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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 18 Dec 2010 13:37:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/18/t1292679695il88z8rxdmyp7g7.htm/, Retrieved Tue, 30 Apr 2024 01:11:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111958, Retrieved Tue, 30 Apr 2024 01:11:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact234

Tree of Dependent Computations

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 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [baby's over de la...] [2010-11-26 13:30:35] [95e8426e0df851c9330605aa1e892ab5]
-    D        [Multiple Regression] [Faillissementen o...] [2010-12-18 13:37:07] [dc77c696707133dea0955379c56a2acd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
58	59	66	62	46
61	58	59	66	62
41	61	58	59	66
27	41	61	58	59
58	27	41	61	58
70	58	27	41	61
49	70	58	27	41
59	49	70	58	27
44	59	49	70	58
36	44	59	49	70
72	36	44	59	49
45	72	36	44	59
56	45	72	36	44
54	56	45	72	36
53	54	56	45	72
35	53	54	56	45
61	35	53	54	56
52	61	35	53	54
47	52	61	35	53
51	47	52	61	35
52	51	47	52	61
63	52	51	47	52
74	63	52	51	47
45	74	63	52	51
51	45	74	63	52
64	51	45	74	63
36	64	51	45	74
30	36	64	51	45
55	30	36	64	51
64	55	30	36	64
39	64	55	30	36
40	39	64	55	30
63	40	39	64	55
45	63	40	39	64
59	45	63	40	39
55	59	45	63	40
40	55	59	45	63
64	40	55	59	45
27	64	40	55	59
28	27	64	40	55
45	28	27	64	40
57	45	28	27	64
45	57	45	28	27
69	45	57	45	28
60	69	45	57	45
56	60	69	45	57
58	56	60	69	45
50	58	56	60	69
51	50	58	56	60
53	51	50	58	56
37	53	51	50	58
22	37	53	51	50
55	22	37	53	51
70	55	22	37	53
62	70	55	22	37
58	62	70	55	22
39	58	62	70	55
49	39	58	62	70
58	49	39	58	62
47	58	49	39	58
42	47	58	49	39
62	42	47	58	49
39	62	42	47	58
40	39	62	42	47
72	40	39	62	42
70	72	40	39	62
54	70	72	40	39
65	54	70	72	40

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 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 & 16 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&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]16 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=111958&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=0

As an alternative you can also use a QR Code:  
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 time16 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
faillissementen[t] = + 44.9943140478135 + 0.119676240683047`Y-1`[t] + 0.118023330461420`Y-2`[t] -0.114070297649060`Y-3`[t] -0.0825692584978275`Y-4`[t] + 0.889367993868057M1[t] + 14.1736152636581M2[t] -8.39507896223981M3[t] -16.831064932218M4[t] + 15.0692743531417M5[t] + 17.4087369750493M6[t] -3.7535077827975M7[t] + 7.3282567821366M8[t] + 5.64905477660003M9[t] + 2.49342334199798M10[t] + 17.1748580182210M11[t] + 0.00847982563409131t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
faillissementen[t] =  +  44.9943140478135 +  0.119676240683047`Y-1`[t] +  0.118023330461420`Y-2`[t] -0.114070297649060`Y-3`[t] -0.0825692584978275`Y-4`[t] +  0.889367993868057M1[t] +  14.1736152636581M2[t] -8.39507896223981M3[t] -16.831064932218M4[t] +  15.0692743531417M5[t] +  17.4087369750493M6[t] -3.7535077827975M7[t] +  7.3282567821366M8[t] +  5.64905477660003M9[t] +  2.49342334199798M10[t] +  17.1748580182210M11[t] +  0.00847982563409131t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]faillissementen[t] =  +  44.9943140478135 +  0.119676240683047`Y-1`[t] +  0.118023330461420`Y-2`[t] -0.114070297649060`Y-3`[t] -0.0825692584978275`Y-4`[t] +  0.889367993868057M1[t] +  14.1736152636581M2[t] -8.39507896223981M3[t] -16.831064932218M4[t] +  15.0692743531417M5[t] +  17.4087369750493M6[t] -3.7535077827975M7[t] +  7.3282567821366M8[t] +  5.64905477660003M9[t] +  2.49342334199798M10[t] +  17.1748580182210M11[t] +  0.00847982563409131t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
faillissementen[t] = + 44.9943140478135 + 0.119676240683047`Y-1`[t] + 0.118023330461420`Y-2`[t] -0.114070297649060`Y-3`[t] -0.0825692584978275`Y-4`[t] + 0.889367993868057M1[t] + 14.1736152636581M2[t] -8.39507896223981M3[t] -16.831064932218M4[t] + 15.0692743531417M5[t] + 17.4087369750493M6[t] -3.7535077827975M7[t] + 7.3282567821366M8[t] + 5.64905477660003M9[t] + 2.49342334199798M10[t] + 17.1748580182210M11[t] + 0.00847982563409131t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)44.994314047813515.2916122.94240.004890.002445
`Y-1`0.1196762406830470.1403340.85280.3977590.198879
`Y-2`0.1180233304614200.1394630.84630.4013540.200677
`Y-3`-0.1140702976490600.138079-0.82610.4125810.20629
`Y-4`-0.08256925849782750.144016-0.57330.5689380.284469
M10.8893679938680575.9629930.14910.8820250.441013
M214.17361526365815.8365222.42840.018730.009365
M3-8.395078962239815.188471-1.6180.1118260.055913
M4-16.8310649322186.524153-2.57980.012810.006405
M515.06927435314177.2711382.07250.0432940.021647
M617.40873697504935.9306432.93540.0049850.002493
M7-3.75350778279756.381471-0.58820.5590020.279501
M87.32825678213667.2572721.00980.3173670.158684
M95.649054776600035.6308191.00320.3204820.160241
M102.493423341997985.6413260.4420.6603630.330182
M1117.17485801822105.8309632.94550.0048490.002424
t0.008479825634091310.0521890.16250.8715670.435784

