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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, 19 Nov 2009 08:32:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258644870gllazrrm9qt3ktc.htm/, Retrieved Fri, 29 Mar 2024 04:47:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57767, Retrieved Fri, 29 Mar 2024 04:47:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 7
Estimated Impact140
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Workshop 7] [2009-11-19 15:32:51] [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 time10 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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&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]10 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=57767&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Britse_pond[t] = -0.191028651774354 + 0.561232545674784Zwitserse_frank[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Britse_pond[t] =  -0.191028651774354 +  0.561232545674784Zwitserse_frank[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Britse_pond[t] =  -0.191028651774354 +  0.561232545674784Zwitserse_frank[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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] = -0.191028651774354 + 0.561232545674784Zwitserse_frank[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.1910286517743540.134703-1.41820.1614970.080749
Zwitserse_frank0.5612325456747840.0887036.327100

\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) & -0.191028651774354 & 0.134703 & -1.4182 & 0.161497 & 0.080749 \tabularnewline
Zwitserse_frank & 0.561232545674784 & 0.088703 & 6.3271 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&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]-0.191028651774354[/C][C]0.134703[/C][C]-1.4182[/C][C]0.161497[/C][C]0.080749[/C][/ROW]
[ROW][C]Zwitserse_frank[/C][C]0.561232545674784[/C][C]0.088703[/C][C]6.3271[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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)-0.1910286517743540.134703-1.41820.1614970.080749
Zwitserse_frank0.5612325456747840.0887036.327100







Multiple Linear Regression - Regression Statistics
Multiple R0.63902661439162
R-squared0.408355013900816
Adjusted R-squared0.398154238278416
F-TEST (value)40.0317612127567
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value3.91446610681356e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0246729447217217
Sum Squared Residuals0.0353077436719859

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.63902661439162 \tabularnewline
R-squared & 0.408355013900816 \tabularnewline
Adjusted R-squared & 0.398154238278416 \tabularnewline
F-TEST (value) & 40.0317612127567 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 3.91446610681356e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0246729447217217 \tabularnewline
Sum Squared Residuals & 0.0353077436719859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.63902661439162[/C][/ROW]
[ROW][C]R-squared[/C][C]0.408355013900816[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.398154238278416[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]40.0317612127567[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]3.91446610681356e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0246729447217217[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0353077436719859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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 R0.63902661439162
R-squared0.408355013900816
Adjusted R-squared0.398154238278416
F-TEST (value)40.0317612127567
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value3.91446610681356e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0246729447217217
Sum Squared Residuals0.0353077436719859







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.63480.667152033816959-0.0323520338169584
20.6340.670912291872979-0.0369122918729788
30.629150.670743922109276-0.0415939221092765
40.621680.666927540798688-0.0452475407986878
50.613280.669565333763359-0.0562853337633594
60.60890.663447899015504-0.0545478990155042
70.608570.658396806104431-0.0498268061044311
80.626720.658901915395538-0.0321819153955384
90.622910.645937443590451-0.0230274435904510
100.623930.639202653042354-0.0152726530423536
110.618380.631906629948581-0.0135266299485813
120.620120.636733229841385-0.0166132298413846
