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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Dec 2012 09:58:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/19/t1355929214y4rvhgav7l9rirx.htm/, Retrieved Thu, 31 Oct 2024 23:12:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202039, Retrieved Thu, 31 Oct 2024 23:12:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2011-12-21 09:12:12] [2417ae1b112c0bd5f0a8e2d9469d5871]
- RMPD      [Multiple Regression] [regressie] [2012-12-19 14:58:48] [69fed4bf76000787e6433dea6d892b14] [Current]
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Dataseries X:
1	4	1	0
0	4	0	0
0	4	0	0
0	4	0	0
0	4	0	0
1	4	0	0
0	4	0	0
0	4	1	0
1	4	0	0
0	4	0	0
0	4	1	0
0	4	0	0
0	4	0	0
0	4	1	0
1	4	0	0
1	4	1	0
0	4	1	1
0	4	1	0
1	4	0	0
1	4	1	1
0	4	0	0
1	4	0	0
1	4	0	0
1	4	0	0
1	4	1	0
0	4	1	0
1	4	0	0
0	4	0	0
1	4	0	0
0	4	0	0
0	4	0	0
0	4	0	0
0	4	0	0
1	4	1	0
0	4	0	0
0	4	0	0
0	4	1	0
1	4	0	0
1	4	0	0
0	4	1	0
1	4	0	1
1	4	0	0
1	4	0	0
0	4	1	0
0	4	0	0
1	4	0	0
0	4	0	0
1	4	0	0
1	4	0	0
0	4	0	0
0	4	1	0
0	4	1	1
1	4	0	0
0	4	0	1
0	4	0	0
1	4	1	0
1	4	0	0
1	4	0	0
1	4	0	0
1	4	1	1
1	4	1	0
0	4	0	0
0	4	0	0
1	4	1	0
0	4	0	0
0	4	0	0
0	4	1	1
0	4	0	0
1	4	0	0
0	4	0	0
0	4	0	0
1	4	0	0
1	4	0	0
0	4	0	0
1	4	0	0
1	4	1	0
1	4	0	0
1	4	0	0
1	4	1	1
0	4	1	0
0	4	0	0
1	4	0	0
0	4	0	0
0	4	0	1
1	4	0	0
0	4	0	0
1	2	0	0
1	2	1	0
0	2	0	0
1	2	0	0
0	2	0	0
0	2	1	0
0	2	0	0
0	2	0	0
0	2	1	0
1	2	0	0
0	2	1	0
0	2	0	0
0	2	0	0
1	2	0	0
1	2	0	0
0	2	0	0
0	2	0	0
0	2	0	0
0	2	1	0
0	2	0	0
0	2	0	0
0	2	1	0
0	2	0	0
0	2	0	0
0	2	1	0
0	2	1	0
0	2	0	0
0	2	1	0
0	2	0	0
0	2	0	0
1	2	0	0
0	2	0	0
0	2	0	0
1	2	0	0
0	2	0	0
0	2	0	0
0	2	1	0
1	2	0	0
1	2	0	0
0	2	1	0
0	2	0	0
1	2	0	0
0	2	0	0
1	2	0	0
0	2	0	0
1	2	0	0
0	2	0	0
0	2	0	0
0	2	0	0
0	2	0	0
1	2	0	0
1	2	1	0
0	2	1	0
0	2	0	0
1	2	0	1
1	2	1	0
0	2	0	0
1	2	0	0
0	2	0	0
1	2	1	0
0	2	1	0
0	2	1	0
0	2	0	0
1	2	0	0
1	2	0	0
0	2	0	1
0	2	0	1
0	2	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
O[t] = -0.259645147048716 + 0.161510344711488`#`[t] -0.0365584034265199T[t] -0.033617071042175C[t] + 0.0021250679997471t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  -0.259645147048716 +  0.161510344711488`#`[t] -0.0365584034265199T[t] -0.033617071042175C[t] +  0.0021250679997471t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  -0.259645147048716 +  0.161510344711488`#`[t] -0.0365584034265199T[t] -0.033617071042175C[t] +  0.0021250679997471t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
O[t] = -0.259645147048716 + 0.161510344711488`#`[t] -0.0365584034265199T[t] -0.033617071042175C[t] + 0.0021250679997471t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2596451470487160.375947-0.69060.4908640.245432
`#`0.1615103447114880.0796572.02760.0443850.022193
T-0.03655840342651990.090507-0.40390.6868430.343421
C-0.0336170710421750.152233-0.22080.825530.412765
t0.00212506799974710.0017721.19950.2322340.116117

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.259645147048716 & 0.375947 & -0.6906 & 0.490864 & 0.245432 \tabularnewline
`#` & 0.161510344711488 & 0.079657 & 2.0276 & 0.044385 & 0.022193 \tabularnewline
T & -0.0365584034265199 & 0.090507 & -0.4039 & 0.686843 & 0.343421 \tabularnewline
C & -0.033617071042175 & 0.152233 & -0.2208 & 0.82553 & 0.412765 \tabularnewline
t & 0.0021250679997471 & 0.001772 & 1.1995 & 0.232234 & 0.116117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.259645147048716[/C][C]0.375947[/C][C]-0.6906[/C][C]0.490864[/C][C]0.245432[/C][/ROW]
[ROW][C]`#`[/C][C]0.161510344711488[/C][C]0.079657[/C][C]2.0276[/C][C]0.044385[/C][C]0.022193[/C][/ROW]
[ROW][C]T[/C][C]-0.0365584034265199[/C][C]0.090507[/C][C]-0.4039[/C][C]0.686843[/C][C]0.343421[/C][/ROW]
[ROW][C]C[/C][C]-0.033617071042175[/C][C]0.152233[/C][C]-0.2208[/C][C]0.82553[/C][C]0.412765[/C][/ROW]
[ROW][C]t[/C][C]0.0021250679997471[/C][C]0.001772[/C][C]1.1995[/C][C]0.232234[/C][C]0.116117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2596451470487160.375947-0.69060.4908640.245432
`#`0.1615103447114880.0796572.02760.0443850.022193
T-0.03655840342651990.090507-0.40390.6868430.343421
C-0.0336170710421750.152233-0.22080.825530.412765
t0.00212506799974710.0017721.19950.2322340.116117







Multiple Linear Regression - Regression Statistics
Multiple R0.190501092399281
R-squared0.0362906662053192
Adjusted R-squared0.0104192746940527
F-TEST (value)1.40273344746514
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.235837218553024
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.48811924380665
Sum Squared Residuals35.500799029982

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.190501092399281 \tabularnewline
R-squared & 0.0362906662053192 \tabularnewline
Adjusted R-squared & 0.0104192746940527 \tabularnewline
F-TEST (value) & 1.40273344746514 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.235837218553024 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.48811924380665 \tabularnewline
Sum Squared Residuals & 35.500799029982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.190501092399281[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0362906662053192[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0104192746940527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.40273344746514[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.235837218553024[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.48811924380665[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.500799029982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.190501092399281
R-squared0.0362906662053192
Adjusted R-squared0.0104192746940527
F-TEST (value)1.40273344746514
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.235837218553024
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.48811924380665
Sum Squared Residuals35.500799029982







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3519628963704620.648037103629538
200.390646367796728-0.390646367796728
300.392771435796475-0.392771435796475
400.394896503796223-0.394896503796223
500.39702157179597-0.39702157179597
610.3991466397957170.600853360204283
700.401271707795464-0.401271707795464
800.366838372368691-0.366838372368691
910.4055218437949580.594478156205042
1000.407646911794705-0.407646911794705
1100.373213576367932-0.373213576367932
1200.411897047794199-0.411897047794199
1300.414022115793946-0.414022115793946
1400.379588780367174-0.379588780367174
1510.4182722517934410.581727748206559
1610.3838389163666680.616161083633332
1700.35234691332424-0.35234691332424
1800.388089052366162-0.388089052366162
1910.4267725237924290.573227476207571
2010.3587221173234810.641277882676519
2100.431022659791923-0.431022659791923
2210.4331477277916710.566852272208329
2310.4352727957914180.564727204208582
2410.4373978637911650.562602136208835
2510.4029645283643920.597035471635608
2600.405089596364139-0.405089596364139
2710.4437730677904060.556226932209594
2800.445898135790153-0.445898135790153
2910.44802320378990.5519767962101
3000.450148271789647-0.450148271789647
3100.452273339789394-0.452273339789394
3200.454398407789141-0.454398407789141
3300.456523475788889-0.456523475788889
3410.4220901403621160.577909859637884
3500.460773611788383-0.460773611788383
3600.46289867978813-0.46289867978813
3700.428465344361357-0.428465344361357
3810.4671488157876240.532851184212376
3910.4692738837873710.530726116212629
4000.434840548360598-0.434840548360598
4110.439906948744690.56009305125531
4210.4756490877866130.524350912213387
4310.477774155786360.52222584421364
4400.443340820359587-0.443340820359587
4500.482024291785854-0.482024291785854
4610.4841493597856010.515850640214399
4700.486274427785348-0.486274427785348
4810.4883994957850950.511600504214905
4910.4905245637848420.509475436215158
5000.492649631784589-0.492649631784589
5100.458216296357817-0.458216296357817
5200.426724293315389-0.426724293315389
5310.4990248357838310.500975164216169
5400.467532832741403-0.467532832741403
5500.503274971783325-0.503274971783325
5610.4688416363565520.531158363643448
5710.5075251077828190.492474892217181
5810.5096501757825660.490349824217434
5910.5117752437823130.488224756217687
6010.4437248373133660.556275162686634
6110.4794669763552880.520533023644712
6200.518150447781555-0.518150447781555
6300.520275515781302-0.520275515781302
6410.4858421803545290.514157819645471
6500.524525651780796-0.524525651780796
6600.526650719780543-0.526650719780543
6700.458600313311595-0.458600313311595
6800.530900855780037-0.530900855780037
6910.5330259237797840.466974076220216
7000.535150991779531-0.535150991779531
7100.537276059779279-0.537276059779279
7210.5394011277790260.460598872220974
7310.5415261957787730.458473804221227
7400.54365126377852-0.54365126377852
7510.5457763317782670.454223668221733
7610.5113429963514940.488657003648506
7710.5500264677777610.449973532222239
7810.5521515357775080.447848464222492
7910.484101129308560.51589887069144
8000.519843268350483-0.519843268350483
8100.558526739776749-0.558526739776749
8210.5606518077764970.439348192223503
8300.562776875776244-0.562776875776244
8400.531284872733816-0.531284872733816
8510.5670270117757380.432972988224262
8600.569152079775485-0.569152079775485
8710.2482564583522570.751743541647743
8810.2138231229254840.786176877074516
8900.252506594351751-0.252506594351751
9010.2546316623514980.745368337648502
9100.256756730351245-0.256756730351245
9200.222323394924472-0.222323394924472
9300.261006866350739-0.261006866350739
9400.263131934350486-0.263131934350486
9500.228698598923714-0.228698598923714
9610.2673820703499810.732617929650019
