Free Statistics

of Irreproducible Research!

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 06:38:53 -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/t1258724369ku4vxmffl1l7hox.htm/, Retrieved Fri, 29 Mar 2024 11:31:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58150, Retrieved Fri, 29 Mar 2024 11:31:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
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]
-   PD    [Multiple Regression] [] [2009-11-19 18:44:20] [85be98bd9ebcfd4d73e77f8552419c9a]
-    D        [Multiple Regression] [] [2009-11-20 13:38:53] [5cd0e65b1f56b3935a0672588b930e12] [Current]
Feedback Forum

Post a new message
Dataseries X:
 2.09 	0 	 2.11 
 2.05 	0 	 2.09 
 2.08 	0 	 2.05 
 2.06 	0 	 2.08 
 2.06 	0 	 2.06 
 2.08 	0 	 2.06 
 2.07 	0 	 2.08 
 2.06 	0 	 2.07 
 2.07 	0 	 2.06 
 2.06 	0 	 2.07 
 2.09 	0 	 2.06 
 2.07 	0 	 2.09 
 2.09 	0 	 2.07 
 2.28 	0 	 2.09 
 2.33 	0 	 2.28 
 2.35 	0 	 2.33 
 2.52 	0 	 2.35 
 2.63 	0 	 2.52 
 2.58 	0 	 2.63 
 2.70 	0 	 2.58 
 2.81 	0 	 2.70 
 2.97 	0 	 2.81 
 3.04 	0 	 2.97 
 3.28 	0 	 3.04 
 3.33 	0 	 3.28 
 3.50 	0 	 3.33 
 3.56 	0 	 3.50 
 3.57 	0 	 3.56 
 3.69 	0 	 3.57 
 3.82 	0 	 3.69 
 3.79 	0 	 3.82 
 3.96 	0 	 3.79 
 4.06 	0 	 3.96 
 4.05 	0 	 4.06 
 4.03 	0 	 4.05 
 3.94 	0 	 4.03 
 4.02 	0 	 3.94 
 3.88 	0 	 4.02 
 4.02 	0 	 3.88 
 4.03 	0 	 4.02 
 4.09 	0 	 4.03 
 3.99 	0 	 4.09 
 4.01 	0 	 3.99 
 4.01 	0 	 4.01 
 4.19 	0 	 4.01 
 4.30 	0 	 4.19 
 4.27 	0 	 4.30 
 3.82 	0 	 4.27 
 3.15 	1 	 3.82 
 2.49 	1 	 3.15 
 1.81 	1 	 2.49 
 1.26 	1 	 1.81 
 1.06 	1 	 1.26 
 0.84 	1 	 1.06 
 0.78 	1 	 0.84 
 0.70 	1 	 0.78 
 0.36 	1 	 0.70 
 0.35 	1 	 0.36 
 0.36 	1 	 0.35 




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&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]3 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=58150&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.335653443287973 -0.898644896907781X[t] + 0.828817234617628Y1[t] + 0.00939051102773215t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.335653443287973 -0.898644896907781X[t] +  0.828817234617628Y1[t] +  0.00939051102773215t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.335653443287973 -0.898644896907781X[t] +  0.828817234617628Y1[t] +  0.00939051102773215t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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] = + 0.335653443287973 -0.898644896907781X[t] + 0.828817234617628Y1[t] + 0.00939051102773215t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3356534432879730.0563195.959800
X-0.8986448969077810.09671-9.292100
Y10.8288172346176280.02550532.496500
t0.009390511027732150.0018315.12854e-062e-06

\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) & 0.335653443287973 & 0.056319 & 5.9598 & 0 & 0 \tabularnewline
X & -0.898644896907781 & 0.09671 & -9.2921 & 0 & 0 \tabularnewline
Y1 & 0.828817234617628 & 0.025505 & 32.4965 & 0 & 0 \tabularnewline
t & 0.00939051102773215 & 0.001831 & 5.1285 & 4e-06 & 2e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&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]0.335653443287973[/C][C]0.056319[/C][C]5.9598[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.898644896907781[/C][C]0.09671[/C][C]-9.2921[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y1[/C][C]0.828817234617628[/C][C]0.025505[/C][C]32.4965[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00939051102773215[/C][C]0.001831[/C][C]5.1285[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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)0.3356534432879730.0563195.959800
X-0.8986448969077810.09671-9.292100
Y10.8288172346176280.02550532.496500
t0.009390511027732150.0018315.12854e-062e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.99514204947928
R-squared0.990307698641821
Adjusted R-squared0.989779027658647
F-TEST (value)1873.20229435968
F-TEST (DF numerator)3
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.115711745031606
Sum Squared Residuals0.736406436604271

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99514204947928 \tabularnewline
R-squared & 0.990307698641821 \tabularnewline
Adjusted R-squared & 0.989779027658647 \tabularnewline
