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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 27 Nov 2011 12:13:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/27/t1322414027419258mna0g0yll.htm/, Retrieved Fri, 29 Mar 2024 10:29:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147567, Retrieved Fri, 29 Mar 2024 10:29:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
-   PD    [Multiple Regression] [] [2010-12-02 19:29:24] [94f4aa1c01e87d8321fffb341ed4df07]
- R         [Multiple Regression] [] [2011-11-25 00:52:53] [74be16979710d4c4e7c6647856088456]
- R P           [Multiple Regression] [] [2011-11-27 17:13:38] [5f9ad3d6882448a3cbf5628cc61fe2a1] [Current]
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Dataseries X:
9	24	14	11	12	24	26
9	25	11	7	8	25	23
9	17	6	17	8	30	25
9	18	12	10	8	19	23
9	18	8	12	9	22	19
9	16	10	12	7	22	29
10	20	10	11	4	25	25
10	16	11	11	11	23	21
10	18	16	12	7	17	22
10	17	11	13	7	21	25
10	23	13	14	12	19	24
10	30	12	16	10	19	18
10	23	8	11	10	15	22
10	18	12	10	8	16	15
10	15	11	11	8	23	22
10	12	4	15	4	27	28
10	21	9	9	9	22	20
10	15	8	11	8	14	12
10	20	8	17	7	22	24
10	31	14	17	11	23	20
10	27	15	11	9	23	21
10	34	16	18	11	21	20
10	21	9	14	13	19	21
10	31	14	10	8	18	23
10	19	11	11	8	20	28
10	16	8	15	9	23	24
10	20	9	15	6	25	24
10	21	9	13	9	19	24
10	22	9	16	9	24	23
10	17	9	13	6	22	23
10	24	10	9	6	25	29
10	25	16	18	16	26	24
10	26	11	18	5	29	18
10	25	8	12	7	32	25
10	17	9	17	9	25	21
10	32	16	9	6	29	26
10	33	11	9	6	28	22
10	13	16	12	5	17	22
10	32	12	18	12	28	22
10	25	12	12	7	29	23
10	29	14	18	10	26	30
10	22	9	14	9	25	23
10	18	10	15	8	14	17
10	17	9	16	5	25	23
10	20	10	10	8	26	23
10	15	12	11	8	20	25
10	20	14	14	10	18	24
10	33	14	9	6	32	24
10	29	10	12	8	25	23
10	23	14	17	7	25	21
10	26	16	5	4	23	24
10	18	9	12	8	21	24
10	20	10	12	8	20	28
10	11	6	6	4	15	16
10	28	8	24	20	30	20
10	26	13	12	8	24	29
10	22	10	12	8	26	27
10	17	8	14	6	24	22
10	12	7	7	4	22	28
10	14	15	13	8	14	16
10	17	9	12	9	24	25
10	21	10	13	6	24	24
10	19	12	14	7	24	28
10	18	13	8	9	24	24
10	10	10	11	5	19	23
10	29	11	9	5	31	30
10	31	8	11	8	22	24
10	19	9	13	8	27	21
10	9	13	10	6	19	25
10	20	11	11	8	25	25
10	28	8	12	7	20	22
10	19	9	9	7	21	23
10	30	9	15	9	27	26
10	29	15	18	11	23	23
10	26	9	15	6	25	25
10	23	10	12	8	20	21
10	13	14	13	6	21	25
10	21	12	14	9	22	24
10	19	12	10	8	23	29
10	28	11	13	6	25	22
10	23	14	13	10	25	27
10	18	6	11	8	17	26
10	21	12	13	8	19	22
10	20	8	16	10	25	24
10	23	14	8	5	19	27
10	21	11	16	7	20	24
10	21	10	11	5	26	24
10	15	14	9	8	23	29
10	28	12	16	14	27	22
10	19	10	12	7	17	21
10	26	14	14	8	17	24
10	10	5	8	6	19	24
10	16	11	9	5	17	23
10	22	10	15	6	22	20
10	19	9	11	10	21	27
10	31	10	21	12	32	26
10	31	16	14	9	21	25
10	29	13	18	12	21	21
10	19	9	12	7	18	21
10	22	10	13	8	18	19
10	23	10	15	10	23	21
10	15	7	12	6	19	21
10	20	9	19	10	20	16
10	18	8	15	10	21	22
10	23	14	11	10	20	29
10	25	14	11	5	17	15
10	21	8	10	7	18	17
10	24	9	13	10	19	15
10	25	14	15	11	22	21
10	17	14	12	6	15	21
10	13	8	12	7	14	19
10	28	8	16	12	18	24
10	21	8	9	11	24	20
10	25	7	18	11	35	17
10	9	6	8	11	29	23
10	16	8	13	5	21	24
10	19	6	17	8	25	14
10	17	11	9	6	20	19
10	25	14	15	9	22	24
10	20	11	8	4	13	13
10	29	11	7	4	26	22
10	14	11	12	7	17	16
10	22	14	14	11	25	19
10	15	8	6	6	20	25
10	19	20	8	7	19	25
10	20	11	17	8	21	23
10	15	8	10	4	22	24
10	20	11	11	8	24	26
10	18	10	14	9	21	26
10	33	14	11	8	26	25
10	22	11	13	11	24	18
10	16	9	12	8	16	21
10	17	9	11	5	23	26
10	16	8	9	4	18	23
10	21	10	12	8	16	23
10	26	13	20	10	26	22
10	18	13	12	6	19	20
10	18	12	13	9	21	13
10	17	8	12	9	21	24
10	22	13	12	13	22	15
10	30	14	9	9	23	14
10	30	12	15	10	29	22
10	24	14	24	20	21	10
10	21	15	7	5	21	24
10	21	13	17	11	23	22
10	29	16	11	6	27	24
10	31	9	17	9	25	19
10	20	9	11	7	21	20
10	16	9	12	9	10	13
10	22	8	14	10	20	20
10	20	7	11	9	26	22
10	28	16	16	8	24	24
10	38	11	21	7	29	29
10	22	9	14	6	19	12
10	20	11	20	13	24	20
10	17	9	13	6	19	21
10	28	14	11	8	24	24
10	22	13	15	10	22	22
10	31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=147567&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=147567&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
PersonalStandards[t] = + 21.0638481525468 -1.332265146131month[t] + 0.331360289672297ConcernoverMistakes[t] -0.355719150002528Doubtsaboutactions[t] + 0.197976188475ParentalExpectations[t] + 0.00683989436930237ParentalCriticism[t] + 0.387508988376619`Organization `[t] -0.00225008961774507t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PersonalStandards[t] =  +  21.0638481525468 -1.332265146131month[t] +  0.331360289672297ConcernoverMistakes[t] -0.355719150002528Doubtsaboutactions[t] +  0.197976188475ParentalExpectations[t] +  0.00683989436930237ParentalCriticism[t] +  0.387508988376619`Organization
`[t] -0.00225008961774507t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PersonalStandards[t] =  +  21.0638481525468 -1.332265146131month[t] +  0.331360289672297ConcernoverMistakes[t] -0.355719150002528Doubtsaboutactions[t] +  0.197976188475ParentalExpectations[t] +  0.00683989436930237ParentalCriticism[t] +  0.387508988376619`Organization
`[t] -0.00225008961774507t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
PersonalStandards[t] = + 21.0638481525468 -1.332265146131month[t] + 0.331360289672297ConcernoverMistakes[t] -0.355719150002528Doubtsaboutactions[t] + 0.197976188475ParentalExpectations[t] + 0.00683989436930237ParentalCriticism[t] + 0.387508988376619`Organization `[t] -0.00225008961774507t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21.063848152546815.1650431.3890.1668860.083443
month-1.3322651461311.523395-0.87450.3832150.191607
ConcernoverMistakes0.3313602896722970.0557695.941600
Doubtsaboutactions-0.3557191500025280.107595-3.30610.0011820.000591
ParentalExpectations0.1979761884750.1020081.94080.0541470.027074
ParentalCriticism0.006839894369302370.1300450.05260.9581230.479062
`Organization `0.3875089883766190.0741045.22931e-060
t-0.002250089617745070.006424-0.35030.7266330.363316

\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) & 21.0638481525468 & 15.165043 & 1.389 & 0.166886 & 0.083443 \tabularnewline
month & -1.332265146131 & 1.523395 & -0.8745 & 0.383215 & 0.191607 \tabularnewline
ConcernoverMistakes & 0.331360289672297 & 0.055769 & 5.9416 & 0 & 0 \tabularnewline
Doubtsaboutactions & -0.355719150002528 & 0.107595 & -3.3061 & 0.001182 & 0.000591 \tabularnewline
ParentalExpectations & 0.197976188475 & 0.102008 & 1.9408 & 0.054147 & 0.027074 \tabularnewline
ParentalCriticism & 0.00683989436930237 & 0.130045 & 0.0526 & 0.958123 & 0.479062 \tabularnewline
`Organization
` & 0.387508988376619 & 0.074104 & 5.2293 & 1e-06 & 0 \tabularnewline
t & -0.00225008961774507 & 0.006424 & -0.3503 & 0.726633 & 0.363316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&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]21.0638481525468[/C][C]15.165043[/C][C]1.389[/C][C]0.166886[/C][C]0.083443[/C][/ROW]
[ROW][C]month[/C][C]-1.332265146131[/C][C]1.523395[/C][C]-0.8745[/C][C]0.383215[/C][C]0.191607[/C][/ROW]
[ROW][C]ConcernoverMistakes[/C][C]0.331360289672297[/C][C]0.055769[/C][C]5.9416[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.355719150002528[/C][C]0.107595[/C][C]-3.3061[/C][C]0.001182[/C][C]0.000591[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.197976188475[/C][C]0.102008[/C][C]1.9408[/C][C]0.054147[/C][C]0.027074[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.00683989436930237[/C][C]0.130045[/C][C]0.0526[/C][C]0.958123[/C][C]0.479062[/C][/ROW]
[ROW][C]`Organization
`[/C][C]0.387508988376619[/C][C]0.074104[/C][C]5.2293[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.00225008961774507[/C][C]0.006424[/C][C]-0.3503[/C][C]0.726633[/C][C]0.363316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21.063848152546815.1650431.3890.1668860.083443
month-1.3322651461311.523395-0.87450.3832150.191607
ConcernoverMistakes0.3313602896722970.0557695.941600
Doubtsaboutactions-0.3557191500025280.107595-3.30610.0011820.000591
ParentalExpectations0.1979761884750.1020081.94080.0541470.027074
ParentalCriticism0.006839894369302370.1300450.05260.9581230.479062
`Organization `0.3875089883766190.0741045.22931e-060
t-0.002250089617745070.006424-0.35030.7266330.363316







Multiple Linear Regression - Regression Statistics
Multiple R0.609951812691802
R-squared0.372041213806015
Adjusted R-squared0.342930541598346
F-TEST (value)12.780234381122
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value7.8070883091641e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.41825913153243
Sum Squared Residuals1764.35881903603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.609951812691802 \tabularnewline
R-squared & 0.372041213806015 \tabularnewline
Adjusted R-squared & 0.342930541598346 \tabularnewline
F-TEST (value) & 12.780234381122 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 7.8070883091641e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.41825913153243 \tabularnewline
Sum Squared Residuals & 1764.35881903603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.609951812691802[/C][/ROW]
[ROW][C]R-squared[/C][C]0.372041213806015[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.342930541598346[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.780234381122[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]7.8070883091641e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.41825913153243[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1764.35881903603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.609951812691802
R-squared0.372041213806015
Adjusted R-squared0.342930541598346
F-TEST (value)12.780234381122
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value7.8070883091641e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.41825913153243
Sum Squared Residuals1764.35881903603







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.3788411032986-0.378841103298631
22523.79331745685371.20668254314634
33025.67356066137344.32643933862657
41921.7075046653346-2.70750466533457
52221.98088749353980.0191125064602387
62224.4658886196-2.46588861959995
72522.6882827174512.31171728254899
82319.50271562622023.4972843737798
91718.9427159653088-1.94271596530881
102121.7482044896363-0.74820448963627
111922.8673445099921-3.86734450999214
121923.5975542560347-4.59755425603468
131523.2588137498525-8.25881374985245
141617.2526667160132-1.25266671601317
152319.52259401449243.4774059855076
162724.1058962125582.89410378744204
172221.05356341396180.946436586038195
181416.7079113118806-2.70791131188055
192224.1935877676244-2.19358776762442
202324.1793095883575-1.1793095883575
212321.68187125883611.31812874116394
222124.6554281666088-3.65542816660885
231922.4448123844842-3.44481238448417
241823.9264831925835-5.92648319258347
252023.1505882072638-3.15058820726385
262322.47012339339960.529876606600377
272523.41707562936061.58292437063937
281923.3707531355731-4.37075313557309
292423.9062829126760.0937170873239792
