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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Mar 2012 16:49:25 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Mar/07/t133115718583vbifip7s5vpc7.htm/, Retrieved Mon, 29 Apr 2024 00:14:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=163722, Retrieved Mon, 29 Apr 2024 00:14:07 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

199

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

-     [Multiple Regression] [15th bird enterin...] [2012-03-06 03:20:16] [74be16979710d4c4e7c6647856088456]
-    D  [Multiple Regression] [Reduced model ] [2012-03-06 15:35:32] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [Chimney swift ent...] [2012-03-07 21:49:25] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D        [Multiple Regression] [Chimney swift ent...] [2012-03-08 21:24:40] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [TimeIn vs Sunset ...] [2012-03-09 17:37:07] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [TimeIn vs Temp Rain] [2012-03-09 17:38:55] [74be16979710d4c4e7c6647856088456] 
-    D        [Multiple Regression] [Poster regression...] [2012-04-02 17:00:19] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [Including SeasonD...] [2012-04-09 18:04:16] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Chimney Swift Roo...] [2012-05-08 00:52:59] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Chimney Swift Roo...] [2012-05-08 01:12:12] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Fixed 5-7-2012] [2012-05-08 04:26:12] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Full Model] [2012-06-06 13:44:04] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Final model] [2012-06-06 13:46:11] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Final Chimney Swi...] [2012-06-08 15:27:31] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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1217 1210 31.00 48 961.00 2304 19.00 30.00 1488.00 10 0
1202 1209 34.40 38 1183.36 1444 18.30 29.95 1307.20 10 0
1180 1207 35.60 37 1267.36 1369 18.90 29.94 1317.20 10 0
1167 1206 32.80 48 1075.84 2304 20.60 29.83 1574.40 10 0
1186 1204 23.30 81 542.89 6561 20.00 29.85 1887.30 9 1
1168 1201 20.00 58 400.00 3364 11.76 29.92 1160.00 10 1
1142 1199 16.70 93 278.89 8649 15.60 29.95 1553.10 6 1
1147 1198 17.80 86 316.84 7396 15.60 29.94 1530.80 10 0
1183 1196 21.20 68 449.44 4624 15.80 29.94 1441.60 10 0
1149 1195 23.90 68 571.21 4624 17.80 30.00 1625.20 10 0
1197 1193 28.80 68 829.44 4624 16.70 30.03 1958.40 10 0
1210 1191 25.60 59 655.36 3481 17.20 29.99 1510.40 10 0
1206 1190 29.40 43 864.36 1849 15.60 29.89 1264.20 10 0
1196 1188 22.80 59 519.84 3481 14.40 29.98 1345.20 6 0
1190 1187 16.10 31 259.21 961 -0.60 30.26 499.10 10 0
1175 1185 16.10 49 259.21 2401 5.60 30.26 788.90 10 0
1186 1183 20.00 52 400.00 2704 10.08 30.23 1040.00 10 0
1172 1182 20.60 75 424.36 5625 16.10 30.16 1545.00 10 0
1152 1185 18.30 90 334.89 8100 16.70 30.00 1647.00 10 1
1154 1179 21.60 86 466.56 7396 18.30 30.60 1857.60 8 1
1168 1177 22.80 87 519.84 7569 20.60 30.00 1983.60 10 0
1180 1175 22.80 47 519.84 2209 11.10 30.06 1071.60 10 0
1169 1174 17.20 70 295.84 4900 11.70 30.01 1204.00 10 0
1166 1170 22.20 61 492.84 3721 14.40 29.86 1354.20 10 0
1177 1169 20.60 48 424.36 2304 9.40 29.82 988.80 10 0
1168 1167 18.30 67 334.89 4489 12.20 29.83 1226.10 10 0
1160 1166 16.70 74 278.89 5476 12.20 29.83 1235.80 10 0
1147 1164 22.80 55 519.84 3025 13.30 29.71 1254.00 10 1
1161 1162 13.90 47 193.21 2209 2.80 29.98 653.30 10 0
1143 1161 10.00 65 100.00 4225 3.90 30.18 650.00 10 0
1161 1159 16.10 28 259.21 784 -2.20 30.88 450.80 10 0
1161 1158 20.60 30 424.36 900 5.00 30.13 618.00 10 0
1168 1156 19.40 67 376.36 4489 13.30 30.24 1299.80 10 0
1172 1155 25.60 32 655.36 1024 7.80 30.24 819.20 10 0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&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]4 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=163722&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&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	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Time[t] = + 334.098988484479 + 0.518791686932565Sunset[t] + 6.68021081402233T[t] + 1.480973757804H[t] -0.106900150895852`T^2`[t] -0.0140914830828158`H^2`[t] -2.03629874989499Dewpoint[t] + 4.19325585866879Pressure[t] + 0.0163565845795424TxH[t] -1.83300479004516Visibility[t] -13.740393697781Rain[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time[t] =  +  334.098988484479 +  0.518791686932565Sunset[t] +  6.68021081402233T[t] +  1.480973757804H[t] -0.106900150895852`T^2`[t] -0.0140914830828158`H^2`[t] -2.03629874989499Dewpoint[t] +  4.19325585866879Pressure[t] +  0.0163565845795424TxH[t] -1.83300479004516Visibility[t] -13.740393697781Rain[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time[t] =  +  334.098988484479 +  0.518791686932565Sunset[t] +  6.68021081402233T[t] +  1.480973757804H[t] -0.106900150895852`T^2`[t] -0.0140914830828158`H^2`[t] -2.03629874989499Dewpoint[t] +  4.19325585866879Pressure[t] +  0.0163565845795424TxH[t] -1.83300479004516Visibility[t] -13.740393697781Rain[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Time[t] = + 334.098988484479 + 0.518791686932565Sunset[t] + 6.68021081402233T[t] + 1.480973757804H[t] -0.106900150895852`T^2`[t] -0.0140914830828158`H^2`[t] -2.03629874989499Dewpoint[t] + 4.19325585866879Pressure[t] + 0.0163565845795424TxH[t] -1.83300479004516Visibility[t] -13.740393697781Rain[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	334.098988484479	618.211333	0.5404	0.594097	0.297048
Sunset	0.518791686932565	0.224312	2.3128	0.030027	0.015013
T	6.68021081402233	7.470964	0.8942	0.3805	0.19025
H	1.480973757804	2.279789	0.6496	0.52238	0.26119
`T^2`	-0.106900150895852	0.090168	-1.1856	0.247897	0.123949
`H^2`	-0.0140914830828158	0.010065	-1.4	0.17486	0.08743
Dewpoint	-2.03629874989499	2.977672	-0.6839	0.500899	0.250449
Pressure	4.19325585866879	15.017784	0.2792	0.782572	0.391286
TxH	0.0163565845795424	0.048117	0.3399	0.736991	0.368496
Visibility	-1.83300479004516	2.893244	-0.6335	0.532627	0.266313
Rain	-13.740393697781	8.028928	-1.7114	0.100469	0.050235

