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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 02:03:34 -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/t1258707988rm7jtmmccfjs6ym.htm/, Retrieved Fri, 29 Mar 2024 15:03:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57989, Retrieved Fri, 29 Mar 2024 15:03:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsworkshop 7
Estimated Impact167
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7] [2009-11-20 09:03:34] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataseries X:
0.6348	1.5291
0.634	1.5358
0.62915	1.5355
0.62168	1.5287
0.61328	1.5334
0.6089	1.5225
0.60857	1.5135
0.62672	1.5144
0.62291	1.4913
0.62393	1.4793
0.61838	1.4663
0.62012	1.4749
0.61659	1.4745
0.6116	1.4775
0.61573	1.4678
0.61407	1.4658
0.62823	1.4572
0.64405	1.4721
0.6387	1.4624
0.63633	1.4636
0.63059	1.4649
0.62994	1.465
0.63709	1.4673
0.64217	1.4679
0.65711	1.4621
0.66977	1.4674
0.68255	1.4695
0.68902	1.4964
0.71322	1.5155
0.70224	1.5411
0.70045	1.5476
0.69919	1.54
0.69693	1.5474
0.69763	1.5485
0.69278	1.559
0.70196	1.5544
0.69215	1.5657
0.6769	1.5734
0.67124	1.567
0.66532	1.5547
0.67157	1.54
0.66428	1.5192
0.66576	1.527
0.66942	1.5387
0.6813	1.5431
0.69144	1.5426
0.69862	1.5216
0.695	1.5364
0.69867	1.5469
0.68968	1.5501
0.69233	1.5494
0.68293	1.5475
0.68399	1.5448
0.66895	1.5391
0.68756	1.5578
0.68527	1.5528
0.6776	1.5496
0.68137	1.549
0.67933	1.5449
0.67922	1.5479




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=57989&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=57989&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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
Britse_pond[t] = + 0.261141331976786 + 0.241000719518527Zwitserse_frank[t] + 0.00488968621576873M1[t] -0.000975010761816689M2[t] + 0.000415678146259142M3[t] -0.00451061566544505M4[t] + 0.00190711140066335M5[t] -0.00375862229591785M6[t] -0.00306619760422058M7[t] -0.00108835102738477M8[t] -0.00311442237833564M9[t] -0.00068715391636125M10[t] -0.000232003526077248M11[t] + 0.00114231325047974t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Britse_pond[t] =  +  0.261141331976786 +  0.241000719518527Zwitserse_frank[t] +  0.00488968621576873M1[t] -0.000975010761816689M2[t] +  0.000415678146259142M3[t] -0.00451061566544505M4[t] +  0.00190711140066335M5[t] -0.00375862229591785M6[t] -0.00306619760422058M7[t] -0.00108835102738477M8[t] -0.00311442237833564M9[t] -0.00068715391636125M10[t] -0.000232003526077248M11[t] +  0.00114231325047974t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Britse_pond[t] =  +  0.261141331976786 +  0.241000719518527Zwitserse_frank[t] +  0.00488968621576873M1[t] -0.000975010761816689M2[t] +  0.000415678146259142M3[t] -0.00451061566544505M4[t] +  0.00190711140066335M5[t] -0.00375862229591785M6[t] -0.00306619760422058M7[t] -0.00108835102738477M8[t] -0.00311442237833564M9[t] -0.00068715391636125M10[t] -0.000232003526077248M11[t] +  0.00114231325047974t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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
Britse_pond[t] = + 0.261141331976786 + 0.241000719518527Zwitserse_frank[t] + 0.00488968621576873M1[t] -0.000975010761816689M2[t] + 0.000415678146259142M3[t] -0.00451061566544505M4[t] + 0.00190711140066335M5[t] -0.00375862229591785M6[t] -0.00306619760422058M7[t] -0.00108835102738477M8[t] -0.00311442237833564M9[t] -0.00068715391636125M10[t] -0.000232003526077248M11[t] + 0.00114231325047974t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2611413319767860.1380321.89190.0648110.032406
Zwitserse_frank0.2410007195185270.093582.57540.0132940.006647
M10.004889686215768730.0132630.36870.7140660.357033
M2-0.0009750107618166890.013283-0.07340.9418040.470902
M30.0004156781462591420.0132220.03140.9750570.487528
M4-0.004510615665445050.013202-0.34170.7341680.367084
M50.001907111400663350.0131750.14480.8855390.442769
M6-0.003758622295917850.013158-0.28560.776430.388215
M7-0.003066197604220580.013159-0.2330.8167920.408396
M8-0.001088351027384770.013143-0.08280.9343640.467182
M9-0.003114422378335640.013114-0.23750.8133290.406665