302221.63278312616390.367216873836116
312525.1274850906094-0.1274850906094
322623.23492008873372.76507991126628
332922.94233327047896.05766672952113
343225.21426591772136.78573408227865
352521.65893913832983.34106086167017
362924.4702750947544.52972490524598
372825.02794509131472.97205490868526
381717.2069821292941-0.206982129294117
392826.15919053489521.84080946510476
402923.00287080325155.99712919674848
412627.5355633049122-1.53556330491215
422523.48107937069531.51892062930467
431419.6637513362319-5.66375133623186
442522.18837054257122.81162945742885
452621.65714472422574.34285527577433
462020.2596490514696-0.259649051469627
471821.4228614759953-3.4228614759953
483224.71105463226527.2889453677348
492525.0263393497554-0.0263393497553644
502521.82107399334623.17892600665381
512320.86775949306222.13224050693778
522122.117854032886-1.11785403288601
532023.9726413261168-3.97264132611681
541516.5457006606119-1.54570066061186
553026.68818285138343.31181714861657
562425.2744043336664-1.27440433366639
572624.23885255861381.7611474413862
582421.73596696696792.26403303303207
592221.35817540118730.641824598812662
601415.7380015387017-1.73800153870174
612422.16059181939991.83940818060007
622422.91801125545931.08198874454068
632423.29645432284270.703545677157296
642419.88291149793234.11708850206774
651918.47599654051490.524003459485101
663126.73048334635464.26951665364541
672226.5495294158872-4.54952941588722
682721.44866211201955.55133788798047
691917.65236012501161.34763987498842
702522.21816749900782.78183250099224
712024.9625665057518-4.96256650575181
722121.4159350820325-0.415935082032486
732727.4227120635285-0.422712063528468
742324.399868173257-1.39986817325701
752525.6847420541193-0.684742054119265
762022.2024072152892-2.20240721528923
772119.19800998218131.80199001781872
782222.3890673931533-0.38906739315325
792322.86289701780470.137102982195297
802524.06629454329020.933705456709781
812523.30499007466371.69500992533629
821723.6945505826395-6.69455058263948
831921.397982885467-2.39798288546698
842523.86987543710391.1301245628961
851922.2719093019712-3.27190930197122
862023.1090584144252-3.10905841442521
872622.45896674369643.54103325630361
882320.60779056407582.39220943592425
892724.33897230710742.66102769289259
901720.8386249075823-3.83862490758231
911723.2983394821097-6.29833948210969
921919.9942601881693-0.994260188169343
931719.6494842422993-2.64948424229929
942222.0232851008073-0.0232851008072989
952123.3306910343887-2.33069103438873
963228.5549779559483.44502204405199
972124.6245509755056-3.62455097550557
982124.2891262400522-3.28912624005225
991821.1740932510251-3.17409325102513
1001821.2400029865128-3.24000298651281
1012322.75376332900920.246236670990791
1021920.5465002291185-1.54650022911846
1032020.9652612427763-0.965261242776263
1042122.1891589001762-1.18915890017617
1052023.6300535236411-3.63005352364107
1061718.8311987042487-1.83119870424873
1071820.2285439329738-2.22854393297381
1081920.7040858341501-1.7040858341501
1092221.9824464857710.0175535142289692
1101518.7011860415034-3.7011860415034
1111418.7396316108277-4.7396316108277
1121826.471435033924-8.47143503392402
1132421.20695374939942.79304625060059
1143523.505122699618511.4948773003815
1152918.902119170756310.0978808292437
1162121.8443033733754-0.844303373375357
1172520.48490700602134.51509299397872
1182018.38139623239081.61860376760922
1192223.1087927659848-1.10879276598483
1201316.8342670146989-3.83426701469886
1212623.10386423904642.89613576095364
1221716.81655649956740.183443500432641
1232519.98387019687755.01612980312253
1242020.503457930182-0.503457930182018
1251917.96081147054241.03918852945758
1262122.5050016345108-1.5050016345108
1272220.88742363811361.11257636188644
1282422.47517128955521.52482871044484
1292122.7666882303897-1.76668823038966
1302625.32368843767530.676311562324675
1312420.44754175309153.55245824690852
1321620.112599318992-4.11259931899196
1332322.16075858934670.839241410653303
1341820.61754812361-2.61754812361002
1351622.1819493252509-6.18194932525091
1362623.97932354214912.02067645785094
1371918.94000407312250.0599959268775115
1382116.79940608645384.20059391354615
1392121.9532949908417-0.953294990841726
1402218.36902928166053.63097071833954
1412319.65314522813973.34685477186027
1422924.65710237075934.3428976292407
1432119.15532902255131.84467097744868
1442119.76021113157231.23978886842772
1452321.71518261617221.28481738382783
1462722.84961876798194.15038123201806
1472525.2709551798013-0.270955179801297
1482120.80971397257630.190286027423699
1491016.9811157828466-6.98111578284664
1502022.4381017712208-2.43810177122084
1512622.303099769223.69690023078004
1522423.50831867171680.491681328283223
1532931.5188532187234-2.51885321872343
1541918.94595077825720.0540492217428303
1552421.90535010773782.09464989226215
1561920.5742538575748-1.57425385757477
1572423.21862558125810.781374418741894
1582221.61449946949450.385500530505523
1591723.5852606802824-6.58526068028239

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.3788411032986 & -0.378841103298631 \tabularnewline
2 & 25 & 23.7933174568537 & 1.20668254314634 \tabularnewline
3 & 30 & 25.6735606613734 & 4.32643933862657 \tabularnewline
4 & 19 & 21.7075046653346 & -2.70750466533457 \tabularnewline
5 & 22 & 21.9808874935398 & 0.0191125064602387 \tabularnewline
6 & 22 & 24.4658886196 & -2.46588861959995 \tabularnewline
7 & 25 & 22.688282717451 & 2.31171728254899 \tabularnewline
8 & 23 & 19.5027156262202 & 3.4972843737798 \tabularnewline
9 & 17 & 18.9427159653088 & -1.94271596530881 \tabularnewline
10 & 21 & 21.7482044896363 & -0.74820448963627 \tabularnewline
11 & 19 & 22.8673445099921 & -3.86734450999214 \tabularnewline
12 & 19 & 23.5975542560347 & -4.59755425603468 \tabularnewline
13 & 15 & 23.2588137498525 & -8.25881374985245 \tabularnewline
14 & 16 & 17.2526667160132 & -1.25266671601317 \tabularnewline
15 & 23 & 19.5225940144924 & 3.4774059855076 \tabularnewline
16 & 27 & 24.105896212558 & 2.89410378744204 \tabularnewline
17 & 22 & 21.0535634139618 & 0.946436586038195 \tabularnewline
18 & 14 & 16.7079113118806 & -2.70791131188055 \tabularnewline
19 & 22 & 24.1935877676244 & -2.19358776762442 \tabularnewline
20 & 23 & 24.1793095883575 & -1.1793095883575 \tabularnewline
21 & 23 & 21.6818712588361 & 1.31812874116394 \tabularnewline
22 & 21 & 24.6554281666088 & -3.65542816660885 \tabularnewline
23 & 19 & 22.4448123844842 & -3.44481238448417 \tabularnewline
24 & 18 & 23.9264831925835 & -5.92648319258347 \tabularnewline
25 & 20 & 23.1505882072638 & -3.15058820726385 \tabularnewline
26 & 23 & 22.4701233933996 & 0.529876606600377 \tabularnewline
27 & 25 & 23.4170756293606 & 1.58292437063937 \tabularnewline
28 & 19 & 23.3707531355731 & -4.37075313557309 \tabularnewline
29 & 24 & 23.906282912676 & 0.0937170873239792 \tabularnewline
30 & 22 & 21.6327831261639 & 0.367216873836116 \tabularnewline
31 & 25 & 25.1274850906094 & -0.1274850906094 \tabularnewline
32 & 26 & 23.2349200887337 & 2.76507991126628 \tabularnewline
33 & 29 & 22.9423332704789 & 6.05766672952113 \tabularnewline
34 & 32 & 25.2142659177213 & 6.78573408227865 \tabularnewline
35 & 25 & 21.6589391383298 & 3.34106086167017 \tabularnewline
36 & 29 & 24.470275094754 & 4.52972490524598 \tabularnewline
37 & 28 & 25.0279450913147 & 2.97205490868526 \tabularnewline
38 & 17 & 17.2069821292941 & -0.206982129294117 \tabularnewline
39 & 28 & 26.1591905348952 & 1.84080946510476 \tabularnewline
40 & 29 & 23.0028708032515 & 5.99712919674848 \tabularnewline
41 & 26 & 27.5355633049122 & -1.53556330491215 \tabularnewline
42 & 25 & 23.4810793706953 & 1.51892062930467 \tabularnewline
43 & 14 & 19.6637513362319 & -5.66375133623186 \tabularnewline
44 & 25 & 22.1883705425712 & 2.81162945742885 \tabularnewline
45 & 26 & 21.6571447242257 & 4.34285527577433 \tabularnewline
46 & 20 & 20.2596490514696 & -0.259649051469627 \tabularnewline
47 & 18 & 21.4228614759953 & -3.4228614759953 \tabularnewline
48 & 32 & 24.7110546322652 & 7.2889453677348 \tabularnewline
49 & 25 & 25.0263393497554 & -0.0263393497553644 \tabularnewline
50 & 25 & 21.8210739933462 & 3.17892600665381 \tabularnewline
51 & 23 & 20.8677594930622 & 2.13224050693778 \tabularnewline
52 & 21 & 22.117854032886 & -1.11785403288601 \tabularnewline
53 & 20 & 23.9726413261168 & -3.97264132611681 \tabularnewline
54 & 15 & 16.5457006606119 & -1.54570066061186 \tabularnewline
55 & 30 & 26.6881828513834 & 3.31181714861657 \tabularnewline
56 & 24 & 25.2744043336664 & -1.27440433366639 \tabularnewline
57 & 26 & 24.2388525586138 & 1.7611474413862 \tabularnewline
58 & 24 & 21.7359669669679 & 2.26403303303207 \tabularnewline
59 & 22 & 21.3581754011873 & 0.641824598812662 \tabularnewline
60 & 14 & 15.7380015387017 & -1.73800153870174 \tabularnewline
61 & 24 & 22.1605918193999 & 1.83940818060007 \tabularnewline
62 & 24 & 22.9180112554593 & 1.08198874454068 \tabularnewline
63 & 24 & 23.2964543228427 & 0.703545677157296 \tabularnewline
64 & 24 & 19.8829114979323 & 4.11708850206774 \tabularnewline
65 & 19 & 18.4759965405149 & 0.524003459485101 \tabularnewline
66 & 31 & 26.7304833463546 & 4.26951665364541 \tabularnewline
67 & 22 & 26.5495294158872 & -4.54952941588722 \tabularnewline
68 & 27 & 21.4486621120195 & 5.55133788798047 \tabularnewline
69 & 19 & 17.6523601250116 & 1.34763987498842 \tabularnewline
70 & 25 & 22.2181674990078 & 2.78183250099224 \tabularnewline
71 & 20 & 24.9625665057518 & -4.96256650575181 \tabularnewline
72 & 21 & 21.4159350820325 & -0.415935082032486 \tabularnewline
73 & 27 & 27.4227120635285 & -0.422712063528468 \tabularnewline
74 & 23 & 24.399868173257 & -1.39986817325701 \tabularnewline
75 & 25 & 25.6847420541193 & -0.684742054119265 \tabularnewline
76 & 20 & 22.2024072152892 & -2.20240721528923 \tabularnewline
77 & 21 & 19.1980099821813 & 1.80199001781872 \tabularnewline
78 & 22 & 22.3890673931533 & -0.38906739315325 \tabularnewline
79 & 23 & 22.8628970178047 & 0.137102982195297 \tabularnewline
80 & 25 & 24.0662945432902 & 0.933705456709781 \tabularnewline
81 & 25 & 23.3049900746637 & 1.69500992533629 \tabularnewline
82 & 17 & 23.6945505826395 & -6.69455058263948 \tabularnewline
83 & 19 & 21.397982885467 & -2.39798288546698 \tabularnewline
84 & 25 & 23.8698754371039 & 1.1301245628961 \tabularnewline
85 & 19 & 22.2719093019712 & -3.27190930197122 \tabularnewline
86 & 20 & 23.1090584144252 & -3.10905841442521 \tabularnewline
87 & 26 & 22.4589667436964 & 3.54103325630361 \tabularnewline
88 & 23 & 20.6077905640758 & 2.39220943592425 \tabularnewline
89 & 27 & 24.3389723071074 & 2.66102769289259 \tabularnewline
90 & 17 & 20.8386249075823 & -3.83862490758231 \tabularnewline
91 & 17 & 23.2983394821097 & -6.29833948210969 \tabularnewline
92 & 19 & 19.9942601881693 & -0.994260188169343 \tabularnewline
93 & 17 & 19.6494842422993 & -2.64948424229929 \tabularnewline
94 & 22 & 22.0232851008073 & -0.0232851008072989 \tabularnewline