F-TEST (value) & 1873.20229435968 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.115711745031606 \tabularnewline
Sum Squared Residuals & 0.736406436604271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99514204947928[/C][/ROW]
[ROW][C]R-squared[/C][C]0.990307698641821[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.989779027658647[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1873.20229435968[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/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]0.115711745031606[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.736406436604271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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 R0.99514204947928
R-squared0.990307698641821
Adjusted R-squared0.989779027658647
F-TEST (value)1873.20229435968
F-TEST (DF numerator)3
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.115711745031606
Sum Squared Residuals0.736406436604271







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.092.09384831935891-0.00384831935890569
22.052.08666248569428-0.0366624856942818
32.082.062900307337310.0170996926626892
42.062.09715533540357-0.0371553354035694
52.062.08996950173895-0.0299695017389490
62.082.09936001276668-0.0193600127666811
72.072.12532686848677-0.0553268684867661
82.062.12642920716832-0.0664292071683215
92.072.12753154584988-0.0575315458498778
102.062.14521022922379-0.0852102292237858
112.092.14631256790534-0.0563125679053421
122.072.18056759597160-0.110567595971603
132.092.17338176230698-0.0833817623069825
142.282.199348618027070.0806513819729328
152.332.36621440363215-0.0362144036321483
162.352.41704577639076-0.067045776390762
172.522.443012632110850.0769873678891531
182.632.593302073023580.0366979269764243
192.582.69386247985925-0.113862479859247
202.72.66181212915610.0381878708439025
212.812.770660708337950.0393392916620549
222.972.871221115173620.098778884826384
233.043.013222383740170.0267776162598311
243.283.080630101191130.199369898808865
253.333.28893674852710.0410632514729026
263.53.339768121285710.160231878714289
273.563.490057562198440.06994243780156
283.573.549177107303230.0208228926967699
293.693.566855790677140.123144209322862
303.823.675704369858990.144295630141014
313.793.79284112138701-0.00284112138700933
323.963.777367115376210.182632884623787
334.063.927656556288940.132343443711058
344.054.019928790778440.0300712092215637
354.034.021031129459990.00896887054000805
363.944.01384529579537-0.0738452957953723
374.023.948642255707520.071357744292482
383.884.02433814550466-0.144338145504660
394.023.917694243685920.102305756314075
404.034.04311916756012-0.0131191675601236
414.094.060797850934030.0292021490659669
423.994.11991739603882-0.129917396038822
434.014.04642618360479-0.0364261836047924
444.014.07239303932488-0.0623930393248766
454.194.081783550352610.108216449647392
464.34.240361163611510.0596388363884856
474.274.34092157044719-0.0709215704471853
483.824.32544756443639-0.505447564436388
493.153.063225422978410.0867745770215936
502.492.51730838681233-0.0273083868123280
511.811.97967952299243-0.169679522992426
521.261.42547431448017-0.165474314480172
531.060.9790153464682090.0809846535317915
540.840.8226424105724150.0173575894275846
550.780.6496931299842690.130306870015731
560.70.6093546069349440.0906453930650561
570.360.552439739193266-0.192439739193266
580.350.2800323904510040.0699676095489957
590.360.2811347291325600.0788652708674396

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.09 & 2.09384831935891 & -0.00384831935890569 \tabularnewline
2 & 2.05 & 2.08666248569428 & -0.0366624856942818 \tabularnewline
3 & 2.08 & 2.06290030733731 & 0.0170996926626892 \tabularnewline
4 & 2.06 & 2.09715533540357 & -0.0371553354035694 \tabularnewline
5 & 2.06 & 2.08996950173895 & -0.0299695017389490 \tabularnewline
6 & 2.08 & 2.09936001276668 & -0.0193600127666811 \tabularnewline
7 & 2.07 & 2.12532686848677 & -0.0553268684867661 \tabularnewline
8 & 2.06 & 2.12642920716832 & -0.0664292071683215 \tabularnewline
9 & 2.07 & 2.12753154584988 & -0.0575315458498778 \tabularnewline
10 & 2.06 & 2.14521022922379 & -0.0852102292237858 \tabularnewline
11 & 2.09 & 2.14631256790534 & -0.0563125679053421 \tabularnewline
12 & 2.07 & 2.18056759597160 & -0.110567595971603 \tabularnewline
