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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 computationWed, 19 Dec 2012 20:08:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/19/t1355965778bufc9lztiqqwsez.htm/, Retrieved Thu, 31 Oct 2024 23:19:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202513, Retrieved Thu, 31 Oct 2024 23:19:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Two-Way ANOVA] [] [2010-11-02 14:42:14] [b98453cac15ba1066b407e146608df68]
- RMPD  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-18 22:24:51] [8fb02a06fa197bf14378f27a41ead604]
- RMPD      [Multiple Regression] [] [2012-12-20 01:08:06] [fec1d01507e8c19b843fb6b1ea3b7bfc] [Current]
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Dataseries X:
1	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
1	0
1	1
1	0
0	0
1	1
0	0
0	0
0	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
0	0
1	0
0	1
0	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
1	1
0	0
0	1
0	0
1	0
0	0
0	0
0	0
1	1
1	0
0	0
0	0
1	0
0	0
0	0
1	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	1
1	0
0	0
0	0
0	0
0	1
0	0
0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0347325778834464 + 0.220866418350756T40[t] + 0.00184631758244026t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0347325778834464 +  0.220866418350756T40[t] +  0.00184631758244026t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0347325778834464 +  0.220866418350756T40[t] +  0.00184631758244026t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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
CorrectAnalysis[t] = -0.0347325778834464 + 0.220866418350756T40[t] + 0.00184631758244026t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03473257788344640.067781-0.51240.6097170.304859
T400.2208664183507560.0715083.08870.0027350.001367
t0.001846317582440260.0012751.44810.1513620.075681

\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) & -0.0347325778834464 & 0.067781 & -0.5124 & 0.609717 & 0.304859 \tabularnewline
T40 & 0.220866418350756 & 0.071508 & 3.0887 & 0.002735 & 0.001367 \tabularnewline
t & 0.00184631758244026 & 0.001275 & 1.4481 & 0.151362 & 0.075681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&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]-0.0347325778834464[/C][C]0.067781[/C][C]-0.5124[/C][C]0.609717[/C][C]0.304859[/C][/ROW]
[ROW][C]T40[/C][C]0.220866418350756[/C][C]0.071508[/C][C]3.0887[/C][C]0.002735[/C][C]0.001367[/C][/ROW]
[ROW][C]t[/C][C]0.00184631758244026[/C][C]0.001275[/C][C]1.4481[/C][C]0.151362[/C][C]0.075681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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)-0.03473257788344640.067781-0.51240.6097170.304859
T400.2208664183507560.0715083.08870.0027350.001367
t0.001846317582440260.0012751.44810.1513620.075681







Multiple Linear Regression - Regression Statistics
Multiple R0.342613978712953
R-squared0.11738433840952
Adjusted R-squared0.0961164911422794
F-TEST (value)5.51933333611674
F-TEST (DF numerator)2
F-TEST (DF denominator)83
p-value0.00561723541576875
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.29272792904506
Sum Squared Residuals7.1122401567698

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.342613978712953 \tabularnewline
R-squared & 0.11738433840952 \tabularnewline
Adjusted R-squared & 0.0961164911422794 \tabularnewline
F-TEST (value) & 5.51933333611674 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 83 \tabularnewline
p-value & 0.00561723541576875 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.29272792904506 \tabularnewline
Sum Squared Residuals & 7.1122401567698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.342613978712953[/C][/ROW]
[ROW][C]R-squared[/C][C]0.11738433840952[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0961164911422794[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.51933333611674[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]83[/C][/ROW]
[ROW][C]p-value[/C][C]0.00561723541576875[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.29272792904506[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.1122401567698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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 R0.342613978712953
R-squared0.11738433840952
Adjusted R-squared0.0961164911422794
F-TEST (value)5.51933333611674
F-TEST (DF numerator)2
F-TEST (DF denominator)83
p-value0.00561723541576875
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.29272792904506
Sum Squared Residuals7.1122401567698







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.18798015804975-0.18798015804975
20-0.0310399427185660.031039942718566
30-0.02919362513612570.0291936251361257
40-0.02734730755368540.0273473075536854
50-0.02550098997124510.0255009899712451
60-0.02365467238880480.0236546723888048
70-0.02180835480636460.0218083548063646
800.200904381126832-0.200904381126832
90-0.01811571964148410.0181157196414841
100-0.01626940205904380.0162694020590438
1100.206443333874152-0.206443333874152
120-0.01257676689416330.0125767668941633
130-0.0107304493117230.010730449311723
1400.211982286621473-0.211982286621473
150-0.007037814146842530.00703781414684253
1600.215674921786354-0.215674921786354
1710.2175212393687940.782478760631206
1800.219367556951234-0.219367556951234
1900.000347456182918498-0.000347456182918498
2010.2230601921161150.776939807883885
2100.00404009134779901-0.00404009134779901
2200.00588640893023927-0.00588640893023927
2300.00773272651267953-0.00773272651267953
2400.00957904409511979-0.00957904409511979
2500.232291780028316-0.232291780028316