\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) & 44.9943140478135 & 15.291612 & 2.9424 & 0.00489 & 0.002445 \tabularnewline
`Y-1` & 0.119676240683047 & 0.140334 & 0.8528 & 0.397759 & 0.198879 \tabularnewline
`Y-2` & 0.118023330461420 & 0.139463 & 0.8463 & 0.401354 & 0.200677 \tabularnewline
`Y-3` & -0.114070297649060 & 0.138079 & -0.8261 & 0.412581 & 0.20629 \tabularnewline
`Y-4` & -0.0825692584978275 & 0.144016 & -0.5733 & 0.568938 & 0.284469 \tabularnewline
M1 & 0.889367993868057 & 5.962993 & 0.1491 & 0.882025 & 0.441013 \tabularnewline
M2 & 14.1736152636581 & 5.836522 & 2.4284 & 0.01873 & 0.009365 \tabularnewline
M3 & -8.39507896223981 & 5.188471 & -1.618 & 0.111826 & 0.055913 \tabularnewline
M4 & -16.831064932218 & 6.524153 & -2.5798 & 0.01281 & 0.006405 \tabularnewline
M5 & 15.0692743531417 & 7.271138 & 2.0725 & 0.043294 & 0.021647 \tabularnewline
M6 & 17.4087369750493 & 5.930643 & 2.9354 & 0.004985 & 0.002493 \tabularnewline
M7 & -3.7535077827975 & 6.381471 & -0.5882 & 0.559002 & 0.279501 \tabularnewline
M8 & 7.3282567821366 & 7.257272 & 1.0098 & 0.317367 & 0.158684 \tabularnewline
M9 & 5.64905477660003 & 5.630819 & 1.0032 & 0.320482 & 0.160241 \tabularnewline
M10 & 2.49342334199798 & 5.641326 & 0.442 & 0.660363 & 0.330182 \tabularnewline
M11 & 17.1748580182210 & 5.830963 & 2.9455 & 0.004849 & 0.002424 \tabularnewline
t & 0.00847982563409131 & 0.052189 & 0.1625 & 0.871567 & 0.435784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&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]44.9943140478135[/C][C]15.291612[/C][C]2.9424[/C][C]0.00489[/C][C]0.002445[/C][/ROW]
[ROW][C]`Y-1`[/C][C]0.119676240683047[/C][C]0.140334[/C][C]0.8528[/C][C]0.397759[/C][C]0.198879[/C][/ROW]
[ROW][C]`Y-2`[/C][C]0.118023330461420[/C][C]0.139463[/C][C]0.8463[/C][C]0.401354[/C][C]0.200677[/C][/ROW]
[ROW][C]`Y-3`[/C][C]-0.114070297649060[/C][C]0.138079[/C][C]-0.8261[/C][C]0.412581[/C][C]0.20629[/C][/ROW]
[ROW][C]`Y-4`[/C][C]-0.0825692584978275[/C][C]0.144016[/C][C]-0.5733[/C][C]0.568938[/C][C]0.284469[/C][/ROW]
[ROW][C]M1[/C][C]0.889367993868057[/C][C]5.962993[/C][C]0.1491[/C][C]0.882025[/C][C]0.441013[/C][/ROW]
[ROW][C]M2[/C][C]14.1736152636581[/C][C]5.836522[/C][C]2.4284[/C][C]0.01873[/C][C]0.009365[/C][/ROW]
[ROW][C]M3[/C][C]-8.39507896223981[/C][C]5.188471[/C][C]-1.618[/C][C]0.111826[/C][C]0.055913[/C][/ROW]
[ROW][C]M4[/C][C]-16.831064932218[/C][C]6.524153[/C][C]-2.5798[/C][C]0.01281[/C][C]0.006405[/C][/ROW]
[ROW][C]M5[/C][C]15.0692743531417[/C][C]7.271138[/C][C]2.0725[/C][C]0.043294[/C][C]0.021647[/C][/ROW]
[ROW][C]M6[/C][C]17.4087369750493[/C][C]5.930643[/C][C]2.9354[/C][C]0.004985[/C][C]0.002493[/C][/ROW]
[ROW][C]M7[/C][C]-3.7535077827975[/C][C]6.381471[/C][C]-0.5882[/C][C]0.559002[/C][C]0.279501[/C][/ROW]
[ROW][C]M8[/C][C]7.3282567821366[/C][C]7.257272[/C][C]1.0098[/C][C]0.317367[/C][C]0.158684[/C][/ROW]
[ROW][C]M9[/C][C]5.64905477660003[/C][C]5.630819[/C][C]1.0032[/C][C]0.320482[/C][C]0.160241[/C][/ROW]
[ROW][C]M10[/C][C]2.49342334199798[/C][C]5.641326[/C][C]0.442[/C][C]0.660363[/C][C]0.330182[/C][/ROW]
[ROW][C]M11[/C][C]17.1748580182210[/C][C]5.830963[/C][C]2.9455[/C][C]0.004849[/C][C]0.002424[/C][/ROW]
[ROW][C]t[/C][C]0.00847982563409131[/C][C]0.052189[/C][C]0.1625[/C][C]0.871567[/C][C]0.435784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=2

