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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:39:19 -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/t1258717224p2lj2zuy40y1u1s.htm/, Retrieved Thu, 28 Mar 2024 11:28:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58044, Retrieved Thu, 28 Mar 2024 11:28:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
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]
-    D      [Multiple Regression] [link2] [2009-11-20 11:39:19] [454b2df2fae01897bad5ff38ed3cc924] [Current]
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Dataseries X:
1.58	0.55
1.59	0.55
1.6	0.55
1.6	0.55
1.6	0.55
1.6	0.56
1.61	0.56
1.61	0.56
1.62	0.56
1.63	0.56
1.63	0.55
1.63	0.56
1.63	0.55
1.63	0.55
1.64	0.56
1.64	0.55
1.64	0.55
1.65	0.55
1.65	0.55
1.65	0.53
1.65	0.53
1.65	0.53
1.66	0.53
1.67	0.54
1.68	0.54
1.68	0.54
1.68	0.55
1.68	0.55
1.69	0.54
1.7	0.55
1.7	0.56
1.71	0.58
1.73	0.59
1.73	0.6
1.73	0.6
1.74	0.6
1.74	0.59
1.74	0.6
1.75	0.6
1.78	0.62
1.82	0.65
1.83	0.68
1.84	0.73
1.85	0.78
1.86	0.78
1.86	0.82
1.87	0.82
1.87	0.81
1.87	0.83
1.87	0.85
1.87	0.86
1.87	0.85
1.87	0.85
1.88	0.82
1.88	0.8
1.87	0.81
1.87	0.8
1.87	0.8
1.87	0.8
1.87	0.8
1.87	0.79




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=58044&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=58044&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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
Y[t] = + 1.21294728270632 + 0.820321325217045X[t] -0.0109867997205874M1[t] -0.0179058616904500M2[t] -0.0168277896417523M3[t] -0.0108277896417522M4[t] -0.00410907494262042M5[t] + 0.000609639756511355M6[t] -0.001952930845225M7[t] -0.0097967867478295M8[t] -0.00179678674782949M9[t] -0.00799999999999997M10[t] -0.00235935734956588M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.21294728270632 +  0.820321325217045X[t] -0.0109867997205874M1[t] -0.0179058616904500M2[t] -0.0168277896417523M3[t] -0.0108277896417522M4[t] -0.00410907494262042M5[t] +  0.000609639756511355M6[t] -0.001952930845225M7[t] -0.0097967867478295M8[t] -0.00179678674782949M9[t] -0.00799999999999997M10[t] -0.00235935734956588M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.21294728270632 +  0.820321325217045X[t] -0.0109867997205874M1[t] -0.0179058616904500M2[t] -0.0168277896417523M3[t] -0.0108277896417522M4[t] -0.00410907494262042M5[t] +  0.000609639756511355M6[t] -0.001952930845225M7[t] -0.0097967867478295M8[t] -0.00179678674782949M9[t] -0.00799999999999997M10[t] -0.00235935734956588M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.21294728270632 + 0.820321325217045X[t] -0.0109867997205874M1[t] -0.0179058616904500M2[t] -0.0168277896417523M3[t] -0.0108277896417522M4[t] -0.00410907494262042M5[t] + 0.000609639756511355M6[t] -0.001952930845225M7[t] -0.0097967867478295M8[t] -0.00179678674782949M9[t] -0.00799999999999997M10[t] -0.00235935734956588M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.212947282706320.03510534.552200
X0.8203213252170450.04512318.179800
M1-0.01098679972058740.024986-0.43970.6621080.331054
M2-0.01790586169045000.026154-0.68460.4968750.248438
M3-0.01682778964175230.026135-0.64390.5227220.261361
M4-0.01082778964175220.026135-0.41430.6805010.34025
M5-0.004109074942620420.026124-0.15730.8756750.437838
M60.0006096397565113550.0261140.02330.9814720.490736
M7-0.0019529308452250.026098-0.07480.940660.47033
M8-0.00979678674782950.026083-0.37560.7088670.354433
M9-0.001796786747829490.026083-0.06890.9453650.472683
M10-0.007999999999999970.026079-0.30680.7603520.380176
M11-0.002359357349565880.026079-0.09050.9282910.464145

\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) & 1.21294728270632 & 0.035105 & 34.5522 & 0 & 0 \tabularnewline
X & 0.820321325217045 & 0.045123 & 18.1798 & 0 & 0 \tabularnewline
M1 & -0.0109867997205874 & 0.024986 & -0.4397 & 0.662108 & 0.331054 \tabularnewline
M2 & -0.0179058616904500 & 0.026154 & -0.6846 & 0.496875 & 0.248438 \tabularnewline
M3 & -0.0168277896417523 & 0.026135 & -0.6439 & 0.522722 & 0.261361 \tabularnewline