2600.0132716792600003-0.0132716792600003
2700.0151179968424406-0.0151179968424406
2800.0169643144248808-0.0169643144248808
2900.0188106320073211-0.0188106320073211
3000.0206569495897613-0.0206569495897613
3100.0225032671722016-0.0225032671722016
3200.0243495847546418-0.0243495847546418
3300.0261959023370821-0.0261959023370821
3400.248908638270278-0.248908638270278
3500.0298885375019626-0.0298885375019626
3600.0317348550844029-0.0317348550844029
3700.254447591017599-0.254447591017599
3800.0354274902492834-0.0354274902492834
3900.0372738078317236-0.0372738078317236
4000.25998654376492-0.25998654376492
4110.04096644299660430.959033557003396
4200.0428127605790444-0.0428127605790444
4300.0446590781614847-0.0446590781614847
4400.267371814094681-0.267371814094681
4500.0483517133263652-0.0483517133263652
4600.0501980309088054-0.0501980309088054
4700.0520443484912457-0.0520443484912457
4800.0538906660736859-0.0538906660736859
4900.0557369836561262-0.0557369836561262
5000.0575833012385665-0.0575833012385665
5100.280296037171763-0.280296037171763
5210.2821423547542030.717857645245797
5300.0631222539858873-0.0631222539858873
5410.06496857156832760.935031428431672
5500.0668148891507678-0.0668148891507678
5600.289527625083964-0.289527625083964
5700.0705075243156483-0.0705075243156483
5800.0723538418980885-0.0723538418980885
5900.0742001594805288-0.0742001594805288
6010.2969128954137250.703087104586275
6100.298759212996165-0.298759212996165
6200.0797391122278496-0.0797391122278496
6300.0815854298102898-0.0815854298102898
6400.304298165743486-0.304298165743486
6500.0852780649751703-0.0852780649751703
6600.0871243825576106-0.0871243825576106
6710.3098371184908070.690162881509193
6800.0908170177224911-0.0908170177224911
6900.0926633353049313-0.0926633353049313
7000.0945096528873716-0.0945096528873716
7100.0963559704698119-0.0963559704698119
7200.0982022880522521-0.0982022880522521
7300.100048605634692-0.100048605634692
7400.101894923217133-0.101894923217133
7500.103741240799573-0.103741240799573
7600.326453976732769-0.326453976732769
7700.107433875964453-0.107433875964453
7800.109280193546894-0.109280193546894
7910.331992929480090.66800707051991
8000.33383924706253-0.33383924706253
8100.114819146294214-0.114819146294214
8200.116665463876655-0.116665463876655
8300.118511781459095-0.118511781459095
8410.1203580990415350.879641900958465
8500.122204416623975-0.122204416623975
8600.124050734206416-0.124050734206416

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.18798015804975 & -0.18798015804975 \tabularnewline
2 & 0 & -0.031039942718566 & 0.031039942718566 \tabularnewline
3 & 0 & -0.0291936251361257 & 0.0291936251361257 \tabularnewline
4 & 0 & -0.0273473075536854 & 0.0273473075536854 \tabularnewline
5 & 0 & -0.0255009899712451 & 0.0255009899712451 \tabularnewline
6 & 0 & -0.0236546723888048 & 0.0236546723888048 \tabularnewline
7 & 0 & -0.0218083548063646 & 0.0218083548063646 \tabularnewline
8 & 0 & 0.200904381126832 & -0.200904381126832 \tabularnewline
9 & 0 & -0.0181157196414841 & 0.0181157196414841 \tabularnewline
10 & 0 & -0.0162694020590438 & 0.0162694020590438 \tabularnewline
11 & 0 & 0.206443333874152 & -0.206443333874152 \tabularnewline
12 & 0 & -0.0125767668941633 & 0.0125767668941633 \tabularnewline
13 & 0 & -0.010730449311723 & 0.010730449311723 \tabularnewline
14 & 0 & 0.211982286621473 & -0.211982286621473 \tabularnewline
15 & 0 & -0.00703781414684253 & 0.00703781414684253 \tabularnewline
16 & 0 & 0.215674921786354 & -0.215674921786354 \tabularnewline
17 & 1 & 0.217521239368794 & 0.782478760631206 \tabularnewline
18 & 0 & 0.219367556951234 & -0.219367556951234 \tabularnewline
19 & 0 & 0.000347456182918498 & -0.000347456182918498 \tabularnewline
20 & 1 & 0.223060192116115 & 0.776939807883885 \tabularnewline
21 & 0 & 0.00404009134779901 & -0.00404009134779901 \tabularnewline
22 & 0 & 0.00588640893023927 & -0.00588640893023927 \tabularnewline
23 & 0 & 0.00773272651267953 & -0.00773272651267953 \tabularnewline
24 & 0 & 0.00957904409511979 & -0.00957904409511979 \tabularnewline
25 & 0 & 0.232291780028316 & -0.232291780028316 \tabularnewline
26 & 0 & 0.0132716792600003 & -0.0132716792600003 \tabularnewline
27 & 0 & 0.0151179968424406 & -0.0151179968424406 \tabularnewline
28 & 0 & 0.0169643144248808 & -0.0169643144248808 \tabularnewline
29 & 0 & 0.0188106320073211 & -0.0188106320073211 \tabularnewline
30 & 0 & 0.0206569495897613 & -0.0206569495897613 \tabularnewline
31 & 0 & 0.0225032671722016 & -0.0225032671722016 \tabularnewline
32 & 0 & 0.0243495847546418 & -0.0243495847546418 \tabularnewline
33 & 0 & 0.0261959023370821 & -0.0261959023370821 \tabularnewline
34 & 0 & 0.248908638270278 & -0.248908638270278 \tabularnewline
35 & 0 & 0.0298885375019626 & -0.0298885375019626 \tabularnewline
36 & 0 & 0.0317348550844029 & -0.0317348550844029 \tabularnewline
37 & 0 & 0.254447591017599 & -0.254447591017599 \tabularnewline
38 & 0 & 0.0354274902492834 & -0.0354274902492834 \tabularnewline
39 & 0 & 0.0372738078317236 & -0.0372738078317236 \tabularnewline
40 & 0 & 0.25998654376492 & -0.25998654376492 \tabularnewline
41 & 1 & 0.0409664429966043 & 0.959033557003396 \tabularnewline
42 & 0 & 0.0428127605790444 & -0.0428127605790444 \tabularnewline
43 & 0 & 0.0446590781614847 & -0.0446590781614847 \tabularnewline
44 & 0 & 0.267371814094681 & -0.267371814094681 \tabularnewline
45 & 0 & 0.0483517133263652 & -0.0483517133263652 \tabularnewline
46 & 0 & 0.0501980309088054 & -0.0501980309088054 \tabularnewline
47 & 0 & 0.0520443484912457 & -0.0520443484912457 \tabularnewline
48 & 0 & 0.0538906660736859 & -0.0538906660736859 \tabularnewline
