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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 13 Dec 2012 14:48:09 -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/13/t1355428119gz5bl5j82v3o2vr.htm/, Retrieved Tue, 30 Apr 2024 01:50:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199376, Retrieved Tue, 30 Apr 2024 01:50:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R P   [Multiple Regression] [Competence to learn] [2012-11-23 20:32:31] [ec67509cb0a58a77552cc42e4bdf733e]
-   PD      [Multiple Regression] [deel 5 paper: mul...] [2012-12-13 19:48:09] [6c45f368330652e778bc9af460dd8da6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=0

As an alternative you can also use a QR Code:  
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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Used[t] = + 1.61487115955421 -0.360489672730471Weeks[t] + 0.052264453573244UseLimit[t] -0.0180598419378511T40[t] -0.426359829580634T20[t] + 0.727157918038104CorrectAnalysis[t] + 0.168326965126598Useful[t] + 0.0742966761115331`Outcome\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Used[t] =  +  1.61487115955421 -0.360489672730471Weeks[t] +  0.052264453573244UseLimit[t] -0.0180598419378511T40[t] -0.426359829580634T20[t] +  0.727157918038104CorrectAnalysis[t] +  0.168326965126598Useful[t] +  0.0742966761115331`Outcome\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Used[t] =  +  1.61487115955421 -0.360489672730471Weeks[t] +  0.052264453573244UseLimit[t] -0.0180598419378511T40[t] -0.426359829580634T20[t] +  0.727157918038104CorrectAnalysis[t] +  0.168326965126598Useful[t] +  0.0742966761115331`Outcome\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Used[t] = + 1.61487115955421 -0.360489672730471Weeks[t] + 0.052264453573244UseLimit[t] -0.0180598419378511T40[t] -0.426359829580634T20[t] + 0.727157918038104CorrectAnalysis[t] + 0.168326965126598Useful[t] + 0.0742966761115331`Outcome\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.614871159554210.4331513.72820.0002750.000138
Weeks-0.3604896727304710.134036-2.68950.0079890.003995
UseLimit0.0522644535732440.0687730.760.4485070.224254
T40-0.01805984193785110.099436-0.18160.8561310.428066
T20-0.4263598295806340.109061-3.90940.0001417.1e-05
CorrectAnalysis0.7271579180381040.1235355.886300
Useful0.1683269651265980.0745632.25750.0254590.012729
`Outcome\r`0.07429667611153310.0655441.13350.2588450.129422

\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) & 1.61487115955421 & 0.433151 & 3.7282 & 0.000275 & 0.000138 \tabularnewline
Weeks & -0.360489672730471 & 0.134036 & -2.6895 & 0.007989 & 0.003995 \tabularnewline
UseLimit & 0.052264453573244 & 0.068773 & 0.76 & 0.448507 & 0.224254 \tabularnewline
T40 & -0.0180598419378511 & 0.099436 & -0.1816 & 0.856131 & 0.428066 \tabularnewline
T20 & -0.426359829580634 & 0.109061 & -3.9094 & 0.000141 & 7.1e-05 \tabularnewline
CorrectAnalysis & 0.727157918038104 & 0.123535 & 5.8863 & 0 & 0 \tabularnewline
Useful & 0.168326965126598 & 0.074563 & 2.2575 & 0.025459 & 0.012729 \tabularnewline
`Outcome\r` & 0.0742966761115331 & 0.065544 & 1.1335 & 0.258845 & 0.129422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&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]1.61487115955421[/C][C]0.433151[/C][C]3.7282[/C][C]0.000275[/C][C]0.000138[/C][/ROW]
[ROW][C]Weeks[/C][C]-0.360489672730471[/C][C]0.134036[/C][C]-2.6895[/C][C]0.007989[/C][C]0.003995[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.052264453573244[/C][C]0.068773[/C][C]0.76[/C][C]0.448507[/C][C]0.224254[/C][/ROW]
[ROW][C]T40[/C][C]-0.0180598419378511[/C][C]0.099436[/C][C]-0.1816[/C][C]0.856131[/C][C]0.428066[/C][/ROW]
[ROW][C]T20[/C][C]-0.426359829580634[/C][C]0.109061[/C][C]-3.9094[/C][C]0.000141[/C][C]7.1e-05[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.727157918038104[/C][C]0.123535[/C][C]5.8863[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.168326965126598[/C][C]0.074563[/C][C]2.2575[/C][C]0.025459[/C][C]0.012729[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]0.0742966761115331[/C][C]0.065544[/C][C]1.1335[/C][C]0.258845[/C][C]0.129422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=2

As an alternative you can also use a QR Code:  
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)1.614871159554210.4331513.72820.0002750.000138
Weeks-0.3604896727304710.134036-2.68950.0079890.003995
UseLimit0.0522644535732440.0687730.760.4485070.224254
T40-0.01805984193785110.099436-0.18160.8561310.428066
T20-0.4263598295806340.109061-3.90940.0001417.1e-05
CorrectAnalysis0.7271579180381040.1235355.886300
Useful0.1683269651265980.0745632.25750.0254590.012729
`Outcome\r`0.07429667611153310.0655441.13350.2588450.129422






Multiple Linear Regression - Regression Statistics
Multiple R0.560608866339958
R-squared0.314282301018973
Adjusted R-squared0.281405425040431
F-TEST (value)9.55937240582391
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value9.47382283733589e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.386772330325392
Sum Squared Residuals21.8405539837788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.560608866339958 \tabularnewline
R-squared & 0.314282301018973 \tabularnewline
Adjusted R-squared & 0.281405425040431 \tabularnewline
F-TEST (value) & 9.55937240582391 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 9.47382283733589e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.386772330325392 \tabularnewline
Sum Squared Residuals & 21.8405539837788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.560608866339958[/C][/ROW]
[ROW][C]R-squared[/C][C]0.314282301018973[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.281405425040431[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.55937240582391[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]9.47382283733589e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.386772330325392[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]21.8405539837788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.560608866339958
R-squared0.314282301018973
Adjusted R-squared0.281405425040431
F-TEST (value)9.55937240582391
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value9.47382283733589e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.386772330325392
Sum Squared Residuals21.8405539837788






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.281413756379248-0.281413756379248
200.136792784756621-0.136792784756621
300.136792784756621-0.136792784756621
400.13679278475662-0.13679278475662
500.13679278475662-0.13679278475662
600.431680879567996-0.431680879567996
700.136792784756621-0.136792784756621
800.154852626694472-0.154852626694472
900.211089460868154-0.211089460868154
1000.189057238329865-0.189057238329865
1100.207117080267716-0.207117080267716
1200.136792784756621-0.136792784756621
1310.3051197498832190.694880250116781
1400.207117080267716-0.207117080267716
1510.3794164259947520.620583574005248
1610.3974762679326030.602523732067397
1711.10260196343242-0.102601963432418
1800.207117080267716-0.207117080267716
1900.211089460868154-0.211089460868154
2011.12463418597071-0.124634185970707
2100.357384203456463-0.357384203456463
2210.4316808795679960.568319120432004
2300.379416425994752-0.379416425994752
2400.431680879567996-0.431680879567996
2510.2291493028060050.770850697193995
2610.3051197498832190.694880250116781
2700.263353914441398-0.263353914441398
2810.1367927847566210.863207215243379
2900.211089460868154-0.211089460868154
3000.305119749883219-0.305119749883219
3100.136792784756621-0.136792784756621
3200.189057238329865-0.189057238329865
3300.357384203456463-0.357384203456463
3400.229149302806005-0.229149302806005
3500.136792784756621-0.136792784756621
3600.136792784756621-0.136792784756621
3710.3754440453943140.624555954605686
3810.2110894608681540.788910539131846
3900.379416425994752-0.379416425994752
4000.32317959182107-0.32317959182107
4111.10657434403286-0.106574344032856
4210.2110894608681540.788910539131846
4300.431680879567996-0.431680879567996
4400.207117080267716-0.207117080267716
4500.305119749883219-0.305119749883219
4600.379416425994752-0.379416425994752
4700.136792784756621-0.136792784756621
4800.211089460868154-0.211089460868154
4900.379416425994752-0.379416425994752
5000.136792784756621-0.136792784756621
5110.1548526266944720.845147373305528
5211.10260196343242-0.102601963432418
5300.211089460868154-0.211089460868154
5410.8639507027947240.136049297205276
5500.136792784756621-0.136792784756621
5610.2291493028060050.770850697193995
5710.3794164259947520.620583574005248
5800.211089460868154-0.211089460868154
5900.211089460868154-0.211089460868154
6011.17689863954395-0.176898639543951
6100.281413756379249-0.281413756379249
6210.3051197498832190.694880250116781
6300.136792784756621-0.136792784756621
6400.281413756379249-0.281413756379249
6500.136792784756621-0.136792784756621
6600.136792784756621-0.136792784756621
6711.05033750985917-0.0503375098591735
6800.189057238329865-0.189057238329865
6900.211089460868154-0.211089460868154
7010.1367927847566210.863207215243379
7100.136792784756621-0.136792784756621
7200.211089460868154-0.211089460868154
7310.2110894608681540.788910539131846
7410.1890572383298650.810942761670135
7500.211089460868154-0.211089460868154
7600.397476267932603-0.397476267932603
7700.211089460868154-0.211089460868154
7810.3794164259947520.620583574005248
7910.9563072208441080.0436927791558916
8000.32317959182107-0.32317959182107
