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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 14:15:53 -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/16/t13556853912s3styg16z6vxlz.htm/, Retrieved Fri, 29 Mar 2024 11:38:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200554, Retrieved Fri, 29 Mar 2024 11:38:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:15:53] [c63d55528b56cf8bb48e0b5d1a959d8e] [Current]
- R  D      [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:54:44] [86dcce9422b96d4554cb918e531c1d5d]
- R P         [Multiple Regression] [Paper Multiple Re...] [2012-12-19 10:59:45] [86dcce9422b96d4554cb918e531c1d5d]
- R P         [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-19 18:07:34] [74be16979710d4c4e7c6647856088456]
-   P           [Multiple Regression] [Multiple Regressi...] [2012-12-19 20:22:16] [f4325a5733446a4ce20d70c276c6a563]
-  MP             [Multiple Regression] [Paper 2012 (deel5.7)] [2012-12-22 02:04:24] [74be16979710d4c4e7c6647856088456]
- R P         [Multiple Regression] [Multiple Regressi...] [2012-12-19 19:43:52] [f4325a5733446a4ce20d70c276c6a563]
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Dataseries X:
4	1	0	0	0	1
4	0	0	0	0	0
4	0	0	0	0	0
4	0	0	0	0	0
4	0	0	0	0	0
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4	0	0	0	0	0
4	0	0	0	0	0
4	0	0	0	0	1
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4	1	0	0	0	0
4	0	0	0	0	0
4	0	1	0	1	0
4	1	0	0	0	0
4	0	1	0	1	1
4	0	1	0	1	1
4	1	1	1	1	0
4	1	0	0	0	0
4	0	0	0	0	1
4	0	1	1	1	1
4	1	0	0	1	0
4	1	1	0	1	1
4	0	0	0	1	1
4	1	0	0	1	1
4	0	1	0	0	1
4	0	1	0	1	0
4	1	0	0	0	1
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4	0	0	0	0	0
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4	0	0	0	0	1
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4	1	0	0	1	1
4	1	0	0	0	0
4	0	0	0	1	0
4	0	0	0	1	1
4	0	0	0	0	0
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4	0	0	0	1	1
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2	1	0	0	0	1
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2	1	1	1	1	0
2	1	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Outcome[t] = -0.220056289536311 + 0.137632477255932Weeks[t] -0.0711600979701897USELIMIT[t] + 0.0766740138589019Used[t] -0.141202916535705CorrectAN[t] + 0.163921975435742Useful[t] + 0.00200304883000975t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Outcome[t] =  -0.220056289536311 +  0.137632477255932Weeks[t] -0.0711600979701897USELIMIT[t] +  0.0766740138589019Used[t] -0.141202916535705CorrectAN[t] +  0.163921975435742Useful[t] +  0.00200304883000975t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Outcome[t] =  -0.220056289536311 +  0.137632477255932Weeks[t] -0.0711600979701897USELIMIT[t] +  0.0766740138589019Used[t] -0.141202916535705CorrectAN[t] +  0.163921975435742Useful[t] +  0.00200304883000975t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Outcome[t] = -0.220056289536311 + 0.137632477255932Weeks[t] -0.0711600979701897USELIMIT[t] + 0.0766740138589019Used[t] -0.141202916535705CorrectAN[t] + 0.163921975435742Useful[t] + 0.00200304883000975t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2200562895363110.377984-0.58220.5613340.280667
Weeks0.1376324772559320.0803381.71320.0887910.044395
USELIMIT-0.07116009797018970.085046-0.83670.4041050.202053
Used0.07667401385890190.0988440.77570.4391690.219585
CorrectAN-0.1412029165357050.165823-0.85150.3958610.197931
Useful0.1639219754357420.0937711.74810.0825340.041267
t0.002003048830009750.001771.13170.259610.129805

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.220056289536311 & 0.377984 & -0.5822 & 0.561334 & 0.280667 \tabularnewline
Weeks & 0.137632477255932 & 0.080338 & 1.7132 & 0.088791 & 0.044395 \tabularnewline
USELIMIT & -0.0711600979701897 & 0.085046 & -0.8367 & 0.404105 & 0.202053 \tabularnewline
Used & 0.0766740138589019 & 0.098844 & 0.7757 & 0.439169 & 0.219585 \tabularnewline
CorrectAN & -0.141202916535705 & 0.165823 & -0.8515 & 0.395861 & 0.197931 \tabularnewline
Useful & 0.163921975435742 & 0.093771 & 1.7481 & 0.082534 & 0.041267 \tabularnewline
t & 0.00200304883000975 & 0.00177 & 1.1317 & 0.25961 & 0.129805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.220056289536311[/C][C]0.377984[/C][C]-0.5822[/C][C]0.561334[/C][C]0.280667[/C][/ROW]
[ROW][C]Weeks[/C][C]0.137632477255932[/C][C]0.080338[/C][C]1.7132[/C][C]0.088791[/C][C]0.044395[/C][/ROW]
[ROW][C]USELIMIT[/C][C]-0.0711600979701897[/C][C]0.085046[/C][C]-0.8367[/C][C]0.404105[/C][C]0.202053[/C][/ROW]
[ROW][C]Used[/C][C]0.0766740138589019[/C][C]0.098844[/C][C]0.7757[/C][C]0.439169[/C][C]0.219585[/C][/ROW]
[ROW][C]CorrectAN[/C][C]-0.141202916535705[/C][C]0.165823[/C][C]-0.8515[/C][C]0.395861[/C][C]0.197931[/C][/ROW]
[ROW][C]Useful[/C][C]0.163921975435742[/C][C]0.093771[/C][C]1.7481[/C][C]0.082534[/C][C]0.041267[/C][/ROW]
[ROW][C]t[/C][C]0.00200304883000975[/C][C]0.00177[/C][C]1.1317[/C][C]0.25961[/C][C]0.129805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2200562895363110.377984-0.58220.5613340.280667
Weeks0.1376324772559320.0803381.71320.0887910.044395
USELIMIT-0.07116009797018970.085046-0.83670.4041050.202053
Used0.07667401385890190.0988440.77570.4391690.219585
CorrectAN-0.1412029165357050.165823-0.85150.3958610.197931
Useful0.1639219754357420.0937711.74810.0825340.041267
t0.002003048830009750.001771.13170.259610.129805







Multiple Linear Regression - Regression Statistics
Multiple R0.255470591027869
R-squared0.0652652228801285
Adjusted R-squared0.0271127829976847
F-TEST (value)1.71064348915103
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.122354378768584
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483984624023571
Sum Squared Residuals34.4334440948119

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.255470591027869 \tabularnewline
R-squared & 0.0652652228801285 \tabularnewline
Adjusted R-squared & 0.0271127829976847 \tabularnewline
F-TEST (value) & 1.71064348915103 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0.122354378768584 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.483984624023571 \tabularnewline
Sum Squared Residuals & 34.4334440948119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.255470591027869[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0652652228801285[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0271127829976847[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.71064348915103[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0.122354378768584[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.483984624023571[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.4334440948119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.255470591027869
R-squared0.0652652228801285
Adjusted R-squared0.0271127829976847
F-TEST (value)1.71064348915103
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.122354378768584
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483984624023571
Sum Squared Residuals34.4334440948119







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.2613165703472380.738683429652762
200.334479717147436-0.334479717147436
300.336482765977446-0.336482765977446
400.338485814807456-0.338485814807456
500.340488863637466-0.340488863637466
610.4352537899330280.564746210066972
700.344494961297486-0.344494961297486
800.346498010127495-0.346498010127495
910.3485010589575050.651498941042495
1000.279344009817325-0.279344009817325
1100.281347058647335-0.281347058647335
1200.354510205447534-0.354510205447534
1300.597109243572188-0.597109243572188
1400.287356205137364-0.287356205137364
1510.6011153412322070.398884658767792
1610.6031183900622170.396881609937783
1700.392758424386332-0.392758424386332
1800.295368400457403-0.295368400457403
1910.3685315472576030.631468452742397
2010.4699276688465520.530072331153448
2100.465299522383174-0.465299522383174
2210.5439765850720860.456023414927914
2310.5404657180133840.459534281986616
2410.4713086688732040.528691331126796
2510.4572238540965630.542776145903437
2600.623148878362315-0.623148878362315
2710.3133958399274910.686604160072509
2800.463233000586592-0.463233000586592
2910.38856203555770.6114379644423
3000.554487059823452-0.554487059823452
3100.39256813321772-0.39256813321772
3200.32341108407754-0.32341108407754
3300.489336108343291-0.489336108343291
3410.3985772797077490.601422720292251
3500.400580328537759-0.400580328537759
3600.402583377367769-0.402583377367769
3700.574022317522232-0.574022317522232
3810.483263488886690.51673651111331
3910.572514499293540.42748550070646
4000.57451754812355-0.57451754812355
4110.5119916942767560.488008305723244
4210.4912756842067290.508724315793271
4310.5093665966433890.490633403356611
4400.347447670037657-0.347447670037657
4500.584532792273598-0.584532792273598
4610.5865358411036080.413464158896392
4700.424616914497876-0.424616914497876
4810.4266199633278860.573380036672114
4910.5925449875936370.407455012406363
5000.430626060987905-0.430626060987905
5100.509303123676817-0.509303123676817
5200.462865133436674-0.462865133436674
5310.4366352074779340.563364792522066
5400.374109353631141-0.374109353631141
5500.440641305137954-0.440641305137954
5610.5193183678268660.480681632173134
5710.6852433920926170.314756607907383
5810.4466504516279830.553349548372017
5910.4486535004579930.551346499542007
6010.4788895240767520.521110475923248
6110.3814995001478230.618500499852177
6200.695258636242666-0.695258636242666
6300.456665695778032-0.456665695778032
6410.3875086466378520.612491353362148
6500.460671793438051-0.460671793438051
6600.462674842268061-0.462674842268061
6700.56407096385701-0.56407096385701
6800.395520841957891-0.395520841957891
6910.4686839887580910.531316011241909
7000.547361051447002-0.547361051447002
7100.47269008641811-0.47269008641811
7210.474693135248120.52530686475188
7310.5533701979370310.446629802062969
7400.484213148796851-0.484213148796851
7510.4807022817381490.519297718261851
7610.6466273060039010.353372693996099
7710.4847083793981690.515291620601831
7810.7273074175228220.272692582477178
7910.4241855743813850.575814425618615
8000.65463950132394-0.65463950132394
8100.492720574718207-0.492720574718207
8210.5002375394369290.499762460563071
8300.496726672378227-0.496726672378227
8400.434200818531434-0.434200818531434