As an alternative you can also use a QR Code:  
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)44.994314047813515.2916122.94240.004890.002445
`Y-1`0.1196762406830470.1403340.85280.3977590.198879
`Y-2`0.1180233304614200.1394630.84630.4013540.200677
`Y-3`-0.1140702976490600.138079-0.82610.4125810.20629
`Y-4`-0.08256925849782750.144016-0.57330.5689380.284469
M10.8893679938680575.9629930.14910.8820250.441013
M214.17361526365815.8365222.42840.018730.009365
M3-8.395078962239815.188471-1.6180.1118260.055913
M4-16.8310649322186.524153-2.57980.012810.006405
M515.06927435314177.2711382.07250.0432940.021647
M617.40873697504935.9306432.93540.0049850.002493
M7-3.75350778279756.381471-0.58820.5590020.279501
M87.32825678213667.2572721.00980.3173670.158684
M95.649054776600035.6308191.00320.3204820.160241
M102.493423341997985.6413260.4420.6603630.330182
M1117.17485801822105.8309632.94550.0048490.002424
t0.008479825634091310.0521890.16250.8715670.435784






Multiple Linear Regression - Regression Statistics
Multiple R0.80610013552331
R-squared0.649797428490698
Adjusted R-squared0.539929955076015
F-TEST (value)5.91437491274698
F-TEST (DF numerator)16
F-TEST (DF denominator)51
p-value5.17198614380376e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.26768004305843
Sum Squared Residuals3486.08119801372

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.80610013552331 \tabularnewline
R-squared & 0.649797428490698 \tabularnewline
Adjusted R-squared & 0.539929955076015 \tabularnewline
F-TEST (value) & 5.91437491274698 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 5.17198614380376e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.26768004305843 \tabularnewline
Sum Squared Residuals & 3486.08119801372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.80610013552331[/C][/ROW]
[ROW][C]R-squared[/C][C]0.649797428490698[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.539929955076015[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.91437491274698[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]5.17198614380376e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.26768004305843[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3486.08119801372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.80610013552331
R-squared0.649797428490698
Adjusted R-squared0.539929955076015
F-TEST (value)5.91437491274698
F-TEST (DF numerator)16
F-TEST (DF denominator)51
p-value5.17198614380376e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.26768004305843
Sum Squared Residuals3486.08119801372






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15849.87205553292748.12794446707255
26160.4415537478770.558446252122982
34138.59055978875312.40944021124695
42728.8156539292661-1.81565392926611
55856.42889742701951.57110257298047
67062.86817488676357.13182511323652
74950.0576174240948-1.05761742409475
85957.67073111770471.32926888229526
94450.7897908197215-6.78979081972154
103648.4323740537784-12.4323740537784
117260.987780125213611.0122198747864
124548.0709218332826-3.07092183328265
135652.13745230961363.86254769038645
145460.1140114827093-6.11401148270931
155338.720105966757914.2798940332421
163530.91147362610954.08852637389046
176159.86797582616911.13202417383088
185263.4822893978091-11.4822893978091
194746.45587950762690.544120492373131
205154.4059516347124-3.40595163471238
215251.5036497231330.496350276866989
226350.261742491419512.7382575085805
237466.34268407314457.65731592685548
244551.3466538315063-6.34665383150633
255148.73480477363832.26519522636174
266457.15987761216376.8401223878363
273639.2633711118947-3.26337111189466
283030.7293202349663-0.729320234966344
295556.6370992285174-1.63709922851738
306463.38937568406860.610624315931397
313949.2596412033724-10.2596412033724
324056.0638476607776-16.0638476607776
336348.471354318735514.5286456812645
344550.3034136906851-5.30341369068506
355967.5038536256567-8.50385362565667
365547.18233674990067.81766325009939
374049.4079786453635-9.40797864536349
386460.32273129457033.67726870542972
392738.1647082854049-11.1647082854049
402830.1830726655894-2.18307266558940
414556.3455565240837-11.3455565240837
425763.084957202766-6.08495720276593
434548.3146960433645-3.31469604336451
446957.363341192741311.6366588072587
456054.37604785744315.62395214255686
465653.36238248321662.63761751678336
475864.7645260065853-6.76452600658531
485046.41037744841253.58962255158754
495147.78626652044983.21373347955025
505360.3566196515587-7.35661965155874
513738.9012049273193-1.90120492731928
522229.3414093633028-7.34140936330281
535557.2560017228723-2.25600172287228
547063.44289640142256.55710359857752
556251.011207585383510.9887924146165
565860.388611062457-2.38861106245705
573952.8591572809668-13.8591572809668
584946.64008728090042.35991271909964
595861.4011561693998-3.40115616939985
604748.989710136898-1.98971013689797
614250.0614422180075-8.0614422180075
626259.6052062111212.39479378887905
633939.3600499198702-0.360049919870194
644032.01907018076587.9809298192342
657259.46446927133812.5355307286620
667066.73230642717043.26769357282964
675450.9009582361583.09904176384200
686556.10751733160698.89248266839315