8100.136792784756621-0.136792784756621
8210.2633539144413980.736646085558602
8300.136792784756621-0.136792784756621
8410.8639507027947240.136049297205276
8500.379416425994752-0.379416425994752
8600.189057238329865-0.189057238329865
8700.167733284616774-0.167733284616774
8810.5940931141974080.405906885802592
8900.0411721549319971-0.0411721549319971
9000.11546883104353-0.11546883104353
9100.209499120058595-0.209499120058595
9200.519796438085875-0.519796438085875
9300.261763573631839-0.261763573631839
9400.0411721549319971-0.0411721549319971
9500.467531984512631-0.467531984512631
9600.11546883104353-0.11546883104353
9700.519796438085875-0.519796438085875
9800.0411721549319971-0.0411721549319971
9900.0934366085052411-0.0934366085052411
10000.11546883104353-0.11546883104353
10100.167733284616774-0.167733284616774
10200.0411721549319971-0.0411721549319971
10300.0411721549319971-0.0411721549319971
10400.0411721549319971-0.0411721549319971
10510.4675319845126310.532468015487369
10600.0411721549319971-0.0411721549319971
10700.0411721549319971-0.0411721549319971
10810.5197964380858750.480203561914125
10900.0411721549319971-0.0411721549319971
11000.0934366085052411-0.0934366085052411
11110.6881234032124730.311876596787527
11200.467531984512631-0.467531984512631
11310.04117215493199710.958827845068003
11410.5197964380858750.480203561914125
11500.0934366085052411-0.0934366085052411
11600.0411721549319971-0.0411721549319971
11700.167733284616774-0.167733284616774
11800.0934366085052411-0.0934366085052411
11900.0411721549319971-0.0411721549319971
12000.11546883104353-0.11546883104353
12100.0934366085052411-0.0934366085052411
12200.0411721549319971-0.0411721549319971
12310.5197964380858750.480203561914125
12410.2837957961701280.716204203829872
12500.11546883104353-0.11546883104353
12600.467531984512631-0.467531984512631
12700.209499120058595-0.209499120058595
12800.11546883104353-0.11546883104353
12900.0411721549319971-0.0411721549319971
13000.11546883104353-0.11546883104353
13100.0934366085052411-0.0934366085052411
13200.167733284616774-0.167733284616774
13310.0934366085052410.906563391494759
13400.0411721549319971-0.0411721549319971
13500.0411721549319971-0.0411721549319971
13600.0411721549319971-0.0411721549319971
13710.3360602497433720.663939750256628
13810.7624200793240060.237579920675994
13900.467531984512631-0.467531984512631
14000.0411721549319971-0.0411721549319971
14110.8426267490816340.157373250918366
14210.5418286606241640.458171339375836
14300.0934366085052411-0.0934366085052411
14400.283795796170128-0.283795796170128
14500.209499120058595-0.209499120058595
14600.541828660624164-0.541828660624164
14710.4675319845126310.532468015487369
14800.467531984512631-0.467531984512631
14900.0934366085052411-0.0934366085052411
15000.283795796170128-0.283795796170128
15100.11546883104353-0.11546883104353
15210.8205945265433450.179405473456655
15310.9889214916699430.0110785083300572
15410.0934366085052410.906563391494759

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.281413756379248 & -0.281413756379248 \tabularnewline
2 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
3 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
4 & 0 & 0.13679278475662 & -0.13679278475662 \tabularnewline
5 & 0 & 0.13679278475662 & -0.13679278475662 \tabularnewline
6 & 0 & 0.431680879567996 & -0.431680879567996 \tabularnewline
7 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
8 & 0 & 0.154852626694472 & -0.154852626694472 \tabularnewline
9 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
10 & 0 & 0.189057238329865 & -0.189057238329865 \tabularnewline
11 & 0 & 0.207117080267716 & -0.207117080267716 \tabularnewline
12 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
13 & 1 & 0.305119749883219 & 0.694880250116781 \tabularnewline
14 & 0 & 0.207117080267716 & -0.207117080267716 \tabularnewline
15 & 1 & 0.379416425994752 & 0.620583574005248 \tabularnewline
16 & 1 & 0.397476267932603 & 0.602523732067397 \tabularnewline
17 & 1 & 1.10260196343242 & -0.102601963432418 \tabularnewline
18 & 0 & 0.207117080267716 & -0.207117080267716 \tabularnewline
19 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
20 & 1 & 1.12463418597071 & -0.124634185970707 \tabularnewline
21 & 0 & 0.357384203456463 & -0.357384203456463 \tabularnewline
22 & 1 & 0.431680879567996 & 0.568319120432004 \tabularnewline
23 & 0 & 0.379416425994752 & -0.379416425994752 \tabularnewline
24 & 0 & 0.431680879567996 & -0.431680879567996 \tabularnewline
25 & 1 & 0.229149302806005 & 0.770850697193995 \tabularnewline
26 & 1 & 0.305119749883219 & 0.694880250116781 \tabularnewline
27 & 0 & 0.263353914441398 & -0.263353914441398 \tabularnewline
28 & 1 & 0.136792784756621 & 0.863207215243379 \tabularnewline
29 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
30 & 0 & 0.305119749883219 & -0.305119749883219 \tabularnewline
31 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
32 & 0 & 0.189057238329865 & -0.189057238329865 \tabularnewline
33 & 0 & 0.357384203456463 & -0.357384203456463 \tabularnewline
34 & 0 & 0.229149302806005 & -0.229149302806005 \tabularnewline
35 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
36 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
37 & 1 & 0.375444045394314 & 0.624555954605686 \tabularnewline
38 & 1 & 0.211089460868154 & 0.788910539131846 \tabularnewline
39 & 0 & 0.379416425994752 & -0.379416425994752 \tabularnewline
40 & 0 & 0.32317959182107 & -0.32317959182107 \tabularnewline
41 & 1 & 1.10657434403286 & -0.106574344032856 \tabularnewline
42 & 1 & 0.211089460868154 & 0.788910539131846 \tabularnewline
43 & 0 & 0.431680879567996 & -0.431680879567996 \tabularnewline
44 & 0 & 0.207117080267716 & -0.207117080267716 \tabularnewline
45 & 0 & 0.305119749883219 & -0.305119749883219 \tabularnewline
46 & 0 & 0.379416425994752 & -0.379416425994752 \tabularnewline
47 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
48 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
49 & 0 & 0.379416425994752 & -0.379416425994752 \tabularnewline
50 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
51 & 1 & 0.154852626694472 & 0.845147373305528 \tabularnewline
52 & 1 & 1.10260196343242 & -0.102601963432418 \tabularnewline
53 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
54 & 1 & 0.863950702794724 & 0.136049297205276 \tabularnewline
55 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
56 & 1 & 0.229149302806005 & 0.770850697193995 \tabularnewline
57 & 1 & 0.379416425994752 & 0.620583574005248 \tabularnewline
58 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
59 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
60 & 1 & 1.17689863954395 & -0.176898639543951 \tabularnewline
61 & 0 & 0.281413756379249 & -0.281413756379249 \tabularnewline
62 & 1 & 0.305119749883219 & 0.694880250116781 \tabularnewline
63 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
64 & 0 & 0.281413756379249 & -0.281413756379249 \tabularnewline
65 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
66 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
67 & 1 & 1.05033750985917 & -0.0503375098591735 \tabularnewline
68 & 0 & 0.189057238329865 & -0.189057238329865 \tabularnewline
69 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
70 & 1 & 0.136792784756621 & 0.863207215243379 \tabularnewline
71 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
72 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
73 & 1 & 0.211089460868154 & 0.788910539131846 \tabularnewline
74 & 1 & 0.189057238329865 & 0.810942761670135 \tabularnewline
75 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
76 & 0 & 0.397476267932603 & -0.397476267932603 \tabularnewline
77 & 0 & 0.211089460868154 & -0.211089460868154 \tabularnewline
78 & 1 & 0.379416425994752 & 0.620583574005248 \tabularnewline
79 & 1 & 0.956307220844108 & 0.0436927791558916 \tabularnewline
80 & 0 & 0.32317959182107 & -0.32317959182107 \tabularnewline
81 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
82 & 1 & 0.263353914441398 & 0.736646085558602 \tabularnewline
83 & 0 & 0.136792784756621 & -0.136792784756621 \tabularnewline
84 & 1 & 0.863950702794724 & 0.136049297205276 \tabularnewline
85 & 0 & 0.379416425994752 & -0.379416425994752 \tabularnewline
86 & 0 & 0.189057238329865 & -0.189057238329865 \tabularnewline
87 & 0 & 0.167733284616774 & -0.167733284616774 \tabularnewline
88 & 1 & 0.594093114197408 & 0.405906885802592 \tabularnewline
89 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
90 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
91 & 0 & 0.209499120058595 & -0.209499120058595 \tabularnewline
92 & 0 & 0.519796438085875 & -0.519796438085875 \tabularnewline
93 & 0 & 0.261763573631839 & -0.261763573631839 \tabularnewline
94 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
95 & 0 & 0.467531984512631 & -0.467531984512631 \tabularnewline
96 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
97 & 0 & 0.519796438085875 & -0.519796438085875 \tabularnewline
98 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
99 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
100 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
101 & 0 & 0.167733284616774 & -0.167733284616774 \tabularnewline
102 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
103 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
104 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
105 & 1 & 0.467531984512631 & 0.532468015487369 \tabularnewline