8510.6646547454739890.335345254526011
8600.431575720898066-0.431575720898066
8710.1583138152162120.841686184783788
8810.2369908779051240.763009122094876
8900.233480010846421-0.233480010846421
9010.2354830596764310.764516940323569
9100.401408083942183-0.401408083942183
9200.168329059366261-0.168329059366261
9300.334254083632013-0.334254083632013
9400.24349525499647-0.24349525499647
9500.24549830382648-0.24549830382648
9610.247501352656490.75249864734351
9700.17834430351631-0.17834430351631
9800.251507450316509-0.251507450316509
9900.182350401176329-0.182350401176329
10010.2555135479765290.744486452023471
10110.1863564988363490.813643501163651
10200.259519645636548-0.259519645636548
10300.261522694466558-0.261522694466558
10400.263525743296568-0.263525743296568
10500.342202805985479-0.342202805985479
10600.267531840956587-0.267531840956587
10700.269534889786597-0.269534889786597
10800.277051854505319-0.277051854505319
10900.273540987446616-0.273540987446616
11000.204383938306437-0.204383938306437
11100.44698297643109-0.44698297643109
11200.279550133936646-0.279550133936646
11300.358227196625557-0.358227196625557
11400.289070147485378-0.289070147485378
11500.214399182456485-0.214399182456485
11600.287562329256685-0.287562329256685
11710.2184052801165050.781594719883495
11800.220408328946515-0.220408328946515
11900.293571475746714-0.293571475746714
12010.2955745245767240.704425475423276
12100.226417475436544-0.226417475436544
12200.299580622236743-0.299580622236743
12300.307097586955465-0.307097586955465
12410.5441827091914070.455817290808593
12510.3055897687267730.694410231273227
12600.307592817556782-0.307592817556782
12700.473517841822534-0.473517841822534
12810.3115989152168020.688401084783198
12900.313601964046812-0.313601964046812
13010.3156050128768210.684394987123179
13100.246447963736641-0.246447963736641
13210.2484510125666510.751548987433349
13300.327128075255563-0.327128075255563
13400.32361720819686-0.32361720819686
13500.32562025702687-0.32562025702687
13600.32762330585688-0.32762330585688
13710.4990622460113440.500937753988656
13810.5010652948413540.498934705158646
13900.333632452346909-0.333632452346909
14000.335635501176919-0.335635501176919
14110.2731096473301260.726890352669874
14210.4163156126958410.583684387304159
14300.270484549696758-0.270484549696758
14410.50756967193270.4924303280673
14500.50957272076271-0.50957272076271
14610.3476537941569780.652346205843022
14700.426330856845889-0.426330856845889
14800.351659891816997-0.351659891816997
14900.282502842676817-0.282502842676817
15010.5195879649127580.480412035087242
15110.3576690383070260.642330961692974
15200.223983086490043-0.223983086490043
15300.389908110755795-0.389908110755795
15400.369192100685768-0.369192100685768

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.261316570347238 & 0.738683429652762 \tabularnewline
2 & 0 & 0.334479717147436 & -0.334479717147436 \tabularnewline
3 & 0 & 0.336482765977446 & -0.336482765977446 \tabularnewline
4 & 0 & 0.338485814807456 & -0.338485814807456 \tabularnewline
5 & 0 & 0.340488863637466 & -0.340488863637466 \tabularnewline
6 & 1 & 0.435253789933028 & 0.564746210066972 \tabularnewline
7 & 0 & 0.344494961297486 & -0.344494961297486 \tabularnewline
8 & 0 & 0.346498010127495 & -0.346498010127495 \tabularnewline
9 & 1 & 0.348501058957505 & 0.651498941042495 \tabularnewline
10 & 0 & 0.279344009817325 & -0.279344009817325 \tabularnewline
11 & 0 & 0.281347058647335 & -0.281347058647335 \tabularnewline
12 & 0 & 0.354510205447534 & -0.354510205447534 \tabularnewline
13 & 0 & 0.597109243572188 & -0.597109243572188 \tabularnewline
14 & 0 & 0.287356205137364 & -0.287356205137364 \tabularnewline
15 & 1 & 0.601115341232207 & 0.398884658767792 \tabularnewline
16 & 1 & 0.603118390062217 & 0.396881609937783 \tabularnewline
17 & 0 & 0.392758424386332 & -0.392758424386332 \tabularnewline
18 & 0 & 0.295368400457403 & -0.295368400457403 \tabularnewline
19 & 1 & 0.368531547257603 & 0.631468452742397 \tabularnewline
20 & 1 & 0.469927668846552 & 0.530072331153448 \tabularnewline
21 & 0 & 0.465299522383174 & -0.465299522383174 \tabularnewline
22 & 1 & 0.543976585072086 & 0.456023414927914 \tabularnewline
23 & 1 & 0.540465718013384 & 0.459534281986616 \tabularnewline
24 & 1 & 0.471308668873204 & 0.528691331126796 \tabularnewline
25 & 1 & 0.457223854096563 & 0.542776145903437 \tabularnewline
26 & 0 & 0.623148878362315 & -0.623148878362315 \tabularnewline
27 & 1 & 0.313395839927491 & 0.686604160072509 \tabularnewline
28 & 0 & 0.463233000586592 & -0.463233000586592 \tabularnewline
29 & 1 & 0.3885620355577 & 0.6114379644423 \tabularnewline
30 & 0 & 0.554487059823452 & -0.554487059823452 \tabularnewline
31 & 0 & 0.39256813321772 & -0.39256813321772 \tabularnewline
32 & 0 & 0.32341108407754 & -0.32341108407754 \tabularnewline
33 & 0 & 0.489336108343291 & -0.489336108343291 \tabularnewline
34 & 1 & 0.398577279707749 & 0.601422720292251 \tabularnewline
35 & 0 & 0.400580328537759 & -0.400580328537759 \tabularnewline
36 & 0 & 0.402583377367769 & -0.402583377367769 \tabularnewline
37 & 0 & 0.574022317522232 & -0.574022317522232 \tabularnewline
38 & 1 & 0.48326348888669 & 0.51673651111331 \tabularnewline
39 & 1 & 0.57251449929354 & 0.42748550070646 \tabularnewline
40 & 0 & 0.57451754812355 & -0.57451754812355 \tabularnewline
41 & 1 & 0.511991694276756 & 0.488008305723244 \tabularnewline
42 & 1 & 0.491275684206729 & 0.508724315793271 \tabularnewline
43 & 1 & 0.509366596643389 & 0.490633403356611 \tabularnewline
44 & 0 & 0.347447670037657 & -0.347447670037657 \tabularnewline
45 & 0 & 0.584532792273598 & -0.584532792273598 \tabularnewline
46 & 1 & 0.586535841103608 & 0.413464158896392 \tabularnewline
47 & 0 & 0.424616914497876 & -0.424616914497876 \tabularnewline
48 & 1 & 0.426619963327886 & 0.573380036672114 \tabularnewline
49 & 1 & 0.592544987593637 & 0.407455012406363 \tabularnewline
50 & 0 & 0.430626060987905 & -0.430626060987905 \tabularnewline
51 & 0 & 0.509303123676817 & -0.509303123676817 \tabularnewline
52 & 0 & 0.462865133436674 & -0.462865133436674 \tabularnewline
53 & 1 & 0.436635207477934 & 0.563364792522066 \tabularnewline
54 & 0 & 0.374109353631141 & -0.374109353631141 \tabularnewline
55 & 0 & 0.440641305137954 & -0.440641305137954 \tabularnewline
56 & 1 & 0.519318367826866 & 0.480681632173134 \tabularnewline
57 & 1 & 0.685243392092617 & 0.314756607907383 \tabularnewline
58 & 1 & 0.446650451627983 & 0.553349548372017 \tabularnewline
59 & 1 & 0.448653500457993 & 0.551346499542007 \tabularnewline
60 & 1 & 0.478889524076752 & 0.521110475923248 \tabularnewline
61 & 1 & 0.381499500147823 & 0.618500499852177 \tabularnewline
62 & 0 & 0.695258636242666 & -0.695258636242666 \tabularnewline
63 & 0 & 0.456665695778032 & -0.456665695778032 \tabularnewline
64 & 1 & 0.387508646637852 & 0.612491353362148 \tabularnewline
65 & 0 & 0.460671793438051 & -0.460671793438051 \tabularnewline
66 & 0 & 0.462674842268061 & -0.462674842268061 \tabularnewline
67 & 0 & 0.56407096385701 & -0.56407096385701 \tabularnewline
68 & 0 & 0.395520841957891 & -0.395520841957891 \tabularnewline
69 & 1 & 0.468683988758091 & 0.531316011241909 \tabularnewline
70 & 0 & 0.547361051447002 & -0.547361051447002 \tabularnewline
71 & 0 & 0.47269008641811 & -0.47269008641811 \tabularnewline
72 & 1 & 0.47469313524812 & 0.52530686475188 \tabularnewline
73 & 1 & 0.553370197937031 & 0.446629802062969 \tabularnewline
74 & 0 & 0.484213148796851 & -0.484213148796851 \tabularnewline
75 & 1 & 0.480702281738149 & 0.519297718261851 \tabularnewline
76 & 1 & 0.646627306003901 & 0.353372693996099 \tabularnewline
77 & 1 & 0.484708379398169 & 0.515291620601831 \tabularnewline
78 & 1 & 0.727307417522822 & 0.272692582477178 \tabularnewline
79 & 1 & 0.424185574381385 & 0.575814425618615 \tabularnewline
80 & 0 & 0.65463950132394 & -0.65463950132394 \tabularnewline
81 & 0 & 0.492720574718207 & -0.492720574718207 \tabularnewline
82 & 1 & 0.500237539436929 & 0.499762460563071 \tabularnewline
83 & 0 & 0.496726672378227 & -0.496726672378227 \tabularnewline
84 & 0 & 0.434200818531434 & -0.434200818531434 \tabularnewline
85 & 1 & 0.664654745473989 & 0.335345254526011 \tabularnewline
86 & 0 & 0.431575720898066 & -0.431575720898066 \tabularnewline
87 & 1 & 0.158313815216212 & 0.841686184783788 \tabularnewline
88 & 1 & 0.236990877905124 & 0.763009122094876 \tabularnewline
89 & 0 & 0.233480010846421 & -0.233480010846421 \tabularnewline
90 & 1 & 0.235483059676431 & 0.764516940323569 \tabularnewline
91 & 0 & 0.401408083942183 & -0.401408083942183 \tabularnewline
92 & 0 & 0.168329059366261 & -0.168329059366261 \tabularnewline
93 & 0 & 0.334254083632013 & -0.334254083632013 \tabularnewline
94 & 0 & 0.24349525499647 & -0.24349525499647 \tabularnewline
95 & 0 & 0.24549830382648 & -0.24549830382648 \tabularnewline
96 & 1 & 0.24750135265649 & 0.75249864734351 \tabularnewline
97 & 0 & 0.17834430351631 & -0.17834430351631 \tabularnewline
98 & 0 & 0.251507450316509 & -0.251507450316509 \tabularnewline
99 & 0 & 0.182350401176329 & -0.182350401176329 \tabularnewline
100 & 1 & 0.255513547976529 & 0.744486452023471 \tabularnewline
101 & 1 & 0.186356498836349 & 0.813643501163651 \tabularnewline
102 & 0 & 0.259519645636548 & -0.259519645636548 \tabularnewline
103 & 0 & 0.261522694466558 & -0.261522694466558 \tabularnewline
104 & 0 & 0.263525743296568 & -0.263525743296568 \tabularnewline
105 & 0 & 0.342202805985479 & -0.342202805985479 \tabularnewline
106 & 0 & 0.267531840956587 & -0.267531840956587 \tabularnewline
107 & 0 & 0.269534889786597 & -0.269534889786597 \tabularnewline
108 & 0 & 0.277051854505319 & -0.277051854505319 \tabularnewline
109 & 0 & 0.273540987446616 & -0.273540987446616 \tabularnewline
110 & 0 & 0.204383938306437 & -0.204383938306437 \tabularnewline
111 & 0 & 0.44698297643109 & -0.44698297643109 \tabularnewline
112 & 0 & 0.279550133936646 & -0.279550133936646 \tabularnewline
113 & 0 & 0.358227196625557 & -0.358227196625557 \tabularnewline
114 & 0 & 0.289070147485378 & -0.289070147485378 \tabularnewline
115 & 0 & 0.214399182456485 & -0.214399182456485 \tabularnewline
116 & 0 & 0.287562329256685 & -0.287562329256685 \tabularnewline
117 & 1 & 0.218405280116505 & 0.781594719883495 \tabularnewline
