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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 computationWed, 01 Dec 2010 16:37:19 +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/01/t1291223247yzvd40mevpd2vd2.htm/, Retrieved Sat, 04 May 2024 22:50:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104120, Retrieved Sat, 04 May 2024 22:50:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [multiple regressi...] [2010-12-01 16:37:19] [03bcd8c83ef1a42b4029a16ba47a4880] [Current]
-    D      [Multiple Regression] [multiple regressi...] [2010-12-01 17:31:46] [96348ef82925ade81ab3c243141d80f1]
-             [Multiple Regression] [] [2010-12-03 15:31:57] [30b3e197115d238a51c18bcedc33a6a5]
-           [Multiple Regression] [] [2010-12-03 15:54:51] [30b3e197115d238a51c18bcedc33a6a5]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 12 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
aantalVrienden[t] = + 0.766368778596676 + 0.048718031746747maand[t] + 0.0991533235522943Popularity[t] -0.0609920300506682FindingFriends[t] + 0.128326230337218KnowingPeople[t] -0.076124742332118Liked[t] + 0.0313954332780401`Celebrity `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
aantalVrienden[t] =  +  0.766368778596676 +  0.048718031746747maand[t] +  0.0991533235522943Popularity[t] -0.0609920300506682FindingFriends[t] +  0.128326230337218KnowingPeople[t] -0.076124742332118Liked[t] +  0.0313954332780401`Celebrity
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]aantalVrienden[t] =  +  0.766368778596676 +  0.048718031746747maand[t] +  0.0991533235522943Popularity[t] -0.0609920300506682FindingFriends[t] +  0.128326230337218KnowingPeople[t] -0.076124742332118Liked[t] +  0.0313954332780401`Celebrity
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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
aantalVrienden[t] = + 0.766368778596676 + 0.048718031746747maand[t] + 0.0991533235522943Popularity[t] -0.0609920300506682FindingFriends[t] + 0.128326230337218KnowingPeople[t] -0.076124742332118Liked[t] + 0.0313954332780401`Celebrity `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7663687785966762.4517660.31260.7550440.377522
maand0.0487180317467470.246460.19770.8435750.421788
Popularity0.09915332355229430.0549951.8030.0734440.036722
FindingFriends-0.06099203005066820.065252-0.93470.3514690.175734
KnowingPeople0.1283262303372180.0439922.9170.0040890.002045
Liked-0.0761247423321180.068512-1.11110.2683350.134168
`Celebrity `0.03139543327804010.1131380.27750.7817880.390894

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.766368778596676 & 2.451766 & 0.3126 & 0.755044 & 0.377522 \tabularnewline
maand & 0.048718031746747 & 0.24646 & 0.1977 & 0.843575 & 0.421788 \tabularnewline
Popularity & 0.0991533235522943 & 0.054995 & 1.803 & 0.073444 & 0.036722 \tabularnewline
FindingFriends & -0.0609920300506682 & 0.065252 & -0.9347 & 0.351469 & 0.175734 \tabularnewline
KnowingPeople & 0.128326230337218 & 0.043992 & 2.917 & 0.004089 & 0.002045 \tabularnewline
Liked & -0.076124742332118 & 0.068512 & -1.1111 & 0.268335 & 0.134168 \tabularnewline
`Celebrity
` & 0.0313954332780401 & 0.113138 & 0.2775 & 0.781788 & 0.390894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.766368778596676[/C][C]2.451766[/C][C]0.3126[/C][C]0.755044[/C][C]0.377522[/C][/ROW]
[ROW][C]maand[/C][C]0.048718031746747[/C][C]0.24646[/C][C]0.1977[/C][C]0.843575[/C][C]0.421788[/C][/ROW]
[ROW][C]Popularity[/C][C]0.0991533235522943[/C][C]0.054995[/C][C]1.803[/C][C]0.073444[/C][C]0.036722[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0609920300506682[/C][C]0.065252[/C][C]-0.9347[/C][C]0.351469[/C][C]0.175734[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.128326230337218[/C][C]0.043992[/C][C]2.917[/C][C]0.004089[/C][C]0.002045[/C][/ROW]
[ROW][C]Liked[/C][C]-0.076124742332118[/C][C]0.068512[/C][C]-1.1111[/C][C]0.268335[/C][C]0.134168[/C][/ROW]
[ROW][C]`Celebrity
`[/C][C]0.0313954332780401[/C][C]0.113138[/C][C]0.2775[/C][C]0.781788[/C][C]0.390894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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)0.7663687785966762.4517660.31260.7550440.377522
maand0.0487180317467470.246460.19770.8435750.421788
Popularity0.09915332355229430.0549951.8030.0734440.036722
FindingFriends-0.06099203005066820.065252-0.93470.3514690.175734
KnowingPeople0.1283262303372180.0439922.9170.0040890.002045
Liked-0.0761247423321180.068512-1.11110.2683350.134168
`Celebrity `0.03139543327804010.1131380.27750.7817880.390894






Multiple Linear Regression - Regression Statistics
Multiple R0.402657535519121
R-squared0.162133090910332
Adjusted R-squared0.127934441559734
F-TEST (value)4.74092088398498
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.000191518186576811
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.41891364789336
Sum Squared Residuals295.957443206173

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.402657535519121 \tabularnewline
R-squared & 0.162133090910332 \tabularnewline
Adjusted R-squared & 0.127934441559734 \tabularnewline
F-TEST (value) & 4.74092088398498 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0.000191518186576811 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.41891364789336 \tabularnewline
Sum Squared Residuals & 295.957443206173 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.402657535519121[/C][/ROW]
[ROW][C]R-squared[/C][C]0.162133090910332[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.127934441559734[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.74092088398498[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0.000191518186576811[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.41891364789336[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]295.957443206173[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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 R0.402657535519121
R-squared0.162133090910332
Adjusted R-squared0.127934441559734
F-TEST (value)4.74092088398498
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.000191518186576811
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.41891364789336
Sum Squared Residuals295.957443206173






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
102.59250210349606-2.59250210349606
232.018901980532260.981098019467736
332.189311303442740.810688696557262
411.74173959054614-0.74173959054614
532.833372316584810.166627683415192
612.06510471684829-1.06510471684829
742.519169151520471.48083084847953
802.13998187723503-2.13998187723503
933.16640336555337-0.166403365553368
1022.25007748817684-0.250077488176837
1143.394043273176850.60595672682315
1231.797015497647461.20298450235254
1311.53866376439451-0.538663764394512
1412.52006382462731-1.52006382462731
1522.15092416462454-0.150924164624543
1632.817335440904790.182664559095212
1711.86419463376661-0.864194633766614
1811.81595243577031-0.815952435770309
1921.718042181535690.281957818464308
2032.895682709730020.104317290269977
2142.945228490962571.05477150903743
2221.84925976615630.150740233843702
2312.98471763785047-1.98471763785047
2423.17314122513152-1.17314122513152
2522.42908899875347-0.429088998753474
2642.893686028553481.10631397144652
2722.86408943178085-0.864089431780848
2832.145825063706340.85417493629366
2932.42709760714350.5729023928565
3032.811464253788040.188535746211956
3142.735537356127061.26446264387294
3221.970151308479660.0298486915203416
3322.06510471684829-0.0651047168482865
3443.220093060678710.779906939321287
3531.992530354395561.00746964560444
3642.267884417584541.73211558241546
3722.81823011401163-0.81823011401163
3853.175363751624631.82463624837537
3932.728065177176040.271934822823962
4012.04164793301271-1.04164793301271
4110.9930164658652930.00698353413470687
4211.82249245067732-0.822492450677325
4321.926090827215850.073909172784148
4432.942634545277220.0573654547227828
4593.235649462947875.76435053705213
4601.46887170200655-1.46887170200655
4702.13356892584998-2.13356892584998
4822.78551631763904-0.785516317639044
4921.304842040269120.695157959730882
5032.526716624248360.473283375751640
5112.29391212412407-1.29391212412407
5221.477441249366850.52255875063315
5302.68439574559408-2.68439574559408
5452.736399388928042.26360061107196
5522.54730697385673-0.547306973856727
5643.314670410194680.685329589805322
5731.580045388871641.41995461112836
5803.10412240509203-3.10412240509203
5901.42301723486863-1.42301723486863
6043.283943804706910.71605619529309
6113.80584142343022-2.80584142343022
6213.01215863175102-2.01215863175102
6341.610438378006872.38956162199313
6422.74538777564474-0.745387775644745
6542.812100441272461.18789955872754
6611.95240181240126-0.952401812401262
6742.875936532475081.12406346752492
6821.090899369360620.909100630639377
6953.033859359584931.96614064041507
7042.646234452092451.35376554790755
7142.176142632031981.82385736796802
7242.669102679578661.33089732042134
7342.572134391582321.42786560841768
7432.835129022492630.164870977507368
7532.690540071158820.309459928841176
7632.791049248742830.208950751257171
7721.99623644834850.00376355165150205
7811.11194075969598-0.111940759695981
7911.32608643780946-0.326086437809456
8053.218860131558911.78113986844109
8142.60940101197711.3905989880229
8222.29023082319459-0.290230823194586
8331.632958485170751.36704151482925
8422.46528304376243-0.465283043762429
8521.865960829966170.134039170033834
8622.44313933345483-0.443139333454826
8723.07494949830710-1.07494949830710
8831.941190899191441.05880910080856
8922.22442232096545-0.224422320965446
9032.288912460100040.71108753989996
9143.163089753320710.836910246679286
9233.38289928358807-0.382899283588070
9332.427545440248510.572454559751493
9401.82799738113567-1.82799738113567
9511.3318873448274-0.331887344827399
9621.485101782697280.514898217302723
9722.66797267086352-0.667972670863517
9832.420977424696060.579022575303945
9943.35240801370860.6475919862914
10043.110888265315610.889111734684386
10112.78748035850973-1.78748035850973
10221.480511155835380.519488844164624
10320.8550904543504111.14490954564959
10432.165229127420190.83477087257981
10532.052161890754090.947838109245906
10633.00496908153973-0.00496908153973336
