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Description of Statistical Computation

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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 07 Dec 2012 07:55:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/07/t135488510711bt996xt1ubvjf.htm/, Retrieved Fri, 01 Nov 2024 01:01:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197345, Retrieved Fri, 01 Nov 2024 01:01:06 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202

Tree of Dependent Computations

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-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
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Dataset

Dataseries X:
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Histogram
Boxplots 
2,7	8,4	4,3	1,5	2,2	2,1
2,5	7,5	3,1	1,7	2,3	2,2
2,2	4,0	5,7	1,6	2,1	2,2
2,9	8,5	6,7	1,7	2,8	2,7
3,1	7,6	9,5	1,8	3,1	3,1
3,0	5,5	9,0	1,7	2,9	3,2
2,8	3,3	6,9	2,2	2,6	3,1
2,5	1,4	7,5	2,7	2,7	3,1
1,9	-4,4	7,0	3,0	2,3	2,8
1,9	-6,5	9,3	2,8	2,3	3,0
1,8	-8,5	7,2	2,7	2,1	2,8
2,0	-6,7	6,6	2,7	2,2	2,7
2,6	-3,3	10,4	2,5	2,9	3,2
2,5	-5,1	8,7	2,0	2,6	3,1
2,5	-3,5	7,9	1,8	2,7	3,0
1,6	-3,6	4,1	1,4	1,8	2,0
1,4	-6,3	2,2	1,5	1,3	1,7
0,8	-8,0	-0,5	1,6	0,9	1,2
1,1	-5,3	1,7	1,3	1,3	1,4
1,3	-4,0	0,4	1,1	1,3	1,3
1,2	-4,0	2,6	0,8	1,3	1,3
1,3	0,1	0,7	1,1	1,3	1,1
1,1	-0,9	0,7	1,3	1,1	0,9
1,3	1,1	0,5	1,5	1,4	1,2
1,2	3,1	-2,3	1,8	1,2	0,9
1,6	5,7	0,3	2,7	1,7	1,3
1,7	6,2	-0,2	3,0	1,8	1,4
1,5	-2,2	0,6	3,2	1,5	1,5
0,9	-4,2	-0,6	3,2	1,0	1,1
1,5	-1,6	2,7	3,3	1,6	1,6
1,4	-1,9	2,3	3,2	1,5	1,5
1,6	0,2	4,3	2,9	1,8	1,6
1,7	-1,2	5,4	2,7	1,8	1,7
1,4	-2,4	2,6	2,6	1,6	1,6
1,8	0,8	2,9	2,3	1,9	1,7
1,7	-0,1	2,9	2,2	1,7	1,6
1,4	-1,5	2,9	2,1	1,6	1,6
1,2	-4,4	1,4	2,4	1,3	1,3
1,0	-4,2	1,1	2,5	1,1	1,1
1,7	3,5	1,9	2,4	1,9	1,6
2,4	10,0	2,8	2,3	2,6	1,9
2,0	8,6	1,4	2,1	2,3	1,6
2,1	9,5	0,7	2,3	2,4	1,7
2,0	9,9	-0,8	2,2	2,2	1,6
1,8	10,4	-3,1	2,1	2,0	1,4
2,7	16,0	0,1	2,0	2,9	2,1
2,3	12,7	1,0	2,1	2,6	1,9
1,9	10,2	1,9	2,1	2,3	1,7
2,0	8,9	-0,5	2,5	2,3	1,8
2,3	12,6	1,5	2,2	2,6	2,0
2,8	13,6	3,9	2,3	3,1	2,5
2,4	14,8	1,9	2,3	2,8	2,1
2,3	9,5	2,6	2,2	2,5	2,1
2,7	13,7	1,7	2,2	2,9	2,3
2,7	17,0	1,4	1,6	3,1	2,4
2,9	14,7	2,8	1,8	3,1	2,4
3,0	17,4	0,5	1,7	3,2	2,3
2,2	9,0	1,0	1,9	2,5	1,7
2,3	9,1	1,5	1,8	2,6	2,0
2,8	12,2	1,8	1,9	2,9	2,3
2,8	15,9	2,7	1,5	2,6	2,0
2,8	12,9	3,0	1,0	2,4	2,0
2,2	10,9	-0,3	0,8	1,7	1,3
2,6	10,6	1,1	1,1	2,0	1,7
2,8	13,2	1,7	1,5	2,2	1,9
2,5	9,6	1,6	1,7	1,9	1,7
2,4	6,4	3,0	2,3	1,6	1,6
2,3	5,8	3,3	2,4	1,6	1,7
1,9	-1,0	6,7	3,0	1,2	1,8
1,7	-0,2	5,6	3,0	1,2	1,9
2,0	2,7	6,0	3,2	1,5	1,9
2,1	3,6	4,8	3,2	1,6	1,9
1,7	-0,9	5,9	3,2	1,7	2,0
1,8	0,3	4,3	3,5	1,8	2,1
1,8	-1,1	3,7	4,0	1,8	1,9
1,8	-2,5	5,6	4,3	1,8	1,9
1,3	-3,4	1,7	4,1	1,3	1,3
1,3	-3,5	3,2	4,0	1,3	1,3
1,3	-3,9	3,6	4,1	1,4	1,4
1,2	-4,6	1,7	4,2	1,1	1,2
1,4	-0,1	0,5	4,5	1,5	1,3
2,2	4,3	2,1	5,6	2,2	1,8
2,9	10,2	1,5	6,5	2,9	2,2
3,1	8,7	2,7	7,6	3,1	2,6
3,5	13,3	1,4	8,5	3,5	2,8
3,6	15,0	1,2	8,7	3,6	3,1
4,4	20,7	2,3	8,3	4,4	3,9
4,1	20,7	1,6	8,3	4,2	3,7
5,1	26,4	4,7	8,5	5,2	4,6
5,8	31,2	3,5	8,7	5,8	5,1
5,9	31,4	4,4	8,7	5,9	5,2
5,4	26,6	3,9	8,5	5,4	4,9
5,5	26,6	3,5	7,9	5,5	5,1
4,8	19,2	3,0	7,0	4,7	4,8
3,2	6,5	1,6	5,8	3,1	3,9
2,7	3,1	2,2	4,5	2,6	3,5
2,1	-0,2	4,1	3,7	2,3	3,3
1,9	-4,0	4,3	3,1	1,9	2,8
0,6	-12,6	3,5	2,7	0,6	1,6
0,7	-13,0	1,8	2,3	0,6	1,5
-0,2	-17,6	0,6	1,8	-0,4	0,7
-1,0	-21,7	-0,4	1,5	-1,1	-0,1
-1,7	-23,2	-2,5	1,2	-1,7	-0,7
-0,7	-16,8	-1,6	1,0	-0,8	-0,2
-1,0	-19,8	-1,9	0,9	-1,2	-0,6
-0,9	-17,2	-1,6	0,6	-1,0	-0,6
0,0	-10,4	-0,7	0,6	-0,1	-0,3
0,3	-6,8	-1,1	0,7	0,3	-0,3
0,8	-2,9	0,3	0,5	0,6	-0,1
0,8	-1,9	1,3	0,5	0,7	0,1
1,9	7,0	3,3	0,5	1,7	0,9
2,1	9,8	2,4	0,5	1,8	1,1
2,5	12,5	2,0	0,8	2,3	1,6
2,7	13,7	3,9	0,8	2,5	2,0
2,4	13,7	4,2	1,1	2,6	2,2
2,4	9,7	4,9	1,2	2,3	2,1
2,9	14,0	5,8	1,5	2,9	2,6
3,1	15,3	4,8	1,7	3,0	2,5
3,0	13,4	4,4	1,8	2,9	2,5
3,4	17,1	5,3	1,8	3,1	2,6
3,7	15,7	2,1	2,1	3,2	2,7
3,5	18,3	2,0	2,2	3,4	2,8
3,5	18,1	-0,9	2,5	3,5	2,9
3,3	16,3	0,1	2,7	3,4	2,9
3,1	15,8	-0,5	3,0	3,3	2,9
3,4	17,3	-0,1	3,4	3,7	3,3
4,0	18,0	0,7	3,4	3,8	3,3
3,4	17,6	-0,4	3,5	3,6	3,1
3,4	18,4	-1,5	3,5	3,6	3,0
3,4	17,4	-0,3	3,4	3,6	3,1
3,7	17,9	1,0	3,6	3,8	3,4
3,2	13,5	0,4	3,8	3,5	3,2
3,3	13,7	0,3	3,5	3,6	3,4
3,3	12,6	1,8	3,5	3,7	3,4
3,1	10,4	3,0	3,5	3,4	3,1
2,9	8,8	2,2	3,2	3,2	3,0
2,6	5,4	3,4	2,9	2,8	2,7
2,2	2,1	3,4	2,5	2,3	2,2
2,0	2,8	3,1	2,3	2,3	2,2
2,6	5,6	4,5	2,7	2,9	2,6
2,6	4,8	4,6	3,0	2,8	2,4
2,6	4,5	5,7	3,3	2,8	2,5
2,2	1,5	4,3	3,2	2,3	2,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
HICP[t] = + 0.553904779893441 + 0.0592152076229676Energiedragers[t] + 0.0437540064226119`Niet-bewerkte_levensmiddelen`[t] + 0.0419357359945791Bewerkte_levensmiddelen[t] + 0.175129444142094Algemene_index[t] + 0.349896886357395Gezondheidsindex[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HICP[t] =  +  0.553904779893441 +  0.0592152076229676Energiedragers[t] +  0.0437540064226119`Niet-bewerkte_levensmiddelen`[t] +  0.0419357359945791Bewerkte_levensmiddelen[t] +  0.175129444142094Algemene_index[t] +  0.349896886357395Gezondheidsindex[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HICP[t] =  +  0.553904779893441 +  0.0592152076229676Energiedragers[t] +  0.0437540064226119`Niet-bewerkte_levensmiddelen`[t] +  0.0419357359945791Bewerkte_levensmiddelen[t] +  0.175129444142094Algemene_index[t] +  0.349896886357395Gezondheidsindex[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
HICP[t] = + 0.553904779893441 + 0.0592152076229676Energiedragers[t] + 0.0437540064226119`Niet-bewerkte_levensmiddelen`[t] + 0.0419357359945791Bewerkte_levensmiddelen[t] + 0.175129444142094Algemene_index[t] + 0.349896886357395Gezondheidsindex[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5539047798934410.05036710.997400
Energiedragers0.05921520762296760.00475412.455900
`Niet-bewerkte_levensmiddelen`0.04375400642261190.0073715.935700
Bewerkte_levensmiddelen0.04193573599457910.0106293.94550.0001276.3e-05
Algemene_index0.1751294441420940.0698942.50560.0133940.006697
Gezondheidsindex0.3498968863573950.0526366.647500

\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) & 0.553904779893441 & 0.050367 & 10.9974 & 0 & 0 \tabularnewline
Energiedragers & 0.0592152076229676 & 0.004754 & 12.4559 & 0 & 0 \tabularnewline
`Niet-bewerkte_levensmiddelen` & 0.0437540064226119 & 0.007371 & 5.9357 & 0 & 0 \tabularnewline
Bewerkte_levensmiddelen & 0.0419357359945791 & 0.010629 & 3.9455 & 0.000127 & 6.3e-05 \tabularnewline
Algemene_index & 0.175129444142094 & 0.069894 & 2.5056 & 0.013394 & 0.006697 \tabularnewline
Gezondheidsindex & 0.349896886357395 & 0.052636 & 6.6475 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&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]0.553904779893441[/C][C]0.050367[/C][C]10.9974[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Energiedragers[/C][C]0.0592152076229676[/C][C]0.004754[/C][C]12.4559[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Niet-bewerkte_levensmiddelen`[/C][C]0.0437540064226119[/C][C]0.007371[/C][C]5.9357[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bewerkte_levensmiddelen[/C][C]0.0419357359945791[/C][C]0.010629[/C][C]3.9455[/C][C]0.000127[/C][C]6.3e-05[/C][/ROW]
[ROW][C]Algemene_index[/C][C]0.175129444142094[/C][C]0.069894[/C][C]2.5056[/C][C]0.013394[/C][C]0.006697[/C][/ROW]
[ROW][C]Gezondheidsindex[/C][C]0.349896886357395[/C][C]0.052636[/C][C]6.6475[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5539047798934410.05036710.997400
Energiedragers0.05921520762296760.00475412.455900
`Niet-bewerkte_levensmiddelen`0.04375400642261190.0073715.935700
Bewerkte_levensmiddelen0.04193573599457910.0106293.94550.0001276.3e-05
Algemene_index0.1751294441420940.0698942.50560.0133940.006697
Gezondheidsindex0.3498968863573950.0526366.647500






Multiple Linear Regression - Regression Statistics
Multiple R0.990988112487621
R-squared0.982057439091777
Adjusted R-squared0.981402601102426
F-TEST (value)1499.69527587243
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.167419644218962
Sum Squared Residuals3.84001920604531

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990988112487621 \tabularnewline
R-squared & 0.982057439091777 \tabularnewline
Adjusted R-squared & 0.981402601102426 \tabularnewline
F-TEST (value) & 1499.69527587243 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.167419644218962 \tabularnewline
Sum Squared Residuals & 3.84001920604531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990988112487621[/C][/ROW]
[ROW][C]R-squared[/C][C]0.982057439091777[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.981402601102426[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1499.69527587243[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/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]0.167419644218962[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.84001920604531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.990988112487621
R-squared0.982057439091777
Adjusted R-squared0.981402601102426
F-TEST (value)1499.69527587243
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.167419644218962
Sum Squared Residuals3.84001920604531






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.72.42242659399860.277573406001396
22.52.377517879679660.122482120320337
32.22.24480560727019-0.044805607270191
42.92.856760675673780.0432393243262219
53.13.12266936818146-0.0226693681814646
632.972210655169790.0277893448302106
72.82.68349313103070.116506868969302
82.52.63571745281213-0.135717452812125
91.92.10795212262192-0.207952122621925
101.92.14582663145826-0.245826631458264
111.81.82631396302549-0.0263139630254875
1221.889172188671730.110827811328268
132.62.545921025874990.0540789741250056
142.52.256455451359560.243544548640445
152.52.290332686997770.209667313002232
161.61.593858261346440.00614173865356485
171.41.162504374182650.237495625817347
180.80.7028960566464790.0971039433535213
191.11.086486365488180.0135136345118198
201.31.063209091213990.236790908786013
