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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 computationFri, 20 Nov 2009 07:41:25 -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/t1258728335vg35zpgfbtd770s.htm/, Retrieved Thu, 28 Mar 2024 20:30:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58225, Retrieved Thu, 28 Mar 2024 20:30:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [ws7] [2009-11-20 14:41:25] [557d56ec4b06cd0135c259898de8ce95] [Current]
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Dataseries X:
10284.5	1.038351422	1.4
12792	0.933031106	1.3
12823.61538	0.932783124	1.3
13845.66667	0.953755367	1.2
15335.63636	1.009865664	1.1
11188.5	0.979532493	1.4
13633.25	0.98651077	1.2
12298.46667	0.964661281	1.5
15353.63636	0.946761816	1.1
12696.15385	0.959068881	1.3
12213.93333	0.985710058	1.5
13683.72727	0.92582159	1.1
11214.14286	1.036865325	1.4
13950.23077	0.944443576	1.3
11179.13333	0.944901812	1.5
11801.875	0.989151445	1.6
11188.82353	1.054361624	1.7
16456.27273	1.033478919	1.1
11110.0625	1.001368875	1.6
16530.69231	1.019812646	1.3
10038.41176	0.993902155	1.7
11681.25	0.961444482	1.6
11148.88235	0.957449711	1.7
8631	0.93308639	1.9
9386.444444	1.045170549	1.8
9764.736842	0.953166261	1.9
12043.75	0.966782226	1.6
12948.06667	0.972992606	1.5
10987.125	1.013607482	1.6
11648.3125	0.984839518	1.6
10633.35294	0.973220775	1.7
10219.3	0.957284573	2
9037.6	0.972067159	2
10296.31579	0.986878944	1.9
11705.41176	0.954654488	1.7
10681.94444	0.978986976	1.8
9362.947368	1.003056035	1.9
11306.35294	0.970081156	1.7
10984.45	0.991376354	2
10062.61905	1.022609041	2.1
8118.583333	1.089901216	2.4
8867.48	1.060373568	2.5
8346.72	0.985952627	2.5
8529.307692	1.037512164	2.6
10697.18182	1.025335152	2.2
8591.84	1.006376649	2.5
8695.607143	1.018762056	2.8
8125.571429	1.01601847	2.8
7009.758621	1.112410461	2.9
7883.466667	1.037903689	3
7527.645161	1.045436015	3.1
6763.758621	1.09935434	2.9
6682.333333	1.101920787	2.7
7855.681818	1.080574973	2.2
6738.88	1.024388761	2.5
7895.434783	1.024282249	2.3
6361.884615	0.993865289	2.6
6935.956522	0.984935203	2.3
8344.454545	1.005791114	2.2
9107.944444	0.94742834	1.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&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]4 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=58225&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Invoer/inflatie[t] = + 54.4678682869157 + 19736.9890382847`Uitvoer/inflatie`[t] -4260.49052841623Inflatie[t] -2600.41110213936M1[t] + 511.415961808663M2[t] + 397.466380062199M3[t] -191.940314138286M4[t] -1531.96966104220M5[t] -844.712461834646M6[t] -671.981889680457M7[t] + 400.300576732874M8[t] -172.657737688075M9[t] -272.504655250035M10[t] + 297.501985184866M11[t] -26.4309477551256t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Invoer/inflatie[t] =  +  54.4678682869157 +  19736.9890382847`Uitvoer/inflatie`[t] -4260.49052841623Inflatie[t] -2600.41110213936M1[t] +  511.415961808663M2[t] +  397.466380062199M3[t] -191.940314138286M4[t] -1531.96966104220M5[t] -844.712461834646M6[t] -671.981889680457M7[t] +  400.300576732874M8[t] -172.657737688075M9[t] -272.504655250035M10[t] +  297.501985184866M11[t] -26.4309477551256t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Invoer/inflatie[t] =  +  54.4678682869157 +  19736.9890382847`Uitvoer/inflatie`[t] -4260.49052841623Inflatie[t] -2600.41110213936M1[t] +  511.415961808663M2[t] +  397.466380062199M3[t] -191.940314138286M4[t] -1531.96966104220M5[t] -844.712461834646M6[t] -671.981889680457M7[t] +  400.300576732874M8[t] -172.657737688075M9[t] -272.504655250035M10[t] +  297.501985184866M11[t] -26.4309477551256t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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
Invoer/inflatie[t] = + 54.4678682869157 + 19736.9890382847`Uitvoer/inflatie`[t] -4260.49052841623Inflatie[t] -2600.41110213936M1[t] + 511.415961808663M2[t] + 397.466380062199M3[t] -191.940314138286M4[t] -1531.96966104220M5[t] -844.712461834646M6[t] -671.981889680457M7[t] + 400.300576732874M8[t] -172.657737688075M9[t] -272.504655250035M10[t] + 297.501985184866M11[t] -26.4309477551256t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)54.46786828691576051.679420.0090.9928590.496429