M4 & -0.0108277896417522 & 0.026135 & -0.4143 & 0.680501 & 0.34025 \tabularnewline
M5 & -0.00410907494262042 & 0.026124 & -0.1573 & 0.875675 & 0.437838 \tabularnewline
M6 & 0.000609639756511355 & 0.026114 & 0.0233 & 0.981472 & 0.490736 \tabularnewline
M7 & -0.001952930845225 & 0.026098 & -0.0748 & 0.94066 & 0.47033 \tabularnewline
M8 & -0.0097967867478295 & 0.026083 & -0.3756 & 0.708867 & 0.354433 \tabularnewline
M9 & -0.00179678674782949 & 0.026083 & -0.0689 & 0.945365 & 0.472683 \tabularnewline
M10 & -0.00799999999999997 & 0.026079 & -0.3068 & 0.760352 & 0.380176 \tabularnewline
M11 & -0.00235935734956588 & 0.026079 & -0.0905 & 0.928291 & 0.464145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&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]1.21294728270632[/C][C]0.035105[/C][C]34.5522[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.820321325217045[/C][C]0.045123[/C][C]18.1798[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0109867997205874[/C][C]0.024986[/C][C]-0.4397[/C][C]0.662108[/C][C]0.331054[/C][/ROW]
[ROW][C]M2[/C][C]-0.0179058616904500[/C][C]0.026154[/C][C]-0.6846[/C][C]0.496875[/C][C]0.248438[/C][/ROW]
[ROW][C]M3[/C][C]-0.0168277896417523[/C][C]0.026135[/C][C]-0.6439[/C][C]0.522722[/C][C]0.261361[/C][/ROW]
[ROW][C]M4[/C][C]-0.0108277896417522[/C][C]0.026135[/C][C]-0.4143[/C][C]0.680501[/C][C]0.34025[/C][/ROW]
[ROW][C]M5[/C][C]-0.00410907494262042[/C][C]0.026124[/C][C]-0.1573[/C][C]0.875675[/C][C]0.437838[/C][/ROW]
[ROW][C]M6[/C][C]0.000609639756511355[/C][C]0.026114[/C][C]0.0233[/C][C]0.981472[/C][C]0.490736[/C][/ROW]
[ROW][C]M7[/C][C]-0.001952930845225[/C][C]0.026098[/C][C]-0.0748[/C][C]0.94066[/C][C]0.47033[/C][/ROW]
[ROW][C]M8[/C][C]-0.0097967867478295[/C][C]0.026083[/C][C]-0.3756[/C][C]0.708867[/C][C]0.354433[/C][/ROW]
[ROW][C]M9[/C][C]-0.00179678674782949[/C][C]0.026083[/C][C]-0.0689[/C][C]0.945365[/C][C]0.472683[/C][/ROW]
[ROW][C]M10[/C][C]-0.00799999999999997[/C][C]0.026079[/C][C]-0.3068[/C][C]0.760352[/C][C]0.380176[/C][/ROW]
[ROW][C]M11[/C][C]-0.00235935734956588[/C][C]0.026079[/C][C]-0.0905[/C][C]0.928291[/C][C]0.464145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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)1.212947282706320.03510534.552200
X0.8203213252170450.04512318.179800
M1-0.01098679972058740.024986-0.43970.6621080.331054
M2-0.01790586169045000.026154-0.68460.4968750.248438
M3-0.01682778964175230.026135-0.64390.5227220.261361
M4-0.01082778964175220.026135-0.41430.6805010.34025
M5-0.004109074942620420.026124-0.15730.8756750.437838
M60.0006096397565113550.0261140.02330.9814720.490736
M7-0.0019529308452250.026098-0.07480.940660.47033
M8-0.00979678674782950.026083-0.37560.7088670.354433
M9-0.001796786747829490.026083-0.06890.9453650.472683
M10-0.007999999999999970.026079-0.30680.7603520.380176
M11-0.002359357349565880.026079-0.09050.9282910.464145







Multiple Linear Regression - Regression Statistics
Multiple R0.936168716070138
R-squared0.876411864948412
Adjusted R-squared0.845514831185514
F-TEST (value)28.3655664706835
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0412343776253169
Sum Squared Residuals0.0816131471110674

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.936168716070138 \tabularnewline
R-squared & 0.876411864948412 \tabularnewline
Adjusted R-squared & 0.845514831185514 \tabularnewline
F-TEST (value) & 28.3655664706835 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0412343776253169 \tabularnewline
Sum Squared Residuals & 0.0816131471110674 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.936168716070138[/C][/ROW]
[ROW][C]R-squared[/C][C]0.876411864948412[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.845514831185514[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.3655664706835[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0412343776253169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0816131471110674[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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 R0.936168716070138
R-squared0.876411864948412
Adjusted R-squared0.845514831185514
