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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, 29 Nov 2015 12:32:21 +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/2015/Nov/29/t1448800381z76ol90hv2e8old.htm/, Retrieved Wed, 15 May 2024 23:51:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284419, Retrieved Wed, 15 May 2024 23:51:47 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

120

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

-       [Exponential Smoothing] [Opdracht 10] [2015-11-29 12:32:21] [b43493158838656c32486372ca9c54cf] [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	'George Udny Yule' @ yule.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284419&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284419&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284419&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	'George Udny Yule' @ yule.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	0.999945152993895
beta	FALSE
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.999945152993895 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284419&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]0.999945152993895[/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=284419&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284419&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	0.999945152993895
beta	FALSE
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
2	85.17	84.71	0.460000000000008
3	84.93	85.1699747703772	-0.239974770377188
4	85.1	84.9300131618977	0.16998683810229
5	85.19	85.0999906767308	0.0900093232691574
6	85.38	85.1899950632581	0.1900049367419
7	85.95	85.3799895787981	0.570010421201943
8	86.04	85.949968736635	0.0900312633650486
9	85.68	86.0399950620548	-0.35999506205475
10	85.79	85.6800197446514	0.109980255348631
11	85.79	85.7899939679123	6.03208773952701e-06
12	86.05	85.7899999996692	0.260000000330834
13	86.14	86.0499857397784	0.0900142602216079
14	86.82	86.1399950629873	0.680004937012669
15	86.93	86.8199627037651	0.110037296234935
16	87.03	86.9299939647837	0.100006035216254
17	87.13	87.0299945149684	0.100005485031616
18	87.09	87.1299945149986	-0.0399945149985541
19	87.65	87.0900021935794	0.559997806420597
20	87.6	87.6499692857969	-0.0499692857969052
21	87.03	87.6000027406657	-0.570002740665714
22	87.12	87.0300312629438	0.0899687370562106
23	87.08	87.1199950654841	-0.0399950654841348
24	87.56	87.0800021936096	0.479997806390401
25	87.31	87.5599736735574	-0.249973673557378
26	87.89	87.3100137103076	0.5799862896924
27	88.2	87.8899681894884	0.310031810511575
28	87.7	88.1999829956834	-0.499982995683396
29	88.19	87.7000274225704	0.489972577429583
30	88.65	88.1899731264711	0.460026873528946
31	89.48	88.6499747689033	0.830025231096741
32	89.65	89.4799544756011	0.17004552439893
33	89.34	89.6499906735121	-0.30999067351209
34	89.73	89.3400170020604	0.38998299793964
35	89.77	89.7299786106001	0.040021389399854
36	90.26	89.7699978049466	0.490002195053407
37	90.03	90.2599731248466	-0.229973124846623
38	91.09	90.0300126133374	1.05998738666263
39	90.94	91.0899418628653	-0.149941862865333
40	91.03	90.9400082238623	0.0899917761377367
41	91.14	91.0299950642205	0.1100049357795
42	91.51	91.1399939665586	0.370006033441399
43	91.99	91.5099797062768	0.480020293723172
44	91.91	91.989973672324	-0.0799736723240159
45	91.8	91.9100043863165	-0.11000438631649
46	91.8	91.8000060334113	-6.03341125327006e-06
47	91.44	91.8000000003309	-0.360000000330913
48	91.83	91.4400197449222	0.389980255077788
49	91.46	91.8299786107506	-0.369978610750579
50	92.17	91.4600202922191	0.709979707780889
51	91.91	92.1699610597386	-0.259961059738643
52	92.06	91.9100142580858	0.149985741914179
53	92.33	92.0599917737311	0.270008226268899
54	92.73	92.3299851908572	0.400014809142846
55	93.35	92.7299780603853	0.620021939614674