106 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
107 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
108 & 1 & 0.519796438085875 & 0.480203561914125 \tabularnewline
109 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
110 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
111 & 1 & 0.688123403212473 & 0.311876596787527 \tabularnewline
112 & 0 & 0.467531984512631 & -0.467531984512631 \tabularnewline
113 & 1 & 0.0411721549319971 & 0.958827845068003 \tabularnewline
114 & 1 & 0.519796438085875 & 0.480203561914125 \tabularnewline
115 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
116 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
117 & 0 & 0.167733284616774 & -0.167733284616774 \tabularnewline
118 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
119 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
120 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
121 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
122 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
123 & 1 & 0.519796438085875 & 0.480203561914125 \tabularnewline
124 & 1 & 0.283795796170128 & 0.716204203829872 \tabularnewline
125 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
126 & 0 & 0.467531984512631 & -0.467531984512631 \tabularnewline
127 & 0 & 0.209499120058595 & -0.209499120058595 \tabularnewline
128 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
129 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
130 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
131 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
132 & 0 & 0.167733284616774 & -0.167733284616774 \tabularnewline
133 & 1 & 0.093436608505241 & 0.906563391494759 \tabularnewline
134 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
135 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
136 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
137 & 1 & 0.336060249743372 & 0.663939750256628 \tabularnewline
138 & 1 & 0.762420079324006 & 0.237579920675994 \tabularnewline
139 & 0 & 0.467531984512631 & -0.467531984512631 \tabularnewline
140 & 0 & 0.0411721549319971 & -0.0411721549319971 \tabularnewline
141 & 1 & 0.842626749081634 & 0.157373250918366 \tabularnewline
142 & 1 & 0.541828660624164 & 0.458171339375836 \tabularnewline
143 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
144 & 0 & 0.283795796170128 & -0.283795796170128 \tabularnewline
145 & 0 & 0.209499120058595 & -0.209499120058595 \tabularnewline
146 & 0 & 0.541828660624164 & -0.541828660624164 \tabularnewline
147 & 1 & 0.467531984512631 & 0.532468015487369 \tabularnewline
148 & 0 & 0.467531984512631 & -0.467531984512631 \tabularnewline
149 & 0 & 0.0934366085052411 & -0.0934366085052411 \tabularnewline
150 & 0 & 0.283795796170128 & -0.283795796170128 \tabularnewline
151 & 0 & 0.11546883104353 & -0.11546883104353 \tabularnewline
152 & 1 & 0.820594526543345 & 0.179405473456655 \tabularnewline
153 & 1 & 0.988921491669943 & 0.0110785083300572 \tabularnewline
154 & 1 & 0.093436608505241 & 0.906563391494759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&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]0.281413756379248[/C][C]-0.281413756379248[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.13679278475662[/C][C]-0.13679278475662[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.13679278475662[/C][C]-0.13679278475662[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.431680879567996[/C][C]-0.431680879567996[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.154852626694472[/C][C]-0.154852626694472[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.189057238329865[/C][C]-0.189057238329865[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.207117080267716[/C][C]-0.207117080267716[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.305119749883219[/C][C]0.694880250116781[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.207117080267716[/C][C]-0.207117080267716[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.379416425994752[/C][C]0.620583574005248[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.397476267932603[/C][C]0.602523732067397[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.10260196343242[/C][C]-0.102601963432418[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.207117080267716[/C][C]-0.207117080267716[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.12463418597071[/C][C]-0.124634185970707[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.357384203456463[/C][C]-0.357384203456463[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.431680879567996[/C][C]0.568319120432004[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.379416425994752[/C][C]-0.379416425994752[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.431680879567996[/C][C]-0.431680879567996[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.229149302806005[/C][C]0.770850697193995[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.305119749883219[/C][C]0.694880250116781[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.263353914441398[/C][C]-0.263353914441398[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.136792784756621[/C][C]0.863207215243379[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.305119749883219[/C][C]-0.305119749883219[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.189057238329865[/C][C]-0.189057238329865[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.357384203456463[/C][C]-0.357384203456463[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.229149302806005[/C][C]-0.229149302806005[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.375444045394314[/C][C]0.624555954605686[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.211089460868154[/C][C]0.788910539131846[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.379416425994752[/C][C]-0.379416425994752[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.32317959182107[/C][C]-0.32317959182107[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.10657434403286[/C][C]-0.106574344032856[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.211089460868154[/C][C]0.788910539131846[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.431680879567996[/C][C]-0.431680879567996[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.207117080267716[/C][C]-0.207117080267716[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.305119749883219[/C][C]-0.305119749883219[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.379416425994752[/C][C]-0.379416425994752[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.379416425994752[/C][C]-0.379416425994752[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.154852626694472[/C][C]0.845147373305528[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.10260196343242[/C][C]-0.102601963432418[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.863950702794724[/C][C]0.136049297205276[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.229149302806005[/C][C]0.770850697193995[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.379416425994752[/C][C]0.620583574005248[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.17689863954395[/C][C]-0.176898639543951[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.281413756379249[/C][C]-0.281413756379249[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.305119749883219[/C][C]0.694880250116781[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.281413756379249[/C][C]-0.281413756379249[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.05033750985917[/C][C]-0.0503375098591735[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.189057238329865[/C][C]-0.189057238329865[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.136792784756621[/C][C]0.863207215243379[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.211089460868154[/C][C]0.788910539131846[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.189057238329865[/C][C]0.810942761670135[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.397476267932603[/C][C]-0.397476267932603[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.211089460868154[/C][C]-0.211089460868154[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.379416425994752[/C][C]0.620583574005248[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.956307220844108[/C][C]0.0436927791558916[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.32317959182107[/C][C]-0.32317959182107[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.263353914441398[/C][C]0.736646085558602[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.136792784756621[/C][C]-0.136792784756621[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.863950702794724[/C][C]0.136049297205276[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.379416425994752[/C][C]-0.379416425994752[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.189057238329865[/C][C]-0.189057238329865[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.167733284616774[/C][C]-0.167733284616774[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.594093114197408[/C][C]0.405906885802592[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.209499120058595[/C][C]-0.209499120058595[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.519796438085875[/C][C]-0.519796438085875[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.261763573631839[/C][C]-0.261763573631839[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.467531984512631[/C][C]-0.467531984512631[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