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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:03:57 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258740280pn3rdt3crnj0chd.htm/, Retrieved Fri, 29 Mar 2024 12:46:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58379, Retrieved Fri, 29 Mar 2024 12:46:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2009-11-17 17:31:01] [78d53abea600e0825abda35dbfc51d4c]
- R  D    [Multiple Regression] [] [2009-11-20 18:03:57] [18c0746232b29e9668aa6bedcb8dd698] [Current]
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Dataseries X:
12.6	18
15.7	16
13.2	19
20.3	18
12.8	23
8	20
0.9	20
3.6	15
14.1	17
21.7	16
24.5	15
18.9	10
13.9	13
11	10
5.8	19
15.5	21
22.4	17
31.7	16
30.3	17
31.4	14
20.2	18
19.7	17
10.8	14
13.2	15
15.1	16
15.6	11
15.5	15
12.7	13
10.9	17
10	16
9.1	9
10.3	17
16.9	15
22	12
27.6	12
28.9	12
31	12
32.9	4
38.1	7
28.8	4
29	3
21.8	3
28.8	0
25.6	5
28.2	3
20.2	4
17.9	3
16.3	10
13.2	4
8.1	1
4.5	1
-0.1	8
0	5
2.3	4
2.8	0
2.9	2
0.1	7
3.5	6
8.6	9
13.8	10




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Rvnp[t] = + 18.4525616663621 -0.0204001461721176Svdg[t] -1.03551982459347M1[t] -1.62120043851635M2[t] -2.78367988306231M3[t] -2.75143979535904M4[t] -3.16735976612461M5[t] -3.45183994153115M6[t] -3.88488032157866M7[t] -3.47632011693769M8[t] -2.30775991229673M9[t] -0.808160058468847M10[t] -0.356320116937693M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rvnp[t] =  +  18.4525616663621 -0.0204001461721176Svdg[t] -1.03551982459347M1[t] -1.62120043851635M2[t] -2.78367988306231M3[t] -2.75143979535904M4[t] -3.16735976612461M5[t] -3.45183994153115M6[t] -3.88488032157866M7[t] -3.47632011693769M8[t] -2.30775991229673M9[t] -0.808160058468847M10[t] -0.356320116937693M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rvnp[t] =  +  18.4525616663621 -0.0204001461721176Svdg[t] -1.03551982459347M1[t] -1.62120043851635M2[t] -2.78367988306231M3[t] -2.75143979535904M4[t] -3.16735976612461M5[t] -3.45183994153115M6[t] -3.88488032157866M7[t] -3.47632011693769M8[t] -2.30775991229673M9[t] -0.808160058468847M10[t] -0.356320116937693M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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
Rvnp[t] = + 18.4525616663621 -0.0204001461721176Svdg[t] -1.03551982459347M1[t] -1.62120043851635M2[t] -2.78367988306231M3[t] -2.75143979535904M4[t] -3.16735976612461M5[t] -3.45183994153115M6[t] -3.88488032157866M7[t] -3.47632011693769M8[t] -2.30775991229673M9[t] -0.808160058468847M10[t] -0.356320116937693M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.45256166636215.4813373.36640.0015260.000763
Svdg-0.02040014617211760.230032-0.08870.929710.464855
M1-1.035519824593476.812685-0.1520.8798390.439919
M2-1.621200438516356.841982-0.23690.8137260.406863
M3-2.783679883062316.809578-0.40880.6845510.342276
M4-2.751439795359046.814705-0.40380.6882270.344114
M5-3.167359766124616.817034-0.46460.6443460.322173
M6-3.451839941531156.807713-0.5070.6144920.307246
M7-3.884880321578666.825877-0.56910.5719710.285986
M8-3.476320116937696.809578-0.51050.6120880.306044
M9-2.307759912296736.80849-0.3390.7361550.368077
M10-0.8081600584688476.807713-0.11870.9060090.453005
M11-0.3563201169376936.809578-0.05230.958490.479245

\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) & 18.4525616663621 & 5.481337 & 3.3664 & 0.001526 & 0.000763 \tabularnewline
Svdg & -0.0204001461721176 & 0.230032 & -0.0887 & 0.92971 & 0.464855 \tabularnewline
M1 & -1.03551982459347 & 6.812685 & -0.152 & 0.879839 & 0.439919 \tabularnewline
M2 & -1.62120043851635 & 6.841982 & -0.2369 & 0.813726 & 0.406863 \tabularnewline
M3 & -2.78367988306231 & 6.809578 & -0.4088 & 0.684551 & 0.342276 \tabularnewline
M4 & -2.75143979535904 & 6.814705 & -0.4038 & 0.688227 & 0.344114 \tabularnewline
M5 & -3.16735976612461 & 6.817034 & -0.4646 & 0.644346 & 0.322173 \tabularnewline
