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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 computationFri, 20 Nov 2009 09:54:01 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258736155pr5jmnpftdev2q2.htm/, Retrieved Fri, 29 Mar 2024 00:11:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58327, Retrieved Fri, 29 Mar 2024 00:11:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [link 1] [2009-11-20 16:54:01] [9a3898f49d4e2f0208d1968305d88f0a] [Current]
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Dataseries X:
3956.2	3977.7
3142.7	3983.4
3884.3	4152.9
3892.2	4286.1
3613	4348.1
3730.5	3949.3
3481.3	4166.7
3649.5	4217.9
4215.2	4528.2
4066.6	4232.2
4196.8	4470.9
4536.6	5121.2
4441.6	4170.8
3548.3	4398.6
4735.9	4491.4
4130.6	4251.8
4356.2	4901.9
4159.6	4745.2
3988	4666.9
4167.8	4210.4
4902.2	5273.6
3909.4	4095.3
4697.6	4610.1
4308.9	4718.1
4420.4	4185.5
3544.2	4314.7
4433	4422.6
4479.7	5059.2
4533.2	5043.6
4237.5	4436.6
4207.4	4922.6
4394	4454.8
5148.4	5058.7
4202.2	4768.9
4682.5	5171.8
4884.3	4989.3
5288.9	5202.1
4505.2	4838.4
4611.5	4876.5
5104	5875.5
4586.6	5717.9
4529.3	4778.8
4504.1	6195.9
4604.9	4625.4
4795.4	5549.8
5391.1	6397.6
5213.9	5856.7
5415	6343.8
5990.3	6615.5
4241.8	5904.6
5677.6	6861
5164.2	6553.5
3962.3	5481
4011	5435.3
3310.3	5278
3837.3	4671.8
4145.3	4891.5
3796.7	4241.6
3849.6	4152.1
4285	4484.4
4189.6	4124.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1538.6271090769 + 0.577774022625357X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1538.6271090769 +  0.577774022625357X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1538.6271090769 +  0.577774022625357X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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
Y[t] = + 1538.6271090769 + 0.577774022625357X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1538.6271090769343.6922434.47683.5e-051.8e-05
X0.5777740226253570.069648.296600

\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) & 1538.6271090769 & 343.692243 & 4.4768 & 3.5e-05 & 1.8e-05 \tabularnewline
X & 0.577774022625357 & 0.06964 & 8.2966 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&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]1538.6271090769[/C][C]343.692243[/C][C]4.4768[/C][C]3.5e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]X[/C][C]0.577774022625357[/C][C]0.06964[/C][C]8.2966[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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)1538.6271090769343.6922434.47683.5e-051.8e-05
X0.5777740226253570.069648.296600







Multiple Linear Regression - Regression Statistics
Multiple R0.733797991705475
R-squared0.538459492630988
Adjusted R-squared0.530636772167106
F-TEST (value)68.8327666976111
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.72734049286305e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation396.36172774853
Sum Squared Residuals9269054.5342042

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.733797991705475 \tabularnewline
R-squared & 0.538459492630988 \tabularnewline
Adjusted R-squared & 0.530636772167106 \tabularnewline
F-TEST (value) & 68.8327666976111 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 1.72734049286305e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 396.36172774853 \tabularnewline
Sum Squared Residuals & 9269054.5342042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.733797991705475[/C][/ROW]
[ROW][C]R-squared[/C][C]0.538459492630988[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.530636772167106[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]68.8327666976111[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]1.72734049286305e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]396.36172774853[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9269054.5342042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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 R0.733797991705475
R-squared0.538459492630988
Adjusted R-squared0.530636772167106
F-TEST (value)68.8327666976111
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.72734049286305e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation396.36172774853
Sum Squared Residuals9269054.5342042







