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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 02 Dec 2010 11:31:15 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t1291291026jg8ozfvprf7wr9q.htm/, Retrieved Sun, 05 May 2024 17:43:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104222, Retrieved Sun, 05 May 2024 17:43:52 +0000
QR Codes:

Original text written by user:Not popular test
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Mini tutorial - M...] [2010-12-02 11:31:15] [25b2a837ac14189684edc0746bbb952e] [Current]
-    D    [Multiple Regression] [Mini tutorial ] [2010-12-02 13:53:30] [ed447cc2ebcc70947ad11d93fa385845]
-           [Multiple Regression] [Mini tutorial - ] [2010-12-02 15:06:31] [ed447cc2ebcc70947ad11d93fa385845]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	13	14	13	3	5
3	12	12	8	13	5	3
3	15	10	12	16	6	0
3	12	9	7	12	6	7
3	10	10	10	11	5	4
3	12	12	7	12	3	1
2	15	13	16	18	8	6
3	9	12	11	11	4	3
3	12	12	14	14	4	12
4	11	6	6	9	4	0
3	11	5	16	14	6	5
3	11	12	11	12	6	6
2	15	11	16	11	5	6
3	7	14	12	12	4	6
3	11	14	7	13	6	2
3	11	12	13	11	4	1
3	10	12	11	12	6	5
3	14	11	15	16	6	7
2	10	11	7	9	4	3
4	6	7	9	11	4	3
3	11	9	7	13	2	3
2	15	11	14	15	7	7
3	11	11	15	10	5	8
3	12	12	7	11	4	6
2	14	12	15	13	6	3
2	15	11	17	16	6	5
4	9	11	15	15	7	5
2	13	8	14	14	5	10
3	13	9	14	14	6	2
2	16	12	8	14	4	6
4	13	10	8	8	4	4
3	12	10	14	13	7	6
2	14	12	14	15	7	8
3	11	8	8	13	4	4
3	9	12	11	11	4	5
1	16	11	16	15	6	10
3	12	12	10	15	6	6
3	10	7	8	9	5	7
3	13	11	14	13	6	4
2	16	11	16	16	7	10
3	14	12	13	13	6	4
	15	9	5	11	3	3
5	5	15	8	12	3	3
4	8	11	10	12	4	3
3	11	11	8	12	6	3
2	16	11	13	14	7	7
2	17	11	15	14	5	15
3	9	15	6	8	4	0
4	9	11	12	13	5	0
2	13	12	16	16	6	4
3	10	12	5	13	6	5
4	6	9	15	11	6	5
3	12	12	12	14	5	2
4	8	12	8	13	4	3
3	14	13	13	13	5	0
3	12	11	14	13	5	9
4	11	9	12	12	4	2
2	16	9	16	16	6	7
4	8	11	10	15	2	7
2	15	11	15	15	8	0
4	7	12	8	12	3	0
2	16	12	16	14	6	10
3	14	9	19	12	6	2
2	16	11	14	15	6	1
3	9	9	6	12	5	8
3	14	12	13	13	5	6
3	11	12	15	12	6	11
3	13	12	7	12	5	3
2	15	12	13	13	6	8
4	5	14	4	5	2	6
2	15	11	14	13	5	9
3	13	12	13	13	5	9
4	11	11	11	14	5	8
2	11	6	14	17	6	8
3	12	10	12	13	6	7
3	12	12	15	13	6	6
3	12	13	14	12	5	5
3	12	8	13	13	5	4
2	14	12	8	14	4	6
4	6	12	6	11	2	3
4	7	12	7	12	4	2
2	14	6	13	12	6	12
2	14	11	13	16	6	8
4	10	10	11	12	5	5
3	13	12	5	12	3	9
3	12	13	12	12	6	6
3	9	11	8	10	4	5
3	12	7	11	15	5	2
2	16	11	14	15	8	4
4	10	11	9	12	4	7
3	14	11	10	16	6	5
4	10	11	13	15	6	6
2	16	12	16	16	7	7
2	15	10	16	13	6	8
3	12	11	11	12	5	6
3	10	12	8	11	4	0
3	8	7	4	13	6	1
4	8	13	7	10	3	5
2	11	8	14	15	5	5
3	13	12	11	13	6	5
2	16	11	17	16	7	7
2	16	12	15	15	7	7
3	14	14	17	18	6	1
3	11	10	5	13	3	3
5	4	10	4	10	2	4
2	14	13	10	16	8	8
4	9	10	11	13	3	6
3	14	11	15	15	8	6
4	8	10	10	14	3	2
4	8	7	9	15	4	2
3	11	10	12	14	5	3
3	12	8	15	13	7	3
3	11	12	7	13	6	0
3	14	12	13	15	6	2
2	15	12	12	16	7	8
2	16	11	14	14	6	8
2	16	12	14	14	6	0
2	11	12	8	16	6	5
1	14	12	15	14	6	9
2	14	11	12	12	4	6
3	12	12	12	13	4	6
3	14	11	16	12	5	3
4	8	11	9	12	4	9
3	13	13	15	14	6	7
2	16	12	15	14	6	8
3	12	12	6	14	5	0
2	16	12	14	16	8	7
3	12	12	15	13	6	0
3	11	8	10	14	5	5
5	4	8	6	4	4	0
2	16	12	14	16	8	14
2	15	11	12	13	6	5
3	10	12	8	16	4	2
1	13	13	11	15	6	8
3	15	12	13	14	6	4
3	12	12	9	13	4	2
3	14	11	15	14	6	6
4	7	12	13	12	3	3
1	19	12	15	15	6	5
2	12	10	14	14	5	9
4	12	11	16	13	4	3
2	13	12	14	14	6	3
2	15	12	14	16	4	0
4	8	10	10	6	4	10
3	12	12	10	13	4	4
3	10	13	4	13	6	2
4	8	12	8	14	5	3
1	10	15	15	15	6	10
2	15	11	16	14	6	7
2	16	12	12	15	8	0
3	13	11	12	13	7	6
2	16	12	15	16	7	8
3	9	11	9	12	4	0
3	14	10	12	15	6	4
3	14	11	14	12	6	10
3	12	11	11	14	2	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
NotPopular[t] = + 2.98670740628656 -0.0111064541653232Popularity[t] + 0.496005575693039FindingFriends[t] + 0.560172927756955KnowingPeople[t] -0.80285171304812Liked[t] + 0.137898841611536Celebrity[t] -0.293925773283954WeightedSum[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NotPopular[t] =  +  2.98670740628656 -0.0111064541653232Popularity[t] +  0.496005575693039FindingFriends[t] +  0.560172927756955KnowingPeople[t] -0.80285171304812Liked[t] +  0.137898841611536Celebrity[t] -0.293925773283954WeightedSum[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NotPopular[t] =  +  2.98670740628656 -0.0111064541653232Popularity[t] +  0.496005575693039FindingFriends[t] +  0.560172927756955KnowingPeople[t] -0.80285171304812Liked[t] +  0.137898841611536Celebrity[t] -0.293925773283954WeightedSum[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
NotPopular[t] = + 2.98670740628656 -0.0111064541653232Popularity[t] + 0.496005575693039FindingFriends[t] + 0.560172927756955KnowingPeople[t] -0.80285171304812Liked[t] + 0.137898841611536Celebrity[t] -0.293925773283954WeightedSum[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.986707406286561.6454631.81510.0715170.035759
Popularity-0.01110645416532320.118792-0.09350.9256360.462818
FindingFriends0.4960055756930390.0762396.505900
KnowingPeople0.5601729277569550.0905296.187800
Liked-0.802851713048120.071574-11.217100
Celebrity0.1378988416115360.0782461.76240.0800560.040028
WeightedSum-0.2939257732839540.141543-2.07660.0395580.019779

\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) & 2.98670740628656 & 1.645463 & 1.8151 & 0.071517 & 0.035759 \tabularnewline
Popularity & -0.0111064541653232 & 0.118792 & -0.0935 & 0.925636 & 0.462818 \tabularnewline
FindingFriends & 0.496005575693039 & 0.076239 & 6.5059 & 0 & 0 \tabularnewline
KnowingPeople & 0.560172927756955 & 0.090529 & 6.1878 & 0 & 0 \tabularnewline
Liked & -0.80285171304812 & 0.071574 & -11.2171 & 0 & 0 \tabularnewline
Celebrity & 0.137898841611536 & 0.078246 & 1.7624 & 0.080056 & 0.040028 \tabularnewline
WeightedSum & -0.293925773283954 & 0.141543 & -2.0766 & 0.039558 & 0.019779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&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]2.98670740628656[/C][C]1.645463[/C][C]1.8151[/C][C]0.071517[/C][C]0.035759[/C][/ROW]
[ROW][C]Popularity[/C][C]-0.0111064541653232[/C][C]0.118792[/C][C]-0.0935[/C][C]0.925636[/C][C]0.462818[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.496005575693039[/C][C]0.076239[/C][C]6.5059[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.560172927756955[/C][C]0.090529[/C][C]6.1878[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Liked[/C][C]-0.80285171304812[/C][C]0.071574[/C][C]-11.2171[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.137898841611536[/C][C]0.078246[/C][C]1.7624[/C][C]0.080056[/C][C]0.040028[/C][/ROW]
[ROW][C]WeightedSum[/C][C]-0.293925773283954[/C][C]0.141543[/C][C]-2.0766[/C][C]0.039558[/C][C]0.019779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.986707406286561.6454631.81510.0715170.035759
Popularity-0.01110645416532320.118792-0.09350.9256360.462818
FindingFriends0.4960055756930390.0762396.505900
KnowingPeople0.5601729277569550.0905296.187800
Liked-0.802851713048120.071574-11.217100
Celebrity0.1378988416115360.0782461.76240.0800560.040028
WeightedSum-0.2939257732839540.141543-2.07660.0395580.019779






Multiple Linear Regression - Regression Statistics
Multiple R0.84624116396135
R-squared0.716124107582662
Adjusted R-squared0.704692863592702
F-TEST (value)62.6462096523909
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65918914845553
Sum Squared Residuals1053.62175216228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.84624116396135 \tabularnewline
R-squared & 0.716124107582662 \tabularnewline
Adjusted R-squared & 0.704692863592702 \tabularnewline
F-TEST (value) & 62.6462096523909 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.65918914845553 \tabularnewline
Sum Squared Residuals & 1053.62175216228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.84624116396135[/C][/ROW]
[ROW][C]R-squared[/C][C]0.716124107582662[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.704692863592702[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.6462096523909[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.65918914845553[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1053.62175216228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.84624116396135
R-squared0.716124107582662
Adjusted R-squared0.704692863592702
F-TEST (value)62.6462096523909
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65918914845553
Sum Squared Residuals1053.62175216228






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.63981236353336-3.63981236353336
232.657524905255030.342475094744966
332.484007124719860.515992875280144
430.3743827119428082.62561728805719
534.11985017052585-1.11985017052585
633.21225755389104-0.212257553891044
723.1192551802499-1.11925518024990
835.83916763550658-2.83916763550658
932.432479957581530.567520042418473
1042.527537380181021.47246261981898
1132.425171333620240.574828666379757
1234.40812337749902-1.40812337749902
1327.33350949536604-5.33350949536604
1435.72893559008028-2.72893559008028
1533.53229419794498-0.532294197944976
1637.52515212925775-4.52515212925775
1734.7131556049483-1.7131556049483
1832.61415752486140.3858424751386
1924.69706732071663-2.69706732071663
2042.272113264023451.72788673597655
2130.2067451797496892.79325482025031
2222.98362869759878-0.983628697598777
2337.0327625507506-4.03276255075059
2432.683379242130920.316620757869077
2526.69442133283461-4.69442133283461
2624.3112484727779-2.31124847277790
2744.19829189691558-0.198291896915585
2821.163101588823490.836898411176508
2934.1484121923997-1.14841219239970
3020.7905712140822261.20942878591777
3145.23684125004874-1.23684125004874
3234.4205716837819-1.4205716837819
3323.19681495417318-1.19681495417318
3430.252784441752722.74721555824728
3535.25131608893867-2.25131608893867
3613.07319193748396-2.07319193748396
3731.428288856432391.57171114356761
3832.235413694177150.76458630582285
3935.35542351026599-2.35542351026599
4022.40823906604738-0.408239066047379
4135.28014970403675-2.28014970403675
42158.064191921860816.93580807813919
43510.3396686249889-5.33966862498889
44810.8671796532721-2.8671796532721
45118.563390849073742.43660915092626
461611.91250823645094.08749176354913
471715.31948777354151.68051222645849
4895.890417524692273.10958247530773
49911.4967412678164-2.49674126781636
501314.6049937798081-1.60499377980813
51107.312386732241422.68761326775858
52611.4793417694378-5.47934176943782
531211.73375387806320.266246121936845
54810.4242349754777-2.42423497547765
551411.67660816189482.3233918381052
561213.1419904471384-1.14199044713835
571112.0374306446612-1.03743064466125
581614.75808389385481.24191610614525
59814.9989230023693-6.9989230023693
601511.10869716469713.89130283530288
61710.5471430092182-3.54714300921817
621614.31204097396341.68795902603657
631413.90376624841630.096233751583721
641612.65021962999583.34978037000419
6598.792012753840150.207987246159846
661412.51510766572931.48489233427067