10711.74855759453246-0.748557594532464
10811.75847814529317-0.758478145293173
10912.36546089241986-1.36546089241986
11013.11049257597335-2.11049257597335
11101.70936585624260-1.70936585624260
11212.62453372425855-1.62453372425855
11332.550631508419550.449368491580452
11433.08828337408314-0.088283374083142
11503.02729134403247-3.02729134403247
11621.609317859583460.390682140416541
11752.95731092726512.04268907273490
11822.72278288441227-0.722782884412273
11932.38735946492490.612640535075101
12033.26748323903918-0.267483239039185
12151.742884252086853.25711574791315
12242.797165573662141.20283442633786
12343.155617574369690.844382425630308
12401.57267277384751-1.57267277384751
12532.937832725924320.0621672740756817
12602.83512902249263-2.83512902249263
12722.23079249184676-0.230792491846763
12801.75326629567497-1.75326629567497
12962.937832725924323.06216727407568
13032.808602332188530.19139766781147
13111.44737366947508-0.447373669475085
13262.207735909981153.79226409001885
13322.79981179014296-0.799811790142962
13412.00238077391325-1.00238077391325
13533.01830295731577-0.0183029573157713
13612.06464838655472-1.06464838655472
13723.37695280269446-1.37695280269446
13842.721266676646591.27873332335341
13912.96165641632444-1.96165641632444
14022.72983137337559-0.729831373375591
14102.71309766925986-2.71309766925986
14252.388950966467452.61104903353255
14322.13070700425046-0.130707004250463
14411.16424181162798-0.164241811627979
14511.43271194031277-0.432711940312772
14642.301596800571801.69840319942820
14733.24578251120528-0.245782511205283
14802.757305007582-2.757305007582
14932.641691118361980.358308881638018
15033.03476352298350-0.0347635229834959
15101.84203757563915-1.84203757563915
15222.61819155402267-0.618191554022668
15353.042226211642791.95777378835721
15422.18110965575015-0.181109655750151

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 2.59250210349606 & -2.59250210349606 \tabularnewline
2 & 3 & 2.01890198053226 & 0.981098019467736 \tabularnewline
3 & 3 & 2.18931130344274 & 0.810688696557262 \tabularnewline
4 & 1 & 1.74173959054614 & -0.74173959054614 \tabularnewline
5 & 3 & 2.83337231658481 & 0.166627683415192 \tabularnewline
6 & 1 & 2.06510471684829 & -1.06510471684829 \tabularnewline
7 & 4 & 2.51916915152047 & 1.48083084847953 \tabularnewline
8 & 0 & 2.13998187723503 & -2.13998187723503 \tabularnewline
9 & 3 & 3.16640336555337 & -0.166403365553368 \tabularnewline
10 & 2 & 2.25007748817684 & -0.250077488176837 \tabularnewline
11 & 4 & 3.39404327317685 & 0.60595672682315 \tabularnewline
12 & 3 & 1.79701549764746 & 1.20298450235254 \tabularnewline
13 & 1 & 1.53866376439451 & -0.538663764394512 \tabularnewline
14 & 1 & 2.52006382462731 & -1.52006382462731 \tabularnewline
15 & 2 & 2.15092416462454 & -0.150924164624543 \tabularnewline
16 & 3 & 2.81733544090479 & 0.182664559095212 \tabularnewline
17 & 1 & 1.86419463376661 & -0.864194633766614 \tabularnewline
18 & 1 & 1.81595243577031 & -0.815952435770309 \tabularnewline
19 & 2 & 1.71804218153569 & 0.281957818464308 \tabularnewline
20 & 3 & 2.89568270973002 & 0.104317290269977 \tabularnewline
21 & 4 & 2.94522849096257 & 1.05477150903743 \tabularnewline
22 & 2 & 1.8492597661563 & 0.150740233843702 \tabularnewline
23 & 1 & 2.98471763785047 & -1.98471763785047 \tabularnewline
24 & 2 & 3.17314122513152 & -1.17314122513152 \tabularnewline
25 & 2 & 2.42908899875347 & -0.429088998753474 \tabularnewline
26 & 4 & 2.89368602855348 & 1.10631397144652 \tabularnewline
27 & 2 & 2.86408943178085 & -0.864089431780848 \tabularnewline
28 & 3 & 2.14582506370634 & 0.85417493629366 \tabularnewline
29 & 3 & 2.4270976071435 & 0.5729023928565 \tabularnewline
30 & 3 & 2.81146425378804 & 0.188535746211956 \tabularnewline
31 & 4 & 2.73553735612706 & 1.26446264387294 \tabularnewline
32 & 2 & 1.97015130847966 & 0.0298486915203416 \tabularnewline
33 & 2 & 2.06510471684829 & -0.0651047168482865 \tabularnewline
34 & 4 & 3.22009306067871 & 0.779906939321287 \tabularnewline
35 & 3 & 1.99253035439556 & 1.00746964560444 \tabularnewline
36 & 4 & 2.26788441758454 & 1.73211558241546 \tabularnewline
37 & 2 & 2.81823011401163 & -0.81823011401163 \tabularnewline
38 & 5 & 3.17536375162463 & 1.82463624837537 \tabularnewline
39 & 3 & 2.72806517717604 & 0.271934822823962 \tabularnewline
40 & 1 & 2.04164793301271 & -1.04164793301271 \tabularnewline
41 & 1 & 0.993016465865293 & 0.00698353413470687 \tabularnewline
42 & 1 & 1.82249245067732 & -0.822492450677325 \tabularnewline
43 & 2 & 1.92609082721585 & 0.073909172784148 \tabularnewline
44 & 3 & 2.94263454527722 & 0.0573654547227828 \tabularnewline
45 & 9 & 3.23564946294787 & 5.76435053705213 \tabularnewline
46 & 0 & 1.46887170200655 & -1.46887170200655 \tabularnewline
47 & 0 & 2.13356892584998 & -2.13356892584998 \tabularnewline
48 & 2 & 2.78551631763904 & -0.785516317639044 \tabularnewline
49 & 2 & 1.30484204026912 & 0.695157959730882 \tabularnewline
50 & 3 & 2.52671662424836 & 0.473283375751640 \tabularnewline
51 & 1 & 2.29391212412407 & -1.29391212412407 \tabularnewline
52 & 2 & 1.47744124936685 & 0.52255875063315 \tabularnewline
53 & 0 & 2.68439574559408 & -2.68439574559408 \tabularnewline
54 & 5 & 2.73639938892804 & 2.26360061107196 \tabularnewline
55 & 2 & 2.54730697385673 & -0.547306973856727 \tabularnewline
56 & 4 & 3.31467041019468 & 0.685329589805322 \tabularnewline
57 & 3 & 1.58004538887164 & 1.41995461112836 \tabularnewline
58 & 0 & 3.10412240509203 & -3.10412240509203 \tabularnewline
59 & 0 & 1.42301723486863 & -1.42301723486863 \tabularnewline
60 & 4 & 3.28394380470691 & 0.71605619529309 \tabularnewline
61 & 1 & 3.80584142343022 & -2.80584142343022 \tabularnewline
62 & 1 & 3.01215863175102 & -2.01215863175102 \tabularnewline
63 & 4 & 1.61043837800687 & 2.38956162199313 \tabularnewline
64 & 2 & 2.74538777564474 & -0.745387775644745 \tabularnewline
65 & 4 & 2.81210044127246 & 1.18789955872754 \tabularnewline
66 & 1 & 1.95240181240126 & -0.952401812401262 \tabularnewline
67 & 4 & 2.87593653247508 & 1.12406346752492 \tabularnewline
68 & 2 & 1.09089936936062 & 0.909100630639377 \tabularnewline
69 & 5 & 3.03385935958493 & 1.96614064041507 \tabularnewline
70 & 4 & 2.64623445209245 & 1.35376554790755 \tabularnewline
71 & 4 & 2.17614263203198 & 1.82385736796802 \tabularnewline
72 & 4 & 2.66910267957866 & 1.33089732042134 \tabularnewline
73 & 4 & 2.57213439158232 & 1.42786560841768 \tabularnewline
74 & 3 & 2.83512902249263 & 0.164870977507368 \tabularnewline
75 & 3 & 2.69054007115882 & 0.309459928841176 \tabularnewline
76 & 3 & 2.79104924874283 & 0.208950751257171 \tabularnewline
77 & 2 & 1.9962364483485 & 0.00376355165150205 \tabularnewline
78 & 1 & 1.11194075969598 & -0.111940759695981 \tabularnewline
79 & 1 & 1.32608643780946 & -0.326086437809456 \tabularnewline
80 & 5 & 3.21886013155891 & 1.78113986844109 \tabularnewline
81 & 4 & 2.6094010119771 & 1.3905989880229 \tabularnewline
82 & 2 & 2.29023082319459 & -0.290230823194586 \tabularnewline
83 & 3 & 1.63295848517075 & 1.36704151482925 \tabularnewline
84 & 2 & 2.46528304376243 & -0.465283043762429 \tabularnewline
85 & 2 & 1.86596082996617 & 0.134039170033834 \tabularnewline
86 & 2 & 2.44313933345483 & -0.443139333454826 \tabularnewline
87 & 2 & 3.07494949830710 & -1.07494949830710 \tabularnewline
88 & 3 & 1.94119089919144 & 1.05880910080856 \tabularnewline
89 & 2 & 2.22442232096545 & -0.224422320965446 \tabularnewline
90 & 3 & 2.28891246010004 & 0.71108753989996 \tabularnewline
91 & 4 & 3.16308975332071 & 0.836910246679286 \tabularnewline
92 & 3 & 3.38289928358807 & -0.382899283588070 \tabularnewline
93 & 3 & 2.42754544024851 & 0.572454559751493 \tabularnewline
94 & 0 & 1.82799738113567 & -1.82799738113567 \tabularnewline
95 & 1 & 1.3318873448274 & -0.331887344827399 \tabularnewline
96 & 2 & 1.48510178269728 & 0.514898217302723 \tabularnewline
97 & 2 & 2.66797267086352 & -0.667972670863517 \tabularnewline
98 & 3 & 2.42097742469606 & 0.579022575303945 \tabularnewline
99 & 4 & 3.3524080137086 & 0.6475919862914 \tabularnewline
100 & 4 & 3.11088826531561 & 0.889111734684386 \tabularnewline
101 & 1 & 2.78748035850973 & -1.78748035850973 \tabularnewline
102 & 2 & 1.48051115583538 & 0.519488844164624 \tabularnewline
103 & 2 & 0.855090454350411 & 1.14490954564959 \tabularnewline
104 & 3 & 2.16522912742019 & 0.83477087257981 \tabularnewline
105 & 3 & 2.05216189075409 & 0.947838109245906 \tabularnewline
106 & 3 & 3.00496908153973 & -0.00496908153973336 \tabularnewline
107 & 1 & 1.74855759453246 & -0.748557594532464 \tabularnewline
108 & 1 & 1.75847814529317 & -0.758478145293173 \tabularnewline
109 & 1 & 2.36546089241986 & -1.36546089241986 \tabularnewline
110 & 1 & 3.11049257597335 & -2.11049257597335 \tabularnewline
111 & 0 & 1.70936585624260 & -1.70936585624260 \tabularnewline
112 & 1 & 2.62453372425855 & -1.62453372425855 \tabularnewline
113 & 3 & 2.55063150841955 & 0.449368491580452 \tabularnewline
114 & 3 & 3.08828337408314 & -0.088283374083142 \tabularnewline
115 & 0 & 3.02729134403247 & -3.02729134403247 \tabularnewline
116 & 2 & 1.60931785958346 & 0.390682140416541 \tabularnewline
117 & 5 & 2.9573109272651 & 2.04268907273490 \tabularnewline
118 & 2 & 2.72278288441227 & -0.722782884412273 \tabularnewline
119 & 3 & 2.3873594649249 & 0.612640535075101 \tabularnewline
120 & 3 & 3.26748323903918 & -0.267483239039185 \tabularnewline
121 & 5 & 1.74288425208685 & 3.25711574791315 \tabularnewline
122 & 4 & 2.79716557366214 & 1.20283442633786 \tabularnewline
123 & 4 & 3.15561757436969 & 0.844382425630308 \tabularnewline
124 & 0 & 1.57267277384751 & -1.57267277384751 \tabularnewline
125 & 3 & 2.93783272592432 & 0.0621672740756817 \tabularnewline
126 & 0 & 2.83512902249263 & -2.83512902249263 \tabularnewline
127 & 2 & 2.23079249184676 & -0.230792491846763 \tabularnewline
128 & 0 & 1.75326629567497 & -1.75326629567497 \tabularnewline
129 & 6 & 2.93783272592432 & 3.06216727407568 \tabularnewline
130 & 3 & 2.80860233218853 & 0.19139766781147 \tabularnewline
131 & 1 & 1.44737366947508 & -0.447373669475085 \tabularnewline
132 & 6 & 2.20773590998115 & 3.79226409001885 \tabularnewline