211.21.146887184545360.0531128154546401
221.31.249138267123460.0508617328765409
231.11.093304940599510.00669505940049038
241.31.36887960090968-0.0688796009096847
251.21.23738456423504-0.0373845642350434
261.61.77037015976268-0.170370159762676
271.71.84318411421118-0.143184114211176
281.51.371617577908370.128382422091635
290.90.973158878341291-0.0731588783412908
301.51.55572632261904-0.0557263226190375
311.41.4637639511137-0.0637639511136957
321.61.75057170104715-0.150571701047145
331.71.74240235887669-0.0424023588766875
341.41.4746237406822-0.074623740682197
351.81.752186408082470.0478135919175295
361.71.624683570158180.0753164298418163
371.41.52007576147236-0.120075761472362
381.21.137793471380370.0622065286196349
3911.03569861847774-0.0356986184777352
401.71.83751734720559-0.13751734720559
412.42.48516090574246-0.085160905742456
4222.17510895972988-0.175108959729882
432.12.25866462234359-0.158664622343589
4422.14251054469524-0.142510544695243
451.81.96228509403536-0.162285094035363
462.72.83225382385494-0.132253823854943
472.32.55789760756485-0.257897607564851
481.92.32671998377368-0.426719983773676
4922.19649458148312-0.196494581483121
502.32.61303635224906-0.313036352249058
512.83.0439679141355-0.243967914135496
522.42.83502056265225-0.435020562652248
532.32.49507535990426-0.195075359904262
542.72.84443178106869-0.144431781068691
552.73.07156990016511-0.371569900165111
562.93.00501767882286-0.105017678822858
5733.04259420681188-0.0425942068118754
582.22.24292187047527-0.0429218704752668
592.32.38900883117084-0.0890088311708398
602.82.747403649478130.0525963505218728
612.82.83159632991578-0.0315963299157801
622.82.611083152147950.188916847852047
632.21.972358937158840.22764106284116
642.62.220928292447570.379071707552434
652.82.522919796618580.277080203381421
662.52.191238585218440.308761414781558
672.42.000638449534980.399361550465017
682.32.017418789123180.282581210876817
691.91.753618351699530.146381648300467
701.71.78785079936877-0.0878507993687736
7122.03800248448597-0.0380024844859684
722.12.056304308053710.0436956919462857
731.71.89046791386518-0.190467913865182
741.81.95660310658489-0.156603106584887
751.81.798437902784980.00156209721502444
761.81.81124994511416-0.0112499451141571
771.31.28142563212090.0185743678790995
781.31.33694154739306-0.0369415473930637
791.31.38745327356233-0.0874532735623282
801.21.144545379108640.0554546208913608
811.41.47613119279581-0.0761311927958099
822.22.150352880285250.049647119714754
832.92.773761729244730.126238270755267
843.12.958557678482830.141442321517171
853.53.351840742522520.148159257477478
863.63.574624951717390.0253750482826118
874.44.363527812234940.0364721877650646
884.14.22789474163921-0.12789474163921
895.15.19948263406289-0.0994826340628868
905.85.719624079808870.0803759201911336
915.95.823348360163760.0766516398362408
925.45.316317425185030.0836825748149722
935.55.361146702704920.138853297295076
944.84.618262379467640.181737620532357
953.23.15953644212180.0404635578782022
962.72.70241720665032-0.00241720665031751
972.12.43407283438772-0.334072834387717
981.91.94764418427268-0.04764418427268
990.60.739071358165642-0.139071358165642
1000.70.5892394811644440.110760518835556
101-0.2-0.2116701028336410.0116701028336408
102-1-0.913295301294175-0.086704698705825
103-1.7-1.42159804531418-0.278401954685822
104-0.7-0.679064315039169-0.0209356849608312
105-1-1.084040245634110.0840402456341088
106-0.9-0.894509335857564-0.00549066414243618
1070-0.1898817526059310.189881752605931
1080.30.08003674352400340.219963256475997
1090.80.4863627255604250.313637274439575
1100.80.6768242612916930.123175738708307
1111.91.746394575209340.153605424790662
1122.11.960310872458980.139689127541015
1132.52.377784216520070.12221578347993
1142.72.70695972124197-0.00695972124197038
1152.42.82015896565282-0.420158965652816
1162.42.53059099137786-0.130590991377864
1172.93.1172018203993-0.217201820399303
1183.13.14133798686393-0.0413379868639347
11932.99800811899650.00199188100349973
1203.43.326498570445990.0735014295540104
1213.73.16866781306980.531332186930201
1223.53.392461103310870.10753889668913
1233.53.318814797009020.181185202990976
1243.33.2468556324950.0531443675049988
1253.13.18606340121411-0.0860634012141143
1263.43.51917264181524-0.119172641815238
12743.613139436703610.386860563296386
1283.43.44051225408911-0.0405122540891142
1293.43.40476532448688-0.00476532448687548
1303.43.42885103960732-0.0288510396073238
1313.73.663720953702760.0362790462972444
1323.23.26279057299294-0.0627905729929399
1333.33.34516981476259-0.0451698147625871
1343.33.36317704042545-0.0631770404254501
1353.13.12790049221221-0.0279004922122089
1362.92.91555665661484-0.0155566566148394
1372.62.579128194041450.020871805958546
1382.22.104430549238090.095569450761915
13922.12436784544846-0.124367845448463
1402.62.61323675121047-0.0132367512104747
1412.62.495328384867050.104671615132953
1422.62.573263639079140.0267363609208568
1432.22.137635045640860.0623649543591395

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.7 & 2.4224265939986 & 0.277573406001396 \tabularnewline
2 & 2.5 & 2.37751787967966 & 0.122482120320337 \tabularnewline
3 & 2.2 & 2.24480560727019 & -0.044805607270191 \tabularnewline
4 & 2.9 & 2.85676067567378 & 0.0432393243262219 \tabularnewline
5 & 3.1 & 3.12266936818146 & -0.0226693681814646 \tabularnewline
6 & 3 & 2.97221065516979 & 0.0277893448302106 \tabularnewline
7 & 2.8 & 2.6834931310307 & 0.116506868969302 \tabularnewline
8 & 2.5 & 2.63571745281213 & -0.135717452812125 \tabularnewline
9 & 1.9 & 2.10795212262192 & -0.207952122621925 \tabularnewline
10 & 1.9 & 2.14582663145826 & -0.245826631458264 \tabularnewline
11 & 1.8 & 1.82631396302549 & -0.0263139630254875 \tabularnewline
12 & 2 & 1.88917218867173 & 0.110827811328268 \tabularnewline
13 & 2.6 & 2.54592102587499 & 0.0540789741250056 \tabularnewline
14 & 2.5 & 2.25645545135956 & 0.243544548640445 \tabularnewline
15 & 2.5 & 2.29033268699777 & 0.209667313002232 \tabularnewline
16 & 1.6 & 1.59385826134644 & 0.00614173865356485 \tabularnewline
17 & 1.4 & 1.16250437418265 & 0.237495625817347 \tabularnewline
18 & 0.8 & 0.702896056646479 & 0.0971039433535213 \tabularnewline
19 & 1.1 & 1.08648636548818 & 0.0135136345118198 \tabularnewline
20 & 1.3 & 1.06320909121399 & 0.236790908786013 \tabularnewline
21 & 1.2 & 1.14688718454536 & 0.0531128154546401 \tabularnewline
22 & 1.3 & 1.24913826712346 & 0.0508617328765409 \tabularnewline
23 & 1.1 & 1.09330494059951 & 0.00669505940049038 \tabularnewline
24 & 1.3 & 1.36887960090968 & -0.0688796009096847 \tabularnewline
25 & 1.2 & 1.23738456423504 & -0.0373845642350434 \tabularnewline
26 & 1.6 & 1.77037015976268 & -0.170370159762676 \tabularnewline
27 & 1.7 & 1.84318411421118 & -0.143184114211176 \tabularnewline
28 & 1.5 & 1.37161757790837 & 0.128382422091635 \tabularnewline
29 & 0.9 & 0.973158878341291 & -0.0731588783412908 \tabularnewline
30 & 1.5 & 1.55572632261904 & -0.0557263226190375 \tabularnewline
31 & 1.4 & 1.4637639511137 & -0.0637639511136957 \tabularnewline
32 & 1.6 & 1.75057170104715 & -0.150571701047145 \tabularnewline
33 & 1.7 & 1.74240235887669 & -0.0424023588766875 \tabularnewline
34 & 1.4 & 1.4746237406822 & -0.074623740682197 \tabularnewline
35 & 1.8 & 1.75218640808247 & 0.0478135919175295 \tabularnewline
36 & 1.7 & 1.62468357015818 & 0.0753164298418163 \tabularnewline
37 & 1.4 & 1.52007576147236 & -0.120075761472362 \tabularnewline
38 & 1.2 & 1.13779347138037 & 0.0622065286196349 \tabularnewline
39 & 1 & 1.03569861847774 & -0.0356986184777352 \tabularnewline
40 & 1.7 & 1.83751734720559 & -0.13751734720559 \tabularnewline
41 & 2.4 & 2.48516090574246 & -0.085160905742456 \tabularnewline
42 & 2 & 2.17510895972988 & -0.175108959729882 \tabularnewline
43 & 2.1 & 2.25866462234359 & -0.158664622343589 \tabularnewline
44 & 2 & 2.14251054469524 & -0.142510544695243 \tabularnewline
45 & 1.8 & 1.96228509403536 & -0.162285094035363 \tabularnewline
46 & 2.7 & 2.83225382385494 & -0.132253823854943 \tabularnewline
47 & 2.3 & 2.55789760756485 & -0.257897607564851 \tabularnewline
48 & 1.9 & 2.32671998377368 & -0.426719983773676 \tabularnewline
49 & 2 & 2.19649458148312 & -0.196494581483121 \tabularnewline
50 & 2.3 & 2.61303635224906 & -0.313036352249058 \tabularnewline
51 & 2.8 & 3.0439679141355 & -0.243967914135496 \tabularnewline
52 & 2.4 & 2.83502056265225 & -0.435020562652248 \tabularnewline
53 & 2.3 & 2.49507535990426 & -0.195075359904262 \tabularnewline
54 & 2.7 & 2.84443178106869 & -0.144431781068691 \tabularnewline
55 & 2.7 & 3.07156990016511 & -0.371569900165111 \tabularnewline
56 & 2.9 & 3.00501767882286 & -0.105017678822858 \tabularnewline
57 & 3 & 3.04259420681188 & -0.0425942068118754 \tabularnewline
58 & 2.2 & 2.24292187047527 & -0.0429218704752668 \tabularnewline
59 & 2.3 & 2.38900883117084 & -0.0890088311708398 \tabularnewline
60 & 2.8 & 2.74740364947813 & 0.0525963505218728 \tabularnewline
61 & 2.8 & 2.83159632991578 & -0.0315963299157801 \tabularnewline
62 & 2.8 & 2.61108315214795 & 0.188916847852047 \tabularnewline
63 & 2.2 & 1.97235893715884 & 0.22764106284116 \tabularnewline
64 & 2.6 & 2.22092829244757 & 0.379071707552434 \tabularnewline
65 & 2.8 & 2.52291979661858 & 0.277080203381421 \tabularnewline
66 & 2.5 & 2.19123858521844 & 0.308761414781558 \tabularnewline
67 & 2.4 & 2.00063844953498 & 0.399361550465017 \tabularnewline
68 & 2.3 & 2.01741878912318 & 0.282581210876817 \tabularnewline
69 & 1.9 & 1.75361835169953 & 0.146381648300467 \tabularnewline
70 & 1.7 & 1.78785079936877 & -0.0878507993687736 \tabularnewline
71 & 2 & 2.03800248448597 & -0.0380024844859684 \tabularnewline
72 & 2.1 & 2.05630430805371 & 0.0436956919462857 \tabularnewline
73 & 1.7 & 1.89046791386518 & -0.190467913865182 \tabularnewline
74 & 1.8 & 1.95660310658489 & -0.156603106584887 \tabularnewline
75 & 1.8 & 1.79843790278498 & 0.00156209721502444 \tabularnewline
76 & 1.8 & 1.81124994511416 & -0.0112499451141571 \tabularnewline