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 334.098988484479 & 618.211333 & 0.5404 & 0.594097 & 0.297048 \tabularnewline
Sunset & 0.518791686932565 & 0.224312 & 2.3128 & 0.030027 & 0.015013 \tabularnewline
T & 6.68021081402233 & 7.470964 & 0.8942 & 0.3805 & 0.19025 \tabularnewline
H & 1.480973757804 & 2.279789 & 0.6496 & 0.52238 & 0.26119 \tabularnewline
`T^2` & -0.106900150895852 & 0.090168 & -1.1856 & 0.247897 & 0.123949 \tabularnewline
`H^2` & -0.0140914830828158 & 0.010065 & -1.4 & 0.17486 & 0.08743 \tabularnewline
Dewpoint & -2.03629874989499 & 2.977672 & -0.6839 & 0.500899 & 0.250449 \tabularnewline
Pressure & 4.19325585866879 & 15.017784 & 0.2792 & 0.782572 & 0.391286 \tabularnewline
TxH & 0.0163565845795424 & 0.048117 & 0.3399 & 0.736991 & 0.368496 \tabularnewline
Visibility & -1.83300479004516 & 2.893244 & -0.6335 & 0.532627 & 0.266313 \tabularnewline
Rain & -13.740393697781 & 8.028928 & -1.7114 & 0.100469 & 0.050235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]334.098988484479[/C][C]618.211333[/C][C]0.5404[/C][C]0.594097[/C][C]0.297048[/C][/ROW]
[ROW][C]Sunset[/C][C]0.518791686932565[/C][C]0.224312[/C][C]2.3128[/C][C]0.030027[/C][C]0.015013[/C][/ROW]
[ROW][C]T[/C][C]6.68021081402233[/C][C]7.470964[/C][C]0.8942[/C][C]0.3805[/C][C]0.19025[/C][/ROW]
[ROW][C]H[/C][C]1.480973757804[/C][C]2.279789[/C][C]0.6496[/C][C]0.52238[/C][C]0.26119[/C][/ROW]
[ROW][C]`T^2`[/C][C]-0.106900150895852[/C][C]0.090168[/C][C]-1.1856[/C][C]0.247897[/C][C]0.123949[/C][/ROW]
[ROW][C]`H^2`[/C][C]-0.0140914830828158[/C][C]0.010065[/C][C]-1.4[/C][C]0.17486[/C][C]0.08743[/C][/ROW]
[ROW][C]Dewpoint[/C][C]-2.03629874989499[/C][C]2.977672[/C][C]-0.6839[/C][C]0.500899[/C][C]0.250449[/C][/ROW]
[ROW][C]Pressure[/C][C]4.19325585866879[/C][C]15.017784[/C][C]0.2792[/C][C]0.782572[/C][C]0.391286[/C][/ROW]
[ROW][C]TxH[/C][C]0.0163565845795424[/C][C]0.048117[/C][C]0.3399[/C][C]0.736991[/C][C]0.368496[/C][/ROW]
[ROW][C]Visibility[/C][C]-1.83300479004516[/C][C]2.893244[/C][C]-0.6335[/C][C]0.532627[/C][C]0.266313[/C][/ROW]
[ROW][C]Rain[/C][C]-13.740393697781[/C][C]8.028928[/C][C]-1.7114[/C][C]0.100469[/C][C]0.050235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	334.098988484479	618.211333	0.5404	0.594097	0.297048
Sunset	0.518791686932565	0.224312	2.3128	0.030027	0.015013
T	6.68021081402233	7.470964	0.8942	0.3805	0.19025
H	1.480973757804	2.279789	0.6496	0.52238	0.26119
`T^2`	-0.106900150895852	0.090168	-1.1856	0.247897	0.123949
`H^2`	-0.0140914830828158	0.010065	-1.4	0.17486	0.08743
Dewpoint	-2.03629874989499	2.977672	-0.6839	0.500899	0.250449
Pressure	4.19325585866879	15.017784	0.2792	0.782572	0.391286
TxH	0.0163565845795424	0.048117	0.3399	0.736991	0.368496
Visibility	-1.83300479004516	2.893244	-0.6335	0.532627	0.266313
Rain	-13.740393697781	8.028928	-1.7114	0.100469	0.050235