13 & 2.09 & 2.17338176230698 & -0.0833817623069825 \tabularnewline
14 & 2.28 & 2.19934861802707 & 0.0806513819729328 \tabularnewline
15 & 2.33 & 2.36621440363215 & -0.0362144036321483 \tabularnewline
16 & 2.35 & 2.41704577639076 & -0.067045776390762 \tabularnewline
17 & 2.52 & 2.44301263211085 & 0.0769873678891531 \tabularnewline
18 & 2.63 & 2.59330207302358 & 0.0366979269764243 \tabularnewline
19 & 2.58 & 2.69386247985925 & -0.113862479859247 \tabularnewline
20 & 2.7 & 2.6618121291561 & 0.0381878708439025 \tabularnewline
21 & 2.81 & 2.77066070833795 & 0.0393392916620549 \tabularnewline
22 & 2.97 & 2.87122111517362 & 0.098778884826384 \tabularnewline
23 & 3.04 & 3.01322238374017 & 0.0267776162598311 \tabularnewline
24 & 3.28 & 3.08063010119113 & 0.199369898808865 \tabularnewline
25 & 3.33 & 3.2889367485271 & 0.0410632514729026 \tabularnewline
26 & 3.5 & 3.33976812128571 & 0.160231878714289 \tabularnewline
27 & 3.56 & 3.49005756219844 & 0.06994243780156 \tabularnewline
28 & 3.57 & 3.54917710730323 & 0.0208228926967699 \tabularnewline
29 & 3.69 & 3.56685579067714 & 0.123144209322862 \tabularnewline
30 & 3.82 & 3.67570436985899 & 0.144295630141014 \tabularnewline
31 & 3.79 & 3.79284112138701 & -0.00284112138700933 \tabularnewline
32 & 3.96 & 3.77736711537621 & 0.182632884623787 \tabularnewline
33 & 4.06 & 3.92765655628894 & 0.132343443711058 \tabularnewline
34 & 4.05 & 4.01992879077844 & 0.0300712092215637 \tabularnewline
35 & 4.03 & 4.02103112945999 & 0.00896887054000805 \tabularnewline
36 & 3.94 & 4.01384529579537 & -0.0738452957953723 \tabularnewline
37 & 4.02 & 3.94864225570752 & 0.071357744292482 \tabularnewline
38 & 3.88 & 4.02433814550466 & -0.144338145504660 \tabularnewline
39 & 4.02 & 3.91769424368592 & 0.102305756314075 \tabularnewline
40 & 4.03 & 4.04311916756012 & -0.0131191675601236 \tabularnewline
41 & 4.09 & 4.06079785093403 & 0.0292021490659669 \tabularnewline
42 & 3.99 & 4.11991739603882 & -0.129917396038822 \tabularnewline
43 & 4.01 & 4.04642618360479 & -0.0364261836047924 \tabularnewline
44 & 4.01 & 4.07239303932488 & -0.0623930393248766 \tabularnewline
45 & 4.19 & 4.08178355035261 & 0.108216449647392 \tabularnewline
46 & 4.3 & 4.24036116361151 & 0.0596388363884856 \tabularnewline
47 & 4.27 & 4.34092157044719 & -0.0709215704471853 \tabularnewline
48 & 3.82 & 4.32544756443639 & -0.505447564436388 \tabularnewline
49 & 3.15 & 3.06322542297841 & 0.0867745770215936 \tabularnewline
50 & 2.49 & 2.51730838681233 & -0.0273083868123280 \tabularnewline
51 & 1.81 & 1.97967952299243 & -0.169679522992426 \tabularnewline
52 & 1.26 & 1.42547431448017 & -0.165474314480172 \tabularnewline
53 & 1.06 & 0.979015346468209 & 0.0809846535317915 \tabularnewline
54 & 0.84 & 0.822642410572415 & 0.0173575894275846 \tabularnewline
55 & 0.78 & 0.649693129984269 & 0.130306870015731 \tabularnewline
56 & 0.7 & 0.609354606934944 & 0.0906453930650561 \tabularnewline
57 & 0.36 & 0.552439739193266 & -0.192439739193266 \tabularnewline
58 & 0.35 & 0.280032390451004 & 0.0699676095489957 \tabularnewline
59 & 0.36 & 0.281134729132560 & 0.0788652708674396 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&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]2.09[/C][C]2.09384831935891[/C][C]-0.00384831935890569[/C][/ROW]
[ROW][C]2[/C][C]2.05[/C][C]2.08666248569428[/C][C]-0.0366624856942818[/C][/ROW]
[ROW][C]3[/C][C]2.08[/C][C]2.06290030733731[/C][C]0.0170996926626892[/C][/ROW]
[ROW][C]4[/C][C]2.06[/C][C]2.09715533540357[/C][C]-0.0371553354035694[/C][/ROW]
[ROW][C]5[/C][C]2.06[/C][C]2.08996950173895[/C][C]-0.0299695017389490[/C][/ROW]
[ROW][C]6[/C][C]2.08[/C][C]2.09936001276668[/C][C]-0.0193600127666811[/C][/ROW]
[ROW][C]7[/C][C]2.07[/C][C]2.12532686848677[/C][C]-0.0553268684867661[/C][/ROW]
[ROW][C]8[/C][C]2.06[/C][C]2.12642920716832[/C][C]-0.0664292071683215[/C][/ROW]
[ROW][C]9[/C][C]2.07[/C][C]2.12753154584988[/C][C]-0.0575315458498778[/C][/ROW]
[ROW][C]10[/C][C]2.06[/C][C]2.14521022922379[/C][C]-0.0852102292237858[/C][/ROW]
[ROW][C]11[/C][C]2.09[/C][C]2.14631256790534[/C][C]-0.0563125679053421[/C][/ROW]
[ROW][C]12[/C][C]2.07[/C][C]2.18056759597160[/C][C]-0.110567595971603[/C][/ROW]
[ROW][C]13[/C][C]2.09[/C][C]2.17338176230698[/C][C]-0.0833817623069825[/C][/ROW]