95 & 21 & 23.3306910343887 & -2.33069103438873 \tabularnewline
96 & 32 & 28.554977955948 & 3.44502204405199 \tabularnewline
97 & 21 & 24.6245509755056 & -3.62455097550557 \tabularnewline
98 & 21 & 24.2891262400522 & -3.28912624005225 \tabularnewline
99 & 18 & 21.1740932510251 & -3.17409325102513 \tabularnewline
100 & 18 & 21.2400029865128 & -3.24000298651281 \tabularnewline
101 & 23 & 22.7537633290092 & 0.246236670990791 \tabularnewline
102 & 19 & 20.5465002291185 & -1.54650022911846 \tabularnewline
103 & 20 & 20.9652612427763 & -0.965261242776263 \tabularnewline
104 & 21 & 22.1891589001762 & -1.18915890017617 \tabularnewline
105 & 20 & 23.6300535236411 & -3.63005352364107 \tabularnewline
106 & 17 & 18.8311987042487 & -1.83119870424873 \tabularnewline
107 & 18 & 20.2285439329738 & -2.22854393297381 \tabularnewline
108 & 19 & 20.7040858341501 & -1.7040858341501 \tabularnewline
109 & 22 & 21.982446485771 & 0.0175535142289692 \tabularnewline
110 & 15 & 18.7011860415034 & -3.7011860415034 \tabularnewline
111 & 14 & 18.7396316108277 & -4.7396316108277 \tabularnewline
112 & 18 & 26.471435033924 & -8.47143503392402 \tabularnewline
113 & 24 & 21.2069537493994 & 2.79304625060059 \tabularnewline
114 & 35 & 23.5051226996185 & 11.4948773003815 \tabularnewline
115 & 29 & 18.9021191707563 & 10.0978808292437 \tabularnewline
116 & 21 & 21.8443033733754 & -0.844303373375357 \tabularnewline
117 & 25 & 20.4849070060213 & 4.51509299397872 \tabularnewline
118 & 20 & 18.3813962323908 & 1.61860376760922 \tabularnewline
119 & 22 & 23.1087927659848 & -1.10879276598483 \tabularnewline
120 & 13 & 16.8342670146989 & -3.83426701469886 \tabularnewline
121 & 26 & 23.1038642390464 & 2.89613576095364 \tabularnewline
122 & 17 & 16.8165564995674 & 0.183443500432641 \tabularnewline
123 & 25 & 19.9838701968775 & 5.01612980312253 \tabularnewline
124 & 20 & 20.503457930182 & -0.503457930182018 \tabularnewline
125 & 19 & 17.9608114705424 & 1.03918852945758 \tabularnewline
126 & 21 & 22.5050016345108 & -1.5050016345108 \tabularnewline
127 & 22 & 20.8874236381136 & 1.11257636188644 \tabularnewline
128 & 24 & 22.4751712895552 & 1.52482871044484 \tabularnewline
129 & 21 & 22.7666882303897 & -1.76668823038966 \tabularnewline
130 & 26 & 25.3236884376753 & 0.676311562324675 \tabularnewline
131 & 24 & 20.4475417530915 & 3.55245824690852 \tabularnewline
132 & 16 & 20.112599318992 & -4.11259931899196 \tabularnewline
133 & 23 & 22.1607585893467 & 0.839241410653303 \tabularnewline
134 & 18 & 20.61754812361 & -2.61754812361002 \tabularnewline
135 & 16 & 22.1819493252509 & -6.18194932525091 \tabularnewline
136 & 26 & 23.9793235421491 & 2.02067645785094 \tabularnewline
137 & 19 & 18.9400040731225 & 0.0599959268775115 \tabularnewline
138 & 21 & 16.7994060864538 & 4.20059391354615 \tabularnewline
139 & 21 & 21.9532949908417 & -0.953294990841726 \tabularnewline
140 & 22 & 18.3690292816605 & 3.63097071833954 \tabularnewline
141 & 23 & 19.6531452281397 & 3.34685477186027 \tabularnewline
142 & 29 & 24.6571023707593 & 4.3428976292407 \tabularnewline
143 & 21 & 19.1553290225513 & 1.84467097744868 \tabularnewline
144 & 21 & 19.7602111315723 & 1.23978886842772 \tabularnewline
145 & 23 & 21.7151826161722 & 1.28481738382783 \tabularnewline
146 & 27 & 22.8496187679819 & 4.15038123201806 \tabularnewline
147 & 25 & 25.2709551798013 & -0.270955179801297 \tabularnewline
148 & 21 & 20.8097139725763 & 0.190286027423699 \tabularnewline
149 & 10 & 16.9811157828466 & -6.98111578284664 \tabularnewline
150 & 20 & 22.4381017712208 & -2.43810177122084 \tabularnewline
151 & 26 & 22.30309976922 & 3.69690023078004 \tabularnewline
152 & 24 & 23.5083186717168 & 0.491681328283223 \tabularnewline
153 & 29 & 31.5188532187234 & -2.51885321872343 \tabularnewline
154 & 19 & 18.9459507782572 & 0.0540492217428303 \tabularnewline
155 & 24 & 21.9053501077378 & 2.09464989226215 \tabularnewline
156 & 19 & 20.5742538575748 & -1.57425385757477 \tabularnewline
157 & 24 & 23.2186255812581 & 0.781374418741894 \tabularnewline
158 & 22 & 21.6144994694945 & 0.385500530505523 \tabularnewline
159 & 17 & 23.5852606802824 & -6.58526068028239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&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]24[/C][C]24.3788411032986[/C][C]-0.378841103298631[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]23.7933174568537[/C][C]1.20668254314634[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]25.6735606613734[/C][C]4.32643933862657[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]21.7075046653346[/C][C]-2.70750466533457[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]21.9808874935398[/C][C]0.0191125064602387[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]24.4658886196[/C][C]-2.46588861959995[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.688282717451[/C][C]2.31171728254899[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]19.5027156262202[/C][C]3.4972843737798[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]18.9427159653088[/C][C]-1.94271596530881[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]21.7482044896363[/C][C]-0.74820448963627[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.8673445099921[/C][C]-3.86734450999214[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.5975542560347[/C][C]-4.59755425603468[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]23.2588137498525[/C][C]-8.25881374985245[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]17.2526667160132[/C][C]-1.25266671601317[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]19.5225940144924[/C][C]3.4774059855076[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]24.105896212558[/C][C]2.89410378744204[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]21.0535634139618[/C][C]0.946436586038195[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]16.7079113118806[/C][C]-2.70791131188055[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]24.1935877676244[/C][C]-2.19358776762442[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]24.1793095883575[/C][C]-1.1793095883575[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.6818712588361[/C][C]1.31812874116394[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]24.6554281666088[/C][C]-3.65542816660885[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.4448123844842[/C][C]-3.44481238448417[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]23.9264831925835[/C][C]-5.92648319258347[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]23.1505882072638[/C][C]-3.15058820726385[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.4701233933996[/C][C]0.529876606600377[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.4170756293606[/C][C]1.58292437063937[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]23.3707531355731[/C][C]-4.37075313557309[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.906282912676[/C][C]0.0937170873239792[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.6327831261639[/C][C]0.367216873836116[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]25.1274850906094[/C][C]-0.1274850906094[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.2349200887337[/C][C]2.76507991126628[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]22.9423332704789[/C][C]6.05766672952113[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]25.2142659177213[/C][C]6.78573408227865[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]21.6589391383298[/C][C]3.34106086167017[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]24.470275094754[/C][C]4.52972490524598[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]25.0279450913147[/C][C]2.97205490868526[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]17.2069821292941[/C][C]-0.206982129294117[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]26.1591905348952[/C][C]1.84080946510476[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]23.0028708032515[/C][C]5.99712919674848[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]27.5355633049122[/C][C]-1.53556330491215[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]23.4810793706953[/C][C]1.51892062930467[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]19.6637513362319[/C][C]-5.66375133623186[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]22.1883705425712[/C][C]2.81162945742885[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.6571447242257[/C][C]4.34285527577433[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]20.2596490514696[/C][C]-0.259649051469627[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]21.4228614759953[/C][C]-3.4228614759953[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]24.7110546322652[/C][C]7.2889453677348[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]25.0263393497554[/C][C]-0.0263393497553644[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]21.8210739933462[/C][C]3.17892600665381[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]20.8677594930622[/C][C]2.13224050693778[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.117854032886[/C][C]-1.11785403288601[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]23.9726413261168[/C][C]-3.97264132611681[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]16.5457006606119[/C][C]-1.54570066061186[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]26.6881828513834[/C][C]3.31181714861657[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]25.2744043336664[/C][C]-1.27440433366639[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]24.2388525586138[/C][C]1.7611474413862[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]21.7359669669679[/C][C]2.26403303303207[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]21.3581754011873[/C][C]0.641824598812662[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]15.7380015387017[/C][C]-1.73800153870174[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]22.1605918193999[/C][C]1.83940818060007[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.9180112554593[/C][C]1.08198874454068[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]23.2964543228427[/C][C]0.703545677157296[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]19.8829114979323[/C][C]4.11708850206774[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]18.4759965405149[/C][C]0.524003459485101[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]26.7304833463546[/C][C]4.26951665364541[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]26.5495294158872[/C][C]-4.54952941588722[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]21.4486621120195[/C][C]5.55133788798047[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]17.6523601250116[/C][C]1.34763987498842[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]22.2181674990078[/C][C]2.78183250099224[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]24.9625665057518[