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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 computationTue, 23 Nov 2010 14:08:19 +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/23/t1290521217hfyxbkaxljt71fi.htm/, Retrieved Fri, 19 Apr 2024 19:43:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99093, Retrieved Fri, 19 Apr 2024 19:43:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [ws 7] [2010-11-23 14:08:19] [de8ccb310fbbdc3d90ae577a3e011cf9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3
12	12	8	13	5
15	10	12	16	6
12	9	7	12	6
10	10	10	11	5
12	12	7	12	3
15	13	16	18	8
9	12	11	11	4
12	12	14	14	4
11	6	6	9	4
11	5	16	14	6
11	12	11	12	6
15	11	16	11	5
7	14	12	12	4
11	14	7	13	6
11	12	13	11	4
10	12	11	12	6
14	11	15	16	6
10	11	7	9	4
6	7	9	11	4
11	9	7	13	2
15	11	14	15	7
11	11	15	10	5
12	12	7	11	4
14	12	15	13	6
15	11	17	16	6
9	11	15	15	7
13	8	14	14	5
13	9	14	14	6
16	12	8	14	4
13	10	8	8	4
12	10	14	13	7
14	12	14	15	7
11	8	8	13	4
9	12	11	11	4
16	11	16	15	6
12	12	10	15	6
10	7	8	9	5
13	11	14	13	6
16	11	16	16	7
14	12	13	13	6
15	9	5	11	3
5	15	8	12	3
8	11	10	12	4
11	11	8	12	6
16	11	13	14	7
17	11	15	14	5
9	15	6	8	4
9	11	12	13	5
13	12	16	16	6
10	12	5	13	6
6	9	15	11	6
12	12	12	14	5
8	12	8	13	4
14	13	13	13	5
12	11	14	13	5
11	9	12	12	4
16	9	16	16	6
8	11	10	15	2
15	11	15	15	8
7	12	8	12	3
16	12	16	14	6
14	9	19	12	6
16	11	14	15	6
9	9	6	12	5
14	12	13	13	5
11	12	15	12	6
13	12	7	12	5
15	12	13	13	6
5	14	4	5	2
15	11	14	13	5
13	12	13	13	5
11	11	11	14	5
11	6	14	17	6
12	10	12	13	6
12	12	15	13	6
12	13	14	12	5
12	8	13	13	5
14	12	8	14	4
6	12	6	11	2
7	12	7	12	4
14	6	13	12	6
14	11	13	16	6
10	10	11	12	5
13	12	5	12	3
12	13	12	12	6
9	11	8	10	4
12	7	11	15	5
16	11	14	15	8
10	11	9	12	4
14	11	10	16	6
10	11	13	15	6
16	12	16	16	7
15	10	16	13	6
12	11	11	12	5
10	12	8	11	4
8	7	4	13	6
8	13	7	10	3
11	8	14	15	5
13	12	11	13	6
16	11	17	16	7
16	12	15	15	7
14	14	17	18	6
11	10	5	13	3
4	10	4	10	2
14	13	10	16	8
9	10	11	13	3
14	11	15	15	8
8	10	10	14	3
8	7	9	15	4
11	10	12	14	5
12	8	15	13	7
11	12	7	13	6
14	12	13	15	6
15	12	12	16	7
16	11	14	14	6
16	12	14	14	6
11	12	8	16	6
14	12	15	14	6
14	11	12	12	4
12	12	12	13	4
14	11	16	12	5
8	11	9	12	4
13	13	15	14	6
16	12	15	14	6
12	12	6	14	5
16	12	14	16	8
12	12	15	13	6
11	8	10	14	5
4	8	6	4	4
16	12	14	16	8
15	11	12	13	6
10	12	8	16	4
13	13	11	15	6
15	12	13	14	6
12	12	9	13	4
14	11	15	14	6
7	12	13	12	3
19	12	15	15	6
12	10	14	14	5
12	11	16	13	4
13	12	14	14	6
15	12	14	16	4
8	10	10	6	4
12	12	10	13	4
10	13	4	13	6
8	12	8	14	5
10	15	15	15	6
15	11	16	14	6
16	12	12	15	8
13	11	12	13	7
16	12	15	16	7
9	11	9	12	4
14	10	12	15	6
14	11	14	12	6
12	11	11	14	2

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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Celebrity [t] = + 0.423591992490597 + 0.154094039023444Popularity[t] -0.0194376966141408FindingFriends[t] + 0.103356510337141KnowingPeople[t] + 0.147677870542411Liked[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Celebrity



[t] =  +  0.423591992490597 +  0.154094039023444Popularity[t] -0.0194376966141408FindingFriends[t] +  0.103356510337141KnowingPeople[t] +  0.147677870542411Liked[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Celebrity



[t] =  +  0.423591992490597 +  0.154094039023444Popularity[t] -0.0194376966141408FindingFriends[t] +  0.103356510337141KnowingPeople[t] +  0.147677870542411Liked[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&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