M10-0.000687153916361250.013097-0.05250.9583840.479192
M11-0.0002320035260772480.013094-0.01770.9859410.49297
t0.001142313250479740.0001975.78521e-060

\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) & 0.261141331976786 & 0.138032 & 1.8919 & 0.064811 & 0.032406 \tabularnewline
Zwitserse_frank & 0.241000719518527 & 0.09358 & 2.5754 & 0.013294 & 0.006647 \tabularnewline
M1 & 0.00488968621576873 & 0.013263 & 0.3687 & 0.714066 & 0.357033 \tabularnewline
M2 & -0.000975010761816689 & 0.013283 & -0.0734 & 0.941804 & 0.470902 \tabularnewline
M3 & 0.000415678146259142 & 0.013222 & 0.0314 & 0.975057 & 0.487528 \tabularnewline
M4 & -0.00451061566544505 & 0.013202 & -0.3417 & 0.734168 & 0.367084 \tabularnewline
M5 & 0.00190711140066335 & 0.013175 & 0.1448 & 0.885539 & 0.442769 \tabularnewline
M6 & -0.00375862229591785 & 0.013158 & -0.2856 & 0.77643 & 0.388215 \tabularnewline
M7 & -0.00306619760422058 & 0.013159 & -0.233 & 0.816792 & 0.408396 \tabularnewline
M8 & -0.00108835102738477 & 0.013143 & -0.0828 & 0.934364 & 0.467182 \tabularnewline
M9 & -0.00311442237833564 & 0.013114 & -0.2375 & 0.813329 & 0.406665 \tabularnewline
M10 & -0.00068715391636125 & 0.013097 & -0.0525 & 0.958384 & 0.479192 \tabularnewline
M11 & -0.000232003526077248 & 0.013094 & -0.0177 & 0.985941 & 0.49297 \tabularnewline
t & 0.00114231325047974 & 0.000197 & 5.7852 & 1e-06 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&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]0.261141331976786[/C][C]0.138032[/C][C]1.8919[/C][C]0.064811[/C][C]0.032406[/C][/ROW]
[ROW][C]Zwitserse_frank[/C][C]0.241000719518527[/C][C]0.09358[/C][C]2.5754[/C][C]0.013294[/C][C]0.006647[/C][/ROW]
[ROW][C]M1[/C][C]0.00488968621576873[/C][C]0.013263[/C][C]0.3687[/C][C]0.714066[/C][C]0.357033[/C][/ROW]
[ROW][C]M2[/C][C]-0.000975010761816689[/C][C]0.013283[/C][C]-0.0734[/C][C]0.941804[/C][C]0.470902[/C][/ROW]
[ROW][C]M3[/C][C]0.000415678146259142[/C][C]0.013222[/C][C]0.0314[/C][C]0.975057[/C][C]0.487528[/C][/ROW]
[ROW][C]M4[/C][C]-0.00451061566544505[/C][C]0.013202[/C][C]-0.3417[/C][C]0.734168[/C][C]0.367084[/C][/ROW]
[ROW][C]M5[/C][C]0.00190711140066335[/C][C]0.013175[/C][C]0.1448[/C][C]0.885539[/C][C]0.442769[/C][/ROW]
[ROW][C]M6[/C][C]-0.00375862229591785[/C][C]0.013158[/C][C]-0.2856[/C][C]0.77643[/C][C]0.388215[/C][/ROW]
[ROW][C]M7[/C][C]-0.00306619760422058[/C][C]0.013159[/C][C]-0.233[/C][C]0.816792[/C][C]0.408396[/C][/ROW]
[ROW][C]M8[/C][C]-0.00108835102738477[/C][C]0.013143[/C][C]-0.0828[/C][C]0.934364[/C][C]0.467182[/C][/ROW]
[ROW][C]M9[/C][C]-0.00311442237833564[/C][C]0.013114[/C][C]-0.2375[/C][C]0.813329[/C][C]0.406665[/C][/ROW]
[ROW][C]M10[/C][C]-0.00068715391636125[/C][C]0.013097[/C][C]-0.0525[/C][C]0.958384[/C][C]0.479192[/C][/ROW]
[ROW][C]M11[/C][C]-0.000232003526077248[/C][C]0.013094[/C][C]-0.0177[/C][C]0.985941[/C][C]0.49297[/C][/ROW]
[ROW][C]t[/C][C]0.00114231325047974[/C][C]0.000197[/C][C]5.7852[/C][C]1e-06[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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)0.2611413319767860.1380321.89190.0648110.032406
Zwitserse_frank0.2410007195185270.093582.57540.0132940.006647
M10.004889686215768730.0132630.36870.7140660.357033
M2-0.0009750107618166890.013283-0.07340.9418040.470902
M30.0004156781462591420.0132220.03140.9750570.487528
M4-0.004510615665445050.013202-0.34170.7341680.367084
M50.001907111400663350.0131750.14480.8855390.442769
M6-0.003758622295917850.013158-0.28560.776430.388215
M7-0.003066197604220580.013159-0.2330.8167920.408396
M8-0.001088351027384770.013143-0.08280.9343640.467182
M9-0.003114422378335640.013114-0.23750.8133290.406665
M10-0.000687153916361250.013097-0.05250.9583840.479192
M11-0.0002320035260772480.013094-0.01770.9859410.49297
t0.001142313250479740.0001975.78521e-060







Multiple Linear Regression - Regression Statistics
Multiple R0.818420057509127
R-squared0.669811390533243
Adjusted R-squared0.576497218292637
F-TEST (value)7.17802424272887
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value2.32630924967836e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0206969578926357
Sum Squared Residuals0.0197047470364387