`Uitvoer/inflatie`19736.98903828476944.0960512.84230.006710.003355
Inflatie-4260.49052841623623.664301-6.831400
M1-2600.41110213936935.528045-2.77960.0079150.003958
M2511.415961808663707.6785160.72270.4736240.236812
M3397.466380062199714.5499040.55620.5807980.290399
M4-191.940314138286776.844708-0.24710.8059730.402986
M5-1531.96966104220952.047675-1.60910.1145820.057291
M6-844.712461834646859.821057-0.98240.3311420.165571
M7-671.981889680457732.053175-0.91790.3635440.181772
M8400.300576732874742.5378610.53910.5924770.296238
M9-172.657737688075712.221312-0.24240.8095550.404778
M10-272.504655250035701.299285-0.38860.6994260.349713
M11297.501985184866704.6799530.42220.6749040.337452
t-26.430947755125616.55801-1.59630.117430.058715

\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) & 54.4678682869157 & 6051.67942 & 0.009 & 0.992859 & 0.496429 \tabularnewline
`Uitvoer/inflatie` & 19736.9890382847 & 6944.096051 & 2.8423 & 0.00671 & 0.003355 \tabularnewline
Inflatie & -4260.49052841623 & 623.664301 & -6.8314 & 0 & 0 \tabularnewline
M1 & -2600.41110213936 & 935.528045 & -2.7796 & 0.007915 & 0.003958 \tabularnewline
M2 & 511.415961808663 & 707.678516 & 0.7227 & 0.473624 & 0.236812 \tabularnewline
M3 & 397.466380062199 & 714.549904 & 0.5562 & 0.580798 & 0.290399 \tabularnewline
M4 & -191.940314138286 & 776.844708 & -0.2471 & 0.805973 & 0.402986 \tabularnewline
M5 & -1531.96966104220 & 952.047675 & -1.6091 & 0.114582 & 0.057291 \tabularnewline
M6 & -844.712461834646 & 859.821057 & -0.9824 & 0.331142 & 0.165571 \tabularnewline
M7 & -671.981889680457 & 732.053175 & -0.9179 & 0.363544 & 0.181772 \tabularnewline
M8 & 400.300576732874 & 742.537861 & 0.5391 & 0.592477 & 0.296238 \tabularnewline
M9 & -172.657737688075 & 712.221312 & -0.2424 & 0.809555 & 0.404778 \tabularnewline
M10 & -272.504655250035 & 701.299285 & -0.3886 & 0.699426 & 0.349713 \tabularnewline
M11 & 297.501985184866 & 704.679953 & 0.4222 & 0.674904 & 0.337452 \tabularnewline
t & -26.4309477551256 & 16.55801 & -1.5963 & 0.11743 & 0.058715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&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]54.4678682869157[/C][C]6051.67942[/C][C]0.009[/C][C]0.992859[/C][C]0.496429[/C][/ROW]
[ROW][C]`Uitvoer/inflatie`[/C][C]19736.9890382847[/C][C]6944.096051[/C][C]2.8423[/C][C]0.00671[/C][C]0.003355[/C][/ROW]
[ROW][C]Inflatie[/C][C]-4260.49052841623[/C][C]623.664301[/C][C]-6.8314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-2600.41110213936[/C][C]935.528045[/C][C]-2.7796[/C][C]0.007915[/C][C]0.003958[/C][/ROW]
[ROW][C]M2[/C][C]511.415961808663[/C][C]707.678516[/C][C]0.7227[/C][C]0.473624[/C][C]0.236812[/C][/ROW]
[ROW][C]M3[/C][C]397.466380062199[/C][C]714.549904[/C][C]0.5562[/C][C]0.580798[/C][C]0.290399[/C][/ROW]
[ROW][C]M4[/C][C]-191.940314138286[/C][C]776.844708[/C][C]-0.2471[/C][C]0.805973[/C][C]0.402986[/C][/ROW]
[ROW][C]M5[/C][C]-1531.96966104220[/C][C]952.047675[/C][C]-1.6091[/C][C]0.114582[/C][C]0.057291[/C][/ROW]
[ROW][C]M6[/C][C]-844.712461834646[/C][C]859.821057[/C][C]-0.9824[/C][C]0.331142[/C][C]0.165571[/C][/ROW]
[ROW][C]M7[/C][C]-671.981889680457[/C][C]732.053175[/C][C]-0.9179[/C][C]0.363544[/C][C]0.181772[/C][/ROW]
[ROW][C]M8[/C][C]400.300576732874[/C][C]742.537861[/C][C]0.5391[/C][C]0.592477[/C][C]0.296238[/C][/ROW]
[ROW][C]M9[/C][C]-172.657737688075[/C][C]712.221312[/C][C]-0.2424[/C][C]0.809555[/C][C]0.404778[/C][/ROW]
[ROW][C]M10[/C][C]-272.504655250035[/C][C]701.299285[/C][C]-0.3886[/C][C]0.699426[/C][C]0.349713[/C][/ROW]
[ROW][C]M11[/C][C]297.501985184866[/C][C]704.679953[/C][C]0.4222[/C][C]0.674904[/C][C]0.337452[/C][/ROW]
[ROW][C]t[/C][C]-26.4309477551256[/C][C]16.55801[/C][C]-1.5963[/C][C]0.11743[/C][C]0.058715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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)54.46786828691576051.679420.0090.9928590.496429
`Uitvoer/inflatie`19736.98903828476944.0960512.84230.006710.003355
Inflatie-4260.49052841623623.664301-6.831400
M1-2600.41110213936935.528045-2.77960.0079150.003958
M2511.415961808663707.6785160.72270.4736240.236812
M3397.466380062199714.5499040.55620.5807980.290399