118 & 0 & 0.220408328946515 & -0.220408328946515 \tabularnewline
119 & 0 & 0.293571475746714 & -0.293571475746714 \tabularnewline
120 & 1 & 0.295574524576724 & 0.704425475423276 \tabularnewline
121 & 0 & 0.226417475436544 & -0.226417475436544 \tabularnewline
122 & 0 & 0.299580622236743 & -0.299580622236743 \tabularnewline
123 & 0 & 0.307097586955465 & -0.307097586955465 \tabularnewline
124 & 1 & 0.544182709191407 & 0.455817290808593 \tabularnewline
125 & 1 & 0.305589768726773 & 0.694410231273227 \tabularnewline
126 & 0 & 0.307592817556782 & -0.307592817556782 \tabularnewline
127 & 0 & 0.473517841822534 & -0.473517841822534 \tabularnewline
128 & 1 & 0.311598915216802 & 0.688401084783198 \tabularnewline
129 & 0 & 0.313601964046812 & -0.313601964046812 \tabularnewline
130 & 1 & 0.315605012876821 & 0.684394987123179 \tabularnewline
131 & 0 & 0.246447963736641 & -0.246447963736641 \tabularnewline
132 & 1 & 0.248451012566651 & 0.751548987433349 \tabularnewline
133 & 0 & 0.327128075255563 & -0.327128075255563 \tabularnewline
134 & 0 & 0.32361720819686 & -0.32361720819686 \tabularnewline
135 & 0 & 0.32562025702687 & -0.32562025702687 \tabularnewline
136 & 0 & 0.32762330585688 & -0.32762330585688 \tabularnewline
137 & 1 & 0.499062246011344 & 0.500937753988656 \tabularnewline
138 & 1 & 0.501065294841354 & 0.498934705158646 \tabularnewline
139 & 0 & 0.333632452346909 & -0.333632452346909 \tabularnewline
140 & 0 & 0.335635501176919 & -0.335635501176919 \tabularnewline
141 & 1 & 0.273109647330126 & 0.726890352669874 \tabularnewline
142 & 1 & 0.416315612695841 & 0.583684387304159 \tabularnewline
143 & 0 & 0.270484549696758 & -0.270484549696758 \tabularnewline
144 & 1 & 0.5075696719327 & 0.4924303280673 \tabularnewline
145 & 0 & 0.50957272076271 & -0.50957272076271 \tabularnewline
146 & 1 & 0.347653794156978 & 0.652346205843022 \tabularnewline
147 & 0 & 0.426330856845889 & -0.426330856845889 \tabularnewline
148 & 0 & 0.351659891816997 & -0.351659891816997 \tabularnewline
149 & 0 & 0.282502842676817 & -0.282502842676817 \tabularnewline
150 & 1 & 0.519587964912758 & 0.480412035087242 \tabularnewline
151 & 1 & 0.357669038307026 & 0.642330961692974 \tabularnewline
152 & 0 & 0.223983086490043 & -0.223983086490043 \tabularnewline
153 & 0 & 0.389908110755795 & -0.389908110755795 \tabularnewline
154 & 0 & 0.369192100685768 & -0.369192100685768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.261316570347238[/C][C]0.738683429652762[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.334479717147436[/C][C]-0.334479717147436[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.336482765977446[/C][C]-0.336482765977446[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.338485814807456[/C][C]-0.338485814807456[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.340488863637466[/C][C]-0.340488863637466[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.435253789933028[/C][C]0.564746210066972[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.344494961297486[/C][C]-0.344494961297486[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.346498010127495[/C][C]-0.346498010127495[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.348501058957505[/C][C]0.651498941042495[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.279344009817325[/C][C]-0.279344009817325[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.281347058647335[/C][C]-0.281347058647335[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.354510205447534[/C][C]-0.354510205447534[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.597109243572188[/C][C]-0.597109243572188[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.287356205137364[/C][C]-0.287356205137364[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.601115341232207[/C][C]0.398884658767792[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.603118390062217[/C][C]0.396881609937783[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.392758424386332[/C][C]-0.392758424386332[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.295368400457403[/C][C]-0.295368400457403[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.368531547257603[/C][C]0.631468452742397[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.469927668846552[/C][C]0.530072331153448[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.465299522383174[/C][C]-0.465299522383174[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.543976585072086[/C][C]0.456023414927914[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.540465718013384[/C][C]0.459534281986616[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.471308668873204[/C][C]0.528691331126796[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.457223854096563[/C][C]0.542776145903437[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.623148878362315[/C][C]-0.623148878362315[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.313395839927491[/C][C]0.686604160072509[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.463233000586592[/C][C]-0.463233000586592[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.3885620355577[/C][C]0.6114379644423[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.554487059823452[/C][C]-0.554487059823452[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.39256813321772[/C][C]-0.39256813321772[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.32341108407754[/C][C]-0.32341108407754[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.489336108343291[/C][C]-0.489336108343291[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.398577279707749[/C][C]0.601422720292251[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.400580328537759[/C][C]-0.400580328537759[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.402583377367769[/C][C]-0.402583377367769[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.574022317522232[/C][C]-0.574022317522232[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.48326348888669[/C][C]0.51673651111331[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.57251449929354[/C][C]0.42748550070646[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.57451754812355[/C][C]-0.57451754812355[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.511991694276756[/C][C]0.488008305723244[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.491275684206729[/C][C]0.508724315793271[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.509366596643389[/C][C]0.490633403356611[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.347447670037657[/C][C]-0.347447670037657[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.584532792273598[/C][C]-0.584532792273598[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.586535841103608[/C][C]0.413464158896392[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.424616914497876[/C][C]-0.424616914497876[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.426619963327886[/C][C]0.573380036672114[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.592544987593637[/C][C]0.407455012406363[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.430626060987905[/C][C]-0.430626060987905[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.509303123676817[/C][C]-0.509303123676817[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.462865133436674[/C][C]-0.462865133436674[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.436635207477934[/C][C]0.563364792522066[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.374109353631141[/C][C]-0.374109353631141[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.440641305137954[/C][C]-0.440641305137954[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.519318367826866[/C][C]0.480681632173134[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.685243392092617[/C][C]0.314756607907383[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.446650451627983[/C][C]0.553349548372017[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.448653500457993[/C][C]0.551346499542007[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.478889524076752[/C][C]0.521110475923248[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.381499500147823[/C][C]0.618500499852177[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.695258636242666[/C][C]-0.695258636242666[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.456665695778032[/C][C]-0.456665695778032[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.387508646637852[/C][C]0.612491353362148[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.460671793438051[/C][C]-0.460671793438051[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.462674842268061[/C][C]-0.462674842268061[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.56407096385701[/C][C]-0.56407096385701[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.395520841957891[/C][C]-0.395520841957891[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.468683988758091[/C][C]0.531316011241909[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.547361051447002[/C][C]-0.547361051447002[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.47269008641811[/C][C]-0.47269008641811[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.47469313524812[/C][C]0.52530686475188[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.553370197937031[/C][C]0.446629802062969[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.484213148796851[/C][C]-0.484213148796851[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.480702281738149[/C][C]0.519297718261851[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.646627306003901[/C][C]0.353372693996099[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.484708379398169[/C][C]0.515291620601831[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.727307417522822[/C][C]0.272692582477178[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.424185574381385[/C][C]0.575814425618615[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.65463950132394[/C][C]-0.65463950132394[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.492720574718207[/C][C]-0.492720574718207[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.500237539436929[/C][C]0.499762460563071[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.496726672378227[/C][C]-0.496726672378227[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.434