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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 20 Nov 2010 15:19:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/20/t1290266267jc4g6huud6z8xev.htm/, Retrieved Wed, 05 Nov 2025 09:36:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98229, Retrieved Wed, 05 Nov 2025 09:36:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact455

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [ws7] [2010-11-20 15:19:19] [0cadca125c925bcc9e6efbdd1941e458] [Current]
-           [Multiple Regression] [] [2010-11-23 20:56:56] [427b5b758ea29b39381b0cf845587015]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 9 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
ConcernoverMistakes[t] = -20.5406175778344 + 1.85205065543305Month[t] + 0.800753683595023Doubtsaboutactions[t] + 0.233892111076654ParentalExpectations[t] + 0.20844977461566ParentalCriticism[t] + 0.571250810176502PersonalStandards[t] -0.108334921483045Organization[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ConcernoverMistakes[t] =  -20.5406175778344 +  1.85205065543305Month[t] +  0.800753683595023Doubtsaboutactions[t] +  0.233892111076654ParentalExpectations[t] +  0.20844977461566ParentalCriticism[t] +  0.571250810176502PersonalStandards[t] -0.108334921483045Organization[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ConcernoverMistakes[t] =  -20.5406175778344 +  1.85205065543305Month[t] +  0.800753683595023Doubtsaboutactions[t] +  0.233892111076654ParentalExpectations[t] +  0.20844977461566ParentalCriticism[t] +  0.571250810176502PersonalStandards[t] -0.108334921483045Organization[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
ConcernoverMistakes[t] = -20.5406175778344 + 1.85205065543305Month[t] + 0.800753683595023Doubtsaboutactions[t] + 0.233892111076654ParentalExpectations[t] + 0.20844977461566ParentalCriticism[t] + 0.571250810176502PersonalStandards[t] -0.108334921483045Organization[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-20.540617577834419.256187-1.06670.2877980.143899
Month1.852050655433051.8962850.97670.3302830.165141
Doubtsaboutactions0.8007536835950230.1307076.126300
ParentalExpectations0.2338921110766540.1339641.74590.0828440.041422
ParentalCriticism0.208449774615660.1695171.22970.220720.11036
PersonalStandards0.5712508101765020.0959755.952100
Organization-0.1083349214830450.103317-1.04860.2960390.148019

\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) & -20.5406175778344 & 19.256187 & -1.0667 & 0.287798 & 0.143899 \tabularnewline
Month & 1.85205065543305 & 1.896285 & 0.9767 & 0.330283 & 0.165141 \tabularnewline
Doubtsaboutactions & 0.800753683595023 & 0.130707 & 6.1263 & 0 & 0 \tabularnewline
ParentalExpectations & 0.233892111076654 & 0.133964 & 1.7459 & 0.082844 & 0.041422 \tabularnewline
ParentalCriticism & 0.20844977461566 & 0.169517 & 1.2297 & 0.22072 & 0.11036 \tabularnewline
PersonalStandards & 0.571250810176502 & 0.095975 & 5.9521 & 0 & 0 \tabularnewline
Organization & -0.108334921483045 & 0.103317 & -1.0486 & 0.296039 & 0.148019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&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]-20.5406175778344[/C][C]19.256187[/C][C]-1.0667[/C][C]0.287798[/C][C]0.143899[/C][/ROW]
[ROW][C]Month[/C][C]1.85205065543305[/C][C]1.896285[/C][C]0.9767[/C][C]0.330283[/C][C]0.165141[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]0.800753683595023[/C][C]0.130707[/C][C]6.1263[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.233892111076654[/C][C]0.133964[/C][C]1.7459[/C][C]0.082844[/C][C]0.041422[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.20844977461566[/C][C]0.169517[/C][C]1.2297[/C][C]0.22072[/C][C]0.11036[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.571250810176502[/C][C]0.095975[/C][C]5.9521[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Organization[/C][C]-0.108334921483045[/C][C]0.103317[/C][C]-1.0486[/C][C]0.296039[/C][C]0.148019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-20.540617577834419.256187-1.06670.2877980.143899
Month1.852050655433051.8962850.97670.3302830.165141
Doubtsaboutactions0.8007536835950230.1307076.126300
ParentalExpectations0.2338921110766540.1339641.74590.0828440.041422
ParentalCriticism0.208449774615660.1695171.22970.220720.11036
PersonalStandards0.5712508101765020.0959755.952100
Organization-0.1083349214830450.103317-1.04860.2960390.148019






Multiple Linear Regression - Regression Statistics
Multiple R0.64099331025021
R-squared0.410872423785522
Adjusted R-squared0.387617387882318
F-TEST (value)17.6681053297763
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value1.77635683940025e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.47838506122403
Sum Squared Residuals3048.50177900237

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.64099331025021 \tabularnewline
R-squared & 0.410872423785522 \tabularnewline
Adjusted R-squared & 0.387617387882318 \tabularnewline
F-TEST (value) & 17.6681053297763 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.77635683940025e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.47838506122403 \tabularnewline
Sum Squared Residuals & 3048.50177900237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.64099331025021[/C][/ROW]
[ROW][C]R-squared[/C][C]0.410872423785522[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.387617387882318[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.6681053297763[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.77635683940025e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.47838506122403[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3048.50177900237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.64099331025021
R-squared0.410872423785522
Adjusted R-squared0.387617387882318
F-TEST (value)17.6681053297763
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value1.77635683940025e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.47838506122403
Sum Squared Residuals3048.50177900237






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.30591189430130.694088105698661
22520.03053887537274.9694611246273
31721.0052757760805-4.00527577608054
41818.1054640311387-0.105464031138674
51817.72577540998920.274224590010765
61617.8270340131175-1.82703401311751
72020.9669353500886-0.96693535008861
81622.5176755215724-6.51767552157243
91822.3856971696195-4.38569716961951
101720.5758193389779-3.57581933897792
112322.4193009914530.580699008547041
123022.31944150967827.6805584903218
132315.22862329327667.77137670672336
141819.1104416279066-1.11044162790658
151521.7839912762424-6.7839912762424
161217.915478548729-5.91547854872895
172119.56856849430431.4314315056957
181515.3238221486993-0.323822148699272
192019.7887124641590.21128753584099
203126.43162416030054.56837583969953
212725.30379070672121.69620929327879
223427.12452201821426.87547798178583
232119.74974079613771.25025920386234
243120.987771243585310.0122287564147
251919.4202293168146-0.420229316814631
261620.3090786014135-4.30907860141352
272021.6269845815146-1.62698458151457
282118.35704482214922.64295517785077
292222.0233101277447-0.0233101277447478
301719.5537828503148-2.5537828503148
312420.48271099123443.51728900876556
322530.5896852562428-5.5896852562428
332626.6567312769232-0.656731276923196
342524.22342508905770.77657491094229
351723.045122891964-6.04512289196399
363227.89724109795974.10275890204028
373323.75556155574039.24443844425972
381321.9687976203882-8.96879762038819
393227.91204288671924.08795711328084
402525.9293572358744-0.929357235874388
412927.08746971246051.91253028753948
422222.1267767157679-0.126776715767941
431817.31922335278070.68077664721929
441721.7607618394586-4.76076183945861
452022.3547629906172-2.35476299061719
461520.5459877648588-5.54598776485879
472022.2319043156402-2.23190431564016
483328.22615600426534.77384399573473
492922.2512964025946.748703597406
502326.6319917607078-3.63199176070779
512623.33393808632892.66606191367113
521819.0572045568099-1.05720455680992
532018.85336774429631.14663225570374
541111.8569662519076-0.856966251907634
552829.1391504790434-1.13915047904337
562623.43229711430432.56770288569571
572222.3892075268383-0.389207526838317
581720.2377578196325-3.23775781963248
591215.5903486600183-3.59034866001828
601420.9635424700856-6.96354247008556
611720.871071840472-3.87107184047204
622121.3887032327798-0.388703232779782
631922.99921279973-3.99921279972996
641823.2468530520286-5.24685305202856
651017.9645501066113-7.96455010661135
662924.39418483978984.60581516021023
673118.593809572314712.4061904276853
681923.0436062933947-4.04360629339472
69920.1246989779693-11.1246989779693
702022.6014881321463-2.60148813214627
712817.693420131388810.3065798686112
721918.25541337044730.744586629552658
733023.17816568274856.82183431725154
742927.1412651905231.85873480947699
752621.51864966003154.48135033996848
762319.61171219467763.38828780532242
771322.7696306151473-9.76963061514733
782122.7069504145405-1.70695041454046
791921.5925083983795-2.59250839837946
802822.97737756951745.0226224304826
812325.6717631113499-2.67176311134988
821813.91937831127614.0806216887239
832120.76752594128470.232474058715269
842021.8939221074588-1.89392210745884
852320.03254882182932.9674511781707
862120.81457978351440.185420216485577
872121.8549708563638-0.854970856363817
881522.9601236544929-7.96012365449285
892827.28990740362070.710092596379332
901917.68950998953241.31049001046759
912621.24375395623234.75624604376766
921013.3592202085389-3.35922020853889
931617.1550179477001-1.15501794770006
942221.14732552051230.852674479487732
951918.91520723051550.084792769484549
963128.86387540753292.1361245924671
973125.2303794172615.76962058273899
982924.82237582056174.17762417943828
991917.46000711611391.53999288388611
1002218.91977252836733.08022747163268
1012322.44404050766840.555959492331635
1021516.2213007844847-1.22130078448468
1032021.4067774452657-1.40677744526568
1041819.5916965986423-1.59169659864227
1052322.1310549953480.868945004652024
1062520.89174259250284.1082574074972
1072116.62480889629774.37519110370226
1082419.54050889011234.4594911098877
1092525.2842542064876-0.284254206487617
1101719.5415733289438-2.54157332894384
1111314.590920034779-1.59092003477895
1122818.31206598545469.68793401454535
1132120.32721598029360.6727840197064
1142528.2402549727791-3.24025497277912
115921.0230657884603-12.0230657884603