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13956.23836.83883887379119.361161126214
23142.73840.13215080275-697.432150802746
33884.33938.06484763775-53.7648476377447
43892.24015.02434745144-122.824347451443
536134050.84633685422-437.846336854215
63730.53820.43005663122-89.9300566312224
73481.33946.03812914997-464.738129149975
83649.53975.62015910839-326.120159108393
94215.24154.9034383290460.2965616709584
104066.63983.8823276319482.7176723680641
114196.84121.7969868326175.0030131673918
124536.64497.5234337458839.0765662541222
134441.63948.40700264274493.192997357261
143548.34080.02392499680-531.723924996795
154735.94133.64135429643602.258645703571
164130.63995.20669847539135.393301524607
174356.24370.81759058414-14.6175905841375
184159.64280.28040123874-120.680401238744
1939884235.04069526718-247.040695267178
204167.83971.2868539387196.513146061297
214902.24585.57619479398316.623805206017
223909.43904.785063934524.61493606547546
234697.64202.22313078206495.376869217942
244308.94264.622725225644.2772747744026
254420.43956.90028077533463.499719224668
263544.24031.54868449853-487.348684498528
2744334093.89050153980339.109498460196
284479.74461.7014443431117.9985556568938
294533.24452.6881695901580.511830409849
304237.54101.97933785656135.520662143441
314207.44382.77751285248-175.377512852483
3243944112.49482506834281.505174931659
335148.44461.41255733179686.987442668206
344202.24293.97364557496-91.773645574965
354682.54526.75879929072155.741200709279
364884.34421.31504016159462.984959838406
375288.94544.26535217627744.63464782373
384505.24334.12894014743171.071059852573
394611.54356.14213040945255.357869590547
4051044933.33837901219170.661620987815
414586.64842.28119304643-255.681193046428
424529.34299.69360839896229.606391601044
434504.15118.45717586135-614.357175861349
444604.94211.06307332823393.836926671774
454795.44745.1573798431150.2426201568932
465391.15234.99419622488156.105803775116
475213.94922.47622738683291.423772613171
4854155203.90995380764211.09004619236
495990.35360.89115575495629.408844245051
504241.84950.15160307058-708.351603070583
515677.65502.73467830947174.865321690526
525164.25325.06916635218-160.869166352177
533962.34705.40652708648-743.106527086482
5440114679.0022542525-668.002254252503
553310.34588.11840049353-1277.81840049353
563837.34237.87178797804-400.571787978043
574145.34364.80874074883-219.508740748834
583796.73989.31340344461-192.613403444615
593849.63937.60262841965-88.0026284196451
6042854129.59693613805155.403063861949
614189.63921.77162019971267.828379800290

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3956.2 & 3836.83883887379 & 119.361161126214 \tabularnewline
2 & 3142.7 & 3840.13215080275 & -697.432150802746 \tabularnewline
3 & 3884.3 & 3938.06484763775 & -53.7648476377447 \tabularnewline
4 & 3892.2 & 4015.02434745144 & -122.824347451443 \tabularnewline
5 & 3613 & 4050.84633685422 & -437.846336854215 \tabularnewline
6 & 3730.5 & 3820.43005663122 & -89.9300566312224 \tabularnewline
7 & 3481.3 & 3946.03812914997 & -464.738129149975 \tabularnewline
8 & 3649.5 & 3975.62015910839 & -326.120159108393 \tabularnewline
9 & 4215.2 & 4154.90343832904 & 60.2965616709584 \tabularnewline
10 & 4066.6 & 3983.88232763194 & 82.7176723680641 \tabularnewline
11 & 4196.8 & 4121.79698683261 & 75.0030131673918 \tabularnewline
12 & 4536.6 & 4497.52343374588 & 39.0765662541222 \tabularnewline
13 & 4441.6 & 3948.40700264274 & 493.192997357261 \tabularnewline
14 & 3548.3 & 4080.02392499680 & -531.723924996795 \tabularnewline
15 & 4735.9 & 4133.64135429643 & 602.258645703571 \tabularnewline
16 & 4130.6 & 3995.20669847539 & 135.393301524607 \tabularnewline
17 & 4356.2 & 4370.81759058414 & -14.6175905841375 \tabularnewline
18 & 4159.6 & 4280.28040123874 & -120.680401238744 \tabularnewline
19 & 3988 & 4235.04069526718 & -247.040695267178 \tabularnewline
20 & 4167.8 & 3971.2868539387 & 196.513146061297 \tabularnewline
21 & 4902.2 & 4585.57619479398 & 316.623805206017 \tabularnewline
22 & 3909.4 & 3904.78506393452 & 4.61493606547546 \tabularnewline
23 & 4697.6 & 4202.22313078206 & 495.376869217942 \tabularnewline
24 & 4308.9 & 4264.6227252256 & 44.2772747744026 \tabularnewline
25 & 4420.4 & 3956.90028077533 & 463.499719224668 \tabularnewline
26 & 3544.2 & 4031.54868449853 & -487.348684498528 \tabularnewline