Celebrity [t] = + 0.423591992490597 + 0.154094039023444Popularity[t] -0.0194376966141408FindingFriends[t] + 0.103356510337141KnowingPeople[t] + 0.147677870542411Liked[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4235919924905970.7057130.60020.5492510.274625
Popularity0.1540940390234440.0383424.01899.2e-054.6e-05
FindingFriends-0.01943769661414080.047694-0.40760.684180.34209
KnowingPeople0.1033565103371410.0308483.35050.0010190.00051
Liked0.1476778705424110.0483843.05220.0026850.001342

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.423591992490597 & 0.705713 & 0.6002 & 0.549251 & 0.274625 \tabularnewline
Popularity & 0.154094039023444 & 0.038342 & 4.0189 & 9.2e-05 & 4.6e-05 \tabularnewline
FindingFriends & -0.0194376966141408 & 0.047694 & -0.4076 & 0.68418 & 0.34209 \tabularnewline
KnowingPeople & 0.103356510337141 & 0.030848 & 3.3505 & 0.001019 & 0.00051 \tabularnewline
Liked & 0.147677870542411 & 0.048384 & 3.0522 & 0.002685 & 0.001342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.423591992490597[/C][C]0.705713[/C][C]0.6002[/C][C]0.549251[/C][C]0.274625[/C][/ROW]
[ROW][C]Popularity[/C][C]0.154094039023444[/C][C]0.038342[/C][C]4.0189[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0194376966141408[/C][C]0.047694[/C][C]-0.4076[/C][C]0.68418[/C][C]0.34209[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.103356510337141[/C][C]0.030848[/C][C]3.3505[/C][C]0.001019[/C][C]0.00051[/C][/ROW]
[ROW][C]Liked[/C][C]0.147677870542411[/C][C]0.048384[/C][C]3.0522[/C][C]0.002685[/C][C]0.001342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4235919924905970.7057130.60020.5492510.274625
Popularity0.1540940390234440.0383424.01899.2e-054.6e-05
FindingFriends-0.01943769661414080.047694-0.40760.684180.34209
KnowingPeople0.1033565103371410.0308483.35050.0010190.00051
Liked0.1476778705424110.0483843.05220.0026850.001342






Multiple Linear Regression - Regression Statistics
Multiple R0.677446083231663
R-squared0.458933195685921
Adjusted R-squared0.444600300207403
F-TEST (value)32.0195731821072
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04372839595306
Sum Squared Residuals164.494713642332

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.677446083231663 \tabularnewline
R-squared & 0.458933195685921 \tabularnewline
Adjusted R-squared & 0.444600300207403 \tabularnewline
F-TEST (value) & 32.0195731821072 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.04372839595306 \tabularnewline
Sum Squared Residuals & 164.494713642332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.677446083231663[/C][/ROW]
[ROW][C]R-squared[/C][C]0.458933195685921[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.444600300207403[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.0195731821072[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.04372839595306[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]164.494713642332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&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 R0.677446083231663
R-squared0.458933195685921
Adjusted R-squared0.444600300207403
F-TEST (value)32.0195731821072
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04372839595306
Sum Squared Residuals164.494713642332






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
135.54092790558286-2.54092790558286
254.786132501150710.213867498849285
366.14374966442513-0.143749664425127
464.593411210113581.40658878988641
554.428177095921570.571822904078432
634.53509812027116-1.53509812027116
786.79421835701611.20578164298391