1161617.8649936604446-1.86499366044457
1171921.1927565169446-2.19275651694458
1181719.5105598387774-2.51055983877741
1192524.54234989280720.457650107192838
1202015.51102203613224.48897796386781
1212921.72837616400277.27162383599735
1221419.0319382805427-5.03193828054266
1232226.9807843689066-4.98078436890656
1241515.7566129258641-0.756612925864104
1251925.4706403155968-6.47064031559685
1262021.9365074008663-1.93650740086628
1271517.5261183627754-2.52611836277545
1282021.9219024004867-1.92190240048673
1291820.3175223942078-2.31752239420782
1303325.57499999310787.42500000689215
1312223.8817153183514-1.88171531835138
1321616.5259552703765-0.525955270376547
1331719.1237948992732-2.1237948992732
1341615.11555793247580.884442067524166
1352117.11003911100553.88996088899452
1362627.6211796228832-1.62117962288317
1371821.1341578075379-3.13415780753787
1381823.0934916296008-5.0934916296008
1391718.4649006478306-1.46490064783056
1402224.8487332677922-2.84873326779222
1413024.79359725135425.20640274864581
1423027.36471781443442.63528218556562
1432429.8857645038554-5.88576450385544
1442122.0669167791498-1.06691677914981
1452125.4142006337394-4.41420063373936
1462927.43919354272611.56080645727388
1473123.26179273493017.73820726506992
1482019.04820235704980.951797642950214
1491614.17357955579761.82642044420244
1502219.00322352035522.9967764796448
1512020.5031787470075-0.503178747007477
1522827.31180121681120.688198783188794
1533826.58362302307111.416376976929
1542219.26560666717542.73439333282455
1552025.7191898021532-5.71918980215319
1561718.0567002627514-1.05670026275139
1572824.54083329423793.45916670576211
1582224.1667158267939-2.16671582679389
1593126.11565976166314.88434023833688

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.3059118943013 & 0.694088105698661 \tabularnewline
2 & 25 & 20.0305388753727 & 4.9694611246273 \tabularnewline
3 & 17 & 21.0052757760805 & -4.00527577608054 \tabularnewline
4 & 18 & 18.1054640311387 & -0.105464031138674 \tabularnewline
5 & 18 & 17.7257754099892 & 0.274224590010765 \tabularnewline
6 & 16 & 17.8270340131175 & -1.82703401311751 \tabularnewline
7 & 20 & 20.9669353500886 & -0.96693535008861 \tabularnewline
8 & 16 & 22.5176755215724 & -6.51767552157243 \tabularnewline
9 & 18 & 22.3856971696195 & -4.38569716961951 \tabularnewline
10 & 17 & 20.5758193389779 & -3.57581933897792 \tabularnewline
11 & 23 & 22.419300991453 & 0.580699008547041 \tabularnewline
12 & 30 & 22.3194415096782 & 7.6805584903218 \tabularnewline
13 & 23 & 15.2286232932766 & 7.77137670672336 \tabularnewline
14 & 18 & 19.1104416279066 & -1.11044162790658 \tabularnewline
15 & 15 & 21.7839912762424 & -6.7839912762424 \tabularnewline
16 & 12 & 17.915478548729 & -5.91547854872895 \tabularnewline
17 & 21 & 19.5685684943043 & 1.4314315056957 \tabularnewline
18 & 15 & 15.3238221486993 & -0.323822148699272 \tabularnewline
19 & 20 & 19.788712464159 & 0.21128753584099 \tabularnewline
20 & 31 & 26.4316241603005 & 4.56837583969953 \tabularnewline
21 & 27 & 25.3037907067212 & 1.69620929327879 \tabularnewline
22 & 34 & 27.1245220182142 & 6.87547798178583 \tabularnewline
23 & 21 & 19.7497407961377 & 1.25025920386234 \tabularnewline
24 & 31 & 20.9877712435853 & 10.0122287564147 \tabularnewline
25 & 19 & 19.4202293168146 & -0.420229316814631 \tabularnewline
26 & 16 & 20.3090786014135 & -4.30907860141352 \tabularnewline
27 & 20 & 21.6269845815146 & -1.62698458151457 \tabularnewline
28 & 21 & 18.3570448221492 & 2.64295517785077 \tabularnewline
29 & 22 & 22.0233101277447 & -0.0233101277447478 \tabularnewline
30 & 17 & 19.5537828503148 & -2.5537828503148 \tabularnewline
31 & 24 & 20.4827109912344 & 3.51728900876556 \tabularnewline
32 & 25 & 30.5896852562428 & -5.5896852562428 \tabularnewline
33 & 26 & 26.6567312769232 & -0.656731276923196 \tabularnewline
34 & 25 & 24.2234250890577 & 0.77657491094229 \tabularnewline
35 & 17 & 23.045122891964 & -6.04512289196399 \tabularnewline
36 & 32 & 27.8972410979597 & 4.10275890204028 \tabularnewline
37 & 33 & 23.7555615557403 & 9.24443844425972 \tabularnewline
38 & 13 & 21.9687976203882 & -8.96879762038819 \tabularnewline
39 & 32 & 27.9120428867192 & 4.08795711328084 \tabularnewline
40 & 25 & 25.9293572358744 & -0.929357235874388 \tabularnewline
41 & 29 & 27.0874697124605 & 1.91253028753948 \tabularnewline
42 & 22 & 22.1267767157679 & -0.126776715767941 \tabularnewline
43 & 18 & 17.3192233527807 & 0.68077664721929 \tabularnewline
44 & 17 & 21.7607618394586 & -4.76076183945861 \tabularnewline
45 & 20 & 22.3547629906172 & -2.35476299061719 \tabularnewline
46 & 15 & 20.5459877648588 & -5.54598776485879 \tabularnewline
47 & 20 & 22.2319043156402 & -2.23190431564016 \tabularnewline
48 & 33 & 28.2261560042653 & 4.77384399573473 \tabularnewline
49 & 29 & 22.251296402594 & 6.748703597406 \tabularnewline
50 & 23 & 26.6319917607078 & -3.63199176070779 \tabularnewline
51 & 26 & 23.3339380863289 & 2.66606191367113 \tabularnewline
52 & 18 & 19.0572045568099 & -1.05720455680992 \tabularnewline
53 & 20 & 18.8533677442963 & 1.14663225570374 \tabularnewline
54 & 11 & 11.8569662519076 & -0.856966251907634 \tabularnewline
55 & 28 & 29.1391504790434 & -1.13915047904337 \tabularnewline
56 & 26 & 23.4322971143043 & 2.56770288569571 \tabularnewline
57 & 22 & 22.3892075268383 & -0.389207526838317 \tabularnewline
58 & 17 & 20.2377578196325 & -3.23775781963248 \tabularnewline
59 & 12 & 15.5903486600183 & -3.59034866001828 \tabularnewline
60 & 14 & 20.9635424700856 & -6.96354247008556 \tabularnewline
61 & 17 & 20.871071840472 & -3.87107184047204 \tabularnewline
62 & 21 & 21.3887032327798 & -0.388703232779782 \tabularnewline
63 & 19 & 22.99921279973 & -3.99921279972996 \tabularnewline
64 & 18 & 23.2468530520286 & -5.24685305202856 \tabularnewline
65 & 10 & 17.9645501066113 & -7.96455010661135 \tabularnewline
66 & 29 & 24.3941848397898 & 4.60581516021023 \tabularnewline
67 & 31 & 18.5938095723147 & 12.4061904276853 \tabularnewline
68 & 19 & 23.0436062933947 & -4.04360629339472 \tabularnewline
69 & 9 & 20.1246989779693 & -11.1246989779693 \tabularnewline
70 & 20 & 22.6014881321463 & -2.60148813214627 \tabularnewline
71 & 28 & 17.6934201313888 & 10.3065798686112 \tabularnewline
72 & 19 & 18.2554133704473 & 0.744586629552658 \tabularnewline
73 & 30 & 23.1781656827485 & 6.82183431725154 \tabularnewline
74 & 29 & 27.141265190523 & 1.85873480947699 \tabularnewline
75 & 26 & 21.5186496600315 & 4.48135033996848 \tabularnewline
76 & 23 & 19.6117121946776 & 3.38828780532242 \tabularnewline
77 & 13 & 22.7696306151473 & -9.76963061514733 \tabularnewline
78 & 21 & 22.7069504145405 & -1.70695041454046 \tabularnewline
79 & 19 & 21.5925083983795 & -2.59250839837946 \tabularnewline
80 & 28 & 22.9773775695174 & 5.0226224304826 \tabularnewline
81 & 23 & 25.6717631113499 & -2.67176311134988 \tabularnewline
82 & 18 & 13.9193783112761 & 4.0806216887239 \tabularnewline
83 & 21 & 20.7675259412847 & 0.232474058715269 \tabularnewline
84 & 20 & 21.8939221074588 & -1.89392210745884 \tabularnewline
85 & 23 & 20.0325488218293 & 2.9674511781707 \tabularnewline
86 & 21 & 20.8145797835144 & 0.185420216485577 \tabularnewline
87 & 21 & 21.8549708563638 & -0.854970856363817 \tabularnewline
88 & 15 & 22.9601236544929 & -7.96012365449285 \tabularnewline
89 & 28 & 27.2899074036207 & 0.710092596379332 \tabularnewline
90 & 19 & 17.6895099895324 & 1.31049001046759 \tabularnewline
91 & 26 & 21.2437539562323 & 4.75624604376766 \tabularnewline
92 & 10 & 13.3592202085389 & -3.35922020853889 \tabularnewline
93 & 16 & 17.1550179477001 & -1.15501794770006 \tabularnewline
94 & 22 & 21.1473255205123 & 0.852674479487732 \tabularnewline
95 & 19 & 18.9152072305155 & 0.084792769484549 \tabularnewline
96 & 31 & 28.8638754075329 & 2.1361245924671 \tabularnewline
97 & 31 & 25.230379417261 & 5.76962058273899 \tabularnewline
98 & 29 & 24.8223758205617 & 4.17762417943828 \tabularnewline
99 & 19 & 17.4600071161139 & 1.53999288388611 \tabularnewline
100 & 22 & 18.9197725283673 & 3.08022747163268 \tabularnewline
101 & 23 & 22.4440405076684 & 0.555959492331635 \tabularnewline
102 & 15 & 16.2213007844847 & -1.22130078448468 \tabularnewline
103 & 20 & 21.4067774452657 & -1.40677744526568 \tabularnewline
104 & 18 & 19.5916965986423 & -1.59169659864227 \tabularnewline
105 & 23 & 22.131054995348 & 0.868945004652024 \tabularnewline
106 & 25 & 20.8917425925028 & 4.1082574074972 \tabularnewline
107 & 21 & 16.6248088962977 & 4.37519110370226 \tabularnewline
108 & 24 & 19.5405088901123 & 4.4594911098877 \tabularnewline
109 & 25 & 25.2842542064876 & -0.284254206487617 \tabularnewline
110 & 17 & 19.5415733289438 & -2.54157332894384 \tabularnewline
111 & 13 & 14.590920034779 & -1.59092003477895 \tabularnewline
112 & 28 & 18.3120659854546 & 9.68793401454535 \tabularnewline
113 & 21 & 20.3272159802936 & 0.6727840197064 \tabularnewline
114 & 25 & 28.2402549727791 & -3.24025497277912 \tabularnewline
115 & 9 & 21.0230657884603 & -12.0230657884603 \tabularnewline
116 & 16 & 17.8649936604446 & -1.86499366044457 \tabularnewline
117 & 19 & 21.1927565169446 & -2.19275651694458 \tabularnewline
118 & 17 & 19.5105598387774 & -2.51055983877741 \tabularnewline
119 & 25 & 24.5423498928072 & 0.457650107192838 \tabularnewline
120 & 20 & 15.5110220361322 & 4.48897796386781 \tabularnewline
121 & 29 & 21.7283761640027 & 7.27162383599735 \tabularnewline
122 & 14 & 19.0319382805427 & -5.03193828054266 \tabularnewline
123 & 22 & 26.9807843689066 & -4.98078436890656 \tabularnewline
124 & 15 & 15.7566129258641 & -0.756612925864104 \tabularnewline
125 & 19 & 25.4706403155968 & -6.47064031559685 \tabularnewline
126 & 20 & 21.9365074008663 & -1.93650740086628 \tabularnewline
127 & 15 & 17.5261183627754 & -2.52611836277545 \tabularnewline
128 & 20 & 21.9219024004867 & -1.92190240048673 \tabularnewline
129 & 18 & 20.3175223942078 & -2.31752239420782 \tabularnewline
130 & 33 & 25.5749999931078 & 7.42500000689215 \tabularnewline
131 & 22 & 23.8817153183514 & -1.88171531835138 \tabularnewline
132 & 16 & 16.5259552703765 & -0.525955270376547 \tabularnewline
133 & 17 & 19.1237948992732 & -2.1237948992732 \tabularnewline
134 & 16 & 15.1155579324758 & 0.884442067524166 \tabularnewline
135 & 21 & 17.1100391110055 & 3.88996088899452 \tabularnewline
136 & 26 & 27.6211796228832 & -1.62117962288317 \tabularnewline
137 & 18 & 21.1341578075379 & -3.13415780753787 \tabularnewline