M6 & -3.45183994153115 & 6.807713 & -0.507 & 0.614492 & 0.307246 \tabularnewline
M7 & -3.88488032157866 & 6.825877 & -0.5691 & 0.571971 & 0.285986 \tabularnewline
M8 & -3.47632011693769 & 6.809578 & -0.5105 & 0.612088 & 0.306044 \tabularnewline
M9 & -2.30775991229673 & 6.80849 & -0.339 & 0.736155 & 0.368077 \tabularnewline
M10 & -0.808160058468847 & 6.807713 & -0.1187 & 0.906009 & 0.453005 \tabularnewline
M11 & -0.356320116937693 & 6.809578 & -0.0523 & 0.95849 & 0.479245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&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]18.4525616663621[/C][C]5.481337[/C][C]3.3664[/C][C]0.001526[/C][C]0.000763[/C][/ROW]
[ROW][C]Svdg[/C][C]-0.0204001461721176[/C][C]0.230032[/C][C]-0.0887[/C][C]0.92971[/C][C]0.464855[/C][/ROW]
[ROW][C]M1[/C][C]-1.03551982459347[/C][C]6.812685[/C][C]-0.152[/C][C]0.879839[/C][C]0.439919[/C][/ROW]
[ROW][C]M2[/C][C]-1.62120043851635[/C][C]6.841982[/C][C]-0.2369[/C][C]0.813726[/C][C]0.406863[/C][/ROW]
[ROW][C]M3[/C][C]-2.78367988306231[/C][C]6.809578[/C][C]-0.4088[/C][C]0.684551[/C][C]0.342276[/C][/ROW]
[ROW][C]M4[/C][C]-2.75143979535904[/C][C]6.814705[/C][C]-0.4038[/C][C]0.688227[/C][C]0.344114[/C][/ROW]
[ROW][C]M5[/C][C]-3.16735976612461[/C][C]6.817034[/C][C]-0.4646[/C][C]0.644346[/C][C]0.322173[/C][/ROW]
[ROW][C]M6[/C][C]-3.45183994153115[/C][C]6.807713[/C][C]-0.507[/C][C]0.614492[/C][C]0.307246[/C][/ROW]
[ROW][C]M7[/C][C]-3.88488032157866[/C][C]6.825877[/C][C]-0.5691[/C][C]0.571971[/C][C]0.285986[/C][/ROW]
[ROW][C]M8[/C][C]-3.47632011693769[/C][C]6.809578[/C][C]-0.5105[/C][C]0.612088[/C][C]0.306044[/C][/ROW]
[ROW][C]M9[/C][C]-2.30775991229673[/C][C]6.80849[/C][C]-0.339[/C][C]0.736155[/C][C]0.368077[/C][/ROW]
[ROW][C]M10[/C][C]-0.808160058468847[/C][C]6.807713[/C][C]-0.1187[/C][C]0.906009[/C][C]0.453005[/C][/ROW]
[ROW][C]M11[/C][C]-0.356320116937693[/C][C]6.809578[/C][C]-0.0523[/C][C]0.95849[/C][C]0.479245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.45256166636215.4813373.36640.0015260.000763
Svdg-0.02040014617211760.230032-0.08870.929710.464855
M1-1.035519824593476.812685-0.1520.8798390.439919
M2-1.621200438516356.841982-0.23690.8137260.406863
M3-2.783679883062316.809578-0.40880.6845510.342276
M4-2.751439795359046.814705-0.40380.6882270.344114
M5-3.167359766124616.817034-0.46460.6443460.322173
M6-3.451839941531156.807713-0.5070.6144920.307246
M7-3.884880321578666.825877-0.56910.5719710.285986
M8-3.476320116937696.809578-0.51050.6120880.306044
M9-2.307759912296736.80849-0.3390.7361550.368077
M10-0.8081600584688476.807713-0.11870.9060090.453005
M11-0.3563201169376936.809578-0.05230.958490.479245







Multiple Linear Regression - Regression Statistics
Multiple R0.133224252760056
R-squared0.0177487015234754
Adjusted R-squared-0.233038864044999
F-TEST (value)0.0707718561853072
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.999990611475983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.7629555079151
Sum Squared Residuals5444.53692947195

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.133224252760056 \tabularnewline
R-squared & 0.0177487015234754 \tabularnewline
Adjusted R-squared & -0.233038864044999 \tabularnewline
F-TEST (value) & 0.0707718561853072 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.999990611475983 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.7629555079151 \tabularnewline
Sum Squared Residuals & 5444.53692947195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.133224252760056[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0177487015234754[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.233038864044999[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.0707718561853072[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.999990611475983[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.7629555079151[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5444.53692947195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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 R0.133224252760056
