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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 computationThu, 24 Nov 2011 07:24:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322137493bfd94waj7pdsfdw.htm/, Retrieved Thu, 28 Mar 2024 14:30:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146644, Retrieved Thu, 28 Mar 2024 14:30:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7_Multiple regr...] [2011-11-24 12:24:12] [63b8a9573feb7e5c5af439dd6e45c15a] [Current]
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Dataseries X:
127	13	1235
115	12	1080
127	7	845
150	9	1522
156	6	1047
182	11	1979
156	12	1822
132	10	1253
137	9	1297
113	9	946
137	15	1713
117	11	1024
137	8	1147
153	6	1092
117	13	1152
126	10	1336
170	14	2131
182	8	1550
162	11	1884
184	10	2041
143	6	845
159	9	1483
108	14	1055
175	8	1545
108	6	729
179	9	1792
111	15	1175
187	8	1593
111	7	785
115	7	744
194	5	1356
168	7	1262




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
veilingprijs[t] = -1294.27677878245 + 12.8537823990847Ouderdom[t] + 83.8868925815814Aantal_bieders[t] -2.50850015776336t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
veilingprijs[t] =  -1294.27677878245 +  12.8537823990847Ouderdom[t] +  83.8868925815814Aantal_bieders[t] -2.50850015776336t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]veilingprijs[t] =  -1294.27677878245 +  12.8537823990847Ouderdom[t] +  83.8868925815814Aantal_bieders[t] -2.50850015776336t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&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
veilingprijs[t] = -1294.27677878245 + 12.8537823990847Ouderdom[t] + 83.8868925815814Aantal_bieders[t] -2.50850015776336t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1294.27677878245180.698473-7.162600
Ouderdom12.85378239908470.91494314.048700
Aantal_bieders83.88689258158149.0257439.294200
t-2.508500157763362.694479-0.9310.3598230.179912

\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) & -1294.27677878245 & 180.698473 & -7.1626 & 0 & 0 \tabularnewline
Ouderdom & 12.8537823990847 & 0.914943 & 14.0487 & 0 & 0 \tabularnewline
Aantal_bieders & 83.8868925815814 & 9.025743 & 9.2942 & 0 & 0 \tabularnewline
t & -2.50850015776336 & 2.694479 & -0.931 & 0.359823 & 0.179912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&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]-1294.27677878245[/C][C]180.698473[/C][C]-7.1626[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Ouderdom[/C][C]12.8537823990847[/C][C]0.914943[/C][C]14.0487[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Aantal_bieders[/C][C]83.8868925815814[/C][C]9.025743[/C][C]9.2942[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-2.50850015776336[/C][C]2.694479[/C][C]-0.931[/C][C]0.359823[/C][C]0.179912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&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)-1294.27677878245180.698473-7.162600
Ouderdom12.85378239908470.91494314.048700
Aantal_bieders83.88689258158149.0257439.294200
t-2.508500157763362.694479-0.9310.3598230.179912







Multiple Linear Regression - Regression Statistics
Multiple R0.94634891894696
R-squared0.89557627639208
Adjusted R-squared0.884388020291231
F-TEST (value)80.046100868584
F-TEST (DF numerator)3
F-TEST (DF denominator)28
p-value7.54951656745106e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation133.792468841566
Sum Squared Residuals501211.8921242

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94634891894696 \tabularnewline
R-squared & 0.89557627639208 \tabularnewline
Adjusted R-squared & 0.884388020291231 \tabularnewline
F-TEST (value) & 80.046100868584 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 28 \tabularnewline
p-value & 7.54951656745106e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 133.792468841566 \tabularnewline
Sum Squared Residuals & 501211.8921242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94634891894696[/C][/ROW]
[ROW][C]R-squared[/C][C]0.89557627639208[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.884388020291231[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]80.046100868584[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]28[/C][/ROW]
[ROW][C]p-value[/C][C]7.54951656745106e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]133.792468841566[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]501211.8921242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&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 R0.94634891894696
R-squared0.89557627639208
Adjusted R-squared0.884388020291231
F-TEST (value)80.046100868584
F-TEST (DF numerator)3
F-TEST (DF denominator)28
p-value7.54951656745106e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation133.792468841566
Sum Squared Residuals501211.8921242