.519796438085875[/C][C]-0.519796438085875[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.167733284616774[/C][C]-0.167733284616774[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0.467531984512631[/C][C]0.532468015487369[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0.519796438085875[/C][C]0.480203561914125[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.688123403212473[/C][C]0.311876596787527[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.467531984512631[/C][C]-0.467531984512631[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0.0411721549319971[/C][C]0.958827845068003[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.519796438085875[/C][C]0.480203561914125[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.167733284616774[/C][C]-0.167733284616774[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0.519796438085875[/C][C]0.480203561914125[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.283795796170128[/C][C]0.716204203829872[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.467531984512631[/C][C]-0.467531984512631[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.209499120058595[/C][C]-0.209499120058595[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.167733284616774[/C][C]-0.167733284616774[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0.093436608505241[/C][C]0.906563391494759[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.336060249743372[/C][C]0.663939750256628[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.762420079324006[/C][C]0.237579920675994[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.467531984512631[/C][C]-0.467531984512631[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0411721549319971[/C][C]-0.0411721549319971[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.842626749081634[/C][C]0.157373250918366[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.541828660624164[/C][C]0.458171339375836[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.283795796170128[/C][C]-0.283795796170128[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.209499120058595[/C][C]-0.209499120058595[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.541828660624164[/C][C]-0.541828660624164[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]0.467531984512631[/C][C]0.532468015487369[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.467531984512631[/C][C]-0.467531984512631[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0934366085052411[/C][C]-0.0934366085052411[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.283795796170128[/C][C]-0.283795796170128[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.11546883104353[/C][C]-0.11546883104353[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.820594526543345[/C][C]0.179405473456655[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.988921491669943[/C][C]0.0110785083300572[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]0.093436608505241[/C][C]0.906563391494759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.281413756379248-0.281413756379248
200.136792784756621-0.136792784756621
300.136792784756621-0.136792784756621
400.13679278475662-0.13679278475662
500.13679278475662-0.13679278475662
600.431680879567996-0.431680879567996
700.136792784756621-0.136792784756621
800.154852626694472-0.154852626694472
900.211089460868154-0.211089460868154
1000.189057238329865-0.189057238329865
1100.207117080267716-0.207117080267716
1200.136792784756621-0.136792784756621
1310.3051197498832190.694880250116781
1400.207117080267716-0.207117080267716
1510.3794164259947520.620583574005248
1610.3974762679326030.602523732067397
1711.10260196343242-0.102601963432418
1800.207117080267716-0.207117080267716
1900.211089460868154-0.211089460868154
2011.12463418597071-0.124634185970707
2100.357384203456463-0.357384203456463
2210.4316808795679960.568319120432004
2300.379416425994752-0.379416425994752
2400.431680879567996-0.431680879567996
2510.2291493028060050.770850697193995
2610.3051197498832190.694880250116781
2700.263353914441398-0.263353914441398
2810.1367927847566210.863207215243379
2900.211089460868154-0.211089460868154
3000.305119749883219-0.305119749883219
3100.136792784756621-0.136792784756621
3200.189057238329865-0.189057238329865
3300.357384203456463-0.357384203456463
3400.229149302806005-0.229149302806005
3500.136792784756621-0.136792784756621
3600.136792784756621-0.136792784756621
3710.3754440453943140.624555954605686
3810.2110894608681540.788910539131846
3900.379416425994752-0.379416425994752
4000.32317959182107-0.32317959182107
4111.10657434403286-0.106574344032856
4210.2110894608681540.788910539131846
4300.431680879567996-0.431680879567996
4400.207117080267716-0.207117080267716
4500.305119749883219-0.305119749883219
4600.379416425994752-0.379416425994752
4700.136792784756621-0.136792784756621
4800.211089460868154-0.211089460868154
4900.379416425994752-0.379416425994752
5000.136792784756621-0.136792784756621
5110.1548526266944720.845147373305528
5211.10260196343242-0.102601963432418
5300.211089460868154-0.211089460868154
5410.8639507027947240.136049297205276
5500.136792784756621-0.136792784756621
5610.2291493028060050.770850697193995
5710.3794164259947520.620583574005248
5800.211089460868154-0.211089460868154
5900.211089460868154-0.211089460868154
6011.17689863954395-0.176898639543951
6100.281413756379249-0.281413756379249
6210.3051197498832190.694880250116781
6300.136792784756621-0.136792784756621
6400.281413756379249-0.281413756379249
6500.136792784756621-0.136792784756621
6600.136792784756621-0.136792784756621
6711.05033750985917-0.0503375098591735
6800.189057238329865-0.189057238329865
6900.211089460868154-0.211089460868154
7010.1367927847566210.863207215243379
7100.136792784756621-0.136792784756621
7200.211089460868154-0.211089460868154
7310.2110894608681540.788910539131846
7410.1890572383298650.810942761670135
7500.211089460868154-0.211089460868154
7600.397476267932603-0.397476267932603
7700.211089460868154-0.211089460868154
7810.3794164259947520.620583574005248
7910.9563072208441080.0436927791558916
8000.32317959182107-0.32317959182107
8100.136792784756621-0.136792784756621
8210.2633539144413980.736646085558602
8300.136792784756621-0.136792784756621
8410.8639507027947240.136049297205276
8500.379416425994752-0.379416425994752
8600.189057238329865-0.189057238329865
8700.167733284616774-0.167733284616774
8810.5940931141974080.405906885802592
8900.0411721549319971-0.0411721549319971
9000.11546883104353-0.11546883104353
9100.209499120058595-0.209499120058595
9200.519796438085875-0.519796438085875
9300.261763573631839-0.261763573631839
9400.0411721549319971-0.0411721549319971
9500.467531984512631-0.467531984512631
9600.11546883104353-0.11546883104353
9700.519796438085875-0.519796438085875
9800.0411721549319971-0.0411721549319971
9900.0934366085052411-0.0934366085052411
10000.11546883104353-0.11546883104353
10100.167733284616774-0.167733284616774
10200.0411721549319971-0.0411721549319971
10300.0411721549319971-0.0411721549319971
10400.0411721549319971-0.0411721549319971
10510.4675319845126310.532468015487369
10600.0411721549319971-0.0411721549319971
10700.0411721549319971-0.0411721549319971
10810.5197964380858750.480203561914125
10900.0411721549319971-0.0411721549319971
11000.0934366085052411-0.0934366085052411
11110.6881234032124730.311876596787527
11200.467531984512631-0.467531984512631
11310.04117215493199710.958827845068003
11410.5197964380858750.480203561914125
11500.0934366085052411-0.0934366085052411
11600.0411721549319971-0.0411721549319971
11700.167733284616774-0.167733284616774
11800.0934366085052411-0.0934366085052411
11900.0411721549319971-0.0411721549319971
12000.11546883104353-0.11546883104353
12100.0934366085052411-0.0934366085052411
12200.0411721549319971-0.0411721549319971
12310.5197964380858750.480203561914125
12410.2837957961701280.716204203829872
12500.11546883104353-0.11546883104353
12600.467531984512631-0.467531984512631
12700.209499120058595-0.209499120058595
12800.11546883104353-0.11546883104353
12900.0411721549319971-0.0411721549319971
13000.11546883104353-0.11546883104353
13100.0934366085052411-0.0934366085052411
13200.167733284616774-0.167733284616774
13310.0934366085052410.906563391494759
13400.0411721549319971-0.0411721549319971
13500.0411721549319971-0.0411721549319971
13600.0411721549319971-0.0411721549319971
13710.3360602497433720.663939750256628
13810.7624200793240060.237579920675994
13900.467531984512631-0.467531984512631
14000.0411721549319971-0.0411721549319971
14110.8426267490816340.157373250918366
14210.5418286606241640.458171339375836
14300.0934366085052411-0.0934366085052411
14400.283795796170128-0.283795796170128
14500.209499120058595-0.209499120058595
14600.541828660624164-0.541828660624164
14710.4675319845126310.532468015487369
14800.467531984512631-0.467531984512631
14900.0934366085052411-0.0934366085052411
15000.283795796170128-0.283795796170128
15100.11546883104353-0.11546883104353
15210.8205945265433450.179405473456655
15310.9889214916699430.0110785083300572
15410.0934366085052410.906563391494759






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11001
12001
130.1574630465375020.3149260930750030.842536953462498
140.08473720172018320.1694744034403660.915262798279817
150.1116798801027570.2233597602055140.888320119897243
160.06781887382598690.1356377476519740.932181126174013
170.03670454974422210.07340909948844410.963295450255778