R-squared0.0177487015234754
Adjusted R-squared-0.233038864044999
F-TEST (value)0.0707718561853072
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.999990611475983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.7629555079151
Sum Squared Residuals5444.53692947195







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.617.0498392106706-4.44983921067059
215.716.5049588890919-0.804958889091902
313.215.2812790060296-2.0812790060296
420.315.3339192399054.96608076009501
512.814.8159985382788-2.01599853827882
6814.5927188013886-6.59271880138864
70.914.1596784213411-13.2596784213411
83.614.6702393568427-11.0702393568427
914.115.7979992691394-1.69799926913941
1021.717.31799926913944.38200073086059
1124.517.79023935684276.70976064315731
1218.918.24856020464100.651439795359037
1313.917.1518399415311-3.25183994153114
141116.6273597661246-5.62735976612461
155.815.2812790060296-9.4812790060296
1615.515.27271880138860.227281198611366
1722.414.93839941531157.46160058468847
1831.714.674319386077117.0256806139229
1930.314.220878859857516.0791211401425
2031.414.690639503014816.7093604969852
2120.215.77759912296734.42240087703271
2219.717.29759912296732.40240087703271
2310.817.8106395030148-7.0106395030148
2413.218.1465594737804-4.94655947378038
2515.117.0906395030148-1.99063950301479
2615.616.6069596199525-1.00695961995249
2715.515.36287959071810.137120409281927
2812.715.4359199707656-2.73591997076558
2910.914.9383994153115-4.03839941531153
301014.6743193860771-4.6743193860771
319.114.3840800292344-5.28408002923442
3210.314.6294390644984-4.32943906449844
3316.915.83879956148361.06120043851635
342217.39959985382794.60040014617212
3527.617.85143979535909.74856020464096
3628.918.207759912296710.6922400877033
373117.172240087703313.8277599122967
3832.916.749760643157316.1502393568427
3938.115.526080760095022.573919239905
4028.815.619521286314613.1804787136854
412915.224001461721213.7759985382788
4221.814.93952128631466.86047871368536
4328.814.567681344783514.2323186552165
4425.614.874240818563910.7257591814361
4528.216.083601315549112.1163986844509
4620.217.56280102320482.63719897679517
4717.918.0350411109081-0.135041110908095
4816.318.2485602046410-1.94856020464096
4913.217.3354412570802-4.1354412570802
508.116.8109610816737-8.71096108167367
514.515.6484816371277-11.1484816371277
52-0.115.5379207016262-15.6379207016262
53015.1832011693770-15.1832011693770
542.314.9191211401425-12.6191211401425
552.814.5676813447835-11.7676813447835
562.914.9354412570802-12.0354412570802
570.116.0020007308606-15.9020007308606
583.517.5220007308606-14.0220007308606
598.617.9126402338754-9.31264023387539
6013.818.2485602046410-4.44856020464096

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.6 & 17.0498392106706 & -4.44983921067059 \tabularnewline
2 & 15.7 & 16.5049588890919 & -0.804958889091902 \tabularnewline
3 & 13.2 & 15.2812790060296 & -2.0812790060296 \tabularnewline
4 & 20.3 & 15.333919239905 & 4.96608076009501 \tabularnewline
5 & 12.8 & 14.8159985382788 & -2.01599853827882 \tabularnewline
6 & 8 & 14.5927188013886 & -6.59271880138864 \tabularnewline
7 & 0.9 & 14.1596784213411 & -13.2596784213411 \tabularnewline
8 & 3.6 & 14.6702393568427 & -11.0702393568427 \tabularnewline
9 & 14.1 & 15.7979992691394 & -1.69799926913941 \tabularnewline
10 & 21.7 & 17.3179992691394 & 4.38200073086059 \tabularnewline
11 & 24.5 & 17.7902393568427 & 6.70976064315731 \tabularnewline
12 & 18.9 & 18.2485602046410 & 0.651439795359037 \tabularnewline
13 & 13.9 & 17.1518399415311 & -3.25183994153114 \tabularnewline
14 & 11 & 16.6273597661246 & -5.62735976612461 \tabularnewline
15 & 5.8 & 15.2812790060296 & -9.4812790060296 \tabularnewline
16 & 15.5 & 15.2727188013886 & 0.227281198611366 \tabularnewline
17 & 22.4 & 14.9383994153115 & 7.46160058468847 \tabularnewline
18 & 31.7 & 14.6743193860771 & 17.0256806139229 \tabularnewline