/C][C]-4.96256650575181[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]21.4159350820325[/C][C]-0.415935082032486[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]27.4227120635285[/C][C]-0.422712063528468[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]24.399868173257[/C][C]-1.39986817325701[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.6847420541193[/C][C]-0.684742054119265[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]22.2024072152892[/C][C]-2.20240721528923[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]19.1980099821813[/C][C]1.80199001781872[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]22.3890673931533[/C][C]-0.38906739315325[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]22.8628970178047[/C][C]0.137102982195297[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]24.0662945432902[/C][C]0.933705456709781[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.3049900746637[/C][C]1.69500992533629[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]23.6945505826395[/C][C]-6.69455058263948[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]21.397982885467[/C][C]-2.39798288546698[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]23.8698754371039[/C][C]1.1301245628961[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]22.2719093019712[/C][C]-3.27190930197122[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]23.1090584144252[/C][C]-3.10905841442521[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]22.4589667436964[/C][C]3.54103325630361[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]20.6077905640758[/C][C]2.39220943592425[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]24.3389723071074[/C][C]2.66102769289259[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]20.8386249075823[/C][C]-3.83862490758231[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]23.2983394821097[/C][C]-6.29833948210969[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]19.9942601881693[/C][C]-0.994260188169343[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]19.6494842422993[/C][C]-2.64948424229929[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]22.0232851008073[/C][C]-0.0232851008072989[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]23.3306910343887[/C][C]-2.33069103438873[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]28.554977955948[/C][C]3.44502204405199[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]24.6245509755056[/C][C]-3.62455097550557[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]24.2891262400522[/C][C]-3.28912624005225[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.1740932510251[/C][C]-3.17409325102513[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]21.2400029865128[/C][C]-3.24000298651281[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.7537633290092[/C][C]0.246236670990791[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]20.5465002291185[/C][C]-1.54650022911846[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.9652612427763[/C][C]-0.965261242776263[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]22.1891589001762[/C][C]-1.18915890017617[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]23.6300535236411[/C][C]-3.63005352364107[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]18.8311987042487[/C][C]-1.83119870424873[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]20.2285439329738[/C][C]-2.22854393297381[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]20.7040858341501[/C][C]-1.7040858341501[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]21.982446485771[/C][C]0.0175535142289692[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]18.7011860415034[/C][C]-3.7011860415034[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]18.7396316108277[/C][C]-4.7396316108277[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]26.471435033924[/C][C]-8.47143503392402[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]21.2069537493994[/C][C]2.79304625060059[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]23.5051226996185[/C][C]11.4948773003815[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]18.9021191707563[/C][C]10.0978808292437[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]21.8443033733754[/C][C]-0.844303373375357[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]20.4849070060213[/C][C]4.51509299397872[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]18.3813962323908[/C][C]1.61860376760922[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]23.1087927659848[/C][C]-1.10879276598483[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]16.8342670146989[/C][C]-3.83426701469886[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]23.1038642390464[/C][C]2.89613576095364[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]16.8165564995674[/C][C]0.183443500432641[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]19.9838701968775[/C][C]5.01612980312253[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]20.503457930182[/C][C]-0.503457930182018[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]17.9608114705424[/C][C]1.03918852945758[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]22.5050016345108[/C][C]-1.5050016345108[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]20.8874236381136[/C][C]1.11257636188644[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]22.4751712895552[/C][C]1.52482871044484[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]22.7666882303897[/C][C]-1.76668823038966[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]25.3236884376753[/C][C]0.676311562324675[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]20.4475417530915[/C][C]3.55245824690852[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]20.112599318992[/C][C]-4.11259931899196[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]22.1607585893467[/C][C]0.839241410653303[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.61754812361[/C][C]-2.61754812361002[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]22.1819493252509[/C][C]-6.18194932525091[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]23.9793235421491[/C][C]2.02067645785094[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]18.9400040731225[/C][C]0.0599959268775115[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]16.7994060864538[/C][C]4.20059391354615[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]21.9532949908417[/C][C]-0.953294990841726[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]18.3690292816605[/C][C]3.63097071833954[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]19.6531452281397[/C][C]3.34685477186027[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]24.6571023707593[/C][C]4.3428976292407[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]19.1553290225513[/C][C]1.84467097744868[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]19.7602111315723[/C][C]1.23978886842772[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]21.7151826161722[/C][C]1.28481738382783[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]22.8496187679819[/C][C]4.15038123201806[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]25.2709551798013[/C][C]-0.270955179801297[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]20.8097139725763[/C][C]0.190286027423699[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]16.9811157828466[/C][C]-6.98111578284664[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]22.4381017712208[/C][C]-2.43810177122084[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]22.30309976922[/C][C]3.69690023078004[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.5083186717168[/C][C]0.491681328283223[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]31.5188532187234[/C][C]-2.51885321872343[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]18.9459507782572[/C][C]0.0540492217428303[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]21.9053501077378[/C][C]2.09464989226215[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]20.5742538575748[/C][C]-1.57425385757477[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.2186255812581[/C][C]0.781374418741894[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.6144994694945[/C][C]0.385500530505523[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]23.5852606802824[/C][C]-6.58526068028239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.3788411032986-0.378841103298631
22523.79331745685371.20668254314634
33025.67356066137344.32643933862657
41921.7075046653346-2.70750466533457
52221.98088749353980.0191125064602387
62224.4658886196-2.46588861959995
72522.6882827174512.31171728254899
82319.50271562622023.4972843737798
91718.9427159653088-1.94271596530881
102121.7482044896363-0.74820448963627
111922.8673445099921-3.86734450999214
121923.5975542560347-4.59755425603468
131523.2588137498525-8.25881374985245
141617.2526667160132-1.25266671601317
152319.52259401449243.4774059855076
162724.1058962125582.89410378744204
172221.05356341396180.946436586038195
181416.7079113118806-2.70791131188055
192224.1935877676244-2.19358776762442
202324.1793095883575-1.1793095883575
212321.68187125883611.31812874116394
222124.6554281666088-3.65542816660885
231922.4448123844842-3.44481238448417
241823.9264831925835-5.92648319258347
252023.1505882072638-3.15058820726385
262322.47012339339960.529876606600377
272523.41707562936061.58292437063937
281923.3707531355731-4.37075313557309
292423.9062829126760.0937170873239792
302221.63278312616390.367216873836116
312525.1274850906094-0.1274850906094
322623.23492008873372.76507991126628
332922.94233327047896.05766672952113
343225.21426591772136.78573408227865
352521.65893913832983.34106086167017
362924.4702750947544.52972490524598
372825.02794509131472.97205490868526
381717.2069821292941-0.206982129294117
392826.15919053489521.84080946510476
402923.00287080325155.99712919674848
412627.5355633049122-1.53556330491215
422523.48107937069531.51892062930467
431419.6637513362319-5.66375133623186
442522.18837054257122.81162945742885
452621.65714472422574.34285527577433
462020.2596490514696-0.259649051469627
471821.4228614759953-3.4228614759953
483224.71105463226527.2889453677348
492525.0263393497554-0.0263393497553644
502521.82107399334623.17892600665381
512320.86775949306222.13224050693778
522122.117854032886-1.11785403288601
532023.9726413261168-3.97264132611681
541516.5457006606119-1.54570066061186
553026.68818285138343.31181714861657
562425.2744043336664-1.27440433366639
572624.23885255861381.7611474413862
582421.73596696696792.26403303303207
592221.35817540118730.641824598812662
601415.7380015387017-1.73800153870174
612422.16059181939991.83940818060007
622422.91801125545931.08198874454068
632423.29645432284270.703545677157296
642419.88291149793234.11708850206774
651918.47599654051490.524003459485101
663126.73048334635464.26951665364541