27 & 4433 & 4093.89050153980 & 339.109498460196 \tabularnewline
28 & 4479.7 & 4461.70144434311 & 17.9985556568938 \tabularnewline
29 & 4533.2 & 4452.68816959015 & 80.511830409849 \tabularnewline
30 & 4237.5 & 4101.97933785656 & 135.520662143441 \tabularnewline
31 & 4207.4 & 4382.77751285248 & -175.377512852483 \tabularnewline
32 & 4394 & 4112.49482506834 & 281.505174931659 \tabularnewline
33 & 5148.4 & 4461.41255733179 & 686.987442668206 \tabularnewline
34 & 4202.2 & 4293.97364557496 & -91.773645574965 \tabularnewline
35 & 4682.5 & 4526.75879929072 & 155.741200709279 \tabularnewline
36 & 4884.3 & 4421.31504016159 & 462.984959838406 \tabularnewline
37 & 5288.9 & 4544.26535217627 & 744.63464782373 \tabularnewline
38 & 4505.2 & 4334.12894014743 & 171.071059852573 \tabularnewline
39 & 4611.5 & 4356.14213040945 & 255.357869590547 \tabularnewline
40 & 5104 & 4933.33837901219 & 170.661620987815 \tabularnewline
41 & 4586.6 & 4842.28119304643 & -255.681193046428 \tabularnewline
42 & 4529.3 & 4299.69360839896 & 229.606391601044 \tabularnewline
43 & 4504.1 & 5118.45717586135 & -614.357175861349 \tabularnewline
44 & 4604.9 & 4211.06307332823 & 393.836926671774 \tabularnewline
45 & 4795.4 & 4745.15737984311 & 50.2426201568932 \tabularnewline
46 & 5391.1 & 5234.99419622488 & 156.105803775116 \tabularnewline
47 & 5213.9 & 4922.47622738683 & 291.423772613171 \tabularnewline
48 & 5415 & 5203.90995380764 & 211.09004619236 \tabularnewline
49 & 5990.3 & 5360.89115575495 & 629.408844245051 \tabularnewline
50 & 4241.8 & 4950.15160307058 & -708.351603070583 \tabularnewline
51 & 5677.6 & 5502.73467830947 & 174.865321690526 \tabularnewline
52 & 5164.2 & 5325.06916635218 & -160.869166352177 \tabularnewline
53 & 3962.3 & 4705.40652708648 & -743.106527086482 \tabularnewline
54 & 4011 & 4679.0022542525 & -668.002254252503 \tabularnewline
55 & 3310.3 & 4588.11840049353 & -1277.81840049353 \tabularnewline
56 & 3837.3 & 4237.87178797804 & -400.571787978043 \tabularnewline
57 & 4145.3 & 4364.80874074883 & -219.508740748834 \tabularnewline
58 & 3796.7 & 3989.31340344461 & -192.613403444615 \tabularnewline
59 & 3849.6 & 3937.60262841965 & -88.0026284196451 \tabularnewline
60 & 4285 & 4129.59693613805 & 155.403063861949 \tabularnewline
61 & 4189.6 & 3921.77162019971 & 267.828379800290 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&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]3956.2[/C][C]3836.83883887379[/C][C]119.361161126214[/C][/ROW]
[ROW][C]2[/C][C]3142.7[/C][C]3840.13215080275[/C][C]-697.432150802746[/C][/ROW]
[ROW][C]3[/C][C]3884.3[/C][C]3938.06484763775[/C][C]-53.7648476377447[/C][/ROW]
[ROW][C]4[/C][C]3892.2[/C][C]4015.02434745144[/C][C]-122.824347451443[/C][/ROW]
[ROW][C]5[/C][C]3613[/C][C]4050.84633685422[/C][C]-437.846336854215[/C][/ROW]
[ROW][C]6[/C][C]3730.5[/C][C]3820.43005663122[/C][C]-89.9300566312224[/C][/ROW]
[ROW][C]7[/C][C]3481.3[/C][C]3946.03812914997[/C][C]-464.738129149975[/C][/ROW]
[ROW][C]8[/C][C]3649.5[/C][C]3975.62015910839[/C][C]-326.120159108393[/C][/ROW]
[ROW][C]9[/C][C]4215.2[/C][C]4154.90343832904[/C][C]60.2965616709584[/C][/ROW]
[ROW][C]10[/C][C]4066.6[/C][C]3983.88232763194[/C][C]82.7176723680641[/C][/ROW]
[ROW][C]11[/C][C]4196.8[/C][C]4121.79698683261[/C][C]75.0030131673918[/C][/ROW]
[ROW][C]12[/C][C]4536.6[/C][C]4497.52343374588[/C][C]39.0765662541222[/C][/ROW]
[ROW][C]13[/C][C]4441.6[/C][C]3948.40700264274[/C][C]493.192997357261[/C][/ROW]
[ROW][C]14[/C][C]3548.3[/C][C]4080.02392499680[/C][C]-531.723924996795[/C][/ROW]
[ROW][C]15[/C][C]4735.9[/C][C]4133.64135429643[/C][C]602.258645703571[/C][/ROW]
[ROW][C]16[/C][C]4130.6[/C][C]3995.20669847539[/C][C]135.393301524607[/C][/ROW]
[ROW][C]17[/C][C]4356.2[/C][C]4370.81759058414[/C][C]-14.6175905841375[/C][/ROW]
[ROW][C]18[/C][C]4159.6[/C][C]4280.28040123874[/C][C]-120.680401238744[/C][/ROW]
[ROW][C]19[/C][C]3988[/C][C]4235.04069526718[/C][C]-247.040695267178[/C][/ROW]
[ROW][C]20[/C][C]4167.8[/C][C]3971.2868539387[/C][C]196.513146061297[/C][/ROW]
[ROW][C]21[/C][C]4902.2[/C][C]4585.57619479398[/C][C]316.623805206017[/C][/ROW]
[ROW][C]22[/C][C]3909.4[/C][C]3904.78506393452[/C][C]4.61493606547546[/C][/ROW]
[ROW][C]23[/C][C]4697.6[/C][C]4202.22313078206[/C][C]495.376869217942[/C][/ROW]