138 & 18 & 23.0934916296008 & -5.0934916296008 \tabularnewline
139 & 17 & 18.4649006478306 & -1.46490064783056 \tabularnewline
140 & 22 & 24.8487332677922 & -2.84873326779222 \tabularnewline
141 & 30 & 24.7935972513542 & 5.20640274864581 \tabularnewline
142 & 30 & 27.3647178144344 & 2.63528218556562 \tabularnewline
143 & 24 & 29.8857645038554 & -5.88576450385544 \tabularnewline
144 & 21 & 22.0669167791498 & -1.06691677914981 \tabularnewline
145 & 21 & 25.4142006337394 & -4.41420063373936 \tabularnewline
146 & 29 & 27.4391935427261 & 1.56080645727388 \tabularnewline
147 & 31 & 23.2617927349301 & 7.73820726506992 \tabularnewline
148 & 20 & 19.0482023570498 & 0.951797642950214 \tabularnewline
149 & 16 & 14.1735795557976 & 1.82642044420244 \tabularnewline
150 & 22 & 19.0032235203552 & 2.9967764796448 \tabularnewline
151 & 20 & 20.5031787470075 & -0.503178747007477 \tabularnewline
152 & 28 & 27.3118012168112 & 0.688198783188794 \tabularnewline
153 & 38 & 26.583623023071 & 11.416376976929 \tabularnewline
154 & 22 & 19.2656066671754 & 2.73439333282455 \tabularnewline
155 & 20 & 25.7191898021532 & -5.71918980215319 \tabularnewline
156 & 17 & 18.0567002627514 & -1.05670026275139 \tabularnewline
157 & 28 & 24.5408332942379 & 3.45916670576211 \tabularnewline
158 & 22 & 24.1667158267939 & -2.16671582679389 \tabularnewline
159 & 31 & 26.1156597616631 & 4.88434023833688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&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]23.3059118943013[/C][C]0.694088105698661[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.0305388753727[/C][C]4.9694611246273[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]21.0052757760805[/C][C]-4.00527577608054[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]18.1054640311387[/C][C]-0.105464031138674[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]17.7257754099892[/C][C]0.274224590010765[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.8270340131175[/C][C]-1.82703401311751[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.9669353500886[/C][C]-0.96693535008861[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.5176755215724[/C][C]-6.51767552157243[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.3856971696195[/C][C]-4.38569716961951[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.5758193389779[/C][C]-3.57581933897792[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]22.419300991453[/C][C]0.580699008547041[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.3194415096782[/C][C]7.6805584903218[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]15.2286232932766[/C][C]7.77137670672336[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]19.1104416279066[/C][C]-1.11044162790658[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.7839912762424[/C][C]-6.7839912762424[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.915478548729[/C][C]-5.91547854872895[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.5685684943043[/C][C]1.4314315056957[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.3238221486993[/C][C]-0.323822148699272[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.788712464159[/C][C]0.21128753584099[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.4316241603005[/C][C]4.56837583969953[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.3037907067212[/C][C]1.69620929327879[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]27.1245220182142[/C][C]6.87547798178583[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.7497407961377[/C][C]1.25025920386234[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.9877712435853[/C][C]10.0122287564147[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.4202293168146[/C][C]-0.420229316814631[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.3090786014135[/C][C]-4.30907860141352[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.6269845815146[/C][C]-1.62698458151457[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]18.3570448221492[/C][C]2.64295517785077[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]22.0233101277447[/C][C]-0.0233101277447478[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.5537828503148[/C][C]-2.5537828503148[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.4827109912344[/C][C]3.51728900876556[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.5896852562428[/C][C]-5.5896852562428[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.6567312769232[/C][C]-0.656731276923196[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.2234250890577[/C][C]0.77657491094229[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]23.045122891964[/C][C]-6.04512289196399[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.8972410979597[/C][C]4.10275890204028[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.7555615557403[/C][C]9.24443844425972[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]21.9687976203882[/C][C]-8.96879762038819[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.9120428867192[/C][C]4.08795711328084[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.9293572358744[/C][C]-0.929357235874388[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]27.0874697124605[/C][C]1.91253028753948[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.1267767157679[/C][C]-0.126776715767941[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.3192233527807[/C][C]0.68077664721929[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.7607618394586[/C][C]-4.76076183945861[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.3547629906172[/C][C]-2.35476299061719[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.5459877648588[/C][C]-5.54598776485879[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]22.2319043156402[/C][C]-2.23190431564016[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]28.2261560042653[/C][C]4.77384399573473[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]22.251296402594[/C][C]6.748703597406[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.6319917607078[/C][C]-3.63199176070779[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.3339380863289[/C][C]2.66606191367113[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]19.0572045568099[/C][C]-1.05720455680992[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.8533677442963[/C][C]1.14663225570374[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.8569662519076[/C][C]-0.856966251907634[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.1391504790434[/C][C]-1.13915047904337[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.4322971143043[/C][C]2.56770288569571[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.3892075268383[/C][C]-0.389207526838317[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.2377578196325[/C][C]-3.23775781963248[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.5903486600183[/C][C]-3.59034866001828[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]20.9635424700856[/C][C]-6.96354247008556[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.871071840472[/C][C]-3.87107184047204[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.3887032327798[/C][C]-0.388703232779782[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.99921279973[/C][C]-3.99921279972996[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.2468530520286[/C][C]-5.24685305202856[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.9645501066113[/C][C]-7.96455010661135[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.3941848397898[/C][C]4.60581516021023[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.5938095723147[/C][C]12.4061904276853[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]23.0436062933947[/C][C]-4.04360629339472[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.1246989779693[/C][C]-11.1246989779693[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.6014881321463[/C][C]-2.60148813214627[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.6934201313888[/C][C]10.3065798686112[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.2554133704473[/C][C]0.744586629552658[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]23.1781656827485[/C][C]6.82183431725154[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.141265190523[/C][C]1.85873480947699[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.5186496600315[/C][C]4.48135033996848[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.6117121946776[/C][C]3.38828780532242[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7696306151473[/C][C]-9.76963061514733[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.7069504145405[/C][C]-1.70695041454046[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.5925083983795[/C][C]-2.59250839837946[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.9773775695174[/C][C]5.0226224304826[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6717631113499[/C][C]-2.67176311134988[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.9193783112761[/C][C]4.0806216887239[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7675259412847[/C][C]0.232474058715269[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.8939221074588[/C][C]-1.89392210745884[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.0325488218293[/C][C]2.9674511781707[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.8145797835144[/C][C]0.185420216485577[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.8549708563638[/C][C]-0.854970856363817[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.9601236544929[/C][C]-7.96012365449285[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.2899074036207[/C][C]0.710092596379332[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.6895099895324[/C][C]1.31049001046759[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.2437539562323[/C][C]4.75624604376766[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.3592202085389[/C][C]-3.35922020853889[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1550179477001[/C][C]-1.15501794770006[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.1473255205123[/C][C]0.852674479487732[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.9152072305155[/C][C]0.084792769484549[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.8638754075329[/C][C]2.1361245924671[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.230379417261[/C][C]5.76962058273899[