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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 computationSat, 22 Dec 2012 09:08:57 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/22/t1356185375tph1x5ca616xi3a.htm/, Retrieved Thu, 31 Oct 2024 22:50:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204518, Retrieved Thu, 31 Oct 2024 22:50:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [Workshop 5 - Ques...] [2012-10-29 13:40:23] [f055db2f1c47e4197bf514e64f7886e5]
- RMPD  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Workshop 5 - Ques...] [2012-10-29 15:25:54] [f055db2f1c47e4197bf514e64f7886e5]
- RMPD      [Multiple Regression] [Paper2012: PR Exp...] [2012-12-22 14:08:57] [86f0addf4b5362ca5a545029cdfac14b] [Current]
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Dataseries X:
1	1	1	1	1	1
0	0	1	1	1	0
0	0	1	1	1	0
0	0	1	1	1	0
0	0	1	1	1	0
1	0	1	1	0	1
0	0	1	1	1	0
0	1	1	1	1	0
0	0	1	1	1	1
1	0	1	1	1	0
1	1	1	1	1	0
0	0	1	1	1	0
0	0	0	1	0	0
1	1	1	1	1	0
0	0	0	1	0	1
0	1	0	1	0	1
1	1	0	0	0	0
1	1	1	1	1	0
0	0	1	1	1	1
0	1	0	0	0	1
1	0	1	1	0	0
1	0	0	1	0	1
0	0	1	1	0	1
1	0	1	1	0	1
0	1	0	1	1	1
0	0	0	1	0	0
1	0	1	1	1	1
0	0	0	1	1	0
0	0	1	1	1	1
0	0	1	1	0	0
0	0	1	1	1	0
1	0	1	1	1	0
1	0	1	1	0	0
0	1	1	1	1	1
0	0	1	1	1	0
0	0	1	1	1	0
1	1	0	1	0	0
0	0	0	1	1	1
0	0	1	1	0	1
0	1	1	1	0	0
0	0	0	0	0	1
0	0	0	1	1	1
1	0	1	1	0	1
1	1	1	1	1	0
0	0	1	1	0	0
0	0	1	1	0	1
0	0	1	1	1	0
0	0	1	1	1	1
0	0	1	1	0	1
0	0	1	1	1	0
0	1	0	1	1	0
1	1	0	0	0	0
0	0	1	1	1	1
0	0	0	0	1	0
0	0	1	1	1	0
0	1	0	1	1	1
0	0	0	1	0	1
0	0	1	1	1	1
0	0	1	1	1	1
1	1	0	0	0	1
1	1	1	1	1	1
0	0	0	1	0	0
0	0	1	1	1	0
1	1	1	1	1	1
0	0	1	1	1	0
0	0	1	1	1	0
0	1	0	0	0	0
1	0	1	1	1	0
0	0	1	1	1	1
0	0	0	1	1	0
0	0	1	1	1	0
0	0	1	1	1	1
0	0	0	1	1	1
1	0	0	1	1	0
0	0	1	1	1	1
0	1	1	1	0	1
0	0	1	1	1	1
0	0	0	1	0	1
0	1	0	0	1	1
0	1	1	1	0	0
0	0	1	1	1	0
1	0	0	1	1	1
0	0	1	1	1	0
0	0	0	0	1	0
0	0	1	1	0	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
T40[t] = + 0.626002101165399 + 0.265016549781742UseLimit[t] -0.0762327475882909Used[t] -0.385441889900005CorrectAnalysis[t] + 0.0128204114906089Useful[t] + 0.025477697016292Outcome[t] -0.00120135143960086t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
T40[t] =  +  0.626002101165399 +  0.265016549781742UseLimit[t] -0.0762327475882909Used[t] -0.385441889900005CorrectAnalysis[t] +  0.0128204114906089Useful[t] +  0.025477697016292Outcome[t] -0.00120135143960086t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]T40[t] =  +  0.626002101165399 +  0.265016549781742UseLimit[t] -0.0762327475882909Used[t] -0.385441889900005CorrectAnalysis[t] +  0.0128204114906089Useful[t] +  0.025477697016292Outcome[t] -0.00120135143960086t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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
T40[t] = + 0.626002101165399 + 0.265016549781742UseLimit[t] -0.0762327475882909Used[t] -0.385441889900005CorrectAnalysis[t] + 0.0128204114906089Useful[t] + 0.025477697016292Outcome[t] -0.00120135143960086t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6260021011653990.1852593.37910.0011380.000569
UseLimit0.2650165497817420.1054182.5140.0139980.006999
Used-0.07623274758829090.114676-0.66480.5081610.254081
CorrectAnalysis-0.3854418899000050.173557-2.22080.0292630.014632
Useful0.01282041149060890.1021050.12560.9004030.450201
Outcome0.0254776970162920.0938640.27140.7867770.393388
t-0.001201351439600860.001945-0.61770.5385970.269298

\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) & 0.626002101165399 & 0.185259 & 3.3791 & 0.001138 & 0.000569 \tabularnewline
UseLimit & 0.265016549781742 & 0.105418 & 2.514 & 0.013998 & 0.006999 \tabularnewline
Used & -0.0762327475882909 & 0.114676 & -0.6648 & 0.508161 & 0.254081 \tabularnewline
CorrectAnalysis & -0.385441889900005 & 0.173557 & -2.2208 & 0.029263 & 0.014632 \tabularnewline
Useful & 0.0128204114906089 & 0.102105 & 0.1256 & 0.900403 & 0.450201 \tabularnewline
Outcome & 0.025477697016292 & 0.093864 & 0.2714 & 0.786777 & 0.393388 \tabularnewline
t & -0.00120135143960086 & 0.001945 & -0.6177 & 0.538597 & 0.269298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&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]0.626002101165399[/C][C]0.185259[/C][C]3.3791[/C][C]0.001138[/C][C]0.000569[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.265016549781742[/C][C]0.105418[/C][C]2.514[/C][C]0.013998[/C][C]0.006999[/C][/ROW]
[ROW][C]Used[/C][C]-0.0762327475882909[/C][C]0.114676[/C][C]-0.6648[/C][C]0.508161[/C][C]0.254081[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]-0.385441889900005[/C][C]0.173557[/C][C]-2.2208[/C][C]0.029263[/C][C]0.014632[/C][/ROW]
[ROW][C]Useful[/C][C]0.0128204114906089[/C][C]0.102105[/C][C]0.1256[/C][C]0.900403[/C][C]0.450201[/C][/ROW]
[ROW][C]Outcome[/C][C]0.025477697016292[/C][C]0.093864[/C][C]0.2714[/C][C]0.786777[/C][C]0.393388[/C][/ROW]
[ROW][C]t[/C][C]-0.00120135143960086[/C][C]0.001945[/C][C]-0.6177[/C][C]0.538597[/C][C]0.269298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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)0.6260021011653990.1852593.37910.0011380.000569
UseLimit0.2650165497817420.1054182.5140.0139980.006999
Used-0.07623274758829090.114676-0.66480.5081610.254081
CorrectAnalysis-0.3854418899000050.173557-2.22080.0292630.014632
Useful0.01282041149060890.1021050.12560.9004030.450201
Outcome0.0254776970162920.0938640.27140.7867770.393388
t-0.001201351439600860.001945-0.61770.5385970.269298







Multiple Linear Regression - Regression Statistics
Multiple R0.419395564211147
R-squared0.175892639279986
Adjusted R-squared0.112499765378447
F-TEST (value)2.77464371710264
F-TEST (DF numerator)6
F-TEST (DF denominator)78
p-value0.0169201509594193
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.421012407677986
Sum Squared Residuals13.8256128986675