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.818420057509127 \tabularnewline
R-squared & 0.669811390533243 \tabularnewline
Adjusted R-squared & 0.576497218292637 \tabularnewline
F-TEST (value) & 7.17802424272887 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 2.32630924967836e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0206969578926357 \tabularnewline
Sum Squared Residuals & 0.0197047470364387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.818420057509127[/C][/ROW]
[ROW][C]R-squared[/C][C]0.669811390533243[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.576497218292637[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.17802424272887[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]2.32630924967836e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0206969578926357[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0197047470364387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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 R0.818420057509127
R-squared0.669811390533243
Adjusted R-squared0.576497218292637
F-TEST (value)7.17802424272887
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value2.32630924967836e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0206969578926357
Sum Squared Residuals0.0197047470364387







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.63480.635687531658816-0.000887531658816348
20.6340.6325798527524830.00142014724751673
30.629150.635040554695183-0.00589055469518334
40.621680.629617769241233-0.00793776924123293
50.613280.638310512939558-0.0250305129395581
60.60890.631160184650705-0.0222601846507047
70.608570.630825916117215-0.0222559161172149
80.626720.634162976592097-0.00744297659209714
90.622910.627712101870748-0.00480210187074816
100.623930.62838967494898-0.00445967494897994
110.618380.626854129236003-0.00847412923600273
120.620120.63030105220042-0.0101810522004191
130.616590.63623665137886-0.0196466513788602
140.61160.63223726981031-0.0206372698103101
150.615730.632432564989536-0.0167025649895359
160.614070.628166582989274-0.0140965829892745
170.628230.633654017118003-0.00542401711800331
180.644050.6327215073927280.0113284926072722
190.63870.6322185383555750.0064814616444249
200.636330.6356278990463130.000702100953687009
210.630590.635057441881216-0.00446744188121595
220.629940.638651123665622-0.00871112366562185
230.637090.640802888961278-0.00371288896127822
240.642170.642321806169546-0.000151806169546329
250.657110.6469560014625870.0101539985374126
260.669770.643510921548930.0262590784510701
270.682550.6465500252184740.0359999747815256
280.689020.6492489640122980.0397710359877017
290.713220.661412118071690.0518078819283096
300.702240.6630583160452630.0391816839547368
310.700450.666459558664310.0339904413356894
320.699190.6677481130232850.0314418869767146
330.696930.6686477602472510.0282822397527487
340.697630.6724824427511760.0251475572488241
350.692780.6766104139468840.0161695860531158
360.701960.6768761274136560.0250838725863441
370.692150.6856314350104640.00651856498953627
380.67690.68276475682365-0.00586475682365074
390.671240.683755354377288-0.0125153543772878
400.665320.677007064965985-0.0116870649659854
410.671570.681024394705651-0.00945439470565118
420.664280.671488159293564-0.0072081592935644
430.665760.675202702847986-0.00944270284798583
440.669420.681142571093668-0.0117225710936682
450.68130.681319216159079-1.92161590785556e-05
460.691440.6847682975117730.0066717024882266
470.698620.6813047460426480.0173152539573519
480.6950.686245873468080.0087541265319206
490.698670.6948083804892720.00386161951072767
500.689680.690857199064626-0.00117719906462599
510.692330.693221500719519-0.000891500719518575
520.682930.688979618791209-0.00604961879120892
530.683990.695888957165097-0.0118989571650970
540.668950.68999183261774-0.0210418326177399
550.687560.696333284014913-0.00877328401491351
560.685270.698248440244636-0.0129784402446363
570.67760.696593479841706-0.018993479841706
580.681370.700018461122449-0.0186484611224489
590.679330.700627821813187-0.0212978218131867
600.679220.7027251407483-0.0235051407482993