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.8223758205617[/C][C]4.17762417943828[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.4600071161139[/C][C]1.53999288388611[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.9197725283673[/C][C]3.08022747163268[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.4440405076684[/C][C]0.555959492331635[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.2213007844847[/C][C]-1.22130078448468[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4067774452657[/C][C]-1.40677744526568[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.5916965986423[/C][C]-1.59169659864227[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]22.131054995348[/C][C]0.868945004652024[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.8917425925028[/C][C]4.1082574074972[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.6248088962977[/C][C]4.37519110370226[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.5405088901123[/C][C]4.4594911098877[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.2842542064876[/C][C]-0.284254206487617[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.5415733289438[/C][C]-2.54157332894384[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.590920034779[/C][C]-1.59092003477895[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.3120659854546[/C][C]9.68793401454535[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.3272159802936[/C][C]0.6727840197064[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.2402549727791[/C][C]-3.24025497277912[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]21.0230657884603[/C][C]-12.0230657884603[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]17.8649936604446[/C][C]-1.86499366044457[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.1927565169446[/C][C]-2.19275651694458[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.5105598387774[/C][C]-2.51055983877741[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.5423498928072[/C][C]0.457650107192838[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5110220361322[/C][C]4.48897796386781[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.7283761640027[/C][C]7.27162383599735[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]19.0319382805427[/C][C]-5.03193828054266[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]26.9807843689066[/C][C]-4.98078436890656[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.7566129258641[/C][C]-0.756612925864104[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.4706403155968[/C][C]-6.47064031559685[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]21.9365074008663[/C][C]-1.93650740086628[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.5261183627754[/C][C]-2.52611836277545[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.9219024004867[/C][C]-1.92190240048673[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.3175223942078[/C][C]-2.31752239420782[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.5749999931078[/C][C]7.42500000689215[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]23.8817153183514[/C][C]-1.88171531835138[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.5259552703765[/C][C]-0.525955270376547[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.1237948992732[/C][C]-2.1237948992732[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.1155579324758[/C][C]0.884442067524166[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.1100391110055[/C][C]3.88996088899452[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.6211796228832[/C][C]-1.62117962288317[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.1341578075379[/C][C]-3.13415780753787[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.0934916296008[/C][C]-5.0934916296008[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.4649006478306[/C][C]-1.46490064783056[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.8487332677922[/C][C]-2.84873326779222[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.7935972513542[/C][C]5.20640274864581[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.3647178144344[/C][C]2.63528218556562[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]29.8857645038554[/C][C]-5.88576450385544[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]22.0669167791498[/C][C]-1.06691677914981[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.4142006337394[/C][C]-4.41420063373936[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.4391935427261[/C][C]1.56080645727388[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.2617927349301[/C][C]7.73820726506992[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.0482023570498[/C][C]0.951797642950214[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.1735795557976[/C][C]1.82642044420244[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]19.0032235203552[/C][C]2.9967764796448[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.5031787470075[/C][C]-0.503178747007477[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.3118012168112[/C][C]0.688198783188794[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]26.583623023071[/C][C]11.416376976929[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.2656066671754[/C][C]2.73439333282455[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]25.7191898021532[/C][C]-5.71918980215319[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.0567002627514[/C][C]-1.05670026275139[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.5408332942379[/C][C]3.45916670576211[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.1667158267939[/C][C]-2.16671582679389[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.1156597616631[/C][C]4.88434023833688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.30591189430130.694088105698661
22520.03053887537274.9694611246273
31721.0052757760805-4.00527577608054
41818.1054640311387-0.105464031138674
51817.72577540998920.274224590010765
61617.8270340131175-1.82703401311751
72020.9669353500886-0.96693535008861
81622.5176755215724-6.51767552157243
91822.3856971696195-4.38569716961951
101720.5758193389779-3.57581933897792
112322.4193009914530.580699008547041
123022.31944150967827.6805584903218
132315.22862329327667.77137670672336
141819.1104416279066-1.11044162790658
151521.7839912762424-6.7839912762424
161217.915478548729-5.91547854872895
172119.56856849430431.4314315056957
181515.3238221486993-0.323822148699272
192019.7887124641590.21128753584099
203126.43162416030054.56837583969953
212725.30379070672121.69620929327879
223427.12452201821426.87547798178583
232119.74974079613771.25025920386234
243120.987771243585310.0122287564147
251919.4202293168146-0.420229316814631
261620.3090786014135-4.30907860141352
272021.6269845815146-1.62698458151457
282118.35704482214922.64295517785077
292222.0233101277447-0.0233101277447478
301719.5537828503148-2.5537828503148
312420.48271099123443.51728900876556
322530.5896852562428-5.5896852562428
332626.6567312769232-0.656731276923196
342524.22342508905770.77657491094229
351723.045122891964-6.04512289196399
363227.89724109795974.10275890204028
373323.75556155574039.24443844425972
381321.9687976203882-8.96879762038819
393227.91204288671924.08795711328084
402525.9293572358744-0.929357235874388
412927.08746971246051.91253028753948
422222.1267767157679-0.126776715767941
431817.31922335278070.68077664721929
441721.7607618394586-4.76076183945861
452022.3547629906172-2.35476299061719
461520.5459877648588-5.54598776485879
472022.2319043156402-2.23190431564016
483328.22615600426534.77384399573473
492922.2512964025946.748703597406
502326.6319917607078-3.63199176070779
512623.33393808632892.66606191367113
521819.0572045568099-1.05720455680992
532018.85336774429631.14663225570374
541111.8569662519076-0.856966251907634
552829.1391504790434-1.13915047904337
562623.43229711430432.56770288569571
572222.3892075268383-0.389207526838317
581720.2377578196325-3.23775781963248
591215.5903486600183-3.59034866001828
601420.9635424700856-6.96354247008556
611720.871071840472-3.87107184047204
622121.3887032327798-0.388703232779782
631922.99921279973-3.99921279972996
641823.2468530520286-5.24685305202856
651017.9645501066113-7.96455010661135
662924.39418483978984.60581516021023
673118.593809572314712.4061904276853
681923.0436062933947-4.04360629339472
69920.1246989779693-11.1246989779693
702022.6014881321463-2.60148813214627
712817.693420131388810.3065798686112
721918.25541337044730.744586629552658
733023.17816568274856.82183431725154
742927.1412651905231.85873480947699
752621.51864966003154.48135033996848
762319.61171219467763.38828780532242
771322.7696306151473-9.76963061514733
782122.7069504145405-1.70695041454046
791921.5925083983795-2.59250839837946
802822.97737756951745.0226224304826
812325.6717631113499-2.67176311134988
821813.91937831127614.0806216887239
832120.76752594128470.232474058715269
842021.8939221074588-1.89392210745884
852320.03254882182932.9674511781707
862120.81457978351440.185420216485577
872121.8549708563638-0.854970856363817
881522.9601236544929-7.96012365449285
892827.28990740362070.710092596379332
901917.68950998953241.31049001046759
912621.24375395623234.75624604376766
921013.3592202085389-3.35922020853889
931617.1550179477001-1.15501794770006
942221.14732552051230.852674479487732
951918.91520723051550.084792769484549
963128.86387540753292.1361245924671
973125.2303794172615.76962058273899
982924.82237582056174.17762417943828
991917.46000711611391.53999288388611
1002218.91977252836733.08022747163268
1012322.44404050766840.555959492331635
1021516.2213007844847-1.22130078448468
1032021.4067774452657-1.40677744526568
1041819.5916965986423-1.59169659864227
1052322.1310549953480.868945004652024
1062520.89174259250284.1082574074972
1072116.62480889629774.37519110370226
1082419.54050889011234.4594911098877
1092525.2842542064876-0.284254206487617
1101719.5415733289438-2.54157332894384
1111314.590920034779-1.59092003477895
1122818.31206598545469.68793401454535
1132120.32721598029360.6727840197064
1142528.2402549727791-3.24025497277912
115921.0230657884603-12.0230657884603
1161617.8649936604446-1.86499366044457
1171921.1927565169446-2.19275651694458
1181719.5105598387774-2.51055983877741
1192524.54234989280720.457650107192838
1202015.51102203613224.48897796386781
1212921.72837616400277.27162383599735
1221419.0319382805427-5.03193828054266
1232226.9807843689066-4.98078436890656