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.419395564211147 \tabularnewline
R-squared & 0.175892639279986 \tabularnewline
Adjusted R-squared & 0.112499765378447 \tabularnewline
F-TEST (value) & 2.77464371710264 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value & 0.0169201509594193 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.421012407677986 \tabularnewline
Sum Squared Residuals & 13.8256128986675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.419395564211147[/C][/ROW]
[ROW][C]R-squared[/C][C]0.175892639279986[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.112499765378447[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.77464371710264[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C]0.0169201509594193[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.421012407677986[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.8256128986675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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 R0.419395564211147
R-squared0.175892639279986
Adjusted R-squared0.112499765378447
F-TEST (value)2.77464371710264
F-TEST (DF numerator)6
F-TEST (DF denominator)78
p-value0.0169201509594193
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.421012407677986
Sum Squared Residuals13.8256128986675







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.4664407705261450.533559229473855
200.17474517228851-0.17474517228851
300.173543820848909-0.173543820848909
400.172342469409308-0.172342469409308
500.171141117969707-0.171141117969707
600.447613601837531-0.447613601837531
700.168738415090505-0.168738415090505
810.1675370636509050.832462936349095
900.191813409227596-0.191813409227596
1000.430150910553444-0.430150910553444
1110.4289495591138440.571050440886156
1200.162731657892501-0.162731657892501
1300.224942642550582-0.224942642550582
1410.4253455047950410.574654495204959
1500.248017636687672-0.248017636687672
1610.2468162852480720.753183714751928
1710.8705956764739260.129404323526074
1810.4205400990366380.579459900963362
1900.179799894831587-0.179799894831587
2010.6274527693896730.372547230610327
2100.404115633227226-0.404115633227226
2200.504624726392208-0.504624726392208
2300.162174077582575-0.162174077582575
2400.425989275924716-0.425989275924716
2510.2488245337822730.751175466217727
2600.209325073835771-0.209325073835771
2700.435205633096522-0.435205633096522
2800.219742782447178-0.219742782447178
2900.167786380435579-0.167786380435579
3000.128286920489077-0.128286920489077
3100.139905980540085-0.139905980540085
3200.403721178882226-0.403721178882226
3300.389699415952016-0.389699415952016
3410.1617796232375740.838220376762426
3500.135100574781681-0.135100574781681
3600.133899223342081-0.133899223342081
3710.4611267577819030.538873242218097
3800.233206965067462-0.233206965067462
3900.142952454548961-0.142952454548961
4010.1162734060930680.883726593906932
4100.602224389158055-0.602224389158055
4200.228401559309058-0.228401559309058
4300.403163598572299-0.403163598572299
4410.3893049616070150.610695038392985
4500.110266648895064-0.110266648895064
4600.134542994471755-0.134542994471755
4700.120684357506471-0.120684357506471
4800.144960703083162-0.144960703083162
4900.130938940152952-0.130938940152952
5000.117080303187669-0.117080303187669
5110.1921116993363590.807888300663641
5210.8285483760878960.171451623912104
5300.138953945885158-0.138953945885158
5400.573949534917561-0.573949534917561
5500.111073545989664-0.111073545989664
5610.2115826391546460.788417360845354
5700.197560876224436-0.197560876224436
5800.132947188687154-0.132947188687154
5900.131745837247553-0.131745837247553
6010.8444152615873810.155584738412619
6110.3943596841500930.605640315849907
6200.16607642201014-0.16607642201014
6300.101462734472857-0.101462734472857
6410.390755629831290.60924437016871
6500.0990600315936557-0.0990600315936557
6600.0978586801540549-0.0978586801540549
6710.5455115547121410.454488445287859
6800.360472527056595-0.360472527056595
6900.119732322851544-0.119732322851544
7000.169286021983942-0.169286021983942
7100.0918519229560505-0.0918519229560505
7200.116128268532742-0.116128268532742
7300.191159664681432-0.191159664681432
7400.429497166007281-0.429497166007281
7500.112524214213939-0.112524214213939
7610.09850245128372940.901497548716271
7700.110121511334737-0.110121511334737
7800.172332495992818-0.172332495992818
7910.5693934459438320.430606554056168
8010.06821934850903410.931780651490966
8100.079838408560042-0.079838408560042
8200.445364051506766-0.445364051506766
8300.0774357056808403-0.0774357056808403
8400.537908991729536-0.537908991729536
8500.0876902883273216-0.0876902883273216

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.466440770526145 & 0.533559229473855 \tabularnewline
2 & 0 & 0.17474517228851 & -0.17474517228851 \tabularnewline
3 & 0 & 0.173543820848909 & -0.173543820848909 \tabularnewline
4 & 0 & 0.172342469409308 & -0.172342469409308 \tabularnewline
5 & 0 & 0.171141117969707 & -0.171141117969707 \tabularnewline
6 & 0 & 0.447613601837531 & -0.447613601837531 \tabularnewline
7 & 0 & 0.168738415090505 & -0.168738415090505 \tabularnewline
8 & 1 & 0.167537063650905 & 0.832462936349095 \tabularnewline
9 & 0 & 0.191813409227596 & -0.191813409227596 \tabularnewline
10 & 0 & 0.430150910553444 & -0.430150910553444 \tabularnewline
11 & 1 & 0.428949559113844 & 0.571050440886156 \tabularnewline
12 & 0 & 0.162731657892501 & -0.162731657892501 \tabularnewline
13 & 0 & 0.224942642550582 & -0.224942642550582 \tabularnewline
14 & 1 & 0.425345504795041 & 0.574654495204959 \tabularnewline
15 & 0 & 0.248017636687672 & -0.248017636687672 \tabularnewline
16 & 1 & 0.246816285248072 & 0.753183714751928 \tabularnewline
17 & 1 & 0.870595676473926 & 0.129404323526074 \tabularnewline
18 & 1 & 0.420540099036638 & 0.579459900963362 \tabularnewline