[ROW][C]14[/C][C]2.28[/C][C]2.19934861802707[/C][C]0.0806513819729328[/C][/ROW]
[ROW][C]15[/C][C]2.33[/C][C]2.36621440363215[/C][C]-0.0362144036321483[/C][/ROW]
[ROW][C]16[/C][C]2.35[/C][C]2.41704577639076[/C][C]-0.067045776390762[/C][/ROW]
[ROW][C]17[/C][C]2.52[/C][C]2.44301263211085[/C][C]0.0769873678891531[/C][/ROW]
[ROW][C]18[/C][C]2.63[/C][C]2.59330207302358[/C][C]0.0366979269764243[/C][/ROW]
[ROW][C]19[/C][C]2.58[/C][C]2.69386247985925[/C][C]-0.113862479859247[/C][/ROW]
[ROW][C]20[/C][C]2.7[/C][C]2.6618121291561[/C][C]0.0381878708439025[/C][/ROW]
[ROW][C]21[/C][C]2.81[/C][C]2.77066070833795[/C][C]0.0393392916620549[/C][/ROW]
[ROW][C]22[/C][C]2.97[/C][C]2.87122111517362[/C][C]0.098778884826384[/C][/ROW]
[ROW][C]23[/C][C]3.04[/C][C]3.01322238374017[/C][C]0.0267776162598311[/C][/ROW]
[ROW][C]24[/C][C]3.28[/C][C]3.08063010119113[/C][C]0.199369898808865[/C][/ROW]
[ROW][C]25[/C][C]3.33[/C][C]3.2889367485271[/C][C]0.0410632514729026[/C][/ROW]
[ROW][C]26[/C][C]3.5[/C][C]3.33976812128571[/C][C]0.160231878714289[/C][/ROW]
[ROW][C]27[/C][C]3.56[/C][C]3.49005756219844[/C][C]0.06994243780156[/C][/ROW]
[ROW][C]28[/C][C]3.57[/C][C]3.54917710730323[/C][C]0.0208228926967699[/C][/ROW]
[ROW][C]29[/C][C]3.69[/C][C]3.56685579067714[/C][C]0.123144209322862[/C][/ROW]
[ROW][C]30[/C][C]3.82[/C][C]3.67570436985899[/C][C]0.144295630141014[/C][/ROW]
[ROW][C]31[/C][C]3.79[/C][C]3.79284112138701[/C][C]-0.00284112138700933[/C][/ROW]
[ROW][C]32[/C][C]3.96[/C][C]3.77736711537621[/C][C]0.182632884623787[/C][/ROW]
[ROW][C]33[/C][C]4.06[/C][C]3.92765655628894[/C][C]0.132343443711058[/C][/ROW]
[ROW][C]34[/C][C]4.05[/C][C]4.01992879077844[/C][C]0.0300712092215637[/C][/ROW]
[ROW][C]35[/C][C]4.03[/C][C]4.02103112945999[/C][C]0.00896887054000805[/C][/ROW]
[ROW][C]36[/C][C]3.94[/C][C]4.01384529579537[/C][C]-0.0738452957953723[/C][/ROW]
[ROW][C]37[/C][C]4.02[/C][C]3.94864225570752[/C][C]0.071357744292482[/C][/ROW]
[ROW][C]38[/C][C]3.88[/C][C]4.02433814550466[/C][C]-0.144338145504660[/C][/ROW]
[ROW][C]39[/C][C]4.02[/C][C]3.91769424368592[/C][C]0.102305756314075[/C][/ROW]
[ROW][C]40[/C][C]4.03[/C][C]4.04311916756012[/C][C]-0.0131191675601236[/C][/ROW]
[ROW][C]41[/C][C]4.09[/C][C]4.06079785093403[/C][C]0.0292021490659669[/C][/ROW]
[ROW][C]42[/C][C]3.99[/C][C]4.11991739603882[/C][C]-0.129917396038822[/C][/ROW]
[ROW][C]43[/C][C]4.01[/C][C]4.04642618360479[/C][C]-0.0364261836047924[/C][/ROW]
[ROW][C]44[/C][C]4.01[/C][C]4.07239303932488[/C][C]-0.0623930393248766[/C][/ROW]
[ROW][C]45[/C][C]4.19[/C][C]4.08178355035261[/C][C]0.108216449647392[/C][/ROW]
[ROW][C]46[/C][C]4.3[/C][C]4.24036116361151[/C][C]0.0596388363884856[/C][/ROW]
[ROW][C]47[/C][C]4.27[/C][C]4.34092157044719[/C][C]-0.0709215704471853[/C][/ROW]
[ROW][C]48[/C][C]3.82[/C][C]4.32544756443639[/C][C]-0.505447564436388[/C][/ROW]
[ROW][C]49[/C][C]3.15[/C][C]3.06322542297841[/C][C]0.0867745770215936[/C][/ROW]
[ROW][C]50[/C][C]2.49[/C][C]2.51730838681233[/C][C]-0.0273083868123280[/C][/ROW]
[ROW][C]51[/C][C]1.81[/C][C]1.97967952299243[/C][C]-0.169679522992426[/C][/ROW]
[ROW][C]52[/C][C]1.26[/C][C]1.42547431448017[/C][C]-0.165474314480172[/C][/ROW]
[ROW][C]53[/C][C]1.06[/C][C]0.979015346468209[/C][C]0.0809846535317915[/C][/ROW]
[ROW][C]54[/C][C]0.84[/C][C]0.822642410572415[/C][C]0.0173575894275846[/C][/ROW]
[ROW][C]55[/C][C]0.78[/C][C]0.649693129984269[/C][C]0.130306870015731[/C][/ROW]
[ROW][C]56[/C][C]0.7[/C][C]0.609354606934944[/C][C]0.0906453930650561[/C][/ROW]
[ROW][C]57[/C][C]0.36[/C][C]0.552439739193266[/C][C]-0.192439739193266[/C][/ROW]
[ROW][C]58[/C][C]0.35[/C][C]0.280032390451004[/C][C]0.0699676095489957[/C][/ROW]
[ROW][C]59[/C][C]0.36[/C][C]0.281134729132560[/C][C]0.0788652708674396[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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
12.092.09384831935891-0.00384831935890569
22.052.08666248569428-0.0366624856942818
32.082.062900307337310.0170996926626892
42.062.09715533540357-0.0371553354035694
52.062.08996950173895-0.0299695017389490
62.082.09936001276668-0.0193600127666811
72.072.12532686848677-0.0553268684867661
82.062.12642920716832-0.0664292071683215
92.072.12753154584988-0.0575315458498778
102.062.14521022922379-0.0852102292237858