9700.232948734923208-0.232948734923208
9800.271632206349475-0.271632206349475
9900.273757274349222-0.273757274349222
10010.2758823423489690.724117657651031
10110.2780074103487160.721992589651284
10200.280132478348463-0.280132478348463
10300.28225754634821-0.28225754634821
10400.284382614347958-0.284382614347958
10500.249949278921185-0.249949278921185
10600.288632750347452-0.288632750347452
10700.290757818347199-0.290757818347199
10800.256324482920426-0.256324482920426
10900.295007954346693-0.295007954346693
11000.29713302234644-0.29713302234644
11100.262699686919667-0.262699686919667
11200.264824754919414-0.264824754919414
11300.303508226345681-0.303508226345681
11400.269074890918909-0.269074890918909
11500.307758362345176-0.307758362345176
11600.309883430344923-0.309883430344923
11710.312008498344670.68799150165533
11800.314133566344417-0.314133566344417
11900.316258634344164-0.316258634344164
12010.3183837023439110.681616297656089
12100.320508770343658-0.320508770343658
12200.322633838343405-0.322633838343405
12300.288200502916633-0.288200502916633
12410.32688397434290.6731160256571
12510.3290090423426470.670990957657353
12600.294575706915874-0.294575706915874
12700.333259178342141-0.333259178342141
12810.3353842463418880.664615753658112
12900.337509314341635-0.337509314341635
13010.3396343823413820.660365617658618
13100.341759450341129-0.341759450341129
13210.3438845183408770.656115481659123
13300.346009586340624-0.346009586340624
13400.348134654340371-0.348134654340371
13500.350259722340118-0.350259722340118
13600.352384790339865-0.352384790339865
13710.3545098583396120.645490141660388
13810.3200765229128390.679923477087161
13900.322201590912586-0.322201590912586
14000.360885062338853-0.360885062338853
14110.3293930592964250.670606940703575
14210.3285767949118280.671423205088172
14300.367260266338095-0.367260266338095
14410.3693853343378420.630614665662158
14500.371510402337589-0.371510402337589
14610.3370770669108160.662922933089184
14700.339202134910563-0.339202134910563
14800.34132720291031-0.34132720291031
14900.380010674336577-0.380010674336577
15010.3821357423363240.617864257663676
15110.3842608103360720.615739189663928
15200.352768807293644-0.352768807293644
15300.354893875293391-0.354893875293391
15400.390636014335313-0.390636014335313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.351962896370462 & 0.648037103629538 \tabularnewline
2 & 0 & 0.390646367796728 & -0.390646367796728 \tabularnewline
3 & 0 & 0.392771435796475 & -0.392771435796475 \tabularnewline
4 & 0 & 0.394896503796223 & -0.394896503796223 \tabularnewline
5 & 0 & 0.39702157179597 & -0.39702157179597 \tabularnewline
6 & 1 & 0.399146639795717 & 0.600853360204283 \tabularnewline
7 & 0 & 0.401271707795464 & -0.401271707795464 \tabularnewline
8 & 0 & 0.366838372368691 & -0.366838372368691 \tabularnewline
9 & 1 & 0.405521843794958 & 0.594478156205042 \tabularnewline
10 & 0 & 0.407646911794705 & -0.407646911794705 \tabularnewline
11 & 0 & 0.373213576367932 & -0.373213576367932 \tabularnewline
12 & 0 & 0.411897047794199 & -0.411897047794199 \tabularnewline
13 & 0 & 0.414022115793946 & -0.414022115793946 \tabularnewline
14 & 0 & 0.379588780367174 & -0.379588780367174 \tabularnewline
15 & 1 & 0.418272251793441 & 0.581727748206559 \tabularnewline
16 & 1 & 0.383838916366668 & 0.616161083633332 \tabularnewline
17 & 0 & 0.35234691332424 & -0.35234691332424 \tabularnewline
18 & 0 & 0.388089052366162 & -0.388089052366162 \tabularnewline
19 & 1 & 0.426772523792429 & 0.573227476207571 \tabularnewline
20 & 1 & 0.358722117323481 & 0.641277882676519 \tabularnewline
21 & 0 & 0.431022659791923 & -0.431022659791923 \tabularnewline
22 & 1 & 0.433147727791671 & 0.566852272208329 \tabularnewline
23 & 1 & 0.435272795791418 & 0.564727204208582 \tabularnewline
24 & 1 & 0.437397863791165 & 0.562602136208835 \tabularnewline
25 & 1 & 0.402964528364392 & 0.597035471635608 \tabularnewline
26 & 0 & 0.405089596364139 & -0.405089596364139 \tabularnewline
27 & 1 & 0.443773067790406 & 0.556226932209594 \tabularnewline
28 & 0 & 0.445898135790153 & -0.445898135790153 \tabularnewline
29 & 1 & 0.4480232037899 & 0.5519767962101 \tabularnewline
30 & 0 & 0.450148271789647 & -0.450148271789647 \tabularnewline
31 & 0 & 0.452273339789394 & -0.452273339789394 \tabularnewline
32 & 0 & 0.454398407789141 & -0.454398407789141 \tabularnewline
33 & 0 & 0.456523475788889 & -0.456523475788889 \tabularnewline
34 & 1 & 0.422090140362116 & 0.577909859637884 \tabularnewline
35 & 0 & 0.460773611788383 & -0.460773611788383 \tabularnewline
36 & 0 & 0.46289867978813 & -0.46289867978813 \tabularnewline
37 & 0 & 0.428465344361357 & -0.428465344361357 \tabularnewline
38 & 1 & 0.467148815787624 & 0.532851184212376 \tabularnewline
39 & 1 & 0.469273883787371 & 0.530726116212629 \tabularnewline
40 & 0 & 0.434840548360598 & -0.434840548360598 \tabularnewline
41 & 1 & 0.43990694874469 & 0.56009305125531 \tabularnewline
42 & 1 & 0.475649087786613 & 0.524350912213387 \tabularnewline
43 & 1 & 0.47777415578636 & 0.52222584421364 \tabularnewline
44 & 0 & 0.443340820359587 & -0.443340820359587 \tabularnewline
45 & 0 & 0.482024291785854 & -0.482024291785854 \tabularnewline
46 & 1 & 0.484149359785601 & 0.515850640214399 \tabularnewline
47 & 0 & 0.486274427785348 & -0.486274427785348 \tabularnewline
48 & 1 & 0.488399495785095 & 0.511600504214905 \tabularnewline
49 & 1 & 0.490524563784842 & 0.509475436215158 \tabularnewline
50 & 0 & 0.492649631784589 & -0.492649631784589 \tabularnewline
51 & 0 & 0.458216296357817 & -0.458216296357817 \tabularnewline
52 & 0 & 0.426724293315389 & -0.426724293315389 \tabularnewline
53 & 1 & 0.499024835783831 & 0.500975164216169 \tabularnewline
54 & 0 & 0.467532832741403 & -0.467532832741403 \tabularnewline
55 & 0 & 0.503274971783325 & -0.503274971783325 \tabularnewline
56 & 1 & 0.468841636356552 & 0.531158363643448 \tabularnewline
57 & 1 & 0.507525107782819 & 0.492474892217181 \tabularnewline
58 & 1 & 0.509650175782566 & 0.490349824217434 \tabularnewline
59 & 1 & 0.511775243782313 & 0.488224756217687 \tabularnewline
60 & 1 & 0.443724837313366 & 0.556275162686634 \tabularnewline
61 & 1 & 0.479466976355288 & 0.520533023644712 \tabularnewline
62 & 0 & 0.518150447781555 & -0.518150447781555 \tabularnewline
63 & 0 & 0.520275515781302 & -0.520275515781302 \tabularnewline
64 & 1 & 0.485842180354529 & 0.514157819645471 \tabularnewline
65 & 0 & 0.524525651780796 & -0.524525651780796 \tabularnewline
66 & 0 & 0.526650719780543 & -0.526650719780543 \tabularnewline
67 & 0 & 0.458600313311595 & -0.458600313311595 \tabularnewline
68 & 0 & 0.530900855780037 & -0.530900855780037 \tabularnewline
69 & 1 & 0.533025923779784 & 0.466974076220216 \tabularnewline
70 & 0 & 0.535150991779531 & -0.535150991779531 \tabularnewline
71 & 0 & 0.537276059779279 & -0.537276059779279 \tabularnewline
72 & 1 & 0.539401127779026 & 0.460598872220974 \tabularnewline
73 & 1 & 0.541526195778773 & 0.458473804221227 \tabularnewline
74 & 0 & 0.54365126377852 & -0.54365126377852 \tabularnewline
75 & 1 & 0.545776331778267 & 0.454223668221733 \tabularnewline
76 & 1 & 0.511342996351494 & 0.488657003648506 \tabularnewline
77 & 1 & 0.550026467777761 & 0.449973532222239 \tabularnewline
78 & 1 & 0.552151535777508 & 0.447848464222492 \tabularnewline
79 & 1 & 0.48410112930856 & 0.51589887069144 \tabularnewline
80 & 0 & 0.519843268350483 & -0.519843268350483 \tabularnewline
81 & 0 & 0.558526739776749 & -0.558526739776749 \tabularnewline
82 & 1 & 0.560651807776497 & 0.439348192223503 \tabularnewline
83 & 0 & 0.562776875776244 & -0.562776875776244 \tabularnewline
84 & 0 & 0.531284872733816 & -0.531284872733816 \tabularnewline
85 & 1 & 0.567027011775738 & 0.432972988224262 \tabularnewline
86 & 0 & 0.569152079775485 & -0.569152079775485 \tabularnewline
87 & 1 & 0.248256458352257 & 0.751743541647743 \tabularnewline
88 & 1 & 0.213823122925484 & 0.786176877074516 \tabularnewline
89 & 0 & 0.252506594351751 & -0.252506594351751 \tabularnewline
90 & 1 & 0.254631662351498 & 0.745368337648502 \tabularnewline
91 & 0 & 0.256756730351245 & -0.256756730351245 \tabularnewline
92 & 0 & 0.222323394924472 & -0.222323394924472 \tabularnewline
93 & 0 & 0.261006866350739 & -0.261006866350739 \tabularnewline
94 & 0 & 0.263131934350486 & -0.263131934350486 \tabularnewline
95 & 0 & 0.228698598923714 & -0.228698598923714 \tabularnewline
96 & 1 & 0.267382070349981 & 0.732617929650019 \tabularnewline
97 & 0 & 0.232948734923208 & -0.232948734923208 \tabularnewline
98 & 0 & 0.271632206349475 & -0.271632206349475 \tabularnewline
99 & 0 & 0.273757274349222 & -0.273757274349222 \tabularnewline
100 & 1 & 0.275882342348969 & 0.724117657651031 \tabularnewline
101 & 1 & 0.278007410348716 & 0.721992589651284 \tabularnewline
102 & 0 & 0.280132478348463 & -0.280132478348463 \tabularnewline
103 & 0 & 0.28225754634821 & -0.28225754634821 \tabularnewline
104 & 0 & 0.284382614347958 & -0.284382614347958 \tabularnewline
105 & 0 & 0.249949278921185 & -0.249949278921185 \tabularnewline
106 & 0 & 0.288632750347452 & -0.288632750347452 \tabularnewline
107 & 0 & 0.290757818347199 & -0.290757818347199 \tabularnewline
108 & 0 & 0.256324482920426 & -0.256324482920426 \tabularnewline
109 & 0 & 0.295007954346693 & -0.295007954346693 \tabularnewline
110 & 0 & 0.29713302234644 & -0.29713302234644 \tabularnewline
111 & 0 & 0.262699686919667 & -0.262699686919667 \tabularnewline
112 & 0 & 0.264824754919414 & -0.264824754919414 \tabularnewline