Multiple Linear Regression - Regression Statistics
Multiple R	0.789376487884753
R-squared	0.623115239625268
Adjusted R-squared	0.459252300331906
F-TEST (value)	3.80266118935967
F-TEST (DF numerator)	10
F-TEST (DF denominator)	23
p-value	0.00389413488510493
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.3574945527332
Sum Squared Residuals	4741.16594613057

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.789376487884753 \tabularnewline
R-squared & 0.623115239625268 \tabularnewline
Adjusted R-squared & 0.459252300331906 \tabularnewline
F-TEST (value) & 3.80266118935967 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 23 \tabularnewline
p-value & 0.00389413488510493 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.3574945527332 \tabularnewline
Sum Squared Residuals & 4741.16594613057 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.789376487884753[/C][/ROW]
[ROW][C]R-squared[/C][C]0.623115239625268[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.459252300331906[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.80266118935967[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]23[/C][/ROW]
[ROW][C]p-value[/C][C]0.00389413488510493[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.3574945527332[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4741.16594613057[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.789376487884753
R-squared	0.623115239625268
Adjusted R-squared	0.459252300331906
F-TEST (value)	3.80266118935967
F-TEST (DF numerator)	10
F-TEST (DF denominator)	23
p-value	0.00389413488510493
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.3574945527332
Sum Squared Residuals	4741.16594613057