671112.8335883843680-1.83358838436802
68138.85913053226354.1408694677365
691511.69412786262033.30587213737967
7056.24994206653401-1.24994206653401
711513.43591622042231.56408377957769
721312.63487841728000.365121582720013
731112.6640993527726-1.66409935277256
741114.7913896476171-3.79138964761709
751211.37636212693040.623637873069637
761212.7042671040673-0.704267104067294
771212.3019350178886-0.301935017888561
781212.5776615724515-0.577661572451516
791411.10417865478532.89582134521474
8069.62365562138995-3.62365562138995
8179.52408340370008-2.52408340370008
821412.34004057286941.65995942713063
831413.38575310005650.614246899943477
841010.8472376533054-0.847237653305417
851310.00629008335892.99370991664108
861210.64497099506591.3550290049341
8799.03062032959518-0.0306203295951838
881212.4413050475216-0.441305047521602
891611.16428695545024.83571304454977
901010.9227694440252-0.922769444025204
911411.48403984814282.5159601518572
921013.1376340356444-3.13763403564441
931614.50976436487861.49023563512143
941513.49828327131411.50171672868595
951210.97403004075161.02596995924837
96108.890192595129141.10980740487086
9786.320318060928861.67968193907114
9889.60917933190357-1.60917933190357
991114.0379860719860-3.03798607198605
1001310.87627173296762.12372826703244
1011615.01687639473690.983123605263068
1021613.15966008814462.84033991185538
1031415.7854357778498-1.78543577784982
104119.17343132320941.82656867679060
10548.81943483875701-4.81943483875701
1061410.26982003855053.73017996144948
107913.1510128487701-4.15101284877014
1081411.93609021436632.06390978563366
109812.3696590611040-4.36965906110396
110811.9582198358997-3.95821983589969
1111112.1877914012893-1.18779140128929
1121211.53214468284590.467855317154133
113117.908829448853773.09117055114623
1141412.57493221503291.42506778496706
1151512.66364090371802.33635909628205
1161613.34926436680362.65073563319644
1171612.23496717974603.76503282025405
1181111.3626995624433-0.362699562443263
1191413.97206232994280.0279376700571903
1201412.27288732949281.72711267050722
1211212.8219538030844-0.821953803084415
1221412.74643562109831.25356437890174
123811.1985671272483-3.19856712724827
1241313.6851581925544-0.685158192554415
1251613.54023771504732.45976228495268
126129.069774287249762.93022571275024
1271612.42097572716043.57902427283958
1281211.87687405439810.123125945601918
1291110.90593929488900.0940607051109729
13044.31532253938963-0.315322539389629
1311613.68019339172512.31980660827488
1321511.08945798954203.91054201045803
1331012.5547064637049-2.55470646370489
1341312.10528188586680.894718114133203
1351511.99663119721513.0033688027849
1361210.78234170955921.21765829044084
1371412.98162071270561.01837928729438
138713.7347931858019-6.73479318580192
1391913.98063989125365.01936010874637
1401213.7132698290606-1.71326982906063
1411214.6973118084712-2.69731180847124
1421312.64866370458060.351336295419442
1431514.37316491478820.62683508521181
14488.48254043217644-0.482540432176445
1451211.55414496847530.445855031524733
146106.391578177548453.60842182245155
147810.7694077367544-2.7694077367544
1481014.6368147368153-4.63681473681533
1491514.20337667657810.796623323421898
150169.903499756736646.09650024326336
1511310.71843089138932.28156910861066
1521613.85773185751312.14226814248689
15399.95747755274446-0.957477552744455
1541412.08301145760971.91698854239033
1551412.21079042122881.78920957877123
1561214.6589579670823-2.65895796708231

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 5.63981236353336 & -3.63981236353336 \tabularnewline
2 & 3 & 2.65752490525503 & 0.342475094744966 \tabularnewline
3 & 3 & 2.48400712471986 & 0.515992875280144 \tabularnewline
4 & 3 & 0.374382711942808 & 2.62561728805719 \tabularnewline
5 & 3 & 4.11985017052585 & -1.11985017052585 \tabularnewline
6 & 3 & 3.21225755389104 & -0.212257553891044 \tabularnewline
7 & 2 & 3.1192551802499 & -1.11925518024990 \tabularnewline
8 & 3 & 5.83916763550658 & -2.83916763550658 \tabularnewline
9 & 3 & 2.43247995758153 & 0.567520042418473 \tabularnewline
10 & 4 & 2.52753738018102 & 1.47246261981898 \tabularnewline
11 & 3 & 2.42517133362024 & 0.574828666379757 \tabularnewline
12 & 3 & 4.40812337749902 & -1.40812337749902 \tabularnewline
13 & 2 & 7.33350949536604 & -5.33350949536604 \tabularnewline
14 & 3 & 5.72893559008028 & -2.72893559008028 \tabularnewline
15 & 3 & 3.53229419794498 & -0.532294197944976 \tabularnewline
16 & 3 & 7.52515212925775 & -4.52515212925775 \tabularnewline
17 & 3 & 4.7131556049483 & -1.7131556049483 \tabularnewline
18 & 3 & 2.6141575248614 & 0.3858424751386 \tabularnewline
19 & 2 & 4.69706732071663 & -2.69706732071663 \tabularnewline
20 & 4 & 2.27211326402345 & 1.72788673597655 \tabularnewline
21 & 3 & 0.206745179749689 & 2.79325482025031 \tabularnewline
22 & 2 & 2.98362869759878 & -0.983628697598777 \tabularnewline
23 & 3 & 7.0327625507506 & -4.03276255075059 \tabularnewline
24 & 3 & 2.68337924213092 & 0.316620757869077 \tabularnewline
25 & 2 & 6.69442133283461 & -4.69442133283461 \tabularnewline
26 & 2 & 4.3112484727779 & -2.31124847277790 \tabularnewline
27 & 4 & 4.19829189691558 & -0.198291896915585 \tabularnewline
28 & 2 & 1.16310158882349 & 0.836898411176508 \tabularnewline
29 & 3 & 4.1484121923997 & -1.14841219239970 \tabularnewline
30 & 2 & 0.790571214082226 & 1.20942878591777 \tabularnewline
31 & 4 & 5.23684125004874 & -1.23684125004874 \tabularnewline
32 & 3 & 4.4205716837819 & -1.4205716837819 \tabularnewline
33 & 2 & 3.19681495417318 & -1.19681495417318 \tabularnewline
34 & 3 & 0.25278444175272 & 2.74721555824728 \tabularnewline
35 & 3 & 5.25131608893867 & -2.25131608893867 \tabularnewline
36 & 1 & 3.07319193748396 & -2.07319193748396 \tabularnewline
37 & 3 & 1.42828885643239 & 1.57171114356761 \tabularnewline
38 & 3 & 2.23541369417715 & 0.76458630582285 \tabularnewline
39 & 3 & 5.35542351026599 & -2.35542351026599 \tabularnewline
40 & 2 & 2.40823906604738 & -0.408239066047379 \tabularnewline
41 & 3 & 5.28014970403675 & -2.28014970403675 \tabularnewline
42 & 15 & 8.06419192186081 & 6.93580807813919 \tabularnewline
43 & 5 & 10.3396686249889 & -5.33966862498889 \tabularnewline
44 & 8 & 10.8671796532721 & -2.8671796532721 \tabularnewline
45 & 11 & 8.56339084907374 & 2.43660915092626 \tabularnewline
46 & 16 & 11.9125082364509 & 4.08749176354913 \tabularnewline
47 & 17 & 15.3194877735415 & 1.68051222645849 \tabularnewline
48 & 9 & 5.89041752469227 & 3.10958247530773 \tabularnewline
49 & 9 & 11.4967412678164 & -2.49674126781636 \tabularnewline
50 & 13 & 14.6049937798081 & -1.60499377980813 \tabularnewline
51 & 10 & 7.31238673224142 & 2.68761326775858 \tabularnewline
52 & 6 & 11.4793417694378 & -5.47934176943782 \tabularnewline
53 & 12 & 11.7337538780632 & 0.266246121936845 \tabularnewline
54 & 8 & 10.4242349754777 & -2.42423497547765 \tabularnewline
55 & 14 & 11.6766081618948 & 2.3233918381052 \tabularnewline
56 & 12 & 13.1419904471384 & -1.14199044713835 \tabularnewline
57 & 11 & 12.0374306446612 & -1.03743064466125 \tabularnewline
58 & 16 & 14.7580838938548 & 1.24191610614525 \tabularnewline
59 & 8 & 14.9989230023693 & -6.9989230023693 \tabularnewline
60 & 15 & 11.1086971646971 & 3.89130283530288 \tabularnewline
61 & 7 & 10.5471430092182 & -3.54714300921817 \tabularnewline
62 & 16 & 14.3120409739634 & 1.68795902603657 \tabularnewline
63 & 14 & 13.9037662484163 & 0.096233751583721 \tabularnewline
64 & 16 & 12.6502196299958 & 3.34978037000419 \tabularnewline
65 & 9 & 8.79201275384015 & 0.207987246159846 \tabularnewline
66 & 14 & 12.5151076657293 & 1.48489233427067 \tabularnewline
67 & 11 & 12.8335883843680 & -1.83358838436802 \tabularnewline
68 & 13 & 8.8591305322635 & 4.1408694677365 \tabularnewline
69 & 15 & 11.6941278626203 & 3.30587213737967 \tabularnewline
70 & 5 & 6.24994206653401 & -1.24994206653401 \tabularnewline
71 & 15 & 13.4359162204223 & 1.56408377957769 \tabularnewline
72 & 13 & 12.6348784172800 & 0.365121582720013 \tabularnewline
73 & 11 & 12.6640993527726 & -1.66409935277256 \tabularnewline
74 & 11 & 14.7913896476171 & -3.79138964761709 \tabularnewline
75 & 12 & 11.3763621269304 & 0.623637873069637 \tabularnewline
76 & 12 & 12.7042671040673 & -0.704267104067294 \tabularnewline
77 & 12 & 12.3019350178886 & -0.301935017888561 \tabularnewline
78 & 12 & 12.5776615724515 & -0.577661572451516 \tabularnewline
79 & 14 & 11.1041786547853 & 2.89582134521474 \tabularnewline
80 & 6 & 9.62365562138995 & -3.62365562138995 \tabularnewline
81 & 7 & 9.52408340370008 & -2.52408340370008 \tabularnewline
82 & 14 & 12.3400405728694 & 1.65995942713063 \tabularnewline
83 & 14 & 13.3857531000565 & 0.614246899943477 \tabularnewline
84 & 10 & 10.8472376533054 & -0.847237653305417 \tabularnewline
85 & 13 & 10.0062900833589 & 2.99370991664108 \tabularnewline
86 & 12 & 10.6449709950659 & 1.3550290049341 \tabularnewline
87 & 9 & 9.03062032959518 & -0.0306203295951838 \tabularnewline
88 & 12 & 12.4413050475216 & -0.441305047521602 \tabularnewline
89 & 16 & 11.1642869554502 & 4.83571304454977 \tabularnewline
90 & 10 & 10.9227694440252 & -0.922769444025204 \tabularnewline
91 & 14 & 11.4840398481428 & 2.5159601518572 \tabularnewline
92 & 10 & 13.1376340356444 & -3.13763403564441 \tabularnewline
93 & 16 & 14.5097643648786 & 1.49023563512143 \tabularnewline
94 & 15 & 13.4982832713141 & 1.50171672868595 \tabularnewline
95 & 12 & 10.9740300407516 & 1.02596995924837 \tabularnewline
96 & 10 & 8.89019259512914 & 1.10980740487086 \tabularnewline
97 & 8 & 6.32031806092886 & 1.67968193907114 \tabularnewline
98 & 8 & 9.60917933190357 & -1.60917933190357 \tabularnewline
99 & 11 & 14.0379860719860 & -3.03798607198605 \tabularnewline
100 & 13 & 10.8762717329676 & 2.12372826703244 \tabularnewline
101 & 16 & 15.0168763947369 & 0.983123605263068 \tabularnewline
102 & 16 & 13.1596600881446 & 2.84033991185538 \tabularnewline
103 & 14 & 15.7854357778498 & -1.78543577784982 \tabularnewline
104 & 11 & 9.1734313232094 & 1.82656867679060 \tabularnewline
105 & 4 & 8.81943483875701 & -4.81943483875701 \tabularnewline
106 & 14 & 10.2698200385505 & 3.73017996144948 \tabularnewline
107 & 9 & 13.1510128487701 & -4.15101284877014 \tabularnewline
108 & 14 & 11.9360902143663 & 2.06390978563366 \tabularnewline
109 & 8 & 12.3696590611040 & -4.36965906110396 \tabularnewline
110 & 8 & 11.9582198358997 & -3.95821983589969 \tabularnewline
111 & 11 & 12.1877914012893 & -1.18779140128929 \tabularnewline
112 & 12 & 11.5321446828459 & 0.467855317154133 \tabularnewline
113 & 11 & 7.90882944885377 & 3.09117055114623 \tabularnewline
114 & 14 & 12.5749322150329 & 1.42506778496706 \tabularnewline
115 & 15 & 12.6636409037180 & 2.33635909628205 \tabularnewline
116 & 16 & 13.3492643668036 & 2.65073563319644 \tabularnewline
117 & 16 & 12.2349671797460 & 3.76503282025405 \tabularnewline