112.092.14631256790534-0.0563125679053421
122.072.18056759597160-0.110567595971603
132.092.17338176230698-0.0833817623069825
142.282.199348618027070.0806513819729328
152.332.36621440363215-0.0362144036321483
162.352.41704577639076-0.067045776390762
172.522.443012632110850.0769873678891531
182.632.593302073023580.0366979269764243
192.582.69386247985925-0.113862479859247
202.72.66181212915610.0381878708439025
212.812.770660708337950.0393392916620549
222.972.871221115173620.098778884826384
233.043.013222383740170.0267776162598311
243.283.080630101191130.199369898808865
253.333.28893674852710.0410632514729026
263.53.339768121285710.160231878714289
273.563.490057562198440.06994243780156
283.573.549177107303230.0208228926967699
293.693.566855790677140.123144209322862
303.823.675704369858990.144295630141014
313.793.79284112138701-0.00284112138700933
323.963.777367115376210.182632884623787
334.063.927656556288940.132343443711058
344.054.019928790778440.0300712092215637
354.034.021031129459990.00896887054000805
363.944.01384529579537-0.0738452957953723
374.023.948642255707520.071357744292482
383.884.02433814550466-0.144338145504660
394.023.917694243685920.102305756314075
404.034.04311916756012-0.0131191675601236
414.094.060797850934030.0292021490659669
423.994.11991739603882-0.129917396038822
434.014.04642618360479-0.0364261836047924
444.014.07239303932488-0.0623930393248766
454.194.081783550352610.108216449647392
464.34.240361163611510.0596388363884856
474.274.34092157044719-0.0709215704471853
483.824.32544756443639-0.505447564436388
493.153.063225422978410.0867745770215936
502.492.51730838681233-0.0273083868123280
511.811.97967952299243-0.169679522992426
521.261.42547431448017-0.165474314480172
531.060.9790153464682090.0809846535317915
540.840.8226424105724150.0173575894275846
550.780.6496931299842690.130306870015731
560.70.6093546069349440.0906453930650561
570.360.552439739193266-0.192439739193266
580.350.2800323904510040.0699676095489957
590.360.2811347291325600.0788652708674396







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.005783860835782610.01156772167156520.994216139164217
80.0007988416620703640.001597683324140730.99920115833793
90.0001029791870069640.0002059583740139270.999897020812993
101.23242226228452e-052.46484452456903e-050.999987675777377
114.55603627608217e-069.11207255216434e-060.999995443963724
126.20208765365553e-071.24041753073111e-060.999999379791235
131.50053060105216e-073.00106120210431e-070.99999984994694
140.003580204902018340.007160409804036680.996419795097982
150.001582663702201280.003165327404402550.998417336297799
160.0008318059944787310.001663611988957460.999168194005521
170.001509112891165060.003018225782330110.998490887108835
180.0006483236102822550.001296647220564510.999351676389718
190.003226195418394450.00645239083678890.996773804581605
200.002355098031760370.004710196063520740.99764490196824
210.001460063597987770.002920127195975540.998539936402012
220.001080151839583930.002160303679167870.998919848160416
230.0007316867971348110.001463373594269620.999268313202865
240.00126419402613110.00252838805226220.998735805973869
250.001305645176313450.002611290352626890.998694354823687
260.0007187814104806450.001437562820961290.99928121858952
270.000538517122929140.001077034245858280.99946148287707
280.0007192770712583330.001438554142516670.999280722928742
290.0003478258595340040.0006956517190680090.999652174140466
300.0001652048811482220.0003304097622964440.999834795118852
310.0003597856017715940.0007195712035431880.999640214398228
320.0002575370824810480.0005150741649620960.999742462917519
330.0001300760704877110.0002601521409754230.999869923929512
340.0001300186802807090.0002600373605614190.99986998131972
350.0001347010193158640.0002694020386317280.999865298980684
360.0004833017753999370.0009666035507998730.9995166982246
370.0002368805250547060.0004737610501094120.999763119474945
380.002249979899523750.00449995979904750.997750020100476
390.001261291930458190.002522583860916390.998738708069542
400.0008296447872566950.001659289574513390.999170355212743
410.0004166778145108440.0008333556290216880.99958332218549
420.0009597268630650560.001919453726130110.999040273136935
430.0005722146993020670.001144429398604130.999427785300698