56	93.28	93.3499659936529	-0.069965993652886
57	93.22	93.2800038374253	-0.0600038374252847
58	93.31	93.2200032910308	0.0899967089691671
59	93.21	93.30999506395	-0.0999950639499616
60	93.14	93.2100054844299	-0.0700054844298847
61	93.82	93.1400038395912	0.679996160408763
62	94.18	93.8199627042464	0.360037295753571
63	94.44	94.1799802530322	0.260019746967757
64	94.35	94.4399857386953	-0.0899857386953471
65	94.38	94.3500049354484	0.0299950645516418
66	94.72	94.3799983548605	0.340001645139495
67	95.25	94.7199813519277	0.5300186480723
68	95.16	95.249970930064	-0.0899709300639842
69	94.9	95.1600049346361	-0.260004934636143
70	95.09	94.9000142604922	0.189985739507762
71	95.22	95.089989579851	0.130010420149006
72	95.39	95.2199928693177	0.170007130682308
73	96.57	95.3899906756179	1.18000932438213
74	97.05	96.5699352800214	0.480064719978628
75	97.11	97.0499736698874	0.0600263301126347
76	97.08	97.1099967077355	-0.0299967077355063
77	97.5	97.0800016452296	0.419998354770385
78	97.92	97.4999769643477	0.420023035652321
79	98.44	97.919976962994	0.520023037005998
80	98.44	98.4399714782933	2.85217066817722e-05
81	98.06	98.4399999984357	-0.379999998435679
82	98.2	98.0600208418622	0.139979158137777
83	98.19	98.1999923225623	-0.00999232256225469
84	98.36	98.190000548049	0.169999451951028
85	98.41	98.359990676039	0.0500093239609782
86	98.97	98.4099972571383	0.560002742861698
87	99.45	98.9699692855261	0.480030714473855
88	98.95	99.4499736717525	-0.499973671752485
89	99.7	98.950027422059	0.74997257794098
90	100.12	99.6999588662494	0.420041133750573
91	100.62	100.119976962001	0.500023037998631
92	100.75	100.619972575233	0.13002742476661
93	100.47	100.749992868385	-0.279992868385051
94	100.71	100.470015356771	0.239984643229434
95	100.85	100.709986837561	0.140013162439203
96	101.03	100.849992320697	0.180007679302776
97	101.13	101.029990127118	0.100009872882282
98	101.38	101.129994514758	0.250005485242113
99	101.73	101.379986287948	0.350013712052373
100	101.89	101.729980802796	0.160019197204193
101	102.02	101.889991223426	0.130008776573888
102	102.11	102.019992869408	0.0900071305921699
103	102.77	102.109995063378	0.660004936621647
104	102.49	102.769963800705	-0.279963800705218
105	102.52	102.490015355176	0.0299846448237275
106	102.69	102.519998355432	0.170001644568003
107	102.32	102.689990675919	-0.369990675918771
108	102.6	102.320020292881	0.279979707119139

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 85.17 & 84.71 & 0.460000000000008 \tabularnewline
3 & 84.93 & 85.1699747703772 & -0.239974770377188 \tabularnewline
4 & 85.1 & 84.9300131618977 & 0.16998683810229 \tabularnewline
5 & 85.19 & 85.0999906767308 & 0.0900093232691574 \tabularnewline
6 & 85.38 & 85.1899950632581 & 0.1900049367419 \tabularnewline
7 & 85.95 & 85.3799895787981 & 0.570010421201943 \tabularnewline
8 & 86.04 & 85.949968736635 & 0.0900312633650486 \tabularnewline
9 & 85.68 & 86.0399950620548 & -0.35999506205475 \tabularnewline
10 & 85.79 & 85.6800197446514 & 0.109980255348631 \tabularnewline
11 & 85.79 & 85.7899939679123 & 6.03208773952701e-06 \tabularnewline
12 & 86.05 & 85.7899999996692 & 0.260000000330834 \tabularnewline
13 & 86.14 & 86.0499857397784 & 0.0900142602216079 \tabularnewline
14 & 86.82 & 86.1399950629873 & 0.680004937012669 \tabularnewline
15 & 86.93 & 86.8199627037651 & 0.110037296234935 \tabularnewline
16 & 87.03 & 86.9299939647837 & 0.100006035216254 \tabularnewline
17 & 87.13 & 87.0299945149684 & 0.100005485031616 \tabularnewline
18 & 87.09 & 87.1299945149986 & -0.0399945149985541 \tabularnewline
19 & 87.65 & 87.0900021935794 & 0.559997806420597 \tabularnewline
20 & 87.6 & 87.6499692857969 & -0.0499692857969052 \tabularnewline
21 & 87.03 & 87.6000027406657 & -0.570002740665714 \tabularnewline
22 & 87.12 & 87.0300312629438 & 0.0899687370562106 \tabularnewline
23 & 87.08 & 87.1199950654841 & -0.0399950654841348 \tabularnewline