19 & 30.3 & 14.2208788598575 & 16.0791211401425 \tabularnewline
20 & 31.4 & 14.6906395030148 & 16.7093604969852 \tabularnewline
21 & 20.2 & 15.7775991229673 & 4.42240087703271 \tabularnewline
22 & 19.7 & 17.2975991229673 & 2.40240087703271 \tabularnewline
23 & 10.8 & 17.8106395030148 & -7.0106395030148 \tabularnewline
24 & 13.2 & 18.1465594737804 & -4.94655947378038 \tabularnewline
25 & 15.1 & 17.0906395030148 & -1.99063950301479 \tabularnewline
26 & 15.6 & 16.6069596199525 & -1.00695961995249 \tabularnewline
27 & 15.5 & 15.3628795907181 & 0.137120409281927 \tabularnewline
28 & 12.7 & 15.4359199707656 & -2.73591997076558 \tabularnewline
29 & 10.9 & 14.9383994153115 & -4.03839941531153 \tabularnewline
30 & 10 & 14.6743193860771 & -4.6743193860771 \tabularnewline
31 & 9.1 & 14.3840800292344 & -5.28408002923442 \tabularnewline
32 & 10.3 & 14.6294390644984 & -4.32943906449844 \tabularnewline
33 & 16.9 & 15.8387995614836 & 1.06120043851635 \tabularnewline
34 & 22 & 17.3995998538279 & 4.60040014617212 \tabularnewline
35 & 27.6 & 17.8514397953590 & 9.74856020464096 \tabularnewline
36 & 28.9 & 18.2077599122967 & 10.6922400877033 \tabularnewline
37 & 31 & 17.1722400877033 & 13.8277599122967 \tabularnewline
38 & 32.9 & 16.7497606431573 & 16.1502393568427 \tabularnewline
39 & 38.1 & 15.5260807600950 & 22.573919239905 \tabularnewline
40 & 28.8 & 15.6195212863146 & 13.1804787136854 \tabularnewline
41 & 29 & 15.2240014617212 & 13.7759985382788 \tabularnewline
42 & 21.8 & 14.9395212863146 & 6.86047871368536 \tabularnewline
43 & 28.8 & 14.5676813447835 & 14.2323186552165 \tabularnewline
44 & 25.6 & 14.8742408185639 & 10.7257591814361 \tabularnewline
45 & 28.2 & 16.0836013155491 & 12.1163986844509 \tabularnewline
46 & 20.2 & 17.5628010232048 & 2.63719897679517 \tabularnewline
47 & 17.9 & 18.0350411109081 & -0.135041110908095 \tabularnewline
48 & 16.3 & 18.2485602046410 & -1.94856020464096 \tabularnewline
49 & 13.2 & 17.3354412570802 & -4.1354412570802 \tabularnewline
50 & 8.1 & 16.8109610816737 & -8.71096108167367 \tabularnewline
51 & 4.5 & 15.6484816371277 & -11.1484816371277 \tabularnewline
52 & -0.1 & 15.5379207016262 & -15.6379207016262 \tabularnewline
53 & 0 & 15.1832011693770 & -15.1832011693770 \tabularnewline
54 & 2.3 & 14.9191211401425 & -12.6191211401425 \tabularnewline
55 & 2.8 & 14.5676813447835 & -11.7676813447835 \tabularnewline
56 & 2.9 & 14.9354412570802 & -12.0354412570802 \tabularnewline
57 & 0.1 & 16.0020007308606 & -15.9020007308606 \tabularnewline
58 & 3.5 & 17.5220007308606 & -14.0220007308606 \tabularnewline
59 & 8.6 & 17.9126402338754 & -9.31264023387539 \tabularnewline
60 & 13.8 & 18.2485602046410 & -4.44856020464096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&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]12.6[/C][C]17.0498392106706[/C][C]-4.44983921067059[/C][/ROW]
[ROW][C]2[/C][C]15.7[/C][C]16.5049588890919[/C][C]-0.804958889091902[/C][/ROW]
[ROW][C]3[/C][C]13.2[/C][C]15.2812790060296[/C][C]-2.0812790060296[/C][/ROW]
[ROW][C]4[/C][C]20.3[/C][C]15.333919239905[/C][C]4.96608076009501[/C][/ROW]
[ROW][C]5[/C][C]12.8[/C][C]14.8159985382788[/C][C]-2.01599853827882[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]14.5927188013886[/C][C]-6.59271880138864[/C][/ROW]
[ROW][C]7[/C][C]0.9[/C][C]14.1596784213411[/C][C]-13.2596784213411[/C][/ROW]
[ROW][C]8[/C][C]3.6[/C][C]14.6702393568427[/C][C]-11.0702393568427[/C][/ROW]
[ROW][C]9[/C][C]14.1[/C][C]15.7979992691394[/C][C]-1.69799926913941[/C][/ROW]
[ROW][C]10[/C][C]21.7[/C][C]17.3179992691394[/C][C]4.38200073086059[/C][/ROW]
[ROW][C]11[/C][C]24.5[/C][C]17.7902393568427[/C][C]6.70976064315731[/C][/ROW]
[ROW][C]12[/C][C]18.9[/C][C]18.2485602046410[/C][C]0.651439795359037[/C][/ROW]
[ROW][C]13[/C][C]13.9[/C][C]17.1518399415311[/C][C]-3.25183994153114[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]16.6273597661246[/C][C]-5.62735976612461[/C][/ROW]