672226.5495294158872-4.54952941588722
682721.44866211201955.55133788798047
691917.65236012501161.34763987498842
702522.21816749900782.78183250099224
712024.9625665057518-4.96256650575181
722121.4159350820325-0.415935082032486
732727.4227120635285-0.422712063528468
742324.399868173257-1.39986817325701
752525.6847420541193-0.684742054119265
762022.2024072152892-2.20240721528923
772119.19800998218131.80199001781872
782222.3890673931533-0.38906739315325
792322.86289701780470.137102982195297
802524.06629454329020.933705456709781
812523.30499007466371.69500992533629
821723.6945505826395-6.69455058263948
831921.397982885467-2.39798288546698
842523.86987543710391.1301245628961
851922.2719093019712-3.27190930197122
862023.1090584144252-3.10905841442521
872622.45896674369643.54103325630361
882320.60779056407582.39220943592425
892724.33897230710742.66102769289259
901720.8386249075823-3.83862490758231
911723.2983394821097-6.29833948210969
921919.9942601881693-0.994260188169343
931719.6494842422993-2.64948424229929
942222.0232851008073-0.0232851008072989
952123.3306910343887-2.33069103438873
963228.5549779559483.44502204405199
972124.6245509755056-3.62455097550557
982124.2891262400522-3.28912624005225
991821.1740932510251-3.17409325102513
1001821.2400029865128-3.24000298651281
1012322.75376332900920.246236670990791
1021920.5465002291185-1.54650022911846
1032020.9652612427763-0.965261242776263
1042122.1891589001762-1.18915890017617
1052023.6300535236411-3.63005352364107
1061718.8311987042487-1.83119870424873
1071820.2285439329738-2.22854393297381
1081920.7040858341501-1.7040858341501
1092221.9824464857710.0175535142289692
1101518.7011860415034-3.7011860415034
1111418.7396316108277-4.7396316108277
1121826.471435033924-8.47143503392402
1132421.20695374939942.79304625060059
1143523.505122699618511.4948773003815
1152918.902119170756310.0978808292437
1162121.8443033733754-0.844303373375357
1172520.48490700602134.51509299397872
1182018.38139623239081.61860376760922
1192223.1087927659848-1.10879276598483
1201316.8342670146989-3.83426701469886
1212623.10386423904642.89613576095364
1221716.81655649956740.183443500432641
1232519.98387019687755.01612980312253
1242020.503457930182-0.503457930182018
1251917.96081147054241.03918852945758
1262122.5050016345108-1.5050016345108
1272220.88742363811361.11257636188644
1282422.47517128955521.52482871044484
1292122.7666882303897-1.76668823038966
1302625.32368843767530.676311562324675
1312420.44754175309153.55245824690852
1321620.112599318992-4.11259931899196
1332322.16075858934670.839241410653303
1341820.61754812361-2.61754812361002
1351622.1819493252509-6.18194932525091
1362623.97932354214912.02067645785094
1371918.94000407312250.0599959268775115
1382116.79940608645384.20059391354615
1392121.9532949908417-0.953294990841726
1402218.36902928166053.63097071833954
1412319.65314522813973.34685477186027
1422924.65710237075934.3428976292407
1432119.15532902255131.84467097744868
1442119.76021113157231.23978886842772
1452321.71518261617221.28481738382783
1462722.84961876798194.15038123201806
1472525.2709551798013-0.270955179801297
1482120.80971397257630.190286027423699
1491016.9811157828466-6.98111578284664
1502022.4381017712208-2.43810177122084
1512622.303099769223.69690023078004
1522423.50831867171680.491681328283223
1532931.5188532187234-2.51885321872343
1541918.94595077825720.0540492217428303
1552421.90535010773782.09464989226215
1561920.5742538575748-1.57425385757477
1572423.21862558125810.781374418741894
1582221.61449946949450.385500530505523
1591723.5852606802824-6.58526068028239







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04722829156910380.09445658313820750.952771708430896
120.01423475691522690.02846951383045370.985765243084773
130.06428842912713660.1285768582542730.935711570872863
140.1235894120732940.2471788241465880.876410587926706
150.5752572659621580.8494854680756840.424742734037842
160.5190109299240040.9619781401519920.480989070075996
170.5652978009178060.8694043981643890.434702199082194
180.5237397530751640.9525204938496720.476260246924836
190.445228556854440.890457113708880.55477144314556
200.5534619081530660.8930761836938680.446538091846934
210.5678230267072470.8643539465855060.432176973292753
220.5002354966453140.9995290067093720.499764503354686
230.4407071025270510.8814142050541030.559292897472949
240.4538287454854840.9076574909709680.546171254514516
250.4004178210394180.8008356420788360.599582178960582
260.3481241895193250.696248379038650.651875810480675
270.3140563948690620.6281127897381240.685943605130938
280.2969530275009180.5939060550018350.703046972499083
290.2589750824994850.517950164998970.741024917500515
300.2079097080670840.4158194161341670.792090291932916
310.1776962699788220.3553925399576430.822303730021178
320.2182863609328530.4365727218657070.781713639067147
330.3035728642013540.6071457284027090.696427135798646
340.5199764599805480.9600470800389040.480023540019452
350.4703273849669170.9406547699338330.529672615033083
360.477197878354430.954395756708860.52280212164557
370.4390928171321680.8781856342643360.560907182867832
380.4688913116812890.9377826233625780.531108688318711
390.4160642342853650.832128468570730.583935765714635
400.4422206903109180.8844413806218360.557779309689082
410.4325939786516780.8651879573033560.567406021348322
420.3823175596910780.7646351193821560.617682440308922
430.5935699033805340.8128601932389330.406430096619466
440.555139652780360.8897206944392810.44486034721964
450.5417994624942510.9164010750114970.458200537505749
460.503485339228550.99302932154290.49651466077145
470.5275450363200120.9449099273599760.472454963679988
480.6040246424317870.7919507151364260.395975357568213
490.5738966906887210.8522066186225580.426103309311279
500.5445301986974390.9109396026051210.455469801302561
510.5067728358781860.9864543282436280.493227164121814
520.4822346953744590.9644693907489180.517765304625541
530.5420383593664410.9159232812671170.457961640633559
540.5196499448668810.9607001102662380.480350055133119
550.5415717180010030.9168565639979940.458428281998997
560.5172282634915090.9655434730169820.482771736508491
570.4730438873092590.9460877746185180.526956112690741
580.4370269246820280.8740538493640560.562973075317972
590.3911213756227720.7822427512455450.608878624377228
600.3588281344382490.7176562688764990.641171865561751
610.3214090744543710.6428181489087420.678590925545629
620.287047696003950.57409539200790.71295230399605
630.2511225547303470.5022451094606940.748877445269653
640.2666858087665330.5333716175330660.733314191233467
650.2309913417720860.4619826835441720.769008658227914
660.2393880594829660.4787761189659320.760611940517034
670.325787745537040.6515754910740810.67421225446296
680.3835115225924250.7670230451848490.616488477407575
690.3460888537582040.6921777075164080.653911146241796
700.3280864792706640.6561729585413290.671913520729336
710.4219110962239030.8438221924478060.578088903776097
720.3805870270403220.7611740540806430.619412972959678
730.3468214166331670.6936428332663340.653178583366833
740.3197232658866620.6394465317733240.680276734113338
750.2955486889979140.5910973779958290.704451311002086
760.2742375205903490.5484750411806980.725762479409651
770.2554859001749830.5109718003499660.744514099825017
780.2220829921412310.4441659842824620.777917007858769
790.1914497904946980.3828995809893960.808550209505302
800.1691749821725430.3383499643450870.830825017827457
810.151373914628040.3027478292560790.84862608537196
820.2370008817382360.4740017634764720.762999118261764
830.2165460647169560.4330921294339110.783453935283044
840.1905245078201370.3810490156402750.809475492179862
850.1882707148200230.3765414296400470.811729285179977
860.1805176779551490.3610353559102980.819482322044851
870.1969384581087520.3938769162175030.803061541891248
880.1920268820690320.3840537641380640.807973117930968
890.1869700163354740.3739400326709480.813029983664526
900.1844095204967170.3688190409934340.815590479503283
910.2439201077217890.4878402154435780.756079892278211
920.2084096284178740.4168192568357470.791590371582126
930.186249858358830.372499716717660.81375014164117
940.1590043181548980.3180086363097970.840995681845102
950.1393464503664770.2786929007329540.860653549633523
960.1541916192471470.3083832384942940.845808380752853
970.1456983301142270.2913966602284550.854301669885773
980.1327881914340420.2655763828680850.867211808565958
990.1199699406733070.2399398813466130.880030059326693
1000.1097075361899590.2194150723799180.890292463810041
1010.08985471714296060.1797094342859210.910145282857039
1020.07245056457998350.1449011291599670.927549435420017
1030.05714926471001750.1142985294200350.942850735289983
1040.04502665378141750.09005330756283510.954973346218582
1050.04595455373318260.09190910746636510.954045446266817
1060.03685709577249710.07371419154499410.963142904227503
1070.03236092348534670.06472184697069340.967639076514653
1080.02962589002216280.05925178004432560.970374109977837
1090.02333455766980110.04666911533960220.976665442330199
1100.02422052390895330.04844104781790670.975779476091047
1110.03404209635046580.06808419270093170.965957903649534
1120.2349767701501450.469953540300290.765023229849855
1130.2474928219535890.4949856439071780.752507178046411
1140.6059989473663670.7880021052672660.394001052633633
1150.8651558243698180.2696883512603640.134844175630182
1160.8336914448481460.3326171103037080.166308555151854
1170.8631133490050830.2737733019898350.136886650994917
1180.8344859960190110.3310280079619770.165514003980989
1190.8128580040534680.3742839918930630.187141995946532
1200.8690668927486690.2618662145026620.130933107251331
1210.8416952732787810.3166094534424390.158304726721219
1220.8069660577661390.3860678844677220.193033942233861
1230.8151220725298970.3697558549402070.184877927470103
1240.7714562090075410.4570875819849170.228543790992459
1250.7367995567907320.5264008864185370.263200443209268
1260.6976967462481020.6046065075037950.302303253751898
1270.6488824923881050.702235015223790.351117507611895
1280.596281405803020.807437188393960.40371859419698
1290.5409657309752050.9180685380495910.459034269024796
1300.4897388388030710.9794776776061430.510261161196929
1310.4612472291117720.9224944582235440.538752770888228
1320.4846459758603470.9692919517206940.515354024139653
1330.4196619159123530.8393238318247050.580338084087647
1340.3903036743217820.7806073486435640.609696325678218
1350.6910763844065840.6178472311868310.308923615593416
1360.6219947205760280.7560105588479430.378005279423972
1370.6079209324675140.7841581350649720.392079067532486
1380.552063011598180.8958739768036410.44793698840182
1390.5756137373693290.8487725252613410.424386262630671
1400.498889616226110.9977792324522210.50111038377389
1410.4366529084895710.8733058169791420.563347091510429
1420.393752184975430.787504369950860.60624781502457
1430.4121081428384770.8242162856769540.587891857161523
