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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, 21 Dec 2012 17:05:41 -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/21/t1356127605zifquiu3ga8bw4u.htm/, Retrieved Thu, 28 Mar 2024 11:45:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204340, Retrieved Thu, 28 Mar 2024 11:45:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-12-20 14:49:50] [e7dc4e9838ed4d1d0327624ddfd2c7d1]
- RMPD  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Chi-kwadraat Corr...] [2012-12-21 20:17:26] [545953f2d86c37fd130a351e27542a1f]
- R P     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Chi-kwadraat T40 ...] [2012-12-21 20:41:54] [545953f2d86c37fd130a351e27542a1f]
- RMPD      [Multiple Regression] [Regression T20] [2012-12-21 22:02:58] [545953f2d86c37fd130a351e27542a1f]
- R  D          [Multiple Regression] [Regression T40] [2012-12-21 22:05:41] [e648b5f17d70022458d6b50366bba98f] [Current]
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Dataseries X:
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	1	1
0	0	0	0	0	0
0	1	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	1	0	0	0	0
0	0	0	0	0	0
0	0	1	0	1	0
1	1	0	0	0	0
0	0	1	0	1	1
0	1	1	0	1	1
1	1	1	1	1	0
1	1	0	0	0	0
0	0	0	0	0	1
0	1	1	1	1	1
1	0	0	0	1	0
1	0	1	0	1	1
0	0	0	0	1	1
1	0	0	0	1	1
0	1	1	0	0	1
0	0	1	0	1	0
1	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	1
0	0	0	0	1	0
0	0	0	0	0	0
1	0	0	0	0	0
1	0	0	0	1	0
0	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	1	0
0	0	1	0	0	1
0	0	0	0	1	1
0	1	0	0	1	0
0	0	1	1	1	1
0	0	1	0	0	1
1	0	0	0	1	1
1	1	0	0	0	0
0	0	0	0	1	0
0	0	0	0	1	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	1
0	0	0	0	0	0
0	1	1	0	0	0
1	1	1	1	1	0
0	0	0	0	0	1
0	0	1	1	0	0
0	0	0	0	0	0
0	1	1	0	0	1
0	0	1	0	1	1
0	0	0	0	0	1
0	0	0	0	0	1
1	1	1	1	1	1
1	1	0	0	0	1
0	0	1	0	1	0
0	0	0	0	0	0
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	1	1	1	1	0
1	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	1
1	0	1	0	0	0
0	0	0	0	0	1
0	1	0	0	1	1
0	0	0	0	0	1
0	0	1	0	1	1
0	1	1	1	0	1
0	1	0	0	1	0
0	0	0	0	0	0
1	0	1	0	0	1
0	0	0	0	0	0
0	0	1	1	0	0
0	0	0	0	1	1
1	0	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Useful[t] = + 0.17063333590264 + 0.106578497505225UseLimit[t] + 0.00138847919926387T40[t] + 0.204411120819957Used[t] + 0.196952252470963CorrectAnalysis[t] + 0.130990812386676Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Useful[t] =  +  0.17063333590264 +  0.106578497505225UseLimit[t] +  0.00138847919926387T40[t] +  0.204411120819957Used[t] +  0.196952252470963CorrectAnalysis[t] +  0.130990812386676Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Useful[t] =  +  0.17063333590264 +  0.106578497505225UseLimit[t] +  0.00138847919926387T40[t] +  0.204411120819957Used[t] +  0.196952252470963CorrectAnalysis[t] +  0.130990812386676Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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
Useful[t] = + 0.17063333590264 + 0.106578497505225UseLimit[t] + 0.00138847919926387T40[t] + 0.204411120819957Used[t] + 0.196952252470963CorrectAnalysis[t] + 0.130990812386676Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.170633335902640.0842942.02430.0462810.023141
UseLimit0.1065784975052250.1169640.91120.3649240.182462
T400.001388479199263870.1241750.01120.9911060.495553
Used0.2044111208199570.124561.64110.1047090.052354
CorrectAnalysis0.1969522524709630.1949091.01050.3153110.157655
Outcome0.1309908123866760.1015471.28990.2007840.100392

\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) & 0.17063333590264 & 0.084294 & 2.0243 & 0.046281 & 0.023141 \tabularnewline
UseLimit & 0.106578497505225 & 0.116964 & 0.9112 & 0.364924 & 0.182462 \tabularnewline
T40 & 0.00138847919926387 & 0.124175 & 0.0112 & 0.991106 & 0.495553 \tabularnewline
Used & 0.204411120819957 & 0.12456 & 1.6411 & 0.104709 & 0.052354 \tabularnewline
CorrectAnalysis & 0.196952252470963 & 0.194909 & 1.0105 & 0.315311 & 0.157655 \tabularnewline
Outcome & 0.130990812386676 & 0.101547 & 1.2899 & 0.200784 & 0.100392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&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]0.17063333590264[/C][C]0.084294[/C][C]2.0243[/C][C]0.046281[/C][C]0.023141[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.106578497505225[/C][C]0.116964[/C][C]0.9112[/C][C]0.364924[/C][C]0.182462[/C][/ROW]
[ROW][C]T40[/C][C]0.00138847919926387[/C][C]0.124175[/C][C]0.0112[/C][C]0.991106[/C][C]0.495553[/C][/ROW]
[ROW][C]Used[/C][C]0.204411120819957[/C][C]0.12456[/C][C]1.6411[/C][C]0.104709[/C][C]0.052354[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.196952252470963[/C][C]0.194909[/C][C]1.0105[/C][C]0.315311[/C][C]0.157655[/C][/ROW]
[ROW][C]Outcome[/C][C]0.130990812386676[/C][C]0.101547[/C][C]1.2899[/C][C]0.200784[/C][C]0.100392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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)0.170633335902640.0842942.02430.0462810.023141
UseLimit0.1065784975052250.1169640.91120.3649240.182462
T400.001388479199263870.1241750.01120.9911060.495553
Used0.2044111208199570.124561.64110.1047090.052354
CorrectAnalysis0.1969522524709630.1949091.01050.3153110.157655
Outcome0.1309908123866760.1015471.28990.2007840.100392







Multiple Linear Regression - Regression Statistics
Multiple R0.336218362325341
R-squared0.113042787164734
Adjusted R-squared0.0576079613625298
F-TEST (value)2.03920163054322
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.0819123275241831
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.46538433066361
Sum Squared Residuals17.3266060181773

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.336218362325341 \tabularnewline