200818531434[/C][C]-0.434200818531434[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.664654745473989[/C][C]0.335345254526011[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.431575720898066[/C][C]-0.431575720898066[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.158313815216212[/C][C]0.841686184783788[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.236990877905124[/C][C]0.763009122094876[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.233480010846421[/C][C]-0.233480010846421[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.235483059676431[/C][C]0.764516940323569[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.401408083942183[/C][C]-0.401408083942183[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.168329059366261[/C][C]-0.168329059366261[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.334254083632013[/C][C]-0.334254083632013[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.24349525499647[/C][C]-0.24349525499647[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.24549830382648[/C][C]-0.24549830382648[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.24750135265649[/C][C]0.75249864734351[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.17834430351631[/C][C]-0.17834430351631[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.251507450316509[/C][C]-0.251507450316509[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.182350401176329[/C][C]-0.182350401176329[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.255513547976529[/C][C]0.744486452023471[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.186356498836349[/C][C]0.813643501163651[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.259519645636548[/C][C]-0.259519645636548[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.261522694466558[/C][C]-0.261522694466558[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.263525743296568[/C][C]-0.263525743296568[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.342202805985479[/C][C]-0.342202805985479[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.267531840956587[/C][C]-0.267531840956587[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.269534889786597[/C][C]-0.269534889786597[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.277051854505319[/C][C]-0.277051854505319[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.273540987446616[/C][C]-0.273540987446616[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.204383938306437[/C][C]-0.204383938306437[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.44698297643109[/C][C]-0.44698297643109[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.279550133936646[/C][C]-0.279550133936646[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.358227196625557[/C][C]-0.358227196625557[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.289070147485378[/C][C]-0.289070147485378[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.214399182456485[/C][C]-0.214399182456485[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.287562329256685[/C][C]-0.287562329256685[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.218405280116505[/C][C]0.781594719883495[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.220408328946515[/C][C]-0.220408328946515[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.293571475746714[/C][C]-0.293571475746714[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.295574524576724[/C][C]0.704425475423276[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.226417475436544[/C][C]-0.226417475436544[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.299580622236743[/C][C]-0.299580622236743[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.307097586955465[/C][C]-0.307097586955465[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.544182709191407[/C][C]0.455817290808593[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.305589768726773[/C][C]0.694410231273227[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.307592817556782[/C][C]-0.307592817556782[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.473517841822534[/C][C]-0.473517841822534[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.311598915216802[/C][C]0.688401084783198[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.313601964046812[/C][C]-0.313601964046812[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.315605012876821[/C][C]0.684394987123179[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.246447963736641[/C][C]-0.246447963736641[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.248451012566651[/C][C]0.751548987433349[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.327128075255563[/C][C]-0.327128075255563[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.32361720819686[/C][C]-0.32361720819686[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.32562025702687[/C][C]-0.32562025702687[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.32762330585688[/C][C]-0.32762330585688[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.499062246011344[/C][C]0.500937753988656[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.501065294841354[/C][C]0.498934705158646[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.333632452346909[/C][C]-0.333632452346909[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.335635501176919[/C][C]-0.335635501176919[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.273109647330126[/C][C]0.726890352669874[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.416315612695841[/C][C]0.583684387304159[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.270484549696758[/C][C]-0.270484549696758[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.5075696719327[/C][C]0.4924303280673[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.50957272076271[/C][C]-0.50957272076271[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.347653794156978[/C][C]0.652346205843022[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.426330856845889[/C][C]-0.426330856845889[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.351659891816997[/C][C]-0.351659891816997[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.282502842676817[/C][C]-0.282502842676817[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.519587964912758[/C][C]0.480412035087242[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.357669038307026[/C][C]0.642330961692974[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.223983086490043[/C][C]-0.223983086490043[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.389908110755795[/C][C]-0.389908110755795[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.369192100685768[/C][C]-0.369192100685768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.2613165703472380.738683429652762
200.334479717147436-0.334479717147436
300.336482765977446-0.336482765977446
400.338485814807456-0.338485814807456
500.340488863637466-0.340488863637466
610.4352537899330280.564746210066972
700.344494961297486-0.344494961297486
800.346498010127495-0.346498010127495
910.3485010589575050.651498941042495
1000.279344009817325-0.279344009817325
1100.281347058647335-0.281347058647335
1200.354510205447534-0.354510205447534
1300.597109243572188-0.597109243572188
1400.287356205137364-0.287356205137364
1510.6011153412322070.398884658767792
1610.6031183900622170.396881609937783
1700.392758424386332-0.392758424386332
1800.295368400457403-0.295368400457403
1910.3685315472576030.631468452742397
2010.4699276688465520.530072331153448
2100.465299522383174-0.465299522383174
2210.5439765850720860.456023414927914
2310.5404657180133840.459534281986616
2410.4713086688732040.528691331126796
2510.4572238540965630.542776145903437
2600.623148878362315-0.623148878362315
2710.3133958399274910.686604160072509
2800.463233000586592-0.463233000586592
2910.38856203555770.6114379644423
3000.554487059823452-0.554487059823452
3100.39256813321772-0.39256813321772
3200.32341108407754-0.32341108407754
3300.489336108343291-0.489336108343291
3410.3985772797077490.601422720292251
3500.400580328537759-0.400580328537759
3600.402583377367769-0.402583377367769
3700.574022317522232-0.574022317522232
3810.483263488886690.51673651111331
3910.572514499293540.42748550070646
4000.57451754812355-0.57451754812355
4110.5119916942767560.488008305723244
4210.4912756842067290.508724315793271
4310.5093665966433890.490633403356611
4400.347447670037657-0.347447670037657
4500.584532792273598-0.584532792273598
4610.5865358411036080.413464158896392
4700.424616914497876-0.424616914497876
4810.4266199633278860.573380036672114
4910.5925449875936370.407455012406363
5000.430626060987905-0.430626060987905
5100.509303123676817-0.509303123676817
5200.462865133436674-0.462865133436674
5310.4366352074779340.563364792522066
5400.374109353631141-0.374109353631141
5500.440641305137954-0.440641305137954
5610.5193183678268660.480681632173134
5710.6852433920926170.314756607907383
5810.4466504516279830.553349548372017
5910.4486535004579930.551346499542007
6010.4788895240767520.521110475923248
6110.3814995001478230.618500499852177
6200.695258636242666-0.695258636242666
6300.456665695778032-0.456665695778032
6410.3875086466378520.612491353362148
6500.460671793438051-0.460671793438051
6600.462674842268061-0.462674842268061
6700.56407096385701-0.56407096385701
6800.395520841957891-0.395520841957891
6910.4686839887580910.531316011241909
7000.547361051447002-0.547361051447002
7100.47269008641811-0.47269008641811
7210.474693135248120.52530686475188
7310.5533701979370310.446629802062969
7400.484213148796851-0.484213148796851
7510.4807022817381490.519297718261851
7610.6466273060039010.353372693996099
7710.4847083793981690.515291620601831
7810.7273074175228220.272692582477178
7910.4241855743813850.575814425618615
8000.65463950132394-0.65463950132394
8100.492720574718207-0.492720574718207
8210.5002375394369290.499762460563071
8300.496726672378227-0.496726672378227
8400.434200818531434-0.434200818531434
8510.6646547454739890.335345254526011
8600.431575720898066-0.431575720898066
8710.1583138152162120.841686184783788
8810.2369908779051240.763009122094876
8900.233480010846421-0.233480010846421
9010.2354830596764310.764516940323569
9100.401408083942183-0.401408083942183
9200.168329059366261-0.168329059366261