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.6348 & 0.635687531658816 & -0.000887531658816348 \tabularnewline
2 & 0.634 & 0.632579852752483 & 0.00142014724751673 \tabularnewline
3 & 0.62915 & 0.635040554695183 & -0.00589055469518334 \tabularnewline
4 & 0.62168 & 0.629617769241233 & -0.00793776924123293 \tabularnewline
5 & 0.61328 & 0.638310512939558 & -0.0250305129395581 \tabularnewline
6 & 0.6089 & 0.631160184650705 & -0.0222601846507047 \tabularnewline
7 & 0.60857 & 0.630825916117215 & -0.0222559161172149 \tabularnewline
8 & 0.62672 & 0.634162976592097 & -0.00744297659209714 \tabularnewline
9 & 0.62291 & 0.627712101870748 & -0.00480210187074816 \tabularnewline
10 & 0.62393 & 0.62838967494898 & -0.00445967494897994 \tabularnewline
11 & 0.61838 & 0.626854129236003 & -0.00847412923600273 \tabularnewline
12 & 0.62012 & 0.63030105220042 & -0.0101810522004191 \tabularnewline
13 & 0.61659 & 0.63623665137886 & -0.0196466513788602 \tabularnewline
14 & 0.6116 & 0.63223726981031 & -0.0206372698103101 \tabularnewline
15 & 0.61573 & 0.632432564989536 & -0.0167025649895359 \tabularnewline
16 & 0.61407 & 0.628166582989274 & -0.0140965829892745 \tabularnewline
17 & 0.62823 & 0.633654017118003 & -0.00542401711800331 \tabularnewline
18 & 0.64405 & 0.632721507392728 & 0.0113284926072722 \tabularnewline
19 & 0.6387 & 0.632218538355575 & 0.0064814616444249 \tabularnewline
20 & 0.63633 & 0.635627899046313 & 0.000702100953687009 \tabularnewline
21 & 0.63059 & 0.635057441881216 & -0.00446744188121595 \tabularnewline
22 & 0.62994 & 0.638651123665622 & -0.00871112366562185 \tabularnewline
23 & 0.63709 & 0.640802888961278 & -0.00371288896127822 \tabularnewline
24 & 0.64217 & 0.642321806169546 & -0.000151806169546329 \tabularnewline
25 & 0.65711 & 0.646956001462587 & 0.0101539985374126 \tabularnewline
26 & 0.66977 & 0.64351092154893 & 0.0262590784510701 \tabularnewline
27 & 0.68255 & 0.646550025218474 & 0.0359999747815256 \tabularnewline
28 & 0.68902 & 0.649248964012298 & 0.0397710359877017 \tabularnewline
29 & 0.71322 & 0.66141211807169 & 0.0518078819283096 \tabularnewline
30 & 0.70224 & 0.663058316045263 & 0.0391816839547368 \tabularnewline
31 & 0.70045 & 0.66645955866431 & 0.0339904413356894 \tabularnewline
32 & 0.69919 & 0.667748113023285 & 0.0314418869767146 \tabularnewline
33 & 0.69693 & 0.668647760247251 & 0.0282822397527487 \tabularnewline
34 & 0.69763 & 0.672482442751176 & 0.0251475572488241 \tabularnewline
35 & 0.69278 & 0.676610413946884 & 0.0161695860531158 \tabularnewline
36 & 0.70196 & 0.676876127413656 & 0.0250838725863441 \tabularnewline
37 & 0.69215 & 0.685631435010464 & 0.00651856498953627 \tabularnewline
38 & 0.6769 & 0.68276475682365 & -0.00586475682365074 \tabularnewline
39 & 0.67124 & 0.683755354377288 & -0.0125153543772878 \tabularnewline
40 & 0.66532 & 0.677007064965985 & -0.0116870649659854 \tabularnewline
41 & 0.67157 & 0.681024394705651 & -0.00945439470565118 \tabularnewline
42 & 0.66428 & 0.671488159293564 & -0.0072081592935644 \tabularnewline
43 & 0.66576 & 0.675202702847986 & -0.00944270284798583 \tabularnewline
44 & 0.66942 & 0.681142571093668 & -0.0117225710936682 \tabularnewline
45 & 0.6813 & 0.681319216159079 & -1.92161590785556e-05 \tabularnewline
46 & 0.69144 & 0.684768297511773 & 0.0066717024882266 \tabularnewline
47 & 0.69862 & 0.681304746042648 & 0.0173152539573519 \tabularnewline
48 & 0.695 & 0.68624587346808 & 0.0087541265319206 \tabularnewline
49 & 0.69867 & 0.694808380489272 & 0.00386161951072767 \tabularnewline
50 & 0.68968 & 0.690857199064626 & -0.00117719906462599 \tabularnewline
51 & 0.69233 & 0.693221500719519 & -0.000891500719518575 \tabularnewline
52 & 0.68293 & 0.688979618791209 & -0.00604961879120892 \tabularnewline
53 & 0.68399 & 0.695888957165097 & -0.0118989571650970 \tabularnewline
54 & 0.66895 & 0.68999183261774 & -0.0210418326177399 \tabularnewline
55 & 0.68756 & 0.696333284014913 & -0.00877328401491351 \tabularnewline
56 & 0.68527 & 0.698248440244636 & -0.0129784402446363 \tabularnewline
57 & 0.6776 & 0.696593479841706 & -0.018993479841706 \tabularnewline
58 & 0.68137 & 0.700018461122449 & -0.0186484611224489 \tabularnewline