19 & 0 & 0.179799894831587 & -0.179799894831587 \tabularnewline
20 & 1 & 0.627452769389673 & 0.372547230610327 \tabularnewline
21 & 0 & 0.404115633227226 & -0.404115633227226 \tabularnewline
22 & 0 & 0.504624726392208 & -0.504624726392208 \tabularnewline
23 & 0 & 0.162174077582575 & -0.162174077582575 \tabularnewline
24 & 0 & 0.425989275924716 & -0.425989275924716 \tabularnewline
25 & 1 & 0.248824533782273 & 0.751175466217727 \tabularnewline
26 & 0 & 0.209325073835771 & -0.209325073835771 \tabularnewline
27 & 0 & 0.435205633096522 & -0.435205633096522 \tabularnewline
28 & 0 & 0.219742782447178 & -0.219742782447178 \tabularnewline
29 & 0 & 0.167786380435579 & -0.167786380435579 \tabularnewline
30 & 0 & 0.128286920489077 & -0.128286920489077 \tabularnewline
31 & 0 & 0.139905980540085 & -0.139905980540085 \tabularnewline
32 & 0 & 0.403721178882226 & -0.403721178882226 \tabularnewline
33 & 0 & 0.389699415952016 & -0.389699415952016 \tabularnewline
34 & 1 & 0.161779623237574 & 0.838220376762426 \tabularnewline
35 & 0 & 0.135100574781681 & -0.135100574781681 \tabularnewline
36 & 0 & 0.133899223342081 & -0.133899223342081 \tabularnewline
37 & 1 & 0.461126757781903 & 0.538873242218097 \tabularnewline
38 & 0 & 0.233206965067462 & -0.233206965067462 \tabularnewline
39 & 0 & 0.142952454548961 & -0.142952454548961 \tabularnewline
40 & 1 & 0.116273406093068 & 0.883726593906932 \tabularnewline
41 & 0 & 0.602224389158055 & -0.602224389158055 \tabularnewline
42 & 0 & 0.228401559309058 & -0.228401559309058 \tabularnewline
43 & 0 & 0.403163598572299 & -0.403163598572299 \tabularnewline
44 & 1 & 0.389304961607015 & 0.610695038392985 \tabularnewline
45 & 0 & 0.110266648895064 & -0.110266648895064 \tabularnewline
46 & 0 & 0.134542994471755 & -0.134542994471755 \tabularnewline
47 & 0 & 0.120684357506471 & -0.120684357506471 \tabularnewline
48 & 0 & 0.144960703083162 & -0.144960703083162 \tabularnewline
49 & 0 & 0.130938940152952 & -0.130938940152952 \tabularnewline
50 & 0 & 0.117080303187669 & -0.117080303187669 \tabularnewline
51 & 1 & 0.192111699336359 & 0.807888300663641 \tabularnewline
52 & 1 & 0.828548376087896 & 0.171451623912104 \tabularnewline
53 & 0 & 0.138953945885158 & -0.138953945885158 \tabularnewline
54 & 0 & 0.573949534917561 & -0.573949534917561 \tabularnewline
55 & 0 & 0.111073545989664 & -0.111073545989664 \tabularnewline
56 & 1 & 0.211582639154646 & 0.788417360845354 \tabularnewline
57 & 0 & 0.197560876224436 & -0.197560876224436 \tabularnewline
58 & 0 & 0.132947188687154 & -0.132947188687154 \tabularnewline
59 & 0 & 0.131745837247553 & -0.131745837247553 \tabularnewline
60 & 1 & 0.844415261587381 & 0.155584738412619 \tabularnewline
61 & 1 & 0.394359684150093 & 0.605640315849907 \tabularnewline
62 & 0 & 0.16607642201014 & -0.16607642201014 \tabularnewline
63 & 0 & 0.101462734472857 & -0.101462734472857 \tabularnewline
64 & 1 & 0.39075562983129 & 0.60924437016871 \tabularnewline
65 & 0 & 0.0990600315936557 & -0.0990600315936557 \tabularnewline
66 & 0 & 0.0978586801540549 & -0.0978586801540549 \tabularnewline
67 & 1 & 0.545511554712141 & 0.454488445287859 \tabularnewline
68 & 0 & 0.360472527056595 & -0.360472527056595 \tabularnewline
69 & 0 & 0.119732322851544 & -0.119732322851544 \tabularnewline
70 & 0 & 0.169286021983942 & -0.169286021983942 \tabularnewline
71 & 0 & 0.0918519229560505 & -0.0918519229560505 \tabularnewline
72 & 0 & 0.116128268532742 & -0.116128268532742 \tabularnewline
73 & 0 & 0.191159664681432 & -0.191159664681432 \tabularnewline
74 & 0 & 0.429497166007281 & -0.429497166007281 \tabularnewline
75 & 0 & 0.112524214213939 & -0.112524214213939 \tabularnewline
76 & 1 & 0.0985024512837294 & 0.901497548716271 \tabularnewline
77 & 0 & 0.110121511334737 & -0.110121511334737 \tabularnewline
78 & 0 & 0.172332495992818 & -0.172332495992818 \tabularnewline
79 & 1 & 0.569393445943832 & 0.430606554056168 \tabularnewline
80 & 1 & 0.0682193485090341 & 0.931780651490966 \tabularnewline
81 & 0 & 0.079838408560042 & -0.079838408560042 \tabularnewline
82 & 0 & 0.445364051506766 & -0.445364051506766 \tabularnewline
83 & 0 & 0.0774357056808403 & -0.0774357056808403 \tabularnewline
84 & 0 & 0.537908991729536 & -0.537908991729536 \tabularnewline
85 & 0 & 0.0876902883273216 & -0.0876902883273216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&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]1[/C][C]0.466440770526145[/C][C]0.533559229473855[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.17474517228851[/C][C]-0.17474517228851[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.173543820848909[/C][C]-0.173543820848909[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.172342469409308[/C][C]-0.172342469409308[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.171141117969707[/C][C]-0.171141117969707[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.447613601837531[/C][C]-0.447613601837531[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.168738415090505[/C][C]-0.168738415090505[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.167537063650905[/C][C]0.832462936349095[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.191813409227596[/C][C]-0.191813409227596[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.430150910553444[/C][C]-0.430150910553444[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.428949559113844[/C][C]0.571050440886156[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.162731657892501[/C][C]-0.162731657892501[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.224942642550582[/C][C]-0.224942642550582[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.425345504795041[/C][C]0.574654495204959[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.248017636687672[/C][C]-0.248017636687672[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.246