R-squared & 0.113042787164734 \tabularnewline
Adjusted R-squared & 0.0576079613625298 \tabularnewline
F-TEST (value) & 2.03920163054322 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 0.0819123275241831 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.46538433066361 \tabularnewline
Sum Squared Residuals & 17.3266060181773 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.336218362325341[/C][/ROW]
[ROW][C]R-squared[/C][C]0.113042787164734[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0576079613625298[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.03920163054322[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]0.0819123275241831[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.46538433066361[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]17.3266060181773[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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 R0.336218362325341
R-squared0.113042787164734
Adjusted R-squared0.0576079613625298
F-TEST (value)2.03920163054322
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.0819123275241831
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.46538433066361
Sum Squared Residuals17.3266060181773







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.409591124993804-0.409591124993804
200.17063333590264-0.17063333590264
300.17063333590264-0.17063333590264
400.17063333590264-0.17063333590264
500.17063333590264-0.17063333590264
610.4082026457945410.591797354205459
700.17063333590264-0.17063333590264
800.172021815101904-0.172021815101904
900.301624148289316-0.301624148289316
1000.277211833407864-0.277211833407864
1100.278600312607128-0.278600312607128
1200.17063333590264-0.17063333590264
1310.3750444567225970.624955543277403
1400.278600312607128-0.278600312607128
1510.5060352691092730.493964730890727
1610.5074237483085370.492576251691463
1710.6799636858980480.320036314101952
1800.278600312607128-0.278600312607128
1900.301624148289316-0.301624148289316
2010.70437600077950.2956239992205
2110.2772118334078640.722788166592136
2210.6126137666144980.387386233385502
2310.3016241482893160.698375851710684
2410.4082026457945410.591797354205459
2500.507423748308537-0.507423748308537
2610.3750444567225970.624955543277403
2700.408202645794541-0.408202645794541
2800.375044456722597-0.375044456722597
2900.301624148289316-0.301624148289316
3010.170633335902640.82936666409736
3100.17063333590264-0.17063333590264
3200.277211833407864-0.277211833407864
3310.2772118334078640.722788166592136
3400.30301262748858-0.30301262748858
3500.17063333590264-0.17063333590264
3600.17063333590264-0.17063333590264
3710.4830114334270850.516988566572915
3800.506035269109273-0.506035269109273
3910.3016241482893160.698375851710684
4010.1720218151019040.827978184898096
4110.7029875215802360.297012478419764
4200.506035269109273-0.506035269109273
4310.4082026457945410.591797354205459
4400.278600312607128-0.278600312607128
4510.170633335902640.82936666409736
4610.3016241482893160.698375851710684
4700.17063333590264-0.17063333590264
4800.301624148289316-0.301624148289316
4910.3016241482893160.698375851710684
5000.17063333590264-0.17063333590264
5100.376432935921861-0.376432935921861
5210.6799636858980480.320036314101952
5300.301624148289316-0.301624148289316
5400.57199670919356-0.57199670919356
5500.17063333590264-0.17063333590264
5600.507423748308537-0.507423748308537
5710.5060352691092730.493964730890727
5800.301624148289316-0.301624148289316
5900.301624148289316-0.301624148289316
6010.8109544982847240.189045501715276
6100.409591124993805-0.409591124993805
6210.3750444567225970.624955543277403
6300.17063333590264-0.17063333590264
6400.409591124993805-0.409591124993805
6500.17063333590264-0.17063333590264
6600.17063333590264-0.17063333590264
6710.5733851883928240.426614811607176
6800.277211833407864-0.277211833407864
6900.301624148289316-0.301624148289316
7000.375044456722597-0.375044456722597
7100.17063333590264-0.17063333590264
7200.301624148289316-0.301624148289316
7300.506035269109273-0.506035269109273
7400.481622954227822-0.481622954227822
7500.301624148289316-0.301624148289316
7610.303012627488580.69698737251142
7700.301624148289316-0.301624148289316
7810.5060352691092730.493964730890727
7900.7043760007795-0.7043760007795
8010.1720218151019040.827978184898096
8100.17063333590264-0.17063333590264
8200.612613766614498-0.612613766614498
8300.17063333590264-0.17063333590264
8400.57199670919356-0.57199670919356
8510.3016241482893160.698375851710684
8600.277211833407864-0.277211833407864

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.409591124993804 & -0.409591124993804 \tabularnewline
2 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
3 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
4 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
5 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
6 & 1 & 0.408202645794541 & 0.591797354205459 \tabularnewline
7 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
8 & 0 & 0.172021815101904 & -0.172021815101904 \tabularnewline
9 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
10 & 0 & 0.277211833407864 & -0.277211833407864 \tabularnewline
11 & 0 & 0.278600312607128 & -0.278600312607128 \tabularnewline
12 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
13 & 1 & 0.375044456722597 & 0.624955543277403 \tabularnewline
14 & 0 & 0.278600312607128 & -0.278600312607128 \tabularnewline
15 & 1 & 0.506035269109273 & 0.493964730890727 \tabularnewline
16 & 1 & 0.507423748308537 & 0.492576251691463 \tabularnewline
17 & 1 & 0.679963685898048 & 0.320036314101952 \tabularnewline