24 & 87.56 & 87.0800021936096 & 0.479997806390401 \tabularnewline
25 & 87.31 & 87.5599736735574 & -0.249973673557378 \tabularnewline
26 & 87.89 & 87.3100137103076 & 0.5799862896924 \tabularnewline
27 & 88.2 & 87.8899681894884 & 0.310031810511575 \tabularnewline
28 & 87.7 & 88.1999829956834 & -0.499982995683396 \tabularnewline
29 & 88.19 & 87.7000274225704 & 0.489972577429583 \tabularnewline
30 & 88.65 & 88.1899731264711 & 0.460026873528946 \tabularnewline
31 & 89.48 & 88.6499747689033 & 0.830025231096741 \tabularnewline
32 & 89.65 & 89.4799544756011 & 0.17004552439893 \tabularnewline
33 & 89.34 & 89.6499906735121 & -0.30999067351209 \tabularnewline
34 & 89.73 & 89.3400170020604 & 0.38998299793964 \tabularnewline
35 & 89.77 & 89.7299786106001 & 0.040021389399854 \tabularnewline
36 & 90.26 & 89.7699978049466 & 0.490002195053407 \tabularnewline
37 & 90.03 & 90.2599731248466 & -0.229973124846623 \tabularnewline
38 & 91.09 & 90.0300126133374 & 1.05998738666263 \tabularnewline
39 & 90.94 & 91.0899418628653 & -0.149941862865333 \tabularnewline
40 & 91.03 & 90.9400082238623 & 0.0899917761377367 \tabularnewline
41 & 91.14 & 91.0299950642205 & 0.1100049357795 \tabularnewline
42 & 91.51 & 91.1399939665586 & 0.370006033441399 \tabularnewline
43 & 91.99 & 91.5099797062768 & 0.480020293723172 \tabularnewline
44 & 91.91 & 91.989973672324 & -0.0799736723240159 \tabularnewline
45 & 91.8 & 91.9100043863165 & -0.11000438631649 \tabularnewline
46 & 91.8 & 91.8000060334113 & -6.03341125327006e-06 \tabularnewline
47 & 91.44 & 91.8000000003309 & -0.360000000330913 \tabularnewline
48 & 91.83 & 91.4400197449222 & 0.389980255077788 \tabularnewline
49 & 91.46 & 91.8299786107506 & -0.369978610750579 \tabularnewline
50 & 92.17 & 91.4600202922191 & 0.709979707780889 \tabularnewline
51 & 91.91 & 92.1699610597386 & -0.259961059738643 \tabularnewline
52 & 92.06 & 91.9100142580858 & 0.149985741914179 \tabularnewline
53 & 92.33 & 92.0599917737311 & 0.270008226268899 \tabularnewline
54 & 92.73 & 92.3299851908572 & 0.400014809142846 \tabularnewline
55 & 93.35 & 92.7299780603853 & 0.620021939614674 \tabularnewline
56 & 93.28 & 93.3499659936529 & -0.069965993652886 \tabularnewline
57 & 93.22 & 93.2800038374253 & -0.0600038374252847 \tabularnewline
58 & 93.31 & 93.2200032910308 & 0.0899967089691671 \tabularnewline
59 & 93.21 & 93.30999506395 & -0.0999950639499616 \tabularnewline
60 & 93.14 & 93.2100054844299 & -0.0700054844298847 \tabularnewline
61 & 93.82 & 93.1400038395912 & 0.679996160408763 \tabularnewline
62 & 94.18 & 93.8199627042464 & 0.360037295753571 \tabularnewline
63 & 94.44 & 94.1799802530322 & 0.260019746967757 \tabularnewline
64 & 94.35 & 94.4399857386953 & -0.0899857386953471 \tabularnewline
65 & 94.38 & 94.3500049354484 & 0.0299950645516418 \tabularnewline
66 & 94.72 & 94.3799983548605 & 0.340001645139495 \tabularnewline
67 & 95.25 & 94.7199813519277 & 0.5300186480723 \tabularnewline
68 & 95.16 & 95.249970930064 & -0.0899709300639842 \tabularnewline
69 & 94.9 & 95.1600049346361 & -0.260004934636143 \tabularnewline
70 & 95.09 & 94.9000142604922 & 0.189985739507762 \tabularnewline
71 & 95.22 & 95.089989579851 & 0.130010420149006 \tabularnewline
72 & 95.39 & 95.2199928693177 & 0.170007130682308 \tabularnewline
73 & 96.57 & 95.3899906756179 & 1.18000932438213 \tabularnewline
74 & 97.05 & 96.5699352800214 & 0.480064719978628 \tabularnewline
75 & 97.11 & 97.0499736698874 & 0.0600263301126347 \tabularnewline
76 & 97.08 & 97.1099967077355 & -0.0299967077355063 \tabularnewline
77 & 97.5 & 97.0800016452296 & 0.419998354770385 \tabularnewline
78 & 97.92 & 97.4999769643477 & 0.420023035652321 \tabularnewline
79 & 98.44 & 97.919976962994 & 0.520023037005998 \tabularnewline
80 & 98.44 & 98.4399714782933 & 2.85217066817722e-05 \tabularnewline