816285248072[/C][C]0.753183714751928[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.870595676473926[/C][C]0.129404323526074[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.420540099036638[/C][C]0.579459900963362[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.179799894831587[/C][C]-0.179799894831587[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.627452769389673[/C][C]0.372547230610327[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.404115633227226[/C][C]-0.404115633227226[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.504624726392208[/C][C]-0.504624726392208[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.162174077582575[/C][C]-0.162174077582575[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.425989275924716[/C][C]-0.425989275924716[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.248824533782273[/C][C]0.751175466217727[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.209325073835771[/C][C]-0.209325073835771[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.435205633096522[/C][C]-0.435205633096522[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.219742782447178[/C][C]-0.219742782447178[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.167786380435579[/C][C]-0.167786380435579[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.128286920489077[/C][C]-0.128286920489077[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.139905980540085[/C][C]-0.139905980540085[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.403721178882226[/C][C]-0.403721178882226[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.389699415952016[/C][C]-0.389699415952016[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.161779623237574[/C][C]0.838220376762426[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.135100574781681[/C][C]-0.135100574781681[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.133899223342081[/C][C]-0.133899223342081[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.461126757781903[/C][C]0.538873242218097[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.233206965067462[/C][C]-0.233206965067462[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.142952454548961[/C][C]-0.142952454548961[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.116273406093068[/C][C]0.883726593906932[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.602224389158055[/C][C]-0.602224389158055[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.228401559309058[/C][C]-0.228401559309058[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.403163598572299[/C][C]-0.403163598572299[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.389304961607015[/C][C]0.610695038392985[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.110266648895064[/C][C]-0.110266648895064[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.134542994471755[/C][C]-0.134542994471755[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.120684357506471[/C][C]-0.120684357506471[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.144960703083162[/C][C]-0.144960703083162[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.130938940152952[/C][C]-0.130938940152952[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.117080303187669[/C][C]-0.117080303187669[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.192111699336359[/C][C]0.807888300663641[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.828548376087896[/C][C]0.171451623912104[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.138953945885158[/C][C]-0.138953945885158[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.573949534917561[/C][C]-0.573949534917561[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.111073545989664[/C][C]-0.111073545989664[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.211582639154646[/C][C]0.788417360845354[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.197560876224436[/C][C]-0.197560876224436[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.132947188687154[/C][C]-0.132947188687154[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.131745837247553[/C][C]-0.131745837247553[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.844415261587381[/C][C]0.155584738412619[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.394359684150093[/C][C]0.605640315849907[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.16607642201014[/C][C]-0.16607642201014[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.101462734472857[/C][C]-0.101462734472857[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.39075562983129[/C][C]0.60924437016871[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0990600315936557[/C][C]-0.0990600315936557[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0978586801540549[/C][C]-0.0978586801540549[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.545511554712141[/C][C]0.454488445287859[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.360472527056595[/C][C]-0.360472527056595[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.119732322851544[/C][C]-0.119732322851544[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.169286021983942[/C][C]-0.169286021983942[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0918519229560505[/C][C]-0.0918519229560505[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.116128268532742[/C][C]-0.116128268532742[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.191159664681432[/C][C]-0.191159664681432[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.429497166007281[/C][C]-0.429497166007281[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.112524214213939[/C][C]-0.112524214213939[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.0985024512837294[/C][C]0.901497548716271[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.110121511334737[/