118 & 11 & 11.3626995624433 & -0.362699562443263 \tabularnewline
119 & 14 & 13.9720623299428 & 0.0279376700571903 \tabularnewline
120 & 14 & 12.2728873294928 & 1.72711267050722 \tabularnewline
121 & 12 & 12.8219538030844 & -0.821953803084415 \tabularnewline
122 & 14 & 12.7464356210983 & 1.25356437890174 \tabularnewline
123 & 8 & 11.1985671272483 & -3.19856712724827 \tabularnewline
124 & 13 & 13.6851581925544 & -0.685158192554415 \tabularnewline
125 & 16 & 13.5402377150473 & 2.45976228495268 \tabularnewline
126 & 12 & 9.06977428724976 & 2.93022571275024 \tabularnewline
127 & 16 & 12.4209757271604 & 3.57902427283958 \tabularnewline
128 & 12 & 11.8768740543981 & 0.123125945601918 \tabularnewline
129 & 11 & 10.9059392948890 & 0.0940607051109729 \tabularnewline
130 & 4 & 4.31532253938963 & -0.315322539389629 \tabularnewline
131 & 16 & 13.6801933917251 & 2.31980660827488 \tabularnewline
132 & 15 & 11.0894579895420 & 3.91054201045803 \tabularnewline
133 & 10 & 12.5547064637049 & -2.55470646370489 \tabularnewline
134 & 13 & 12.1052818858668 & 0.894718114133203 \tabularnewline
135 & 15 & 11.9966311972151 & 3.0033688027849 \tabularnewline
136 & 12 & 10.7823417095592 & 1.21765829044084 \tabularnewline
137 & 14 & 12.9816207127056 & 1.01837928729438 \tabularnewline
138 & 7 & 13.7347931858019 & -6.73479318580192 \tabularnewline
139 & 19 & 13.9806398912536 & 5.01936010874637 \tabularnewline
140 & 12 & 13.7132698290606 & -1.71326982906063 \tabularnewline
141 & 12 & 14.6973118084712 & -2.69731180847124 \tabularnewline
142 & 13 & 12.6486637045806 & 0.351336295419442 \tabularnewline
143 & 15 & 14.3731649147882 & 0.62683508521181 \tabularnewline
144 & 8 & 8.48254043217644 & -0.482540432176445 \tabularnewline
145 & 12 & 11.5541449684753 & 0.445855031524733 \tabularnewline
146 & 10 & 6.39157817754845 & 3.60842182245155 \tabularnewline
147 & 8 & 10.7694077367544 & -2.7694077367544 \tabularnewline
148 & 10 & 14.6368147368153 & -4.63681473681533 \tabularnewline
149 & 15 & 14.2033766765781 & 0.796623323421898 \tabularnewline
150 & 16 & 9.90349975673664 & 6.09650024326336 \tabularnewline
151 & 13 & 10.7184308913893 & 2.28156910861066 \tabularnewline
152 & 16 & 13.8577318575131 & 2.14226814248689 \tabularnewline
153 & 9 & 9.95747755274446 & -0.957477552744455 \tabularnewline
154 & 14 & 12.0830114576097 & 1.91698854239033 \tabularnewline
155 & 14 & 12.2107904212288 & 1.78920957877123 \tabularnewline
156 & 12 & 14.6589579670823 & -2.65895796708231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&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]2[/C][C]5.63981236353336[/C][C]-3.63981236353336[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]2.65752490525503[/C][C]0.342475094744966[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.48400712471986[/C][C]0.515992875280144[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]0.374382711942808[/C][C]2.62561728805719[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]4.11985017052585[/C][C]-1.11985017052585[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]3.21225755389104[/C][C]-0.212257553891044[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]3.1192551802499[/C][C]-1.11925518024990[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]5.83916763550658[/C][C]-2.83916763550658[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]2.43247995758153[/C][C]0.567520042418473[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]2.52753738018102[/C][C]1.47246261981898[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.42517133362024[/C][C]0.574828666379757[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.40812337749902[/C][C]-1.40812337749902[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]7.33350949536604[/C][C]-5.33350949536604[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]5.72893559008028[/C][C]-2.72893559008028[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]3.53229419794498[/C][C]-0.532294197944976[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]7.52515212925775[/C][C]-4.52515212925775[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]4.7131556049483[/C][C]-1.7131556049483[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.6141575248614[/C][C]0.3858424751386[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]4.69706732071663[/C][C]-2.69706732071663[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]2.27211326402345[/C][C]1.72788673597655[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]0.206745179749689[/C][C]2.79325482025031[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]2.98362869759878[/C][C]-0.983628697598777[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]7.0327625507506[/C][C]-4.03276255075059[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]2.68337924213092[/C][C]0.316620757869077[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]6.69442133283461[/C][C]-4.69442133283461[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]4.3112484727779[/C][C]-2.31124847277790[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]4.19829189691558[/C][C]-0.198291896915585[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.16310158882349[/C][C]0.836898411176508[/C][/ROW]
[ROW][C]29[/C][C]3[/C][C]4.1484121923997[/C][C]-1.14841219239970[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]0.790571214082226[/C][C]1.20942878591777[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]5.23684125004874[/C][C]-1.23684125004874[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]4.4205716837819[/C][C]-1.4205716837819[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]3.19681495417318[/C][C]-1.19681495417318[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]0.25278444175272[/C][C]2.74721555824728[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]5.25131608893867[/C][C]-2.25131608893867[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]3.07319193748396[/C][C]-2.07319193748396[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]1.42828885643239[/C][C]1.57171114356761[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]2.23541369417715[/C][C]0.76458630582285[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]5.35542351026599[/C][C]-2.35542351026599[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]2.40823906604738[/C][C]-0.408239066047379[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]5.28014970403675[/C][C]-2.28014970403675[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.06419192186081[/C][C]6.93580807813919[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]10.3396686249889[/C][C]-5.33966862498889[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.8671796532721[/C][C]-2.8671796532721[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]8.56339084907374[/C][C]2.43660915092626[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]11.9125082364509[/C][C]4.08749176354913[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]15.3194877735415[/C][C]1.68051222645849[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]5.89041752469227[/C][C]3.10958247530773[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4967412678164[/C][C]-2.49674126781636[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6049937798081[/C][C]-1.60499377980813[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]7.31238673224142[/C][C]2.68761326775858[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]11.4793417694378[/C][C]-5.47934176943782[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.7337538780632[/C][C]0.266246121936845[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4242349754777[/C][C]-2.42423497547765[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.6766081618948[/C][C]2.3233918381052[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.1419904471384[/C][C]-1.14199044713835[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]12.0374306446612[/C][C]-1.03743064466125[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7580838938548[/C][C]1.24191610614525[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]14.9989230023693[/C][C]-6.9989230023693[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]11.1086971646971[/C][C]3.89130283530288[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]10.5471430092182[/C][C]-3.54714300921817[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.3120409739634[/C][C]1.68795902603657[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.9037662484163[/C][C]0.096233751583721[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]12.6502196299958[/C][C]3.34978037000419[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]8.79201275384015[/C][C]0.207987246159846[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.5151076657293[/C][C]1.48489233427067[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.8335883843680[/C][C]-1.83358838436802[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]8.8591305322635[/C][C]4.1408694677365[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]11.6941278626203[/C][C]3.30587213737967[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.24994206653401[/C][C]-1.24994206653401[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]13.4359162204223[/C][C]1.56408377957769[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.6348784172800[/C][C]0.365121582720013[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.6640993527726[/C][C]-1.66409935277256[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.7913896476171[/C][C]-3.79138964761709[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.3763621269304[/C][C]0.623637873069637[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.7042671040673[/C][C]-0.704267104067294[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.3019350178886[/C][C]-0.301935017888561[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.5776615724515[/C][C]-0.577661572451516[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.1041786547853[/C][C]2.89582134521474[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]9.62365562138995[/C][C]-3.62365562138995[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.52408340370008[/C][C]-2.52408340370008[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.3400405728694[/C][C]1.65995942713063[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.3857531000565[/C][C]0.614246899943477[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.8472376533054[/C][C]-0.847237653305417[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]10.0062900833589[/C][C]2.99370991664108[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]10.6449709950659[/C][C]1.3550290049341[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.03062032959518[/C][C]-0.0306203295951838[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.4413050475216[/