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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 10:36:35 -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/t1258739871adavkx60fmo0b0q.htm/, Retrieved Thu, 28 Mar 2024 23:44:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58374, Retrieved Thu, 28 Mar 2024 23:44:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7_4] [2009-11-18 19:13:17] [8b1aef4e7013bd33fbc2a5833375c5f5]
-           [Multiple Regression] [] [2009-11-19 14:06:12] [08fc5c07292c885b941f0cb515ce13f3]
-    D          [Multiple Regression] [] [2009-11-20 17:36:35] [91df150cd527c563f0151b3a845ecd72] [Current]
-    D            [Multiple Regression] [verbetering] [2009-11-26 18:08:09] [42ad1186d39724f834063794eac7cea3]
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Dataseries X:
8	5560	0	0	0	0
8.1	3922	8	0	0	0
7.7	3759	8.1	8	0	0
7.5	4138	7.7	8.1	8	0
7.6	4634	7.5	7.7	8.1	8
7.8	3996	7.6	7.5	7.7	8.1
7.8	4308	7.8	7.6	7.5	7.7
7.8	4143	7.8	7.8	7.6	7.5
7.5	4429	7.8	7.8	7.8	7.6
7.5	5219	7.5	7.8	7.8	7.8
7.1	4929	7.5	7.5	7.8	7.8
7.5	5755	7.1	7.5	7.5	7.8
7.5	5592	7.5	7.1	7.5	7.5
7.6	4163	7.5	7.5	7.1	7.5
7.7	4962	7.6	7.5	7.5	7.1
7.7	5208	7.7	7.6	7.5	7.5
7.9	4755	7.7	7.7	7.6	7.5
8.1	4491	7.9	7.7	7.7	7.6
8.2	5732	8.1	7.9	7.7	7.7
8.2	5731	8.2	8.1	7.9	7.7
8.2	5040	8.2	8.2	8.1	7.9
7.9	6102	8.2	8.2	8.2	8.1
7.3	4904	7.9	8.2	8.2	8.2
6.9	5369	7.3	7.9	8.2	8.2
6.7	5578	6.9	7.3	7.9	8.2
6.7	4619	6.7	6.9	7.3	7.9
6.9	4731	6.7	6.7	6.9	7.3
7	5011	6.9	6.7	6.7	6.9
7.1	5299	7	6.9	6.7	6.7
7.2	4146	7.1	7	6.9	6.7
7.1	4625	7.2	7.1	7	6.9
6.9	4736	7.1	7.2	7.1	7
7	4219	6.9	7.1	7.2	7.1
6.8	5116	7	6.9	7.1	7.2
6.4	4205	6.8	7	6.9	7.1
6.7	4121	6.4	6.8	7	6.9
6.6	5103	6.7	6.4	6.8	7
6.4	4300	6.6	6.7	6.4	6.8
6.3	4578	6.4	6.6	6.7	6.4
6.2	3809	6.3	6.4	6.6	6.7
6.5	5526	6.2	6.3	6.4	6.6
6.8	4247	6.5	6.2	6.3	6.4
6.8	3830	6.8	6.5	6.2	6.3
6.4	4394	6.8	6.8	6.5	6.2
6.1	4826	6.4	6.8	6.8	6.5
5.8	4409	6.1	6.4	6.8	6.8
6.1	4569	5.8	6.1	6.4	6.8
7.2	4106	6.1	5.8	6.1	6.4
7.3	4794	7.2	6.1	5.8	6.1
6.9	3914	7.3	7.2	6.1	5.8
6.1	3793	6.9	7.3	7.2	6.1
5.8	4405	6.1	6.9	7.3	7.2
6.2	4022	5.8	6.1	6.9	7.3
7.1	4100	6.2	5.8	6.1	6.9
7.7	4788	7.1	6.2	5.8	6.1
7.9	3163	7.7	7.1	6.2	5.8
7.7	3585	7.9	7.7	7.1	6.2
7.4	3903	7.7	7.9	7.7	7.1
7.5	4178	7.4	7.7	7.9	7.7
8	3863	7.5	7.4	7.7	7.9
8.1	4187	8	7.5	7.4	7.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&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]5 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=58374&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.45334600611849 + 2.86270785610798e-05X[t] + 0.313663967704978Y1[t] -0.0391313044914567Y2[t] -0.0475363981301766Y3[t] -0.0511292781289009Y4[t] + 0.0976468238655872M1[t] -0.587691273323679M2[t] -0.691345384808646M3[t] -0.634225040597112M4[t] -0.31093127283165M5[t] -0.0254784574698382M6[t] -0.0183527603020163M7[t] -0.0976254621655117M8[t] -0.166708756365665M9[t] -0.324682647520785M10[t] -0.434249952247223M11[t] -0.0133276870234248t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.45334600611849 +  2.86270785610798e-05X[t] +  0.313663967704978Y1[t] -0.0391313044914567Y2[t] -0.0475363981301766Y3[t] -0.0511292781289009Y4[t] +  0.0976468238655872M1[t] -0.587691273323679M2[t] -0.691345384808646M3[t] -0.634225040597112M4[t] -0.31093127283165M5[t] -0.0254784574698382M6[t] -0.0183527603020163M7[t] -0.0976254621655117M8[t] -0.166708756365665M9[t] -0.324682647520785M10[t] -0.434249952247223M11[t] -0.0133276870234248t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.45334600611849 +  2.86270785610798e-05X[t] +  0.313663967704978Y1[t] -0.0391313044914567Y2[t] -0.0475363981301766Y3[t] -0.0511292781289009Y4[t] +  0.0976468238655872M1[t] -0.587691273323679M2[t] -0.691345384808646M3[t] -0.634225040597112M4[t] -0.31093127283165M5[t] -0.0254784574698382M6[t] -0.0183527603020163M7[t] -0.0976254621655117M8[t] -0.166708756365665M9[t] -0.324682647520785M10[t] -0.434249952247223M11[t] -0.0133276870234248t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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
