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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 computationFri, 20 Nov 2009 10:41:18 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258739719zply9go0510kk3c.htm/, Retrieved Fri, 29 Mar 2024 07:24:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58372, Retrieved Fri, 29 Mar 2024 07:24:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-20 14:20:51] [a542c511726eba04a1fc2f4bd37a90f8]
-   PD        [Multiple Regression] [] [2009-11-20 17:41:18] [bcaf453a09027aa0f995cb78bdc3c98a] [Current]
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Dataseries X:
9.3	8.1	10.9	25.6
8.7	7.7	10	23.7
8.2	7.5	9.2	22
8.3	7.6	9.2	21.3
8.5	7.8	9.5	20.7
8.6	7.8	9.6	20.4
8.5	7.8	9.5	20.3
8.2	7.5	9.1	20.4
8.1	7.5	8.9	19.8
7.9	7.1	9	19.5
8.6	7.5	10.1	23.1
8.7	7.5	10.3	23.5
8.7	7.6	10.2	23.5
8.5	7.7	9.6	22.9
8.4	7.7	9.2	21.9
8.5	7.9	9.3	21.5
8.7	8.1	9.4	20.5
8.7	8.2	9.4	20.2
8.6	8.2	9.2	19.4
8.5	8.2	9	19.2
8.3	7.9	9	18.8
8	7.3	9	18.8
8.2	6.9	9.8	22.6
8.1	6.6	10	23.3
8.1	6.7	9.8	23
8	6.9	9.3	21.4
7.9	7	9	19.9
7.9	7.1	9	18.8
8	7.2	9.1	18.6
8	7.1	9.1	18.4
7.9	6.9	9.1	18.6
8	7	9.2	19.9
7.7	6.8	8.8	19.2
7.2	6.4	8.3	18.4
7.5	6.7	8.4	21.1
7.3	6.6	8.1	20.5
7	6.4	7.7	19.1
7	6.3	7.9	18.1
7	6.2	7.9	17
7.2	6.5	8	17.1
7.3	6.8	7.9	17.4
7.1	6.8	7.6	16.8
6.8	6.4	7.1	15.3
6.4	6.1	6.8	14.3
6.1	5.8	6.5	13.4
6.5	6.1	6.9	15.3
7.7	7.2	8.2	22.1
7.9	7.3	8.7	23.7
7.5	6.9	8.3	22.2
6.9	6.1	7.9	19.5
6.6	5.8	7.5	16.6
6.9	6.2	7.8	17.3
7.7	7.1	8.3	19.8
8	7.7	8.4	21.2
8	7.9	8.2	21.5
7.7	7.7	7.7	20.6
7.3	7.4	7.2	19.1
7.4	7.5	7.3	19.6
8.1	8	8.1	23.5
8.3	8.1	8.5	24
8.2	8	8.4	23.2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
TW[t] = + 0.198077214550677 + 0.533258589434412WM[t] + 0.42373357822937WV[t] + 0.0067473415919117WJ[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TW[t] =  +  0.198077214550677 +  0.533258589434412WM[t] +  0.42373357822937WV[t] +  0.0067473415919117WJ[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TW[t] =  +  0.198077214550677 +  0.533258589434412WM[t] +  0.42373357822937WV[t] +  0.0067473415919117WJ[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&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
TW[t] = + 0.198077214550677 + 0.533258589434412WM[t] + 0.42373357822937WV[t] + 0.0067473415919117WJ[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1980772145506770.0488734.05290.0001557.7e-05
WM0.5332585894344120.00840163.479400
WV0.423733578229370.00656364.566500
WJ0.00674734159191170.0025782.61760.0113210.005661

\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) & 0.198077214550677 & 0.048873 & 4.0529 & 0.000155 & 7.7e-05 \tabularnewline
WM & 0.533258589434412 & 0.008401 & 63.4794 & 0 & 0 \tabularnewline
WV & 0.42373357822937 & 0.006563 & 64.5665 & 0 & 0 \tabularnewline
WJ & 0.0067473415919117 & 0.002578 & 2.6176 & 0.011321 & 0.005661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&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]0.198077214550677[/C][C]0.048873[/C][C]4.0529[/C][C]0.000155[/C][C]7.7e-05[/C][/ROW]
[ROW][C]WM[/C][C]0.533258589434412[/C][C]0.008401[/C][C]63.4794[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]WV[/C][C]0.42373357822937[/C][C]0.006563[/C][C]64.5665[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]WJ[/C][C]0.0067473415919117[/C][C]0.002578[/C][C]2.6176[/C][C]0.011321[/C][C]0.005661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&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)0.1980772145506770.0488734.05290.0001557.7e-05
WM0.5332585894344120.00840163.479400
WV0.423733578229370.00656364.566500
WJ0.00674734159191170.0025782.61760.0113210.005661







Multiple Linear Regression - Regression Statistics
Multiple R0.99889653819086
R-squared0.997794294009685
Adjusted R-squared0.99767820422072
F-TEST (value)8595.02203350076
F-TEST (DF numerator)3
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0328062112908612
Sum Squared Residuals0.0613461074578558

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99889653819086 \tabularnewline
R-squared & 0.997794294009685 \tabularnewline
Adjusted R-squared & 0.99767820422072 \tabularnewline
F-TEST (value) & 8595.02203350076 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0328062112908612 \tabularnewline