113 & 0 & 0.303508226345681 & -0.303508226345681 \tabularnewline
114 & 0 & 0.269074890918909 & -0.269074890918909 \tabularnewline
115 & 0 & 0.307758362345176 & -0.307758362345176 \tabularnewline
116 & 0 & 0.309883430344923 & -0.309883430344923 \tabularnewline
117 & 1 & 0.31200849834467 & 0.68799150165533 \tabularnewline
118 & 0 & 0.314133566344417 & -0.314133566344417 \tabularnewline
119 & 0 & 0.316258634344164 & -0.316258634344164 \tabularnewline
120 & 1 & 0.318383702343911 & 0.681616297656089 \tabularnewline
121 & 0 & 0.320508770343658 & -0.320508770343658 \tabularnewline
122 & 0 & 0.322633838343405 & -0.322633838343405 \tabularnewline
123 & 0 & 0.288200502916633 & -0.288200502916633 \tabularnewline
124 & 1 & 0.3268839743429 & 0.6731160256571 \tabularnewline
125 & 1 & 0.329009042342647 & 0.670990957657353 \tabularnewline
126 & 0 & 0.294575706915874 & -0.294575706915874 \tabularnewline
127 & 0 & 0.333259178342141 & -0.333259178342141 \tabularnewline
128 & 1 & 0.335384246341888 & 0.664615753658112 \tabularnewline
129 & 0 & 0.337509314341635 & -0.337509314341635 \tabularnewline
130 & 1 & 0.339634382341382 & 0.660365617658618 \tabularnewline
131 & 0 & 0.341759450341129 & -0.341759450341129 \tabularnewline
132 & 1 & 0.343884518340877 & 0.656115481659123 \tabularnewline
133 & 0 & 0.346009586340624 & -0.346009586340624 \tabularnewline
134 & 0 & 0.348134654340371 & -0.348134654340371 \tabularnewline
135 & 0 & 0.350259722340118 & -0.350259722340118 \tabularnewline
136 & 0 & 0.352384790339865 & -0.352384790339865 \tabularnewline
137 & 1 & 0.354509858339612 & 0.645490141660388 \tabularnewline
138 & 1 & 0.320076522912839 & 0.679923477087161 \tabularnewline
139 & 0 & 0.322201590912586 & -0.322201590912586 \tabularnewline
140 & 0 & 0.360885062338853 & -0.360885062338853 \tabularnewline
141 & 1 & 0.329393059296425 & 0.670606940703575 \tabularnewline
142 & 1 & 0.328576794911828 & 0.671423205088172 \tabularnewline
143 & 0 & 0.367260266338095 & -0.367260266338095 \tabularnewline
144 & 1 & 0.369385334337842 & 0.630614665662158 \tabularnewline
145 & 0 & 0.371510402337589 & -0.371510402337589 \tabularnewline
146 & 1 & 0.337077066910816 & 0.662922933089184 \tabularnewline
147 & 0 & 0.339202134910563 & -0.339202134910563 \tabularnewline
148 & 0 & 0.34132720291031 & -0.34132720291031 \tabularnewline
149 & 0 & 0.380010674336577 & -0.380010674336577 \tabularnewline
150 & 1 & 0.382135742336324 & 0.617864257663676 \tabularnewline
151 & 1 & 0.384260810336072 & 0.615739189663928 \tabularnewline
152 & 0 & 0.352768807293644 & -0.352768807293644 \tabularnewline
153 & 0 & 0.354893875293391 & -0.354893875293391 \tabularnewline
154 & 0 & 0.390636014335313 & -0.390636014335313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.351962896370462[/C][C]0.648037103629538[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.390646367796728[/C][C]-0.390646367796728[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.392771435796475[/C][C]-0.392771435796475[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.394896503796223[/C][C]-0.394896503796223[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.39702157179597[/C][C]-0.39702157179597[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.399146639795717[/C][C]0.600853360204283[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.401271707795464[/C][C]-0.401271707795464[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.366838372368691[/C][C]-0.366838372368691[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.405521843794958[/C][C]0.594478156205042[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.407646911794705[/C][C]-0.407646911794705[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.373213576367932[/C][C]-0.373213576367932[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.411897047794199[/C][C]-0.411897047794199[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.414022115793946[/C][C]-0.414022115793946[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.379588780367174[/C][C]-0.379588780367174[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.418272251793441[/C][C]0.581727748206559[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.383838916366668[/C][C]0.616161083633332[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.35234691332424[/C][C]-0.35234691332424[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.388089052366162[/C][C]-0.388089052366162[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.426772523792429[/C][C]0.573227476207571[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.358722117323481[/C][C]0.641277882676519[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.431022659791923[/C][C]-0.431022659791923[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.433147727791671[/C][C]0.566852272208329[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.435272795791418[/C][C]0.564727204208582[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.437397863791165[/C][C]0.562602136208835[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.402964528364392[/C][C]0.597035471635608[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.405089596364139[/C][C]-0.405089596364139[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.443773067790406[/C][C]0.556226932209594[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.445898135790153[/C][C]-0.445898135790153[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.4480232037899[/C][C]0.5519767962101[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.450148271789647[/C][C]-0.450148271789647[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.452273339789394[/C][C]-0.452273339789394[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.454398407789141[/C][C]-0.454398407789141[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.456523475788889[/C][C]-0.456523475788889[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.422090140362116[/C][C]0.577909859637884[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.460773611788383[/C][C]-0.460773611788383[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.46289867978813[/C][C]-0.46289867978813[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.428465344361357[/C][C]-0.428465344361357[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.467148815787624[/C][C]0.532851184212376[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.469273883787371[/C][C]0.530726116212629[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.434840548360598[/C][C]-0.434840548360598[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.43990694874469[/C][C]0.56009305125531[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.475649087786613[/C][C]0.524350912213387[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.47777415578636[/C][C]0.52222584421364[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.443340820359587[/C][C]-0.443340820359587[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.482024291785854[/C][C]-0.482024291785854[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.484149359785601[/C][C]0.515850640214399[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.486274427785348[/C][C]-0.486274427785348[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.488399495785095[/C][C]0.511600504214905[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.490524563784842[/C][C]0.509475436215158[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.492649631784589[/C][C]-0.492649631784589[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.458216296357817[/C][C]-0.458216296357817[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.426724293315389[/C][C]-0.426724293315389[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.499024835783831[/C][C]0.500975164216169[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.467532832741403[/C][C]-0.467532832741403[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.503274971783325[/C][C]-0.503274971783325[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.468841636356552[/C][C]0.531158363643448[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.507525107782819[/C][C]0.492474892217181[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.509650175782566[/C][C]0.490349824217434[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.511775243782313[/C][C]0.488224756217687[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.443724837313366[/C][C]0.556275162686634[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.479466976355288[/C][C]0.520533023644712[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.518150447781555[/C][C]-0.518150447781555[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.520275515781302[/C][C]-0.520275515781302[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.485842180354529[/C][C]0.514157819645471[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.524525651780796[/C][C]-0.524525651780796[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.526650719780543[/C][C]-0.526650719780543[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.458600313311595[/C][C]-0.458600313311595[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.530900855780037[/C][C]-0.530900855780037[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.533025923779784[/C][C]0.466974076220216[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.535150991779531[/C][C]-0.535150991779531[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.537276059779279[/C][C]-0.537276059779279[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.539401127779026[/C][C]0.460598872220974[