Y[t] = + 6.45334600611849 + 2.86270785610798e-05X[t] + 0.313663967704978Y1[t] -0.0391313044914567Y2[t] -0.0475363981301766Y3[t] -0.0511292781289009Y4[t] + 0.0976468238655872M1[t] -0.587691273323679M2[t] -0.691345384808646M3[t] -0.634225040597112M4[t] -0.31093127283165M5[t] -0.0254784574698382M6[t] -0.0183527603020163M7[t] -0.0976254621655117M8[t] -0.166708756365665M9[t] -0.324682647520785M10[t] -0.434249952247223M11[t] -0.0133276870234248t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.453346006118491.0098166.390600
X2.86270785610798e-050.0001630.17560.8614130.430707
Y10.3136639677049780.0963693.25480.0022150.001107
Y2-0.03913130449145670.106875-0.36610.7160560.358028
Y3-0.04753639813017660.106688-0.44560.6581480.329074
Y4-0.05112927812890090.079544-0.64280.5237820.261891
M10.09764682386558720.3556870.27450.7849920.392496
M2-0.5876912733236790.384-1.53040.1332310.066615
M3-0.6913453848086460.378884-1.82470.0750.0375
M4-0.6342250405971120.375584-1.68860.098530.049265
M5-0.310931272831650.354505-0.87710.3853140.192657
M6-0.02547845746983820.364483-0.06990.9445950.472298
M7-0.01835276030201630.357641-0.05130.9593110.479656
M8-0.09762546216551170.358981-0.2720.7869610.39348
M9-0.1667087563656650.357814-0.46590.6436320.321816
M10-0.3246826475207850.35839-0.90590.3700120.185006
M11-0.4342499522472230.353522-1.22840.2259950.112998
t-0.01332768702342480.005246-2.54040.0147660.007383

\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) & 6.45334600611849 & 1.009816 & 6.3906 & 0 & 0 \tabularnewline
X & 2.86270785610798e-05 & 0.000163 & 0.1756 & 0.861413 & 0.430707 \tabularnewline
Y1 & 0.313663967704978 & 0.096369 & 3.2548 & 0.002215 & 0.001107 \tabularnewline
Y2 & -0.0391313044914567 & 0.106875 & -0.3661 & 0.716056 & 0.358028 \tabularnewline
Y3 & -0.0475363981301766 & 0.106688 & -0.4456 & 0.658148 & 0.329074 \tabularnewline
Y4 & -0.0511292781289009 & 0.079544 & -0.6428 & 0.523782 & 0.261891 \tabularnewline
M1 & 0.0976468238655872 & 0.355687 & 0.2745 & 0.784992 & 0.392496 \tabularnewline
M2 & -0.587691273323679 & 0.384 & -1.5304 & 0.133231 & 0.066615 \tabularnewline
M3 & -0.691345384808646 & 0.378884 & -1.8247 & 0.075 & 0.0375 \tabularnewline
M4 & -0.634225040597112 & 0.375584 & -1.6886 & 0.09853 & 0.049265 \tabularnewline
M5 & -0.31093127283165 & 0.354505 & -0.8771 & 0.385314 & 0.192657 \tabularnewline
M6 & -0.0254784574698382 & 0.364483 & -0.0699 & 0.944595 & 0.472298 \tabularnewline
M7 & -0.0183527603020163 & 0.357641 & -0.0513 & 0.959311 & 0.479656 \tabularnewline
M8 & -0.0976254621655117 & 0.358981 & -0.272 & 0.786961 & 0.39348 \tabularnewline
M9 & -0.166708756365665 & 0.357814 & -0.4659 & 0.643632 & 0.321816 \tabularnewline
M10 & -0.324682647520785 & 0.35839 & -0.9059 & 0.370012 & 0.185006 \tabularnewline
M11 & -0.434249952247223 & 0.353522 & -1.2284 & 0.225995 & 0.112998 \tabularnewline
t & -0.0133276870234248 & 0.005246 & -2.5404 & 0.014766 & 0.007383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&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]6.45334600611849[/C][C]1.009816[/C][C]6.3906[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]2.86270785610798e-05[/C][C]0.000163[/C][C]0.1756[/C][C]0.861413[/C][C]0.430707[/C][/ROW]
[ROW][C]Y1[/C][C]0.313663967704978[/C][C]0.096369[/C][C]3.2548[/C][C]0.002215[/C][C]0.001107[/C][/ROW]
[ROW][C]Y2[/C][C]-0.0391313044914567[/C][C]0.106875[/C][C]-0.3661[/C][C]0.716056[/C][C]0.358028[/C][/ROW]
[ROW][C]Y3[/C][C]-0.0475363981301766[/C][C]0.106688[/C][C]-0.4456[/C][C]0.658148[/C][C]0.329074[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0511292781289009[/C][C]0.079544[/C][C]-0.6428[/C][C]0.523782[/C][C]0.261891[/C][/ROW]
[ROW][C]M1[/C][C]0.0976468238655872[/C][C]0.355687[/C][C]0.2745[/C][C]0.784992[/C][C]0.392496[/C][/ROW]
[ROW][C]M2[/C][C]-0.587691273323679[/C][C]0.384[/C][C]-1.5304[/C][C]0.133231[/C][C]0.066615[/C][/ROW]
[ROW][C]M3[/C][C]-0.691345384808646[/C][C]0.378884[/C][C]-1.8247[/C][C]0.075[/C][C]0.0375[/C][/ROW]
[ROW][C]M4[/C][C]-0.634225040597112[/C][C]0.375584[/C][C]-1.6886[/C][C]0.09853[/C][C]0.049265[/C][/ROW]