49 & 0 & 0.0557369836561262 & -0.0557369836561262 \tabularnewline
50 & 0 & 0.0575833012385665 & -0.0575833012385665 \tabularnewline
51 & 0 & 0.280296037171763 & -0.280296037171763 \tabularnewline
52 & 1 & 0.282142354754203 & 0.717857645245797 \tabularnewline
53 & 0 & 0.0631222539858873 & -0.0631222539858873 \tabularnewline
54 & 1 & 0.0649685715683276 & 0.935031428431672 \tabularnewline
55 & 0 & 0.0668148891507678 & -0.0668148891507678 \tabularnewline
56 & 0 & 0.289527625083964 & -0.289527625083964 \tabularnewline
57 & 0 & 0.0705075243156483 & -0.0705075243156483 \tabularnewline
58 & 0 & 0.0723538418980885 & -0.0723538418980885 \tabularnewline
59 & 0 & 0.0742001594805288 & -0.0742001594805288 \tabularnewline
60 & 1 & 0.296912895413725 & 0.703087104586275 \tabularnewline
61 & 0 & 0.298759212996165 & -0.298759212996165 \tabularnewline
62 & 0 & 0.0797391122278496 & -0.0797391122278496 \tabularnewline
63 & 0 & 0.0815854298102898 & -0.0815854298102898 \tabularnewline
64 & 0 & 0.304298165743486 & -0.304298165743486 \tabularnewline
65 & 0 & 0.0852780649751703 & -0.0852780649751703 \tabularnewline
66 & 0 & 0.0871243825576106 & -0.0871243825576106 \tabularnewline
67 & 1 & 0.309837118490807 & 0.690162881509193 \tabularnewline
68 & 0 & 0.0908170177224911 & -0.0908170177224911 \tabularnewline
69 & 0 & 0.0926633353049313 & -0.0926633353049313 \tabularnewline
70 & 0 & 0.0945096528873716 & -0.0945096528873716 \tabularnewline
71 & 0 & 0.0963559704698119 & -0.0963559704698119 \tabularnewline
72 & 0 & 0.0982022880522521 & -0.0982022880522521 \tabularnewline
73 & 0 & 0.100048605634692 & -0.100048605634692 \tabularnewline
74 & 0 & 0.101894923217133 & -0.101894923217133 \tabularnewline
75 & 0 & 0.103741240799573 & -0.103741240799573 \tabularnewline
76 & 0 & 0.326453976732769 & -0.326453976732769 \tabularnewline
77 & 0 & 0.107433875964453 & -0.107433875964453 \tabularnewline
78 & 0 & 0.109280193546894 & -0.109280193546894 \tabularnewline
79 & 1 & 0.33199292948009 & 0.66800707051991 \tabularnewline
80 & 0 & 0.33383924706253 & -0.33383924706253 \tabularnewline
81 & 0 & 0.114819146294214 & -0.114819146294214 \tabularnewline
82 & 0 & 0.116665463876655 & -0.116665463876655 \tabularnewline
83 & 0 & 0.118511781459095 & -0.118511781459095 \tabularnewline
84 & 1 & 0.120358099041535 & 0.879641900958465 \tabularnewline
85 & 0 & 0.122204416623975 & -0.122204416623975 \tabularnewline
86 & 0 & 0.124050734206416 & -0.124050734206416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&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[/C][C]0.18798015804975[/C][C]-0.18798015804975[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.031039942718566[/C][C]0.031039942718566[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0291936251361257[/C][C]0.0291936251361257[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0273473075536854[/C][C]0.0273473075536854[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0255009899712451[/C][C]0.0255009899712451[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.0236546723888048[/C][C]0.0236546723888048[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0218083548063646[/C][C]0.0218083548063646[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.200904381126832[/C][C]-0.200904381126832[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0181157196414841[/C][C]0.0181157196414841[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0162694020590438[/C][C]0.0162694020590438[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.206443333874152[/C][C]-0.206443333874152[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0125767668941633[/C][C]0.0125767668941633[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]-0.010730449311723[/C][C]0.010730449311723[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.211982286621473[/C][C]-0.211982286621473[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]-0.00703781414684253[/C][C]0.00703781414684253[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.215674921786354[/C][C]-0.215674921786354[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.217521239368794[/C][C]0.782478760631206[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.219367556951234[/C][C]-0.219367556951234[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.000347456182918498[/C][C]-0.000347456182918498[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.223060192116115[/C][C]0.776939807883885[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.00404009134779901[/C][C]-0.00404009134779901[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.00588640893023927[/C][C]-0.00588640893023927[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.00773272651267953[/C][C]-0.00773272651267953[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.00957904409511979[/C][C]-0.00957904409511979[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.232291780028316[/C][C]-0.232291780028316[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.0132716792600003[/C][C]-0.0132716792600003[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.0151179968424406[/C][C]-0.0151179968424406[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.0169643144248808[/C][C]-0.0169643144248808[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0188106320073211[/C][C]-0.0188106320073211[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0206569495897613[/C][C]-0.0206569495897613[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0225032671722016[/C][C]-0.0225032671722016[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0243495847546418[/C][C]-0.0243495847546418[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