[ROW][C]24[/C][C]4308.9[/C][C]4264.6227252256[/C][C]44.2772747744026[/C][/ROW]
[ROW][C]25[/C][C]4420.4[/C][C]3956.90028077533[/C][C]463.499719224668[/C][/ROW]
[ROW][C]26[/C][C]3544.2[/C][C]4031.54868449853[/C][C]-487.348684498528[/C][/ROW]
[ROW][C]27[/C][C]4433[/C][C]4093.89050153980[/C][C]339.109498460196[/C][/ROW]
[ROW][C]28[/C][C]4479.7[/C][C]4461.70144434311[/C][C]17.9985556568938[/C][/ROW]
[ROW][C]29[/C][C]4533.2[/C][C]4452.68816959015[/C][C]80.511830409849[/C][/ROW]
[ROW][C]30[/C][C]4237.5[/C][C]4101.97933785656[/C][C]135.520662143441[/C][/ROW]
[ROW][C]31[/C][C]4207.4[/C][C]4382.77751285248[/C][C]-175.377512852483[/C][/ROW]
[ROW][C]32[/C][C]4394[/C][C]4112.49482506834[/C][C]281.505174931659[/C][/ROW]
[ROW][C]33[/C][C]5148.4[/C][C]4461.41255733179[/C][C]686.987442668206[/C][/ROW]
[ROW][C]34[/C][C]4202.2[/C][C]4293.97364557496[/C][C]-91.773645574965[/C][/ROW]
[ROW][C]35[/C][C]4682.5[/C][C]4526.75879929072[/C][C]155.741200709279[/C][/ROW]
[ROW][C]36[/C][C]4884.3[/C][C]4421.31504016159[/C][C]462.984959838406[/C][/ROW]
[ROW][C]37[/C][C]5288.9[/C][C]4544.26535217627[/C][C]744.63464782373[/C][/ROW]
[ROW][C]38[/C][C]4505.2[/C][C]4334.12894014743[/C][C]171.071059852573[/C][/ROW]
[ROW][C]39[/C][C]4611.5[/C][C]4356.14213040945[/C][C]255.357869590547[/C][/ROW]
[ROW][C]40[/C][C]5104[/C][C]4933.33837901219[/C][C]170.661620987815[/C][/ROW]
[ROW][C]41[/C][C]4586.6[/C][C]4842.28119304643[/C][C]-255.681193046428[/C][/ROW]
[ROW][C]42[/C][C]4529.3[/C][C]4299.69360839896[/C][C]229.606391601044[/C][/ROW]
[ROW][C]43[/C][C]4504.1[/C][C]5118.45717586135[/C][C]-614.357175861349[/C][/ROW]
[ROW][C]44[/C][C]4604.9[/C][C]4211.06307332823[/C][C]393.836926671774[/C][/ROW]
[ROW][C]45[/C][C]4795.4[/C][C]4745.15737984311[/C][C]50.2426201568932[/C][/ROW]
[ROW][C]46[/C][C]5391.1[/C][C]5234.99419622488[/C][C]156.105803775116[/C][/ROW]
[ROW][C]47[/C][C]5213.9[/C][C]4922.47622738683[/C][C]291.423772613171[/C][/ROW]
[ROW][C]48[/C][C]5415[/C][C]5203.90995380764[/C][C]211.09004619236[/C][/ROW]
[ROW][C]49[/C][C]5990.3[/C][C]5360.89115575495[/C][C]629.408844245051[/C][/ROW]
[ROW][C]50[/C][C]4241.8[/C][C]4950.15160307058[/C][C]-708.351603070583[/C][/ROW]
[ROW][C]51[/C][C]5677.6[/C][C]5502.73467830947[/C][C]174.865321690526[/C][/ROW]
[ROW][C]52[/C][C]5164.2[/C][C]5325.06916635218[/C][C]-160.869166352177[/C][/ROW]
[ROW][C]53[/C][C]3962.3[/C][C]4705.40652708648[/C][C]-743.106527086482[/C][/ROW]
[ROW][C]54[/C][C]4011[/C][C]4679.0022542525[/C][C]-668.002254252503[/C][/ROW]
[ROW][C]55[/C][C]3310.3[/C][C]4588.11840049353[/C][C]-1277.81840049353[/C][/ROW]
[ROW][C]56[/C][C]3837.3[/C][C]4237.87178797804[/C][C]-400.571787978043[/C][/ROW]
[ROW][C]57[/C][C]4145.3[/C][C]4364.80874074883[/C][C]-219.508740748834[/C][/ROW]
[ROW][C]58[/C][C]3796.7[/C][C]3989.31340344461[/C][C]-192.613403444615[/C][/ROW]
[ROW][C]59[/C][C]3849.6[/C][C]3937.60262841965[/C][C]-88.0026284196451[/C][/ROW]
[ROW][C]60[/C][C]4285[/C][C]4129.59693613805[/C][C]155.403063861949[/C][/ROW]
[ROW][C]61[/C][C]4189.6[/C][C]3921.77162019971[/C][C]267.828379800290[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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
13956.23836.83883887379119.361161126214
23142.73840.13215080275-697.432150802746
33884.33938.06484763775-53.7648476377447
43892.24015.02434745144-122.824347451443
536134050.84633685422-437.846336854215
63730.53820.43005663122-89.9300566312224
73481.33946.03812914997-464.738129149975
83649.53975.62015910839-326.120159108393
94215.24154.9034383290460.2965616709584
104066.63983.8823276319482.7176723680641
114196.84121.7969868326175.0030131673918
124536.64497.5234337458839.0765662541222
134441.63948.40700264274493.192997357261
143548.34080.02392499680-531.723924996795
154735.94133.64135429643602.258645703571
164130.63995.20669847539135.393301524607
174356.24370.81759058414-14.6175905841375
184159.64280.28040123874-120.680401238744
1939884235.04069526718-247.040695267178
204167.83971.2868539387196.513146061297
214902.24585.57619479398316.623805206017
223909.43904.785063934524.61493606547546
234697.64202.22313078206495.376869217942
244308.94264.622725225644.2772747744026
254420.43956.90028077533463.499719224668
263544.24031.54868449853-487.348684498528
2744334093.89050153980339.109498460196
284479.74461.7014443431117.9985556568938