C][C]-0.110121511334737[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.172332495992818[/C][C]-0.172332495992818[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.569393445943832[/C][C]0.430606554056168[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.0682193485090341[/C][C]0.931780651490966[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.079838408560042[/C][C]-0.079838408560042[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.445364051506766[/C][C]-0.445364051506766[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0774357056808403[/C][C]-0.0774357056808403[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.537908991729536[/C][C]-0.537908991729536[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0876902883273216[/C][C]-0.0876902883273216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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
110.4664407705261450.533559229473855
200.17474517228851-0.17474517228851
300.173543820848909-0.173543820848909
400.172342469409308-0.172342469409308
500.171141117969707-0.171141117969707
600.447613601837531-0.447613601837531
700.168738415090505-0.168738415090505
810.1675370636509050.832462936349095
900.191813409227596-0.191813409227596
1000.430150910553444-0.430150910553444
1110.4289495591138440.571050440886156
1200.162731657892501-0.162731657892501
1300.224942642550582-0.224942642550582
1410.4253455047950410.574654495204959
1500.248017636687672-0.248017636687672
1610.2468162852480720.753183714751928
1710.8705956764739260.129404323526074
1810.4205400990366380.579459900963362
1900.179799894831587-0.179799894831587
2010.6274527693896730.372547230610327
2100.404115633227226-0.404115633227226
2200.504624726392208-0.504624726392208
2300.162174077582575-0.162174077582575
2400.425989275924716-0.425989275924716
2510.2488245337822730.751175466217727
2600.209325073835771-0.209325073835771
2700.435205633096522-0.435205633096522
2800.219742782447178-0.219742782447178
2900.167786380435579-0.167786380435579
3000.128286920489077-0.128286920489077
3100.139905980540085-0.139905980540085
3200.403721178882226-0.403721178882226
3300.389699415952016-0.389699415952016
3410.1617796232375740.838220376762426
3500.135100574781681-0.135100574781681
3600.133899223342081-0.133899223342081
3710.4611267577819030.538873242218097
3800.233206965067462-0.233206965067462
3900.142952454548961-0.142952454548961
4010.1162734060930680.883726593906932
4100.602224389158055-0.602224389158055
4200.228401559309058-0.228401559309058
4300.403163598572299-0.403163598572299
4410.3893049616070150.610695038392985
4500.110266648895064-0.110266648895064
4600.134542994471755-0.134542994471755
4700.120684357506471-0.120684357506471
4800.144960703083162-0.144960703083162
4900.130938940152952-0.130938940152952
5000.117080303187669-0.117080303187669
5110.1921116993363590.807888300663641
5210.8285483760878960.171451623912104
5300.138953945885158-0.138953945885158
5400.573949534917561-0.573949534917561
5500.111073545989664-0.111073545989664
5610.2115826391546460.788417360845354
5700.197560876224436-0.197560876224436
5800.132947188687154-0.132947188687154
5900.131745837247553-0.131745837247553
6010.8444152615873810.155584738412619
6110.3943596841500930.605640315849907
6200.16607642201014-0.16607642201014
6300.101462734472857-0.101462734472857
6410.390755629831290.60924437016871
6500.0990600315936557-0.0990600315936557
6600.0978586801540549-0.0978586801540549
6710.5455115547121410.454488445287859
6800.360472527056595-0.360472527056595
6900.119732322851544-0.119732322851544
7000.169286021983942-0.169286021983942
7100.0918519229560505-0.0918519229560505
7200.116128268532742-0.116128268532742
7300.191159664681432-0.191159664681432
7400.429497166007281-0.429497166007281
7500.112524214213939-0.112524214213939
7610.09850245128372940.901497548716271
7700.110121511334737-0.110121511334737
7800.172332495992818-0.172332495992818
7910.5693934459438320.430606554056168
8010.06821934850903410.931780651490966
8100.079838408560042-0.079838408560042
8200.445364051506766-0.445364051506766
8300.0774357056808403-0.0774357056808403
8400.537908991729536-0.537908991729536
8500.0876902883273216-0.0876902883273216







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9120315739030080.1759368521939840.0879684260969921
110.9100833398808350.179833320238330.0899166601191648
120.8473756591251750.305248681749650.152624340874825
130.7655481916906670.4689036166186650.234451808309333
140.7269428029739830.5461143940520340.273057197026017
150.6362489164091960.7275021671816090.363751083590804
160.7783615041432810.4432769917134390.221638495856719
170.7010945137888610.5978109724222780.298905486211139
180.6598243503056030.6803512993887930.340175649694397
190.6280756202408360.7438487595183290.371924379759164
200.6038882463708710.7922235072582590.396111753629129
210.52671283833070.9465743233385990.4732871616693
220.6807277415505610.6385445168988770.319272258449439
230.6285300914003940.7429398171992130.371469908599606
240.5802806744664010.8394386510671990.419719325533599
250.587617643960350.82476471207930.41238235603965
260.5221358899119790.9557282201760420.477864110088021
270.5982593504779720.8034812990440570.401740649522028
280.6040937706935340.7918124586129310.395906229306466
290.5353734632429030.9292530735141930.464626536757097
300.5054314682071630.9891370635856740.494568531792837
310.4356840391609910.8713680783219810.564315960839009
320.4141287832359060.8282575664718120.585871216764094
330.3980753619943150.796150723988630.601924638005685
340.6192485687024830.7615028625950330.380751431297517
350.5562081209490390.8875837581019210.443791879050961
360.4915601381257080.9831202762514160.508439861874292
370.5417017230079270.9165965539841470.458298276992073
380.5319400480029150.9361199039941710.468059951997085