[ROW][C]M5[/C][C]-0.31093127283165[/C][C]0.354505[/C][C]-0.8771[/C][C]0.385314[/C][C]0.192657[/C][/ROW]
[ROW][C]M6[/C][C]-0.0254784574698382[/C][C]0.364483[/C][C]-0.0699[/C][C]0.944595[/C][C]0.472298[/C][/ROW]
[ROW][C]M7[/C][C]-0.0183527603020163[/C][C]0.357641[/C][C]-0.0513[/C][C]0.959311[/C][C]0.479656[/C][/ROW]
[ROW][C]M8[/C][C]-0.0976254621655117[/C][C]0.358981[/C][C]-0.272[/C][C]0.786961[/C][C]0.39348[/C][/ROW]
[ROW][C]M9[/C][C]-0.166708756365665[/C][C]0.357814[/C][C]-0.4659[/C][C]0.643632[/C][C]0.321816[/C][/ROW]
[ROW][C]M10[/C][C]-0.324682647520785[/C][C]0.35839[/C][C]-0.9059[/C][C]0.370012[/C][C]0.185006[/C][/ROW]
[ROW][C]M11[/C][C]-0.434249952247223[/C][C]0.353522[/C][C]-1.2284[/C][C]0.225995[/C][C]0.112998[/C][/ROW]
[ROW][C]t[/C][C]-0.0133276870234248[/C][C]0.005246[/C][C]-2.5404[/C][C]0.014766[/C][C]0.007383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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)6.453346006118491.0098166.390600
X2.86270785610798e-050.0001630.17560.8614130.430707
Y10.3136639677049780.0963693.25480.0022150.001107
Y2-0.03913130449145670.106875-0.36610.7160560.358028
Y3-0.04753639813017660.106688-0.44560.6581480.329074
Y4-0.05112927812890090.079544-0.64280.5237820.261891
M10.09764682386558720.3556870.27450.7849920.392496
M2-0.5876912733236790.384-1.53040.1332310.066615
M3-0.6913453848086460.378884-1.82470.0750.0375
M4-0.6342250405971120.375584-1.68860.098530.049265
M5-0.310931272831650.354505-0.87710.3853140.192657
M6-0.02547845746983820.364483-0.06990.9445950.472298
M7-0.01835276030201630.357641-0.05130.9593110.479656
M8-0.09762546216551170.358981-0.2720.7869610.39348
M9-0.1667087563656650.357814-0.46590.6436320.321816
M10-0.3246826475207850.35839-0.90590.3700120.185006
M11-0.4342499522472230.353522-1.22840.2259950.112998
t-0.01332768702342480.005246-2.54040.0147660.007383







Multiple Linear Regression - Regression Statistics
Multiple R0.700175703647055
R-squared0.490246015977649
Adjusted R-squared0.288715371131603
F-TEST (value)2.43261274905442
F-TEST (DF numerator)17
F-TEST (DF denominator)43
p-value0.00948963500605382
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.557968541564462
Sum Squared Residuals13.3871424151496

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.700175703647055 \tabularnewline
R-squared & 0.490246015977649 \tabularnewline
Adjusted R-squared & 0.288715371131603 \tabularnewline
F-TEST (value) & 2.43261274905442 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0.00948963500605382 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.557968541564462 \tabularnewline
Sum Squared Residuals & 13.3871424151496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.700175703647055[/C][/ROW]
[ROW][C]R-squared[/C][C]0.490246015977649[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.288715371131603[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.43261274905442[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]0.00948963500605382[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.557968541564462[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.3871424151496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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 R0.700175703647055
R-squared0.490246015977649
Adjusted R-squared0.288715371131603
F-TEST (value)2.43261274905442
F-TEST (DF numerator)17
F-TEST (DF denominator)43
p-value0.00948963500605382
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.557968541564462
Sum Squared Residuals13.3871424151496







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
186.696831699760241.30316830023976
28.18.46058650250434-0.36058650250434
37.78.05725445102933-0.357254451029332
47.57.60222686841954-0.102226868419543
57.67.465523843539240.13447615646076
67.87.772479184863630.0275208151363730
77.87.86398749748853-0.0639874974885262
87.87.76430959555350.0356904044465012
97.57.67546575135946-0.175465751359464
107.57.42245451930690.0775454806930998
117.17.30299706612176-0.202997066121761
127.57.63636063059407-0.136360630594072
