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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 08:14:54 -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/19/t12586439007vrqp4s5qwj3z5l.htm/, Retrieved Thu, 28 Mar 2024 14:15:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57755, Retrieved Thu, 28 Mar 2024 14:15:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
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]
-   PD      [Multiple Regression] [Multiple Regression] [2009-11-19 15:14:54] [d45d8d97b86162be82506c3c0ea6e4a6] [Current]
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Dataseries X:
1.4	1.9
1	1.6
-0.8	0
-2.9	-1.3
-0.7	-0.4
-0.7	-0.3
1.5	1.4
3	2.6
3.2	2.8
3.1	2.6
3.9	3.4
1	1.7
1.3	1.2
0.8	0
1.2	0
2.9	1.6
3.9	2.5
4.5	3.2
4.5	3.4
3.3	2.3
2	1.9
1.5	1.7
1	1.9
2.1	3.3
3	3.8
4	4.4
5.1	4.5
4.5	3.5
4.2	3
3.3	2.8
2.7	2.9
1.8	2.6
1.4	2.1
0.5	1.5
-0.4	1.1
0.8	1.5
0.7	1.7
1.9	2.3
2	2.3
1.1	1.9
0.9	2
0.4	1.6
0.7	1.2
2.1	1.9
2.8	2.1
3.9	2.4
3.5	2.9
2	2.5
2	2.3
1.5	2.5
2.5	2.6
3.1	2.4
2.7	2.5
2.8	2.1
2.5	2.2
3	2.7
3.2	3
2.8	3.2
2.4	3
2	2.7
1.8	2.5
1.1	1.6
-1.5	0.1
-3.7	-1.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&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]3 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=57755&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
bbp[t] = -1.43203385352092 + 1.28719395449612dnst[t] + 0.257300688479588M1[t] + 0.488499680895607M2[t] + 0.810643425568731M3[t] + 0.935266767208264M4[t] + 1.16062146088837M5[t] + 1.07210921906822M6[t] + 0.954463274539534M7[t] + 0.95702448364031M8[t] + 0.888512241820156M9[t] + 0.857231637269767M10[t] + 0.345536725460465M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bbp[t] =  -1.43203385352092 +  1.28719395449612dnst[t] +  0.257300688479588M1[t] +  0.488499680895607M2[t] +  0.810643425568731M3[t] +  0.935266767208264M4[t] +  1.16062146088837M5[t] +  1.07210921906822M6[t] +  0.954463274539534M7[t] +  0.95702448364031M8[t] +  0.888512241820156M9[t] +  0.857231637269767M10[t] +  0.345536725460465M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bbp[t] =  -1.43203385352092 +  1.28719395449612dnst[t] +  0.257300688479588M1[t] +  0.488499680895607M2[t] +  0.810643425568731M3[t] +  0.935266767208264M4[t] +  1.16062146088837M5[t] +  1.07210921906822M6[t] +  0.954463274539534M7[t] +  0.95702448364031M8[t] +  0.888512241820156M9[t] +  0.857231637269767M10[t] +  0.345536725460465M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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
bbp[t] = -1.43203385352092 + 1.28719395449612dnst[t] + 0.257300688479588M1[t] + 0.488499680895607M2[t] + 0.810643425568731M3[t] + 0.935266767208264M4[t] + 1.16062146088837M5[t] + 1.07210921906822M6[t] + 0.954463274539534M7[t] + 0.95702448364031M8[t] + 0.888512241820156M9[t] + 0.857231637269767M10[t] + 0.345536725460465M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.432033853520920.395309-3.62260.0006720.000336
dnst1.287193954496120.08451915.229600
M10.2573006884795880.4635340.55510.5812630.290631
M20.4884996808956070.4640211.05280.2974170.148708
M30.8106434255687310.4678381.73270.0891850.044592
M40.9352667672082640.4764231.96310.0550990.02755
M51.160621460888370.4853532.39130.0205160.010258
M61.072109219068220.4856122.20770.0317860.015893
M70.9544632745395340.484161.97140.0541180.027059
M80.957024483640310.4841011.97690.053470.026735
M90.8885122418201560.4840651.83550.0722620.036131
M100.8572316372697670.484081.77080.0825620.041281
M110.3455367254604650.484160.71370.4786770.239338

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.43203385352092 & 0.395309 & -3.6226 & 0.000672 & 0.000336 \tabularnewline
dnst & 1.28719395449612 & 0.084519 & 15.2296 & 0 & 0 \tabularnewline
M1 & 0.257300688479588 & 0.463534 & 0.5551 & 0.581263 & 0.290631 \tabularnewline
M2 & 0.488499680895607 & 0.464021 & 1.0528 & 0.297417 & 0.148708 \tabularnewline
M3 & 0.810643425568731 & 0.467838 & 1.7327 & 0.089185 & 0.044592 \tabularnewline
M4 & 0.935266767208264 & 0.476423 & 1.9631 & 0.055099 & 0.02755 \tabularnewline
M5 & 1.16062146088837 & 0.485353 & 2.3913 & 0.020516 & 0.010258 \tabularnewline