59 & 0.67933 & 0.700627821813187 & -0.0212978218131867 \tabularnewline
60 & 0.67922 & 0.7027251407483 & -0.0235051407482993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&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]0.6348[/C][C]0.635687531658816[/C][C]-0.000887531658816348[/C][/ROW]
[ROW][C]2[/C][C]0.634[/C][C]0.632579852752483[/C][C]0.00142014724751673[/C][/ROW]
[ROW][C]3[/C][C]0.62915[/C][C]0.635040554695183[/C][C]-0.00589055469518334[/C][/ROW]
[ROW][C]4[/C][C]0.62168[/C][C]0.629617769241233[/C][C]-0.00793776924123293[/C][/ROW]
[ROW][C]5[/C][C]0.61328[/C][C]0.638310512939558[/C][C]-0.0250305129395581[/C][/ROW]
[ROW][C]6[/C][C]0.6089[/C][C]0.631160184650705[/C][C]-0.0222601846507047[/C][/ROW]
[ROW][C]7[/C][C]0.60857[/C][C]0.630825916117215[/C][C]-0.0222559161172149[/C][/ROW]
[ROW][C]8[/C][C]0.62672[/C][C]0.634162976592097[/C][C]-0.00744297659209714[/C][/ROW]
[ROW][C]9[/C][C]0.62291[/C][C]0.627712101870748[/C][C]-0.00480210187074816[/C][/ROW]
[ROW][C]10[/C][C]0.62393[/C][C]0.62838967494898[/C][C]-0.00445967494897994[/C][/ROW]
[ROW][C]11[/C][C]0.61838[/C][C]0.626854129236003[/C][C]-0.00847412923600273[/C][/ROW]
[ROW][C]12[/C][C]0.62012[/C][C]0.63030105220042[/C][C]-0.0101810522004191[/C][/ROW]
[ROW][C]13[/C][C]0.61659[/C][C]0.63623665137886[/C][C]-0.0196466513788602[/C][/ROW]
[ROW][C]14[/C][C]0.6116[/C][C]0.63223726981031[/C][C]-0.0206372698103101[/C][/ROW]
[ROW][C]15[/C][C]0.61573[/C][C]0.632432564989536[/C][C]-0.0167025649895359[/C][/ROW]
[ROW][C]16[/C][C]0.61407[/C][C]0.628166582989274[/C][C]-0.0140965829892745[/C][/ROW]
[ROW][C]17[/C][C]0.62823[/C][C]0.633654017118003[/C][C]-0.00542401711800331[/C][/ROW]
[ROW][C]18[/C][C]0.64405[/C][C]0.632721507392728[/C][C]0.0113284926072722[/C][/ROW]
[ROW][C]19[/C][C]0.6387[/C][C]0.632218538355575[/C][C]0.0064814616444249[/C][/ROW]
[ROW][C]20[/C][C]0.63633[/C][C]0.635627899046313[/C][C]0.000702100953687009[/C][/ROW]
[ROW][C]21[/C][C]0.63059[/C][C]0.635057441881216[/C][C]-0.00446744188121595[/C][/ROW]
[ROW][C]22[/C][C]0.62994[/C][C]0.638651123665622[/C][C]-0.00871112366562185[/C][/ROW]
[ROW][C]23[/C][C]0.63709[/C][C]0.640802888961278[/C][C]-0.00371288896127822[/C][/ROW]
[ROW][C]24[/C][C]0.64217[/C][C]0.642321806169546[/C][C]-0.000151806169546329[/C][/ROW]
[ROW][C]25[/C][C]0.65711[/C][C]0.646956001462587[/C][C]0.0101539985374126[/C][/ROW]
[ROW][C]26[/C][C]0.66977[/C][C]0.64351092154893[/C][C]0.0262590784510701[/C][/ROW]
[ROW][C]27[/C][C]0.68255[/C][C]0.646550025218474[/C][C]0.0359999747815256[/C][/ROW]
[ROW][C]28[/C][C]0.68902[/C][C]0.649248964012298[/C][C]0.0397710359877017[/C][/ROW]
[ROW][C]29[/C][C]0.71322[/C][C]0.66141211807169[/C][C]0.0518078819283096[/C][/ROW]
[ROW][C]30[/C][C]0.70224[/C][C]0.663058316045263[/C][C]0.0391816839547368[/C][/ROW]
[ROW][C]31[/C][C]0.70045[/C][C]0.66645955866431[/C][C]0.0339904413356894[/C][/ROW]
[ROW][C]32[/C][C]0.69919[/C][C]0.667748113023285[/C][C]0.0314418869767146[/C][/ROW]
[ROW][C]33[/C][C]0.69693[/C][C]0.668647760247251[/C][C]0.0282822397527487[/C][/ROW]
[ROW][C]34[/C][C]0.69763[/C][C]0.672482442751176[/C][C]0.0251475572488241[/C][/ROW]
[ROW][C]35[/C][C]0.69278[/C][C]0.676610413946884[/C][C]0.0161695860531158[/C][/ROW]
[ROW][C]36[/C][C]0.70196[/C][C]0.676876127413656[/C][C]0.0250838725863441[/C][/ROW]
[ROW][C]37[/C][C]0.69215[/C][C]0.685631435010464[/C][C]0.00651856498953627[/C][/ROW]
[ROW][C]38[/C][C]0.6769[/C][C]0.68276475682365[/C][C]-0.00586475682365074[/C][/ROW]
[ROW][C]39[/C][C]0.67124[/C][C]0.683755354377288[/C][C]-0.0125153543772878[/C][/ROW]
[ROW][C]40[/C][C]0.66532[/C][C]0.677007064965985[/C][C]-0.0116870649659854[/C][/ROW]
[ROW][C]41[/C][C]0.67157[/C][C]0.681024394705651[/C][C]-0.00945439470565118[/C][/ROW]
[ROW][C]42[/C][C]0.66428[/C][C]0.671488159293564[/C][C]-0.0072081592935644[/C][/ROW]
[ROW][C]43[/C][C]0.66576[/C][C]0.675202702847986[/C][C]-0.00944270284798583[/C][/ROW]
[ROW][C]44[/C][C]0.66942[/C][C]0.681142571093668[/C][C]-0.0117225710936682[/C][/ROW]
[ROW][C]45[/C][C]0.6813[/C][C]0.681319216159079[/C][C]-1.92161590785556e-05[/C][/ROW]
[ROW][C]46[/C][C]0.69144[/C][C]0.684768297511773[/C][C]0.0066717024882266[/C][/ROW]