[ROW][C]15[/C][C]5.8[/C][C]15.2812790060296[/C][C]-9.4812790060296[/C][/ROW]
[ROW][C]16[/C][C]15.5[/C][C]15.2727188013886[/C][C]0.227281198611366[/C][/ROW]
[ROW][C]17[/C][C]22.4[/C][C]14.9383994153115[/C][C]7.46160058468847[/C][/ROW]
[ROW][C]18[/C][C]31.7[/C][C]14.6743193860771[/C][C]17.0256806139229[/C][/ROW]
[ROW][C]19[/C][C]30.3[/C][C]14.2208788598575[/C][C]16.0791211401425[/C][/ROW]
[ROW][C]20[/C][C]31.4[/C][C]14.6906395030148[/C][C]16.7093604969852[/C][/ROW]
[ROW][C]21[/C][C]20.2[/C][C]15.7775991229673[/C][C]4.42240087703271[/C][/ROW]
[ROW][C]22[/C][C]19.7[/C][C]17.2975991229673[/C][C]2.40240087703271[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]17.8106395030148[/C][C]-7.0106395030148[/C][/ROW]
[ROW][C]24[/C][C]13.2[/C][C]18.1465594737804[/C][C]-4.94655947378038[/C][/ROW]
[ROW][C]25[/C][C]15.1[/C][C]17.0906395030148[/C][C]-1.99063950301479[/C][/ROW]
[ROW][C]26[/C][C]15.6[/C][C]16.6069596199525[/C][C]-1.00695961995249[/C][/ROW]
[ROW][C]27[/C][C]15.5[/C][C]15.3628795907181[/C][C]0.137120409281927[/C][/ROW]
[ROW][C]28[/C][C]12.7[/C][C]15.4359199707656[/C][C]-2.73591997076558[/C][/ROW]
[ROW][C]29[/C][C]10.9[/C][C]14.9383994153115[/C][C]-4.03839941531153[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]14.6743193860771[/C][C]-4.6743193860771[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]14.3840800292344[/C][C]-5.28408002923442[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]14.6294390644984[/C][C]-4.32943906449844[/C][/ROW]
[ROW][C]33[/C][C]16.9[/C][C]15.8387995614836[/C][C]1.06120043851635[/C][/ROW]
[ROW][C]34[/C][C]22[/C][C]17.3995998538279[/C][C]4.60040014617212[/C][/ROW]
[ROW][C]35[/C][C]27.6[/C][C]17.8514397953590[/C][C]9.74856020464096[/C][/ROW]
[ROW][C]36[/C][C]28.9[/C][C]18.2077599122967[/C][C]10.6922400877033[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]17.1722400877033[/C][C]13.8277599122967[/C][/ROW]
[ROW][C]38[/C][C]32.9[/C][C]16.7497606431573[/C][C]16.1502393568427[/C][/ROW]
[ROW][C]39[/C][C]38.1[/C][C]15.5260807600950[/C][C]22.573919239905[/C][/ROW]
[ROW][C]40[/C][C]28.8[/C][C]15.6195212863146[/C][C]13.1804787136854[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]15.2240014617212[/C][C]13.7759985382788[/C][/ROW]
[ROW][C]42[/C][C]21.8[/C][C]14.9395212863146[/C][C]6.86047871368536[/C][/ROW]
[ROW][C]43[/C][C]28.8[/C][C]14.5676813447835[/C][C]14.2323186552165[/C][/ROW]
[ROW][C]44[/C][C]25.6[/C][C]14.8742408185639[/C][C]10.7257591814361[/C][/ROW]
[ROW][C]45[/C][C]28.2[/C][C]16.0836013155491[/C][C]12.1163986844509[/C][/ROW]
[ROW][C]46[/C][C]20.2[/C][C]17.5628010232048[/C][C]2.63719897679517[/C][/ROW]
[ROW][C]47[/C][C]17.9[/C][C]18.0350411109081[/C][C]-0.135041110908095[/C][/ROW]
[ROW][C]48[/C][C]16.3[/C][C]18.2485602046410[/C][C]-1.94856020464096[/C][/ROW]
[ROW][C]49[/C][C]13.2[/C][C]17.3354412570802[/C][C]-4.1354412570802[/C][/ROW]
[ROW][C]50[/C][C]8.1[/C][C]16.8109610816737[/C][C]-8.71096108167367[/C][/ROW]
[ROW][C]51[/C][C]4.5[/C][C]15.6484816371277[/C][C]-11.1484816371277[/C][/ROW]
[ROW][C]52[/C][C]-0.1[/C][C]15.5379207016262[/C][C]-15.6379207016262[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]15.1832011693770[/C][C]-15.1832011693770[/C][/ROW]
[ROW][C]54[/C][C]2.3[/C][C]14.9191211401425[/C][C]-12.6191211401425[/C][/ROW]
[ROW][C]55[/C][C]2.8[/C][C]14.5676813447835[/C][C]-11.7676813447835[/C][/ROW]
[ROW][C]56[/C][C]2.9[/C][C]14.9354412570802[/C][C]-12.0354412570802[/C][/ROW]
[ROW][C]57[/C][C]0.1[/C][C]16.0020007308606[/C][C]-15.9020007308606[/C][/ROW]
[ROW][C]58[/C][C]3.5[/C][C]17.5220007308606[/C][C]-14.0220007308606[/C][/ROW]
[ROW][C]59[/C][C]8.6[/C][C]17.9126402338754[/C][C]-9.31264023387539[/C][/ROW]
[ROW][C]60[/C][C]13.8[/C][C]18.2485602046410[/C][C]-4.44856020464096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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 IndexActualsInterpolationForecastResidualsPrediction Error
112.617.0498392106706-4.44983921067059