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.541526195778773[/C][C]0.458473804221227[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.54365126377852[/C][C]-0.54365126377852[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.545776331778267[/C][C]0.454223668221733[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.511342996351494[/C][C]0.488657003648506[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.550026467777761[/C][C]0.449973532222239[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.552151535777508[/C][C]0.447848464222492[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.48410112930856[/C][C]0.51589887069144[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.519843268350483[/C][C]-0.519843268350483[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.558526739776749[/C][C]-0.558526739776749[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.560651807776497[/C][C]0.439348192223503[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.562776875776244[/C][C]-0.562776875776244[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.531284872733816[/C][C]-0.531284872733816[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.567027011775738[/C][C]0.432972988224262[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.569152079775485[/C][C]-0.569152079775485[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.248256458352257[/C][C]0.751743541647743[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.213823122925484[/C][C]0.786176877074516[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.252506594351751[/C][C]-0.252506594351751[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.254631662351498[/C][C]0.745368337648502[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.256756730351245[/C][C]-0.256756730351245[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.222323394924472[/C][C]-0.222323394924472[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.261006866350739[/C][C]-0.261006866350739[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.263131934350486[/C][C]-0.263131934350486[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.228698598923714[/C][C]-0.228698598923714[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.267382070349981[/C][C]0.732617929650019[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.232948734923208[/C][C]-0.232948734923208[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.271632206349475[/C][C]-0.271632206349475[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.273757274349222[/C][C]-0.273757274349222[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.275882342348969[/C][C]0.724117657651031[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.278007410348716[/C][C]0.721992589651284[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.280132478348463[/C][C]-0.280132478348463[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.28225754634821[/C][C]-0.28225754634821[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.284382614347958[/C][C]-0.284382614347958[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.249949278921185[/C][C]-0.249949278921185[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.288632750347452[/C][C]-0.288632750347452[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.290757818347199[/C][C]-0.290757818347199[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.256324482920426[/C][C]-0.256324482920426[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.295007954346693[/C][C]-0.295007954346693[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.29713302234644[/C][C]-0.29713302234644[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.262699686919667[/C][C]-0.262699686919667[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.264824754919414[/C][C]-0.264824754919414[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.303508226345681[/C][C]-0.303508226345681[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.269074890918909[/C][C]-0.269074890918909[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.307758362345176[/C][C]-0.307758362345176[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.309883430344923[/C][C]-0.309883430344923[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.31200849834467[/C][C]0.68799150165533[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.314133566344417[/C][C]-0.314133566344417[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.316258634344164[/C][C]-0.316258634344164[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.318383702343911[/C][C]0.681616297656089[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.320508770343658[/C][C]-0.320508770343658[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.322633838343405[/C][C]-0.322633838343405[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.288200502916633[/C][C]-0.288200502916633[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.3268839743429[/C][C]0.6731160256571[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.329009042342647[/C][C]0.670990957657353[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.294575706915874[/C][C]-0.294575706915874[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.333259178342141[/C][C]-0.333259178342141[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.335384246341888[/C][C]0.664615753658112[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.337509314341635[/C][C]-0.337509314341635[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.339634382341382[/C][C]0.660365617658618[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.341759450341129[/C][C]-0.341759450341129[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.343884518340877[/C][C]0.656115481659123[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.346009586340624[/C][C]-0.346009586340624[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.348134654340371[/C][C]-0.348134654340371[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.350259722340118[/C][C]-0.350259722340118[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.352384790339865[/C][C]-0.352384790339865[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.354509858339612[/C][C]0.645490141660388[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.320076522912839[/C][C]0.679923477087161[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.322201590912586[/C][C]-0.322201590912586[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.360885062338853[/C][C]-0.360885062338853[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.329393059296425[/C][C]0.670606940703575[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.328576794911828[/C][C]0.671423205088172[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.367260266338095[/C][C]-0.367260266338095[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.369385334337842[/C][C]0.630614665662158[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.371510402337589[/C][C]-0.371510402337589[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.337077066910816[/C][C]0.662922933089184[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.339202134910563[/C][C]-0.339202134910563[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.34132720291031[/C][C]-0.34132720291031[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.380010674336577[/C][C]-0.380010674336577[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.382135742336324[/C][C]0.617864257663676[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.384260810336072[/C][C]0.615739189663928[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.352768807293644[/C][C]-0.352768807293644[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.354893875293391[/C][C]-0.354893875293391[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.390636014335313[/C][C]-0.390636014335313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3519628963704620.648037103629538
200.390646367796728-0.390646367796728
300.392771435796475-0.392771435796475
400.394896503796223-0.394896503796223
500.39702157179597-0.39702157179597
610.3991466397957170.600853360204283
700.401271707795464-0.401271707795464
800.366838372368691-0.366838372368691
910.4055218437949580.594478156205042
1000.407646911794705-0.407646911794705
1100.373213576367932-0.373213576367932
1200.411897047794199-0.411897047794199
1300.414022115793946-0.414022115793946
1400.379588780367174-0.379588780367174
1510.4182722517934410.581727748206559
1610.3838389163666680.616161083633332
1700.35234691332424-0.35234691332424
1800.388089052366162-0.388089052366162
1910.4267725237924290.573227476207571
2010.3587221173234810.641277882676519
2100.431022659791923-0.431022659791923
2210.4331477277916710.566852272208329
2310.4352727957914180.564727204208582
2410.4373978637911650.562602136208835
2510.4029645283643920.597035471635608
2600.405089596364139-0.405089596364139
2710.4437730677904060.556226932209594
2800.445898135790153-0.445898135790153
2910.44802320378990.5519767962101
3000.450148271789647-0.450148271789647
3100.452273339789394-0.452273339789394
3200.454398407789141-0.454398407789141
3300.456523475788889-0.456523475788889
3410.4220901403621160.577909859637884
3500.460773611788383-0.460773611788383
3600.46289867978813-0.46289867978813
3700.428465344361357-0.428465344361357
3810.4671488157876240.532851184212376
3910.4692738837873710.530726116212629
4000.434840548360598-0.434840548360598
4110.439906948744690.56009305125531
4210.4756490877866130.524350912213387
4310.477774155786360.52222584421364
4400.443340820359587-0.443340820359587