1241515.7566129258641-0.756612925864104
1251925.4706403155968-6.47064031559685
1262021.9365074008663-1.93650740086628
1271517.5261183627754-2.52611836277545
1282021.9219024004867-1.92190240048673
1291820.3175223942078-2.31752239420782
1303325.57499999310787.42500000689215
1312223.8817153183514-1.88171531835138
1321616.5259552703765-0.525955270376547
1331719.1237948992732-2.1237948992732
1341615.11555793247580.884442067524166
1352117.11003911100553.88996088899452
1362627.6211796228832-1.62117962288317
1371821.1341578075379-3.13415780753787
1381823.0934916296008-5.0934916296008
1391718.4649006478306-1.46490064783056
1402224.8487332677922-2.84873326779222
1413024.79359725135425.20640274864581
1423027.36471781443442.63528218556562
1432429.8857645038554-5.88576450385544
1442122.0669167791498-1.06691677914981
1452125.4142006337394-4.41420063373936
1462927.43919354272611.56080645727388
1473123.26179273493017.73820726506992
1482019.04820235704980.951797642950214
1491614.17357955579761.82642044420244
1502219.00322352035522.9967764796448
1512020.5031787470075-0.503178747007477
1522827.31180121681120.688198783188794
1533826.58362302307111.416376976929
1542219.26560666717542.73439333282455
1552025.7191898021532-5.71918980215319
1561718.0567002627514-1.05670026275139
1572824.54083329423793.45916670576211
1582224.1667158267939-2.16671582679389
1593126.11565976166314.88434023833688






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09706704118348950.1941340823669790.90293295881651
110.3523937315236130.7047874630472260.647606268476387
120.741566177881040.516867644237920.25843382211896
130.7546971831618020.4906056336763960.245302816838198
140.7254484767998230.5491030464003530.274551523200177
150.7222331260299960.5555337479400070.277766873970004
160.6523287250372220.6953425499255550.347671274962778
170.5723867571705250.855226485658950.427613242829475
180.5418594976379090.9162810047241810.458140502362091
190.4746768820354030.9493537640708060.525323117964597
200.5058427191870520.9883145616258960.494157280812948
210.4595367988993250.919073597798650.540463201100675
220.4615022656115020.9230045312230050.538497734388498
230.3959674323419820.7919348646839640.604032567658018
240.6503967961502250.699206407699550.349603203849775
250.5814704078358780.8370591843282440.418529592164122
260.5529692208946710.8940615582106570.447030779105329
270.4892333828740320.9784667657480640.510766617125968
280.4394438196187580.8788876392375150.560556180381242
290.375597317633610.751194635267220.624402682366391
300.3202727906892250.6405455813784490.679727209310775
310.3629620800756560.7259241601513110.637037919924344
320.4384682808619950.876936561723990.561531719138005
330.3923908743408210.7847817486816420.607609125659179
340.3861762224596610.7723524449193230.613823777540339
350.4024822347189310.8049644694378620.597517765281069
360.3831703545198490.7663407090396970.616829645480152
370.5397889760306880.9204220479386230.460211023969312
380.737672203095430.5246555938091390.26232779690457
390.7285821679184440.5428356641631120.271417832081556
400.6868421397651520.6263157204696960.313157860234848
410.6576620025556670.6846759948886660.342337997444333
420.6057467149323730.7885065701352530.394253285067627
430.5565640123475030.8868719753049930.443435987652497
440.5418557683634410.9162884632731180.458144231636559
450.5155875849873650.968824830025270.484412415012635
460.5469266133009430.9061467733981140.453073386699057
470.5065035837407180.9869928325185630.493496416259282
480.4915276700125150.983055340025030.508472329987485
490.547042409487490.905915181025020.45295759051251
500.5224991616397490.9550016767205020.477500838360251
510.4855414435183180.9710828870366370.514458556481682
520.4368307603278960.8736615206557920.563169239672104
530.3948040599089570.7896081198179150.605195940091043
540.3502596862864780.7005193725729550.649740313713522
550.3118524346005220.6237048692010440.688147565399478
560.2822231878659350.564446375731870.717776812134065
570.241965379222740.4839307584454790.75803462077726
580.2202492497407240.4404984994814470.779750750259276
590.2061169261575810.4122338523151620.79388307384242
600.2587333467365410.5174666934730820.741266653263459
610.2525009302931990.5050018605863980.747499069706801
620.2165920987111340.4331841974222680.783407901288866
630.2054603211361670.4109206422723350.794539678863833
640.2463127265104330.4926254530208660.753687273489567
650.3219235874853270.6438471749706530.678076412514673
660.3202506915878110.6405013831756220.679749308412189
670.6420223536470220.7159552927059570.357977646352978
680.6372001251777710.7255997496444580.362799874822229
690.8266019964675010.3467960070649980.173398003532499
700.806821968698160.386356062603680.19317803130184
710.919416165358110.161167669283780.0805838346418899
720.9009752033729130.1980495932541740.0990247966270871
730.9254246520089460.1491506959821090.0745753479910544
740.9118300878247510.1763398243504980.0881699121752488
750.91353332191340.1729333561732010.0864666780866007
760.905613102717340.188773794565320.09438689728266
770.962709409449630.07458118110073870.0372905905503694
780.9539360111987650.09212797760246980.0460639888012349
790.9456252896303450.1087494207393090.0543747103696547
800.9485718978755230.1028562042489530.0514281021244765
810.939915985905970.1201680281880620.060084014094031
820.9385156749491290.1229686501017420.0614843250508711
830.9233385316367650.1533229367264690.0766614683632347
840.908823036675180.1823539266496420.0911769633248208
850.8985615779932490.2028768440135030.101438422006751
860.8810927302868740.2378145394262520.118907269713126
870.8573424373495980.2853151253008040.142657562650402
880.909265767454570.1814684650908580.090734232545429
890.891232397727670.2175352045446590.108767602272329
900.869291299282840.261417401434320.13070870071716
910.8715605388889320.2568789222221360.128439461111068
920.8608194275341910.2783611449316170.139180572465808
930.837228729993420.3255425400131610.162771270006581
940.8079727354703180.3840545290593650.192027264529682
950.7732820940468610.4534358119062780.226717905953139
960.7420484505183240.5159030989633530.257951549481676
970.762131802224180.4757363955516390.237868197775819
980.756060624930660.487878750138680.24393937506934
990.7192751377394320.5614497245211350.280724862260568
1000.6937075540770250.612584891845950.306292445922975
1010.6499134249314420.7001731501371160.350086575068558
1020.6104304153024740.7791391693950520.389569584697526
1030.5708582117825880.8582835764348240.429141788217412
1040.5284045659273720.9431908681452560.471595434072628
1050.4818238902379330.9636477804758670.518176109762067
1060.4624159195561080.9248318391122160.537584080443892
1070.4578279697095260.9156559394190520.542172030290474
1080.4678946111208110.9357892222416230.532105388879189
1090.4180484714399170.8360969428798330.581951528560083
1100.3946709846202370.7893419692404740.605329015379763
1110.3558516400834660.7117032801669320.644148359916534
1120.5752234395915220.8495531208169560.424776560408478
1130.5673438708425450.865312258314910.432656129157455
1140.5393894291358540.9212211417282930.460610570864147
1150.7571702979817530.4856594040364950.242829702018248
1160.7329947012418040.5340105975163920.267005298758196
1170.7205939496000640.5588121007998730.279406050399936
1180.6917327277600390.6165345444799220.308267272239961
1190.6408490525831830.7183018948336350.359150947416817
1200.6497517109130540.7004965781738920.350248289086946
1210.7020471644376520.5959056711246960.297952835562348
1220.706619746256260.5867605074874820.293380253743741
1230.7123835515225260.5752328969549480.287616448477474
1240.6591790318696720.6816419362606570.340820968130328
1250.7051892455662040.5896215088675930.294810754433796
1260.6753406703454740.6493186593090520.324659329654526
1270.6636775751517140.6726448496965720.336322424848286
1280.6271862085351140.7456275829297720.372813791464886
1290.5997998025579540.8004003948840930.400200197442046
1300.6706639022525950.658672195494810.329336097747405
1310.6129750712256810.7740498575486380.387024928774319
1320.5486312494142140.9027375011715720.451368750585786
1330.5465045273716840.9069909452566310.453495472628316
1340.4850634851079970.9701269702159930.514936514892003
1350.4425242411045130.8850484822090250.557475758895487
1360.4136292895515210.8272585791030430.586370710448479
1370.4172169583978590.8344339167957180.582783041602141
1380.4570914219548550.914182843909710.542908578045145
1390.3892987441054670.7785974882109340.610701255894533
1400.3171946465217060.6343892930434120.682805353478294
1410.4194161283338520.8388322566677040.580583871666148
1420.3650626141949540.7301252283899080.634937385805046
1430.2968768573865530.5937537147731060.703123142613447
1440.2228583556791680.4457167113583360.777141644320832
1450.2664017075775650.532803415155130.733598292422435
1460.1837295032859990.3674590065719990.816270496714
1470.2387194256747620.4774388513495240.761280574325238
1480.1475795712728910.2951591425457830.852420428727109
1490.07898494244129470.1579698848825890.921015057558705

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0970670411834895 & 0.194134082366979 & 0.90293295881651 \tabularnewline
11 & 0.352393731523613 & 0.704787463047226 & 0.647606268476387 \tabularnewline
12 & 0.74156617788104 & 0.51686764423792 & 0.25843382211896 \tabularnewline
13 & 0.754697183161802 & 0.490605633676396 & 0.245302816838198 \tabularnewline
14 & 0.725448476799823 & 0.549103046400353 & 0.274551523200177 \tabularnewline
15 & 0.722233126029996 & 0.555533747940007 & 0.277766873970004 \tabularnewline
16 & 0.652328725037222 & 0.695342549925555 & 0.347671274962778 \tabularnewline
17 & 0.572386757170525 & 0.85522648565895 & 0.427613242829475 \tabularnewline
18 & 0.541859497637909 & 0.916281004724181 & 0.458140502362091 \tabularnewline
19 & 0.474676882035403 & 0.949353764070806 & 0.525323117964597 \tabularnewline
20 & 0.505842719187052 & 0.988314561625896 & 0.494157280812948 \tabularnewline
21 & 0.459536798899325 & 0.91907359779865 & 0.540463201100675 \tabularnewline
22 & 0.461502265611502 & 0.923004531223005 & 0.538497734388498 \tabularnewline
23 & 0.395967432341982 & 0.791934864683964 & 0.604032567658018 \tabularnewline
24 & 0.650396796150225 & 0.69920640769955 & 0.349603203849775 \tabularnewline
25 & 0.581470407835878 & 0.837059184328244 & 0.418529592164122 \tabularnewline