18 & 0 & 0.278600312607128 & -0.278600312607128 \tabularnewline
19 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
20 & 1 & 0.7043760007795 & 0.2956239992205 \tabularnewline
21 & 1 & 0.277211833407864 & 0.722788166592136 \tabularnewline
22 & 1 & 0.612613766614498 & 0.387386233385502 \tabularnewline
23 & 1 & 0.301624148289316 & 0.698375851710684 \tabularnewline
24 & 1 & 0.408202645794541 & 0.591797354205459 \tabularnewline
25 & 0 & 0.507423748308537 & -0.507423748308537 \tabularnewline
26 & 1 & 0.375044456722597 & 0.624955543277403 \tabularnewline
27 & 0 & 0.408202645794541 & -0.408202645794541 \tabularnewline
28 & 0 & 0.375044456722597 & -0.375044456722597 \tabularnewline
29 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
30 & 1 & 0.17063333590264 & 0.82936666409736 \tabularnewline
31 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
32 & 0 & 0.277211833407864 & -0.277211833407864 \tabularnewline
33 & 1 & 0.277211833407864 & 0.722788166592136 \tabularnewline
34 & 0 & 0.30301262748858 & -0.30301262748858 \tabularnewline
35 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
36 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
37 & 1 & 0.483011433427085 & 0.516988566572915 \tabularnewline
38 & 0 & 0.506035269109273 & -0.506035269109273 \tabularnewline
39 & 1 & 0.301624148289316 & 0.698375851710684 \tabularnewline
40 & 1 & 0.172021815101904 & 0.827978184898096 \tabularnewline
41 & 1 & 0.702987521580236 & 0.297012478419764 \tabularnewline
42 & 0 & 0.506035269109273 & -0.506035269109273 \tabularnewline
43 & 1 & 0.408202645794541 & 0.591797354205459 \tabularnewline
44 & 0 & 0.278600312607128 & -0.278600312607128 \tabularnewline
45 & 1 & 0.17063333590264 & 0.82936666409736 \tabularnewline
46 & 1 & 0.301624148289316 & 0.698375851710684 \tabularnewline
47 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
48 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
49 & 1 & 0.301624148289316 & 0.698375851710684 \tabularnewline
50 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
51 & 0 & 0.376432935921861 & -0.376432935921861 \tabularnewline
52 & 1 & 0.679963685898048 & 0.320036314101952 \tabularnewline
53 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
54 & 0 & 0.57199670919356 & -0.57199670919356 \tabularnewline
55 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
56 & 0 & 0.507423748308537 & -0.507423748308537 \tabularnewline
57 & 1 & 0.506035269109273 & 0.493964730890727 \tabularnewline
58 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
59 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
60 & 1 & 0.810954498284724 & 0.189045501715276 \tabularnewline
61 & 0 & 0.409591124993805 & -0.409591124993805 \tabularnewline
62 & 1 & 0.375044456722597 & 0.624955543277403 \tabularnewline
63 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
64 & 0 & 0.409591124993805 & -0.409591124993805 \tabularnewline
65 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
66 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
67 & 1 & 0.573385188392824 & 0.426614811607176 \tabularnewline
68 & 0 & 0.277211833407864 & -0.277211833407864 \tabularnewline
69 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
70 & 0 & 0.375044456722597 & -0.375044456722597 \tabularnewline
71 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
72 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
73 & 0 & 0.506035269109273 & -0.506035269109273 \tabularnewline
74 & 0 & 0.481622954227822 & -0.481622954227822 \tabularnewline
75 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
76 & 1 & 0.30301262748858 & 0.69698737251142 \tabularnewline
77 & 0 & 0.301624148289316 & -0.301624148289316 \tabularnewline
78 & 1 & 0.506035269109273 & 0.493964730890727 \tabularnewline
79 & 0 & 0.7043760007795 & -0.7043760007795 \tabularnewline
80 & 1 & 0.172021815101904 & 0.827978184898096 \tabularnewline
81 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
82 & 0 & 0.612613766614498 & -0.612613766614498 \tabularnewline
83 & 0 & 0.17063333590264 & -0.17063333590264 \tabularnewline
84 & 0 & 0.57199670919356 & -0.57199670919356 \tabularnewline
85 & 1 & 0.301624148289316 & 0.698375851710684 \tabularnewline
86 & 0 & 0.277211833407864 & -0.277211833407864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&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]0[/C][C]0.409591124993804[/C][C]-0.409591124993804[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.408202645794541[/C][C]0.591797354205459[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.172021815101904[/C][C]-0.172021815101904[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.277211833407864[/C][C]-0.277211833407864[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.278600312607128[/C][C]-0.278600312607128[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.375044456722597[/C][C]0.624955543277403[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.278600312607128[/C][C]-0.278600312607128[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.506035269109273[/C][C]0.493964730890727[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.507423748308537[/C][C]0.492576251691463[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.679963685898048[/C][C]0.320036314101952[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.278600312607128[/C][C]-0.278600312607128[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.7043760007795[/C][C]0.2956239992205[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.277211833407864[/C][C]0.722788166592136[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.612613766614498[/C][C]0.387386233385502[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.301624148289