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 58 & 49.8720555329274 & 8.12794446707255 \tabularnewline
2 & 61 & 60.441553747877 & 0.558446252122982 \tabularnewline
3 & 41 & 38.5905597887531 & 2.40944021124695 \tabularnewline
4 & 27 & 28.8156539292661 & -1.81565392926611 \tabularnewline
5 & 58 & 56.4288974270195 & 1.57110257298047 \tabularnewline
6 & 70 & 62.8681748867635 & 7.13182511323652 \tabularnewline
7 & 49 & 50.0576174240948 & -1.05761742409475 \tabularnewline
8 & 59 & 57.6707311177047 & 1.32926888229526 \tabularnewline
9 & 44 & 50.7897908197215 & -6.78979081972154 \tabularnewline
10 & 36 & 48.4323740537784 & -12.4323740537784 \tabularnewline
11 & 72 & 60.9877801252136 & 11.0122198747864 \tabularnewline
12 & 45 & 48.0709218332826 & -3.07092183328265 \tabularnewline
13 & 56 & 52.1374523096136 & 3.86254769038645 \tabularnewline
14 & 54 & 60.1140114827093 & -6.11401148270931 \tabularnewline
15 & 53 & 38.7201059667579 & 14.2798940332421 \tabularnewline
16 & 35 & 30.9114736261095 & 4.08852637389046 \tabularnewline
17 & 61 & 59.8679758261691 & 1.13202417383088 \tabularnewline
18 & 52 & 63.4822893978091 & -11.4822893978091 \tabularnewline
19 & 47 & 46.4558795076269 & 0.544120492373131 \tabularnewline
20 & 51 & 54.4059516347124 & -3.40595163471238 \tabularnewline
21 & 52 & 51.503649723133 & 0.496350276866989 \tabularnewline
22 & 63 & 50.2617424914195 & 12.7382575085805 \tabularnewline
23 & 74 & 66.3426840731445 & 7.65731592685548 \tabularnewline
24 & 45 & 51.3466538315063 & -6.34665383150633 \tabularnewline
25 & 51 & 48.7348047736383 & 2.26519522636174 \tabularnewline
26 & 64 & 57.1598776121637 & 6.8401223878363 \tabularnewline
27 & 36 & 39.2633711118947 & -3.26337111189466 \tabularnewline
28 & 30 & 30.7293202349663 & -0.729320234966344 \tabularnewline
29 & 55 & 56.6370992285174 & -1.63709922851738 \tabularnewline
30 & 64 & 63.3893756840686 & 0.610624315931397 \tabularnewline
31 & 39 & 49.2596412033724 & -10.2596412033724 \tabularnewline
32 & 40 & 56.0638476607776 & -16.0638476607776 \tabularnewline
33 & 63 & 48.4713543187355 & 14.5286456812645 \tabularnewline
34 & 45 & 50.3034136906851 & -5.30341369068506 \tabularnewline
35 & 59 & 67.5038536256567 & -8.50385362565667 \tabularnewline
36 & 55 & 47.1823367499006 & 7.81766325009939 \tabularnewline
37 & 40 & 49.4079786453635 & -9.40797864536349 \tabularnewline
38 & 64 & 60.3227312945703 & 3.67726870542972 \tabularnewline
39 & 27 & 38.1647082854049 & -11.1647082854049 \tabularnewline
40 & 28 & 30.1830726655894 & -2.18307266558940 \tabularnewline
41 & 45 & 56.3455565240837 & -11.3455565240837 \tabularnewline
42 & 57 & 63.084957202766 & -6.08495720276593 \tabularnewline
43 & 45 & 48.3146960433645 & -3.31469604336451 \tabularnewline
44 & 69 & 57.3633411927413 & 11.6366588072587 \tabularnewline
45 & 60 & 54.3760478574431 & 5.62395214255686 \tabularnewline
46 & 56 & 53.3623824832166 & 2.63761751678336 \tabularnewline
47 & 58 & 64.7645260065853 & -6.76452600658531 \tabularnewline
48 & 50 & 46.4103774484125 & 3.58962255158754 \tabularnewline
49 & 51 & 47.7862665204498 & 3.21373347955025 \tabularnewline
50 & 53 & 60.3566196515587 & -7.35661965155874 \tabularnewline
51 & 37 & 38.9012049273193 & -1.90120492731928 \tabularnewline
52 & 22 & 29.3414093633028 & -7.34140936330281 \tabularnewline
53 & 55 & 57.2560017228723 & -2.25600172287228 \tabularnewline
54 & 70 & 63.4428964014225 & 6.55710359857752 \tabularnewline
55 & 62 & 51.0112075853835 & 10.9887924146165 \tabularnewline
56 & 58 & 60.388611062457 & -2.38861106245705 \tabularnewline
57 & 39 & 52.8591572809668 & -13.8591572809668 \tabularnewline
58 & 49 & 46.6400872809004 & 2.35991271909964 \tabularnewline
59 & 58 & 61.4011561693998 & -3.40115616939985 \tabularnewline
60 & 47 & 48.989710136898 & -1.98971013689797 \tabularnewline
61 & 42 & 50.0614422180075 & -8.0614422180075 \tabularnewline
62 & 62 & 59.605206211121 & 2.39479378887905 \tabularnewline
63 & 39 & 39.3600499198702 & -0.360049919870194 \tabularnewline
64 & 40 & 32.0190701807658 & 7.9809298192342 \tabularnewline