[ROW][C]47[/C][C]0.69862[/C][C]0.681304746042648[/C][C]0.0173152539573519[/C][/ROW]
[ROW][C]48[/C][C]0.695[/C][C]0.68624587346808[/C][C]0.0087541265319206[/C][/ROW]
[ROW][C]49[/C][C]0.69867[/C][C]0.694808380489272[/C][C]0.00386161951072767[/C][/ROW]
[ROW][C]50[/C][C]0.68968[/C][C]0.690857199064626[/C][C]-0.00117719906462599[/C][/ROW]
[ROW][C]51[/C][C]0.69233[/C][C]0.693221500719519[/C][C]-0.000891500719518575[/C][/ROW]
[ROW][C]52[/C][C]0.68293[/C][C]0.688979618791209[/C][C]-0.00604961879120892[/C][/ROW]
[ROW][C]53[/C][C]0.68399[/C][C]0.695888957165097[/C][C]-0.0118989571650970[/C][/ROW]
[ROW][C]54[/C][C]0.66895[/C][C]0.68999183261774[/C][C]-0.0210418326177399[/C][/ROW]
[ROW][C]55[/C][C]0.68756[/C][C]0.696333284014913[/C][C]-0.00877328401491351[/C][/ROW]
[ROW][C]56[/C][C]0.68527[/C][C]0.698248440244636[/C][C]-0.0129784402446363[/C][/ROW]
[ROW][C]57[/C][C]0.6776[/C][C]0.696593479841706[/C][C]-0.018993479841706[/C][/ROW]
[ROW][C]58[/C][C]0.68137[/C][C]0.700018461122449[/C][C]-0.0186484611224489[/C][/ROW]
[ROW][C]59[/C][C]0.67933[/C][C]0.700627821813187[/C][C]-0.0212978218131867[/C][/ROW]
[ROW][C]60[/C][C]0.67922[/C][C]0.7027251407483[/C][C]-0.0235051407482993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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
10.63480.635687531658816-0.000887531658816348
20.6340.6325798527524830.00142014724751673
30.629150.635040554695183-0.00589055469518334
40.621680.629617769241233-0.00793776924123293
50.613280.638310512939558-0.0250305129395581
60.60890.631160184650705-0.0222601846507047
70.608570.630825916117215-0.0222559161172149
80.626720.634162976592097-0.00744297659209714
90.622910.627712101870748-0.00480210187074816
100.623930.62838967494898-0.00445967494897994
110.618380.626854129236003-0.00847412923600273
120.620120.63030105220042-0.0101810522004191
130.616590.63623665137886-0.0196466513788602
140.61160.63223726981031-0.0206372698103101
150.615730.632432564989536-0.0167025649895359
160.614070.628166582989274-0.0140965829892745
170.628230.633654017118003-0.00542401711800331
180.644050.6327215073927280.0113284926072722
190.63870.6322185383555750.0064814616444249
200.636330.6356278990463130.000702100953687009
210.630590.635057441881216-0.00446744188121595
220.629940.638651123665622-0.00871112366562185
230.637090.640802888961278-0.00371288896127822
240.642170.642321806169546-0.000151806169546329
250.657110.6469560014625870.0101539985374126
260.669770.643510921548930.0262590784510701
270.682550.6465500252184740.0359999747815256
280.689020.6492489640122980.0397710359877017
290.713220.661412118071690.0518078819283096
300.702240.6630583160452630.0391816839547368
310.700450.666459558664310.0339904413356894
320.699190.6677481130232850.0314418869767146
330.696930.6686477602472510.0282822397527487
340.697630.6724824427511760.0251475572488241
350.692780.6766104139468840.0161695860531158
360.701960.6768761274136560.0250838725863441
370.692150.6856314350104640.00651856498953627
380.67690.68276475682365-0.00586475682365074
390.671240.683755354377288-0.0125153543772878
400.665320.677007064965985-0.0116870649659854
410.671570.681024394705651-0.00945439470565118
420.664280.671488159293564-0.0072081592935644
430.665760.675202702847986-0.00944270284798583
440.669420.681142571093668-0.0117225710936682
450.68130.681319216159079-1.92161590785556e-05
460.691440.6847682975117730.0066717024882266
470.698620.6813047460426480.0173152539573519
480.6950.686245873468080.0087541265319206
490.698670.6948083804892720.00386161951072767
500.689680.690857199064626-0.00117719906462599
510.692330.693221500719519-0.000891500719518575
520.682930.688979618791209-0.00604961879120892
530.683990.695888957165097-0.0118989571650970
540.668950.68999183261774-0.0210418326177399
550.687560.696333284014913-0.00877328401491351
560.685270.698248440244636-0.0129784402446363
570.67760.696593479841706-0.018993479841706
580.681370.700018461122449-0.0186484611224489
590.679330.700627821813187-0.0212978218131867
600.679220.7027251407483-0.0235051407482993







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.04137849452453260.08275698904906520.958621505475467
180.5701041676881760.859791664623650.429895832311824