4500.482024291785854-0.482024291785854
4610.4841493597856010.515850640214399
4700.486274427785348-0.486274427785348
4810.4883994957850950.511600504214905
4910.4905245637848420.509475436215158
5000.492649631784589-0.492649631784589
5100.458216296357817-0.458216296357817
5200.426724293315389-0.426724293315389
5310.4990248357838310.500975164216169
5400.467532832741403-0.467532832741403
5500.503274971783325-0.503274971783325
5610.4688416363565520.531158363643448
5710.5075251077828190.492474892217181
5810.5096501757825660.490349824217434
5910.5117752437823130.488224756217687
6010.4437248373133660.556275162686634
6110.4794669763552880.520533023644712
6200.518150447781555-0.518150447781555
6300.520275515781302-0.520275515781302
6410.4858421803545290.514157819645471
6500.524525651780796-0.524525651780796
6600.526650719780543-0.526650719780543
6700.458600313311595-0.458600313311595
6800.530900855780037-0.530900855780037
6910.5330259237797840.466974076220216
7000.535150991779531-0.535150991779531
7100.537276059779279-0.537276059779279
7210.5394011277790260.460598872220974
7310.5415261957787730.458473804221227
7400.54365126377852-0.54365126377852
7510.5457763317782670.454223668221733
7610.5113429963514940.488657003648506
7710.5500264677777610.449973532222239
7810.5521515357775080.447848464222492
7910.484101129308560.51589887069144
8000.519843268350483-0.519843268350483
8100.558526739776749-0.558526739776749
8210.5606518077764970.439348192223503
8300.562776875776244-0.562776875776244
8400.531284872733816-0.531284872733816
8510.5670270117757380.432972988224262
8600.569152079775485-0.569152079775485
8710.2482564583522570.751743541647743
8810.2138231229254840.786176877074516
8900.252506594351751-0.252506594351751
9010.2546316623514980.745368337648502
9100.256756730351245-0.256756730351245
9200.222323394924472-0.222323394924472
9300.261006866350739-0.261006866350739
9400.263131934350486-0.263131934350486
9500.228698598923714-0.228698598923714
9610.2673820703499810.732617929650019
9700.232948734923208-0.232948734923208
9800.271632206349475-0.271632206349475
9900.273757274349222-0.273757274349222
10010.2758823423489690.724117657651031
10110.2780074103487160.721992589651284
10200.280132478348463-0.280132478348463
10300.28225754634821-0.28225754634821
10400.284382614347958-0.284382614347958
10500.249949278921185-0.249949278921185
10600.288632750347452-0.288632750347452
10700.290757818347199-0.290757818347199
10800.256324482920426-0.256324482920426
10900.295007954346693-0.295007954346693
11000.29713302234644-0.29713302234644
11100.262699686919667-0.262699686919667
11200.264824754919414-0.264824754919414
11300.303508226345681-0.303508226345681
11400.269074890918909-0.269074890918909
11500.307758362345176-0.307758362345176
11600.309883430344923-0.309883430344923
11710.312008498344670.68799150165533
11800.314133566344417-0.314133566344417
11900.316258634344164-0.316258634344164
12010.3183837023439110.681616297656089
12100.320508770343658-0.320508770343658
12200.322633838343405-0.322633838343405
12300.288200502916633-0.288200502916633
12410.32688397434290.6731160256571
12510.3290090423426470.670990957657353
12600.294575706915874-0.294575706915874
12700.333259178342141-0.333259178342141
12810.3353842463418880.664615753658112
12900.337509314341635-0.337509314341635
13010.3396343823413820.660365617658618
13100.341759450341129-0.341759450341129
13210.3438845183408770.656115481659123
13300.346009586340624-0.346009586340624
13400.348134654340371-0.348134654340371
13500.350259722340118-0.350259722340118
13600.352384790339865-0.352384790339865
13710.3545098583396120.645490141660388
13810.3200765229128390.679923477087161
13900.322201590912586-0.322201590912586
14000.360885062338853-0.360885062338853
14110.3293930592964250.670606940703575
14210.3285767949118280.671423205088172
14300.367260266338095-0.367260266338095
14410.3693853343378420.630614665662158
14500.371510402337589-0.371510402337589
14610.3370770669108160.662922933089184
14700.339202134910563-0.339202134910563
14800.34132720291031-0.34132720291031
14900.380010674336577-0.380010674336577
15010.3821357423363240.617864257663676
15110.3842608103360720.615739189663928
15200.352768807293644-0.352768807293644
15300.354893875293391-0.354893875293391
15400.390636014335313-0.390636014335313







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8353098200124920.3293803599750160.164690179987508
90.9011456382705390.1977087234589220.098854361729461
100.8549283258821990.2901433482356010.145071674117801
110.8094459529936570.3811080940126870.190554047006344
120.7310170959194680.5379658081610630.268982904080532
130.6422910139260440.7154179721479120.357708986073956
140.5523817857322110.8952364285355790.447618214267789
150.7165826473234330.5668347053531350.283417352676567
160.751152556052130.497694887895740.24884744394787
170.6825198525293990.6349602949412020.317480147470601
180.6565919787215070.6868160425569860.343408021278493
190.6769354039369310.6461291921261390.323064596063069
200.7198710270820680.5602579458358650.280128972917932
210.7130985627920530.5738028744158930.286901437207947
220.709975588074750.58004882385050.29002441192525
230.6849727954359530.6300544091280940.315027204564047
240.6478255988274450.7043488023451110.352174401172555
250.6116342224246120.7767315551507760.388365777575388
260.6615820407131520.6768359185736950.338417959286848
270.620968675601150.75806264879770.37903132439885
280.6843376771773120.6313246456453770.315662322822688
290.6522349162624560.6955301674750870.347765083737544
300.6975637671367020.6048724657265960.302436232863298
310.7189176246902510.5621647506194980.281082375309749
320.7255987501371480.5488024997257040.274401249862852
330.7226310524304940.5547378951390120.277368947569506
340.7206101228444040.5587797543111920.279389877155596
350.717356976140640.565286047718720.28264302385936
360.7081658857335020.5836682285329950.291834114266497
370.6993376841847680.6013246316304630.300662315815231
380.7098962762408670.5802074475182660.290103723759133
390.7106647665650510.5786704668698990.289335233434949
400.7039327945036370.5921344109927260.296067205496363
410.6850607009007080.6298785981985840.314939299099292
420.6798237531644310.6403524936711380.320176246835569
430.6697071667255820.6605856665488350.330292833274418
440.665167825438380.669664349123240.33483217456162
450.6727377625584060.6545244748831890.327262237441594
460.6659827630991730.6680344738016540.334017236900827
470.6732079903258110.6535840193483790.326792009674189
480.6667959875093070.6664080249813870.333204012490693
490.6573619890530010.6852760218939970.342638010946999
500.669070177076460.6618596458470810.33092982292354
510.6601764547991710.6796470904016590.339823545200829
520.6671618000130980.6656763999738030.332838199986902
530.6618030385163870.6763939229672260.338196961483613
540.6728170056209130.6543659887581750.327182994379087
550.6758032101363140.6483935797273730.324196789863686
560.685942854920060.6281142901598790.31405714507994
570.679444765530710.641110468938580.32055523446929
580.671242575018690.657514849962620.32875742498131
590.6621678314514450.6756643370971090.337832168548555
600.667480145571750.66503970885650.33251985442825
610.6654881866695950.669023626660810.334511813330405
620.6822514957777860.6354970084444280.317748504222214
630.6939080029050890.6121839941898210.306091997094911
640.6938021231853670.6123957536292650.306197876814633
650.7036023176562430.5927953646875140.296397682343757
660.7108040359329480.5783919281341040.289195964067052
670.7053863563154240.5892272873691510.294613643684576
680.7110390181861610.5779219636276780.288960981813839
690.705471864668790.5890562706624210.29452813533121
700.7120037993362480.5759924013275040.287996200663752
710.7195834907072440.5608330185855120.280416509292756
720.7128157549478920.5743684901042160.287184245052108
730.7055817598096620.5888364803806770.294418240190338
740.7148800118987310.5702399762025380.285119988101269
750.7064709945616170.5870580108767670.293529005438384
760.7055039489023150.588992102195370.294496051097685
770.7014166316471460.5971667367057090.298583368352854
780.7026562927803510.5946874144392990.297343707219649
790.7309928284314990.5380143431370030.269007171568501
800.7239347743723020.5521304512553950.276065225627698
810.7236250282269550.552749943546090.276374971773045
820.733485906206820.5330281875863610.26651409379318
830.72830341584350.5433931683130010.2716965841565
840.7185198894785410.5629602210429170.281480110521459
850.7388445369849710.5223109260300590.261155463015029
860.7208899587193520.5582200825612970.279110041280648
870.7389220977445370.5221558045109270.261077902255463
880.7834268273561080.4331463452877850.216573172643892
890.7879696667443950.424060666511210.212030333255605
900.8227697460655690.3544605078688630.177230253934431
910.8142769025398960.3714461949202080.185723097460104
920.7969547746944320.4060904506111360.203045225305568
930.7752699435828220.4494601128343560.224730056417178
940.7497286137336650.5005427725326690.250271386266335
950.7178418664171980.5643162671656050.282158133582802
960.7748639763645010.4502720472709980.225136023635499
970.7441944234530280.5116111530939440.255805576546972
980.7133413990549720.5733172018900560.286658600945028
990.6802168280768760.6395663438462480.319783171923124
1000.7472276291433490.5055447417133030.252772370856651
1010.8180658238344750.363868352331050.181934176165525
1020.7904925871840460.4190148256319080.209507412815954