M4-191.940314138286776.844708-0.24710.8059730.402986
M5-1531.96966104220952.047675-1.60910.1145820.057291
M6-844.712461834646859.821057-0.98240.3311420.165571
M7-671.981889680457732.053175-0.91790.3635440.181772
M8400.300576732874742.5378610.53910.5924770.296238
M9-172.657737688075712.221312-0.24240.8095550.404778
M10-272.504655250035701.299285-0.38860.6994260.349713
M11297.501985184866704.6799530.42220.6749040.337452
t-26.430947755125616.55801-1.59630.117430.058715







Multiple Linear Regression - Regression Statistics
Multiple R0.923156921377264
R-squared0.852218701486747
Adjusted R-squared0.806242297504847
F-TEST (value)18.5360016808238
F-TEST (DF numerator)14
F-TEST (DF denominator)45
p-value3.50830475781549e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1088.85489387133
Sum Squared Residuals53352224.0958391

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.923156921377264 \tabularnewline
R-squared & 0.852218701486747 \tabularnewline
Adjusted R-squared & 0.806242297504847 \tabularnewline
F-TEST (value) & 18.5360016808238 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 3.50830475781549e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1088.85489387133 \tabularnewline
Sum Squared Residuals & 53352224.0958391 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.923156921377264[/C][/ROW]
[ROW][C]R-squared[/C][C]0.852218701486747[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.806242297504847[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.5360016808238[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]3.50830475781549e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1088.85489387133[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]53352224.0958391[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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 R0.923156921377264
R-squared0.852218701486747
Adjusted R-squared0.806242297504847
F-TEST (value)18.5360016808238
F-TEST (DF numerator)14
F-TEST (DF denominator)45
p-value3.50830475781549e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1088.85489387133
Sum Squared Residuals53352224.0958391







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110284.511956.8697125110-1672.36971251104
21279213389.6089591449-597.608959144895
312823.6153813244.3340116276-420.718631627615
413845.6666713468.4743527129377.192317287128
515335.6363613635.51142771941700.12493228065
611188.512419.5050571235-1231.0050571235
713633.2513555.632963860977.6170361390786
812298.4666712892.0941991091-593.627529109136
915353.6363613643.61960380341710.01675619661
1012696.1538512908.1480398015-211.994189801517
1112213.9333313125.4422452141-911.508915214052
1213683.7272713323.6874872049360.039782795114
1311214.1428611610.3672592507-396.224399250729
1413950.2307713297.6853813731652.545388626859
1511179.1333312314.2509450973-1135.11761509725
1611801.87512145.7187717691-343.843771769142
1711188.8235311640.2620123761-451.438482376064
1816456.2727314445.22086120352011.0518687965
1911110.062511827.5196349476-717.457134947606
2016530.6923114515.54281818232015.14949181768
2110038.4117611700.5622677962-1662.15050779617
2211681.2511359.7167191115321.533280888521
2311148.8823511398.3986075122-249.516257512174
2486319741.50896937573-1110.50896937573
259386.4444449752.91978987123-366.475345871229
269764.73684210596.3792294913-831.642387491307
2712043.7512002.884010465340.8659895347464
2812948.0666711935.66962333491012.39704666515
2910987.12510944.775638237542.3493617625198
3011648.312511037.8088995681610.50360043186
3110633.3529410528.7404678959104.612472104067
3210219.39981.91218384338237.387816156622
339037.69674.2866595068-636.686659506803
3410296.3157910266.397885213829.9179047862289
3511705.4117611026.0579487401679.353811259897
3610681.9444410756.3260118887-74.381571888684
379362.9473688178.48566279741184.46170520259
3811306.3529411465.1550593118-158.802119311781
3910984.4510466.9304607794517.519539220574
4010062.6190510041.482966937421.1360830626299
418118.5833338725.0204340908-606.437101090796
428867.488377.01076779927490.469232200726
438346.727054.46509546251292.25490453750
448529.3076928691.89757786712-162.589885867121