9300.334254083632013-0.334254083632013
9400.24349525499647-0.24349525499647
9500.24549830382648-0.24549830382648
9610.247501352656490.75249864734351
9700.17834430351631-0.17834430351631
9800.251507450316509-0.251507450316509
9900.182350401176329-0.182350401176329
10010.2555135479765290.744486452023471
10110.1863564988363490.813643501163651
10200.259519645636548-0.259519645636548
10300.261522694466558-0.261522694466558
10400.263525743296568-0.263525743296568
10500.342202805985479-0.342202805985479
10600.267531840956587-0.267531840956587
10700.269534889786597-0.269534889786597
10800.277051854505319-0.277051854505319
10900.273540987446616-0.273540987446616
11000.204383938306437-0.204383938306437
11100.44698297643109-0.44698297643109
11200.279550133936646-0.279550133936646
11300.358227196625557-0.358227196625557
11400.289070147485378-0.289070147485378
11500.214399182456485-0.214399182456485
11600.287562329256685-0.287562329256685
11710.2184052801165050.781594719883495
11800.220408328946515-0.220408328946515
11900.293571475746714-0.293571475746714
12010.2955745245767240.704425475423276
12100.226417475436544-0.226417475436544
12200.299580622236743-0.299580622236743
12300.307097586955465-0.307097586955465
12410.5441827091914070.455817290808593
12510.3055897687267730.694410231273227
12600.307592817556782-0.307592817556782
12700.473517841822534-0.473517841822534
12810.3115989152168020.688401084783198
12900.313601964046812-0.313601964046812
13010.3156050128768210.684394987123179
13100.246447963736641-0.246447963736641
13210.2484510125666510.751548987433349
13300.327128075255563-0.327128075255563
13400.32361720819686-0.32361720819686
13500.32562025702687-0.32562025702687
13600.32762330585688-0.32762330585688
13710.4990622460113440.500937753988656
13810.5010652948413540.498934705158646
13900.333632452346909-0.333632452346909
14000.335635501176919-0.335635501176919
14110.2731096473301260.726890352669874
14210.4163156126958410.583684387304159
14300.270484549696758-0.270484549696758
14410.50756967193270.4924303280673
14500.50957272076271-0.50957272076271
14610.3476537941569780.652346205843022
14700.426330856845889-0.426330856845889
14800.351659891816997-0.351659891816997
14900.282502842676817-0.282502842676817
15010.5195879649127580.480412035087242
15110.3576690383070260.642330961692974
15200.223983086490043-0.223983086490043
15300.389908110755795-0.389908110755795
15400.369192100685768-0.369192100685768







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8681103987990220.2637792024019560.131889601200978
110.8029007194736790.3941985610526410.19709928052632
120.6976186509952920.6047626980094160.302381349004708
130.5874149833666030.8251700332667940.412585016633397
140.4788192966974020.9576385933948040.521180703302598
150.606633764721820.7867324705563610.39336623527818
160.5827472079008620.8345055841982770.417252792099138
170.4904052337650140.9808104675300280.509594766234986
180.4016246543115120.8032493086230240.598375345688488
190.5775512767501780.8448974464996430.422448723249822
200.6034754522461590.7930490955076820.396524547753841
210.6972625388837980.6054749222324040.302737461116202
220.6677771408691360.6644457182617280.332222859130864
230.6229750346311920.7540499307376160.377024965368808
240.580798314284540.8384033714309190.41920168571546
250.5693660321796970.8612679356406060.430633967820303
260.6978773168832360.6042453662335280.302122683116764
270.7036071713215660.5927856573568670.296392828678434
280.7073709304385160.5852581391229680.292629069561484
290.6912429624888480.6175140750223050.308757037511152
300.7639810897257160.4720378205485690.236018910274284
310.7530824767898620.4938350464202760.246917523210138
320.7334873917852250.533025216429550.266512608214775
330.7384927374766270.5230145250467460.261507262523373
340.7596081453587750.4807837092824490.240391854641225
350.7422955860063880.5154088279872240.257704413993612
360.7189764137600960.5620471724798070.281023586239903
370.7311444254190090.5377111491619810.268855574580991
380.7371693666049040.5256612667901930.262830633395096
390.7261101492293320.5477797015413360.273889850770668
400.7360275773183480.5279448453633040.263972422681652
410.7171934412844620.5656131174310750.282806558715538
420.7065793214322790.5868413571354420.293420678567721
430.7040586892649940.5918826214700110.295941310735006
440.6856855545203820.6286288909592350.314314445479618
450.6973167058528660.6053665882942680.302683294147134
460.6866455672143110.6267088655713780.313354432785689
470.6738474718322250.6523050563355510.326152528167775
480.6860843843924970.6278312312150060.313915615607503
490.6678853317406620.6642293365186760.332114668259338
500.6610897309098190.6778205381803620.338910269090181
510.6687103378874340.6625793242251320.331289662112566
520.6719502413645320.6560995172709360.328049758635468
530.6849311387248710.6301377225502570.315068861275129
540.6647869335964250.6704261328071490.335213066403575
550.6530016593853220.6939966812293550.346998340614678
560.6496765584790560.7006468830418880.350323441520944
570.6173797145149460.7652405709701090.382620285485054
580.6261225421705140.7477549156589730.373877457829486
590.6305422716294440.7389154567411130.369457728370556
600.6312664921861530.7374670156276940.368733507813847
610.6455525857746280.7088948284507430.354447414225372
620.7000499178812250.5999001642375510.299950082118775
630.6995418175425390.6009163649149220.300458182457461
640.7139035215334850.572192956933030.286096478466515
650.7126079217785280.5747841564429440.287392078221472
660.7093096726298060.5813806547403880.290690327370194
670.7218634695049410.5562730609901170.278136530495059
680.7098896651971280.5802206696057430.290110334802872
690.7146443528632090.5707112942735810.285355647136791
700.7267508133287630.5464983733424740.273249186671237
710.724632007690210.5507359846195810.275367992309791
720.7274620876290430.5450758247419140.272537912370957
730.7164844710717670.5670310578564650.283515528928233
740.7179153727858140.5641692544283720.282084627214186
750.7193174874976630.5613650250046730.280682512502337
760.6976750042462530.6046499915074940.302324995753747
770.7049200647540780.5901598704918440.295079935245922
780.6789630379776410.6420739240447180.321036962022359
790.7055872018264070.5888255963471860.294412798173593
800.7255536062424590.5488927875150810.274446393757541
810.7185117059293130.5629765881413740.281488294070687
820.7359805356156230.5280389287687550.264019464384377
830.7231600365339830.5536799269320350.276839963466017
840.7072717822346930.5854564355306140.292728217765307
850.6996444879800190.6007110240399630.300355512019981
860.6726160460694670.6547679078610670.327383953930533
870.7092947957986090.5814104084027820.290705204201391
880.7521068573322040.4957862853355920.247893142667796
890.7571456695342740.4857086609314520.242854330465726
900.79522947550570.4095410489886010.2047705244943
910.8053852768177440.3892294463645110.194614723182256
920.783270462913440.4334590741731190.21672953708656
930.7708058140917960.4583883718164070.229194185908204
940.7441562600316480.5116874799367050.255843739968352
950.7147882026931820.5704235946136350.285211797306818
960.7706119878908680.4587760242182640.229388012109132
970.7375752300037770.5248495399924470.262424769996223
980.706126303774210.5877473924515790.29387369622579
990.6670963997864720.6658072004270570.332903600213528
1000.7350426871876340.5299146256247320.264957312812366
1010.8332355095035360.3335289809929280.166764490496464
1020.8062772864919790.3874454270160420.193722713508021
1030.7760746022729770.4478507954540460.223925397727023
1040.7428951569009220.5142096861981550.257104843099078
1050.7147804676111490.5704390647777020.285219532388851
1060.677309661826490.645380676347020.32269033817351
1070.6386164787204890.7227670425590210.361383521279511
1080.5969233444847220.8061533110305560.403076655515278
1090.5569759458100060.8860481083799880.443024054189994
1100.5069609783956490.9860780432087030.493039021604351
1110.4945729976587280.9891459953174560.505427002341272
1120.4585803038864450.917160607772890.541419696113555
1130.4423863553444370.8847727106888740.557613644655563
1140.4084498933145540.8168997866291080.591550106685446
1150.3625891292575940.7251782585151890.637410870742406
1160.3425274870666090.6850549741332190.657472512933391
1170.4375084871575650.8750169743151310.562491512842435
1180.3846309579031340.7692619158062670.615369042096866
1190.3625142496401280.7250284992802550.637485750359872
1200.3965382870191230.7930765740382460.603461712980877
1210.3431909212439490.6863818424878980.656809078756051
1220.3189671710456550.6379343420913110.681032828954345
1230.2954333221826690.5908666443653380.704566677817331
1240.2555335505402310.5110671010804610.744466449459769
1250.279359564606660.5587191292133190.72064043539334
1260.2546364493963220.5092728987926440.745363550603678
1270.3177223806028390.6354447612056770.682277619397161
1280.3295884732584040.6591769465168090.670411526741596
1290.3156112050607370.6312224101214740.684388794939263
1300.332998722250910.665997444501820.66700127774909
1310.2773305257832160.5546610515664310.722669474216784
1320.4599502937241970.9199005874483940.540049706275803
1330.3976122027182730.7952244054365460.602387797281727
1340.3457857686267730.6915715372535470.654214231373227
1350.3071052486636480.6142104973272970.692894751336352
1360.2908844734270150.581768946854030.709115526572985
1370.2455568627096850.4911137254193710.754443137290315
1380.3079112815958470.6158225631916950.692088718404153
1390.2876446739362140.5752893478724270.712355326063786
1400.3295650268448660.6591300536897310.670434973155134
1410.2593625822642670.5187251645285340.740637417735733
1420.3550612493261840.7101224986523670.644938750673816
1430.2458745500882180.4917491001764350.754125449911782
1440.3120095071151350.624019014230270.687990492884865

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.868110398799022 & 0.263779202401956 & 0.131889601200978 \tabularnewline
11 & 0.802900719473679 & 0.394198561052641 & 0.19709928052632 \tabularnewline
12 & 0.697618650995292 & 0.604762698009416 & 0.302381349004708 \tabularnewline
13 & 0.587414983366603 & 0.825170033266794 & 0.412585016633397 \tabularnewline
14 & 0.478819296697402 & 0.957638593394804 & 0.521180703302598 \tabularnewline