Sum Squared Residuals & 0.0613461074578558 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99889653819086[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997794294009685[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99767820422072[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8595.02203350076[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0328062112908612[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0613461074578558[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&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 R0.99889653819086
R-squared0.997794294009685
Adjusted R-squared0.99767820422072
F-TEST (value)8595.02203350076
F-TEST (DF numerator)3
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0328062112908612
Sum Squared Residuals0.0613461074578558







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.39.3088997364225-0.00889973642249405
28.78.70141613121766-0.00141613121766539
38.28.24430707004103-0.0443070700410328
48.38.292909789870140.00709021012986453
58.58.52263317627068-0.0226331762706832
68.68.562982331616050.0370176683839531
78.58.51993423963392-0.0199342396339185
88.28.191137965671040.00886203432896158
98.18.10234284507002-0.00234284507001730
107.97.92938856464161-0.0293885646416147
118.68.63308936619857-0.0330893661985699
128.78.72053501848121-0.0205350184812094
138.78.73148751960171-0.0314875196017129
148.58.52652482665238-0.0265248266523847
158.48.350284053768720.0497159462312756
168.58.496610192841780.00338980715821969
178.78.638887926959690.0611120730403116
188.78.690189583425560.00981041657444402
198.68.60004499450615-4.49945061518061e-05
208.58.5139488105419-0.0139488105418954
218.38.3512722970748-0.0512722970748066
2288.03131714341416-0.0313171434141595
238.28.182640468273160.0173595317268437
248.18.11213274620304-0.0121327462030437
258.18.078687687023040.0213123129769619
2687.962676869248180.0373231307518236
277.97.878761642334940.0212383576650617
287.97.92466542552728-0.0246654255272765
2988.01901517397527-0.0190151739752730
3087.964339846713450.0356601532865509
317.97.859037597144950.0409624028550512
3287.963508357980810.0364916420191876
337.77.682640069687840.0173599303121561
347.27.25207197152587-0.0520719715258647
357.57.472640728477290.0273592715227130
367.37.288146391109890.0118536088901127
3777.00255496370258-0.00255496370258068
3877.0272284788131-0.0272284788131016
3976.966480544118560.0335194558814424
407.27.169506212931010.0304937870689908
417.37.289134634415970.0108653655840302
427.17.15796615599201-0.0579661559920117
436.86.722674918715690.077325081284306
446.46.42882992682465-0.0288299268246466
456.16.13565966909279-0.035659669092792
466.56.47795062623950.0220493737605041
477.77.661270649140530.0387293508594697
487.97.93725904374572-0.0372590437457151
497.57.54434116429234-0.0443411642923356
506.96.9300230391549-0.0300230391548949
516.66.580984740416280.0190152595837203
526.96.9261313887732-0.0261313887731935
537.77.634799262358630.0652007376413705
5488.00657405207089-0.0065740520708908
5588.03050325678947-0.0305032567894724
567.77.70591214235518-0.00591214235518432
577.37.32394676402231-0.0239467640223084
587.47.42301965158464-0.0230196515846415
598.18.05495044109380.0450495589061997
608.38.281143402124950.0188565978750555
618.28.180046312085040.0199536879149614

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.3 & 9.3088997364225 & -0.00889973642249405 \tabularnewline
2 & 8.7 & 8.70141613121766 & -0.00141613121766539 \tabularnewline
3 & 8.2 & 8.24430707004103 & -0.0443070700410328 \tabularnewline
4 & 8.3 & 8.29290978987014 & 0.00709021012986453 \tabularnewline
5 & 8.5 & 8.52263317627068 & -0.0226331762706832 \tabularnewline
6 & 8.6 & 8.56298233161605 & 0.0370176683839531 \tabularnewline
7 & 8.5 & 8.51993423963392 & -0.0199342396339185 \tabularnewline
8 & 8.2 & 8.19113796567104 & 0.00886203432896158 \tabularnewline
9 & 8.1 & 8.10234284507002 & -0.00234284507001730 \tabularnewline
10 & 7.9 & 7.92938856464161 & -0.0293885646416147 \tabularnewline
11 & 8.6 & 8.63308936619857 & -0.0330893661985699 \tabularnewline
12 & 8.7 & 8.72053501848121 & -0.0205350184812094 \tabularnewline
13 & 8.7 & 8.73148751960171 & -0.0314875196017129 \tabularnewline