294533.24452.6881695901580.511830409849
304237.54101.97933785656135.520662143441
314207.44382.77751285248-175.377512852483
3243944112.49482506834281.505174931659
335148.44461.41255733179686.987442668206
344202.24293.97364557496-91.773645574965
354682.54526.75879929072155.741200709279
364884.34421.31504016159462.984959838406
375288.94544.26535217627744.63464782373
384505.24334.12894014743171.071059852573
394611.54356.14213040945255.357869590547
4051044933.33837901219170.661620987815
414586.64842.28119304643-255.681193046428
424529.34299.69360839896229.606391601044
434504.15118.45717586135-614.357175861349
444604.94211.06307332823393.836926671774
454795.44745.1573798431150.2426201568932
465391.15234.99419622488156.105803775116
475213.94922.47622738683291.423772613171
4854155203.90995380764211.09004619236
495990.35360.89115575495629.408844245051
504241.84950.15160307058-708.351603070583
515677.65502.73467830947174.865321690526
525164.25325.06916635218-160.869166352177
533962.34705.40652708648-743.106527086482
5440114679.0022542525-668.002254252503
553310.34588.11840049353-1277.81840049353
563837.34237.87178797804-400.571787978043
574145.34364.80874074883-219.508740748834
583796.73989.31340344461-192.613403444615
593849.63937.60262841965-88.0026284196451
6042854129.59693613805155.403063861949
614189.63921.77162019971267.828379800290







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.5528906280895950.894218743820810.447109371910405
60.392835942836220.785671885672440.60716405716378
70.3115670375052790.6231340750105570.688432962494721
80.2061943850748900.4123887701497790.79380561492511
90.1824432943275110.3648865886550220.817556705672489
100.1515371160523850.303074232104770.848462883947615
110.1034747944963840.2069495889927670.896525205503616
120.06279221992623030.1255844398524610.93720778007377
130.1739592805397830.3479185610795660.826040719460217
140.2132168102745460.4264336205490930.786783189725454
150.3610051202147680.7220102404295370.638994879785232
160.3004742425175370.6009484850350730.699525757482463
170.2306681990218360.4613363980436720.769331800978164
180.1772249627584820.3544499255169640.822775037241518
190.1463325798281940.2926651596563870.853667420171806
200.1226319004281410.2452638008562830.877368099571859
210.09799449242550350.1959889848510070.902005507574497
220.06895855580584370.1379171116116870.931041444194156
230.08598882079703390.1719776415940680.914011179202966
240.05841466360434140.1168293272086830.941585336395659
250.0768981426749290.1537962853498580.923101857325071
260.0941185182517620.1882370365035240.905881481748238
270.08543358381779960.1708671676355990.9145664161822
280.06025042511346380.1205008502269280.939749574886536
290.04064863409769370.08129726819538750.959351365902306
300.0277389884203870.0554779768407740.972261011579613
310.02094778945479320.04189557890958630.979052210545207
320.01652714045102850.03305428090205690.983472859548971
330.03171139079177780.06342278158355560.968288609208222
340.02176125070990640.04352250141981270.978238749290094
350.01414920110874730.02829840221749450.985850798891253
360.01446963156662090.02893926313324180.985530368433379
370.03310286876765890.06620573753531790.96689713123234
380.02350474743540480.04700949487080970.976495252564595
390.01829775062657750.03659550125315510.981702249373422
400.01453482420912860.02906964841825730.985465175790871
410.01571137207863340.03142274415726680.984288627921367
420.01235064337276900.02470128674553790.987649356627231
430.03209750245196680.06419500490393370.967902497548033
440.04011038485431940.08022076970863880.95988961514568
450.02684277973702470.05368555947404950.973157220262975
460.01776377460083340.03552754920166690.982236225399167
470.01562662707647920.03125325415295850.98437337292352
480.01195627422164090.02391254844328180.98804372577836
490.05361936371545480.1072387274309100.946380636284545
500.07726254506666160.1545250901333230.922737454933338
510.1600648424999070.3201296849998150.839935157500093
520.652285420397460.6954291592050790.347714579602540
530.6199872639602780.7600254720794440.380012736039722
540.6413704736349380.7172590527301230.358629526365062
550.8607133334997480.2785733330005040.139286666500252
560.8217522010338740.3564955979322510.178247798966126

