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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 16:47:57 +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/t1291223334vfyyfwu2taps6m3.htm/, Retrieved Sun, 05 May 2024 12:38:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104124, Retrieved Sun, 05 May 2024 12:38:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [multiple regressi...] [2010-12-01 16:47:57] [03bcd8c83ef1a42b4029a16ba47a4880] [Current]
-           [Multiple Regression] [] [2010-12-03 16:02:12] [30b3e197115d238a51c18bcedc33a6a5]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	13	14	13	3
1	12	12	8	13	5
0	15	10	12	16	6
3	12	9	7	12	6
3	10	10	10	11	5
1	12	12	7	12	3
3	15	13	16	18	8
1	9	12	11	11	4
4	12	12	14	14	4
0	11	6	6	9	4
3	11	5	16	14	6
2	11	12	11	12	6
4	15	11	16	11	5
3	7	14	12	12	4
1	11	14	7	13	6
1	11	12	13	11	4
2	10	12	11	12	6
3	14	11	15	16	6
1	10	11	7	9	4
1	6	7	9	11	4
2	11	9	7	13	2
3	15	11	14	15	7
4	11	11	15	10	5
2	12	12	7	11	4
1	14	12	15	13	6
2	15	11	17	16	6
2	9	11	15	15	7
4	13	8	14	14	5
2	13	9	14	14	6
3	16	12	8	14	4
3	13	10	8	8	4
3	12	10	14	13	7
4	14	12	14	15	7
2	11	8	8	13	4
2	9	12	11	11	4
4	16	11	16	15	6
3	12	12	10	15	6
4	10	7	8	9	5
2	13	11	14	13	6
5	16	11	16	16	7
3	14	12	13	13	6
1	15	9	5	11	3
1	5	15	8	12	3
1	8	11	10	12	4
2	11	11	8	12	6
3	16	11	13	14	7
9	17	11	15	14	5
0	9	15	6	8	4
0	9	11	12	13	5
2	13	12	16	16	6
2	10	12	5	13	6
3	6	9	15	11	6
1	12	12	12	14	5
2	8	12	8	13	4
0	14	13	13	13	5
5	12	11	14	13	5
2	11	9	12	12	4
4	16	9	16	16	6
3	8	11	10	15	2
0	15	11	15	15	8
0	7	12	8	12	3
4	16	12	16	14	6
1	14	9	19	12	6
1	16	11	14	15	6
4	9	9	6	12	5
2	14	12	13	13	5
4	11	12	15	12	6
1	13	12	7	12	5
4	15	12	13	13	6
2	5	14	4	5	2
5	15	11	14	13	5
4	13	12	13	13	5
4	11	11	11	14	5
4	11	6	14	17	6
4	12	10	12	13	6
3	12	12	15	13	6
3	12	13	14	12	5
3	12	8	13	13	5
2	14	12	8	14	4
1	6	12	6	11	2
1	7	12	7	12	4
5	14	6	13	12	6
4	14	11	13	16	6
2	10	10	11	12	5
3	13	12	5	12	3
2	12	13	12	12	6
2	9	11	8	10	4
2	12	7	11	15	5
2	16	11	14	15	8
3	10	11	9	12	4
2	14	11	10	16	6
3	10	11	13	15	6
4	16	12	16	16	7
3	15	10	16	13	6
3	12	11	11	12	5
0	10	12	8	11	4
1	8	7	4	13	6
2	8	13	7	10	3
2	11	8	14	15	5
3	13	12	11	13	6
4	16	11	17	16	7
4	16	12	15	15	7
1	14	14	17	18	6
2	11	10	5	13	3
2	4	10	4	10	2
3	14	13	10	16	8
3	9	10	11	13	3
3	14	11	15	15	8
1	8	10	10	14	3
1	8	7	9	15	4
1	11	10	12	14	5
1	12	8	15	13	7
0	11	12	7	13	6
1	14	12	13	15	6
3	15	12	12	16	7
3	16	11	14	14	6
0	16	12	14	14	6
2	11	12	8	16	6
5	14	12	15	14	6
2	14	11	12	12	4
3	12	12	12	13	4
3	14	11	16	12	5
5	8	11	9	12	4
4	13	13	15	14	6
4	16	12	15	14	6
0	12	12	6	14	5
3	16	12	14	16	8
0	12	12	15	13	6
2	11	8	10	14	5
0	4	8	6	4	4
6	16	12	14	16	8
3	15	11	12	13	6
1	10	12	8	16	4
6	13	13	11	15	6
2	15	12	13	14	6
1	12	12	9	13	4
3	14	11	15	14	6
1	7	12	13	12	3
2	19	12	15	15	6
4	12	10	14	14	5
1	12	11	16	13	4
2	13	12	14	14	6
0	15	12	14	16	4
5	8	10	10	6	4
2	12	12	10	13	4
1	10	13	4	13	6
1	8	12	8	14	5
4	10	15	15	15	6
3	15	11	16	14	6
0	16	12	12	15	8
3	13	11	12	13	7
3	16	12	15	16	7
0	9	11	9	12	4
2	14	10	12	15	6
5	14	11	14	12	6
2	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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=104124&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=104124&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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] = + 1.23901678587798 + 0.096316083193947Popularity[t] -0.0640953209811908FindingFriends[t] + 0.128730344656522KnowingPeople[t] -0.0742946234905384Liked[t] + 0.0388673886095463`Celebrity `[t] -0.000136327489515619t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
aantalVrienden[t] =  +  1.23901678587798 +  0.096316083193947Popularity[t] -0.0640953209811908FindingFriends[t] +  0.128730344656522KnowingPeople[t] -0.0742946234905384Liked[t] +  0.0388673886095463`Celebrity
`[t] -0.000136327489515619t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]aantalVrienden[t] =  +  1.23901678587798 +  0.096316083193947Popularity[t] -0.0640953209811908FindingFriends[t] +  0.128730344656522KnowingPeople[t] -0.0742946234905384Liked[t] +  0.0388673886095463`Celebrity
`[t] -0.000136327489515619t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&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] = + 1.23901678587798 + 0.096316083193947Popularity[t] -0.0640953209811908FindingFriends[t] + 0.128730344656522KnowingPeople[t] -0.0742946234905384Liked[t] + 0.0388673886095463`Celebrity `[t] -0.000136327489515619t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.239016785877980.9565071.29540.1972010.0986
Popularity0.0963160831939470.0545921.76430.0797330.039867
FindingFriends-0.06409532098119080.064855-0.98830.3246170.162309
KnowingPeople0.1287303446565220.0432872.97390.0034310.001715
Liked-0.07429462349053840.068054-1.09170.276730.138365
`Celebrity `0.03886738860954630.110140.35290.7246680.362334
t-0.0001363274895156190.002557-0.05330.9575520.478776

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.23901678587798 & 0.956507 & 1.2954 & 0.197201 & 0.0986 \tabularnewline
Popularity & 0.096316083193947 & 0.054592 & 1.7643 & 0.079733 & 0.039867 \tabularnewline
FindingFriends & -0.0640953209811908 & 0.064855 & -0.9883 & 0.324617 & 0.162309 \tabularnewline
KnowingPeople & 0.128730344656522 & 0.043287 & 2.9739 & 0.003431 & 0.001715 \tabularnewline
Liked & -0.0742946234905384 & 0.068054 & -1.0917 & 0.27673 & 0.138365 \tabularnewline
`Celebrity
` & 0.0388673886095463 & 0.11014 & 0.3529 & 0.724668 & 0.362334 \tabularnewline
t & -0.000136327489515619 & 0.002557 & -0.0533 & 0.957552 & 0.478776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.23901678587798[/C][C]0.956507[/C][C]1.2954[/C][C]0.197201[/C][C]0.0986[/C][/ROW]
[ROW][C]Popularity[/C][C]0.096316083193947[/C][C]0.054592[/C][C]1.7643[/C][C]0.079733[/C][C]0.039867[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0640953209811908[/C][C]0.064855[/C][C]-0.9883[/C][C]0.324617[/C][C]0.162309[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.128730344656522[/C][C]0.043287[/C][C]2.9739[/C][C]0.003431[/C][C]0.001715[/C][/ROW]
[ROW][C]Liked[/C][C]-0.0742946234905384[/C][C]0.068054[/C][C]-1.0917[/C][C]0.27673[/C][C]0.138365[/C][/ROW]
[ROW][C]`Celebrity
`[/C][C]0.0388673886095463[/C][C]0.11014[/C][C]0.3529[/C][C]0.724668[/C][C]0.362334[/C][/ROW]
[ROW][C]t[/C][C]-0.000136327489515619[/C][C]0.002557[/C][C]-0.0533[/C][C]0.957552[/C][C]0.478776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.239016785877980.9565071.29540.1972010.0986
Popularity0.0963160831939470.0545921.76430.0797330.039867
FindingFriends-0.06409532098119080.064855-0.98830.3246170.162309
KnowingPeople0.1287303446565220.0432872.97390.0034310.001715
Liked-0.07429462349053840.068054-1.09170.276730.138365
`Celebrity `0.03886738860954630.110140.35290.7246680.362334
t-0.0001363274895156190.002557-0.05330.9575520.478776






Multiple Linear Regression - Regression Statistics
Multiple R0.404585261523168
R-squared0.163689233841770
Adjusted R-squared0.130012290238083
F-TEST (value)4.86057273391784
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.000145740995116217
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.41229152671296
Sum Squared Residuals297.190536107357

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.404585261523168 \tabularnewline
R-squared & 0.163689233841770 \tabularnewline
Adjusted R-squared & 0.130012290238083 \tabularnewline
F-TEST (value) & 4.86057273391784 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.000145740995116217 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.41229152671296 \tabularnewline
Sum Squared Residuals & 297.190536107357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.404585261523168[/C][/ROW]
[ROW][C]R-squared[/C][C]0.163689233841770[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.130012290238083[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.86057273391784[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.000145740995116217[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.41229152671296[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]297.190536107357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&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 R0.404585261523168
R-squared0.163689233841770
Adjusted R-squared0.130012290238083
F-TEST (value)4.86057273391784
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.000145740995116217
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.41229152671296
Sum Squared Residuals297.190536107357






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
122.61074725279726-0.610747252797261
211.88374287237492-0.883742872374924
302.63165033319365-2.63165033319365
432.060187847783030.93981215221697
532.224942301774990.775057698225014
611.75102706403179-0.75102706403179
732.882885969156120.117114030843881
812.08988955019709-1.08988955019709
942.542008635787361.45799136421264
1002.17175851119156-2.17175851119156
1133.22928261101486-0.229282611014856
1222.28541656035547-0.285416560355473
1343.413718844786540.586281155213463
1431.82268449807571.1773155019243
1511.56760093380792-0.567600933807919
1612.5388917859819-1.5388917859819
1722.18841883971395-0.188418839713948
1832.855385050645340.144614949354656
1911.88246922034254-0.882469220342543
2011.86232128633397-0.862321286333969
2121.731790019338590.268209980661405
2232.935587491324790.0644125086752079
2342.972655515949611.02734448405039
2421.861735181320590.138264818679408
2513.01321930770916-2.01321930770916
2623.20807120323621-1.20807120323621
2722.48573969937005-0.485739699370053
2842.930983169214821.06901683078518
2922.90561890935366-0.905618909353662
3032.152028023344190.847971976655807
3132.436901829178450.563098170821552
3232.857960534810060.142039465189938
3342.773676484764981.22632351523502
3422.00057820483170-0.000578204831696509
3522.08620870798017-0.086208707980165
3643.248588290369020.751411709630982
3732.026710241183390.973289758816608
3842.303858015232581.69614198476742
3922.85035961598666-0.850359615986663
4053.212615745529961.78738425447004
4132.753577378563870.246422621436134
4212.04418742111214-1.04418742111214
4311.00821474627503-0.00821474627502965
4411.84973603021471-0.849736030214708
4521.958822040213080.0411779597869168
4632.974195993604380.0258040063956189
4793.250101661402765.74989833859724
4801.47138263486190-1.47138263486190
4902.16740393039313-2.16740393039313
5022.81934151146223-0.819341511462228
5121.337107013640750.662892986359252
5232.579885009865310.42011499013469
5312.31741692554518-1.31741692554518
5421.452522121534780.547477878465221
5502.64870608411991-2.64870608411991
5652.712858576861402.28714142313860
5722.52256335370827-0.522563353708271