316[/C][C]0.698375851710684[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.408202645794541[/C][C]0.591797354205459[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.507423748308537[/C][C]-0.507423748308537[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.375044456722597[/C][C]0.624955543277403[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.408202645794541[/C][C]-0.408202645794541[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.375044456722597[/C][C]-0.375044456722597[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.17063333590264[/C][C]0.82936666409736[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.277211833407864[/C][C]-0.277211833407864[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.277211833407864[/C][C]0.722788166592136[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.30301262748858[/C][C]-0.30301262748858[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.483011433427085[/C][C]0.516988566572915[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.506035269109273[/C][C]-0.506035269109273[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.301624148289316[/C][C]0.698375851710684[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.172021815101904[/C][C]0.827978184898096[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.702987521580236[/C][C]0.297012478419764[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.506035269109273[/C][C]-0.506035269109273[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.408202645794541[/C][C]0.591797354205459[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.278600312607128[/C][C]-0.278600312607128[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]0.17063333590264[/C][C]0.82936666409736[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.301624148289316[/C][C]0.698375851710684[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.301624148289316[/C][C]0.698375851710684[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.376432935921861[/C][C]-0.376432935921861[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.679963685898048[/C][C]0.320036314101952[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.57199670919356[/C][C]-0.57199670919356[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.507423748308537[/C][C]-0.507423748308537[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.506035269109273[/C][C]0.493964730890727[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.810954498284724[/C][C]0.189045501715276[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.409591124993805[/C][C]-0.409591124993805[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.375044456722597[/C][C]0.624955543277403[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.409591124993805[/C][C]-0.409591124993805[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.573385188392824[/C][C]0.426614811607176[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.277211833407864[/C][C]-0.277211833407864[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.375044456722597[/C][C]-0.375044456722597[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.506035269109273[/C][C]-0.506035269109273[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.481622954227822[/C][C]-0.481622954227822[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.30301262748858[/C][C]0.69698737251142[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.301624148289316[/C][C]-0.301624148289316[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.506035269109273[/C][C]0.493964730890727[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.7043760007795[/C][C]-0.7043760007795[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.172021815101904[/C][C]0.827978184898096[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.612613766614498[/C][C]-0.612613766614498[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.17063333590264[/C][C]-0.17063333590264[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.57199670919356[/C][C]-0.57199670919356[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.301624148289316[/C][C]0.698375851710684[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.277211833407864[/C][C]-0.277211833407864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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
100.409591124993804-0.409591124993804
200.17063333590264-0.17063333590264
300.17063333590264-0.17063333590264
400.17063333590264-0.17063333590264
500.17063333590264-0.17063333590264
610.4082026457945410.591797354205459
700.17063333590264-0.17063333590264
800.172021815101904-0.172021815101904
900.301624148289316-0.301624148289316
1000.277211833407864-0.277211833407864
1100.278600312607128-0.278600312607128
1200.17063333590264-0.17063333590264
1310.3750444567225970.624955543277403
1400.278600312607128-0.278600312607128
1510.5060352691092730.493964730890727
1610.5074237483085370.492576251691463
1710.6799636858980480.320036314101952
1800.278600312607128-0.278600312607128
1900.301624148289316-0.301624148289316
2010.70437600077950.2956239992205
2110.2772118334078640.722788166592136
2210.6126137666144980.387386233385502
2310.3016241482893160.698375851710684
2410.4082026457945410.591797354205459
2500.507423748308537-0.507423748308537
2610.3750444567225970.624955543277403
2700.408202645794541-0.408202645794541
2800.375044456722597-0.375044456722597
2900.301624148289316-0.301624148289316
3010.170633335902640.82936666409736
3100.17063333590264-0.17063333590264
3200.277211833407864-0.277211833407864
3310.2772118334078640.722788166592136
3400.30301262748858-0.30301262748858
3500.17063333590264-0.17063333590264
3600.17063333590264-0.17063333590264