14 & 8.5 & 8.52652482665238 & -0.0265248266523847 \tabularnewline
15 & 8.4 & 8.35028405376872 & 0.0497159462312756 \tabularnewline
16 & 8.5 & 8.49661019284178 & 0.00338980715821969 \tabularnewline
17 & 8.7 & 8.63888792695969 & 0.0611120730403116 \tabularnewline
18 & 8.7 & 8.69018958342556 & 0.00981041657444402 \tabularnewline
19 & 8.6 & 8.60004499450615 & -4.49945061518061e-05 \tabularnewline
20 & 8.5 & 8.5139488105419 & -0.0139488105418954 \tabularnewline
21 & 8.3 & 8.3512722970748 & -0.0512722970748066 \tabularnewline
22 & 8 & 8.03131714341416 & -0.0313171434141595 \tabularnewline
23 & 8.2 & 8.18264046827316 & 0.0173595317268437 \tabularnewline
24 & 8.1 & 8.11213274620304 & -0.0121327462030437 \tabularnewline
25 & 8.1 & 8.07868768702304 & 0.0213123129769619 \tabularnewline
26 & 8 & 7.96267686924818 & 0.0373231307518236 \tabularnewline
27 & 7.9 & 7.87876164233494 & 0.0212383576650617 \tabularnewline
28 & 7.9 & 7.92466542552728 & -0.0246654255272765 \tabularnewline
29 & 8 & 8.01901517397527 & -0.0190151739752730 \tabularnewline
30 & 8 & 7.96433984671345 & 0.0356601532865509 \tabularnewline
31 & 7.9 & 7.85903759714495 & 0.0409624028550512 \tabularnewline
32 & 8 & 7.96350835798081 & 0.0364916420191876 \tabularnewline
33 & 7.7 & 7.68264006968784 & 0.0173599303121561 \tabularnewline
34 & 7.2 & 7.25207197152587 & -0.0520719715258647 \tabularnewline
35 & 7.5 & 7.47264072847729 & 0.0273592715227130 \tabularnewline
36 & 7.3 & 7.28814639110989 & 0.0118536088901127 \tabularnewline
37 & 7 & 7.00255496370258 & -0.00255496370258068 \tabularnewline
38 & 7 & 7.0272284788131 & -0.0272284788131016 \tabularnewline
39 & 7 & 6.96648054411856 & 0.0335194558814424 \tabularnewline
40 & 7.2 & 7.16950621293101 & 0.0304937870689908 \tabularnewline
41 & 7.3 & 7.28913463441597 & 0.0108653655840302 \tabularnewline
42 & 7.1 & 7.15796615599201 & -0.0579661559920117 \tabularnewline
43 & 6.8 & 6.72267491871569 & 0.077325081284306 \tabularnewline
44 & 6.4 & 6.42882992682465 & -0.0288299268246466 \tabularnewline
45 & 6.1 & 6.13565966909279 & -0.035659669092792 \tabularnewline
46 & 6.5 & 6.4779506262395 & 0.0220493737605041 \tabularnewline
47 & 7.7 & 7.66127064914053 & 0.0387293508594697 \tabularnewline
48 & 7.9 & 7.93725904374572 & -0.0372590437457151 \tabularnewline
49 & 7.5 & 7.54434116429234 & -0.0443411642923356 \tabularnewline
50 & 6.9 & 6.9300230391549 & -0.0300230391548949 \tabularnewline
51 & 6.6 & 6.58098474041628 & 0.0190152595837203 \tabularnewline
52 & 6.9 & 6.9261313887732 & -0.0261313887731935 \tabularnewline
53 & 7.7 & 7.63479926235863 & 0.0652007376413705 \tabularnewline
54 & 8 & 8.00657405207089 & -0.0065740520708908 \tabularnewline
55 & 8 & 8.03050325678947 & -0.0305032567894724 \tabularnewline
56 & 7.7 & 7.70591214235518 & -0.00591214235518432 \tabularnewline
57 & 7.3 & 7.32394676402231 & -0.0239467640223084 \tabularnewline
58 & 7.4 & 7.42301965158464 & -0.0230196515846415 \tabularnewline
59 & 8.1 & 8.0549504410938 & 0.0450495589061997 \tabularnewline
60 & 8.3 & 8.28114340212495 & 0.0188565978750555 \tabularnewline
61 & 8.2 & 8.18004631208504 & 0.0199536879149614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&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]9.3[/C][C]9.3088997364225[/C][C]-0.00889973642249405[/C][/ROW]
[ROW][C]2[/C][C]8.7[/C][C]8.70141613121766[/C][C]-0.00141613121766539[/C][/ROW]
[ROW][C]3[/C][C]8.2[/C][C]8.24430707004103[/C][C]-0.0443070700410328[/C][/ROW]
[ROW][C]4[/C][C]8.3[/C][C]8.29290978987014[/C][C]0.00709021012986453[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]8.52263317627068[/C][C]-0.0226331762706832[/C][/ROW]
[ROW][C]6[/C][C]8.6[/C][C]8.56298233161605[/C][C]0.0370176683839531[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]8.51993423963392[/C][C]-0.0199342396339185[/C][/ROW]
[ROW][C]8[/C][C]8.2[/C][C]8.19113796567104[/C][C]0.00886203432896158[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.10234284507002[/C][C]-0.00234284507001730[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]7.92938856464161[/C][C]-0.0293885646416147[/C][/ROW]
[ROW][C]11[/C][C]8.6[/C][C]8.63308936619857[/C][C]-0.0330893661985699[/C][/ROW]
[ROW][C]12[/C][C]8.7[/C][C]8.72053501848121[/C][C]-0.0205350184812094[/C][/ROW]