5843.299485104071520.700514895928483
5931.547072470181271.45292752981873
6003.09800477998927-3.09800477998927
6101.39067898079522-1.39067898079522
6243.255243078150960.744756921849041
6313.78954082816776-2.78954082816776
6412.98731043134954-1.98731043134954
6541.595325888074672.40467411192533
6622.71130180271643-0.71130180271643
6742.79283992705821.2071600729418
6811.91662562009486-0.91662562009486
6942.846076292051381.15392370794862
7021.034902822237430.965097177762566
7152.999761914100512.00023808589949
7242.614167754585391.38583224541461
7342.153739268885591.84626073111441
7442.676254098409521.32374590159048
7542.55577037483831.4442296251617
7632.813634439355970.186365560644032
7732.656099681109730.343900318890267
7832.773414990379110.226585009620889
7921.952715809970030.0472841900299665
8011.06973922086842-0.06973922086842
8111.29808947495793-0.298089474957927
8253.206854500871411.79314549912859
8342.589063074513791.41093692548621
8422.26860815126924-0.268608151269236
8531.579112586240961.42088741375904
8622.43627943300059-0.436279433000595
8721.831318589027520.168681410972483
8822.43009710017103-0.430097100171026
8923.06163702133074-1.06163702133074
9031.907366787428361.09263321257164
9122.20178142062810-0.201781420628096
9232.276866417822900.723133582177105
9343.141295067604440.858704932395556
9433.35704978074543-0.357049780745432
9532.395645394291270.604354605708732
9601.78801778034409-1.78801778034409
9711.32995004298457-0.329950042984568
9821.437714528220450.562285471779551
9922.65437712758078-0.654377127580781
10032.391757284175450.608242715824546
10143.333030113326030.666969886673968
10243.085632399032820.91436760096718
10312.76038269342491-1.76038269342491
10421.437786216024110.56221378397589
10520.8187234433825121.18127655661749
10632.149280643542070.850719356457928
10732.01712713510680.9828728648932
10832.995144977298570.00485502270143044
10911.71751342878676-0.717513428786761
11011.74550548470330-0.745505484703304
11112.34138448992171-1.34138448992171
11213.10397532226772-2.10397532226772
11301.68243148179777-1.68243148179777
11412.59503622484815-1.59503622484815
11532.527058401015070.472941598984933
11633.05451602538526-0.0545160253852632
11702.99028437691456-2.99028437691456
11821.58759631853510.412403681464901
11952.926109900204152.07389009979585
12022.67473232948825-0.674732329488247
12132.343573891139110.656426108860892
12233.22824844174485-0.228248441744849
12351.710235813886453.28976418611355
12442.765016858581441.23498314141856
12543.117924101654950.882075898345046
12601.53508295087141-1.53508295087141
12732.918066632257420.0819333677425837
12802.80654540990116-2.80654540990116
12922.20966054775976-0.209660547759764
13001.72446910558237-1.72446910558237
13162.917521322299353.08247867770065
13232.772852636536560.227147363463439
13311.41150054577932-0.411500545779325
13462.174437581569663.82556241843034
13522.76278405425281-0.762784054252807
13611.95533794482681-0.95533794482681
13732.987751326374060.0122486736259375
13812.02383348738512-1.02383348738512
13923.33066914289304-1.33066914289304
14042.691207765232741.30879223476726
14112.91986404095607-1.91986404095607
14222.69792794009483-0.697927940094825
14302.66409975479303-2.66409975479303
14452.345966343187572.65403365681243
14522.08284134207769-0.0828413420776901
14611.13133023649905-0.131330236499052
14711.40441643012883-0.404416430128835
14842.27031148379831.7296885162017
14933.21116182435034-0.211161824350344
15002.73176503417605-2.73176503417605
15132.616497636457420.383502363542584
15233.0045214010665-0.00452140106650126
15301.80246207239493-1.80246207239493
15422.58904342257338-0.589043422573384
15553.005156333887341.99484366611266
15622.1221380046211-0.122138004621100

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 2.61074725279726 & -0.610747252797261 \tabularnewline
2 & 1 & 1.88374287237492 & -0.883742872374924 \tabularnewline
3 & 0 & 2.63165033319365 & -2.63165033319365 \tabularnewline
4 & 3 & 2.06018784778303 & 0.93981215221697 \tabularnewline
5 & 3 & 2.22494230177499 & 0.775057698225014 \tabularnewline
6 & 1 & 1.75102706403179 & -0.75102706403179 \tabularnewline
7 & 3 & 2.88288596915612 & 0.117114030843881 \tabularnewline
8 & 1 & 2.08988955019709 & -1.08988955019709 \tabularnewline
9 & 4 & 2.54200863578736 & 1.45799136421264 \tabularnewline
10 & 0 & 2.17175851119156 & -2.17175851119156 \tabularnewline
11 & 3 & 3.22928261101486 & -0.229282611014856 \tabularnewline
12 & 2 & 2.28541656035547 & -0.285416560355473 \tabularnewline
13 & 4 & 3.41371884478654 & 0.586281155213463 \tabularnewline
14 & 3 & 1.8226844980757 & 1.1773155019243 \tabularnewline
15 & 1 & 1.56760093380792 & -0.567600933807919 \tabularnewline
16 & 1 & 2.5388917859819 & -1.5388917859819 \tabularnewline
17 & 2 & 2.18841883971395 & -0.188418839713948 \tabularnewline
18 & 3 & 2.85538505064534 & 0.144614949354656 \tabularnewline
19 & 1 & 1.88246922034254 & -0.882469220342543 \tabularnewline
20 & 1 & 1.86232128633397 & -0.862321286333969 \tabularnewline
21 & 2 & 1.73179001933859 & 0.268209980661405 \tabularnewline
22 & 3 & 2.93558749132479 & 0.0644125086752079 \tabularnewline
23 & 4 & 2.97265551594961 & 1.02734448405039 \tabularnewline
24 & 2 & 1.86173518132059 & 0.138264818679408 \tabularnewline
25 & 1 & 3.01321930770916 & -2.01321930770916 \tabularnewline
26 & 2 & 3.20807120323621 & -1.20807120323621 \tabularnewline
27 & 2 & 2.48573969937005 & -0.485739699370053 \tabularnewline
28 & 4 & 2.93098316921482 & 1.06901683078518 \tabularnewline
29 & 2 & 2.90561890935366 & -0.905618909353662 \tabularnewline
30 & 3 & 2.15202802334419 & 0.847971976655807 \tabularnewline
31 & 3 & 2.43690182917845 & 0.563098170821552 \tabularnewline
32 & 3 & 2.85796053481006 & 0.142039465189938 \tabularnewline
33 & 4 & 2.77367648476498 & 1.22632351523502 \tabularnewline
34 & 2 & 2.00057820483170 & -0.000578204831696509 \tabularnewline
35 & 2 & 2.08620870798017 & -0.086208707980165 \tabularnewline
36 & 4 & 3.24858829036902 & 0.751411709630982 \tabularnewline
37 & 3 & 2.02671024118339 & 0.973289758816608 \tabularnewline
38 & 4 & 2.30385801523258 & 1.69614198476742 \tabularnewline
39 & 2 & 2.85035961598666 & -0.850359615986663 \tabularnewline
40 & 5 & 3.21261574552996 & 1.78738425447004 \tabularnewline
41 & 3 & 2.75357737856387 & 0.246422621436134 \tabularnewline
42 & 1 & 2.04418742111214 & -1.04418742111214 \tabularnewline
43 & 1 & 1.00821474627503 & -0.00821474627502965 \tabularnewline
44 & 1 & 1.84973603021471 & -0.849736030214708 \tabularnewline
45 & 2 & 1.95882204021308 & 0.0411779597869168 \tabularnewline
46 & 3 & 2.97419599360438 & 0.0258040063956189 \tabularnewline
47 & 9 & 3.25010166140276 & 5.74989833859724 \tabularnewline
48 & 0 & 1.47138263486190 & -1.47138263486190 \tabularnewline
49 & 0 & 2.16740393039313 & -2.16740393039313 \tabularnewline
50 & 2 & 2.81934151146223 & -0.819341511462228 \tabularnewline
51 & 2 & 1.33710701364075 & 0.662892986359252 \tabularnewline
52 & 3 & 2.57988500986531 & 0.42011499013469 \tabularnewline
53 & 1 & 2.31741692554518 & -1.31741692554518 \tabularnewline
54 & 2 & 1.45252212153478 & 0.547477878465221 \tabularnewline
55 & 0 & 2.64870608411991 & -2.64870608411991 \tabularnewline
56 & 5 & 2.71285857686140 & 2.28714142313860 \tabularnewline
57 & 2 & 2.52256335370827 & -0.522563353708271 \tabularnewline
58 & 4 & 3.29948510407152 & 0.700514895928483 \tabularnewline
59 & 3 & 1.54707247018127 & 1.45292752981873 \tabularnewline
60 & 0 & 3.09800477998927 & -3.09800477998927 \tabularnewline
61 & 0 & 1.39067898079522 & -1.39067898079522 \tabularnewline
62 & 4 & 3.25524307815096 & 0.744756921849041 \tabularnewline
63 & 1 & 3.78954082816776 & -2.78954082816776 \tabularnewline
64 & 1 & 2.98731043134954 & -1.98731043134954 \tabularnewline
65 & 4 & 1.59532588807467 & 2.40467411192533 \tabularnewline
66 & 2 & 2.71130180271643 & -0.71130180271643 \tabularnewline
67 & 4 & 2.7928399270582 & 1.2071600729418 \tabularnewline
68 & 1 & 1.91662562009486 & -0.91662562009486 \tabularnewline
69 & 4 & 2.84607629205138 & 1.15392370794862 \tabularnewline
70 & 2 & 1.03490282223743 & 0.965097177762566 \tabularnewline
71 & 5 & 2.99976191410051 & 2.00023808589949 \tabularnewline
72 & 4 & 2.61416775458539 & 1.38583224541461 \tabularnewline
73 & 4 & 2.15373926888559 & 1.84626073111441 \tabularnewline
74 & 4 & 2.67625409840952 & 1.32374590159048 \tabularnewline
75 & 4 & 2.5557703748383 & 1.4442296251617 \tabularnewline
76 & 3 & 2.81363443935597 & 0.186365560644032 \tabularnewline
77 & 3 & 2.65609968110973 & 0.343900318890267 \tabularnewline
78 & 3 & 2.77341499037911 & 0.226585009620889 \tabularnewline
79 & 2 & 1.95271580997003 & 0.0472841900299665 \tabularnewline
80 & 1 & 1.06973922086842 & -0.06973922086842 \tabularnewline
81 & 1 & 1.29808947495793 & -0.298089474957927 \tabularnewline
82 & 5 & 3.20685450087141 & 1.79314549912859 \tabularnewline
83 & 4 & 2.58906307451379 & 1.41093692548621 \tabularnewline
84 & 2 & 2.26860815126924 & -0.268608151269236 \tabularnewline
85 & 3 & 1.57911258624096 & 1.42088741375904 \tabularnewline
86 & 2 & 2.43627943300059 & -0.436279433000595 \tabularnewline
87 & 2 & 1.83131858902752 & 0.168681410972483 \tabularnewline
88 & 2 & 2.43009710017103 & -0.430097100171026 \tabularnewline
89 & 2 & 3.06163702133074 & -1.06163702133074 \tabularnewline
90 & 3 & 1.90736678742836 & 1.09263321257164 \tabularnewline
91 & 2 & 2.20178142062810 & -0.201781420628096 \tabularnewline
92 & 3 & 2.27686641782290 & 0.723133582177105 \tabularnewline
93 & 4 & 3.14129506760444 & 0.858704932395556 \tabularnewline
94 & 3 & 3.35704978074543 & -0.357049780745432 \tabularnewline
95 & 3 & 2.39564539429127 & 0.604354605708732 \tabularnewline
96 & 0 & 1.78801778034409 & -1.78801778034409 \tabularnewline
97 & 1 & 1.32995004298457 & -0.329950042984568 \tabularnewline
98 & 2 & 1.43771452822045 & 0.562285471779551 \tabularnewline
99 & 2 & 2.65437712758078 & -0.654377127580781 \tabularnewline
100 & 3 & 2.39175728417545 & 0.608242715824546 \tabularnewline
101 & 4 & 3.33303011332603 & 0.666969886673968 \tabularnewline
102 & 4 & 3.08563239903282 & 0.91436760096718 \tabularnewline
103 & 1 & 2.76038269342491 & -1.76038269342491 \tabularnewline
104 & 2 & 1.43778621602411 & 0.56221378397589 \tabularnewline
105 & 2 & 0.818723443382512 & 1.18127655661749 \tabularnewline
106 & 3 & 2.14928064354207 & 0.850719356457928 \tabularnewline
107 & 3 & 2.0171271351068 & 0.9828728648932 \tabularnewline
108 & 3 & 2.99514497729857 & 0.00485502270143044 \tabularnewline
109 & 1 & 1.71751342878676 & -0.717513428786761 \tabularnewline
110 & 1 & 1.74550548470330 & -0.745505484703304 \tabularnewline