133 & 2 & 2.79981179014296 & -0.799811790142962 \tabularnewline
134 & 1 & 2.00238077391325 & -1.00238077391325 \tabularnewline
135 & 3 & 3.01830295731577 & -0.0183029573157713 \tabularnewline
136 & 1 & 2.06464838655472 & -1.06464838655472 \tabularnewline
137 & 2 & 3.37695280269446 & -1.37695280269446 \tabularnewline
138 & 4 & 2.72126667664659 & 1.27873332335341 \tabularnewline
139 & 1 & 2.96165641632444 & -1.96165641632444 \tabularnewline
140 & 2 & 2.72983137337559 & -0.729831373375591 \tabularnewline
141 & 0 & 2.71309766925986 & -2.71309766925986 \tabularnewline
142 & 5 & 2.38895096646745 & 2.61104903353255 \tabularnewline
143 & 2 & 2.13070700425046 & -0.130707004250463 \tabularnewline
144 & 1 & 1.16424181162798 & -0.164241811627979 \tabularnewline
145 & 1 & 1.43271194031277 & -0.432711940312772 \tabularnewline
146 & 4 & 2.30159680057180 & 1.69840319942820 \tabularnewline
147 & 3 & 3.24578251120528 & -0.245782511205283 \tabularnewline
148 & 0 & 2.757305007582 & -2.757305007582 \tabularnewline
149 & 3 & 2.64169111836198 & 0.358308881638018 \tabularnewline
150 & 3 & 3.03476352298350 & -0.0347635229834959 \tabularnewline
151 & 0 & 1.84203757563915 & -1.84203757563915 \tabularnewline
152 & 2 & 2.61819155402267 & -0.618191554022668 \tabularnewline
153 & 5 & 3.04222621164279 & 1.95777378835721 \tabularnewline
154 & 2 & 2.18110965575015 & -0.181109655750151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0[/C][C]2.59250210349606[/C][C]-2.59250210349606[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]2.01890198053226[/C][C]0.981098019467736[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.18931130344274[/C][C]0.810688696557262[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.74173959054614[/C][C]-0.74173959054614[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.83337231658481[/C][C]0.166627683415192[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]2.06510471684829[/C][C]-1.06510471684829[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]2.51916915152047[/C][C]1.48083084847953[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]2.13998187723503[/C][C]-2.13998187723503[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.16640336555337[/C][C]-0.166403365553368[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]2.25007748817684[/C][C]-0.250077488176837[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.39404327317685[/C][C]0.60595672682315[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]1.79701549764746[/C][C]1.20298450235254[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.53866376439451[/C][C]-0.538663764394512[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]2.52006382462731[/C][C]-1.52006382462731[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]2.15092416462454[/C][C]-0.150924164624543[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.81733544090479[/C][C]0.182664559095212[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.86419463376661[/C][C]-0.864194633766614[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.81595243577031[/C][C]-0.815952435770309[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.71804218153569[/C][C]0.281957818464308[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]2.89568270973002[/C][C]0.104317290269977[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]2.94522849096257[/C][C]1.05477150903743[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.8492597661563[/C][C]0.150740233843702[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.98471763785047[/C][C]-1.98471763785047[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]3.17314122513152[/C][C]-1.17314122513152[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]2.42908899875347[/C][C]-0.429088998753474[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]2.89368602855348[/C][C]1.10631397144652[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]2.86408943178085[/C][C]-0.864089431780848[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]2.14582506370634[/C][C]0.85417493629366[/C][/ROW]
[ROW][C]29[/C][C]3[/C][C]2.4270976071435[/C][C]0.5729023928565[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.81146425378804[/C][C]0.188535746211956[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]2.73553735612706[/C][C]1.26446264387294[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]1.97015130847966[/C][C]0.0298486915203416[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]2.06510471684829[/C][C]-0.0651047168482865[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.22009306067871[/C][C]0.779906939321287[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]1.99253035439556[/C][C]1.00746964560444[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]2.26788441758454[/C][C]1.73211558241546[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]2.81823011401163[/C][C]-0.81823011401163[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]3.17536375162463[/C][C]1.82463624837537[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]2.72806517717604[/C][C]0.271934822823962[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]2.04164793301271[/C][C]-1.04164793301271[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.993016465865293[/C][C]0.00698353413470687[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.82249245067732[/C][C]-0.822492450677325[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.92609082721585[/C][C]0.073909172784148[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]2.94263454527722[/C][C]0.0573654547227828[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]3.23564946294787[/C][C]5.76435053705213[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]1.46887170200655[/C][C]-1.46887170200655[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]2.13356892584998[/C][C]-2.13356892584998[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.78551631763904[/C][C]-0.785516317639044[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.30484204026912[/C][C]0.695157959730882[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.52671662424836[/C][C]0.473283375751640[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]2.29391212412407[/C][C]-1.29391212412407[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.47744124936685[/C][C]0.52255875063315[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]2.68439574559408[/C][C]-2.68439574559408[/C][/ROW]
[ROW][C]54[/C][C]5[/C][C]2.73639938892804[/C][C]2.26360061107196[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]2.54730697385673[/C][C]-0.547306973856727[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.31467041019468[/C][C]0.685329589805322[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]1.58004538887164[/C][C]1.41995461112836[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]3.10412240509203[/C][C]-3.10412240509203[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]1.42301723486863[/C][C]-1.42301723486863[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.28394380470691[/C][C]0.71605619529309[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]3.80584142343022[/C][C]-2.80584142343022[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]3.01215863175102[/C][C]-2.01215863175102[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]1.61043837800687[/C][C]2.38956162199313[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]2.74538777564474[/C][C]-0.745387775644745[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]2.81210044127246[/C][C]1.18789955872754[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.95240181240126[/C][C]-0.952401812401262[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]2.87593653247508[/C][C]1.12406346752492[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.09089936936062[/C][C]0.909100630639377[/C][/ROW]
[ROW][C]69[/C][C]5[/C][C]3.03385935958493[/C][C]1.96614064041507[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]2.64623445209245[/C][C]1.35376554790755[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]2.17614263203198[/C][C]1.82385736796802[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]2.66910267957866[/C][C]1.33089732042134[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]2.57213439158232[/C][C]1.42786560841768[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.83512902249263[/C][C]0.164870977507368[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]2.69054007115882[/C][C]0.309459928841176[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.79104924874283[/C][C]0.208950751257171[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.9962364483485[/C][C]0.00376355165150205[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.11194075969598[/C][C]-0.111940759695981[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.32608643780946[/C][C]-0.326086437809456[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]3.21886013155891[/C][C]1.78113986844109[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]2.6094010119771[/C][C]1.3905989880229[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]2.29023082319459[/C][C]-0.290230823194586[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]1.63295848517075[/C][C]1.36704151482925[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]2.46528304376243[/C][C]-0.465283043762429[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.86596082996617[/C][C]0.134039170033834[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]2.44313933345483[/C][C]-0.443139333454826[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]3.07494949830710[/C][C]-1.07494949830710[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]1.94119089919144[/C][C]1.05880910080856[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.22442232096545[/C][C]-0.224422320965446[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.28891246010004[/C][C]0.71108753989996[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.16308975332071[/C][C]0.836910246679286[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]3.38289928358807[/C][C]-0.382899283588070[/C][/ROW]