81 & 98.06 & 98.4399999984357 & -0.379999998435679 \tabularnewline
82 & 98.2 & 98.0600208418622 & 0.139979158137777 \tabularnewline
83 & 98.19 & 98.1999923225623 & -0.00999232256225469 \tabularnewline
84 & 98.36 & 98.190000548049 & 0.169999451951028 \tabularnewline
85 & 98.41 & 98.359990676039 & 0.0500093239609782 \tabularnewline
86 & 98.97 & 98.4099972571383 & 0.560002742861698 \tabularnewline
87 & 99.45 & 98.9699692855261 & 0.480030714473855 \tabularnewline
88 & 98.95 & 99.4499736717525 & -0.499973671752485 \tabularnewline
89 & 99.7 & 98.950027422059 & 0.74997257794098 \tabularnewline
90 & 100.12 & 99.6999588662494 & 0.420041133750573 \tabularnewline
91 & 100.62 & 100.119976962001 & 0.500023037998631 \tabularnewline
92 & 100.75 & 100.619972575233 & 0.13002742476661 \tabularnewline
93 & 100.47 & 100.749992868385 & -0.279992868385051 \tabularnewline
94 & 100.71 & 100.470015356771 & 0.239984643229434 \tabularnewline
95 & 100.85 & 100.709986837561 & 0.140013162439203 \tabularnewline
96 & 101.03 & 100.849992320697 & 0.180007679302776 \tabularnewline
97 & 101.13 & 101.029990127118 & 0.100009872882282 \tabularnewline
98 & 101.38 & 101.129994514758 & 0.250005485242113 \tabularnewline
99 & 101.73 & 101.379986287948 & 0.350013712052373 \tabularnewline
100 & 101.89 & 101.729980802796 & 0.160019197204193 \tabularnewline
101 & 102.02 & 101.889991223426 & 0.130008776573888 \tabularnewline
102 & 102.11 & 102.019992869408 & 0.0900071305921699 \tabularnewline
103 & 102.77 & 102.109995063378 & 0.660004936621647 \tabularnewline
104 & 102.49 & 102.769963800705 & -0.279963800705218 \tabularnewline
105 & 102.52 & 102.490015355176 & 0.0299846448237275 \tabularnewline
106 & 102.69 & 102.519998355432 & 0.170001644568003 \tabularnewline
107 & 102.32 & 102.689990675919 & -0.369990675918771 \tabularnewline
108 & 102.6 & 102.320020292881 & 0.279979707119139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284419&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]85.17[/C][C]84.71[/C][C]0.460000000000008[/C][/ROW]
[ROW][C]3[/C][C]84.93[/C][C]85.1699747703772[/C][C]-0.239974770377188[/C][/ROW]
[ROW][C]4[/C][C]85.1[/C][C]84.9300131618977[/C][C]0.16998683810229[/C][/ROW]
[ROW][C]5[/C][C]85.19[/C][C]85.0999906767308[/C][C]0.0900093232691574[/C][/ROW]
[ROW][C]6[/C][C]85.38[/C][C]85.1899950632581[/C][C]0.1900049367419[/C][/ROW]
[ROW][C]7[/C][C]85.95[/C][C]85.3799895787981[/C][C]0.570010421201943[/C][/ROW]
[ROW][C]8[/C][C]86.04[/C][C]85.949968736635[/C][C]0.0900312633650486[/C][/ROW]
[ROW][C]9[/C][C]85.68[/C][C]86.0399950620548[/C][C]-0.35999506205475[/C][/ROW]
[ROW][C]10[/C][C]85.79[/C][C]85.6800197446514[/C][C]0.109980255348631[/C][/ROW]
[ROW][C]11[/C][C]85.79[/C][C]85.7899939679123[/C][C]6.03208773952701e-06[/C][/ROW]
[ROW][C]12[/C][C]86.05[/C][C]85.7899999996692[/C][C]0.260000000330834[/C][/ROW]
[ROW][C]13[/C][C]86.14[/C][C]86.0499857397784[/C][C]0.0900142602216079[/C][/ROW]
[ROW][C]14[/C][C]86.82[/C][C]86.1399950629873[/C][C]0.680004937012669[/C][/ROW]
[ROW][C]15[/C][C]86.93[/C][C]86.8199627037651[/C][C]0.110037296234935[/C][/ROW]
[ROW][C]16[/C][C]87.03[/C][C]86.9299939647837[/C][C]0.100006035216254[/C][/ROW]
[ROW][C]17[/C][C]87.13[/C][C]87.0299945149684[/C][C]0.100005485031616[/C][/ROW]
[ROW][C]18[/C][C]87.09[/C][C]87.1299945149986[/C][C]-0.0399945149985541[/C][/ROW]
[ROW][C]19[/C][C]87.65[/C][C]87.0900021935794[/C][C]0.559997806420597[/C][/ROW]
[ROW][C]20[/C][C]87.6[/C][C]87.6499692857969[/C][C]-0.0499692857969052[/C][/ROW]
[ROW][C]21[/C][C]87.03[/C][C]87.6000027406657[/C][C]-0.570002740665714[/C][/ROW]
[ROW][C]22[/C][C]87.12[/C][C]87.0300312629438[/C][C]0.0899687370562106[/C][/ROW]
[ROW][C]23[/C][C]87.08[/C][C]87.1199950654841[/C][C]-0.0399950654841348[/C][/ROW]
[ROW][C]24[/C][C]87.56[/C][C]87.0800021936096[/C][C]0.479997806390401[/C][/ROW]
[ROW][C]25[/C][C]87.31[/C][C]87.5599736735574[/C][C]-0.249973673557378[/C][/ROW]