0261959023370821[/C][C]-0.0261959023370821[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.248908638270278[/C][C]-0.248908638270278[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0298885375019626[/C][C]-0.0298885375019626[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0317348550844029[/C][C]-0.0317348550844029[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.254447591017599[/C][C]-0.254447591017599[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0354274902492834[/C][C]-0.0354274902492834[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0372738078317236[/C][C]-0.0372738078317236[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.25998654376492[/C][C]-0.25998654376492[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.0409664429966043[/C][C]0.959033557003396[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0428127605790444[/C][C]-0.0428127605790444[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0446590781614847[/C][C]-0.0446590781614847[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.267371814094681[/C][C]-0.267371814094681[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0483517133263652[/C][C]-0.0483517133263652[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0501980309088054[/C][C]-0.0501980309088054[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0520443484912457[/C][C]-0.0520443484912457[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0538906660736859[/C][C]-0.0538906660736859[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0557369836561262[/C][C]-0.0557369836561262[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0575833012385665[/C][C]-0.0575833012385665[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.280296037171763[/C][C]-0.280296037171763[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.282142354754203[/C][C]0.717857645245797[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0631222539858873[/C][C]-0.0631222539858873[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.0649685715683276[/C][C]0.935031428431672[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0668148891507678[/C][C]-0.0668148891507678[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.289527625083964[/C][C]-0.289527625083964[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0705075243156483[/C][C]-0.0705075243156483[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0723538418980885[/C][C]-0.0723538418980885[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0742001594805288[/C][C]-0.0742001594805288[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.296912895413725[/C][C]0.703087104586275[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.298759212996165[/C][C]-0.298759212996165[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.0797391122278496[/C][C]-0.0797391122278496[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0815854298102898[/C][C]-0.0815854298102898[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.304298165743486[/C][C]-0.304298165743486[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0852780649751703[/C][C]-0.0852780649751703[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0871243825576106[/C][C]-0.0871243825576106[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.309837118490807[/C][C]0.690162881509193[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0908170177224911[/C][C]-0.0908170177224911[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0926633353049313[/C][C]-0.0926633353049313[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0945096528873716[/C][C]-0.0945096528873716[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0963559704698119[/C][C]-0.0963559704698119[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.0982022880522521[/C][C]-0.0982022880522521[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.100048605634692[/C][C]-0.100048605634692[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.101894923217133[/C][C]-0.101894923217133[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.103741240799573[/C][C]-0.103741240799573[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.326453976732769[/C][C]-0.326453976732769[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.107433875964453[/C][C]-0.107433875964453[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.109280193546894[/C][C]-0.109280193546894[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.33199292948009[/C][C]0.66800707051991[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.33383924706253[/C][C]-0.33383924706253[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.114819146294214[/C][C]-0.114819146294214[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.116665463876655[/C][C]-0.116665463876655[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.118511781459095[/C][C]-0.118511781459095[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.120358099041535[/C][C]0.879641900958465[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.122204416623975[/C][C]-0.122204416623975[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.124050734206416[/C][C]-0.124050734206416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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
100.18798015804975-0.18798015804975
20-0.0310399427185660.031039942718566
30-0.02919362513612570.0291936251361257
40-0.02734730755368540.0273473075536854
50-0.02550098997124510.0255009899712451
60-0.02365467238880480.0236546723888048
70-0.02180835480636460.0218083548063646
800.200904381126832-0.200904381126832
90-0.01811571964148410.0181157196414841
100-0.01626940205904380.0162694020590438