440.0003809717505543650.000761943501108730.999619028249446
450.0004143919737798850.000828783947559770.99958560802622
460.001497244383838480.002994488767676960.998502755616162
470.02708363399291270.05416726798582540.972916366007087
480.1707000431689930.3414000863379860.829299956831007
490.2255600960271780.4511201920543550.774439903972822
500.3729708110829090.7459416221658170.627029188917091
510.4289710151064530.8579420302129060.571028984893547
520.314705191594470.629410383188940.68529480840553

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.00578386083578261 & 0.0115677216715652 & 0.994216139164217 \tabularnewline
8 & 0.000798841662070364 & 0.00159768332414073 & 0.99920115833793 \tabularnewline
9 & 0.000102979187006964 & 0.000205958374013927 & 0.999897020812993 \tabularnewline
10 & 1.23242226228452e-05 & 2.46484452456903e-05 & 0.999987675777377 \tabularnewline
11 & 4.55603627608217e-06 & 9.11207255216434e-06 & 0.999995443963724 \tabularnewline
12 & 6.20208765365553e-07 & 1.24041753073111e-06 & 0.999999379791235 \tabularnewline
13 & 1.50053060105216e-07 & 3.00106120210431e-07 & 0.99999984994694 \tabularnewline
14 & 0.00358020490201834 & 0.00716040980403668 & 0.996419795097982 \tabularnewline
15 & 0.00158266370220128 & 0.00316532740440255 & 0.998417336297799 \tabularnewline
16 & 0.000831805994478731 & 0.00166361198895746 & 0.999168194005521 \tabularnewline
17 & 0.00150911289116506 & 0.00301822578233011 & 0.998490887108835 \tabularnewline
18 & 0.000648323610282255 & 0.00129664722056451 & 0.999351676389718 \tabularnewline
19 & 0.00322619541839445 & 0.0064523908367889 & 0.996773804581605 \tabularnewline
20 & 0.00235509803176037 & 0.00471019606352074 & 0.99764490196824 \tabularnewline
21 & 0.00146006359798777 & 0.00292012719597554 & 0.998539936402012 \tabularnewline
22 & 0.00108015183958393 & 0.00216030367916787 & 0.998919848160416 \tabularnewline
23 & 0.000731686797134811 & 0.00146337359426962 & 0.999268313202865 \tabularnewline
24 & 0.0012641940261311 & 0.0025283880522622 & 0.998735805973869 \tabularnewline
25 & 0.00130564517631345 & 0.00261129035262689 & 0.998694354823687 \tabularnewline
26 & 0.000718781410480645 & 0.00143756282096129 & 0.99928121858952 \tabularnewline
27 & 0.00053851712292914 & 0.00107703424585828 & 0.99946148287707 \tabularnewline
28 & 0.000719277071258333 & 0.00143855414251667 & 0.999280722928742 \tabularnewline
29 & 0.000347825859534004 & 0.000695651719068009 & 0.999652174140466 \tabularnewline
30 & 0.000165204881148222 & 0.000330409762296444 & 0.999834795118852 \tabularnewline
31 & 0.000359785601771594 & 0.000719571203543188 & 0.999640214398228 \tabularnewline
32 & 0.000257537082481048 & 0.000515074164962096 & 0.999742462917519 \tabularnewline
33 & 0.000130076070487711 & 0.000260152140975423 & 0.999869923929512 \tabularnewline
34 & 0.000130018680280709 & 0.000260037360561419 & 0.99986998131972 \tabularnewline
35 & 0.000134701019315864 & 0.000269402038631728 & 0.999865298980684 \tabularnewline
36 & 0.000483301775399937 & 0.000966603550799873 & 0.9995166982246 \tabularnewline
37 & 0.000236880525054706 & 0.000473761050109412 & 0.999763119474945 \tabularnewline
38 & 0.00224997989952375 & 0.0044999597990475 & 0.997750020100476 \tabularnewline
39 & 0.00126129193045819 & 0.00252258386091639 & 0.998738708069542 \tabularnewline
40 & 0.000829644787256695 & 0.00165928957451339 & 0.999170355212743 \tabularnewline
41 & 0.000416677814510844 & 0.000833355629021688 & 0.99958332218549 \tabularnewline
42 & 0.000959726863065056 & 0.00191945372613011 & 0.999040273136935 \tabularnewline
43 & 0.000572214699302067 & 0.00114442939860413 & 0.999427785300698 \tabularnewline
44 & 0.000380971750554365 & 0.00076194350110873 & 0.999619028249446 \tabularnewline
45 & 0.000414391973779885 & 0.00082878394755977 & 0.99958560802622 \tabularnewline
46 & 0.00149724438383848 & 0.00299448876767696 & 0.998502755616162 \tabularnewline
47 & 0.0270836339929127 & 0.0541672679858254 & 0.972916366007087 \tabularnewline
48 & 0.170700043168993 & 0.341400086337986 & 0.829299956831007 \tabularnewline
49 & 0.225560096027178 & 0.451120192054355 & 0.774439903972822 \tabularnewline
50 & 0.372970811082909 & 0.745941622165817 & 0.627029188917091 \tabularnewline