390.4782526942513740.9565053885027470.521747305748626
400.7238276155421120.5523447689157770.276172384457888
410.7855477179497770.4289045641004460.214452282050223
420.7488181506646020.5023636986707960.251181849335398
430.7673029455649330.4653941088701340.232697054435067
440.8057786273468060.3884427453063890.194221372653194
450.7651171327801730.4697657344396530.234882867219827
460.7383568022417890.5232863955164230.261643197758211
470.6859275939000530.6281448121998940.314072406099947
480.6345556783725660.7308886432548680.365444321627434
490.6319702431445650.736059513710870.368029756855435
500.5759925120584320.8480149758831360.424007487941568
510.7943437919593250.411312416081350.205656208040675
520.7413232880280130.5173534239439740.258676711971987
530.6993456372197180.6013087255605630.300654362780282
540.7496724222626120.5006551554747770.250327577737388
550.6965190463555480.6069619072889040.303480953644452
560.922194926243380.1556101475132410.0778050737566205
570.9028800129221360.1942399741557280.097119987077864
580.8759457698930340.2481084602139310.124054230106966
590.8509455944773850.2981088110452290.149054405522615
600.883217651388860.233564697222280.11678234861114
610.8795606308395340.2408787383209330.120439369160466
620.8641792035534970.2716415928930070.135820796446503
630.8191519756963550.3616960486072890.180848024303645
640.8813014868415950.237397026316810.118698513158405
650.8334337219934630.3331325560130740.166566278006537
660.7743652460375580.4512695079248840.225634753962442
670.7337465432307680.5325069135384630.266253456769232
680.7430597995538590.5138804008922810.256940200446141
690.6968861360690320.6062277278619370.303113863930968
700.6106135472619730.7787729054760540.389386452738027
710.5584417721925340.8831164556149310.441558227807466
720.535226065125170.929547869749660.46477393487483
730.4666019042937220.9332038085874440.533398095706278
740.5874048851893810.8251902296212370.412595114810619
750.5465169999869770.9069660000260470.453483000013023

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.912031573903008 & 0.175936852193984 & 0.0879684260969921 \tabularnewline
11 & 0.910083339880835 & 0.17983332023833 & 0.0899166601191648 \tabularnewline
12 & 0.847375659125175 & 0.30524868174965 & 0.152624340874825 \tabularnewline
13 & 0.765548191690667 & 0.468903616618665 & 0.234451808309333 \tabularnewline
14 & 0.726942802973983 & 0.546114394052034 & 0.273057197026017 \tabularnewline
15 & 0.636248916409196 & 0.727502167181609 & 0.363751083590804 \tabularnewline
16 & 0.778361504143281 & 0.443276991713439 & 0.221638495856719 \tabularnewline
17 & 0.701094513788861 & 0.597810972422278 & 0.298905486211139 \tabularnewline
18 & 0.659824350305603 & 0.680351299388793 & 0.340175649694397 \tabularnewline
19 & 0.628075620240836 & 0.743848759518329 & 0.371924379759164 \tabularnewline
20 & 0.603888246370871 & 0.792223507258259 & 0.396111753629129 \tabularnewline
21 & 0.5267128383307 & 0.946574323338599 & 0.4732871616693 \tabularnewline
22 & 0.680727741550561 & 0.638544516898877 & 0.319272258449439 \tabularnewline
23 & 0.628530091400394 & 0.742939817199213 & 0.371469908599606 \tabularnewline
24 & 0.580280674466401 & 0.839438651067199 & 0.419719325533599 \tabularnewline
25 & 0.58761764396035 & 0.8247647120793 & 0.41238235603965 \tabularnewline
26 & 0.522135889911979 & 0.955728220176042 & 0.477864110088021 \tabularnewline
27 & 0.598259350477972 & 0.803481299044057 & 0.401740649522028 \tabularnewline
28 & 0.604093770693534 & 0.791812458612931 & 0.395906229306466 \tabularnewline
29 & 0.535373463242903 & 0.929253073514193 & 0.464626536757097 \tabularnewline
30 & 0.505431468207163 & 0.989137063585674 & 0.494568531792837 \tabularnewline
31 & 0.435684039160991 & 0.871368078321981 & 0.564315960839009 \tabularnewline
32 & 0.414128783235906 & 0.828257566471812 & 0.585871216764094 \tabularnewline
33 & 0.398075361994315 & 0.79615072398863 & 0.601924638005685 \tabularnewline
34 & 0.619248568702483 & 0.761502862595033 & 0.380751431297517 \tabularnewline
35 & 0.556208120949039 & 0.887583758101921 & 0.443791879050961 \tabularnewline
36 & 0.491560138125708 & 0.983120276251416 & 0.508439861874292 \tabularnewline
37 & 0.541701723007927 & 0.916596553984147 & 0.458298276992073 \tabularnewline
38 & 0.531940048002915 & 0.936119903994171 & 0.468059951997085 \tabularnewline
39 & 0.478252694251374 & 0.956505388502747 & 0.521747305748626 \tabularnewline
40 & 0.723827615542112 & 0.552344768915777 & 0.276172384457888 \tabularnewline
41 & 0.785547717949777 & 0.428904564100446 & 0.214452282050223 \tabularnewline
42 & 0.748818150664602 & 0.502363698670796 & 0.251181849335398 \tabularnewline
43 & 0.767302945564933 & 0.465394108870134 & 0.232697054435067 \tabularnewline
44 & 0.805778627346806 & 0.388442745306389 & 0.194221372653194 \tabularnewline
45 & 0.765117132780173 & 0.469765734439653 & 0.234882867219827 \tabularnewline
46 & 0.738356802241789 & 0.523286395516423 & 0.261643197758211 \tabularnewline
47 & 0.685927593900053 & 0.628144812199894 & 0.314072406099947 \tabularnewline
48 & 0.634555678372566 & 0.730888643254868 & 0.365444321627434 \tabularnewline
49 & 0.631970243144565 & 0.73605951371087 & 0.368029756855435 \tabularnewline
50 & 0.575992512058432 & 0.848014975883136 & 0.424007487941568 \tabularnewline
51 & 0.794343791959325 & 0.41131241608135 & 0.205656208040675 \tabularnewline
52 & 0.741323288028013 & 0.517353423943974 & 0.258676711971987 \tabularnewline
53 & 0.699345637219718 & 0.601308725560563 & 0.300654362780282 \tabularnewline
54 & 0.749672422262612 & 0.500655155474777 & 0.250327577737388 \tabularnewline
55 & 0.696519046355548 & 0.606961907288904 & 0.303480953644452 \tabularnewline
56 & 0.92219492624338 & 0.155610147513241 & 0.0778050737566205 \tabularnewline
57 & 0.902880012922136 & 0.194239974155728 & 0.097119987077864 \tabularnewline