1100.206443333874152-0.206443333874152
120-0.01257676689416330.0125767668941633
130-0.0107304493117230.010730449311723
1400.211982286621473-0.211982286621473
150-0.007037814146842530.00703781414684253
1600.215674921786354-0.215674921786354
1710.2175212393687940.782478760631206
1800.219367556951234-0.219367556951234
1900.000347456182918498-0.000347456182918498
2010.2230601921161150.776939807883885
2100.00404009134779901-0.00404009134779901
2200.00588640893023927-0.00588640893023927
2300.00773272651267953-0.00773272651267953
2400.00957904409511979-0.00957904409511979
2500.232291780028316-0.232291780028316
2600.0132716792600003-0.0132716792600003
2700.0151179968424406-0.0151179968424406
2800.0169643144248808-0.0169643144248808
2900.0188106320073211-0.0188106320073211
3000.0206569495897613-0.0206569495897613
3100.0225032671722016-0.0225032671722016
3200.0243495847546418-0.0243495847546418
3300.0261959023370821-0.0261959023370821
3400.248908638270278-0.248908638270278
3500.0298885375019626-0.0298885375019626
3600.0317348550844029-0.0317348550844029
3700.254447591017599-0.254447591017599
3800.0354274902492834-0.0354274902492834
3900.0372738078317236-0.0372738078317236
4000.25998654376492-0.25998654376492
4110.04096644299660430.959033557003396
4200.0428127605790444-0.0428127605790444
4300.0446590781614847-0.0446590781614847
4400.267371814094681-0.267371814094681
4500.0483517133263652-0.0483517133263652
4600.0501980309088054-0.0501980309088054
4700.0520443484912457-0.0520443484912457
4800.0538906660736859-0.0538906660736859
4900.0557369836561262-0.0557369836561262
5000.0575833012385665-0.0575833012385665
5100.280296037171763-0.280296037171763
5210.2821423547542030.717857645245797
5300.0631222539858873-0.0631222539858873
5410.06496857156832760.935031428431672
5500.0668148891507678-0.0668148891507678
5600.289527625083964-0.289527625083964
5700.0705075243156483-0.0705075243156483
5800.0723538418980885-0.0723538418980885
5900.0742001594805288-0.0742001594805288
6010.2969128954137250.703087104586275
6100.298759212996165-0.298759212996165
6200.0797391122278496-0.0797391122278496
6300.0815854298102898-0.0815854298102898
6400.304298165743486-0.304298165743486
6500.0852780649751703-0.0852780649751703
6600.0871243825576106-0.0871243825576106
6710.3098371184908070.690162881509193
6800.0908170177224911-0.0908170177224911
6900.0926633353049313-0.0926633353049313
7000.0945096528873716-0.0945096528873716
7100.0963559704698119-0.0963559704698119
7200.0982022880522521-0.0982022880522521
7300.100048605634692-0.100048605634692
7400.101894923217133-0.101894923217133
7500.103741240799573-0.103741240799573
7600.326453976732769-0.326453976732769
7700.107433875964453-0.107433875964453
7800.109280193546894-0.109280193546894
7910.331992929480090.66800707051991
8000.33383924706253-0.33383924706253
8100.114819146294214-0.114819146294214
8200.116665463876655-0.116665463876655
8300.118511781459095-0.118511781459095
8410.1203580990415350.879641900958465
8500.122204416623975-0.122204416623975
8600.124050734206416-0.124050734206416







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1107570940163950.221514188032790.889242905983605
180.1034751938302420.2069503876604830.896524806169758
190.07424766107737260.1484953221547450.925752338922627
200.3754327091884140.7508654183768290.624567290811586
210.3297938504167020.6595877008334040.670206149583298
220.2794406540818490.5588813081636990.720559345918151
230.2298335794948020.4596671589896040.770166420505198
240.1839805064845190.3679610129690370.816019493515481
250.1900753597433860.3801507194867710.809924640256614
260.1469076055749240.2938152111498480.853092394425076
270.1107963396297390.2215926792594790.889203660370261
280.08155701539651310.1631140307930260.918442984603487
290.05860206448278350.1172041289655670.941397935517217
300.04110818968085170.08221637936170330.958891810319148
310.0281547686644620.0563095373289240.971845231335538
320.01882907163346650.03765814326693310.981170928366534
330.0122972513009870.02459450260197390.987702748699013
340.01138158939642010.02276317879284020.98861841060358
350.007219284462870820.01443856892574160.992780715537129
360.004475886502742170.008951773005484330.995524113497258
370.00385331722437930.007706634448758590.996146682775621
380.002330365059314310.004660730118628620.997669634940686
390.001380015213154570.002760030426309150.998619984786845
400.001171477900472360.002342955800944710.998828522099528
410.09092119543663760.1818423908732750.909078804563362
420.06856117952074090.1371223590414820.931438820479259
430.05060176491765590.1012035298353120.949398235082344
440.04828229918863350.0965645983772670.951717700811367
450.03470732573882410.06941465147764820.965292674261176
460.0244262779282840.0488525558565680.975573722071716
470.01683226685877020.03366453371754030.98316773314123
480.01136041607429160.02272083214858310.988639583925708
490.007513307081541130.01502661416308230.992486692918459
500.004873303366945120.009746606733890250.995126696633055
510.005133223470558680.01026644694111740.994866776529441
520.03556818931685930.07113637863371850.964431810683141
530.02529953585300890.05059907170601790.974700464146991
540.2989686244466530.5979372488933050.701031375553347
550.2503924054396160.5007848108792330.749607594560384
560.2573511723996010.5147023447992020.742648827600399
570.2097675882695790.4195351765391590.790232411730421
580.167186180977750.3343723619555010.83281381902225
590.1301221616995470.2602443233990950.869877838300453
600.3472979400566550.6945958801133090.652702059943346
610.3428493089557160.6856986179114310.657150691044284
620.2842085049134060.5684170098268120.715791495086594