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112351426.17468930411-191.174689304109
210801185.53390777575-105.533907775748
3845917.836333499095-72.8363334990947
415221378.73861368344143.261386316557
510471201.69213017544-154.692130175444
619791952.8164353017926.1835646982086
718221699.99648534941122.003514650594
812531221.2234224504531.776577549554
912971199.0969417065297.903058293475
10946888.09766397072857.9023360292722
1117131697.4012968804915.5987031195132
1210241102.2695784147-78.2695784147029
1311471105.1760484938941.8239515061099
1410921140.55428155832-48.5542815583197
1511521262.51786310458-110.517863104576
1613361124.03272679383211.967273206169
1721312022.63822252212108.361777477878
1815501671.05375566389-121.053755663887
1918841663.13028526917220.869714730827
2020411859.51810530969181.481894690308
21845994.456956463129-149.456956463129
2214831449.2696524354733.7303475645344
2310551210.65271283229-155.652712832287
2415451566.02627792371-21.0262779237134
25729534.540571864109194.459428135891
2617921696.3112997861195.688700213893
2711751323.06695198007-148.06695198007
2815931710.23766608168-117.237666081677
29785646.954811011892138.045188988108
30744695.86144045046748.1385595495328
3113561541.02796465724-185.027964657236
3212621372.09490728643-110.094907286432