130.616590.636508736823115-0.0199187368231146
140.61160.638192434460139-0.0265924344601389
150.615730.632748478767094-0.0170184787670936
160.614070.631626013675744-0.0175560136757440
170.628230.6267994137829410.00143058621705908
180.644050.6351617787134950.0088882212865049
190.63870.629717823020450.00898217697955036
200.636330.630391302075260.00593869792474048
210.630590.631120904384637-0.000530904384636748
220.629940.631177027639204-0.00123702763920415
230.637090.6324678624942560.00462213750574385
240.642170.6328046020216610.009365397978339
250.657110.6295494532567470.0275605467432527
260.669770.6325239857488240.0372460142511763
270.682550.6337025740947410.0488474259052593
280.689020.6487997295733920.0402202704266076
290.713220.6595192711957810.0537007288042192
300.702240.6738868243650550.0283531756349448
310.700450.6775348359119410.0229151640880587
320.699190.6732694685648130.0259205314351870
330.696930.6774225894028060.0195074105971937
340.697630.6780399452030490.0195900547969514
350.692780.6839328869326340.00884711306736617
360.701960.681351217222530.0206087827774702
370.692150.6876931449886550.00445685501134513
380.67690.69201463559035-0.0151146355903507
390.671240.688422747298032-0.0171827472980321
400.665320.681519586986232-0.0161995869862322
410.671570.673269468564813-0.00169946856481294
420.664280.6615958316147780.00268416838522250
430.665760.665973445471041-0.000213445471040670
440.669420.672539866255436-0.00311986625543566
450.68130.6750092894564050.00629071054359531
460.691440.6747286731835670.0167113268164327
470.698620.6629427897243970.0356772102756031
480.6950.6712490314003840.0237509685996163
490.698670.6771419731299690.0215280268700311
500.689680.6789379172761280.0107420827238717
510.692330.6785450544941560.0137849455058441
520.682930.6774787126573740.00545128734262618
530.683990.6759633847840520.00802661521594813
540.668950.672764359273706-0.00381435927370552
550.687560.6832594078778240.00430059212217584
560.685270.680453245149450.00481675485054992
570.67760.678657301003291-0.00105730100329091
580.681370.6783205614758860.00304943852411409
590.679330.676019508038620.00331049196138066
600.679220.6777032056756440.00151679432435630

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.6348 & 0.667152033816959 & -0.0323520338169584 \tabularnewline
2 & 0.634 & 0.670912291872979 & -0.0369122918729788 \tabularnewline
3 & 0.62915 & 0.670743922109276 & -0.0415939221092765 \tabularnewline
4 & 0.62168 & 0.666927540798688 & -0.0452475407986878 \tabularnewline
5 & 0.61328 & 0.669565333763359 & -0.0562853337633594 \tabularnewline
6 & 0.6089 & 0.663447899015504 & -0.0545478990155042 \tabularnewline
7 & 0.60857 & 0.658396806104431 & -0.0498268061044311 \tabularnewline
8 & 0.62672 & 0.658901915395538 & -0.0321819153955384 \tabularnewline
9 & 0.62291 & 0.645937443590451 & -0.0230274435904510 \tabularnewline
10 & 0.62393 & 0.639202653042354 & -0.0152726530423536 \tabularnewline
11 & 0.61838 & 0.631906629948581 & -0.0135266299485813 \tabularnewline
12 & 0.62012 & 0.636733229841385 & -0.0166132298413846 \tabularnewline
13 & 0.61659 & 0.636508736823115 & -0.0199187368231146 \tabularnewline
14 & 0.6116 & 0.638192434460139 & -0.0265924344601389 \tabularnewline
15 & 0.61573 & 0.632748478767094 & -0.0170184787670936 \tabularnewline
16 & 0.61407 & 0.631626013675744 & -0.0175560136757440 \tabularnewline
17 & 0.62823 & 0.626799413782941 & 0.00143058621705908 \tabularnewline
18 & 0.64405 & 0.635161778713495 & 0.0088882212865049 \tabularnewline
19 & 0.6387 & 0.62971782302045 & 0.00898217697955036 \tabularnewline
20 & 0.63633 & 0.63039130207526 & 0.00593869792474048 \tabularnewline
21 & 0.63059 & 0.631120904384637 & -0.000530904384636748 \tabularnewline
22 & 0.62994 & 0.631177027639204 & -0.00123702763920415 \tabularnewline
23 & 0.63709 & 0.632467862494256 & 0.00462213750574385 \tabularnewline
24 & 0.64217 & 0.632804602021661 & 0.009365397978339 \tabularnewline
25 & 0.65711 & 0.629549453256747 & 0.0275605467432527 \tabularnewline
26 & 0.66977 & 0.632523985748824 & 0.0372460142511763 \tabularnewline
27 & 0.68255 & 0.633702574094741 & 0.0488474259052593 \tabularnewline
28 & 0.68902 & 0.648799729573392 & 0.0402202704266076 \tabularnewline
29 & 0.71322 & 0.659519271195781 & 0.0537007288042192 \tabularnewline
30 & 0.70224 & 0.673886824365055 & 0.0283531756349448 \tabularnewline
31 & 0.70045 & 0.677534835911941 & 0.0229151640880587 \tabularnewline