[ROW][C]26[/C][C]87.89[/C][C]87.3100137103076[/C][C]0.5799862896924[/C][/ROW]
[ROW][C]27[/C][C]88.2[/C][C]87.8899681894884[/C][C]0.310031810511575[/C][/ROW]
[ROW][C]28[/C][C]87.7[/C][C]88.1999829956834[/C][C]-0.499982995683396[/C][/ROW]
[ROW][C]29[/C][C]88.19[/C][C]87.7000274225704[/C][C]0.489972577429583[/C][/ROW]
[ROW][C]30[/C][C]88.65[/C][C]88.1899731264711[/C][C]0.460026873528946[/C][/ROW]
[ROW][C]31[/C][C]89.48[/C][C]88.6499747689033[/C][C]0.830025231096741[/C][/ROW]
[ROW][C]32[/C][C]89.65[/C][C]89.4799544756011[/C][C]0.17004552439893[/C][/ROW]
[ROW][C]33[/C][C]89.34[/C][C]89.6499906735121[/C][C]-0.30999067351209[/C][/ROW]
[ROW][C]34[/C][C]89.73[/C][C]89.3400170020604[/C][C]0.38998299793964[/C][/ROW]
[ROW][C]35[/C][C]89.77[/C][C]89.7299786106001[/C][C]0.040021389399854[/C][/ROW]
[ROW][C]36[/C][C]90.26[/C][C]89.7699978049466[/C][C]0.490002195053407[/C][/ROW]
[ROW][C]37[/C][C]90.03[/C][C]90.2599731248466[/C][C]-0.229973124846623[/C][/ROW]
[ROW][C]38[/C][C]91.09[/C][C]90.0300126133374[/C][C]1.05998738666263[/C][/ROW]
[ROW][C]39[/C][C]90.94[/C][C]91.0899418628653[/C][C]-0.149941862865333[/C][/ROW]
[ROW][C]40[/C][C]91.03[/C][C]90.9400082238623[/C][C]0.0899917761377367[/C][/ROW]
[ROW][C]41[/C][C]91.14[/C][C]91.0299950642205[/C][C]0.1100049357795[/C][/ROW]
[ROW][C]42[/C][C]91.51[/C][C]91.1399939665586[/C][C]0.370006033441399[/C][/ROW]
[ROW][C]43[/C][C]91.99[/C][C]91.5099797062768[/C][C]0.480020293723172[/C][/ROW]
[ROW][C]44[/C][C]91.91[/C][C]91.989973672324[/C][C]-0.0799736723240159[/C][/ROW]
[ROW][C]45[/C][C]91.8[/C][C]91.9100043863165[/C][C]-0.11000438631649[/C][/ROW]
[ROW][C]46[/C][C]91.8[/C][C]91.8000060334113[/C][C]-6.03341125327006e-06[/C][/ROW]
[ROW][C]47[/C][C]91.44[/C][C]91.8000000003309[/C][C]-0.360000000330913[/C][/ROW]
[ROW][C]48[/C][C]91.83[/C][C]91.4400197449222[/C][C]0.389980255077788[/C][/ROW]
[ROW][C]49[/C][C]91.46[/C][C]91.8299786107506[/C][C]-0.369978610750579[/C][/ROW]
[ROW][C]50[/C][C]92.17[/C][C]91.4600202922191[/C][C]0.709979707780889[/C][/ROW]
[ROW][C]51[/C][C]91.91[/C][C]92.1699610597386[/C][C]-0.259961059738643[/C][/ROW]
[ROW][C]52[/C][C]92.06[/C][C]91.9100142580858[/C][C]0.149985741914179[/C][/ROW]
[ROW][C]53[/C][C]92.33[/C][C]92.0599917737311[/C][C]0.270008226268899[/C][/ROW]
[ROW][C]54[/C][C]92.73[/C][C]92.3299851908572[/C][C]0.400014809142846[/C][/ROW]
[ROW][C]55[/C][C]93.35[/C][C]92.7299780603853[/C][C]0.620021939614674[/C][/ROW]
[ROW][C]56[/C][C]93.28[/C][C]93.3499659936529[/C][C]-0.069965993652886[/C][/ROW]
[ROW][C]57[/C][C]93.22[/C][C]93.2800038374253[/C][C]-0.0600038374252847[/C][/ROW]
[ROW][C]58[/C][C]93.31[/C][C]93.2200032910308[/C][C]0.0899967089691671[/C][/ROW]
[ROW][C]59[/C][C]93.21[/C][C]93.30999506395[/C][C]-0.0999950639499616[/C][/ROW]
[ROW][C]60[/C][C]93.14[/C][C]93.2100054844299[/C][C]-0.0700054844298847[/C][/ROW]
[ROW][C]61[/C][C]93.82[/C][C]93.1400038395912[/C][C]0.679996160408763[/C][/ROW]
[ROW][C]62[/C][C]94.18[/C][C]93.8199627042464[/C][C]0.360037295753571[/C][/ROW]
[ROW][C]63[/C][C]94.44[/C][C]94.1799802530322[/C][C]0.260019746967757[/C][/ROW]
[ROW][C]64[/C][C]94.35[/C][C]94.4399857386953[/C][C]-0.0899857386953471[/C][/ROW]
[ROW][C]65[/C][C]94.38[/C][C]94.3500049354484[/C][C]0.0299950645516418[/C][/ROW]
[ROW][C]66[/C][C]94.72[/C][C]94.3799983548605[/C][C]0.340001645139495[/C][/ROW]
[ROW][C]67[/C][C]95.25[/C][C]94.7199813519277[/C][C]0.5300186480723[/C][/ROW]
[ROW][C]68[/C][C]95.16[/C][C]95.249970930064[/C][C]-0.0899709300639842[/C][/ROW]
[ROW][C]69[/C][C]94.9[/C][C]95.1600049346361[/C][C]-0.260004934636143[/C][/ROW]
[ROW][C]70[/C][C]95.09[/C][C]94.9000142604922[/C][C]0.189985739507762[/C][/ROW]
[ROW][C]71[/C][C]95.22[/C][C]95.089989579851[/C][C]0.130010420149006[/C][/ROW]
[ROW][C]72[/C][C]95.39[/C][C]95.2199928693177[/C][C]0.170007130682308[/C][/ROW]
[ROW][C]73[/C][C]96.57[/C][C]95.3899906756179[/C][C]1.18000932438213[/C][/ROW]