65 & 72 & 59.464469271338 & 12.5355307286620 \tabularnewline
66 & 70 & 66.7323064271704 & 3.26769357282964 \tabularnewline
67 & 54 & 50.900958236158 & 3.09904176384200 \tabularnewline
68 & 65 & 56.1075173316069 & 8.89248266839315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&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]58[/C][C]49.8720555329274[/C][C]8.12794446707255[/C][/ROW]
[ROW][C]2[/C][C]61[/C][C]60.441553747877[/C][C]0.558446252122982[/C][/ROW]
[ROW][C]3[/C][C]41[/C][C]38.5905597887531[/C][C]2.40944021124695[/C][/ROW]
[ROW][C]4[/C][C]27[/C][C]28.8156539292661[/C][C]-1.81565392926611[/C][/ROW]
[ROW][C]5[/C][C]58[/C][C]56.4288974270195[/C][C]1.57110257298047[/C][/ROW]
[ROW][C]6[/C][C]70[/C][C]62.8681748867635[/C][C]7.13182511323652[/C][/ROW]
[ROW][C]7[/C][C]49[/C][C]50.0576174240948[/C][C]-1.05761742409475[/C][/ROW]
[ROW][C]8[/C][C]59[/C][C]57.6707311177047[/C][C]1.32926888229526[/C][/ROW]
[ROW][C]9[/C][C]44[/C][C]50.7897908197215[/C][C]-6.78979081972154[/C][/ROW]
[ROW][C]10[/C][C]36[/C][C]48.4323740537784[/C][C]-12.4323740537784[/C][/ROW]
[ROW][C]11[/C][C]72[/C][C]60.9877801252136[/C][C]11.0122198747864[/C][/ROW]
[ROW][C]12[/C][C]45[/C][C]48.0709218332826[/C][C]-3.07092183328265[/C][/ROW]
[ROW][C]13[/C][C]56[/C][C]52.1374523096136[/C][C]3.86254769038645[/C][/ROW]
[ROW][C]14[/C][C]54[/C][C]60.1140114827093[/C][C]-6.11401148270931[/C][/ROW]
[ROW][C]15[/C][C]53[/C][C]38.7201059667579[/C][C]14.2798940332421[/C][/ROW]
[ROW][C]16[/C][C]35[/C][C]30.9114736261095[/C][C]4.08852637389046[/C][/ROW]
[ROW][C]17[/C][C]61[/C][C]59.8679758261691[/C][C]1.13202417383088[/C][/ROW]
[ROW][C]18[/C][C]52[/C][C]63.4822893978091[/C][C]-11.4822893978091[/C][/ROW]
[ROW][C]19[/C][C]47[/C][C]46.4558795076269[/C][C]0.544120492373131[/C][/ROW]
[ROW][C]20[/C][C]51[/C][C]54.4059516347124[/C][C]-3.40595163471238[/C][/ROW]
[ROW][C]21[/C][C]52[/C][C]51.503649723133[/C][C]0.496350276866989[/C][/ROW]
[ROW][C]22[/C][C]63[/C][C]50.2617424914195[/C][C]12.7382575085805[/C][/ROW]
[ROW][C]23[/C][C]74[/C][C]66.3426840731445[/C][C]7.65731592685548[/C][/ROW]
[ROW][C]24[/C][C]45[/C][C]51.3466538315063[/C][C]-6.34665383150633[/C][/ROW]
[ROW][C]25[/C][C]51[/C][C]48.7348047736383[/C][C]2.26519522636174[/C][/ROW]
[ROW][C]26[/C][C]64[/C][C]57.1598776121637[/C][C]6.8401223878363[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]39.2633711118947[/C][C]-3.26337111189466[/C][/ROW]
[ROW][C]28[/C][C]30[/C][C]30.7293202349663[/C][C]-0.729320234966344[/C][/ROW]
[ROW][C]29[/C][C]55[/C][C]56.6370992285174[/C][C]-1.63709922851738[/C][/ROW]
[ROW][C]30[/C][C]64[/C][C]63.3893756840686[/C][C]0.610624315931397[/C][/ROW]
[ROW][C]31[/C][C]39[/C][C]49.2596412033724[/C][C]-10.2596412033724[/C][/ROW]
[ROW][C]32[/C][C]40[/C][C]56.0638476607776[/C][C]-16.0638476607776[/C][/ROW]
[ROW][C]33[/C][C]63[/C][C]48.4713543187355[/C][C]14.5286456812645[/C][/ROW]
[ROW][C]34[/C][C]45[/C][C]50.3034136906851[/C][C]-5.30341369068506[/C][/ROW]
[ROW][C]35[/C][C]59[/C][C]67.5038536256567[/C][C]-8.50385362565667[/C][/ROW]
[ROW][C]36[/C][C]55[/C][C]47.1823367499006[/C][C]7.81766325009939[/C][/ROW]
[ROW][C]37[/C][C]40[/C][C]49.4079786453635[/C][C]-9.40797864536349[/C][/ROW]
[ROW][C]38[/C][C]64[/C][C]60.3227312945703[/C][C]3.67726870542972[/C][/ROW]
[ROW][C]39[/C][C]27[/C][C]38.1647082854049[/C][C]-11.1647082854049[/C][/ROW]
[ROW][C]40[/C][C]28[/C][C]30.1830726655894[/C][C]-2.18307266558940[/C][/ROW]
[ROW][C]41[/C][C]45[/C][C]56.3455565240837[/C][C]-11.3455565240837[/C][/ROW]
[ROW][C]42[/C][C]57[/C][C]63.084957202766[/C][C]-6.08495720276593[/C][/ROW]
[ROW][C]43[/C][C]45[/C][C]48.3146960433645[/C][C]-3.31469604336451[/C][/ROW]
[ROW][C]44[/C][C]69[/C][C]57.3633411927413[/C][C]11.6366588072587[/C][/ROW]
[ROW][C]45[/C][C]60[/C][C]54.3760478574431[/C][C]5.62395214255686[/C][/ROW]
[ROW][C]46[/C][C]56[/C][C]53.3623824832166[/C][C]2.63761751678336[/C][/ROW]
[ROW][C]47[/C][C]58[/C][C]64.7645260065853[/C][C]-6.76452600658531[/C][/ROW]
[ROW][C]48[/C][C]50[/C][C]46.4103774484125[/C][C]3.58962255158754[/C][/ROW]
[ROW][C]49[/C][C]51[/C][C]47.7862665204498[/C][C]3.21373347955025[/C][/ROW]