190.550870199660420.8982596006791610.449129800339580
200.4278354447927910.8556708895855820.572164555207209
210.3593401707694280.7186803415388550.640659829230572
220.3391697081768230.6783394163536470.660830291823177
230.3447113877338750.689422775467750.655288612266125
240.4285645986960340.8571291973920690.571435401303966
250.5079417232096320.9841165535807350.492058276790368
260.5906632393193940.8186735213612120.409336760680606
270.6751630095281250.649673980943750.324836990471875
280.6264488491291520.7471023017416950.373551150870848
290.6728916660235590.6542166679528810.327108333976441
300.7647339824793760.4705320350412480.235266017520624
310.7878905710988820.4242188578022350.212109428901118
320.806347278175090.3873054436498190.193652721824910
330.8220232395611950.3559535208776090.177976760438804
340.8070976780248530.3858046439502930.192902321975147
350.7938359027934810.4123281944130380.206164097206519
360.8616378154663570.2767243690672860.138362184533643
370.870576563416420.258846873167160.12942343658358
380.8894306932058620.2211386135882770.110569306794138
390.9021929313104640.1956141373790710.0978070686895356
400.9155963319543140.1688073360913720.084403668045686
410.9162368485103650.167526302979270.083763151489635
420.8492479299627060.3015041400745880.150752070037294
430.8463766939447040.3072466121105930.153623306055296

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0413784945245326 & 0.0827569890490652 & 0.958621505475467 \tabularnewline
18 & 0.570104167688176 & 0.85979166462365 & 0.429895832311824 \tabularnewline
19 & 0.55087019966042 & 0.898259600679161 & 0.449129800339580 \tabularnewline
20 & 0.427835444792791 & 0.855670889585582 & 0.572164555207209 \tabularnewline
21 & 0.359340170769428 & 0.718680341538855 & 0.640659829230572 \tabularnewline
22 & 0.339169708176823 & 0.678339416353647 & 0.660830291823177 \tabularnewline
23 & 0.344711387733875 & 0.68942277546775 & 0.655288612266125 \tabularnewline
24 & 0.428564598696034 & 0.857129197392069 & 0.571435401303966 \tabularnewline
25 & 0.507941723209632 & 0.984116553580735 & 0.492058276790368 \tabularnewline
26 & 0.590663239319394 & 0.818673521361212 & 0.409336760680606 \tabularnewline
27 & 0.675163009528125 & 0.64967398094375 & 0.324836990471875 \tabularnewline
28 & 0.626448849129152 & 0.747102301741695 & 0.373551150870848 \tabularnewline
29 & 0.672891666023559 & 0.654216667952881 & 0.327108333976441 \tabularnewline
30 & 0.764733982479376 & 0.470532035041248 & 0.235266017520624 \tabularnewline
31 & 0.787890571098882 & 0.424218857802235 & 0.212109428901118 \tabularnewline
32 & 0.80634727817509 & 0.387305443649819 & 0.193652721824910 \tabularnewline
33 & 0.822023239561195 & 0.355953520877609 & 0.177976760438804 \tabularnewline
34 & 0.807097678024853 & 0.385804643950293 & 0.192902321975147 \tabularnewline
35 & 0.793835902793481 & 0.412328194413038 & 0.206164097206519 \tabularnewline
36 & 0.861637815466357 & 0.276724369067286 & 0.138362184533643 \tabularnewline
37 & 0.87057656341642 & 0.25884687316716 & 0.12942343658358 \tabularnewline
38 & 0.889430693205862 & 0.221138613588277 & 0.110569306794138 \tabularnewline
39 & 0.902192931310464 & 0.195614137379071 & 0.0978070686895356 \tabularnewline
40 & 0.915596331954314 & 0.168807336091372 & 0.084403668045686 \tabularnewline
41 & 0.916236848510365 & 0.16752630297927 & 0.083763151489635 \tabularnewline
42 & 0.849247929962706 & 0.301504140074588 & 0.150752070037294 \tabularnewline
43 & 0.846376693944704 & 0.307246612110593 & 0.153623306055296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&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]17[/C][C]0.0413784945245326[/C][C]0.0827569890490652[/C][C]0.958621505475467[/C][/ROW]
[ROW][C]18[/C][C]0.570104167688176[/C][C]0.85979166462365[/C][C]0.429895832311824[/C][/ROW]
[ROW][C]19[/C][C]0.55087019966042[/C][C]0.898259600679161[/C][C]0.449129800339580[/C][/ROW]
[ROW][C]20[/C][C]0.427835444792791[/C][C]0.855670889585582[/C][C]0.572164555207209[/C][/ROW]
[ROW][C]21[/C][C]0.359340170769428[/C][C]0.718680341538855[/C][C]0.640659829230572[/C][/ROW]