M6 & 1.07210921906822 & 0.485612 & 2.2077 & 0.031786 & 0.015893 \tabularnewline
M7 & 0.954463274539534 & 0.48416 & 1.9714 & 0.054118 & 0.027059 \tabularnewline
M8 & 0.95702448364031 & 0.484101 & 1.9769 & 0.05347 & 0.026735 \tabularnewline
M9 & 0.888512241820156 & 0.484065 & 1.8355 & 0.072262 & 0.036131 \tabularnewline
M10 & 0.857231637269767 & 0.48408 & 1.7708 & 0.082562 & 0.041281 \tabularnewline
M11 & 0.345536725460465 & 0.48416 & 0.7137 & 0.478677 & 0.239338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.43203385352092[/C][C]0.395309[/C][C]-3.6226[/C][C]0.000672[/C][C]0.000336[/C][/ROW]
[ROW][C]dnst[/C][C]1.28719395449612[/C][C]0.084519[/C][C]15.2296[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.257300688479588[/C][C]0.463534[/C][C]0.5551[/C][C]0.581263[/C][C]0.290631[/C][/ROW]
[ROW][C]M2[/C][C]0.488499680895607[/C][C]0.464021[/C][C]1.0528[/C][C]0.297417[/C][C]0.148708[/C][/ROW]
[ROW][C]M3[/C][C]0.810643425568731[/C][C]0.467838[/C][C]1.7327[/C][C]0.089185[/C][C]0.044592[/C][/ROW]
[ROW][C]M4[/C][C]0.935266767208264[/C][C]0.476423[/C][C]1.9631[/C][C]0.055099[/C][C]0.02755[/C][/ROW]
[ROW][C]M5[/C][C]1.16062146088837[/C][C]0.485353[/C][C]2.3913[/C][C]0.020516[/C][C]0.010258[/C][/ROW]
[ROW][C]M6[/C][C]1.07210921906822[/C][C]0.485612[/C][C]2.2077[/C][C]0.031786[/C][C]0.015893[/C][/ROW]
[ROW][C]M7[/C][C]0.954463274539534[/C][C]0.48416[/C][C]1.9714[/C][C]0.054118[/C][C]0.027059[/C][/ROW]
[ROW][C]M8[/C][C]0.95702448364031[/C][C]0.484101[/C][C]1.9769[/C][C]0.05347[/C][C]0.026735[/C][/ROW]
[ROW][C]M9[/C][C]0.888512241820156[/C][C]0.484065[/C][C]1.8355[/C][C]0.072262[/C][C]0.036131[/C][/ROW]
[ROW][C]M10[/C][C]0.857231637269767[/C][C]0.48408[/C][C]1.7708[/C][C]0.082562[/C][C]0.041281[/C][/ROW]
[ROW][C]M11[/C][C]0.345536725460465[/C][C]0.48416[/C][C]0.7137[/C][C]0.478677[/C][C]0.239338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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)-1.432033853520920.395309-3.62260.0006720.000336
dnst1.287193954496120.08451915.229600
M10.2573006884795880.4635340.55510.5812630.290631
M20.4884996808956070.4640211.05280.2974170.148708
M30.8106434255687310.4678381.73270.0891850.044592
M40.9352667672082640.4764231.96310.0550990.02755
M51.160621460888370.4853532.39130.0205160.010258
M61.072109219068220.4856122.20770.0317860.015893
M70.9544632745395340.484161.97140.0541180.027059
M80.957024483640310.4841011.97690.053470.026735
M90.8885122418201560.4840651.83550.0722620.036131
M100.8572316372697670.484081.77080.0825620.041281
M110.3455367254604650.484160.71370.4786770.239338







Multiple Linear Regression - Regression Statistics
Multiple R0.914494865846938
R-squared0.836300859660409
Adjusted R-squared0.797783414874622
F-TEST (value)21.7122621791626
F-TEST (DF numerator)12
F-TEST (DF denominator)51
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.765355857745887
Sum Squared Residuals29.8742490382830

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.914494865846938 \tabularnewline
R-squared & 0.836300859660409 \tabularnewline
Adjusted R-squared & 0.797783414874622 \tabularnewline
F-TEST (value) & 21.7122621791626 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 6.66133814775094e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.765355857745887 \tabularnewline
Sum Squared Residuals & 29.8742490382830 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.914494865846938[/C][/ROW]
[ROW][C]R-squared[/C][C]0.836300859660409[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.797783414874622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.7122621791626[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]6.66133814775094e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.765355857745887[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29.8742490382830[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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 R0.914494865846938
R-squared0.836300859660409
Adjusted R-squared0.797783414874622
F-TEST (value)21.7122621791626
F-TEST (DF numerator)12
F-TEST (DF denominator)51
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.765355857745887
Sum Squared Residuals29.8742490382830







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.270935348501280.129064651498718