630.2299319986850580.4598639973701170.770068001314942
640.2520774535835830.5041549071671650.747922546416417
650.1984624058236240.3969248116472470.801537594176376
660.1517248457158420.3034496914316840.848275154284158
670.3816047280959020.7632094561918040.618395271904098
680.3132995304476370.6265990608952730.686700469552363
690.2494202389633030.4988404779266070.750579761036697
700.1918799270201560.3837598540403120.808120072979844
710.1420979920854620.2841959841709250.857902007914538
720.1008673670401880.2017347340803770.899132632959811
730.06830764415797930.1366152883159590.931692355842021
740.04391499935861370.08782999871722750.956085000641386
750.02670851898912460.05341703797824930.973291481010875
760.0247376742398130.04947534847962590.975262325760187
770.01310521054103370.02621042108206750.986894789458966
780.006537364545481720.01307472909096340.993462635454518
790.04722402816169640.09444805632339280.952775971838304
800.02306370306440260.04612740612880520.976936296935597

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0 & 0 & 1 \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.110757094016395 & 0.22151418803279 & 0.889242905983605 \tabularnewline
18 & 0.103475193830242 & 0.206950387660483 & 0.896524806169758 \tabularnewline
19 & 0.0742476610773726 & 0.148495322154745 & 0.925752338922627 \tabularnewline
20 & 0.375432709188414 & 0.750865418376829 & 0.624567290811586 \tabularnewline
21 & 0.329793850416702 & 0.659587700833404 & 0.670206149583298 \tabularnewline
22 & 0.279440654081849 & 0.558881308163699 & 0.720559345918151 \tabularnewline
23 & 0.229833579494802 & 0.459667158989604 & 0.770166420505198 \tabularnewline
24 & 0.183980506484519 & 0.367961012969037 & 0.816019493515481 \tabularnewline
25 & 0.190075359743386 & 0.380150719486771 & 0.809924640256614 \tabularnewline
26 & 0.146907605574924 & 0.293815211149848 & 0.853092394425076 \tabularnewline
27 & 0.110796339629739 & 0.221592679259479 & 0.889203660370261 \tabularnewline
28 & 0.0815570153965131 & 0.163114030793026 & 0.918442984603487 \tabularnewline
29 & 0.0586020644827835 & 0.117204128965567 & 0.941397935517217 \tabularnewline
30 & 0.0411081896808517 & 0.0822163793617033 & 0.958891810319148 \tabularnewline
31 & 0.028154768664462 & 0.056309537328924 & 0.971845231335538 \tabularnewline
32 & 0.0188290716334665 & 0.0376581432669331 & 0.981170928366534 \tabularnewline
33 & 0.012297251300987 & 0.0245945026019739 & 0.987702748699013 \tabularnewline
34 & 0.0113815893964201 & 0.0227631787928402 & 0.98861841060358 \tabularnewline
35 & 0.00721928446287082 & 0.0144385689257416 & 0.992780715537129 \tabularnewline
36 & 0.00447588650274217 & 0.00895177300548433 & 0.995524113497258 \tabularnewline
37 & 0.0038533172243793 & 0.00770663444875859 & 0.996146682775621 \tabularnewline
38 & 0.00233036505931431 & 0.00466073011862862 & 0.997669634940686 \tabularnewline
39 & 0.00138001521315457 & 0.00276003042630915 & 0.998619984786845 \tabularnewline
40 & 0.00117147790047236 & 0.00234295580094471 & 0.998828522099528 \tabularnewline
41 & 0.0909211954366376 & 0.181842390873275 & 0.909078804563362 \tabularnewline
42 & 0.0685611795207409 & 0.137122359041482 & 0.931438820479259 \tabularnewline
43 & 0.0506017649176559 & 0.101203529835312 & 0.949398235082344 \tabularnewline
44 & 0.0482822991886335 & 0.096564598377267 & 0.951717700811367 \tabularnewline
45 & 0.0347073257388241 & 0.0694146514776482 & 0.965292674261176 \tabularnewline
46 & 0.024426277928284 & 0.048852555856568 & 0.975573722071716 \tabularnewline
47 & 0.0168322668587702 & 0.0336645337175403 & 0.98316773314123 \tabularnewline
48 & 0.0113604160742916 & 0.0227208321485831 & 0.988639583925708 \tabularnewline
49 & 0.00751330708154113 & 0.0150266141630823 & 0.992486692918459 \tabularnewline
50 & 0.00487330336694512 & 0.00974660673389025 & 0.995126696633055 \tabularnewline
51 & 0.00513322347055868 & 0.0102664469411174 & 0.994866776529441 \tabularnewline
52 & 0.0355681893168593 & 0.0711363786337185 & 0.964431810683141 \tabularnewline
53 & 0.0252995358530089 & 0.0505990717060179 & 0.974700464146991 \tabularnewline
54 & 0.298968624446653 & 0.597937248893305 & 0.701031375553347 \tabularnewline
55 & 0.250392405439616 & 0.500784810879233 & 0.749607594560384 \tabularnewline
56 & 0.257351172399601 & 0.514702344799202 & 0.742648827600399 \tabularnewline
57 & 0.209767588269579 & 0.419535176539159 & 0.790232411730421 \tabularnewline
58 & 0.16718618097775 & 0.334372361955501 & 0.83281381902225 \tabularnewline
59 & 0.130122161699547 & 0.260244323399095 & 0.869877838300453 \tabularnewline
60 & 0.347297940056655 & 0.694595880113309 & 0.652702059943346 \tabularnewline
61 & 0.342849308955716 & 0.685698617911431 & 0.657150691044284 \tabularnewline
62 & 0.284208504913406 & 0.568417009826812 & 0.715791495086594 \tabularnewline
63 & 0.229931998685058 & 0.459863997370117 & 0.770068001314942 \tabularnewline
64 & 0.252077453583583 & 0.504154907167165 & 0.747922546416417 \tabularnewline
65 & 0.198462405823624 & 0.396924811647247 & 0.801537594176376 \tabularnewline
66 & 0.151724845715842 & 0.303449691431684 & 0.848275154284158 \tabularnewline
67 & 0.381604728095902 & 0.763209456191804 & 0.618395271904098 \tabularnewline
68 & 0.313299530447637 & 0.626599060895273 & 0.686700469552363 \tabularnewline
69 & 0.249420238963303 & 0.498840477926607 & 0.750579761036697 \tabularnewline
70 & 0.191879927020156 & 0.383759854040312 & 0.808120072979844 \tabularnewline
71 & 0.142097992085462 & 0.284195984170925 & 0.857902007914538 \tabularnewline
72 & 0.100867367040188 & 0.201734734080377 & 0.899132632959811 \tabularnewline