215.716.5049588890919-0.804958889091902
313.215.2812790060296-2.0812790060296
420.315.3339192399054.96608076009501
512.814.8159985382788-2.01599853827882
6814.5927188013886-6.59271880138864
70.914.1596784213411-13.2596784213411
83.614.6702393568427-11.0702393568427
914.115.7979992691394-1.69799926913941
1021.717.31799926913944.38200073086059
1124.517.79023935684276.70976064315731
1218.918.24856020464100.651439795359037
1313.917.1518399415311-3.25183994153114
141116.6273597661246-5.62735976612461
155.815.2812790060296-9.4812790060296
1615.515.27271880138860.227281198611366
1722.414.93839941531157.46160058468847
1831.714.674319386077117.0256806139229
1930.314.220878859857516.0791211401425
2031.414.690639503014816.7093604969852
2120.215.77759912296734.42240087703271
2219.717.29759912296732.40240087703271
2310.817.8106395030148-7.0106395030148
2413.218.1465594737804-4.94655947378038
2515.117.0906395030148-1.99063950301479
2615.616.6069596199525-1.00695961995249
2715.515.36287959071810.137120409281927
2812.715.4359199707656-2.73591997076558
2910.914.9383994153115-4.03839941531153
301014.6743193860771-4.6743193860771
319.114.3840800292344-5.28408002923442
3210.314.6294390644984-4.32943906449844
3316.915.83879956148361.06120043851635
342217.39959985382794.60040014617212
3527.617.85143979535909.74856020464096
3628.918.207759912296710.6922400877033
373117.172240087703313.8277599122967
3832.916.749760643157316.1502393568427
3938.115.526080760095022.573919239905
4028.815.619521286314613.1804787136854
412915.224001461721213.7759985382788
4221.814.93952128631466.86047871368536
4328.814.567681344783514.2323186552165
4425.614.874240818563910.7257591814361
4528.216.083601315549112.1163986844509
4620.217.56280102320482.63719897679517
4717.918.0350411109081-0.135041110908095
4816.318.2485602046410-1.94856020464096
4913.217.3354412570802-4.1354412570802
508.116.8109610816737-8.71096108167367
514.515.6484816371277-11.1484816371277
52-0.115.5379207016262-15.6379207016262
53015.1832011693770-15.1832011693770
542.314.9191211401425-12.6191211401425
552.814.5676813447835-11.7676813447835
562.914.9354412570802-12.0354412570802
570.116.0020007308606-15.9020007308606
583.517.5220007308606-14.0220007308606
598.617.9126402338754-9.31264023387539
6013.818.2485602046410-4.44856020464096







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.04962335494638090.09924670989276180.95037664505362
170.03494740730560310.06989481461120620.965052592694397
180.1551437901241960.3102875802483920.844856209875804
190.3425255945190040.6850511890380080.657474405480996
200.5151166049999250.969766790000150.484883395000075
210.4193133918750390.8386267837500780.580686608124961
220.3118395498528800.6236790997057610.68816045014712
230.2877616887860720.5755233775721450.712238311213928
240.2067903576954690.4135807153909370.793209642304531
250.1427393502402270.2854787004804540.857260649759773
260.09227859678503780.1845571935700760.907721403214962
270.05740913962951780.1148182792590360.942590860370482
280.05174897129772510.1034979425954500.948251028702275
290.0375243809760950.075048761952190.962475619023905
300.02887169102736950.0577433820547390.97112830897263
310.02330696617994260.04661393235988530.976693033820057
320.01580681752669250.03161363505338500.984193182473307
330.008624663496151740.01724932699230350.991375336503848
340.004279806467425780.008559612934851560.995720193532574
350.0027381774269880.0054763548539760.997261822573012
360.002297874797399830.004595749594799660.9977021252026
370.002542411717386090.005084823434772170.997457588282614
380.003521853523492370.007043707046984740.996478146476508
390.03980249853727440.07960499707454880.960197501462726
400.03022732017672920.06045464035345850.96977267982327
410.03912869890653640.07825739781307270.960871301093464
420.03808350120716800.07616700241433610.961916498792832
430.0729296727005230.1458593454010460.927070327299477