[ROW][C]93[/C][C]3[/C][C]2.42754544024851[/C][C]0.572454559751493[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]1.82799738113567[/C][C]-1.82799738113567[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.3318873448274[/C][C]-0.331887344827399[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.48510178269728[/C][C]0.514898217302723[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]2.66797267086352[/C][C]-0.667972670863517[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]2.42097742469606[/C][C]0.579022575303945[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.3524080137086[/C][C]0.6475919862914[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.11088826531561[/C][C]0.889111734684386[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]2.78748035850973[/C][C]-1.78748035850973[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.48051115583538[/C][C]0.519488844164624[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]0.855090454350411[/C][C]1.14490954564959[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.16522912742019[/C][C]0.83477087257981[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]2.05216189075409[/C][C]0.947838109245906[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]3.00496908153973[/C][C]-0.00496908153973336[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.74855759453246[/C][C]-0.748557594532464[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.75847814529317[/C][C]-0.758478145293173[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]2.36546089241986[/C][C]-1.36546089241986[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]3.11049257597335[/C][C]-2.11049257597335[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]1.70936585624260[/C][C]-1.70936585624260[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]2.62453372425855[/C][C]-1.62453372425855[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.55063150841955[/C][C]0.449368491580452[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]3.08828337408314[/C][C]-0.088283374083142[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]3.02729134403247[/C][C]-3.02729134403247[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.60931785958346[/C][C]0.390682140416541[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]2.9573109272651[/C][C]2.04268907273490[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]2.72278288441227[/C][C]-0.722782884412273[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.3873594649249[/C][C]0.612640535075101[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]3.26748323903918[/C][C]-0.267483239039185[/C][/ROW]
[ROW][C]121[/C][C]5[/C][C]1.74288425208685[/C][C]3.25711574791315[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]2.79716557366214[/C][C]1.20283442633786[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]3.15561757436969[/C][C]0.844382425630308[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]1.57267277384751[/C][C]-1.57267277384751[/C][/ROW]
[ROW][C]125[/C][C]3[/C][C]2.93783272592432[/C][C]0.0621672740756817[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]2.83512902249263[/C][C]-2.83512902249263[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]2.23079249184676[/C][C]-0.230792491846763[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]1.75326629567497[/C][C]-1.75326629567497[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]2.93783272592432[/C][C]3.06216727407568[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]2.80860233218853[/C][C]0.19139766781147[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.44737366947508[/C][C]-0.447373669475085[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]2.20773590998115[/C][C]3.79226409001885[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]2.79981179014296[/C][C]-0.799811790142962[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]2.00238077391325[/C][C]-1.00238077391325[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]3.01830295731577[/C][C]-0.0183029573157713[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]2.06464838655472[/C][C]-1.06464838655472[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]3.37695280269446[/C][C]-1.37695280269446[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]2.72126667664659[/C][C]1.27873332335341[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]2.96165641632444[/C][C]-1.96165641632444[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]2.72983137337559[/C][C]-0.729831373375591[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]2.71309766925986[/C][C]-2.71309766925986[/C][/ROW]
[ROW][C]142[/C][C]5[/C][C]2.38895096646745[/C][C]2.61104903353255[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]2.13070700425046[/C][C]-0.130707004250463[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.16424181162798[/C][C]-0.164241811627979[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.43271194031277[/C][C]-0.432711940312772[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]2.30159680057180[/C][C]1.69840319942820[/C][/ROW]
[ROW][C]147[/C][C]3[/C][C]3.24578251120528[/C][C]-0.245782511205283[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]2.757305007582[/C][C]-2.757305007582[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.64169111836198[/C][C]0.358308881638018[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]3.03476352298350[/C][C]-0.0347635229834959[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]1.84203757563915[/C][C]-1.84203757563915[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.61819155402267[/C][C]-0.618191554022668[/C][/ROW]
[ROW][C]153[/C][C]5[/C][C]3.04222621164279[/C][C]1.95777378835721[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]2.18110965575015[/C][C]-0.181109655750151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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
102.59250210349606-2.59250210349606
232.018901980532260.981098019467736
332.189311303442740.810688696557262
411.74173959054614-0.74173959054614
532.833372316584810.166627683415192
612.06510471684829-1.06510471684829
742.519169151520471.48083084847953
802.13998187723503-2.13998187723503
933.16640336555337-0.166403365553368
1022.25007748817684-0.250077488176837
1143.394043273176850.60595672682315
1231.797015497647461.20298450235254
1311.53866376439451-0.538663764394512
1412.52006382462731-1.52006382462731
1522.15092416462454-0.150924164624543
1632.817335440904790.182664559095212
1711.86419463376661-0.864194633766614
1811.81595243577031-0.815952435770309
1921.718042181535690.281957818464308
2032.895682709730020.104317290269977
2142.945228490962571.05477150903743
2221.84925976615630.150740233843702
2312.98471763785047-1.98471763785047
2423.17314122513152-1.17314122513152
2522.42908899875347-0.429088998753474
2642.893686028553481.10631397144652
2722.86408943178085-0.864089431780848
2832.145825063706340.85417493629366
2932.42709760714350.5729023928565
3032.811464253788040.188535746211956
3142.735537356127061.26446264387294
3221.970151308479660.0298486915203416
3322.06510471684829-0.0651047168482865
3443.220093060678710.779906939321287
3531.992530354395561.00746964560444
3642.267884417584541.73211558241546
3722.81823011401163-0.81823011401163
3853.175363751624631.82463624837537
3932.728065177176040.271934822823962
4012.04164793301271-1.04164793301271
4110.9930164658652930.00698353413470687
4211.82249245067732-0.822492450677325
4321.926090827215850.073909172784148
4432.942634545277220.0573654547227828
4593.235649462947875.76435053705213
4601.46887170200655-1.46887170200655
4702.13356892584998-2.13356892584998
4822.78551631763904-0.785516317639044
4921.304842040269120.695157959730882
5032.526716624248360.473283375751640
5112.29391212412407-1.29391212412407
5221.477441249366850.52255875063315
5302.68439574559408-2.68439574559408
5452.736399388928042.26360061107196
5522.54730697385673-0.547306973856727
5643.314670410194680.685329589805322
5731.580045388871641.41995461112836
5803.10412240509203-3.10412240509203
5901.42301723486863-1.42301723486863
6043.283943804706910.71605619529309
6113.80584142343022-2.80584142343022
6213.01215863175102-2.01215863175102
6341.610438378006872.38956162199313
6422.74538777564474-0.745387775644745
6542.812100441272461.18789955872754
6611.95240181240126-0.952401812401262
6742.875936532475081.12406346752492
6821.090899369360620.909100630639377
6953.033859359584931.96614064041507
7042.646234452092451.35376554790755
7142.176142632031981.82385736796802
7242.669102679578661.33089732042134
7342.572134391582321.42786560841768
7432.835129022492630.164870977507368
7532.690540071158820.309459928841176
7632.791049248742830.208950751257171
7721.99623644834850.00376355165150205
7811.11194075969598-0.111940759695981
7911.32608643780946-0.326086437809456
8053.218860131558911.78113986844109
8142.60940101197711.3905989880229
8222.29023082319459-0.290230823194586
8331.632958485170751.36704151482925
8422.46528304376243-0.465283043762429
8521.865960829966170.134039170033834
8622.44313933345483-0.443139333454826
8723.07494949830710-1.07494949830710
8831.941190899191441.05880910080856
8922.22442232096545-0.224422320965446
9032.288912460100040.71108753989996
9143.163089753320710.836910246679286
9233.38289928358807-0.382899283588070
9332.427545440248510.572454559751493
9401.82799738113567-1.82799738113567
9511.3318873448274-0.331887344827399
9621.485101782697280.514898217302723
9722.66797267086352-0.667972670863517
9832.420977424696060.579022575303945
9943.35240801370860.6475919862914
10043.110888265315610.889111734684386
10112.78748035850973-1.78748035850973
10221.480511155835380.519488844164624
10320.8550904543504111.14490954564959
10432.165229127420190.83477087257981
10532.052161890754090.947838109245906
10633.00496908153973-0.00496908153973336
10711.74855759453246-0.748557594532464
10811.75847814529317-0.758478145293173
10912.36546089241986-1.36546089241986
11013.11049257597335-2.11049257597335
11101.70936585624260-1.70936585624260
11212.62453372425855-1.62453372425855
11332.550631508419550.449368491580452
11433.08828337408314-0.088283374083142
11503.02729134403247-3.02729134403247
11621.609317859583460.390682140416541
11752.95731092726512.04268907273490
11822.72278288441227-0.722782884412273
11932.38735946492490.612640535075101