73 & 0.0683076441579793 & 0.136615288315959 & 0.931692355842021 \tabularnewline
74 & 0.0439149993586137 & 0.0878299987172275 & 0.956085000641386 \tabularnewline
75 & 0.0267085189891246 & 0.0534170379782493 & 0.973291481010875 \tabularnewline
76 & 0.024737674239813 & 0.0494753484796259 & 0.975262325760187 \tabularnewline
77 & 0.0131052105410337 & 0.0262104210820675 & 0.986894789458966 \tabularnewline
78 & 0.00653736454548172 & 0.0130747290909634 & 0.993462635454518 \tabularnewline
79 & 0.0472240281616964 & 0.0944480563233928 & 0.952775971838304 \tabularnewline
80 & 0.0230637030644026 & 0.0461274061288052 & 0.976936296935597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&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]6[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.110757094016395[/C][C]0.22151418803279[/C][C]0.889242905983605[/C][/ROW]
[ROW][C]18[/C][C]0.103475193830242[/C][C]0.206950387660483[/C][C]0.896524806169758[/C][/ROW]
[ROW][C]19[/C][C]0.0742476610773726[/C][C]0.148495322154745[/C][C]0.925752338922627[/C][/ROW]
[ROW][C]20[/C][C]0.375432709188414[/C][C]0.750865418376829[/C][C]0.624567290811586[/C][/ROW]
[ROW][C]21[/C][C]0.329793850416702[/C][C]0.659587700833404[/C][C]0.670206149583298[/C][/ROW]
[ROW][C]22[/C][C]0.279440654081849[/C][C]0.558881308163699[/C][C]0.720559345918151[/C][/ROW]
[ROW][C]23[/C][C]0.229833579494802[/C][C]0.459667158989604[/C][C]0.770166420505198[/C][/ROW]
[ROW][C]24[/C][C]0.183980506484519[/C][C]0.367961012969037[/C][C]0.816019493515481[/C][/ROW]
[ROW][C]25[/C][C]0.190075359743386[/C][C]0.380150719486771[/C][C]0.809924640256614[/C][/ROW]
[ROW][C]26[/C][C]0.146907605574924[/C][C]0.293815211149848[/C][C]0.853092394425076[/C][/ROW]
[ROW][C]27[/C][C]0.110796339629739[/C][C]0.221592679259479[/C][C]0.889203660370261[/C][/ROW]
[ROW][C]28[/C][C]0.0815570153965131[/C][C]0.163114030793026[/C][C]0.918442984603487[/C][/ROW]
[ROW][C]29[/C][C]0.0586020644827835[/C][C]0.117204128965567[/C][C]0.941397935517217[/C][/ROW]
[ROW][C]30[/C][C]0.0411081896808517[/C][C]0.0822163793617033[/C][C]0.958891810319148[/C][/ROW]
[ROW][C]31[/C][C]0.028154768664462[/C][C]0.056309537328924[/C][C]0.971845231335538[/C][/ROW]
[ROW][C]32[/C][C]0.0188290716334665[/C][C]0.0376581432669331[/C][C]0.981170928366534[/C][/ROW]
[ROW][C]33[/C][C]0.012297251300987[/C][C]0.0245945026019739[/C][C]0.987702748699013[/C][/ROW]
[ROW][C]34[/C][C]0.0113815893964201[/C][C]0.0227631787928402[/C][C]0.98861841060358[/C][/ROW]
[ROW][C]35[/C][C]0.00721928446287082[/C][C]0.0144385689257416[/C][C]0.992780715537129[/C][/ROW]
[ROW][C]36[/C][C]0.00447588650274217[/C][C]0.00895177300548433[/C][C]0.995524113497258[/C][/ROW]
[ROW][C]37[/C][C]0.0038533172243793[/C][C]0.00770663444875859[/C][C]0.996146682775621[/C][/ROW]
[ROW][C]38[/C][C]0.00233036505931431[/C][C]0.00466073011862862[/C][C]0.997669634940686[/C][/ROW]
[ROW][C]39[/C][C]0.00138001521315457[/C][C]0.00276003042630915[/C][C]0.998619984786845[/C][/ROW]
[ROW][C]40[/C][C]0.00117147790047236[/C][C]0.00234295580094471[/C][C]0.998828522099528[/C][/ROW]
[ROW][C]41[/C][C]0.0909211954366376[/C][C]0.181842390873275[/C][C]0.909078804563362[/C][/ROW]
[ROW][C]42[/C][C]0.0685611795207409[/C][C]0.137122359041482[/C][C]0.931438820479259[/C][/ROW]
[ROW][C]43[/C][C]0.0506017649176559[/C][C]0.101203529835312[/C][C]0.949398235082344[/C][/ROW]
[ROW][C]44[/C][C]0.0482822991886335[/C][C]0.096564598377267[/C][C]0.951717700811367[/C][/ROW]
[ROW][C]45[/C][C]0.0347073257388241[/C][C]0.0694146514776482[/C][C]0.965292674261176[/C][/ROW]
[ROW][C]46[/C][C]0.024426277928284[/C][C]0.048852555856568[/C][C]0.975573722071716[/C][/ROW]
[ROW][C]47[/C][C]0.0168322668587702[/C][C]0.0336645337175403[/C][C]0.98316773314123[/C][/ROW]
[ROW][C]48[/C][C]0.0113604160742916[/C][C]0.0227208321485831[/C][C]0.988639583925708[/C][/ROW]
[ROW][C]49[/C][C]0.00751330708154113[/C][C]0.0150266141630823[/C][C]0.992486692918459[/C][/ROW]
[ROW][C]50[/C][C]0.00487330336694512[/C][C]0.00974660673389025[/C][C]0.995126696633055[/C][/ROW]
[ROW][C]51[/C][C]0.00513322347055868[/C][C]0.0102664469411174[/C][C]0.994866776529441[/C][/ROW]
[ROW][C]52[/C][C]0.0355681893168593[/C][C]0.0711363786337185[/C][C]0.964431810683141[/C][/ROW]
[ROW][C]53[/C][C]0.0252995358530089[/C][C]0.0505990717060179[/C][C]0.974700464146991[/C][/ROW]
[ROW][C]54[/C][C]0.298968624446653[/C][C]0.597937248893305[/C][C]0.701031375553347[/C][/ROW]
[ROW][C]55[/C][C]0.250392405439616[/C][C]0.500784810879233[/C][C]0.749607594560384[/C][/ROW]
[ROW][C]56[/C][C]0.257351172399601[/C][C]0.514702344799202[/C][C]0.742648827600399[/C][/ROW]
[ROW][C]57[/C][C]0.209767588269579[/C][C]0.419535176539159[/C][C]0.790232411730421[/C][/ROW]
[ROW][C]58[/C][C]0.16718618097775[/C][C]0.334372361955501[/C][C]0.83281381902225[/C][/ROW]
[ROW][C]59[/C][C]0.130122161699547[/C][C]0.260244323399095[/C][C]0.869877838300453[/C][/ROW]
[ROW][C]60[/C][C]0.347297940056655[/C][C]0.694595880113309[/C][C]0.652702059943346[/C][/ROW]
[ROW][C]61[/C][C]0.342849308955716[/C][C]0.685698617911431[/C][C]0.657150691044284[/C][/ROW]
[ROW][C]62[/C][C]0.284208504913406[/C][C]0.568417009826812[/C][C]0.715791495086594[/C][/ROW]
[ROW][C]63[/C][C]0.229931998685058[/C][C]0.459863997370117[/C][C]0.770068001314942[/C][/ROW]
[ROW][C]64[/C][C]0.252077453583583[/C][C]0.504154907167165[/C][C]0.747922546416417[/C][/ROW]
[ROW][C]65[/C][C]0.198462405823624[/C][C]0.396924811647247[/C][C]0.801537594176376[/C][/ROW]
[ROW][C]66[/C][C]0.151724845715842[/C][C]0.303449691431684[/C][C]0.848275154284158[/C][/ROW]
[ROW][C]67[/C][C]0.381604728095902[/C][C]0.763209456191804[/C][C]0.618395271904098[/C][/ROW]
[ROW][C]68[/C][C]0.313299530447637[/C][C]0.626599060895273[/C][C]0.686700469552363[/C][/ROW]