180.01943754958792450.0388750991758490.980562450412075
190.009834793893602970.01966958778720590.990165206106397
200.005549150929786120.01109830185957220.994450849070214
210.01568299126938050.03136598253876090.98431700873062
220.05302941669302350.1060588333860470.946970583306977
230.1638753681612580.3277507363225160.836124631838742
240.1723152780481480.3446305560962960.827684721951852
250.3297026674678320.6594053349356650.670297332532168
260.3497036222821620.6994072445643240.650296377717838
270.3200151697352880.6400303394705760.679984830264712
280.6266367310280710.7467265379438580.373363268971929
290.5703593135628290.8592813728743420.429640686437171
300.6492125894038250.701574821192350.350787410596175
310.5922074817720080.8155850364559830.407792518227992
320.5465948611353070.9068102777293860.453405138864693
330.5204091721617110.9591816556765780.479590827838289
340.5118282988918460.9763434022163070.488171701108154
350.455901248715760.9118024974315190.54409875128424
360.401129492042850.8022589840856990.59887050795715
370.4380345256856080.8760690513712160.561965474314392
380.6588252847227590.6823494305544820.341174715277241
390.7044613692466720.5910772615066560.295538630753328
400.7597356066913780.4805287866172440.240264393308622
410.7148598032479440.5702803935041110.285140196752056
420.8286312522035420.3427374955929150.171368747796458
430.82684668554170.34630662891660.1731533144583
440.7957649427207450.4084701145585110.204235057279255
450.790613110348780.4187737793024410.20938688965122
460.8032347924226550.393530415154690.196765207577345
470.769347149147070.461305701705860.23065285085293
480.7420941439450970.5158117121098060.257905856054903
490.7517173755811280.4965652488377450.248282624418872
500.7146347666002530.5707304667994930.285365233399747
510.834384323687360.331231352625280.16561567631264
520.8036994259580140.3926011480839710.196300574041986
530.7792745073198270.4414509853603470.220725492680174
540.7519329283165950.4961341433668090.248067071683405
550.7173104211475380.5653791577049240.282689578852462
560.8345607878646430.3308784242707150.165439212135357
570.863532668031660.2729346639366810.13646733196834
580.846131000669940.307737998660120.15386899933006
590.8274687794541830.3450624410916330.172531220545817
600.7974466985666130.4051066028667740.202553301433387
610.769179031629770.461641936740460.23082096837023
620.8224186884467620.3551626231064750.177581311553238
630.7944061678322830.4111876643354350.205593832167717
640.7648517217790020.4702965564419960.235148278220998
650.7325584148344220.5348831703311560.267441585165578
660.698717330235810.6025653395283810.30128266976419
670.6635791994784490.6728416010431010.33642080052155
680.6483583367037230.7032833265925530.351641663296277
690.6241074467118710.7517851065762580.375892553288129
700.7801370961381810.4397258077236390.219862903861819
710.7507631548425930.4984736903148130.249236845157406
720.7302038413326110.5395923173347780.269796158667389
730.8353016692815910.3293966614368180.164698330718409
740.9291795120458910.1416409759082170.0708204879541087
750.9171678343656370.1656643312687260.0828321656343631
760.9191758925647820.1616482148704350.0808241074352177
770.9069073894761780.1861852210476440.0930926105238221
780.932634214883250.13473157023350.0673657851167498
790.9170436375130460.1659127249739080.082956362486954
800.9115665526989190.1768668946021620.0884334473010811
810.8928197905757810.2143604188484370.107180209424219
820.9479577722216880.1040844555566240.0520422277783122
830.9352830363620470.1294339272759060.0647169636379528
840.927101533198040.145796933603920.0728984668019601
850.9169440852608920.1661118294782150.0830559147391076
860.8968300885617260.2063398228765480.103169911438274
870.8803424894963380.2393150210073230.119657510503662
880.8675389606417020.2649220787165970.132461039358298
890.8391711567435840.3216576865128320.160828843256416
900.807836148801220.3843277023975610.19216385119878
910.78025293379950.4394941324009990.2197470662005
920.8319863836436050.3360272327127890.168013616356395
930.8291580462809660.3416839074380680.170841953719034
940.7955100388441360.4089799223117290.204489961155864
950.80334933329510.3933013334097990.1966506667049
960.7665748804874640.4668502390250730.233425119512536
970.8153401077800970.3693197844398050.184659892219903
980.7794456846584320.4411086306831360.220554315341568
990.752579616380490.4948407672390210.24742038361951
1000.7102867966679530.5794264066640940.289713203332047
1010.6839316111846550.632136777630690.316068388815345
1020.6363191453316580.7273617093366830.363680854668342
1030.5862455483128820.8275089033742360.413754451687118
1040.5344658456912370.9310683086175250.465534154308763
1050.6040975936727390.7918048126545230.395902406327261
1060.5519461685679570.8961076628640850.448053831432043
1070.4986056198573720.9972112397147450.501394380142627
1080.5064835135139940.9870329729720130.493516486486006
1090.4523183667072150.904636733414430.547681633292785
1100.4184988915982670.8369977831965340.581501108401733
1110.3798280106306230.7596560212612460.620171989369377
1120.3934512925573160.7869025851146310.606548707442684
1130.7201656395822040.5596687208355920.279834360417796
1140.7166822360562860.5666355278874280.283317763943714
1150.6825361390932390.6349277218135210.317463860906761
1160.6291793845399740.7416412309200520.370820615460026
1170.6193330760379060.7613338479241880.380666923962094
1180.5900894705089210.8198210589821590.409910529491079
1190.5312247353816580.9375505292366830.468775264618342
1200.4719615408639480.9439230817278960.528038459136052
1210.4476775406987190.8953550813974380.552322459301281
1220.3869701671339260.7739403342678520.613029832866074
1230.3694370670170720.7388741340341440.630562932982928
1240.561684554752260.876630890495480.43831544524774
1250.4963421700980640.9926843401961280.503657829901936
1260.4944977271544840.9889954543089680.505502272845516
1270.4287657129971330.8575314259942650.571234287002867
1280.3617107462819090.7234214925638180.638289253718091
1290.2971147458332950.594229491666590.702885254166705
1300.2379036500243190.4758073000486370.762096349975681
1310.2252278338915960.4504556677831920.774772166108404
1320.2846833374266510.5693666748533010.715316662573349
1330.3776319459093870.7552638918187750.622368054090613
1340.3026232647886030.6052465295772060.697376735211397
1350.2340140972584560.4680281945169120.765985902741544
1360.1742880224933780.3485760449867560.825711977506622
1370.187090305659910.374180611319820.81290969434009
1380.1378993052767060.2757986105534130.862100694723294
1390.1334335919388460.2668671838776910.866566408061154
1400.0844751104155940.1689502208311880.915524889584406
1410.05255220315642920.1051044063128580.947447796843571
1420.08225226386985040.1645045277397010.91774773613015
1430.06515019875288990.130300397505780.93484980124711

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0.157463046537502 & 0.314926093075003 & 0.842536953462498 \tabularnewline
14 & 0.0847372017201832 & 0.169474403440366 & 0.915262798279817 \tabularnewline
15 & 0.111679880102757 & 0.223359760205514 & 0.888320119897243 \tabularnewline
16 & 0.0678188738259869 & 0.135637747651974 & 0.932181126174013 \tabularnewline
17 & 0.0367045497442221 & 0.0734090994884441 & 0.963295450255778 \tabularnewline
18 & 0.0194375495879245 & 0.038875099175849 & 0.980562450412075 \tabularnewline
19 & 0.00983479389360297 & 0.0196695877872059 & 0.990165206106397 \tabularnewline
20 & 0.00554915092978612 & 0.0110983018595722 & 0.994450849070214 \tabularnewline
21 & 0.0156829912693805 & 0.0313659825387609 & 0.98431700873062 \tabularnewline
22 & 0.0530294166930235 & 0.106058833386047 & 0.946970583306977 \tabularnewline
23 & 0.163875368161258 & 0.327750736322516 & 0.836124631838742 \tabularnewline
24 & 0.172315278048148 & 0.344630556096296 & 0.827684721951852 \tabularnewline
25 & 0.329702667467832 & 0.659405334935665 & 0.670297332532168 \tabularnewline
26 & 0.349703622282162 & 0.699407244564324 & 0.650296377717838 \tabularnewline
27 & 0.320015169735288 & 0.640030339470576 & 0.679984830264712 \tabularnewline
28 & 0.626636731028071 & 0.746726537943858 & 0.373363268971929 \tabularnewline
29 & 0.570359313562829 & 0.859281372874342 & 0.429640686437171 \tabularnewline
30 & 0.649212589403825 & 0.70157482119235 & 0.350787410596175 \tabularnewline
31 & 0.592207481772008 & 0.815585036455983 & 0.407792518227992 \tabularnewline
32 & 0.546594861135307 & 0.906810277729386 & 0.453405138864693 \tabularnewline
33 & 0.520409172161711 & 0.959181655676578 & 0.479590827838289 \tabularnewline
34 & 0.511828298891846 & 0.976343402216307 & 0.488171701108154 \tabularnewline
35 & 0.45590124871576 & 0.911802497431519 & 0.54409875128424 \tabularnewline
36 & 0.40112949204285 & 0.802258984085699 & 0.59887050795715 \tabularnewline
37 & 0.438034525685608 & 0.876069051371216 & 0.561965474314392 \tabularnewline
38 & 0.658825284722759 & 0.682349430554482 & 0.341174715277241 \tabularnewline
39 & 0.704461369246672 & 0.591077261506656 & 0.295538630753328 \tabularnewline
40 & 0.759735606691378 & 0.480528786617244 & 0.240264393308622 \tabularnewline
41 & 0.714859803247944 & 0.570280393504111 & 0.285140196752056 \tabularnewline
42 & 0.828631252203542 & 0.342737495592915 & 0.171368747796458 \tabularnewline
43 & 0.8268466855417 & 0.3463066289166 & 0.1731533144583 \tabularnewline
44 & 0.795764942720745 & 0.408470114558511 & 0.204235057279255 \tabularnewline
45 & 0.79061311034878 & 0.418773779302441 & 0.20938688965122 \tabularnewline