[ROW][C]13[/C][C]8.7[/C][C]8.73148751960171[/C][C]-0.0314875196017129[/C][/ROW]
[ROW][C]14[/C][C]8.5[/C][C]8.52652482665238[/C][C]-0.0265248266523847[/C][/ROW]
[ROW][C]15[/C][C]8.4[/C][C]8.35028405376872[/C][C]0.0497159462312756[/C][/ROW]
[ROW][C]16[/C][C]8.5[/C][C]8.49661019284178[/C][C]0.00338980715821969[/C][/ROW]
[ROW][C]17[/C][C]8.7[/C][C]8.63888792695969[/C][C]0.0611120730403116[/C][/ROW]
[ROW][C]18[/C][C]8.7[/C][C]8.69018958342556[/C][C]0.00981041657444402[/C][/ROW]
[ROW][C]19[/C][C]8.6[/C][C]8.60004499450615[/C][C]-4.49945061518061e-05[/C][/ROW]
[ROW][C]20[/C][C]8.5[/C][C]8.5139488105419[/C][C]-0.0139488105418954[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]8.3512722970748[/C][C]-0.0512722970748066[/C][/ROW]
[ROW][C]22[/C][C]8[/C][C]8.03131714341416[/C][C]-0.0313171434141595[/C][/ROW]
[ROW][C]23[/C][C]8.2[/C][C]8.18264046827316[/C][C]0.0173595317268437[/C][/ROW]
[ROW][C]24[/C][C]8.1[/C][C]8.11213274620304[/C][C]-0.0121327462030437[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]8.07868768702304[/C][C]0.0213123129769619[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]7.96267686924818[/C][C]0.0373231307518236[/C][/ROW]
[ROW][C]27[/C][C]7.9[/C][C]7.87876164233494[/C][C]0.0212383576650617[/C][/ROW]
[ROW][C]28[/C][C]7.9[/C][C]7.92466542552728[/C][C]-0.0246654255272765[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]8.01901517397527[/C][C]-0.0190151739752730[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.96433984671345[/C][C]0.0356601532865509[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.85903759714495[/C][C]0.0409624028550512[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]7.96350835798081[/C][C]0.0364916420191876[/C][/ROW]
[ROW][C]33[/C][C]7.7[/C][C]7.68264006968784[/C][C]0.0173599303121561[/C][/ROW]
[ROW][C]34[/C][C]7.2[/C][C]7.25207197152587[/C][C]-0.0520719715258647[/C][/ROW]
[ROW][C]35[/C][C]7.5[/C][C]7.47264072847729[/C][C]0.0273592715227130[/C][/ROW]
[ROW][C]36[/C][C]7.3[/C][C]7.28814639110989[/C][C]0.0118536088901127[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]7.00255496370258[/C][C]-0.00255496370258068[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.0272284788131[/C][C]-0.0272284788131016[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]6.96648054411856[/C][C]0.0335194558814424[/C][/ROW]
[ROW][C]40[/C][C]7.2[/C][C]7.16950621293101[/C][C]0.0304937870689908[/C][/ROW]
[ROW][C]41[/C][C]7.3[/C][C]7.28913463441597[/C][C]0.0108653655840302[/C][/ROW]
[ROW][C]42[/C][C]7.1[/C][C]7.15796615599201[/C][C]-0.0579661559920117[/C][/ROW]
[ROW][C]43[/C][C]6.8[/C][C]6.72267491871569[/C][C]0.077325081284306[/C][/ROW]
[ROW][C]44[/C][C]6.4[/C][C]6.42882992682465[/C][C]-0.0288299268246466[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]6.13565966909279[/C][C]-0.035659669092792[/C][/ROW]
[ROW][C]46[/C][C]6.5[/C][C]6.4779506262395[/C][C]0.0220493737605041[/C][/ROW]
[ROW][C]47[/C][C]7.7[/C][C]7.66127064914053[/C][C]0.0387293508594697[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]7.93725904374572[/C][C]-0.0372590437457151[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]7.54434116429234[/C][C]-0.0443411642923356[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]6.9300230391549[/C][C]-0.0300230391548949[/C][/ROW]
[ROW][C]51[/C][C]6.6[/C][C]6.58098474041628[/C][C]0.0190152595837203[/C][/ROW]
[ROW][C]52[/C][C]6.9[/C][C]6.9261313887732[/C][C]-0.0261313887731935[/C][/ROW]
[ROW][C]53[/C][C]7.7[/C][C]7.63479926235863[/C][C]0.0652007376413705[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]8.00657405207089[/C][C]-0.0065740520708908[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]8.03050325678947[/C][C]-0.0305032567894724[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.70591214235518[/C][C]-0.00591214235518432[/C][/ROW]
[ROW][C]57[/C][C]7.3[/C][C]7.32394676402231[/C][C]-0.0239467640223084[/C][/ROW]
[ROW][C]58[/C][C]7.4[/C][C]7.42301965158464[/C][C]-0.0230196515846415[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]8.0549504410938[/C][C]0.0450495589061997[/C][/ROW]
[ROW][C]60[/C][C]8.3[/C][C]8.28114340212495[/C][C]0.0188565978750555[/C][/ROW]