111 & 1 & 2.34138448992171 & -1.34138448992171 \tabularnewline
112 & 1 & 3.10397532226772 & -2.10397532226772 \tabularnewline
113 & 0 & 1.68243148179777 & -1.68243148179777 \tabularnewline
114 & 1 & 2.59503622484815 & -1.59503622484815 \tabularnewline
115 & 3 & 2.52705840101507 & 0.472941598984933 \tabularnewline
116 & 3 & 3.05451602538526 & -0.0545160253852632 \tabularnewline
117 & 0 & 2.99028437691456 & -2.99028437691456 \tabularnewline
118 & 2 & 1.5875963185351 & 0.412403681464901 \tabularnewline
119 & 5 & 2.92610990020415 & 2.07389009979585 \tabularnewline
120 & 2 & 2.67473232948825 & -0.674732329488247 \tabularnewline
121 & 3 & 2.34357389113911 & 0.656426108860892 \tabularnewline
122 & 3 & 3.22824844174485 & -0.228248441744849 \tabularnewline
123 & 5 & 1.71023581388645 & 3.28976418611355 \tabularnewline
124 & 4 & 2.76501685858144 & 1.23498314141856 \tabularnewline
125 & 4 & 3.11792410165495 & 0.882075898345046 \tabularnewline
126 & 0 & 1.53508295087141 & -1.53508295087141 \tabularnewline
127 & 3 & 2.91806663225742 & 0.0819333677425837 \tabularnewline
128 & 0 & 2.80654540990116 & -2.80654540990116 \tabularnewline
129 & 2 & 2.20966054775976 & -0.209660547759764 \tabularnewline
130 & 0 & 1.72446910558237 & -1.72446910558237 \tabularnewline
131 & 6 & 2.91752132229935 & 3.08247867770065 \tabularnewline
132 & 3 & 2.77285263653656 & 0.227147363463439 \tabularnewline
133 & 1 & 1.41150054577932 & -0.411500545779325 \tabularnewline
134 & 6 & 2.17443758156966 & 3.82556241843034 \tabularnewline
135 & 2 & 2.76278405425281 & -0.762784054252807 \tabularnewline
136 & 1 & 1.95533794482681 & -0.95533794482681 \tabularnewline
137 & 3 & 2.98775132637406 & 0.0122486736259375 \tabularnewline
138 & 1 & 2.02383348738512 & -1.02383348738512 \tabularnewline
139 & 2 & 3.33066914289304 & -1.33066914289304 \tabularnewline
140 & 4 & 2.69120776523274 & 1.30879223476726 \tabularnewline
141 & 1 & 2.91986404095607 & -1.91986404095607 \tabularnewline
142 & 2 & 2.69792794009483 & -0.697927940094825 \tabularnewline
143 & 0 & 2.66409975479303 & -2.66409975479303 \tabularnewline
144 & 5 & 2.34596634318757 & 2.65403365681243 \tabularnewline
145 & 2 & 2.08284134207769 & -0.0828413420776901 \tabularnewline
146 & 1 & 1.13133023649905 & -0.131330236499052 \tabularnewline
147 & 1 & 1.40441643012883 & -0.404416430128835 \tabularnewline
148 & 4 & 2.2703114837983 & 1.7296885162017 \tabularnewline
149 & 3 & 3.21116182435034 & -0.211161824350344 \tabularnewline
150 & 0 & 2.73176503417605 & -2.73176503417605 \tabularnewline
151 & 3 & 2.61649763645742 & 0.383502363542584 \tabularnewline
152 & 3 & 3.0045214010665 & -0.00452140106650126 \tabularnewline
153 & 0 & 1.80246207239493 & -1.80246207239493 \tabularnewline
154 & 2 & 2.58904342257338 & -0.589043422573384 \tabularnewline
155 & 5 & 3.00515633388734 & 1.99484366611266 \tabularnewline
156 & 2 & 2.1221380046211 & -0.122138004621100 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2[/C][C]2.61074725279726[/C][C]-0.610747252797261[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.88374287237492[/C][C]-0.883742872374924[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]2.63165033319365[/C][C]-2.63165033319365[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]2.06018784778303[/C][C]0.93981215221697[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.22494230177499[/C][C]0.775057698225014[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.75102706403179[/C][C]-0.75102706403179[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]2.88288596915612[/C][C]0.117114030843881[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]2.08988955019709[/C][C]-1.08988955019709[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.54200863578736[/C][C]1.45799136421264[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]2.17175851119156[/C][C]-2.17175851119156[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]3.22928261101486[/C][C]-0.229282611014856[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]2.28541656035547[/C][C]-0.285416560355473[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]3.41371884478654[/C][C]0.586281155213463[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]1.8226844980757[/C][C]1.1773155019243[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.56760093380792[/C][C]-0.567600933807919[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]2.5388917859819[/C][C]-1.5388917859819[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.18841883971395[/C][C]-0.188418839713948[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.85538505064534[/C][C]0.144614949354656[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.88246922034254[/C][C]-0.882469220342543[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.86232128633397[/C][C]-0.862321286333969[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.73179001933859[/C][C]0.268209980661405[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.93558749132479[/C][C]0.0644125086752079[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]2.97265551594961[/C][C]1.02734448405039[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.86173518132059[/C][C]0.138264818679408[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]3.01321930770916[/C][C]-2.01321930770916[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]3.20807120323621[/C][C]-1.20807120323621[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]2.48573969937005[/C][C]-0.485739699370053[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]2.93098316921482[/C][C]1.06901683078518[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]2.90561890935366[/C][C]-0.905618909353662[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.15202802334419[/C][C]0.847971976655807[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.43690182917845[/C][C]0.563098170821552[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]2.85796053481006[/C][C]0.142039465189938[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]2.77367648476498[/C][C]1.22632351523502[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]2.00057820483170[/C][C]-0.000578204831696509[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.08620870798017[/C][C]-0.086208707980165[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.24858829036902[/C][C]0.751411709630982[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]2.02671024118339[/C][C]0.973289758816608[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]2.30385801523258[/C][C]1.69614198476742[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.85035961598666[/C][C]-0.850359615986663[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]3.21261574552996[/C][C]1.78738425447004[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]2.75357737856387[/C][C]0.246422621436134[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]2.04418742111214[/C][C]-1.04418742111214[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.00821474627503[/C][C]-0.00821474627502965[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.84973603021471[/C][C]-0.849736030214708[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.95882204021308[/C][C]0.0411779597869168[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.97419599360438[/C][C]0.0258040063956189[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]3.25010166140276[/C][C]5.74989833859724[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]1.47138263486190[/C][C]-1.47138263486190[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]2.16740393039313[/C][C]-2.16740393039313[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.81934151146223[/C][C]-0.819341511462228[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.33710701364075[/C][C]0.662892986359252[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.57988500986531[/C][C]0.42011499013469[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]2.31741692554518[/C][C]-1.31741692554518[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.45252212153478[/C][C]0.547477878465221[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]2.64870608411991[/C][C]-2.64870608411991[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]2.71285857686140[/C][C]2.28714142313860[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]2.52256335370827[/C][C]-0.522563353708271[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.29948510407152[/C][C]0.700514895928483[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]1.54707247018127[/C][C]1.45292752981873[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]3.09800477998927[/C][C]-3.09800477998927[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]1.39067898079522[/C][C]-1.39067898079522[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.25524307815096[/C][C]0.744756921849041[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]3.78954082816776[/C][C]-2.78954082816776[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]2.98731043134954[/C][C]-1.98731043134954[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]1.59532588807467[/C][C]2.40467411192533[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]2.71130180271643[/C][C]-0.71130180271643[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]2.7928399270582[/C][C]1.2071600729418[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.91662562009486[/C][C]-0.91662562009486[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]2.84607629205138[/C][C]1.15392370794862[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]1.03490282223743[/C][C]0.965097177762566[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]2.99976191410051[/C][C]2.00023808589949[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]2.61416775458539[/C][C]1.38583224541461[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]2.15373926888559[/C][C]1.84626073111441[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]2.67625409840952[/C][C]1.32374590159048[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]2.5557703748383[/C][C]1.4442296251617[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.81363443935597[/C][C]0.186365560644032[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.65609968110973[/C][C]0.343900318890267[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]2.77341499037911[/C][C]0.226585009620889[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.95271580997003[/C][C]0.0472841900299665[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.06973922086842[/C][C]-0.06973922086842[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.29808947495793[/C][C]-0.298089474957927[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]3.20685450087141