C][C]-0.441305047521602[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]11.1642869554502[/C][C]4.83571304454977[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.9227694440252[/C][C]-0.922769444025204[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]11.4840398481428[/C][C]2.5159601518572[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.1376340356444[/C][C]-3.13763403564441[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.5097643648786[/C][C]1.49023563512143[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4982832713141[/C][C]1.50171672868595[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.9740300407516[/C][C]1.02596995924837[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]8.89019259512914[/C][C]1.10980740487086[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]6.32031806092886[/C][C]1.67968193907114[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]9.60917933190357[/C][C]-1.60917933190357[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]14.0379860719860[/C][C]-3.03798607198605[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]10.8762717329676[/C][C]2.12372826703244[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.0168763947369[/C][C]0.983123605263068[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.1596600881446[/C][C]2.84033991185538[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.7854357778498[/C][C]-1.78543577784982[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.1734313232094[/C][C]1.82656867679060[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]8.81943483875701[/C][C]-4.81943483875701[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]10.2698200385505[/C][C]3.73017996144948[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]13.1510128487701[/C][C]-4.15101284877014[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]11.9360902143663[/C][C]2.06390978563366[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]12.3696590611040[/C][C]-4.36965906110396[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]11.9582198358997[/C][C]-3.95821983589969[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1877914012893[/C][C]-1.18779140128929[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.5321446828459[/C][C]0.467855317154133[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]7.90882944885377[/C][C]3.09117055114623[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.5749322150329[/C][C]1.42506778496706[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]12.6636409037180[/C][C]2.33635909628205[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3492643668036[/C][C]2.65073563319644[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.2349671797460[/C][C]3.76503282025405[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]11.3626995624433[/C][C]-0.362699562443263[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.9720623299428[/C][C]0.0279376700571903[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]12.2728873294928[/C][C]1.72711267050722[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.8219538030844[/C][C]-0.821953803084415[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.7464356210983[/C][C]1.25356437890174[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]11.1985671272483[/C][C]-3.19856712724827[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.6851581925544[/C][C]-0.685158192554415[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.5402377150473[/C][C]2.45976228495268[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]9.06977428724976[/C][C]2.93022571275024[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]12.4209757271604[/C][C]3.57902427283958[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.8768740543981[/C][C]0.123125945601918[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]10.9059392948890[/C][C]0.0940607051109729[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]4.31532253938963[/C][C]-0.315322539389629[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.6801933917251[/C][C]2.31980660827488[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]11.0894579895420[/C][C]3.91054201045803[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.5547064637049[/C][C]-2.55470646370489[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]12.1052818858668[/C][C]0.894718114133203[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]11.9966311972151[/C][C]3.0033688027849[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.7823417095592[/C][C]1.21765829044084[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]12.9816207127056[/C][C]1.01837928729438[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]13.7347931858019[/C][C]-6.73479318580192[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.9806398912536[/C][C]5.01936010874637[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.7132698290606[/C][C]-1.71326982906063[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.6973118084712[/C][C]-2.69731180847124[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]12.6486637045806[/C][C]0.351336295419442[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]14.3731649147882[/C][C]0.62683508521181[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.48254043217644[/C][C]-0.482540432176445[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.5541449684753[/C][C]0.445855031524733[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]6.39157817754845[/C][C]3.60842182245155[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]10.7694077367544[/C][C]-2.7694077367544[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.6368147368153[/C][C]-4.63681473681533[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]14.2033766765781[/C][C]0.796623323421898[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]9.90349975673664[/C][C]6.09650024326336[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]10.7184308913893[/C][C]2.28156910861066[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8577318575131[/C][C]2.14226814248689[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.95747755274446[/C][C]-0.957477552744455[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.0830114576097[/C][C]1.91698854239033[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.2107904212288[/C][C]1.78920957877123[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]14.6589579670823[/C][C]-2.65895796708231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.63981236353336-3.63981236353336
232.657524905255030.342475094744966
332.484007124719860.515992875280144
430.3743827119428082.62561728805719
534.11985017052585-1.11985017052585
633.21225755389104-0.212257553891044
723.1192551802499-1.11925518024990
835.83916763550658-2.83916763550658
932.432479957581530.567520042418473
1042.527537380181021.47246261981898
1132.425171333620240.574828666379757
1234.40812337749902-1.40812337749902
1327.33350949536604-5.33350949536604
1435.72893559008028-2.72893559008028
1533.53229419794498-0.532294197944976
1637.52515212925775-4.52515212925775
1734.7131556049483-1.7131556049483
1832.61415752486140.3858424751386
1924.69706732071663-2.69706732071663
2042.272113264023451.72788673597655
2130.2067451797496892.79325482025031
2222.98362869759878-0.983628697598777
2337.0327625507506-4.03276255075059
2432.683379242130920.316620757869077
2526.69442133283461-4.69442133283461
2624.3112484727779-2.31124847277790
2744.19829189691558-0.198291896915585
2821.163101588823490.836898411176508
2934.1484121923997-1.14841219239970
3020.7905712140822261.20942878591777
3145.23684125004874-1.23684125004874
3234.4205716837819-1.4205716837819
3323.19681495417318-1.19681495417318
3430.252784441752722.74721555824728
3535.25131608893867-2.25131608893867
3613.07319193748396-2.07319193748396
3731.428288856432391.57171114356761
3832.235413694177150.76458630582285
3935.35542351026599-2.35542351026599
4022.40823906604738-0.408239066047379
4135.28014970403675-2.28014970403675
42158.064191921860816.93580807813919
43510.3396686249889-5.33966862498889
44810.8671796532721-2.8671796532721
45118.563390849073742.43660915092626
461611.91250823645094.08749176354913
471715.31948777354151.68051222645849
4895.890417524692273.10958247530773
49911.4967412678164-2.49674126781636
501314.6049937798081-1.60499377980813
51107.312386732241422.68761326775858
52611.4793417694378-5.47934176943782
531211.73375387806320.266246121936845
54810.4242349754777-2.42423497547765
551411.67660816189482.3233918381052
561213.1419904471384-1.14199044713835
571112.0374306446612-1.03743064466125
581614.75808389385481.24191610614525
59814.9989230023693-6.9989230023693
601511.10869716469713.89130283530288
61710.5471430092182-3.54714300921817
621614.31204097396341.68795902603657
631413.90376624841630.096233751583721
641612.65021962999583.34978037000419
6598.792012753840150.207987246159846
661412.51510766572931.48489233427067
671112.8335883843680-1.83358838436802
68138.85913053226354.1408694677365
691511.69412786262033.30587213737967
7056.24994206653401-1.24994206653401
711513.43591622042231.56408377957769
721312.63487841728000.365121582720013
731112.6640993527726-1.66409935277256
741114.7913896476171-3.79138964761709
751211.37636212693040.623637873069637
761212.7042671040673-0.704267104067294
771212.3019350178886-0.301935017888561
781212.5776615724515-0.577661572451516
791411.10417865478532.89582134521474
8069.62365562138995-3.62365562138995
8179.52408340370008-2.52408340370008
821412.34004057286941.65995942713063
831413.38575310005650.614246899943477
841010.8472376533054-0.847237653305417
851310.00629008335892.99370991664108
861210.64497099506591.3550290049341
8799.03062032959518-0.0306203295951838
881212.4413050475216-0.441305047521602
891611.16428695545024.83571304454977
901010.9227694440252-0.922769444025204
911411.48403984814282.5159601518572
921013.1376340356444-3.13763403564441
931614.50976436487861.49023563512143
941513.49828327131411.50171672868595
951210.97403004075161.02596995924837
96108.890192595129141.10980740487086
9786.320318060928861.67968193907114
9889.60917933190357-1.60917933190357
991114.0379860719860-3.03798607198605
1001310.87627173296762.12372826703244
1011615.01687639473690.983123605263068
1021613.15966008814462.84033991185538
1031415.7854357778498-1.78543577784982
104119.17343132320941.82656867679060
10548.81943483875701-4.81943483875701
1061410.26982003855053.73017996144948
107913.1510128487701-4.15101284877014
1081411.93609021436632.06390978563366
109812.3696590611040-4.36965906110396
110811.9582198358997-3.95821983589969
1111112.1877914012893-1.18779140128929
1121211.53214468284590.467855317154133
113117.908829448853773.09117055114623
1141412.57493221503291.42506778496706
1151512.66364090371802.33635909628205
1161613.34926436680362.65073563319644