77 & 1.3 & 1.2814256321209 & 0.0185743678790995 \tabularnewline
78 & 1.3 & 1.33694154739306 & -0.0369415473930637 \tabularnewline
79 & 1.3 & 1.38745327356233 & -0.0874532735623282 \tabularnewline
80 & 1.2 & 1.14454537910864 & 0.0554546208913608 \tabularnewline
81 & 1.4 & 1.47613119279581 & -0.0761311927958099 \tabularnewline
82 & 2.2 & 2.15035288028525 & 0.049647119714754 \tabularnewline
83 & 2.9 & 2.77376172924473 & 0.126238270755267 \tabularnewline
84 & 3.1 & 2.95855767848283 & 0.141442321517171 \tabularnewline
85 & 3.5 & 3.35184074252252 & 0.148159257477478 \tabularnewline
86 & 3.6 & 3.57462495171739 & 0.0253750482826118 \tabularnewline
87 & 4.4 & 4.36352781223494 & 0.0364721877650646 \tabularnewline
88 & 4.1 & 4.22789474163921 & -0.12789474163921 \tabularnewline
89 & 5.1 & 5.19948263406289 & -0.0994826340628868 \tabularnewline
90 & 5.8 & 5.71962407980887 & 0.0803759201911336 \tabularnewline
91 & 5.9 & 5.82334836016376 & 0.0766516398362408 \tabularnewline
92 & 5.4 & 5.31631742518503 & 0.0836825748149722 \tabularnewline
93 & 5.5 & 5.36114670270492 & 0.138853297295076 \tabularnewline
94 & 4.8 & 4.61826237946764 & 0.181737620532357 \tabularnewline
95 & 3.2 & 3.1595364421218 & 0.0404635578782022 \tabularnewline
96 & 2.7 & 2.70241720665032 & -0.00241720665031751 \tabularnewline
97 & 2.1 & 2.43407283438772 & -0.334072834387717 \tabularnewline
98 & 1.9 & 1.94764418427268 & -0.04764418427268 \tabularnewline
99 & 0.6 & 0.739071358165642 & -0.139071358165642 \tabularnewline
100 & 0.7 & 0.589239481164444 & 0.110760518835556 \tabularnewline
101 & -0.2 & -0.211670102833641 & 0.0116701028336408 \tabularnewline
102 & -1 & -0.913295301294175 & -0.086704698705825 \tabularnewline
103 & -1.7 & -1.42159804531418 & -0.278401954685822 \tabularnewline
104 & -0.7 & -0.679064315039169 & -0.0209356849608312 \tabularnewline
105 & -1 & -1.08404024563411 & 0.0840402456341088 \tabularnewline
106 & -0.9 & -0.894509335857564 & -0.00549066414243618 \tabularnewline
107 & 0 & -0.189881752605931 & 0.189881752605931 \tabularnewline
108 & 0.3 & 0.0800367435240034 & 0.219963256475997 \tabularnewline
109 & 0.8 & 0.486362725560425 & 0.313637274439575 \tabularnewline
110 & 0.8 & 0.676824261291693 & 0.123175738708307 \tabularnewline
111 & 1.9 & 1.74639457520934 & 0.153605424790662 \tabularnewline
112 & 2.1 & 1.96031087245898 & 0.139689127541015 \tabularnewline
113 & 2.5 & 2.37778421652007 & 0.12221578347993 \tabularnewline
114 & 2.7 & 2.70695972124197 & -0.00695972124197038 \tabularnewline
115 & 2.4 & 2.82015896565282 & -0.420158965652816 \tabularnewline
116 & 2.4 & 2.53059099137786 & -0.130590991377864 \tabularnewline
117 & 2.9 & 3.1172018203993 & -0.217201820399303 \tabularnewline
118 & 3.1 & 3.14133798686393 & -0.0413379868639347 \tabularnewline
119 & 3 & 2.9980081189965 & 0.00199188100349973 \tabularnewline
120 & 3.4 & 3.32649857044599 & 0.0735014295540104 \tabularnewline
121 & 3.7 & 3.1686678130698 & 0.531332186930201 \tabularnewline
122 & 3.5 & 3.39246110331087 & 0.10753889668913 \tabularnewline
123 & 3.5 & 3.31881479700902 & 0.181185202990976 \tabularnewline
124 & 3.3 & 3.246855632495 & 0.0531443675049988 \tabularnewline
125 & 3.1 & 3.18606340121411 & -0.0860634012141143 \tabularnewline
126 & 3.4 & 3.51917264181524 & -0.119172641815238 \tabularnewline
127 & 4 & 3.61313943670361 & 0.386860563296386 \tabularnewline
128 & 3.4 & 3.44051225408911 & -0.0405122540891142 \tabularnewline
129 & 3.4 & 3.40476532448688 & -0.00476532448687548 \tabularnewline
130 & 3.4 & 3.42885103960732 & -0.0288510396073238 \tabularnewline
131 & 3.7 & 3.66372095370276 & 0.0362790462972444 \tabularnewline
132 & 3.2 & 3.26279057299294 & -0.0627905729929399 \tabularnewline
133 & 3.3 & 3.34516981476259 & -0.0451698147625871 \tabularnewline
134 & 3.3 & 3.36317704042545 & -0.0631770404254501 \tabularnewline
135 & 3.1 & 3.12790049221221 & -0.0279004922122089 \tabularnewline
136 & 2.9 & 2.91555665661484 & -0.0155566566148394 \tabularnewline
137 & 2.6 & 2.57912819404145 & 0.020871805958546 \tabularnewline
138 & 2.2 & 2.10443054923809 & 0.095569450761915 \tabularnewline
139 & 2 & 2.12436784544846 & -0.124367845448463 \tabularnewline
140 & 2.6 & 2.61323675121047 & -0.0132367512104747 \tabularnewline
141 & 2.6 & 2.49532838486705 & 0.104671615132953 \tabularnewline
142 & 2.6 & 2.57326363907914 & 0.0267363609208568 \tabularnewline
143 & 2.2 & 2.13763504564086 & 0.0623649543591395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&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]2.7[/C][C]2.4224265939986[/C][C]0.277573406001396[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]2.37751787967966[/C][C]0.122482120320337[/C][/ROW]
[ROW][C]3[/C][C]2.2[/C][C]2.24480560727019[/C][C]-0.044805607270191[/C][/ROW]
[ROW][C]4[/C][C]2.9[/C][C]2.85676067567378[/C][C]0.0432393243262219[/C][/ROW]
[ROW][C]5[/C][C]3.1[/C][C]3.12266936818146[/C][C]-0.0226693681814646[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]2.97221065516979[/C][C]0.0277893448302106[/C][/ROW]
[ROW][C]7[/C][C]2.8[/C][C]2.6834931310307[/C][C]0.116506868969302[/C][/ROW]
[ROW][C]8[/C][C]2.5[/C][C]2.63571745281213[/C][C]-0.135717452812125[/C][/ROW]
[ROW][C]9[/C][C]1.9[/C][C]2.10795212262192[/C][C]-0.207952122621925[/C][/ROW]
[ROW][C]10[/C][C]1.9[/C][C]2.14582663145826[/C][C]-0.245826631458264[/C][/ROW]
[ROW][C]11[/C][C]1.8[/C][C]1.82631396302549[/C][C]-0.0263139630254875[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.88917218867173[/C][C]0.110827811328268[/C][/ROW]
[ROW][C]13[/C][C]2.6[/C][C]2.54592102587499[/C][C]0.0540789741250056[/C][/ROW]
[ROW][C]14[/C][C]2.5[/C][C]2.25645545135956[/C][C]0.243544548640445[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]2.29033268699777[/C][C]0.209667313002232[/C][/ROW]
[ROW][C]16[/C][C]1.6[/C][C]1.59385826134644[/C][C]0.00614173865356485[/C][/ROW]
[ROW][C]17[/C][C]1.4[/C][C]1.16250437418265[/C][C]0.237495625817347[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.702896056646479[/C][C]0.0971039433535213[/C][/ROW]
[ROW][C]19[/C][C]1.1[/C][C]1.08648636548818[/C][C]0.0135136345118198[/C][/ROW]
[ROW][C]20[/C][C]1.3[/C][C]1.06320909121399[/C][C]0.236790908786013[/C][/ROW]
[ROW][C]21[/C][C]1.2[/C][C]1.14688718454536[/C][C]0.0531128154546401[/C][/ROW]
[ROW][C]22[/C][C]1.3[/C][C]1.24913826712346[/C][C]0.0508617328765409[/C][/ROW]
[ROW][C]23[/C][C]1.1[/C][C]1.09330494059951[/C][C]0.00669505940049038[/C][/ROW]
[ROW][C]24[/C][C]1.3[/C][C]1.36887960090968[/C][C]-0.0688796009096847[/C][/ROW]
[ROW][C]25[/C][C]1.2[/C][C]1.23738456423504[/C][C]-0.0373845642350434[/C][/ROW]
[ROW][C]26[/C][C]1.6[/C][C]1.77037015976268[/C][C]-0.170370159762676[/C][/ROW]
[ROW][C]27[/C][C]1.7[/C][C]1.84318411421118[/C][C]-0.143184114211176[/C][/ROW]
[ROW][C]28[/C][C]1.5[/C][C]1.37161757790837[/C][C]0.128382422091635[/C][/ROW]
[ROW][C]29[/C][C]0.9[/C][C]0.973158878341291[/C][C]-0.0731588783412908[/C][/ROW]
[ROW][C]30[/C][C]1.5[/C][C]1.55572632261904[/C][C]-0.0557263226190375[/C][/ROW]
[ROW][C]31[/C][C]1.4[/C][C]1.4637639511137[/C][C]-0.0637639511136957[/C][/ROW]
[ROW][C]32[/C][C]1.6[/C][C]1.75057170104715[/C][C]-0.150571701047145[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]1.74240235887669[/C][C]-0.0424023588766875[/C][/ROW]
[ROW][C]34[/C][C]1.4[/C][C]1.4746237406822[/C][C]-0.074623740682197[/C][/ROW]
[ROW][C]35[/C][C]1.8[/C][C]1.75218640808247[/C][C]0.0478135919175295[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]1.62468357015818[/C][C]0.0753164298418163[/C][/ROW]
[ROW][C]37[/C][C]1.4[/C][C]1.52007576147236[/C][C]-0.120075761472362[/C][/ROW]
[ROW][C]38[/C][C]1.2[/C][C]1.13779347138037[/C][C]0.0622065286196349[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.03569861847774[/C][C]-0.0356986184777352[/C][/ROW]
[ROW][C]40[/C][C]1.7[/C][C]1.83751734720559[/C][C]-0.13751734720559[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.48516090574246[/C][C]-0.085160905742456[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]2.17510895972988[/C][C]-0.175108959729882[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]2.25866462234359[/C][C]-0.158664622343589[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.14251054469524[/C][C]-0.142510544695243[/C][/ROW]
[ROW][C]45[/C][C]1.8[/C][C]1.96228509403536[/C][C]-0.162285094035363[/C][/ROW]
[ROW][C]46[/C][C]2.7[/C][C]2.83225382385494[/C][C]-0.132253823854943[/C][/ROW]
[ROW][C]47[/C][C]2.3[/C][C]2.55789760756485[/C][C]-0.257897607564851[/C][/ROW]
[ROW][C]48[/C][C]1.9[/C][C]2.32671998377368[/C][C]-0.426719983773676[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]2.19649458148312[/C][C]-0.196494581483121[/C][/ROW]
[ROW][C]50[/C][C]2.3[/C][C]2.61303635224906[/C][C]-0.313036352249058[/C][/ROW]
[ROW][C]51[/C][C]2.8[/C][C]3.0439679141355[/C][C]-0.243967914135496[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.83502056265225[/C][C]-0.435020562652248[/C][/ROW]
[ROW][C]53[/C][C]2.3[/C][C]2.49507535990426[/C][C]-0.195075359904262[/C][/ROW]
[ROW][C]54[/C][C]2.7[/C][C]2.84443178106869[/C][C]-0.144431781068691[/C][/ROW]
[ROW][C]55[/C][C]2.7[/C][C]3.07156990016511[/C][C]-0.371569900165111[/C][/ROW]
[ROW][C]56[/C][C]2.9[/C][C]3.00501767882286[/C][C]-0.105017678822858[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]3.04259420681188[/C][C]-0.0425942068118754[/C][/ROW]
[ROW][C]58[/C][C]2.2[/C][C]2.24292187047527[/C][C]-0.0429218704752668[/C][/ROW]
[ROW][C]59[/C][C]2.3[/C][C]2.38900883117084[/C][C]-0.0890088311708398[/C][/ROW]
[ROW][C]60[/C][C]2.8[/C][C]2.74740364947813[/C][C]0.0525963505218728[/C][/ROW]
[ROW][C]61[/C][C]2.8[/C][C]2.83159632991578[/C][C]-0.0315963299157801[/C][/ROW]
[ROW][C]62[/C][C]2.8[/C][C]2.61108315214795[/C][C]0.188916847852047[/C][/ROW]
[ROW][C]63[/C][C]2.2[/C][C]1.97235893715884[/C][C]0.22764106284116[/C][/ROW]
[ROW][C]64[/C][C]2.6[/C][C]2.22092829244757[/C][C]0.379071707552434[/C][/ROW]
[ROW][C]65[/C][C]2.8[/C][C]2.52291979661858[/C][C]0.277080203381421[/C][/ROW]