[ROW][C]61[/C][C]8.2[/C][C]8.18004631208504[/C][C]0.0199536879149614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&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
19.39.3088997364225-0.00889973642249405
28.78.70141613121766-0.00141613121766539
38.28.24430707004103-0.0443070700410328
48.38.292909789870140.00709021012986453
58.58.52263317627068-0.0226331762706832
68.68.562982331616050.0370176683839531
78.58.51993423963392-0.0199342396339185
88.28.191137965671040.00886203432896158
98.18.10234284507002-0.00234284507001730
107.97.92938856464161-0.0293885646416147
118.68.63308936619857-0.0330893661985699
128.78.72053501848121-0.0205350184812094
138.78.73148751960171-0.0314875196017129
148.58.52652482665238-0.0265248266523847
158.48.350284053768720.0497159462312756
168.58.496610192841780.00338980715821969
178.78.638887926959690.0611120730403116
188.78.690189583425560.00981041657444402
198.68.60004499450615-4.49945061518061e-05
208.58.5139488105419-0.0139488105418954
218.38.3512722970748-0.0512722970748066
2288.03131714341416-0.0313171434141595
238.28.182640468273160.0173595317268437
248.18.11213274620304-0.0121327462030437
258.18.078687687023040.0213123129769619
2687.962676869248180.0373231307518236
277.97.878761642334940.0212383576650617
287.97.92466542552728-0.0246654255272765
2988.01901517397527-0.0190151739752730
3087.964339846713450.0356601532865509
317.97.859037597144950.0409624028550512
3287.963508357980810.0364916420191876
337.77.682640069687840.0173599303121561
347.27.25207197152587-0.0520719715258647
357.57.472640728477290.0273592715227130
367.37.288146391109890.0118536088901127
3777.00255496370258-0.00255496370258068
3877.0272284788131-0.0272284788131016
3976.966480544118560.0335194558814424
407.27.169506212931010.0304937870689908
417.37.289134634415970.0108653655840302
427.17.15796615599201-0.0579661559920117
436.86.722674918715690.077325081284306
446.46.42882992682465-0.0288299268246466
456.16.13565966909279-0.035659669092792
466.56.47795062623950.0220493737605041
477.77.661270649140530.0387293508594697
487.97.93725904374572-0.0372590437457151
497.57.54434116429234-0.0443411642923356
506.96.9300230391549-0.0300230391548949
516.66.580984740416280.0190152595837203
526.96.9261313887732-0.0261313887731935
537.77.634799262358630.0652007376413705
5488.00657405207089-0.0065740520708908
5588.03050325678947-0.0305032567894724
567.77.70591214235518-0.00591214235518432
577.37.32394676402231-0.0239467640223084
587.47.42301965158464-0.0230196515846415
598.18.05495044109380.0450495589061997
608.38.281143402124950.0188565978750555
618.28.180046312085040.0199536879149614







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.4987300927023260.9974601854046530.501269907297674
80.3264598970473360.6529197940946720.673540102952664
90.1965393791851710.3930787583703430.803460620814829
100.2327459648401860.4654919296803730.767254035159814
110.1610762535937260.3221525071874510.838923746406274
120.1014090310327280.2028180620654560.898590968967272
130.06891167742489630.1378233548497930.931088322575104
140.04589706866125040.09179413732250090.95410293133875
150.1459098693599200.2918197387198390.85409013064008
160.1010040907516380.2020081815032770.898995909248361
170.1192965356507440.2385930713014880.880703464349256
180.1139472951316920.2278945902633840.886052704868308
190.1134573202904650.2269146405809300.886542679709535
200.1308346559473320.2616693118946640.869165344052668
210.2480034837525080.4960069675050160.751996516247492
220.2193912400938740.4387824801877480.780608759906126
230.2653757517340470.5307515034680950.734624248265953
240.2245985856559240.4491971713118490.775401414344076
250.2160675151727720.4321350303455450.783932484827228
260.232281462405090.464562924810180.76771853759491
270.1931395754682800.3862791509365590.80686042453172
280.1808869587619250.3617739175238490.819113041238075
290.1729822460975730.3459644921951460.827017753902427
300.1833928838844430.3667857677688860.816607116115557
310.1807194200091530.3614388400183050.819280579990847
320.1610258479610480.3220516959220960.838974152038952
330.1187075255138810.2374150510277610.881292474486119
340.2273122949166930.4546245898333850.772687705083307
350.2018569769383990.4037139538767970.798143023061601
360.1564249719060000.3128499438119990.843575028094