844.33856417400698-0.338564174006983
945.55394943371597-1.55394943371597
1043.951240138968190.048759861031813
1165.74263229166580.257367708334203
1264.794430122596281.20556987740372
1355.79934865644749-0.799348656447494
1444.24253508361137-0.242535083611366
1564.489806558561851.51019344143815
1644.85346527272815-0.853465272728154
1764.640336083572841.35966391642716
1866.28028745979897-0.280287459798966
1943.803314127211180.19668587278882
2043.766757519333070.233242480666927
2124.58699504163255-2.58699504163255
2276.183347117942860.816652882057142
2354.931938119474170.0680618805258348
2444.38742024972875-0.38742024972875
2565.817816151557590.182183848442409
2666.6410945194967-0.641094519496693
2775.362139394139341.63786060586067
2855.78579425919598-0.78579425919598
2965.766356562581840.23364343741816
3045.5501865277869-1.5501865277869
3144.24071258069038-0.240712580690383
3275.445146956401841.55485304359816
3376.009815382305270.990184617694727
3444.70978924858383-0.709789248583834
3544.33856417400698-0.338564174006983
3666.54415417764058-0.544154177640584
3765.288201262909820.71179873709018
3853.984421424004881.01557857599512
3965.579803298811150.420196701188853
4076.6918320481830.308167951817004
4165.611103130883310.388896869116691
4234.70130243596722-1.70130243596722
4333.50148326760177-0.501483267601773
4444.24822919180295-0.24822919180295
4564.5037982881991.496201711801
4676.086406776086750.91359322391325
4756.44721383578448-1.44721383578448
4843.320434920851620.67956507914838
4954.756714122043090.243285877956912
5066.21011223449852-0.210112234498523
5164.16787489209241.8321251079076
5264.348021188127641.65197881187236
5355.34723641304169-0.347236413041691
5444.16975634505694-0.169756345056939
5555.59166543426917-0.591665434269168
5655.4257092597877-0.425709259787703
5744.95609972277585-0.956099722775846
5866.73070744141128-0.730707441411277
5924.69126280343018-2.69126280343019
6086.286703628281.71329637172
6133.86798443549108-0.867984435491083
6266.37703861048403-0.377038610484032
6366.14187741220617-0.141877412206167
6466.3374411569663-0.337441156966302
6554.027772582706110.972227417293889
6655.61110313088331-0.611103130883309
6765.207856163944850.792143836055152
6854.689192159294610.310807840705394
6965.765197169906750.234802830093247
7022.07374982907047-0.0737498290704678
7155.88799137685803-0.887991376858035
7255.45700909185986-0.457009091859865
7355.10922356029525-0.109223560295247
7465.959515186004610.0404848139953915
7565.238433935727560.761566064272439
7665.50962807351070.490371926489297
7755.23915599601701-0.23915599601701
7855.38066583929298-0.380665839292984
7945.24199844974001-1.24199844974001
8023.35949950525094-1.35949950525094
8143.764627925153940.235372074846058
8265.580051440025740.419948559974258
8366.07357443912468-0.0735744391246839
8454.679211476801120.32078852319888
8534.48247913862032-1.48247913862032
8665.032442975342730.967557024657273
8743.900254469067290.099745530932711
8855.48874625631767-0.488746256317665
8986.33744115696631.6625588430337
9044.4530607595127-0.453060759512697
9165.763504908113260.23649509188674
9265.30952041248850.690479587511504
9376.672394351568850.327605648431145
9466.11414209414646-0.114142094146458
9554.967961858233870.0320381417661324
9644.182588682019-0.182588682019004
9763.853518786779082.14648121322092
9833.60392852647842-0.603928526478422
9955.6252840516915-0.625284051691504
10065.250296071185580.749703928814418
10176.795188558520140.204811441479863
10276.42135997068930.578640029310698