58 & 0.875945769893034 & 0.248108460213931 & 0.124054230106966 \tabularnewline
59 & 0.850945594477385 & 0.298108811045229 & 0.149054405522615 \tabularnewline
60 & 0.88321765138886 & 0.23356469722228 & 0.11678234861114 \tabularnewline
61 & 0.879560630839534 & 0.240878738320933 & 0.120439369160466 \tabularnewline
62 & 0.864179203553497 & 0.271641592893007 & 0.135820796446503 \tabularnewline
63 & 0.819151975696355 & 0.361696048607289 & 0.180848024303645 \tabularnewline
64 & 0.881301486841595 & 0.23739702631681 & 0.118698513158405 \tabularnewline
65 & 0.833433721993463 & 0.333132556013074 & 0.166566278006537 \tabularnewline
66 & 0.774365246037558 & 0.451269507924884 & 0.225634753962442 \tabularnewline
67 & 0.733746543230768 & 0.532506913538463 & 0.266253456769232 \tabularnewline
68 & 0.743059799553859 & 0.513880400892281 & 0.256940200446141 \tabularnewline
69 & 0.696886136069032 & 0.606227727861937 & 0.303113863930968 \tabularnewline
70 & 0.610613547261973 & 0.778772905476054 & 0.389386452738027 \tabularnewline
71 & 0.558441772192534 & 0.883116455614931 & 0.441558227807466 \tabularnewline
72 & 0.53522606512517 & 0.92954786974966 & 0.46477393487483 \tabularnewline
73 & 0.466601904293722 & 0.933203808587444 & 0.533398095706278 \tabularnewline
74 & 0.587404885189381 & 0.825190229621237 & 0.412595114810619 \tabularnewline
75 & 0.546516999986977 & 0.906966000026047 & 0.453483000013023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&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]10[/C][C]0.912031573903008[/C][C]0.175936852193984[/C][C]0.0879684260969921[/C][/ROW]
[ROW][C]11[/C][C]0.910083339880835[/C][C]0.17983332023833[/C][C]0.0899166601191648[/C][/ROW]
[ROW][C]12[/C][C]0.847375659125175[/C][C]0.30524868174965[/C][C]0.152624340874825[/C][/ROW]
[ROW][C]13[/C][C]0.765548191690667[/C][C]0.468903616618665[/C][C]0.234451808309333[/C][/ROW]
[ROW][C]14[/C][C]0.726942802973983[/C][C]0.546114394052034[/C][C]0.273057197026017[/C][/ROW]
[ROW][C]15[/C][C]0.636248916409196[/C][C]0.727502167181609[/C][C]0.363751083590804[/C][/ROW]
[ROW][C]16[/C][C]0.778361504143281[/C][C]0.443276991713439[/C][C]0.221638495856719[/C][/ROW]
[ROW][C]17[/C][C]0.701094513788861[/C][C]0.597810972422278[/C][C]0.298905486211139[/C][/ROW]
[ROW][C]18[/C][C]0.659824350305603[/C][C]0.680351299388793[/C][C]0.340175649694397[/C][/ROW]
[ROW][C]19[/C][C]0.628075620240836[/C][C]0.743848759518329[/C][C]0.371924379759164[/C][/ROW]
[ROW][C]20[/C][C]0.603888246370871[/C][C]0.792223507258259[/C][C]0.396111753629129[/C][/ROW]
[ROW][C]21[/C][C]0.5267128383307[/C][C]0.946574323338599[/C][C]0.4732871616693[/C][/ROW]
[ROW][C]22[/C][C]0.680727741550561[/C][C]0.638544516898877[/C][C]0.319272258449439[/C][/ROW]
[ROW][C]23[/C][C]0.628530091400394[/C][C]0.742939817199213[/C][C]0.371469908599606[/C][/ROW]
[ROW][C]24[/C][C]0.580280674466401[/C][C]0.839438651067199[/C][C]0.419719325533599[/C][/ROW]
[ROW][C]25[/C][C]0.58761764396035[/C][C]0.8247647120793[/C][C]0.41238235603965[/C][/ROW]
[ROW][C]26[/C][C]0.522135889911979[/C][C]0.955728220176042[/C][C]0.477864110088021[/C][/ROW]
[ROW][C]27[/C][C]0.598259350477972[/C][C]0.803481299044057[/C][C]0.401740649522028[/C][/ROW]
[ROW][C]28[/C][C]0.604093770693534[/C][C]0.791812458612931[/C][C]0.395906229306466[/C][/ROW]
[ROW][C]29[/C][C]0.535373463242903[/C][C]0.929253073514193[/C][C]0.464626536757097[/C][/ROW]
[ROW][C]30[/C][C]0.505431468207163[/C][C]0.989137063585674[/C][C]0.494568531792837[/C][/ROW]
[ROW][C]31[/C][C]0.435684039160991[/C][C]0.871368078321981[/C][C]0.564315960839009[/C][/ROW]
[ROW][C]32[/C][C]0.414128783235906[/C][C]0.828257566471812[/C][C]0.585871216764094[/C][/ROW]
[ROW][C]33[/C][C]0.398075361994315[/C][C]0.79615072398863[/C][C]0.601924638005685[/C][/ROW]
[ROW][C]34[/C][C]0.619248568702483[/C][C]0.761502862595033[/C][C]0.380751431297517[/C][/ROW]
[ROW][C]35[/C][C]0.556208120949039[/C][C]0.887583758101921[/C][C]0.443791879050961[/C][/ROW]
[ROW][C]36[/C][C]0.491560138125708[/C][C]0.983120276251416[/C][C]0.508439861874292[/C][/ROW]
[ROW][C]37[/C][C]0.541701723007927[/C][C]0.916596553984147[/C][C]0.458298276992073[/C][/ROW]
[ROW][C]38[/C][C]0.531940048002915[/C][C]0.936119903994171[/C][C]0.468059951997085[/C][/ROW]
[ROW][C]39[/C][C]0.478252694251374[/C][C]0.956505388502747[/C][C]0.521747305748626[/C][/ROW]
[ROW][C]40[/C][C]0.723827615542112[/C][C]0.552344768915777[/C][C]0.276172384457888[/C][/ROW]
[ROW][C]41[/C][C]0.785547717949777[/C][C]0.428904564100446[/C][C]0.214452282050223[/C][/ROW]
[ROW][C]42[/C][C]0.748818150664602[/C][C]0.502363698670796[/C][C]0.251181849335398[/C][/ROW]
[ROW][C]43[/C][C]0.767302945564933[/C][C]0.465394108870134[/C][C]0.232697054435067[/C][/ROW]
[ROW][C]44[/C][C]0.805778627346806[/C][C]0.388442745306389[/C][C]0.194221372653194[/C][/ROW]
[ROW][C]45[/C][C]0.765117132780173[/C][C]0.469765734439653[/C][C]0.234882867219827[/C][/ROW]
[ROW][C]46[/C][C]0.738356802241789[/C][C]0.523286395516423[/C][C]0.261643197758211[/C][/ROW]
[ROW][C]47[/C][C]0.685927593900053[/C][C]0.628144812199894[/C][C]0.314072406099947[/C][/ROW]
[ROW][C]48[/C][C]0.634555678372566[/C][C]0.730888643254868[/C][C]0.365444321627434[/C][/ROW]
[ROW][C]49[/C][C]0.631970243144565[/C][C]0.73605951371087[/C][C]0.368029756855435[/C][/ROW]
[ROW][C]50[/C][C]0.575992512058432[/C][C]0.848014975883136[/C][C]0.424007487941568[/C][/ROW]
[ROW][C]51[/C][C]0.794343791959325[/C][C]0.41131241608135[/C][C]0.205656208040675[/C][/ROW]
[ROW][C]52[/C][C]0.741323288028013[/C][C]0.517353423943974[/C][C]0.258676711971987[/C][/ROW]
[ROW][C]53[/C][C]0.699345637219718[/C][C]0.601308725560563[/C][C]0.300654362780282[/C][/ROW]
[ROW][C]54[/C][C]0.749672422262612[/C][C]0.500655155474777[/C][C]0.250327577737388[/C][/ROW]
[ROW][C]55[/C][C]0.696519046355548[/C][C]0.606961907288904[/C][C]0.303480953644452[/C][/ROW]