370.1163346659740120.2326693319480250.883665334025988
380.1065489854725960.2130979709451910.893451014527404
390.09443152086457490.1888630417291500.905568479135425
400.07666613771884120.1533322754376820.923333862281159
410.05149424150750110.1029884830150020.948505758492499
420.1350896460946650.2701792921893300.864910353905335
430.3736377965391320.7472755930782640.626362203460868
440.3401049175525080.6802098351050170.659895082447492
450.3218638151982440.6437276303964880.678136184801756
460.2945124601336490.5890249202672980.705487539866351
470.3500248345950000.7000496691899990.649975165405
480.3583509037060780.7167018074121560.641649096293922
490.4553621088411210.9107242176822430.544637891158879
500.6304406245912690.7391187508174620.369559375408731
510.5142659595419280.9714680809161430.485734040458072
520.9902668164824880.01946636703502360.0097331835175118
530.9790828832472850.04183423350542930.0209171167527146
540.9604925538855370.07901489222892580.0395074461144629

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.498730092702326 & 0.997460185404653 & 0.501269907297674 \tabularnewline
8 & 0.326459897047336 & 0.652919794094672 & 0.673540102952664 \tabularnewline
9 & 0.196539379185171 & 0.393078758370343 & 0.803460620814829 \tabularnewline
10 & 0.232745964840186 & 0.465491929680373 & 0.767254035159814 \tabularnewline
11 & 0.161076253593726 & 0.322152507187451 & 0.838923746406274 \tabularnewline
12 & 0.101409031032728 & 0.202818062065456 & 0.898590968967272 \tabularnewline
13 & 0.0689116774248963 & 0.137823354849793 & 0.931088322575104 \tabularnewline
14 & 0.0458970686612504 & 0.0917941373225009 & 0.95410293133875 \tabularnewline
15 & 0.145909869359920 & 0.291819738719839 & 0.85409013064008 \tabularnewline
16 & 0.101004090751638 & 0.202008181503277 & 0.898995909248361 \tabularnewline
17 & 0.119296535650744 & 0.238593071301488 & 0.880703464349256 \tabularnewline
18 & 0.113947295131692 & 0.227894590263384 & 0.886052704868308 \tabularnewline
19 & 0.113457320290465 & 0.226914640580930 & 0.886542679709535 \tabularnewline
20 & 0.130834655947332 & 0.261669311894664 & 0.869165344052668 \tabularnewline
21 & 0.248003483752508 & 0.496006967505016 & 0.751996516247492 \tabularnewline
22 & 0.219391240093874 & 0.438782480187748 & 0.780608759906126 \tabularnewline
23 & 0.265375751734047 & 0.530751503468095 & 0.734624248265953 \tabularnewline
24 & 0.224598585655924 & 0.449197171311849 & 0.775401414344076 \tabularnewline
25 & 0.216067515172772 & 0.432135030345545 & 0.783932484827228 \tabularnewline
26 & 0.23228146240509 & 0.46456292481018 & 0.76771853759491 \tabularnewline
27 & 0.193139575468280 & 0.386279150936559 & 0.80686042453172 \tabularnewline
28 & 0.180886958761925 & 0.361773917523849 & 0.819113041238075 \tabularnewline
29 & 0.172982246097573 & 0.345964492195146 & 0.827017753902427 \tabularnewline
30 & 0.183392883884443 & 0.366785767768886 & 0.816607116115557 \tabularnewline
31 & 0.180719420009153 & 0.361438840018305 & 0.819280579990847 \tabularnewline
32 & 0.161025847961048 & 0.322051695922096 & 0.838974152038952 \tabularnewline
33 & 0.118707525513881 & 0.237415051027761 & 0.881292474486119 \tabularnewline
34 & 0.227312294916693 & 0.454624589833385 & 0.772687705083307 \tabularnewline
35 & 0.201856976938399 & 0.403713953876797 & 0.798143023061601 \tabularnewline
36 & 0.156424971906000 & 0.312849943811999 & 0.843575028094 \tabularnewline
37 & 0.116334665974012 & 0.232669331948025 & 0.883665334025988 \tabularnewline
38 & 0.106548985472596 & 0.213097970945191 & 0.893451014527404 \tabularnewline
39 & 0.0944315208645749 & 0.188863041729150 & 0.905568479135425 \tabularnewline
40 & 0.0766661377188412 & 0.153332275437682 & 0.923333862281159 \tabularnewline
41 & 0.0514942415075011 & 0.102988483015002 & 0.948505758492499 \tabularnewline
42 & 0.135089646094665 & 0.270179292189330 & 0.864910353905335 \tabularnewline
43 & 0.373637796539132 & 0.747275593078264 & 0.626362203460868 \tabularnewline
44 & 0.340104917552508 & 0.680209835105017 & 0.659895082447492 \tabularnewline
45 & 0.321863815198244 & 0.643727630396488 & 0.678136184801756 \tabularnewline
46 & 0.294512460133649 & 0.589024920267298 & 0.705487539866351 \tabularnewline
47 & 0.350024834595000 & 0.700049669189999 & 0.649975165405 \tabularnewline