[ROW][C]50[/C][C]53[/C][C]60.3566196515587[/C][C]-7.35661965155874[/C][/ROW]
[ROW][C]51[/C][C]37[/C][C]38.9012049273193[/C][C]-1.90120492731928[/C][/ROW]
[ROW][C]52[/C][C]22[/C][C]29.3414093633028[/C][C]-7.34140936330281[/C][/ROW]
[ROW][C]53[/C][C]55[/C][C]57.2560017228723[/C][C]-2.25600172287228[/C][/ROW]
[ROW][C]54[/C][C]70[/C][C]63.4428964014225[/C][C]6.55710359857752[/C][/ROW]
[ROW][C]55[/C][C]62[/C][C]51.0112075853835[/C][C]10.9887924146165[/C][/ROW]
[ROW][C]56[/C][C]58[/C][C]60.388611062457[/C][C]-2.38861106245705[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]52.8591572809668[/C][C]-13.8591572809668[/C][/ROW]
[ROW][C]58[/C][C]49[/C][C]46.6400872809004[/C][C]2.35991271909964[/C][/ROW]
[ROW][C]59[/C][C]58[/C][C]61.4011561693998[/C][C]-3.40115616939985[/C][/ROW]
[ROW][C]60[/C][C]47[/C][C]48.989710136898[/C][C]-1.98971013689797[/C][/ROW]
[ROW][C]61[/C][C]42[/C][C]50.0614422180075[/C][C]-8.0614422180075[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]59.605206211121[/C][C]2.39479378887905[/C][/ROW]
[ROW][C]63[/C][C]39[/C][C]39.3600499198702[/C][C]-0.360049919870194[/C][/ROW]
[ROW][C]64[/C][C]40[/C][C]32.0190701807658[/C][C]7.9809298192342[/C][/ROW]
[ROW][C]65[/C][C]72[/C][C]59.464469271338[/C][C]12.5355307286620[/C][/ROW]
[ROW][C]66[/C][C]70[/C][C]66.7323064271704[/C][C]3.26769357282964[/C][/ROW]
[ROW][C]67[/C][C]54[/C][C]50.900958236158[/C][C]3.09904176384200[/C][/ROW]
[ROW][C]68[/C][C]65[/C][C]56.1075173316069[/C][C]8.89248266839315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15849.87205553292748.12794446707255
26160.4415537478770.558446252122982
34138.59055978875312.40944021124695
42728.8156539292661-1.81565392926611
55856.42889742701951.57110257298047
67062.86817488676357.13182511323652
74950.0576174240948-1.05761742409475
85957.67073111770471.32926888229526
94450.7897908197215-6.78979081972154
103648.4323740537784-12.4323740537784
117260.987780125213611.0122198747864
124548.0709218332826-3.07092183328265
135652.13745230961363.86254769038645
145460.1140114827093-6.11401148270931
155338.720105966757914.2798940332421
163530.91147362610954.08852637389046
176159.86797582616911.13202417383088
185263.4822893978091-11.4822893978091
194746.45587950762690.544120492373131
205154.4059516347124-3.40595163471238
215251.5036497231330.496350276866989
226350.261742491419512.7382575085805
237466.34268407314457.65731592685548
244551.3466538315063-6.34665383150633
255148.73480477363832.26519522636174
266457.15987761216376.8401223878363
273639.2633711118947-3.26337111189466
283030.7293202349663-0.729320234966344
295556.6370992285174-1.63709922851738
306463.38937568406860.610624315931397
313949.2596412033724-10.2596412033724
324056.0638476607776-16.0638476607776
336348.471354318735514.5286456812645
344550.3034136906851-5.30341369068506
355967.5038536256567-8.50385362565667
365547.18233674990067.81766325009939
374049.4079786453635-9.40797864536349
386460.32273129457033.67726870542972
392738.1647082854049-11.1647082854049
402830.1830726655894-2.18307266558940
414556.3455565240837-11.3455565240837
425763.084957202766-6.08495720276593
434548.3146960433645-3.31469604336451
446957.363341192741311.6366588072587
456054.37604785744315.62395214255686
465653.36238248321662.63761751678336
475864.7645260065853-6.76452600658531
485046.41037744841253.58962255158754
495147.78626652044983.21373347955025
505360.3566196515587-7.35661965155874
513738.9012049273193-1.90120492731928
522229.3414093633028-7.34140936330281
535557.2560017228723-2.25600172287228
547063.44289640142256.55710359857752
556251.011207585383510.9887924146165
565860.388611062457-2.38861106245705
573952.8591572809668-13.8591572809668
584946.64008728090042.35991271909964
595861.4011561693998-3.40115616939985
604748.989710136898-1.98971013689797
614250.0614422180075-8.0614422180075
626259.6052062111212.39479378887905
633939.3600499198702-0.360049919870194
644032.01907018076587.9809298192342
657259.46446927133812.5355307286620
667066.73230642717043.26769357282964
675450.9009582361583.09904176384200
686556.10751733160698.89248266839315