1171612.23496717974603.76503282025405
1181111.3626995624433-0.362699562443263
1191413.97206232994280.0279376700571903
1201412.27288732949281.72711267050722
1211212.8219538030844-0.821953803084415
1221412.74643562109831.25356437890174
123811.1985671272483-3.19856712724827
1241313.6851581925544-0.685158192554415
1251613.54023771504732.45976228495268
126129.069774287249762.93022571275024
1271612.42097572716043.57902427283958
1281211.87687405439810.123125945601918
1291110.90593929488900.0940607051109729
13044.31532253938963-0.315322539389629
1311613.68019339172512.31980660827488
1321511.08945798954203.91054201045803
1331012.5547064637049-2.55470646370489
1341312.10528188586680.894718114133203
1351511.99663119721513.0033688027849
1361210.78234170955921.21765829044084
1371412.98162071270561.01837928729438
138713.7347931858019-6.73479318580192
1391913.98063989125365.01936010874637
1401213.7132698290606-1.71326982906063
1411214.6973118084712-2.69731180847124
1421312.64866370458060.351336295419442
1431514.37316491478820.62683508521181
14488.48254043217644-0.482540432176445
1451211.55414496847530.445855031524733
146106.391578177548453.60842182245155
147810.7694077367544-2.7694077367544
1481014.6368147368153-4.63681473681533
1491514.20337667657810.796623323421898
150169.903499756736646.09650024326336
1511310.71843089138932.28156910861066
1521613.85773185751312.14226814248689
15399.95747755274446-0.957477552744455
1541412.08301145760971.91698854239033
1551412.21079042122881.78920957877123
1561214.6589579670823-2.65895796708231






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0004293881229472940.0008587762458945880.999570611877053
110.0007305274672893610.001461054934578720.99926947253271
120.0001385576203380370.0002771152406760750.999861442379662
131.79910848727217e-053.59821697454433e-050.999982008915127
142.12852836940137e-064.25705673880274e-060.99999787147163
152.25805436844015e-074.51610873688031e-070.999999774194563
165.01683168676068e-081.00336633735214e-070.999999949831683
175.51575124385806e-091.10315024877161e-080.999999994484249
182.43208360751691e-094.86416721503383e-090.999999997567916
192.21510726346730e-084.43021452693460e-080.999999977848927
203.37144747107402e-096.74289494214805e-090.999999996628553
219.41760948767846e-101.88352189753569e-090.99999999905824
222.3334810497393e-104.6669620994786e-100.999999999766652
237.85517199778525e-111.57103439955705e-100.999999999921448
241.37859176788035e-112.75718353576069e-110.999999999986214
253.57235974493121e-127.14471948986243e-120.999999999996428
267.8881926041566e-131.57763852083132e-120.999999999999211
276.32062223754689e-131.26412444750938e-120.999999999999368
283.79816315907879e-137.59632631815759e-130.99999999999962
297.40780612979165e-141.48156122595833e-130.999999999999926
301.20067499811604e-142.40134999623207e-140.999999999999988
311.03416084774483e-132.06832169548966e-130.999999999999897
322.17651337368183e-144.35302674736367e-140.999999999999978
336.13804271494524e-151.22760854298905e-140.999999999999994
341.22119633916234e-152.44239267832467e-150.999999999999999
352.75990869642768e-165.51981739285537e-161
363.86687701497779e-167.73375402995558e-161
378.89025431016517e-171.77805086203303e-161
382.27582297035992e-174.55164594071984e-171
391.30450771407335e-172.60901542814669e-171
403.721601180822e-177.443202361644e-171
411.96523337595439e-153.93046675190878e-150.999999999999998
420.05147267407672120.1029453481534420.948527325923279
430.08592245458169910.1718449091633980.9140775454183
440.08289278119355190.1657855623871040.917107218806448
450.1997507346741560.3995014693483130.800249265325844
460.83385723951610.3322855209678000.166142760483900
470.8334078910317130.3331842179365750.166592108968287
480.8540391728893350.291921654221330.145960827110665
490.8546584208087660.2906831583824680.145341579191234
500.8881726390323060.2236547219353870.111827360967694
510.8689736266838910.2620527466322180.131026373316109
520.9576873904172540.08462521916549120.0423126095827456
530.963338845276270.07332230944746130.0366611547237306
540.9652740006415860.06945199871682730.0347259993584136
550.9877924051888370.02441518962232590.0122075948111630
560.9842862548653960.03142749026920740.0157137451346037
570.9798463437621420.04030731247571660.0201536562378583
580.9854849058232140.02903018835357150.0145150941767857
590.9964649570388680.007070085922265020.00353504296113251
600.9991487591120260.001702481775947880.000851240887973938
610.9992375352489510.001524929502097560.000762464751048781
620.9993491346682260.001301730663547680.00065086533177384
630.9991768399316010.001646320136796960.000823160068398482
640.9996221991846140.000755601630772510.000377800815386255
650.9994267923226480.001146415354704630.000573207677352313
660.9993569905079360.001286018984127520.000643009492063762
670.9994038976143710.001192204771258280.000596102385629139
680.999731939481260.0005361210374786940.000268060518739347
690.9997735113799930.0004529772400129230.000226488620006462
700.9997293372749680.0005413254500649040.000270662725032452
710.9997241225906960.0005517548186074280.000275877409303714
720.9995820269699360.0008359460601275480.000417973030063774
730.9994056392594630.001188721481073340.000594360740536668
740.999559650940660.000880698118680290.000440349059340145
750.9993662251005810.001267549798837920.00063377489941896
760.9992230953803640.001553809239272950.000776904619636477
770.9988693684322360.002261263135528290.00113063156776414
780.998375314956970.003249370086058890.00162468504302944
790.9988710266192550.002257946761490530.00112897338074526
800.9991433738450680.001713252309863420.000856626154931712
810.9991169658520180.00176606829596460.0008830341479823
820.9992041627621340.001591674475732770.000795837237866387
830.9988915494675140.002216901064971890.00110845053248595
840.998472337337290.003055325325419550.00152766266270978
850.9995696228488420.0008607543023170450.000430377151158522
860.9994223819945660.001155236010868510.000577618005434254
870.9991214148549530.001757170290093200.000878585145046599
880.9988661241407250.002267751718550930.00113387585927547
890.999246406329270.001507187341460440.000753593670730222
900.9988832214266360.002233557146727960.00111677857336398
910.99868967916310.002620641673799770.00131032083689988
920.9991378523881770.0017242952236460.000862147611823
930.9988819392593470.002236121481306770.00111806074065339
940.9985507799968950.002898440006209750.00144922000310487
950.997941697683870.004116604632258130.00205830231612906
960.997077085690290.005845828619419680.00292291430970984
970.9960497243875560.007900551224888620.00395027561244431
980.9946147773127940.0107704453744130.0053852226872065
990.994170877250710.01165824549857840.00582912274928922
1000.9928670161287950.01426596774240960.00713298387120479
1010.9904603498863220.01907930022735510.00953965011367755
1020.9890860742064430.02182785158711490.0109139257935575
1030.9915894045228720.01682119095425540.00841059547712768
1040.9927800005873990.01443999882520240.0072199994126012
1050.9934696786817140.01306064263657120.00653032131828561
1060.9931217746266680.01375645074666430.00687822537333217
1070.9924532971957780.01509340560844450.00754670280422226
1080.9933364886508580.01332702269828340.0066635113491417
1090.9948056188490240.01038876230195240.00519438115097622
1100.9958284734750530.008343053049894350.00417152652494717
1110.9950044580936040.009991083812791530.00499554190639576
1120.9967385651265880.006522869746824960.00326143487341248
1130.995786361447820.008427277104360530.00421363855218027
1140.9939148187650180.01217036246996510.00608518123498253
1150.9917000494427920.01659990111441510.00829995055720754
1160.9922917511005620.01541649779887660.00770824889943831
1170.9931035280477630.01379294390447390.00689647195223694
1180.989938915966120.02012216806776140.0100610840338807
1190.9849523853228990.03009522935420300.0150476146771015
1200.9893222114219220.0213555771561560.010677788578078
1210.984830012581230.03033997483753870.0151699874187694
1220.9779565054869330.04408698902613450.0220434945130673
1230.975198272001540.04960345599692070.0248017279984604
1240.964658967804780.070682064390440.03534103219522
1250.9611292712205040.07774145755899140.0388707287794957
1260.9593438757664370.08131224846712630.0406561242335632
1270.9472449461711570.1055101076576850.0527550538288426
1280.9448850749086930.1102298501826150.0551149250913074
1290.9623379240462180.07532415190756440.0376620759537822
1300.950774226493320.09845154701335930.0492257735066797
1310.9323813191632650.135237361673470.067618680836735
1320.9257895654597970.1484208690804060.0742104345402028
1330.8962081190987580.2075837618024840.103791880901242
1340.8561075880512930.2877848238974150.143892411948707
1350.8270296980501060.3459406038997880.172970301949894
1360.8015016935531920.3969966128936170.198498306446808
1370.7612291582073780.4775416835852440.238770841792622
1380.7887231877944140.4225536244111720.211276812205586
1390.9631823950902260.07363520981954820.0368176049097741
1400.9922415378165930.01551692436681510.00775846218340754
1410.9845447087115580.03091058257688410.0154552912884420
1420.9667492081052460.0665015837895070.0332507918947535
1430.935690789924250.1286184201515010.0643092100757505
1440.8743748336572870.2512503326854250.125625166342713
1450.8057602271101240.3884795457797520.194239772889876
1460.7352739582060970.5294520835878060.264726041793903

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.000429388122947294 & 0.000858776245894588 & 0.999570611877053 \tabularnewline
11 & 0.000730527467289361 & 0.00146105493457872 & 0.99926947253271 \tabularnewline
12 & 0.000138557620338037 & 0.000277115240676075 & 0.999861442379662 \tabularnewline
13 & 1.79910848727217e-05 & 3.59821697454433e-05 & 0.999982008915127 \tabularnewline
14 & 2.12852836940137e-06 & 4.25705673880274e-06 & 0.99999787147163 \tabularnewline
15 & 2.25805436844015e-07 & 4.51610873688031e-07 & 0.999999774194563 \tabularnewline
16 & 5.01683168676068e-08 & 1.00336633735214e-07 & 0.999999949831683 \tabularnewline
17 & 5.51575124385806e-09 & 1.10315024877161e-08 & 0.999999994484249 \tabularnewline
18 & 2.43208360751691e-09 & 4.86416721503383e-09 & 0.999999997567916 \tabularnewline
19 & 2.21510726346730e-08 & 4.43021452693460e-08 & 0.999999977848927 \tabularnewline
20 & 3.37144747107402e-09 & 6.74289494214805e-09 & 0.999999996628553 \tabularnewline
21 & 9.41760948767846e-10 & 1.88352189753569e-09 & 0.99999999905824 \tabularnewline
22 & 2.3334810497393e-10 & 4.6669620994786e-10 & 0.999999999766652 \tabularnewline
23 & 7.85517199778525e-11 & 1.57103439955705e-10 & 0.999999999921448 \tabularnewline
24 & 1.37859176788035e-11 & 2.75718353576069e-11 & 0.999999999986214 \tabularnewline
25 & 3.57235974493121e-12 & 7.14471948986243e-12 & 0.999999999996428 \tabularnewline