4510697.181829556.366974694481140.81484530552
468591.847777.75818495923814.081815040767
478695.6071437287.637361307831407.96978169217
488125.5714296909.554301560251216.01712743975
497009.7586215759.150868569591250.60775243041
507883.4666676947.95858967888935.508077321123
517527.6451616530.19444303045997.45071796955
526763.7586217830.64029624576-1066.88167524576
536682.3333337366.9320435763-684.598710576306
547855.6818189736.70146230559-1881.01964430559
556738.887495.90727783304-757.027277833038
567895.4347839391.75467599805-1496.31989299805
576361.8846156913.87904919916-551.994434199156
586935.9565227889.495332914-953.538810914
598344.4545459270.75296522584-926.298420225837
609107.9444449499.11081297045-391.16636897045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10284.5 & 11956.8697125110 & -1672.36971251104 \tabularnewline
2 & 12792 & 13389.6089591449 & -597.608959144895 \tabularnewline
3 & 12823.61538 & 13244.3340116276 & -420.718631627615 \tabularnewline
4 & 13845.66667 & 13468.4743527129 & 377.192317287128 \tabularnewline
5 & 15335.63636 & 13635.5114277194 & 1700.12493228065 \tabularnewline
6 & 11188.5 & 12419.5050571235 & -1231.0050571235 \tabularnewline
7 & 13633.25 & 13555.6329638609 & 77.6170361390786 \tabularnewline
8 & 12298.46667 & 12892.0941991091 & -593.627529109136 \tabularnewline
9 & 15353.63636 & 13643.6196038034 & 1710.01675619661 \tabularnewline
10 & 12696.15385 & 12908.1480398015 & -211.994189801517 \tabularnewline
11 & 12213.93333 & 13125.4422452141 & -911.508915214052 \tabularnewline
12 & 13683.72727 & 13323.6874872049 & 360.039782795114 \tabularnewline
13 & 11214.14286 & 11610.3672592507 & -396.224399250729 \tabularnewline
14 & 13950.23077 & 13297.6853813731 & 652.545388626859 \tabularnewline
15 & 11179.13333 & 12314.2509450973 & -1135.11761509725 \tabularnewline
16 & 11801.875 & 12145.7187717691 & -343.843771769142 \tabularnewline
17 & 11188.82353 & 11640.2620123761 & -451.438482376064 \tabularnewline
18 & 16456.27273 & 14445.2208612035 & 2011.0518687965 \tabularnewline
19 & 11110.0625 & 11827.5196349476 & -717.457134947606 \tabularnewline
20 & 16530.69231 & 14515.5428181823 & 2015.14949181768 \tabularnewline
21 & 10038.41176 & 11700.5622677962 & -1662.15050779617 \tabularnewline
22 & 11681.25 & 11359.7167191115 & 321.533280888521 \tabularnewline
23 & 11148.88235 & 11398.3986075122 & -249.516257512174 \tabularnewline
24 & 8631 & 9741.50896937573 & -1110.50896937573 \tabularnewline
25 & 9386.444444 & 9752.91978987123 & -366.475345871229 \tabularnewline
26 & 9764.736842 & 10596.3792294913 & -831.642387491307 \tabularnewline
27 & 12043.75 & 12002.8840104653 & 40.8659895347464 \tabularnewline
28 & 12948.06667 & 11935.6696233349 & 1012.39704666515 \tabularnewline
29 & 10987.125 & 10944.7756382375 & 42.3493617625198 \tabularnewline
30 & 11648.3125 & 11037.8088995681 & 610.50360043186 \tabularnewline
31 & 10633.35294 & 10528.7404678959 & 104.612472104067 \tabularnewline
32 & 10219.3 & 9981.91218384338 & 237.387816156622 \tabularnewline
33 & 9037.6 & 9674.2866595068 & -636.686659506803 \tabularnewline
34 & 10296.31579 & 10266.3978852138 & 29.9179047862289 \tabularnewline
35 & 11705.41176 & 11026.0579487401 & 679.353811259897 \tabularnewline
36 & 10681.94444 & 10756.3260118887 & -74.381571888684 \tabularnewline
37 & 9362.947368 & 8178.4856627974 & 1184.46170520259 \tabularnewline
38 & 11306.35294 & 11465.1550593118 & -158.802119311781 \tabularnewline
39 & 10984.45 & 10466.9304607794 & 517.519539220574 \tabularnewline
40 & 10062.61905 & 10041.4829669374 & 21.1360830626299 \tabularnewline
41 & 8118.583333 & 8725.0204340908 & -606.437101090796 \tabularnewline
42 & 8867.48 & 8377.01076779927 & 490.469232200726 \tabularnewline
43 & 8346.72 & 7054.4650954625 & 1292.25490453750 \tabularnewline
44 & 8529.307692 & 8691.89757786712 & -162.589885867121 \tabularnewline
45 & 10697.18182 & 9556.36697469448 & 1140.81484530552 \tabularnewline
46 & 8591.84 & 7777.75818495923 & 814.081815040767 \tabularnewline
47 & 8695.607143 & 7287.63736130783 & 1407.96978169217 \tabularnewline
48 & 8125.571429 & 6909.55430156025 & 1216.01712743975 \tabularnewline
49 & 7009.758621 & 5759.15086856959 & 1250.60775243041 \tabularnewline