F-TEST (value)28.3655664706835
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0412343776253169
Sum Squared Residuals0.0816131471110674







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.581.65313721185511-0.0731372118551079
21.591.64621814988524-0.0562181498852411
31.61.64729622193394-0.0472962219339386
41.61.65329622193394-0.0532962219339386
51.61.66001493663307-0.0600149366330704
61.61.67293686458437-0.0729368645843727
71.611.67037429398264-0.0603742939826363
81.611.66253043808003-0.0525304380800318
91.621.67053043808003-0.0505304380800318
101.631.66432722482786-0.0343272248278615
111.631.66176465422613-0.0317646542261252
121.631.67232722482786-0.0423272248278615
131.631.65313721185510-0.0231372118551036
141.631.64621814988524-0.016218149885241
151.641.65549943518611-0.0154994351861092
161.641.65329622193394-0.0132962219339388
171.641.66001493663307-0.0200149366330706
181.651.66473365133220-0.0147336513322024
191.651.66217108073047-0.012171080730466
201.651.637920798323520.0120792016764794
211.651.645920798323520.00407920167647939
221.651.639717585071350.0102824149286499
231.661.645358227721780.0146417722782158
241.671.655920798323520.0140792016764795
251.681.644933998602930.0350660013970669
261.681.638014936633070.0419850633669295
271.681.647296221933940.0327037780660613
281.681.653296221933940.0267037780660613
291.691.65181172338090.0381882766190999
301.71.664733651332200.0352663486677977
311.71.670374293982640.0296257060173636
321.711.678936864584370.0310631354156273
331.731.695140077836540.0348599221634568
341.731.697140077836540.0328599221634568
351.731.702780720486980.0272192795130227
361.741.705140077836540.0348599221634569
371.741.685950064863790.0540499351362148
381.741.687234216146090.0527657838539069
391.751.688312288194790.0616877118052091
401.781.710718714699130.0692812853008682
411.821.742047069154780.077952930845225
421.831.771375423610420.0586245763895819
431.841.809828919269530.0301710807304660
441.851.843001129627780.00699887037221826
451.861.851001129627780.00899887037221827
461.861.87761076938429-0.017610769384293
471.871.88325141203473-0.0132514120347271
481.871.87740755613212-0.0074075561321226
491.871.88282718291588-0.0128271829158760
501.871.89231454745035-0.0223145474503543
511.871.90159583275122-0.0315958327512226
521.871.89939261949905-0.0293926194990522
531.871.90611133419818-0.0361113341981840
541.881.88622040914080-0.00622040914080454
551.881.867251412034730.0127485879652727
561.871.867610769384290.00238923061570689
571.871.867407556132120.00259244386787736
581.871.861204342879950.00879565712004784
591.871.866844985530390.00315501446961374
601.871.869204342879950.000795657120047871
611.871.850014329907190.0199856700928058

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.58 & 1.65313721185511 & -0.0731372118551079 \tabularnewline
2 & 1.59 & 1.64621814988524 & -0.0562181498852411 \tabularnewline
3 & 1.6 & 1.64729622193394 & -0.0472962219339386 \tabularnewline
4 & 1.6 & 1.65329622193394 & -0.0532962219339386 \tabularnewline
5 & 1.6 & 1.66001493663307 & -0.0600149366330704 \tabularnewline
6 & 1.6 & 1.67293686458437 & -0.0729368645843727 \tabularnewline
7 & 1.61 & 1.67037429398264 & -0.0603742939826363 \tabularnewline
8 & 1.61 & 1.66253043808003 & -0.0525304380800318 \tabularnewline
9 & 1.62 & 1.67053043808003 & -0.0505304380800318 \tabularnewline
10 & 1.63 & 1.66432722482786 & -0.0343272248278615 \tabularnewline
11 & 1.63 & 1.66176465422613 & -0.0317646542261252 \tabularnewline
12 & 1.63 & 1.67232722482786 & -0.0423272248278615 \tabularnewline
13 & 1.63 & 1.65313721185510 & -0.0231372118551036 \tabularnewline
14 & 1.63 & 1.64621814988524 & -0.016218149885241 \tabularnewline
15 & 1.64 & 1.65549943518611 & -0.0154994351861092 \tabularnewline
16 & 1.64 & 1.65329622193394 & -0.0132962219339388 \tabularnewline
17 & 1.64 & 1.66001493663307 & -0.0200149366330706 \tabularnewline
18 & 1.65 & 1.66473365133220 & -0.0147336513322024 \tabularnewline
19 & 1.65 & 1.66217108073047 & -0.012171080730466 \tabularnewline
20 & 1.65 & 1.63792079832352 & 0.0120792016764794 \tabularnewline