12033.26748323903918-0.267483239039185
12151.742884252086853.25711574791315
12242.797165573662141.20283442633786
12343.155617574369690.844382425630308
12401.57267277384751-1.57267277384751
12532.937832725924320.0621672740756817
12602.83512902249263-2.83512902249263
12722.23079249184676-0.230792491846763
12801.75326629567497-1.75326629567497
12962.937832725924323.06216727407568
13032.808602332188530.19139766781147
13111.44737366947508-0.447373669475085
13262.207735909981153.79226409001885
13322.79981179014296-0.799811790142962
13412.00238077391325-1.00238077391325
13533.01830295731577-0.0183029573157713
13612.06464838655472-1.06464838655472
13723.37695280269446-1.37695280269446
13842.721266676646591.27873332335341
13912.96165641632444-1.96165641632444
14022.72983137337559-0.729831373375591
14102.71309766925986-2.71309766925986
14252.388950966467452.61104903353255
14322.13070700425046-0.130707004250463
14411.16424181162798-0.164241811627979
14511.43271194031277-0.432711940312772
14642.301596800571801.69840319942820
14733.24578251120528-0.245782511205283
14802.757305007582-2.757305007582
14932.641691118361980.358308881638018
15033.03476352298350-0.0347635229834959
15101.84203757563915-1.84203757563915
15222.61819155402267-0.618191554022668
15353.042226211642791.95777378835721
15422.18110965575015-0.181109655750151






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8638776565532830.2722446868934350.136122343446717
110.7964780963816420.4070438072367150.203521903618358
120.6902942753205770.6194114493588460.309705724679423
130.5805403859113470.8389192281773050.419459614088653
140.6299260301317430.7401479397365150.370073969868257
150.5321390558599010.9357218882801980.467860944140099
160.435541390513570.871082781027140.56445860948643
170.3455950106389540.6911900212779080.654404989361046
180.2708996975011950.5417993950023890.729100302498805
190.2588208239637660.5176416479275320.741179176036234
200.1957883788133960.3915767576267920.804211621186604
210.162456389474960.324912778949920.83754361052504
220.1266026630383690.2532053260767380.873397336961631
230.1907593560806090.3815187121612180.809240643919391
240.1698209473836550.3396418947673090.830179052616346
250.1340173723350320.2680347446700640.865982627664968
260.1438590003872190.2877180007744390.85614099961278
270.1129270670011290.2258541340022580.88707293299887
280.1087855909886130.2175711819772250.891214409011387
290.09240962854460960.1848192570892190.90759037145539
300.07049416045359750.1409883209071950.929505839546402
310.07315640447688050.1463128089537610.92684359552312
320.05446324678988230.1089264935797650.945536753210118
330.03853775591041950.0770755118208390.96146224408958
340.02990603936661530.05981207873323060.970093960633385
350.02662948794678790.05325897589357570.973370512053212
360.04275635414516140.08551270829032270.957243645854839
370.03498397789586870.06996795579173740.965016022104131
380.04500048629809370.09000097259618730.954999513701906
390.03250523873927960.06501047747855920.96749476126072
400.02713640564379660.05427281128759330.972863594356203
410.01904108603062500.03808217206125010.980958913969375
420.01482094755751670.02964189511503340.985179052442483
430.01016060674348090.02032121348696190.98983939325652
440.006842770411512510.01368554082302500.993157229588488
450.3664063629706810.7328127259413620.633593637029319
460.3476250689175050.695250137835010.652374931082495
470.3834986025402410.7669972050804820.616501397459759
480.3566000044223290.7132000088446580.643399995577671
490.3449712888610020.6899425777220050.655028711138998
500.3200827986963860.6401655973927710.679917201303614
510.3030747538088990.6061495076177980.696925246191101
520.2596467757098270.5192935514196540.740353224290173
530.3827934108370670.7655868216741350.617206589162933
540.4807338777705760.9614677555411520.519266122229424
550.4373395310040320.8746790620080640.562660468995968
560.3964754095083850.792950819016770.603524590491615
570.3981126012975920.7962252025951830.601887398702408
580.5668566963244760.8662866073510480.433143303675524
590.5506833578693350.8986332842613310.449316642130665
600.5177743256091380.9644513487817230.482225674390862
610.6289853543696160.7420292912607690.371014645630384
620.6475862136135290.7048275727729420.352413786386471
630.7689144500564160.4621710998871670.231085549943584
640.7360151810070750.527969637985850.263984818992925
650.7394783669053620.5210432661892770.260521633094638
660.710288592354210.5794228152915790.289711407645790
670.703076698757610.593846602484780.29692330124239
680.6863860667741330.6272278664517330.313613933225866
690.724629385445340.5507412291093190.275370614554659
700.71977939859790.5604412028041990.280220601402099
710.7411641415462440.5176717169075120.258835858453756
720.7343736595028540.5312526809942920.265626340497146
730.7310086368421130.5379827263157750.268991363157887
740.6900668579167230.6198662841665540.309933142083277
750.6474861446590.7050277106820.352513855341
760.6045693544307210.7908612911385580.395430645569279
770.5594723002854790.8810553994290430.440527699714521
780.5132579566902620.9734840866194760.486742043309738
790.4696173508263380.9392347016526770.530382649173662
800.5108431534762860.9783136930474270.489156846523714
810.5080700693900290.9838598612199420.491929930609971
820.4631494138334570.9262988276669140.536850586166543
830.461305693702320.922611387404640.53869430629768
840.4239680094621620.8479360189243240.576031990537838
850.3774029635409730.7548059270819470.622597036459027
860.3442258952231470.6884517904462930.655774104776853
870.3245607940526010.6491215881052020.675439205947399
880.3047770265612210.6095540531224420.695222973438779
890.2659566624299880.5319133248599750.734043337570012
900.2335834070392580.4671668140785150.766416592960742
910.2094848760679570.4189697521359140.790515123932043
920.1789469054208610.3578938108417210.82105309457914
930.1538391377264590.3076782754529170.846160862273541
940.1769989758180130.3539979516360260.823001024181987
950.1475310937476410.2950621874952820.85246890625236
960.1220847649060540.2441695298121080.877915235093946
970.1038102917651840.2076205835303680.896189708234816
980.08559000343510030.1711800068702010.9144099965649
990.07332538003756250.1466507600751250.926674619962437
1000.06392762446143590.1278552489228720.936072375538564
1010.07813956489861590.1562791297972320.921860435101384
1020.07036688017634070.1407337603526810.929633119823659
1030.0655125874912550.131025174982510.934487412508745
1040.05401656559422510.1080331311884500.945983434405775
1050.05023357714684720.1004671542936940.949766422853153
1060.03837731753907410.07675463507814820.961622682460926
1070.03063702185634220.06127404371268430.969362978143658
1080.02496329634745090.04992659269490180.97503670365255
1090.02205050363336210.04410100726672420.977949496366638
1100.02885426487858740.05770852975717470.971145735121413
1110.03136979616872180.06273959233744360.968630203831278
1120.0333252484727740.0666504969455480.966674751527226
1130.02509753639079240.05019507278158480.974902463609208
1140.01843881541223380.03687763082446760.981561184587766
1150.04371740851914950.0874348170382990.95628259148085
1160.03296036374573040.06592072749146090.96703963625427
1170.03925213212652680.07850426425305370.960747867873473
1180.02972644190423380.05945288380846750.970273558095766
1190.02352475152132490.04704950304264980.976475248478675
1200.01669263781746240.03338527563492470.983307362182538
1210.0624068053902720.1248136107805440.937593194609728
1220.05184913692717310.1036982738543460.948150863072827
1230.04225693086958310.08451386173916630.957743069130417
1240.03793398759826330.07586797519652650.962066012401737
1250.02711436155000800.05422872310001610.972885638449992
1260.07632724550376490.1526544910075300.923672754496235
1270.06201954390288110.1240390878057620.937980456097119
1280.08681371802920530.1736274360584110.913186281970795
1290.1928331914471990.3856663828943980.8071668085528
1300.1509531515027910.3019063030055810.84904684849721
1310.1217863614741640.2435727229483280.878213638525836
1320.5948326205853290.8103347588293430.405167379414671
1330.5212879363651760.9574241272696490.478712063634824
1340.4451298865158270.8902597730316550.554870113484173
1350.3643537180473310.7287074360946610.63564628195267
1360.3895361689730820.7790723379461630.610463831026918
1370.3137134513735480.6274269027470960.686286548626452
1380.3644801430780960.7289602861561920.635519856921904
1390.496490543603130.992981087206260.50350945639687
1400.4295231932444930.8590463864889850.570476806755507
1410.5014188430338810.9971623139322390.498581156966119
1420.3916305003876870.7832610007753740.608369499612313
1430.2668675710307820.5337351420615630.733132428969218
1440.2922326967238760.5844653934477520.707767303276124

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.863877656553283 & 0.272244686893435 & 0.136122343446717 \tabularnewline
11 & 0.796478096381642 & 0.407043807236715 & 0.203521903618358 \tabularnewline
12 & 0.690294275320577 & 0.619411449358846 & 0.309705724679423 \tabularnewline
13 & 0.580540385911347 & 0.838919228177305 & 0.419459614088653 \tabularnewline
14 & 0.629926030131743 & 0.740147939736515 & 0.370073969868257 \tabularnewline
15 & 0.532139055859901 & 0.935721888280198 & 0.467860944140099 \tabularnewline
16 & 0.43554139051357 & 0.87108278102714 & 0.56445860948643 \tabularnewline
17 & 0.345595010638954 & 0.691190021277908 & 0.654404989361046 \tabularnewline
18 & 0.270899697501195 & 0.541799395002389 & 0.729100302498805 \tabularnewline
19 & 0.258820823963766 & 0.517641647927532 & 0.741179176036234 \tabularnewline
20 & 0.195788378813396 & 0.391576757626792 & 0.804211621186604 \tabularnewline
21 & 0.16245638947496 & 0.32491277894992 & 0.83754361052504 \tabularnewline
22 & 0.126602663038369 & 0.253205326076738 & 0.873397336961631 \tabularnewline
23 & 0.190759356080609 & 0.381518712161218 & 0.809240643919391 \tabularnewline
24 & 0.169820947383655 & 0.339641894767309 & 0.830179052616346 \tabularnewline
25 & 0.134017372335032 & 0.268034744670064 & 0.865982627664968 \tabularnewline
26 & 0.143859000387219 & 0.287718000774439 & 0.85614099961278 \tabularnewline