15 & 0.60663376472182 & 0.786732470556361 & 0.39336623527818 \tabularnewline
16 & 0.582747207900862 & 0.834505584198277 & 0.417252792099138 \tabularnewline
17 & 0.490405233765014 & 0.980810467530028 & 0.509594766234986 \tabularnewline
18 & 0.401624654311512 & 0.803249308623024 & 0.598375345688488 \tabularnewline
19 & 0.577551276750178 & 0.844897446499643 & 0.422448723249822 \tabularnewline
20 & 0.603475452246159 & 0.793049095507682 & 0.396524547753841 \tabularnewline
21 & 0.697262538883798 & 0.605474922232404 & 0.302737461116202 \tabularnewline
22 & 0.667777140869136 & 0.664445718261728 & 0.332222859130864 \tabularnewline
23 & 0.622975034631192 & 0.754049930737616 & 0.377024965368808 \tabularnewline
24 & 0.58079831428454 & 0.838403371430919 & 0.41920168571546 \tabularnewline
25 & 0.569366032179697 & 0.861267935640606 & 0.430633967820303 \tabularnewline
26 & 0.697877316883236 & 0.604245366233528 & 0.302122683116764 \tabularnewline
27 & 0.703607171321566 & 0.592785657356867 & 0.296392828678434 \tabularnewline
28 & 0.707370930438516 & 0.585258139122968 & 0.292629069561484 \tabularnewline
29 & 0.691242962488848 & 0.617514075022305 & 0.308757037511152 \tabularnewline
30 & 0.763981089725716 & 0.472037820548569 & 0.236018910274284 \tabularnewline
31 & 0.753082476789862 & 0.493835046420276 & 0.246917523210138 \tabularnewline
32 & 0.733487391785225 & 0.53302521642955 & 0.266512608214775 \tabularnewline
33 & 0.738492737476627 & 0.523014525046746 & 0.261507262523373 \tabularnewline
34 & 0.759608145358775 & 0.480783709282449 & 0.240391854641225 \tabularnewline
35 & 0.742295586006388 & 0.515408827987224 & 0.257704413993612 \tabularnewline
36 & 0.718976413760096 & 0.562047172479807 & 0.281023586239903 \tabularnewline
37 & 0.731144425419009 & 0.537711149161981 & 0.268855574580991 \tabularnewline
38 & 0.737169366604904 & 0.525661266790193 & 0.262830633395096 \tabularnewline
39 & 0.726110149229332 & 0.547779701541336 & 0.273889850770668 \tabularnewline
40 & 0.736027577318348 & 0.527944845363304 & 0.263972422681652 \tabularnewline
41 & 0.717193441284462 & 0.565613117431075 & 0.282806558715538 \tabularnewline
42 & 0.706579321432279 & 0.586841357135442 & 0.293420678567721 \tabularnewline
43 & 0.704058689264994 & 0.591882621470011 & 0.295941310735006 \tabularnewline
44 & 0.685685554520382 & 0.628628890959235 & 0.314314445479618 \tabularnewline
45 & 0.697316705852866 & 0.605366588294268 & 0.302683294147134 \tabularnewline
46 & 0.686645567214311 & 0.626708865571378 & 0.313354432785689 \tabularnewline
47 & 0.673847471832225 & 0.652305056335551 & 0.326152528167775 \tabularnewline
48 & 0.686084384392497 & 0.627831231215006 & 0.313915615607503 \tabularnewline
49 & 0.667885331740662 & 0.664229336518676 & 0.332114668259338 \tabularnewline
50 & 0.661089730909819 & 0.677820538180362 & 0.338910269090181 \tabularnewline
51 & 0.668710337887434 & 0.662579324225132 & 0.331289662112566 \tabularnewline
52 & 0.671950241364532 & 0.656099517270936 & 0.328049758635468 \tabularnewline
53 & 0.684931138724871 & 0.630137722550257 & 0.315068861275129 \tabularnewline
54 & 0.664786933596425 & 0.670426132807149 & 0.335213066403575 \tabularnewline
55 & 0.653001659385322 & 0.693996681229355 & 0.346998340614678 \tabularnewline
56 & 0.649676558479056 & 0.700646883041888 & 0.350323441520944 \tabularnewline
57 & 0.617379714514946 & 0.765240570970109 & 0.382620285485054 \tabularnewline
58 & 0.626122542170514 & 0.747754915658973 & 0.373877457829486 \tabularnewline
59 & 0.630542271629444 & 0.738915456741113 & 0.369457728370556 \tabularnewline
60 & 0.631266492186153 & 0.737467015627694 & 0.368733507813847 \tabularnewline
61 & 0.645552585774628 & 0.708894828450743 & 0.354447414225372 \tabularnewline
62 & 0.700049917881225 & 0.599900164237551 & 0.299950082118775 \tabularnewline
63 & 0.699541817542539 & 0.600916364914922 & 0.300458182457461 \tabularnewline
64 & 0.713903521533485 & 0.57219295693303 & 0.286096478466515 \tabularnewline
65 & 0.712607921778528 & 0.574784156442944 & 0.287392078221472 \tabularnewline
66 & 0.709309672629806 & 0.581380654740388 & 0.290690327370194 \tabularnewline
67 & 0.721863469504941 & 0.556273060990117 & 0.278136530495059 \tabularnewline
68 & 0.709889665197128 & 0.580220669605743 & 0.290110334802872 \tabularnewline
69 & 0.714644352863209 & 0.570711294273581 & 0.285355647136791 \tabularnewline
70 & 0.726750813328763 & 0.546498373342474 & 0.273249186671237 \tabularnewline
71 & 0.72463200769021 & 0.550735984619581 & 0.275367992309791 \tabularnewline
72 & 0.727462087629043 & 0.545075824741914 & 0.272537912370957 \tabularnewline
73 & 0.716484471071767 & 0.567031057856465 & 0.283515528928233 \tabularnewline
74 & 0.717915372785814 & 0.564169254428372 & 0.282084627214186 \tabularnewline
75 & 0.719317487497663 & 0.561365025004673 & 0.280682512502337 \tabularnewline
76 & 0.697675004246253 & 0.604649991507494 & 0.302324995753747 \tabularnewline
77 & 0.704920064754078 & 0.590159870491844 & 0.295079935245922 \tabularnewline
78 & 0.678963037977641 & 0.642073924044718 & 0.321036962022359 \tabularnewline
79 & 0.705587201826407 & 0.588825596347186 & 0.294412798173593 \tabularnewline
80 & 0.725553606242459 & 0.548892787515081 & 0.274446393757541 \tabularnewline
81 & 0.718511705929313 & 0.562976588141374 & 0.281488294070687 \tabularnewline
82 & 0.735980535615623 & 0.528038928768755 & 0.264019464384377 \tabularnewline
83 & 0.723160036533983 & 0.553679926932035 & 0.276839963466017 \tabularnewline
84 & 0.707271782234693 & 0.585456435530614 & 0.292728217765307 \tabularnewline
85 & 0.699644487980019 & 0.600711024039963 & 0.300355512019981 \tabularnewline
86 & 0.672616046069467 & 0.654767907861067 & 0.327383953930533 \tabularnewline
87 & 0.709294795798609 & 0.581410408402782 & 0.290705204201391 \tabularnewline
88 & 0.752106857332204 & 0.495786285335592 & 0.247893142667796 \tabularnewline
89 & 0.757145669534274 & 0.485708660931452 & 0.242854330465726 \tabularnewline
90 & 0.7952294755057 & 0.409541048988601 & 0.2047705244943 \tabularnewline
91 & 0.805385276817744 & 0.389229446364511 & 0.194614723182256 \tabularnewline
92 & 0.78327046291344 & 0.433459074173119 & 0.21672953708656 \tabularnewline
93 & 0.770805814091796 & 0.458388371816407 & 0.229194185908204 \tabularnewline
94 & 0.744156260031648 & 0.511687479936705 & 0.255843739968352 \tabularnewline
95 & 0.714788202693182 & 0.570423594613635 & 0.285211797306818 \tabularnewline
96 & 0.770611987890868 & 0.458776024218264 & 0.229388012109132 \tabularnewline
97 & 0.737575230003777 & 0.524849539992447 & 0.262424769996223 \tabularnewline
98 & 0.70612630377421 & 0.587747392451579 & 0.29387369622579 \tabularnewline
99 & 0.667096399786472 & 0.665807200427057 & 0.332903600213528 \tabularnewline
100 & 0.735042687187634 & 0.529914625624732 & 0.264957312812366 \tabularnewline
101 & 0.833235509503536 & 0.333528980992928 & 0.166764490496464 \tabularnewline
102 & 0.806277286491979 & 0.387445427016042 & 0.193722713508021 \tabularnewline
103 & 0.776074602272977 & 0.447850795454046 & 0.223925397727023 \tabularnewline
104 & 0.742895156900922 & 0.514209686198155 & 0.257104843099078 \tabularnewline
105 & 0.714780467611149 & 0.570439064777702 & 0.285219532388851 \tabularnewline
106 & 0.67730966182649 & 0.64538067634702 & 0.32269033817351 \tabularnewline
107 & 0.638616478720489 & 0.722767042559021 & 0.361383521279511 \tabularnewline
108 & 0.596923344484722 & 0.806153311030556 & 0.403076655515278 \tabularnewline
109 & 0.556975945810006 & 0.886048108379988 & 0.443024054189994 \tabularnewline
110 & 0.506960978395649 & 0.986078043208703 & 0.493039021604351 \tabularnewline
111 & 0.494572997658728 & 0.989145995317456 & 0.505427002341272 \tabularnewline
112 & 0.458580303886445 & 0.91716060777289 & 0.541419696113555 \tabularnewline
113 & 0.442386355344437 & 0.884772710688874 & 0.557613644655563 \tabularnewline
114 & 0.408449893314554 & 0.816899786629108 & 0.591550106685446 \tabularnewline
115 & 0.362589129257594 & 0.725178258515189 & 0.637410870742406 \tabularnewline
116 & 0.342527487066609 & 0.685054974133219 & 0.657472512933391 \tabularnewline
117 & 0.437508487157565 & 0.875016974315131 & 0.562491512842435 \tabularnewline
118 & 0.384630957903134 & 0.769261915806267 & 0.615369042096866 \tabularnewline
119 & 0.362514249640128 & 0.725028499280255 & 0.637485750359872 \tabularnewline
120 & 0.396538287019123 & 0.793076574038246 & 0.603461712980877 \tabularnewline
121 & 0.343190921243949 & 0.686381842487898 & 0.656809078756051 \tabularnewline
122 & 0.318967171045655 & 0.637934342091311 & 0.681032828954345 \tabularnewline
123 & 0.295433322182669 & 0.590866644365338 & 0.704566677817331 \tabularnewline
124 & 0.255533550540231 & 0.511067101080461 & 0.744466449459769 \tabularnewline
125 & 0.27935956460666 & 0.558719129213319 & 0.72064043539334 \tabularnewline
126 & 0.254636449396322 & 0.509272898792644 & 0.745363550603678 \tabularnewline
127 & 0.317722380602839 & 0.635444761205677 & 0.682277619397161 \tabularnewline
128 & 0.329588473258404 & 0.659176946516809 & 0.670411526741596 \tabularnewline
129 & 0.315611205060737 & 0.631222410121474 & 0.684388794939263 \tabularnewline
130 & 0.33299872225091 & 0.66599744450182 & 0.66700127774909 \tabularnewline
131 & 0.277330525783216 & 0.554661051566431 & 0.722669474216784 \tabularnewline
132 & 0.459950293724197 & 0.919900587448394 & 0.540049706275803 \tabularnewline
133 & 0.397612202718273 & 0.795224405436546 & 0.602387797281727 \tabularnewline
134 & 0.345785768626773 & 0.691571537253547 & 0.654214231373227 \tabularnewline
135 & 0.307105248663648 & 0.614210497327297 & 0.692894751336352 \tabularnewline
136 & 0.290884473427015 & 0.58176894685403 & 0.709115526572985 \tabularnewline
137 & 0.245556862709685 & 0.491113725419371 & 0.754443137290315 \tabularnewline
138 & 0.307911281595847 & 0.615822563191695 & 0.692088718404153 \tabularnewline
139 & 0.287644673936214 & 0.575289347872427 & 0.712355326063786 \tabularnewline
140 & 0.329565026844866 & 0.659130053689731 & 0.670434973155134 \tabularnewline
141 & 0.259362582264267 & 0.518725164528534 & 0.740637417735733 \tabularnewline
142 & 0.355061249326184 & 0.710122498652367 & 0.644938750673816 \tabularnewline
143 & 0.245874550088218 & 0.491749100176435 & 0.754125449911782 \tabularnewline