[ROW][C]22[/C][C]0.339169708176823[/C][C]0.678339416353647[/C][C]0.660830291823177[/C][/ROW]
[ROW][C]23[/C][C]0.344711387733875[/C][C]0.68942277546775[/C][C]0.655288612266125[/C][/ROW]
[ROW][C]24[/C][C]0.428564598696034[/C][C]0.857129197392069[/C][C]0.571435401303966[/C][/ROW]
[ROW][C]25[/C][C]0.507941723209632[/C][C]0.984116553580735[/C][C]0.492058276790368[/C][/ROW]
[ROW][C]26[/C][C]0.590663239319394[/C][C]0.818673521361212[/C][C]0.409336760680606[/C][/ROW]
[ROW][C]27[/C][C]0.675163009528125[/C][C]0.64967398094375[/C][C]0.324836990471875[/C][/ROW]
[ROW][C]28[/C][C]0.626448849129152[/C][C]0.747102301741695[/C][C]0.373551150870848[/C][/ROW]
[ROW][C]29[/C][C]0.672891666023559[/C][C]0.654216667952881[/C][C]0.327108333976441[/C][/ROW]
[ROW][C]30[/C][C]0.764733982479376[/C][C]0.470532035041248[/C][C]0.235266017520624[/C][/ROW]
[ROW][C]31[/C][C]0.787890571098882[/C][C]0.424218857802235[/C][C]0.212109428901118[/C][/ROW]
[ROW][C]32[/C][C]0.80634727817509[/C][C]0.387305443649819[/C][C]0.193652721824910[/C][/ROW]
[ROW][C]33[/C][C]0.822023239561195[/C][C]0.355953520877609[/C][C]0.177976760438804[/C][/ROW]
[ROW][C]34[/C][C]0.807097678024853[/C][C]0.385804643950293[/C][C]0.192902321975147[/C][/ROW]
[ROW][C]35[/C][C]0.793835902793481[/C][C]0.412328194413038[/C][C]0.206164097206519[/C][/ROW]
[ROW][C]36[/C][C]0.861637815466357[/C][C]0.276724369067286[/C][C]0.138362184533643[/C][/ROW]
[ROW][C]37[/C][C]0.87057656341642[/C][C]0.25884687316716[/C][C]0.12942343658358[/C][/ROW]
[ROW][C]38[/C][C]0.889430693205862[/C][C]0.221138613588277[/C][C]0.110569306794138[/C][/ROW]
[ROW][C]39[/C][C]0.902192931310464[/C][C]0.195614137379071[/C][C]0.0978070686895356[/C][/ROW]
[ROW][C]40[/C][C]0.915596331954314[/C][C]0.168807336091372[/C][C]0.084403668045686[/C][/ROW]
[ROW][C]41[/C][C]0.916236848510365[/C][C]0.16752630297927[/C][C]0.083763151489635[/C][/ROW]
[ROW][C]42[/C][C]0.849247929962706[/C][C]0.301504140074588[/C][C]0.150752070037294[/C][/ROW]
[ROW][C]43[/C][C]0.846376693944704[/C][C]0.307246612110593[/C][C]0.153623306055296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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
170.04137849452453260.08275698904906520.958621505475467
180.5701041676881760.859791664623650.429895832311824
190.550870199660420.8982596006791610.449129800339580
200.4278354447927910.8556708895855820.572164555207209
210.3593401707694280.7186803415388550.640659829230572
220.3391697081768230.6783394163536470.660830291823177
230.3447113877338750.689422775467750.655288612266125
240.4285645986960340.8571291973920690.571435401303966
250.5079417232096320.9841165535807350.492058276790368
260.5906632393193940.8186735213612120.409336760680606
270.6751630095281250.649673980943750.324836990471875
280.6264488491291520.7471023017416950.373551150870848
290.6728916660235590.6542166679528810.327108333976441
300.7647339824793760.4705320350412480.235266017520624
310.7878905710988820.4242188578022350.212109428901118
320.806347278175090.3873054436498190.193652721824910
330.8220232395611950.3559535208776090.177976760438804
340.8070976780248530.3858046439502930.192902321975147
350.7938359027934810.4123281944130380.206164097206519
360.8616378154663570.2767243690672860.138362184533643
370.870576563416420.258846873167160.12942343658358
380.8894306932058620.2211386135882770.110569306794138
390.9021929313104640.1956141373790710.0978070686895356
400.9155963319543140.1688073360913720.084403668045686
410.9162368485103650.167526302979270.083763151489635
420.8492479299627060.3015041400745880.150752070037294
430.8463766939447040.3072466121105930.153623306055296







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 level10.0370370370370370OK

\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 & 1 & 0.0370370370370370 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57989&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]1[/C][C]0.0370370370370370[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57989&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57989&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 level10.0370370370370370OK



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')
}