32 & 0.69919 & 0.673269468564813 & 0.0259205314351870 \tabularnewline
33 & 0.69693 & 0.677422589402806 & 0.0195074105971937 \tabularnewline
34 & 0.69763 & 0.678039945203049 & 0.0195900547969514 \tabularnewline
35 & 0.69278 & 0.683932886932634 & 0.00884711306736617 \tabularnewline
36 & 0.70196 & 0.68135121722253 & 0.0206087827774702 \tabularnewline
37 & 0.69215 & 0.687693144988655 & 0.00445685501134513 \tabularnewline
38 & 0.6769 & 0.69201463559035 & -0.0151146355903507 \tabularnewline
39 & 0.67124 & 0.688422747298032 & -0.0171827472980321 \tabularnewline
40 & 0.66532 & 0.681519586986232 & -0.0161995869862322 \tabularnewline
41 & 0.67157 & 0.673269468564813 & -0.00169946856481294 \tabularnewline
42 & 0.66428 & 0.661595831614778 & 0.00268416838522250 \tabularnewline
43 & 0.66576 & 0.665973445471041 & -0.000213445471040670 \tabularnewline
44 & 0.66942 & 0.672539866255436 & -0.00311986625543566 \tabularnewline
45 & 0.6813 & 0.675009289456405 & 0.00629071054359531 \tabularnewline
46 & 0.69144 & 0.674728673183567 & 0.0167113268164327 \tabularnewline
47 & 0.69862 & 0.662942789724397 & 0.0356772102756031 \tabularnewline
48 & 0.695 & 0.671249031400384 & 0.0237509685996163 \tabularnewline
49 & 0.69867 & 0.677141973129969 & 0.0215280268700311 \tabularnewline
50 & 0.68968 & 0.678937917276128 & 0.0107420827238717 \tabularnewline
51 & 0.69233 & 0.678545054494156 & 0.0137849455058441 \tabularnewline
52 & 0.68293 & 0.677478712657374 & 0.00545128734262618 \tabularnewline
53 & 0.68399 & 0.675963384784052 & 0.00802661521594813 \tabularnewline
54 & 0.66895 & 0.672764359273706 & -0.00381435927370552 \tabularnewline
55 & 0.68756 & 0.683259407877824 & 0.00430059212217584 \tabularnewline
56 & 0.68527 & 0.68045324514945 & 0.00481675485054992 \tabularnewline
57 & 0.6776 & 0.678657301003291 & -0.00105730100329091 \tabularnewline
58 & 0.68137 & 0.678320561475886 & 0.00304943852411409 \tabularnewline
59 & 0.67933 & 0.67601950803862 & 0.00331049196138066 \tabularnewline
60 & 0.67922 & 0.677703205675644 & 0.00151679432435630 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&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]0.667152033816959[/C][C]-0.0323520338169584[/C][/ROW]
[ROW][C]2[/C][C]0.634[/C][C]0.670912291872979[/C][C]-0.0369122918729788[/C][/ROW]
[ROW][C]3[/C][C]0.62915[/C][C]0.670743922109276[/C][C]-0.0415939221092765[/C][/ROW]
[ROW][C]4[/C][C]0.62168[/C][C]0.666927540798688[/C][C]-0.0452475407986878[/C][/ROW]
[ROW][C]5[/C][C]0.61328[/C][C]0.669565333763359[/C][C]-0.0562853337633594[/C][/ROW]
[ROW][C]6[/C][C]0.6089[/C][C]0.663447899015504[/C][C]-0.0545478990155042[/C][/ROW]
[ROW][C]7[/C][C]0.60857[/C][C]0.658396806104431[/C][C]-0.0498268061044311[/C][/ROW]
[ROW][C]8[/C][C]0.62672[/C][C]0.658901915395538[/C][C]-0.0321819153955384[/C][/ROW]
[ROW][C]9[/C][C]0.62291[/C][C]0.645937443590451[/C][C]-0.0230274435904510[/C][/ROW]
[ROW][C]10[/C][C]0.62393[/C][C]0.639202653042354[/C][C]-0.0152726530423536[/C][/ROW]
[ROW][C]11[/C][C]0.61838[/C][C]0.631906629948581[/C][C]-0.0135266299485813[/C][/ROW]
[ROW][C]12[/C][C]0.62012[/C][C]0.636733229841385[/C][C]-0.0166132298413846[/C][/ROW]
[ROW][C]13[/C][C]0.61659[/C][C]0.636508736823115[/C][C]-0.0199187368231146[/C][/ROW]
[ROW][C]14[/C][C]0.6116[/C][C]0.638192434460139[/C][C]-0.0265924344601389[/C][/ROW]
[ROW][C]15[/C][C]0.61573[/C][C]0.632748478767094[/C][C]-0.0170184787670936[/C][/ROW]
[ROW][C]16[/C][C]0.61407[/C][C]0.631626013675744[/C][C]-0.0175560136757440[/C][/ROW]
[ROW][C]17[/C][C]0.62823[/C][C]0.626799413782941[/C][C]0.00143058621705908[/C][/ROW]
[ROW][C]18[/C][C]0.64405[/C][C]0.635161778713495[/C][C]0.0088882212865049[/C][/ROW]
[ROW][C]19[/C][C]0.6387[/C][C]0.62971782302045[/C][C]0.00898217697955036[/C][/ROW]
[ROW][C]20[/C][C]0.63633[/C][C]0.63039130207526[/C][C]0.00593869792474048[/C][/ROW]
[ROW][C]21[/C][C]0.63059[/C][C]0.631120904384637[/C][C]-0.000530904384636748[/C][/ROW]
[ROW][C]22[/C][C]0.62994[/C][C]0.631177027639204[/C][C]-0.00123702763920415[/C][/ROW]
[ROW][C]23[/C][C]0.63709[/C][C]0.632467862494256[/C][C]0.00462213750574385[/C][/ROW]
[ROW][C]24[/C][C]0.64217[/C][C]0.632804602021661[/C][C]0.009365397978339[/C][/ROW]
[ROW][C]25[/C][C]0.65711[/C][C]0.629549453256747[/C][C]0.0275605467432527[/C][/ROW]
[ROW][C]26[/C][C]0.66977[/C][C]0.632523985748824[/C][C]0.0372460142511763[/C][/ROW]
[ROW][C]27[/C][C]0.68255[/C][C]0.633702574094741[/C][C]0.0488474259052593[/C][/ROW]
[ROW][C]28[/C][C]0.68902[/C][C]0.648799729573392[/C][C]0.0402202704266076[/C][/ROW]