3710.4830114334270850.516988566572915
3800.506035269109273-0.506035269109273
3910.3016241482893160.698375851710684
4010.1720218151019040.827978184898096
4110.7029875215802360.297012478419764
4200.506035269109273-0.506035269109273
4310.4082026457945410.591797354205459
4400.278600312607128-0.278600312607128
4510.170633335902640.82936666409736
4610.3016241482893160.698375851710684
4700.17063333590264-0.17063333590264
4800.301624148289316-0.301624148289316
4910.3016241482893160.698375851710684
5000.17063333590264-0.17063333590264
5100.376432935921861-0.376432935921861
5210.6799636858980480.320036314101952
5300.301624148289316-0.301624148289316
5400.57199670919356-0.57199670919356
5500.17063333590264-0.17063333590264
5600.507423748308537-0.507423748308537
5710.5060352691092730.493964730890727
5800.301624148289316-0.301624148289316
5900.301624148289316-0.301624148289316
6010.8109544982847240.189045501715276
6100.409591124993805-0.409591124993805
6210.3750444567225970.624955543277403
6300.17063333590264-0.17063333590264
6400.409591124993805-0.409591124993805
6500.17063333590264-0.17063333590264
6600.17063333590264-0.17063333590264
6710.5733851883928240.426614811607176
6800.277211833407864-0.277211833407864
6900.301624148289316-0.301624148289316
7000.375044456722597-0.375044456722597
7100.17063333590264-0.17063333590264
7200.301624148289316-0.301624148289316
7300.506035269109273-0.506035269109273
7400.481622954227822-0.481622954227822
7500.301624148289316-0.301624148289316
7610.303012627488580.69698737251142
7700.301624148289316-0.301624148289316
7810.5060352691092730.493964730890727
7900.7043760007795-0.7043760007795
8010.1720218151019040.827978184898096
8100.17063333590264-0.17063333590264
8200.612613766614498-0.612613766614498
8300.17063333590264-0.17063333590264
8400.57199670919356-0.57199670919356
8510.3016241482893160.698375851710684
8600.277211833407864-0.277211833407864







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2813299409464620.5626598818929230.718670059053538
100.2888104972532670.5776209945065340.711189502746733
110.1695719988355580.3391439976711170.830428001164442
120.09209328083100310.1841865616620060.907906719168997
130.04961306912578070.09922613825156140.950386930874219
140.02446972672872010.04893945345744020.97553027327128
150.01430452442202170.02860904884404350.985695475577978
160.007511804301229970.01502360860245990.99248819569877
170.003326062158531380.006652124317062760.996673937841469
180.001463025079491660.002926050158983320.998536974920508
190.0008496271344493260.001699254268898650.999150372865551
200.0003657239968606710.0007314479937213420.999634276003139
210.004761633310474620.009523266620949240.995238366689525
220.006581976969831520.0131639539396630.993418023030168
230.0330358526651570.06607170533031410.966964147334843
240.03580529257258750.0716105851451750.964194707427413
250.06120922794505020.12241845589010.93879077205495
260.05736352939218660.1147270587843730.942636470607813
270.08383472137641360.1676694427528270.916165278623586
280.1337236058483470.2674472116966940.866276394151653
290.1127872530256990.2255745060513990.887212746974301
300.2450556721627340.4901113443254690.754944327837266
310.1988295417340070.3976590834680130.801170458265993
320.1804550583191210.3609101166382420.819544941680879
330.242997169150780.485994338301560.75700283084922
340.2140437595169090.4280875190338170.785956240483091
350.1727050559362510.3454101118725020.827294944063749
360.136884924507030.273769849014060.86311507549297
370.143211144661170.2864222893223410.85678885533883
380.2004563005073280.4009126010146550.799543699492672
390.279653246323340.559306492646680.72034675367666
400.4664742079813830.9329484159627670.533525792018617
410.4297762883111480.8595525766222950.570223711688852
420.453963940324230.907927880648460.54603605967577
430.5269953418662780.9460093162674430.473004658133721
440.4808203247372530.9616406494745050.519179675262747
450.6263642813503450.747271437299310.373635718649655
460.7215508844869790.5568982310260410.278449115513021
470.6718362015196230.6563275969607550.328163798480377
480.6315876678712850.7368246642574310.368412332128715
490.7427882110406530.5144235779186940.257211788959347
500.6926913259538440.6146173480923130.307308674046156
510.7335624674756560.5328750650486890.266437532524344
520.7319980782439790.5360038435120430.268001921756021
530.689854028352560.620291943294880.31014597164744
540.7092730093232760.5814539813534470.290726990676724
550.6530639145423420.6938721709153160.346936085457658
560.7967661915735640.4064676168528720.203233808426436
570.8124128440139690.3751743119720620.187587155986031
580.7697110654104080.4605778691791850.230288934589592
590.720525673929050.5589486521418990.27947432607095
600.8084950231251660.3830099537496670.191504976874834
610.7838161356999710.4323677286000580.216183864300029
620.8252511000582270.3494977998835460.174748899941773
630.7765495674189720.4469008651620570.223450432581028
640.8080989028082570.3838021943834870.191901097191743
650.751915500184090.496168999631820.24808449981591
660.6882506591307280.6234986817385440.311749340869272
670.7348834239621660.5302331520756690.265116576037834
680.6683476475522370.6633047048955250.331652352447763
690.6070139534530880.7859720930938230.392986046546912
700.5719293601363360.8561412797273280.428070639863664
710.4945314880183540.9890629760367080.505468511981646
720.4256153690803280.8512307381606560.574384630919672
730.4977589634260790.9955179268521590.502241036573921
740.4163521013909720.8327042027819430.583647898609029
750.358793695138020.7175873902760390.64120630486198
760.2697594762866940.5395189525733890.730240523713306