50 & 7883.466667 & 6947.95858967888 & 935.508077321123 \tabularnewline
51 & 7527.645161 & 6530.19444303045 & 997.45071796955 \tabularnewline
52 & 6763.758621 & 7830.64029624576 & -1066.88167524576 \tabularnewline
53 & 6682.333333 & 7366.9320435763 & -684.598710576306 \tabularnewline
54 & 7855.681818 & 9736.70146230559 & -1881.01964430559 \tabularnewline
55 & 6738.88 & 7495.90727783304 & -757.027277833038 \tabularnewline
56 & 7895.434783 & 9391.75467599805 & -1496.31989299805 \tabularnewline
57 & 6361.884615 & 6913.87904919916 & -551.994434199156 \tabularnewline
58 & 6935.956522 & 7889.495332914 & -953.538810914 \tabularnewline
59 & 8344.454545 & 9270.75296522584 & -926.298420225837 \tabularnewline
60 & 9107.944444 & 9499.11081297045 & -391.16636897045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&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]10284.5[/C][C]11956.8697125110[/C][C]-1672.36971251104[/C][/ROW]
[ROW][C]2[/C][C]12792[/C][C]13389.6089591449[/C][C]-597.608959144895[/C][/ROW]
[ROW][C]3[/C][C]12823.61538[/C][C]13244.3340116276[/C][C]-420.718631627615[/C][/ROW]
[ROW][C]4[/C][C]13845.66667[/C][C]13468.4743527129[/C][C]377.192317287128[/C][/ROW]
[ROW][C]5[/C][C]15335.63636[/C][C]13635.5114277194[/C][C]1700.12493228065[/C][/ROW]
[ROW][C]6[/C][C]11188.5[/C][C]12419.5050571235[/C][C]-1231.0050571235[/C][/ROW]
[ROW][C]7[/C][C]13633.25[/C][C]13555.6329638609[/C][C]77.6170361390786[/C][/ROW]
[ROW][C]8[/C][C]12298.46667[/C][C]12892.0941991091[/C][C]-593.627529109136[/C][/ROW]
[ROW][C]9[/C][C]15353.63636[/C][C]13643.6196038034[/C][C]1710.01675619661[/C][/ROW]
[ROW][C]10[/C][C]12696.15385[/C][C]12908.1480398015[/C][C]-211.994189801517[/C][/ROW]
[ROW][C]11[/C][C]12213.93333[/C][C]13125.4422452141[/C][C]-911.508915214052[/C][/ROW]
[ROW][C]12[/C][C]13683.72727[/C][C]13323.6874872049[/C][C]360.039782795114[/C][/ROW]
[ROW][C]13[/C][C]11214.14286[/C][C]11610.3672592507[/C][C]-396.224399250729[/C][/ROW]
[ROW][C]14[/C][C]13950.23077[/C][C]13297.6853813731[/C][C]652.545388626859[/C][/ROW]
[ROW][C]15[/C][C]11179.13333[/C][C]12314.2509450973[/C][C]-1135.11761509725[/C][/ROW]
[ROW][C]16[/C][C]11801.875[/C][C]12145.7187717691[/C][C]-343.843771769142[/C][/ROW]
[ROW][C]17[/C][C]11188.82353[/C][C]11640.2620123761[/C][C]-451.438482376064[/C][/ROW]
[ROW][C]18[/C][C]16456.27273[/C][C]14445.2208612035[/C][C]2011.0518687965[/C][/ROW]
[ROW][C]19[/C][C]11110.0625[/C][C]11827.5196349476[/C][C]-717.457134947606[/C][/ROW]
[ROW][C]20[/C][C]16530.69231[/C][C]14515.5428181823[/C][C]2015.14949181768[/C][/ROW]
[ROW][C]21[/C][C]10038.41176[/C][C]11700.5622677962[/C][C]-1662.15050779617[/C][/ROW]
[ROW][C]22[/C][C]11681.25[/C][C]11359.7167191115[/C][C]321.533280888521[/C][/ROW]
[ROW][C]23[/C][C]11148.88235[/C][C]11398.3986075122[/C][C]-249.516257512174[/C][/ROW]
[ROW][C]24[/C][C]8631[/C][C]9741.50896937573[/C][C]-1110.50896937573[/C][/ROW]
[ROW][C]25[/C][C]9386.444444[/C][C]9752.91978987123[/C][C]-366.475345871229[/C][/ROW]
[ROW][C]26[/C][C]9764.736842[/C][C]10596.3792294913[/C][C]-831.642387491307[/C][/ROW]
[ROW][C]27[/C][C]12043.75[/C][C]12002.8840104653[/C][C]40.8659895347464[/C][/ROW]
[ROW][C]28[/C][C]12948.06667[/C][C]11935.6696233349[/C][C]1012.39704666515[/C][/ROW]
[ROW][C]29[/C][C]10987.125[/C][C]10944.7756382375[/C][C]42.3493617625198[/C][/ROW]
[ROW][C]30[/C][C]11648.3125[/C][C]11037.8088995681[/C][C]610.50360043186[/C][/ROW]
[ROW][C]31[/C][C]10633.35294[/C][C]10528.7404678959[/C][C]104.612472104067[/C][/ROW]
[ROW][C]32[/C][C]10219.3[/C][C]9981.91218384338[/C][C]237.387816156622[/C][/ROW]
[ROW][C]33[/C][C]9037.6[/C][C]9674.2866595068[/C][C]-636.686659506803[/C][/ROW]
[ROW][C]34[/C][C]10296.31579[/C][C]10266.3978852138[/C][C]29.9179047862289[/C][/ROW]
[ROW][C]35[/C][C]11705.41176[/C][C]11026.0579487401[/C][C]679.353811259897[/C][/ROW]
[ROW][C]36[/C][C]10681.94444[/C][C]10756.3260118887[/C][C]-74.381571888684[/C][/ROW]
[ROW][C]37[/C][C]9362.947368[/C][C]8178.4856627974[/C][C]1184.46170520259[/C][/ROW]
[ROW][C]38[/C][C]11306.35294[/C][C]11465.1550593118[/C][C]-158.802119311781[/C][/ROW]
[ROW][C]39[/C][C]10984.45[/C][C]10466.9304607794[/C][C]517.519539220574[/C][/ROW]