48 & 0.358350903706078 & 0.716701807412156 & 0.641649096293922 \tabularnewline
49 & 0.455362108841121 & 0.910724217682243 & 0.544637891158879 \tabularnewline
50 & 0.630440624591269 & 0.739118750817462 & 0.369559375408731 \tabularnewline
51 & 0.514265959541928 & 0.971468080916143 & 0.485734040458072 \tabularnewline
52 & 0.990266816482488 & 0.0194663670350236 & 0.0097331835175118 \tabularnewline
53 & 0.979082883247285 & 0.0418342335054293 & 0.0209171167527146 \tabularnewline
54 & 0.960492553885537 & 0.0790148922289258 & 0.0395074461144629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.498730092702326[/C][C]0.997460185404653[/C][C]0.501269907297674[/C][/ROW]
[ROW][C]8[/C][C]0.326459897047336[/C][C]0.652919794094672[/C][C]0.673540102952664[/C][/ROW]
[ROW][C]9[/C][C]0.196539379185171[/C][C]0.393078758370343[/C][C]0.803460620814829[/C][/ROW]
[ROW][C]10[/C][C]0.232745964840186[/C][C]0.465491929680373[/C][C]0.767254035159814[/C][/ROW]
[ROW][C]11[/C][C]0.161076253593726[/C][C]0.322152507187451[/C][C]0.838923746406274[/C][/ROW]
[ROW][C]12[/C][C]0.101409031032728[/C][C]0.202818062065456[/C][C]0.898590968967272[/C][/ROW]
[ROW][C]13[/C][C]0.0689116774248963[/C][C]0.137823354849793[/C][C]0.931088322575104[/C][/ROW]
[ROW][C]14[/C][C]0.0458970686612504[/C][C]0.0917941373225009[/C][C]0.95410293133875[/C][/ROW]
[ROW][C]15[/C][C]0.145909869359920[/C][C]0.291819738719839[/C][C]0.85409013064008[/C][/ROW]
[ROW][C]16[/C][C]0.101004090751638[/C][C]0.202008181503277[/C][C]0.898995909248361[/C][/ROW]
[ROW][C]17[/C][C]0.119296535650744[/C][C]0.238593071301488[/C][C]0.880703464349256[/C][/ROW]
[ROW][C]18[/C][C]0.113947295131692[/C][C]0.227894590263384[/C][C]0.886052704868308[/C][/ROW]
[ROW][C]19[/C][C]0.113457320290465[/C][C]0.226914640580930[/C][C]0.886542679709535[/C][/ROW]
[ROW][C]20[/C][C]0.130834655947332[/C][C]0.261669311894664[/C][C]0.869165344052668[/C][/ROW]
[ROW][C]21[/C][C]0.248003483752508[/C][C]0.496006967505016[/C][C]0.751996516247492[/C][/ROW]
[ROW][C]22[/C][C]0.219391240093874[/C][C]0.438782480187748[/C][C]0.780608759906126[/C][/ROW]
[ROW][C]23[/C][C]0.265375751734047[/C][C]0.530751503468095[/C][C]0.734624248265953[/C][/ROW]
[ROW][C]24[/C][C]0.224598585655924[/C][C]0.449197171311849[/C][C]0.775401414344076[/C][/ROW]
[ROW][C]25[/C][C]0.216067515172772[/C][C]0.432135030345545[/C][C]0.783932484827228[/C][/ROW]
[ROW][C]26[/C][C]0.23228146240509[/C][C]0.46456292481018[/C][C]0.76771853759491[/C][/ROW]
[ROW][C]27[/C][C]0.193139575468280[/C][C]0.386279150936559[/C][C]0.80686042453172[/C][/ROW]
[ROW][C]28[/C][C]0.180886958761925[/C][C]0.361773917523849[/C][C]0.819113041238075[/C][/ROW]
[ROW][C]29[/C][C]0.172982246097573[/C][C]0.345964492195146[/C][C]0.827017753902427[/C][/ROW]
[ROW][C]30[/C][C]0.183392883884443[/C][C]0.366785767768886[/C][C]0.816607116115557[/C][/ROW]
[ROW][C]31[/C][C]0.180719420009153[/C][C]0.361438840018305[/C][C]0.819280579990847[/C][/ROW]
[ROW][C]32[/C][C]0.161025847961048[/C][C]0.322051695922096[/C][C]0.838974152038952[/C][/ROW]
[ROW][C]33[/C][C]0.118707525513881[/C][C]0.237415051027761[/C][C]0.881292474486119[/C][/ROW]
[ROW][C]34[/C][C]0.227312294916693[/C][C]0.454624589833385[/C][C]0.772687705083307[/C][/ROW]
[ROW][C]35[/C][C]0.201856976938399[/C][C]0.403713953876797[/C][C]0.798143023061601[/C][/ROW]
[ROW][C]36[/C][C]0.156424971906000[/C][C]0.312849943811999[/C][C]0.843575028094[/C][/ROW]
[ROW][C]37[/C][C]0.116334665974012[/C][C]0.232669331948025[/C][C]0.883665334025988[/C][/ROW]
[ROW][C]38[/C][C]0.106548985472596[/C][C]0.213097970945191[/C][C]0.893451014527404[/C][/ROW]
[ROW][C]39[/C][C]0.0944315208645749[/C][C]0.188863041729150[/C][C]0.905568479135425[/C][/ROW]
[ROW][C]40[/C][C]0.0766661377188412[/C][C]0.153332275437682[/C][C]0.923333862281159[/C][/ROW]
[ROW][C]41[/C][C]0.0514942415075011[/C][C]0.102988483015002[/C][C]0.948505758492499[/C][/ROW]
[ROW][C]42[/C][C]0.135089646094665[/C][C]0.270179292189330[/C][C]0.864910353905335[/C][/ROW]
[ROW][C]43[/C][C]0.373637796539132[/C][C]0.747275593078264[/C][C]0.626362203460868[/C][/ROW]
[ROW][C]44[/C][C]0.340104917552508[/C][C]0.680209835105017[/C][C]0.659895082447492[/C][/ROW]
[ROW][C]45[/C][C]0.321863815198244[/C][C]0.643727630396488[/C][C]0.678136184801756[/C][/ROW]