770.2707385921908070.5414771843816140.729261407809193

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.281329940946462 & 0.562659881892923 & 0.718670059053538 \tabularnewline
10 & 0.288810497253267 & 0.577620994506534 & 0.711189502746733 \tabularnewline
11 & 0.169571998835558 & 0.339143997671117 & 0.830428001164442 \tabularnewline
12 & 0.0920932808310031 & 0.184186561662006 & 0.907906719168997 \tabularnewline
13 & 0.0496130691257807 & 0.0992261382515614 & 0.950386930874219 \tabularnewline
14 & 0.0244697267287201 & 0.0489394534574402 & 0.97553027327128 \tabularnewline
15 & 0.0143045244220217 & 0.0286090488440435 & 0.985695475577978 \tabularnewline
16 & 0.00751180430122997 & 0.0150236086024599 & 0.99248819569877 \tabularnewline
17 & 0.00332606215853138 & 0.00665212431706276 & 0.996673937841469 \tabularnewline
18 & 0.00146302507949166 & 0.00292605015898332 & 0.998536974920508 \tabularnewline
19 & 0.000849627134449326 & 0.00169925426889865 & 0.999150372865551 \tabularnewline
20 & 0.000365723996860671 & 0.000731447993721342 & 0.999634276003139 \tabularnewline
21 & 0.00476163331047462 & 0.00952326662094924 & 0.995238366689525 \tabularnewline
22 & 0.00658197696983152 & 0.013163953939663 & 0.993418023030168 \tabularnewline
23 & 0.033035852665157 & 0.0660717053303141 & 0.966964147334843 \tabularnewline
24 & 0.0358052925725875 & 0.071610585145175 & 0.964194707427413 \tabularnewline
25 & 0.0612092279450502 & 0.1224184558901 & 0.93879077205495 \tabularnewline
26 & 0.0573635293921866 & 0.114727058784373 & 0.942636470607813 \tabularnewline
27 & 0.0838347213764136 & 0.167669442752827 & 0.916165278623586 \tabularnewline
28 & 0.133723605848347 & 0.267447211696694 & 0.866276394151653 \tabularnewline
29 & 0.112787253025699 & 0.225574506051399 & 0.887212746974301 \tabularnewline
30 & 0.245055672162734 & 0.490111344325469 & 0.754944327837266 \tabularnewline
31 & 0.198829541734007 & 0.397659083468013 & 0.801170458265993 \tabularnewline
32 & 0.180455058319121 & 0.360910116638242 & 0.819544941680879 \tabularnewline
33 & 0.24299716915078 & 0.48599433830156 & 0.75700283084922 \tabularnewline
34 & 0.214043759516909 & 0.428087519033817 & 0.785956240483091 \tabularnewline
35 & 0.172705055936251 & 0.345410111872502 & 0.827294944063749 \tabularnewline
36 & 0.13688492450703 & 0.27376984901406 & 0.86311507549297 \tabularnewline
37 & 0.14321114466117 & 0.286422289322341 & 0.85678885533883 \tabularnewline
38 & 0.200456300507328 & 0.400912601014655 & 0.799543699492672 \tabularnewline
39 & 0.27965324632334 & 0.55930649264668 & 0.72034675367666 \tabularnewline
40 & 0.466474207981383 & 0.932948415962767 & 0.533525792018617 \tabularnewline
41 & 0.429776288311148 & 0.859552576622295 & 0.570223711688852 \tabularnewline
42 & 0.45396394032423 & 0.90792788064846 & 0.54603605967577 \tabularnewline
43 & 0.526995341866278 & 0.946009316267443 & 0.473004658133721 \tabularnewline
44 & 0.480820324737253 & 0.961640649474505 & 0.519179675262747 \tabularnewline
45 & 0.626364281350345 & 0.74727143729931 & 0.373635718649655 \tabularnewline
46 & 0.721550884486979 & 0.556898231026041 & 0.278449115513021 \tabularnewline
47 & 0.671836201519623 & 0.656327596960755 & 0.328163798480377 \tabularnewline
48 & 0.631587667871285 & 0.736824664257431 & 0.368412332128715 \tabularnewline
49 & 0.742788211040653 & 0.514423577918694 & 0.257211788959347 \tabularnewline
50 & 0.692691325953844 & 0.614617348092313 & 0.307308674046156 \tabularnewline
51 & 0.733562467475656 & 0.532875065048689 & 0.266437532524344 \tabularnewline
52 & 0.731998078243979 & 0.536003843512043 & 0.268001921756021 \tabularnewline
53 & 0.68985402835256 & 0.62029194329488 & 0.31014597164744 \tabularnewline
54 & 0.709273009323276 & 0.581453981353447 & 0.290726990676724 \tabularnewline
55 & 0.653063914542342 & 0.693872170915316 & 0.346936085457658 \tabularnewline
56 & 0.796766191573564 & 0.406467616852872 & 0.203233808426436 \tabularnewline
57 & 0.812412844013969 & 0.375174311972062 & 0.187587155986031 \tabularnewline
58 & 0.769711065410408 & 0.460577869179185 & 0.230288934589592 \tabularnewline
59 & 0.72052567392905 & 0.558948652141899 & 0.27947432607095 \tabularnewline
60 & 0.808495023125166 & 0.383009953749667 & 0.191504976874834 \tabularnewline
61 & 0.783816135699971 & 0.432367728600058 & 0.216183864300029 \tabularnewline
62 & 0.825251100058227 & 0.349497799883546 & 0.174748899941773 \tabularnewline
63 & 0.776549567418972 & 0.446900865162057 & 0.223450432581028 \tabularnewline
64 & 0.808098902808257 & 0.383802194383487 & 0.191901097191743 \tabularnewline
65 & 0.75191550018409 & 0.49616899963182 & 0.24808449981591 \tabularnewline
66 & 0.688250659130728 & 0.623498681738544 & 0.311749340869272 \tabularnewline
67 & 0.734883423962166 & 0.530233152075669 & 0.265116576037834 \tabularnewline
68 & 0.668347647552237 & 0.663304704895525 & 0.331652352447763 \tabularnewline
69 & 0.607013953453088 & 0.785972093093823 & 0.392986046546912 \tabularnewline
70 & 0.571929360136336 & 0.856141279727328 & 0.428070639863664 \tabularnewline
71 & 0.494531488018354 & 0.989062976036708 & 0.505468511981646 \tabularnewline
72 & 0.425615369080328 & 0.851230738160656 & 0.574384630919672 \tabularnewline
73 & 0.497758963426079 & 0.995517926852159 & 0.502241036573921 \tabularnewline
74 & 0.416352101390972 & 0.832704202781943 & 0.583647898609029 \tabularnewline
75 & 0.35879369513802 & 0.717587390276039 & 0.64120630486198 \tabularnewline
76 & 0.269759476286694 & 0.539518952573389 & 0.730240523713306 \tabularnewline
77 & 0.270738592190807 & 0.541477184381614 & 0.729261407809193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&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]9[/C][C]0.281329940946462[/C][C]0.562659881892923[/C][C]0.718670059053538[/C][/ROW]