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.552890628089595 & 0.89421874382081 & 0.447109371910405 \tabularnewline
6 & 0.39283594283622 & 0.78567188567244 & 0.60716405716378 \tabularnewline
7 & 0.311567037505279 & 0.623134075010557 & 0.688432962494721 \tabularnewline
8 & 0.206194385074890 & 0.412388770149779 & 0.79380561492511 \tabularnewline
9 & 0.182443294327511 & 0.364886588655022 & 0.817556705672489 \tabularnewline
10 & 0.151537116052385 & 0.30307423210477 & 0.848462883947615 \tabularnewline
11 & 0.103474794496384 & 0.206949588992767 & 0.896525205503616 \tabularnewline
12 & 0.0627922199262303 & 0.125584439852461 & 0.93720778007377 \tabularnewline
13 & 0.173959280539783 & 0.347918561079566 & 0.826040719460217 \tabularnewline
14 & 0.213216810274546 & 0.426433620549093 & 0.786783189725454 \tabularnewline
15 & 0.361005120214768 & 0.722010240429537 & 0.638994879785232 \tabularnewline
16 & 0.300474242517537 & 0.600948485035073 & 0.699525757482463 \tabularnewline
17 & 0.230668199021836 & 0.461336398043672 & 0.769331800978164 \tabularnewline
18 & 0.177224962758482 & 0.354449925516964 & 0.822775037241518 \tabularnewline
19 & 0.146332579828194 & 0.292665159656387 & 0.853667420171806 \tabularnewline
20 & 0.122631900428141 & 0.245263800856283 & 0.877368099571859 \tabularnewline
21 & 0.0979944924255035 & 0.195988984851007 & 0.902005507574497 \tabularnewline
22 & 0.0689585558058437 & 0.137917111611687 & 0.931041444194156 \tabularnewline
23 & 0.0859888207970339 & 0.171977641594068 & 0.914011179202966 \tabularnewline
24 & 0.0584146636043414 & 0.116829327208683 & 0.941585336395659 \tabularnewline
25 & 0.076898142674929 & 0.153796285349858 & 0.923101857325071 \tabularnewline
26 & 0.094118518251762 & 0.188237036503524 & 0.905881481748238 \tabularnewline
27 & 0.0854335838177996 & 0.170867167635599 & 0.9145664161822 \tabularnewline
28 & 0.0602504251134638 & 0.120500850226928 & 0.939749574886536 \tabularnewline
29 & 0.0406486340976937 & 0.0812972681953875 & 0.959351365902306 \tabularnewline
30 & 0.027738988420387 & 0.055477976840774 & 0.972261011579613 \tabularnewline
31 & 0.0209477894547932 & 0.0418955789095863 & 0.979052210545207 \tabularnewline
32 & 0.0165271404510285 & 0.0330542809020569 & 0.983472859548971 \tabularnewline
33 & 0.0317113907917778 & 0.0634227815835556 & 0.968288609208222 \tabularnewline
34 & 0.0217612507099064 & 0.0435225014198127 & 0.978238749290094 \tabularnewline
35 & 0.0141492011087473 & 0.0282984022174945 & 0.985850798891253 \tabularnewline
36 & 0.0144696315666209 & 0.0289392631332418 & 0.985530368433379 \tabularnewline
37 & 0.0331028687676589 & 0.0662057375353179 & 0.96689713123234 \tabularnewline
38 & 0.0235047474354048 & 0.0470094948708097 & 0.976495252564595 \tabularnewline
39 & 0.0182977506265775 & 0.0365955012531551 & 0.981702249373422 \tabularnewline
40 & 0.0145348242091286 & 0.0290696484182573 & 0.985465175790871 \tabularnewline
41 & 0.0157113720786334 & 0.0314227441572668 & 0.984288627921367 \tabularnewline
42 & 0.0123506433727690 & 0.0247012867455379 & 0.987649356627231 \tabularnewline
43 & 0.0320975024519668 & 0.0641950049039337 & 0.967902497548033 \tabularnewline
44 & 0.0401103848543194 & 0.0802207697086388 & 0.95988961514568 \tabularnewline
45 & 0.0268427797370247 & 0.0536855594740495 & 0.973157220262975 \tabularnewline
46 & 0.0177637746008334 & 0.0355275492016669 & 0.982236225399167 \tabularnewline
47 & 0.0156266270764792 & 0.0312532541529585 & 0.98437337292352 \tabularnewline
48 & 0.0119562742216409 & 0.0239125484432818 & 0.98804372577836 \tabularnewline
49 & 0.0536193637154548 & 0.107238727430910 & 0.946380636284545 \tabularnewline
50 & 0.0772625450666616 & 0.154525090133323 & 0.922737454933338 \tabularnewline
51 & 0.160064842499907 & 0.320129684999815 & 0.839935157500093 \tabularnewline
52 & 0.65228542039746 & 0.695429159205079 & 0.347714579602540 \tabularnewline
53 & 0.619987263960278 & 0.760025472079444 & 0.380012736039722 \tabularnewline
54 & 0.641370473634938 & 0.717259052730123 & 0.358629526365062 \tabularnewline
55 & 0.860713333499748 & 0.278573333000504 & 0.139286666500252 \tabularnewline
56 & 0.821752201033874 & 0.356495597932251 & 0.178247798966126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&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]5[/C][C]0.552890628089595[/C][C]0.89421874382081[/C][C]0.447109371910405[/C][/ROW]
