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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 02 Dec 2010 09:26:04 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t1291283274dpjzh2q7wwj1p7q.htm/, Retrieved Sun, 05 May 2024 18:41:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104200, Retrieved Sun, 05 May 2024 18:41:38 +0000
QR Codes:

Original text written by user:Member of Sports clu data
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact179

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7 Multip...] [2010-12-02 09:26:04] [f6fdc0236f011c1845380977efc505f8] [Current]
-    D    [Multiple Regression] [Workshop 7 multip...] [2010-12-03 12:20:20] [82c18f3ebe9df70882495121eb816e07]
-   PD    [Multiple Regression] [Workshop 7 Multip...] [2010-12-03 12:39:24] [82c18f3ebe9df70882495121eb816e07]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	5	2	2	1	1
1	4	1	3	1	4
1	7	3	3	1	5
1	7	2	2	1	2
2	5	2	1	1	1
2	5	1	1	1	1
1	4	1	3	1	2
2	4	3	3	2	1
1	6	1	1	1	1
2	5	1	1	1	1
1	1	1	1	1	3
2	5	1	1	1	1
1	4	2	1	2	1
2	6	3	1	1	1
2	7	2	2	1	2
2	7	3	3	1	4
1	2	2	1	1	1
1	6	1	1	1	1
1	3	1	1	1	2
2	6	1	1	1	3
2	6	1	3	1	1
1	5	1	1	1	1
2	6	3	2	1	1
2	4	1	3	2	1
2	3	3	1	2	2
2	4	1	1	1	1
2	5	1	1	2	1
2	6	1	1	2	1
1	6	3	3	2	1
1	4	1	1	1	1
2	6	1	3	1	1
1	6	1	1	1	1
2	5	1	3	1	1
2	6	3	1	1	1
2	4	1	1	1	1
1	6	1	1	1	1
2	7	1	3	1	1
1	5	2	3	1	1
1	6	1	3	1	1
2	6	1	1	2	1
1	5	2	2	2	4
2	7	2	3	1	1
2	6	2	2	1	1
1	3	1	1	1	4
1	4	1	1	1	2
2	5	2	3	1	2
2	4	2	3	2	1
1	3	1	1	1	1
2	5	3	1	1	2
2	5	1	1	1	1
1	4	1	2	1	1
1	5	1	1	1	1
2	1	2	2	1	1
2	2	1	1	2	1
2	3	3	3	1	1
1	4	2	2	1	2
1	3	3	3	1	1
1	7	1	1	1	1
1	2	1	1	1	1
1	4	3	1	1	2
1	2	1	1	1	1
2	5	1	1	1	2
2	6	1	2	1	4
2	6	1	2	1	1
2	6	1	1	1	1
1	6	2	2	1	1
2	6	3	3	1	2
2	6	1	1	1	3
1	6	1	3	1	1
1	4	2	2	1	1
1	4	2	2	1	1
2	5	3	3	1	1
1	6	2	3	1	1
1	6	1	1	1	1
1	7	3	2	1	1
1	6	2	3	1	1
2	6	1	3	2	1
1	6	1	2	1	2
2	3	2	1	1	1
2	5	2	3	1	1
2	6	2	3	1	1
2	4	2	3	1	1
1	5	3	3	1	1
2	6	3	2	1	1
2	6	2	2	1	1
1	3	1	3	1	1
2	6	2	3	1	2
2	5	1	1	1	1
1	6	1	1	1	1
1	4	1	1	1	1
2	7	2	1	1	1
2	5	2	2	1	1
2	6	2	2	1	2
1	6	2	2	1	1
2	6	2	3	1	5
1	7	1	1	2	1
2	6	2	3	1	1
1	6	2	2	1	1
1	6	3	3	1	2
2	6	1	1	1	1
2	2	1	1	1	3
1	4	1	1	1	1
2	4	3	3	1	1
2	6	2	3	1	1
1	5	3	3	1	3
1	6	3	1	1	1
1	6	1	2	1	1
1	2	1	1	2	1
2	7	2	2	1	1
1	1	1	2	1	1
1	4	1	1	1	1
1	1	2	3	1	2
1	6	2	2	1	2
2	6	2	2	1	4
1	6	2	3	1	4
2	7	2	2	1	1
1	6	1	3	1	1
2	4	1	1	1	1
2	4	3	3	1	1
1	6	1	1	1	4
1	5	3	1	2	1
2	7	1	1	1	1
2	4	1	1	1	1
1	4	2	2	1	1
2	6	2	2	1	3
2	7	3	1	1	1
2	5	1	1	1	1
2	6	3	2	1	1
1	6	2	2	2	4
2	6	3	3	1	4
2	5	2	3	1	1
2	7	1	1	1	2
2	4	3	3	1	1
1	6	2	1	1	2
1	6	3	3	1	1
2	7	2	2	1	3
2	6	3	3	1	2
2	6	3	3	1	2
2	5	1	2	1	1
1	5	2	1	1	1
2	5	1	1	1	2
2	6	2	3	1	1
2	6	3	1	2	2
1	7	1	3	2	2
1	4	1	2	1	1
2	6	2	2	1	1
2	6	2	2	1	2
2	7	1	1	1	1
1	6	1	1	1	2
2	7	1	2	1	1
2	4	1	2	2	1
2	6	1	2	1	1
1	4	3	3	1	1
1	4	1	1	1	1
2	7	3	1	1	1
1	4	1	2	1	1
2	7	3	1	2	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Member[t] = + 1.04782670721478 + 0.0729570089347279Provison[t] + 0.0654005174473116Mother[t] + 0.00562364643803958Father[t] + 0.063112559403901Illness[t] -0.0432767813249698Tobacco[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Member[t] =  +  1.04782670721478 +  0.0729570089347279Provison[t] +  0.0654005174473116Mother[t] +  0.00562364643803958Father[t] +  0.063112559403901Illness[t] -0.0432767813249698Tobacco[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Member[t] =  +  1.04782670721478 +  0.0729570089347279Provison[t] +  0.0654005174473116Mother[t] +  0.00562364643803958Father[t] +  0.063112559403901Illness[t] -0.0432767813249698Tobacco[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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
Member[t] = + 1.04782670721478 + 0.0729570089347279Provison[t] + 0.0654005174473116Mother[t] + 0.00562364643803958Father[t] + 0.063112559403901Illness[t] -0.0432767813249698Tobacco[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.047826707214780.2237464.68316e-063e-06
Provison0.07295700893472790.0278022.62420.0095780.004789
Mother0.06540051744731160.0539321.21270.2271560.113578
Father0.005623646438039580.0498860.11270.9103950.455197
Illness0.0631125594039010.117830.53560.5930060.296503
Tobacco-0.04327678132496980.04231-1.02290.3080120.154006

\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) & 1.04782670721478 & 0.223746 & 4.6831 & 6e-06 & 3e-06 \tabularnewline
Provison & 0.0729570089347279 & 0.027802 & 2.6242 & 0.009578 & 0.004789 \tabularnewline
Mother & 0.0654005174473116 & 0.053932 & 1.2127 & 0.227156 & 0.113578 \tabularnewline
Father & 0.00562364643803958 & 0.049886 & 0.1127 & 0.910395 & 0.455197 \tabularnewline
Illness & 0.063112559403901 & 0.11783 & 0.5356 & 0.593006 & 0.296503 \tabularnewline
Tobacco & -0.0432767813249698 & 0.04231 & -1.0229 & 0.308012 & 0.154006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&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]1.04782670721478[/C][C]0.223746[/C][C]4.6831[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Provison[/C][C]0.0729570089347279[/C][C]0.027802[/C][C]2.6242[/C][C]0.009578[/C][C]0.004789[/C][/ROW]
[ROW][C]Mother[/C][C]0.0654005174473116[/C][C]0.053932[/C][C]1.2127[/C][C]0.227156[/C][C]0.113578[/C][/ROW]
[ROW][C]Father[/C][C]0.00562364643803958[/C][C]0.049886[/C][C]0.1127[/C][C]0.910395[/C][C]0.455197[/C][/ROW]
[ROW][C]Illness[/C][C]0.063112559403901[/C][C]0.11783[/C][C]0.5356[/C][C]0.593006[/C][C]0.296503[/C][/ROW]
[ROW][C]Tobacco[/C][C]-0.0432767813249698[/C][C]0.04231[/C][C]-1.0229[/C][C]0.308012[/C][C]0.154006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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)1.047826707214780.2237464.68316e-063e-06
Provison0.07295700893472790.0278022.62420.0095780.004789
Mother0.06540051744731160.0539321.21270.2271560.113578
Father0.005623646438039580.0498860.11270.9103950.455197
Illness0.0631125594039010.117830.53560.5930060.296503
Tobacco-0.04327678132496980.04231-1.02290.3080120.154006






Multiple Linear Regression - Regression Statistics
Multiple R0.256259646347524
R-squared0.0656690063461582
Adjusted R-squared0.0347308939735144
F-TEST (value)2.12259253425637
F-TEST (DF numerator)5
F-TEST (DF denominator)151
p-value0.0657160719619755
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.489914985929934
Sum Squared Residuals36.2425207092478

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.256259646347524 \tabularnewline
R-squared & 0.0656690063461582 \tabularnewline
Adjusted R-squared & 0.0347308939735144 \tabularnewline
F-TEST (value) & 2.12259253425637 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0.0657160719619755 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.489914985929934 \tabularnewline
Sum Squared Residuals & 36.2425207092478 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.256259646347524[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0656690063461582[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0347308939735144[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.12259253425637[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0.0657160719619755[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.489914985929934[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36.2425207092478[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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 R0.256259646347524
R-squared0.0656690063461582
Adjusted R-squared0.0347308939735144
F-TEST (value)2.12259253425637
F-TEST (DF numerator)5
F-TEST (DF denominator)151
p-value0.0657160719619755
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.489914985929934
Sum Squared Residuals36.2425207092478






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.574495857738010.42550414226199
211.31193163381913-0.311931633819132
311.61832691419297-0.618326914192972
411.67713309428253-0.677133094282532
521.568872211300010.431127788699995
621.503471693852690.496528306147307
711.39848519646908-0.398485196469075
821.635675572092570.364324427907431
911.57642870278742-0.576428702787422
1021.503471693852690.496528306147306
1111.12509009546384-0.125090095463842
1221.503471693852690.496528306147306
1311.55902776176918-0.559027761769178
1421.707229737682040.292770262317955
1521.677133094282530.322866905717469
1621.661603695517940.338396304482057
1711.35000118449582-0.350001184495821
1811.57642870278742-0.576428702787422
1911.31428089465827-0.314280894658268
2021.489875140137480.510124859862518
2121.58767599566350.412324004336499
2211.50347169385269-0.503471693852694
2321.712853384120080.287146615879916
2421.504874537197950.495125462802054
2521.508194488956790.491805511043208
2621.430514684917970.569485315082034
2721.566584253256600.433415746743405
2821.639541262191320.360458737808677
2911.78158958996203-0.781589589962025
3011.43051468491797-0.430514684917966
3121.58767599566350.412324004336499
3211.57642870278742-0.576428702787422
3321.514718986728770.485281013271227
3421.707229737682040.292770262317955
3521.430514684917970.569485315082034
3611.57642870278742-0.576428702787422
3721.660633004598230.339366995401771
3811.58011950417608-0.580119504176085
3911.5876759956635-0.587675995663501
4021.639541262191320.360458737808677
4111.50777807316704-0.507778073167037
4221.726033522045540.273966477954459
4321.647452866672770.352547133327227
4411.22772733200833-0.227727332008328
4511.38723790359300-0.387237903592996
4621.536842722851120.463157277148885
4721.570275054645260.429724945354742
4811.35755767598324-0.357557675983238
4921.590995947422350.409004052577653
5021.503471693852690.496528306147306
5111.43613833135601-0.436138331356005
5211.50347169385269-0.503471693852694
5321.282667821999130.717332178000867
5421.347713226452410.652286773547589
5521.499606003753940.50039399624606
5611.45826206747835-0.458262067478347
5711.49960600375394-0.49960600375394
5811.64938571172215-0.64938571172215
5911.28460066704851-0.284600667048510
6011.51803893848762-0.518038938487619
6111.28460066704851-0.284600667048510
6221.460194912527720.539805087472276
6321.452222005250550.547777994749448
6421.582052349225460.417947650774538
6521.576428702787420.423571297212578
6611.64745286667277-0.647452866672773
6721.675200249233150.324799750766846
6821.489875140137480.510124859862518
6911.5876759956635-0.587675995663501
7011.50153884880332-0.501538848803317
7111.50153884880332-0.501538848803317
7221.645520021623400.354479978376604
7311.65307651311081-0.653076513110813
7411.57642870278742-0.576428702787422
7511.78581039305481-0.785810393054812
7611.65307651311081-0.653076513110813