[ROW][C]10[/C][C]0.288810497253267[/C][C]0.577620994506534[/C][C]0.711189502746733[/C][/ROW]
[ROW][C]11[/C][C]0.169571998835558[/C][C]0.339143997671117[/C][C]0.830428001164442[/C][/ROW]
[ROW][C]12[/C][C]0.0920932808310031[/C][C]0.184186561662006[/C][C]0.907906719168997[/C][/ROW]
[ROW][C]13[/C][C]0.0496130691257807[/C][C]0.0992261382515614[/C][C]0.950386930874219[/C][/ROW]
[ROW][C]14[/C][C]0.0244697267287201[/C][C]0.0489394534574402[/C][C]0.97553027327128[/C][/ROW]
[ROW][C]15[/C][C]0.0143045244220217[/C][C]0.0286090488440435[/C][C]0.985695475577978[/C][/ROW]
[ROW][C]16[/C][C]0.00751180430122997[/C][C]0.0150236086024599[/C][C]0.99248819569877[/C][/ROW]
[ROW][C]17[/C][C]0.00332606215853138[/C][C]0.00665212431706276[/C][C]0.996673937841469[/C][/ROW]
[ROW][C]18[/C][C]0.00146302507949166[/C][C]0.00292605015898332[/C][C]0.998536974920508[/C][/ROW]
[ROW][C]19[/C][C]0.000849627134449326[/C][C]0.00169925426889865[/C][C]0.999150372865551[/C][/ROW]
[ROW][C]20[/C][C]0.000365723996860671[/C][C]0.000731447993721342[/C][C]0.999634276003139[/C][/ROW]
[ROW][C]21[/C][C]0.00476163331047462[/C][C]0.00952326662094924[/C][C]0.995238366689525[/C][/ROW]
[ROW][C]22[/C][C]0.00658197696983152[/C][C]0.013163953939663[/C][C]0.993418023030168[/C][/ROW]
[ROW][C]23[/C][C]0.033035852665157[/C][C]0.0660717053303141[/C][C]0.966964147334843[/C][/ROW]
[ROW][C]24[/C][C]0.0358052925725875[/C][C]0.071610585145175[/C][C]0.964194707427413[/C][/ROW]
[ROW][C]25[/C][C]0.0612092279450502[/C][C]0.1224184558901[/C][C]0.93879077205495[/C][/ROW]
[ROW][C]26[/C][C]0.0573635293921866[/C][C]0.114727058784373[/C][C]0.942636470607813[/C][/ROW]
[ROW][C]27[/C][C]0.0838347213764136[/C][C]0.167669442752827[/C][C]0.916165278623586[/C][/ROW]
[ROW][C]28[/C][C]0.133723605848347[/C][C]0.267447211696694[/C][C]0.866276394151653[/C][/ROW]
[ROW][C]29[/C][C]0.112787253025699[/C][C]0.225574506051399[/C][C]0.887212746974301[/C][/ROW]
[ROW][C]30[/C][C]0.245055672162734[/C][C]0.490111344325469[/C][C]0.754944327837266[/C][/ROW]
[ROW][C]31[/C][C]0.198829541734007[/C][C]0.397659083468013[/C][C]0.801170458265993[/C][/ROW]
[ROW][C]32[/C][C]0.180455058319121[/C][C]0.360910116638242[/C][C]0.819544941680879[/C][/ROW]
[ROW][C]33[/C][C]0.24299716915078[/C][C]0.48599433830156[/C][C]0.75700283084922[/C][/ROW]
[ROW][C]34[/C][C]0.214043759516909[/C][C]0.428087519033817[/C][C]0.785956240483091[/C][/ROW]
[ROW][C]35[/C][C]0.172705055936251[/C][C]0.345410111872502[/C][C]0.827294944063749[/C][/ROW]
[ROW][C]36[/C][C]0.13688492450703[/C][C]0.27376984901406[/C][C]0.86311507549297[/C][/ROW]
[ROW][C]37[/C][C]0.14321114466117[/C][C]0.286422289322341[/C][C]0.85678885533883[/C][/ROW]
[ROW][C]38[/C][C]0.200456300507328[/C][C]0.400912601014655[/C][C]0.799543699492672[/C][/ROW]
[ROW][C]39[/C][C]0.27965324632334[/C][C]0.55930649264668[/C][C]0.72034675367666[/C][/ROW]
[ROW][C]40[/C][C]0.466474207981383[/C][C]0.932948415962767[/C][C]0.533525792018617[/C][/ROW]
[ROW][C]41[/C][C]0.429776288311148[/C][C]0.859552576622295[/C][C]0.570223711688852[/C][/ROW]
[ROW][C]42[/C][C]0.45396394032423[/C][C]0.90792788064846[/C][C]0.54603605967577[/C][/ROW]
[ROW][C]43[/C][C]0.526995341866278[/C][C]0.946009316267443[/C][C]0.473004658133721[/C][/ROW]
[ROW][C]44[/C][C]0.480820324737253[/C][C]0.961640649474505[/C][C]0.519179675262747[/C][/ROW]
[ROW][C]45[/C][C]0.626364281350345[/C][C]0.74727143729931[/C][C]0.373635718649655[/C][/ROW]
[ROW][C]46[/C][C]0.721550884486979[/C][C]0.556898231026041[/C][C]0.278449115513021[/C][/ROW]
[ROW][C]47[/C][C]0.671836201519623[/C][C]0.656327596960755[/C][C]0.328163798480377[/C][/ROW]
[ROW][C]48[/C][C]0.631587667871285[/C][C]0.736824664257431[/C][C]0.368412332128715[/C][/ROW]
[ROW][C]49[/C][C]0.742788211040653[/C][C]0.514423577918694[/C][C]0.257211788959347[/C][/ROW]
[ROW][C]50[/C][C]0.692691325953844[/C][C]0.614617348092313[/C][C]0.307308674046156[/C][/ROW]
[ROW][C]51[/C][C]0.733562467475656[/C][C]0.532875065048689[/C][C]0.266437532524344[/C][/ROW]
[ROW][C]52[/C][C]0.731998078243979[/C][C]0.536003843512043[/C][C]0.268001921756021[/C][/ROW]
[ROW][C]53[/C][C]0.68985402835256[/C][C]0.62029194329488[/C][C]0.31014597164744[/C][/ROW]
[ROW][C]54[/C][C]0.709273009323276[/C][C]0.581453981353447[/C][C]0.290726990676724[/C][/ROW]
[ROW][C]55[/C][C]0.653063914542342[/C][C]0.693872170915316[/C][C]0.346936085457658[/C][/ROW]
[ROW][C]56[/C][C]0.796766191573564[/C][C]0.406467616852872[/C][C]0.203233808426436[/C][/ROW]
[ROW][C]57[/C][C]0.812412844013969[/C][C]0.375174311972062[/C][C]0.187587155986031[/C][/ROW]
[ROW][C]58[/C][C]0.769711065410408[/C][C]0.460577869179185[/C][C]0.230288934589592[/C][/ROW]
[ROW][C]59[/C][C]0.72052567392905[/C][C]0.558948652141899[/C][C]0.27947432607095[/C][/ROW]
[ROW][C]60[/C][C]0.808495023125166[/C][C]0.383009953749667[/C][C]0.191504976874834[/C][/ROW]
[ROW][C]61[/C][C]0.783816135699971[/C][C]0.432367728600058[/C][C]0.216183864300029[/C][/ROW]
[ROW][C]62[/C][C]0.825251100058227[/C][C]0.349497799883546[/C][C]0.174748899941773[/C][/ROW]
[ROW][C]63[/C][C]0.776549567418972[/C][C]0.446900865162057[/C][C]0.223450432581028[/C][/ROW]
[ROW][C]64[/C][C]0.808098902808257[/C][C]0.383802194383487[/C][C]0.191901097191743[/C][/ROW]
[ROW][C]65[/C][C]0.75191550018409[/C][C]0.49616899963182[/C][C]0.24808449981591[/C][/ROW]
[ROW][C]66[/C][C]0.688250659130728[/C][C]0.623498681738544[/C][C]0.311749340869272[/C][/ROW]
[ROW][C]67[/C][C]0.734883423962166[/C][C]0.530233152075669[/C][C]0.265116576037834[/C][/ROW]
[ROW][C]68[/C][C]0.668347647552237[/C][C]0.663304704895525[/C][C]0.331652352447763[/C][/ROW]