1440.3524302428042630.7048604856085250.647569757195737
1450.2529538611569670.5059077223139350.747046138843033
1460.2160731281626150.4321462563252290.783926871837386
1470.1748569856346320.3497139712692640.825143014365368
1480.09686044374374830.1937208874874970.903139556256252

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0472282915691038 & 0.0944565831382075 & 0.952771708430896 \tabularnewline
12 & 0.0142347569152269 & 0.0284695138304537 & 0.985765243084773 \tabularnewline
13 & 0.0642884291271366 & 0.128576858254273 & 0.935711570872863 \tabularnewline
14 & 0.123589412073294 & 0.247178824146588 & 0.876410587926706 \tabularnewline
15 & 0.575257265962158 & 0.849485468075684 & 0.424742734037842 \tabularnewline
16 & 0.519010929924004 & 0.961978140151992 & 0.480989070075996 \tabularnewline
17 & 0.565297800917806 & 0.869404398164389 & 0.434702199082194 \tabularnewline
18 & 0.523739753075164 & 0.952520493849672 & 0.476260246924836 \tabularnewline
19 & 0.44522855685444 & 0.89045711370888 & 0.55477144314556 \tabularnewline
20 & 0.553461908153066 & 0.893076183693868 & 0.446538091846934 \tabularnewline
21 & 0.567823026707247 & 0.864353946585506 & 0.432176973292753 \tabularnewline
22 & 0.500235496645314 & 0.999529006709372 & 0.499764503354686 \tabularnewline
23 & 0.440707102527051 & 0.881414205054103 & 0.559292897472949 \tabularnewline
24 & 0.453828745485484 & 0.907657490970968 & 0.546171254514516 \tabularnewline
25 & 0.400417821039418 & 0.800835642078836 & 0.599582178960582 \tabularnewline
26 & 0.348124189519325 & 0.69624837903865 & 0.651875810480675 \tabularnewline
27 & 0.314056394869062 & 0.628112789738124 & 0.685943605130938 \tabularnewline
28 & 0.296953027500918 & 0.593906055001835 & 0.703046972499083 \tabularnewline
29 & 0.258975082499485 & 0.51795016499897 & 0.741024917500515 \tabularnewline
30 & 0.207909708067084 & 0.415819416134167 & 0.792090291932916 \tabularnewline
31 & 0.177696269978822 & 0.355392539957643 & 0.822303730021178 \tabularnewline
32 & 0.218286360932853 & 0.436572721865707 & 0.781713639067147 \tabularnewline
33 & 0.303572864201354 & 0.607145728402709 & 0.696427135798646 \tabularnewline
34 & 0.519976459980548 & 0.960047080038904 & 0.480023540019452 \tabularnewline
35 & 0.470327384966917 & 0.940654769933833 & 0.529672615033083 \tabularnewline
36 & 0.47719787835443 & 0.95439575670886 & 0.52280212164557 \tabularnewline
37 & 0.439092817132168 & 0.878185634264336 & 0.560907182867832 \tabularnewline
38 & 0.468891311681289 & 0.937782623362578 & 0.531108688318711 \tabularnewline
39 & 0.416064234285365 & 0.83212846857073 & 0.583935765714635 \tabularnewline
40 & 0.442220690310918 & 0.884441380621836 & 0.557779309689082 \tabularnewline
41 & 0.432593978651678 & 0.865187957303356 & 0.567406021348322 \tabularnewline
42 & 0.382317559691078 & 0.764635119382156 & 0.617682440308922 \tabularnewline
43 & 0.593569903380534 & 0.812860193238933 & 0.406430096619466 \tabularnewline
44 & 0.55513965278036 & 0.889720694439281 & 0.44486034721964 \tabularnewline
45 & 0.541799462494251 & 0.916401075011497 & 0.458200537505749 \tabularnewline
46 & 0.50348533922855 & 0.9930293215429 & 0.49651466077145 \tabularnewline
47 & 0.527545036320012 & 0.944909927359976 & 0.472454963679988 \tabularnewline
48 & 0.604024642431787 & 0.791950715136426 & 0.395975357568213 \tabularnewline
49 & 0.573896690688721 & 0.852206618622558 & 0.426103309311279 \tabularnewline
50 & 0.544530198697439 & 0.910939602605121 & 0.455469801302561 \tabularnewline
51 & 0.506772835878186 & 0.986454328243628 & 0.493227164121814 \tabularnewline
52 & 0.482234695374459 & 0.964469390748918 & 0.517765304625541 \tabularnewline
53 & 0.542038359366441 & 0.915923281267117 & 0.457961640633559 \tabularnewline
54 & 0.519649944866881 & 0.960700110266238 & 0.480350055133119 \tabularnewline
55 & 0.541571718001003 & 0.916856563997994 & 0.458428281998997 \tabularnewline
56 & 0.517228263491509 & 0.965543473016982 & 0.482771736508491 \tabularnewline
57 & 0.473043887309259 & 0.946087774618518 & 0.526956112690741 \tabularnewline
58 & 0.437026924682028 & 0.874053849364056 & 0.562973075317972 \tabularnewline
59 & 0.391121375622772 & 0.782242751245545 & 0.608878624377228 \tabularnewline
60 & 0.358828134438249 & 0.717656268876499 & 0.641171865561751 \tabularnewline
61 & 0.321409074454371 & 0.642818148908742 & 0.678590925545629 \tabularnewline
62 & 0.28704769600395 & 0.5740953920079 & 0.71295230399605 \tabularnewline
63 & 0.251122554730347 & 0.502245109460694 & 0.748877445269653 \tabularnewline
64 & 0.266685808766533 & 0.533371617533066 & 0.733314191233467 \tabularnewline
65 & 0.230991341772086 & 0.461982683544172 & 0.769008658227914 \tabularnewline
66 & 0.239388059482966 & 0.478776118965932 & 0.760611940517034 \tabularnewline
67 & 0.32578774553704 & 0.651575491074081 & 0.67421225446296 \tabularnewline
68 & 0.383511522592425 & 0.767023045184849 & 0.616488477407575 \tabularnewline
69 & 0.346088853758204 & 0.692177707516408 & 0.653911146241796 \tabularnewline
70 & 0.328086479270664 & 0.656172958541329 & 0.671913520729336 \tabularnewline
71 & 0.421911096223903 & 0.843822192447806 & 0.578088903776097 \tabularnewline
72 & 0.380587027040322 & 0.761174054080643 & 0.619412972959678 \tabularnewline
73 & 0.346821416633167 & 0.693642833266334 & 0.653178583366833 \tabularnewline
74 & 0.319723265886662 & 0.639446531773324 & 0.680276734113338 \tabularnewline
75 & 0.295548688997914 & 0.591097377995829 & 0.704451311002086 \tabularnewline
76 & 0.274237520590349 & 0.548475041180698 & 0.725762479409651 \tabularnewline
77 & 0.255485900174983 & 0.510971800349966 & 0.744514099825017 \tabularnewline
78 & 0.222082992141231 & 0.444165984282462 & 0.777917007858769 \tabularnewline
79 & 0.191449790494698 & 0.382899580989396 & 0.808550209505302 \tabularnewline
80 & 0.169174982172543 & 0.338349964345087 & 0.830825017827457 \tabularnewline
81 & 0.15137391462804 & 0.302747829256079 & 0.84862608537196 \tabularnewline
82 & 0.237000881738236 & 0.474001763476472 & 0.762999118261764 \tabularnewline
83 & 0.216546064716956 & 0.433092129433911 & 0.783453935283044 \tabularnewline
84 & 0.190524507820137 & 0.381049015640275 & 0.809475492179862 \tabularnewline
85 & 0.188270714820023 & 0.376541429640047 & 0.811729285179977 \tabularnewline
86 & 0.180517677955149 & 0.361035355910298 & 0.819482322044851 \tabularnewline
87 & 0.196938458108752 & 0.393876916217503 & 0.803061541891248 \tabularnewline
88 & 0.192026882069032 & 0.384053764138064 & 0.807973117930968 \tabularnewline
89 & 0.186970016335474 & 0.373940032670948 & 0.813029983664526 \tabularnewline
90 & 0.184409520496717 & 0.368819040993434 & 0.815590479503283 \tabularnewline
91 & 0.243920107721789 & 0.487840215443578 & 0.756079892278211 \tabularnewline
92 & 0.208409628417874 & 0.416819256835747 & 0.791590371582126 \tabularnewline
93 & 0.18624985835883 & 0.37249971671766 & 0.81375014164117 \tabularnewline
94 & 0.159004318154898 & 0.318008636309797 & 0.840995681845102 \tabularnewline
95 & 0.139346450366477 & 0.278692900732954 & 0.860653549633523 \tabularnewline
96 & 0.154191619247147 & 0.308383238494294 & 0.845808380752853 \tabularnewline
97 & 0.145698330114227 & 0.291396660228455 & 0.854301669885773 \tabularnewline
98 & 0.132788191434042 & 0.265576382868085 & 0.867211808565958 \tabularnewline
99 & 0.119969940673307 & 0.239939881346613 & 0.880030059326693 \tabularnewline
100 & 0.109707536189959 & 0.219415072379918 & 0.890292463810041 \tabularnewline
101 & 0.0898547171429606 & 0.179709434285921 & 0.910145282857039 \tabularnewline
102 & 0.0724505645799835 & 0.144901129159967 & 0.927549435420017 \tabularnewline
103 & 0.0571492647100175 & 0.114298529420035 & 0.942850735289983 \tabularnewline
104 & 0.0450266537814175 & 0.0900533075628351 & 0.954973346218582 \tabularnewline
105 & 0.0459545537331826 & 0.0919091074663651 & 0.954045446266817 \tabularnewline
106 & 0.0368570957724971 & 0.0737141915449941 & 0.963142904227503 \tabularnewline
107 & 0.0323609234853467 & 0.0647218469706934 & 0.967639076514653 \tabularnewline
108 & 0.0296258900221628 & 0.0592517800443256 & 0.970374109977837 \tabularnewline
109 & 0.0233345576698011 & 0.0466691153396022 & 0.976665442330199 \tabularnewline
110 & 0.0242205239089533 & 0.0484410478179067 & 0.975779476091047 \tabularnewline
111 & 0.0340420963504658 & 0.0680841927009317 & 0.965957903649534 \tabularnewline
112 & 0.234976770150145 & 0.46995354030029 & 0.765023229849855 \tabularnewline
113 & 0.247492821953589 & 0.494985643907178 & 0.752507178046411 \tabularnewline
114 & 0.605998947366367 & 0.788002105267266 & 0.394001052633633 \tabularnewline
115 & 0.865155824369818 & 0.269688351260364 & 0.134844175630182 \tabularnewline
116 & 0.833691444848146 & 0.332617110303708 & 0.166308555151854 \tabularnewline
117 & 0.863113349005083 & 0.273773301989835 & 0.136886650994917 \tabularnewline
118 & 0.834485996019011 & 0.331028007961977 & 0.165514003980989 \tabularnewline
119 & 0.812858004053468 & 0.374283991893063 & 0.187141995946532 \tabularnewline
120 & 0.869066892748669 & 0.261866214502662 & 0.130933107251331 \tabularnewline
121 & 0.841695273278781 & 0.316609453442439 & 0.158304726721219 \tabularnewline
122 & 0.806966057766139 & 0.386067884467722 & 0.193033942233861 \tabularnewline
123 & 0.815122072529897 & 0.369755854940207 & 0.184877927470103 \tabularnewline
124 & 0.771456209007541 & 0.457087581984917 & 0.228543790992459 \tabularnewline
125 & 0.736799556790732 & 0.526400886418537 & 0.263200443209268 \tabularnewline
126 & 0.697696746248102 & 0.604606507503795 & 0.302303253751898 \tabularnewline
127 & 0.648882492388105 & 0.70223501522379 & 0.351117507611895 \tabularnewline
128 & 0.59628140580302 & 0.80743718839396 & 0.40371859419698 \tabularnewline
129 & 0.540965730975205 & 0.918068538049591 & 0.459034269024796 \tabularnewline
130 & 0.489738838803071 & 0.979477677606143 & 0.510261161196929 \tabularnewline
131 & 0.461247229111772 & 0.922494458223544 & 0.538752770888228 \tabularnewline
132 & 0.484645975860347 & 0.969291951720694 & 0.515354024139653 \tabularnewline
133 & 0.419661915912353 & 0.839323831824705 & 0.580338084087647 \tabularnewline
134 & 0.390303674321782 & 0.780607348643564 & 0.609696325678218 \tabularnewline
135 & 0.691076384406584 & 0.617847231186831 & 0.308923615593416 \tabularnewline
136 & 0.621994720576028 & 0.756010558847943 & 0.378005279423972 \tabularnewline
137 & 0.607920932467514 & 0.784158135064972 & 0.392079067532486 \tabularnewline
138 & 0.55206301159818 & 0.895873976803641 & 0.44793698840182 \tabularnewline
139 & 0.575613737369329 & 0.848772525261341 & 0.424386262630671 \tabularnewline
140 & 0.49888961622611 & 0.997779232452221 & 0.50111038377389 \tabularnewline
141 & 0.436652908489571 & 0.873305816979142 & 0.563347091510429 \tabularnewline
142 & 0.39375218497543 & 0.78750436995086 & 0.60624781502457 \tabularnewline
143 & 0.412108142838477 & 0.824216285676954 & 0.587891857161523 \tabularnewline