137.57.87247044594802-0.372470445948023
147.67.136258603927040.463741396072962
157.77.074953389958930.625046610041066
167.77.132789863542860.567210136457139
177.97.421121107434570.478878892565434
188.17.738554912947920.361445087052084
198.27.817672732416430.382327267583572
208.27.739076572697120.460923427302882
218.27.613238014486870.586761985513128
227.97.45735889830140.442641101698604
237.37.200956548310980.0990434516890234
246.97.45873141579013-0.558731415790127
256.77.46130772710349-0.761307727103491
266.76.73196892512309-0.0319689251230881
276.96.675711746441240.224288253558761
2876.820211770045040.179788229954959
297.17.17218844091066-0.0721884409106568
307.27.42925253436344-0.229252534363435
317.17.44923668602114-0.349236686021144
326.97.31466780800895-0.414667808008952
3377.14877039645154-0.148770396451538
346.87.0419806774112-0.241980677411199
356.46.84098070054098-0.440980700540977
366.77.1473311807947-0.447331180794707
376.67.37390817270507-0.773908172705071
386.46.63838947116775-0.238389471167749
396.36.47673712922-0.176737129219994
406.26.46439028349677-0.264390283496774
416.56.81067599924576-0.310675999245759
426.87.15917891030396-0.359178910303962
436.87.23326579527835-0.433265795278353
446.47.13592369572628-0.735923695726281
456.16.91081432248138-0.810814322481376
465.86.63378980058928-0.83378980058928
476.16.4521299016972-0.352129901697204
487.27.000349041896770.199650958103235
497.37.46725428479471-0.167254284794713
506.96.732796497277780.167203502722216
516.16.4153432833505-0.315343283350501
525.86.18038121449578-0.380381214495782
536.26.43049060886978-0.230490608869778
547.16.900534457521060.199465542478940
557.77.235837288795550.46416271120445
567.97.246022328014150.65397767198585
577.77.151711515220750.54828848477925
587.46.844416104391230.555583895608774
597.56.602935783329080.897064216670918
6087.057227730924330.94277226907567
618.17.328227669688460.771772330311541

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8 & 6.69683169976024 & 1.30316830023976 \tabularnewline
2 & 8.1 & 8.46058650250434 & -0.36058650250434 \tabularnewline
3 & 7.7 & 8.05725445102933 & -0.357254451029332 \tabularnewline
4 & 7.5 & 7.60222686841954 & -0.102226868419543 \tabularnewline
5 & 7.6 & 7.46552384353924 & 0.13447615646076 \tabularnewline
6 & 7.8 & 7.77247918486363 & 0.0275208151363730 \tabularnewline
7 & 7.8 & 7.86398749748853 & -0.0639874974885262 \tabularnewline
8 & 7.8 & 7.7643095955535 & 0.0356904044465012 \tabularnewline
9 & 7.5 & 7.67546575135946 & -0.175465751359464 \tabularnewline
10 & 7.5 & 7.4224545193069 & 0.0775454806930998 \tabularnewline
11 & 7.1 & 7.30299706612176 & -0.202997066121761 \tabularnewline
12 & 7.5 & 7.63636063059407 & -0.136360630594072 \tabularnewline
13 & 7.5 & 7.87247044594802 & -0.372470445948023 \tabularnewline
14 & 7.6 & 7.13625860392704 & 0.463741396072962 \tabularnewline
15 & 7.7 & 7.07495338995893 & 0.625046610041066 \tabularnewline
16 & 7.7 & 7.13278986354286 & 0.567210136457139 \tabularnewline
17 & 7.9 & 7.42112110743457 & 0.478878892565434 \tabularnewline
18 & 8.1 & 7.73855491294792 & 0.361445087052084 \tabularnewline
19 & 8.2 & 7.81767273241643 & 0.382327267583572 \tabularnewline
20 & 8.2 & 7.73907657269712 & 0.460923427302882 \tabularnewline
21 & 8.2 & 7.61323801448687 & 0.586761985513128 \tabularnewline
22 & 7.9 & 7.4573588983014 & 0.442641101698604 \tabularnewline
23 & 7.3 & 7.20095654831098 & 0.0990434516890234 \tabularnewline
24 & 6.9 & 7.45873141579013 & -0.558731415790127 \tabularnewline
25 & 6.7 & 7.46130772710349 & -0.761307727103491 \tabularnewline
26 & 6.7 & 6.73196892512309 & -0.0319689251230881 \tabularnewline
27 & 6.9 & 6.67571174644124 & 0.224288253558761 \tabularnewline
28 & 7 & 6.82021177004504 & 0.179788229954959 \tabularnewline
29 & 7.1 & 7.17218844091066 & -0.0721884409106568 \tabularnewline
30 & 7.2 & 7.42925253436344 & -0.229252534363435 \tabularnewline
31 & 7.1 & 7.44923668602114 & -0.349236686021144 \tabularnewline
32 & 6.9 & 7.31466780800895 & -0.414667808008952 \tabularnewline