[ROW][C]66[/C][C]2.5[/C][C]2.19123858521844[/C][C]0.308761414781558[/C][/ROW]
[ROW][C]67[/C][C]2.4[/C][C]2.00063844953498[/C][C]0.399361550465017[/C][/ROW]
[ROW][C]68[/C][C]2.3[/C][C]2.01741878912318[/C][C]0.282581210876817[/C][/ROW]
[ROW][C]69[/C][C]1.9[/C][C]1.75361835169953[/C][C]0.146381648300467[/C][/ROW]
[ROW][C]70[/C][C]1.7[/C][C]1.78785079936877[/C][C]-0.0878507993687736[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]2.03800248448597[/C][C]-0.0380024844859684[/C][/ROW]
[ROW][C]72[/C][C]2.1[/C][C]2.05630430805371[/C][C]0.0436956919462857[/C][/ROW]
[ROW][C]73[/C][C]1.7[/C][C]1.89046791386518[/C][C]-0.190467913865182[/C][/ROW]
[ROW][C]74[/C][C]1.8[/C][C]1.95660310658489[/C][C]-0.156603106584887[/C][/ROW]
[ROW][C]75[/C][C]1.8[/C][C]1.79843790278498[/C][C]0.00156209721502444[/C][/ROW]
[ROW][C]76[/C][C]1.8[/C][C]1.81124994511416[/C][C]-0.0112499451141571[/C][/ROW]
[ROW][C]77[/C][C]1.3[/C][C]1.2814256321209[/C][C]0.0185743678790995[/C][/ROW]
[ROW][C]78[/C][C]1.3[/C][C]1.33694154739306[/C][C]-0.0369415473930637[/C][/ROW]
[ROW][C]79[/C][C]1.3[/C][C]1.38745327356233[/C][C]-0.0874532735623282[/C][/ROW]
[ROW][C]80[/C][C]1.2[/C][C]1.14454537910864[/C][C]0.0554546208913608[/C][/ROW]
[ROW][C]81[/C][C]1.4[/C][C]1.47613119279581[/C][C]-0.0761311927958099[/C][/ROW]
[ROW][C]82[/C][C]2.2[/C][C]2.15035288028525[/C][C]0.049647119714754[/C][/ROW]
[ROW][C]83[/C][C]2.9[/C][C]2.77376172924473[/C][C]0.126238270755267[/C][/ROW]
[ROW][C]84[/C][C]3.1[/C][C]2.95855767848283[/C][C]0.141442321517171[/C][/ROW]
[ROW][C]85[/C][C]3.5[/C][C]3.35184074252252[/C][C]0.148159257477478[/C][/ROW]
[ROW][C]86[/C][C]3.6[/C][C]3.57462495171739[/C][C]0.0253750482826118[/C][/ROW]
[ROW][C]87[/C][C]4.4[/C][C]4.36352781223494[/C][C]0.0364721877650646[/C][/ROW]
[ROW][C]88[/C][C]4.1[/C][C]4.22789474163921[/C][C]-0.12789474163921[/C][/ROW]
[ROW][C]89[/C][C]5.1[/C][C]5.19948263406289[/C][C]-0.0994826340628868[/C][/ROW]
[ROW][C]90[/C][C]5.8[/C][C]5.71962407980887[/C][C]0.0803759201911336[/C][/ROW]
[ROW][C]91[/C][C]5.9[/C][C]5.82334836016376[/C][C]0.0766516398362408[/C][/ROW]
[ROW][C]92[/C][C]5.4[/C][C]5.31631742518503[/C][C]0.0836825748149722[/C][/ROW]
[ROW][C]93[/C][C]5.5[/C][C]5.36114670270492[/C][C]0.138853297295076[/C][/ROW]
[ROW][C]94[/C][C]4.8[/C][C]4.61826237946764[/C][C]0.181737620532357[/C][/ROW]
[ROW][C]95[/C][C]3.2[/C][C]3.1595364421218[/C][C]0.0404635578782022[/C][/ROW]
[ROW][C]96[/C][C]2.7[/C][C]2.70241720665032[/C][C]-0.00241720665031751[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]2.43407283438772[/C][C]-0.334072834387717[/C][/ROW]
[ROW][C]98[/C][C]1.9[/C][C]1.94764418427268[/C][C]-0.04764418427268[/C][/ROW]
[ROW][C]99[/C][C]0.6[/C][C]0.739071358165642[/C][C]-0.139071358165642[/C][/ROW]
[ROW][C]100[/C][C]0.7[/C][C]0.589239481164444[/C][C]0.110760518835556[/C][/ROW]
[ROW][C]101[/C][C]-0.2[/C][C]-0.211670102833641[/C][C]0.0116701028336408[/C][/ROW]
[ROW][C]102[/C][C]-1[/C][C]-0.913295301294175[/C][C]-0.086704698705825[/C][/ROW]
[ROW][C]103[/C][C]-1.7[/C][C]-1.42159804531418[/C][C]-0.278401954685822[/C][/ROW]
[ROW][C]104[/C][C]-0.7[/C][C]-0.679064315039169[/C][C]-0.0209356849608312[/C][/ROW]
[ROW][C]105[/C][C]-1[/C][C]-1.08404024563411[/C][C]0.0840402456341088[/C][/ROW]
[ROW][C]106[/C][C]-0.9[/C][C]-0.894509335857564[/C][C]-0.00549066414243618[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]-0.189881752605931[/C][C]0.189881752605931[/C][/ROW]
[ROW][C]108[/C][C]0.3[/C][C]0.0800367435240034[/C][C]0.219963256475997[/C][/ROW]
[ROW][C]109[/C][C]0.8[/C][C]0.486362725560425[/C][C]0.313637274439575[/C][/ROW]
[ROW][C]110[/C][C]0.8[/C][C]0.676824261291693[/C][C]0.123175738708307[/C][/ROW]
[ROW][C]111[/C][C]1.9[/C][C]1.74639457520934[/C][C]0.153605424790662[/C][/ROW]
[ROW][C]112[/C][C]2.1[/C][C]1.96031087245898[/C][C]0.139689127541015[/C][/ROW]
[ROW][C]113[/C][C]2.5[/C][C]2.37778421652007[/C][C]0.12221578347993[/C][/ROW]
[ROW][C]114[/C][C]2.7[/C][C]2.70695972124197[/C][C]-0.00695972124197038[/C][/ROW]
[ROW][C]115[/C][C]2.4[/C][C]2.82015896565282[/C][C]-0.420158965652816[/C][/ROW]
[ROW][C]116[/C][C]2.4[/C][C]2.53059099137786[/C][C]-0.130590991377864[/C][/ROW]
[ROW][C]117[/C][C]2.9[/C][C]3.1172018203993[/C][C]-0.217201820399303[/C][/ROW]
[ROW][C]118[/C][C]3.1[/C][C]3.14133798686393[/C][C]-0.0413379868639347[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.9980081189965[/C][C]0.00199188100349973[/C][/ROW]
[ROW][C]120[/C][C]3.4[/C][C]3.32649857044599[/C][C]0.0735014295540104[/C][/ROW]
[ROW][C]121[/C][C]3.7[/C][C]3.1686678130698[/C][C]0.531332186930201[/C][/ROW]
[ROW][C]122[/C][C]3.5[/C][C]3.39246110331087[/C][C]0.10753889668913[/C][/ROW]
[ROW][C]123[/C][C]3.5[/C][C]3.31881479700902[/C][C]0.181185202990976[/C][/ROW]
[ROW][C]124[/C][C]3.3[/C][C]3.246855632495[/C][C]0.0531443675049988[/C][/ROW]
[ROW][C]125[/C][C]3.1[/C][C]3.18606340121411[/C][C]-0.0860634012141143[/C][/ROW]
[ROW][C]126[/C][C]3.4[/C][C]3.51917264181524[/C][C]-0.119172641815238[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]3.61313943670361[/C][C]0.386860563296386[/C][/ROW]
[ROW][C]128[/C][C]3.4[/C][C]3.44051225408911[/C][C]-0.0405122540891142[/C][/ROW]
[ROW][C]129[/C][C]3.4[/C][C]3.40476532448688[/C][C]-0.00476532448687548[/C][/ROW]
[ROW][C]130[/C][C]3.4[/C][C]3.42885103960732[/C][C]-0.0288510396073238[/C][/ROW]
[ROW][C]131[/C][C]3.7[/C][C]3.66372095370276[/C][C]0.0362790462972444[/C][/ROW]
[ROW][C]132[/C][C]3.2[/C][C]3.26279057299294[/C][C]-0.0627905729929399[/C][/ROW]
[ROW][C]133[/C][C]3.3[/C][C]3.34516981476259[/C][C]-0.0451698147625871[/C][/ROW]
[ROW][C]134[/C][C]3.3[/C][C]3.36317704042545[/C][C]-0.0631770404254501[/C][/ROW]
[ROW][C]135[/C][C]3.1[/C][C]3.12790049221221[/C][C]-0.0279004922122089[/C][/ROW]
[ROW][C]136[/C][C]2.9[/C][C]2.91555665661484[/C][C]-0.0155566566148394[/C][/ROW]
[ROW][C]137[/C][C]2.6[/C][C]2.57912819404145[/C][C]0.020871805958546[/C][/ROW]
[ROW][C]138[/C][C]2.2[/C][C]2.10443054923809[/C][C]0.095569450761915[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]2.12436784544846[/C][C]-0.124367845448463[/C][/ROW]
[ROW][C]140[/C][C]2.6[/C][C]2.61323675121047[/C][C]-0.0132367512104747[/C][/ROW]
[ROW][C]141[/C][C]2.6[/C][C]2.49532838486705[/C][C]0.104671615132953[/C][/ROW]
[ROW][C]142[/C][C]2.6[/C][C]2.57326363907914[/C][C]0.0267363609208568[/C][/ROW]
[ROW][C]143[/C][C]2.2[/C][C]2.13763504564086[/C][C]0.0623649543591395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.72.42242659399860.277573406001396
22.52.377517879679660.122482120320337
32.22.24480560727019-0.044805607270191
42.92.856760675673780.0432393243262219
53.13.12266936818146-0.0226693681814646
632.972210655169790.0277893448302106
72.82.68349313103070.116506868969302
82.52.63571745281213-0.135717452812125
91.92.10795212262192-0.207952122621925
101.92.14582663145826-0.245826631458264
111.81.82631396302549-0.0263139630254875
1221.889172188671730.110827811328268
132.62.545921025874990.0540789741250056
142.52.256455451359560.243544548640445
152.52.290332686997770.209667313002232
161.61.593858261346440.00614173865356485
171.41.162504374182650.237495625817347
180.80.7028960566464790.0971039433535213
191.11.086486365488180.0135136345118198
201.31.063209091213990.236790908786013
211.21.146887184545360.0531128154546401
221.31.249138267123460.0508617328765409
231.11.093304940599510.00669505940049038
241.31.36887960090968-0.0688796009096847
251.21.23738456423504-0.0373845642350434
261.61.77037015976268-0.170370159762676
271.71.84318411421118-0.143184114211176
281.51.371617577908370.128382422091635
290.90.973158878341291-0.0731588783412908
301.51.55572632261904-0.0557263226190375
311.41.4637639511137-0.0637639511136957
321.61.75057170104715-0.150571701047145
331.71.74240235887669-0.0424023588766875
341.41.4746237406822-0.074623740682197
351.81.752186408082470.0478135919175295
361.71.624683570158180.0753164298418163
371.41.52007576147236-0.120075761472362
381.21.137793471380370.0622065286196349
3911.03569861847774-0.0356986184777352
401.71.83751734720559-0.13751734720559
412.42.48516090574246-0.085160905742456
4222.17510895972988-0.175108959729882
432.12.25866462234359-0.158664622343589
4422.14251054469524-0.142510544695243
451.81.96228509403536-0.162285094035363
462.72.83225382385494-0.132253823854943
472.32.55789760756485-0.257897607564851
481.92.32671998377368-0.426719983773676
4922.19649458148312-0.196494581483121
502.32.61303635224906-0.313036352249058
512.83.0439679141355-0.243967914135496
522.42.83502056265225-0.435020562652248
532.32.49507535990426-0.195075359904262
542.72.84443178106869-0.144431781068691
552.73.07156990016511-0.371569900165111
562.93.00501767882286-0.105017678822858
5733.04259420681188-0.0425942068118754
582.22.24292187047527-0.0429218704752668
592.32.38900883117084-0.0890088311708398
602.82.747403649478130.0525963505218728
612.82.83159632991578-0.0315963299157801
622.82.611083152147950.188916847852047
632.21.972358937158840.22764106284116
642.62.220928292447570.379071707552434
652.82.522919796618580.277080203381421
662.52.191238585218440.308761414781558
672.42.000638449534980.399361550465017
682.32.017418789123180.282581210876817
691.91.753618351699530.146381648300467
701.71.78785079936877-0.0878507993687736
7122.03800248448597-0.0380024844859684
722.12.056304308053710.0436956919462857
731.71.89046791386518-0.190467913865182
741.81.95660310658489-0.156603106584887
751.81.798437902784980.00156209721502444
761.81.81124994511416-0.0112499451141571
771.31.28142563212090.0185743678790995
781.31.33694154739306-0.0369415473930637
791.31.38745327356233-0.0874532735623282
801.21.144545379108640.0554546208913608
811.41.47613119279581-0.0761311927958099
822.22.150352880285250.049647119714754
832.92.773761729244730.126238270755267
843.12.958557678482830.141442321517171
853.53.351840742522520.148159257477478
863.63.574624951717390.0253750482826118
874.44.363527812234940.0364721877650646
884.14.22789474163921-0.12789474163921
895.15.19948263406289-0.0994826340628868