440.3960132441996290.7920264883992570.603986755800371

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0496233549463809 & 0.0992467098927618 & 0.95037664505362 \tabularnewline
17 & 0.0349474073056031 & 0.0698948146112062 & 0.965052592694397 \tabularnewline
18 & 0.155143790124196 & 0.310287580248392 & 0.844856209875804 \tabularnewline
19 & 0.342525594519004 & 0.685051189038008 & 0.657474405480996 \tabularnewline
20 & 0.515116604999925 & 0.96976679000015 & 0.484883395000075 \tabularnewline
21 & 0.419313391875039 & 0.838626783750078 & 0.580686608124961 \tabularnewline
22 & 0.311839549852880 & 0.623679099705761 & 0.68816045014712 \tabularnewline
23 & 0.287761688786072 & 0.575523377572145 & 0.712238311213928 \tabularnewline
24 & 0.206790357695469 & 0.413580715390937 & 0.793209642304531 \tabularnewline
25 & 0.142739350240227 & 0.285478700480454 & 0.857260649759773 \tabularnewline
26 & 0.0922785967850378 & 0.184557193570076 & 0.907721403214962 \tabularnewline
27 & 0.0574091396295178 & 0.114818279259036 & 0.942590860370482 \tabularnewline
28 & 0.0517489712977251 & 0.103497942595450 & 0.948251028702275 \tabularnewline
29 & 0.037524380976095 & 0.07504876195219 & 0.962475619023905 \tabularnewline
30 & 0.0288716910273695 & 0.057743382054739 & 0.97112830897263 \tabularnewline
31 & 0.0233069661799426 & 0.0466139323598853 & 0.976693033820057 \tabularnewline
32 & 0.0158068175266925 & 0.0316136350533850 & 0.984193182473307 \tabularnewline
33 & 0.00862466349615174 & 0.0172493269923035 & 0.991375336503848 \tabularnewline
34 & 0.00427980646742578 & 0.00855961293485156 & 0.995720193532574 \tabularnewline
35 & 0.002738177426988 & 0.005476354853976 & 0.997261822573012 \tabularnewline
36 & 0.00229787479739983 & 0.00459574959479966 & 0.9977021252026 \tabularnewline
37 & 0.00254241171738609 & 0.00508482343477217 & 0.997457588282614 \tabularnewline
38 & 0.00352185352349237 & 0.00704370704698474 & 0.996478146476508 \tabularnewline
39 & 0.0398024985372744 & 0.0796049970745488 & 0.960197501462726 \tabularnewline
40 & 0.0302273201767292 & 0.0604546403534585 & 0.96977267982327 \tabularnewline
41 & 0.0391286989065364 & 0.0782573978130727 & 0.960871301093464 \tabularnewline
42 & 0.0380835012071680 & 0.0761670024143361 & 0.961916498792832 \tabularnewline
43 & 0.072929672700523 & 0.145859345401046 & 0.927070327299477 \tabularnewline
44 & 0.396013244199629 & 0.792026488399257 & 0.603986755800371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&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]16[/C][C]0.0496233549463809[/C][C]0.0992467098927618[/C][C]0.95037664505362[/C][/ROW]
[ROW][C]17[/C][C]0.0349474073056031[/C][C]0.0698948146112062[/C][C]0.965052592694397[/C][/ROW]
[ROW][C]18[/C][C]0.155143790124196[/C][C]0.310287580248392[/C][C]0.844856209875804[/C][/ROW]
[ROW][C]19[/C][C]0.342525594519004[/C][C]0.685051189038008[/C][C]0.657474405480996[/C][/ROW]
[ROW][C]20[/C][C]0.515116604999925[/C][C]0.96976679000015[/C][C]0.484883395000075[/C][/ROW]
[ROW][C]21[/C][C]0.419313391875039[/C][C]0.838626783750078[/C][C]0.580686608124961[/C][/ROW]
[ROW][C]22[/C][C]0.311839549852880[/C][C]0.623679099705761[/C][C]0.68816045014712[/C][/ROW]
[ROW][C]23[/C][C]0.287761688786072[/C][C]0.575523377572145[/C][C]0.712238311213928[/C][/ROW]
[ROW][C]24[/C][C]0.206790357695469[/C][C]0.413580715390937[/C][C]0.793209642304531[/C][/ROW]
[ROW][C]25[/C][C]0.142739350240227[/C][C]0.285478700480454[/C][C]0.857260649759773[/C][/ROW]
[ROW][C]26[/C][C]0.0922785967850378[/C][C]0.184557193570076[/C][C]0.907721403214962[/C][/ROW]
[ROW][C]27[/C][C]0.0574091396295178[/C][C]0.114818279259036[/C][C]0.942590860370482[/C][/ROW]
[ROW][C]28[/C][C]0.0517489712977251[/C][C]0.103497942595450[/C][C]0.948251028702275[/C][/ROW]
[ROW][C]29[/C][C]0.037524380976095[/C][C]0.07504876195219[/C][C]0.962475619023905[/C][/ROW]
[ROW][C]30[/C][C]0.0288716910273695[/C][C]0.057743382054739[/C][C]0.97112830897263[/C][/ROW]