[ROW][C]6[/C][C]0.39283594283622[/C][C]0.78567188567244[/C][C]0.60716405716378[/C][/ROW]
[ROW][C]7[/C][C]0.311567037505279[/C][C]0.623134075010557[/C][C]0.688432962494721[/C][/ROW]
[ROW][C]8[/C][C]0.206194385074890[/C][C]0.412388770149779[/C][C]0.79380561492511[/C][/ROW]
[ROW][C]9[/C][C]0.182443294327511[/C][C]0.364886588655022[/C][C]0.817556705672489[/C][/ROW]
[ROW][C]10[/C][C]0.151537116052385[/C][C]0.30307423210477[/C][C]0.848462883947615[/C][/ROW]
[ROW][C]11[/C][C]0.103474794496384[/C][C]0.206949588992767[/C][C]0.896525205503616[/C][/ROW]
[ROW][C]12[/C][C]0.0627922199262303[/C][C]0.125584439852461[/C][C]0.93720778007377[/C][/ROW]
[ROW][C]13[/C][C]0.173959280539783[/C][C]0.347918561079566[/C][C]0.826040719460217[/C][/ROW]
[ROW][C]14[/C][C]0.213216810274546[/C][C]0.426433620549093[/C][C]0.786783189725454[/C][/ROW]
[ROW][C]15[/C][C]0.361005120214768[/C][C]0.722010240429537[/C][C]0.638994879785232[/C][/ROW]
[ROW][C]16[/C][C]0.300474242517537[/C][C]0.600948485035073[/C][C]0.699525757482463[/C][/ROW]
[ROW][C]17[/C][C]0.230668199021836[/C][C]0.461336398043672[/C][C]0.769331800978164[/C][/ROW]
[ROW][C]18[/C][C]0.177224962758482[/C][C]0.354449925516964[/C][C]0.822775037241518[/C][/ROW]
[ROW][C]19[/C][C]0.146332579828194[/C][C]0.292665159656387[/C][C]0.853667420171806[/C][/ROW]
[ROW][C]20[/C][C]0.122631900428141[/C][C]0.245263800856283[/C][C]0.877368099571859[/C][/ROW]
[ROW][C]21[/C][C]0.0979944924255035[/C][C]0.195988984851007[/C][C]0.902005507574497[/C][/ROW]
[ROW][C]22[/C][C]0.0689585558058437[/C][C]0.137917111611687[/C][C]0.931041444194156[/C][/ROW]
[ROW][C]23[/C][C]0.0859888207970339[/C][C]0.171977641594068[/C][C]0.914011179202966[/C][/ROW]
[ROW][C]24[/C][C]0.0584146636043414[/C][C]0.116829327208683[/C][C]0.941585336395659[/C][/ROW]
[ROW][C]25[/C][C]0.076898142674929[/C][C]0.153796285349858[/C][C]0.923101857325071[/C][/ROW]
[ROW][C]26[/C][C]0.094118518251762[/C][C]0.188237036503524[/C][C]0.905881481748238[/C][/ROW]
[ROW][C]27[/C][C]0.0854335838177996[/C][C]0.170867167635599[/C][C]0.9145664161822[/C][/ROW]
[ROW][C]28[/C][C]0.0602504251134638[/C][C]0.120500850226928[/C][C]0.939749574886536[/C][/ROW]
[ROW][C]29[/C][C]0.0406486340976937[/C][C]0.0812972681953875[/C][C]0.959351365902306[/C][/ROW]
[ROW][C]30[/C][C]0.027738988420387[/C][C]0.055477976840774[/C][C]0.972261011579613[/C][/ROW]
[ROW][C]31[/C][C]0.0209477894547932[/C][C]0.0418955789095863[/C][C]0.979052210545207[/C][/ROW]
[ROW][C]32[/C][C]0.0165271404510285[/C][C]0.0330542809020569[/C][C]0.983472859548971[/C][/ROW]
[ROW][C]33[/C][C]0.0317113907917778[/C][C]0.0634227815835556[/C][C]0.968288609208222[/C][/ROW]
[ROW][C]34[/C][C]0.0217612507099064[/C][C]0.0435225014198127[/C][C]0.978238749290094[/C][/ROW]
[ROW][C]35[/C][C]0.0141492011087473[/C][C]0.0282984022174945[/C][C]0.985850798891253[/C][/ROW]
[ROW][C]36[/C][C]0.0144696315666209[/C][C]0.0289392631332418[/C][C]0.985530368433379[/C][/ROW]
[ROW][C]37[/C][C]0.0331028687676589[/C][C]0.0662057375353179[/C][C]0.96689713123234[/C][/ROW]
[ROW][C]38[/C][C]0.0235047474354048[/C][C]0.0470094948708097[/C][C]0.976495252564595[/C][/ROW]
[ROW][C]39[/C][C]0.0182977506265775[/C][C]0.0365955012531551[/C][C]0.981702249373422[/C][/ROW]
[ROW][C]40[/C][C]0.0145348242091286[/C][C]0.0290696484182573[/C][C]0.985465175790871[/C][/ROW]
[ROW][C]41[/C][C]0.0157113720786334[/C][C]0.0314227441572668[/C][C]0.984288627921367[/C][/ROW]
[ROW][C]42[/C][C]0.0123506433727690[/C][C]0.0247012867455379[/C][C]0.987649356627231[/C][/ROW]
[ROW][C]43[/C][C]0.0320975024519668[/C][C]0.0641950049039337[/C][C]0.967902497548033[/C][/ROW]
[ROW][C]44[/C][C]0.0401103848543194[/C][C]0.0802207697086388[/C][C]0.95988961514568[/C][/ROW]
[ROW][C]45[/C][C]0.0268427797370247[/C][C]0.0536855594740495[/C][C]0.973157220262975[/C][/ROW]
[ROW][C]46[/C][C]0.0177637746008334[/C][C]0.0355275492016669[/C][C]0.982236225399167[/C][/ROW]
[ROW][C]47[/C][C]0.0156266270764792[/C][C]0.0312532541529585[/C][C]0.98437337292352[/C][/ROW]
[ROW][C]48[/C][C]0.0119562742216409[/C][C]0.0239125484432818[/C][C]0.98804372577836[/C][/ROW]
[ROW][C]49[/C][C]0.0536193637154548[/C][C]0.107238727430910[/C][C]0.946380636284545[/C][/ROW]
[ROW][C]50[/C][C]0.0772625450666616[/C][C]0.154525090133323[/C][C]0.922737454933338[/C][/ROW]