33 & 7 & 7.14877039645154 & -0.148770396451538 \tabularnewline
34 & 6.8 & 7.0419806774112 & -0.241980677411199 \tabularnewline
35 & 6.4 & 6.84098070054098 & -0.440980700540977 \tabularnewline
36 & 6.7 & 7.1473311807947 & -0.447331180794707 \tabularnewline
37 & 6.6 & 7.37390817270507 & -0.773908172705071 \tabularnewline
38 & 6.4 & 6.63838947116775 & -0.238389471167749 \tabularnewline
39 & 6.3 & 6.47673712922 & -0.176737129219994 \tabularnewline
40 & 6.2 & 6.46439028349677 & -0.264390283496774 \tabularnewline
41 & 6.5 & 6.81067599924576 & -0.310675999245759 \tabularnewline
42 & 6.8 & 7.15917891030396 & -0.359178910303962 \tabularnewline
43 & 6.8 & 7.23326579527835 & -0.433265795278353 \tabularnewline
44 & 6.4 & 7.13592369572628 & -0.735923695726281 \tabularnewline
45 & 6.1 & 6.91081432248138 & -0.810814322481376 \tabularnewline
46 & 5.8 & 6.63378980058928 & -0.83378980058928 \tabularnewline
47 & 6.1 & 6.4521299016972 & -0.352129901697204 \tabularnewline
48 & 7.2 & 7.00034904189677 & 0.199650958103235 \tabularnewline
49 & 7.3 & 7.46725428479471 & -0.167254284794713 \tabularnewline
50 & 6.9 & 6.73279649727778 & 0.167203502722216 \tabularnewline
51 & 6.1 & 6.4153432833505 & -0.315343283350501 \tabularnewline
52 & 5.8 & 6.18038121449578 & -0.380381214495782 \tabularnewline
53 & 6.2 & 6.43049060886978 & -0.230490608869778 \tabularnewline
54 & 7.1 & 6.90053445752106 & 0.199465542478940 \tabularnewline
55 & 7.7 & 7.23583728879555 & 0.46416271120445 \tabularnewline
56 & 7.9 & 7.24602232801415 & 0.65397767198585 \tabularnewline
57 & 7.7 & 7.15171151522075 & 0.54828848477925 \tabularnewline
58 & 7.4 & 6.84441610439123 & 0.555583895608774 \tabularnewline
59 & 7.5 & 6.60293578332908 & 0.897064216670918 \tabularnewline
60 & 8 & 7.05722773092433 & 0.94277226907567 \tabularnewline
61 & 8.1 & 7.32822766968846 & 0.771772330311541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&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]8[/C][C]6.69683169976024[/C][C]1.30316830023976[/C][/ROW]
[ROW][C]2[/C][C]8.1[/C][C]8.46058650250434[/C][C]-0.36058650250434[/C][/ROW]
[ROW][C]3[/C][C]7.7[/C][C]8.05725445102933[/C][C]-0.357254451029332[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]7.60222686841954[/C][C]-0.102226868419543[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.46552384353924[/C][C]0.13447615646076[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.77247918486363[/C][C]0.0275208151363730[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.86398749748853[/C][C]-0.0639874974885262[/C][/ROW]
[ROW][C]8[/C][C]7.8[/C][C]7.7643095955535[/C][C]0.0356904044465012[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.67546575135946[/C][C]-0.175465751359464[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]7.4224545193069[/C][C]0.0775454806930998[/C][/ROW]
[ROW][C]11[/C][C]7.1[/C][C]7.30299706612176[/C][C]-0.202997066121761[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.63636063059407[/C][C]-0.136360630594072[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.87247044594802[/C][C]-0.372470445948023[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.13625860392704[/C][C]0.463741396072962[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.07495338995893[/C][C]0.625046610041066[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]7.13278986354286[/C][C]0.567210136457139[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.42112110743457[/C][C]0.478878892565434[/C][/ROW]
[ROW][C]18[/C][C]8.1[/C][C]7.73855491294792[/C][C]0.361445087052084[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]7.81767273241643[/C][C]0.382327267583572[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]7.73907657269712[/C][C]0.460923427302882[/C][/ROW]
[ROW][C]21[/C][C]8.2[/C][C]7.61323801448687[/C][C]0.586761985513128[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.4573588983014[/C][C]0.442641101698604[/C][/ROW]
[ROW][C]23[/C][C]7.3[/C][C]7.20095654831098[/C][C]0.0990434516890234[/C][/ROW]
[ROW][C]24[/C][C]6.9[/C][C]7.45873141579013[/C][C]-0.558731415790127[/C][/ROW]