[ROW][C]40[/C][C]10062.61905[/C][C]10041.4829669374[/C][C]21.1360830626299[/C][/ROW]
[ROW][C]41[/C][C]8118.583333[/C][C]8725.0204340908[/C][C]-606.437101090796[/C][/ROW]
[ROW][C]42[/C][C]8867.48[/C][C]8377.01076779927[/C][C]490.469232200726[/C][/ROW]
[ROW][C]43[/C][C]8346.72[/C][C]7054.4650954625[/C][C]1292.25490453750[/C][/ROW]
[ROW][C]44[/C][C]8529.307692[/C][C]8691.89757786712[/C][C]-162.589885867121[/C][/ROW]
[ROW][C]45[/C][C]10697.18182[/C][C]9556.36697469448[/C][C]1140.81484530552[/C][/ROW]
[ROW][C]46[/C][C]8591.84[/C][C]7777.75818495923[/C][C]814.081815040767[/C][/ROW]
[ROW][C]47[/C][C]8695.607143[/C][C]7287.63736130783[/C][C]1407.96978169217[/C][/ROW]
[ROW][C]48[/C][C]8125.571429[/C][C]6909.55430156025[/C][C]1216.01712743975[/C][/ROW]
[ROW][C]49[/C][C]7009.758621[/C][C]5759.15086856959[/C][C]1250.60775243041[/C][/ROW]
[ROW][C]50[/C][C]7883.466667[/C][C]6947.95858967888[/C][C]935.508077321123[/C][/ROW]
[ROW][C]51[/C][C]7527.645161[/C][C]6530.19444303045[/C][C]997.45071796955[/C][/ROW]
[ROW][C]52[/C][C]6763.758621[/C][C]7830.64029624576[/C][C]-1066.88167524576[/C][/ROW]
[ROW][C]53[/C][C]6682.333333[/C][C]7366.9320435763[/C][C]-684.598710576306[/C][/ROW]
[ROW][C]54[/C][C]7855.681818[/C][C]9736.70146230559[/C][C]-1881.01964430559[/C][/ROW]
[ROW][C]55[/C][C]6738.88[/C][C]7495.90727783304[/C][C]-757.027277833038[/C][/ROW]
[ROW][C]56[/C][C]7895.434783[/C][C]9391.75467599805[/C][C]-1496.31989299805[/C][/ROW]
[ROW][C]57[/C][C]6361.884615[/C][C]6913.87904919916[/C][C]-551.994434199156[/C][/ROW]
[ROW][C]58[/C][C]6935.956522[/C][C]7889.495332914[/C][C]-953.538810914[/C][/ROW]
[ROW][C]59[/C][C]8344.454545[/C][C]9270.75296522584[/C][C]-926.298420225837[/C][/ROW]
[ROW][C]60[/C][C]9107.944444[/C][C]9499.11081297045[/C][C]-391.16636897045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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
110284.511956.8697125110-1672.36971251104
21279213389.6089591449-597.608959144895
312823.6153813244.3340116276-420.718631627615
413845.6666713468.4743527129377.192317287128
515335.6363613635.51142771941700.12493228065
611188.512419.5050571235-1231.0050571235
713633.2513555.632963860977.6170361390786
812298.4666712892.0941991091-593.627529109136
915353.6363613643.61960380341710.01675619661
1012696.1538512908.1480398015-211.994189801517
1112213.9333313125.4422452141-911.508915214052
1213683.7272713323.6874872049360.039782795114
1311214.1428611610.3672592507-396.224399250729
1413950.2307713297.6853813731652.545388626859
1511179.1333312314.2509450973-1135.11761509725
1611801.87512145.7187717691-343.843771769142
1711188.8235311640.2620123761-451.438482376064
1816456.2727314445.22086120352011.0518687965
1911110.062511827.5196349476-717.457134947606
2016530.6923114515.54281818232015.14949181768
2110038.4117611700.5622677962-1662.15050779617
2211681.2511359.7167191115321.533280888521
2311148.8823511398.3986075122-249.516257512174
2486319741.50896937573-1110.50896937573
259386.4444449752.91978987123-366.475345871229
269764.73684210596.3792294913-831.642387491307
2712043.7512002.884010465340.8659895347464
2812948.0666711935.66962333491012.39704666515
2910987.12510944.775638237542.3493617625198
3011648.312511037.8088995681610.50360043186
3110633.3529410528.7404678959104.612472104067
3210219.39981.91218384338237.387816156622
339037.69674.2866595068-636.686659506803
3410296.3157910266.397885213829.9179047862289
3511705.4117611026.0579487401679.353811259897
3610681.9444410756.3260118887-74.381571888684
379362.9473688178.48566279741184.46170520259
3811306.3529411465.1550593118-158.802119311781
3910984.4510466.9304607794517.519539220574
4010062.6190510041.482966937421.1360830626299
418118.5833338725.0204340908-606.437101090796
428867.488377.01076779927490.469232200726
438346.727054.46509546251292.25490453750
448529.3076928691.89757786712-162.589885867121
4510697.181829556.366974694481140.81484530552
468591.847777.75818495923814.081815040767
478695.6071437287.637361307831407.96978169217
488125.5714296909.554301560251216.01712743975
497009.7586215759.150868569591250.60775243041
507883.4666676947.95858967888935.508077321123
517527.6451616530.19444303045997.45071796955
526763.7586217830.64029624576-1066.88167524576