46 & 0.803234792422655 & 0.39353041515469 & 0.196765207577345 \tabularnewline
47 & 0.76934714914707 & 0.46130570170586 & 0.23065285085293 \tabularnewline
48 & 0.742094143945097 & 0.515811712109806 & 0.257905856054903 \tabularnewline
49 & 0.751717375581128 & 0.496565248837745 & 0.248282624418872 \tabularnewline
50 & 0.714634766600253 & 0.570730466799493 & 0.285365233399747 \tabularnewline
51 & 0.83438432368736 & 0.33123135262528 & 0.16561567631264 \tabularnewline
52 & 0.803699425958014 & 0.392601148083971 & 0.196300574041986 \tabularnewline
53 & 0.779274507319827 & 0.441450985360347 & 0.220725492680174 \tabularnewline
54 & 0.751932928316595 & 0.496134143366809 & 0.248067071683405 \tabularnewline
55 & 0.717310421147538 & 0.565379157704924 & 0.282689578852462 \tabularnewline
56 & 0.834560787864643 & 0.330878424270715 & 0.165439212135357 \tabularnewline
57 & 0.86353266803166 & 0.272934663936681 & 0.13646733196834 \tabularnewline
58 & 0.84613100066994 & 0.30773799866012 & 0.15386899933006 \tabularnewline
59 & 0.827468779454183 & 0.345062441091633 & 0.172531220545817 \tabularnewline
60 & 0.797446698566613 & 0.405106602866774 & 0.202553301433387 \tabularnewline
61 & 0.76917903162977 & 0.46164193674046 & 0.23082096837023 \tabularnewline
62 & 0.822418688446762 & 0.355162623106475 & 0.177581311553238 \tabularnewline
63 & 0.794406167832283 & 0.411187664335435 & 0.205593832167717 \tabularnewline
64 & 0.764851721779002 & 0.470296556441996 & 0.235148278220998 \tabularnewline
65 & 0.732558414834422 & 0.534883170331156 & 0.267441585165578 \tabularnewline
66 & 0.69871733023581 & 0.602565339528381 & 0.30128266976419 \tabularnewline
67 & 0.663579199478449 & 0.672841601043101 & 0.33642080052155 \tabularnewline
68 & 0.648358336703723 & 0.703283326592553 & 0.351641663296277 \tabularnewline
69 & 0.624107446711871 & 0.751785106576258 & 0.375892553288129 \tabularnewline
70 & 0.780137096138181 & 0.439725807723639 & 0.219862903861819 \tabularnewline
71 & 0.750763154842593 & 0.498473690314813 & 0.249236845157406 \tabularnewline
72 & 0.730203841332611 & 0.539592317334778 & 0.269796158667389 \tabularnewline
73 & 0.835301669281591 & 0.329396661436818 & 0.164698330718409 \tabularnewline
74 & 0.929179512045891 & 0.141640975908217 & 0.0708204879541087 \tabularnewline
75 & 0.917167834365637 & 0.165664331268726 & 0.0828321656343631 \tabularnewline
76 & 0.919175892564782 & 0.161648214870435 & 0.0808241074352177 \tabularnewline
77 & 0.906907389476178 & 0.186185221047644 & 0.0930926105238221 \tabularnewline
78 & 0.93263421488325 & 0.1347315702335 & 0.0673657851167498 \tabularnewline
79 & 0.917043637513046 & 0.165912724973908 & 0.082956362486954 \tabularnewline
80 & 0.911566552698919 & 0.176866894602162 & 0.0884334473010811 \tabularnewline
81 & 0.892819790575781 & 0.214360418848437 & 0.107180209424219 \tabularnewline
82 & 0.947957772221688 & 0.104084455556624 & 0.0520422277783122 \tabularnewline
83 & 0.935283036362047 & 0.129433927275906 & 0.0647169636379528 \tabularnewline
84 & 0.92710153319804 & 0.14579693360392 & 0.0728984668019601 \tabularnewline
85 & 0.916944085260892 & 0.166111829478215 & 0.0830559147391076 \tabularnewline
86 & 0.896830088561726 & 0.206339822876548 & 0.103169911438274 \tabularnewline
87 & 0.880342489496338 & 0.239315021007323 & 0.119657510503662 \tabularnewline
88 & 0.867538960641702 & 0.264922078716597 & 0.132461039358298 \tabularnewline
89 & 0.839171156743584 & 0.321657686512832 & 0.160828843256416 \tabularnewline
90 & 0.80783614880122 & 0.384327702397561 & 0.19216385119878 \tabularnewline
91 & 0.7802529337995 & 0.439494132400999 & 0.2197470662005 \tabularnewline
92 & 0.831986383643605 & 0.336027232712789 & 0.168013616356395 \tabularnewline
93 & 0.829158046280966 & 0.341683907438068 & 0.170841953719034 \tabularnewline
94 & 0.795510038844136 & 0.408979922311729 & 0.204489961155864 \tabularnewline
95 & 0.8033493332951 & 0.393301333409799 & 0.1966506667049 \tabularnewline
96 & 0.766574880487464 & 0.466850239025073 & 0.233425119512536 \tabularnewline
97 & 0.815340107780097 & 0.369319784439805 & 0.184659892219903 \tabularnewline
98 & 0.779445684658432 & 0.441108630683136 & 0.220554315341568 \tabularnewline
99 & 0.75257961638049 & 0.494840767239021 & 0.24742038361951 \tabularnewline
100 & 0.710286796667953 & 0.579426406664094 & 0.289713203332047 \tabularnewline
101 & 0.683931611184655 & 0.63213677763069 & 0.316068388815345 \tabularnewline
102 & 0.636319145331658 & 0.727361709336683 & 0.363680854668342 \tabularnewline
103 & 0.586245548312882 & 0.827508903374236 & 0.413754451687118 \tabularnewline
104 & 0.534465845691237 & 0.931068308617525 & 0.465534154308763 \tabularnewline
105 & 0.604097593672739 & 0.791804812654523 & 0.395902406327261 \tabularnewline
106 & 0.551946168567957 & 0.896107662864085 & 0.448053831432043 \tabularnewline
107 & 0.498605619857372 & 0.997211239714745 & 0.501394380142627 \tabularnewline
108 & 0.506483513513994 & 0.987032972972013 & 0.493516486486006 \tabularnewline
109 & 0.452318366707215 & 0.90463673341443 & 0.547681633292785 \tabularnewline
110 & 0.418498891598267 & 0.836997783196534 & 0.581501108401733 \tabularnewline
111 & 0.379828010630623 & 0.759656021261246 & 0.620171989369377 \tabularnewline
112 & 0.393451292557316 & 0.786902585114631 & 0.606548707442684 \tabularnewline
113 & 0.720165639582204 & 0.559668720835592 & 0.279834360417796 \tabularnewline
114 & 0.716682236056286 & 0.566635527887428 & 0.283317763943714 \tabularnewline
115 & 0.682536139093239 & 0.634927721813521 & 0.317463860906761 \tabularnewline
116 & 0.629179384539974 & 0.741641230920052 & 0.370820615460026 \tabularnewline
117 & 0.619333076037906 & 0.761333847924188 & 0.380666923962094 \tabularnewline
118 & 0.590089470508921 & 0.819821058982159 & 0.409910529491079 \tabularnewline
119 & 0.531224735381658 & 0.937550529236683 & 0.468775264618342 \tabularnewline
120 & 0.471961540863948 & 0.943923081727896 & 0.528038459136052 \tabularnewline
121 & 0.447677540698719 & 0.895355081397438 & 0.552322459301281 \tabularnewline
122 & 0.386970167133926 & 0.773940334267852 & 0.613029832866074 \tabularnewline
123 & 0.369437067017072 & 0.738874134034144 & 0.630562932982928 \tabularnewline
124 & 0.56168455475226 & 0.87663089049548 & 0.43831544524774 \tabularnewline
125 & 0.496342170098064 & 0.992684340196128 & 0.503657829901936 \tabularnewline
126 & 0.494497727154484 & 0.988995454308968 & 0.505502272845516 \tabularnewline
127 & 0.428765712997133 & 0.857531425994265 & 0.571234287002867 \tabularnewline
128 & 0.361710746281909 & 0.723421492563818 & 0.638289253718091 \tabularnewline
129 & 0.297114745833295 & 0.59422949166659 & 0.702885254166705 \tabularnewline
130 & 0.237903650024319 & 0.475807300048637 & 0.762096349975681 \tabularnewline
131 & 0.225227833891596 & 0.450455667783192 & 0.774772166108404 \tabularnewline
132 & 0.284683337426651 & 0.569366674853301 & 0.715316662573349 \tabularnewline
133 & 0.377631945909387 & 0.755263891818775 & 0.622368054090613 \tabularnewline
134 & 0.302623264788603 & 0.605246529577206 & 0.697376735211397 \tabularnewline
135 & 0.234014097258456 & 0.468028194516912 & 0.765985902741544 \tabularnewline
136 & 0.174288022493378 & 0.348576044986756 & 0.825711977506622 \tabularnewline
137 & 0.18709030565991 & 0.37418061131982 & 0.81290969434009 \tabularnewline
138 & 0.137899305276706 & 0.275798610553413 & 0.862100694723294 \tabularnewline
139 & 0.133433591938846 & 0.266867183877691 & 0.866566408061154 \tabularnewline
140 & 0.084475110415594 & 0.168950220831188 & 0.915524889584406 \tabularnewline
141 & 0.0525522031564292 & 0.105104406312858 & 0.947447796843571 \tabularnewline
142 & 0.0822522638698504 & 0.164504527739701 & 0.91774773613015 \tabularnewline
143 & 0.0651501987528899 & 0.13030039750578 & 0.93484980124711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&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]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0.157463046537502[/C][C]0.314926093075003[/C][C]0.842536953462498[/C][/ROW]
[ROW][C]14[/C][C]0.0847372017201832[/C][C]0.169474403440366[/C][C]0.915262798279817[/C][/ROW]
[ROW][C]15[/C][C]0.111679880102757[/C][C]0.223359760205514[/C][C]0.888320119897243[/C][/ROW]
[ROW][C]16[/C][C]0.0678188738259869[/C][C]0.135637747651974[/C][C]0.932181126174013[/C][/ROW]
[ROW][C]17[/C][C]0.0367045497442221[/C][C]0.0734090994884441[/C][C]0.963295450255778[/C][/ROW]
[ROW][C]18[/C][C]0.0194375495879245[/C][C]0.038875099175849[/C][C]0.980562450412075[/C][/ROW]
[ROW][C]19[/C][C]0.00983479389360297[/C][C]0.0196695877872059[/C][C]0.990165206106397[/C][/ROW]
[ROW][C]20[/C][C]0.00554915092978612[/C][C]0.0110983018595722[/C][C]0.994450849070214[/C][/ROW]
[ROW][C]21[/C][C]0.0156829912693805[/C][C]0.0313659825387609[/C][C]0.98431700873062[/C][/ROW]
[ROW][C]22[/C][C]0.0530294166930235[/C][C]0.106058833386047[/C][C]0.946970583306977[/C][/ROW]
[ROW][C]23[/C][C]0.163875368161258[/C][C]0.327750736322516[/C][C]0.836124631838742[/C][/ROW]
[ROW][C]24[/C][C]0.172315278048148[/C][C]0.344630556096296[/C][C]0.827684721951852[/C][/ROW]
[ROW][C]25[/C][C]0.329702667467832[/C][C]0.659405334935665[/C][C]0.670297332532168[/C][/ROW]
[ROW][C]26[/C][C]0.349703622282162[/C][C]0.699407244564324[/C][C]0.650296377717838[/C][/ROW]
[ROW][C]27[/C][C]0.320015169735288[/C][C]0.640030339470576[/C][C]0.679984830264712[/C][/ROW]
[ROW][C]28[/C][C]0.626636731028071[/C][C]0.746726537943858[/C][C]0.373363268971929[/C][/ROW]