[ROW][C]31[/C][C]0.0233069661799426[/C][C]0.0466139323598853[/C][C]0.976693033820057[/C][/ROW]
[ROW][C]32[/C][C]0.0158068175266925[/C][C]0.0316136350533850[/C][C]0.984193182473307[/C][/ROW]
[ROW][C]33[/C][C]0.00862466349615174[/C][C]0.0172493269923035[/C][C]0.991375336503848[/C][/ROW]
[ROW][C]34[/C][C]0.00427980646742578[/C][C]0.00855961293485156[/C][C]0.995720193532574[/C][/ROW]
[ROW][C]35[/C][C]0.002738177426988[/C][C]0.005476354853976[/C][C]0.997261822573012[/C][/ROW]
[ROW][C]36[/C][C]0.00229787479739983[/C][C]0.00459574959479966[/C][C]0.9977021252026[/C][/ROW]
[ROW][C]37[/C][C]0.00254241171738609[/C][C]0.00508482343477217[/C][C]0.997457588282614[/C][/ROW]
[ROW][C]38[/C][C]0.00352185352349237[/C][C]0.00704370704698474[/C][C]0.996478146476508[/C][/ROW]
[ROW][C]39[/C][C]0.0398024985372744[/C][C]0.0796049970745488[/C][C]0.960197501462726[/C][/ROW]
[ROW][C]40[/C][C]0.0302273201767292[/C][C]0.0604546403534585[/C][C]0.96977267982327[/C][/ROW]
[ROW][C]41[/C][C]0.0391286989065364[/C][C]0.0782573978130727[/C][C]0.960871301093464[/C][/ROW]
[ROW][C]42[/C][C]0.0380835012071680[/C][C]0.0761670024143361[/C][C]0.961916498792832[/C][/ROW]
[ROW][C]43[/C][C]0.072929672700523[/C][C]0.145859345401046[/C][C]0.927070327299477[/C][/ROW]
[ROW][C]44[/C][C]0.396013244199629[/C][C]0.792026488399257[/C][C]0.603986755800371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.04962335494638090.09924670989276180.95037664505362
170.03494740730560310.06989481461120620.965052592694397
180.1551437901241960.3102875802483920.844856209875804
190.3425255945190040.6850511890380080.657474405480996
200.5151166049999250.969766790000150.484883395000075
210.4193133918750390.8386267837500780.580686608124961
220.3118395498528800.6236790997057610.68816045014712
230.2877616887860720.5755233775721450.712238311213928
240.2067903576954690.4135807153909370.793209642304531
250.1427393502402270.2854787004804540.857260649759773
260.09227859678503780.1845571935700760.907721403214962
270.05740913962951780.1148182792590360.942590860370482
280.05174897129772510.1034979425954500.948251028702275
290.0375243809760950.075048761952190.962475619023905
300.02887169102736950.0577433820547390.97112830897263
310.02330696617994260.04661393235988530.976693033820057
320.01580681752669250.03161363505338500.984193182473307
330.008624663496151740.01724932699230350.991375336503848
340.004279806467425780.008559612934851560.995720193532574
350.0027381774269880.0054763548539760.997261822573012
360.002297874797399830.004595749594799660.9977021252026
370.002542411717386090.005084823434772170.997457588282614
380.003521853523492370.007043707046984740.996478146476508
390.03980249853727440.07960499707454880.960197501462726
400.03022732017672920.06045464035345850.96977267982327
410.03912869890653640.07825739781307270.960871301093464
420.03808350120716800.07616700241433610.961916498792832
430.0729296727005230.1458593454010460.927070327299477
440.3960132441996290.7920264883992570.603986755800371







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.172413793103448NOK
5% type I error level80.275862068965517NOK
10% type I error level160.551724137931034NOK

\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 & 5 & 0.172413793103448 & NOK \tabularnewline
5% type I error level & 8 & 0.275862068965517 & NOK \tabularnewline
10% type I error level & 16 & 0.551724137931034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58379&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]5[/C][C]0.172413793103448[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.275862068965517[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58379&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58379&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 testsOK/NOK
1% type I error level50.172413793103448NOK
5% type I error level80.275862068965517NOK
10% type I error level160.551724137931034NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}