144 & 0.352430242804263 & 0.704860485608525 & 0.647569757195737 \tabularnewline
145 & 0.252953861156967 & 0.505907722313935 & 0.747046138843033 \tabularnewline
146 & 0.216073128162615 & 0.432146256325229 & 0.783926871837386 \tabularnewline
147 & 0.174856985634632 & 0.349713971269264 & 0.825143014365368 \tabularnewline
148 & 0.0968604437437483 & 0.193720887487497 & 0.903139556256252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&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]11[/C][C]0.0472282915691038[/C][C]0.0944565831382075[/C][C]0.952771708430896[/C][/ROW]
[ROW][C]12[/C][C]0.0142347569152269[/C][C]0.0284695138304537[/C][C]0.985765243084773[/C][/ROW]
[ROW][C]13[/C][C]0.0642884291271366[/C][C]0.128576858254273[/C][C]0.935711570872863[/C][/ROW]
[ROW][C]14[/C][C]0.123589412073294[/C][C]0.247178824146588[/C][C]0.876410587926706[/C][/ROW]
[ROW][C]15[/C][C]0.575257265962158[/C][C]0.849485468075684[/C][C]0.424742734037842[/C][/ROW]
[ROW][C]16[/C][C]0.519010929924004[/C][C]0.961978140151992[/C][C]0.480989070075996[/C][/ROW]
[ROW][C]17[/C][C]0.565297800917806[/C][C]0.869404398164389[/C][C]0.434702199082194[/C][/ROW]
[ROW][C]18[/C][C]0.523739753075164[/C][C]0.952520493849672[/C][C]0.476260246924836[/C][/ROW]
[ROW][C]19[/C][C]0.44522855685444[/C][C]0.89045711370888[/C][C]0.55477144314556[/C][/ROW]
[ROW][C]20[/C][C]0.553461908153066[/C][C]0.893076183693868[/C][C]0.446538091846934[/C][/ROW]
[ROW][C]21[/C][C]0.567823026707247[/C][C]0.864353946585506[/C][C]0.432176973292753[/C][/ROW]
[ROW][C]22[/C][C]0.500235496645314[/C][C]0.999529006709372[/C][C]0.499764503354686[/C][/ROW]
[ROW][C]23[/C][C]0.440707102527051[/C][C]0.881414205054103[/C][C]0.559292897472949[/C][/ROW]
[ROW][C]24[/C][C]0.453828745485484[/C][C]0.907657490970968[/C][C]0.546171254514516[/C][/ROW]
[ROW][C]25[/C][C]0.400417821039418[/C][C]0.800835642078836[/C][C]0.599582178960582[/C][/ROW]
[ROW][C]26[/C][C]0.348124189519325[/C][C]0.69624837903865[/C][C]0.651875810480675[/C][/ROW]
[ROW][C]27[/C][C]0.314056394869062[/C][C]0.628112789738124[/C][C]0.685943605130938[/C][/ROW]
[ROW][C]28[/C][C]0.296953027500918[/C][C]0.593906055001835[/C][C]0.703046972499083[/C][/ROW]
[ROW][C]29[/C][C]0.258975082499485[/C][C]0.51795016499897[/C][C]0.741024917500515[/C][/ROW]
[ROW][C]30[/C][C]0.207909708067084[/C][C]0.415819416134167[/C][C]0.792090291932916[/C][/ROW]
[ROW][C]31[/C][C]0.177696269978822[/C][C]0.355392539957643[/C][C]0.822303730021178[/C][/ROW]
[ROW][C]32[/C][C]0.218286360932853[/C][C]0.436572721865707[/C][C]0.781713639067147[/C][/ROW]
[ROW][C]33[/C][C]0.303572864201354[/C][C]0.607145728402709[/C][C]0.696427135798646[/C][/ROW]
[ROW][C]34[/C][C]0.519976459980548[/C][C]0.960047080038904[/C][C]0.480023540019452[/C][/ROW]
[ROW][C]35[/C][C]0.470327384966917[/C][C]0.940654769933833[/C][C]0.529672615033083[/C][/ROW]
[ROW][C]36[/C][C]0.47719787835443[/C][C]0.95439575670886[/C][C]0.52280212164557[/C][/ROW]
[ROW][C]37[/C][C]0.439092817132168[/C][C]0.878185634264336[/C][C]0.560907182867832[/C][/ROW]
[ROW][C]38[/C][C]0.468891311681289[/C][C]0.937782623362578[/C][C]0.531108688318711[/C][/ROW]
[ROW][C]39[/C][C]0.416064234285365[/C][C]0.83212846857073[/C][C]0.583935765714635[/C][/ROW]
[ROW][C]40[/C][C]0.442220690310918[/C][C]0.884441380621836[/C][C]0.557779309689082[/C][/ROW]
[ROW][C]41[/C][C]0.432593978651678[/C][C]0.865187957303356[/C][C]0.567406021348322[/C][/ROW]
[ROW][C]42[/C][C]0.382317559691078[/C][C]0.764635119382156[/C][C]0.617682440308922[/C][/ROW]
[ROW][C]43[/C][C]0.593569903380534[/C][C]0.812860193238933[/C][C]0.406430096619466[/C][/ROW]
[ROW][C]44[/C][C]0.55513965278036[/C][C]0.889720694439281[/C][C]0.44486034721964[/C][/ROW]
[ROW][C]45[/C][C]0.541799462494251[/C][C]0.916401075011497[/C][C]0.458200537505749[/C][/ROW]
[ROW][C]46[/C][C]0.50348533922855[/C][C]0.9930293215429[/C][C]0.49651466077145[/C][/ROW]
[ROW][C]47[/C][C]0.527545036320012[/C][C]0.944909927359976[/C][C]0.472454963679988[/C][/ROW]
[ROW][C]48[/C][C]0.604024642431787[/C][C]0.791950715136426[/C][C]0.395975357568213[/C][/ROW]
[ROW][C]49[/C][C]0.573896690688721[/C][C]0.852206618622558[/C][C]0.426103309311279[/C][/ROW]
[ROW][C]50[/C][C]0.544530198697439[/C][C]0.910939602605121[/C][C]0.455469801302561[/C][/ROW]
[ROW][C]51[/C][C]0.506772835878186[/C][C]0.986454328243628[/C][C]0.493227164121814[/C][/ROW]
[ROW][C]52[/C][C]0.482234695374459[/C][C]0.964469390748918[/C][C]0.517765304625541[/C][/ROW]
[ROW][C]53[/C][C]0.542038359366441[/C][C]0.915923281267117[/C][C]0.457961640633559[/C][/ROW]
[ROW][C]54[/C][C]0.519649944866881[/C][C]0.960700110266238[/C][C]0.480350055133119[/C][/ROW]
[ROW][C]55[/C][C]0.541571718001003[/C][C]0.916856563997994[/C][C]0.458428281998997[/C][/ROW]
[ROW][C]56[/C][C]0.517228263491509[/C][C]0.965543473016982[/C][C]0.482771736508491[/C][/ROW]
[ROW][C]57[/C][C]0.473043887309259[/C][C]0.946087774618518[/C][C]0.526956112690741[/C][/ROW]
[ROW][C]58[/C][C]0.437026924682028[/C][C]0.874053849364056[/C][C]0.562973075317972[/C][/ROW]
[ROW][C]59[/C][C]0.391121375622772[/C][C]0.782242751245545[/C][C]0.608878624377228[/C][/ROW]
[ROW][C]60[/C][C]0.358828134438249[/C][C]0.717656268876499[/C][C]0.641171865561751[/C][/ROW]
[ROW][C]61[/C][C]0.321409074454371[/C][C]0.642818148908742[/C][C]0.678590925545629[/C][/ROW]
[ROW][C]62[/C][C]0.28704769600395[/C][C]0.5740953920079[/C][C]0.71295230399605[/C][/ROW]
[ROW][C]63[/C][C]0.251122554730347[/C][C]0.502245109460694[/C][C]0.748877445269653[/C][/ROW]
[ROW][C]64[/C][C]0.266685808766533[/C][C]0.533371617533066[/C][C]0.733314191233467[/C][/ROW]
[ROW][C]65[/C][C]0.230991341772086[/C][C]0.461982683544172[/C][C]0.769008658227914[/C][/ROW]
[ROW][C]66[/C][C]0.239388059482966[/C][C]0.478776118965932[/C][C]0.760611940517034[/C][/ROW]
[ROW][C]67[/C][C]0.32578774553704[/C][C]0.651575491074081[/C][C]0.67421225446296[/C][/ROW]
[ROW][C]68[/C][C]0.383511522592425[/C][C]0.767023045184849[/C][C]0.616488477407575[/C][/ROW]
[ROW][C]69[/C][C]0.346088853758204[/C][C]0.692177707516408[/C][C]0.653911146241796[/C][/ROW]
[ROW][C]70[/C][C]0.328086479270664[/C][C]0.656172958541329[/C][C]0.671913520729336[/C][/ROW]
[ROW][C]71[/C][C]0.421911096223903[/C][C]0.843822192447806[/C][C]0.578088903776097[/C][/ROW]
[ROW][C]72[/C][C]0.380587027040322[/C][C]0.761174054080643[/C][C]0.619412972959678[/C][/ROW]
[ROW][C]73[/C][C]0.346821416633167[/C][C]0.693642833266334[/C][C]0.653178583366833[/C][/ROW]
[ROW][C]74[/C][C]0.319723265886662[/C][C]0.639446531773324[/C][C]0.680276734113338[/C][/ROW]
[ROW][C]75[/C][C]0.295548688997914[/C][C]0.591097377995829[/C][C]0.704451311002086[/C][/ROW]
[ROW][C]76[/C][C]0.274237520590349[/C][C]0.548475041180698[/C][C]0.725762479409651[/C][/ROW]
[ROW][C]77[/C][C]0.255485900174983[/C][C]0.510971800349966[/C][C]0.744514099825017[/C][/ROW]
[ROW][C]78[/C][C]0.222082992141231[/C][C]0.444165984282462[/C][C]0.777917007858769[/C][/ROW]
[ROW][C]79[/C][C]0.191449790494698[/C][C]0.382899580989396[/C][C]0.808550209505302[/C][/ROW]
[ROW][C]80[/C][C]0.169174982172543[/C][C]0.338349964345087[/C][C]0.830825017827457[/C][/ROW]
[ROW][C]81[/C][C]0.15137391462804[/C][C]0.302747829256079[/C][C]0.84862608537196[/C][/ROW]
[ROW][C]82[/C][C]0.237000881738236[/C][C]0.474001763476472[/C][C]0.762999118261764[/C][/ROW]
[ROW][C]83[/C][C]0.216546064716956[/C][C]0.433092129433911[/C][C]0.783453935283044[/C][/ROW]
[ROW][C]84[/C][C]0.190524507820137[/C][C]0.381049015640275[/C][C]0.809475492179862[/C][/ROW]
[ROW][C]85[/C][C]0.188270714820023[/C][C]0.376541429640047[/C][C]0.811729285179977[/C][/ROW]
[ROW][C]86[/C][C]0.180517677955149[/C][C]0.361035355910298[/C][C]0.819482322044851[/C][/ROW]
[ROW][C]87[/C][C]0.196938458108752[/C][C]0.393876916217503[/C][C]0.803061541891248[/C][/ROW]
[ROW][C]88[/C][C]0.192026882069032[/C][C]0.384053764138064[/C][C]0.807973117930968[/C][/ROW]
[ROW][C]89[/C][C]0.186970016335474[/C][C]0.373940032670948[/C][C]0.813029983664526[/C][/ROW]
[ROW][C]90[/C][C]0.184409520496717[/C][C]0.368819040993434[/C][C]0.815590479503283[/C][/ROW]
[ROW][C]91[/C][C]0.243920107721789[/C][C]0.487840215443578[/C][C]0.756079892278211[/C][/ROW]
[ROW][C]92[/C][C]0.208409628417874[/C][C]0.416819256835747[/C][C]0.791590371582126[/C][/ROW]
[ROW][C]93[/C][C]0.18624985835883[/C][C]0.37249971671766[/C][C]0.81375014164117[/C][/ROW]
[ROW][C]94[/C][C]0.159004318154898[/C][C]0.318008636309797[/C][C]0.840995681845102[/C][/ROW]
[ROW][C]95[/C][C]0.139346450366477[/C][C]0.278692900732954[/C][C]0.860653549633523[/C][/ROW]
[ROW][C]96[/C][C]0.154191619247147[/C][C]0.308383238494294[/C][C]0.845808380752853[/C][/ROW]
[ROW][C]97[/C][C]0.145698330114227[/C][C]0.291396660228455[/C][C]0.854301669885773[/C][/ROW]
[ROW][C]98[/C][C]0.132788191434042[/C][C]0.265576382868085[/C][C]0.867211808565958[/C][/ROW]
[ROW][C]99[/C][C]0.119969940673307[/C][C]0.239939881346613[/C][C]0.880030059326693[/C][/ROW]
[ROW][C]100[/C][C]0.109707536189959[/C][C]0.219415072379918[/C][C]0.890292463810041[/C][/ROW]
[ROW][C]101[/C][C]0.0898547171429606[/C][C]0.179709434285921[/C][C]0.910145282857039[/C][/ROW]
[ROW][C]102[/C][C]0.0724505645799835[/C][C]0.144901129159967[/C][C]0.927549435420017[/C][/ROW]
[ROW][C]103[/C][C]0.0571492647100175[/C][C]0.114298529420035[/C][C]0.942850735289983[/C][/ROW]
[ROW][C]104[/C][C]0.0450266537814175[/C][C]0.0900533075628351[/C][C]0.954973346218582[/C][/ROW]
[ROW][C]105[/C][C]0.0459545537331826[/C][C]0.0919091074663651[/C][C]0.954045446266817[/C][/ROW]
[ROW][C]106[/C][C]0.0368570957724971[/C][C]0.0737141915449941[/C][C]0.963142904227503[/C][/ROW]
[ROW][C]107[/C][C]0.0323609234853467[/C][C]0.0647218469706934[/C][C]0.967639076514653[/C][/ROW]
[ROW][C]108[/C][C]0.0296258900221628[/C][C]0.0592517800443256[/C][C]0.970374109977837[/C][/ROW]
[ROW][C]109[/C][C]0.0233345576698011[/C][C]0.0466691153396022[/C][C]0.976665442330199[/C][/ROW]
[ROW][C]110[/C][C]0.0242205239089533[/C][C]0.0484410478179067[/C][C]0.975779476091047[/C][/ROW]
[ROW][C]111[/C][C]0.0340420963504658[/C][C]0.0680841927009317[/C][C]0.965957903649534[/C][/ROW]
[ROW][C]112[/C][C]0.234976770150145[/C][C]0.46995354030029[/C][C]0.765023229849855[/C][/ROW]
[ROW][C]113[/C][C]0.247492821953589[/C][C]0.494985643907178[/C][C]0.752507178046411[/C][/ROW]
[ROW][C]114[/C][C]0.605998947366367[/C][C]0.788002105267266[/C][C]0.394001052633633[/C][/ROW]
[ROW][C]115[/C][C]0.865155824369818[/C][C]0.269688351260364[/C][C]0.134844175630182[/C][/ROW]
[ROW][C]116[/C][C]0.833691444848146[/C][C]0.332617110303708[/C][C]0.166308555151854[/C][/ROW]
[ROW][C]117[/C][C]0.863113349005083[/C][C]0.273773301989835[/C][C]0.136886650994917[/C][/ROW]
[ROW][C]118[/C][C]0.834485996019011[/C][C]0.331028007961977[/C][C]0.165514003980989[/C][/ROW]
[ROW][C]119[/C][C]0.812858004053468[/C][C]0.374283991893063[/C][C]0.187141995946532[/C][/ROW]
[ROW][C]120[/C][C]0.869066892748669[/C][C]0.261866214502662[/C][C]0.130933107251331[/C][/ROW]
[ROW][C]121[/C][C]0.841695273278781[/C][C]0.316609453442439[/C][C]0.158304726721219[/C][/ROW]