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4997750945753560.9995501891507120.500224905424644
210.3293580849779600.6587161699559210.67064191502204
220.6784837645584130.6430324708831750.321516235441587
230.6470873996472950.705825200705410.352912600352705
240.5760681976027180.8478636047945630.423931802397282
250.489340159676090.978680319352180.51065984032391
260.4495359928070910.8990719856141820.550464007192909
270.5320783325427430.9358433349145130.467921667457257
280.435339100133040.870678200266080.56466089986696
290.3492827701733040.6985655403466070.650717229826696
300.26973380473050.5394676094610.7302661952695
310.2500296203821130.5000592407642260.749970379617887
320.3678915040958730.7357830081917460.632108495904127
330.6146073498828640.7707853002342720.385392650117136
340.5614481655817170.8771036688365660.438551834418283
350.5376195912890990.9247608174218020.462380408710901
360.5459429342797720.9081141314404560.454057065720228
370.5187311046885560.9625377906228880.481268895311444
380.542100554798250.91579889040350.45789944520175
390.5614737629307260.8770524741385490.438526237069274
400.4663636888416310.9327273776832620.533636311158369
410.5299121470771730.9401757058456530.470087852922826
420.4373638283394270.8747276566788550.562636171660573
430.413294557195160.826589114390320.58670544280484
440.5160323441829790.9679353116340420.483967655817021
450.5524152023774550.895169595245090.447584797622545
460.4572928467513720.9145856935027440.542707153248628
470.3399540651726120.6799081303452240.660045934827388
480.2585256136501230.5170512273002460.741474386349877