[ROW][C]25[/C][C]6.7[/C][C]7.46130772710349[/C][C]-0.761307727103491[/C][/ROW]
[ROW][C]26[/C][C]6.7[/C][C]6.73196892512309[/C][C]-0.0319689251230881[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]6.67571174644124[/C][C]0.224288253558761[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]6.82021177004504[/C][C]0.179788229954959[/C][/ROW]
[ROW][C]29[/C][C]7.1[/C][C]7.17218844091066[/C][C]-0.0721884409106568[/C][/ROW]
[ROW][C]30[/C][C]7.2[/C][C]7.42925253436344[/C][C]-0.229252534363435[/C][/ROW]
[ROW][C]31[/C][C]7.1[/C][C]7.44923668602114[/C][C]-0.349236686021144[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]7.31466780800895[/C][C]-0.414667808008952[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]7.14877039645154[/C][C]-0.148770396451538[/C][/ROW]
[ROW][C]34[/C][C]6.8[/C][C]7.0419806774112[/C][C]-0.241980677411199[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]6.84098070054098[/C][C]-0.440980700540977[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]7.1473311807947[/C][C]-0.447331180794707[/C][/ROW]
[ROW][C]37[/C][C]6.6[/C][C]7.37390817270507[/C][C]-0.773908172705071[/C][/ROW]
[ROW][C]38[/C][C]6.4[/C][C]6.63838947116775[/C][C]-0.238389471167749[/C][/ROW]
[ROW][C]39[/C][C]6.3[/C][C]6.47673712922[/C][C]-0.176737129219994[/C][/ROW]
[ROW][C]40[/C][C]6.2[/C][C]6.46439028349677[/C][C]-0.264390283496774[/C][/ROW]
[ROW][C]41[/C][C]6.5[/C][C]6.81067599924576[/C][C]-0.310675999245759[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]7.15917891030396[/C][C]-0.359178910303962[/C][/ROW]
[ROW][C]43[/C][C]6.8[/C][C]7.23326579527835[/C][C]-0.433265795278353[/C][/ROW]
[ROW][C]44[/C][C]6.4[/C][C]7.13592369572628[/C][C]-0.735923695726281[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]6.91081432248138[/C][C]-0.810814322481376[/C][/ROW]
[ROW][C]46[/C][C]5.8[/C][C]6.63378980058928[/C][C]-0.83378980058928[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]6.4521299016972[/C][C]-0.352129901697204[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.00034904189677[/C][C]0.199650958103235[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.46725428479471[/C][C]-0.167254284794713[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]6.73279649727778[/C][C]0.167203502722216[/C][/ROW]
[ROW][C]51[/C][C]6.1[/C][C]6.4153432833505[/C][C]-0.315343283350501[/C][/ROW]
[ROW][C]52[/C][C]5.8[/C][C]6.18038121449578[/C][C]-0.380381214495782[/C][/ROW]
[ROW][C]53[/C][C]6.2[/C][C]6.43049060886978[/C][C]-0.230490608869778[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]6.90053445752106[/C][C]0.199465542478940[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.23583728879555[/C][C]0.46416271120445[/C][/ROW]
[ROW][C]56[/C][C]7.9[/C][C]7.24602232801415[/C][C]0.65397767198585[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.15171151522075[/C][C]0.54828848477925[/C][/ROW]
[ROW][C]58[/C][C]7.4[/C][C]6.84441610439123[/C][C]0.555583895608774[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]6.60293578332908[/C][C]0.897064216670918[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.05722773092433[/C][C]0.94277226907567[/C][/ROW]
[ROW][C]61[/C][C]8.1[/C][C]7.32822766968846[/C][C]0.771772330311541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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
186.696831699760241.30316830023976
28.18.46058650250434-0.36058650250434
37.78.05725445102933-0.357254451029332
47.57.60222686841954-0.102226868419543
57.67.465523843539240.13447615646076
67.87.772479184863630.0275208151363730
77.87.86398749748853-0.0639874974885262
87.87.76430959555350.0356904044465012
97.57.67546575135946-0.175465751359464
107.57.42245451930690.0775454806930998
117.17.30299706612176-0.202997066121761
127.57.63636063059407-0.136360630594072
137.57.87247044594802-0.372470445948023
147.67.136258603927040.463741396072962
157.77.074953389958930.625046610041066
167.77.132789863542860.567210136457139
177.97.421121107434570.478878892565434
188.17.738554912947920.361445087052084
198.27.817672732416430.382327267583572
208.27.739076572697120.460923427302882
218.27.613238014486870.586761985513128