[ROW][C]74[/C][C]97.05[/C][C]96.5699352800214[/C][C]0.480064719978628[/C][/ROW]
[ROW][C]75[/C][C]97.11[/C][C]97.0499736698874[/C][C]0.0600263301126347[/C][/ROW]
[ROW][C]76[/C][C]97.08[/C][C]97.1099967077355[/C][C]-0.0299967077355063[/C][/ROW]
[ROW][C]77[/C][C]97.5[/C][C]97.0800016452296[/C][C]0.419998354770385[/C][/ROW]
[ROW][C]78[/C][C]97.92[/C][C]97.4999769643477[/C][C]0.420023035652321[/C][/ROW]
[ROW][C]79[/C][C]98.44[/C][C]97.919976962994[/C][C]0.520023037005998[/C][/ROW]
[ROW][C]80[/C][C]98.44[/C][C]98.4399714782933[/C][C]2.85217066817722e-05[/C][/ROW]
[ROW][C]81[/C][C]98.06[/C][C]98.4399999984357[/C][C]-0.379999998435679[/C][/ROW]
[ROW][C]82[/C][C]98.2[/C][C]98.0600208418622[/C][C]0.139979158137777[/C][/ROW]
[ROW][C]83[/C][C]98.19[/C][C]98.1999923225623[/C][C]-0.00999232256225469[/C][/ROW]
[ROW][C]84[/C][C]98.36[/C][C]98.190000548049[/C][C]0.169999451951028[/C][/ROW]
[ROW][C]85[/C][C]98.41[/C][C]98.359990676039[/C][C]0.0500093239609782[/C][/ROW]
[ROW][C]86[/C][C]98.97[/C][C]98.4099972571383[/C][C]0.560002742861698[/C][/ROW]
[ROW][C]87[/C][C]99.45[/C][C]98.9699692855261[/C][C]0.480030714473855[/C][/ROW]
[ROW][C]88[/C][C]98.95[/C][C]99.4499736717525[/C][C]-0.499973671752485[/C][/ROW]
[ROW][C]89[/C][C]99.7[/C][C]98.950027422059[/C][C]0.74997257794098[/C][/ROW]
[ROW][C]90[/C][C]100.12[/C][C]99.6999588662494[/C][C]0.420041133750573[/C][/ROW]
[ROW][C]91[/C][C]100.62[/C][C]100.119976962001[/C][C]0.500023037998631[/C][/ROW]
[ROW][C]92[/C][C]100.75[/C][C]100.619972575233[/C][C]0.13002742476661[/C][/ROW]
[ROW][C]93[/C][C]100.47[/C][C]100.749992868385[/C][C]-0.279992868385051[/C][/ROW]
[ROW][C]94[/C][C]100.71[/C][C]100.470015356771[/C][C]0.239984643229434[/C][/ROW]
[ROW][C]95[/C][C]100.85[/C][C]100.709986837561[/C][C]0.140013162439203[/C][/ROW]
[ROW][C]96[/C][C]101.03[/C][C]100.849992320697[/C][C]0.180007679302776[/C][/ROW]
[ROW][C]97[/C][C]101.13[/C][C]101.029990127118[/C][C]0.100009872882282[/C][/ROW]
[ROW][C]98[/C][C]101.38[/C][C]101.129994514758[/C][C]0.250005485242113[/C][/ROW]
[ROW][C]99[/C][C]101.73[/C][C]101.379986287948[/C][C]0.350013712052373[/C][/ROW]
[ROW][C]100[/C][C]101.89[/C][C]101.729980802796[/C][C]0.160019197204193[/C][/ROW]
[ROW][C]101[/C][C]102.02[/C][C]101.889991223426[/C][C]0.130008776573888[/C][/ROW]
[ROW][C]102[/C][C]102.11[/C][C]102.019992869408[/C][C]0.0900071305921699[/C][/ROW]
[ROW][C]103[/C][C]102.77[/C][C]102.109995063378[/C][C]0.660004936621647[/C][/ROW]
[ROW][C]104[/C][C]102.49[/C][C]102.769963800705[/C][C]-0.279963800705218[/C][/ROW]
[ROW][C]105[/C][C]102.52[/C][C]102.490015355176[/C][C]0.0299846448237275[/C][/ROW]
[ROW][C]106[/C][C]102.69[/C][C]102.519998355432[/C][C]0.170001644568003[/C][/ROW]
[ROW][C]107[/C][C]102.32[/C][C]102.689990675919[/C][C]-0.369990675918771[/C][/ROW]
[ROW][C]108[/C][C]102.6[/C][C]102.320020292881[/C][C]0.279979707119139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284419&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284419&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	85.17	84.71	0.460000000000008
3	84.93	85.1699747703772	-0.239974770377188
4	85.1	84.9300131618977	0.16998683810229
5	85.19	85.0999906767308	0.0900093232691574
6	85.38	85.1899950632581	0.1900049367419
7	85.95	85.3799895787981	0.570010421201943
8	86.04	85.949968736635	0.0900312633650486
9	85.68	86.0399950620548	-0.35999506205475
10	85.79	85.6800197446514	0.109980255348631
11	85.79	85.7899939679123	6.03208773952701e-06
12	86.05	85.7899999996692	0.260000000330834
13	86.14	86.0499857397784	0.0900142602216079
14	86.82	86.1399950629873	0.680004937012669
15	86.93	86.8199627037651	0.110037296234935
16	87.03	86.9299939647837	0.100006035216254
17	87.13	87.0299945149684	0.100005485031616
18	87.09	87.1299945149986	-0.0399945149985541
19	87.65	87.0900021935794	0.559997806420597
20	87.6	87.6499692857969	-0.0499692857969052