21 & 1.65 & 1.64592079832352 & 0.00407920167647939 \tabularnewline
22 & 1.65 & 1.63971758507135 & 0.0102824149286499 \tabularnewline
23 & 1.66 & 1.64535822772178 & 0.0146417722782158 \tabularnewline
24 & 1.67 & 1.65592079832352 & 0.0140792016764795 \tabularnewline
25 & 1.68 & 1.64493399860293 & 0.0350660013970669 \tabularnewline
26 & 1.68 & 1.63801493663307 & 0.0419850633669295 \tabularnewline
27 & 1.68 & 1.64729622193394 & 0.0327037780660613 \tabularnewline
28 & 1.68 & 1.65329622193394 & 0.0267037780660613 \tabularnewline
29 & 1.69 & 1.6518117233809 & 0.0381882766190999 \tabularnewline
30 & 1.7 & 1.66473365133220 & 0.0352663486677977 \tabularnewline
31 & 1.7 & 1.67037429398264 & 0.0296257060173636 \tabularnewline
32 & 1.71 & 1.67893686458437 & 0.0310631354156273 \tabularnewline
33 & 1.73 & 1.69514007783654 & 0.0348599221634568 \tabularnewline
34 & 1.73 & 1.69714007783654 & 0.0328599221634568 \tabularnewline
35 & 1.73 & 1.70278072048698 & 0.0272192795130227 \tabularnewline
36 & 1.74 & 1.70514007783654 & 0.0348599221634569 \tabularnewline
37 & 1.74 & 1.68595006486379 & 0.0540499351362148 \tabularnewline
38 & 1.74 & 1.68723421614609 & 0.0527657838539069 \tabularnewline
39 & 1.75 & 1.68831228819479 & 0.0616877118052091 \tabularnewline
40 & 1.78 & 1.71071871469913 & 0.0692812853008682 \tabularnewline
41 & 1.82 & 1.74204706915478 & 0.077952930845225 \tabularnewline
42 & 1.83 & 1.77137542361042 & 0.0586245763895819 \tabularnewline
43 & 1.84 & 1.80982891926953 & 0.0301710807304660 \tabularnewline
44 & 1.85 & 1.84300112962778 & 0.00699887037221826 \tabularnewline
45 & 1.86 & 1.85100112962778 & 0.00899887037221827 \tabularnewline
46 & 1.86 & 1.87761076938429 & -0.017610769384293 \tabularnewline
47 & 1.87 & 1.88325141203473 & -0.0132514120347271 \tabularnewline
48 & 1.87 & 1.87740755613212 & -0.0074075561321226 \tabularnewline
49 & 1.87 & 1.88282718291588 & -0.0128271829158760 \tabularnewline
50 & 1.87 & 1.89231454745035 & -0.0223145474503543 \tabularnewline
51 & 1.87 & 1.90159583275122 & -0.0315958327512226 \tabularnewline
52 & 1.87 & 1.89939261949905 & -0.0293926194990522 \tabularnewline
53 & 1.87 & 1.90611133419818 & -0.0361113341981840 \tabularnewline
54 & 1.88 & 1.88622040914080 & -0.00622040914080454 \tabularnewline
55 & 1.88 & 1.86725141203473 & 0.0127485879652727 \tabularnewline
56 & 1.87 & 1.86761076938429 & 0.00238923061570689 \tabularnewline
57 & 1.87 & 1.86740755613212 & 0.00259244386787736 \tabularnewline
58 & 1.87 & 1.86120434287995 & 0.00879565712004784 \tabularnewline
59 & 1.87 & 1.86684498553039 & 0.00315501446961374 \tabularnewline
60 & 1.87 & 1.86920434287995 & 0.000795657120047871 \tabularnewline
61 & 1.87 & 1.85001432990719 & 0.0199856700928058 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&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]1.58[/C][C]1.65313721185511[/C][C]-0.0731372118551079[/C][/ROW]
[ROW][C]2[/C][C]1.59[/C][C]1.64621814988524[/C][C]-0.0562181498852411[/C][/ROW]
[ROW][C]3[/C][C]1.6[/C][C]1.64729622193394[/C][C]-0.0472962219339386[/C][/ROW]
[ROW][C]4[/C][C]1.6[/C][C]1.65329622193394[/C][C]-0.0532962219339386[/C][/ROW]
[ROW][C]5[/C][C]1.6[/C][C]1.66001493663307[/C][C]-0.0600149366330704[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.67293686458437[/C][C]-0.0729368645843727[/C][/ROW]
[ROW][C]7[/C][C]1.61[/C][C]1.67037429398264[/C][C]-0.0603742939826363[/C][/ROW]
[ROW][C]8[/C][C]1.61[/C][C]1.66253043808003[/C][C]-0.0525304380800318[/C][/ROW]
[ROW][C]9[/C][C]1.62[/C][C]1.67053043808003[/C][C]-0.0505304380800318[/C][/ROW]
[ROW][C]10[/C][C]1.63[/C][C]1.66432722482786[/C][C]-0.0343272248278615[/C][/ROW]
[ROW][C]11[/C][C]1.63[/C][C]1.66176465422613[/C][C]-0.0317646542261252[/C][/ROW]
[ROW][C]12[/C][C]1.63[/C][C]1.67232722482786[/C][C]-0.0423272248278615[/C][/ROW]
[ROW][C]13[/C][C]1.63[/C][C]1.65313721185510[/C][C]-0.0231372118551036[/C][/ROW]
[ROW][C]14[/C][C]1.63[/C][C]1.64621814988524[/C][C]-0.016218149885241[/C][/ROW]
[ROW][C]15[/C][C]1.64[/C][C]1.65549943518611[/C][C]-0.0154994351861092[/C][/ROW]
[ROW][C]16[/C][C]1.64[/C][C]1.65329622193394[/C][C]-0.0132962219339388[/C][/ROW]