211.11597615456847-0.115976154568473
3-0.8-0.62139042795219-0.17860957204781
4-2.9-2.17011922715761-0.729880772842387
5-0.7-0.7862899744309980.0862899744309977
6-0.7-0.746082820801540.0460828208015405
71.51.324500957313180.175499042686818
832.87169491180930.128305088190698
93.23.060621460888370.139378539111630
103.12.771902065438760.328097934561242
113.93.289962317226350.610037682773647
1210.7561958691224840.243804130877516
131.30.3698995803540120.930100419645988
140.8-0.9435341726253131.74353417262531
151.2-0.621390427952191.82139042795219
162.91.562743240881131.33725675911887
173.92.946572493607750.95342750639225
184.53.759096019934880.740903980065122
194.53.898888866305420.601111133694579
203.32.485536725460470.814463274539534
2121.902146901841860.0978530981581372
221.51.61342750639225-0.113427506392250
2311.35917138548217-0.359171385482173
242.12.81570619631627-0.715706196316275
2533.71660386204392-0.716603862043924
2644.72011922715761-0.720119227157614
275.15.17098236728035-0.0709823672803501
284.54.008411754423760.491588245576238
294.23.590169470855810.609830529144191
303.33.244218438136430.0557815618635703
312.73.25529188905736-0.555291889057361
321.82.8716949118093-1.07169491180930
331.42.15958569274109-0.759585692741087
340.51.35598871549303-0.855988715493027
35-0.40.329416221885277-0.729416221885277
360.80.4987570782232590.301242921776741
370.71.01349655760207-0.313496557602072
381.92.01701192271576-0.117011922715762
3922.33915566738889-0.339155667388886
401.11.94890142722997-0.84890142722997
410.92.30297551635969-1.40297551635969
420.41.69958569274109-1.29958569274109
430.71.06706216641396-0.367062166413958
442.11.970659143662020.129340856337983
452.82.159585692741090.640414307258913
463.92.514463274539531.38553672546047
473.52.646365339978290.853634660021707
4821.785951032719380.214048967280621
4921.785812930299740.214187069700257
501.52.27445071361499-0.774450713614986
512.52.72531385373772-0.225313853737722
523.12.592498404478030.50750159552197
532.72.94657249360775-0.246572493607749
542.82.343182669989150.456817330010854
552.52.354256120910080.145743879089922
5633.00041430725891-0.000414307258913817
573.23.31806025178759-0.118060251787595
582.83.54421843813643-0.744218438136431
592.42.77508473542790-0.375084735427905
6022.04338982361860-0.0433898236186033
611.82.04325172119897-0.243251721198968
621.11.11597615456848-0.0159761545684783
63-1.5-0.492671032502578-1.00732896749742
64-3.7-2.94243559985529-0.757564400144715

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.27093534850128 & 0.129064651498718 \tabularnewline
2 & 1 & 1.11597615456847 & -0.115976154568473 \tabularnewline
3 & -0.8 & -0.62139042795219 & -0.17860957204781 \tabularnewline
4 & -2.9 & -2.17011922715761 & -0.729880772842387 \tabularnewline
5 & -0.7 & -0.786289974430998 & 0.0862899744309977 \tabularnewline
6 & -0.7 & -0.74608282080154 & 0.0460828208015405 \tabularnewline
7 & 1.5 & 1.32450095731318 & 0.175499042686818 \tabularnewline
8 & 3 & 2.8716949118093 & 0.128305088190698 \tabularnewline
9 & 3.2 & 3.06062146088837 & 0.139378539111630 \tabularnewline
10 & 3.1 & 2.77190206543876 & 0.328097934561242 \tabularnewline
11 & 3.9 & 3.28996231722635 & 0.610037682773647 \tabularnewline
12 & 1 & 0.756195869122484 & 0.243804130877516 \tabularnewline
13 & 1.3 & 0.369899580354012 & 0.930100419645988 \tabularnewline
14 & 0.8 & -0.943534172625313 & 1.74353417262531 \tabularnewline
15 & 1.2 & -0.62139042795219 & 1.82139042795219 \tabularnewline
16 & 2.9 & 1.56274324088113 & 1.33725675911887 \tabularnewline
17 & 3.9 & 2.94657249360775 & 0.95342750639225 \tabularnewline
18 & 4.5 & 3.75909601993488 & 0.740903980065122 \tabularnewline
19 & 4.5 & 3.89888886630542 & 0.601111133694579 \tabularnewline
20 & 3.3 & 2.48553672546047 & 0.814463274539534 \tabularnewline
21 & 2 & 1.90214690184186 & 0.0978530981581372 \tabularnewline
22 & 1.5 & 1.61342750639225 & -0.113427506392250 \tabularnewline
23 & 1 & 1.35917138548217 & -0.359171385482173 \tabularnewline
24 & 2.1 & 2.81570619631627 & -0.715706196316275 \tabularnewline
25 & 3 & 3.71660386204392 & -0.716603862043924 \tabularnewline