26 & 7.8881926041566e-13 & 1.57763852083132e-12 & 0.999999999999211 \tabularnewline
27 & 6.32062223754689e-13 & 1.26412444750938e-12 & 0.999999999999368 \tabularnewline
28 & 3.79816315907879e-13 & 7.59632631815759e-13 & 0.99999999999962 \tabularnewline
29 & 7.40780612979165e-14 & 1.48156122595833e-13 & 0.999999999999926 \tabularnewline
30 & 1.20067499811604e-14 & 2.40134999623207e-14 & 0.999999999999988 \tabularnewline
31 & 1.03416084774483e-13 & 2.06832169548966e-13 & 0.999999999999897 \tabularnewline
32 & 2.17651337368183e-14 & 4.35302674736367e-14 & 0.999999999999978 \tabularnewline
33 & 6.13804271494524e-15 & 1.22760854298905e-14 & 0.999999999999994 \tabularnewline
34 & 1.22119633916234e-15 & 2.44239267832467e-15 & 0.999999999999999 \tabularnewline
35 & 2.75990869642768e-16 & 5.51981739285537e-16 & 1 \tabularnewline
36 & 3.86687701497779e-16 & 7.73375402995558e-16 & 1 \tabularnewline
37 & 8.89025431016517e-17 & 1.77805086203303e-16 & 1 \tabularnewline
38 & 2.27582297035992e-17 & 4.55164594071984e-17 & 1 \tabularnewline
39 & 1.30450771407335e-17 & 2.60901542814669e-17 & 1 \tabularnewline
40 & 3.721601180822e-17 & 7.443202361644e-17 & 1 \tabularnewline
41 & 1.96523337595439e-15 & 3.93046675190878e-15 & 0.999999999999998 \tabularnewline
42 & 0.0514726740767212 & 0.102945348153442 & 0.948527325923279 \tabularnewline
43 & 0.0859224545816991 & 0.171844909163398 & 0.9140775454183 \tabularnewline
44 & 0.0828927811935519 & 0.165785562387104 & 0.917107218806448 \tabularnewline
45 & 0.199750734674156 & 0.399501469348313 & 0.800249265325844 \tabularnewline
46 & 0.8338572395161 & 0.332285520967800 & 0.166142760483900 \tabularnewline
47 & 0.833407891031713 & 0.333184217936575 & 0.166592108968287 \tabularnewline
48 & 0.854039172889335 & 0.29192165422133 & 0.145960827110665 \tabularnewline
49 & 0.854658420808766 & 0.290683158382468 & 0.145341579191234 \tabularnewline
50 & 0.888172639032306 & 0.223654721935387 & 0.111827360967694 \tabularnewline
51 & 0.868973626683891 & 0.262052746632218 & 0.131026373316109 \tabularnewline
52 & 0.957687390417254 & 0.0846252191654912 & 0.0423126095827456 \tabularnewline
53 & 0.96333884527627 & 0.0733223094474613 & 0.0366611547237306 \tabularnewline
54 & 0.965274000641586 & 0.0694519987168273 & 0.0347259993584136 \tabularnewline
55 & 0.987792405188837 & 0.0244151896223259 & 0.0122075948111630 \tabularnewline
56 & 0.984286254865396 & 0.0314274902692074 & 0.0157137451346037 \tabularnewline
57 & 0.979846343762142 & 0.0403073124757166 & 0.0201536562378583 \tabularnewline
58 & 0.985484905823214 & 0.0290301883535715 & 0.0145150941767857 \tabularnewline
59 & 0.996464957038868 & 0.00707008592226502 & 0.00353504296113251 \tabularnewline
60 & 0.999148759112026 & 0.00170248177594788 & 0.000851240887973938 \tabularnewline
61 & 0.999237535248951 & 0.00152492950209756 & 0.000762464751048781 \tabularnewline
62 & 0.999349134668226 & 0.00130173066354768 & 0.00065086533177384 \tabularnewline
63 & 0.999176839931601 & 0.00164632013679696 & 0.000823160068398482 \tabularnewline
64 & 0.999622199184614 & 0.00075560163077251 & 0.000377800815386255 \tabularnewline
65 & 0.999426792322648 & 0.00114641535470463 & 0.000573207677352313 \tabularnewline
66 & 0.999356990507936 & 0.00128601898412752 & 0.000643009492063762 \tabularnewline
67 & 0.999403897614371 & 0.00119220477125828 & 0.000596102385629139 \tabularnewline
68 & 0.99973193948126 & 0.000536121037478694 & 0.000268060518739347 \tabularnewline
69 & 0.999773511379993 & 0.000452977240012923 & 0.000226488620006462 \tabularnewline
70 & 0.999729337274968 & 0.000541325450064904 & 0.000270662725032452 \tabularnewline
71 & 0.999724122590696 & 0.000551754818607428 & 0.000275877409303714 \tabularnewline
72 & 0.999582026969936 & 0.000835946060127548 & 0.000417973030063774 \tabularnewline
73 & 0.999405639259463 & 0.00118872148107334 & 0.000594360740536668 \tabularnewline
74 & 0.99955965094066 & 0.00088069811868029 & 0.000440349059340145 \tabularnewline
75 & 0.999366225100581 & 0.00126754979883792 & 0.00063377489941896 \tabularnewline
76 & 0.999223095380364 & 0.00155380923927295 & 0.000776904619636477 \tabularnewline
77 & 0.998869368432236 & 0.00226126313552829 & 0.00113063156776414 \tabularnewline
78 & 0.99837531495697 & 0.00324937008605889 & 0.00162468504302944 \tabularnewline
79 & 0.998871026619255 & 0.00225794676149053 & 0.00112897338074526 \tabularnewline
80 & 0.999143373845068 & 0.00171325230986342 & 0.000856626154931712 \tabularnewline
81 & 0.999116965852018 & 0.0017660682959646 & 0.0008830341479823 \tabularnewline
82 & 0.999204162762134 & 0.00159167447573277 & 0.000795837237866387 \tabularnewline
83 & 0.998891549467514 & 0.00221690106497189 & 0.00110845053248595 \tabularnewline
84 & 0.99847233733729 & 0.00305532532541955 & 0.00152766266270978 \tabularnewline
85 & 0.999569622848842 & 0.000860754302317045 & 0.000430377151158522 \tabularnewline
86 & 0.999422381994566 & 0.00115523601086851 & 0.000577618005434254 \tabularnewline
87 & 0.999121414854953 & 0.00175717029009320 & 0.000878585145046599 \tabularnewline
88 & 0.998866124140725 & 0.00226775171855093 & 0.00113387585927547 \tabularnewline
89 & 0.99924640632927 & 0.00150718734146044 & 0.000753593670730222 \tabularnewline
90 & 0.998883221426636 & 0.00223355714672796 & 0.00111677857336398 \tabularnewline
91 & 0.9986896791631 & 0.00262064167379977 & 0.00131032083689988 \tabularnewline
92 & 0.999137852388177 & 0.001724295223646 & 0.000862147611823 \tabularnewline
93 & 0.998881939259347 & 0.00223612148130677 & 0.00111806074065339 \tabularnewline
94 & 0.998550779996895 & 0.00289844000620975 & 0.00144922000310487 \tabularnewline
95 & 0.99794169768387 & 0.00411660463225813 & 0.00205830231612906 \tabularnewline
96 & 0.99707708569029 & 0.00584582861941968 & 0.00292291430970984 \tabularnewline
97 & 0.996049724387556 & 0.00790055122488862 & 0.00395027561244431 \tabularnewline
98 & 0.994614777312794 & 0.010770445374413 & 0.0053852226872065 \tabularnewline
99 & 0.99417087725071 & 0.0116582454985784 & 0.00582912274928922 \tabularnewline
100 & 0.992867016128795 & 0.0142659677424096 & 0.00713298387120479 \tabularnewline
101 & 0.990460349886322 & 0.0190793002273551 & 0.00953965011367755 \tabularnewline
102 & 0.989086074206443 & 0.0218278515871149 & 0.0109139257935575 \tabularnewline
103 & 0.991589404522872 & 0.0168211909542554 & 0.00841059547712768 \tabularnewline
104 & 0.992780000587399 & 0.0144399988252024 & 0.0072199994126012 \tabularnewline
105 & 0.993469678681714 & 0.0130606426365712 & 0.00653032131828561 \tabularnewline
106 & 0.993121774626668 & 0.0137564507466643 & 0.00687822537333217 \tabularnewline
107 & 0.992453297195778 & 0.0150934056084445 & 0.00754670280422226 \tabularnewline
108 & 0.993336488650858 & 0.0133270226982834 & 0.0066635113491417 \tabularnewline
109 & 0.994805618849024 & 0.0103887623019524 & 0.00519438115097622 \tabularnewline
110 & 0.995828473475053 & 0.00834305304989435 & 0.00417152652494717 \tabularnewline
111 & 0.995004458093604 & 0.00999108381279153 & 0.00499554190639576 \tabularnewline
112 & 0.996738565126588 & 0.00652286974682496 & 0.00326143487341248 \tabularnewline
113 & 0.99578636144782 & 0.00842727710436053 & 0.00421363855218027 \tabularnewline
114 & 0.993914818765018 & 0.0121703624699651 & 0.00608518123498253 \tabularnewline
115 & 0.991700049442792 & 0.0165999011144151 & 0.00829995055720754 \tabularnewline
116 & 0.992291751100562 & 0.0154164977988766 & 0.00770824889943831 \tabularnewline
117 & 0.993103528047763 & 0.0137929439044739 & 0.00689647195223694 \tabularnewline
118 & 0.98993891596612 & 0.0201221680677614 & 0.0100610840338807 \tabularnewline
119 & 0.984952385322899 & 0.0300952293542030 & 0.0150476146771015 \tabularnewline
120 & 0.989322211421922 & 0.021355577156156 & 0.010677788578078 \tabularnewline
121 & 0.98483001258123 & 0.0303399748375387 & 0.0151699874187694 \tabularnewline
122 & 0.977956505486933 & 0.0440869890261345 & 0.0220434945130673 \tabularnewline
123 & 0.97519827200154 & 0.0496034559969207 & 0.0248017279984604 \tabularnewline
124 & 0.96465896780478 & 0.07068206439044 & 0.03534103219522 \tabularnewline
125 & 0.961129271220504 & 0.0777414575589914 & 0.0388707287794957 \tabularnewline
126 & 0.959343875766437 & 0.0813122484671263 & 0.0406561242335632 \tabularnewline
127 & 0.947244946171157 & 0.105510107657685 & 0.0527550538288426 \tabularnewline
128 & 0.944885074908693 & 0.110229850182615 & 0.0551149250913074 \tabularnewline
129 & 0.962337924046218 & 0.0753241519075644 & 0.0376620759537822 \tabularnewline
130 & 0.95077422649332 & 0.0984515470133593 & 0.0492257735066797 \tabularnewline
131 & 0.932381319163265 & 0.13523736167347 & 0.067618680836735 \tabularnewline
132 & 0.925789565459797 & 0.148420869080406 & 0.0742104345402028 \tabularnewline
133 & 0.896208119098758 & 0.207583761802484 & 0.103791880901242 \tabularnewline
134 & 0.856107588051293 & 0.287784823897415 & 0.143892411948707 \tabularnewline
135 & 0.827029698050106 & 0.345940603899788 & 0.172970301949894 \tabularnewline
136 & 0.801501693553192 & 0.396996612893617 & 0.198498306446808 \tabularnewline
137 & 0.761229158207378 & 0.477541683585244 & 0.238770841792622 \tabularnewline
138 & 0.788723187794414 & 0.422553624411172 & 0.211276812205586 \tabularnewline
139 & 0.963182395090226 & 0.0736352098195482 & 0.0368176049097741 \tabularnewline
140 & 0.992241537816593 & 0.0155169243668151 & 0.00775846218340754 \tabularnewline
141 & 0.984544708711558 & 0.0309105825768841 & 0.0154552912884420 \tabularnewline
142 & 0.966749208105246 & 0.066501583789507 & 0.0332507918947535 \tabularnewline
143 & 0.93569078992425 & 0.128618420151501 & 0.0643092100757505 \tabularnewline
144 & 0.874374833657287 & 0.251250332685425 & 0.125625166342713 \tabularnewline
145 & 0.805760227110124 & 0.388479545779752 & 0.194239772889876 \tabularnewline
146 & 0.735273958206097 & 0.529452083587806 & 0.264726041793903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&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]0.000429388122947294[/C][C]0.000858776245894588[/C][C]0.999570611877053[/C][/ROW]
[ROW][C]11[/C][C]0.000730527467289361[/C][C]0.00146105493457872[/C][C]0.99926947253271[/C][/ROW]
[ROW][C]12[/C][C]0.000138557620338037[/C][C]0.000277115240676075[/C][C]0.999861442379662[/C][/ROW]
[ROW][C]13[/C][C]1.79910848727217e-05[/C][C]3.59821697454433e-05[/C][C]0.999982008915127[/C][/ROW]
[ROW][C]14[/C][C]2.12852836940137e-06[/C][C]4.25705673880274e-06[/C][C]0.99999787147163[/C][/ROW]
[ROW][C]15[/C][C]2.25805436844015e-07[/C][C]4.51610873688031e-07[/C][C]0.999999774194563[/C][/ROW]
[ROW][C]16[/C][C]5.01683168676068e-08[/C][C]1.00336633735214e-07[/C][C]0.999999949831683[/C][/ROW]
[ROW][C]17[/C][C]5.51575124385806e-09[/C][C]1.10315024877161e-08[/C][C]0.999999994484249[/C][/ROW]