[/C][C]1.79314549912859[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]2.58906307451379[/C][C]1.41093692548621[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]2.26860815126924[/C][C]-0.268608151269236[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]1.57911258624096[/C][C]1.42088741375904[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]2.43627943300059[/C][C]-0.436279433000595[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.83131858902752[/C][C]0.168681410972483[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.43009710017103[/C][C]-0.430097100171026[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]3.06163702133074[/C][C]-1.06163702133074[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]1.90736678742836[/C][C]1.09263321257164[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.20178142062810[/C][C]-0.201781420628096[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.27686641782290[/C][C]0.723133582177105[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.14129506760444[/C][C]0.858704932395556[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]3.35704978074543[/C][C]-0.357049780745432[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]2.39564539429127[/C][C]0.604354605708732[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]1.78801778034409[/C][C]-1.78801778034409[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.32995004298457[/C][C]-0.329950042984568[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.43771452822045[/C][C]0.562285471779551[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]2.65437712758078[/C][C]-0.654377127580781[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]2.39175728417545[/C][C]0.608242715824546[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.33303011332603[/C][C]0.666969886673968[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.08563239903282[/C][C]0.91436760096718[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]2.76038269342491[/C][C]-1.76038269342491[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.43778621602411[/C][C]0.56221378397589[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]0.818723443382512[/C][C]1.18127655661749[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.14928064354207[/C][C]0.850719356457928[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]2.0171271351068[/C][C]0.9828728648932[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]2.99514497729857[/C][C]0.00485502270143044[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.71751342878676[/C][C]-0.717513428786761[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.74550548470330[/C][C]-0.745505484703304[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]2.34138448992171[/C][C]-1.34138448992171[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]3.10397532226772[/C][C]-2.10397532226772[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]1.68243148179777[/C][C]-1.68243148179777[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]2.59503622484815[/C][C]-1.59503622484815[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.52705840101507[/C][C]0.472941598984933[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]3.05451602538526[/C][C]-0.0545160253852632[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]2.99028437691456[/C][C]-2.99028437691456[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.5875963185351[/C][C]0.412403681464901[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]2.92610990020415[/C][C]2.07389009979585[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.67473232948825[/C][C]-0.674732329488247[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.34357389113911[/C][C]0.656426108860892[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]3.22824844174485[/C][C]-0.228248441744849[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]1.71023581388645[/C][C]3.28976418611355[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]2.76501685858144[/C][C]1.23498314141856[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]3.11792410165495[/C][C]0.882075898345046[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]1.53508295087141[/C][C]-1.53508295087141[/C][/ROW]
[ROW][C]127[/C][C]3[/C][C]2.91806663225742[/C][C]0.0819333677425837[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]2.80654540990116[/C][C]-2.80654540990116[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]2.20966054775976[/C][C]-0.209660547759764[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]1.72446910558237[/C][C]-1.72446910558237[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]2.91752132229935[/C][C]3.08247867770065[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]2.77285263653656[/C][C]0.227147363463439[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.41150054577932[/C][C]-0.411500545779325[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]2.17443758156966[/C][C]3.82556241843034[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]2.76278405425281[/C][C]-0.762784054252807[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.95533794482681[/C][C]-0.95533794482681[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.98775132637406[/C][C]0.0122486736259375[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]2.02383348738512[/C][C]-1.02383348738512[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]3.33066914289304[/C][C]-1.33066914289304[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]2.69120776523274[/C][C]1.30879223476726[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]2.91986404095607[/C][C]-1.91986404095607[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]2.69792794009483[/C][C]-0.697927940094825[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]2.66409975479303[/C][C]-2.66409975479303[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]2.34596634318757[/C][C]2.65403365681243[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.08284134207769[/C][C]-0.0828413420776901[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.13133023649905[/C][C]-0.131330236499052[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.40441643012883[/C][C]-0.404416430128835[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]2.2703114837983[/C][C]1.7296885162017[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]3.21116182435034[/C][C]-0.211161824350344[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]2.73176503417605[/C][C]-2.73176503417605[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]2.61649763645742[/C][C]0.383502363542584[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]3.0045214010665[/C][C]-0.00452140106650126[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]1.80246207239493[/C][C]-1.80246207239493[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]2.58904342257338[/C][C]-0.589043422573384[/C][/ROW]
[ROW][C]155[/C][C]5[/C][C]3.00515633388734[/C][C]1.99484366611266[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]2.1221380046211[/C][C]-0.122138004621100[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&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
122.61074725279726-0.610747252797261
211.88374287237492-0.883742872374924
302.63165033319365-2.63165033319365
432.060187847783030.93981215221697
532.224942301774990.775057698225014
611.75102706403179-0.75102706403179
732.882885969156120.117114030843881
812.08988955019709-1.08988955019709
942.542008635787361.45799136421264
1002.17175851119156-2.17175851119156
1133.22928261101486-0.229282611014856
1222.28541656035547-0.285416560355473
1343.413718844786540.586281155213463
1431.82268449807571.1773155019243
1511.56760093380792-0.567600933807919
1612.5388917859819-1.5388917859819
1722.18841883971395-0.188418839713948
1832.855385050645340.144614949354656
1911.88246922034254-0.882469220342543
2011.86232128633397-0.862321286333969
2121.731790019338590.268209980661405
2232.935587491324790.0644125086752079
2342.972655515949611.02734448405039
2421.861735181320590.138264818679408
2513.01321930770916-2.01321930770916
2623.20807120323621-1.20807120323621
2722.48573969937005-0.485739699370053
2842.930983169214821.06901683078518
2922.90561890935366-0.905618909353662
3032.152028023344190.847971976655807
3132.436901829178450.563098170821552
3232.857960534810060.142039465189938
3342.773676484764981.22632351523502
3422.00057820483170-0.000578204831696509
3522.08620870798017-0.086208707980165
3643.248588290369020.751411709630982
3732.026710241183390.973289758816608
3842.303858015232581.69614198476742
3922.85035961598666-0.850359615986663
4053.212615745529961.78738425447004
4132.753577378563870.246422621436134
4212.04418742111214-1.04418742111214
4311.00821474627503-0.00821474627502965
4411.84973603021471-0.849736030214708
4521.958822040213080.0411779597869168
4632.974195993604380.0258040063956189
4793.250101661402765.74989833859724
4801.47138263486190-1.47138263486190
4902.16740393039313-2.16740393039313
5022.81934151146223-0.819341511462228
5121.337107013640750.662892986359252
5232.579885009865310.42011499013469
5312.31741692554518-1.31741692554518
5421.452522121534780.547477878465221
5502.64870608411991-2.64870608411991
5652.712858576861402.28714142313860
5722.52256335370827-0.522563353708271
5843.299485104071520.700514895928483
5931.547072470181271.45292752981873
6003.09800477998927-3.09800477998927
6101.39067898079522-1.39067898079522
6243.255243078150960.744756921849041
6313.78954082816776-2.78954082816776
6412.98731043134954-1.98731043134954
6541.595325888074672.40467411192533
6622.71130180271643-0.71130180271643
6742.79283992705821.2071600729418
6811.91662562009486-0.91662562009486
6942.846076292051381.15392370794862
7021.034902822237430.965097177762566
7152.999761914100512.00023808589949
7242.614167754585391.38583224541461
7342.153739268885591.84626073111441
7442.676254098409521.32374590159048
7542.55577037483831.4442296251617
7632.813634439355970.186365560644032
7732.656099681109730.343900318890267
7832.773414990379110.226585009620889
7921.952715809970030.0472841900299665
8011.06973922086842-0.06973922086842
8111.29808947495793-0.298089474957927
8253.206854500871411.79314549912859
8342.589063074513791.41093692548621
8422.26860815126924-0.268608151269236
8531.579112586240961.42088741375904
8622.43627943300059-0.436279433000595
8721.831318589027520.168681410972483
8822.43009710017103-0.430097100171026
8923.06163702133074-1.06163702133074
9031.907366787428361.09263321257164
9122.20178142062810-0.201781420628096
9232.276866417822900.723133582177105
9343.141295067604440.858704932395556
9433.35704978074543-0.357049780745432
9532.395645394291270.604354605708732