[ROW][C]29[/C][C]0.71322[/C][C]0.659519271195781[/C][C]0.0537007288042192[/C][/ROW]
[ROW][C]30[/C][C]0.70224[/C][C]0.673886824365055[/C][C]0.0283531756349448[/C][/ROW]
[ROW][C]31[/C][C]0.70045[/C][C]0.677534835911941[/C][C]0.0229151640880587[/C][/ROW]
[ROW][C]32[/C][C]0.69919[/C][C]0.673269468564813[/C][C]0.0259205314351870[/C][/ROW]
[ROW][C]33[/C][C]0.69693[/C][C]0.677422589402806[/C][C]0.0195074105971937[/C][/ROW]
[ROW][C]34[/C][C]0.69763[/C][C]0.678039945203049[/C][C]0.0195900547969514[/C][/ROW]
[ROW][C]35[/C][C]0.69278[/C][C]0.683932886932634[/C][C]0.00884711306736617[/C][/ROW]
[ROW][C]36[/C][C]0.70196[/C][C]0.68135121722253[/C][C]0.0206087827774702[/C][/ROW]
[ROW][C]37[/C][C]0.69215[/C][C]0.687693144988655[/C][C]0.00445685501134513[/C][/ROW]
[ROW][C]38[/C][C]0.6769[/C][C]0.69201463559035[/C][C]-0.0151146355903507[/C][/ROW]
[ROW][C]39[/C][C]0.67124[/C][C]0.688422747298032[/C][C]-0.0171827472980321[/C][/ROW]
[ROW][C]40[/C][C]0.66532[/C][C]0.681519586986232[/C][C]-0.0161995869862322[/C][/ROW]
[ROW][C]41[/C][C]0.67157[/C][C]0.673269468564813[/C][C]-0.00169946856481294[/C][/ROW]
[ROW][C]42[/C][C]0.66428[/C][C]0.661595831614778[/C][C]0.00268416838522250[/C][/ROW]
[ROW][C]43[/C][C]0.66576[/C][C]0.665973445471041[/C][C]-0.000213445471040670[/C][/ROW]
[ROW][C]44[/C][C]0.66942[/C][C]0.672539866255436[/C][C]-0.00311986625543566[/C][/ROW]
[ROW][C]45[/C][C]0.6813[/C][C]0.675009289456405[/C][C]0.00629071054359531[/C][/ROW]
[ROW][C]46[/C][C]0.69144[/C][C]0.674728673183567[/C][C]0.0167113268164327[/C][/ROW]
[ROW][C]47[/C][C]0.69862[/C][C]0.662942789724397[/C][C]0.0356772102756031[/C][/ROW]
[ROW][C]48[/C][C]0.695[/C][C]0.671249031400384[/C][C]0.0237509685996163[/C][/ROW]
[ROW][C]49[/C][C]0.69867[/C][C]0.677141973129969[/C][C]0.0215280268700311[/C][/ROW]
[ROW][C]50[/C][C]0.68968[/C][C]0.678937917276128[/C][C]0.0107420827238717[/C][/ROW]
[ROW][C]51[/C][C]0.69233[/C][C]0.678545054494156[/C][C]0.0137849455058441[/C][/ROW]
[ROW][C]52[/C][C]0.68293[/C][C]0.677478712657374[/C][C]0.00545128734262618[/C][/ROW]
[ROW][C]53[/C][C]0.68399[/C][C]0.675963384784052[/C][C]0.00802661521594813[/C][/ROW]
[ROW][C]54[/C][C]0.66895[/C][C]0.672764359273706[/C][C]-0.00381435927370552[/C][/ROW]
[ROW][C]55[/C][C]0.68756[/C][C]0.683259407877824[/C][C]0.00430059212217584[/C][/ROW]
[ROW][C]56[/C][C]0.68527[/C][C]0.68045324514945[/C][C]0.00481675485054992[/C][/ROW]
[ROW][C]57[/C][C]0.6776[/C][C]0.678657301003291[/C][C]-0.00105730100329091[/C][/ROW]
[ROW][C]58[/C][C]0.68137[/C][C]0.678320561475886[/C][C]0.00304943852411409[/C][/ROW]
[ROW][C]59[/C][C]0.67933[/C][C]0.67601950803862[/C][C]0.00331049196138066[/C][/ROW]
[ROW][C]60[/C][C]0.67922[/C][C]0.677703205675644[/C][C]0.00151679432435630[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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
10.63480.667152033816959-0.0323520338169584
20.6340.670912291872979-0.0369122918729788
30.629150.670743922109276-0.0415939221092765
40.621680.666927540798688-0.0452475407986878
50.613280.669565333763359-0.0562853337633594
60.60890.663447899015504-0.0545478990155042
70.608570.658396806104431-0.0498268061044311
80.626720.658901915395538-0.0321819153955384
90.622910.645937443590451-0.0230274435904510
100.623930.639202653042354-0.0152726530423536
110.618380.631906629948581-0.0135266299485813
120.620120.636733229841385-0.0166132298413846
130.616590.636508736823115-0.0199187368231146
140.61160.638192434460139-0.0265924344601389
150.615730.632748478767094-0.0170184787670936
160.614070.631626013675744-0.0175560136757440
170.628230.6267994137829410.00143058621705908
180.644050.6351617787134950.0088882212865049
190.63870.629717823020450.00898217697955036
200.636330.630391302075260.00593869792474048
210.630590.631120904384637-0.000530904384636748
220.629940.631177027639204-0.00123702763920415
230.637090.6324678624942560.00462213750574385
240.642170.6328046020216610.009365397978339
250.657110.6295494532567470.0275605467432527
260.669770.6325239857488240.0372460142511763
270.682550.6337025740947410.0488474259052593
280.689020.6487997295733920.0402202704266076
290.713220.6595192711957810.0537007288042192
300.702240.6738868243650550.0283531756349448
310.700450.6775348359119410.0229151640880587
320.699190.6732694685648130.0259205314351870
330.696930.6774225894028060.0195074105971937
340.697630.6780399452030490.0195900547969514
350.692780.6839328869326340.00884711306736617