1030.759744589567270.480510820865460.24025541043273
1040.7260640388720620.5478719222558760.273935961127938
1050.6873361892392580.6253276215214850.312663810760742
1060.6487069155718140.7025861688563720.351293084428186
1070.6085689266374140.7828621467251720.391431073362586
1080.5631405550678710.8737188898642590.43685944493213
1090.5216631218076470.9566737563847060.478336878192353
1100.4810313109478970.9620626218957950.518968689052103
1110.4354201495078040.8708402990156080.564579850492196
1120.3920306383693510.7840612767387030.607969361630649
1130.3571322926738760.7142645853477510.642867707326124
1140.3212037458889880.6424074917779760.678796254111012
1150.2938082744456210.5876165488912410.706191725554379
1160.2712791765901190.5425583531802370.728720823409881
1170.3019346306178410.6038692612356820.698065369382159
1180.2767330929925480.5534661859850960.723266907007452
1190.2572462861912780.5144925723825550.742753713808722
1200.2817241182261760.5634482364523530.718275881773824
1210.2595445681595910.5190891363191820.740455431840409
1220.2444020279706480.4888040559412950.755597972029352
1230.2342064130413110.4684128260826210.765793586958689
1240.2434859367167760.4869718734335530.756514063283224
1250.2643102155819440.5286204311638870.735689784418057
1260.2554163492001380.5108326984002750.744583650799862
1270.2411160515850630.4822321031701270.758883948414937
1280.2553244048950140.5106488097900290.744675595104986
1290.2365256802130530.4730513604261050.763474319786947
1300.2562109058928030.5124218117856070.743789094107197
1310.2311472629521460.4622945259042920.768852737047854
1320.2609405033205920.5218810066411850.739059496679408
1330.2269675073620180.4539350147240360.773032492637982
1340.2038586818813970.4077173637627930.796141318118603
1350.196443867039380.3928877340787610.80355613296062
1360.2183122905487560.4366245810975130.781687709451244
1370.1913239783145080.3826479566290160.808676021685492
1380.1792623398378120.3585246796756250.820737660162188
1390.1818865541372350.3637731082744710.818113445862765
1400.213824963277270.427649926554540.78617503672273
1410.2014488492800480.4028976985600950.798551150719952
1420.2015866745932970.4031733491865930.798413325406703
1430.198612346489180.3972246929783590.80138765351082
1440.176921782551510.353843565103020.82307821744849
1450.208536827502030.417073655004060.79146317249797
1460.2787090649422310.5574181298844630.721290935057769

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.835309820012492 & 0.329380359975016 & 0.164690179987508 \tabularnewline
9 & 0.901145638270539 & 0.197708723458922 & 0.098854361729461 \tabularnewline
10 & 0.854928325882199 & 0.290143348235601 & 0.145071674117801 \tabularnewline
11 & 0.809445952993657 & 0.381108094012687 & 0.190554047006344 \tabularnewline
12 & 0.731017095919468 & 0.537965808161063 & 0.268982904080532 \tabularnewline
13 & 0.642291013926044 & 0.715417972147912 & 0.357708986073956 \tabularnewline
14 & 0.552381785732211 & 0.895236428535579 & 0.447618214267789 \tabularnewline
15 & 0.716582647323433 & 0.566834705353135 & 0.283417352676567 \tabularnewline
16 & 0.75115255605213 & 0.49769488789574 & 0.24884744394787 \tabularnewline
17 & 0.682519852529399 & 0.634960294941202 & 0.317480147470601 \tabularnewline
18 & 0.656591978721507 & 0.686816042556986 & 0.343408021278493 \tabularnewline
19 & 0.676935403936931 & 0.646129192126139 & 0.323064596063069 \tabularnewline
20 & 0.719871027082068 & 0.560257945835865 & 0.280128972917932 \tabularnewline
21 & 0.713098562792053 & 0.573802874415893 & 0.286901437207947 \tabularnewline
22 & 0.70997558807475 & 0.5800488238505 & 0.29002441192525 \tabularnewline
23 & 0.684972795435953 & 0.630054409128094 & 0.315027204564047 \tabularnewline
24 & 0.647825598827445 & 0.704348802345111 & 0.352174401172555 \tabularnewline
25 & 0.611634222424612 & 0.776731555150776 & 0.388365777575388 \tabularnewline
26 & 0.661582040713152 & 0.676835918573695 & 0.338417959286848 \tabularnewline
27 & 0.62096867560115 & 0.7580626487977 & 0.37903132439885 \tabularnewline
28 & 0.684337677177312 & 0.631324645645377 & 0.315662322822688 \tabularnewline
29 & 0.652234916262456 & 0.695530167475087 & 0.347765083737544 \tabularnewline
30 & 0.697563767136702 & 0.604872465726596 & 0.302436232863298 \tabularnewline
31 & 0.718917624690251 & 0.562164750619498 & 0.281082375309749 \tabularnewline
32 & 0.725598750137148 & 0.548802499725704 & 0.274401249862852 \tabularnewline
33 & 0.722631052430494 & 0.554737895139012 & 0.277368947569506 \tabularnewline
34 & 0.720610122844404 & 0.558779754311192 & 0.279389877155596 \tabularnewline
35 & 0.71735697614064 & 0.56528604771872 & 0.28264302385936 \tabularnewline
36 & 0.708165885733502 & 0.583668228532995 & 0.291834114266497 \tabularnewline
37 & 0.699337684184768 & 0.601324631630463 & 0.300662315815231 \tabularnewline
38 & 0.709896276240867 & 0.580207447518266 & 0.290103723759133 \tabularnewline
39 & 0.710664766565051 & 0.578670466869899 & 0.289335233434949 \tabularnewline
40 & 0.703932794503637 & 0.592134410992726 & 0.296067205496363 \tabularnewline
41 & 0.685060700900708 & 0.629878598198584 & 0.314939299099292 \tabularnewline
42 & 0.679823753164431 & 0.640352493671138 & 0.320176246835569 \tabularnewline
43 & 0.669707166725582 & 0.660585666548835 & 0.330292833274418 \tabularnewline
44 & 0.66516782543838 & 0.66966434912324 & 0.33483217456162 \tabularnewline
45 & 0.672737762558406 & 0.654524474883189 & 0.327262237441594 \tabularnewline
46 & 0.665982763099173 & 0.668034473801654 & 0.334017236900827 \tabularnewline
47 & 0.673207990325811 & 0.653584019348379 & 0.326792009674189 \tabularnewline
48 & 0.666795987509307 & 0.666408024981387 & 0.333204012490693 \tabularnewline
49 & 0.657361989053001 & 0.685276021893997 & 0.342638010946999 \tabularnewline
50 & 0.66907017707646 & 0.661859645847081 & 0.33092982292354 \tabularnewline
51 & 0.660176454799171 & 0.679647090401659 & 0.339823545200829 \tabularnewline
52 & 0.667161800013098 & 0.665676399973803 & 0.332838199986902 \tabularnewline
53 & 0.661803038516387 & 0.676393922967226 & 0.338196961483613 \tabularnewline
54 & 0.672817005620913 & 0.654365988758175 & 0.327182994379087 \tabularnewline
55 & 0.675803210136314 & 0.648393579727373 & 0.324196789863686 \tabularnewline
56 & 0.68594285492006 & 0.628114290159879 & 0.31405714507994 \tabularnewline
57 & 0.67944476553071 & 0.64111046893858 & 0.32055523446929 \tabularnewline
58 & 0.67124257501869 & 0.65751484996262 & 0.32875742498131 \tabularnewline
59 & 0.662167831451445 & 0.675664337097109 & 0.337832168548555 \tabularnewline
60 & 0.66748014557175 & 0.6650397088565 & 0.33251985442825 \tabularnewline
61 & 0.665488186669595 & 0.66902362666081 & 0.334511813330405 \tabularnewline
62 & 0.682251495777786 & 0.635497008444428 & 0.317748504222214 \tabularnewline
63 & 0.693908002905089 & 0.612183994189821 & 0.306091997094911 \tabularnewline
64 & 0.693802123185367 & 0.612395753629265 & 0.306197876814633 \tabularnewline
65 & 0.703602317656243 & 0.592795364687514 & 0.296397682343757 \tabularnewline
66 & 0.710804035932948 & 0.578391928134104 & 0.289195964067052 \tabularnewline
67 & 0.705386356315424 & 0.589227287369151 & 0.294613643684576 \tabularnewline
68 & 0.711039018186161 & 0.577921963627678 & 0.288960981813839 \tabularnewline
69 & 0.70547186466879 & 0.589056270662421 & 0.29452813533121 \tabularnewline
70 & 0.712003799336248 & 0.575992401327504 & 0.287996200663752 \tabularnewline
71 & 0.719583490707244 & 0.560833018585512 & 0.280416509292756 \tabularnewline
72 & 0.712815754947892 & 0.574368490104216 & 0.287184245052108 \tabularnewline
73 & 0.705581759809662 & 0.588836480380677 & 0.294418240190338 \tabularnewline
74 & 0.714880011898731 & 0.570239976202538 & 0.285119988101269 \tabularnewline
75 & 0.706470994561617 & 0.587058010876767 & 0.293529005438384 \tabularnewline
76 & 0.705503948902315 & 0.58899210219537 & 0.294496051097685 \tabularnewline
77 & 0.701416631647146 & 0.597166736705709 & 0.298583368352854 \tabularnewline
78 & 0.702656292780351 & 0.594687414439299 & 0.297343707219649 \tabularnewline
79 & 0.730992828431499 & 0.538014343137003 & 0.269007171568501 \tabularnewline
80 & 0.723934774372302 & 0.552130451255395 & 0.276065225627698 \tabularnewline
81 & 0.723625028226955 & 0.55274994354609 & 0.276374971773045 \tabularnewline
82 & 0.73348590620682 & 0.533028187586361 & 0.26651409379318 \tabularnewline
83 & 0.7283034158435 & 0.543393168313001 & 0.2716965841565 \tabularnewline
84 & 0.718519889478541 & 0.562960221042917 & 0.281480110521459 \tabularnewline
85 & 0.738844536984971 & 0.522310926030059 & 0.261155463015029 \tabularnewline
86 & 0.720889958719352 & 0.558220082561297 & 0.279110041280648 \tabularnewline
87 & 0.738922097744537 & 0.522155804510927 & 0.261077902255463 \tabularnewline
88 & 0.783426827356108 & 0.433146345287785 & 0.216573172643892 \tabularnewline
89 & 0.787969666744395 & 0.42406066651121 & 0.212030333255605 \tabularnewline
90 & 0.822769746065569 & 0.354460507868863 & 0.177230253934431 \tabularnewline
91 & 0.814276902539896 & 0.371446194920208 & 0.185723097460104 \tabularnewline
92 & 0.796954774694432 & 0.406090450611136 & 0.203045225305568 \tabularnewline
93 & 0.775269943582822 & 0.449460112834356 & 0.224730056417178 \tabularnewline
94 & 0.749728613733665 & 0.500542772532669 & 0.250271386266335 \tabularnewline
95 & 0.717841866417198 & 0.564316267165605 & 0.282158133582802 \tabularnewline
96 & 0.774863976364501 & 0.450272047270998 & 0.225136023635499 \tabularnewline
97 & 0.744194423453028 & 0.511611153093944 & 0.255805576546972 \tabularnewline