26 & 4 & 4.72011922715761 & -0.720119227157614 \tabularnewline
27 & 5.1 & 5.17098236728035 & -0.0709823672803501 \tabularnewline
28 & 4.5 & 4.00841175442376 & 0.491588245576238 \tabularnewline
29 & 4.2 & 3.59016947085581 & 0.609830529144191 \tabularnewline
30 & 3.3 & 3.24421843813643 & 0.0557815618635703 \tabularnewline
31 & 2.7 & 3.25529188905736 & -0.555291889057361 \tabularnewline
32 & 1.8 & 2.8716949118093 & -1.07169491180930 \tabularnewline
33 & 1.4 & 2.15958569274109 & -0.759585692741087 \tabularnewline
34 & 0.5 & 1.35598871549303 & -0.855988715493027 \tabularnewline
35 & -0.4 & 0.329416221885277 & -0.729416221885277 \tabularnewline
36 & 0.8 & 0.498757078223259 & 0.301242921776741 \tabularnewline
37 & 0.7 & 1.01349655760207 & -0.313496557602072 \tabularnewline
38 & 1.9 & 2.01701192271576 & -0.117011922715762 \tabularnewline
39 & 2 & 2.33915566738889 & -0.339155667388886 \tabularnewline
40 & 1.1 & 1.94890142722997 & -0.84890142722997 \tabularnewline
41 & 0.9 & 2.30297551635969 & -1.40297551635969 \tabularnewline
42 & 0.4 & 1.69958569274109 & -1.29958569274109 \tabularnewline
43 & 0.7 & 1.06706216641396 & -0.367062166413958 \tabularnewline
44 & 2.1 & 1.97065914366202 & 0.129340856337983 \tabularnewline
45 & 2.8 & 2.15958569274109 & 0.640414307258913 \tabularnewline
46 & 3.9 & 2.51446327453953 & 1.38553672546047 \tabularnewline
47 & 3.5 & 2.64636533997829 & 0.853634660021707 \tabularnewline
48 & 2 & 1.78595103271938 & 0.214048967280621 \tabularnewline
49 & 2 & 1.78581293029974 & 0.214187069700257 \tabularnewline
50 & 1.5 & 2.27445071361499 & -0.774450713614986 \tabularnewline
51 & 2.5 & 2.72531385373772 & -0.225313853737722 \tabularnewline
52 & 3.1 & 2.59249840447803 & 0.50750159552197 \tabularnewline
53 & 2.7 & 2.94657249360775 & -0.246572493607749 \tabularnewline
54 & 2.8 & 2.34318266998915 & 0.456817330010854 \tabularnewline
55 & 2.5 & 2.35425612091008 & 0.145743879089922 \tabularnewline
56 & 3 & 3.00041430725891 & -0.000414307258913817 \tabularnewline
57 & 3.2 & 3.31806025178759 & -0.118060251787595 \tabularnewline
58 & 2.8 & 3.54421843813643 & -0.744218438136431 \tabularnewline
59 & 2.4 & 2.77508473542790 & -0.375084735427905 \tabularnewline
60 & 2 & 2.04338982361860 & -0.0433898236186033 \tabularnewline
61 & 1.8 & 2.04325172119897 & -0.243251721198968 \tabularnewline
62 & 1.1 & 1.11597615456848 & -0.0159761545684783 \tabularnewline
63 & -1.5 & -0.492671032502578 & -1.00732896749742 \tabularnewline
64 & -3.7 & -2.94243559985529 & -0.757564400144715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&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]1.4[/C][C]1.27093534850128[/C][C]0.129064651498718[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.11597615456847[/C][C]-0.115976154568473[/C][/ROW]
[ROW][C]3[/C][C]-0.8[/C][C]-0.62139042795219[/C][C]-0.17860957204781[/C][/ROW]
[ROW][C]4[/C][C]-2.9[/C][C]-2.17011922715761[/C][C]-0.729880772842387[/C][/ROW]
[ROW][C]5[/C][C]-0.7[/C][C]-0.786289974430998[/C][C]0.0862899744309977[/C][/ROW]
[ROW][C]6[/C][C]-0.7[/C][C]-0.74608282080154[/C][C]0.0460828208015405[/C][/ROW]
[ROW][C]7[/C][C]1.5[/C][C]1.32450095731318[/C][C]0.175499042686818[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.8716949118093[/C][C]0.128305088190698[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.06062146088837[/C][C]0.139378539111630[/C][/ROW]
[ROW][C]10[/C][C]3.1[/C][C]2.77190206543876[/C][C]0.328097934561242[/C][/ROW]
[ROW][C]11[/C][C]3.9[/C][C]3.28996231722635[/C][C]0.610037682773647[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.756195869122484[/C][C]0.243804130877516[/C][/ROW]
[ROW][C]13[/C][C]1.3[/C][C]0.369899580354012[/C][C]0.930100419645988[/C][/ROW]
[ROW][C]14[/C][C]0.8[/C][C]-0.943534172625313[/C][C]1.74353417262531[/C][/ROW]
[ROW][C]15[/C][C]1.2[/C][C]-0.62139042795219[/C][C]1.82139042795219[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]1.56274324088113[/C][C]1.33725675911887[/C][/ROW]
[ROW][C]17[/C][C]3.9[/C][C]2.94657249360775[/C][C]0.95342750639225[/C][/ROW]
[ROW][C]18[/C][C]4.5[/C][C]3.75909601993488[/C][C]0.740903980065122[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]3.89888886630542[/C][C]0.601111133694579[/C][/ROW]