[ROW][C]18[/C][C]2.43208360751691e-09[/C][C]4.86416721503383e-09[/C][C]0.999999997567916[/C][/ROW]
[ROW][C]19[/C][C]2.21510726346730e-08[/C][C]4.43021452693460e-08[/C][C]0.999999977848927[/C][/ROW]
[ROW][C]20[/C][C]3.37144747107402e-09[/C][C]6.74289494214805e-09[/C][C]0.999999996628553[/C][/ROW]
[ROW][C]21[/C][C]9.41760948767846e-10[/C][C]1.88352189753569e-09[/C][C]0.99999999905824[/C][/ROW]
[ROW][C]22[/C][C]2.3334810497393e-10[/C][C]4.6669620994786e-10[/C][C]0.999999999766652[/C][/ROW]
[ROW][C]23[/C][C]7.85517199778525e-11[/C][C]1.57103439955705e-10[/C][C]0.999999999921448[/C][/ROW]
[ROW][C]24[/C][C]1.37859176788035e-11[/C][C]2.75718353576069e-11[/C][C]0.999999999986214[/C][/ROW]
[ROW][C]25[/C][C]3.57235974493121e-12[/C][C]7.14471948986243e-12[/C][C]0.999999999996428[/C][/ROW]
[ROW][C]26[/C][C]7.8881926041566e-13[/C][C]1.57763852083132e-12[/C][C]0.999999999999211[/C][/ROW]
[ROW][C]27[/C][C]6.32062223754689e-13[/C][C]1.26412444750938e-12[/C][C]0.999999999999368[/C][/ROW]
[ROW][C]28[/C][C]3.79816315907879e-13[/C][C]7.59632631815759e-13[/C][C]0.99999999999962[/C][/ROW]
[ROW][C]29[/C][C]7.40780612979165e-14[/C][C]1.48156122595833e-13[/C][C]0.999999999999926[/C][/ROW]
[ROW][C]30[/C][C]1.20067499811604e-14[/C][C]2.40134999623207e-14[/C][C]0.999999999999988[/C][/ROW]
[ROW][C]31[/C][C]1.03416084774483e-13[/C][C]2.06832169548966e-13[/C][C]0.999999999999897[/C][/ROW]
[ROW][C]32[/C][C]2.17651337368183e-14[/C][C]4.35302674736367e-14[/C][C]0.999999999999978[/C][/ROW]
[ROW][C]33[/C][C]6.13804271494524e-15[/C][C]1.22760854298905e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]34[/C][C]1.22119633916234e-15[/C][C]2.44239267832467e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]35[/C][C]2.75990869642768e-16[/C][C]5.51981739285537e-16[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]3.86687701497779e-16[/C][C]7.73375402995558e-16[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]8.89025431016517e-17[/C][C]1.77805086203303e-16[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]2.27582297035992e-17[/C][C]4.55164594071984e-17[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.30450771407335e-17[/C][C]2.60901542814669e-17[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]3.721601180822e-17[/C][C]7.443202361644e-17[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]1.96523337595439e-15[/C][C]3.93046675190878e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]42[/C][C]0.0514726740767212[/C][C]0.102945348153442[/C][C]0.948527325923279[/C][/ROW]
[ROW][C]43[/C][C]0.0859224545816991[/C][C]0.171844909163398[/C][C]0.9140775454183[/C][/ROW]
[ROW][C]44[/C][C]0.0828927811935519[/C][C]0.165785562387104[/C][C]0.917107218806448[/C][/ROW]
[ROW][C]45[/C][C]0.199750734674156[/C][C]0.399501469348313[/C][C]0.800249265325844[/C][/ROW]
[ROW][C]46[/C][C]0.8338572395161[/C][C]0.332285520967800[/C][C]0.166142760483900[/C][/ROW]
[ROW][C]47[/C][C]0.833407891031713[/C][C]0.333184217936575[/C][C]0.166592108968287[/C][/ROW]
[ROW][C]48[/C][C]0.854039172889335[/C][C]0.29192165422133[/C][C]0.145960827110665[/C][/ROW]
[ROW][C]49[/C][C]0.854658420808766[/C][C]0.290683158382468[/C][C]0.145341579191234[/C][/ROW]
[ROW][C]50[/C][C]0.888172639032306[/C][C]0.223654721935387[/C][C]0.111827360967694[/C][/ROW]
[ROW][C]51[/C][C]0.868973626683891[/C][C]0.262052746632218[/C][C]0.131026373316109[/C][/ROW]
[ROW][C]52[/C][C]0.957687390417254[/C][C]0.0846252191654912[/C][C]0.0423126095827456[/C][/ROW]
[ROW][C]53[/C][C]0.96333884527627[/C][C]0.0733223094474613[/C][C]0.0366611547237306[/C][/ROW]
[ROW][C]54[/C][C]0.965274000641586[/C][C]0.0694519987168273[/C][C]0.0347259993584136[/C][/ROW]
[ROW][C]55[/C][C]0.987792405188837[/C][C]0.0244151896223259[/C][C]0.0122075948111630[/C][/ROW]
[ROW][C]56[/C][C]0.984286254865396[/C][C]0.0314274902692074[/C][C]0.0157137451346037[/C][/ROW]
[ROW][C]57[/C][C]0.979846343762142[/C][C]0.0403073124757166[/C][C]0.0201536562378583[/C][/ROW]
[ROW][C]58[/C][C]0.985484905823214[/C][C]0.0290301883535715[/C][C]0.0145150941767857[/C][/ROW]
[ROW][C]59[/C][C]0.996464957038868[/C][C]0.00707008592226502[/C][C]0.00353504296113251[/C][/ROW]
[ROW][C]60[/C][C]0.999148759112026[/C][C]0.00170248177594788[/C][C]0.000851240887973938[/C][/ROW]
[ROW][C]61[/C][C]0.999237535248951[/C][C]0.00152492950209756[/C][C]0.000762464751048781[/C][/ROW]
[ROW][C]62[/C][C]0.999349134668226[/C][C]0.00130173066354768[/C][C]0.00065086533177384[/C][/ROW]
[ROW][C]63[/C][C]0.999176839931601[/C][C]0.00164632013679696[/C][C]0.000823160068398482[/C][/ROW]
[ROW][C]64[/C][C]0.999622199184614[/C][C]0.00075560163077251[/C][C]0.000377800815386255[/C][/ROW]
[ROW][C]65[/C][C]0.999426792322648[/C][C]0.00114641535470463[/C][C]0.000573207677352313[/C][/ROW]
[ROW][C]66[/C][C]0.999356990507936[/C][C]0.00128601898412752[/C][C]0.000643009492063762[/C][/ROW]
[ROW][C]67[/C][C]0.999403897614371[/C][C]0.00119220477125828[/C][C]0.000596102385629139[/C][/ROW]
[ROW][C]68[/C][C]0.99973193948126[/C][C]0.000536121037478694[/C][C]0.000268060518739347[/C][/ROW]
[ROW][C]69[/C][C]0.999773511379993[/C][C]0.000452977240012923[/C][C]0.000226488620006462[/C][/ROW]
[ROW][C]70[/C][C]0.999729337274968[/C][C]0.000541325450064904[/C][C]0.000270662725032452[/C][/ROW]
[ROW][C]71[/C][C]0.999724122590696[/C][C]0.000551754818607428[/C][C]0.000275877409303714[/C][/ROW]
[ROW][C]72[/C][C]0.999582026969936[/C][C]0.000835946060127548[/C][C]0.000417973030063774[/C][/ROW]
[ROW][C]73[/C][C]0.999405639259463[/C][C]0.00118872148107334[/C][C]0.000594360740536668[/C][/ROW]
[ROW][C]74[/C][C]0.99955965094066[/C][C]0.00088069811868029[/C][C]0.000440349059340145[/C][/ROW]
[ROW][C]75[/C][C]0.999366225100581[/C][C]0.00126754979883792[/C][C]0.00063377489941896[/C][/ROW]
[ROW][C]76[/C][C]0.999223095380364[/C][C]0.00155380923927295[/C][C]0.000776904619636477[/C][/ROW]
[ROW][C]77[/C][C]0.998869368432236[/C][C]0.00226126313552829[/C][C]0.00113063156776414[/C][/ROW]
[ROW][C]78[/C][C]0.99837531495697[/C][C]0.00324937008605889[/C][C]0.00162468504302944[/C][/ROW]
[ROW][C]79[/C][C]0.998871026619255[/C][C]0.00225794676149053[/C][C]0.00112897338074526[/C][/ROW]
[ROW][C]80[/C][C]0.999143373845068[/C][C]0.00171325230986342[/C][C]0.000856626154931712[/C][/ROW]
[ROW][C]81[/C][C]0.999116965852018[/C][C]0.0017660682959646[/C][C]0.0008830341479823[/C][/ROW]
[ROW][C]82[/C][C]0.999204162762134[/C][C]0.00159167447573277[/C][C]0.000795837237866387[/C][/ROW]
[ROW][C]83[/C][C]0.998891549467514[/C][C]0.00221690106497189[/C][C]0.00110845053248595[/C][/ROW]
[ROW][C]84[/C][C]0.99847233733729[/C][C]0.00305532532541955[/C][C]0.00152766266270978[/C][/ROW]
[ROW][C]85[/C][C]0.999569622848842[/C][C]0.000860754302317045[/C][C]0.000430377151158522[/C][/ROW]
[ROW][C]86[/C][C]0.999422381994566[/C][C]0.00115523601086851[/C][C]0.000577618005434254[/C][/ROW]
[ROW][C]87[/C][C]0.999121414854953[/C][C]0.00175717029009320[/C][C]0.000878585145046599[/C][/ROW]
[ROW][C]88[/C][C]0.998866124140725[/C][C]0.00226775171855093[/C][C]0.00113387585927547[/C][/ROW]
[ROW][C]89[/C][C]0.99924640632927[/C][C]0.00150718734146044[/C][C]0.000753593670730222[/C][/ROW]
[ROW][C]90[/C][C]0.998883221426636[/C][C]0.00223355714672796[/C][C]0.00111677857336398[/C][/ROW]
[ROW][C]91[/C][C]0.9986896791631[/C][C]0.00262064167379977[/C][C]0.00131032083689988[/C][/ROW]
[ROW][C]92[/C][C]0.999137852388177[/C][C]0.001724295223646[/C][C]0.000862147611823[/C][/ROW]
[ROW][C]93[/C][C]0.998881939259347[/C][C]0.00223612148130677[/C][C]0.00111806074065339[/C][/ROW]
[ROW][C]94[/C][C]0.998550779996895[/C][C]0.00289844000620975[/C][C]0.00144922000310487[/C][/ROW]
[ROW][C]95[/C][C]0.99794169768387[/C][C]0.00411660463225813[/C][C]0.00205830231612906[/C][/ROW]
[ROW][C]96[/C][C]0.99707708569029[/C][C]0.00584582861941968[/C][C]0.00292291430970984[/C][/ROW]
[ROW][C]97[/C][C]0.996049724387556[/C][C]0.00790055122488862[/C][C]0.00395027561244431[/C][/ROW]
[ROW][C]98[/C][C]0.994614777312794[/C][C]0.010770445374413[/C][C]0.0053852226872065[/C][/ROW]
[ROW][C]99[/C][C]0.99417087725071[/C][C]0.0116582454985784[/C][C]0.00582912274928922[/C][/ROW]
[ROW][C]100[/C][C]0.992867016128795[/C][C]0.0142659677424096[/C][C]0.00713298387120479[/C][/ROW]
[ROW][C]101[/C][C]0.990460349886322[/C][C]0.0190793002273551[/C][C]0.00953965011367755[/C][/ROW]
[ROW][C]102[/C][C]0.989086074206443[/C][C]0.0218278515871149[/C][C]0.0109139257935575[/C][/ROW]
[ROW][C]103[/C][C]0.991589404522872[/C][C]0.0168211909542554[/C][C]0.00841059547712768[/C][/ROW]
[ROW][C]104[/C][C]0.992780000587399[/C][C]0.0144399988252024[/C][C]0.0072199994126012[/C][/ROW]
[ROW][C]105[/C][C]0.993469678681714[/C][C]0.0130606426365712[/C][C]0.00653032131828561[/C][/ROW]
[ROW][C]106[/C][C]0.993121774626668[/C][C]0.0137564507466643[/C][C]0.00687822537333217[/C][/ROW]
[ROW][C]107[/C][C]0.992453297195778[/C][C]0.0150934056084445[/C][C]0.00754670280422226[/C][/ROW]
[ROW][C]108[/C][C]0.993336488650858[/C][C]0.0133270226982834[/C][C]0.0066635113491417[/C][/ROW]
[ROW][C]109[/C][C]0.994805618849024[/C][C]0.0103887623019524[/C][C]0.00519438115097622[/C][/ROW]
[ROW][C]110[/C][C]0.995828473475053[/C][C]0.00834305304989435[/C][C]0.00417152652494717[/C][/ROW]
[ROW][C]111[/C][C]0.995004458093604[/C][C]0.00999108381279153[/C][C]0.00499554190639576[/C][/ROW]
[ROW][C]112[/C][C]0.996738565126588[/C][C]0.00652286974682496[/C][C]0.00326143487341248[/C][/ROW]
[ROW][C]113[/C][C]0.99578636144782[/C][C]0.00842727710436053[/C][C]0.00421363855218027[/C][/ROW]
[ROW][C]114[/C][C]0.993914818765018[/C][C]0.0121703624699651[/C][C]0.00608518123498253[/C][/ROW]
[ROW][C]115[/C][C]0.991700049442792[/C][C]0.0165999011144151[/C][C]0.00829995055720754[/C][/ROW]
[ROW][C]116[/C][C]0.992291751100562[/C][C]0.0154164977988766[/C][C]0.00770824889943831[/C][/ROW]
[ROW][C]117[/C][C]0.993103528047763[/C][C]0.0137929439044739[/C][C]0.00689647195223694[/C][/ROW]
[ROW][C]118[/C][C]0.98993891596612[/C][C]0.0201221680677614[/C][C]0.0100610840338807[/C][/ROW]
[ROW][C]119[/C][C]0.984952385322899[/C][C]0.0300952293542030[/C][C]0.0150476146771015[/C][/ROW]
[ROW][C]120[/C][C]0.989322211421922[/C][C]0.021355577156156[/C][C]0.010677788578078[/C][/ROW]
[ROW][C]121[/C][C]0.98483001258123[/C][C]0.0303399748375387[/C][C]0.0151699874187694[/C][/ROW]
[ROW][C]122[/C][C]0.977956505486933[/C][C]0.0440869890261345[/C][C]0.0220434945130673[/C][/ROW]
[ROW][C]123[/C][C]0.97519827200154[/C][C]0.0496034559969207[/C][C]0.0248017279984604[/C][/ROW]
[ROW][C]124[/C][C]0.96465896780478[/C][C]0.07068206439044[/C][C]0.03534103219522[/C][/ROW]
[ROW][C]125[/C][C]0.961129271220504[/C][C]0.0777414575589914[/C][C]0.0388707287794957[/C][/ROW]
[ROW][C]126[/C][C]0.959343875766437[/C][C]0.0813122484671263[/C][C]0.0406561242335632[/C][/ROW]
[ROW][C]127[/C][C]0.947244946171157[/C][C]0.105510107657685[/C][C]0.0527550538288426[/C][/ROW]