360.701960.681351217222530.0206087827774702
370.692150.6876931449886550.00445685501134513
380.67690.69201463559035-0.0151146355903507
390.671240.688422747298032-0.0171827472980321
400.665320.681519586986232-0.0161995869862322
410.671570.673269468564813-0.00169946856481294
420.664280.6615958316147780.00268416838522250
430.665760.665973445471041-0.000213445471040670
440.669420.672539866255436-0.00311986625543566
450.68130.6750092894564050.00629071054359531
460.691440.6747286731835670.0167113268164327
470.698620.6629427897243970.0356772102756031
480.6950.6712490314003840.0237509685996163
490.698670.6771419731299690.0215280268700311
500.689680.6789379172761280.0107420827238717
510.692330.6785450544941560.0137849455058441
520.682930.6774787126573740.00545128734262618
530.683990.6759633847840520.00802661521594813
540.668950.672764359273706-0.00381435927370552
550.687560.6832594078778240.00430059212217584
560.685270.680453245149450.00481675485054992
570.67760.678657301003291-0.00105730100329091
580.681370.6783205614758860.00304943852411409
590.679330.676019508038620.00331049196138066
600.679220.6777032056756440.00151679432435630







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1260433549697630.2520867099395250.873956645030237
60.08614297665504550.1722859533100910.913857023344955
70.05041292560642360.1008258512128470.949587074393576
80.07033724552353650.1406744910470730.929662754476464
90.06297517668720910.1259503533744180.93702482331279
100.03965522909231630.07931045818463260.960344770907684
110.0211271019554910.0422542039109820.978872898044509
120.01156114305898390.02312228611796780.988438856941016
130.007019169880133950.01403833976026790.992980830119866
140.00674395113014250.0134879022602850.993256048869858
150.004634296104558360.009268592209116710.995365703895442
160.003834436729201130.007668873458402260.996165563270799
170.004852821548072050.00970564309614410.995147178451928
180.0276916649990130.0553833299980260.972308335000987
190.03854258386478240.07708516772956470.961457416135218
200.04075619171568130.08151238343136260.959243808284319
210.04043175246953240.08086350493906480.959568247530468
220.04887068732478550.0977413746495710.951129312675214
230.07653512529772670.1530702505954530.923464874702273
240.1551669667432710.3103339334865420.844833033256729
250.385275979736670.770551959473340.61472402026333
260.737827715099520.5243445698009610.262172284900481
270.9411972987912980.1176054024174040.0588027012087018
280.9915423438103510.01691531237929740.0084576561896487
290.9999272633627450.0001454732745094597.27366372547294e-05
300.9999902972921581.94054156843845e-059.70270784219224e-06
310.9999963288528367.34229432767475e-063.67114716383737e-06
320.9999980515301863.89693962802466e-061.94846981401233e-06
330.9999981313181833.73736363461377e-061.86868181730689e-06
340.9999981545503883.69089922397126e-061.84544961198563e-06
350.9999964750539947.0498920114714e-063.5249460057357e-06
360.9999978025840464.39483190830195e-062.19741595415098e-06
370.9999957116353798.57672924248656e-064.28836462124328e-06
380.9999891507128262.16985743481075e-051.08492871740538e-05
390.9999836475848363.27048303285935e-051.63524151642967e-05
400.9999892504699282.14990601447246e-051.07495300723623e-05
410.9999792903996334.14192007349759e-052.07096003674879e-05
420.9999723984134495.52031731026553e-052.76015865513276e-05
430.9999848260004623.03479990752284e-051.51739995376142e-05
440.9999916990232511.66019534971663e-058.30097674858314e-06
450.9999765974935634.68050128736922e-052.34025064368461e-05
460.9999363657774820.0001272684450368506.36342225184251e-05
470.9999178405128230.0001643189743545688.21594871772838e-05
480.999960930133997.81397320197246e-053.90698660098623e-05
490.9999955304309188.9391381636776e-064.4695690818388e-06
500.9999871329980272.57340039459862e-051.28670019729931e-05
510.9999948335334641.03329330728023e-055.16646653640115e-06
520.9999700569338535.98861322940471e-052.99430661470236e-05
530.999982844404913.43111901788382e-051.71555950894191e-05
540.999894569246760.0002108615064808530.000105430753240426
550.9986859429053440.002628114189311980.00131405709465599

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.126043354969763 & 0.252086709939525 & 0.873956645030237 \tabularnewline
6 & 0.0861429766550455 & 0.172285953310091 & 0.913857023344955 \tabularnewline
7 & 0.0504129256064236 & 0.100825851212847 & 0.949587074393576 \tabularnewline