26 & 0.552969220894671 & 0.894061558210657 & 0.447030779105329 \tabularnewline
27 & 0.489233382874032 & 0.978466765748064 & 0.510766617125968 \tabularnewline
28 & 0.439443819618758 & 0.878887639237515 & 0.560556180381242 \tabularnewline
29 & 0.37559731763361 & 0.75119463526722 & 0.624402682366391 \tabularnewline
30 & 0.320272790689225 & 0.640545581378449 & 0.679727209310775 \tabularnewline
31 & 0.362962080075656 & 0.725924160151311 & 0.637037919924344 \tabularnewline
32 & 0.438468280861995 & 0.87693656172399 & 0.561531719138005 \tabularnewline
33 & 0.392390874340821 & 0.784781748681642 & 0.607609125659179 \tabularnewline
34 & 0.386176222459661 & 0.772352444919323 & 0.613823777540339 \tabularnewline
35 & 0.402482234718931 & 0.804964469437862 & 0.597517765281069 \tabularnewline
36 & 0.383170354519849 & 0.766340709039697 & 0.616829645480152 \tabularnewline
37 & 0.539788976030688 & 0.920422047938623 & 0.460211023969312 \tabularnewline
38 & 0.73767220309543 & 0.524655593809139 & 0.26232779690457 \tabularnewline
39 & 0.728582167918444 & 0.542835664163112 & 0.271417832081556 \tabularnewline
40 & 0.686842139765152 & 0.626315720469696 & 0.313157860234848 \tabularnewline
41 & 0.657662002555667 & 0.684675994888666 & 0.342337997444333 \tabularnewline
42 & 0.605746714932373 & 0.788506570135253 & 0.394253285067627 \tabularnewline
43 & 0.556564012347503 & 0.886871975304993 & 0.443435987652497 \tabularnewline
44 & 0.541855768363441 & 0.916288463273118 & 0.458144231636559 \tabularnewline
45 & 0.515587584987365 & 0.96882483002527 & 0.484412415012635 \tabularnewline
46 & 0.546926613300943 & 0.906146773398114 & 0.453073386699057 \tabularnewline
47 & 0.506503583740718 & 0.986992832518563 & 0.493496416259282 \tabularnewline
48 & 0.491527670012515 & 0.98305534002503 & 0.508472329987485 \tabularnewline
49 & 0.54704240948749 & 0.90591518102502 & 0.45295759051251 \tabularnewline
50 & 0.522499161639749 & 0.955001676720502 & 0.477500838360251 \tabularnewline
51 & 0.485541443518318 & 0.971082887036637 & 0.514458556481682 \tabularnewline
52 & 0.436830760327896 & 0.873661520655792 & 0.563169239672104 \tabularnewline
53 & 0.394804059908957 & 0.789608119817915 & 0.605195940091043 \tabularnewline
54 & 0.350259686286478 & 0.700519372572955 & 0.649740313713522 \tabularnewline
55 & 0.311852434600522 & 0.623704869201044 & 0.688147565399478 \tabularnewline
56 & 0.282223187865935 & 0.56444637573187 & 0.717776812134065 \tabularnewline
57 & 0.24196537922274 & 0.483930758445479 & 0.75803462077726 \tabularnewline
58 & 0.220249249740724 & 0.440498499481447 & 0.779750750259276 \tabularnewline
59 & 0.206116926157581 & 0.412233852315162 & 0.79388307384242 \tabularnewline
60 & 0.258733346736541 & 0.517466693473082 & 0.741266653263459 \tabularnewline
61 & 0.252500930293199 & 0.505001860586398 & 0.747499069706801 \tabularnewline
62 & 0.216592098711134 & 0.433184197422268 & 0.783407901288866 \tabularnewline
63 & 0.205460321136167 & 0.410920642272335 & 0.794539678863833 \tabularnewline
64 & 0.246312726510433 & 0.492625453020866 & 0.753687273489567 \tabularnewline
65 & 0.321923587485327 & 0.643847174970653 & 0.678076412514673 \tabularnewline
66 & 0.320250691587811 & 0.640501383175622 & 0.679749308412189 \tabularnewline
67 & 0.642022353647022 & 0.715955292705957 & 0.357977646352978 \tabularnewline
68 & 0.637200125177771 & 0.725599749644458 & 0.362799874822229 \tabularnewline
69 & 0.826601996467501 & 0.346796007064998 & 0.173398003532499 \tabularnewline
70 & 0.80682196869816 & 0.38635606260368 & 0.19317803130184 \tabularnewline
71 & 0.91941616535811 & 0.16116766928378 & 0.0805838346418899 \tabularnewline
72 & 0.900975203372913 & 0.198049593254174 & 0.0990247966270871 \tabularnewline
73 & 0.925424652008946 & 0.149150695982109 & 0.0745753479910544 \tabularnewline
74 & 0.911830087824751 & 0.176339824350498 & 0.0881699121752488 \tabularnewline
75 & 0.9135333219134 & 0.172933356173201 & 0.0864666780866007 \tabularnewline
76 & 0.90561310271734 & 0.18877379456532 & 0.09438689728266 \tabularnewline
77 & 0.96270940944963 & 0.0745811811007387 & 0.0372905905503694 \tabularnewline
78 & 0.953936011198765 & 0.0921279776024698 & 0.0460639888012349 \tabularnewline
79 & 0.945625289630345 & 0.108749420739309 & 0.0543747103696547 \tabularnewline
80 & 0.948571897875523 & 0.102856204248953 & 0.0514281021244765 \tabularnewline
81 & 0.93991598590597 & 0.120168028188062 & 0.060084014094031 \tabularnewline
82 & 0.938515674949129 & 0.122968650101742 & 0.0614843250508711 \tabularnewline
83 & 0.923338531636765 & 0.153322936726469 & 0.0766614683632347 \tabularnewline
84 & 0.90882303667518 & 0.182353926649642 & 0.0911769633248208 \tabularnewline
85 & 0.898561577993249 & 0.202876844013503 & 0.101438422006751 \tabularnewline
86 & 0.881092730286874 & 0.237814539426252 & 0.118907269713126 \tabularnewline
87 & 0.857342437349598 & 0.285315125300804 & 0.142657562650402 \tabularnewline
88 & 0.90926576745457 & 0.181468465090858 & 0.090734232545429 \tabularnewline
89 & 0.89123239772767 & 0.217535204544659 & 0.108767602272329 \tabularnewline
90 & 0.86929129928284 & 0.26141740143432 & 0.13070870071716 \tabularnewline
91 & 0.871560538888932 & 0.256878922222136 & 0.128439461111068 \tabularnewline
92 & 0.860819427534191 & 0.278361144931617 & 0.139180572465808 \tabularnewline
93 & 0.83722872999342 & 0.325542540013161 & 0.162771270006581 \tabularnewline
94 & 0.807972735470318 & 0.384054529059365 & 0.192027264529682 \tabularnewline
95 & 0.773282094046861 & 0.453435811906278 & 0.226717905953139 \tabularnewline
96 & 0.742048450518324 & 0.515903098963353 & 0.257951549481676 \tabularnewline
97 & 0.76213180222418 & 0.475736395551639 & 0.237868197775819 \tabularnewline
98 & 0.75606062493066 & 0.48787875013868 & 0.24393937506934 \tabularnewline
99 & 0.719275137739432 & 0.561449724521135 & 0.280724862260568 \tabularnewline
100 & 0.693707554077025 & 0.61258489184595 & 0.306292445922975 \tabularnewline
101 & 0.649913424931442 & 0.700173150137116 & 0.350086575068558 \tabularnewline
102 & 0.610430415302474 & 0.779139169395052 & 0.389569584697526 \tabularnewline
103 & 0.570858211782588 & 0.858283576434824 & 0.429141788217412 \tabularnewline
104 & 0.528404565927372 & 0.943190868145256 & 0.471595434072628 \tabularnewline
105 & 0.481823890237933 & 0.963647780475867 & 0.518176109762067 \tabularnewline
106 & 0.462415919556108 & 0.924831839112216 & 0.537584080443892 \tabularnewline
107 & 0.457827969709526 & 0.915655939419052 & 0.542172030290474 \tabularnewline
108 & 0.467894611120811 & 0.935789222241623 & 0.532105388879189 \tabularnewline
109 & 0.418048471439917 & 0.836096942879833 & 0.581951528560083 \tabularnewline
110 & 0.394670984620237 & 0.789341969240474 & 0.605329015379763 \tabularnewline
111 & 0.355851640083466 & 0.711703280166932 & 0.644148359916534 \tabularnewline
112 & 0.575223439591522 & 0.849553120816956 & 0.424776560408478 \tabularnewline
113 & 0.567343870842545 & 0.86531225831491 & 0.432656129157455 \tabularnewline
114 & 0.539389429135854 & 0.921221141728293 & 0.460610570864147 \tabularnewline
115 & 0.757170297981753 & 0.485659404036495 & 0.242829702018248 \tabularnewline
116 & 0.732994701241804 & 0.534010597516392 & 0.267005298758196 \tabularnewline
117 & 0.720593949600064 & 0.558812100799873 & 0.279406050399936 \tabularnewline
118 & 0.691732727760039 & 0.616534544479922 & 0.308267272239961 \tabularnewline
119 & 0.640849052583183 & 0.718301894833635 & 0.359150947416817 \tabularnewline
120 & 0.649751710913054 & 0.700496578173892 & 0.350248289086946 \tabularnewline
121 & 0.702047164437652 & 0.595905671124696 & 0.297952835562348 \tabularnewline
122 & 0.70661974625626 & 0.586760507487482 & 0.293380253743741 \tabularnewline
123 & 0.712383551522526 & 0.575232896954948 & 0.287616448477474 \tabularnewline
124 & 0.659179031869672 & 0.681641936260657 & 0.340820968130328 \tabularnewline
125 & 0.705189245566204 & 0.589621508867593 & 0.294810754433796 \tabularnewline
126 & 0.675340670345474 & 0.649318659309052 & 0.324659329654526 \tabularnewline
127 & 0.663677575151714 & 0.672644849696572 & 0.336322424848286 \tabularnewline
128 & 0.627186208535114 & 0.745627582929772 & 0.372813791464886 \tabularnewline
129 & 0.599799802557954 & 0.800400394884093 & 0.400200197442046 \tabularnewline
130 & 0.670663902252595 & 0.65867219549481 & 0.329336097747405 \tabularnewline
131 & 0.612975071225681 & 0.774049857548638 & 0.387024928774319 \tabularnewline
132 & 0.548631249414214 & 0.902737501171572 & 0.451368750585786 \tabularnewline
133 & 0.546504527371684 & 0.906990945256631 & 0.453495472628316 \tabularnewline
134 & 0.485063485107997 & 0.970126970215993 & 0.514936514892003 \tabularnewline
135 & 0.442524241104513 & 0.885048482209025 & 0.557475758895487 \tabularnewline
136 & 0.413629289551521 & 0.827258579103043 & 0.586370710448479 \tabularnewline
137 & 0.417216958397859 & 0.834433916795718 & 0.582783041602141 \tabularnewline
138 & 0.457091421954855 & 0.91418284390971 & 0.542908578045145 \tabularnewline
139 & 0.389298744105467 & 0.778597488210934 & 0.610701255894533 \tabularnewline
140 & 0.317194646521706 & 0.634389293043412 & 0.682805353478294 \tabularnewline
141 & 0.419416128333852 & 0.838832256667704 & 0.580583871666148 \tabularnewline
142 & 0.365062614194954 & 0.730125228389908 & 0.634937385805046 \tabularnewline
143 & 0.296876857386553 & 0.593753714773106 & 0.703123142613447 \tabularnewline
144 & 0.222858355679168 & 0.445716711358336 & 0.777141644320832 \tabularnewline
145 & 0.266401707577565 & 0.53280341515513 & 0.733598292422435 \tabularnewline
146 & 0.183729503285999 & 0.367459006571999 & 0.816270496714 \tabularnewline
147 & 0.238719425674762 & 0.477438851349524 & 0.761280574325238 \tabularnewline
148 & 0.147579571272891 & 0.295159142545783 & 0.852420428727109 \tabularnewline
149 & 0.0789849424412947 & 0.157969884882589 & 0.921015057558705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&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]10[/C][C]0.0970670411834895[/C][C]0.194134082366979[/C][C]0.90293295881651[/C][/ROW]
[ROW][C]11[/C][C]0.352393731523613[/C][C]0.704787463047226[/C][C]0.647606268476387[/C][/ROW]
[ROW][C]12[/C][C]0.74156617788104[/C][C]0.51686764423792[/C][C]0.25843382211896[/C][/ROW]
[ROW][C]13[/C][C]0.754697183161802[/C][C]0.490605633676396[/C][C]0.245302816838198[/C][/ROW]
[ROW][C]14[/C][C]0.725448476799823[/C][C]0.549103046400353[/C][C]0.274551523200177[/C][/ROW]