7721.650788555067400.349211444932598
7811.53877556790049-0.538775567900492
7921.422958193430550.577041806569451
8021.580119504176080.419880495823915
8121.653076513110810.346923486889187
8221.507162495241360.492837504758643
8311.64552002162340-0.645520021623396
8421.712853384120080.287146615879916
8521.647452866672770.352547133327227
8611.36880496885932-0.368804968859317
8721.609799731785840.390200268214157
8821.503471693852690.496528306147306
8911.57642870278742-0.576428702787422
9011.43051468491797-0.430514684917966
9121.714786229169460.285213770830539
9221.574495857738050.425504142261955
9321.604176085347800.395823914652197
9411.64745286667277-0.647452866672773
9521.479969387810930.520030612189066
9611.71249827112605-0.712498271126051
9721.653076513110810.346923486889187
9811.64745286667277-0.647452866672773
9911.67520024923315-0.675200249233154
10021.576428702787420.423571297212578
10121.198047104398570.80195289560143
10211.43051468491797-0.430514684917966
10321.572563012688670.427436987311332
10421.653076513110810.346923486889187
10511.55896645897346-0.558966458973456
10611.70722973768204-0.707229737682045
10711.58205234922546-0.582052349225461
10811.34771322645241-0.347713226452411
10921.72040987560750.279590124392499
11011.21726730455182-0.217267304551821
11111.43051468491797-0.430514684917966
11211.24501468711220-0.245014687112202
11311.60417608534780-0.604176085347803
11421.517622522697860.482377477302136
11511.52324616913590-0.523246169135903
11621.72040987560750.279590124392499
11711.5876759956635-0.587675995663501
11821.430514684917970.569485315082034
11921.572563012688670.427436987311332
12011.44659835881251-0.446598358812513
12111.69738528815122-0.697385288151218
12221.649385711722150.35061428827785
12321.430514684917970.569485315082034
12411.50153884880332-0.501538848803317
12521.560899304022830.439100695977166
12621.780186746616770.219813253383227
12721.503471693852690.496528306147306
12821.712853384120080.287146615879916
12911.58073508210176-0.580735082101765
13021.588646686583210.411353313416785
13121.580119504176080.419880495823915
13221.606108930397180.39389106960282
13321.572563012688670.427436987311332
13411.59855243890976-0.598552438909764
13511.71847703055812-0.718477030558124
13621.633856312957560.366143687042438
13721.675200249233150.324799750766846
13821.675200249233150.324799750766846
13921.509095340290730.490904659709266
14011.56887221130001-0.568872211300005
14121.460194912527720.539805087472276
14221.653076513110810.346923486889187
14321.727065515760980.272934484239024
14411.68046878267716-0.68046878267716
14511.43613833135601-0.436138331356005
14621.647452866672770.352547133327227
14721.604176085347800.395823914652197
14821.649385711722150.35061428827785
14911.53315192146245-0.533151921462452
15021.655009358160190.34499064183981
15121.499250890759910.500749109240094
15221.582052349225460.417947650774538
15311.57256301268867-0.572563012688668
15411.43051468491797-0.430514684917966
15521.780186746616770.219813253383227
15611.43613833135601-0.436138331356005
15721.843299306020670.156700693979326

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.57449585773801 & 0.42550414226199 \tabularnewline
2 & 1 & 1.31193163381913 & -0.311931633819132 \tabularnewline
3 & 1 & 1.61832691419297 & -0.618326914192972 \tabularnewline
4 & 1 & 1.67713309428253 & -0.677133094282532 \tabularnewline
5 & 2 & 1.56887221130001 & 0.431127788699995 \tabularnewline
6 & 2 & 1.50347169385269 & 0.496528306147307 \tabularnewline
7 & 1 & 1.39848519646908 & -0.398485196469075 \tabularnewline
8 & 2 & 1.63567557209257 & 0.364324427907431 \tabularnewline
9 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
10 & 2 & 1.50347169385269 & 0.496528306147306 \tabularnewline
11 & 1 & 1.12509009546384 & -0.125090095463842 \tabularnewline
12 & 2 & 1.50347169385269 & 0.496528306147306 \tabularnewline
13 & 1 & 1.55902776176918 & -0.559027761769178 \tabularnewline
14 & 2 & 1.70722973768204 & 0.292770262317955 \tabularnewline
15 & 2 & 1.67713309428253 & 0.322866905717469 \tabularnewline
16 & 2 & 1.66160369551794 & 0.338396304482057 \tabularnewline
17 & 1 & 1.35000118449582 & -0.350001184495821 \tabularnewline
18 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
19 & 1 & 1.31428089465827 & -0.314280894658268 \tabularnewline
20 & 2 & 1.48987514013748 & 0.510124859862518 \tabularnewline
21 & 2 & 1.5876759956635 & 0.412324004336499 \tabularnewline
22 & 1 & 1.50347169385269 & -0.503471693852694 \tabularnewline
23 & 2 & 1.71285338412008 & 0.287146615879916 \tabularnewline
24 & 2 & 1.50487453719795 & 0.495125462802054 \tabularnewline
25 & 2 & 1.50819448895679 & 0.491805511043208 \tabularnewline
26 & 2 & 1.43051468491797 & 0.569485315082034 \tabularnewline
27 & 2 & 1.56658425325660 & 0.433415746743405 \tabularnewline
28 & 2 & 1.63954126219132 & 0.360458737808677 \tabularnewline
29 & 1 & 1.78158958996203 & -0.781589589962025 \tabularnewline
30 & 1 & 1.43051468491797 & -0.430514684917966 \tabularnewline
31 & 2 & 1.5876759956635 & 0.412324004336499 \tabularnewline
32 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
33 & 2 & 1.51471898672877 & 0.485281013271227 \tabularnewline
34 & 2 & 1.70722973768204 & 0.292770262317955 \tabularnewline
35 & 2 & 1.43051468491797 & 0.569485315082034 \tabularnewline
36 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
37 & 2 & 1.66063300459823 & 0.339366995401771 \tabularnewline
38 & 1 & 1.58011950417608 & -0.580119504176085 \tabularnewline
39 & 1 & 1.5876759956635 & -0.587675995663501 \tabularnewline
40 & 2 & 1.63954126219132 & 0.360458737808677 \tabularnewline
41 & 1 & 1.50777807316704 & -0.507778073167037 \tabularnewline
42 & 2 & 1.72603352204554 & 0.273966477954459 \tabularnewline
43 & 2 & 1.64745286667277 & 0.352547133327227 \tabularnewline
44 & 1 & 1.22772733200833 & -0.227727332008328 \tabularnewline
45 & 1 & 1.38723790359300 & -0.387237903592996 \tabularnewline
46 & 2 & 1.53684272285112 & 0.463157277148885 \tabularnewline
47 & 2 & 1.57027505464526 & 0.429724945354742 \tabularnewline
48 & 1 & 1.35755767598324 & -0.357557675983238 \tabularnewline
49 & 2 & 1.59099594742235 & 0.409004052577653 \tabularnewline
50 & 2 & 1.50347169385269 & 0.496528306147306 \tabularnewline
51 & 1 & 1.43613833135601 & -0.436138331356005 \tabularnewline
52 & 1 & 1.50347169385269 & -0.503471693852694 \tabularnewline
53 & 2 & 1.28266782199913 & 0.717332178000867 \tabularnewline
54 & 2 & 1.34771322645241 & 0.652286773547589 \tabularnewline
55 & 2 & 1.49960600375394 & 0.50039399624606 \tabularnewline
56 & 1 & 1.45826206747835 & -0.458262067478347 \tabularnewline
57 & 1 & 1.49960600375394 & -0.49960600375394 \tabularnewline
58 & 1 & 1.64938571172215 & -0.64938571172215 \tabularnewline
59 & 1 & 1.28460066704851 & -0.284600667048510 \tabularnewline
60 & 1 & 1.51803893848762 & -0.518038938487619 \tabularnewline
61 & 1 & 1.28460066704851 & -0.284600667048510 \tabularnewline
62 & 2 & 1.46019491252772 & 0.539805087472276 \tabularnewline
63 & 2 & 1.45222200525055 & 0.547777994749448 \tabularnewline
64 & 2 & 1.58205234922546 & 0.417947650774538 \tabularnewline
65 & 2 & 1.57642870278742 & 0.423571297212578 \tabularnewline
66 & 1 & 1.64745286667277 & -0.647452866672773 \tabularnewline
67 & 2 & 1.67520024923315 & 0.324799750766846 \tabularnewline
68 & 2 & 1.48987514013748 & 0.510124859862518 \tabularnewline
69 & 1 & 1.5876759956635 & -0.587675995663501 \tabularnewline
70 & 1 & 1.50153884880332 & -0.501538848803317 \tabularnewline
71 & 1 & 1.50153884880332 & -0.501538848803317 \tabularnewline
72 & 2 & 1.64552002162340 & 0.354479978376604 \tabularnewline
73 & 1 & 1.65307651311081 & -0.653076513110813 \tabularnewline
74 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
75 & 1 & 1.78581039305481 & -0.785810393054812 \tabularnewline
76 & 1 & 1.65307651311081 & -0.653076513110813 \tabularnewline
77 & 2 & 1.65078855506740 & 0.349211444932598 \tabularnewline
78 & 1 & 1.53877556790049 & -0.538775567900492 \tabularnewline
79 & 2 & 1.42295819343055 & 0.577041806569451 \tabularnewline
80 & 2 & 1.58011950417608 & 0.419880495823915 \tabularnewline
81 & 2 & 1.65307651311081 & 0.346923486889187 \tabularnewline
82 & 2 & 1.50716249524136 & 0.492837504758643 \tabularnewline
83 & 1 & 1.64552002162340 & -0.645520021623396 \tabularnewline
84 & 2 & 1.71285338412008 & 0.287146615879916 \tabularnewline
85 & 2 & 1.64745286667277 & 0.352547133327227 \tabularnewline
86 & 1 & 1.36880496885932 & -0.368804968859317 \tabularnewline
87 & 2 & 1.60979973178584 & 0.390200268214157 \tabularnewline
88 & 2 & 1.50347169385269 & 0.496528306147306 \tabularnewline
89 & 1 & 1.57642870278742 & -0.576428702787422 \tabularnewline
90 & 1 & 1.43051468491797 & -0.430514684917966 \tabularnewline
91 & 2 & 1.71478622916946 & 0.285213770830539 \tabularnewline
92 & 2 & 1.57449585773805 & 0.425504142261955 \tabularnewline
93 & 2 & 1.60417608534780 & 0.395823914652197 \tabularnewline
94 & 1 & 1.64745286667277 & -0.647452866672773 \tabularnewline
95 & 2 & 1.47996938781093 & 0.520030612189066 \tabularnewline
96 & 1 & 1.71249827112605 & -0.712498271126051 \tabularnewline
97 & 2 & 1.65307651311081 & 0.346923486889187 \tabularnewline
98 & 1 & 1.64745286667277 & -0.647452866672773 \tabularnewline
99 & 1 & 1.67520024923315 & -0.675200249233154 \tabularnewline
100 & 2 & 1.57642870278742 & 0.423571297212578 \tabularnewline
101 & 2 & 1.19804710439857 & 0.80195289560143 \tabularnewline
102 & 1 & 1.43051468491797 & -0.430514684917966 \tabularnewline
103 & 2 & 1.57256301268867 & 0.427436987311332 \tabularnewline
104 & 2 & 1.65307651311081 & 0.346923486889187 \tabularnewline
105 & 1 & 1.55896645897346 & -0.558966458973456 \tabularnewline
106 & 1 & 1.70722973768204 & -0.707229737682045 \tabularnewline
107 & 1 & 1.58205234922546 & -0.582052349225461 \tabularnewline
108 & 1 & 1.34771322645241 & -0.347713226452411 \tabularnewline
109 & 2 & 1.7204098756075 & 0.279590124392499 \tabularnewline
110 & 1 & 1.21726730455182 & -0.217267304551821 \tabularnewline
111 & 1 & 1.43051468491797 & -0.430514684917966 \tabularnewline
112 & 1 & 1.24501468711220 & -0.245014687112202 \tabularnewline
113 & 1 & 1.60417608534780 & -0.604176085347803 \tabularnewline
114 & 2 & 1.51762252269786 & 0.482377477302136 \tabularnewline
115 & 1 & 1.52324616913590 & -0.523246169135903 \tabularnewline
116 & 2 & 1.7204098756075 & 0.279590124392499 \tabularnewline
117 & 1 & 1.5876759956635 & -0.587675995663501 \tabularnewline