10366.72404313171565-0.72404313171565
10434.36084432434413-1.36084432434413
10522.73579592921564-0.735795929215645
10685.724629514884982.27537048511502
10734.67279530832009-1.67279530832009
10886.132609589256561.86739041074345
10934.56302262950191-1.56302262950191
11044.66565707954961-0.665657079549607
11155.23201776724653-0.232017767246529
11275.587378859967271.41262114003273
11364.528681951790131.47131804820987
11465.906458871968130.0935411280318684
11576.104874271196850.895125728803154
11666.18976328642389-0.18976328642389
11766.17032558980975-0.170325589809749
11865.07507207375450.924927926245495
11965.96549402210.0345059778999974
12045.3795064466179-1.3795064466179
12145.19955854249928-1.19955854249928
12255.79293248796646-0.792932487966462
12344.14487268146581-0.144872681465809
12465.791962286462420.208037713537582
12566.27368210014689-0.273682100146891
12654.727097351018840.272902648981156
12786.465681330894571.53431866910543
12865.50962807351070.490371926489297
12955.06418013980053-0.0641801398005278
13042.095317119863741.90468288013626
13186.465681330894571.53431866910543
13265.681278356183750.318721643816248
13344.92097803473106-0.920978034731061
13465.526214115656260.473785884343736
13565.912875040449160.0871249595508358
13644.88948901148786-0.889489011487856
13765.984931718714140.0150682812858566
13834.38476698717679-1.38476698717679
13966.88364208775963-0.883642087759634
14055.59282482694425-0.592824826944255
14145.63242228046199-1.63242228046199
14265.708043472739420.291956527260583
14346.31158729187113-2.31158729187113
14443.381599665162620.618400334837378
14544.992845521825-0.992845521824997
14664.045080685141121.95491931485888
14754.317434215599350.68256578440065
14865.438482646706220.561517353293784
14966.24238226807473-0.242382268074729
15086.111290439677881.88870956032212
15175.373090278136861.62690972186314
15276.569037841231710.430962158768286
15344.29896672048925-0.298966720489253
15465.841977754859270.158022245140728
15565.586219467292180.413780532707821
15625.26331759931869-3.26331759931869

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 5.54092790558286 & -2.54092790558286 \tabularnewline
2 & 5 & 4.78613250115071 & 0.213867498849285 \tabularnewline
3 & 6 & 6.14374966442513 & -0.143749664425127 \tabularnewline
4 & 6 & 4.59341121011358 & 1.40658878988641 \tabularnewline
5 & 5 & 4.42817709592157 & 0.571822904078432 \tabularnewline
6 & 3 & 4.53509812027116 & -1.53509812027116 \tabularnewline
7 & 8 & 6.7942183570161 & 1.20578164298391 \tabularnewline
8 & 4 & 4.33856417400698 & -0.338564174006983 \tabularnewline
9 & 4 & 5.55394943371597 & -1.55394943371597 \tabularnewline
10 & 4 & 3.95124013896819 & 0.048759861031813 \tabularnewline
11 & 6 & 5.7426322916658 & 0.257367708334203 \tabularnewline
12 & 6 & 4.79443012259628 & 1.20556987740372 \tabularnewline
13 & 5 & 5.79934865644749 & -0.799348656447494 \tabularnewline
14 & 4 & 4.24253508361137 & -0.242535083611366 \tabularnewline
15 & 6 & 4.48980655856185 & 1.51019344143815 \tabularnewline
16 & 4 & 4.85346527272815 & -0.853465272728154 \tabularnewline
17 & 6 & 4.64033608357284 & 1.35966391642716 \tabularnewline
18 & 6 & 6.28028745979897 & -0.280287459798966 \tabularnewline
19 & 4 & 3.80331412721118 & 0.19668587278882 \tabularnewline
20 & 4 & 3.76675751933307 & 0.233242480666927 \tabularnewline
21 & 2 & 4.58699504163255 & -2.58699504163255 \tabularnewline
22 & 7 & 6.18334711794286 & 0.816652882057142 \tabularnewline