98 & 0.713341399054972 & 0.573317201890056 & 0.286658600945028 \tabularnewline
99 & 0.680216828076876 & 0.639566343846248 & 0.319783171923124 \tabularnewline
100 & 0.747227629143349 & 0.505544741713303 & 0.252772370856651 \tabularnewline
101 & 0.818065823834475 & 0.36386835233105 & 0.181934176165525 \tabularnewline
102 & 0.790492587184046 & 0.419014825631908 & 0.209507412815954 \tabularnewline
103 & 0.75974458956727 & 0.48051082086546 & 0.24025541043273 \tabularnewline
104 & 0.726064038872062 & 0.547871922255876 & 0.273935961127938 \tabularnewline
105 & 0.687336189239258 & 0.625327621521485 & 0.312663810760742 \tabularnewline
106 & 0.648706915571814 & 0.702586168856372 & 0.351293084428186 \tabularnewline
107 & 0.608568926637414 & 0.782862146725172 & 0.391431073362586 \tabularnewline
108 & 0.563140555067871 & 0.873718889864259 & 0.43685944493213 \tabularnewline
109 & 0.521663121807647 & 0.956673756384706 & 0.478336878192353 \tabularnewline
110 & 0.481031310947897 & 0.962062621895795 & 0.518968689052103 \tabularnewline
111 & 0.435420149507804 & 0.870840299015608 & 0.564579850492196 \tabularnewline
112 & 0.392030638369351 & 0.784061276738703 & 0.607969361630649 \tabularnewline
113 & 0.357132292673876 & 0.714264585347751 & 0.642867707326124 \tabularnewline
114 & 0.321203745888988 & 0.642407491777976 & 0.678796254111012 \tabularnewline
115 & 0.293808274445621 & 0.587616548891241 & 0.706191725554379 \tabularnewline
116 & 0.271279176590119 & 0.542558353180237 & 0.728720823409881 \tabularnewline
117 & 0.301934630617841 & 0.603869261235682 & 0.698065369382159 \tabularnewline
118 & 0.276733092992548 & 0.553466185985096 & 0.723266907007452 \tabularnewline
119 & 0.257246286191278 & 0.514492572382555 & 0.742753713808722 \tabularnewline
120 & 0.281724118226176 & 0.563448236452353 & 0.718275881773824 \tabularnewline
121 & 0.259544568159591 & 0.519089136319182 & 0.740455431840409 \tabularnewline
122 & 0.244402027970648 & 0.488804055941295 & 0.755597972029352 \tabularnewline
123 & 0.234206413041311 & 0.468412826082621 & 0.765793586958689 \tabularnewline
124 & 0.243485936716776 & 0.486971873433553 & 0.756514063283224 \tabularnewline
125 & 0.264310215581944 & 0.528620431163887 & 0.735689784418057 \tabularnewline
126 & 0.255416349200138 & 0.510832698400275 & 0.744583650799862 \tabularnewline
127 & 0.241116051585063 & 0.482232103170127 & 0.758883948414937 \tabularnewline
128 & 0.255324404895014 & 0.510648809790029 & 0.744675595104986 \tabularnewline
129 & 0.236525680213053 & 0.473051360426105 & 0.763474319786947 \tabularnewline
130 & 0.256210905892803 & 0.512421811785607 & 0.743789094107197 \tabularnewline
131 & 0.231147262952146 & 0.462294525904292 & 0.768852737047854 \tabularnewline
132 & 0.260940503320592 & 0.521881006641185 & 0.739059496679408 \tabularnewline
133 & 0.226967507362018 & 0.453935014724036 & 0.773032492637982 \tabularnewline
134 & 0.203858681881397 & 0.407717363762793 & 0.796141318118603 \tabularnewline
135 & 0.19644386703938 & 0.392887734078761 & 0.80355613296062 \tabularnewline
136 & 0.218312290548756 & 0.436624581097513 & 0.781687709451244 \tabularnewline
137 & 0.191323978314508 & 0.382647956629016 & 0.808676021685492 \tabularnewline
138 & 0.179262339837812 & 0.358524679675625 & 0.820737660162188 \tabularnewline
139 & 0.181886554137235 & 0.363773108274471 & 0.818113445862765 \tabularnewline
140 & 0.21382496327727 & 0.42764992655454 & 0.78617503672273 \tabularnewline
141 & 0.201448849280048 & 0.402897698560095 & 0.798551150719952 \tabularnewline
142 & 0.201586674593297 & 0.403173349186593 & 0.798413325406703 \tabularnewline
143 & 0.19861234648918 & 0.397224692978359 & 0.80138765351082 \tabularnewline
144 & 0.17692178255151 & 0.35384356510302 & 0.82307821744849 \tabularnewline
145 & 0.20853682750203 & 0.41707365500406 & 0.79146317249797 \tabularnewline
146 & 0.278709064942231 & 0.557418129884463 & 0.721290935057769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&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]8[/C][C]0.835309820012492[/C][C]0.329380359975016[/C][C]0.164690179987508[/C][/ROW]
[ROW][C]9[/C][C]0.901145638270539[/C][C]0.197708723458922[/C][C]0.098854361729461[/C][/ROW]
[ROW][C]10[/C][C]0.854928325882199[/C][C]0.290143348235601[/C][C]0.145071674117801[/C][/ROW]
[ROW][C]11[/C][C]0.809445952993657[/C][C]0.381108094012687[/C][C]0.190554047006344[/C][/ROW]
[ROW][C]12[/C][C]0.731017095919468[/C][C]0.537965808161063[/C][C]0.268982904080532[/C][/ROW]
[ROW][C]13[/C][C]0.642291013926044[/C][C]0.715417972147912[/C][C]0.357708986073956[/C][/ROW]
[ROW][C]14[/C][C]0.552381785732211[/C][C]0.895236428535579[/C][C]0.447618214267789[/C][/ROW]
[ROW][C]15[/C][C]0.716582647323433[/C][C]0.566834705353135[/C][C]0.283417352676567[/C][/ROW]
[ROW][C]16[/C][C]0.75115255605213[/C][C]0.49769488789574[/C][C]0.24884744394787[/C][/ROW]
[ROW][C]17[/C][C]0.682519852529399[/C][C]0.634960294941202[/C][C]0.317480147470601[/C][/ROW]
[ROW][C]18[/C][C]0.656591978721507[/C][C]0.686816042556986[/C][C]0.343408021278493[/C][/ROW]
[ROW][C]19[/C][C]0.676935403936931[/C][C]0.646129192126139[/C][C]0.323064596063069[/C][/ROW]
[ROW][C]20[/C][C]0.719871027082068[/C][C]0.560257945835865[/C][C]0.280128972917932[/C][/ROW]
[ROW][C]21[/C][C]0.713098562792053[/C][C]0.573802874415893[/C][C]0.286901437207947[/C][/ROW]
[ROW][C]22[/C][C]0.70997558807475[/C][C]0.5800488238505[/C][C]0.29002441192525[/C][/ROW]
[ROW][C]23[/C][C]0.684972795435953[/C][C]0.630054409128094[/C][C]0.315027204564047[/C][/ROW]
[ROW][C]24[/C][C]0.647825598827445[/C][C]0.704348802345111[/C][C]0.352174401172555[/C][/ROW]
[ROW][C]25[/C][C]0.611634222424612[/C][C]0.776731555150776[/C][C]0.388365777575388[/C][/ROW]
[ROW][C]26[/C][C]0.661582040713152[/C][C]0.676835918573695[/C][C]0.338417959286848[/C][/ROW]
[ROW][C]27[/C][C]0.62096867560115[/C][C]0.7580626487977[/C][C]0.37903132439885[/C][/ROW]
[ROW][C]28[/C][C]0.684337677177312[/C][C]0.631324645645377[/C][C]0.315662322822688[/C][/ROW]
[ROW][C]29[/C][C]0.652234916262456[/C][C]0.695530167475087[/C][C]0.347765083737544[/C][/ROW]
[ROW][C]30[/C][C]0.697563767136702[/C][C]0.604872465726596[/C][C]0.302436232863298[/C][/ROW]
[ROW][C]31[/C][C]0.718917624690251[/C][C]0.562164750619498[/C][C]0.281082375309749[/C][/ROW]
[ROW][C]32[/C][C]0.725598750137148[/C][C]0.548802499725704[/C][C]0.274401249862852[/C][/ROW]
[ROW][C]33[/C][C]0.722631052430494[/C][C]0.554737895139012[/C][C]0.277368947569506[/C][/ROW]
[ROW][C]34[/C][C]0.720610122844404[/C][C]0.558779754311192[/C][C]0.279389877155596[/C][/ROW]
[ROW][C]35[/C][C]0.71735697614064[/C][C]0.56528604771872[/C][C]0.28264302385936[/C][/ROW]
[ROW][C]36[/C][C]0.708165885733502[/C][C]0.583668228532995[/C][C]0.291834114266497[/C][/ROW]
[ROW][C]37[/C][C]0.699337684184768[/C][C]0.601324631630463[/C][C]0.300662315815231[/C][/ROW]
[ROW][C]38[/C][C]0.709896276240867[/C][C]0.580207447518266[/C][C]0.290103723759133[/C][/ROW]
[ROW][C]39[/C][C]0.710664766565051[/C][C]0.578670466869899[/C][C]0.289335233434949[/C][/ROW]
[ROW][C]40[/C][C]0.703932794503637[/C][C]0.592134410992726[/C][C]0.296067205496363[/C][/ROW]
[ROW][C]41[/C][C]0.685060700900708[/C][C]0.629878598198584[/C][C]0.314939299099292[/C][/ROW]
[ROW][C]42[/C][C]0.679823753164431[/C][C]0.640352493671138[/C][C]0.320176246835569[/C][/ROW]
[ROW][C]43[/C][C]0.669707166725582[/C][C]0.660585666548835[/C][C]0.330292833274418[/C][/ROW]
[ROW][C]44[/C][C]0.66516782543838[/C][C]0.66966434912324[/C][C]0.33483217456162[/C][/ROW]
[ROW][C]45[/C][C]0.672737762558406[/C][C]0.654524474883189[/C][C]0.327262237441594[/C][/ROW]
[ROW][C]46[/C][C]0.665982763099173[/C][C]0.668034473801654[/C][C]0.334017236900827[/C][/ROW]
[ROW][C]47[/C][C]0.673207990325811[/C][C]0.653584019348379[/C][C]0.326792009674189[/C][/ROW]
[ROW][C]48[/C][C]0.666795987509307[/C][C]0.666408024981387[/C][C]0.333204012490693[/C][/ROW]
[ROW][C]49[/C][C]0.657361989053001[/C][C]0.685276021893997[/C][C]0.342638010946999[/C][/ROW]
[ROW][C]50[/C][C]0.66907017707646[/C][C]0.661859645847081[/C][C]0.33092982292354[/C][/ROW]
[ROW][C]51[/C][C]0.660176454799171[/C][C]0.679647090401659[/C][C]0.339823545200829[/C][/ROW]
[ROW][C]52[/C][C]0.667161800013098[/C][C]0.665676399973803[/C][C]0.332838199986902[/C][/ROW]
[ROW][C]53[/C][C]0.661803038516387[/C][C]0.676393922967226[/C][C]0.338196961483613[/C][/ROW]
[ROW][C]54[/C][C]0.672817005620913[/C][C]0.654365988758175[/C][C]0.327182994379087[/C][/ROW]
[ROW][C]55[/C][C]0.675803210136314[/C][C]0.648393579727373[/C][C]0.324196789863686[/C][/ROW]
[ROW][C]56[/C][C]0.68594285492006[/C][C]0.628114290159879[/C][C]0.31405714507994[/C][/ROW]
[ROW][C]57[/C][C]0.67944476553071[/C][C]0.64111046893858[/C][C]0.32055523446929[/C][/ROW]
[ROW][C]58[/C][C]0.67124257501869[/C][C]0.65751484996262[/C][C]0.32875742498131[/C][/ROW]
[ROW][C]59[/C][C]0.662167831451445[/C][C]0.675664337097109[/C][C]0.337832168548555[/C][/ROW]
[ROW][C]60[/C][C]0.66748014557175[/C][C]0.6650397088565[/C][C]0.33251985442825[/C][/ROW]
[ROW][C]61[/C][C]0.665488186669595[/C][C]0.66902362666081[/C][C]0.334511813330405[/C][/ROW]
[ROW][C]62[/C][C]0.682251495777786[/C][C]0.635497008444428[/C][C]0.317748504222214[/C][/ROW]
[ROW][C]63[/C][C]0.693908002905089[/C][C]0.612183994189821[/C][C]0.306091997094911[/C][/ROW]
[ROW][C]64[/C][C]0.693802123185367[/C][C]0.612395753629265[/C][C]0.306197876814633[/C][/ROW]
[ROW][C]65[/C][C]0.703602317656243[/C][C]0.592795364687514[/C][C]0.296397682343757[/C][/ROW]
[ROW][C]66[/C][C]0.710804035932948[/C][C]0.578391928134104[/C][C]0.289195964067052[/C][/ROW]