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1235 & 1426.17468930411 & -191.174689304109 \tabularnewline
2 & 1080 & 1185.53390777575 & -105.533907775748 \tabularnewline
3 & 845 & 917.836333499095 & -72.8363334990947 \tabularnewline
4 & 1522 & 1378.73861368344 & 143.261386316557 \tabularnewline
5 & 1047 & 1201.69213017544 & -154.692130175444 \tabularnewline
6 & 1979 & 1952.81643530179 & 26.1835646982086 \tabularnewline
7 & 1822 & 1699.99648534941 & 122.003514650594 \tabularnewline
8 & 1253 & 1221.22342245045 & 31.776577549554 \tabularnewline
9 & 1297 & 1199.09694170652 & 97.903058293475 \tabularnewline
10 & 946 & 888.097663970728 & 57.9023360292722 \tabularnewline
11 & 1713 & 1697.40129688049 & 15.5987031195132 \tabularnewline
12 & 1024 & 1102.2695784147 & -78.2695784147029 \tabularnewline
13 & 1147 & 1105.17604849389 & 41.8239515061099 \tabularnewline
14 & 1092 & 1140.55428155832 & -48.5542815583197 \tabularnewline
15 & 1152 & 1262.51786310458 & -110.517863104576 \tabularnewline
16 & 1336 & 1124.03272679383 & 211.967273206169 \tabularnewline
17 & 2131 & 2022.63822252212 & 108.361777477878 \tabularnewline
18 & 1550 & 1671.05375566389 & -121.053755663887 \tabularnewline
19 & 1884 & 1663.13028526917 & 220.869714730827 \tabularnewline
20 & 2041 & 1859.51810530969 & 181.481894690308 \tabularnewline
21 & 845 & 994.456956463129 & -149.456956463129 \tabularnewline
22 & 1483 & 1449.26965243547 & 33.7303475645344 \tabularnewline
23 & 1055 & 1210.65271283229 & -155.652712832287 \tabularnewline
24 & 1545 & 1566.02627792371 & -21.0262779237134 \tabularnewline
25 & 729 & 534.540571864109 & 194.459428135891 \tabularnewline
26 & 1792 & 1696.31129978611 & 95.688700213893 \tabularnewline
27 & 1175 & 1323.06695198007 & -148.06695198007 \tabularnewline
28 & 1593 & 1710.23766608168 & -117.237666081677 \tabularnewline
29 & 785 & 646.954811011892 & 138.045188988108 \tabularnewline
30 & 744 & 695.861440450467 & 48.1385595495328 \tabularnewline
31 & 1356 & 1541.02796465724 & -185.027964657236 \tabularnewline
32 & 1262 & 1372.09490728643 & -110.094907286432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&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]1235[/C][C]1426.17468930411[/C][C]-191.174689304109[/C][/ROW]
[ROW][C]2[/C][C]1080[/C][C]1185.53390777575[/C][C]-105.533907775748[/C][/ROW]
[ROW][C]3[/C][C]845[/C][C]917.836333499095[/C][C]-72.8363334990947[/C][/ROW]
[ROW][C]4[/C][C]1522[/C][C]1378.73861368344[/C][C]143.261386316557[/C][/ROW]
[ROW][C]5[/C][C]1047[/C][C]1201.69213017544[/C][C]-154.692130175444[/C][/ROW]
[ROW][C]6[/C][C]1979[/C][C]1952.81643530179[/C][C]26.1835646982086[/C][/ROW]
[ROW][C]7[/C][C]1822[/C][C]1699.99648534941[/C][C]122.003514650594[/C][/ROW]
[ROW][C]8[/C][C]1253[/C][C]1221.22342245045[/C][C]31.776577549554[/C][/ROW]
[ROW][C]9[/C][C]1297[/C][C]1199.09694170652[/C][C]97.903058293475[/C][/ROW]
[ROW][C]10[/C][C]946[/C][C]888.097663970728[/C][C]57.9023360292722[/C][/ROW]
[ROW][C]11[/C][C]1713[/C][C]1697.40129688049[/C][C]15.5987031195132[/C][/ROW]