118 & 2 & 1.43051468491797 & 0.569485315082034 \tabularnewline
119 & 2 & 1.57256301268867 & 0.427436987311332 \tabularnewline
120 & 1 & 1.44659835881251 & -0.446598358812513 \tabularnewline
121 & 1 & 1.69738528815122 & -0.697385288151218 \tabularnewline
122 & 2 & 1.64938571172215 & 0.35061428827785 \tabularnewline
123 & 2 & 1.43051468491797 & 0.569485315082034 \tabularnewline
124 & 1 & 1.50153884880332 & -0.501538848803317 \tabularnewline
125 & 2 & 1.56089930402283 & 0.439100695977166 \tabularnewline
126 & 2 & 1.78018674661677 & 0.219813253383227 \tabularnewline
127 & 2 & 1.50347169385269 & 0.496528306147306 \tabularnewline
128 & 2 & 1.71285338412008 & 0.287146615879916 \tabularnewline
129 & 1 & 1.58073508210176 & -0.580735082101765 \tabularnewline
130 & 2 & 1.58864668658321 & 0.411353313416785 \tabularnewline
131 & 2 & 1.58011950417608 & 0.419880495823915 \tabularnewline
132 & 2 & 1.60610893039718 & 0.39389106960282 \tabularnewline
133 & 2 & 1.57256301268867 & 0.427436987311332 \tabularnewline
134 & 1 & 1.59855243890976 & -0.598552438909764 \tabularnewline
135 & 1 & 1.71847703055812 & -0.718477030558124 \tabularnewline
136 & 2 & 1.63385631295756 & 0.366143687042438 \tabularnewline
137 & 2 & 1.67520024923315 & 0.324799750766846 \tabularnewline
138 & 2 & 1.67520024923315 & 0.324799750766846 \tabularnewline
139 & 2 & 1.50909534029073 & 0.490904659709266 \tabularnewline
140 & 1 & 1.56887221130001 & -0.568872211300005 \tabularnewline
141 & 2 & 1.46019491252772 & 0.539805087472276 \tabularnewline
142 & 2 & 1.65307651311081 & 0.346923486889187 \tabularnewline
143 & 2 & 1.72706551576098 & 0.272934484239024 \tabularnewline
144 & 1 & 1.68046878267716 & -0.68046878267716 \tabularnewline
145 & 1 & 1.43613833135601 & -0.436138331356005 \tabularnewline
146 & 2 & 1.64745286667277 & 0.352547133327227 \tabularnewline
147 & 2 & 1.60417608534780 & 0.395823914652197 \tabularnewline
148 & 2 & 1.64938571172215 & 0.35061428827785 \tabularnewline
149 & 1 & 1.53315192146245 & -0.533151921462452 \tabularnewline
150 & 2 & 1.65500935816019 & 0.34499064183981 \tabularnewline
151 & 2 & 1.49925089075991 & 0.500749109240094 \tabularnewline
152 & 2 & 1.58205234922546 & 0.417947650774538 \tabularnewline
153 & 1 & 1.57256301268867 & -0.572563012688668 \tabularnewline
154 & 1 & 1.43051468491797 & -0.430514684917966 \tabularnewline
155 & 2 & 1.78018674661677 & 0.219813253383227 \tabularnewline
156 & 1 & 1.43613833135601 & -0.436138331356005 \tabularnewline
157 & 2 & 1.84329930602067 & 0.156700693979326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&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[/C][C]1.57449585773801[/C][C]0.42550414226199[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.31193163381913[/C][C]-0.311931633819132[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.61832691419297[/C][C]-0.618326914192972[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.67713309428253[/C][C]-0.677133094282532[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.56887221130001[/C][C]0.431127788699995[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147307[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.39848519646908[/C][C]-0.398485196469075[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.63567557209257[/C][C]0.364324427907431[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147306[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.12509009546384[/C][C]-0.125090095463842[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147306[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.55902776176918[/C][C]-0.559027761769178[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.70722973768204[/C][C]0.292770262317955[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.67713309428253[/C][C]0.322866905717469[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.66160369551794[/C][C]0.338396304482057[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.35000118449582[/C][C]-0.350001184495821[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.31428089465827[/C][C]-0.314280894658268[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.48987514013748[/C][C]0.510124859862518[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.5876759956635[/C][C]0.412324004336499[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.50347169385269[/C][C]-0.503471693852694[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.71285338412008[/C][C]0.287146615879916[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.50487453719795[/C][C]0.495125462802054[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]1.50819448895679[/C][C]0.491805511043208[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.43051468491797[/C][C]0.569485315082034[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.56658425325660[/C][C]0.433415746743405[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.63954126219132[/C][C]0.360458737808677[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.78158958996203[/C][C]-0.781589589962025[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.43051468491797[/C][C]-0.430514684917966[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.5876759956635[/C][C]0.412324004336499[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.51471898672877[/C][C]0.485281013271227[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.70722973768204[/C][C]0.292770262317955[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.43051468491797[/C][C]0.569485315082034[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.66063300459823[/C][C]0.339366995401771[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.58011950417608[/C][C]-0.580119504176085[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.5876759956635[/C][C]-0.587675995663501[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]1.63954126219132[/C][C]0.360458737808677[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.50777807316704[/C][C]-0.507778073167037[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.72603352204554[/C][C]0.273966477954459[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.64745286667277[/C][C]0.352547133327227[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.22772733200833[/C][C]-0.227727332008328[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.38723790359300[/C][C]-0.387237903592996[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.53684272285112[/C][C]0.463157277148885[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.57027505464526[/C][C]0.429724945354742[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.35755767598324[/C][C]-0.357557675983238[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.59099594742235[/C][C]0.409004052577653[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147306[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.43613833135601[/C][C]-0.436138331356005[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.50347169385269[/C][C]-0.503471693852694[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]1.28266782199913[/C][C]0.717332178000867[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.34771322645241[/C][C]0.652286773547589[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.49960600375394[/C][C]0.50039399624606[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.45826206747835[/C][C]-0.458262067478347[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.49960600375394[/C][C]-0.49960600375394[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.64938571172215[/C][C]-0.64938571172215[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.28460066704851[/C][C]-0.284600667048510[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.51803893848762[/C][C]-0.518038938487619[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.28460066704851[/C][C]-0.284600667048510[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.46019491252772[/C][C]0.539805087472276[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.45222200525055[/C][C]0.547777994749448[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.58205234922546[/C][C]0.417947650774538[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.57642870278742[/C][C]0.423571297212578[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.64745286667277[/C][C]-0.647452866672773[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.67520024923315[/C][C]0.324799750766846[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.48987514013748[/C][C]0.510124859862518[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.5876759956635[/C][C]-0.587675995663501[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.50153884880332[/C][C]-0.501538848803317[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.50153884880332[/C][C]-0.501538848803317[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.64552002162340[/C][C]0.354479978376604[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.65307651311081[/C][C]-0.653076513110813[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.78581039305481[/C][C]-0.785810393054812[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.65307651311081[/C][C]-0.653076513110813[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.65078855506740[/C][C]0.349211444932598[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.53877556790049[/C][C]-0.538775567900492[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.42295819343055[/C][C]0.577041806569451[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.58011950417608[/C][C]0.419880495823915[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.65307651311081[/C][C]0.346923486889187[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.50716249524136[/C][C]0.492837504758643[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.64552002162340[/C][C]-0.645520021623396[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.71285338412008[/C][C]0.287146615879916[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.64745286667277[/C][C]0.352547133327227[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.36880496885932[/C][C]-0.368804968859317[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.60979973178584[/C][C]0.390200268214157[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147306[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.57642870278742[/C][C]-0.576428702787422[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.43051468491797[/C][C]-0.430514684917966[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