27 & 0.112927067001129 & 0.225854134002258 & 0.88707293299887 \tabularnewline
28 & 0.108785590988613 & 0.217571181977225 & 0.891214409011387 \tabularnewline
29 & 0.0924096285446096 & 0.184819257089219 & 0.90759037145539 \tabularnewline
30 & 0.0704941604535975 & 0.140988320907195 & 0.929505839546402 \tabularnewline
31 & 0.0731564044768805 & 0.146312808953761 & 0.92684359552312 \tabularnewline
32 & 0.0544632467898823 & 0.108926493579765 & 0.945536753210118 \tabularnewline
33 & 0.0385377559104195 & 0.077075511820839 & 0.96146224408958 \tabularnewline
34 & 0.0299060393666153 & 0.0598120787332306 & 0.970093960633385 \tabularnewline
35 & 0.0266294879467879 & 0.0532589758935757 & 0.973370512053212 \tabularnewline
36 & 0.0427563541451614 & 0.0855127082903227 & 0.957243645854839 \tabularnewline
37 & 0.0349839778958687 & 0.0699679557917374 & 0.965016022104131 \tabularnewline
38 & 0.0450004862980937 & 0.0900009725961873 & 0.954999513701906 \tabularnewline
39 & 0.0325052387392796 & 0.0650104774785592 & 0.96749476126072 \tabularnewline
40 & 0.0271364056437966 & 0.0542728112875933 & 0.972863594356203 \tabularnewline
41 & 0.0190410860306250 & 0.0380821720612501 & 0.980958913969375 \tabularnewline
42 & 0.0148209475575167 & 0.0296418951150334 & 0.985179052442483 \tabularnewline
43 & 0.0101606067434809 & 0.0203212134869619 & 0.98983939325652 \tabularnewline
44 & 0.00684277041151251 & 0.0136855408230250 & 0.993157229588488 \tabularnewline
45 & 0.366406362970681 & 0.732812725941362 & 0.633593637029319 \tabularnewline
46 & 0.347625068917505 & 0.69525013783501 & 0.652374931082495 \tabularnewline
47 & 0.383498602540241 & 0.766997205080482 & 0.616501397459759 \tabularnewline
48 & 0.356600004422329 & 0.713200008844658 & 0.643399995577671 \tabularnewline
49 & 0.344971288861002 & 0.689942577722005 & 0.655028711138998 \tabularnewline
50 & 0.320082798696386 & 0.640165597392771 & 0.679917201303614 \tabularnewline
51 & 0.303074753808899 & 0.606149507617798 & 0.696925246191101 \tabularnewline
52 & 0.259646775709827 & 0.519293551419654 & 0.740353224290173 \tabularnewline
53 & 0.382793410837067 & 0.765586821674135 & 0.617206589162933 \tabularnewline
54 & 0.480733877770576 & 0.961467755541152 & 0.519266122229424 \tabularnewline
55 & 0.437339531004032 & 0.874679062008064 & 0.562660468995968 \tabularnewline
56 & 0.396475409508385 & 0.79295081901677 & 0.603524590491615 \tabularnewline
57 & 0.398112601297592 & 0.796225202595183 & 0.601887398702408 \tabularnewline
58 & 0.566856696324476 & 0.866286607351048 & 0.433143303675524 \tabularnewline
59 & 0.550683357869335 & 0.898633284261331 & 0.449316642130665 \tabularnewline
60 & 0.517774325609138 & 0.964451348781723 & 0.482225674390862 \tabularnewline
61 & 0.628985354369616 & 0.742029291260769 & 0.371014645630384 \tabularnewline
62 & 0.647586213613529 & 0.704827572772942 & 0.352413786386471 \tabularnewline
63 & 0.768914450056416 & 0.462171099887167 & 0.231085549943584 \tabularnewline
64 & 0.736015181007075 & 0.52796963798585 & 0.263984818992925 \tabularnewline
65 & 0.739478366905362 & 0.521043266189277 & 0.260521633094638 \tabularnewline
66 & 0.71028859235421 & 0.579422815291579 & 0.289711407645790 \tabularnewline
67 & 0.70307669875761 & 0.59384660248478 & 0.29692330124239 \tabularnewline
68 & 0.686386066774133 & 0.627227866451733 & 0.313613933225866 \tabularnewline
69 & 0.72462938544534 & 0.550741229109319 & 0.275370614554659 \tabularnewline
70 & 0.7197793985979 & 0.560441202804199 & 0.280220601402099 \tabularnewline
71 & 0.741164141546244 & 0.517671716907512 & 0.258835858453756 \tabularnewline
72 & 0.734373659502854 & 0.531252680994292 & 0.265626340497146 \tabularnewline
73 & 0.731008636842113 & 0.537982726315775 & 0.268991363157887 \tabularnewline
74 & 0.690066857916723 & 0.619866284166554 & 0.309933142083277 \tabularnewline
75 & 0.647486144659 & 0.705027710682 & 0.352513855341 \tabularnewline
76 & 0.604569354430721 & 0.790861291138558 & 0.395430645569279 \tabularnewline
77 & 0.559472300285479 & 0.881055399429043 & 0.440527699714521 \tabularnewline
78 & 0.513257956690262 & 0.973484086619476 & 0.486742043309738 \tabularnewline
79 & 0.469617350826338 & 0.939234701652677 & 0.530382649173662 \tabularnewline
80 & 0.510843153476286 & 0.978313693047427 & 0.489156846523714 \tabularnewline
81 & 0.508070069390029 & 0.983859861219942 & 0.491929930609971 \tabularnewline
82 & 0.463149413833457 & 0.926298827666914 & 0.536850586166543 \tabularnewline
83 & 0.46130569370232 & 0.92261138740464 & 0.53869430629768 \tabularnewline
84 & 0.423968009462162 & 0.847936018924324 & 0.576031990537838 \tabularnewline
85 & 0.377402963540973 & 0.754805927081947 & 0.622597036459027 \tabularnewline
86 & 0.344225895223147 & 0.688451790446293 & 0.655774104776853 \tabularnewline
87 & 0.324560794052601 & 0.649121588105202 & 0.675439205947399 \tabularnewline
88 & 0.304777026561221 & 0.609554053122442 & 0.695222973438779 \tabularnewline
89 & 0.265956662429988 & 0.531913324859975 & 0.734043337570012 \tabularnewline
90 & 0.233583407039258 & 0.467166814078515 & 0.766416592960742 \tabularnewline
91 & 0.209484876067957 & 0.418969752135914 & 0.790515123932043 \tabularnewline
92 & 0.178946905420861 & 0.357893810841721 & 0.82105309457914 \tabularnewline
93 & 0.153839137726459 & 0.307678275452917 & 0.846160862273541 \tabularnewline
94 & 0.176998975818013 & 0.353997951636026 & 0.823001024181987 \tabularnewline
95 & 0.147531093747641 & 0.295062187495282 & 0.85246890625236 \tabularnewline
96 & 0.122084764906054 & 0.244169529812108 & 0.877915235093946 \tabularnewline
97 & 0.103810291765184 & 0.207620583530368 & 0.896189708234816 \tabularnewline
98 & 0.0855900034351003 & 0.171180006870201 & 0.9144099965649 \tabularnewline
99 & 0.0733253800375625 & 0.146650760075125 & 0.926674619962437 \tabularnewline
100 & 0.0639276244614359 & 0.127855248922872 & 0.936072375538564 \tabularnewline
101 & 0.0781395648986159 & 0.156279129797232 & 0.921860435101384 \tabularnewline
102 & 0.0703668801763407 & 0.140733760352681 & 0.929633119823659 \tabularnewline
103 & 0.065512587491255 & 0.13102517498251 & 0.934487412508745 \tabularnewline
104 & 0.0540165655942251 & 0.108033131188450 & 0.945983434405775 \tabularnewline
105 & 0.0502335771468472 & 0.100467154293694 & 0.949766422853153 \tabularnewline
106 & 0.0383773175390741 & 0.0767546350781482 & 0.961622682460926 \tabularnewline
107 & 0.0306370218563422 & 0.0612740437126843 & 0.969362978143658 \tabularnewline
108 & 0.0249632963474509 & 0.0499265926949018 & 0.97503670365255 \tabularnewline
109 & 0.0220505036333621 & 0.0441010072667242 & 0.977949496366638 \tabularnewline
110 & 0.0288542648785874 & 0.0577085297571747 & 0.971145735121413 \tabularnewline
111 & 0.0313697961687218 & 0.0627395923374436 & 0.968630203831278 \tabularnewline
112 & 0.033325248472774 & 0.066650496945548 & 0.966674751527226 \tabularnewline
113 & 0.0250975363907924 & 0.0501950727815848 & 0.974902463609208 \tabularnewline
114 & 0.0184388154122338 & 0.0368776308244676 & 0.981561184587766 \tabularnewline
115 & 0.0437174085191495 & 0.087434817038299 & 0.95628259148085 \tabularnewline
116 & 0.0329603637457304 & 0.0659207274914609 & 0.96703963625427 \tabularnewline
117 & 0.0392521321265268 & 0.0785042642530537 & 0.960747867873473 \tabularnewline
118 & 0.0297264419042338 & 0.0594528838084675 & 0.970273558095766 \tabularnewline
119 & 0.0235247515213249 & 0.0470495030426498 & 0.976475248478675 \tabularnewline
120 & 0.0166926378174624 & 0.0333852756349247 & 0.983307362182538 \tabularnewline
121 & 0.062406805390272 & 0.124813610780544 & 0.937593194609728 \tabularnewline
122 & 0.0518491369271731 & 0.103698273854346 & 0.948150863072827 \tabularnewline
123 & 0.0422569308695831 & 0.0845138617391663 & 0.957743069130417 \tabularnewline
124 & 0.0379339875982633 & 0.0758679751965265 & 0.962066012401737 \tabularnewline
125 & 0.0271143615500080 & 0.0542287231000161 & 0.972885638449992 \tabularnewline
126 & 0.0763272455037649 & 0.152654491007530 & 0.923672754496235 \tabularnewline
127 & 0.0620195439028811 & 0.124039087805762 & 0.937980456097119 \tabularnewline
128 & 0.0868137180292053 & 0.173627436058411 & 0.913186281970795 \tabularnewline
129 & 0.192833191447199 & 0.385666382894398 & 0.8071668085528 \tabularnewline
130 & 0.150953151502791 & 0.301906303005581 & 0.84904684849721 \tabularnewline
131 & 0.121786361474164 & 0.243572722948328 & 0.878213638525836 \tabularnewline
132 & 0.594832620585329 & 0.810334758829343 & 0.405167379414671 \tabularnewline
133 & 0.521287936365176 & 0.957424127269649 & 0.478712063634824 \tabularnewline
134 & 0.445129886515827 & 0.890259773031655 & 0.554870113484173 \tabularnewline
135 & 0.364353718047331 & 0.728707436094661 & 0.63564628195267 \tabularnewline
136 & 0.389536168973082 & 0.779072337946163 & 0.610463831026918 \tabularnewline
137 & 0.313713451373548 & 0.627426902747096 & 0.686286548626452 \tabularnewline
138 & 0.364480143078096 & 0.728960286156192 & 0.635519856921904 \tabularnewline
139 & 0.49649054360313 & 0.99298108720626 & 0.50350945639687 \tabularnewline
140 & 0.429523193244493 & 0.859046386488985 & 0.570476806755507 \tabularnewline
141 & 0.501418843033881 & 0.997162313932239 & 0.498581156966119 \tabularnewline
142 & 0.391630500387687 & 0.783261000775374 & 0.608369499612313 \tabularnewline
143 & 0.266867571030782 & 0.533735142061563 & 0.733132428969218 \tabularnewline
144 & 0.292232696723876 & 0.584465393447752 & 0.707767303276124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&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]0.863877656553283[/C][C]0.272244686893435[/C][C]0.136122343446717[/C][/ROW]
[ROW][C]11[/C][C]0.796478096381642[/C][C]0.407043807236715[/C][C]0.203521903618358[/C][/ROW]
[ROW][C]12[/C][C]0.690294275320577[/C][C]0.619411449358846[/C][C]0.309705724679423[/C][/ROW]
[ROW][C]13[/C][C]0.580540385911347[/C][C]0.838919228177305[/C][C]0.419459614088653[/C][/ROW]
[ROW][C]14[/C][C]0.629926030131743[/C][C]0.740147939736515[/C][C]0.370073969868257[/C][/ROW]
[ROW][C]15[/C][C]0.532139055859901[/C][C]0.935721888280198[/C][C]0.467860944140099[/C][/ROW]
[ROW][C]16[/C][C]0.43554139051357[/C][C]0.87108278102714[/C][C]0.56445860948643[/C][/ROW]