905.85.719624079808870.0803759201911336
915.95.823348360163760.0766516398362408
925.45.316317425185030.0836825748149722
935.55.361146702704920.138853297295076
944.84.618262379467640.181737620532357
953.23.15953644212180.0404635578782022
962.72.70241720665032-0.00241720665031751
972.12.43407283438772-0.334072834387717
981.91.94764418427268-0.04764418427268
990.60.739071358165642-0.139071358165642
1000.70.5892394811644440.110760518835556
101-0.2-0.2116701028336410.0116701028336408
102-1-0.913295301294175-0.086704698705825
103-1.7-1.42159804531418-0.278401954685822
104-0.7-0.679064315039169-0.0209356849608312
105-1-1.084040245634110.0840402456341088
106-0.9-0.894509335857564-0.00549066414243618
1070-0.1898817526059310.189881752605931
1080.30.08003674352400340.219963256475997
1090.80.4863627255604250.313637274439575
1100.80.6768242612916930.123175738708307
1111.91.746394575209340.153605424790662
1122.11.960310872458980.139689127541015
1132.52.377784216520070.12221578347993
1142.72.70695972124197-0.00695972124197038
1152.42.82015896565282-0.420158965652816
1162.42.53059099137786-0.130590991377864
1172.93.1172018203993-0.217201820399303
1183.13.14133798686393-0.0413379868639347
11932.99800811899650.00199188100349973
1203.43.326498570445990.0735014295540104
1213.73.16866781306980.531332186930201
1223.53.392461103310870.10753889668913
1233.53.318814797009020.181185202990976
1243.33.2468556324950.0531443675049988
1253.13.18606340121411-0.0860634012141143
1263.43.51917264181524-0.119172641815238
12743.613139436703610.386860563296386
1283.43.44051225408911-0.0405122540891142
1293.43.40476532448688-0.00476532448687548
1303.43.42885103960732-0.0288510396073238
1313.73.663720953702760.0362790462972444
1323.23.26279057299294-0.0627905729929399
1333.33.34516981476259-0.0451698147625871
1343.33.36317704042545-0.0631770404254501
1353.13.12790049221221-0.0279004922122089
1362.92.91555665661484-0.0155566566148394
1372.62.579128194041450.020871805958546
1382.22.104430549238090.095569450761915
13922.12436784544846-0.124367845448463
1402.62.61323675121047-0.0132367512104747
1412.62.495328384867050.104671615132953
1422.62.573263639079140.0267363609208568
1432.22.137635045640860.0623649543591395






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1420175710288620.2840351420577250.857982428971138
100.07492376295475690.1498475259095140.925076237045243
110.1100000968943070.2200001937886150.889999903105693
120.1331796974106050.266359394821210.866820302589395
130.1123683352494660.2247366704989310.887631664750534
140.0667418174269230.1334836348538460.933258182573077
150.06426111867799180.1285222373559840.935738881322008
160.1486014787051760.2972029574103510.851398521294824
170.1149884979241320.2299769958482630.885011502075868
180.08728414780692170.1745682956138430.912715852193078
190.07949456323170480.158989126463410.920505436768295
200.0558601246598350.111720249319670.944139875340165
210.03841174459615720.07682348919231450.961588255403843
220.02385032800385960.04770065600771920.97614967199614
230.01576930803938850.03153861607877710.984230691960611
240.01227707015683340.02455414031366670.987722929843167
250.007424178451579760.01484835690315950.99257582154842
260.005052097570836880.01010419514167380.994947902429163
270.003211867989740040.006423735979480080.99678813201026
280.0158399766058470.0316799532116940.984160023394153
290.01278518743882880.02557037487765760.987214812561171
300.01269762869909410.02539525739818820.987302371300906
310.009953107153849430.01990621430769890.990046892846151
320.00657711028945950.0131542205789190.99342288971054
330.005574455152349040.01114891030469810.994425544847651
340.003510184771613940.007020369543227880.996489815228386
350.002232534003114560.004465068006229120.997767465996885
360.001688986354689830.003377972709379660.99831101364531
370.001380331654288060.002760663308576120.998619668345712
380.0009809701110387770.001961940222077550.999019029888961
390.0005911138752136490.00118222775042730.999408886124786
400.0005704495333291610.001140899066658320.999429550466671
410.0004123969674959750.000824793934991950.999587603032504
420.000512060035762140.001024120071524280.999487939964238
430.0004524323391714570.0009048646783429130.999547567660829
440.0003816112510655950.0007632225021311910.999618388748934
450.0003796190993576660.0007592381987153320.999620380900642
460.0002556195514810120.0005112391029620240.999744380448519
470.0003547984460562380.0007095968921124760.999645201553944
480.003387809791902760.006775619583805520.996612190208097
490.003023267934533670.006046535869067330.996976732065466
500.005219448450986940.01043889690197390.994780551549013
510.004849366951688890.009698733903377780.995150633048311
520.02002223178274110.04004446356548210.979977768217259
530.01879690164975720.03759380329951430.981203098350243
540.01669196963171080.03338393926342170.983308030368289
550.04962268697034130.09924537394068260.950377313029659
560.04647269771196190.09294539542392370.953527302288038
570.05247287870291130.1049457574058230.947527121297089
580.04840917650222390.09681835300444780.951590823497776
590.04183345515513620.08366691031027250.958166544844864
600.04481284653768460.08962569307536910.955187153462315
610.04383407540462490.08766815080924980.956165924595375
620.0527815745502930.1055631491005860.947218425449707
630.0549510704187610.1099021408375220.945048929581239
640.1317054696350230.2634109392700460.868294530364977
650.1759549375324180.3519098750648350.824045062467582
660.2491793520507730.4983587041015470.750820647949227
670.4845821677867930.9691643355735850.515417832213207
680.5781756902717320.8436486194565360.421824309728268
690.6333278890507990.7333442218984010.366672110949201
700.6988133418632370.6023733162735270.301186658136763
710.678207327117050.6435853457659010.32179267288295
720.6771321810562120.6457356378875760.322867818943788
730.665886876099470.668226247801060.33411312390053
740.6330992976560990.7338014046878020.366900702343901
750.644564586066040.710870827867920.35543541393396
760.6561176326527470.6877647346945060.343882367347253
770.6531409820500470.6937180358999060.346859017949953
780.6231204755183980.7537590489632050.376879524481602
790.5990539373404320.8018921253191370.400946062659568
800.5835150590988950.832969881802210.416484940901105
810.5836204064637850.8327591870724310.416379593536215
820.6735527501287070.6528944997425860.326447249871293
830.791739450035890.4165210999282190.20826054996411
840.8265872741046180.3468254517907640.173412725895382
850.8287195441138140.3425609117723710.171280455886186
860.7976300134113870.4047399731772270.202369986588613
870.7599102610155650.4801794779688710.240089738984435
880.7808375181844760.4383249636310470.219162481815524
890.7878933458920270.4242133082159450.212106654107973
900.7588785778695610.4822428442608780.241121422130439
910.7298602955828650.540279408834270.270139704417135
920.7039054861625220.5921890276749570.296094513837478
930.6629930059018950.6740139881962110.337006994098105
940.6400420881652160.7199158236695670.359957911834784
950.6824465646701010.6351068706597970.317553435329899
960.7407905309162180.5184189381675640.259209469083782
970.8105042785859620.3789914428280750.189495721414038
980.7946316665625970.4107366668748050.205368333437403
990.7728718358370870.4542563283258250.227128164162913
1000.7995420540814070.4009158918371850.200457945918593
1010.8291876702781930.3416246594436130.170812329721807
1020.8116743386032510.3766513227934980.188325661396749
1030.8155482495560480.3689035008879030.184451750443952
1040.7758109481067340.4483781037865320.224189051893266
1050.7751200002783010.4497599994433990.224879999721699
1060.7565376629415580.4869246741168850.243462337058442
1070.7737497333562060.4525005332875870.226250266643794
1080.7568401994751360.4863196010497280.243159800524864
1090.7608979969180140.4782040061639710.239102003081986
1100.7165141506835190.5669716986329620.283485849316481
1110.6823413961753630.6353172076492740.317658603824637
1120.6306518694624360.7386962610751270.369348130537564
1130.5760416933959940.8479166132080120.423958306604006
1140.5112575500592290.9774848998815410.488742449940771
1150.8003565882580480.3992868234839050.199643411741952
1160.7568670372503930.4862659254992140.243132962749607
1170.7882159354735690.4235681290528610.211784064526431
1180.7975221861623090.4049556276753820.202477813837691
1190.784146963984520.4317060720309610.21585303601548
1200.8505502754729770.2988994490540450.149449724527023
1210.9758834600208150.048233079958370.024116539979185
1220.9681763821965250.06364723560694930.0318236178034746
1230.9638647631031690.07227047379366260.0361352368968313
1240.9419024866104220.1161950267791550.0580975133895775
1250.9229437906053040.1541124187893930.0770562093946964
1260.9218213961649840.1563572076700310.0781786038350156
1270.9996363577525760.0007272844948475410.00036364224742377
1280.9989912879078970.002017424184205970.00100871209210298
1290.9970460948469290.005907810306142650.00295390515307132
1300.9920866277353930.01582674452921310.00791337226460655
1310.9953112311541460.009377537691708690.00468876884585434
1320.9851917085221640.02961658295567110.0148082914778355
1330.9785469045882660.04290619082346730.0214530954117337
1340.9404088215657770.1191823568684470.0595911784342233

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.142017571028862 & 0.284035142057725 & 0.857982428971138 \tabularnewline
10 & 0.0749237629547569 & 0.149847525909514 & 0.925076237045243 \tabularnewline
11 & 0.110000096894307 & 0.220000193788615 & 0.889999903105693 \tabularnewline
12 & 0.133179697410605 & 0.26635939482121 & 0.866820302589395 \tabularnewline
13 & 0.112368335249466 & 0.224736670498931 & 0.887631664750534 \tabularnewline
14 & 0.066741817426923 & 0.133483634853846 & 0.933258182573077 \tabularnewline
15 & 0.0642611186779918 & 0.128522237355984 & 0.935738881322008 \tabularnewline