.71478622916946[/C][C]0.285213770830539[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.57449585773805[/C][C]0.425504142261955[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.60417608534780[/C][C]0.395823914652197[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.64745286667277[/C][C]-0.647452866672773[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.47996938781093[/C][C]0.520030612189066[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.71249827112605[/C][C]-0.712498271126051[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.65307651311081[/C][C]0.346923486889187[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.64745286667277[/C][C]-0.647452866672773[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.67520024923315[/C][C]-0.675200249233154[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.57642870278742[/C][C]0.423571297212578[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.19804710439857[/C][C]0.80195289560143[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.43051468491797[/C][C]-0.430514684917966[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.57256301268867[/C][C]0.427436987311332[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.65307651311081[/C][C]0.346923486889187[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.55896645897346[/C][C]-0.558966458973456[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.70722973768204[/C][C]-0.707229737682045[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.58205234922546[/C][C]-0.582052349225461[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.34771322645241[/C][C]-0.347713226452411[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.7204098756075[/C][C]0.279590124392499[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.21726730455182[/C][C]-0.217267304551821[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.43051468491797[/C][C]-0.430514684917966[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.24501468711220[/C][C]-0.245014687112202[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.60417608534780[/C][C]-0.604176085347803[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.51762252269786[/C][C]0.482377477302136[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.52324616913590[/C][C]-0.523246169135903[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.7204098756075[/C][C]0.279590124392499[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.5876759956635[/C][C]-0.587675995663501[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.43051468491797[/C][C]0.569485315082034[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.57256301268867[/C][C]0.427436987311332[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.44659835881251[/C][C]-0.446598358812513[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.69738528815122[/C][C]-0.697385288151218[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.64938571172215[/C][C]0.35061428827785[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.43051468491797[/C][C]0.569485315082034[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.50153884880332[/C][C]-0.501538848803317[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.56089930402283[/C][C]0.439100695977166[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.78018674661677[/C][C]0.219813253383227[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.50347169385269[/C][C]0.496528306147306[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.71285338412008[/C][C]0.287146615879916[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.58073508210176[/C][C]-0.580735082101765[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.58864668658321[/C][C]0.411353313416785[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.58011950417608[/C][C]0.419880495823915[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.60610893039718[/C][C]0.39389106960282[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.57256301268867[/C][C]0.427436987311332[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.59855243890976[/C][C]-0.598552438909764[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.71847703055812[/C][C]-0.718477030558124[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.63385631295756[/C][C]0.366143687042438[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.67520024923315[/C][C]0.324799750766846[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.67520024923315[/C][C]0.324799750766846[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.50909534029073[/C][C]0.490904659709266[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.56887221130001[/C][C]-0.568872211300005[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.46019491252772[/C][C]0.539805087472276[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.65307651311081[/C][C]0.346923486889187[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.72706551576098[/C][C]0.272934484239024[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.68046878267716[/C][C]-0.68046878267716[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.43613833135601[/C][C]-0.436138331356005[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.64745286667277[/C][C]0.352547133327227[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.60417608534780[/C][C]0.395823914652197[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.64938571172215[/C][C]0.35061428827785[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.53315192146245[/C][C]-0.533151921462452[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.65500935816019[/C][C]0.34499064183981[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.49925089075991[/C][C]0.500749109240094[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.58205234922546[/C][C]0.417947650774538[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.57256301268867[/C][C]-0.572563012688668[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.43051468491797[/C][C]-0.430514684917966[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.78018674661677[/C][C]0.219813253383227[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]1.43613833135601[/C][C]-0.436138331356005[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.84329930602067[/C][C]0.156700693979326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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
121.574495857738010.42550414226199
211.31193163381913-0.311931633819132
311.61832691419297-0.618326914192972
411.67713309428253-0.677133094282532
521.568872211300010.431127788699995
621.503471693852690.496528306147307
711.39848519646908-0.398485196469075
821.635675572092570.364324427907431
911.57642870278742-0.576428702787422
1021.503471693852690.496528306147306
1111.12509009546384-0.125090095463842
1221.503471693852690.496528306147306
1311.55902776176918-0.559027761769178
1421.707229737682040.292770262317955
1521.677133094282530.322866905717469
1621.661603695517940.338396304482057
1711.35000118449582-0.350001184495821
1811.57642870278742-0.576428702787422
1911.31428089465827-0.314280894658268
2021.489875140137480.510124859862518
2121.58767599566350.412324004336499
2211.50347169385269-0.503471693852694
2321.712853384120080.287146615879916
2421.504874537197950.495125462802054
2521.508194488956790.491805511043208
2621.430514684917970.569485315082034
2721.566584253256600.433415746743405
2821.639541262191320.360458737808677
2911.78158958996203-0.781589589962025
3011.43051468491797-0.430514684917966
3121.58767599566350.412324004336499
3211.57642870278742-0.576428702787422
3321.514718986728770.485281013271227
3421.707229737682040.292770262317955
3521.430514684917970.569485315082034
3611.57642870278742-0.576428702787422
3721.660633004598230.339366995401771
3811.58011950417608-0.580119504176085
3911.5876759956635-0.587675995663501
4021.639541262191320.360458737808677
4111.50777807316704-0.507778073167037
4221.726033522045540.273966477954459
4321.647452866672770.352547133327227
4411.22772733200833-0.227727332008328
4511.38723790359300-0.387237903592996
4621.536842722851120.463157277148885
4721.570275054645260.429724945354742
4811.35755767598324-0.357557675983238
4921.590995947422350.409004052577653
5021.503471693852690.496528306147306
5111.43613833135601-0.436138331356005
5211.50347169385269-0.503471693852694
5321.282667821999130.717332178000867
5421.347713226452410.652286773547589
5521.499606003753940.50039399624606
5611.45826206747835-0.458262067478347
5711.49960600375394-0.49960600375394
5811.64938571172215-0.64938571172215
5911.28460066704851-0.284600667048510
6011.51803893848762-0.518038938487619
6111.28460066704851-0.284600667048510
6221.460194912527720.539805087472276
6321.452222005250550.547777994749448
6421.582052349225460.417947650774538
6521.576428702787420.423571297212578
6611.64745286667277-0.647452866672773
6721.675200249233150.324799750766846
6821.489875140137480.510124859862518
6911.5876759956635-0.587675995663501
7011.50153884880332-0.501538848803317
7111.50153884880332-0.501538848803317
7221.645520021623400.354479978376604
7311.65307651311081-0.653076513110813
7411.57642870278742-0.576428702787422
7511.78581039305481-0.785810393054812
7611.65307651311081-0.653076513110813
7721.650788555067400.349211444932598
7811.53877556790049-0.538775567900492
7921.422958193430550.577041806569451
8021.580119504176080.419880495823915
8121.653076513110810.346923486889187
8221.507162495241360.492837504758643
8311.64552002162340-0.645520021623396
8421.712853384120080.287146615879916
8521.647452866672770.352547133327227
8611.36880496885932-0.368804968859317
8721.609799731785840.390200268214157
8821.503471693852690.496528306147306
8911.57642870278742-0.576428702787422
9011.43051468491797-0.430514684917966
9121.714786229169460.285213770830539
9221.574495857738050.425504142261955
9321.604176085347800.395823914652197
9411.64745286667277-0.647452866672773
9521.479969387810930.520030612189066
9611.71249827112605-0.712498271126051
9721.653076513110810.346923486889187
9811.64745286667277-0.647452866672773
9911.67520024923315-0.675200249233154
10021.576428702787420.423571297212578
10121.198047104398570.80195289560143
10211.43051468491797-0.430514684917966
10321.572563012688670.427436987311332
10421.653076513110810.346923486889187
10511.55896645897346-0.558966458973456
10611.70722973768204-0.707229737682045
10711.58205234922546-0.582052349225461
10811.34771322645241-0.347713226452411
10921.72040987560750.279590124392499
11011.21726730455182-0.217267304551821