21	87.03	87.6000027406657	-0.570002740665714
22	87.12	87.0300312629438	0.0899687370562106
23	87.08	87.1199950654841	-0.0399950654841348
24	87.56	87.0800021936096	0.479997806390401
25	87.31	87.5599736735574	-0.249973673557378
26	87.89	87.3100137103076	0.5799862896924
27	88.2	87.8899681894884	0.310031810511575
28	87.7	88.1999829956834	-0.499982995683396
29	88.19	87.7000274225704	0.489972577429583
30	88.65	88.1899731264711	0.460026873528946
31	89.48	88.6499747689033	0.830025231096741
32	89.65	89.4799544756011	0.17004552439893
33	89.34	89.6499906735121	-0.30999067351209
34	89.73	89.3400170020604	0.38998299793964
35	89.77	89.7299786106001	0.040021389399854
36	90.26	89.7699978049466	0.490002195053407
37	90.03	90.2599731248466	-0.229973124846623
38	91.09	90.0300126133374	1.05998738666263
39	90.94	91.0899418628653	-0.149941862865333
40	91.03	90.9400082238623	0.0899917761377367
41	91.14	91.0299950642205	0.1100049357795
42	91.51	91.1399939665586	0.370006033441399
43	91.99	91.5099797062768	0.480020293723172
44	91.91	91.989973672324	-0.0799736723240159
45	91.8	91.9100043863165	-0.11000438631649
46	91.8	91.8000060334113	-6.03341125327006e-06
47	91.44	91.8000000003309	-0.360000000330913
48	91.83	91.4400197449222	0.389980255077788
49	91.46	91.8299786107506	-0.369978610750579
50	92.17	91.4600202922191	0.709979707780889
51	91.91	92.1699610597386	-0.259961059738643
52	92.06	91.9100142580858	0.149985741914179
53	92.33	92.0599917737311	0.270008226268899
54	92.73	92.3299851908572	0.400014809142846
55	93.35	92.7299780603853	0.620021939614674
56	93.28	93.3499659936529	-0.069965993652886
57	93.22	93.2800038374253	-0.0600038374252847
58	93.31	93.2200032910308	0.0899967089691671
59	93.21	93.30999506395	-0.0999950639499616
60	93.14	93.2100054844299	-0.0700054844298847
61	93.82	93.1400038395912	0.679996160408763
62	94.18	93.8199627042464	0.360037295753571
63	94.44	94.1799802530322	0.260019746967757
64	94.35	94.4399857386953	-0.0899857386953471
65	94.38	94.3500049354484	0.0299950645516418
66	94.72	94.3799983548605	0.340001645139495
67	95.25	94.7199813519277	0.5300186480723
68	95.16	95.249970930064	-0.0899709300639842
69	94.9	95.1600049346361	-0.260004934636143
70	95.09	94.9000142604922	0.189985739507762
71	95.22	95.089989579851	0.130010420149006
72	95.39	95.2199928693177	0.170007130682308
73	96.57	95.3899906756179	1.18000932438213
74	97.05	96.5699352800214	0.480064719978628
75	97.11	97.0499736698874	0.0600263301126347
76	97.08	97.1099967077355	-0.0299967077355063
77	97.5	97.0800016452296	0.419998354770385
78	97.92	97.4999769643477	0.420023035652321
79	98.44	97.919976962994	0.520023037005998
80	98.44	98.4399714782933	2.85217066817722e-05
81	98.06	98.4399999984357	-0.379999998435679
82	98.2	98.0600208418622	0.139979158137777
83	98.19	98.1999923225623	-0.00999232256225469
84	98.36	98.190000548049	0.169999451951028
85	98.41	98.359990676039	0.0500093239609782
86	98.97	98.4099972571383	0.560002742861698
87	99.45	98.9699692855261	0.480030714473855
88	98.95	99.4499736717525	-0.499973671752485
89	99.7	98.950027422059	0.74997257794098
90	100.12	99.6999588662494	0.420041133750573
91	100.62	100.119976962001	0.500023037998631
92	100.75	100.619972575233	0.13002742476661
93	100.47	100.749992868385	-0.279992868385051
94	100.71	100.470015356771	0.239984643229434
95	100.85	100.709986837561	0.140013162439203
96	101.03	100.849992320697	0.180007679302776
97	101.13	101.029990127118	0.100009872882282
98	101.38	101.129994514758	0.250005485242113
99	101.73	101.379986287948	0.350013712052373
100	101.89	101.729980802796	0.160019197204193
101	102.02	101.889991223426	0.130008776573888
102	102.11	102.019992869408	0.0900071305921699
103	102.77	102.109995063378	0.660004936621647
104	102.49	102.769963800705	-0.279963800705218
105	102.52	102.490015355176	0.0299846448237275
106	102.69	102.519998355432	0.170001644568003