[ROW][C]17[/C][C]1.64[/C][C]1.66001493663307[/C][C]-0.0200149366330706[/C][/ROW]
[ROW][C]18[/C][C]1.65[/C][C]1.66473365133220[/C][C]-0.0147336513322024[/C][/ROW]
[ROW][C]19[/C][C]1.65[/C][C]1.66217108073047[/C][C]-0.012171080730466[/C][/ROW]
[ROW][C]20[/C][C]1.65[/C][C]1.63792079832352[/C][C]0.0120792016764794[/C][/ROW]
[ROW][C]21[/C][C]1.65[/C][C]1.64592079832352[/C][C]0.00407920167647939[/C][/ROW]
[ROW][C]22[/C][C]1.65[/C][C]1.63971758507135[/C][C]0.0102824149286499[/C][/ROW]
[ROW][C]23[/C][C]1.66[/C][C]1.64535822772178[/C][C]0.0146417722782158[/C][/ROW]
[ROW][C]24[/C][C]1.67[/C][C]1.65592079832352[/C][C]0.0140792016764795[/C][/ROW]
[ROW][C]25[/C][C]1.68[/C][C]1.64493399860293[/C][C]0.0350660013970669[/C][/ROW]
[ROW][C]26[/C][C]1.68[/C][C]1.63801493663307[/C][C]0.0419850633669295[/C][/ROW]
[ROW][C]27[/C][C]1.68[/C][C]1.64729622193394[/C][C]0.0327037780660613[/C][/ROW]
[ROW][C]28[/C][C]1.68[/C][C]1.65329622193394[/C][C]0.0267037780660613[/C][/ROW]
[ROW][C]29[/C][C]1.69[/C][C]1.6518117233809[/C][C]0.0381882766190999[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.66473365133220[/C][C]0.0352663486677977[/C][/ROW]
[ROW][C]31[/C][C]1.7[/C][C]1.67037429398264[/C][C]0.0296257060173636[/C][/ROW]
[ROW][C]32[/C][C]1.71[/C][C]1.67893686458437[/C][C]0.0310631354156273[/C][/ROW]
[ROW][C]33[/C][C]1.73[/C][C]1.69514007783654[/C][C]0.0348599221634568[/C][/ROW]
[ROW][C]34[/C][C]1.73[/C][C]1.69714007783654[/C][C]0.0328599221634568[/C][/ROW]
[ROW][C]35[/C][C]1.73[/C][C]1.70278072048698[/C][C]0.0272192795130227[/C][/ROW]
[ROW][C]36[/C][C]1.74[/C][C]1.70514007783654[/C][C]0.0348599221634569[/C][/ROW]
[ROW][C]37[/C][C]1.74[/C][C]1.68595006486379[/C][C]0.0540499351362148[/C][/ROW]
[ROW][C]38[/C][C]1.74[/C][C]1.68723421614609[/C][C]0.0527657838539069[/C][/ROW]
[ROW][C]39[/C][C]1.75[/C][C]1.68831228819479[/C][C]0.0616877118052091[/C][/ROW]
[ROW][C]40[/C][C]1.78[/C][C]1.71071871469913[/C][C]0.0692812853008682[/C][/ROW]
[ROW][C]41[/C][C]1.82[/C][C]1.74204706915478[/C][C]0.077952930845225[/C][/ROW]
[ROW][C]42[/C][C]1.83[/C][C]1.77137542361042[/C][C]0.0586245763895819[/C][/ROW]
[ROW][C]43[/C][C]1.84[/C][C]1.80982891926953[/C][C]0.0301710807304660[/C][/ROW]
[ROW][C]44[/C][C]1.85[/C][C]1.84300112962778[/C][C]0.00699887037221826[/C][/ROW]
[ROW][C]45[/C][C]1.86[/C][C]1.85100112962778[/C][C]0.00899887037221827[/C][/ROW]
[ROW][C]46[/C][C]1.86[/C][C]1.87761076938429[/C][C]-0.017610769384293[/C][/ROW]
[ROW][C]47[/C][C]1.87[/C][C]1.88325141203473[/C][C]-0.0132514120347271[/C][/ROW]
[ROW][C]48[/C][C]1.87[/C][C]1.87740755613212[/C][C]-0.0074075561321226[/C][/ROW]
[ROW][C]49[/C][C]1.87[/C][C]1.88282718291588[/C][C]-0.0128271829158760[/C][/ROW]
[ROW][C]50[/C][C]1.87[/C][C]1.89231454745035[/C][C]-0.0223145474503543[/C][/ROW]
[ROW][C]51[/C][C]1.87[/C][C]1.90159583275122[/C][C]-0.0315958327512226[/C][/ROW]
[ROW][C]52[/C][C]1.87[/C][C]1.89939261949905[/C][C]-0.0293926194990522[/C][/ROW]
[ROW][C]53[/C][C]1.87[/C][C]1.90611133419818[/C][C]-0.0361113341981840[/C][/ROW]
[ROW][C]54[/C][C]1.88[/C][C]1.88622040914080[/C][C]-0.00622040914080454[/C][/ROW]
[ROW][C]55[/C][C]1.88[/C][C]1.86725141203473[/C][C]0.0127485879652727[/C][/ROW]
[ROW][C]56[/C][C]1.87[/C][C]1.86761076938429[/C][C]0.00238923061570689[/C][/ROW]
[ROW][C]57[/C][C]1.87[/C][C]1.86740755613212[/C][C]0.00259244386787736[/C][/ROW]
[ROW][C]58[/C][C]1.87[/C][C]1.86120434287995[/C][C]0.00879565712004784[/C][/ROW]
[ROW][C]59[/C][C]1.87[/C][C]1.86684498553039[/C][C]0.00315501446961374[/C][/ROW]
[ROW][C]60[/C][C]1.87[/C][C]1.86920434287995[/C][C]0.000795657120047871[/C][/ROW]
[ROW][C]61[/C][C]1.87[/C][C]1.85001432990719[/C][C]0.0199856700928058[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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
11.581.65313721185511-0.0731372118551079
21.591.64621814988524-0.0562181498852411
31.61.64729622193394-0.0472962219339386
41.61.65329622193394-0.0532962219339386
51.61.66001493663307-0.0600149366330704
61.61.67293686458437-0.0729368645843727
71.611.67037429398264-0.0603742939826363