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1217	1197.92893271441	19.0710672855877
2	1202	1191.91995395515	10.080046044853
3	1180	1188.39475239354	-8.39475239353605
4	1167	1193.04400951491	-26.044009514909
5	1186	1168.91778012607	17.0822198739253
6	1168	1174.92322778128	-6.92322778127705
7	1142	1148.21642246116	-6.21642246115539
8	1147	1164.28050408418	-17.2805040841799
9	1183	1182.31847383982	0.681526160180086
10	1149	1186.00108675669	-37.0010867566938
11	1197	1197.90745068825	-0.907450688250221
12	1210	1188.36654281996	21.6334571800432
13	1206	1189.00390224585	16.9960977541508
14	1196	1192.88230125806	3.11769874193793
15	1190	1180.05802311127	9.94197688872947
16	1175	1177.50131770043	-2.50131770042502
17	1186	1182.49800846857	3.50199153142783
18	1172	1171.99245876701	0.00754123298872701
19	1152	1141.12216893531	10.8778310646913
20	1154	1156.34366249853	-2.34366249852765
21	1168	1161.60571238099	6.39428761901156
22	1180	1181.53875635784	-1.53875635783599
23	1169	1164.43280312214	4.56719687785506
24	1166	1174.31421945278	-8.3142194527777
25	1177	1175.17965298342	1.82034701658116
26	1168	1163.91226567988	4.08773432011619
27	1160	1155.30872651299	4.6912734870064
28	1147	1159.47673819766	-12.4767381976565
29	1161	1159.98124429584	1.01875570416145
30	1143	1140.16763706247	2.83236293752661
31	1161	1168.65100070642	-7.65100070641982
32	1161	1166.79446128859	-5.79446128859303
33	1168	1161.80542632208	6.19457367791888
34	1172	1173.21037551646	-1.21037551645543