[ROW][C]20[/C][C]3.3[/C][C]2.48553672546047[/C][C]0.814463274539534[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.90214690184186[/C][C]0.0978530981581372[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]1.61342750639225[/C][C]-0.113427506392250[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.35917138548217[/C][C]-0.359171385482173[/C][/ROW]
[ROW][C]24[/C][C]2.1[/C][C]2.81570619631627[/C][C]-0.715706196316275[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.71660386204392[/C][C]-0.716603862043924[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.72011922715761[/C][C]-0.720119227157614[/C][/ROW]
[ROW][C]27[/C][C]5.1[/C][C]5.17098236728035[/C][C]-0.0709823672803501[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.00841175442376[/C][C]0.491588245576238[/C][/ROW]
[ROW][C]29[/C][C]4.2[/C][C]3.59016947085581[/C][C]0.609830529144191[/C][/ROW]
[ROW][C]30[/C][C]3.3[/C][C]3.24421843813643[/C][C]0.0557815618635703[/C][/ROW]
[ROW][C]31[/C][C]2.7[/C][C]3.25529188905736[/C][C]-0.555291889057361[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]2.8716949118093[/C][C]-1.07169491180930[/C][/ROW]
[ROW][C]33[/C][C]1.4[/C][C]2.15958569274109[/C][C]-0.759585692741087[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]1.35598871549303[/C][C]-0.855988715493027[/C][/ROW]
[ROW][C]35[/C][C]-0.4[/C][C]0.329416221885277[/C][C]-0.729416221885277[/C][/ROW]
[ROW][C]36[/C][C]0.8[/C][C]0.498757078223259[/C][C]0.301242921776741[/C][/ROW]
[ROW][C]37[/C][C]0.7[/C][C]1.01349655760207[/C][C]-0.313496557602072[/C][/ROW]
[ROW][C]38[/C][C]1.9[/C][C]2.01701192271576[/C][C]-0.117011922715762[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.33915566738889[/C][C]-0.339155667388886[/C][/ROW]
[ROW][C]40[/C][C]1.1[/C][C]1.94890142722997[/C][C]-0.84890142722997[/C][/ROW]
[ROW][C]41[/C][C]0.9[/C][C]2.30297551635969[/C][C]-1.40297551635969[/C][/ROW]
[ROW][C]42[/C][C]0.4[/C][C]1.69958569274109[/C][C]-1.29958569274109[/C][/ROW]
[ROW][C]43[/C][C]0.7[/C][C]1.06706216641396[/C][C]-0.367062166413958[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]1.97065914366202[/C][C]0.129340856337983[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.15958569274109[/C][C]0.640414307258913[/C][/ROW]
[ROW][C]46[/C][C]3.9[/C][C]2.51446327453953[/C][C]1.38553672546047[/C][/ROW]
[ROW][C]47[/C][C]3.5[/C][C]2.64636533997829[/C][C]0.853634660021707[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.78595103271938[/C][C]0.214048967280621[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.78581293029974[/C][C]0.214187069700257[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]2.27445071361499[/C][C]-0.774450713614986[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.72531385373772[/C][C]-0.225313853737722[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]2.59249840447803[/C][C]0.50750159552197[/C][/ROW]
[ROW][C]53[/C][C]2.7[/C][C]2.94657249360775[/C][C]-0.246572493607749[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]2.34318266998915[/C][C]0.456817330010854[/C][/ROW]
[ROW][C]55[/C][C]2.5[/C][C]2.35425612091008[/C][C]0.145743879089922[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.00041430725891[/C][C]-0.000414307258913817[/C][/ROW]
[ROW][C]57[/C][C]3.2[/C][C]3.31806025178759[/C][C]-0.118060251787595[/C][/ROW]
[ROW][C]58[/C][C]2.8[/C][C]3.54421843813643[/C][C]-0.744218438136431[/C][/ROW]
[ROW][C]59[/C][C]2.4[/C][C]2.77508473542790[/C][C]-0.375084735427905[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.04338982361860[/C][C]-0.0433898236186033[/C][/ROW]
[ROW][C]61[/C][C]1.8[/C][C]2.04325172119897[/C][C]-0.243251721198968[/C][/ROW]
[ROW][C]62[/C][C]1.1[/C][C]1.11597615456848[/C][C]-0.0159761545684783[/C][/ROW]
[ROW][C]63[/C][C]-1.5[/C][C]-0.492671032502578[/C][C]-1.00732896749742[/C][/ROW]
[ROW][C]64[/C][C]-3.7[/C][C]-2.94243559985529[/C][C]-0.757564400144715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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
11.41.270935348501280.129064651498718
211.11597615456847-0.115976154568473
3-0.8-0.62139042795219-0.17860957204781
4-2.9-2.17011922715761-0.729880772842387
5-0.7-0.7862899744309980.0862899744309977
6-0.7-0.746082820801540.0460828208015405
71.51.324500957313180.175499042686818