227.97.45735889830140.442641101698604
237.37.200956548310980.0990434516890234
246.97.45873141579013-0.558731415790127
256.77.46130772710349-0.761307727103491
266.76.73196892512309-0.0319689251230881
276.96.675711746441240.224288253558761
2876.820211770045040.179788229954959
297.17.17218844091066-0.0721884409106568
307.27.42925253436344-0.229252534363435
317.17.44923668602114-0.349236686021144
326.97.31466780800895-0.414667808008952
3377.14877039645154-0.148770396451538
346.87.0419806774112-0.241980677411199
356.46.84098070054098-0.440980700540977
366.77.1473311807947-0.447331180794707
376.67.37390817270507-0.773908172705071
386.46.63838947116775-0.238389471167749
396.36.47673712922-0.176737129219994
406.26.46439028349677-0.264390283496774
416.56.81067599924576-0.310675999245759
426.87.15917891030396-0.359178910303962
436.87.23326579527835-0.433265795278353
446.47.13592369572628-0.735923695726281
456.16.91081432248138-0.810814322481376
465.86.63378980058928-0.83378980058928
476.16.4521299016972-0.352129901697204
487.27.000349041896770.199650958103235
497.37.46725428479471-0.167254284794713
506.96.732796497277780.167203502722216
516.16.4153432833505-0.315343283350501
525.86.18038121449578-0.380381214495782
536.26.43049060886978-0.230490608869778
547.16.900534457521060.199465542478940
557.77.235837288795550.46416271120445
567.97.246022328014150.65397767198585
577.77.151711515220750.54828848477925
587.46.844416104391230.555583895608774
597.56.602935783329080.897064216670918
6087.057227730924330.94277226907567
618.17.328227669688460.771772330311541







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.3525572176053420.7051144352106840.647442782394658
220.2036790829610540.4073581659221070.796320917038946
230.1228446936374710.2456893872749430.877155306362529
240.7203727308359620.5592545383280770.279627269164038
250.9427509953117770.1144980093764450.0572490046882226
260.9866735444666860.02665291106662760.0133264555333138
270.9790406119420340.04191877611593230.0209593880579662
280.9646737415833140.07065251683337150.0353262584166857
290.9632443336879430.07351133262411470.0367556663120573
300.9383472046515620.1233055906968760.061652795348438
310.900354443218570.1992911135628600.0996455567814301
320.859337732824440.2813245343511210.140662267175561
330.8714096427024260.2571807145951490.128590357297574
340.7972783464673960.4054433070652080.202721653532604
350.8602412505253680.2795174989492640.139758749474632
360.8757952973775290.2484094052449420.124204702622471
370.87538711564190.2492257687161990.124612884358099
380.8038125349670140.3923749300659720.196187465032986
390.8496423726272040.3007152547455910.150357627372796
400.8735165604605420.2529668790789160.126483439539458

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.352557217605342 & 0.705114435210684 & 0.647442782394658 \tabularnewline
22 & 0.203679082961054 & 0.407358165922107 & 0.796320917038946 \tabularnewline
23 & 0.122844693637471 & 0.245689387274943 & 0.877155306362529 \tabularnewline
24 & 0.720372730835962 & 0.559254538328077 & 0.279627269164038 \tabularnewline
25 & 0.942750995311777 & 0.114498009376445 & 0.0572490046882226 \tabularnewline
26 & 0.986673544466686 & 0.0266529110666276 & 0.0133264555333138 \tabularnewline
27 & 0.979040611942034 & 0.0419187761159323 & 0.0209593880579662 \tabularnewline
28 & 0.964673741583314 & 0.0706525168333715 & 0.0353262584166857 \tabularnewline
29 & 0.963244333687943 & 0.0735113326241147 & 0.0367556663120573 \tabularnewline
30 & 0.938347204651562 & 0.123305590696876 & 0.061652795348438 \tabularnewline
31 & 0.90035444321857 & 0.199291113562860 & 0.0996455567814301 \tabularnewline
32 & 0.85933773282444 & 0.281324534351121 & 0.140662267175561 \tabularnewline
33 & 0.871409642702426 & 0.257180714595149 & 0.128590357297574 \tabularnewline
34 & 0.797278346467396 & 0.405443307065208 & 0.202721653532604 \tabularnewline
35 & 0.860241250525368 & 0.279517498949264 & 0.139758749474632 \tabularnewline
36 & 0.875795297377529 & 0.248409405244942 & 0.124204702622471 \tabularnewline