[ROW][C]56[/C][C]0.92219492624338[/C][C]0.155610147513241[/C][C]0.0778050737566205[/C][/ROW]
[ROW][C]57[/C][C]0.902880012922136[/C][C]0.194239974155728[/C][C]0.097119987077864[/C][/ROW]
[ROW][C]58[/C][C]0.875945769893034[/C][C]0.248108460213931[/C][C]0.124054230106966[/C][/ROW]
[ROW][C]59[/C][C]0.850945594477385[/C][C]0.298108811045229[/C][C]0.149054405522615[/C][/ROW]
[ROW][C]60[/C][C]0.88321765138886[/C][C]0.23356469722228[/C][C]0.11678234861114[/C][/ROW]
[ROW][C]61[/C][C]0.879560630839534[/C][C]0.240878738320933[/C][C]0.120439369160466[/C][/ROW]
[ROW][C]62[/C][C]0.864179203553497[/C][C]0.271641592893007[/C][C]0.135820796446503[/C][/ROW]
[ROW][C]63[/C][C]0.819151975696355[/C][C]0.361696048607289[/C][C]0.180848024303645[/C][/ROW]
[ROW][C]64[/C][C]0.881301486841595[/C][C]0.23739702631681[/C][C]0.118698513158405[/C][/ROW]
[ROW][C]65[/C][C]0.833433721993463[/C][C]0.333132556013074[/C][C]0.166566278006537[/C][/ROW]
[ROW][C]66[/C][C]0.774365246037558[/C][C]0.451269507924884[/C][C]0.225634753962442[/C][/ROW]
[ROW][C]67[/C][C]0.733746543230768[/C][C]0.532506913538463[/C][C]0.266253456769232[/C][/ROW]
[ROW][C]68[/C][C]0.743059799553859[/C][C]0.513880400892281[/C][C]0.256940200446141[/C][/ROW]
[ROW][C]69[/C][C]0.696886136069032[/C][C]0.606227727861937[/C][C]0.303113863930968[/C][/ROW]
[ROW][C]70[/C][C]0.610613547261973[/C][C]0.778772905476054[/C][C]0.389386452738027[/C][/ROW]
[ROW][C]71[/C][C]0.558441772192534[/C][C]0.883116455614931[/C][C]0.441558227807466[/C][/ROW]
[ROW][C]72[/C][C]0.53522606512517[/C][C]0.92954786974966[/C][C]0.46477393487483[/C][/ROW]
[ROW][C]73[/C][C]0.466601904293722[/C][C]0.933203808587444[/C][C]0.533398095706278[/C][/ROW]
[ROW][C]74[/C][C]0.587404885189381[/C][C]0.825190229621237[/C][C]0.412595114810619[/C][/ROW]
[ROW][C]75[/C][C]0.546516999986977[/C][C]0.906966000026047[/C][C]0.453483000013023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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
100.9120315739030080.1759368521939840.0879684260969921
110.9100833398808350.179833320238330.0899166601191648
120.8473756591251750.305248681749650.152624340874825
130.7655481916906670.4689036166186650.234451808309333
140.7269428029739830.5461143940520340.273057197026017
150.6362489164091960.7275021671816090.363751083590804
160.7783615041432810.4432769917134390.221638495856719
170.7010945137888610.5978109724222780.298905486211139
180.6598243503056030.6803512993887930.340175649694397
190.6280756202408360.7438487595183290.371924379759164
200.6038882463708710.7922235072582590.396111753629129
210.52671283833070.9465743233385990.4732871616693
220.6807277415505610.6385445168988770.319272258449439
230.6285300914003940.7429398171992130.371469908599606
240.5802806744664010.8394386510671990.419719325533599
250.587617643960350.82476471207930.41238235603965
260.5221358899119790.9557282201760420.477864110088021
270.5982593504779720.8034812990440570.401740649522028
280.6040937706935340.7918124586129310.395906229306466
290.5353734632429030.9292530735141930.464626536757097
300.5054314682071630.9891370635856740.494568531792837
310.4356840391609910.8713680783219810.564315960839009
320.4141287832359060.8282575664718120.585871216764094
330.3980753619943150.796150723988630.601924638005685
340.6192485687024830.7615028625950330.380751431297517
350.5562081209490390.8875837581019210.443791879050961
360.4915601381257080.9831202762514160.508439861874292
370.5417017230079270.9165965539841470.458298276992073
380.5319400480029150.9361199039941710.468059951997085
390.4782526942513740.9565053885027470.521747305748626
400.7238276155421120.5523447689157770.276172384457888
410.7855477179497770.4289045641004460.214452282050223
420.7488181506646020.5023636986707960.251181849335398
430.7673029455649330.4653941088701340.232697054435067
440.8057786273468060.3884427453063890.194221372653194
450.7651171327801730.4697657344396530.234882867219827
460.7383568022417890.5232863955164230.261643197758211
470.6859275939000530.6281448121998940.314072406099947
480.6345556783725660.7308886432548680.365444321627434
490.6319702431445650.736059513710870.368029756855435
500.5759925120584320.8480149758831360.424007487941568
510.7943437919593250.411312416081350.205656208040675
520.7413232880280130.5173534239439740.258676711971987
530.6993456372197180.6013087255605630.300654362780282
540.7496724222626120.5006551554747770.250327577737388
550.6965190463555480.6069619072889040.303480953644452
560.922194926243380.1556101475132410.0778050737566205
570.9028800129221360.1942399741557280.097119987077864
580.8759457698930340.2481084602139310.124054230106966
590.8509455944773850.2981088110452290.149054405522615
600.883217651388860.233564697222280.11678234861114
610.8795606308395340.2408787383209330.120439369160466
620.8641792035534970.2716415928930070.135820796446503
630.8191519756963550.3616960486072890.180848024303645
640.8813014868415950.237397026316810.118698513158405
650.8334337219934630.3331325560130740.166566278006537
660.7743652460375580.4512695079248840.225634753962442
670.7337465432307680.5325069135384630.266253456769232
680.7430597995538590.5138804008922810.256940200446141
690.6968861360690320.6062277278619370.303113863930968
700.6106135472619730.7787729054760540.389386452738027
710.5584417721925340.8831164556149310.441558227807466
720.535226065125170.929547869749660.46477393487483
730.4666019042937220.9332038085874440.533398095706278
740.5874048851893810.8251902296212370.412595114810619
750.5465169999869770.9069660000260470.453483000013023







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204518&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204518&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204518&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 level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}