832.87169491180930.128305088190698
93.23.060621460888370.139378539111630
103.12.771902065438760.328097934561242
113.93.289962317226350.610037682773647
1210.7561958691224840.243804130877516
131.30.3698995803540120.930100419645988
140.8-0.9435341726253131.74353417262531
151.2-0.621390427952191.82139042795219
162.91.562743240881131.33725675911887
173.92.946572493607750.95342750639225
184.53.759096019934880.740903980065122
194.53.898888866305420.601111133694579
203.32.485536725460470.814463274539534
2121.902146901841860.0978530981581372
221.51.61342750639225-0.113427506392250
2311.35917138548217-0.359171385482173
242.12.81570619631627-0.715706196316275
2533.71660386204392-0.716603862043924
2644.72011922715761-0.720119227157614
275.15.17098236728035-0.0709823672803501
284.54.008411754423760.491588245576238
294.23.590169470855810.609830529144191
303.33.244218438136430.0557815618635703
312.73.25529188905736-0.555291889057361
321.82.8716949118093-1.07169491180930
331.42.15958569274109-0.759585692741087
340.51.35598871549303-0.855988715493027
35-0.40.329416221885277-0.729416221885277
360.80.4987570782232590.301242921776741
370.71.01349655760207-0.313496557602072
381.92.01701192271576-0.117011922715762
3922.33915566738889-0.339155667388886
401.11.94890142722997-0.84890142722997
410.92.30297551635969-1.40297551635969
420.41.69958569274109-1.29958569274109
430.71.06706216641396-0.367062166413958
442.11.970659143662020.129340856337983
452.82.159585692741090.640414307258913
463.92.514463274539531.38553672546047
473.52.646365339978290.853634660021707
4821.785951032719380.214048967280621
4921.785812930299740.214187069700257
501.52.27445071361499-0.774450713614986
512.52.72531385373772-0.225313853737722
523.12.592498404478030.50750159552197
532.72.94657249360775-0.246572493607749
542.82.343182669989150.456817330010854
552.52.354256120910080.145743879089922
5633.00041430725891-0.000414307258913817
573.23.31806025178759-0.118060251787595
582.83.54421843813643-0.744218438136431
592.42.77508473542790-0.375084735427905
6022.04338982361860-0.0433898236186033
611.82.04325172119897-0.243251721198968
621.11.11597615456848-0.0159761545684783
63-1.5-0.492671032502578-1.00732896749742
64-3.7-2.94243559985529-0.757564400144715







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9874716090694750.02505678186104930.0125283909305246
170.9783426284719960.04331474305600870.0216573715280044
180.9603682752991670.07926344940166560.0396317247008328
190.9314305725202430.1371388549595150.0685694274797574
200.9207735738350560.1584528523298880.079226426164944
210.8725472942113250.254905411577350.127452705788675
220.809094220707350.3818115585852990.190905779292650
230.7491845641717130.5016308716565740.250815435828287
240.7851496474838120.4297007050323760.214850352516188
250.8546318290091940.2907363419816120.145368170990806
260.9018590141117420.1962819717765160.098140985888258
270.8654337686758970.2691324626482070.134566231324103
280.815733943224860.3685321135502810.184266056775141
290.8119963509724340.3760072980551320.188003649027566
300.7486073188083010.5027853623833980.251392681191699
310.727925220944450.5441495581111010.272074779055550
320.8053419412722840.3893161174554320.194658058727716
330.802070372893270.3958592542134610.197929627106731
340.8055102389920430.3889795220159150.194489761007957
350.7853954898934490.4292090202131010.214604510106551
360.7241189718313110.5517620563373770.275881028168689
370.6476105179380130.7047789641239740.352389482061987
380.5628880298681810.8742239402636390.437111970131819
390.4900358093649350.980071618729870.509964190635065
400.5117703305326540.9764593389346920.488229669467346
410.58641789075710.8271642184857990.413582109242899
420.715425331509240.569149336981520.28457466849076
430.6239046161654840.7521907676690330.376095383834516
440.5090411381071780.9819177237856440.490958861892822
450.4553935523527250.910787104705450.544606447647275
460.8464672981019310.3070654037961380.153532701898069
470.9185989176132980.1628021647734050.0814010823867023
480.8284720392208020.3430559215583950.171527960779198

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.987471609069475 & 0.0250567818610493 & 0.0125283909305246 \tabularnewline