[ROW][C]17[/C][C]0.345595010638954[/C][C]0.691190021277908[/C][C]0.654404989361046[/C][/ROW]
[ROW][C]18[/C][C]0.270899697501195[/C][C]0.541799395002389[/C][C]0.729100302498805[/C][/ROW]
[ROW][C]19[/C][C]0.258820823963766[/C][C]0.517641647927532[/C][C]0.741179176036234[/C][/ROW]
[ROW][C]20[/C][C]0.195788378813396[/C][C]0.391576757626792[/C][C]0.804211621186604[/C][/ROW]
[ROW][C]21[/C][C]0.16245638947496[/C][C]0.32491277894992[/C][C]0.83754361052504[/C][/ROW]
[ROW][C]22[/C][C]0.126602663038369[/C][C]0.253205326076738[/C][C]0.873397336961631[/C][/ROW]
[ROW][C]23[/C][C]0.190759356080609[/C][C]0.381518712161218[/C][C]0.809240643919391[/C][/ROW]
[ROW][C]24[/C][C]0.169820947383655[/C][C]0.339641894767309[/C][C]0.830179052616346[/C][/ROW]
[ROW][C]25[/C][C]0.134017372335032[/C][C]0.268034744670064[/C][C]0.865982627664968[/C][/ROW]
[ROW][C]26[/C][C]0.143859000387219[/C][C]0.287718000774439[/C][C]0.85614099961278[/C][/ROW]
[ROW][C]27[/C][C]0.112927067001129[/C][C]0.225854134002258[/C][C]0.88707293299887[/C][/ROW]
[ROW][C]28[/C][C]0.108785590988613[/C][C]0.217571181977225[/C][C]0.891214409011387[/C][/ROW]
[ROW][C]29[/C][C]0.0924096285446096[/C][C]0.184819257089219[/C][C]0.90759037145539[/C][/ROW]
[ROW][C]30[/C][C]0.0704941604535975[/C][C]0.140988320907195[/C][C]0.929505839546402[/C][/ROW]
[ROW][C]31[/C][C]0.0731564044768805[/C][C]0.146312808953761[/C][C]0.92684359552312[/C][/ROW]
[ROW][C]32[/C][C]0.0544632467898823[/C][C]0.108926493579765[/C][C]0.945536753210118[/C][/ROW]
[ROW][C]33[/C][C]0.0385377559104195[/C][C]0.077075511820839[/C][C]0.96146224408958[/C][/ROW]
[ROW][C]34[/C][C]0.0299060393666153[/C][C]0.0598120787332306[/C][C]0.970093960633385[/C][/ROW]
[ROW][C]35[/C][C]0.0266294879467879[/C][C]0.0532589758935757[/C][C]0.973370512053212[/C][/ROW]
[ROW][C]36[/C][C]0.0427563541451614[/C][C]0.0855127082903227[/C][C]0.957243645854839[/C][/ROW]
[ROW][C]37[/C][C]0.0349839778958687[/C][C]0.0699679557917374[/C][C]0.965016022104131[/C][/ROW]
[ROW][C]38[/C][C]0.0450004862980937[/C][C]0.0900009725961873[/C][C]0.954999513701906[/C][/ROW]
[ROW][C]39[/C][C]0.0325052387392796[/C][C]0.0650104774785592[/C][C]0.96749476126072[/C][/ROW]
[ROW][C]40[/C][C]0.0271364056437966[/C][C]0.0542728112875933[/C][C]0.972863594356203[/C][/ROW]
[ROW][C]41[/C][C]0.0190410860306250[/C][C]0.0380821720612501[/C][C]0.980958913969375[/C][/ROW]
[ROW][C]42[/C][C]0.0148209475575167[/C][C]0.0296418951150334[/C][C]0.985179052442483[/C][/ROW]
[ROW][C]43[/C][C]0.0101606067434809[/C][C]0.0203212134869619[/C][C]0.98983939325652[/C][/ROW]
[ROW][C]44[/C][C]0.00684277041151251[/C][C]0.0136855408230250[/C][C]0.993157229588488[/C][/ROW]
[ROW][C]45[/C][C]0.366406362970681[/C][C]0.732812725941362[/C][C]0.633593637029319[/C][/ROW]
[ROW][C]46[/C][C]0.347625068917505[/C][C]0.69525013783501[/C][C]0.652374931082495[/C][/ROW]
[ROW][C]47[/C][C]0.383498602540241[/C][C]0.766997205080482[/C][C]0.616501397459759[/C][/ROW]
[ROW][C]48[/C][C]0.356600004422329[/C][C]0.713200008844658[/C][C]0.643399995577671[/C][/ROW]
[ROW][C]49[/C][C]0.344971288861002[/C][C]0.689942577722005[/C][C]0.655028711138998[/C][/ROW]
[ROW][C]50[/C][C]0.320082798696386[/C][C]0.640165597392771[/C][C]0.679917201303614[/C][/ROW]
[ROW][C]51[/C][C]0.303074753808899[/C][C]0.606149507617798[/C][C]0.696925246191101[/C][/ROW]
[ROW][C]52[/C][C]0.259646775709827[/C][C]0.519293551419654[/C][C]0.740353224290173[/C][/ROW]
[ROW][C]53[/C][C]0.382793410837067[/C][C]0.765586821674135[/C][C]0.617206589162933[/C][/ROW]
[ROW][C]54[/C][C]0.480733877770576[/C][C]0.961467755541152[/C][C]0.519266122229424[/C][/ROW]
[ROW][C]55[/C][C]0.437339531004032[/C][C]0.874679062008064[/C][C]0.562660468995968[/C][/ROW]
[ROW][C]56[/C][C]0.396475409508385[/C][C]0.79295081901677[/C][C]0.603524590491615[/C][/ROW]
[ROW][C]57[/C][C]0.398112601297592[/C][C]0.796225202595183[/C][C]0.601887398702408[/C][/ROW]
[ROW][C]58[/C][C]0.566856696324476[/C][C]0.866286607351048[/C][C]0.433143303675524[/C][/ROW]
[ROW][C]59[/C][C]0.550683357869335[/C][C]0.898633284261331[/C][C]0.449316642130665[/C][/ROW]
[ROW][C]60[/C][C]0.517774325609138[/C][C]0.964451348781723[/C][C]0.482225674390862[/C][/ROW]
[ROW][C]61[/C][C]0.628985354369616[/C][C]0.742029291260769[/C][C]0.371014645630384[/C][/ROW]
[ROW][C]62[/C][C]0.647586213613529[/C][C]0.704827572772942[/C][C]0.352413786386471[/C][/ROW]
[ROW][C]63[/C][C]0.768914450056416[/C][C]0.462171099887167[/C][C]0.231085549943584[/C][/ROW]
[ROW][C]64[/C][C]0.736015181007075[/C][C]0.52796963798585[/C][C]0.263984818992925[/C][/ROW]
[ROW][C]65[/C][C]0.739478366905362[/C][C]0.521043266189277[/C][C]0.260521633094638[/C][/ROW]
[ROW][C]66[/C][C]0.71028859235421[/C][C]0.579422815291579[/C][C]0.289711407645790[/C][/ROW]
[ROW][C]67[/C][C]0.70307669875761[/C][C]0.59384660248478[/C][C]0.29692330124239[/C][/ROW]
[ROW][C]68[/C][C]0.686386066774133[/C][C]0.627227866451733[/C][C]0.313613933225866[/C][/ROW]
[ROW][C]69[/C][C]0.72462938544534[/C][C]0.550741229109319[/C][C]0.275370614554659[/C][/ROW]
[ROW][C]70[/C][C]0.7197793985979[/C][C]0.560441202804199[/C][C]0.280220601402099[/C][/ROW]
[ROW][C]71[/C][C]0.741164141546244[/C][C]0.517671716907512[/C][C]0.258835858453756[/C][/ROW]
[ROW][C]72[/C][C]0.734373659502854[/C][C]0.531252680994292[/C][C]0.265626340497146[/C][/ROW]
[ROW][C]73[/C][C]0.731008636842113[/C][C]0.537982726315775[/C][C]0.268991363157887[/C][/ROW]
[ROW][C]74[/C][C]0.690066857916723[/C][C]0.619866284166554[/C][C]0.309933142083277[/C][/ROW]
[ROW][C]75[/C][C]0.647486144659[/C][C]0.705027710682[/C][C]0.352513855341[/C][/ROW]
[ROW][C]76[/C][C]0.604569354430721[/C][C]0.790861291138558[/C][C]0.395430645569279[/C][/ROW]
[ROW][C]77[/C][C]0.559472300285479[/C][C]0.881055399429043[/C][C]0.440527699714521[/C][/ROW]
[ROW][C]78[/C][C]0.513257956690262[/C][C]0.973484086619476[/C][C]0.486742043309738[/C][/ROW]
[ROW][C]79[/C][C]0.469617350826338[/C][C]0.939234701652677[/C][C]0.530382649173662[/C][/ROW]
[ROW][C]80[/C][C]0.510843153476286[/C][C]0.978313693047427[/C][C]0.489156846523714[/C][/ROW]
[ROW][C]81[/C][C]0.508070069390029[/C][C]0.983859861219942[/C][C]0.491929930609971[/C][/ROW]
[ROW][C]82[/C][C]0.463149413833457[/C][C]0.926298827666914[/C][C]0.536850586166543[/C][/ROW]
[ROW][C]83[/C][C]0.46130569370232[/C][C]0.92261138740464[/C][C]0.53869430629768[/C][/ROW]
[ROW][C]84[/C][C]0.423968009462162[/C][C]0.847936018924324[/C][C]0.576031990537838[/C][/ROW]
[ROW][C]85[/C][C]0.377402963540973[/C][C]0.754805927081947[/C][C]0.622597036459027[/C][/ROW]
[ROW][C]86[/C][C]0.344225895223147[/C][C]0.688451790446293[/C][C]0.655774104776853[/C][/ROW]
[ROW][C]87[/C][C]0.324560794052601[/C][C]0.649121588105202[/C][C]0.675439205947399[/C][/ROW]
[ROW][C]88[/C][C]0.304777026561221[/C][C]0.609554053122442[/C][C]0.695222973438779[/C][/ROW]
[ROW][C]89[/C][C]0.265956662429988[/C][C]0.531913324859975[/C][C]0.734043337570012[/C][/ROW]
[ROW][C]90[/C][C]0.233583407039258[/C][C]0.467166814078515[/C][C]0.766416592960742[/C][/ROW]
[ROW][C]91[/C][C]0.209484876067957[/C][C]0.418969752135914[/C][C]0.790515123932043[/C][/ROW]
[ROW][C]92[/C][C]0.178946905420861[/C][C]0.357893810841721[/C][C]0.82105309457914[/C][/ROW]
[ROW][C]93[/C][C]0.153839137726459[/C][C]0.307678275452917[/C][C]0.846160862273541[/C][/ROW]
[ROW][C]94[/C][C]0.176998975818013[/C][C]0.353997951636026[/C][C]0.823001024181987[/C][/ROW]
[ROW][C]95[/C][C]0.147531093747641[/C][C]0.295062187495282[/C][C]0.85246890625236[/C][/ROW]
[ROW][C]96[/C][C]0.122084764906054[/C][C]0.244169529812108[/C][C]0.877915235093946[/C][/ROW]
[ROW][C]97[/C][C]0.103810291765184[/C][C]0.207620583530368[/C][C]0.896189708234816[/C][/ROW]
[ROW][C]98[/C][C]0.0855900034351003[/C][C]0.171180006870201[/C][C]0.9144099965649[/C][/ROW]
[ROW][C]99[/C][C]0.0733253800375625[/C][C]0.146650760075125[/C][C]0.926674619962437[/C][/ROW]
[ROW][C]100[/C][C]0.0639276244614359[/C][C]0.127855248922872[/C][C]0.936072375538564[/C][/ROW]
[ROW][C]101[/C][C]0.0781395648986159[/C][C]0.156279129797232[/C][C]0.921860435101384[/C][/ROW]
[ROW][C]102[/C][C]0.0703668801763407[/C][C]0.140733760352681[/C][C]0.929633119823659[/C][/ROW]
[ROW][C]103[/C][C]0.065512587491255[/C][C]0.13102517498251[/C][C]0.934487412508745[/C][/ROW]
[ROW][C]104[/C][C]0.0540165655942251[/C][C]0.108033131188450[/C][C]0.945983434405775[/C][/ROW]
[ROW][C]105[/C][C]0.0502335771468472[/C][C]0.100467154293694[/C][C]0.949766422853153[/C][/ROW]
[ROW][C]106[/C][C]0.0383773175390741[/C][C]0.0767546350781482[/C][C]0.961622682460926[/C][/ROW]
[ROW][C]107[/C][C]0.0306370218563422[/C][C]0.0612740437126843[/C][C]0.969362978143658[/C][/ROW]
[ROW][C]108[/C][C]0.0249632963474509[/C][C]0.0499265926949018[/C][C]0.97503670365255[/C][/ROW]
[ROW][C]109[/C][C]0.0220505036333621[/C][C]0.0441010072667242[/C][C]0.977949496366638[/C][/ROW]
[ROW][C]110[/C][C]0.0288542648785874[/C][C]0.0577085297571747[/C][C]0.971145735121413[/C][/ROW]
[ROW][C]111[/C][C]0.0313697961687218[/C][C]0.0627395923374436[/C][C]0.968630203831278[/C][/ROW]
[ROW][C]112[/C][C]0.033325248472774[/C][C]0.066650496945548[/C][C]0.966674751527226[/C][/ROW]
[ROW][C]113[/C][C]0.0250975363907924[/C][C]0.0501950727815848[/C][C]0.974902463609208[/C][/ROW]
[ROW][C]114[/C][C]0.0184388154122338[/C][C]0.0368776308244676[/C][C]0.981561184587766[/C][/ROW]
[ROW][C]115[/C][C]0.0437174085191495[/C][C]0.087434817038299[/C][C]0.95628259148085[/C][/ROW]
[ROW][C]116[/C][C]0.0329603637457304[/C][C]0.0659207274914609[/C][C]0.96703963625427[/C][/ROW]
[ROW][C]117[/C][C]0.0392521321265268[/C][C]0.0785042642530537[/C][C]0.960747867873473[/C][/ROW]
[ROW][C]118[/C][C]0.0297264419042338[/C][C]0.0594528838084675[/C][C]0.970273558095766[/C][/ROW]
[ROW][C]119[/C][C]0.0235247515213249[/C][C]0.0470495030426498[/C][C]0.976475248478675[/C][/ROW]
[ROW][C]120[/C][C]0.0166926378174624[/C][C]0.0333852756349247[/C][C]0.983307362182538[/C][/ROW]
[ROW][C]121[/C][C]0.062406805390272[/C][C]0.124813610780544[/C][C]0.937593194609728[/C][/ROW]
[ROW][C]122[/C][C]0.0518491369271731[/C][C]0.103698273854346[/C][C]0.948150863072827[/C][/ROW]
[ROW][C]123[/C][C]0.0422569308695831[/C][C]0.0845138617391663[/C][C]0.957743069130417[/C][/ROW]
[ROW][C]124[/C][C]0.0379339875982633[/C][C]0.0758679751965265[/C][C]0.962066012401737[/C][/ROW]