11111.43051468491797-0.430514684917966
11211.24501468711220-0.245014687112202
11311.60417608534780-0.604176085347803
11421.517622522697860.482377477302136
11511.52324616913590-0.523246169135903
11621.72040987560750.279590124392499
11711.5876759956635-0.587675995663501
11821.430514684917970.569485315082034
11921.572563012688670.427436987311332
12011.44659835881251-0.446598358812513
12111.69738528815122-0.697385288151218
12221.649385711722150.35061428827785
12321.430514684917970.569485315082034
12411.50153884880332-0.501538848803317
12521.560899304022830.439100695977166
12621.780186746616770.219813253383227
12721.503471693852690.496528306147306
12821.712853384120080.287146615879916
12911.58073508210176-0.580735082101765
13021.588646686583210.411353313416785
13121.580119504176080.419880495823915
13221.606108930397180.39389106960282
13321.572563012688670.427436987311332
13411.59855243890976-0.598552438909764
13511.71847703055812-0.718477030558124
13621.633856312957560.366143687042438
13721.675200249233150.324799750766846
13821.675200249233150.324799750766846
13921.509095340290730.490904659709266
14011.56887221130001-0.568872211300005
14121.460194912527720.539805087472276
14221.653076513110810.346923486889187
14321.727065515760980.272934484239024
14411.68046878267716-0.68046878267716
14511.43613833135601-0.436138331356005
14621.647452866672770.352547133327227
14721.604176085347800.395823914652197
14821.649385711722150.35061428827785
14911.53315192146245-0.533151921462452
15021.655009358160190.34499064183981
15121.499250890759910.500749109240094
15221.582052349225460.417947650774538
15311.57256301268867-0.572563012688668
15411.43051468491797-0.430514684917966
15521.780186746616770.219813253383227
15611.43613833135601-0.436138331356005
15721.843299306020670.156700693979326






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2594128849274120.5188257698548250.740587115072588
100.2047083239781470.4094166479562940.795291676021853
110.5251921103917270.9496157792165450.474807889608273
120.4771972147146610.9543944294293220.522802785285339
130.4712780755796620.9425561511593230.528721924420338
140.3832373049857930.7664746099715860.616762695014207
150.3871212383219160.7742424766438330.612878761678084
160.4368278995548170.8736557991096340.563172100445183
170.5551230021928420.8897539956143160.444876997807158
180.5808549727167020.8382900545665960.419145027283298
190.5052689209358580.9894621581282850.494731079064142
200.6222698496175790.7554603007648420.377730150382421
210.5745573168816480.8508853662367040.425442683118352
220.5842144036263720.8315711927472570.415785596373628
230.5133916371980330.9732167256039330.486608362801967
240.5132443578792640.9735112842414720.486755642120736
250.5130786705929970.9738426588140060.486921329407003
260.526493639362540.947012721274920.47350636063746
270.4841397468331410.9682794936662810.515860253166859
280.4283331886428140.8566663772856280.571666811357186
290.6063590565443490.7872818869113030.393640943455651
300.604465694016920.791068611966160.39553430598308
310.5744301717812970.8511396564374050.425569828218703
320.6117576162935920.7764847674128160.388242383706408
330.5908180509555620.8183638980888770.409181949044438
340.5462104769243430.9075790461513140.453789523075657
350.5489069955974580.9021860088050840.451093004402542
360.58014818671630.83970362656740.4198518132837
370.5375709412165980.9248581175668040.462429058783402
380.5831619505484870.8336760989030250.416838049451513
390.6108846100763040.7782307798473910.389115389923696
400.5745969401505160.8508061196989690.425403059849484
410.5627044276516940.8745911446966120.437295572348306
420.5215115852622340.9569768294755330.478488414737766
430.4883139020612530.9766278041225060.511686097938747
440.4417363549651110.8834727099302230.558263645034889
450.4179572943959240.8359145887918480.582042705604076
460.4129643441361130.8259286882722250.587035655863887
470.3896278791235960.7792557582471920.610372120876404
480.3702707052500820.7405414105001640.629729294749918
490.3524333998008320.7048667996016650.647566600199168
500.3487615647752140.6975231295504280.651238435224786
510.343837442048460.687674884096920.65616255795154
520.3503369781328390.7006739562656770.649663021867162
530.3836905056356220.7673810112712440.616309494364378
540.4153206763178150.830641352635630.584679323682185
550.3975590153761250.795118030752250.602440984623875
560.3975499392117650.795099878423530.602450060788235
570.429100907151810.858201814303620.57089909284819
580.4645087826787180.9290175653574350.535491217321282
590.4352816361781090.8705632723562170.564718363821891
600.4412587105672390.8825174211344790.558741289432761
610.4105753571425460.8211507142850930.589424642857454
620.4306867516857570.8613735033715150.569313248314243
630.4615449847981250.923089969596250.538455015201875
640.4457126947509680.8914253895019370.554287305249032
650.4312310979339320.8624621958678640.568768902066068
660.4695322784881210.9390645569762420.530467721511879
670.4418656250948520.8837312501897030.558134374905148
680.4470481199527990.8940962399055970.552951880047201
690.4688629605836240.9377259211672480.531137039416376
700.4691394694766040.9382789389532090.530860530523396
710.4686109860470830.9372219720941660.531389013952917
720.4466857593864980.8933715187729950.553314240613502
730.4801482880092080.9602965760184160.519851711990792
740.4984547216279560.9969094432559110.501545278372044
750.5671328973514890.8657342052970210.432867102648511
760.5989673171153610.8020653657692770.401032682884639
770.5890755766483320.8218488467033350.410924423351668
780.5988325509320680.8023348981358630.401167449067932
790.6180677185757990.7638645628484030.381932281424201
800.6084210397879130.7831579204241740.391578960212087
810.586815613077320.826368773845360.41318438692268
820.590999870331350.81800025933730.40900012966865
830.6195474794299090.7609050411401830.380452520570091
840.5901058176023810.8197883647952380.409894182397619
850.5674624223248380.8650751553503230.432537577675162
860.5433479700489330.9133040599021340.456652029951067
870.5259731097648320.9480537804703370.474026890235168
880.5256437124528770.9487125750942460.474356287547123
890.5461433865200180.9077132269599640.453856613479982
900.5357745019671530.9284509960656950.464225498032847
910.5014055566043640.9971888867912720.498594443395636
920.4890470744401370.9780941488802750.510952925559863
930.4698577106511450.939715421302290.530142289348855
940.5066303459024290.9867393081951430.493369654097571
950.5090727776059440.9818544447881130.490927222394056
960.5468586399193430.9062827201613140.453141360080657
970.5215847768388250.9568304463223510.478415223161175
980.5613450753706780.8773098492586430.438654924629322
990.604369893227560.791260213544880.39563010677244
1000.5861118369534290.8277763260931430.413888163046571
1010.6962876143299660.6074247713400680.303712385670034
1020.6819367712077280.6361264575845440.318063228792272
1030.6739166246791190.6521667506417630.326083375320881
1040.645433616254810.7091327674903790.354566383745190
1050.651739037342030.6965219253159390.348260962657970
1060.7246902005136030.5506195989727940.275309799486397
1070.7553800272565920.4892399454868150.244619972743408
1080.7255790815104490.5488418369791020.274420918489551
1090.6881215950397450.6237568099205090.311878404960254
1100.6443998346575390.7112003306849230.355600165342461
1110.6352552733576830.7294894532846330.364744726642317
1120.5891543400935020.8216913198129960.410845659906498
1130.6335962585479690.7328074829040630.366403741452031
1140.6367874469840130.7264251060319730.363212553015987
1150.6361477918203440.7277044163593130.363852208179656
1160.5900862722205640.8198274555588710.409913727779436
1170.6553627190652480.6892745618695030.344637280934752
1180.6777240016066670.6445519967866660.322275998393333
1190.6675990158195330.6648019683609350.332400984180467
1200.669480336899920.6610393262001610.330519663100081
1210.6778613383540920.6442773232918150.322138661645908
1220.6319307907735220.7361384184529570.368069209226478
1230.6644121863390450.671175627321910.335587813660955
1240.6570889632956670.6858220734086660.342911036704333
1250.6333154383199950.733369123360010.366684561680005
1260.5762968709649270.8474062580701470.423703129035073
1270.5754718486809090.8490563026381820.424528151319091
1280.5198377395918990.9603245208162020.480162260408101
1290.5436426065299410.9127147869401180.456357393470059
1300.4957660760183860.9915321520367710.504233923981614
1310.471646602964080.943293205928160.52835339703592
1320.4228875576199650.845775115239930.577112442380035
1330.4428549116456460.8857098232912930.557145088354354
1340.5104909871256350.979018025748730.489509012874365
1350.6189054145443880.7621891709112250.381094585455612
1360.5516682563417690.8966634873164620.448331743658231
1370.4900243825122770.9800487650245540.509975617487723
1380.4480300387208370.8960600774416750.551969961279163
1390.4521394555600860.9042789111201710.547860544439914
1400.49963472886230.99926945772460.5003652711377
1410.6087703950012380.7824592099975250.391229604998762
1420.5549406712413650.890118657517270.445059328758635
1430.5157614865210940.9684770269578110.484238513478906
1440.8516102378534370.2967795242931250.148389762146562
1450.7812672914700780.4374654170598450.218732708529922
1460.6997388649732730.6005222700534540.300261135026727
1470.8663632008466640.2672735983066730.133636799153336
1480.7363883560698540.5272232878602910.263611643930146

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.259412884927412 & 0.518825769854825 & 0.740587115072588 \tabularnewline
10 & 0.204708323978147 & 0.409416647956294 & 0.795291676021853 \tabularnewline
11 & 0.525192110391727 & 0.949615779216545 & 0.474807889608273 \tabularnewline
12 & 0.477197214714661 & 0.954394429429322 & 0.522802785285339 \tabularnewline
13 & 0.471278075579662 & 0.942556151159323 & 0.528721924420338 \tabularnewline
14 & 0.383237304985793 & 0.766474609971586 & 0.616762695014207 \tabularnewline
15 & 0.387121238321916 & 0.774242476643833 & 0.612878761678084 \tabularnewline
16 & 0.436827899554817 & 0.873655799109634 & 0.563172100445183 \tabularnewline
17 & 0.555123002192842 & 0.889753995614316 & 0.444876997807158 \tabularnewline
18 & 0.580854972716702 & 0.838290054566596 & 0.419145027283298 \tabularnewline
19 & 0.505268920935858 & 0.989462158128285 & 0.494731079064142 \tabularnewline
20 & 0.622269849617579 & 0.755460300764842 & 0.377730150382421 \tabularnewline