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.499775094575356 & 0.999550189150712 & 0.500224905424644 \tabularnewline
21 & 0.329358084977960 & 0.658716169955921 & 0.67064191502204 \tabularnewline
22 & 0.678483764558413 & 0.643032470883175 & 0.321516235441587 \tabularnewline
23 & 0.647087399647295 & 0.70582520070541 & 0.352912600352705 \tabularnewline
24 & 0.576068197602718 & 0.847863604794563 & 0.423931802397282 \tabularnewline
25 & 0.48934015967609 & 0.97868031935218 & 0.51065984032391 \tabularnewline
26 & 0.449535992807091 & 0.899071985614182 & 0.550464007192909 \tabularnewline
27 & 0.532078332542743 & 0.935843334914513 & 0.467921667457257 \tabularnewline
28 & 0.43533910013304 & 0.87067820026608 & 0.56466089986696 \tabularnewline
29 & 0.349282770173304 & 0.698565540346607 & 0.650717229826696 \tabularnewline
30 & 0.2697338047305 & 0.539467609461 & 0.7302661952695 \tabularnewline
31 & 0.250029620382113 & 0.500059240764226 & 0.749970379617887 \tabularnewline
32 & 0.367891504095873 & 0.735783008191746 & 0.632108495904127 \tabularnewline
33 & 0.614607349882864 & 0.770785300234272 & 0.385392650117136 \tabularnewline
34 & 0.561448165581717 & 0.877103668836566 & 0.438551834418283 \tabularnewline
35 & 0.537619591289099 & 0.924760817421802 & 0.462380408710901 \tabularnewline
36 & 0.545942934279772 & 0.908114131440456 & 0.454057065720228 \tabularnewline
37 & 0.518731104688556 & 0.962537790622888 & 0.481268895311444 \tabularnewline
38 & 0.54210055479825 & 0.9157988904035 & 0.45789944520175 \tabularnewline
39 & 0.561473762930726 & 0.877052474138549 & 0.438526237069274 \tabularnewline
40 & 0.466363688841631 & 0.932727377683262 & 0.533636311158369 \tabularnewline
41 & 0.529912147077173 & 0.940175705845653 & 0.470087852922826 \tabularnewline
42 & 0.437363828339427 & 0.874727656678855 & 0.562636171660573 \tabularnewline
43 & 0.41329455719516 & 0.82658911439032 & 0.58670544280484 \tabularnewline
44 & 0.516032344182979 & 0.967935311634042 & 0.483967655817021 \tabularnewline
45 & 0.552415202377455 & 0.89516959524509 & 0.447584797622545 \tabularnewline
46 & 0.457292846751372 & 0.914585693502744 & 0.542707153248628 \tabularnewline
47 & 0.339954065172612 & 0.679908130345224 & 0.660045934827388 \tabularnewline
48 & 0.258525613650123 & 0.517051227300246 & 0.741474386349877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&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]20[/C][C]0.499775094575356[/C][C]0.999550189150712[/C][C]0.500224905424644[/C][/ROW]
[ROW][C]21[/C][C]0.329358084977960[/C][C]0.658716169955921[/C][C]0.67064191502204[/C][/ROW]
[ROW][C]22[/C][C]0.678483764558413[/C][C]0.643032470883175[/C][C]0.321516235441587[/C][/ROW]
[ROW][C]23[/C][C]0.647087399647295[/C][C]0.70582520070541[/C][C]0.352912600352705[/C][/ROW]
[ROW][C]24[/C][C]0.576068197602718[/C][C]0.847863604794563[/C][C]0.423931802397282[/C][/ROW]
[ROW][C]25[/C][C]0.48934015967609[/C][C]0.97868031935218[/C][C]0.51065984032391[/C][/ROW]
[ROW][C]26[/C][C]0.449535992807091[/C][C]0.899071985614182[/C][C]0.550464007192909[/C][/ROW]
[ROW][C]27[/C][C]0.532078332542743[/C][C]0.935843334914513[/C][C]0.467921667457257[/C][/ROW]
[ROW][C]28[/C][C]0.43533910013304[/C][C]0.87067820026608[/C][C]0.56466089986696[/C][/ROW]
[ROW][C]29[/C][C]0.349282770173304[/C][C]0.698565540346607[/C][C]0.650717229826696[/C][/ROW]
[ROW][C]30[/C][C]0.2697338047305[/C][C]0.539467609461[/C][C]0.7302661952695[/C][/ROW]
[ROW][C]31[/C][C]0.250029620382113[/C][C]0.500059240764226[/C][C]0.749970379617887[/C][/ROW]
[ROW][C]32[/C][C]0.367891504095873[/C][C]0.735783008191746[/C][C]0.632108495904127[/C][/ROW]
[ROW][C]33[/C][C]0.614607349882864[/C][C]0.770785300234272[/C][C]0.385392650117136[/C][/ROW]
[ROW][C]34[/C][C]0.561448165581717[/C][C]0.877103668836566[/C][C]0.438551834418283[/C][/ROW]
[ROW][C]35[/C][C]0.537619591289099[/C][C]0.924760817421802[/C][C]0.462380408710901[/C][/ROW]
[ROW][C]36[/C][C]0.545942934279772[/C][C]0.908114131440456[/C][C]0.454057065720228[/C][/ROW]
[ROW][C]37[/C][C]0.518731104688556[/C][C]0.962537790622888[/C][C]0.481268895311444[/C][/ROW]
[ROW][C]38[/C][C]0.54210055479825[/C][C]0.9157988904035[/C][C]0.45789944520175[/C][/ROW]
[ROW][C]39[/C][C]0.561473762930726[/C][C]0.877052474138549[/C][C]0.438526237069274[/C][/ROW]
[ROW][C]40[/C][C]0.466363688841631[/C][C]0.932727377683262[/C][C]0.533636311158369[/C][/ROW]
[ROW][C]41[/C][C]0.529912147077173[/C][C]0.940175705845653[/C][C]0.470087852922826[/C][/ROW]
[ROW][C]42[/C][C]0.437363828339427[/C][C]0.874727656678855[/C][C]0.562636171660573[/C][/ROW]
[ROW][C]43[/C][C]0.41329455719516[/C][C]0.82658911439032[/C][C]0.58670544280484[/C][/ROW]
[ROW][C]44[/C][C]0.516032344182979[/C][C]0.967935311634042[/C][C]0.483967655817021[/C][/ROW]
[ROW][C]45[/C][C]0.552415202377455[/C][C]0.89516959524509[/C][C]0.447584797622545[/C][/ROW]
[ROW][C]46[/C][C]0.457292846751372[/C][C]0.914585693502744[/C][C]0.542707153248628[/C][/ROW]
[ROW][C]47[/C][C]0.339954065172612[/C][C]0.679908130345224[/C][C]0.660045934827388[/C][/ROW]
[ROW][C]48[/C][C]0.258525613650123[/C][C]0.517051227300246[/C][C]0.741474386349877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4997750945753560.9995501891507120.500224905424644
210.3293580849779600.6587161699559210.67064191502204
220.6784837645584130.6430324708831750.321516235441587
230.6470873996472950.705825200705410.352912600352705
240.5760681976027180.8478636047945630.423931802397282
250.489340159676090.978680319352180.51065984032391
260.4495359928070910.8990719856141820.550464007192909
270.5320783325427430.9358433349145130.467921667457257
280.435339100133040.870678200266080.56466089986696
290.3492827701733040.6985655403466070.650717229826696
300.26973380473050.5394676094610.7302661952695
310.2500296203821130.5000592407642260.749970379617887
320.3678915040958730.7357830081917460.632108495904127
330.6146073498828640.7707853002342720.385392650117136
340.5614481655817170.8771036688365660.438551834418283
350.5376195912890990.9247608174218020.462380408710901
360.5459429342797720.9081141314404560.454057065720228
370.5187311046885560.9625377906228880.481268895311444
380.542100554798250.91579889040350.45789944520175
390.5614737629307260.8770524741385490.438526237069274
400.4663636888416310.9327273776832620.533636311158369
410.5299121470771730.9401757058456530.470087852922826
420.4373638283394270.8747276566788550.562636171660573
430.413294557195160.826589114390320.58670544280484
440.5160323441829790.9679353116340420.483967655817021
450.5524152023774550.895169595245090.447584797622545
460.4572928467513720.9145856935027440.542707153248628
470.3399540651726120.6799081303452240.660045934827388
480.2585256136501230.5170512273002460.741474386349877






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111958&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111958&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111958&T=6

As an alternative you can also use a QR Code:  
QR Code


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}