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1217 & 1197.92893271441 & 19.0710672855877 \tabularnewline
2 & 1202 & 1191.91995395515 & 10.080046044853 \tabularnewline
3 & 1180 & 1188.39475239354 & -8.39475239353605 \tabularnewline
4 & 1167 & 1193.04400951491 & -26.044009514909 \tabularnewline
5 & 1186 & 1168.91778012607 & 17.0822198739253 \tabularnewline
6 & 1168 & 1174.92322778128 & -6.92322778127705 \tabularnewline
7 & 1142 & 1148.21642246116 & -6.21642246115539 \tabularnewline
8 & 1147 & 1164.28050408418 & -17.2805040841799 \tabularnewline
9 & 1183 & 1182.31847383982 & 0.681526160180086 \tabularnewline
10 & 1149 & 1186.00108675669 & -37.0010867566938 \tabularnewline
11 & 1197 & 1197.90745068825 & -0.907450688250221 \tabularnewline
12 & 1210 & 1188.36654281996 & 21.6334571800432 \tabularnewline
13 & 1206 & 1189.00390224585 & 16.9960977541508 \tabularnewline
14 & 1196 & 1192.88230125806 & 3.11769874193793 \tabularnewline
15 & 1190 & 1180.05802311127 & 9.94197688872947 \tabularnewline
16 & 1175 & 1177.50131770043 & -2.50131770042502 \tabularnewline
17 & 1186 & 1182.49800846857 & 3.50199153142783 \tabularnewline
18 & 1172 & 1171.99245876701 & 0.00754123298872701 \tabularnewline
19 & 1152 & 1141.12216893531 & 10.8778310646913 \tabularnewline
20 & 1154 & 1156.34366249853 & -2.34366249852765 \tabularnewline
21 & 1168 & 1161.60571238099 & 6.39428761901156 \tabularnewline
22 & 1180 & 1181.53875635784 & -1.53875635783599 \tabularnewline
23 & 1169 & 1164.43280312214 & 4.56719687785506 \tabularnewline
24 & 1166 & 1174.31421945278 & -8.3142194527777 \tabularnewline
25 & 1177 & 1175.17965298342 & 1.82034701658116 \tabularnewline
26 & 1168 & 1163.91226567988 & 4.08773432011619 \tabularnewline
27 & 1160 & 1155.30872651299 & 4.6912734870064 \tabularnewline
28 & 1147 & 1159.47673819766 & -12.4767381976565 \tabularnewline
29 & 1161 & 1159.98124429584 & 1.01875570416145 \tabularnewline
30 & 1143 & 1140.16763706247 & 2.83236293752661 \tabularnewline
31 & 1161 & 1168.65100070642 & -7.65100070641982 \tabularnewline
32 & 1161 & 1166.79446128859 & -5.79446128859303 \tabularnewline
33 & 1168 & 1161.80542632208 & 6.19457367791888 \tabularnewline
34 & 1172 & 1173.21037551646 & -1.21037551645543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1217[/C][C]1197.92893271441[/C][C]19.0710672855877[/C][/ROW]
[ROW][C]2[/C][C]1202[/C][C]1191.91995395515[/C][C]10.080046044853[/C][/ROW]
[ROW][C]3[/C][C]1180[/C][C]1188.39475239354[/C][C]-8.39475239353605[/C][/ROW]
[ROW][C]4[/C][C]1167[/C][C]1193.04400951491[/C][C]-26.044009514909[/C][/ROW]
[ROW][C]5[/C][C]1186[/C][C]1168.91778012607[/C][C]17.0822198739253[/C][/ROW]
[ROW][C]6[/C][C]1168[/C][C]1174.92322778128[/C][C]-6.92322778127705[/C][/ROW]
[ROW][C]7[/C][C]1142[/C][C]1148.21642246116[/C][C]-6.21642246115539[/C][/ROW]
[ROW][C]8[/C][C]1147[/C][C]1164.28050408418[/C][C]-17.2805040841799[/C][/ROW]
[ROW][C]9[/C][C]1183[/C][C]1182.31847383982[/C][C]0.681526160180086[/C][/ROW]
[ROW][C]10[/C][C]1149[/C][C]1186.00108675669[/C][C]-37.0010867566938[/C][/ROW]
[ROW][C]11[/C][C]1197[/C][C]1197.90745068825[/C][C]-0.907450688250221[/C][/ROW]
[ROW][C]12[/C][C]1210[/C][C]1188.36654281996[/C][C]21.6334571800432[/C][/ROW]
[ROW][C]13[/C][C]1206[/C][C]1189.00390224585[/C][C]16.9960977541508[/C][/ROW]
[ROW][C]14[/C][C]1196[/C][C]1192.88230125806[/C][C]3.11769874193793[/C][/ROW]
[ROW][C]15[/C][C]1190[/C][C]1180.05802311127[/C][C]9.94197688872947[/C][/ROW]
[ROW][C]16[/C][C]1175[/C][C]1177.50131770043[/C][C]-2.50131770042502[/C][/ROW]
[ROW][C]17[/C][C]1186[/C][C]1182.49800846857[/C][C]3.50199153142783[/C][/ROW]
[ROW][C]18[/C][C]1172[/C][C]1171.99245876701[/C][C]0.00754123298872701[/C][/ROW]
[ROW][C]19[/C][C]1152[/C][C]1141.12216893531[/C][C]10.8778310646913[/C][/ROW]
[ROW][C]20[/C][C]1154[/C][C]1156.34366249853[/C][C]-2.34366249852765[/C][/ROW]
[ROW][C]21[/C][C]1168[/C][C]1161.60571238099[/C][C]6.39428761901156[/C][/ROW]
[ROW][C]22[/C][C]1180[/C][C]1181.53875635784[/C][C]-1.53875635783599[/C][/ROW]
[ROW][C]23[/C][C]1169[/C][C]1164.43280312214[/C][C]4.56719687785506[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1174.31421945278[/C][C]-8.3142194527777[/C][/ROW]
[ROW][C]25[/C][C]1177[/C][C]1175.17965298342[/C][C]1.82034701658116[/C][/ROW]
[ROW][C]26[/C][C]1168[/C][C]1163.91226567988[/C][C]4.08773432011619[/C][/ROW]
[ROW][C]27[/C][C]1160[/C][C]1155.30872651299[/C][C]4.6912734870064[/C][/ROW]
[ROW][C]28[/C][C]1147[/C][C]1159.47673819766[/C][C]-12.4767381976565[/C][/ROW]
[ROW][C]29[/C][C]1161[/C][C]1159.98124429584[/C][C]1.01875570416145[/C][/ROW]
[ROW][C]30[/C][C]1143[/C][C]1140.16763706247[/C][C]2.83236293752661[/C][/ROW]
[ROW][C]31[/C][C]1161[/C][C]1168.65100070642[/C][C]-7.65100070641982[/C][/ROW]
[ROW][C]32[/C][C]1161[/C][C]1166.79446128859[/C][C]-5.79446128859303[/C][/ROW]
[ROW][C]33[/C][C]1168[/C][C]1161.80542632208[/C][C]6.19457367791888[/C][/ROW]
[ROW][C]34[/C][C]1172[/C][C]1173.21037551646[/C][C]-1.21037551645543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1217	1197.92893271441	19.0710672855877
2	1202	1191.91995395515	10.080046044853
3	1180	1188.39475239354	-8.39475239353605
4	1167	1193.04400951491	-26.044009514909
5	1186	1168.91778012607	17.0822198739253
6	1168	1174.92322778128	-6.92322778127705
7	1142	1148.21642246116	-6.21642246115539
8	1147	1164.28050408418	-17.2805040841799
9	1183	1182.31847383982	0.681526160180086
10	1149	1186.00108675669	-37.0010867566938
11	1197	1197.90745068825	-0.907450688250221
12	1210	1188.36654281996	21.6334571800432
13	1206	1189.00390224585	16.9960977541508
14	1196	1192.88230125806	3.11769874193793
15	1190	1180.05802311127	9.94197688872947
16	1175	1177.50131770043	-2.50131770042502
17	1186	1182.49800846857	3.50199153142783
18	1172	1171.99245876701	0.00754123298872701
19	1152	1141.12216893531	10.8778310646913
20	1154	1156.34366249853	-2.34366249852765
21	1168	1161.60571238099	6.39428761901156
22	1180	1181.53875635784	-1.53875635783599
23	1169	1164.43280312214	4.56719687785506
24	1166	1174.31421945278	-8.3142194527777
25	1177	1175.17965298342	1.82034701658116
26	1168	1163.91226567988	4.08773432011619
27	1160	1155.30872651299	4.6912734870064
28	1147	1159.47673819766	-12.4767381976565
29	1161	1159.98124429584	1.01875570416145
30	1143	1140.16763706247	2.83236293752661
31	1161	1168.65100070642	-7.65100070641982
32	1161	1166.79446128859	-5.79446128859303
33	1168	1161.80542632208	6.19457367791888
34	1172	1173.21037551646	-1.21037551645543