23 & 5 & 4.93193811947417 & 0.0680618805258348 \tabularnewline
24 & 4 & 4.38742024972875 & -0.38742024972875 \tabularnewline
25 & 6 & 5.81781615155759 & 0.182183848442409 \tabularnewline
26 & 6 & 6.6410945194967 & -0.641094519496693 \tabularnewline
27 & 7 & 5.36213939413934 & 1.63786060586067 \tabularnewline
28 & 5 & 5.78579425919598 & -0.78579425919598 \tabularnewline
29 & 6 & 5.76635656258184 & 0.23364343741816 \tabularnewline
30 & 4 & 5.5501865277869 & -1.5501865277869 \tabularnewline
31 & 4 & 4.24071258069038 & -0.240712580690383 \tabularnewline
32 & 7 & 5.44514695640184 & 1.55485304359816 \tabularnewline
33 & 7 & 6.00981538230527 & 0.990184617694727 \tabularnewline
34 & 4 & 4.70978924858383 & -0.709789248583834 \tabularnewline
35 & 4 & 4.33856417400698 & -0.338564174006983 \tabularnewline
36 & 6 & 6.54415417764058 & -0.544154177640584 \tabularnewline
37 & 6 & 5.28820126290982 & 0.71179873709018 \tabularnewline
38 & 5 & 3.98442142400488 & 1.01557857599512 \tabularnewline
39 & 6 & 5.57980329881115 & 0.420196701188853 \tabularnewline
40 & 7 & 6.691832048183 & 0.308167951817004 \tabularnewline
41 & 6 & 5.61110313088331 & 0.388896869116691 \tabularnewline
42 & 3 & 4.70130243596722 & -1.70130243596722 \tabularnewline
43 & 3 & 3.50148326760177 & -0.501483267601773 \tabularnewline
44 & 4 & 4.24822919180295 & -0.24822919180295 \tabularnewline
45 & 6 & 4.503798288199 & 1.496201711801 \tabularnewline
46 & 7 & 6.08640677608675 & 0.91359322391325 \tabularnewline
47 & 5 & 6.44721383578448 & -1.44721383578448 \tabularnewline
48 & 4 & 3.32043492085162 & 0.67956507914838 \tabularnewline
49 & 5 & 4.75671412204309 & 0.243285877956912 \tabularnewline
50 & 6 & 6.21011223449852 & -0.210112234498523 \tabularnewline
51 & 6 & 4.1678748920924 & 1.8321251079076 \tabularnewline
52 & 6 & 4.34802118812764 & 1.65197881187236 \tabularnewline
53 & 5 & 5.34723641304169 & -0.347236413041691 \tabularnewline
54 & 4 & 4.16975634505694 & -0.169756345056939 \tabularnewline
55 & 5 & 5.59166543426917 & -0.591665434269168 \tabularnewline
56 & 5 & 5.4257092597877 & -0.425709259787703 \tabularnewline
57 & 4 & 4.95609972277585 & -0.956099722775846 \tabularnewline
58 & 6 & 6.73070744141128 & -0.730707441411277 \tabularnewline
59 & 2 & 4.69126280343018 & -2.69126280343019 \tabularnewline
60 & 8 & 6.28670362828 & 1.71329637172 \tabularnewline
61 & 3 & 3.86798443549108 & -0.867984435491083 \tabularnewline
62 & 6 & 6.37703861048403 & -0.377038610484032 \tabularnewline
63 & 6 & 6.14187741220617 & -0.141877412206167 \tabularnewline
64 & 6 & 6.3374411569663 & -0.337441156966302 \tabularnewline
65 & 5 & 4.02777258270611 & 0.972227417293889 \tabularnewline
66 & 5 & 5.61110313088331 & -0.611103130883309 \tabularnewline
67 & 6 & 5.20785616394485 & 0.792143836055152 \tabularnewline
68 & 5 & 4.68919215929461 & 0.310807840705394 \tabularnewline
69 & 6 & 5.76519716990675 & 0.234802830093247 \tabularnewline
70 & 2 & 2.07374982907047 & -0.0737498290704678 \tabularnewline
71 & 5 & 5.88799137685803 & -0.887991376858035 \tabularnewline
72 & 5 & 5.45700909185986 & -0.457009091859865 \tabularnewline
73 & 5 & 5.10922356029525 & -0.109223560295247 \tabularnewline
74 & 6 & 5.95951518600461 & 0.0404848139953915 \tabularnewline
75 & 6 & 5.23843393572756 & 0.761566064272439 \tabularnewline
76 & 6 & 5.5096280735107 & 0.490371926489297 \tabularnewline
77 & 5 & 5.23915599601701 & -0.23915599601701 \tabularnewline
78 & 5 & 5.38066583929298 & -0.380665839292984 \tabularnewline
79 & 4 & 5.24199844974001 & -1.24199844974001 \tabularnewline