[ROW][C]12[/C][C]1024[/C][C]1102.2695784147[/C][C]-78.2695784147029[/C][/ROW]
[ROW][C]13[/C][C]1147[/C][C]1105.17604849389[/C][C]41.8239515061099[/C][/ROW]
[ROW][C]14[/C][C]1092[/C][C]1140.55428155832[/C][C]-48.5542815583197[/C][/ROW]
[ROW][C]15[/C][C]1152[/C][C]1262.51786310458[/C][C]-110.517863104576[/C][/ROW]
[ROW][C]16[/C][C]1336[/C][C]1124.03272679383[/C][C]211.967273206169[/C][/ROW]
[ROW][C]17[/C][C]2131[/C][C]2022.63822252212[/C][C]108.361777477878[/C][/ROW]
[ROW][C]18[/C][C]1550[/C][C]1671.05375566389[/C][C]-121.053755663887[/C][/ROW]
[ROW][C]19[/C][C]1884[/C][C]1663.13028526917[/C][C]220.869714730827[/C][/ROW]
[ROW][C]20[/C][C]2041[/C][C]1859.51810530969[/C][C]181.481894690308[/C][/ROW]
[ROW][C]21[/C][C]845[/C][C]994.456956463129[/C][C]-149.456956463129[/C][/ROW]
[ROW][C]22[/C][C]1483[/C][C]1449.26965243547[/C][C]33.7303475645344[/C][/ROW]
[ROW][C]23[/C][C]1055[/C][C]1210.65271283229[/C][C]-155.652712832287[/C][/ROW]
[ROW][C]24[/C][C]1545[/C][C]1566.02627792371[/C][C]-21.0262779237134[/C][/ROW]
[ROW][C]25[/C][C]729[/C][C]534.540571864109[/C][C]194.459428135891[/C][/ROW]
[ROW][C]26[/C][C]1792[/C][C]1696.31129978611[/C][C]95.688700213893[/C][/ROW]
[ROW][C]27[/C][C]1175[/C][C]1323.06695198007[/C][C]-148.06695198007[/C][/ROW]
[ROW][C]28[/C][C]1593[/C][C]1710.23766608168[/C][C]-117.237666081677[/C][/ROW]
[ROW][C]29[/C][C]785[/C][C]646.954811011892[/C][C]138.045188988108[/C][/ROW]
[ROW][C]30[/C][C]744[/C][C]695.861440450467[/C][C]48.1385595495328[/C][/ROW]
[ROW][C]31[/C][C]1356[/C][C]1541.02796465724[/C][C]-185.027964657236[/C][/ROW]
[ROW][C]32[/C][C]1262[/C][C]1372.09490728643[/C][C]-110.094907286432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&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
112351426.17468930411-191.174689304109
210801185.53390777575-105.533907775748
3845917.836333499095-72.8363334990947
415221378.73861368344143.261386316557
510471201.69213017544-154.692130175444
619791952.8164353017926.1835646982086
718221699.99648534941122.003514650594
812531221.2234224504531.776577549554
912971199.0969417065297.903058293475
10946888.09766397072857.9023360292722
1117131697.4012968804915.5987031195132
1210241102.2695784147-78.2695784147029
1311471105.1760484938941.8239515061099
1410921140.55428155832-48.5542815583197
1511521262.51786310458-110.517863104576
1613361124.03272679383211.967273206169
1721312022.63822252212108.361777477878
1815501671.05375566389-121.053755663887
1918841663.13028526917220.869714730827
2020411859.51810530969181.481894690308
21845994.456956463129-149.456956463129
2214831449.2696524354733.7303475645344
2310551210.65271283229-155.652712832287
2415451566.02627792371-21.0262779237134
25729534.540571864109194.459428135891
2617921696.3112997861195.688700213893
2711751323.06695198007-148.06695198007
2815931710.23766608168-117.237666081677
29785646.954811011892138.045188988108
30744695.86144045046748.1385595495328
3113561541.02796465724-185.027964657236
3212621372.09490728643-110.094907286432