21 & 0.574557316881648 & 0.850885366236704 & 0.425442683118352 \tabularnewline
22 & 0.584214403626372 & 0.831571192747257 & 0.415785596373628 \tabularnewline
23 & 0.513391637198033 & 0.973216725603933 & 0.486608362801967 \tabularnewline
24 & 0.513244357879264 & 0.973511284241472 & 0.486755642120736 \tabularnewline
25 & 0.513078670592997 & 0.973842658814006 & 0.486921329407003 \tabularnewline
26 & 0.52649363936254 & 0.94701272127492 & 0.47350636063746 \tabularnewline
27 & 0.484139746833141 & 0.968279493666281 & 0.515860253166859 \tabularnewline
28 & 0.428333188642814 & 0.856666377285628 & 0.571666811357186 \tabularnewline
29 & 0.606359056544349 & 0.787281886911303 & 0.393640943455651 \tabularnewline
30 & 0.60446569401692 & 0.79106861196616 & 0.39553430598308 \tabularnewline
31 & 0.574430171781297 & 0.851139656437405 & 0.425569828218703 \tabularnewline
32 & 0.611757616293592 & 0.776484767412816 & 0.388242383706408 \tabularnewline
33 & 0.590818050955562 & 0.818363898088877 & 0.409181949044438 \tabularnewline
34 & 0.546210476924343 & 0.907579046151314 & 0.453789523075657 \tabularnewline
35 & 0.548906995597458 & 0.902186008805084 & 0.451093004402542 \tabularnewline
36 & 0.5801481867163 & 0.8397036265674 & 0.4198518132837 \tabularnewline
37 & 0.537570941216598 & 0.924858117566804 & 0.462429058783402 \tabularnewline
38 & 0.583161950548487 & 0.833676098903025 & 0.416838049451513 \tabularnewline
39 & 0.610884610076304 & 0.778230779847391 & 0.389115389923696 \tabularnewline
40 & 0.574596940150516 & 0.850806119698969 & 0.425403059849484 \tabularnewline
41 & 0.562704427651694 & 0.874591144696612 & 0.437295572348306 \tabularnewline
42 & 0.521511585262234 & 0.956976829475533 & 0.478488414737766 \tabularnewline
43 & 0.488313902061253 & 0.976627804122506 & 0.511686097938747 \tabularnewline
44 & 0.441736354965111 & 0.883472709930223 & 0.558263645034889 \tabularnewline
45 & 0.417957294395924 & 0.835914588791848 & 0.582042705604076 \tabularnewline
46 & 0.412964344136113 & 0.825928688272225 & 0.587035655863887 \tabularnewline
47 & 0.389627879123596 & 0.779255758247192 & 0.610372120876404 \tabularnewline
48 & 0.370270705250082 & 0.740541410500164 & 0.629729294749918 \tabularnewline
49 & 0.352433399800832 & 0.704866799601665 & 0.647566600199168 \tabularnewline
50 & 0.348761564775214 & 0.697523129550428 & 0.651238435224786 \tabularnewline
51 & 0.34383744204846 & 0.68767488409692 & 0.65616255795154 \tabularnewline
52 & 0.350336978132839 & 0.700673956265677 & 0.649663021867162 \tabularnewline
53 & 0.383690505635622 & 0.767381011271244 & 0.616309494364378 \tabularnewline
54 & 0.415320676317815 & 0.83064135263563 & 0.584679323682185 \tabularnewline
55 & 0.397559015376125 & 0.79511803075225 & 0.602440984623875 \tabularnewline
56 & 0.397549939211765 & 0.79509987842353 & 0.602450060788235 \tabularnewline
57 & 0.42910090715181 & 0.85820181430362 & 0.57089909284819 \tabularnewline
58 & 0.464508782678718 & 0.929017565357435 & 0.535491217321282 \tabularnewline
59 & 0.435281636178109 & 0.870563272356217 & 0.564718363821891 \tabularnewline
60 & 0.441258710567239 & 0.882517421134479 & 0.558741289432761 \tabularnewline
61 & 0.410575357142546 & 0.821150714285093 & 0.589424642857454 \tabularnewline
62 & 0.430686751685757 & 0.861373503371515 & 0.569313248314243 \tabularnewline
63 & 0.461544984798125 & 0.92308996959625 & 0.538455015201875 \tabularnewline
64 & 0.445712694750968 & 0.891425389501937 & 0.554287305249032 \tabularnewline
65 & 0.431231097933932 & 0.862462195867864 & 0.568768902066068 \tabularnewline
66 & 0.469532278488121 & 0.939064556976242 & 0.530467721511879 \tabularnewline
67 & 0.441865625094852 & 0.883731250189703 & 0.558134374905148 \tabularnewline
68 & 0.447048119952799 & 0.894096239905597 & 0.552951880047201 \tabularnewline
69 & 0.468862960583624 & 0.937725921167248 & 0.531137039416376 \tabularnewline
70 & 0.469139469476604 & 0.938278938953209 & 0.530860530523396 \tabularnewline
71 & 0.468610986047083 & 0.937221972094166 & 0.531389013952917 \tabularnewline
72 & 0.446685759386498 & 0.893371518772995 & 0.553314240613502 \tabularnewline
73 & 0.480148288009208 & 0.960296576018416 & 0.519851711990792 \tabularnewline
74 & 0.498454721627956 & 0.996909443255911 & 0.501545278372044 \tabularnewline
75 & 0.567132897351489 & 0.865734205297021 & 0.432867102648511 \tabularnewline
76 & 0.598967317115361 & 0.802065365769277 & 0.401032682884639 \tabularnewline
77 & 0.589075576648332 & 0.821848846703335 & 0.410924423351668 \tabularnewline
78 & 0.598832550932068 & 0.802334898135863 & 0.401167449067932 \tabularnewline
79 & 0.618067718575799 & 0.763864562848403 & 0.381932281424201 \tabularnewline
80 & 0.608421039787913 & 0.783157920424174 & 0.391578960212087 \tabularnewline
81 & 0.58681561307732 & 0.82636877384536 & 0.41318438692268 \tabularnewline
82 & 0.59099987033135 & 0.8180002593373 & 0.40900012966865 \tabularnewline
83 & 0.619547479429909 & 0.760905041140183 & 0.380452520570091 \tabularnewline
84 & 0.590105817602381 & 0.819788364795238 & 0.409894182397619 \tabularnewline
85 & 0.567462422324838 & 0.865075155350323 & 0.432537577675162 \tabularnewline
86 & 0.543347970048933 & 0.913304059902134 & 0.456652029951067 \tabularnewline
87 & 0.525973109764832 & 0.948053780470337 & 0.474026890235168 \tabularnewline
88 & 0.525643712452877 & 0.948712575094246 & 0.474356287547123 \tabularnewline
89 & 0.546143386520018 & 0.907713226959964 & 0.453856613479982 \tabularnewline
90 & 0.535774501967153 & 0.928450996065695 & 0.464225498032847 \tabularnewline
91 & 0.501405556604364 & 0.997188886791272 & 0.498594443395636 \tabularnewline
92 & 0.489047074440137 & 0.978094148880275 & 0.510952925559863 \tabularnewline
93 & 0.469857710651145 & 0.93971542130229 & 0.530142289348855 \tabularnewline
94 & 0.506630345902429 & 0.986739308195143 & 0.493369654097571 \tabularnewline
95 & 0.509072777605944 & 0.981854444788113 & 0.490927222394056 \tabularnewline
96 & 0.546858639919343 & 0.906282720161314 & 0.453141360080657 \tabularnewline
97 & 0.521584776838825 & 0.956830446322351 & 0.478415223161175 \tabularnewline
98 & 0.561345075370678 & 0.877309849258643 & 0.438654924629322 \tabularnewline
99 & 0.60436989322756 & 0.79126021354488 & 0.39563010677244 \tabularnewline
100 & 0.586111836953429 & 0.827776326093143 & 0.413888163046571 \tabularnewline
101 & 0.696287614329966 & 0.607424771340068 & 0.303712385670034 \tabularnewline
102 & 0.681936771207728 & 0.636126457584544 & 0.318063228792272 \tabularnewline
103 & 0.673916624679119 & 0.652166750641763 & 0.326083375320881 \tabularnewline
104 & 0.64543361625481 & 0.709132767490379 & 0.354566383745190 \tabularnewline
105 & 0.65173903734203 & 0.696521925315939 & 0.348260962657970 \tabularnewline
106 & 0.724690200513603 & 0.550619598972794 & 0.275309799486397 \tabularnewline
107 & 0.755380027256592 & 0.489239945486815 & 0.244619972743408 \tabularnewline
108 & 0.725579081510449 & 0.548841836979102 & 0.274420918489551 \tabularnewline
109 & 0.688121595039745 & 0.623756809920509 & 0.311878404960254 \tabularnewline
110 & 0.644399834657539 & 0.711200330684923 & 0.355600165342461 \tabularnewline
111 & 0.635255273357683 & 0.729489453284633 & 0.364744726642317 \tabularnewline
112 & 0.589154340093502 & 0.821691319812996 & 0.410845659906498 \tabularnewline
113 & 0.633596258547969 & 0.732807482904063 & 0.366403741452031 \tabularnewline
114 & 0.636787446984013 & 0.726425106031973 & 0.363212553015987 \tabularnewline
115 & 0.636147791820344 & 0.727704416359313 & 0.363852208179656 \tabularnewline
116 & 0.590086272220564 & 0.819827455558871 & 0.409913727779436 \tabularnewline
117 & 0.655362719065248 & 0.689274561869503 & 0.344637280934752 \tabularnewline
118 & 0.677724001606667 & 0.644551996786666 & 0.322275998393333 \tabularnewline
119 & 0.667599015819533 & 0.664801968360935 & 0.332400984180467 \tabularnewline
120 & 0.66948033689992 & 0.661039326200161 & 0.330519663100081 \tabularnewline
121 & 0.677861338354092 & 0.644277323291815 & 0.322138661645908 \tabularnewline
122 & 0.631930790773522 & 0.736138418452957 & 0.368069209226478 \tabularnewline
123 & 0.664412186339045 & 0.67117562732191 & 0.335587813660955 \tabularnewline
124 & 0.657088963295667 & 0.685822073408666 & 0.342911036704333 \tabularnewline
125 & 0.633315438319995 & 0.73336912336001 & 0.366684561680005 \tabularnewline
126 & 0.576296870964927 & 0.847406258070147 & 0.423703129035073 \tabularnewline
127 & 0.575471848680909 & 0.849056302638182 & 0.424528151319091 \tabularnewline
128 & 0.519837739591899 & 0.960324520816202 & 0.480162260408101 \tabularnewline
129 & 0.543642606529941 & 0.912714786940118 & 0.456357393470059 \tabularnewline
130 & 0.495766076018386 & 0.991532152036771 & 0.504233923981614 \tabularnewline
131 & 0.47164660296408 & 0.94329320592816 & 0.52835339703592 \tabularnewline
132 & 0.422887557619965 & 0.84577511523993 & 0.577112442380035 \tabularnewline
133 & 0.442854911645646 & 0.885709823291293 & 0.557145088354354 \tabularnewline
134 & 0.510490987125635 & 0.97901802574873 & 0.489509012874365 \tabularnewline
135 & 0.618905414544388 & 0.762189170911225 & 0.381094585455612 \tabularnewline
136 & 0.551668256341769 & 0.896663487316462 & 0.448331743658231 \tabularnewline
137 & 0.490024382512277 & 0.980048765024554 & 0.509975617487723 \tabularnewline
138 & 0.448030038720837 & 0.896060077441675 & 0.551969961279163 \tabularnewline
139 & 0.452139455560086 & 0.904278911120171 & 0.547860544439914 \tabularnewline
140 & 0.4996347288623 & 0.9992694577246 & 0.5003652711377 \tabularnewline
141 & 0.608770395001238 & 0.782459209997525 & 0.391229604998762 \tabularnewline
142 & 0.554940671241365 & 0.89011865751727 & 0.445059328758635 \tabularnewline
143 & 0.515761486521094 & 0.968477026957811 & 0.484238513478906 \tabularnewline
144 & 0.851610237853437 & 0.296779524293125 & 0.148389762146562 \tabularnewline
145 & 0.781267291470078 & 0.437465417059845 & 0.218732708529922 \tabularnewline
146 & 0.699738864973273 & 0.600522270053454 & 0.300261135026727 \tabularnewline
147 & 0.866363200846664 & 0.267273598306673 & 0.133636799153336 \tabularnewline
148 & 0.736388356069854 & 0.527223287860291 & 0.263611643930146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&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]0.259412884927412[/C][C]0.518825769854825[/C][C]0.740587115072588[/C][/ROW]
[ROW][C]10[/C][C]0.204708323978147[/C][C]0.409416647956294[/C][C]0.795291676021853[/C][/ROW]