8 & 0.0703372455235365 & 0.140674491047073 & 0.929662754476464 \tabularnewline
9 & 0.0629751766872091 & 0.125950353374418 & 0.93702482331279 \tabularnewline
10 & 0.0396552290923163 & 0.0793104581846326 & 0.960344770907684 \tabularnewline
11 & 0.021127101955491 & 0.042254203910982 & 0.978872898044509 \tabularnewline
12 & 0.0115611430589839 & 0.0231222861179678 & 0.988438856941016 \tabularnewline
13 & 0.00701916988013395 & 0.0140383397602679 & 0.992980830119866 \tabularnewline
14 & 0.0067439511301425 & 0.013487902260285 & 0.993256048869858 \tabularnewline
15 & 0.00463429610455836 & 0.00926859220911671 & 0.995365703895442 \tabularnewline
16 & 0.00383443672920113 & 0.00766887345840226 & 0.996165563270799 \tabularnewline
17 & 0.00485282154807205 & 0.0097056430961441 & 0.995147178451928 \tabularnewline
18 & 0.027691664999013 & 0.055383329998026 & 0.972308335000987 \tabularnewline
19 & 0.0385425838647824 & 0.0770851677295647 & 0.961457416135218 \tabularnewline
20 & 0.0407561917156813 & 0.0815123834313626 & 0.959243808284319 \tabularnewline
21 & 0.0404317524695324 & 0.0808635049390648 & 0.959568247530468 \tabularnewline
22 & 0.0488706873247855 & 0.097741374649571 & 0.951129312675214 \tabularnewline
23 & 0.0765351252977267 & 0.153070250595453 & 0.923464874702273 \tabularnewline
24 & 0.155166966743271 & 0.310333933486542 & 0.844833033256729 \tabularnewline
25 & 0.38527597973667 & 0.77055195947334 & 0.61472402026333 \tabularnewline
26 & 0.73782771509952 & 0.524344569800961 & 0.262172284900481 \tabularnewline
27 & 0.941197298791298 & 0.117605402417404 & 0.0588027012087018 \tabularnewline
28 & 0.991542343810351 & 0.0169153123792974 & 0.0084576561896487 \tabularnewline
29 & 0.999927263362745 & 0.000145473274509459 & 7.27366372547294e-05 \tabularnewline
30 & 0.999990297292158 & 1.94054156843845e-05 & 9.70270784219224e-06 \tabularnewline
31 & 0.999996328852836 & 7.34229432767475e-06 & 3.67114716383737e-06 \tabularnewline
32 & 0.999998051530186 & 3.89693962802466e-06 & 1.94846981401233e-06 \tabularnewline
33 & 0.999998131318183 & 3.73736363461377e-06 & 1.86868181730689e-06 \tabularnewline
34 & 0.999998154550388 & 3.69089922397126e-06 & 1.84544961198563e-06 \tabularnewline
35 & 0.999996475053994 & 7.0498920114714e-06 & 3.5249460057357e-06 \tabularnewline
36 & 0.999997802584046 & 4.39483190830195e-06 & 2.19741595415098e-06 \tabularnewline
37 & 0.999995711635379 & 8.57672924248656e-06 & 4.28836462124328e-06 \tabularnewline
38 & 0.999989150712826 & 2.16985743481075e-05 & 1.08492871740538e-05 \tabularnewline
39 & 0.999983647584836 & 3.27048303285935e-05 & 1.63524151642967e-05 \tabularnewline
40 & 0.999989250469928 & 2.14990601447246e-05 & 1.07495300723623e-05 \tabularnewline
41 & 0.999979290399633 & 4.14192007349759e-05 & 2.07096003674879e-05 \tabularnewline
42 & 0.999972398413449 & 5.52031731026553e-05 & 2.76015865513276e-05 \tabularnewline
43 & 0.999984826000462 & 3.03479990752284e-05 & 1.51739995376142e-05 \tabularnewline
44 & 0.999991699023251 & 1.66019534971663e-05 & 8.30097674858314e-06 \tabularnewline
45 & 0.999976597493563 & 4.68050128736922e-05 & 2.34025064368461e-05 \tabularnewline
46 & 0.999936365777482 & 0.000127268445036850 & 6.36342225184251e-05 \tabularnewline
47 & 0.999917840512823 & 0.000164318974354568 & 8.21594871772838e-05 \tabularnewline
48 & 0.99996093013399 & 7.81397320197246e-05 & 3.90698660098623e-05 \tabularnewline
49 & 0.999995530430918 & 8.9391381636776e-06 & 4.4695690818388e-06 \tabularnewline
50 & 0.999987132998027 & 2.57340039459862e-05 & 1.28670019729931e-05 \tabularnewline
51 & 0.999994833533464 & 1.03329330728023e-05 & 5.16646653640115e-06 \tabularnewline
52 & 0.999970056933853 & 5.98861322940471e-05 & 2.99430661470236e-05 \tabularnewline
53 & 0.99998284440491 & 3.43111901788382e-05 & 1.71555950894191e-05 \tabularnewline
54 & 0.99989456924676 & 0.000210861506480853 & 0.000105430753240426 \tabularnewline
55 & 0.998685942905344 & 0.00262811418931198 & 0.00131405709465599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&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]5[/C][C]0.126043354969763[/C][C]0.252086709939525[/C][C]0.873956645030237[/C][/ROW]
[ROW][C]6[/C][C]0.0861429766550455[/C][C]0.172285953310091[/C][C]0.913857023344955[/C][/ROW]
[ROW][C]7[/C][C]0.0504129256064236[/C][C]0.100825851212847[/C][C]0.949587074393576[/C][/ROW]
[ROW][C]8[/C][C]0.0703372455235365[/C][C]0.140674491047073[/C][C]0.929662754476464[/C][/ROW]