[ROW][C]29[/C][C]0.570359313562829[/C][C]0.859281372874342[/C][C]0.429640686437171[/C][/ROW]
[ROW][C]30[/C][C]0.649212589403825[/C][C]0.70157482119235[/C][C]0.350787410596175[/C][/ROW]
[ROW][C]31[/C][C]0.592207481772008[/C][C]0.815585036455983[/C][C]0.407792518227992[/C][/ROW]
[ROW][C]32[/C][C]0.546594861135307[/C][C]0.906810277729386[/C][C]0.453405138864693[/C][/ROW]
[ROW][C]33[/C][C]0.520409172161711[/C][C]0.959181655676578[/C][C]0.479590827838289[/C][/ROW]
[ROW][C]34[/C][C]0.511828298891846[/C][C]0.976343402216307[/C][C]0.488171701108154[/C][/ROW]
[ROW][C]35[/C][C]0.45590124871576[/C][C]0.911802497431519[/C][C]0.54409875128424[/C][/ROW]
[ROW][C]36[/C][C]0.40112949204285[/C][C]0.802258984085699[/C][C]0.59887050795715[/C][/ROW]
[ROW][C]37[/C][C]0.438034525685608[/C][C]0.876069051371216[/C][C]0.561965474314392[/C][/ROW]
[ROW][C]38[/C][C]0.658825284722759[/C][C]0.682349430554482[/C][C]0.341174715277241[/C][/ROW]
[ROW][C]39[/C][C]0.704461369246672[/C][C]0.591077261506656[/C][C]0.295538630753328[/C][/ROW]
[ROW][C]40[/C][C]0.759735606691378[/C][C]0.480528786617244[/C][C]0.240264393308622[/C][/ROW]
[ROW][C]41[/C][C]0.714859803247944[/C][C]0.570280393504111[/C][C]0.285140196752056[/C][/ROW]
[ROW][C]42[/C][C]0.828631252203542[/C][C]0.342737495592915[/C][C]0.171368747796458[/C][/ROW]
[ROW][C]43[/C][C]0.8268466855417[/C][C]0.3463066289166[/C][C]0.1731533144583[/C][/ROW]
[ROW][C]44[/C][C]0.795764942720745[/C][C]0.408470114558511[/C][C]0.204235057279255[/C][/ROW]
[ROW][C]45[/C][C]0.79061311034878[/C][C]0.418773779302441[/C][C]0.20938688965122[/C][/ROW]
[ROW][C]46[/C][C]0.803234792422655[/C][C]0.39353041515469[/C][C]0.196765207577345[/C][/ROW]
[ROW][C]47[/C][C]0.76934714914707[/C][C]0.46130570170586[/C][C]0.23065285085293[/C][/ROW]
[ROW][C]48[/C][C]0.742094143945097[/C][C]0.515811712109806[/C][C]0.257905856054903[/C][/ROW]
[ROW][C]49[/C][C]0.751717375581128[/C][C]0.496565248837745[/C][C]0.248282624418872[/C][/ROW]
[ROW][C]50[/C][C]0.714634766600253[/C][C]0.570730466799493[/C][C]0.285365233399747[/C][/ROW]
[ROW][C]51[/C][C]0.83438432368736[/C][C]0.33123135262528[/C][C]0.16561567631264[/C][/ROW]
[ROW][C]52[/C][C]0.803699425958014[/C][C]0.392601148083971[/C][C]0.196300574041986[/C][/ROW]
[ROW][C]53[/C][C]0.779274507319827[/C][C]0.441450985360347[/C][C]0.220725492680174[/C][/ROW]
[ROW][C]54[/C][C]0.751932928316595[/C][C]0.496134143366809[/C][C]0.248067071683405[/C][/ROW]
[ROW][C]55[/C][C]0.717310421147538[/C][C]0.565379157704924[/C][C]0.282689578852462[/C][/ROW]
[ROW][C]56[/C][C]0.834560787864643[/C][C]0.330878424270715[/C][C]0.165439212135357[/C][/ROW]
[ROW][C]57[/C][C]0.86353266803166[/C][C]0.272934663936681[/C][C]0.13646733196834[/C][/ROW]
[ROW][C]58[/C][C]0.84613100066994[/C][C]0.30773799866012[/C][C]0.15386899933006[/C][/ROW]
[ROW][C]59[/C][C]0.827468779454183[/C][C]0.345062441091633[/C][C]0.172531220545817[/C][/ROW]
[ROW][C]60[/C][C]0.797446698566613[/C][C]0.405106602866774[/C][C]0.202553301433387[/C][/ROW]
[ROW][C]61[/C][C]0.76917903162977[/C][C]0.46164193674046[/C][C]0.23082096837023[/C][/ROW]
[ROW][C]62[/C][C]0.822418688446762[/C][C]0.355162623106475[/C][C]0.177581311553238[/C][/ROW]
[ROW][C]63[/C][C]0.794406167832283[/C][C]0.411187664335435[/C][C]0.205593832167717[/C][/ROW]
[ROW][C]64[/C][C]0.764851721779002[/C][C]0.470296556441996[/C][C]0.235148278220998[/C][/ROW]
[ROW][C]65[/C][C]0.732558414834422[/C][C]0.534883170331156[/C][C]0.267441585165578[/C][/ROW]
[ROW][C]66[/C][C]0.69871733023581[/C][C]0.602565339528381[/C][C]0.30128266976419[/C][/ROW]
[ROW][C]67[/C][C]0.663579199478449[/C][C]0.672841601043101[/C][C]0.33642080052155[/C][/ROW]
[ROW][C]68[/C][C]0.648358336703723[/C][C]0.703283326592553[/C][C]0.351641663296277[/C][/ROW]
[ROW][C]69[/C][C]0.624107446711871[/C][C]0.751785106576258[/C][C]0.375892553288129[/C][/ROW]
[ROW][C]70[/C][C]0.780137096138181[/C][C]0.439725807723639[/C][C]0.219862903861819[/C][/ROW]
[ROW][C]71[/C][C]0.750763154842593[/C][C]0.498473690314813[/C][C]0.249236845157406[/C][/ROW]
[ROW][C]72[/C][C]0.730203841332611[/C][C]0.539592317334778[/C][C]0.269796158667389[/C][/ROW]
[ROW][C]73[/C][C]0.835301669281591[/C][C]0.329396661436818[/C][C]0.164698330718409[/C][/ROW]
[ROW][C]74[/C][C]0.929179512045891[/C][C]0.141640975908217[/C][C]0.0708204879541087[/C][/ROW]
[ROW][C]75[/C][C]0.917167834365637[/C][C]0.165664331268726[/C][C]0.0828321656343631[/C][/ROW]
[ROW][C]76[/C][C]0.919175892564782[/C][C]0.161648214870435[/C][C]0.0808241074352177[/C][/ROW]
[ROW][C]77[/C][C]0.906907389476178[/C][C]0.186185221047644[/C][C]0.0930926105238221[/C][/ROW]
[ROW][C]78[/C][C]0.93263421488325[/C][C]0.1347315702335[/C][C]0.0673657851167498[/C][/ROW]
[ROW][C]79[/C][C]0.917043637513046[/C][C]0.165912724973908[/C][C]0.082956362486954[/C][/ROW]
[ROW][C]80[/C][C]0.911566552698919[/C][C]0.176866894602162[/C][C]0.0884334473010811[/C][/ROW]
[ROW][C]81[/C][C]0.892819790575781[/C][C]0.214360418848437[/C][C]0.107180209424219[/C][/ROW]
[ROW][C]82[/C][C]0.947957772221688[/C][C]0.104084455556624[/C][C]0.0520422277783122[/C][/ROW]
[ROW][C]83[/C][C]0.935283036362047[/C][C]0.129433927275906[/C][C]0.0647169636379528[/C][/ROW]
[ROW][C]84[/C][C]0.92710153319804[/C][C]0.14579693360392[/C][C]0.0728984668019601[/C][/ROW]
[ROW][C]85[/C][C]0.916944085260892[/C][C]0.166111829478215[/C][C]0.0830559147391076[/C][/ROW]
[ROW][C]86[/C][C]0.896830088561726[/C][C]0.206339822876548[/C][C]0.103169911438274[/C][/ROW]
[ROW][C]87[/C][C]0.880342489496338[/C][C]0.239315021007323[/C][C]0.119657510503662[/C][/ROW]
[ROW][C]88[/C][C]0.867538960641702[/C][C]0.264922078716597[/C][C]0.132461039358298[/C][/ROW]
[ROW][C]89[/C][C]0.839171156743584[/C][C]0.321657686512832[/C][C]0.160828843256416[/C][/ROW]
[ROW][C]90[/C][C]0.80783614880122[/C][C]0.384327702397561[/C][C]0.19216385119878[/C][/ROW]
[ROW][C]91[/C][C]0.7802529337995[/C][C]0.439494132400999[/C][C]0.2197470662005[/C][/ROW]
[ROW][C]92[/C][C]0.831986383643605[/C][C]0.336027232712789[/C][C]0.168013616356395[/C][/ROW]
[ROW][C]93[/C][C]0.829158046280966[/C][C]0.341683907438068[/C][C]0.170841953719034[/C][/ROW]
[ROW][C]94[/C][C]0.795510038844136[/C][C]0.408979922311729[/C][C]0.204489961155864[/C][/ROW]
[ROW][C]95[/C][C]0.8033493332951[/C][C]0.393301333409799[/C][C]0.1966506667049[/C][/ROW]
[ROW][C]96[/C][C]0.766574880487464[/C][C]0.466850239025073[/C][C]0.233425119512536[/C][/ROW]
[ROW][C]97[/C][C]0.815340107780097[/C][C]0.369319784439805[/C][C]0.184659892219903[/C][/ROW]
[ROW][C]98[/C][C]0.779445684658432[/C][C]0.441108630683136[/C][C]0.220554315341568[/C][/ROW]
[ROW][C]99[/C][C]0.75257961638049[/C][C]0.494840767239021[/C][C]0.24742038361951[/C][/ROW]
[ROW][C]100[/C][C]0.710286796667953[/C][C]0.579426406664094[/C][C]0.289713203332047[/C][/ROW]
[ROW][C]101[/C][C]0.683931611184655[/C][C]0.63213677763069[/C][C]0.316068388815345[/C][/ROW]
[ROW][C]102[/C][C]0.636319145331658[/C][C]0.727361709336683[/C][C]0.363680854668342[/C][/ROW]
[ROW][C]103[/C][C]0.586245548312882[/C][C]0.827508903374236[/C][C]0.413754451687118[/C][/ROW]
[ROW][C]104[/C][C]0.534465845691237[/C][C]0.931068308617525[/C][C]0.465534154308763[/C][/ROW]
[ROW][C]105[/C][C]0.604097593672739[/C][C]0.791804812654523[/C][C]0.395902406327261[/C][/ROW]
[ROW][C]106[/C][C]0.551946168567957[/C][C]0.896107662864085[/C][C]0.448053831432043[/C][/ROW]
[ROW][C]107[/C][C]0.498605619857372[/C][C]0.997211239714745[/C][C]0.501394380142627[/C][/ROW]
[ROW][C]108[/C][C]0.506483513513994[/C][C]0.987032972972013[/C][C]0.493516486486006[/C][/ROW]
[ROW][C]109[/C][C]0.452318366707215[/C][C]0.90463673341443[/C][C]0.547681633292785[/C][/ROW]
[ROW][C]110[/C][C]0.418498891598267[/C][C]0.836997783196534[/C][C]0.581501108401733[/C][/ROW]
[ROW][C]111[/C][C]0.379828010630623[/C][C]0.759656021261246[/C][C]0.620171989369377[/C][/ROW]
[ROW][C]112[/C][C]0.393451292557316[/C][C]0.786902585114631[/C][C]0.606548707442684[/C][/ROW]
[ROW][C]113[/C][C]0.720165639582204[/C][C]0.559668720835592[/C][C]0.279834360417796[/C][/ROW]
[ROW][C]114[/C][C]0.716682236056286[/C][C]0.566635527887428[/C][C]0.283317763943714[/C][/ROW]
[ROW][C]115[/C][C]0.682536139093239[/C][C]0.634927721813521[/C][C]0.317463860906761[/C][/ROW]
[ROW][C]116[/C][C]0.629179384539974[/C][C]0.741641230920052[/C][C]0.370820615460026[/C][/ROW]
[ROW][C]117[/C][C]0.619333076037906[/C][C]0.761333847924188[/C][C]0.380666923962094[/C][/ROW]
[ROW][C]118[/C][C]0.590089470508921[/C][C]0.819821058982159[/C][C]0.409910529491079[/C][/ROW]
[ROW][C]119[/C][C]0.531224735381658[/C][C]0.937550529236683[/C][C]0.468775264618342[/C][/ROW]
[ROW][C]120[/C][C]0.471961540863948[/C][C]0.943923081727896[/C][C]0.528038459136052[/C][/ROW]
[ROW][C]121[/C][C]0.447677540698719[/C][C]0.895355081397438[/C][C]0.552322459301281[/C][/ROW]
[ROW][C]122[/C][C]0.386970167133926[/C][C]0.773940334267852[/C][C]0.613029832866074[/C][/ROW]
[ROW][C]123[/C][C]0.369437067017072[/C][C]0.738874134034144[/C][C]0.630562932982928[/C][/ROW]
[ROW][C]124[/C][C]0.56168455475226[/C][C]0.87663089049548[/C][C]0.43831544524774[/C][/ROW]
[ROW][C]125[/C][C]0.496342170098064[/C][C]0.992684340196128[/C][C]0.503657829901936[/C][/ROW]
[ROW][C]126[/C][C]0.494497727154484[/C][C]0.988995454308968[/C][C]0.505502272845516[/C][/ROW]
[ROW][C]127[/C][C]0.428765712997133[/C][C]0.857531425994265[/C][C]0.571234287002867[/C][/ROW]
[ROW][C]128[/C][C]0.361710746281909[/C][C]0.723421492563818[/C][C]0.638289253718091[/C][/ROW]
[ROW][C]129[/C][C]0.297114745833295[/C][C]0.59422949166659[/C][C]0.702885254166705[/C][/ROW]
[ROW][C]130[/C][C]0.237903650024319[/C][C]0.475807300048637[/C][C]0.762096349975681[/C][/ROW]