[ROW][C]11[/C][C]0.525192110391727[/C][C]0.949615779216545[/C][C]0.474807889608273[/C][/ROW]
[ROW][C]12[/C][C]0.477197214714661[/C][C]0.954394429429322[/C][C]0.522802785285339[/C][/ROW]
[ROW][C]13[/C][C]0.471278075579662[/C][C]0.942556151159323[/C][C]0.528721924420338[/C][/ROW]
[ROW][C]14[/C][C]0.383237304985793[/C][C]0.766474609971586[/C][C]0.616762695014207[/C][/ROW]
[ROW][C]15[/C][C]0.387121238321916[/C][C]0.774242476643833[/C][C]0.612878761678084[/C][/ROW]
[ROW][C]16[/C][C]0.436827899554817[/C][C]0.873655799109634[/C][C]0.563172100445183[/C][/ROW]
[ROW][C]17[/C][C]0.555123002192842[/C][C]0.889753995614316[/C][C]0.444876997807158[/C][/ROW]
[ROW][C]18[/C][C]0.580854972716702[/C][C]0.838290054566596[/C][C]0.419145027283298[/C][/ROW]
[ROW][C]19[/C][C]0.505268920935858[/C][C]0.989462158128285[/C][C]0.494731079064142[/C][/ROW]
[ROW][C]20[/C][C]0.622269849617579[/C][C]0.755460300764842[/C][C]0.377730150382421[/C][/ROW]
[ROW][C]21[/C][C]0.574557316881648[/C][C]0.850885366236704[/C][C]0.425442683118352[/C][/ROW]
[ROW][C]22[/C][C]0.584214403626372[/C][C]0.831571192747257[/C][C]0.415785596373628[/C][/ROW]
[ROW][C]23[/C][C]0.513391637198033[/C][C]0.973216725603933[/C][C]0.486608362801967[/C][/ROW]
[ROW][C]24[/C][C]0.513244357879264[/C][C]0.973511284241472[/C][C]0.486755642120736[/C][/ROW]
[ROW][C]25[/C][C]0.513078670592997[/C][C]0.973842658814006[/C][C]0.486921329407003[/C][/ROW]
[ROW][C]26[/C][C]0.52649363936254[/C][C]0.94701272127492[/C][C]0.47350636063746[/C][/ROW]
[ROW][C]27[/C][C]0.484139746833141[/C][C]0.968279493666281[/C][C]0.515860253166859[/C][/ROW]
[ROW][C]28[/C][C]0.428333188642814[/C][C]0.856666377285628[/C][C]0.571666811357186[/C][/ROW]
[ROW][C]29[/C][C]0.606359056544349[/C][C]0.787281886911303[/C][C]0.393640943455651[/C][/ROW]
[ROW][C]30[/C][C]0.60446569401692[/C][C]0.79106861196616[/C][C]0.39553430598308[/C][/ROW]
[ROW][C]31[/C][C]0.574430171781297[/C][C]0.851139656437405[/C][C]0.425569828218703[/C][/ROW]
[ROW][C]32[/C][C]0.611757616293592[/C][C]0.776484767412816[/C][C]0.388242383706408[/C][/ROW]
[ROW][C]33[/C][C]0.590818050955562[/C][C]0.818363898088877[/C][C]0.409181949044438[/C][/ROW]
[ROW][C]34[/C][C]0.546210476924343[/C][C]0.907579046151314[/C][C]0.453789523075657[/C][/ROW]
[ROW][C]35[/C][C]0.548906995597458[/C][C]0.902186008805084[/C][C]0.451093004402542[/C][/ROW]
[ROW][C]36[/C][C]0.5801481867163[/C][C]0.8397036265674[/C][C]0.4198518132837[/C][/ROW]
[ROW][C]37[/C][C]0.537570941216598[/C][C]0.924858117566804[/C][C]0.462429058783402[/C][/ROW]
[ROW][C]38[/C][C]0.583161950548487[/C][C]0.833676098903025[/C][C]0.416838049451513[/C][/ROW]
[ROW][C]39[/C][C]0.610884610076304[/C][C]0.778230779847391[/C][C]0.389115389923696[/C][/ROW]
[ROW][C]40[/C][C]0.574596940150516[/C][C]0.850806119698969[/C][C]0.425403059849484[/C][/ROW]
[ROW][C]41[/C][C]0.562704427651694[/C][C]0.874591144696612[/C][C]0.437295572348306[/C][/ROW]
[ROW][C]42[/C][C]0.521511585262234[/C][C]0.956976829475533[/C][C]0.478488414737766[/C][/ROW]
[ROW][C]43[/C][C]0.488313902061253[/C][C]0.976627804122506[/C][C]0.511686097938747[/C][/ROW]
[ROW][C]44[/C][C]0.441736354965111[/C][C]0.883472709930223[/C][C]0.558263645034889[/C][/ROW]
[ROW][C]45[/C][C]0.417957294395924[/C][C]0.835914588791848[/C][C]0.582042705604076[/C][/ROW]
[ROW][C]46[/C][C]0.412964344136113[/C][C]0.825928688272225[/C][C]0.587035655863887[/C][/ROW]
[ROW][C]47[/C][C]0.389627879123596[/C][C]0.779255758247192[/C][C]0.610372120876404[/C][/ROW]
[ROW][C]48[/C][C]0.370270705250082[/C][C]0.740541410500164[/C][C]0.629729294749918[/C][/ROW]
[ROW][C]49[/C][C]0.352433399800832[/C][C]0.704866799601665[/C][C]0.647566600199168[/C][/ROW]
[ROW][C]50[/C][C]0.348761564775214[/C][C]0.697523129550428[/C][C]0.651238435224786[/C][/ROW]
[ROW][C]51[/C][C]0.34383744204846[/C][C]0.68767488409692[/C][C]0.65616255795154[/C][/ROW]
[ROW][C]52[/C][C]0.350336978132839[/C][C]0.700673956265677[/C][C]0.649663021867162[/C][/ROW]
[ROW][C]53[/C][C]0.383690505635622[/C][C]0.767381011271244[/C][C]0.616309494364378[/C][/ROW]
[ROW][C]54[/C][C]0.415320676317815[/C][C]0.83064135263563[/C][C]0.584679323682185[/C][/ROW]
[ROW][C]55[/C][C]0.397559015376125[/C][C]0.79511803075225[/C][C]0.602440984623875[/C][/ROW]
[ROW][C]56[/C][C]0.397549939211765[/C][C]0.79509987842353[/C][C]0.602450060788235[/C][/ROW]
[ROW][C]57[/C][C]0.42910090715181[/C][C]0.85820181430362[/C][C]0.57089909284819[/C][/ROW]
[ROW][C]58[/C][C]0.464508782678718[/C][C]0.929017565357435[/C][C]0.535491217321282[/C][/ROW]
[ROW][C]59[/C][C]0.435281636178109[/C][C]0.870563272356217[/C][C]0.564718363821891[/C][/ROW]
[ROW][C]60[/C][C]0.441258710567239[/C][C]0.882517421134479[/C][C]0.558741289432761[/C][/ROW]
[ROW][C]61[/C][C]0.410575357142546[/C][C]0.821150714285093[/C][C]0.589424642857454[/C][/ROW]
[ROW][C]62[/C][C]0.430686751685757[/C][C]0.861373503371515[/C][C]0.569313248314243[/C][/ROW]
[ROW][C]63[/C][C]0.461544984798125[/C][C]0.92308996959625[/C][C]0.538455015201875[/C][/ROW]
[ROW][C]64[/C][C]0.445712694750968[/C][C]0.891425389501937[/C][C]0.554287305249032[/C][/ROW]
[ROW][C]65[/C][C]0.431231097933932[/C][C]0.862462195867864[/C][C]0.568768902066068[/C][/ROW]
[ROW][C]66[/C][C]0.469532278488121[/C][C]0.939064556976242[/C][C]0.530467721511879[/C][/ROW]
[ROW][C]67[/C][C]0.441865625094852[/C][C]0.883731250189703[/C][C]0.558134374905148[/C][/ROW]
[ROW][C]68[/C][C]0.447048119952799[/C][C]0.894096239905597[/C][C]0.552951880047201[/C][/ROW]
[ROW][C]69[/C][C]0.468862960583624[/C][C]0.937725921167248[/C][C]0.531137039416376[/C][/ROW]
[ROW][C]70[/C][C]0.469139469476604[/C][C]0.938278938953209[/C][C]0.530860530523396[/C][/ROW]
[ROW][C]71[/C][C]0.468610986047083[/C][C]0.937221972094166[/C][C]0.531389013952917[/C][/ROW]
[ROW][C]72[/C][C]0.446685759386498[/C][C]0.893371518772995[/C][C]0.553314240613502[/C][/ROW]
[ROW][C]73[/C][C]0.480148288009208[/C][C]0.960296576018416[/C][C]0.519851711990792[/C][/ROW]
[ROW][C]74[/C][C]0.498454721627956[/C][C]0.996909443255911[/C][C]0.501545278372044[/C][/ROW]
[ROW][C]75[/C][C]0.567132897351489[/C][C]0.865734205297021[/C][C]0.432867102648511[/C][/ROW]
[ROW][C]76[/C][C]0.598967317115361[/C][C]0.802065365769277[/C][C]0.401032682884639[/C][/ROW]
[ROW][C]77[/C][C]0.589075576648332[/C][C]0.821848846703335[/C][C]0.410924423351668[/C][/ROW]
[ROW][C]78[/C][C]0.598832550932068[/C][C]0.802334898135863[/C][C]0.401167449067932[/C][/ROW]
[ROW][C]79[/C][C]0.618067718575799[/C][C]0.763864562848403[/C][C]0.381932281424201[/C][/ROW]
[ROW][C]80[/C][C]0.608421039787913[/C][C]0.783157920424174[/C][C]0.391578960212087[/C][/ROW]
[ROW][C]81[/C][C]0.58681561307732[/C][C]0.82636877384536[/C][C]0.41318438692268[/C][/ROW]
[ROW][C]82[/C][C]0.59099987033135[/C][C]0.8180002593373[/C][C]0.40900012966865[/C][/ROW]
[ROW][C]83[/C][C]0.619547479429909[/C][C]0.760905041140183[/C][C]0.380452520570091[/C][/ROW]
[ROW][C]84[/C][C]0.590105817602381[/C][C]0.819788364795238[/C][C]0.409894182397619[/C][/ROW]
[ROW][C]85[/C][C]0.567462422324838[/C][C]0.865075155350323[/C][C]0.432537577675162[/C][/ROW]
[ROW][C]86[/C][C]0.543347970048933[/C][C]0.913304059902134[/C][C]0.456652029951067[/C][/ROW]
[ROW][C]87[/C][C]0.525973109764832[/C][C]0.948053780470337[/C][C]0.474026890235168[/C][/ROW]
[ROW][C]88[/C][C]0.525643712452877[/C][C]0.948712575094246[/C][C]0.474356287547123[/C][/ROW]
[ROW][C]89[/C][C]0.546143386520018[/C][C]0.907713226959964[/C][C]0.453856613479982[/C][/ROW]
[ROW][C]90[/C][C]0.535774501967153[/C][C]0.928450996065695[/C][C]0.464225498032847[/C][/ROW]
[ROW][C]91[/C][C]0.501405556604364[/C][C]0.997188886791272[/C][C]0.498594443395636[/C][/ROW]
[ROW][C]92[/C][C]0.489047074440137[/C][C]0.978094148880275[/C][C]0.510952925559863[/C][/ROW]
[ROW][C]93[/C][C]0.469857710651145[/C][C]0.93971542130229[/C][C]0.530142289348855[/C][/ROW]
[ROW][C]94[/C][C]0.506630345902429[/C][C]0.986739308195143[/C][C]0.493369654097571[/C][/ROW]
[ROW][C]95[/C][C]0.509072777605944[/C][C]0.981854444788113[/C][C]0.490927222394056[/C][/ROW]
[ROW][C]96[/C][C]0.546858639919343[/C][C]0.906282720161314[/C][C]0.453141360080657[/C][/ROW]
[ROW][C]97[/C][C]0.521584776838825[/C][C]0.956830446322351[/C][C]0.478415223161175[/C][/ROW]
[ROW][C]98[/C][C]0.561345075370678[/C][C]0.877309849258643[/C][C]0.438654924629322[/C][/ROW]
[ROW][C]99[/C][C]0.60436989322756[/C][C]0.79126021354488[/C][C]0.39563010677244[/C][/ROW]
[ROW][C]100[/C][C]0.586111836953429[/C][C]0.827776326093143[/C][C]0.413888163046571[/C][/ROW]
[ROW][C]101[/C][C]0.696287614329966[/C][C]0.607424771340068[/C][C]0.303712385670034[/C][/ROW]
[ROW][C]102[/C][C]0.681936771207728[/C][C]0.636126457584544[/C][C]0.318063228792272[/C][/ROW]
[ROW][C]103[/C][C]0.673916624679119[/C][C]0.652166750641763[/C][C]0.326083375320881[/C][/ROW]
[ROW][C]104[/C][C]0.64543361625481[/C][C]0.709132767490379[/C][C]0.354566383745190[/C][/ROW]
[ROW][C]105[/C][C]0.65173903734203[/C][C]0.696521925315939[/C][C]0.348260962657970[/C][/ROW]
[ROW][C]106[/C][C]0.724690200513603[/C][C]0.550619598972794[/C][C]0.275309799486397[/C][/ROW]
[ROW][C]107[/C][C]0.755380027256592[/C][C]0.489239945486815[/C][C]0.244619972743408[/C][/ROW]
[ROW][C]108[/C][C]0.725579081510449[/C][C]0.548841836979102[/C][C]0.274420918489551[/C][/ROW]
[ROW][C]109[/C][C]0.688121595039745[/C][C]0.623756809920509[/C][C]0.311878404960254[/C][/ROW]
[ROW][C]110[/C][C]0.644399834657539[/C][C]0.711200330684923[/C][C]0.355600165342461[/C][/ROW]
[ROW][C]111[/C][C]0.635255273357683[/C][C]0.729489453284633[/C][C]0.364744726642317[/C][/ROW]
[ROW][C]112[/C][C]0.589154340093502[/C][C]0.821691319812996[/C][C]0.410845659906498[/C][/ROW]
[ROW][C]113[/C][C]0.633596258547969[/C][C]0.732807482904063[/C][C]0.366403741452031[/C][/ROW]
[ROW][C]114[/C][C]0.636787446984013[/C][C]0.726425106031973[/C][C]0.363212553015987[/C][/ROW]
[ROW][C]115[/C][C]0.636147791820344[/C][C]0.727704416359313[/C][C]0.363852208179656[/C][/ROW]
[ROW][C]116[/C][C]0.590086272220564[/C][C]0.819827455558871[/C][C]0.409913727779436[/C][/ROW]
[ROW][C]117[/C][C]0.655362719065248[/C][C]0.689274561869503[/C][C]0.344637280934752[/C][/ROW]
[ROW][C]118[/C][C]0.677724001606667[/C][C]0.644551996786666[/C][C]0.322275998393333[/C][/ROW]
[ROW][C]119[/C][C]0.667599015819533[/C][C]0.664801968360935[/C][C]0.332400984180467[/C][/ROW]
[ROW][C]120[/C][C]0.66948033689992[/C][C]0.661039326200161[/C][C]0.330519663100081[/C][/ROW]
[ROW][C]121[/C][C]0.677861338354092[/C][C]0.644277323291815[/C][C]0.322138661645908[/C][/ROW]
[ROW][C]122[/C][C]0.631930790773522[/C][C]0.736138418452957[/C][C]0.368069209226478[/C][/ROW]