107	102.32	102.689990675919	-0.369990675918771
108	102.6	102.320020292881	0.279979707119139

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
109	102.599984643951	101.951312154057	103.248657133846
110	102.599984643951	101.682648368123	103.517320919779
111	102.599984643951	101.476492015246	103.723477272656
112	102.599984643951	101.302693030412	103.89727625749
113	102.599984643951	101.149572504467	104.050396783435
114	102.599984643951	101.011140655963	104.18882863194
115	102.599984643951	100.883839235785	104.316130052118
116	102.599984643951	100.765349828579	104.434619459323
117	102.599984643951	100.654062047963	104.54590723994
118	102.599984643951	100.548803376149	104.651165911754
119	102.599984643951	100.448688653812	104.75128063409
120	102.599984643951	100.353030198264	104.846939089638

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
109 & 102.599984643951 & 101.951312154057 & 103.248657133846 \tabularnewline
110 & 102.599984643951 & 101.682648368123 & 103.517320919779 \tabularnewline
111 & 102.599984643951 & 101.476492015246 & 103.723477272656 \tabularnewline
112 & 102.599984643951 & 101.302693030412 & 103.89727625749 \tabularnewline
113 & 102.599984643951 & 101.149572504467 & 104.050396783435 \tabularnewline
114 & 102.599984643951 & 101.011140655963 & 104.18882863194 \tabularnewline
115 & 102.599984643951 & 100.883839235785 & 104.316130052118 \tabularnewline
116 & 102.599984643951 & 100.765349828579 & 104.434619459323 \tabularnewline
117 & 102.599984643951 & 100.654062047963 & 104.54590723994 \tabularnewline
118 & 102.599984643951 & 100.548803376149 & 104.651165911754 \tabularnewline
119 & 102.599984643951 & 100.448688653812 & 104.75128063409 \tabularnewline
120 & 102.599984643951 & 100.353030198264 & 104.846939089638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284419&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]109[/C][C]102.599984643951[/C][C]101.951312154057[/C][C]103.248657133846[/C][/ROW]
[ROW][C]110[/C][C]102.599984643951[/C][C]101.682648368123[/C][C]103.517320919779[/C][/ROW]
[ROW][C]111[/C][C]102.599984643951[/C][C]101.476492015246[/C][C]103.723477272656[/C][/ROW]
[ROW][C]112[/C][C]102.599984643951[/C][C]101.302693030412[/C][C]103.89727625749[/C][/ROW]
[ROW][C]113[/C][C]102.599984643951[/C][C]101.149572504467[/C][C]104.050396783435[/C][/ROW]
[ROW][C]114[/C][C]102.599984643951[/C][C]101.011140655963[/C][C]104.18882863194[/C][/ROW]
[ROW][C]115[/C][C]102.599984643951[/C][C]100.883839235785[/C][C]104.316130052118[/C][/ROW]
[ROW][C]116[/C][C]102.599984643951[/C][C]100.765349828579[/C][C]104.434619459323[/C][/ROW]
[ROW][C]117[/C][C]102.599984643951[/C][C]100.654062047963[/C][C]104.54590723994[/C][/ROW]
[ROW][C]118[/C][C]102.599984643951[/C][C]100.548803376149[/C][C]104.651165911754[/C][/ROW]
[ROW][C]119[/C][C]102.599984643951[/C][C]100.448688653812[/C][C]104.75128063409[/C][/ROW]
[ROW][C]120[/C][C]102.599984643951[/C][C]100.353030198264[/C][C]104.846939089638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284419&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284419&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
109	102.599984643951	101.951312154057	103.248657133846
110	102.599984643951	101.682648368123	103.517320919779
111	102.599984643951	101.476492015246	103.723477272656
112	102.599984643951	101.302693030412	103.89727625749
113	102.599984643951	101.149572504467	104.050396783435
114	102.599984643951	101.011140655963	104.18882863194
115	102.599984643951	100.883839235785	104.316130052118
116	102.599984643951	100.765349828579	104.434619459323
117	102.599984643951	100.654062047963	104.54590723994
118	102.599984643951	100.548803376149	104.651165911754
119	102.599984643951	100.448688653812	104.75128063409
120	102.599984643951	100.353030198264	104.846939089638

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