16 & 0.148601478705176 & 0.297202957410351 & 0.851398521294824 \tabularnewline
17 & 0.114988497924132 & 0.229976995848263 & 0.885011502075868 \tabularnewline
18 & 0.0872841478069217 & 0.174568295613843 & 0.912715852193078 \tabularnewline
19 & 0.0794945632317048 & 0.15898912646341 & 0.920505436768295 \tabularnewline
20 & 0.055860124659835 & 0.11172024931967 & 0.944139875340165 \tabularnewline
21 & 0.0384117445961572 & 0.0768234891923145 & 0.961588255403843 \tabularnewline
22 & 0.0238503280038596 & 0.0477006560077192 & 0.97614967199614 \tabularnewline
23 & 0.0157693080393885 & 0.0315386160787771 & 0.984230691960611 \tabularnewline
24 & 0.0122770701568334 & 0.0245541403136667 & 0.987722929843167 \tabularnewline
25 & 0.00742417845157976 & 0.0148483569031595 & 0.99257582154842 \tabularnewline
26 & 0.00505209757083688 & 0.0101041951416738 & 0.994947902429163 \tabularnewline
27 & 0.00321186798974004 & 0.00642373597948008 & 0.99678813201026 \tabularnewline
28 & 0.015839976605847 & 0.031679953211694 & 0.984160023394153 \tabularnewline
29 & 0.0127851874388288 & 0.0255703748776576 & 0.987214812561171 \tabularnewline
30 & 0.0126976286990941 & 0.0253952573981882 & 0.987302371300906 \tabularnewline
31 & 0.00995310715384943 & 0.0199062143076989 & 0.990046892846151 \tabularnewline
32 & 0.0065771102894595 & 0.013154220578919 & 0.99342288971054 \tabularnewline
33 & 0.00557445515234904 & 0.0111489103046981 & 0.994425544847651 \tabularnewline
34 & 0.00351018477161394 & 0.00702036954322788 & 0.996489815228386 \tabularnewline
35 & 0.00223253400311456 & 0.00446506800622912 & 0.997767465996885 \tabularnewline
36 & 0.00168898635468983 & 0.00337797270937966 & 0.99831101364531 \tabularnewline
37 & 0.00138033165428806 & 0.00276066330857612 & 0.998619668345712 \tabularnewline
38 & 0.000980970111038777 & 0.00196194022207755 & 0.999019029888961 \tabularnewline
39 & 0.000591113875213649 & 0.0011822277504273 & 0.999408886124786 \tabularnewline
40 & 0.000570449533329161 & 0.00114089906665832 & 0.999429550466671 \tabularnewline
41 & 0.000412396967495975 & 0.00082479393499195 & 0.999587603032504 \tabularnewline
42 & 0.00051206003576214 & 0.00102412007152428 & 0.999487939964238 \tabularnewline
43 & 0.000452432339171457 & 0.000904864678342913 & 0.999547567660829 \tabularnewline
44 & 0.000381611251065595 & 0.000763222502131191 & 0.999618388748934 \tabularnewline
45 & 0.000379619099357666 & 0.000759238198715332 & 0.999620380900642 \tabularnewline
46 & 0.000255619551481012 & 0.000511239102962024 & 0.999744380448519 \tabularnewline
47 & 0.000354798446056238 & 0.000709596892112476 & 0.999645201553944 \tabularnewline
48 & 0.00338780979190276 & 0.00677561958380552 & 0.996612190208097 \tabularnewline
49 & 0.00302326793453367 & 0.00604653586906733 & 0.996976732065466 \tabularnewline
50 & 0.00521944845098694 & 0.0104388969019739 & 0.994780551549013 \tabularnewline
51 & 0.00484936695168889 & 0.00969873390337778 & 0.995150633048311 \tabularnewline
52 & 0.0200222317827411 & 0.0400444635654821 & 0.979977768217259 \tabularnewline
53 & 0.0187969016497572 & 0.0375938032995143 & 0.981203098350243 \tabularnewline
54 & 0.0166919696317108 & 0.0333839392634217 & 0.983308030368289 \tabularnewline
55 & 0.0496226869703413 & 0.0992453739406826 & 0.950377313029659 \tabularnewline
56 & 0.0464726977119619 & 0.0929453954239237 & 0.953527302288038 \tabularnewline
57 & 0.0524728787029113 & 0.104945757405823 & 0.947527121297089 \tabularnewline
58 & 0.0484091765022239 & 0.0968183530044478 & 0.951590823497776 \tabularnewline
59 & 0.0418334551551362 & 0.0836669103102725 & 0.958166544844864 \tabularnewline
60 & 0.0448128465376846 & 0.0896256930753691 & 0.955187153462315 \tabularnewline
61 & 0.0438340754046249 & 0.0876681508092498 & 0.956165924595375 \tabularnewline
62 & 0.052781574550293 & 0.105563149100586 & 0.947218425449707 \tabularnewline
63 & 0.054951070418761 & 0.109902140837522 & 0.945048929581239 \tabularnewline
64 & 0.131705469635023 & 0.263410939270046 & 0.868294530364977 \tabularnewline
65 & 0.175954937532418 & 0.351909875064835 & 0.824045062467582 \tabularnewline
66 & 0.249179352050773 & 0.498358704101547 & 0.750820647949227 \tabularnewline
67 & 0.484582167786793 & 0.969164335573585 & 0.515417832213207 \tabularnewline
68 & 0.578175690271732 & 0.843648619456536 & 0.421824309728268 \tabularnewline
69 & 0.633327889050799 & 0.733344221898401 & 0.366672110949201 \tabularnewline
70 & 0.698813341863237 & 0.602373316273527 & 0.301186658136763 \tabularnewline
71 & 0.67820732711705 & 0.643585345765901 & 0.32179267288295 \tabularnewline
72 & 0.677132181056212 & 0.645735637887576 & 0.322867818943788 \tabularnewline
73 & 0.66588687609947 & 0.66822624780106 & 0.33411312390053 \tabularnewline
74 & 0.633099297656099 & 0.733801404687802 & 0.366900702343901 \tabularnewline
75 & 0.64456458606604 & 0.71087082786792 & 0.35543541393396 \tabularnewline
76 & 0.656117632652747 & 0.687764734694506 & 0.343882367347253 \tabularnewline
77 & 0.653140982050047 & 0.693718035899906 & 0.346859017949953 \tabularnewline
78 & 0.623120475518398 & 0.753759048963205 & 0.376879524481602 \tabularnewline
79 & 0.599053937340432 & 0.801892125319137 & 0.400946062659568 \tabularnewline
80 & 0.583515059098895 & 0.83296988180221 & 0.416484940901105 \tabularnewline
81 & 0.583620406463785 & 0.832759187072431 & 0.416379593536215 \tabularnewline
82 & 0.673552750128707 & 0.652894499742586 & 0.326447249871293 \tabularnewline
83 & 0.79173945003589 & 0.416521099928219 & 0.20826054996411 \tabularnewline
84 & 0.826587274104618 & 0.346825451790764 & 0.173412725895382 \tabularnewline
85 & 0.828719544113814 & 0.342560911772371 & 0.171280455886186 \tabularnewline
86 & 0.797630013411387 & 0.404739973177227 & 0.202369986588613 \tabularnewline
87 & 0.759910261015565 & 0.480179477968871 & 0.240089738984435 \tabularnewline
88 & 0.780837518184476 & 0.438324963631047 & 0.219162481815524 \tabularnewline
89 & 0.787893345892027 & 0.424213308215945 & 0.212106654107973 \tabularnewline
90 & 0.758878577869561 & 0.482242844260878 & 0.241121422130439 \tabularnewline
91 & 0.729860295582865 & 0.54027940883427 & 0.270139704417135 \tabularnewline
92 & 0.703905486162522 & 0.592189027674957 & 0.296094513837478 \tabularnewline
93 & 0.662993005901895 & 0.674013988196211 & 0.337006994098105 \tabularnewline
94 & 0.640042088165216 & 0.719915823669567 & 0.359957911834784 \tabularnewline
95 & 0.682446564670101 & 0.635106870659797 & 0.317553435329899 \tabularnewline
96 & 0.740790530916218 & 0.518418938167564 & 0.259209469083782 \tabularnewline
97 & 0.810504278585962 & 0.378991442828075 & 0.189495721414038 \tabularnewline
98 & 0.794631666562597 & 0.410736666874805 & 0.205368333437403 \tabularnewline
99 & 0.772871835837087 & 0.454256328325825 & 0.227128164162913 \tabularnewline
100 & 0.799542054081407 & 0.400915891837185 & 0.200457945918593 \tabularnewline
101 & 0.829187670278193 & 0.341624659443613 & 0.170812329721807 \tabularnewline
102 & 0.811674338603251 & 0.376651322793498 & 0.188325661396749 \tabularnewline
103 & 0.815548249556048 & 0.368903500887903 & 0.184451750443952 \tabularnewline
104 & 0.775810948106734 & 0.448378103786532 & 0.224189051893266 \tabularnewline
105 & 0.775120000278301 & 0.449759999443399 & 0.224879999721699 \tabularnewline
106 & 0.756537662941558 & 0.486924674116885 & 0.243462337058442 \tabularnewline
107 & 0.773749733356206 & 0.452500533287587 & 0.226250266643794 \tabularnewline
108 & 0.756840199475136 & 0.486319601049728 & 0.243159800524864 \tabularnewline
109 & 0.760897996918014 & 0.478204006163971 & 0.239102003081986 \tabularnewline
110 & 0.716514150683519 & 0.566971698632962 & 0.283485849316481 \tabularnewline
111 & 0.682341396175363 & 0.635317207649274 & 0.317658603824637 \tabularnewline
112 & 0.630651869462436 & 0.738696261075127 & 0.369348130537564 \tabularnewline
113 & 0.576041693395994 & 0.847916613208012 & 0.423958306604006 \tabularnewline
114 & 0.511257550059229 & 0.977484899881541 & 0.488742449940771 \tabularnewline
115 & 0.800356588258048 & 0.399286823483905 & 0.199643411741952 \tabularnewline
116 & 0.756867037250393 & 0.486265925499214 & 0.243132962749607 \tabularnewline
117 & 0.788215935473569 & 0.423568129052861 & 0.211784064526431 \tabularnewline
118 & 0.797522186162309 & 0.404955627675382 & 0.202477813837691 \tabularnewline
119 & 0.78414696398452 & 0.431706072030961 & 0.21585303601548 \tabularnewline
120 & 0.850550275472977 & 0.298899449054045 & 0.149449724527023 \tabularnewline
121 & 0.975883460020815 & 0.04823307995837 & 0.024116539979185 \tabularnewline
122 & 0.968176382196525 & 0.0636472356069493 & 0.0318236178034746 \tabularnewline
123 & 0.963864763103169 & 0.0722704737936626 & 0.0361352368968313 \tabularnewline
124 & 0.941902486610422 & 0.116195026779155 & 0.0580975133895775 \tabularnewline
125 & 0.922943790605304 & 0.154112418789393 & 0.0770562093946964 \tabularnewline
126 & 0.921821396164984 & 0.156357207670031 & 0.0781786038350156 \tabularnewline
127 & 0.999636357752576 & 0.000727284494847541 & 0.00036364224742377 \tabularnewline
128 & 0.998991287907897 & 0.00201742418420597 & 0.00100871209210298 \tabularnewline
129 & 0.997046094846929 & 0.00590781030614265 & 0.00295390515307132 \tabularnewline
130 & 0.992086627735393 & 0.0158267445292131 & 0.00791337226460655 \tabularnewline
131 & 0.995311231154146 & 0.00937753769170869 & 0.00468876884585434 \tabularnewline
132 & 0.985191708522164 & 0.0296165829556711 & 0.0148082914778355 \tabularnewline
133 & 0.978546904588266 & 0.0429061908234673 & 0.0214530954117337 \tabularnewline
134 & 0.940408821565777 & 0.119182356868447 & 0.0595911784342233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&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]9[/C][C]0.142017571028862[/C][C]0.284035142057725[/C][C]0.857982428971138[/C][/ROW]
[ROW][C]10[/C][C]0.0749237629547569[/C][C]0.149847525909514[/C][C]0.925076237045243[/C][/ROW]