[ROW][C]46[/C][C]0.294512460133649[/C][C]0.589024920267298[/C][C]0.705487539866351[/C][/ROW]
[ROW][C]47[/C][C]0.350024834595000[/C][C]0.700049669189999[/C][C]0.649975165405[/C][/ROW]
[ROW][C]48[/C][C]0.358350903706078[/C][C]0.716701807412156[/C][C]0.641649096293922[/C][/ROW]
[ROW][C]49[/C][C]0.455362108841121[/C][C]0.910724217682243[/C][C]0.544637891158879[/C][/ROW]
[ROW][C]50[/C][C]0.630440624591269[/C][C]0.739118750817462[/C][C]0.369559375408731[/C][/ROW]
[ROW][C]51[/C][C]0.514265959541928[/C][C]0.971468080916143[/C][C]0.485734040458072[/C][/ROW]
[ROW][C]52[/C][C]0.990266816482488[/C][C]0.0194663670350236[/C][C]0.0097331835175118[/C][/ROW]
[ROW][C]53[/C][C]0.979082883247285[/C][C]0.0418342335054293[/C][C]0.0209171167527146[/C][/ROW]
[ROW][C]54[/C][C]0.960492553885537[/C][C]0.0790148922289258[/C][C]0.0395074461144629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.4987300927023260.9974601854046530.501269907297674
80.3264598970473360.6529197940946720.673540102952664
90.1965393791851710.3930787583703430.803460620814829
100.2327459648401860.4654919296803730.767254035159814
110.1610762535937260.3221525071874510.838923746406274
120.1014090310327280.2028180620654560.898590968967272
130.06891167742489630.1378233548497930.931088322575104
140.04589706866125040.09179413732250090.95410293133875
150.1459098693599200.2918197387198390.85409013064008
160.1010040907516380.2020081815032770.898995909248361
170.1192965356507440.2385930713014880.880703464349256
180.1139472951316920.2278945902633840.886052704868308
190.1134573202904650.2269146405809300.886542679709535
200.1308346559473320.2616693118946640.869165344052668
210.2480034837525080.4960069675050160.751996516247492
220.2193912400938740.4387824801877480.780608759906126
230.2653757517340470.5307515034680950.734624248265953
240.2245985856559240.4491971713118490.775401414344076
250.2160675151727720.4321350303455450.783932484827228
260.232281462405090.464562924810180.76771853759491
270.1931395754682800.3862791509365590.80686042453172
280.1808869587619250.3617739175238490.819113041238075
290.1729822460975730.3459644921951460.827017753902427
300.1833928838844430.3667857677688860.816607116115557
310.1807194200091530.3614388400183050.819280579990847
320.1610258479610480.3220516959220960.838974152038952
330.1187075255138810.2374150510277610.881292474486119
340.2273122949166930.4546245898333850.772687705083307
350.2018569769383990.4037139538767970.798143023061601
360.1564249719060000.3128499438119990.843575028094
370.1163346659740120.2326693319480250.883665334025988
380.1065489854725960.2130979709451910.893451014527404
390.09443152086457490.1888630417291500.905568479135425
400.07666613771884120.1533322754376820.923333862281159
410.05149424150750110.1029884830150020.948505758492499
420.1350896460946650.2701792921893300.864910353905335
430.3736377965391320.7472755930782640.626362203460868
440.3401049175525080.6802098351050170.659895082447492
450.3218638151982440.6437276303964880.678136184801756
460.2945124601336490.5890249202672980.705487539866351
470.3500248345950000.7000496691899990.649975165405
480.3583509037060780.7167018074121560.641649096293922
490.4553621088411210.9107242176822430.544637891158879
500.6304406245912690.7391187508174620.369559375408731
510.5142659595419280.9714680809161430.485734040458072
520.9902668164824880.01946636703502360.0097331835175118
530.9790828832472850.04183423350542930.0209171167527146
540.9604925538855370.07901489222892580.0395074461144629







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0416666666666667OK
10% type I error level40.0833333333333333OK

\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 & 2 & 0.0416666666666667 & OK \tabularnewline
10% type I error level & 4 & 0.0833333333333333 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58372&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]2[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0833333333333333[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58372&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58372&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0416666666666667OK
10% type I error level40.0833333333333333OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}