51 & 0.428971015106453 & 0.857942030212906 & 0.571028984893547 \tabularnewline
52 & 0.31470519159447 & 0.62941038318894 & 0.68529480840553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&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]0.00578386083578261[/C][C]0.0115677216715652[/C][C]0.994216139164217[/C][/ROW]
[ROW][C]8[/C][C]0.000798841662070364[/C][C]0.00159768332414073[/C][C]0.99920115833793[/C][/ROW]
[ROW][C]9[/C][C]0.000102979187006964[/C][C]0.000205958374013927[/C][C]0.999897020812993[/C][/ROW]
[ROW][C]10[/C][C]1.23242226228452e-05[/C][C]2.46484452456903e-05[/C][C]0.999987675777377[/C][/ROW]
[ROW][C]11[/C][C]4.55603627608217e-06[/C][C]9.11207255216434e-06[/C][C]0.999995443963724[/C][/ROW]
[ROW][C]12[/C][C]6.20208765365553e-07[/C][C]1.24041753073111e-06[/C][C]0.999999379791235[/C][/ROW]
[ROW][C]13[/C][C]1.50053060105216e-07[/C][C]3.00106120210431e-07[/C][C]0.99999984994694[/C][/ROW]
[ROW][C]14[/C][C]0.00358020490201834[/C][C]0.00716040980403668[/C][C]0.996419795097982[/C][/ROW]
[ROW][C]15[/C][C]0.00158266370220128[/C][C]0.00316532740440255[/C][C]0.998417336297799[/C][/ROW]
[ROW][C]16[/C][C]0.000831805994478731[/C][C]0.00166361198895746[/C][C]0.999168194005521[/C][/ROW]
[ROW][C]17[/C][C]0.00150911289116506[/C][C]0.00301822578233011[/C][C]0.998490887108835[/C][/ROW]
[ROW][C]18[/C][C]0.000648323610282255[/C][C]0.00129664722056451[/C][C]0.999351676389718[/C][/ROW]
[ROW][C]19[/C][C]0.00322619541839445[/C][C]0.0064523908367889[/C][C]0.996773804581605[/C][/ROW]
[ROW][C]20[/C][C]0.00235509803176037[/C][C]0.00471019606352074[/C][C]0.99764490196824[/C][/ROW]
[ROW][C]21[/C][C]0.00146006359798777[/C][C]0.00292012719597554[/C][C]0.998539936402012[/C][/ROW]
[ROW][C]22[/C][C]0.00108015183958393[/C][C]0.00216030367916787[/C][C]0.998919848160416[/C][/ROW]
[ROW][C]23[/C][C]0.000731686797134811[/C][C]0.00146337359426962[/C][C]0.999268313202865[/C][/ROW]
[ROW][C]24[/C][C]0.0012641940261311[/C][C]0.0025283880522622[/C][C]0.998735805973869[/C][/ROW]
[ROW][C]25[/C][C]0.00130564517631345[/C][C]0.00261129035262689[/C][C]0.998694354823687[/C][/ROW]
[ROW][C]26[/C][C]0.000718781410480645[/C][C]0.00143756282096129[/C][C]0.99928121858952[/C][/ROW]
[ROW][C]27[/C][C]0.00053851712292914[/C][C]0.00107703424585828[/C][C]0.99946148287707[/C][/ROW]
[ROW][C]28[/C][C]0.000719277071258333[/C][C]0.00143855414251667[/C][C]0.999280722928742[/C][/ROW]
[ROW][C]29[/C][C]0.000347825859534004[/C][C]0.000695651719068009[/C][C]0.999652174140466[/C][/ROW]
[ROW][C]30[/C][C]0.000165204881148222[/C][C]0.000330409762296444[/C][C]0.999834795118852[/C][/ROW]
[ROW][C]31[/C][C]0.000359785601771594[/C][C]0.000719571203543188[/C][C]0.999640214398228[/C][/ROW]
[ROW][C]32[/C][C]0.000257537082481048[/C][C]0.000515074164962096[/C][C]0.999742462917519[/C][/ROW]
[ROW][C]33[/C][C]0.000130076070487711[/C][C]0.000260152140975423[/C][C]0.999869923929512[/C][/ROW]
[ROW][C]34[/C][C]0.000130018680280709[/C][C]0.000260037360561419[/C][C]0.99986998131972[/C][/ROW]
[ROW][C]35[/C][C]0.000134701019315864[/C][C]0.000269402038631728[/C][C]0.999865298980684[/C][/ROW]
[ROW][C]36[/C][C]0.000483301775399937[/C][C]0.000966603550799873[/C][C]0.9995166982246[/C][/ROW]
[ROW][C]37[/C][C]0.000236880525054706[/C][C]0.000473761050109412[/C][C]0.999763119474945[/C][/ROW]
[ROW][C]38[/C][C]0.00224997989952375[/C][C]0.0044999597990475[/C][C]0.997750020100476[/C][/ROW]
[ROW][C]39[/C][C]0.00126129193045819[/C][C]0.00252258386091639[/C][C]0.998738708069542[/C][/ROW]
[ROW][C]40[/C][C]0.000829644787256695[/C][C]0.00165928957451339[/C][C]0.999170355212743[/C][/ROW]
[ROW][C]41[/C][C]0.000416677814510844[/C][C]0.000833355629021688[/C][C]0.99958332218549[/C][/ROW]
[ROW][C]42[/C][C]0.000959726863065056[/C][C]0.00191945372613011[/C][C]0.999040273136935[/C][/ROW]
[ROW][C]43[/C][C]0.000572214699302067[/C][C]0.00114442939860413[/C][C]0.999427785300698[/C][/ROW]
[ROW][C]44[/C][C]0.000380971750554365[/C][C]0.00076194350110873[/C][C]0.999619028249446[/C][/ROW]
[ROW][C]45[/C][C]0.000414391973779885[/C][C]0.00082878394755977[/C][C]0.99958560802622[/C][/ROW]
[ROW][C]46[/C][C]0.00149724438383848[/C][C]0.00299448876767696[/C][C]0.998502755616162[/C][/ROW]
[ROW][C]47[/C][C]0.0270836339929127[/C][C]0.0541672679858254[/C][C]0.972916366007087[/C][/ROW]