[ROW][C]123[/C][C]0.664412186339045[/C][C]0.67117562732191[/C][C]0.335587813660955[/C][/ROW]
[ROW][C]124[/C][C]0.657088963295667[/C][C]0.685822073408666[/C][C]0.342911036704333[/C][/ROW]
[ROW][C]125[/C][C]0.633315438319995[/C][C]0.73336912336001[/C][C]0.366684561680005[/C][/ROW]
[ROW][C]126[/C][C]0.576296870964927[/C][C]0.847406258070147[/C][C]0.423703129035073[/C][/ROW]
[ROW][C]127[/C][C]0.575471848680909[/C][C]0.849056302638182[/C][C]0.424528151319091[/C][/ROW]
[ROW][C]128[/C][C]0.519837739591899[/C][C]0.960324520816202[/C][C]0.480162260408101[/C][/ROW]
[ROW][C]129[/C][C]0.543642606529941[/C][C]0.912714786940118[/C][C]0.456357393470059[/C][/ROW]
[ROW][C]130[/C][C]0.495766076018386[/C][C]0.991532152036771[/C][C]0.504233923981614[/C][/ROW]
[ROW][C]131[/C][C]0.47164660296408[/C][C]0.94329320592816[/C][C]0.52835339703592[/C][/ROW]
[ROW][C]132[/C][C]0.422887557619965[/C][C]0.84577511523993[/C][C]0.577112442380035[/C][/ROW]
[ROW][C]133[/C][C]0.442854911645646[/C][C]0.885709823291293[/C][C]0.557145088354354[/C][/ROW]
[ROW][C]134[/C][C]0.510490987125635[/C][C]0.97901802574873[/C][C]0.489509012874365[/C][/ROW]
[ROW][C]135[/C][C]0.618905414544388[/C][C]0.762189170911225[/C][C]0.381094585455612[/C][/ROW]
[ROW][C]136[/C][C]0.551668256341769[/C][C]0.896663487316462[/C][C]0.448331743658231[/C][/ROW]
[ROW][C]137[/C][C]0.490024382512277[/C][C]0.980048765024554[/C][C]0.509975617487723[/C][/ROW]
[ROW][C]138[/C][C]0.448030038720837[/C][C]0.896060077441675[/C][C]0.551969961279163[/C][/ROW]
[ROW][C]139[/C][C]0.452139455560086[/C][C]0.904278911120171[/C][C]0.547860544439914[/C][/ROW]
[ROW][C]140[/C][C]0.4996347288623[/C][C]0.9992694577246[/C][C]0.5003652711377[/C][/ROW]
[ROW][C]141[/C][C]0.608770395001238[/C][C]0.782459209997525[/C][C]0.391229604998762[/C][/ROW]
[ROW][C]142[/C][C]0.554940671241365[/C][C]0.89011865751727[/C][C]0.445059328758635[/C][/ROW]
[ROW][C]143[/C][C]0.515761486521094[/C][C]0.968477026957811[/C][C]0.484238513478906[/C][/ROW]
[ROW][C]144[/C][C]0.851610237853437[/C][C]0.296779524293125[/C][C]0.148389762146562[/C][/ROW]
[ROW][C]145[/C][C]0.781267291470078[/C][C]0.437465417059845[/C][C]0.218732708529922[/C][/ROW]
[ROW][C]146[/C][C]0.699738864973273[/C][C]0.600522270053454[/C][C]0.300261135026727[/C][/ROW]
[ROW][C]147[/C][C]0.866363200846664[/C][C]0.267273598306673[/C][C]0.133636799153336[/C][/ROW]
[ROW][C]148[/C][C]0.736388356069854[/C][C]0.527223287860291[/C][C]0.263611643930146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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
90.2594128849274120.5188257698548250.740587115072588
100.2047083239781470.4094166479562940.795291676021853
110.5251921103917270.9496157792165450.474807889608273
120.4771972147146610.9543944294293220.522802785285339
130.4712780755796620.9425561511593230.528721924420338
140.3832373049857930.7664746099715860.616762695014207
150.3871212383219160.7742424766438330.612878761678084
160.4368278995548170.8736557991096340.563172100445183
170.5551230021928420.8897539956143160.444876997807158
180.5808549727167020.8382900545665960.419145027283298
190.5052689209358580.9894621581282850.494731079064142
200.6222698496175790.7554603007648420.377730150382421
210.5745573168816480.8508853662367040.425442683118352
220.5842144036263720.8315711927472570.415785596373628
230.5133916371980330.9732167256039330.486608362801967
240.5132443578792640.9735112842414720.486755642120736
250.5130786705929970.9738426588140060.486921329407003
260.526493639362540.947012721274920.47350636063746
270.4841397468331410.9682794936662810.515860253166859
280.4283331886428140.8566663772856280.571666811357186
290.6063590565443490.7872818869113030.393640943455651
300.604465694016920.791068611966160.39553430598308
310.5744301717812970.8511396564374050.425569828218703
320.6117576162935920.7764847674128160.388242383706408
330.5908180509555620.8183638980888770.409181949044438
340.5462104769243430.9075790461513140.453789523075657
350.5489069955974580.9021860088050840.451093004402542
360.58014818671630.83970362656740.4198518132837
370.5375709412165980.9248581175668040.462429058783402
380.5831619505484870.8336760989030250.416838049451513
390.6108846100763040.7782307798473910.389115389923696
400.5745969401505160.8508061196989690.425403059849484
410.5627044276516940.8745911446966120.437295572348306
420.5215115852622340.9569768294755330.478488414737766
430.4883139020612530.9766278041225060.511686097938747
440.4417363549651110.8834727099302230.558263645034889
450.4179572943959240.8359145887918480.582042705604076
460.4129643441361130.8259286882722250.587035655863887
470.3896278791235960.7792557582471920.610372120876404
480.3702707052500820.7405414105001640.629729294749918
490.3524333998008320.7048667996016650.647566600199168
500.3487615647752140.6975231295504280.651238435224786
510.343837442048460.687674884096920.65616255795154
520.3503369781328390.7006739562656770.649663021867162
530.3836905056356220.7673810112712440.616309494364378
540.4153206763178150.830641352635630.584679323682185
550.3975590153761250.795118030752250.602440984623875
560.3975499392117650.795099878423530.602450060788235
570.429100907151810.858201814303620.57089909284819
580.4645087826787180.9290175653574350.535491217321282
590.4352816361781090.8705632723562170.564718363821891
600.4412587105672390.8825174211344790.558741289432761
610.4105753571425460.8211507142850930.589424642857454
620.4306867516857570.8613735033715150.569313248314243
630.4615449847981250.923089969596250.538455015201875
640.4457126947509680.8914253895019370.554287305249032
650.4312310979339320.8624621958678640.568768902066068
660.4695322784881210.9390645569762420.530467721511879
670.4418656250948520.8837312501897030.558134374905148
680.4470481199527990.8940962399055970.552951880047201
690.4688629605836240.9377259211672480.531137039416376
700.4691394694766040.9382789389532090.530860530523396
710.4686109860470830.9372219720941660.531389013952917
720.4466857593864980.8933715187729950.553314240613502
730.4801482880092080.9602965760184160.519851711990792
740.4984547216279560.9969094432559110.501545278372044
750.5671328973514890.8657342052970210.432867102648511
760.5989673171153610.8020653657692770.401032682884639
770.5890755766483320.8218488467033350.410924423351668
780.5988325509320680.8023348981358630.401167449067932
790.6180677185757990.7638645628484030.381932281424201
800.6084210397879130.7831579204241740.391578960212087
810.586815613077320.826368773845360.41318438692268
820.590999870331350.81800025933730.40900012966865
830.6195474794299090.7609050411401830.380452520570091
840.5901058176023810.8197883647952380.409894182397619
850.5674624223248380.8650751553503230.432537577675162
860.5433479700489330.9133040599021340.456652029951067
870.5259731097648320.9480537804703370.474026890235168
880.5256437124528770.9487125750942460.474356287547123
890.5461433865200180.9077132269599640.453856613479982
900.5357745019671530.9284509960656950.464225498032847
910.5014055566043640.9971888867912720.498594443395636
920.4890470744401370.9780941488802750.510952925559863
930.4698577106511450.939715421302290.530142289348855
940.5066303459024290.9867393081951430.493369654097571
950.5090727776059440.9818544447881130.490927222394056
960.5468586399193430.9062827201613140.453141360080657
970.5215847768388250.9568304463223510.478415223161175
980.5613450753706780.8773098492586430.438654924629322
990.604369893227560.791260213544880.39563010677244
1000.5861118369534290.8277763260931430.413888163046571
1010.6962876143299660.6074247713400680.303712385670034
1020.6819367712077280.6361264575845440.318063228792272
1030.6739166246791190.6521667506417630.326083375320881
1040.645433616254810.7091327674903790.354566383745190
1050.651739037342030.6965219253159390.348260962657970
1060.7246902005136030.5506195989727940.275309799486397
1070.7553800272565920.4892399454868150.244619972743408
1080.7255790815104490.5488418369791020.274420918489551
1090.6881215950397450.6237568099205090.311878404960254
1100.6443998346575390.7112003306849230.355600165342461
1110.6352552733576830.7294894532846330.364744726642317
1120.5891543400935020.8216913198129960.410845659906498
1130.6335962585479690.7328074829040630.366403741452031
1140.6367874469840130.7264251060319730.363212553015987
1150.6361477918203440.7277044163593130.363852208179656
1160.5900862722205640.8198274555588710.409913727779436
1170.6553627190652480.6892745618695030.344637280934752
1180.6777240016066670.6445519967866660.322275998393333
1190.6675990158195330.6648019683609350.332400984180467
1200.669480336899920.6610393262001610.330519663100081
1210.6778613383540920.6442773232918150.322138661645908
1220.6319307907735220.7361384184529570.368069209226478
1230.6644121863390450.671175627321910.335587813660955
1240.6570889632956670.6858220734086660.342911036704333
1250.6333154383199950.733369123360010.366684561680005
1260.5762968709649270.8474062580701470.423703129035073
1270.5754718486809090.8490563026381820.424528151319091
1280.5198377395918990.9603245208162020.480162260408101
1290.5436426065299410.9127147869401180.456357393470059
1300.4957660760183860.9915321520367710.504233923981614
1310.471646602964080.943293205928160.52835339703592
1320.4228875576199650.845775115239930.577112442380035
1330.4428549116456460.8857098232912930.557145088354354
1340.5104909871256350.979018025748730.489509012874365
1350.6189054145443880.7621891709112250.381094585455612
1360.5516682563417690.8966634873164620.448331743658231
1370.4900243825122770.9800487650245540.509975617487723
1380.4480300387208370.8960600774416750.551969961279163
1390.4521394555600860.9042789111201710.547860544439914
1400.49963472886230.99926945772460.5003652711377
1410.6087703950012380.7824592099975250.391229604998762
1420.5549406712413650.890118657517270.445059328758635
1430.5157614865210940.9684770269578110.484238513478906
1440.8516102378534370.2967795242931250.148389762146562
1450.7812672914700780.4374654170598450.218732708529922
1460.6997388649732730.6005222700534540.300261135026727
1470.8663632008466640.2672735983066730.133636799153336
1480.7363883560698540.5272232878602910.263611643930146






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104200&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104200&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104200&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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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')
}