536682.3333337366.9320435763-684.598710576306
547855.6818189736.70146230559-1881.01964430559
556738.887495.90727783304-757.027277833038
567895.4347839391.75467599805-1496.31989299805
576361.8846156913.87904919916-551.994434199156
586935.9565227889.495332914-953.538810914
598344.4545459270.75296522584-926.298420225837
609107.9444449499.11081297045-391.16636897045







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.03965579730456220.07931159460912430.960344202695438
190.01497346506551890.02994693013103780.985026534934481
200.007917292104785490.01583458420957100.992082707895215
210.02777243679353910.05554487358707820.97222756320646
220.03815545989378140.07631091978756270.961844540106219
230.01925927554847150.0385185510969430.980740724451528
240.1606729051717150.3213458103434300.839327094828285
250.1600487329258690.3200974658517380.83995126707413
260.2380793513404660.4761587026809320.761920648659534
270.1920112465097300.3840224930194610.80798875349027
280.2441460677463560.4882921354927110.755853932253644
290.4304896815060370.8609793630120740.569510318493963
300.3599622589821550.719924517964310.640037741017845
310.2686881130287610.5373762260575210.73131188697124
320.2083101069521110.4166202139042220.791689893047889
330.4483816372759940.8967632745519870.551618362724006
340.3859004106050910.7718008212101820.614099589394909
350.2909946103204890.5819892206409770.709005389679511
360.4645000394249540.9290000788499080.535499960575046
370.4651502174984430.9303004349968860.534849782501557
380.5907768659125170.8184462681749660.409223134087483
390.6908645912062610.6182708175874780.309135408793739
400.5639641215677260.8720717568645490.436035878432274
410.9429043121171550.1141913757656890.0570956878828446
420.9199986201218160.1600027597563680.0800013798781842

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0396557973045622 & 0.0793115946091243 & 0.960344202695438 \tabularnewline
19 & 0.0149734650655189 & 0.0299469301310378 & 0.985026534934481 \tabularnewline
20 & 0.00791729210478549 & 0.0158345842095710 & 0.992082707895215 \tabularnewline
21 & 0.0277724367935391 & 0.0555448735870782 & 0.97222756320646 \tabularnewline
22 & 0.0381554598937814 & 0.0763109197875627 & 0.961844540106219 \tabularnewline
23 & 0.0192592755484715 & 0.038518551096943 & 0.980740724451528 \tabularnewline
24 & 0.160672905171715 & 0.321345810343430 & 0.839327094828285 \tabularnewline
25 & 0.160048732925869 & 0.320097465851738 & 0.83995126707413 \tabularnewline
26 & 0.238079351340466 & 0.476158702680932 & 0.761920648659534 \tabularnewline
27 & 0.192011246509730 & 0.384022493019461 & 0.80798875349027 \tabularnewline
28 & 0.244146067746356 & 0.488292135492711 & 0.755853932253644 \tabularnewline
29 & 0.430489681506037 & 0.860979363012074 & 0.569510318493963 \tabularnewline
30 & 0.359962258982155 & 0.71992451796431 & 0.640037741017845 \tabularnewline
31 & 0.268688113028761 & 0.537376226057521 & 0.73131188697124 \tabularnewline
32 & 0.208310106952111 & 0.416620213904222 & 0.791689893047889 \tabularnewline
33 & 0.448381637275994 & 0.896763274551987 & 0.551618362724006 \tabularnewline
34 & 0.385900410605091 & 0.771800821210182 & 0.614099589394909 \tabularnewline
35 & 0.290994610320489 & 0.581989220640977 & 0.709005389679511 \tabularnewline
36 & 0.464500039424954 & 0.929000078849908 & 0.535499960575046 \tabularnewline
37 & 0.465150217498443 & 0.930300434996886 & 0.534849782501557 \tabularnewline
38 & 0.590776865912517 & 0.818446268174966 & 0.409223134087483 \tabularnewline
39 & 0.690864591206261 & 0.618270817587478 & 0.309135408793739 \tabularnewline
40 & 0.563964121567726 & 0.872071756864549 & 0.436035878432274 \tabularnewline
41 & 0.942904312117155 & 0.114191375765689 & 0.0570956878828446 \tabularnewline
42 & 0.919998620121816 & 0.160002759756368 & 0.0800013798781842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&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]18[/C][C]0.0396557973045622[/C][C]0.0793115946091243[/C][C]0.960344202695438[/C][/ROW]
[ROW][C]19[/C][C]0.0149734650655189[/C][C]0.0299469301310378[/C][C]0.985026534934481[/C][/ROW]
[ROW][C]20[/C][C]0.00791729210478549[/C][C]0.0158345842095710[/C][C]0.992082707895215[/C][/ROW]