81.611.66253043808003-0.0525304380800318
91.621.67053043808003-0.0505304380800318
101.631.66432722482786-0.0343272248278615
111.631.66176465422613-0.0317646542261252
121.631.67232722482786-0.0423272248278615
131.631.65313721185510-0.0231372118551036
141.631.64621814988524-0.016218149885241
151.641.65549943518611-0.0154994351861092
161.641.65329622193394-0.0132962219339388
171.641.66001493663307-0.0200149366330706
181.651.66473365133220-0.0147336513322024
191.651.66217108073047-0.012171080730466
201.651.637920798323520.0120792016764794
211.651.645920798323520.00407920167647939
221.651.639717585071350.0102824149286499
231.661.645358227721780.0146417722782158
241.671.655920798323520.0140792016764795
251.681.644933998602930.0350660013970669
261.681.638014936633070.0419850633669295
271.681.647296221933940.0327037780660613
281.681.653296221933940.0267037780660613
291.691.65181172338090.0381882766190999
301.71.664733651332200.0352663486677977
311.71.670374293982640.0296257060173636
321.711.678936864584370.0310631354156273
331.731.695140077836540.0348599221634568
341.731.697140077836540.0328599221634568
351.731.702780720486980.0272192795130227
361.741.705140077836540.0348599221634569
371.741.685950064863790.0540499351362148
381.741.687234216146090.0527657838539069
391.751.688312288194790.0616877118052091
401.781.710718714699130.0692812853008682
411.821.742047069154780.077952930845225
421.831.771375423610420.0586245763895819
431.841.809828919269530.0301710807304660
441.851.843001129627780.00699887037221826
451.861.851001129627780.00899887037221827
461.861.87761076938429-0.017610769384293
471.871.88325141203473-0.0132514120347271
481.871.87740755613212-0.0074075561321226
491.871.88282718291588-0.0128271829158760
501.871.89231454745035-0.0223145474503543
511.871.90159583275122-0.0315958327512226
521.871.89939261949905-0.0293926194990522
531.871.90611133419818-0.0361113341981840
541.881.88622040914080-0.00622040914080454
551.881.867251412034730.0127485879652727
561.871.867610769384290.00238923061570689
571.871.867407556132120.00259244386787736
581.871.861204342879950.00879565712004784
591.871.866844985530390.00315501446961374
601.871.869204342879950.000795657120047871
611.871.850014329907190.0199856700928058







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7191026435439610.5617947129120790.280897356456039
170.7514211366902330.4971577266195340.248578863309767
180.8938311262963530.2123377474072950.106168873703648
190.9064135520864630.1871728958270740.0935864479135371
200.866972799799670.266054400400660.13302720020033
210.8483510744271270.3032978511457460.151648925572873
220.8206407719727250.3587184560545510.179359228027275
230.778227481626910.443545036746180.22177251837309
240.7564804039177690.4870391921644630.243519596082231
250.8853272801079350.2293454397841290.114672719892064
260.9164453720476450.167109255904710.083554627952355
270.9332277964982530.1335444070034930.0667722035017466
280.9619568602541360.07608627949172910.0380431397458645
290.9716672845684920.05666543086301590.0283327154315080
300.9871793400957430.02564131980851330.0128206599042567
310.9969272904301710.006145419139657170.00307270956982858
320.99957001028120.0008599794376011680.000429989718800584
330.999827303634520.0003453927309588000.000172696365479400
340.999833057385350.0003338852292999680.000166942614649984
350.999895817873440.0002083642531187870.000104182126559394
360.9999416000591130.0001167998817733525.8399940886676e-05
370.9999823302422873.53395154251091e-051.76697577125545e-05
380.9999879898674572.40202650868354e-051.20101325434177e-05
390.9999907138123511.85723752978944e-059.28618764894718e-06
400.999979085851134.18282977424013e-052.09141488712006e-05
410.9999471153145970.0001057693708058265.28846854029131e-05
420.9997039565767840.0005920868464316850.000296043423215842
430.9996302741116360.0007394517767277190.000369725888363860
440.9996799895809220.00064002083815690.00032001041907845
450.9991966406352550.001606718729490270.000803359364745134

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.719102643543961 & 0.561794712912079 & 0.280897356456039 \tabularnewline