[ROW][C]67[/C][C]0.705386356315424[/C][C]0.589227287369151[/C][C]0.294613643684576[/C][/ROW]
[ROW][C]68[/C][C]0.711039018186161[/C][C]0.577921963627678[/C][C]0.288960981813839[/C][/ROW]
[ROW][C]69[/C][C]0.70547186466879[/C][C]0.589056270662421[/C][C]0.29452813533121[/C][/ROW]
[ROW][C]70[/C][C]0.712003799336248[/C][C]0.575992401327504[/C][C]0.287996200663752[/C][/ROW]
[ROW][C]71[/C][C]0.719583490707244[/C][C]0.560833018585512[/C][C]0.280416509292756[/C][/ROW]
[ROW][C]72[/C][C]0.712815754947892[/C][C]0.574368490104216[/C][C]0.287184245052108[/C][/ROW]
[ROW][C]73[/C][C]0.705581759809662[/C][C]0.588836480380677[/C][C]0.294418240190338[/C][/ROW]
[ROW][C]74[/C][C]0.714880011898731[/C][C]0.570239976202538[/C][C]0.285119988101269[/C][/ROW]
[ROW][C]75[/C][C]0.706470994561617[/C][C]0.587058010876767[/C][C]0.293529005438384[/C][/ROW]
[ROW][C]76[/C][C]0.705503948902315[/C][C]0.58899210219537[/C][C]0.294496051097685[/C][/ROW]
[ROW][C]77[/C][C]0.701416631647146[/C][C]0.597166736705709[/C][C]0.298583368352854[/C][/ROW]
[ROW][C]78[/C][C]0.702656292780351[/C][C]0.594687414439299[/C][C]0.297343707219649[/C][/ROW]
[ROW][C]79[/C][C]0.730992828431499[/C][C]0.538014343137003[/C][C]0.269007171568501[/C][/ROW]
[ROW][C]80[/C][C]0.723934774372302[/C][C]0.552130451255395[/C][C]0.276065225627698[/C][/ROW]
[ROW][C]81[/C][C]0.723625028226955[/C][C]0.55274994354609[/C][C]0.276374971773045[/C][/ROW]
[ROW][C]82[/C][C]0.73348590620682[/C][C]0.533028187586361[/C][C]0.26651409379318[/C][/ROW]
[ROW][C]83[/C][C]0.7283034158435[/C][C]0.543393168313001[/C][C]0.2716965841565[/C][/ROW]
[ROW][C]84[/C][C]0.718519889478541[/C][C]0.562960221042917[/C][C]0.281480110521459[/C][/ROW]
[ROW][C]85[/C][C]0.738844536984971[/C][C]0.522310926030059[/C][C]0.261155463015029[/C][/ROW]
[ROW][C]86[/C][C]0.720889958719352[/C][C]0.558220082561297[/C][C]0.279110041280648[/C][/ROW]
[ROW][C]87[/C][C]0.738922097744537[/C][C]0.522155804510927[/C][C]0.261077902255463[/C][/ROW]
[ROW][C]88[/C][C]0.783426827356108[/C][C]0.433146345287785[/C][C]0.216573172643892[/C][/ROW]
[ROW][C]89[/C][C]0.787969666744395[/C][C]0.42406066651121[/C][C]0.212030333255605[/C][/ROW]
[ROW][C]90[/C][C]0.822769746065569[/C][C]0.354460507868863[/C][C]0.177230253934431[/C][/ROW]
[ROW][C]91[/C][C]0.814276902539896[/C][C]0.371446194920208[/C][C]0.185723097460104[/C][/ROW]
[ROW][C]92[/C][C]0.796954774694432[/C][C]0.406090450611136[/C][C]0.203045225305568[/C][/ROW]
[ROW][C]93[/C][C]0.775269943582822[/C][C]0.449460112834356[/C][C]0.224730056417178[/C][/ROW]
[ROW][C]94[/C][C]0.749728613733665[/C][C]0.500542772532669[/C][C]0.250271386266335[/C][/ROW]
[ROW][C]95[/C][C]0.717841866417198[/C][C]0.564316267165605[/C][C]0.282158133582802[/C][/ROW]
[ROW][C]96[/C][C]0.774863976364501[/C][C]0.450272047270998[/C][C]0.225136023635499[/C][/ROW]
[ROW][C]97[/C][C]0.744194423453028[/C][C]0.511611153093944[/C][C]0.255805576546972[/C][/ROW]
[ROW][C]98[/C][C]0.713341399054972[/C][C]0.573317201890056[/C][C]0.286658600945028[/C][/ROW]
[ROW][C]99[/C][C]0.680216828076876[/C][C]0.639566343846248[/C][C]0.319783171923124[/C][/ROW]
[ROW][C]100[/C][C]0.747227629143349[/C][C]0.505544741713303[/C][C]0.252772370856651[/C][/ROW]
[ROW][C]101[/C][C]0.818065823834475[/C][C]0.36386835233105[/C][C]0.181934176165525[/C][/ROW]
[ROW][C]102[/C][C]0.790492587184046[/C][C]0.419014825631908[/C][C]0.209507412815954[/C][/ROW]
[ROW][C]103[/C][C]0.75974458956727[/C][C]0.48051082086546[/C][C]0.24025541043273[/C][/ROW]
[ROW][C]104[/C][C]0.726064038872062[/C][C]0.547871922255876[/C][C]0.273935961127938[/C][/ROW]
[ROW][C]105[/C][C]0.687336189239258[/C][C]0.625327621521485[/C][C]0.312663810760742[/C][/ROW]
[ROW][C]106[/C][C]0.648706915571814[/C][C]0.702586168856372[/C][C]0.351293084428186[/C][/ROW]
[ROW][C]107[/C][C]0.608568926637414[/C][C]0.782862146725172[/C][C]0.391431073362586[/C][/ROW]
[ROW][C]108[/C][C]0.563140555067871[/C][C]0.873718889864259[/C][C]0.43685944493213[/C][/ROW]
[ROW][C]109[/C][C]0.521663121807647[/C][C]0.956673756384706[/C][C]0.478336878192353[/C][/ROW]
[ROW][C]110[/C][C]0.481031310947897[/C][C]0.962062621895795[/C][C]0.518968689052103[/C][/ROW]
[ROW][C]111[/C][C]0.435420149507804[/C][C]0.870840299015608[/C][C]0.564579850492196[/C][/ROW]
[ROW][C]112[/C][C]0.392030638369351[/C][C]0.784061276738703[/C][C]0.607969361630649[/C][/ROW]
[ROW][C]113[/C][C]0.357132292673876[/C][C]0.714264585347751[/C][C]0.642867707326124[/C][/ROW]
[ROW][C]114[/C][C]0.321203745888988[/C][C]0.642407491777976[/C][C]0.678796254111012[/C][/ROW]
[ROW][C]115[/C][C]0.293808274445621[/C][C]0.587616548891241[/C][C]0.706191725554379[/C][/ROW]
[ROW][C]116[/C][C]0.271279176590119[/C][C]0.542558353180237[/C][C]0.728720823409881[/C][/ROW]
[ROW][C]117[/C][C]0.301934630617841[/C][C]0.603869261235682[/C][C]0.698065369382159[/C][/ROW]
[ROW][C]118[/C][C]0.276733092992548[/C][C]0.553466185985096[/C][C]0.723266907007452[/C][/ROW]
[ROW][C]119[/C][C]0.257246286191278[/C][C]0.514492572382555[/C][C]0.742753713808722[/C][/ROW]
[ROW][C]120[/C][C]0.281724118226176[/C][C]0.563448236452353[/C][C]0.718275881773824[/C][/ROW]
[ROW][C]121[/C][C]0.259544568159591[/C][C]0.519089136319182[/C][C]0.740455431840409[/C][/ROW]
[ROW][C]122[/C][C]0.244402027970648[/C][C]0.488804055941295[/C][C]0.755597972029352[/C][/ROW]
[ROW][C]123[/C][C]0.234206413041311[/C][C]0.468412826082621[/C][C]0.765793586958689[/C][/ROW]
[ROW][C]124[/C][C]0.243485936716776[/C][C]0.486971873433553[/C][C]0.756514063283224[/C][/ROW]
[ROW][C]125[/C][C]0.264310215581944[/C][C]0.528620431163887[/C][C]0.735689784418057[/C][/ROW]
[ROW][C]126[/C][C]0.255416349200138[/C][C]0.510832698400275[/C][C]0.744583650799862[/C][/ROW]
[ROW][C]127[/C][C]0.241116051585063[/C][C]0.482232103170127[/C][C]0.758883948414937[/C][/ROW]
[ROW][C]128[/C][C]0.255324404895014[/C][C]0.510648809790029[/C][C]0.744675595104986[/C][/ROW]
[ROW][C]129[/C][C]0.236525680213053[/C][C]0.473051360426105[/C][C]0.763474319786947[/C][/ROW]
[ROW][C]130[/C][C]0.256210905892803[/C][C]0.512421811785607[/C][C]0.743789094107197[/C][/ROW]
[ROW][C]131[/C][C]0.231147262952146[/C][C]0.462294525904292[/C][C]0.768852737047854[/C][/ROW]
[ROW][C]132[/C][C]0.260940503320592[/C][C]0.521881006641185[/C][C]0.739059496679408[/C][/ROW]
[ROW][C]133[/C][C]0.226967507362018[/C][C]0.453935014724036[/C][C]0.773032492637982[/C][/ROW]
[ROW][C]134[/C][C]0.203858681881397[/C][C]0.407717363762793[/C][C]0.796141318118603[/C][/ROW]
[ROW][C]135[/C][C]0.19644386703938[/C][C]0.392887734078761[/C][C]0.80355613296062[/C][/ROW]
[ROW][C]136[/C][C]0.218312290548756[/C][C]0.436624581097513[/C][C]0.781687709451244[/C][/ROW]
[ROW][C]137[/C][C]0.191323978314508[/C][C]0.382647956629016[/C][C]0.808676021685492[/C][/ROW]
[ROW][C]138[/C][C]0.179262339837812[/C][C]0.358524679675625[/C][C]0.820737660162188[/C][/ROW]
[ROW][C]139[/C][C]0.181886554137235[/C][C]0.363773108274471[/C][C]0.818113445862765[/C][/ROW]
[ROW][C]140[/C][C]0.21382496327727[/C][C]0.42764992655454[/C][C]0.78617503672273[/C][/ROW]
[ROW][C]141[/C][C]0.201448849280048[/C][C]0.402897698560095[/C][C]0.798551150719952[/C][/ROW]
[ROW][C]142[/C][C]0.201586674593297[/C][C]0.403173349186593[/C][C]0.798413325406703[/C][/ROW]
[ROW][C]143[/C][C]0.19861234648918[/C][C]0.397224692978359[/C][C]0.80138765351082[/C][/ROW]
[ROW][C]144[/C][C]0.17692178255151[/C][C]0.35384356510302[/C][C]0.82307821744849[/C][/ROW]
[ROW][C]145[/C][C]0.20853682750203[/C][C]0.41707365500406[/C][C]0.79146317249797[/C][/ROW]
[ROW][C]146[/C][C]0.278709064942231[/C][C]0.557418129884463[/C][C]0.721290935057769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8353098200124920.3293803599750160.164690179987508
90.9011456382705390.1977087234589220.098854361729461
100.8549283258821990.2901433482356010.145071674117801
110.8094459529936570.3811080940126870.190554047006344
120.7310170959194680.5379658081610630.268982904080532
130.6422910139260440.7154179721479120.357708986073956
140.5523817857322110.8952364285355790.447618214267789
150.7165826473234330.5668347053531350.283417352676567
160.751152556052130.497694887895740.24884744394787
170.6825198525293990.6349602949412020.317480147470601
180.6565919787215070.6868160425569860.343408021278493
190.6769354039369310.6461291921261390.323064596063069
200.7198710270820680.5602579458358650.280128972917932
210.7130985627920530.5738028744158930.286901437207947
220.709975588074750.58004882385050.29002441192525
230.6849727954359530.6300544091280940.315027204564047
240.6478255988274450.7043488023451110.352174401172555
250.6116342224246120.7767315551507760.388365777575388
260.6615820407131520.6768359185736950.338417959286848
270.620968675601150.75806264879770.37903132439885
280.6843376771773120.6313246456453770.315662322822688
290.6522349162624560.6955301674750870.347765083737544
300.6975637671367020.6048724657265960.302436232863298
310.7189176246902510.5621647506194980.281082375309749
320.7255987501371480.5488024997257040.274401249862852
330.7226310524304940.5547378951390120.277368947569506
340.7206101228444040.5587797543111920.279389877155596
350.717356976140640.565286047718720.28264302385936
360.7081658857335020.5836682285329950.291834114266497
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1460.2787090649422310.5574181298844630.721290935057769







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202039&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202039&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202039&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 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')
}