[ROW][C]51[/C][C]0.160064842499907[/C][C]0.320129684999815[/C][C]0.839935157500093[/C][/ROW]
[ROW][C]52[/C][C]0.65228542039746[/C][C]0.695429159205079[/C][C]0.347714579602540[/C][/ROW]
[ROW][C]53[/C][C]0.619987263960278[/C][C]0.760025472079444[/C][C]0.380012736039722[/C][/ROW]
[ROW][C]54[/C][C]0.641370473634938[/C][C]0.717259052730123[/C][C]0.358629526365062[/C][/ROW]
[ROW][C]55[/C][C]0.860713333499748[/C][C]0.278573333000504[/C][C]0.139286666500252[/C][/ROW]
[ROW][C]56[/C][C]0.821752201033874[/C][C]0.356495597932251[/C][C]0.178247798966126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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
50.5528906280895950.894218743820810.447109371910405
60.392835942836220.785671885672440.60716405716378
70.3115670375052790.6231340750105570.688432962494721
80.2061943850748900.4123887701497790.79380561492511
90.1824432943275110.3648865886550220.817556705672489
100.1515371160523850.303074232104770.848462883947615
110.1034747944963840.2069495889927670.896525205503616
120.06279221992623030.1255844398524610.93720778007377
130.1739592805397830.3479185610795660.826040719460217
140.2132168102745460.4264336205490930.786783189725454
150.3610051202147680.7220102404295370.638994879785232
160.3004742425175370.6009484850350730.699525757482463
170.2306681990218360.4613363980436720.769331800978164
180.1772249627584820.3544499255169640.822775037241518
190.1463325798281940.2926651596563870.853667420171806
200.1226319004281410.2452638008562830.877368099571859
210.09799449242550350.1959889848510070.902005507574497
220.06895855580584370.1379171116116870.931041444194156
230.08598882079703390.1719776415940680.914011179202966
240.05841466360434140.1168293272086830.941585336395659
250.0768981426749290.1537962853498580.923101857325071
260.0941185182517620.1882370365035240.905881481748238
270.08543358381779960.1708671676355990.9145664161822
280.06025042511346380.1205008502269280.939749574886536
290.04064863409769370.08129726819538750.959351365902306
300.0277389884203870.0554779768407740.972261011579613
310.02094778945479320.04189557890958630.979052210545207
320.01652714045102850.03305428090205690.983472859548971
330.03171139079177780.06342278158355560.968288609208222
340.02176125070990640.04352250141981270.978238749290094
350.01414920110874730.02829840221749450.985850798891253
360.01446963156662090.02893926313324180.985530368433379
370.03310286876765890.06620573753531790.96689713123234
380.02350474743540480.04700949487080970.976495252564595
390.01829775062657750.03659550125315510.981702249373422
400.01453482420912860.02906964841825730.985465175790871
410.01571137207863340.03142274415726680.984288627921367
420.01235064337276900.02470128674553790.987649356627231
430.03209750245196680.06419500490393370.967902497548033
440.04011038485431940.08022076970863880.95988961514568
450.02684277973702470.05368555947404950.973157220262975
460.01776377460083340.03552754920166690.982236225399167
470.01562662707647920.03125325415295850.98437337292352
480.01195627422164090.02391254844328180.98804372577836
490.05361936371545480.1072387274309100.946380636284545
500.07726254506666160.1545250901333230.922737454933338
510.1600648424999070.3201296849998150.839935157500093
520.652285420397460.6954291592050790.347714579602540
530.6199872639602780.7600254720794440.380012736039722
540.6413704736349380.7172590527301230.358629526365062
550.8607133334997480.2785733330005040.139286666500252
560.8217522010338740.3564955979322510.178247798966126







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level130.25NOK
10% type I error level200.384615384615385NOK

\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 & 13 & 0.25 & NOK \tabularnewline
10% type I error level & 20 & 0.384615384615385 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58327&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]13[/C][C]0.25[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.384615384615385[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58327&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58327&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 level130.25NOK
10% type I error level200.384615384615385NOK



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