17 & 0.751421136690233 & 0.497157726619534 & 0.248578863309767 \tabularnewline
18 & 0.893831126296353 & 0.212337747407295 & 0.106168873703648 \tabularnewline
19 & 0.906413552086463 & 0.187172895827074 & 0.0935864479135371 \tabularnewline
20 & 0.86697279979967 & 0.26605440040066 & 0.13302720020033 \tabularnewline
21 & 0.848351074427127 & 0.303297851145746 & 0.151648925572873 \tabularnewline
22 & 0.820640771972725 & 0.358718456054551 & 0.179359228027275 \tabularnewline
23 & 0.77822748162691 & 0.44354503674618 & 0.22177251837309 \tabularnewline
24 & 0.756480403917769 & 0.487039192164463 & 0.243519596082231 \tabularnewline
25 & 0.885327280107935 & 0.229345439784129 & 0.114672719892064 \tabularnewline
26 & 0.916445372047645 & 0.16710925590471 & 0.083554627952355 \tabularnewline
27 & 0.933227796498253 & 0.133544407003493 & 0.0667722035017466 \tabularnewline
28 & 0.961956860254136 & 0.0760862794917291 & 0.0380431397458645 \tabularnewline
29 & 0.971667284568492 & 0.0566654308630159 & 0.0283327154315080 \tabularnewline
30 & 0.987179340095743 & 0.0256413198085133 & 0.0128206599042567 \tabularnewline
31 & 0.996927290430171 & 0.00614541913965717 & 0.00307270956982858 \tabularnewline
32 & 0.9995700102812 & 0.000859979437601168 & 0.000429989718800584 \tabularnewline
33 & 0.99982730363452 & 0.000345392730958800 & 0.000172696365479400 \tabularnewline
34 & 0.99983305738535 & 0.000333885229299968 & 0.000166942614649984 \tabularnewline
35 & 0.99989581787344 & 0.000208364253118787 & 0.000104182126559394 \tabularnewline
36 & 0.999941600059113 & 0.000116799881773352 & 5.8399940886676e-05 \tabularnewline
37 & 0.999982330242287 & 3.53395154251091e-05 & 1.76697577125545e-05 \tabularnewline
38 & 0.999987989867457 & 2.40202650868354e-05 & 1.20101325434177e-05 \tabularnewline
39 & 0.999990713812351 & 1.85723752978944e-05 & 9.28618764894718e-06 \tabularnewline
40 & 0.99997908585113 & 4.18282977424013e-05 & 2.09141488712006e-05 \tabularnewline
41 & 0.999947115314597 & 0.000105769370805826 & 5.28846854029131e-05 \tabularnewline
42 & 0.999703956576784 & 0.000592086846431685 & 0.000296043423215842 \tabularnewline
43 & 0.999630274111636 & 0.000739451776727719 & 0.000369725888363860 \tabularnewline
44 & 0.999679989580922 & 0.0006400208381569 & 0.00032001041907845 \tabularnewline
45 & 0.999196640635255 & 0.00160671872949027 & 0.000803359364745134 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&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]0.719102643543961[/C][C]0.561794712912079[/C][C]0.280897356456039[/C][/ROW]
[ROW][C]17[/C][C]0.751421136690233[/C][C]0.497157726619534[/C][C]0.248578863309767[/C][/ROW]
[ROW][C]18[/C][C]0.893831126296353[/C][C]0.212337747407295[/C][C]0.106168873703648[/C][/ROW]
[ROW][C]19[/C][C]0.906413552086463[/C][C]0.187172895827074[/C][C]0.0935864479135371[/C][/ROW]
[ROW][C]20[/C][C]0.86697279979967[/C][C]0.26605440040066[/C][C]0.13302720020033[/C][/ROW]
[ROW][C]21[/C][C]0.848351074427127[/C][C]0.303297851145746[/C][C]0.151648925572873[/C][/ROW]
[ROW][C]22[/C][C]0.820640771972725[/C][C]0.358718456054551[/C][C]0.179359228027275[/C][/ROW]
[ROW][C]23[/C][C]0.77822748162691[/C][C]0.44354503674618[/C][C]0.22177251837309[/C][/ROW]
[ROW][C]24[/C][C]0.756480403917769[/C][C]0.487039192164463[/C][C]0.243519596082231[/C][/ROW]
[ROW][C]25[/C][C]0.885327280107935[/C][C]0.229345439784129[/C][C]0.114672719892064[/C][/ROW]
[ROW][C]26[/C][C]0.916445372047645[/C][C]0.16710925590471[/C][C]0.083554627952355[/C][/ROW]
[ROW][C]27[/C][C]0.933227796498253[/C][C]0.133544407003493[/C][C]0.0667722035017466[/C][/ROW]
[ROW][C]28[/C][C]0.961956860254136[/C][C]0.0760862794917291[/C][C]0.0380431397458645[/C][/ROW]
[ROW][C]29[/C][C]0.971667284568492[/C][C]0.0566654308630159[/C][C]0.0283327154315080[/C][/ROW]
[ROW][C]30[/C][C]0.987179340095743[/C][C]0.0256413198085133[/C][C]0.0128206599042567[/C][/ROW]
[ROW][C]31[/C][C]0.996927290430171[/C][C]0.00614541913965717[/C][C]0.00307270956982858[/C][/ROW]
[ROW][C]32[/C][C]0.9995700102812[/C][C]0.000859979437601168[/C][C]0.000429989718800584[/C][/ROW]