144 & 0.312009507115135 & 0.62401901423027 & 0.687990492884865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.868110398799022[/C][C]0.263779202401956[/C][C]0.131889601200978[/C][/ROW]
[ROW][C]11[/C][C]0.802900719473679[/C][C]0.394198561052641[/C][C]0.19709928052632[/C][/ROW]
[ROW][C]12[/C][C]0.697618650995292[/C][C]0.604762698009416[/C][C]0.302381349004708[/C][/ROW]
[ROW][C]13[/C][C]0.587414983366603[/C][C]0.825170033266794[/C][C]0.412585016633397[/C][/ROW]
[ROW][C]14[/C][C]0.478819296697402[/C][C]0.957638593394804[/C][C]0.521180703302598[/C][/ROW]
[ROW][C]15[/C][C]0.60663376472182[/C][C]0.786732470556361[/C][C]0.39336623527818[/C][/ROW]
[ROW][C]16[/C][C]0.582747207900862[/C][C]0.834505584198277[/C][C]0.417252792099138[/C][/ROW]
[ROW][C]17[/C][C]0.490405233765014[/C][C]0.980810467530028[/C][C]0.509594766234986[/C][/ROW]
[ROW][C]18[/C][C]0.401624654311512[/C][C]0.803249308623024[/C][C]0.598375345688488[/C][/ROW]
[ROW][C]19[/C][C]0.577551276750178[/C][C]0.844897446499643[/C][C]0.422448723249822[/C][/ROW]
[ROW][C]20[/C][C]0.603475452246159[/C][C]0.793049095507682[/C][C]0.396524547753841[/C][/ROW]
[ROW][C]21[/C][C]0.697262538883798[/C][C]0.605474922232404[/C][C]0.302737461116202[/C][/ROW]
[ROW][C]22[/C][C]0.667777140869136[/C][C]0.664445718261728[/C][C]0.332222859130864[/C][/ROW]
[ROW][C]23[/C][C]0.622975034631192[/C][C]0.754049930737616[/C][C]0.377024965368808[/C][/ROW]
[ROW][C]24[/C][C]0.58079831428454[/C][C]0.838403371430919[/C][C]0.41920168571546[/C][/ROW]
[ROW][C]25[/C][C]0.569366032179697[/C][C]0.861267935640606[/C][C]0.430633967820303[/C][/ROW]
[ROW][C]26[/C][C]0.697877316883236[/C][C]0.604245366233528[/C][C]0.302122683116764[/C][/ROW]
[ROW][C]27[/C][C]0.703607171321566[/C][C]0.592785657356867[/C][C]0.296392828678434[/C][/ROW]
[ROW][C]28[/C][C]0.707370930438516[/C][C]0.585258139122968[/C][C]0.292629069561484[/C][/ROW]
[ROW][C]29[/C][C]0.691242962488848[/C][C]0.617514075022305[/C][C]0.308757037511152[/C][/ROW]
[ROW][C]30[/C][C]0.763981089725716[/C][C]0.472037820548569[/C][C]0.236018910274284[/C][/ROW]
[ROW][C]31[/C][C]0.753082476789862[/C][C]0.493835046420276[/C][C]0.246917523210138[/C][/ROW]
[ROW][C]32[/C][C]0.733487391785225[/C][C]0.53302521642955[/C][C]0.266512608214775[/C][/ROW]
[ROW][C]33[/C][C]0.738492737476627[/C][C]0.523014525046746[/C][C]0.261507262523373[/C][/ROW]
[ROW][C]34[/C][C]0.759608145358775[/C][C]0.480783709282449[/C][C]0.240391854641225[/C][/ROW]
[ROW][C]35[/C][C]0.742295586006388[/C][C]0.515408827987224[/C][C]0.257704413993612[/C][/ROW]
[ROW][C]36[/C][C]0.718976413760096[/C][C]0.562047172479807[/C][C]0.281023586239903[/C][/ROW]
[ROW][C]37[/C][C]0.731144425419009[/C][C]0.537711149161981[/C][C]0.268855574580991[/C][/ROW]
[ROW][C]38[/C][C]0.737169366604904[/C][C]0.525661266790193[/C][C]0.262830633395096[/C][/ROW]
[ROW][C]39[/C][C]0.726110149229332[/C][C]0.547779701541336[/C][C]0.273889850770668[/C][/ROW]
[ROW][C]40[/C][C]0.736027577318348[/C][C]0.527944845363304[/C][C]0.263972422681652[/C][/ROW]
[ROW][C]41[/C][C]0.717193441284462[/C][C]0.565613117431075[/C][C]0.282806558715538[/C][/ROW]
[ROW][C]42[/C][C]0.706579321432279[/C][C]0.586841357135442[/C][C]0.293420678567721[/C][/ROW]
[ROW][C]43[/C][C]0.704058689264994[/C][C]0.591882621470011[/C][C]0.295941310735006[/C][/ROW]
[ROW][C]44[/C][C]0.685685554520382[/C][C]0.628628890959235[/C][C]0.314314445479618[/C][/ROW]
[ROW][C]45[/C][C]0.697316705852866[/C][C]0.605366588294268[/C][C]0.302683294147134[/C][/ROW]
[ROW][C]46[/C][C]0.686645567214311[/C][C]0.626708865571378[/C][C]0.313354432785689[/C][/ROW]
[ROW][C]47[/C][C]0.673847471832225[/C][C]0.652305056335551[/C][C]0.326152528167775[/C][/ROW]
[ROW][C]48[/C][C]0.686084384392497[/C][C]0.627831231215006[/C][C]0.313915615607503[/C][/ROW]
[ROW][C]49[/C][C]0.667885331740662[/C][C]0.664229336518676[/C][C]0.332114668259338[/C][/ROW]
[ROW][C]50[/C][C]0.661089730909819[/C][C]0.677820538180362[/C][C]0.338910269090181[/C][/ROW]
[ROW][C]51[/C][C]0.668710337887434[/C][C]0.662579324225132[/C][C]0.331289662112566[/C][/ROW]
[ROW][C]52[/C][C]0.671950241364532[/C][C]0.656099517270936[/C][C]0.328049758635468[/C][/ROW]
[ROW][C]53[/C][C]0.684931138724871[/C][C]0.630137722550257[/C][C]0.315068861275129[/C][/ROW]
[ROW][C]54[/C][C]0.664786933596425[/C][C]0.670426132807149[/C][C]0.335213066403575[/C][/ROW]
[ROW][C]55[/C][C]0.653001659385322[/C][C]0.693996681229355[/C][C]0.346998340614678[/C][/ROW]
[ROW][C]56[/C][C]0.649676558479056[/C][C]0.700646883041888[/C][C]0.350323441520944[/C][/ROW]
[ROW][C]57[/C][C]0.617379714514946[/C][C]0.765240570970109[/C][C]0.382620285485054[/C][/ROW]
[ROW][C]58[/C][C]0.626122542170514[/C][C]0.747754915658973[/C][C]0.373877457829486[/C][/ROW]
[ROW][C]59[/C][C]0.630542271629444[/C][C]0.738915456741113[/C][C]0.369457728370556[/C][/ROW]
[ROW][C]60[/C][C]0.631266492186153[/C][C]0.737467015627694[/C][C]0.368733507813847[/C][/ROW]
[ROW][C]61[/C][C]0.645552585774628[/C][C]0.708894828450743[/C][C]0.354447414225372[/C][/ROW]
[ROW][C]62[/C][C]0.700049917881225[/C][C]0.599900164237551[/C][C]0.299950082118775[/C][/ROW]
[ROW][C]63[/C][C]0.699541817542539[/C][C]0.600916364914922[/C][C]0.300458182457461[/C][/ROW]
[ROW][C]64[/C][C]0.713903521533485[/C][C]0.57219295693303[/C][C]0.286096478466515[/C][/ROW]
[ROW][C]65[/C][C]0.712607921778528[/C][C]0.574784156442944[/C][C]0.287392078221472[/C][/ROW]
[ROW][C]66[/C][C]0.709309672629806[/C][C]0.581380654740388[/C][C]0.290690327370194[/C][/ROW]
[ROW][C]67[/C][C]0.721863469504941[/C][C]0.556273060990117[/C][C]0.278136530495059[/C][/ROW]
[ROW][C]68[/C][C]0.709889665197128[/C][C]0.580220669605743[/C][C]0.290110334802872[/C][/ROW]
[ROW][C]69[/C][C]0.714644352863209[/C][C]0.570711294273581[/C][C]0.285355647136791[/C][/ROW]
[ROW][C]70[/C][C]0.726750813328763[/C][C]0.546498373342474[/C][C]0.273249186671237[/C][/ROW]
[ROW][C]71[/C][C]0.72463200769021[/C][C]0.550735984619581[/C][C]0.275367992309791[/C][/ROW]
[ROW][C]72[/C][C]0.727462087629043[/C][C]0.545075824741914[/C][C]0.272537912370957[/C][/ROW]
[ROW][C]73[/C][C]0.716484471071767[/C][C]0.567031057856465[/C][C]0.283515528928233[/C][/ROW]
[ROW][C]74[/C][C]0.717915372785814[/C][C]0.564169254428372[/C][C]0.282084627214186[/C][/ROW]
[ROW][C]75[/C][C]0.719317487497663[/C][C]0.561365025004673[/C][C]0.280682512502337[/C][/ROW]
[ROW][C]76[/C][C]0.697675004246253[/C][C]0.604649991507494[/C][C]0.302324995753747[/C][/ROW]
[ROW][C]77[/C][C]0.704920064754078[/C][C]0.590159870491844[/C][C]0.295079935245922[/C][/ROW]
[ROW][C]78[/C][C]0.678963037977641[/C][C]0.642073924044718[/C][C]0.321036962022359[/C][/ROW]
[ROW][C]79[/C][C]0.705587201826407[/C][C]0.588825596347186[/C][C]0.294412798173593[/C][/ROW]
[ROW][C]80[/C][C]0.725553606242459[/C][C]0.548892787515081[/C][C]0.274446393757541[/C][/ROW]
[ROW][C]81[/C][C]0.718511705929313[/C][C]0.562976588141374[/C][C]0.281488294070687[/C][/ROW]
[ROW][C]82[/C][C]0.735980535615623[/C][C]0.528038928768755[/C][C]0.264019464384377[/C][/ROW]
[ROW][C]83[/C][C]0.723160036533983[/C][C]0.553679926932035[/C][C]0.276839963466017[/C][/ROW]
[ROW][C]84[/C][C]0.707271782234693[/C][C]0.585456435530614[/C][C]0.292728217765307[/C][/ROW]
[ROW][C]85[/C][C]0.699644487980019[/C][C]0.600711024039963[/C][C]0.300355512019981[/C][/ROW]
[ROW][C]86[/C][C]0.672616046069467[/C][C]0.654767907861067[/C][C]0.327383953930533[/C][/ROW]
[ROW][C]87[/C][C]0.709294795798609[/C][C]0.581410408402782[/C][C]0.290705204201391[/C][/ROW]
[ROW][C]88[/C][C]0.752106857332204[/C][C]0.495786285335592[/C][C]0.247893142667796[/C][/ROW]
[ROW][C]89[/C][C]0.757145669534274[/C][C]0.485708660931452[/C][C]0.242854330465726[/C][/ROW]
[ROW][C]90[/C][C]0.7952294755057[/C][C]0.409541048988601[/C][C]0.2047705244943[/C][/ROW]
[ROW][C]91[/C][C]0.805385276817744[/C][C]0.389229446364511[/C][C]0.194614723182256[/C][/ROW]
[ROW][C]92[/C][C]0.78327046291344[/C][C]0.433459074173119[/C][C]0.21672953708656[/C][/ROW]
[ROW][C]93[/C][C]0.770805814091796[/C][C]0.458388371816407[/C][C]0.229194185908204[/C][/ROW]
[ROW][C]94[/C][C]0.744156260031648[/C][C]0.511687479936705[/C][C]0.255843739968352[/C][/ROW]
[ROW][C]95[/C][C]0.714788202693182[/C][C]0.570423594613635[/C][C]0.285211797306818[/C][/ROW]
[ROW][C]96[/C][C]0.770611987890868[/C][C]0.458776024218264[/C][C]0.229388012109132[/C][/ROW]
[ROW][C]97[/C][C]0.737575230003777[/C][C]0.524849539992447[/C][C]0.262424769996223[/C][/ROW]
[ROW][C]98[/C][C]0.70612630377421[/C][C]0.587747392451579[/C][C]0.29387369622579[/C][/ROW]
[ROW][C]99[/C][C]0.667096399786472[/C][C]0.665807200427057[/C][C]0.332903600213528[/C][/ROW]
[ROW][C]100[/C][C]0.735042687187634[/C][C]0.529914625624732[/C][C]0.264957312812366[/C][/ROW]
[ROW][C]101[/C][C]0.833235509503536[/C][C]0.333528980992928[/C][C]0.166764490496464[/C][/ROW]
[ROW][C]102[/C][C]0.806277286491979[/C][C]0.387445427016042[/C][C]0.193722713508021[/C][/ROW]
[ROW][C]103[/C][C]0.776074602272977[/C][C]0.447850795454046[/C][C]0.223925397727023[/C][/ROW]
[ROW][C]104[/C][C]0.742895156900922[/C][C]0.514209686198155[/C][C]0.257104843099078[/C][/ROW]
[ROW][C]105[/C][C]0.714780467611149[/C][C]0.570439064777702[/C][C]0.285219532388851[/C][/ROW]
[ROW][C]106[/C][C]0.67730966182649[/C][C]0.64538067634702[/C][C]0.32269033817351[/C][/ROW]
[ROW][C]107[/C][C]0.638616478720489[/C][C]0.722767042559021[/C][C]0.361383521279511[/C][/ROW]
[ROW][C]108[/C][C]0.596923344484722[/C][C]0.806153311030556[/C][C]0.403076655515278[/C][/ROW]
[ROW][C]109[/C][C]0.556975945810006[/C][C]0.886048108379988[/C][C]0.443024054189994[/C][/ROW]
[ROW][C]110[/C][C]0.506960978395649[/C][C]0.986078043208703[/C][C]0.493039021604351[/C][/ROW]
[ROW][C]111[/C][C]0.494572997658728[/C][C]0.989145995317456[/C][C]0.505427002341272[/C][/ROW]
[ROW][C]112[/C][C]0.458580303886445[/C][C]0.91716060777289[/C][C]0.541419696113555[/C][/ROW]
[ROW][C]113[/C][C]0.442386355344437[/C][C]0.884772710688874[/C][C]0.557613644655563[/C][/ROW]
[ROW][C]114[/C][C]0.408449893314554[/C][C]0.816899786629108[/C][C]0.591550106685446[/C][/ROW]
[ROW][C]115[/C][C]0.362589129257594[/C][C]0.725178258515189[/C][C]0.637410870742406[/C][/ROW]
[ROW][C]116[/C][C]0.342527487066609[/C][C]0.685054974133219[/C][C]0.657472512933391[/C][/ROW]
[ROW][C]117[/C][C]0.437508487157565[/C][C]0.875016974315131[/C][C]0.562491512842435[/C][/ROW]
[ROW][C]118[/C][C]0.384630957903134[/C][C]0.769261915806267[/C][C]0.615369042096866[/C][/ROW]