[ROW][C]48[/C][C]0.170700043168993[/C][C]0.341400086337986[/C][C]0.829299956831007[/C][/ROW]
[ROW][C]49[/C][C]0.225560096027178[/C][C]0.451120192054355[/C][C]0.774439903972822[/C][/ROW]
[ROW][C]50[/C][C]0.372970811082909[/C][C]0.745941622165817[/C][C]0.627029188917091[/C][/ROW]
[ROW][C]51[/C][C]0.428971015106453[/C][C]0.857942030212906[/C][C]0.571028984893547[/C][/ROW]
[ROW][C]52[/C][C]0.31470519159447[/C][C]0.62941038318894[/C][C]0.68529480840553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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
70.005783860835782610.01156772167156520.994216139164217
80.0007988416620703640.001597683324140730.99920115833793
90.0001029791870069640.0002059583740139270.999897020812993
101.23242226228452e-052.46484452456903e-050.999987675777377
114.55603627608217e-069.11207255216434e-060.999995443963724
126.20208765365553e-071.24041753073111e-060.999999379791235
131.50053060105216e-073.00106120210431e-070.99999984994694
140.003580204902018340.007160409804036680.996419795097982
150.001582663702201280.003165327404402550.998417336297799
160.0008318059944787310.001663611988957460.999168194005521
170.001509112891165060.003018225782330110.998490887108835
180.0006483236102822550.001296647220564510.999351676389718
190.003226195418394450.00645239083678890.996773804581605
200.002355098031760370.004710196063520740.99764490196824
210.001460063597987770.002920127195975540.998539936402012
220.001080151839583930.002160303679167870.998919848160416
230.0007316867971348110.001463373594269620.999268313202865
240.00126419402613110.00252838805226220.998735805973869
250.001305645176313450.002611290352626890.998694354823687
260.0007187814104806450.001437562820961290.99928121858952
270.000538517122929140.001077034245858280.99946148287707
280.0007192770712583330.001438554142516670.999280722928742
290.0003478258595340040.0006956517190680090.999652174140466
300.0001652048811482220.0003304097622964440.999834795118852
310.0003597856017715940.0007195712035431880.999640214398228
320.0002575370824810480.0005150741649620960.999742462917519
330.0001300760704877110.0002601521409754230.999869923929512
340.0001300186802807090.0002600373605614190.99986998131972
350.0001347010193158640.0002694020386317280.999865298980684
360.0004833017753999370.0009666035507998730.9995166982246
370.0002368805250547060.0004737610501094120.999763119474945
380.002249979899523750.00449995979904750.997750020100476
390.001261291930458190.002522583860916390.998738708069542
400.0008296447872566950.001659289574513390.999170355212743
410.0004166778145108440.0008333556290216880.99958332218549
420.0009597268630650560.001919453726130110.999040273136935
430.0005722146993020670.001144429398604130.999427785300698
440.0003809717505543650.000761943501108730.999619028249446
450.0004143919737798850.000828783947559770.99958560802622
460.001497244383838480.002994488767676960.998502755616162
470.02708363399291270.05416726798582540.972916366007087
480.1707000431689930.3414000863379860.829299956831007
490.2255600960271780.4511201920543550.774439903972822
500.3729708110829090.7459416221658170.627029188917091
510.4289710151064530.8579420302129060.571028984893547
520.314705191594470.629410383188940.68529480840553







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.847826086956522NOK
5% type I error level400.869565217391304NOK
10% type I error level410.891304347826087NOK

\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 & 39 & 0.847826086956522 & NOK \tabularnewline
5% type I error level & 40 & 0.869565217391304 & NOK \tabularnewline
10% type I error level & 41 & 0.891304347826087 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58150&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]39[/C][C]0.847826086956522[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.869565217391304[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.891304347826087[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58150&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58150&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 level390.847826086956522NOK
5% type I error level400.869565217391304NOK
10% type I error level410.891304347826087NOK



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