[ROW][C]15[/C][C]0.722233126029996[/C][C]0.555533747940007[/C][C]0.277766873970004[/C][/ROW]
[ROW][C]16[/C][C]0.652328725037222[/C][C]0.695342549925555[/C][C]0.347671274962778[/C][/ROW]
[ROW][C]17[/C][C]0.572386757170525[/C][C]0.85522648565895[/C][C]0.427613242829475[/C][/ROW]
[ROW][C]18[/C][C]0.541859497637909[/C][C]0.916281004724181[/C][C]0.458140502362091[/C][/ROW]
[ROW][C]19[/C][C]0.474676882035403[/C][C]0.949353764070806[/C][C]0.525323117964597[/C][/ROW]
[ROW][C]20[/C][C]0.505842719187052[/C][C]0.988314561625896[/C][C]0.494157280812948[/C][/ROW]
[ROW][C]21[/C][C]0.459536798899325[/C][C]0.91907359779865[/C][C]0.540463201100675[/C][/ROW]
[ROW][C]22[/C][C]0.461502265611502[/C][C]0.923004531223005[/C][C]0.538497734388498[/C][/ROW]
[ROW][C]23[/C][C]0.395967432341982[/C][C]0.791934864683964[/C][C]0.604032567658018[/C][/ROW]
[ROW][C]24[/C][C]0.650396796150225[/C][C]0.69920640769955[/C][C]0.349603203849775[/C][/ROW]
[ROW][C]25[/C][C]0.581470407835878[/C][C]0.837059184328244[/C][C]0.418529592164122[/C][/ROW]
[ROW][C]26[/C][C]0.552969220894671[/C][C]0.894061558210657[/C][C]0.447030779105329[/C][/ROW]
[ROW][C]27[/C][C]0.489233382874032[/C][C]0.978466765748064[/C][C]0.510766617125968[/C][/ROW]
[ROW][C]28[/C][C]0.439443819618758[/C][C]0.878887639237515[/C][C]0.560556180381242[/C][/ROW]
[ROW][C]29[/C][C]0.37559731763361[/C][C]0.75119463526722[/C][C]0.624402682366391[/C][/ROW]
[ROW][C]30[/C][C]0.320272790689225[/C][C]0.640545581378449[/C][C]0.679727209310775[/C][/ROW]
[ROW][C]31[/C][C]0.362962080075656[/C][C]0.725924160151311[/C][C]0.637037919924344[/C][/ROW]
[ROW][C]32[/C][C]0.438468280861995[/C][C]0.87693656172399[/C][C]0.561531719138005[/C][/ROW]
[ROW][C]33[/C][C]0.392390874340821[/C][C]0.784781748681642[/C][C]0.607609125659179[/C][/ROW]
[ROW][C]34[/C][C]0.386176222459661[/C][C]0.772352444919323[/C][C]0.613823777540339[/C][/ROW]
[ROW][C]35[/C][C]0.402482234718931[/C][C]0.804964469437862[/C][C]0.597517765281069[/C][/ROW]
[ROW][C]36[/C][C]0.383170354519849[/C][C]0.766340709039697[/C][C]0.616829645480152[/C][/ROW]
[ROW][C]37[/C][C]0.539788976030688[/C][C]0.920422047938623[/C][C]0.460211023969312[/C][/ROW]
[ROW][C]38[/C][C]0.73767220309543[/C][C]0.524655593809139[/C][C]0.26232779690457[/C][/ROW]
[ROW][C]39[/C][C]0.728582167918444[/C][C]0.542835664163112[/C][C]0.271417832081556[/C][/ROW]
[ROW][C]40[/C][C]0.686842139765152[/C][C]0.626315720469696[/C][C]0.313157860234848[/C][/ROW]
[ROW][C]41[/C][C]0.657662002555667[/C][C]0.684675994888666[/C][C]0.342337997444333[/C][/ROW]
[ROW][C]42[/C][C]0.605746714932373[/C][C]0.788506570135253[/C][C]0.394253285067627[/C][/ROW]
[ROW][C]43[/C][C]0.556564012347503[/C][C]0.886871975304993[/C][C]0.443435987652497[/C][/ROW]
[ROW][C]44[/C][C]0.541855768363441[/C][C]0.916288463273118[/C][C]0.458144231636559[/C][/ROW]
[ROW][C]45[/C][C]0.515587584987365[/C][C]0.96882483002527[/C][C]0.484412415012635[/C][/ROW]
[ROW][C]46[/C][C]0.546926613300943[/C][C]0.906146773398114[/C][C]0.453073386699057[/C][/ROW]
[ROW][C]47[/C][C]0.506503583740718[/C][C]0.986992832518563[/C][C]0.493496416259282[/C][/ROW]
[ROW][C]48[/C][C]0.491527670012515[/C][C]0.98305534002503[/C][C]0.508472329987485[/C][/ROW]
[ROW][C]49[/C][C]0.54704240948749[/C][C]0.90591518102502[/C][C]0.45295759051251[/C][/ROW]
[ROW][C]50[/C][C]0.522499161639749[/C][C]0.955001676720502[/C][C]0.477500838360251[/C][/ROW]
[ROW][C]51[/C][C]0.485541443518318[/C][C]0.971082887036637[/C][C]0.514458556481682[/C][/ROW]
[ROW][C]52[/C][C]0.436830760327896[/C][C]0.873661520655792[/C][C]0.563169239672104[/C][/ROW]
[ROW][C]53[/C][C]0.394804059908957[/C][C]0.789608119817915[/C][C]0.605195940091043[/C][/ROW]
[ROW][C]54[/C][C]0.350259686286478[/C][C]0.700519372572955[/C][C]0.649740313713522[/C][/ROW]
[ROW][C]55[/C][C]0.311852434600522[/C][C]0.623704869201044[/C][C]0.688147565399478[/C][/ROW]
[ROW][C]56[/C][C]0.282223187865935[/C][C]0.56444637573187[/C][C]0.717776812134065[/C][/ROW]
[ROW][C]57[/C][C]0.24196537922274[/C][C]0.483930758445479[/C][C]0.75803462077726[/C][/ROW]
[ROW][C]58[/C][C]0.220249249740724[/C][C]0.440498499481447[/C][C]0.779750750259276[/C][/ROW]
[ROW][C]59[/C][C]0.206116926157581[/C][C]0.412233852315162[/C][C]0.79388307384242[/C][/ROW]
[ROW][C]60[/C][C]0.258733346736541[/C][C]0.517466693473082[/C][C]0.741266653263459[/C][/ROW]
[ROW][C]61[/C][C]0.252500930293199[/C][C]0.505001860586398[/C][C]0.747499069706801[/C][/ROW]
[ROW][C]62[/C][C]0.216592098711134[/C][C]0.433184197422268[/C][C]0.783407901288866[/C][/ROW]
[ROW][C]63[/C][C]0.205460321136167[/C][C]0.410920642272335[/C][C]0.794539678863833[/C][/ROW]
[ROW][C]64[/C][C]0.246312726510433[/C][C]0.492625453020866[/C][C]0.753687273489567[/C][/ROW]
[ROW][C]65[/C][C]0.321923587485327[/C][C]0.643847174970653[/C][C]0.678076412514673[/C][/ROW]
[ROW][C]66[/C][C]0.320250691587811[/C][C]0.640501383175622[/C][C]0.679749308412189[/C][/ROW]
[ROW][C]67[/C][C]0.642022353647022[/C][C]0.715955292705957[/C][C]0.357977646352978[/C][/ROW]
[ROW][C]68[/C][C]0.637200125177771[/C][C]0.725599749644458[/C][C]0.362799874822229[/C][/ROW]
[ROW][C]69[/C][C]0.826601996467501[/C][C]0.346796007064998[/C][C]0.173398003532499[/C][/ROW]
[ROW][C]70[/C][C]0.80682196869816[/C][C]0.38635606260368[/C][C]0.19317803130184[/C][/ROW]
[ROW][C]71[/C][C]0.91941616535811[/C][C]0.16116766928378[/C][C]0.0805838346418899[/C][/ROW]
[ROW][C]72[/C][C]0.900975203372913[/C][C]0.198049593254174[/C][C]0.0990247966270871[/C][/ROW]
[ROW][C]73[/C][C]0.925424652008946[/C][C]0.149150695982109[/C][C]0.0745753479910544[/C][/ROW]
[ROW][C]74[/C][C]0.911830087824751[/C][C]0.176339824350498[/C][C]0.0881699121752488[/C][/ROW]
[ROW][C]75[/C][C]0.9135333219134[/C][C]0.172933356173201[/C][C]0.0864666780866007[/C][/ROW]
[ROW][C]76[/C][C]0.90561310271734[/C][C]0.18877379456532[/C][C]0.09438689728266[/C][/ROW]
[ROW][C]77[/C][C]0.96270940944963[/C][C]0.0745811811007387[/C][C]0.0372905905503694[/C][/ROW]
[ROW][C]78[/C][C]0.953936011198765[/C][C]0.0921279776024698[/C][C]0.0460639888012349[/C][/ROW]
[ROW][C]79[/C][C]0.945625289630345[/C][C]0.108749420739309[/C][C]0.0543747103696547[/C][/ROW]
[ROW][C]80[/C][C]0.948571897875523[/C][C]0.102856204248953[/C][C]0.0514281021244765[/C][/ROW]
[ROW][C]81[/C][C]0.93991598590597[/C][C]0.120168028188062[/C][C]0.060084014094031[/C][/ROW]
[ROW][C]82[/C][C]0.938515674949129[/C][C]0.122968650101742[/C][C]0.0614843250508711[/C][/ROW]
[ROW][C]83[/C][C]0.923338531636765[/C][C]0.153322936726469[/C][C]0.0766614683632347[/C][/ROW]
[ROW][C]84[/C][C]0.90882303667518[/C][C]0.182353926649642[/C][C]0.0911769633248208[/C][/ROW]
[ROW][C]85[/C][C]0.898561577993249[/C][C]0.202876844013503[/C][C]0.101438422006751[/C][/ROW]
[ROW][C]86[/C][C]0.881092730286874[/C][C]0.237814539426252[/C][C]0.118907269713126[/C][/ROW]
[ROW][C]87[/C][C]0.857342437349598[/C][C]0.285315125300804[/C][C]0.142657562650402[/C][/ROW]
[ROW][C]88[/C][C]0.90926576745457[/C][C]0.181468465090858[/C][C]0.090734232545429[/C][/ROW]
[ROW][C]89[/C][C]0.89123239772767[/C][C]0.217535204544659[/C][C]0.108767602272329[/C][/ROW]
[ROW][C]90[/C][C]0.86929129928284[/C][C]0.26141740143432[/C][C]0.13070870071716[/C][/ROW]
[ROW][C]91[/C][C]0.871560538888932[/C][C]0.256878922222136[/C][C]0.128439461111068[/C][/ROW]
[ROW][C]92[/C][C]0.860819427534191[/C][C]0.278361144931617[/C][C]0.139180572465808[/C][/ROW]
[ROW][C]93[/C][C]0.83722872999342[/C][C]0.325542540013161[/C][C]0.162771270006581[/C][/ROW]
[ROW][C]94[/C][C]0.807972735470318[/C][C]0.384054529059365[/C][C]0.192027264529682[/C][/ROW]
[ROW][C]95[/C][C]0.773282094046861[/C][C]0.453435811906278[/C][C]0.226717905953139[/C][/ROW]
[ROW][C]96[/C][C]0.742048450518324[/C][C]0.515903098963353[/C][C]0.257951549481676[/C][/ROW]
[ROW][C]97[/C][C]0.76213180222418[/C][C]0.475736395551639[/C][C]0.237868197775819[/C][/ROW]
[ROW][C]98[/C][C]0.75606062493066[/C][C]0.48787875013868[/C][C]0.24393937506934[/C][/ROW]
[ROW][C]99[/C][C]0.719275137739432[/C][C]0.561449724521135[/C][C]0.280724862260568[/C][/ROW]
[ROW][C]100[/C][C]0.693707554077025[/C][C]0.61258489184595[/C][C]0.306292445922975[/C][/ROW]
[ROW][C]101[/C][C]0.649913424931442[/C][C]0.700173150137116[/C][C]0.350086575068558[/C][/ROW]
[ROW][C]102[/C][C]0.610430415302474[/C][C]0.779139169395052[/C][C]0.389569584697526[/C][/ROW]
[ROW][C]103[/C][C]0.570858211782588[/C][C]0.858283576434824[/C][C]0.429141788217412[/C][/ROW]
[ROW][C]104[/C][C]0.528404565927372[/C][C]0.943190868145256[/C][C]0.471595434072628[/C][/ROW]
[ROW][C]105[/C][C]0.481823890237933[/C][C]0.963647780475867[/C][C]0.518176109762067[/C][/ROW]
[ROW][C]106[/C][C]0.462415919556108[/C][C]0.924831839112216[/C][C]0.537584080443892[/C][/ROW]
[ROW][C]107[/C][C]0.457827969709526[/C][C]0.915655939419052[/C][C]0.542172030290474[/C][/ROW]
[ROW][C]108[/C][C]0.467894611120811[/C][C]0.935789222241623[/C][C]0.532105388879189[/C][/ROW]
[ROW][C]109[/C][C]0.418048471439917[/C][C]0.836096942879833[/C][C]0.581951528560083[/C][/ROW]
[ROW][C]110[/C][C]0.394670984620237[/C][C]0.789341969240474[/C][C]0.605329015379763[/C][/ROW]
[ROW][C]111[/C][C]0.355851640083466[/C][C]0.711703280166932[/C][C]0.644148359916534[/C][/ROW]
[ROW][C]112[/C][C]0.575223439591522[/C][C]0.849553120816956[/C][C]0.424776560408478[/C][/ROW]
[ROW][C]113[/C][C]0.567343870842545[/C][C]0.86531225831491[/C][C]0.432656129157455[/C][/ROW]
[ROW][C]114[/C][C]0.539389429135854[/C][C]0.921221141728293[/C][C]0.460610570864147[/C][/ROW]
[ROW][C]115[/C][C]0.757170297981753[/C][C]0.485659404036495[/C][C]0.242829702018248[/C][/ROW]
[ROW][C]116[/C][C]0.732994701241804[/C][C]0.534010597516392[/C][C]0.267005298758196[/C][/ROW]
[ROW][C]117[/C][C]0.720593949600064[/C][C]0.558812100799873[/C][C]0.279406050399936[/C][/ROW]
[ROW][C]118[/C][C]0.691732727760039[/C][C]0.616534544479922[/C][C]0.308267272239961[/C][/ROW]
[ROW][C]119[/C][C]0.640849052583183[/C][C]0.718301894833635[/C][C]0.359150947416817[/C][/ROW]
[ROW][C]120[/C][C]0.649751710913054[/C][C]0.700496578173892[/C][C]0.350248289086946[/C][/ROW]
[ROW][C]121[/C][C]0.702047164437652[/C][C]0.595905671124696[/C][C]0.297952835562348[/C][/ROW]
[ROW][C]122[/C][C]0.70661974625626[/C][C]0.586760507487482[/C][C]0.293380253743741[/C][/ROW]
[ROW][C]123[/C][C]0.712383551522526[/C][C]0.575232896954948[/C][C]0.287616448477474[/C][/ROW]
[ROW][C]124[/C][C]0.659179031869672[/C][C]0.681641936260657[/C][C]0.340820968130328[/C][/ROW]
[ROW][C]125[/C][C]0.705189245566204[/C][C]0.589621508867593[/C][C]0.294810754433796[/C][/ROW]
[ROW][C]126[/C][C]0.675340670345474[/C][C]0.649318659309052[/C][C]0.324659329654526[/C][/ROW]