9601.78801778034409-1.78801778034409
9711.32995004298457-0.329950042984568
9821.437714528220450.562285471779551
9922.65437712758078-0.654377127580781
10032.391757284175450.608242715824546
10143.333030113326030.666969886673968
10243.085632399032820.91436760096718
10312.76038269342491-1.76038269342491
10421.437786216024110.56221378397589
10520.8187234433825121.18127655661749
10632.149280643542070.850719356457928
10732.01712713510680.9828728648932
10832.995144977298570.00485502270143044
10911.71751342878676-0.717513428786761
11011.74550548470330-0.745505484703304
11112.34138448992171-1.34138448992171
11213.10397532226772-2.10397532226772
11301.68243148179777-1.68243148179777
11412.59503622484815-1.59503622484815
11532.527058401015070.472941598984933
11633.05451602538526-0.0545160253852632
11702.99028437691456-2.99028437691456
11821.58759631853510.412403681464901
11952.926109900204152.07389009979585
12022.67473232948825-0.674732329488247
12132.343573891139110.656426108860892
12233.22824844174485-0.228248441744849
12351.710235813886453.28976418611355
12442.765016858581441.23498314141856
12543.117924101654950.882075898345046
12601.53508295087141-1.53508295087141
12732.918066632257420.0819333677425837
12802.80654540990116-2.80654540990116
12922.20966054775976-0.209660547759764
13001.72446910558237-1.72446910558237
13162.917521322299353.08247867770065
13232.772852636536560.227147363463439
13311.41150054577932-0.411500545779325
13462.174437581569663.82556241843034
13522.76278405425281-0.762784054252807
13611.95533794482681-0.95533794482681
13732.987751326374060.0122486736259375
13812.02383348738512-1.02383348738512
13923.33066914289304-1.33066914289304
14042.691207765232741.30879223476726
14112.91986404095607-1.91986404095607
14222.69792794009483-0.697927940094825
14302.66409975479303-2.66409975479303
14452.345966343187572.65403365681243
14522.08284134207769-0.0828413420776901
14611.13133023649905-0.131330236499052
14711.40441643012883-0.404416430128835
14842.27031148379831.7296885162017
14933.21116182435034-0.211161824350344
15002.73176503417605-2.73176503417605
15132.616497636457420.383502363542584
15233.0045214010665-0.00452140106650126
15301.80246207239493-1.80246207239493
15422.58904342257338-0.589043422573384
15553.005156333887341.99484366611266
15622.1221380046211-0.122138004621100






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8684983617365350.2630032765269300.131501638263465
110.7765731872168130.4468536255663740.223426812783187
120.6784458806340210.6431082387319570.321554119365979
130.5889605430914860.8220789138170270.411039456908514
140.4762138132511840.9524276265023690.523786186748816
150.3743755348532990.7487510697065990.625624465146701
160.3984786079772140.7969572159544290.601521392022786
170.3072861261372390.6145722522744770.692713873862761
180.2617947123216110.5235894246432230.738205287678389
190.1939744243903180.3879488487806360.806025575609682
200.1411297397580820.2822594795161650.858870260241918
210.1614401189535990.3228802379071970.838559881046401
220.1159307197979960.2318614395959910.884069280202004
230.09157820969532830.1831564193906570.908421790304672
240.06564898170168640.1312979634033730.934351018298314
250.1228925561526860.2457851123053710.877107443847314
260.1073604815451010.2147209630902020.892639518454899
270.08298383345132590.1659676669026520.917016166548674
280.09413146667383270.1882629333476650.905868533326167
290.073485939010960.146971878021920.92651406098904
300.0716722632890310.1433445265780620.928327736710969
310.0568606665349210.1137213330698420.94313933346508
320.04046413927842020.08092827855684050.95953586072158
330.0365682954762460.0731365909524920.963431704523754
340.02528917286926460.05057834573852920.974710827130735
350.01815814198444500.03631628396889000.981841858015555
360.01273746252925750.02547492505851500.987262537470742
370.00928959209370840.01857918418741680.990710407906292
380.01093333492331270.02186666984662530.989066665076687
390.01139785620266630.02279571240533270.988602143797334
400.01185357749075750.02370715498151510.988146422509242
410.008209906493553420.01641981298710680.991790093506447
420.00855836038483420.01711672076966840.991441639615166
430.006197254744844840.01239450948968970.993802745255155
440.00575931828965880.01151863657931760.994240681710341
450.003846400327288760.007692800654577520.996153599672711
460.002609291955771710.005218583911543430.997390708044228
470.1721991653429160.3443983306858310.827800834657084
480.1932774237562230.3865548475124460.806722576243777
490.2819746968722120.5639493937444240.718025303127788
500.2799318287669660.5598636575339330.720068171233034
510.2517018823062070.5034037646124140.748298117693793
520.2150853566815310.4301707133630620.78491464331847
530.2325417456712470.4650834913424950.767458254328753
540.2020552495867430.4041104991734870.797944750413257
550.3649562596612580.7299125193225160.635043740338742
560.4071880184093810.8143760368187620.592811981590619
570.3758020324434560.7516040648869120.624197967556544
580.3333210908678950.666642181735790.666678909132105
590.3249248704983710.6498497409967410.67507512950163
600.5545288142901240.8909423714197520.445471185709876
610.5665556453961260.8668887092077470.433444354603874
620.5215530019604120.9568939960791750.478446998039588
630.691526612544540.6169467749109190.308473387455460
640.7509981504607930.4980036990784150.249001849539207
650.8066339053031160.3867321893937690.193366094696884
660.7894401298792520.4211197402414950.210559870120748
670.7751485104330760.4497029791338470.224851489566924
680.7645798771622910.4708402456754170.235420122837709
690.7428896778312660.5142206443374680.257110322168734
700.7174761873305920.5650476253388150.282523812669408
710.7304430929473420.5391138141053160.269556907052658
720.710231386165090.5795372276698190.289768613834909
730.7127072748002850.5745854503994310.287292725199715
740.6943482066825050.6113035866349910.305651793317495
750.6771496611342450.645700677731510.322850338865755
760.6340669015036440.7318661969927110.365933098496356
770.5884783049000560.8230433901998880.411521695099944
780.5443347050266340.9113305899467320.455665294973366
790.5011902477725250.997619504454950.498809752227475
800.4592967234102650.9185934468205310.540703276589735
810.423641576163130.847283152326260.57635842383687
820.4478604173812050.895720834762410.552139582618795
830.4331429268066520.8662858536133040.566857073193348
840.3948796403909850.789759280781970.605120359609015
850.3830924544832860.7661849089665710.616907545516714
860.3551372295013150.710274459002630.644862770498685
870.3122761304883040.6245522609766080.687723869511696
880.2876516650146340.5753033300292680.712348334985366
890.2803874572509650.560774914501930.719612542749035
900.2551249389747630.5102498779495270.744875061025237
910.2220800434183590.4441600868367180.777919956581641
920.1904658816043320.3809317632086640.809534118395668
930.1662695888421200.3325391776842390.83373041115788
940.1428980473666710.2857960947333420.857101952633329
950.1202237288755310.2404474577510620.879776271124469
960.1498070315209250.2996140630418510.850192968479075
970.124723747413420.249447494826840.87527625258658
980.1016197989873130.2032395979746270.898380201012687
990.08784278547941510.1756855709588300.912157214520585
1000.07066588110855820.1413317622171160.929334118891442
1010.05906123510885250.1181224702177050.940938764891148
1020.05018812239662810.1003762447932560.949811877603372
1030.06598090726937310.1319618145387460.934019092730627
1040.05810292642379710.1162058528475940.941897073576203
1050.052736311840770.105472623681540.94726368815923
1060.04267574404004990.08535148808009980.95732425595995
1070.03990464550906150.07980929101812290.960095354490939
1080.03020654512820270.06041309025640540.969793454871797
1090.02449505094773750.04899010189547510.975504949052262
1100.02046013687301130.04092027374602260.979539863126989
1110.01831808267300660.03663616534601310.981681917326994
1120.02505437667495100.05010875334990210.974945623325049
1130.0285726550492320.0571453100984640.971427344950768
1140.03277455217643390.06554910435286780.967225447823566
1150.02433252048747190.04866504097494390.975667479512528
1160.01769760574863020.03539521149726050.98230239425137
1170.0510266858924640.1020533717849280.948973314107536
1180.03854275082370570.07708550164741130.961457249176294
1190.04073695896540410.08147391793080830.959263041034596
1200.03134227666561900.06268455333123810.968657723334381
1210.02362030745940160.04724061491880320.976379692540598
1220.01706003044804010.03412006089608010.98293996955196
1230.05697687408596430.1139537481719290.943023125914036
1240.04712294314706140.09424588629412270.952877056852939
1250.03923037460697080.07846074921394160.96076962539303
1260.03458002633641070.06916005267282150.96541997366359
1270.02449352379641540.04898704759283080.975506476203585
1280.07886598763399420.1577319752679880.921134012366006
1290.0614852171904670.1229704343809340.938514782809533
1300.1110229443375460.2220458886750910.888977055662454
1310.1928868851325450.385773770265090.807113114867455
1320.1487643510389750.2975287020779500.851235648961025
1330.1170436114476550.2340872228953100.882956388552345
1340.6514763100380780.6970473799238450.348523689961922
1350.5796353987797350.840729202440530.420364601220265
1360.509405024737290.981189950525420.49059497526271
1370.4515585286863070.9031170573726140.548441471313693
1380.4340807067798770.8681614135597540.565919293220123
1390.3709269243291840.7418538486583670.629073075670816
1400.6691506096447670.6616987807104670.330849390355233
1410.6501563124107360.6996873751785280.349843687589264
1420.5416382652450330.9167234695099340.458361734754967
1430.5028178867940380.9943642264119240.497182113205962
1440.3965459038075070.7930918076150140.603454096192493
1450.2715902935053620.5431805870107240.728409706494638
1460.3948322384789280.7896644769578560.605167761521072

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.868498361736535 & 0.263003276526930 & 0.131501638263465 \tabularnewline
11 & 0.776573187216813 & 0.446853625566374 & 0.223426812783187 \tabularnewline
12 & 0.678445880634021 & 0.643108238731957 & 0.321554119365979 \tabularnewline
13 & 0.588960543091486 & 0.822078913817027 & 0.411039456908514 \tabularnewline
14 & 0.476213813251184 & 0.952427626502369 & 0.523786186748816 \tabularnewline
15 & 0.374375534853299 & 0.748751069706599 & 0.625624465146701 \tabularnewline
16 & 0.398478607977214 & 0.796957215954429 & 0.601521392022786 \tabularnewline
17 & 0.307286126137239 & 0.614572252274477 & 0.692713873862761 \tabularnewline