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.6163629503723450.767274099255310.383637049627655
80.4821489657902580.9642979315805170.517851034209742
90.3271133306856270.6542266613712540.672886669314373
100.2087150453459770.4174300906919530.791284954654023
110.1786345059371740.3572690118743480.821365494062826
120.1908679744855440.3817359489710880.809132025514456
130.1194347516909450.2388695033818890.880565248309055
140.1257797968601670.2515595937203340.874220203139833
150.1611733283556220.3223466567112430.838826671644378
160.1919165908685180.3838331817370370.808083409131482
170.1325696914853910.2651393829707810.86743030851461
180.2461002736889850.4922005473779690.753899726311015
190.2858000294303850.5716000588607690.714199970569615
200.3901495910960490.7802991821920980.609850408903951
210.7776902078123070.4446195843753870.222309792187693
220.6618023408165360.6763953183669280.338197659183464
230.767290282955680.4654194340886390.23270971704432
240.7054979810462860.5890040379074270.294502018953714
250.7509744849648610.4980510300702780.249025515035139

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.616362950372345 & 0.76727409925531 & 0.383637049627655 \tabularnewline
8 & 0.482148965790258 & 0.964297931580517 & 0.517851034209742 \tabularnewline
9 & 0.327113330685627 & 0.654226661371254 & 0.672886669314373 \tabularnewline
10 & 0.208715045345977 & 0.417430090691953 & 0.791284954654023 \tabularnewline
11 & 0.178634505937174 & 0.357269011874348 & 0.821365494062826 \tabularnewline
12 & 0.190867974485544 & 0.381735948971088 & 0.809132025514456 \tabularnewline
13 & 0.119434751690945 & 0.238869503381889 & 0.880565248309055 \tabularnewline
14 & 0.125779796860167 & 0.251559593720334 & 0.874220203139833 \tabularnewline
15 & 0.161173328355622 & 0.322346656711243 & 0.838826671644378 \tabularnewline
16 & 0.191916590868518 & 0.383833181737037 & 0.808083409131482 \tabularnewline
17 & 0.132569691485391 & 0.265139382970781 & 0.86743030851461 \tabularnewline
18 & 0.246100273688985 & 0.492200547377969 & 0.753899726311015 \tabularnewline
19 & 0.285800029430385 & 0.571600058860769 & 0.714199970569615 \tabularnewline
20 & 0.390149591096049 & 0.780299182192098 & 0.609850408903951 \tabularnewline
21 & 0.777690207812307 & 0.444619584375387 & 0.222309792187693 \tabularnewline
22 & 0.661802340816536 & 0.676395318366928 & 0.338197659183464 \tabularnewline
23 & 0.76729028295568 & 0.465419434088639 & 0.23270971704432 \tabularnewline
24 & 0.705497981046286 & 0.589004037907427 & 0.294502018953714 \tabularnewline
25 & 0.750974484964861 & 0.498051030070278 & 0.249025515035139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&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]7[/C][C]0.616362950372345[/C][C]0.76727409925531[/C][C]0.383637049627655[/C][/ROW]
[ROW][C]8[/C][C]0.482148965790258[/C][C]0.964297931580517[/C][C]0.517851034209742[/C][/ROW]
[ROW][C]9[/C][C]0.327113330685627[/C][C]0.654226661371254[/C][C]0.672886669314373[/C][/ROW]
[ROW][C]10[/C][C]0.208715045345977[/C][C]0.417430090691953[/C][C]0.791284954654023[/C][/ROW]
[ROW][C]11[/C][C]0.178634505937174[/C][C]0.357269011874348[/C][C]0.821365494062826[/C][/ROW]
[ROW][C]12[/C][C]0.190867974485544[/C][C]0.381735948971088[/C][C]0.809132025514456[/C][/ROW]
[ROW][C]13[/C][C]0.119434751690945[/C][C]0.238869503381889[/C][C]0.880565248309055[/C][/ROW]
[ROW][C]14[/C][C]0.125779796860167[/C][C]0.251559593720334[/C][C]0.874220203139833[/C][/ROW]
[ROW][C]15[/C][C]0.161173328355622[/C][C]0.322346656711243[/C][C]0.838826671644378[/C][/ROW]
[ROW][C]16[/C][C]0.191916590868518[/C][C]0.383833181737037[/C][C]0.808083409131482[/C][/ROW]
[ROW][C]17[/C][C]0.132569691485391[/C][C]0.265139382970781[/C][C]0.86743030851461[/C][/ROW]
[ROW][C]18[/C][C]0.246100273688985[/C][C]0.492200547377969[/C][C]0.753899726311015[/C][/ROW]
[ROW][C]19[/C][C]0.285800029430385[/C][C]0.571600058860769[/C][C]0.714199970569615[/C][/ROW]
[ROW][C]20[/C][C]0.390149591096049[/C][C]0.780299182192098[/C][C]0.609850408903951[/C][/ROW]
[ROW][C]21[/C][C]0.777690207812307[/C][C]0.444619584375387[/C][C]0.222309792187693[/C][/ROW]
[ROW][C]22[/C][C]0.661802340816536[/C][C]0.676395318366928[/C][C]0.338197659183464[/C][/ROW]
[ROW][C]23[/C][C]0.76729028295568[/C][C]0.465419434088639[/C][C]0.23270971704432[/C][/ROW]
[ROW][C]24[/C][C]0.705497981046286[/C][C]0.589004037907427[/C][C]0.294502018953714[/C][/ROW]
[ROW][C]25[/C][C]0.750974484964861[/C][C]0.498051030070278[/C][C]0.249025515035139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&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
70.6163629503723450.767274099255310.383637049627655
80.4821489657902580.9642979315805170.517851034209742
90.3271133306856270.6542266613712540.672886669314373
100.2087150453459770.4174300906919530.791284954654023
110.1786345059371740.3572690118743480.821365494062826
120.1908679744855440.3817359489710880.809132025514456
130.1194347516909450.2388695033818890.880565248309055
140.1257797968601670.2515595937203340.874220203139833
150.1611733283556220.3223466567112430.838826671644378
160.1919165908685180.3838331817370370.808083409131482
170.1325696914853910.2651393829707810.86743030851461
180.2461002736889850.4922005473779690.753899726311015
190.2858000294303850.5716000588607690.714199970569615
200.3901495910960490.7802991821920980.609850408903951
210.7776902078123070.4446195843753870.222309792187693
220.6618023408165360.6763953183669280.338197659183464
230.767290282955680.4654194340886390.23270971704432
240.7054979810462860.5890040379074270.294502018953714
250.7509744849648610.4980510300702780.249025515035139







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146644&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146644&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146644&T=6

As an alternative you can also use a QR Code:  

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

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



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