[ROW][C]11[/C][C]0.110000096894307[/C][C]0.220000193788615[/C][C]0.889999903105693[/C][/ROW]
[ROW][C]12[/C][C]0.133179697410605[/C][C]0.26635939482121[/C][C]0.866820302589395[/C][/ROW]
[ROW][C]13[/C][C]0.112368335249466[/C][C]0.224736670498931[/C][C]0.887631664750534[/C][/ROW]
[ROW][C]14[/C][C]0.066741817426923[/C][C]0.133483634853846[/C][C]0.933258182573077[/C][/ROW]
[ROW][C]15[/C][C]0.0642611186779918[/C][C]0.128522237355984[/C][C]0.935738881322008[/C][/ROW]
[ROW][C]16[/C][C]0.148601478705176[/C][C]0.297202957410351[/C][C]0.851398521294824[/C][/ROW]
[ROW][C]17[/C][C]0.114988497924132[/C][C]0.229976995848263[/C][C]0.885011502075868[/C][/ROW]
[ROW][C]18[/C][C]0.0872841478069217[/C][C]0.174568295613843[/C][C]0.912715852193078[/C][/ROW]
[ROW][C]19[/C][C]0.0794945632317048[/C][C]0.15898912646341[/C][C]0.920505436768295[/C][/ROW]
[ROW][C]20[/C][C]0.055860124659835[/C][C]0.11172024931967[/C][C]0.944139875340165[/C][/ROW]
[ROW][C]21[/C][C]0.0384117445961572[/C][C]0.0768234891923145[/C][C]0.961588255403843[/C][/ROW]
[ROW][C]22[/C][C]0.0238503280038596[/C][C]0.0477006560077192[/C][C]0.97614967199614[/C][/ROW]
[ROW][C]23[/C][C]0.0157693080393885[/C][C]0.0315386160787771[/C][C]0.984230691960611[/C][/ROW]
[ROW][C]24[/C][C]0.0122770701568334[/C][C]0.0245541403136667[/C][C]0.987722929843167[/C][/ROW]
[ROW][C]25[/C][C]0.00742417845157976[/C][C]0.0148483569031595[/C][C]0.99257582154842[/C][/ROW]
[ROW][C]26[/C][C]0.00505209757083688[/C][C]0.0101041951416738[/C][C]0.994947902429163[/C][/ROW]
[ROW][C]27[/C][C]0.00321186798974004[/C][C]0.00642373597948008[/C][C]0.99678813201026[/C][/ROW]
[ROW][C]28[/C][C]0.015839976605847[/C][C]0.031679953211694[/C][C]0.984160023394153[/C][/ROW]
[ROW][C]29[/C][C]0.0127851874388288[/C][C]0.0255703748776576[/C][C]0.987214812561171[/C][/ROW]
[ROW][C]30[/C][C]0.0126976286990941[/C][C]0.0253952573981882[/C][C]0.987302371300906[/C][/ROW]
[ROW][C]31[/C][C]0.00995310715384943[/C][C]0.0199062143076989[/C][C]0.990046892846151[/C][/ROW]
[ROW][C]32[/C][C]0.0065771102894595[/C][C]0.013154220578919[/C][C]0.99342288971054[/C][/ROW]
[ROW][C]33[/C][C]0.00557445515234904[/C][C]0.0111489103046981[/C][C]0.994425544847651[/C][/ROW]
[ROW][C]34[/C][C]0.00351018477161394[/C][C]0.00702036954322788[/C][C]0.996489815228386[/C][/ROW]
[ROW][C]35[/C][C]0.00223253400311456[/C][C]0.00446506800622912[/C][C]0.997767465996885[/C][/ROW]
[ROW][C]36[/C][C]0.00168898635468983[/C][C]0.00337797270937966[/C][C]0.99831101364531[/C][/ROW]
[ROW][C]37[/C][C]0.00138033165428806[/C][C]0.00276066330857612[/C][C]0.998619668345712[/C][/ROW]
[ROW][C]38[/C][C]0.000980970111038777[/C][C]0.00196194022207755[/C][C]0.999019029888961[/C][/ROW]
[ROW][C]39[/C][C]0.000591113875213649[/C][C]0.0011822277504273[/C][C]0.999408886124786[/C][/ROW]
[ROW][C]40[/C][C]0.000570449533329161[/C][C]0.00114089906665832[/C][C]0.999429550466671[/C][/ROW]
[ROW][C]41[/C][C]0.000412396967495975[/C][C]0.00082479393499195[/C][C]0.999587603032504[/C][/ROW]
[ROW][C]42[/C][C]0.00051206003576214[/C][C]0.00102412007152428[/C][C]0.999487939964238[/C][/ROW]
[ROW][C]43[/C][C]0.000452432339171457[/C][C]0.000904864678342913[/C][C]0.999547567660829[/C][/ROW]
[ROW][C]44[/C][C]0.000381611251065595[/C][C]0.000763222502131191[/C][C]0.999618388748934[/C][/ROW]
[ROW][C]45[/C][C]0.000379619099357666[/C][C]0.000759238198715332[/C][C]0.999620380900642[/C][/ROW]
[ROW][C]46[/C][C]0.000255619551481012[/C][C]0.000511239102962024[/C][C]0.999744380448519[/C][/ROW]
[ROW][C]47[/C][C]0.000354798446056238[/C][C]0.000709596892112476[/C][C]0.999645201553944[/C][/ROW]
[ROW][C]48[/C][C]0.00338780979190276[/C][C]0.00677561958380552[/C][C]0.996612190208097[/C][/ROW]
[ROW][C]49[/C][C]0.00302326793453367[/C][C]0.00604653586906733[/C][C]0.996976732065466[/C][/ROW]
[ROW][C]50[/C][C]0.00521944845098694[/C][C]0.0104388969019739[/C][C]0.994780551549013[/C][/ROW]
[ROW][C]51[/C][C]0.00484936695168889[/C][C]0.00969873390337778[/C][C]0.995150633048311[/C][/ROW]
[ROW][C]52[/C][C]0.0200222317827411[/C][C]0.0400444635654821[/C][C]0.979977768217259[/C][/ROW]
[ROW][C]53[/C][C]0.0187969016497572[/C][C]0.0375938032995143[/C][C]0.981203098350243[/C][/ROW]
[ROW][C]54[/C][C]0.0166919696317108[/C][C]0.0333839392634217[/C][C]0.983308030368289[/C][/ROW]
[ROW][C]55[/C][C]0.0496226869703413[/C][C]0.0992453739406826[/C][C]0.950377313029659[/C][/ROW]
[ROW][C]56[/C][C]0.0464726977119619[/C][C]0.0929453954239237[/C][C]0.953527302288038[/C][/ROW]
[ROW][C]57[/C][C]0.0524728787029113[/C][C]0.104945757405823[/C][C]0.947527121297089[/C][/ROW]
[ROW][C]58[/C][C]0.0484091765022239[/C][C]0.0968183530044478[/C][C]0.951590823497776[/C][/ROW]
[ROW][C]59[/C][C]0.0418334551551362[/C][C]0.0836669103102725[/C][C]0.958166544844864[/C][/ROW]
[ROW][C]60[/C][C]0.0448128465376846[/C][C]0.0896256930753691[/C][C]0.955187153462315[/C][/ROW]
[ROW][C]61[/C][C]0.0438340754046249[/C][C]0.0876681508092498[/C][C]0.956165924595375[/C][/ROW]
[ROW][C]62[/C][C]0.052781574550293[/C][C]0.105563149100586[/C][C]0.947218425449707[/C][/ROW]
[ROW][C]63[/C][C]0.054951070418761[/C][C]0.109902140837522[/C][C]0.945048929581239[/C][/ROW]
[ROW][C]64[/C][C]0.131705469635023[/C][C]0.263410939270046[/C][C]0.868294530364977[/C][/ROW]
[ROW][C]65[/C][C]0.175954937532418[/C][C]0.351909875064835[/C][C]0.824045062467582[/C][/ROW]
[ROW][C]66[/C][C]0.249179352050773[/C][C]0.498358704101547[/C][C]0.750820647949227[/C][/ROW]
[ROW][C]67[/C][C]0.484582167786793[/C][C]0.969164335573585[/C][C]0.515417832213207[/C][/ROW]
[ROW][C]68[/C][C]0.578175690271732[/C][C]0.843648619456536[/C][C]0.421824309728268[/C][/ROW]
[ROW][C]69[/C][C]0.633327889050799[/C][C]0.733344221898401[/C][C]0.366672110949201[/C][/ROW]
[ROW][C]70[/C][C]0.698813341863237[/C][C]0.602373316273527[/C][C]0.301186658136763[/C][/ROW]
[ROW][C]71[/C][C]0.67820732711705[/C][C]0.643585345765901[/C][C]0.32179267288295[/C][/ROW]
[ROW][C]72[/C][C]0.677132181056212[/C][C]0.645735637887576[/C][C]0.322867818943788[/C][/ROW]
[ROW][C]73[/C][C]0.66588687609947[/C][C]0.66822624780106[/C][C]0.33411312390053[/C][/ROW]
[ROW][C]74[/C][C]0.633099297656099[/C][C]0.733801404687802[/C][C]0.366900702343901[/C][/ROW]
[ROW][C]75[/C][C]0.64456458606604[/C][C]0.71087082786792[/C][C]0.35543541393396[/C][/ROW]
[ROW][C]76[/C][C]0.656117632652747[/C][C]0.687764734694506[/C][C]0.343882367347253[/C][/ROW]
[ROW][C]77[/C][C]0.653140982050047[/C][C]0.693718035899906[/C][C]0.346859017949953[/C][/ROW]
[ROW][C]78[/C][C]0.623120475518398[/C][C]0.753759048963205[/C][C]0.376879524481602[/C][/ROW]
[ROW][C]79[/C][C]0.599053937340432[/C][C]0.801892125319137[/C][C]0.400946062659568[/C][/ROW]
[ROW][C]80[/C][C]0.583515059098895[/C][C]0.83296988180221[/C][C]0.416484940901105[/C][/ROW]
[ROW][C]81[/C][C]0.583620406463785[/C][C]0.832759187072431[/C][C]0.416379593536215[/C][/ROW]
[ROW][C]82[/C][C]0.673552750128707[/C][C]0.652894499742586[/C][C]0.326447249871293[/C][/ROW]
[ROW][C]83[/C][C]0.79173945003589[/C][C]0.416521099928219[/C][C]0.20826054996411[/C][/ROW]
[ROW][C]84[/C][C]0.826587274104618[/C][C]0.346825451790764[/C][C]0.173412725895382[/C][/ROW]
[ROW][C]85[/C][C]0.828719544113814[/C][C]0.342560911772371[/C][C]0.171280455886186[/C][/ROW]
[ROW][C]86[/C][C]0.797630013411387[/C][C]0.404739973177227[/C][C]0.202369986588613[/C][/ROW]
[ROW][C]87[/C][C]0.759910261015565[/C][C]0.480179477968871[/C][C]0.240089738984435[/C][/ROW]
[ROW][C]88[/C][C]0.780837518184476[/C][C]0.438324963631047[/C][C]0.219162481815524[/C][/ROW]
[ROW][C]89[/C][C]0.787893345892027[/C][C]0.424213308215945[/C][C]0.212106654107973[/C][/ROW]
[ROW][C]90[/C][C]0.758878577869561[/C][C]0.482242844260878[/C][C]0.241121422130439[/C][/ROW]
[ROW][C]91[/C][C]0.729860295582865[/C][C]0.54027940883427[/C][C]0.270139704417135[/C][/ROW]
[ROW][C]92[/C][C]0.703905486162522[/C][C]0.592189027674957[/C][C]0.296094513837478[/C][/ROW]
[ROW][C]93[/C][C]0.662993005901895[/C][C]0.674013988196211[/C][C]0.337006994098105[/C][/ROW]
[ROW][C]94[/C][C]0.640042088165216[/C][C]0.719915823669567[/C][C]0.359957911834784[/C][/ROW]
[ROW][C]95[/C][C]0.682446564670101[/C][C]0.635106870659797[/C][C]0.317553435329899[/C][/ROW]
[ROW][C]96[/C][C]0.740790530916218[/C][C]0.518418938167564[/C][C]0.259209469083782[/C][/ROW]
[ROW][C]97[/C][C]0.810504278585962[/C][C]0.378991442828075[/C][C]0.189495721414038[/C][/ROW]
[ROW][C]98[/C][C]0.794631666562597[/C][C]0.410736666874805[/C][C]0.205368333437403[/C][/ROW]
[ROW][C]99[/C][C]0.772871835837087[/C][C]0.454256328325825[/C][C]0.227128164162913[/C][/ROW]
[ROW][C]100[/C][C]0.799542054081407[/C][C]0.400915891837185[/C][C]0.200457945918593[/C][/ROW]
[ROW][C]101[/C][C]0.829187670278193[/C][C]0.341624659443613[/C][C]0.170812329721807[/C][/ROW]
[ROW][C]102[/C][C]0.811674338603251[/C][C]0.376651322793498[/C][C]0.188325661396749[/C][/ROW]
[ROW][C]103[/C][C]0.815548249556048[/C][C]0.368903500887903[/C][C]0.184451750443952[/C][/ROW]
[ROW][C]104[/C][C]0.775810948106734[/C][C]0.448378103786532[/C][C]0.224189051893266[/C][/ROW]
[ROW][C]105[/C][C]0.775120000278301[/C][C]0.449759999443399[/C][C]0.224879999721699[/C][/ROW]
[ROW][C]106[/C][C]0.756537662941558[/C][C]0.486924674116885[/C][C]0.243462337058442[/C][/ROW]
[ROW][C]107[/C][C]0.773749733356206[/C][C]0.452500533287587[/C][C]0.226250266643794[/C][/ROW]
[ROW][C]108[/C][C]0.756840199475136[/C][C]0.486319601049728[/C][C]0.243159800524864[/C][/ROW]
[ROW][C]109[/C][C]0.760897996918014[/C][C]0.478204006163971[/C][C]0.239102003081986[/C][/ROW]
[ROW][C]110[/C][C]0.716514150683519[/C][C]0.566971698632962[/C][C]0.283485849316481[/C][/ROW]
[ROW][C]111[/C][C]0.682341396175363[/C][C]0.635317207649274[/C][C]0.317658603824637[/C][/ROW]
[ROW][C]112[/C][C]0.630651869462436[/C][C]0.738696261075127[/C][C]0.369348130537564[/C][/ROW]
[ROW][C]113[/C][C]0.576041693395994[/C][C]0.847916613208012[/C][C]0.423958306604006[/C][/ROW]
[ROW][C]114[/C][C]0.511257550059229[/C][C]0.977484899881541[/C][C]0.488742449940771[/C][/ROW]
[ROW][C]115[/C][C]0.800356588258048[/C][C]0.399286823483905[/C][C]0.199643411741952[/C][/ROW]