[ROW][C]122[/C][C]0.806966057766139[/C][C]0.386067884467722[/C][C]0.193033942233861[/C][/ROW]
[ROW][C]123[/C][C]0.815122072529897[/C][C]0.369755854940207[/C][C]0.184877927470103[/C][/ROW]
[ROW][C]124[/C][C]0.771456209007541[/C][C]0.457087581984917[/C][C]0.228543790992459[/C][/ROW]
[ROW][C]125[/C][C]0.736799556790732[/C][C]0.526400886418537[/C][C]0.263200443209268[/C][/ROW]
[ROW][C]126[/C][C]0.697696746248102[/C][C]0.604606507503795[/C][C]0.302303253751898[/C][/ROW]
[ROW][C]127[/C][C]0.648882492388105[/C][C]0.70223501522379[/C][C]0.351117507611895[/C][/ROW]
[ROW][C]128[/C][C]0.59628140580302[/C][C]0.80743718839396[/C][C]0.40371859419698[/C][/ROW]
[ROW][C]129[/C][C]0.540965730975205[/C][C]0.918068538049591[/C][C]0.459034269024796[/C][/ROW]
[ROW][C]130[/C][C]0.489738838803071[/C][C]0.979477677606143[/C][C]0.510261161196929[/C][/ROW]
[ROW][C]131[/C][C]0.461247229111772[/C][C]0.922494458223544[/C][C]0.538752770888228[/C][/ROW]
[ROW][C]132[/C][C]0.484645975860347[/C][C]0.969291951720694[/C][C]0.515354024139653[/C][/ROW]
[ROW][C]133[/C][C]0.419661915912353[/C][C]0.839323831824705[/C][C]0.580338084087647[/C][/ROW]
[ROW][C]134[/C][C]0.390303674321782[/C][C]0.780607348643564[/C][C]0.609696325678218[/C][/ROW]
[ROW][C]135[/C][C]0.691076384406584[/C][C]0.617847231186831[/C][C]0.308923615593416[/C][/ROW]
[ROW][C]136[/C][C]0.621994720576028[/C][C]0.756010558847943[/C][C]0.378005279423972[/C][/ROW]
[ROW][C]137[/C][C]0.607920932467514[/C][C]0.784158135064972[/C][C]0.392079067532486[/C][/ROW]
[ROW][C]138[/C][C]0.55206301159818[/C][C]0.895873976803641[/C][C]0.44793698840182[/C][/ROW]
[ROW][C]139[/C][C]0.575613737369329[/C][C]0.848772525261341[/C][C]0.424386262630671[/C][/ROW]
[ROW][C]140[/C][C]0.49888961622611[/C][C]0.997779232452221[/C][C]0.50111038377389[/C][/ROW]
[ROW][C]141[/C][C]0.436652908489571[/C][C]0.873305816979142[/C][C]0.563347091510429[/C][/ROW]
[ROW][C]142[/C][C]0.39375218497543[/C][C]0.78750436995086[/C][C]0.60624781502457[/C][/ROW]
[ROW][C]143[/C][C]0.412108142838477[/C][C]0.824216285676954[/C][C]0.587891857161523[/C][/ROW]
[ROW][C]144[/C][C]0.352430242804263[/C][C]0.704860485608525[/C][C]0.647569757195737[/C][/ROW]
[ROW][C]145[/C][C]0.252953861156967[/C][C]0.505907722313935[/C][C]0.747046138843033[/C][/ROW]
[ROW][C]146[/C][C]0.216073128162615[/C][C]0.432146256325229[/C][C]0.783926871837386[/C][/ROW]
[ROW][C]147[/C][C]0.174856985634632[/C][C]0.349713971269264[/C][C]0.825143014365368[/C][/ROW]
[ROW][C]148[/C][C]0.0968604437437483[/C][C]0.193720887487497[/C][C]0.903139556256252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04722829156910380.09445658313820750.952771708430896
120.01423475691522690.02846951383045370.985765243084773
130.06428842912713660.1285768582542730.935711570872863
140.1235894120732940.2471788241465880.876410587926706
150.5752572659621580.8494854680756840.424742734037842
160.5190109299240040.9619781401519920.480989070075996
170.5652978009178060.8694043981643890.434702199082194
180.5237397530751640.9525204938496720.476260246924836
190.445228556854440.890457113708880.55477144314556
200.5534619081530660.8930761836938680.446538091846934
210.5678230267072470.8643539465855060.432176973292753
220.5002354966453140.9995290067093720.499764503354686
230.4407071025270510.8814142050541030.559292897472949
240.4538287454854840.9076574909709680.546171254514516
250.4004178210394180.8008356420788360.599582178960582
260.3481241895193250.696248379038650.651875810480675
270.3140563948690620.6281127897381240.685943605130938
280.2969530275009180.5939060550018350.703046972499083
290.2589750824994850.517950164998970.741024917500515
300.2079097080670840.4158194161341670.792090291932916
310.1776962699788220.3553925399576430.822303730021178
320.2182863609328530.4365727218657070.781713639067147
330.3035728642013540.6071457284027090.696427135798646
340.5199764599805480.9600470800389040.480023540019452
350.4703273849669170.9406547699338330.529672615033083
360.477197878354430.954395756708860.52280212164557
370.4390928171321680.8781856342643360.560907182867832
380.4688913116812890.9377826233625780.531108688318711
390.4160642342853650.832128468570730.583935765714635
400.4422206903109180.8844413806218360.557779309689082
410.4325939786516780.8651879573033560.567406021348322
420.3823175596910780.7646351193821560.617682440308922
430.5935699033805340.8128601932389330.406430096619466
440.555139652780360.8897206944392810.44486034721964
450.5417994624942510.9164010750114970.458200537505749
460.503485339228550.99302932154290.49651466077145
470.5275450363200120.9449099273599760.472454963679988
480.6040246424317870.7919507151364260.395975357568213
490.5738966906887210.8522066186225580.426103309311279
500.5445301986974390.9109396026051210.455469801302561
510.5067728358781860.9864543282436280.493227164121814
520.4822346953744590.9644693907489180.517765304625541
530.5420383593664410.9159232812671170.457961640633559
540.5196499448668810.9607001102662380.480350055133119
550.5415717180010030.9168565639979940.458428281998997
560.5172282634915090.9655434730169820.482771736508491
570.4730438873092590.9460877746185180.526956112690741
580.4370269246820280.8740538493640560.562973075317972
590.3911213756227720.7822427512455450.608878624377228
600.3588281344382490.7176562688764990.641171865561751
610.3214090744543710.6428181489087420.678590925545629
620.287047696003950.57409539200790.71295230399605
630.2511225547303470.5022451094606940.748877445269653
640.2666858087665330.5333716175330660.733314191233467
650.2309913417720860.4619826835441720.769008658227914
660.2393880594829660.4787761189659320.760611940517034
670.325787745537040.6515754910740810.67421225446296
680.3835115225924250.7670230451848490.616488477407575
690.3460888537582040.6921777075164080.653911146241796
700.3280864792706640.6561729585413290.671913520729336
710.4219110962239030.8438221924478060.578088903776097
720.3805870270403220.7611740540806430.619412972959678
730.3468214166331670.6936428332663340.653178583366833
740.3197232658866620.6394465317733240.680276734113338
750.2955486889979140.5910973779958290.704451311002086
760.2742375205903490.5484750411806980.725762479409651
770.2554859001749830.5109718003499660.744514099825017
780.2220829921412310.4441659842824620.777917007858769
790.1914497904946980.3828995809893960.808550209505302
800.1691749821725430.3383499643450870.830825017827457
810.151373914628040.3027478292560790.84862608537196
820.2370008817382360.4740017634764720.762999118261764
830.2165460647169560.4330921294339110.783453935283044
840.1905245078201370.3810490156402750.809475492179862
850.1882707148200230.3765414296400470.811729285179977
860.1805176779551490.3610353559102980.819482322044851
870.1969384581087520.3938769162175030.803061541891248
880.1920268820690320.3840537641380640.807973117930968
890.1869700163354740.3739400326709480.813029983664526
900.1844095204967170.3688190409934340.815590479503283
910.2439201077217890.4878402154435780.756079892278211
920.2084096284178740.4168192568357470.791590371582126
930.186249858358830.372499716717660.81375014164117
940.1590043181548980.3180086363097970.840995681845102
950.1393464503664770.2786929007329540.860653549633523
960.1541916192471470.3083832384942940.845808380752853
970.1456983301142270.2913966602284550.854301669885773
980.1327881914340420.2655763828680850.867211808565958
990.1199699406733070.2399398813466130.880030059326693
1000.1097075361899590.2194150723799180.890292463810041
1010.08985471714296060.1797094342859210.910145282857039
1020.07245056457998350.1449011291599670.927549435420017
1030.05714926471001750.1142985294200350.942850735289983
1040.04502665378141750.09005330756283510.954973346218582
1050.04595455373318260.09190910746636510.954045446266817
1060.03685709577249710.07371419154499410.963142904227503
1070.03236092348534670.06472184697069340.967639076514653
1080.02962589002216280.05925178004432560.970374109977837
1090.02333455766980110.04666911533960220.976665442330199
1100.02422052390895330.04844104781790670.975779476091047
1110.03404209635046580.06808419270093170.965957903649534
1120.2349767701501450.469953540300290.765023229849855
1130.2474928219535890.4949856439071780.752507178046411
1140.6059989473663670.7880021052672660.394001052633633
1150.8651558243698180.2696883512603640.134844175630182
1160.8336914448481460.3326171103037080.166308555151854
1170.8631133490050830.2737733019898350.136886650994917
1180.8344859960190110.3310280079619770.165514003980989
1190.8128580040534680.3742839918930630.187141995946532
1200.8690668927486690.2618662145026620.130933107251331
1210.8416952732787810.3166094534424390.158304726721219
1220.8069660577661390.3860678844677220.193033942233861
1230.8151220725298970.3697558549402070.184877927470103
1240.7714562090075410.4570875819849170.228543790992459
1250.7367995567907320.5264008864185370.263200443209268
1260.6976967462481020.6046065075037950.302303253751898
1270.6488824923881050.702235015223790.351117507611895
1280.596281405803020.807437188393960.40371859419698
1290.5409657309752050.9180685380495910.459034269024796
1300.4897388388030710.9794776776061430.510261161196929
1310.4612472291117720.9224944582235440.538752770888228
1320.4846459758603470.9692919517206940.515354024139653
1330.4196619159123530.8393238318247050.580338084087647
1340.3903036743217820.7806073486435640.609696325678218
1350.6910763844065840.6178472311868310.308923615593416
1360.6219947205760280.7560105588479430.378005279423972
1370.6079209324675140.7841581350649720.392079067532486
1380.552063011598180.8958739768036410.44793698840182
1390.5756137373693290.8487725252613410.424386262630671
1400.498889616226110.9977792324522210.50111038377389
1410.4366529084895710.8733058169791420.563347091510429
1420.393752184975430.787504369950860.60624781502457
1430.4121081428384770.8242162856769540.587891857161523
1440.3524302428042630.7048604856085250.647569757195737
1450.2529538611569670.5059077223139350.747046138843033
1460.2160731281626150.4321462563252290.783926871837386
1470.1748569856346320.3497139712692640.825143014365368
1480.09686044374374830.1937208874874970.903139556256252







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0217391304347826OK
10% type I error level100.072463768115942OK

\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 & 3 & 0.0217391304347826 & OK \tabularnewline
10% type I error level & 10 & 0.072463768115942 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147567&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]3[/C][C]0.0217391304347826[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.072463768115942[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147567&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147567&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0217391304347826OK
10% type I error level100.072463768115942OK



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