900026705915025
106	101.58	101.449995993994	0.130004006006104
107	101.73	101.57999421354	0.150005786459872
108	101.84	101.729993323264	0.110006676736276
109	102.01	101.839995103618	0.17000489638157
110	102.14	102.009992433106	0.130007566893823
111	102.16	102.139994213382	0.0200057866183556
112	102.32	102.159999109545	0.160000890454711
113	102.41	102.319992878383	0.0900071216169493
114	102.4	102.409995993796	-0.00999599379576921
115	102.43	102.40000044492	0.0299995550797405
116	102.42	102.429998664724	-0.00999866472406552
117	102.3	102.420000445039	-0.120000445039153
118	102.65	102.300005341203	0.349994658797215
119	102.72	102.649984421787	0.0700155782126046
120	102.86	102.719996883617	0.140003116383468

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
121	102.859993768481	102.219511878173	103.500475658789
122	102.859993768481	101.954235750558	103.765751786404
123	102.859993768481	101.750679510819	103.969308026143
124	102.859993768481	101.579072749264	104.140914787698
125	102.859993768481	101.427883719396	104.292103817566
126	102.859993768481	101.291198138735	104.428789398227
127	102.859993768481	101.165502616799	104.554484920164
128	102.859993768481	101.048507969931	104.671479567032
129	102.859993768481	100.938624118057	104.781363418905
130	102.859993768481	100.834693329339	104.885294207623
131	102.859993768481	100.735841607263	104.9841459297
132	102.859993768481	100.641389941921	105.078597595042

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
121 & 102.859993768481 & 102.219511878173 & 103.500475658789 \tabularnewline
122 & 102.859993768481 & 101.954235750558 & 103.765751786404 \tabularnewline
123 & 102.859993768481 & 101.750679510819 & 103.969308026143 \tabularnewline
124 & 102.859993768481 & 101.579072749264 & 104.140914787698 \tabularnewline
125 & 102.859993768481 & 101.427883719396 & 104.292103817566 \tabularnewline
126 & 102.859993768481 & 101.291198138735 & 104.428789398227 \tabularnewline
127 & 102.859993768481 & 101.165502616799 & 104.554484920164 \tabularnewline
128 & 102.859993768481 & 101.048507969931 & 104.671479567032 \tabularnewline
129 & 102.859993768481 & 100.938624118057 & 104.781363418905 \tabularnewline
130 & 102.859993768481 & 100.834693329339 & 104.885294207623 \tabularnewline
131 & 102.859993768481 & 100.735841607263 & 104.9841459297 \tabularnewline
132 & 102.859993768481 & 100.641389941921 & 105.078597595042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]121[/C][C]102.859993768481[/C][C]102.219511878173[/C][C]103.500475658789[/C][/ROW]
[ROW][C]122[/C][C]102.859993768481[/C][C]101.954235750558[/C][C]103.765751786404[/C][/ROW]
[ROW][C]123[/C][C]102.859993768481[/C][C]101.750679510819[/C][C]103.969308026143[/C][/ROW]
[ROW][C]124[/C][C]102.859993768481[/C][C]101.579072749264[/C][C]104.140914787698[/C][/ROW]
[ROW][C]125[/C][C]102.859993768481[/C][C]101.427883719396[/C][C]104.292103817566[/C][/ROW]
[ROW][C]126[/C][C]102.859993768481[/C][C]101.291198138735[/C][C]104.428789398227[/C][/ROW]
[ROW][C]127[/C][C]102.859993768481[/C][C]101.165502616799[/C][C]104.554484920164[/C][/ROW]
[ROW][C]128[/C][C]102.859993768481[/C][C]101.048507969931[/C][C]104.671479567032[/C][/ROW]
[ROW][C]129[/C][C]102.859993768481[/C][C]100.938624118057[/C][C]104.781363418905[/C][/ROW]
[ROW][C]130[/C][C]102.859993768481[/C][C]100.834693329339[/C][C]104.885294207623[/C][/ROW]
[ROW][C]131[/C][C]102.859993768481[/C][C]100.735841607263[/C][C]104.9841459297[/C][/ROW]
[ROW][C]132[/C][C]102.859993768481[/C][C]100.641389941921[/C][C]105.078597595042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
121	102.859993768481	102.219511878173	103.500475658789
122	102.859993768481	101.954235750558	103.765751786404
123	102.859993768481	101.750679510819	103.969308026143
124	102.859993768481	101.579072749264	104.140914787698
125	102.859993768481	101.427883719396	104.292103817566
126	102.859993768481	101.291198138735	104.428789398227
127	102.859993768481	101.165502616799	104.554484920164
128	102.859993768481	101.048507969931	104.671479567032
129	102.859993768481	100.938624118057	104.781363418905
130	102.859993768481	100.834693329339	104.885294207623
131	102.859993768481	100.735841607263	104.9841459297
132	102.859993768481	100.641389941921	105.078597595042

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 ;

Parameters (R input):

par1 = 12 ; par2 = Single ; 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