[ROW][C]127[/C][C]0.663677575151714[/C][C]0.672644849696572[/C][C]0.336322424848286[/C][/ROW]
[ROW][C]128[/C][C]0.627186208535114[/C][C]0.745627582929772[/C][C]0.372813791464886[/C][/ROW]
[ROW][C]129[/C][C]0.599799802557954[/C][C]0.800400394884093[/C][C]0.400200197442046[/C][/ROW]
[ROW][C]130[/C][C]0.670663902252595[/C][C]0.65867219549481[/C][C]0.329336097747405[/C][/ROW]
[ROW][C]131[/C][C]0.612975071225681[/C][C]0.774049857548638[/C][C]0.387024928774319[/C][/ROW]
[ROW][C]132[/C][C]0.548631249414214[/C][C]0.902737501171572[/C][C]0.451368750585786[/C][/ROW]
[ROW][C]133[/C][C]0.546504527371684[/C][C]0.906990945256631[/C][C]0.453495472628316[/C][/ROW]
[ROW][C]134[/C][C]0.485063485107997[/C][C]0.970126970215993[/C][C]0.514936514892003[/C][/ROW]
[ROW][C]135[/C][C]0.442524241104513[/C][C]0.885048482209025[/C][C]0.557475758895487[/C][/ROW]
[ROW][C]136[/C][C]0.413629289551521[/C][C]0.827258579103043[/C][C]0.586370710448479[/C][/ROW]
[ROW][C]137[/C][C]0.417216958397859[/C][C]0.834433916795718[/C][C]0.582783041602141[/C][/ROW]
[ROW][C]138[/C][C]0.457091421954855[/C][C]0.91418284390971[/C][C]0.542908578045145[/C][/ROW]
[ROW][C]139[/C][C]0.389298744105467[/C][C]0.778597488210934[/C][C]0.610701255894533[/C][/ROW]
[ROW][C]140[/C][C]0.317194646521706[/C][C]0.634389293043412[/C][C]0.682805353478294[/C][/ROW]
[ROW][C]141[/C][C]0.419416128333852[/C][C]0.838832256667704[/C][C]0.580583871666148[/C][/ROW]
[ROW][C]142[/C][C]0.365062614194954[/C][C]0.730125228389908[/C][C]0.634937385805046[/C][/ROW]
[ROW][C]143[/C][C]0.296876857386553[/C][C]0.593753714773106[/C][C]0.703123142613447[/C][/ROW]
[ROW][C]144[/C][C]0.222858355679168[/C][C]0.445716711358336[/C][C]0.777141644320832[/C][/ROW]
[ROW][C]145[/C][C]0.266401707577565[/C][C]0.53280341515513[/C][C]0.733598292422435[/C][/ROW]
[ROW][C]146[/C][C]0.183729503285999[/C][C]0.367459006571999[/C][C]0.816270496714[/C][/ROW]
[ROW][C]147[/C][C]0.238719425674762[/C][C]0.477438851349524[/C][C]0.761280574325238[/C][/ROW]
[ROW][C]148[/C][C]0.147579571272891[/C][C]0.295159142545783[/C][C]0.852420428727109[/C][/ROW]
[ROW][C]149[/C][C]0.0789849424412947[/C][C]0.157969884882589[/C][C]0.921015057558705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09706704118348950.1941340823669790.90293295881651
110.3523937315236130.7047874630472260.647606268476387
120.741566177881040.516867644237920.25843382211896
130.7546971831618020.4906056336763960.245302816838198
140.7254484767998230.5491030464003530.274551523200177
150.7222331260299960.5555337479400070.277766873970004
160.6523287250372220.6953425499255550.347671274962778
170.5723867571705250.855226485658950.427613242829475
180.5418594976379090.9162810047241810.458140502362091
190.4746768820354030.9493537640708060.525323117964597
200.5058427191870520.9883145616258960.494157280812948
210.4595367988993250.919073597798650.540463201100675
220.4615022656115020.9230045312230050.538497734388498
230.3959674323419820.7919348646839640.604032567658018
240.6503967961502250.699206407699550.349603203849775
250.5814704078358780.8370591843282440.418529592164122
260.5529692208946710.8940615582106570.447030779105329
270.4892333828740320.9784667657480640.510766617125968
280.4394438196187580.8788876392375150.560556180381242
290.375597317633610.751194635267220.624402682366391
300.3202727906892250.6405455813784490.679727209310775
310.3629620800756560.7259241601513110.637037919924344
320.4384682808619950.876936561723990.561531719138005
330.3923908743408210.7847817486816420.607609125659179
340.3861762224596610.7723524449193230.613823777540339
350.4024822347189310.8049644694378620.597517765281069
360.3831703545198490.7663407090396970.616829645480152
370.5397889760306880.9204220479386230.460211023969312
380.737672203095430.5246555938091390.26232779690457
390.7285821679184440.5428356641631120.271417832081556
400.6868421397651520.6263157204696960.313157860234848
410.6576620025556670.6846759948886660.342337997444333
420.6057467149323730.7885065701352530.394253285067627
430.5565640123475030.8868719753049930.443435987652497
440.5418557683634410.9162884632731180.458144231636559
450.5155875849873650.968824830025270.484412415012635
460.5469266133009430.9061467733981140.453073386699057
470.5065035837407180.9869928325185630.493496416259282
480.4915276700125150.983055340025030.508472329987485
490.547042409487490.905915181025020.45295759051251
500.5224991616397490.9550016767205020.477500838360251
510.4855414435183180.9710828870366370.514458556481682
520.4368307603278960.8736615206557920.563169239672104
530.3948040599089570.7896081198179150.605195940091043
540.3502596862864780.7005193725729550.649740313713522
550.3118524346005220.6237048692010440.688147565399478
560.2822231878659350.564446375731870.717776812134065
570.241965379222740.4839307584454790.75803462077726
580.2202492497407240.4404984994814470.779750750259276
590.2061169261575810.4122338523151620.79388307384242
600.2587333467365410.5174666934730820.741266653263459
610.2525009302931990.5050018605863980.747499069706801
620.2165920987111340.4331841974222680.783407901288866
630.2054603211361670.4109206422723350.794539678863833
640.2463127265104330.4926254530208660.753687273489567
650.3219235874853270.6438471749706530.678076412514673
660.3202506915878110.6405013831756220.679749308412189
670.6420223536470220.7159552927059570.357977646352978
680.6372001251777710.7255997496444580.362799874822229
690.8266019964675010.3467960070649980.173398003532499
700.806821968698160.386356062603680.19317803130184
710.919416165358110.161167669283780.0805838346418899
720.9009752033729130.1980495932541740.0990247966270871
730.9254246520089460.1491506959821090.0745753479910544
740.9118300878247510.1763398243504980.0881699121752488
750.91353332191340.1729333561732010.0864666780866007
760.905613102717340.188773794565320.09438689728266
770.962709409449630.07458118110073870.0372905905503694
780.9539360111987650.09212797760246980.0460639888012349
790.9456252896303450.1087494207393090.0543747103696547
800.9485718978755230.1028562042489530.0514281021244765
810.939915985905970.1201680281880620.060084014094031
820.9385156749491290.1229686501017420.0614843250508711
830.9233385316367650.1533229367264690.0766614683632347
840.908823036675180.1823539266496420.0911769633248208
850.8985615779932490.2028768440135030.101438422006751
860.8810927302868740.2378145394262520.118907269713126
870.8573424373495980.2853151253008040.142657562650402
880.909265767454570.1814684650908580.090734232545429
890.891232397727670.2175352045446590.108767602272329
900.869291299282840.261417401434320.13070870071716
910.8715605388889320.2568789222221360.128439461111068
920.8608194275341910.2783611449316170.139180572465808
930.837228729993420.3255425400131610.162771270006581
940.8079727354703180.3840545290593650.192027264529682
950.7732820940468610.4534358119062780.226717905953139
960.7420484505183240.5159030989633530.257951549481676
970.762131802224180.4757363955516390.237868197775819
980.756060624930660.487878750138680.24393937506934
990.7192751377394320.5614497245211350.280724862260568
1000.6937075540770250.612584891845950.306292445922975
1010.6499134249314420.7001731501371160.350086575068558
1020.6104304153024740.7791391693950520.389569584697526
1030.5708582117825880.8582835764348240.429141788217412
1040.5284045659273720.9431908681452560.471595434072628
1050.4818238902379330.9636477804758670.518176109762067
1060.4624159195561080.9248318391122160.537584080443892
1070.4578279697095260.9156559394190520.542172030290474
1080.4678946111208110.9357892222416230.532105388879189
1090.4180484714399170.8360969428798330.581951528560083
1100.3946709846202370.7893419692404740.605329015379763
1110.3558516400834660.7117032801669320.644148359916534
1120.5752234395915220.8495531208169560.424776560408478
1130.5673438708425450.865312258314910.432656129157455
1140.5393894291358540.9212211417282930.460610570864147
1150.7571702979817530.4856594040364950.242829702018248
1160.7329947012418040.5340105975163920.267005298758196
1170.7205939496000640.5588121007998730.279406050399936
1180.6917327277600390.6165345444799220.308267272239961
1190.6408490525831830.7183018948336350.359150947416817
1200.6497517109130540.7004965781738920.350248289086946
1210.7020471644376520.5959056711246960.297952835562348
1220.706619746256260.5867605074874820.293380253743741
1230.7123835515225260.5752328969549480.287616448477474
1240.6591790318696720.6816419362606570.340820968130328
1250.7051892455662040.5896215088675930.294810754433796
1260.6753406703454740.6493186593090520.324659329654526
1270.6636775751517140.6726448496965720.336322424848286
1280.6271862085351140.7456275829297720.372813791464886
1290.5997998025579540.8004003948840930.400200197442046
1300.6706639022525950.658672195494810.329336097747405
1310.6129750712256810.7740498575486380.387024928774319
1320.5486312494142140.9027375011715720.451368750585786
1330.5465045273716840.9069909452566310.453495472628316
1340.4850634851079970.9701269702159930.514936514892003
1350.4425242411045130.8850484822090250.557475758895487
1360.4136292895515210.8272585791030430.586370710448479
1370.4172169583978590.8344339167957180.582783041602141
1380.4570914219548550.914182843909710.542908578045145
1390.3892987441054670.7785974882109340.610701255894533
1400.3171946465217060.6343892930434120.682805353478294
1410.4194161283338520.8388322566677040.580583871666148
1420.3650626141949540.7301252283899080.634937385805046
1430.2968768573865530.5937537147731060.703123142613447
1440.2228583556791680.4457167113583360.777141644320832
1450.2664017075775650.532803415155130.733598292422435
1460.1837295032859990.3674590065719990.816270496714
1470.2387194256747620.4774388513495240.761280574325238
1480.1475795712728910.2951591425457830.852420428727109
1490.07898494244129470.1579698848825890.921015057558705






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0142857142857143OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0142857142857143 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98229&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0142857142857143[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98229&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0142857142857143OK


Figures (Output of Computation)

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

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