37 & 0.8753871156419 & 0.249225768716199 & 0.124612884358099 \tabularnewline
38 & 0.803812534967014 & 0.392374930065972 & 0.196187465032986 \tabularnewline
39 & 0.849642372627204 & 0.300715254745591 & 0.150357627372796 \tabularnewline
40 & 0.873516560460542 & 0.252966879078916 & 0.126483439539458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&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]21[/C][C]0.352557217605342[/C][C]0.705114435210684[/C][C]0.647442782394658[/C][/ROW]
[ROW][C]22[/C][C]0.203679082961054[/C][C]0.407358165922107[/C][C]0.796320917038946[/C][/ROW]
[ROW][C]23[/C][C]0.122844693637471[/C][C]0.245689387274943[/C][C]0.877155306362529[/C][/ROW]
[ROW][C]24[/C][C]0.720372730835962[/C][C]0.559254538328077[/C][C]0.279627269164038[/C][/ROW]
[ROW][C]25[/C][C]0.942750995311777[/C][C]0.114498009376445[/C][C]0.0572490046882226[/C][/ROW]
[ROW][C]26[/C][C]0.986673544466686[/C][C]0.0266529110666276[/C][C]0.0133264555333138[/C][/ROW]
[ROW][C]27[/C][C]0.979040611942034[/C][C]0.0419187761159323[/C][C]0.0209593880579662[/C][/ROW]
[ROW][C]28[/C][C]0.964673741583314[/C][C]0.0706525168333715[/C][C]0.0353262584166857[/C][/ROW]
[ROW][C]29[/C][C]0.963244333687943[/C][C]0.0735113326241147[/C][C]0.0367556663120573[/C][/ROW]
[ROW][C]30[/C][C]0.938347204651562[/C][C]0.123305590696876[/C][C]0.061652795348438[/C][/ROW]
[ROW][C]31[/C][C]0.90035444321857[/C][C]0.199291113562860[/C][C]0.0996455567814301[/C][/ROW]
[ROW][C]32[/C][C]0.85933773282444[/C][C]0.281324534351121[/C][C]0.140662267175561[/C][/ROW]
[ROW][C]33[/C][C]0.871409642702426[/C][C]0.257180714595149[/C][C]0.128590357297574[/C][/ROW]
[ROW][C]34[/C][C]0.797278346467396[/C][C]0.405443307065208[/C][C]0.202721653532604[/C][/ROW]
[ROW][C]35[/C][C]0.860241250525368[/C][C]0.279517498949264[/C][C]0.139758749474632[/C][/ROW]
[ROW][C]36[/C][C]0.875795297377529[/C][C]0.248409405244942[/C][C]0.124204702622471[/C][/ROW]
[ROW][C]37[/C][C]0.8753871156419[/C][C]0.249225768716199[/C][C]0.124612884358099[/C][/ROW]
[ROW][C]38[/C][C]0.803812534967014[/C][C]0.392374930065972[/C][C]0.196187465032986[/C][/ROW]
[ROW][C]39[/C][C]0.849642372627204[/C][C]0.300715254745591[/C][C]0.150357627372796[/C][/ROW]
[ROW][C]40[/C][C]0.873516560460542[/C][C]0.252966879078916[/C][C]0.126483439539458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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
210.3525572176053420.7051144352106840.647442782394658
220.2036790829610540.4073581659221070.796320917038946
230.1228446936374710.2456893872749430.877155306362529
240.7203727308359620.5592545383280770.279627269164038
250.9427509953117770.1144980093764450.0572490046882226
260.9866735444666860.02665291106662760.0133264555333138
270.9790406119420340.04191877611593230.0209593880579662
280.9646737415833140.07065251683337150.0353262584166857
290.9632443336879430.07351133262411470.0367556663120573
300.9383472046515620.1233055906968760.061652795348438
310.900354443218570.1992911135628600.0996455567814301
320.859337732824440.2813245343511210.140662267175561
330.8714096427024260.2571807145951490.128590357297574
340.7972783464673960.4054433070652080.202721653532604
350.8602412505253680.2795174989492640.139758749474632
360.8757952973775290.2484094052449420.124204702622471
370.87538711564190.2492257687161990.124612884358099
380.8038125349670140.3923749300659720.196187465032986
390.8496423726272040.3007152547455910.150357627372796
400.8735165604605420.2529668790789160.126483439539458







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.1NOK
10% type I error level40.2NOK

\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 & 0.1 & NOK \tabularnewline
10% type I error level & 4 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58374&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]0.1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58374&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58374&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 level20.1NOK
10% type I error level40.2NOK



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