[ROW][C]69[/C][C]0.607013953453088[/C][C]0.785972093093823[/C][C]0.392986046546912[/C][/ROW]
[ROW][C]70[/C][C]0.571929360136336[/C][C]0.856141279727328[/C][C]0.428070639863664[/C][/ROW]
[ROW][C]71[/C][C]0.494531488018354[/C][C]0.989062976036708[/C][C]0.505468511981646[/C][/ROW]
[ROW][C]72[/C][C]0.425615369080328[/C][C]0.851230738160656[/C][C]0.574384630919672[/C][/ROW]
[ROW][C]73[/C][C]0.497758963426079[/C][C]0.995517926852159[/C][C]0.502241036573921[/C][/ROW]
[ROW][C]74[/C][C]0.416352101390972[/C][C]0.832704202781943[/C][C]0.583647898609029[/C][/ROW]
[ROW][C]75[/C][C]0.35879369513802[/C][C]0.717587390276039[/C][C]0.64120630486198[/C][/ROW]
[ROW][C]76[/C][C]0.269759476286694[/C][C]0.539518952573389[/C][C]0.730240523713306[/C][/ROW]
[ROW][C]77[/C][C]0.270738592190807[/C][C]0.541477184381614[/C][C]0.729261407809193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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
90.2813299409464620.5626598818929230.718670059053538
100.2888104972532670.5776209945065340.711189502746733
110.1695719988355580.3391439976711170.830428001164442
120.09209328083100310.1841865616620060.907906719168997
130.04961306912578070.09922613825156140.950386930874219
140.02446972672872010.04893945345744020.97553027327128
150.01430452442202170.02860904884404350.985695475577978
160.007511804301229970.01502360860245990.99248819569877
170.003326062158531380.006652124317062760.996673937841469
180.001463025079491660.002926050158983320.998536974920508
190.0008496271344493260.001699254268898650.999150372865551
200.0003657239968606710.0007314479937213420.999634276003139
210.004761633310474620.009523266620949240.995238366689525
220.006581976969831520.0131639539396630.993418023030168
230.0330358526651570.06607170533031410.966964147334843
240.03580529257258750.0716105851451750.964194707427413
250.06120922794505020.12241845589010.93879077205495
260.05736352939218660.1147270587843730.942636470607813
270.08383472137641360.1676694427528270.916165278623586
280.1337236058483470.2674472116966940.866276394151653
290.1127872530256990.2255745060513990.887212746974301
300.2450556721627340.4901113443254690.754944327837266
310.1988295417340070.3976590834680130.801170458265993
320.1804550583191210.3609101166382420.819544941680879
330.242997169150780.485994338301560.75700283084922
340.2140437595169090.4280875190338170.785956240483091
350.1727050559362510.3454101118725020.827294944063749
360.136884924507030.273769849014060.86311507549297
370.143211144661170.2864222893223410.85678885533883
380.2004563005073280.4009126010146550.799543699492672
390.279653246323340.559306492646680.72034675367666
400.4664742079813830.9329484159627670.533525792018617
410.4297762883111480.8595525766222950.570223711688852
420.453963940324230.907927880648460.54603605967577
430.5269953418662780.9460093162674430.473004658133721
440.4808203247372530.9616406494745050.519179675262747
450.6263642813503450.747271437299310.373635718649655
460.7215508844869790.5568982310260410.278449115513021
470.6718362015196230.6563275969607550.328163798480377
480.6315876678712850.7368246642574310.368412332128715
490.7427882110406530.5144235779186940.257211788959347
500.6926913259538440.6146173480923130.307308674046156
510.7335624674756560.5328750650486890.266437532524344
520.7319980782439790.5360038435120430.268001921756021
530.689854028352560.620291943294880.31014597164744
540.7092730093232760.5814539813534470.290726990676724
550.6530639145423420.6938721709153160.346936085457658
560.7967661915735640.4064676168528720.203233808426436
570.8124128440139690.3751743119720620.187587155986031
580.7697110654104080.4605778691791850.230288934589592
590.720525673929050.5589486521418990.27947432607095
600.8084950231251660.3830099537496670.191504976874834
610.7838161356999710.4323677286000580.216183864300029
620.8252511000582270.3494977998835460.174748899941773
630.7765495674189720.4469008651620570.223450432581028
640.8080989028082570.3838021943834870.191901097191743
650.751915500184090.496168999631820.24808449981591
660.6882506591307280.6234986817385440.311749340869272
670.7348834239621660.5302331520756690.265116576037834
680.6683476475522370.6633047048955250.331652352447763
690.6070139534530880.7859720930938230.392986046546912
700.5719293601363360.8561412797273280.428070639863664
710.4945314880183540.9890629760367080.505468511981646
720.4256153690803280.8512307381606560.574384630919672
730.4977589634260790.9955179268521590.502241036573921
740.4163521013909720.8327042027819430.583647898609029
750.358793695138020.7175873902760390.64120630486198
760.2697594762866940.5395189525733890.730240523713306
770.2707385921908070.5414771843816140.729261407809193







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.072463768115942NOK
5% type I error level90.130434782608696NOK
10% type I error level120.173913043478261NOK

\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 & 0.072463768115942 & NOK \tabularnewline
5% type I error level & 9 & 0.130434782608696 & NOK \tabularnewline
10% type I error level & 12 & 0.173913043478261 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204340&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]0.072463768115942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.173913043478261[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204340&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204340&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 level50.072463768115942NOK
5% type I error level90.130434782608696NOK
10% type I error level120.173913043478261NOK



Parameters (Session):
par1 = 4 ; par2 = 7 ; par3 = Pearson Chi-Squared ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}