[ROW][C]33[/C][C]0.99982730363452[/C][C]0.000345392730958800[/C][C]0.000172696365479400[/C][/ROW]
[ROW][C]34[/C][C]0.99983305738535[/C][C]0.000333885229299968[/C][C]0.000166942614649984[/C][/ROW]
[ROW][C]35[/C][C]0.99989581787344[/C][C]0.000208364253118787[/C][C]0.000104182126559394[/C][/ROW]
[ROW][C]36[/C][C]0.999941600059113[/C][C]0.000116799881773352[/C][C]5.8399940886676e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999982330242287[/C][C]3.53395154251091e-05[/C][C]1.76697577125545e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999987989867457[/C][C]2.40202650868354e-05[/C][C]1.20101325434177e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999990713812351[/C][C]1.85723752978944e-05[/C][C]9.28618764894718e-06[/C][/ROW]
[ROW][C]40[/C][C]0.99997908585113[/C][C]4.18282977424013e-05[/C][C]2.09141488712006e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999947115314597[/C][C]0.000105769370805826[/C][C]5.28846854029131e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999703956576784[/C][C]0.000592086846431685[/C][C]0.000296043423215842[/C][/ROW]
[ROW][C]43[/C][C]0.999630274111636[/C][C]0.000739451776727719[/C][C]0.000369725888363860[/C][/ROW]
[ROW][C]44[/C][C]0.999679989580922[/C][C]0.0006400208381569[/C][C]0.00032001041907845[/C][/ROW]
[ROW][C]45[/C][C]0.999196640635255[/C][C]0.00160671872949027[/C][C]0.000803359364745134[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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
160.7191026435439610.5617947129120790.280897356456039
170.7514211366902330.4971577266195340.248578863309767
180.8938311262963530.2123377474072950.106168873703648
190.9064135520864630.1871728958270740.0935864479135371
200.866972799799670.266054400400660.13302720020033
210.8483510744271270.3032978511457460.151648925572873
220.8206407719727250.3587184560545510.179359228027275
230.778227481626910.443545036746180.22177251837309
240.7564804039177690.4870391921644630.243519596082231
250.8853272801079350.2293454397841290.114672719892064
260.9164453720476450.167109255904710.083554627952355
270.9332277964982530.1335444070034930.0667722035017466
280.9619568602541360.07608627949172910.0380431397458645
290.9716672845684920.05666543086301590.0283327154315080
300.9871793400957430.02564131980851330.0128206599042567
310.9969272904301710.006145419139657170.00307270956982858
320.99957001028120.0008599794376011680.000429989718800584
330.999827303634520.0003453927309588000.000172696365479400
340.999833057385350.0003338852292999680.000166942614649984
350.999895817873440.0002083642531187870.000104182126559394
360.9999416000591130.0001167998817733525.8399940886676e-05
370.9999823302422873.53395154251091e-051.76697577125545e-05
380.9999879898674572.40202650868354e-051.20101325434177e-05
390.9999907138123511.85723752978944e-059.28618764894718e-06
400.999979085851134.18282977424013e-052.09141488712006e-05
410.9999471153145970.0001057693708058265.28846854029131e-05
420.9997039565767840.0005920868464316850.000296043423215842
430.9996302741116360.0007394517767277190.000369725888363860
440.9996799895809220.00064002083815690.00032001041907845
450.9991966406352550.001606718729490270.000803359364745134







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.5NOK
5% type I error level160.533333333333333NOK
10% type I error level180.6NOK

\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 & 15 & 0.5 & NOK \tabularnewline
5% type I error level & 16 & 0.533333333333333 & NOK \tabularnewline
10% type I error level & 18 & 0.6 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58044&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]15[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.533333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.6[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58044&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58044&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 level150.5NOK
5% type I error level160.533333333333333NOK
10% type I error level180.6NOK



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')
}