[ROW][C]69[/C][C]0.249420238963303[/C][C]0.498840477926607[/C][C]0.750579761036697[/C][/ROW]
[ROW][C]70[/C][C]0.191879927020156[/C][C]0.383759854040312[/C][C]0.808120072979844[/C][/ROW]
[ROW][C]71[/C][C]0.142097992085462[/C][C]0.284195984170925[/C][C]0.857902007914538[/C][/ROW]
[ROW][C]72[/C][C]0.100867367040188[/C][C]0.201734734080377[/C][C]0.899132632959811[/C][/ROW]
[ROW][C]73[/C][C]0.0683076441579793[/C][C]0.136615288315959[/C][C]0.931692355842021[/C][/ROW]
[ROW][C]74[/C][C]0.0439149993586137[/C][C]0.0878299987172275[/C][C]0.956085000641386[/C][/ROW]
[ROW][C]75[/C][C]0.0267085189891246[/C][C]0.0534170379782493[/C][C]0.973291481010875[/C][/ROW]
[ROW][C]76[/C][C]0.024737674239813[/C][C]0.0494753484796259[/C][C]0.975262325760187[/C][/ROW]
[ROW][C]77[/C][C]0.0131052105410337[/C][C]0.0262104210820675[/C][C]0.986894789458966[/C][/ROW]
[ROW][C]78[/C][C]0.00653736454548172[/C][C]0.0130747290909634[/C][C]0.993462635454518[/C][/ROW]
[ROW][C]79[/C][C]0.0472240281616964[/C][C]0.0944480563233928[/C][C]0.952775971838304[/C][/ROW]
[ROW][C]80[/C][C]0.0230637030644026[/C][C]0.0461274061288052[/C][C]0.976936296935597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1107570940163950.221514188032790.889242905983605
180.1034751938302420.2069503876604830.896524806169758
190.07424766107737260.1484953221547450.925752338922627
200.3754327091884140.7508654183768290.624567290811586
210.3297938504167020.6595877008334040.670206149583298
220.2794406540818490.5588813081636990.720559345918151
230.2298335794948020.4596671589896040.770166420505198
240.1839805064845190.3679610129690370.816019493515481
250.1900753597433860.3801507194867710.809924640256614
260.1469076055749240.2938152111498480.853092394425076
270.1107963396297390.2215926792594790.889203660370261
280.08155701539651310.1631140307930260.918442984603487
290.05860206448278350.1172041289655670.941397935517217
300.04110818968085170.08221637936170330.958891810319148
310.0281547686644620.0563095373289240.971845231335538
320.01882907163346650.03765814326693310.981170928366534
330.0122972513009870.02459450260197390.987702748699013
340.01138158939642010.02276317879284020.98861841060358
350.007219284462870820.01443856892574160.992780715537129
360.004475886502742170.008951773005484330.995524113497258
370.00385331722437930.007706634448758590.996146682775621
380.002330365059314310.004660730118628620.997669634940686
390.001380015213154570.002760030426309150.998619984786845
400.001171477900472360.002342955800944710.998828522099528
410.09092119543663760.1818423908732750.909078804563362
420.06856117952074090.1371223590414820.931438820479259
430.05060176491765590.1012035298353120.949398235082344
440.04828229918863350.0965645983772670.951717700811367
450.03470732573882410.06941465147764820.965292674261176
460.0244262779282840.0488525558565680.975573722071716
470.01683226685877020.03366453371754030.98316773314123
480.01136041607429160.02272083214858310.988639583925708
490.007513307081541130.01502661416308230.992486692918459
500.004873303366945120.009746606733890250.995126696633055
510.005133223470558680.01026644694111740.994866776529441
520.03556818931685930.07113637863371850.964431810683141
530.02529953585300890.05059907170601790.974700464146991
540.2989686244466530.5979372488933050.701031375553347
550.2503924054396160.5007848108792330.749607594560384
560.2573511723996010.5147023447992020.742648827600399
570.2097675882695790.4195351765391590.790232411730421
580.167186180977750.3343723619555010.83281381902225
590.1301221616995470.2602443233990950.869877838300453
600.3472979400566550.6945958801133090.652702059943346
610.3428493089557160.6856986179114310.657150691044284
620.2842085049134060.5684170098268120.715791495086594
630.2299319986850580.4598639973701170.770068001314942
640.2520774535835830.5041549071671650.747922546416417
650.1984624058236240.3969248116472470.801537594176376
660.1517248457158420.3034496914316840.848275154284158
670.3816047280959020.7632094561918040.618395271904098
680.3132995304476370.6265990608952730.686700469552363
690.2494202389633030.4988404779266070.750579761036697
700.1918799270201560.3837598540403120.808120072979844
710.1420979920854620.2841959841709250.857902007914538
720.1008673670401880.2017347340803770.899132632959811
730.06830764415797930.1366152883159590.931692355842021
740.04391499935861370.08782999871722750.956085000641386
750.02670851898912460.05341703797824930.973291481010875
760.0247376742398130.04947534847962590.975262325760187
770.01310521054103370.02621042108206750.986894789458966
780.006537364545481720.01307472909096340.993462635454518
790.04722402816169640.09444805632339280.952775971838304
800.02306370306440260.04612740612880520.976936296935597







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.226666666666667NOK
5% type I error level300.4NOK
10% type I error level390.52NOK

\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 & 17 & 0.226666666666667 & NOK \tabularnewline
5% type I error level & 30 & 0.4 & NOK \tabularnewline
10% type I error level & 39 & 0.52 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202513&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]17[/C][C]0.226666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.4[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.52[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202513&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202513&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 level170.226666666666667NOK
5% type I error level300.4NOK
10% type I error level390.52NOK



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