[ROW][C]128[/C][C]0.944885074908693[/C][C]0.110229850182615[/C][C]0.0551149250913074[/C][/ROW]
[ROW][C]129[/C][C]0.962337924046218[/C][C]0.0753241519075644[/C][C]0.0376620759537822[/C][/ROW]
[ROW][C]130[/C][C]0.95077422649332[/C][C]0.0984515470133593[/C][C]0.0492257735066797[/C][/ROW]
[ROW][C]131[/C][C]0.932381319163265[/C][C]0.13523736167347[/C][C]0.067618680836735[/C][/ROW]
[ROW][C]132[/C][C]0.925789565459797[/C][C]0.148420869080406[/C][C]0.0742104345402028[/C][/ROW]
[ROW][C]133[/C][C]0.896208119098758[/C][C]0.207583761802484[/C][C]0.103791880901242[/C][/ROW]
[ROW][C]134[/C][C]0.856107588051293[/C][C]0.287784823897415[/C][C]0.143892411948707[/C][/ROW]
[ROW][C]135[/C][C]0.827029698050106[/C][C]0.345940603899788[/C][C]0.172970301949894[/C][/ROW]
[ROW][C]136[/C][C]0.801501693553192[/C][C]0.396996612893617[/C][C]0.198498306446808[/C][/ROW]
[ROW][C]137[/C][C]0.761229158207378[/C][C]0.477541683585244[/C][C]0.238770841792622[/C][/ROW]
[ROW][C]138[/C][C]0.788723187794414[/C][C]0.422553624411172[/C][C]0.211276812205586[/C][/ROW]
[ROW][C]139[/C][C]0.963182395090226[/C][C]0.0736352098195482[/C][C]0.0368176049097741[/C][/ROW]
[ROW][C]140[/C][C]0.992241537816593[/C][C]0.0155169243668151[/C][C]0.00775846218340754[/C][/ROW]
[ROW][C]141[/C][C]0.984544708711558[/C][C]0.0309105825768841[/C][C]0.0154552912884420[/C][/ROW]
[ROW][C]142[/C][C]0.966749208105246[/C][C]0.066501583789507[/C][C]0.0332507918947535[/C][/ROW]
[ROW][C]143[/C][C]0.93569078992425[/C][C]0.128618420151501[/C][C]0.0643092100757505[/C][/ROW]
[ROW][C]144[/C][C]0.874374833657287[/C][C]0.251250332685425[/C][C]0.125625166342713[/C][/ROW]
[ROW][C]145[/C][C]0.805760227110124[/C][C]0.388479545779752[/C][C]0.194239772889876[/C][/ROW]
[ROW][C]146[/C][C]0.735273958206097[/C][C]0.529452083587806[/C][C]0.264726041793903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0004293881229472940.0008587762458945880.999570611877053
110.0007305274672893610.001461054934578720.99926947253271
120.0001385576203380370.0002771152406760750.999861442379662
131.79910848727217e-053.59821697454433e-050.999982008915127
142.12852836940137e-064.25705673880274e-060.99999787147163
152.25805436844015e-074.51610873688031e-070.999999774194563
165.01683168676068e-081.00336633735214e-070.999999949831683
175.51575124385806e-091.10315024877161e-080.999999994484249
182.43208360751691e-094.86416721503383e-090.999999997567916
192.21510726346730e-084.43021452693460e-080.999999977848927
203.37144747107402e-096.74289494214805e-090.999999996628553
219.41760948767846e-101.88352189753569e-090.99999999905824
222.3334810497393e-104.6669620994786e-100.999999999766652
237.85517199778525e-111.57103439955705e-100.999999999921448
241.37859176788035e-112.75718353576069e-110.999999999986214
253.57235974493121e-127.14471948986243e-120.999999999996428
267.8881926041566e-131.57763852083132e-120.999999999999211
276.32062223754689e-131.26412444750938e-120.999999999999368
283.79816315907879e-137.59632631815759e-130.99999999999962
297.40780612979165e-141.48156122595833e-130.999999999999926
301.20067499811604e-142.40134999623207e-140.999999999999988
311.03416084774483e-132.06832169548966e-130.999999999999897
322.17651337368183e-144.35302674736367e-140.999999999999978
336.13804271494524e-151.22760854298905e-140.999999999999994
341.22119633916234e-152.44239267832467e-150.999999999999999
352.75990869642768e-165.51981739285537e-161
363.86687701497779e-167.73375402995558e-161
378.89025431016517e-171.77805086203303e-161
382.27582297035992e-174.55164594071984e-171
391.30450771407335e-172.60901542814669e-171
403.721601180822e-177.443202361644e-171
411.96523337595439e-153.93046675190878e-150.999999999999998
420.05147267407672120.1029453481534420.948527325923279
430.08592245458169910.1718449091633980.9140775454183
440.08289278119355190.1657855623871040.917107218806448
450.1997507346741560.3995014693483130.800249265325844
460.83385723951610.3322855209678000.166142760483900
470.8334078910317130.3331842179365750.166592108968287
480.8540391728893350.291921654221330.145960827110665
490.8546584208087660.2906831583824680.145341579191234
500.8881726390323060.2236547219353870.111827360967694
510.8689736266838910.2620527466322180.131026373316109
520.9576873904172540.08462521916549120.0423126095827456
530.963338845276270.07332230944746130.0366611547237306
540.9652740006415860.06945199871682730.0347259993584136
550.9877924051888370.02441518962232590.0122075948111630
560.9842862548653960.03142749026920740.0157137451346037
570.9798463437621420.04030731247571660.0201536562378583
580.9854849058232140.02903018835357150.0145150941767857
590.9964649570388680.007070085922265020.00353504296113251
600.9991487591120260.001702481775947880.000851240887973938
610.9992375352489510.001524929502097560.000762464751048781
620.9993491346682260.001301730663547680.00065086533177384
630.9991768399316010.001646320136796960.000823160068398482
640.9996221991846140.000755601630772510.000377800815386255
650.9994267923226480.001146415354704630.000573207677352313
660.9993569905079360.001286018984127520.000643009492063762
670.9994038976143710.001192204771258280.000596102385629139
680.999731939481260.0005361210374786940.000268060518739347
690.9997735113799930.0004529772400129230.000226488620006462
700.9997293372749680.0005413254500649040.000270662725032452
710.9997241225906960.0005517548186074280.000275877409303714
720.9995820269699360.0008359460601275480.000417973030063774
730.9994056392594630.001188721481073340.000594360740536668
740.999559650940660.000880698118680290.000440349059340145
750.9993662251005810.001267549798837920.00063377489941896
760.9992230953803640.001553809239272950.000776904619636477
770.9988693684322360.002261263135528290.00113063156776414
780.998375314956970.003249370086058890.00162468504302944
790.9988710266192550.002257946761490530.00112897338074526
800.9991433738450680.001713252309863420.000856626154931712
810.9991169658520180.00176606829596460.0008830341479823
820.9992041627621340.001591674475732770.000795837237866387
830.9988915494675140.002216901064971890.00110845053248595
840.998472337337290.003055325325419550.00152766266270978
850.9995696228488420.0008607543023170450.000430377151158522
860.9994223819945660.001155236010868510.000577618005434254
870.9991214148549530.001757170290093200.000878585145046599
880.9988661241407250.002267751718550930.00113387585927547
890.999246406329270.001507187341460440.000753593670730222
900.9988832214266360.002233557146727960.00111677857336398
910.99868967916310.002620641673799770.00131032083689988
920.9991378523881770.0017242952236460.000862147611823
930.9988819392593470.002236121481306770.00111806074065339
940.9985507799968950.002898440006209750.00144922000310487
950.997941697683870.004116604632258130.00205830231612906
960.997077085690290.005845828619419680.00292291430970984
970.9960497243875560.007900551224888620.00395027561244431
980.9946147773127940.0107704453744130.0053852226872065
990.994170877250710.01165824549857840.00582912274928922
1000.9928670161287950.01426596774240960.00713298387120479
1010.9904603498863220.01907930022735510.00953965011367755
1020.9890860742064430.02182785158711490.0109139257935575
1030.9915894045228720.01682119095425540.00841059547712768
1040.9927800005873990.01443999882520240.0072199994126012
1050.9934696786817140.01306064263657120.00653032131828561
1060.9931217746266680.01375645074666430.00687822537333217
1070.9924532971957780.01509340560844450.00754670280422226
1080.9933364886508580.01332702269828340.0066635113491417
1090.9948056188490240.01038876230195240.00519438115097622
1100.9958284734750530.008343053049894350.00417152652494717
1110.9950044580936040.009991083812791530.00499554190639576
1120.9967385651265880.006522869746824960.00326143487341248
1130.995786361447820.008427277104360530.00421363855218027
1140.9939148187650180.01217036246996510.00608518123498253
1150.9917000494427920.01659990111441510.00829995055720754
1160.9922917511005620.01541649779887660.00770824889943831
1170.9931035280477630.01379294390447390.00689647195223694
1180.989938915966120.02012216806776140.0100610840338807
1190.9849523853228990.03009522935420300.0150476146771015
1200.9893222114219220.0213555771561560.010677788578078
1210.984830012581230.03033997483753870.0151699874187694
1220.9779565054869330.04408698902613450.0220434945130673
1230.975198272001540.04960345599692070.0248017279984604
1240.964658967804780.070682064390440.03534103219522
1250.9611292712205040.07774145755899140.0388707287794957
1260.9593438757664370.08131224846712630.0406561242335632
1270.9472449461711570.1055101076576850.0527550538288426
1280.9448850749086930.1102298501826150.0551149250913074
1290.9623379240462180.07532415190756440.0376620759537822
1300.950774226493320.09845154701335930.0492257735066797
1310.9323813191632650.135237361673470.067618680836735
1320.9257895654597970.1484208690804060.0742104345402028
1330.8962081190987580.2075837618024840.103791880901242
1340.8561075880512930.2877848238974150.143892411948707
1350.8270296980501060.3459406038997880.172970301949894
1360.8015016935531920.3969966128936170.198498306446808
1370.7612291582073780.4775416835852440.238770841792622
1380.7887231877944140.4225536244111720.211276812205586
1390.9631823950902260.07363520981954820.0368176049097741
1400.9922415378165930.01551692436681510.00775846218340754
1410.9845447087115580.03091058257688410.0154552912884420
1420.9667492081052460.0665015837895070.0332507918947535
1430.935690789924250.1286184201515010.0643092100757505
1440.8743748336572870.2512503326854250.125625166342713
1450.8057602271101240.3884795457797520.194239772889876
1460.7352739582060970.5294520835878060.264726041793903






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level750.547445255474453NOK
5% type I error level1030.751824817518248NOK
10% type I error level1130.824817518248175NOK

\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 & 75 & 0.547445255474453 & NOK \tabularnewline
5% type I error level & 103 & 0.751824817518248 & NOK \tabularnewline
10% type I error level & 113 & 0.824817518248175 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104222&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]75[/C][C]0.547445255474453[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]103[/C][C]0.751824817518248[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]113[/C][C]0.824817518248175[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104222&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104222&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level750.547445255474453NOK
5% type I error level1030.751824817518248NOK
10% type I error level1130.824817518248175NOK


Figures (Output of Computation)

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

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