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.999865470157484	0.000269059685031802	0.000134529842515901
15	0.999780642249719	0.000438715500561694	0.000219357750280847
16	0.999372767479902	0.0012544650401959	0.000627232520097948
17	0.998377100529479	0.00324579894104253	0.00162289947052127
18	0.993899849581766	0.012200300836468	0.00610015041823399
19	0.983535330022557	0.0329293399548853	0.0164646699774427
20	0.95080878004846	0.098382439903079	0.0491912199515395

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.999865470157484 & 0.000269059685031802 & 0.000134529842515901 \tabularnewline
15 & 0.999780642249719 & 0.000438715500561694 & 0.000219357750280847 \tabularnewline
16 & 0.999372767479902 & 0.0012544650401959 & 0.000627232520097948 \tabularnewline
17 & 0.998377100529479 & 0.00324579894104253 & 0.00162289947052127 \tabularnewline
18 & 0.993899849581766 & 0.012200300836468 & 0.00610015041823399 \tabularnewline
19 & 0.983535330022557 & 0.0329293399548853 & 0.0164646699774427 \tabularnewline
20 & 0.95080878004846 & 0.098382439903079 & 0.0491912199515395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]14[/C][C]0.999865470157484[/C][C]0.000269059685031802[/C][C]0.000134529842515901[/C][/ROW]
[ROW][C]15[/C][C]0.999780642249719[/C][C]0.000438715500561694[/C][C]0.000219357750280847[/C][/ROW]
[ROW][C]16[/C][C]0.999372767479902[/C][C]0.0012544650401959[/C][C]0.000627232520097948[/C][/ROW]
[ROW][C]17[/C][C]0.998377100529479[/C][C]0.00324579894104253[/C][C]0.00162289947052127[/C][/ROW]
[ROW][C]18[/C][C]0.993899849581766[/C][C]0.012200300836468[/C][C]0.00610015041823399[/C][/ROW]
[ROW][C]19[/C][C]0.983535330022557[/C][C]0.0329293399548853[/C][C]0.0164646699774427[/C][/ROW]
[ROW][C]20[/C][C]0.95080878004846[/C][C]0.098382439903079[/C][C]0.0491912199515395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.999865470157484	0.000269059685031802	0.000134529842515901
15	0.999780642249719	0.000438715500561694	0.000219357750280847
16	0.999372767479902	0.0012544650401959	0.000627232520097948
17	0.998377100529479	0.00324579894104253	0.00162289947052127
18	0.993899849581766	0.012200300836468	0.00610015041823399
19	0.983535330022557	0.0329293399548853	0.0164646699774427
20	0.95080878004846	0.098382439903079	0.0491912199515395

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.571428571428571	NOK
5% type I error level	6	0.857142857142857	NOK
10% type I error level	7	1	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 4 & 0.571428571428571 & NOK \tabularnewline
5% type I error level & 6 & 0.857142857142857 & NOK \tabularnewline
10% type I error level & 7 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163722&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]4[/C][C]0.571428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163722&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163722&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.571428571428571	NOK
5% type I error level	6	0.857142857142857	NOK
10% type I error level	7	1	NOK

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code