18 & 0.261794712321611 & 0.523589424643223 & 0.738205287678389 \tabularnewline
19 & 0.193974424390318 & 0.387948848780636 & 0.806025575609682 \tabularnewline
20 & 0.141129739758082 & 0.282259479516165 & 0.858870260241918 \tabularnewline
21 & 0.161440118953599 & 0.322880237907197 & 0.838559881046401 \tabularnewline
22 & 0.115930719797996 & 0.231861439595991 & 0.884069280202004 \tabularnewline
23 & 0.0915782096953283 & 0.183156419390657 & 0.908421790304672 \tabularnewline
24 & 0.0656489817016864 & 0.131297963403373 & 0.934351018298314 \tabularnewline
25 & 0.122892556152686 & 0.245785112305371 & 0.877107443847314 \tabularnewline
26 & 0.107360481545101 & 0.214720963090202 & 0.892639518454899 \tabularnewline
27 & 0.0829838334513259 & 0.165967666902652 & 0.917016166548674 \tabularnewline
28 & 0.0941314666738327 & 0.188262933347665 & 0.905868533326167 \tabularnewline
29 & 0.07348593901096 & 0.14697187802192 & 0.92651406098904 \tabularnewline
30 & 0.071672263289031 & 0.143344526578062 & 0.928327736710969 \tabularnewline
31 & 0.056860666534921 & 0.113721333069842 & 0.94313933346508 \tabularnewline
32 & 0.0404641392784202 & 0.0809282785568405 & 0.95953586072158 \tabularnewline
33 & 0.036568295476246 & 0.073136590952492 & 0.963431704523754 \tabularnewline
34 & 0.0252891728692646 & 0.0505783457385292 & 0.974710827130735 \tabularnewline
35 & 0.0181581419844450 & 0.0363162839688900 & 0.981841858015555 \tabularnewline
36 & 0.0127374625292575 & 0.0254749250585150 & 0.987262537470742 \tabularnewline
37 & 0.0092895920937084 & 0.0185791841874168 & 0.990710407906292 \tabularnewline
38 & 0.0109333349233127 & 0.0218666698466253 & 0.989066665076687 \tabularnewline
39 & 0.0113978562026663 & 0.0227957124053327 & 0.988602143797334 \tabularnewline
40 & 0.0118535774907575 & 0.0237071549815151 & 0.988146422509242 \tabularnewline
41 & 0.00820990649355342 & 0.0164198129871068 & 0.991790093506447 \tabularnewline
42 & 0.0085583603848342 & 0.0171167207696684 & 0.991441639615166 \tabularnewline
43 & 0.00619725474484484 & 0.0123945094896897 & 0.993802745255155 \tabularnewline
44 & 0.0057593182896588 & 0.0115186365793176 & 0.994240681710341 \tabularnewline
45 & 0.00384640032728876 & 0.00769280065457752 & 0.996153599672711 \tabularnewline
46 & 0.00260929195577171 & 0.00521858391154343 & 0.997390708044228 \tabularnewline
47 & 0.172199165342916 & 0.344398330685831 & 0.827800834657084 \tabularnewline
48 & 0.193277423756223 & 0.386554847512446 & 0.806722576243777 \tabularnewline
49 & 0.281974696872212 & 0.563949393744424 & 0.718025303127788 \tabularnewline
50 & 0.279931828766966 & 0.559863657533933 & 0.720068171233034 \tabularnewline
51 & 0.251701882306207 & 0.503403764612414 & 0.748298117693793 \tabularnewline
52 & 0.215085356681531 & 0.430170713363062 & 0.78491464331847 \tabularnewline
53 & 0.232541745671247 & 0.465083491342495 & 0.767458254328753 \tabularnewline
54 & 0.202055249586743 & 0.404110499173487 & 0.797944750413257 \tabularnewline
55 & 0.364956259661258 & 0.729912519322516 & 0.635043740338742 \tabularnewline
56 & 0.407188018409381 & 0.814376036818762 & 0.592811981590619 \tabularnewline
57 & 0.375802032443456 & 0.751604064886912 & 0.624197967556544 \tabularnewline
58 & 0.333321090867895 & 0.66664218173579 & 0.666678909132105 \tabularnewline
59 & 0.324924870498371 & 0.649849740996741 & 0.67507512950163 \tabularnewline
60 & 0.554528814290124 & 0.890942371419752 & 0.445471185709876 \tabularnewline
61 & 0.566555645396126 & 0.866888709207747 & 0.433444354603874 \tabularnewline
62 & 0.521553001960412 & 0.956893996079175 & 0.478446998039588 \tabularnewline
63 & 0.69152661254454 & 0.616946774910919 & 0.308473387455460 \tabularnewline
64 & 0.750998150460793 & 0.498003699078415 & 0.249001849539207 \tabularnewline
65 & 0.806633905303116 & 0.386732189393769 & 0.193366094696884 \tabularnewline
66 & 0.789440129879252 & 0.421119740241495 & 0.210559870120748 \tabularnewline
67 & 0.775148510433076 & 0.449702979133847 & 0.224851489566924 \tabularnewline
68 & 0.764579877162291 & 0.470840245675417 & 0.235420122837709 \tabularnewline
69 & 0.742889677831266 & 0.514220644337468 & 0.257110322168734 \tabularnewline
70 & 0.717476187330592 & 0.565047625338815 & 0.282523812669408 \tabularnewline
71 & 0.730443092947342 & 0.539113814105316 & 0.269556907052658 \tabularnewline
72 & 0.71023138616509 & 0.579537227669819 & 0.289768613834909 \tabularnewline
73 & 0.712707274800285 & 0.574585450399431 & 0.287292725199715 \tabularnewline
74 & 0.694348206682505 & 0.611303586634991 & 0.305651793317495 \tabularnewline
75 & 0.677149661134245 & 0.64570067773151 & 0.322850338865755 \tabularnewline
76 & 0.634066901503644 & 0.731866196992711 & 0.365933098496356 \tabularnewline
77 & 0.588478304900056 & 0.823043390199888 & 0.411521695099944 \tabularnewline
78 & 0.544334705026634 & 0.911330589946732 & 0.455665294973366 \tabularnewline
79 & 0.501190247772525 & 0.99761950445495 & 0.498809752227475 \tabularnewline
80 & 0.459296723410265 & 0.918593446820531 & 0.540703276589735 \tabularnewline
81 & 0.42364157616313 & 0.84728315232626 & 0.57635842383687 \tabularnewline
82 & 0.447860417381205 & 0.89572083476241 & 0.552139582618795 \tabularnewline
83 & 0.433142926806652 & 0.866285853613304 & 0.566857073193348 \tabularnewline
84 & 0.394879640390985 & 0.78975928078197 & 0.605120359609015 \tabularnewline
85 & 0.383092454483286 & 0.766184908966571 & 0.616907545516714 \tabularnewline
86 & 0.355137229501315 & 0.71027445900263 & 0.644862770498685 \tabularnewline
87 & 0.312276130488304 & 0.624552260976608 & 0.687723869511696 \tabularnewline
88 & 0.287651665014634 & 0.575303330029268 & 0.712348334985366 \tabularnewline
89 & 0.280387457250965 & 0.56077491450193 & 0.719612542749035 \tabularnewline
90 & 0.255124938974763 & 0.510249877949527 & 0.744875061025237 \tabularnewline
91 & 0.222080043418359 & 0.444160086836718 & 0.777919956581641 \tabularnewline
92 & 0.190465881604332 & 0.380931763208664 & 0.809534118395668 \tabularnewline
93 & 0.166269588842120 & 0.332539177684239 & 0.83373041115788 \tabularnewline
94 & 0.142898047366671 & 0.285796094733342 & 0.857101952633329 \tabularnewline
95 & 0.120223728875531 & 0.240447457751062 & 0.879776271124469 \tabularnewline
96 & 0.149807031520925 & 0.299614063041851 & 0.850192968479075 \tabularnewline
97 & 0.12472374741342 & 0.24944749482684 & 0.87527625258658 \tabularnewline
98 & 0.101619798987313 & 0.203239597974627 & 0.898380201012687 \tabularnewline
99 & 0.0878427854794151 & 0.175685570958830 & 0.912157214520585 \tabularnewline
100 & 0.0706658811085582 & 0.141331762217116 & 0.929334118891442 \tabularnewline
101 & 0.0590612351088525 & 0.118122470217705 & 0.940938764891148 \tabularnewline
102 & 0.0501881223966281 & 0.100376244793256 & 0.949811877603372 \tabularnewline
103 & 0.0659809072693731 & 0.131961814538746 & 0.934019092730627 \tabularnewline
104 & 0.0581029264237971 & 0.116205852847594 & 0.941897073576203 \tabularnewline
105 & 0.05273631184077 & 0.10547262368154 & 0.94726368815923 \tabularnewline
106 & 0.0426757440400499 & 0.0853514880800998 & 0.95732425595995 \tabularnewline
107 & 0.0399046455090615 & 0.0798092910181229 & 0.960095354490939 \tabularnewline
108 & 0.0302065451282027 & 0.0604130902564054 & 0.969793454871797 \tabularnewline
109 & 0.0244950509477375 & 0.0489901018954751 & 0.975504949052262 \tabularnewline
110 & 0.0204601368730113 & 0.0409202737460226 & 0.979539863126989 \tabularnewline
111 & 0.0183180826730066 & 0.0366361653460131 & 0.981681917326994 \tabularnewline
112 & 0.0250543766749510 & 0.0501087533499021 & 0.974945623325049 \tabularnewline
113 & 0.028572655049232 & 0.057145310098464 & 0.971427344950768 \tabularnewline
114 & 0.0327745521764339 & 0.0655491043528678 & 0.967225447823566 \tabularnewline
115 & 0.0243325204874719 & 0.0486650409749439 & 0.975667479512528 \tabularnewline
116 & 0.0176976057486302 & 0.0353952114972605 & 0.98230239425137 \tabularnewline
117 & 0.051026685892464 & 0.102053371784928 & 0.948973314107536 \tabularnewline
118 & 0.0385427508237057 & 0.0770855016474113 & 0.961457249176294 \tabularnewline
119 & 0.0407369589654041 & 0.0814739179308083 & 0.959263041034596 \tabularnewline
120 & 0.0313422766656190 & 0.0626845533312381 & 0.968657723334381 \tabularnewline
121 & 0.0236203074594016 & 0.0472406149188032 & 0.976379692540598 \tabularnewline
122 & 0.0170600304480401 & 0.0341200608960801 & 0.98293996955196 \tabularnewline
123 & 0.0569768740859643 & 0.113953748171929 & 0.943023125914036 \tabularnewline
124 & 0.0471229431470614 & 0.0942458862941227 & 0.952877056852939 \tabularnewline
125 & 0.0392303746069708 & 0.0784607492139416 & 0.96076962539303 \tabularnewline
126 & 0.0345800263364107 & 0.0691600526728215 & 0.96541997366359 \tabularnewline
127 & 0.0244935237964154 & 0.0489870475928308 & 0.975506476203585 \tabularnewline
128 & 0.0788659876339942 & 0.157731975267988 & 0.921134012366006 \tabularnewline
129 & 0.061485217190467 & 0.122970434380934 & 0.938514782809533 \tabularnewline
130 & 0.111022944337546 & 0.222045888675091 & 0.888977055662454 \tabularnewline
131 & 0.192886885132545 & 0.38577377026509 & 0.807113114867455 \tabularnewline
132 & 0.148764351038975 & 0.297528702077950 & 0.851235648961025 \tabularnewline
133 & 0.117043611447655 & 0.234087222895310 & 0.882956388552345 \tabularnewline
134 & 0.651476310038078 & 0.697047379923845 & 0.348523689961922 \tabularnewline
135 & 0.579635398779735 & 0.84072920244053 & 0.420364601220265 \tabularnewline
136 & 0.50940502473729 & 0.98118995052542 & 0.49059497526271 \tabularnewline
137 & 0.451558528686307 & 0.903117057372614 & 0.548441471313693 \tabularnewline
138 & 0.434080706779877 & 0.868161413559754 & 0.565919293220123 \tabularnewline
139 & 0.370926924329184 & 0.741853848658367 & 0.629073075670816 \tabularnewline
140 & 0.669150609644767 & 0.661698780710467 & 0.330849390355233 \tabularnewline
141 & 0.650156312410736 & 0.699687375178528 & 0.349843687589264 \tabularnewline
142 & 0.541638265245033 & 0.916723469509934 & 0.458361734754967 \tabularnewline
143 & 0.502817886794038 & 0.994364226411924 & 0.497182113205962 \tabularnewline
144 & 0.396545903807507 & 0.793091807615014 & 0.603454096192493 \tabularnewline
145 & 0.271590293505362 & 0.543180587010724 & 0.728409706494638 \tabularnewline
146 & 0.394832238478928 & 0.789664476957856 & 0.605167761521072 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&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]0.868498361736535[/C][C]0.263003276526930[/C][C]0.131501638263465[/C][/ROW]
[ROW][C]11[/C][C]0.776573187216813[/C][C]0.446853625566374[/C][C]0.223426812783187[/C][/ROW]
[ROW][C]12[/C][C]0.678445880634021[/C][C]0.643108238731957[/C][C]0.321554119365979[/C][/ROW]