[ROW][C]21[/C][C]0.0277724367935391[/C][C]0.0555448735870782[/C][C]0.97222756320646[/C][/ROW]
[ROW][C]22[/C][C]0.0381554598937814[/C][C]0.0763109197875627[/C][C]0.961844540106219[/C][/ROW]
[ROW][C]23[/C][C]0.0192592755484715[/C][C]0.038518551096943[/C][C]0.980740724451528[/C][/ROW]
[ROW][C]24[/C][C]0.160672905171715[/C][C]0.321345810343430[/C][C]0.839327094828285[/C][/ROW]
[ROW][C]25[/C][C]0.160048732925869[/C][C]0.320097465851738[/C][C]0.83995126707413[/C][/ROW]
[ROW][C]26[/C][C]0.238079351340466[/C][C]0.476158702680932[/C][C]0.761920648659534[/C][/ROW]
[ROW][C]27[/C][C]0.192011246509730[/C][C]0.384022493019461[/C][C]0.80798875349027[/C][/ROW]
[ROW][C]28[/C][C]0.244146067746356[/C][C]0.488292135492711[/C][C]0.755853932253644[/C][/ROW]
[ROW][C]29[/C][C]0.430489681506037[/C][C]0.860979363012074[/C][C]0.569510318493963[/C][/ROW]
[ROW][C]30[/C][C]0.359962258982155[/C][C]0.71992451796431[/C][C]0.640037741017845[/C][/ROW]
[ROW][C]31[/C][C]0.268688113028761[/C][C]0.537376226057521[/C][C]0.73131188697124[/C][/ROW]
[ROW][C]32[/C][C]0.208310106952111[/C][C]0.416620213904222[/C][C]0.791689893047889[/C][/ROW]
[ROW][C]33[/C][C]0.448381637275994[/C][C]0.896763274551987[/C][C]0.551618362724006[/C][/ROW]
[ROW][C]34[/C][C]0.385900410605091[/C][C]0.771800821210182[/C][C]0.614099589394909[/C][/ROW]
[ROW][C]35[/C][C]0.290994610320489[/C][C]0.581989220640977[/C][C]0.709005389679511[/C][/ROW]
[ROW][C]36[/C][C]0.464500039424954[/C][C]0.929000078849908[/C][C]0.535499960575046[/C][/ROW]
[ROW][C]37[/C][C]0.465150217498443[/C][C]0.930300434996886[/C][C]0.534849782501557[/C][/ROW]
[ROW][C]38[/C][C]0.590776865912517[/C][C]0.818446268174966[/C][C]0.409223134087483[/C][/ROW]
[ROW][C]39[/C][C]0.690864591206261[/C][C]0.618270817587478[/C][C]0.309135408793739[/C][/ROW]
[ROW][C]40[/C][C]0.563964121567726[/C][C]0.872071756864549[/C][C]0.436035878432274[/C][/ROW]
[ROW][C]41[/C][C]0.942904312117155[/C][C]0.114191375765689[/C][C]0.0570956878828446[/C][/ROW]
[ROW][C]42[/C][C]0.919998620121816[/C][C]0.160002759756368[/C][C]0.0800013798781842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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
180.03965579730456220.07931159460912430.960344202695438
190.01497346506551890.02994693013103780.985026534934481
200.007917292104785490.01583458420957100.992082707895215
210.02777243679353910.05554487358707820.97222756320646
220.03815545989378140.07631091978756270.961844540106219
230.01925927554847150.0385185510969430.980740724451528
240.1606729051717150.3213458103434300.839327094828285
250.1600487329258690.3200974658517380.83995126707413
260.2380793513404660.4761587026809320.761920648659534
270.1920112465097300.3840224930194610.80798875349027
280.2441460677463560.4882921354927110.755853932253644
290.4304896815060370.8609793630120740.569510318493963
300.3599622589821550.719924517964310.640037741017845
310.2686881130287610.5373762260575210.73131188697124
320.2083101069521110.4166202139042220.791689893047889
330.4483816372759940.8967632745519870.551618362724006
340.3859004106050910.7718008212101820.614099589394909
350.2909946103204890.5819892206409770.709005389679511
360.4645000394249540.9290000788499080.535499960575046
370.4651502174984430.9303004349968860.534849782501557
380.5907768659125170.8184462681749660.409223134087483
390.6908645912062610.6182708175874780.309135408793739
400.5639641215677260.8720717568645490.436035878432274
410.9429043121171550.1141913757656890.0570956878828446
420.9199986201218160.1600027597563680.0800013798781842







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.12NOK
10% type I error level60.24NOK

\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 & 3 & 0.12 & NOK \tabularnewline
10% type I error level & 6 & 0.24 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58225&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]3[/C][C]0.12[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.24[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58225&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58225&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 level30.12NOK
10% type I error level60.24NOK



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