[ROW][C]116[/C][C]0.756867037250393[/C][C]0.486265925499214[/C][C]0.243132962749607[/C][/ROW]
[ROW][C]117[/C][C]0.788215935473569[/C][C]0.423568129052861[/C][C]0.211784064526431[/C][/ROW]
[ROW][C]118[/C][C]0.797522186162309[/C][C]0.404955627675382[/C][C]0.202477813837691[/C][/ROW]
[ROW][C]119[/C][C]0.78414696398452[/C][C]0.431706072030961[/C][C]0.21585303601548[/C][/ROW]
[ROW][C]120[/C][C]0.850550275472977[/C][C]0.298899449054045[/C][C]0.149449724527023[/C][/ROW]
[ROW][C]121[/C][C]0.975883460020815[/C][C]0.04823307995837[/C][C]0.024116539979185[/C][/ROW]
[ROW][C]122[/C][C]0.968176382196525[/C][C]0.0636472356069493[/C][C]0.0318236178034746[/C][/ROW]
[ROW][C]123[/C][C]0.963864763103169[/C][C]0.0722704737936626[/C][C]0.0361352368968313[/C][/ROW]
[ROW][C]124[/C][C]0.941902486610422[/C][C]0.116195026779155[/C][C]0.0580975133895775[/C][/ROW]
[ROW][C]125[/C][C]0.922943790605304[/C][C]0.154112418789393[/C][C]0.0770562093946964[/C][/ROW]
[ROW][C]126[/C][C]0.921821396164984[/C][C]0.156357207670031[/C][C]0.0781786038350156[/C][/ROW]
[ROW][C]127[/C][C]0.999636357752576[/C][C]0.000727284494847541[/C][C]0.00036364224742377[/C][/ROW]
[ROW][C]128[/C][C]0.998991287907897[/C][C]0.00201742418420597[/C][C]0.00100871209210298[/C][/ROW]
[ROW][C]129[/C][C]0.997046094846929[/C][C]0.00590781030614265[/C][C]0.00295390515307132[/C][/ROW]
[ROW][C]130[/C][C]0.992086627735393[/C][C]0.0158267445292131[/C][C]0.00791337226460655[/C][/ROW]
[ROW][C]131[/C][C]0.995311231154146[/C][C]0.00937753769170869[/C][C]0.00468876884585434[/C][/ROW]
[ROW][C]132[/C][C]0.985191708522164[/C][C]0.0296165829556711[/C][C]0.0148082914778355[/C][/ROW]
[ROW][C]133[/C][C]0.978546904588266[/C][C]0.0429061908234673[/C][C]0.0214530954117337[/C][/ROW]
[ROW][C]134[/C][C]0.940408821565777[/C][C]0.119182356868447[/C][C]0.0595911784342233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1420175710288620.2840351420577250.857982428971138
100.07492376295475690.1498475259095140.925076237045243
110.1100000968943070.2200001937886150.889999903105693
120.1331796974106050.266359394821210.866820302589395
130.1123683352494660.2247366704989310.887631664750534
140.0667418174269230.1334836348538460.933258182573077
150.06426111867799180.1285222373559840.935738881322008
160.1486014787051760.2972029574103510.851398521294824
170.1149884979241320.2299769958482630.885011502075868
180.08728414780692170.1745682956138430.912715852193078
190.07949456323170480.158989126463410.920505436768295
200.0558601246598350.111720249319670.944139875340165
210.03841174459615720.07682348919231450.961588255403843
220.02385032800385960.04770065600771920.97614967199614
230.01576930803938850.03153861607877710.984230691960611
240.01227707015683340.02455414031366670.987722929843167
250.007424178451579760.01484835690315950.99257582154842
260.005052097570836880.01010419514167380.994947902429163
270.003211867989740040.006423735979480080.99678813201026
280.0158399766058470.0316799532116940.984160023394153
290.01278518743882880.02557037487765760.987214812561171
300.01269762869909410.02539525739818820.987302371300906
310.009953107153849430.01990621430769890.990046892846151
320.00657711028945950.0131542205789190.99342288971054
330.005574455152349040.01114891030469810.994425544847651
340.003510184771613940.007020369543227880.996489815228386
350.002232534003114560.004465068006229120.997767465996885
360.001688986354689830.003377972709379660.99831101364531
370.001380331654288060.002760663308576120.998619668345712
380.0009809701110387770.001961940222077550.999019029888961
390.0005911138752136490.00118222775042730.999408886124786
400.0005704495333291610.001140899066658320.999429550466671
410.0004123969674959750.000824793934991950.999587603032504
420.000512060035762140.001024120071524280.999487939964238
430.0004524323391714570.0009048646783429130.999547567660829
440.0003816112510655950.0007632225021311910.999618388748934
450.0003796190993576660.0007592381987153320.999620380900642
460.0002556195514810120.0005112391029620240.999744380448519
470.0003547984460562380.0007095968921124760.999645201553944
480.003387809791902760.006775619583805520.996612190208097
490.003023267934533670.006046535869067330.996976732065466
500.005219448450986940.01043889690197390.994780551549013
510.004849366951688890.009698733903377780.995150633048311
520.02002223178274110.04004446356548210.979977768217259
530.01879690164975720.03759380329951430.981203098350243
540.01669196963171080.03338393926342170.983308030368289
550.04962268697034130.09924537394068260.950377313029659
560.04647269771196190.09294539542392370.953527302288038
570.05247287870291130.1049457574058230.947527121297089
580.04840917650222390.09681835300444780.951590823497776
590.04183345515513620.08366691031027250.958166544844864
600.04481284653768460.08962569307536910.955187153462315
610.04383407540462490.08766815080924980.956165924595375
620.0527815745502930.1055631491005860.947218425449707
630.0549510704187610.1099021408375220.945048929581239
640.1317054696350230.2634109392700460.868294530364977
650.1759549375324180.3519098750648350.824045062467582
660.2491793520507730.4983587041015470.750820647949227
670.4845821677867930.9691643355735850.515417832213207
680.5781756902717320.8436486194565360.421824309728268
690.6333278890507990.7333442218984010.366672110949201
700.6988133418632370.6023733162735270.301186658136763
710.678207327117050.6435853457659010.32179267288295
720.6771321810562120.6457356378875760.322867818943788
730.665886876099470.668226247801060.33411312390053
740.6330992976560990.7338014046878020.366900702343901
750.644564586066040.710870827867920.35543541393396
760.6561176326527470.6877647346945060.343882367347253
770.6531409820500470.6937180358999060.346859017949953
780.6231204755183980.7537590489632050.376879524481602
790.5990539373404320.8018921253191370.400946062659568
800.5835150590988950.832969881802210.416484940901105
810.5836204064637850.8327591870724310.416379593536215
820.6735527501287070.6528944997425860.326447249871293
830.791739450035890.4165210999282190.20826054996411
840.8265872741046180.3468254517907640.173412725895382
850.8287195441138140.3425609117723710.171280455886186
860.7976300134113870.4047399731772270.202369986588613
870.7599102610155650.4801794779688710.240089738984435
880.7808375181844760.4383249636310470.219162481815524
890.7878933458920270.4242133082159450.212106654107973
900.7588785778695610.4822428442608780.241121422130439
910.7298602955828650.540279408834270.270139704417135
920.7039054861625220.5921890276749570.296094513837478
930.6629930059018950.6740139881962110.337006994098105
940.6400420881652160.7199158236695670.359957911834784
950.6824465646701010.6351068706597970.317553435329899
960.7407905309162180.5184189381675640.259209469083782
970.8105042785859620.3789914428280750.189495721414038
980.7946316665625970.4107366668748050.205368333437403
990.7728718358370870.4542563283258250.227128164162913
1000.7995420540814070.4009158918371850.200457945918593
1010.8291876702781930.3416246594436130.170812329721807
1020.8116743386032510.3766513227934980.188325661396749
1030.8155482495560480.3689035008879030.184451750443952
1040.7758109481067340.4483781037865320.224189051893266
1050.7751200002783010.4497599994433990.224879999721699
1060.7565376629415580.4869246741168850.243462337058442
1070.7737497333562060.4525005332875870.226250266643794
1080.7568401994751360.4863196010497280.243159800524864
1090.7608979969180140.4782040061639710.239102003081986
1100.7165141506835190.5669716986329620.283485849316481
1110.6823413961753630.6353172076492740.317658603824637
1120.6306518694624360.7386962610751270.369348130537564
1130.5760416933959940.8479166132080120.423958306604006
1140.5112575500592290.9774848998815410.488742449940771
1150.8003565882580480.3992868234839050.199643411741952
1160.7568670372503930.4862659254992140.243132962749607
1170.7882159354735690.4235681290528610.211784064526431
1180.7975221861623090.4049556276753820.202477813837691
1190.784146963984520.4317060720309610.21585303601548
1200.8505502754729770.2988994490540450.149449724527023
1210.9758834600208150.048233079958370.024116539979185
1220.9681763821965250.06364723560694930.0318236178034746
1230.9638647631031690.07227047379366260.0361352368968313
1240.9419024866104220.1161950267791550.0580975133895775
1250.9229437906053040.1541124187893930.0770562093946964
1260.9218213961649840.1563572076700310.0781786038350156
1270.9996363577525760.0007272844948475410.00036364224742377
1280.9989912879078970.002017424184205970.00100871209210298
1290.9970460948469290.005907810306142650.00295390515307132
1300.9920866277353930.01582674452921310.00791337226460655
1310.9953112311541460.009377537691708690.00468876884585434
1320.9851917085221640.02961658295567110.0148082914778355
1330.9785469045882660.04290619082346730.0214530954117337
1340.9404088215657770.1191823568684470.0595911784342233






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.174603174603175NOK
5% type I error level410.325396825396825NOK
10% type I error level500.396825396825397NOK

\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 & 22 & 0.174603174603175 & NOK \tabularnewline
5% type I error level & 41 & 0.325396825396825 & NOK \tabularnewline
10% type I error level & 50 & 0.396825396825397 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197345&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]22[/C][C]0.174603174603175[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.325396825396825[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.396825396825397[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197345&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197345&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.174603174603175NOK
5% type I error level410.325396825396825NOK
10% type I error level500.396825396825397NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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