[ROW][C]119[/C][C]0.362514249640128[/C][C]0.725028499280255[/C][C]0.637485750359872[/C][/ROW]
[ROW][C]120[/C][C]0.396538287019123[/C][C]0.793076574038246[/C][C]0.603461712980877[/C][/ROW]
[ROW][C]121[/C][C]0.343190921243949[/C][C]0.686381842487898[/C][C]0.656809078756051[/C][/ROW]
[ROW][C]122[/C][C]0.318967171045655[/C][C]0.637934342091311[/C][C]0.681032828954345[/C][/ROW]
[ROW][C]123[/C][C]0.295433322182669[/C][C]0.590866644365338[/C][C]0.704566677817331[/C][/ROW]
[ROW][C]124[/C][C]0.255533550540231[/C][C]0.511067101080461[/C][C]0.744466449459769[/C][/ROW]
[ROW][C]125[/C][C]0.27935956460666[/C][C]0.558719129213319[/C][C]0.72064043539334[/C][/ROW]
[ROW][C]126[/C][C]0.254636449396322[/C][C]0.509272898792644[/C][C]0.745363550603678[/C][/ROW]
[ROW][C]127[/C][C]0.317722380602839[/C][C]0.635444761205677[/C][C]0.682277619397161[/C][/ROW]
[ROW][C]128[/C][C]0.329588473258404[/C][C]0.659176946516809[/C][C]0.670411526741596[/C][/ROW]
[ROW][C]129[/C][C]0.315611205060737[/C][C]0.631222410121474[/C][C]0.684388794939263[/C][/ROW]
[ROW][C]130[/C][C]0.33299872225091[/C][C]0.66599744450182[/C][C]0.66700127774909[/C][/ROW]
[ROW][C]131[/C][C]0.277330525783216[/C][C]0.554661051566431[/C][C]0.722669474216784[/C][/ROW]
[ROW][C]132[/C][C]0.459950293724197[/C][C]0.919900587448394[/C][C]0.540049706275803[/C][/ROW]
[ROW][C]133[/C][C]0.397612202718273[/C][C]0.795224405436546[/C][C]0.602387797281727[/C][/ROW]
[ROW][C]134[/C][C]0.345785768626773[/C][C]0.691571537253547[/C][C]0.654214231373227[/C][/ROW]
[ROW][C]135[/C][C]0.307105248663648[/C][C]0.614210497327297[/C][C]0.692894751336352[/C][/ROW]
[ROW][C]136[/C][C]0.290884473427015[/C][C]0.58176894685403[/C][C]0.709115526572985[/C][/ROW]
[ROW][C]137[/C][C]0.245556862709685[/C][C]0.491113725419371[/C][C]0.754443137290315[/C][/ROW]
[ROW][C]138[/C][C]0.307911281595847[/C][C]0.615822563191695[/C][C]0.692088718404153[/C][/ROW]
[ROW][C]139[/C][C]0.287644673936214[/C][C]0.575289347872427[/C][C]0.712355326063786[/C][/ROW]
[ROW][C]140[/C][C]0.329565026844866[/C][C]0.659130053689731[/C][C]0.670434973155134[/C][/ROW]
[ROW][C]141[/C][C]0.259362582264267[/C][C]0.518725164528534[/C][C]0.740637417735733[/C][/ROW]
[ROW][C]142[/C][C]0.355061249326184[/C][C]0.710122498652367[/C][C]0.644938750673816[/C][/ROW]
[ROW][C]143[/C][C]0.245874550088218[/C][C]0.491749100176435[/C][C]0.754125449911782[/C][/ROW]
[ROW][C]144[/C][C]0.312009507115135[/C][C]0.62401901423027[/C][C]0.687990492884865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8681103987990220.2637792024019560.131889601200978
110.8029007194736790.3941985610526410.19709928052632
120.6976186509952920.6047626980094160.302381349004708
130.5874149833666030.8251700332667940.412585016633397
140.4788192966974020.9576385933948040.521180703302598
150.606633764721820.7867324705563610.39336623527818
160.5827472079008620.8345055841982770.417252792099138
170.4904052337650140.9808104675300280.509594766234986
180.4016246543115120.8032493086230240.598375345688488
190.5775512767501780.8448974464996430.422448723249822
200.6034754522461590.7930490955076820.396524547753841
210.6972625388837980.6054749222324040.302737461116202
220.6677771408691360.6644457182617280.332222859130864
230.6229750346311920.7540499307376160.377024965368808
240.580798314284540.8384033714309190.41920168571546
250.5693660321796970.8612679356406060.430633967820303
260.6978773168832360.6042453662335280.302122683116764
270.7036071713215660.5927856573568670.296392828678434
280.7073709304385160.5852581391229680.292629069561484
290.6912429624888480.6175140750223050.308757037511152
300.7639810897257160.4720378205485690.236018910274284
310.7530824767898620.4938350464202760.246917523210138
320.7334873917852250.533025216429550.266512608214775
330.7384927374766270.5230145250467460.261507262523373
340.7596081453587750.4807837092824490.240391854641225
350.7422955860063880.5154088279872240.257704413993612
360.7189764137600960.5620471724798070.281023586239903
370.7311444254190090.5377111491619810.268855574580991
380.7371693666049040.5256612667901930.262830633395096
390.7261101492293320.5477797015413360.273889850770668
400.7360275773183480.5279448453633040.263972422681652
410.7171934412844620.5656131174310750.282806558715538
420.7065793214322790.5868413571354420.293420678567721
430.7040586892649940.5918826214700110.295941310735006
440.6856855545203820.6286288909592350.314314445479618
450.6973167058528660.6053665882942680.302683294147134
460.6866455672143110.6267088655713780.313354432785689
470.6738474718322250.6523050563355510.326152528167775
480.6860843843924970.6278312312150060.313915615607503
490.6678853317406620.6642293365186760.332114668259338
500.6610897309098190.6778205381803620.338910269090181
510.6687103378874340.6625793242251320.331289662112566
520.6719502413645320.6560995172709360.328049758635468
530.6849311387248710.6301377225502570.315068861275129
540.6647869335964250.6704261328071490.335213066403575
550.6530016593853220.6939966812293550.346998340614678
560.6496765584790560.7006468830418880.350323441520944
570.6173797145149460.7652405709701090.382620285485054
580.6261225421705140.7477549156589730.373877457829486
590.6305422716294440.7389154567411130.369457728370556
600.6312664921861530.7374670156276940.368733507813847
610.6455525857746280.7088948284507430.354447414225372
620.7000499178812250.5999001642375510.299950082118775
630.6995418175425390.6009163649149220.300458182457461
640.7139035215334850.572192956933030.286096478466515
650.7126079217785280.5747841564429440.287392078221472
660.7093096726298060.5813806547403880.290690327370194
670.7218634695049410.5562730609901170.278136530495059
680.7098896651971280.5802206696057430.290110334802872
690.7146443528632090.5707112942735810.285355647136791
700.7267508133287630.5464983733424740.273249186671237
710.724632007690210.5507359846195810.275367992309791
720.7274620876290430.5450758247419140.272537912370957
730.7164844710717670.5670310578564650.283515528928233
740.7179153727858140.5641692544283720.282084627214186
750.7193174874976630.5613650250046730.280682512502337
760.6976750042462530.6046499915074940.302324995753747
770.7049200647540780.5901598704918440.295079935245922
780.6789630379776410.6420739240447180.321036962022359
790.7055872018264070.5888255963471860.294412798173593
800.7255536062424590.5488927875150810.274446393757541
810.7185117059293130.5629765881413740.281488294070687
820.7359805356156230.5280389287687550.264019464384377
830.7231600365339830.5536799269320350.276839963466017
840.7072717822346930.5854564355306140.292728217765307
850.6996444879800190.6007110240399630.300355512019981
860.6726160460694670.6547679078610670.327383953930533
870.7092947957986090.5814104084027820.290705204201391
880.7521068573322040.4957862853355920.247893142667796
890.7571456695342740.4857086609314520.242854330465726
900.79522947550570.4095410489886010.2047705244943
910.8053852768177440.3892294463645110.194614723182256
920.783270462913440.4334590741731190.21672953708656
930.7708058140917960.4583883718164070.229194185908204
940.7441562600316480.5116874799367050.255843739968352
950.7147882026931820.5704235946136350.285211797306818
960.7706119878908680.4587760242182640.229388012109132
970.7375752300037770.5248495399924470.262424769996223
980.706126303774210.5877473924515790.29387369622579
990.6670963997864720.6658072004270570.332903600213528
1000.7350426871876340.5299146256247320.264957312812366
1010.8332355095035360.3335289809929280.166764490496464
1020.8062772864919790.3874454270160420.193722713508021
1030.7760746022729770.4478507954540460.223925397727023
1040.7428951569009220.5142096861981550.257104843099078
1050.7147804676111490.5704390647777020.285219532388851
1060.677309661826490.645380676347020.32269033817351
1070.6386164787204890.7227670425590210.361383521279511
1080.5969233444847220.8061533110305560.403076655515278
1090.5569759458100060.8860481083799880.443024054189994
1100.5069609783956490.9860780432087030.493039021604351
1110.4945729976587280.9891459953174560.505427002341272
1120.4585803038864450.917160607772890.541419696113555
1130.4423863553444370.8847727106888740.557613644655563
1140.4084498933145540.8168997866291080.591550106685446
1150.3625891292575940.7251782585151890.637410870742406
1160.3425274870666090.6850549741332190.657472512933391
1170.4375084871575650.8750169743151310.562491512842435
1180.3846309579031340.7692619158062670.615369042096866
1190.3625142496401280.7250284992802550.637485750359872
1200.3965382870191230.7930765740382460.603461712980877
1210.3431909212439490.6863818424878980.656809078756051
1220.3189671710456550.6379343420913110.681032828954345
1230.2954333221826690.5908666443653380.704566677817331
1240.2555335505402310.5110671010804610.744466449459769
1250.279359564606660.5587191292133190.72064043539334
1260.2546364493963220.5092728987926440.745363550603678
1270.3177223806028390.6354447612056770.682277619397161
1280.3295884732584040.6591769465168090.670411526741596
1290.3156112050607370.6312224101214740.684388794939263
1300.332998722250910.665997444501820.66700127774909
1310.2773305257832160.5546610515664310.722669474216784
1320.4599502937241970.9199005874483940.540049706275803
1330.3976122027182730.7952244054365460.602387797281727
1340.3457857686267730.6915715372535470.654214231373227
1350.3071052486636480.6142104973272970.692894751336352
1360.2908844734270150.581768946854030.709115526572985
1370.2455568627096850.4911137254193710.754443137290315
1380.3079112815958470.6158225631916950.692088718404153
1390.2876446739362140.5752893478724270.712355326063786
1400.3295650268448660.6591300536897310.670434973155134
1410.2593625822642670.5187251645285340.740637417735733
1420.3550612493261840.7101224986523670.644938750673816
1430.2458745500882180.4917491001764350.754125449911782
1440.3120095071151350.624019014230270.687990492884865







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200554&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200554&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200554&T=6

As an alternative you can also use a QR Code:  

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

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



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