[ROW][C]9[/C][C]0.0629751766872091[/C][C]0.125950353374418[/C][C]0.93702482331279[/C][/ROW]
[ROW][C]10[/C][C]0.0396552290923163[/C][C]0.0793104581846326[/C][C]0.960344770907684[/C][/ROW]
[ROW][C]11[/C][C]0.021127101955491[/C][C]0.042254203910982[/C][C]0.978872898044509[/C][/ROW]
[ROW][C]12[/C][C]0.0115611430589839[/C][C]0.0231222861179678[/C][C]0.988438856941016[/C][/ROW]
[ROW][C]13[/C][C]0.00701916988013395[/C][C]0.0140383397602679[/C][C]0.992980830119866[/C][/ROW]
[ROW][C]14[/C][C]0.0067439511301425[/C][C]0.013487902260285[/C][C]0.993256048869858[/C][/ROW]
[ROW][C]15[/C][C]0.00463429610455836[/C][C]0.00926859220911671[/C][C]0.995365703895442[/C][/ROW]
[ROW][C]16[/C][C]0.00383443672920113[/C][C]0.00766887345840226[/C][C]0.996165563270799[/C][/ROW]
[ROW][C]17[/C][C]0.00485282154807205[/C][C]0.0097056430961441[/C][C]0.995147178451928[/C][/ROW]
[ROW][C]18[/C][C]0.027691664999013[/C][C]0.055383329998026[/C][C]0.972308335000987[/C][/ROW]
[ROW][C]19[/C][C]0.0385425838647824[/C][C]0.0770851677295647[/C][C]0.961457416135218[/C][/ROW]
[ROW][C]20[/C][C]0.0407561917156813[/C][C]0.0815123834313626[/C][C]0.959243808284319[/C][/ROW]
[ROW][C]21[/C][C]0.0404317524695324[/C][C]0.0808635049390648[/C][C]0.959568247530468[/C][/ROW]
[ROW][C]22[/C][C]0.0488706873247855[/C][C]0.097741374649571[/C][C]0.951129312675214[/C][/ROW]
[ROW][C]23[/C][C]0.0765351252977267[/C][C]0.153070250595453[/C][C]0.923464874702273[/C][/ROW]
[ROW][C]24[/C][C]0.155166966743271[/C][C]0.310333933486542[/C][C]0.844833033256729[/C][/ROW]
[ROW][C]25[/C][C]0.38527597973667[/C][C]0.77055195947334[/C][C]0.61472402026333[/C][/ROW]
[ROW][C]26[/C][C]0.73782771509952[/C][C]0.524344569800961[/C][C]0.262172284900481[/C][/ROW]
[ROW][C]27[/C][C]0.941197298791298[/C][C]0.117605402417404[/C][C]0.0588027012087018[/C][/ROW]
[ROW][C]28[/C][C]0.991542343810351[/C][C]0.0169153123792974[/C][C]0.0084576561896487[/C][/ROW]
[ROW][C]29[/C][C]0.999927263362745[/C][C]0.000145473274509459[/C][C]7.27366372547294e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999990297292158[/C][C]1.94054156843845e-05[/C][C]9.70270784219224e-06[/C][/ROW]
[ROW][C]31[/C][C]0.999996328852836[/C][C]7.34229432767475e-06[/C][C]3.67114716383737e-06[/C][/ROW]
[ROW][C]32[/C][C]0.999998051530186[/C][C]3.89693962802466e-06[/C][C]1.94846981401233e-06[/C][/ROW]
[ROW][C]33[/C][C]0.999998131318183[/C][C]3.73736363461377e-06[/C][C]1.86868181730689e-06[/C][/ROW]
[ROW][C]34[/C][C]0.999998154550388[/C][C]3.69089922397126e-06[/C][C]1.84544961198563e-06[/C][/ROW]
[ROW][C]35[/C][C]0.999996475053994[/C][C]7.0498920114714e-06[/C][C]3.5249460057357e-06[/C][/ROW]
[ROW][C]36[/C][C]0.999997802584046[/C][C]4.39483190830195e-06[/C][C]2.19741595415098e-06[/C][/ROW]
[ROW][C]37[/C][C]0.999995711635379[/C][C]8.57672924248656e-06[/C][C]4.28836462124328e-06[/C][/ROW]
[ROW][C]38[/C][C]0.999989150712826[/C][C]2.16985743481075e-05[/C][C]1.08492871740538e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999983647584836[/C][C]3.27048303285935e-05[/C][C]1.63524151642967e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999989250469928[/C][C]2.14990601447246e-05[/C][C]1.07495300723623e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999979290399633[/C][C]4.14192007349759e-05[/C][C]2.07096003674879e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999972398413449[/C][C]5.52031731026553e-05[/C][C]2.76015865513276e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999984826000462[/C][C]3.03479990752284e-05[/C][C]1.51739995376142e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999991699023251[/C][C]1.66019534971663e-05[/C][C]8.30097674858314e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999976597493563[/C][C]4.68050128736922e-05[/C][C]2.34025064368461e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999936365777482[/C][C]0.000127268445036850[/C][C]6.36342225184251e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999917840512823[/C][C]0.000164318974354568[/C][C]8.21594871772838e-05[/C][/ROW]
[ROW][C]48[/C][C]0.99996093013399[/C][C]7.81397320197246e-05[/C][C]3.90698660098623e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999995530430918[/C][C]8.9391381636776e-06[/C][C]4.4695690818388e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999987132998027[/C][C]2.57340039459862e-05[/C][C]1.28670019729931e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999994833533464[/C][C]1.03329330728023e-05[/C][C]5.16646653640115e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999970056933853[/C][C]5.98861322940471e-05[/C][C]2.99430661470236e-05[/C][/ROW]