17 & 0.978342628471996 & 0.0433147430560087 & 0.0216573715280044 \tabularnewline
18 & 0.960368275299167 & 0.0792634494016656 & 0.0396317247008328 \tabularnewline
19 & 0.931430572520243 & 0.137138854959515 & 0.0685694274797574 \tabularnewline
20 & 0.920773573835056 & 0.158452852329888 & 0.079226426164944 \tabularnewline
21 & 0.872547294211325 & 0.25490541157735 & 0.127452705788675 \tabularnewline
22 & 0.80909422070735 & 0.381811558585299 & 0.190905779292650 \tabularnewline
23 & 0.749184564171713 & 0.501630871656574 & 0.250815435828287 \tabularnewline
24 & 0.785149647483812 & 0.429700705032376 & 0.214850352516188 \tabularnewline
25 & 0.854631829009194 & 0.290736341981612 & 0.145368170990806 \tabularnewline
26 & 0.901859014111742 & 0.196281971776516 & 0.098140985888258 \tabularnewline
27 & 0.865433768675897 & 0.269132462648207 & 0.134566231324103 \tabularnewline
28 & 0.81573394322486 & 0.368532113550281 & 0.184266056775141 \tabularnewline
29 & 0.811996350972434 & 0.376007298055132 & 0.188003649027566 \tabularnewline
30 & 0.748607318808301 & 0.502785362383398 & 0.251392681191699 \tabularnewline
31 & 0.72792522094445 & 0.544149558111101 & 0.272074779055550 \tabularnewline
32 & 0.805341941272284 & 0.389316117455432 & 0.194658058727716 \tabularnewline
33 & 0.80207037289327 & 0.395859254213461 & 0.197929627106731 \tabularnewline
34 & 0.805510238992043 & 0.388979522015915 & 0.194489761007957 \tabularnewline
35 & 0.785395489893449 & 0.429209020213101 & 0.214604510106551 \tabularnewline
36 & 0.724118971831311 & 0.551762056337377 & 0.275881028168689 \tabularnewline
37 & 0.647610517938013 & 0.704778964123974 & 0.352389482061987 \tabularnewline
38 & 0.562888029868181 & 0.874223940263639 & 0.437111970131819 \tabularnewline
39 & 0.490035809364935 & 0.98007161872987 & 0.509964190635065 \tabularnewline
40 & 0.511770330532654 & 0.976459338934692 & 0.488229669467346 \tabularnewline
41 & 0.5864178907571 & 0.827164218485799 & 0.413582109242899 \tabularnewline
42 & 0.71542533150924 & 0.56914933698152 & 0.28457466849076 \tabularnewline
43 & 0.623904616165484 & 0.752190767669033 & 0.376095383834516 \tabularnewline
44 & 0.509041138107178 & 0.981917723785644 & 0.490958861892822 \tabularnewline
45 & 0.455393552352725 & 0.91078710470545 & 0.544606447647275 \tabularnewline
46 & 0.846467298101931 & 0.307065403796138 & 0.153532701898069 \tabularnewline
47 & 0.918598917613298 & 0.162802164773405 & 0.0814010823867023 \tabularnewline
48 & 0.828472039220802 & 0.343055921558395 & 0.171527960779198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&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]16[/C][C]0.987471609069475[/C][C]0.0250567818610493[/C][C]0.0125283909305246[/C][/ROW]
[ROW][C]17[/C][C]0.978342628471996[/C][C]0.0433147430560087[/C][C]0.0216573715280044[/C][/ROW]
[ROW][C]18[/C][C]0.960368275299167[/C][C]0.0792634494016656[/C][C]0.0396317247008328[/C][/ROW]
[ROW][C]19[/C][C]0.931430572520243[/C][C]0.137138854959515[/C][C]0.0685694274797574[/C][/ROW]
[ROW][C]20[/C][C]0.920773573835056[/C][C]0.158452852329888[/C][C]0.079226426164944[/C][/ROW]
[ROW][C]21[/C][C]0.872547294211325[/C][C]0.25490541157735[/C][C]0.127452705788675[/C][/ROW]
[ROW][C]22[/C][C]0.80909422070735[/C][C]0.381811558585299[/C][C]0.190905779292650[/C][/ROW]
[ROW][C]23[/C][C]0.749184564171713[/C][C]0.501630871656574[/C][C]0.250815435828287[/C][/ROW]
[ROW][C]24[/C][C]0.785149647483812[/C][C]0.429700705032376[/C][C]0.214850352516188[/C][/ROW]
[ROW][C]25[/C][C]0.854631829009194[/C][C]0.290736341981612[/C][C]0.145368170990806[/C][/ROW]
[ROW][C]26[/C][C]0.901859014111742[/C][C]0.196281971776516[/C][C]0.098140985888258[/C][/ROW]
[ROW][C]27[/C][C]0.865433768675897[/C][C]0.269132462648207[/C][C]0.134566231324103[/C][/ROW]
[ROW][C]28[/C][C]0.81573394322486[/C][C]0.368532113550281[/C][C]0.184266056775141[/C][/ROW]
[ROW][C]29[/C][C]0.811996350972434[/C][C]0.376007298055132[/C][C]0.188003649027566[/C][/ROW]
[ROW][C]30[/C][C]0.748607318808301[/C][C]0.502785362383398[/C][C]0.251392681191699[/C][/ROW]
[ROW][C]31[/C][C]0.72792522094445[/C][C]0.544149558111101[/C][C]0.272074779055550[/C][/ROW]
[ROW][C]32[/C][C]0.805341941272284[/C][C]0.389316117455432[/C][C]0.194658058727716[/C][/ROW]