[ROW][C]125[/C][C]0.0271143615500080[/C][C]0.0542287231000161[/C][C]0.972885638449992[/C][/ROW]
[ROW][C]126[/C][C]0.0763272455037649[/C][C]0.152654491007530[/C][C]0.923672754496235[/C][/ROW]
[ROW][C]127[/C][C]0.0620195439028811[/C][C]0.124039087805762[/C][C]0.937980456097119[/C][/ROW]
[ROW][C]128[/C][C]0.0868137180292053[/C][C]0.173627436058411[/C][C]0.913186281970795[/C][/ROW]
[ROW][C]129[/C][C]0.192833191447199[/C][C]0.385666382894398[/C][C]0.8071668085528[/C][/ROW]
[ROW][C]130[/C][C]0.150953151502791[/C][C]0.301906303005581[/C][C]0.84904684849721[/C][/ROW]
[ROW][C]131[/C][C]0.121786361474164[/C][C]0.243572722948328[/C][C]0.878213638525836[/C][/ROW]
[ROW][C]132[/C][C]0.594832620585329[/C][C]0.810334758829343[/C][C]0.405167379414671[/C][/ROW]
[ROW][C]133[/C][C]0.521287936365176[/C][C]0.957424127269649[/C][C]0.478712063634824[/C][/ROW]
[ROW][C]134[/C][C]0.445129886515827[/C][C]0.890259773031655[/C][C]0.554870113484173[/C][/ROW]
[ROW][C]135[/C][C]0.364353718047331[/C][C]0.728707436094661[/C][C]0.63564628195267[/C][/ROW]
[ROW][C]136[/C][C]0.389536168973082[/C][C]0.779072337946163[/C][C]0.610463831026918[/C][/ROW]
[ROW][C]137[/C][C]0.313713451373548[/C][C]0.627426902747096[/C][C]0.686286548626452[/C][/ROW]
[ROW][C]138[/C][C]0.364480143078096[/C][C]0.728960286156192[/C][C]0.635519856921904[/C][/ROW]
[ROW][C]139[/C][C]0.49649054360313[/C][C]0.99298108720626[/C][C]0.50350945639687[/C][/ROW]
[ROW][C]140[/C][C]0.429523193244493[/C][C]0.859046386488985[/C][C]0.570476806755507[/C][/ROW]
[ROW][C]141[/C][C]0.501418843033881[/C][C]0.997162313932239[/C][C]0.498581156966119[/C][/ROW]
[ROW][C]142[/C][C]0.391630500387687[/C][C]0.783261000775374[/C][C]0.608369499612313[/C][/ROW]
[ROW][C]143[/C][C]0.266867571030782[/C][C]0.533735142061563[/C][C]0.733132428969218[/C][/ROW]
[ROW][C]144[/C][C]0.292232696723876[/C][C]0.584465393447752[/C][C]0.707767303276124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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
100.8638776565532830.2722446868934350.136122343446717
110.7964780963816420.4070438072367150.203521903618358
120.6902942753205770.6194114493588460.309705724679423
130.5805403859113470.8389192281773050.419459614088653
140.6299260301317430.7401479397365150.370073969868257
150.5321390558599010.9357218882801980.467860944140099
160.435541390513570.871082781027140.56445860948643
170.3455950106389540.6911900212779080.654404989361046
180.2708996975011950.5417993950023890.729100302498805
190.2588208239637660.5176416479275320.741179176036234
200.1957883788133960.3915767576267920.804211621186604
210.162456389474960.324912778949920.83754361052504
220.1266026630383690.2532053260767380.873397336961631
230.1907593560806090.3815187121612180.809240643919391
240.1698209473836550.3396418947673090.830179052616346
250.1340173723350320.2680347446700640.865982627664968
260.1438590003872190.2877180007744390.85614099961278
270.1129270670011290.2258541340022580.88707293299887
280.1087855909886130.2175711819772250.891214409011387
290.09240962854460960.1848192570892190.90759037145539
300.07049416045359750.1409883209071950.929505839546402
310.07315640447688050.1463128089537610.92684359552312
320.05446324678988230.1089264935797650.945536753210118
330.03853775591041950.0770755118208390.96146224408958
340.02990603936661530.05981207873323060.970093960633385
350.02662948794678790.05325897589357570.973370512053212
360.04275635414516140.08551270829032270.957243645854839
370.03498397789586870.06996795579173740.965016022104131
380.04500048629809370.09000097259618730.954999513701906
390.03250523873927960.06501047747855920.96749476126072
400.02713640564379660.05427281128759330.972863594356203
410.01904108603062500.03808217206125010.980958913969375
420.01482094755751670.02964189511503340.985179052442483
430.01016060674348090.02032121348696190.98983939325652
440.006842770411512510.01368554082302500.993157229588488
450.3664063629706810.7328127259413620.633593637029319
460.3476250689175050.695250137835010.652374931082495
470.3834986025402410.7669972050804820.616501397459759
480.3566000044223290.7132000088446580.643399995577671
490.3449712888610020.6899425777220050.655028711138998
500.3200827986963860.6401655973927710.679917201303614
510.3030747538088990.6061495076177980.696925246191101
520.2596467757098270.5192935514196540.740353224290173
530.3827934108370670.7655868216741350.617206589162933
540.4807338777705760.9614677555411520.519266122229424
550.4373395310040320.8746790620080640.562660468995968
560.3964754095083850.792950819016770.603524590491615
570.3981126012975920.7962252025951830.601887398702408
580.5668566963244760.8662866073510480.433143303675524
590.5506833578693350.8986332842613310.449316642130665
600.5177743256091380.9644513487817230.482225674390862
610.6289853543696160.7420292912607690.371014645630384
620.6475862136135290.7048275727729420.352413786386471
630.7689144500564160.4621710998871670.231085549943584
640.7360151810070750.527969637985850.263984818992925
650.7394783669053620.5210432661892770.260521633094638
660.710288592354210.5794228152915790.289711407645790
670.703076698757610.593846602484780.29692330124239
680.6863860667741330.6272278664517330.313613933225866
690.724629385445340.5507412291093190.275370614554659
700.71977939859790.5604412028041990.280220601402099
710.7411641415462440.5176717169075120.258835858453756
720.7343736595028540.5312526809942920.265626340497146
730.7310086368421130.5379827263157750.268991363157887
740.6900668579167230.6198662841665540.309933142083277
750.6474861446590.7050277106820.352513855341
760.6045693544307210.7908612911385580.395430645569279
770.5594723002854790.8810553994290430.440527699714521
780.5132579566902620.9734840866194760.486742043309738
790.4696173508263380.9392347016526770.530382649173662
800.5108431534762860.9783136930474270.489156846523714
810.5080700693900290.9838598612199420.491929930609971
820.4631494138334570.9262988276669140.536850586166543
830.461305693702320.922611387404640.53869430629768
840.4239680094621620.8479360189243240.576031990537838
850.3774029635409730.7548059270819470.622597036459027
860.3442258952231470.6884517904462930.655774104776853
870.3245607940526010.6491215881052020.675439205947399
880.3047770265612210.6095540531224420.695222973438779
890.2659566624299880.5319133248599750.734043337570012
900.2335834070392580.4671668140785150.766416592960742
910.2094848760679570.4189697521359140.790515123932043
920.1789469054208610.3578938108417210.82105309457914
930.1538391377264590.3076782754529170.846160862273541
940.1769989758180130.3539979516360260.823001024181987
950.1475310937476410.2950621874952820.85246890625236
960.1220847649060540.2441695298121080.877915235093946
970.1038102917651840.2076205835303680.896189708234816
980.08559000343510030.1711800068702010.9144099965649
990.07332538003756250.1466507600751250.926674619962437
1000.06392762446143590.1278552489228720.936072375538564
1010.07813956489861590.1562791297972320.921860435101384
1020.07036688017634070.1407337603526810.929633119823659
1030.0655125874912550.131025174982510.934487412508745
1040.05401656559422510.1080331311884500.945983434405775
1050.05023357714684720.1004671542936940.949766422853153
1060.03837731753907410.07675463507814820.961622682460926
1070.03063702185634220.06127404371268430.969362978143658
1080.02496329634745090.04992659269490180.97503670365255
1090.02205050363336210.04410100726672420.977949496366638
1100.02885426487858740.05770852975717470.971145735121413
1110.03136979616872180.06273959233744360.968630203831278
1120.0333252484727740.0666504969455480.966674751527226
1130.02509753639079240.05019507278158480.974902463609208
1140.01843881541223380.03687763082446760.981561184587766
1150.04371740851914950.0874348170382990.95628259148085
1160.03296036374573040.06592072749146090.96703963625427
1170.03925213212652680.07850426425305370.960747867873473
1180.02972644190423380.05945288380846750.970273558095766
1190.02352475152132490.04704950304264980.976475248478675
1200.01669263781746240.03338527563492470.983307362182538
1210.0624068053902720.1248136107805440.937593194609728
1220.05184913692717310.1036982738543460.948150863072827
1230.04225693086958310.08451386173916630.957743069130417
1240.03793398759826330.07586797519652650.962066012401737
1250.02711436155000800.05422872310001610.972885638449992
1260.07632724550376490.1526544910075300.923672754496235
1270.06201954390288110.1240390878057620.937980456097119
1280.08681371802920530.1736274360584110.913186281970795
1290.1928331914471990.3856663828943980.8071668085528
1300.1509531515027910.3019063030055810.84904684849721
1310.1217863614741640.2435727229483280.878213638525836
1320.5948326205853290.8103347588293430.405167379414671
1330.5212879363651760.9574241272696490.478712063634824
1340.4451298865158270.8902597730316550.554870113484173
1350.3643537180473310.7287074360946610.63564628195267
1360.3895361689730820.7790723379461630.610463831026918
1370.3137134513735480.6274269027470960.686286548626452
1380.3644801430780960.7289602861561920.635519856921904
1390.496490543603130.992981087206260.50350945639687
1400.4295231932444930.8590463864889850.570476806755507
1410.5014188430338810.9971623139322390.498581156966119
1420.3916305003876870.7832610007753740.608369499612313
1430.2668675710307820.5337351420615630.733132428969218
1440.2922326967238760.5844653934477520.707767303276124






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0666666666666667NOK
10% type I error level300.222222222222222NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 9 & 0.0666666666666667 & NOK \tabularnewline
10% type I error level & 30 & 0.222222222222222 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104120&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.0666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.222222222222222[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104120&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104120&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 level00OK
5% type I error level90.0666666666666667NOK
10% type I error level300.222222222222222NOK


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')
}