[ROW][C]53[/C][C]0.99998284440491[/C][C]3.43111901788382e-05[/C][C]1.71555950894191e-05[/C][/ROW]
[ROW][C]54[/C][C]0.99989456924676[/C][C]0.000210861506480853[/C][C]0.000105430753240426[/C][/ROW]
[ROW][C]55[/C][C]0.998685942905344[/C][C]0.00262811418931198[/C][C]0.00131405709465599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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
50.1260433549697630.2520867099395250.873956645030237
60.08614297665504550.1722859533100910.913857023344955
70.05041292560642360.1008258512128470.949587074393576
80.07033724552353650.1406744910470730.929662754476464
90.06297517668720910.1259503533744180.93702482331279
100.03965522909231630.07931045818463260.960344770907684
110.0211271019554910.0422542039109820.978872898044509
120.01156114305898390.02312228611796780.988438856941016
130.007019169880133950.01403833976026790.992980830119866
140.00674395113014250.0134879022602850.993256048869858
150.004634296104558360.009268592209116710.995365703895442
160.003834436729201130.007668873458402260.996165563270799
170.004852821548072050.00970564309614410.995147178451928
180.0276916649990130.0553833299980260.972308335000987
190.03854258386478240.07708516772956470.961457416135218
200.04075619171568130.08151238343136260.959243808284319
210.04043175246953240.08086350493906480.959568247530468
220.04887068732478550.0977413746495710.951129312675214
230.07653512529772670.1530702505954530.923464874702273
240.1551669667432710.3103339334865420.844833033256729
250.385275979736670.770551959473340.61472402026333
260.737827715099520.5243445698009610.262172284900481
270.9411972987912980.1176054024174040.0588027012087018
280.9915423438103510.01691531237929740.0084576561896487
290.9999272633627450.0001454732745094597.27366372547294e-05
300.9999902972921581.94054156843845e-059.70270784219224e-06
310.9999963288528367.34229432767475e-063.67114716383737e-06
320.9999980515301863.89693962802466e-061.94846981401233e-06
330.9999981313181833.73736363461377e-061.86868181730689e-06
340.9999981545503883.69089922397126e-061.84544961198563e-06
350.9999964750539947.0498920114714e-063.5249460057357e-06
360.9999978025840464.39483190830195e-062.19741595415098e-06
370.9999957116353798.57672924248656e-064.28836462124328e-06
380.9999891507128262.16985743481075e-051.08492871740538e-05
390.9999836475848363.27048303285935e-051.63524151642967e-05
400.9999892504699282.14990601447246e-051.07495300723623e-05
410.9999792903996334.14192007349759e-052.07096003674879e-05
420.9999723984134495.52031731026553e-052.76015865513276e-05
430.9999848260004623.03479990752284e-051.51739995376142e-05
440.9999916990232511.66019534971663e-058.30097674858314e-06
450.9999765974935634.68050128736922e-052.34025064368461e-05
460.9999363657774820.0001272684450368506.36342225184251e-05
470.9999178405128230.0001643189743545688.21594871772838e-05
480.999960930133997.81397320197246e-053.90698660098623e-05
490.9999955304309188.9391381636776e-064.4695690818388e-06
500.9999871329980272.57340039459862e-051.28670019729931e-05
510.9999948335334641.03329330728023e-055.16646653640115e-06
520.9999700569338535.98861322940471e-052.99430661470236e-05
530.999982844404913.43111901788382e-051.71555950894191e-05
540.999894569246760.0002108615064808530.000105430753240426
550.9986859429053440.002628114189311980.00131405709465599







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level300.588235294117647NOK
5% type I error level350.686274509803922NOK
10% type I error level410.80392156862745NOK

\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 & 30 & 0.588235294117647 & NOK \tabularnewline
5% type I error level & 35 & 0.686274509803922 & NOK \tabularnewline
10% type I error level & 41 & 0.80392156862745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57767&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]30[/C][C]0.588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.686274509803922[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.80392156862745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57767&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57767&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 level300.588235294117647NOK
5% type I error level350.686274509803922NOK
10% type I error level410.80392156862745NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}