[ROW][C]33[/C][C]0.80207037289327[/C][C]0.395859254213461[/C][C]0.197929627106731[/C][/ROW]
[ROW][C]34[/C][C]0.805510238992043[/C][C]0.388979522015915[/C][C]0.194489761007957[/C][/ROW]
[ROW][C]35[/C][C]0.785395489893449[/C][C]0.429209020213101[/C][C]0.214604510106551[/C][/ROW]
[ROW][C]36[/C][C]0.724118971831311[/C][C]0.551762056337377[/C][C]0.275881028168689[/C][/ROW]
[ROW][C]37[/C][C]0.647610517938013[/C][C]0.704778964123974[/C][C]0.352389482061987[/C][/ROW]
[ROW][C]38[/C][C]0.562888029868181[/C][C]0.874223940263639[/C][C]0.437111970131819[/C][/ROW]
[ROW][C]39[/C][C]0.490035809364935[/C][C]0.98007161872987[/C][C]0.509964190635065[/C][/ROW]
[ROW][C]40[/C][C]0.511770330532654[/C][C]0.976459338934692[/C][C]0.488229669467346[/C][/ROW]
[ROW][C]41[/C][C]0.5864178907571[/C][C]0.827164218485799[/C][C]0.413582109242899[/C][/ROW]
[ROW][C]42[/C][C]0.71542533150924[/C][C]0.56914933698152[/C][C]0.28457466849076[/C][/ROW]
[ROW][C]43[/C][C]0.623904616165484[/C][C]0.752190767669033[/C][C]0.376095383834516[/C][/ROW]
[ROW][C]44[/C][C]0.509041138107178[/C][C]0.981917723785644[/C][C]0.490958861892822[/C][/ROW]
[ROW][C]45[/C][C]0.455393552352725[/C][C]0.91078710470545[/C][C]0.544606447647275[/C][/ROW]
[ROW][C]46[/C][C]0.846467298101931[/C][C]0.307065403796138[/C][C]0.153532701898069[/C][/ROW]
[ROW][C]47[/C][C]0.918598917613298[/C][C]0.162802164773405[/C][C]0.0814010823867023[/C][/ROW]
[ROW][C]48[/C][C]0.828472039220802[/C][C]0.343055921558395[/C][C]0.171527960779198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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
160.9874716090694750.02505678186104930.0125283909305246
170.9783426284719960.04331474305600870.0216573715280044
180.9603682752991670.07926344940166560.0396317247008328
190.9314305725202430.1371388549595150.0685694274797574
200.9207735738350560.1584528523298880.079226426164944
210.8725472942113250.254905411577350.127452705788675
220.809094220707350.3818115585852990.190905779292650
230.7491845641717130.5016308716565740.250815435828287
240.7851496474838120.4297007050323760.214850352516188
250.8546318290091940.2907363419816120.145368170990806
260.9018590141117420.1962819717765160.098140985888258
270.8654337686758970.2691324626482070.134566231324103
280.815733943224860.3685321135502810.184266056775141
290.8119963509724340.3760072980551320.188003649027566
300.7486073188083010.5027853623833980.251392681191699
310.727925220944450.5441495581111010.272074779055550
320.8053419412722840.3893161174554320.194658058727716
330.802070372893270.3958592542134610.197929627106731
340.8055102389920430.3889795220159150.194489761007957
350.7853954898934490.4292090202131010.214604510106551
360.7241189718313110.5517620563373770.275881028168689
370.6476105179380130.7047789641239740.352389482061987
380.5628880298681810.8742239402636390.437111970131819
390.4900358093649350.980071618729870.509964190635065
400.5117703305326540.9764593389346920.488229669467346
410.58641789075710.8271642184857990.413582109242899
420.715425331509240.569149336981520.28457466849076
430.6239046161654840.7521907676690330.376095383834516
440.5090411381071780.9819177237856440.490958861892822
450.4553935523527250.910787104705450.544606447647275
460.8464672981019310.3070654037961380.153532701898069
470.9185989176132980.1628021647734050.0814010823867023
480.8284720392208020.3430559215583950.171527960779198







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0606060606060606NOK
10% type I error level30.090909090909091OK

\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 & 2 & 0.0606060606060606 & NOK \tabularnewline
10% type I error level & 3 & 0.090909090909091 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57755&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]2[/C][C]0.0606060606060606[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.090909090909091[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57755&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57755&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 level20.0606060606060606NOK
10% type I error level30.090909090909091OK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}