[ROW][C]131[/C][C]0.225227833891596[/C][C]0.450455667783192[/C][C]0.774772166108404[/C][/ROW]
[ROW][C]132[/C][C]0.284683337426651[/C][C]0.569366674853301[/C][C]0.715316662573349[/C][/ROW]
[ROW][C]133[/C][C]0.377631945909387[/C][C]0.755263891818775[/C][C]0.622368054090613[/C][/ROW]
[ROW][C]134[/C][C]0.302623264788603[/C][C]0.605246529577206[/C][C]0.697376735211397[/C][/ROW]
[ROW][C]135[/C][C]0.234014097258456[/C][C]0.468028194516912[/C][C]0.765985902741544[/C][/ROW]
[ROW][C]136[/C][C]0.174288022493378[/C][C]0.348576044986756[/C][C]0.825711977506622[/C][/ROW]
[ROW][C]137[/C][C]0.18709030565991[/C][C]0.37418061131982[/C][C]0.81290969434009[/C][/ROW]
[ROW][C]138[/C][C]0.137899305276706[/C][C]0.275798610553413[/C][C]0.862100694723294[/C][/ROW]
[ROW][C]139[/C][C]0.133433591938846[/C][C]0.266867183877691[/C][C]0.866566408061154[/C][/ROW]
[ROW][C]140[/C][C]0.084475110415594[/C][C]0.168950220831188[/C][C]0.915524889584406[/C][/ROW]
[ROW][C]141[/C][C]0.0525522031564292[/C][C]0.105104406312858[/C][C]0.947447796843571[/C][/ROW]
[ROW][C]142[/C][C]0.0822522638698504[/C][C]0.164504527739701[/C][C]0.91774773613015[/C][/ROW]
[ROW][C]143[/C][C]0.0651501987528899[/C][C]0.13030039750578[/C][C]0.93484980124711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11001
12001
130.1574630465375020.3149260930750030.842536953462498
140.08473720172018320.1694744034403660.915262798279817
150.1116798801027570.2233597602055140.888320119897243
160.06781887382598690.1356377476519740.932181126174013
170.03670454974422210.07340909948844410.963295450255778
180.01943754958792450.0388750991758490.980562450412075
190.009834793893602970.01966958778720590.990165206106397
200.005549150929786120.01109830185957220.994450849070214
210.01568299126938050.03136598253876090.98431700873062
220.05302941669302350.1060588333860470.946970583306977
230.1638753681612580.3277507363225160.836124631838742
240.1723152780481480.3446305560962960.827684721951852
250.3297026674678320.6594053349356650.670297332532168
260.3497036222821620.6994072445643240.650296377717838
270.3200151697352880.6400303394705760.679984830264712
280.6266367310280710.7467265379438580.373363268971929
290.5703593135628290.8592813728743420.429640686437171
300.6492125894038250.701574821192350.350787410596175
310.5922074817720080.8155850364559830.407792518227992
320.5465948611353070.9068102777293860.453405138864693
330.5204091721617110.9591816556765780.479590827838289
340.5118282988918460.9763434022163070.488171701108154
350.455901248715760.9118024974315190.54409875128424
360.401129492042850.8022589840856990.59887050795715
370.4380345256856080.8760690513712160.561965474314392
380.6588252847227590.6823494305544820.341174715277241
390.7044613692466720.5910772615066560.295538630753328
400.7597356066913780.4805287866172440.240264393308622
410.7148598032479440.5702803935041110.285140196752056
420.8286312522035420.3427374955929150.171368747796458
430.82684668554170.34630662891660.1731533144583
440.7957649427207450.4084701145585110.204235057279255
450.790613110348780.4187737793024410.20938688965122
460.8032347924226550.393530415154690.196765207577345
470.769347149147070.461305701705860.23065285085293
480.7420941439450970.5158117121098060.257905856054903
490.7517173755811280.4965652488377450.248282624418872
500.7146347666002530.5707304667994930.285365233399747
510.834384323687360.331231352625280.16561567631264
520.8036994259580140.3926011480839710.196300574041986
530.7792745073198270.4414509853603470.220725492680174
540.7519329283165950.4961341433668090.248067071683405
550.7173104211475380.5653791577049240.282689578852462
560.8345607878646430.3308784242707150.165439212135357
570.863532668031660.2729346639366810.13646733196834
580.846131000669940.307737998660120.15386899933006
590.8274687794541830.3450624410916330.172531220545817
600.7974466985666130.4051066028667740.202553301433387
610.769179031629770.461641936740460.23082096837023
620.8224186884467620.3551626231064750.177581311553238
630.7944061678322830.4111876643354350.205593832167717
640.7648517217790020.4702965564419960.235148278220998
650.7325584148344220.5348831703311560.267441585165578
660.698717330235810.6025653395283810.30128266976419
670.6635791994784490.6728416010431010.33642080052155
680.6483583367037230.7032833265925530.351641663296277
690.6241074467118710.7517851065762580.375892553288129
700.7801370961381810.4397258077236390.219862903861819
710.7507631548425930.4984736903148130.249236845157406
720.7302038413326110.5395923173347780.269796158667389
730.8353016692815910.3293966614368180.164698330718409
740.9291795120458910.1416409759082170.0708204879541087
750.9171678343656370.1656643312687260.0828321656343631
760.9191758925647820.1616482148704350.0808241074352177
770.9069073894761780.1861852210476440.0930926105238221
780.932634214883250.13473157023350.0673657851167498
790.9170436375130460.1659127249739080.082956362486954
800.9115665526989190.1768668946021620.0884334473010811
810.8928197905757810.2143604188484370.107180209424219
820.9479577722216880.1040844555566240.0520422277783122
830.9352830363620470.1294339272759060.0647169636379528
840.927101533198040.145796933603920.0728984668019601
850.9169440852608920.1661118294782150.0830559147391076
860.8968300885617260.2063398228765480.103169911438274
870.8803424894963380.2393150210073230.119657510503662
880.8675389606417020.2649220787165970.132461039358298
890.8391711567435840.3216576865128320.160828843256416
900.807836148801220.3843277023975610.19216385119878
910.78025293379950.4394941324009990.2197470662005
920.8319863836436050.3360272327127890.168013616356395
930.8291580462809660.3416839074380680.170841953719034
940.7955100388441360.4089799223117290.204489961155864
950.80334933329510.3933013334097990.1966506667049
960.7665748804874640.4668502390250730.233425119512536
970.8153401077800970.3693197844398050.184659892219903
980.7794456846584320.4411086306831360.220554315341568
990.752579616380490.4948407672390210.24742038361951
1000.7102867966679530.5794264066640940.289713203332047
1010.6839316111846550.632136777630690.316068388815345
1020.6363191453316580.7273617093366830.363680854668342
1030.5862455483128820.8275089033742360.413754451687118
1040.5344658456912370.9310683086175250.465534154308763
1050.6040975936727390.7918048126545230.395902406327261
1060.5519461685679570.8961076628640850.448053831432043
1070.4986056198573720.9972112397147450.501394380142627
1080.5064835135139940.9870329729720130.493516486486006
1090.4523183667072150.904636733414430.547681633292785
1100.4184988915982670.8369977831965340.581501108401733
1110.3798280106306230.7596560212612460.620171989369377
1120.3934512925573160.7869025851146310.606548707442684
1130.7201656395822040.5596687208355920.279834360417796
1140.7166822360562860.5666355278874280.283317763943714
1150.6825361390932390.6349277218135210.317463860906761
1160.6291793845399740.7416412309200520.370820615460026
1170.6193330760379060.7613338479241880.380666923962094
1180.5900894705089210.8198210589821590.409910529491079
1190.5312247353816580.9375505292366830.468775264618342
1200.4719615408639480.9439230817278960.528038459136052
1210.4476775406987190.8953550813974380.552322459301281
1220.3869701671339260.7739403342678520.613029832866074
1230.3694370670170720.7388741340341440.630562932982928
1240.561684554752260.876630890495480.43831544524774
1250.4963421700980640.9926843401961280.503657829901936
1260.4944977271544840.9889954543089680.505502272845516
1270.4287657129971330.8575314259942650.571234287002867
1280.3617107462819090.7234214925638180.638289253718091
1290.2971147458332950.594229491666590.702885254166705
1300.2379036500243190.4758073000486370.762096349975681
1310.2252278338915960.4504556677831920.774772166108404
1320.2846833374266510.5693666748533010.715316662573349
1330.3776319459093870.7552638918187750.622368054090613
1340.3026232647886030.6052465295772060.697376735211397
1350.2340140972584560.4680281945169120.765985902741544
1360.1742880224933780.3485760449867560.825711977506622
1370.187090305659910.374180611319820.81290969434009
1380.1378993052767060.2757986105534130.862100694723294
1390.1334335919388460.2668671838776910.866566408061154
1400.0844751104155940.1689502208311880.915524889584406
1410.05255220315642920.1051044063128580.947447796843571
1420.08225226386985040.1645045277397010.91774773613015
1430.06515019875288990.130300397505780.93484980124711






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0150375939849624NOK
5% type I error level60.0451127819548872OK
10% type I error level70.0526315789473684OK

\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 & 2 & 0.0150375939849624 & NOK \tabularnewline
5% type I error level & 6 & 0.0451127819548872 & OK \tabularnewline
10% type I error level & 7 & 0.0526315789473684 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199376&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]2[/C][C]0.0150375939849624[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.0451127819548872[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0526315789473684[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199376&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199376&T=6

As an alternative you can also use a QR Code:  
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 level20.0150375939849624NOK
5% type I error level60.0451127819548872OK
10% type I error level70.0526315789473684OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}