[ROW][C]13[/C][C]0.588960543091486[/C][C]0.822078913817027[/C][C]0.411039456908514[/C][/ROW]
[ROW][C]14[/C][C]0.476213813251184[/C][C]0.952427626502369[/C][C]0.523786186748816[/C][/ROW]
[ROW][C]15[/C][C]0.374375534853299[/C][C]0.748751069706599[/C][C]0.625624465146701[/C][/ROW]
[ROW][C]16[/C][C]0.398478607977214[/C][C]0.796957215954429[/C][C]0.601521392022786[/C][/ROW]
[ROW][C]17[/C][C]0.307286126137239[/C][C]0.614572252274477[/C][C]0.692713873862761[/C][/ROW]
[ROW][C]18[/C][C]0.261794712321611[/C][C]0.523589424643223[/C][C]0.738205287678389[/C][/ROW]
[ROW][C]19[/C][C]0.193974424390318[/C][C]0.387948848780636[/C][C]0.806025575609682[/C][/ROW]
[ROW][C]20[/C][C]0.141129739758082[/C][C]0.282259479516165[/C][C]0.858870260241918[/C][/ROW]
[ROW][C]21[/C][C]0.161440118953599[/C][C]0.322880237907197[/C][C]0.838559881046401[/C][/ROW]
[ROW][C]22[/C][C]0.115930719797996[/C][C]0.231861439595991[/C][C]0.884069280202004[/C][/ROW]
[ROW][C]23[/C][C]0.0915782096953283[/C][C]0.183156419390657[/C][C]0.908421790304672[/C][/ROW]
[ROW][C]24[/C][C]0.0656489817016864[/C][C]0.131297963403373[/C][C]0.934351018298314[/C][/ROW]
[ROW][C]25[/C][C]0.122892556152686[/C][C]0.245785112305371[/C][C]0.877107443847314[/C][/ROW]
[ROW][C]26[/C][C]0.107360481545101[/C][C]0.214720963090202[/C][C]0.892639518454899[/C][/ROW]
[ROW][C]27[/C][C]0.0829838334513259[/C][C]0.165967666902652[/C][C]0.917016166548674[/C][/ROW]
[ROW][C]28[/C][C]0.0941314666738327[/C][C]0.188262933347665[/C][C]0.905868533326167[/C][/ROW]
[ROW][C]29[/C][C]0.07348593901096[/C][C]0.14697187802192[/C][C]0.92651406098904[/C][/ROW]
[ROW][C]30[/C][C]0.071672263289031[/C][C]0.143344526578062[/C][C]0.928327736710969[/C][/ROW]
[ROW][C]31[/C][C]0.056860666534921[/C][C]0.113721333069842[/C][C]0.94313933346508[/C][/ROW]
[ROW][C]32[/C][C]0.0404641392784202[/C][C]0.0809282785568405[/C][C]0.95953586072158[/C][/ROW]
[ROW][C]33[/C][C]0.036568295476246[/C][C]0.073136590952492[/C][C]0.963431704523754[/C][/ROW]
[ROW][C]34[/C][C]0.0252891728692646[/C][C]0.0505783457385292[/C][C]0.974710827130735[/C][/ROW]
[ROW][C]35[/C][C]0.0181581419844450[/C][C]0.0363162839688900[/C][C]0.981841858015555[/C][/ROW]
[ROW][C]36[/C][C]0.0127374625292575[/C][C]0.0254749250585150[/C][C]0.987262537470742[/C][/ROW]
[ROW][C]37[/C][C]0.0092895920937084[/C][C]0.0185791841874168[/C][C]0.990710407906292[/C][/ROW]
[ROW][C]38[/C][C]0.0109333349233127[/C][C]0.0218666698466253[/C][C]0.989066665076687[/C][/ROW]
[ROW][C]39[/C][C]0.0113978562026663[/C][C]0.0227957124053327[/C][C]0.988602143797334[/C][/ROW]
[ROW][C]40[/C][C]0.0118535774907575[/C][C]0.0237071549815151[/C][C]0.988146422509242[/C][/ROW]
[ROW][C]41[/C][C]0.00820990649355342[/C][C]0.0164198129871068[/C][C]0.991790093506447[/C][/ROW]
[ROW][C]42[/C][C]0.0085583603848342[/C][C]0.0171167207696684[/C][C]0.991441639615166[/C][/ROW]
[ROW][C]43[/C][C]0.00619725474484484[/C][C]0.0123945094896897[/C][C]0.993802745255155[/C][/ROW]
[ROW][C]44[/C][C]0.0057593182896588[/C][C]0.0115186365793176[/C][C]0.994240681710341[/C][/ROW]
[ROW][C]45[/C][C]0.00384640032728876[/C][C]0.00769280065457752[/C][C]0.996153599672711[/C][/ROW]
[ROW][C]46[/C][C]0.00260929195577171[/C][C]0.00521858391154343[/C][C]0.997390708044228[/C][/ROW]
[ROW][C]47[/C][C]0.172199165342916[/C][C]0.344398330685831[/C][C]0.827800834657084[/C][/ROW]
[ROW][C]48[/C][C]0.193277423756223[/C][C]0.386554847512446[/C][C]0.806722576243777[/C][/ROW]
[ROW][C]49[/C][C]0.281974696872212[/C][C]0.563949393744424[/C][C]0.718025303127788[/C][/ROW]
[ROW][C]50[/C][C]0.279931828766966[/C][C]0.559863657533933[/C][C]0.720068171233034[/C][/ROW]
[ROW][C]51[/C][C]0.251701882306207[/C][C]0.503403764612414[/C][C]0.748298117693793[/C][/ROW]
[ROW][C]52[/C][C]0.215085356681531[/C][C]0.430170713363062[/C][C]0.78491464331847[/C][/ROW]
[ROW][C]53[/C][C]0.232541745671247[/C][C]0.465083491342495[/C][C]0.767458254328753[/C][/ROW]
[ROW][C]54[/C][C]0.202055249586743[/C][C]0.404110499173487[/C][C]0.797944750413257[/C][/ROW]
[ROW][C]55[/C][C]0.364956259661258[/C][C]0.729912519322516[/C][C]0.635043740338742[/C][/ROW]
[ROW][C]56[/C][C]0.407188018409381[/C][C]0.814376036818762[/C][C]0.592811981590619[/C][/ROW]
[ROW][C]57[/C][C]0.375802032443456[/C][C]0.751604064886912[/C][C]0.624197967556544[/C][/ROW]
[ROW][C]58[/C][C]0.333321090867895[/C][C]0.66664218173579[/C][C]0.666678909132105[/C][/ROW]
[ROW][C]59[/C][C]0.324924870498371[/C][C]0.649849740996741[/C][C]0.67507512950163[/C][/ROW]
[ROW][C]60[/C][C]0.554528814290124[/C][C]0.890942371419752[/C][C]0.445471185709876[/C][/ROW]
[ROW][C]61[/C][C]0.566555645396126[/C][C]0.866888709207747[/C][C]0.433444354603874[/C][/ROW]
[ROW][C]62[/C][C]0.521553001960412[/C][C]0.956893996079175[/C][C]0.478446998039588[/C][/ROW]
[ROW][C]63[/C][C]0.69152661254454[/C][C]0.616946774910919[/C][C]0.308473387455460[/C][/ROW]
[ROW][C]64[/C][C]0.750998150460793[/C][C]0.498003699078415[/C][C]0.249001849539207[/C][/ROW]
[ROW][C]65[/C][C]0.806633905303116[/C][C]0.386732189393769[/C][C]0.193366094696884[/C][/ROW]
[ROW][C]66[/C][C]0.789440129879252[/C][C]0.421119740241495[/C][C]0.210559870120748[/C][/ROW]
[ROW][C]67[/C][C]0.775148510433076[/C][C]0.449702979133847[/C][C]0.224851489566924[/C][/ROW]
[ROW][C]68[/C][C]0.764579877162291[/C][C]0.470840245675417[/C][C]0.235420122837709[/C][/ROW]
[ROW][C]69[/C][C]0.742889677831266[/C][C]0.514220644337468[/C][C]0.257110322168734[/C][/ROW]
[ROW][C]70[/C][C]0.717476187330592[/C][C]0.565047625338815[/C][C]0.282523812669408[/C][/ROW]
[ROW][C]71[/C][C]0.730443092947342[/C][C]0.539113814105316[/C][C]0.269556907052658[/C][/ROW]
[ROW][C]72[/C][C]0.71023138616509[/C][C]0.579537227669819[/C][C]0.289768613834909[/C][/ROW]
[ROW][C]73[/C][C]0.712707274800285[/C][C]0.574585450399431[/C][C]0.287292725199715[/C][/ROW]
[ROW][C]74[/C][C]0.694348206682505[/C][C]0.611303586634991[/C][C]0.305651793317495[/C][/ROW]
[ROW][C]75[/C][C]0.677149661134245[/C][C]0.64570067773151[/C][C]0.322850338865755[/C][/ROW]
[ROW][C]76[/C][C]0.634066901503644[/C][C]0.731866196992711[/C][C]0.365933098496356[/C][/ROW]
[ROW][C]77[/C][C]0.588478304900056[/C][C]0.823043390199888[/C][C]0.411521695099944[/C][/ROW]
[ROW][C]78[/C][C]0.544334705026634[/C][C]0.911330589946732[/C][C]0.455665294973366[/C][/ROW]
[ROW][C]79[/C][C]0.501190247772525[/C][C]0.99761950445495[/C][C]0.498809752227475[/C][/ROW]
[ROW][C]80[/C][C]0.459296723410265[/C][C]0.918593446820531[/C][C]0.540703276589735[/C][/ROW]
[ROW][C]81[/C][C]0.42364157616313[/C][C]0.84728315232626[/C][C]0.57635842383687[/C][/ROW]
[ROW][C]82[/C][C]0.447860417381205[/C][C]0.89572083476241[/C][C]0.552139582618795[/C][/ROW]
[ROW][C]83[/C][C]0.433142926806652[/C][C]0.866285853613304[/C][C]0.566857073193348[/C][/ROW]
[ROW][C]84[/C][C]0.394879640390985[/C][C]0.78975928078197[/C][C]0.605120359609015[/C][/ROW]
[ROW][C]85[/C][C]0.383092454483286[/C][C]0.766184908966571[/C][C]0.616907545516714[/C][/ROW]
[ROW][C]86[/C][C]0.355137229501315[/C][C]0.71027445900263[/C][C]0.644862770498685[/C][/ROW]
[ROW][C]87[/C][C]0.312276130488304[/C][C]0.624552260976608[/C][C]0.687723869511696[/C][/ROW]
[ROW][C]88[/C][C]0.287651665014634[/C][C]0.575303330029268[/C][C]0.712348334985366[/C][/ROW]
[ROW][C]89[/C][C]0.280387457250965[/C][C]0.56077491450193[/C][C]0.719612542749035[/C][/ROW]
[ROW][C]90[/C][C]0.255124938974763[/C][C]0.510249877949527[/C][C]0.744875061025237[/C][/ROW]
[ROW][C]91[/C][C]0.222080043418359[/C][C]0.444160086836718[/C][C]0.777919956581641[/C][/ROW]
[ROW][C]92[/C][C]0.190465881604332[/C][C]0.380931763208664[/C][C]0.809534118395668[/C][/ROW]
[ROW][C]93[/C][C]0.166269588842120[/C][C]0.332539177684239[/C][C]0.83373041115788[/C][/ROW]
[ROW][C]94[/C][C]0.142898047366671[/C][C]0.285796094733342[/C][C]0.857101952633329[/C][/ROW]
[ROW][C]95[/C][C]0.120223728875531[/C][C]0.240447457751062[/C][C]0.879776271124469[/C][/ROW]
[ROW][C]96[/C][C]0.149807031520925[/C][C]0.299614063041851[/C][C]0.850192968479075[/C][/ROW]
[ROW][C]97[/C][C]0.12472374741342[/C][C]0.24944749482684[/C][C]0.87527625258658[/C][/ROW]
[ROW][C]98[/C][C]0.101619798987313[/C][C]0.203239597974627[/C][C]0.898380201012687[/C][/ROW]
[ROW][C]99[/C][C]0.0878427854794151[/C][C]0.175685570958830[/C][C]0.912157214520585[/C][/ROW]
[ROW][C]100[/C][C]0.0706658811085582[/C][C]0.141331762217116[/C][C]0.929334118891442[/C][/ROW]
[ROW][C]101[/C][C]0.0590612351088525[/C][C]0.118122470217705[/C][C]0.940938764891148[/C][/ROW]
[ROW][C]102[/C][C]0.0501881223966281[/C][C]0.100376244793256[/C][C]0.949811877603372[/C][/ROW]
[ROW][C]103[/C][C]0.0659809072693731[/C][C]0.131961814538746[/C][C]0.934019092730627[/C][/ROW]
[ROW][C]104[/C][C]0.0581029264237971[/C][C]0.116205852847594[/C][C]0.941897073576203[/C][/ROW]
[ROW][C]105[/C][C]0.05273631184077[/C][C]0.10547262368154[/C][C]0.94726368815923[/C][/ROW]
[ROW][C]106[/C][C]0.0426757440400499[/C][C]0.0853514880800998[/C][C]0.95732425595995[/C][/ROW]
[ROW][C]107[/C][C]0.0399046455090615[/C][C]0.0798092910181229[/C][C]0.960095354490939[/C][/ROW]
[ROW][C]108[/C][C]0.0302065451282027[/C][C]0.0604130902564054[/C][C]0.969793454871797[/C][/ROW]
[ROW][C]109[/C][C]0.0244950509477375[/C][C]0.0489901018954751[/C][C]0.975504949052262[/C][/ROW]
[ROW][C]110[/C][C]0.0204601368730113[/C][C]0.0409202737460226[/C][C]0.979539863126989[/C][/ROW]
[ROW][C]111[/C][C]0.0183180826730066[/C][C]0.0366361653460131[/C][C]0.981681917326994[/C][/ROW]
[ROW][C]112[/C][C]0.0250543766749510[/C][C]0.0501087533499021[/C][C]0.974945623325049[/C][/ROW]
[ROW][C]113[/C][C]0.028572655049232[/C][C]0.057145310098464[/C][C]0.971427344950768[/C][/ROW]
[ROW][C]114[/C][C]0.0327745521764339[/C][C]0.0655491043528678[/C][C]0.967225447823566[/C][/ROW]
[ROW][C]115[/C][C]0.0243325204874719[/C][C]0.0486650409749439[/C][C]0.975667479512528[/C][/ROW]
[ROW][C]116[/C][C]0.0176976057486302[/C][C]0.0353952114972605[/C][C]0.98230239425137[/C][/ROW]
[ROW][C]117[/C][C]0.051026685892464[/C][C]0.102053371784928[/C][C]0.948973314107536[/C][/ROW]
[ROW][C]118[/C][C]0.0385427508237057[/C][C]0.0770855016474113[/C][C]0.961457249176294[/C][/ROW]
[ROW][C]119[/C][C]0.0407369589654041[/C][C]0.0814739179308083[/C][C]0.959263041034596[/C][/ROW]
[ROW][C]120[/C][C]0.0313422766656190[/C][C]0.0626845533312381[/C][C]0.968657723334381[/C][/ROW]
[ROW][C]121[/C][C]0.0236203074594016[/C][C]0.0472406149188032[/C][C]0.976379692540598[/C][/ROW]
[ROW][C]122[/C][C]0.0170600304480401[/C][C]0.0341200608960801[/C][C]0.98293996955196[/C][/ROW]
[ROW][C]123[/C][C]0.0569768740859643[/C][C]0.113953748171929[/C][C]0.943023125914036[/C][/ROW]
[ROW][C]124[/C][C]0.0471229431470614[/C][C]0.0942458862941227[/C][C]0.952877056852939[/C][/ROW]
[ROW][C]125[/C][C]0.0392303746069708[/C][C]0.0784607492139416[/C][C]0.96076962539303[/C][/ROW]
[ROW][C]126[/C][C]0.0345800263364107[/C][C]0.0691600526728215[/C][C]0.96541997366359[/C][/ROW]
[ROW][C]127[/C][C]0.0244935237964154[/C][C]0.0489870475928308[/C][C]0.975506476203585[/C][/ROW]
[ROW][C]128[/C][C]0.0788659876339942[/C][C]0.157731975267988[/C][C]0.921134012366006[/C][/ROW]
[ROW][C]129[/C][C]0.061485217190467[/C][C]0.122970434380934[/C][C]0.938514782809533[/C][/ROW]
[ROW][C]130[/C][C]0.111022944337546[/C][C]0.222045888675091[/C][C]0.888977055662454[/C][/ROW]
[ROW][C]131[/C][C]0.192886885132545[/C][C]0.38577377026509[/C][C]0.807113114867455[/C][/ROW]
[ROW][C]132[/C][C]0.148764351038975[/C][C]0.297528702077950[/C][C]0.851235648961025[/C][/ROW]
[ROW][C]133[/C][C]0.117043611447655[/C][C]0.234087222895310[/C][C]0.882956388552345[/C][/ROW]
[ROW][C]134[/C][C]0.651476310038078[/C][C]0.697047379923845[/C][C]0.348523689961922[/C][/ROW]
[ROW][C]135[/C][C]0.579635398779735[/C][C]0.84072920244053[/C][C]0.420364601220265[/C][/ROW]
[ROW][C]136[/C][C]0.50940502473729[/C][C]0.98118995052542[/C][C]0.49059497526271[/C][/ROW]
[ROW][C]137[/C][C]0.451558528686307[/C][C]0.903117057372614[/C][C]0.548441471313693[/C][/ROW]
[ROW][C]138[/C][C]0.434080706779877[/C][C]0.868161413559754[/C][C]0.565919293220123[/C][/ROW]
[ROW][C]139[/C][C]0.370926924329184[/C][C]0.741853848658367[/C][C]0.629073075670816[/C][/ROW]
[ROW][C]140[/C][C]0.669150609644767[/C][C]0.661698780710467[/C][C]0.330849390355233[/C][/ROW]
[ROW][C]141[/C][C]0.650156312410736[/C][C]0.699687375178528[/C][C]0.349843687589264[/C][/ROW]
[ROW][C]142[/C][C]0.541638265245033[/C][C]0.916723469509934[/C][C]0.458361734754967[/C][/ROW]
[ROW][C]143[/C][C]0.502817886794038[/C][C]0.994364226411924[/C][C]0.497182113205962[/C][/ROW]
[ROW][C]144[/C][C]0.396545903807507[/C][C]0.793091807615014[/C][C]0.603454096192493[/C][/ROW]
[ROW][C]145[/C][C]0.271590293505362[/C][C]0.543180587010724[/C][C]0.728409706494638[/C][/ROW]
[ROW][C]146[/C][C]0.394832238478928[/C][C]0.789664476957856[/C][C]0.605167761521072[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&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
100.8684983617365350.2630032765269300.131501638263465
110.7765731872168130.4468536255663740.223426812783187
120.6784458806340210.6431082387319570.321554119365979
130.5889605430914860.8220789138170270.411039456908514
140.4762138132511840.9524276265023690.523786186748816
150.3743755348532990.7487510697065990.625624465146701
160.3984786079772140.7969572159544290.601521392022786
170.3072861261372390.6145722522744770.692713873862761
180.2617947123216110.5235894246432230.738205287678389
190.1939744243903180.3879488487806360.806025575609682
200.1411297397580820.2822594795161650.858870260241918
210.1614401189535990.3228802379071970.838559881046401
220.1159307197979960.2318614395959910.884069280202004
230.09157820969532830.1831564193906570.908421790304672
240.06564898170168640.1312979634033730.934351018298314
250.1228925561526860.2457851123053710.877107443847314
260.1073604815451010.2147209630902020.892639518454899
270.08298383345132590.1659676669026520.917016166548674
280.09413146667383270.1882629333476650.905868533326167
290.073485939010960.146971878021920.92651406098904
300.0716722632890310.1433445265780620.928327736710969
310.0568606665349210.1137213330698420.94313933346508
320.04046413927842020.08092827855684050.95953586072158
330.0365682954762460.0731365909524920.963431704523754
340.02528917286926460.05057834573852920.974710827130735
350.01815814198444500.03631628396889000.981841858015555
360.01273746252925750.02547492505851500.987262537470742
370.00928959209370840.01857918418741680.990710407906292
380.01093333492331270.02186666984662530.989066665076687
390.01139785620266630.02279571240533270.988602143797334
400.01185357749075750.02370715498151510.988146422509242
410.008209906493553420.01641981298710680.991790093506447
420.00855836038483420.01711672076966840.991441639615166
430.006197254744844840.01239450948968970.993802745255155
440.00575931828965880.01151863657931760.994240681710341
450.003846400327288760.007692800654577520.996153599672711
460.002609291955771710.005218583911543430.997390708044228
470.1721991653429160.3443983306858310.827800834657084
480.1932774237562230.3865548475124460.806722576243777
490.2819746968722120.5639493937444240.718025303127788
500.2799318287669660.5598636575339330.720068171233034
510.2517018823062070.5034037646124140.748298117693793
520.2150853566815310.4301707133630620.78491464331847
530.2325417456712470.4650834913424950.767458254328753
540.2020552495867430.4041104991734870.797944750413257
550.3649562596612580.7299125193225160.635043740338742
560.4071880184093810.8143760368187620.592811981590619
570.3758020324434560.7516040648869120.624197967556544
580.3333210908678950.666642181735790.666678909132105
590.3249248704983710.6498497409967410.67507512950163
600.5545288142901240.8909423714197520.445471185709876
610.5665556453961260.8668887092077470.433444354603874
620.5215530019604120.9568939960791750.478446998039588
630.691526612544540.6169467749109190.308473387455460
640.7509981504607930.4980036990784150.249001849539207
650.8066339053031160.3867321893937690.193366094696884
660.7894401298792520.4211197402414950.210559870120748
670.7751485104330760.4497029791338470.224851489566924
680.7645798771622910.4708402456754170.235420122837709
690.7428896778312660.5142206443374680.257110322168734
700.7174761873305920.5650476253388150.282523812669408
710.7304430929473420.5391138141053160.269556907052658
720.710231386165090.5795372276698190.289768613834909
730.7127072748002850.5745854503994310.287292725199715
740.6943482066825050.6113035866349910.305651793317495
750.6771496611342450.645700677731510.322850338865755
760.6340669015036440.7318661969927110.365933098496356
770.5884783049000560.8230433901998880.411521695099944
780.5443347050266340.9113305899467320.455665294973366
790.5011902477725250.997619504454950.498809752227475
800.4592967234102650.9185934468205310.540703276589735
810.423641576163130.847283152326260.57635842383687
820.4478604173812050.895720834762410.552139582618795
830.4331429268066520.8662858536133040.566857073193348
840.3948796403909850.789759280781970.605120359609015
850.3830924544832860.7661849089665710.616907545516714
860.3551372295013150.710274459002630.644862770498685
870.3122761304883040.6245522609766080.687723869511696
880.2876516650146340.5753033300292680.712348334985366
890.2803874572509650.560774914501930.719612542749035
900.2551249389747630.5102498779495270.744875061025237
910.2220800434183590.4441600868367180.777919956581641
920.1904658816043320.3809317632086640.809534118395668
930.1662695888421200.3325391776842390.83373041115788
940.1428980473666710.2857960947333420.857101952633329
950.1202237288755310.2404474577510620.879776271124469
960.1498070315209250.2996140630418510.850192968479075
970.124723747413420.249447494826840.87527625258658
980.1016197989873130.2032395979746270.898380201012687
990.08784278547941510.1756855709588300.912157214520585
1000.07066588110855820.1413317622171160.929334118891442
1010.05906123510885250.1181224702177050.940938764891148
1020.05018812239662810.1003762447932560.949811877603372
1030.06598090726937310.1319618145387460.934019092730627
1040.05810292642379710.1162058528475940.941897073576203
1050.052736311840770.105472623681540.94726368815923
1060.04267574404004990.08535148808009980.95732425595995
1070.03990464550906150.07980929101812290.960095354490939
1080.03020654512820270.06041309025640540.969793454871797
1090.02449505094773750.04899010189547510.975504949052262
1100.02046013687301130.04092027374602260.979539863126989
1110.01831808267300660.03663616534601310.981681917326994
1120.02505437667495100.05010875334990210.974945623325049
1130.0285726550492320.0571453100984640.971427344950768
1140.03277455217643390.06554910435286780.967225447823566
1150.02433252048747190.04866504097494390.975667479512528
1160.01769760574863020.03539521149726050.98230239425137
1170.0510266858924640.1020533717849280.948973314107536
1180.03854275082370570.07708550164741130.961457249176294
1190.04073695896540410.08147391793080830.959263041034596
1200.03134227666561900.06268455333123810.968657723334381
1210.02362030745940160.04724061491880320.976379692540598
1220.01706003044804010.03412006089608010.98293996955196
1230.05697687408596430.1139537481719290.943023125914036
1240.04712294314706140.09424588629412270.952877056852939
1250.03923037460697080.07846074921394160.96076962539303
1260.03458002633641070.06916005267282150.96541997366359
1270.02449352379641540.04898704759283080.975506476203585
1280.07886598763399420.1577319752679880.921134012366006
1290.0614852171904670.1229704343809340.938514782809533
1300.1110229443375460.2220458886750910.888977055662454
1310.1928868851325450.385773770265090.807113114867455
1320.1487643510389750.2975287020779500.851235648961025
1330.1170436114476550.2340872228953100.882956388552345
1340.6514763100380780.6970473799238450.348523689961922
1350.5796353987797350.840729202440530.420364601220265
1360.509405024737290.981189950525420.49059497526271
1370.4515585286863070.9031170573726140.548441471313693
1380.4340807067798770.8681614135597540.565919293220123
1390.3709269243291840.7418538486583670.629073075670816
1400.6691506096447670.6616987807104670.330849390355233
1410.6501563124107360.6996873751785280.349843687589264
1420.5416382652450330.9167234695099340.458361734754967
1430.5028178867940380.9943642264119240.497182113205962
1440.3965459038075070.7930918076150140.603454096192493
1450.2715902935053620.5431805870107240.728409706494638
1460.3948322384789280.7896644769578560.605167761521072






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0145985401459854NOK
5% type I error level200.145985401459854NOK
10% type I error level350.255474452554745NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 2 & 0.0145985401459854 & NOK \tabularnewline
5% type I error level & 20 & 0.145985401459854 & NOK \tabularnewline
10% type I error level & 35 & 0.255474452554745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104124&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]2[/C][C]0.0145985401459854[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.145985401459854[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.255474452554745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104124&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104124&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0145985401459854NOK
5% type I error level200.145985401459854NOK
10% type I error level350.255474452554745NOK


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