80 & 2 & 3.35949950525094 & -1.35949950525094 \tabularnewline
81 & 4 & 3.76462792515394 & 0.235372074846058 \tabularnewline
82 & 6 & 5.58005144002574 & 0.419948559974258 \tabularnewline
83 & 6 & 6.07357443912468 & -0.0735744391246839 \tabularnewline
84 & 5 & 4.67921147680112 & 0.32078852319888 \tabularnewline
85 & 3 & 4.48247913862032 & -1.48247913862032 \tabularnewline
86 & 6 & 5.03244297534273 & 0.967557024657273 \tabularnewline
87 & 4 & 3.90025446906729 & 0.099745530932711 \tabularnewline
88 & 5 & 5.48874625631767 & -0.488746256317665 \tabularnewline
89 & 8 & 6.3374411569663 & 1.6625588430337 \tabularnewline
90 & 4 & 4.4530607595127 & -0.453060759512697 \tabularnewline
91 & 6 & 5.76350490811326 & 0.23649509188674 \tabularnewline
92 & 6 & 5.3095204124885 & 0.690479587511504 \tabularnewline
93 & 7 & 6.67239435156885 & 0.327605648431145 \tabularnewline
94 & 6 & 6.11414209414646 & -0.114142094146458 \tabularnewline
95 & 5 & 4.96796185823387 & 0.0320381417661324 \tabularnewline
96 & 4 & 4.182588682019 & -0.182588682019004 \tabularnewline
97 & 6 & 3.85351878677908 & 2.14648121322092 \tabularnewline
98 & 3 & 3.60392852647842 & -0.603928526478422 \tabularnewline
99 & 5 & 5.6252840516915 & -0.625284051691504 \tabularnewline
100 & 6 & 5.25029607118558 & 0.749703928814418 \tabularnewline
101 & 7 & 6.79518855852014 & 0.204811441479863 \tabularnewline
102 & 7 & 6.4213599706893 & 0.578640029310698 \tabularnewline
103 & 6 & 6.72404313171565 & -0.72404313171565 \tabularnewline
104 & 3 & 4.36084432434413 & -1.36084432434413 \tabularnewline
105 & 2 & 2.73579592921564 & -0.735795929215645 \tabularnewline
106 & 8 & 5.72462951488498 & 2.27537048511502 \tabularnewline
107 & 3 & 4.67279530832009 & -1.67279530832009 \tabularnewline
108 & 8 & 6.13260958925656 & 1.86739041074345 \tabularnewline
109 & 3 & 4.56302262950191 & -1.56302262950191 \tabularnewline
110 & 4 & 4.66565707954961 & -0.665657079549607 \tabularnewline
111 & 5 & 5.23201776724653 & -0.232017767246529 \tabularnewline
112 & 7 & 5.58737885996727 & 1.41262114003273 \tabularnewline
113 & 6 & 4.52868195179013 & 1.47131804820987 \tabularnewline
114 & 6 & 5.90645887196813 & 0.0935411280318684 \tabularnewline
115 & 7 & 6.10487427119685 & 0.895125728803154 \tabularnewline
116 & 6 & 6.18976328642389 & -0.18976328642389 \tabularnewline
117 & 6 & 6.17032558980975 & -0.170325589809749 \tabularnewline
118 & 6 & 5.0750720737545 & 0.924927926245495 \tabularnewline
119 & 6 & 5.9654940221 & 0.0345059778999974 \tabularnewline
120 & 4 & 5.3795064466179 & -1.3795064466179 \tabularnewline
121 & 4 & 5.19955854249928 & -1.19955854249928 \tabularnewline
122 & 5 & 5.79293248796646 & -0.792932487966462 \tabularnewline
123 & 4 & 4.14487268146581 & -0.144872681465809 \tabularnewline
124 & 6 & 5.79196228646242 & 0.208037713537582 \tabularnewline
125 & 6 & 6.27368210014689 & -0.273682100146891 \tabularnewline
126 & 5 & 4.72709735101884 & 0.272902648981156 \tabularnewline
127 & 8 & 6.46568133089457 & 1.53431866910543 \tabularnewline
128 & 6 & 5.5096280735107 & 0.490371926489297 \tabularnewline
129 & 5 & 5.06418013980053 & -0.0641801398005278 \tabularnewline
130 & 4 & 2.09531711986374 & 1.90468288013626 \tabularnewline
131 & 8 & 6.46568133089457 & 1.53431866910543 \tabularnewline
132 & 6 & 5.68127835618375 & 0.318721643816248 \tabularnewline
133 & 4 & 4.92097803473106 & -0.920978034731061 \tabularnewline
134 & 6 & 5.52621411565626 & 0.473785884343736 \tabularnewline
135 & 6 & 5.91287504044916 & 0.0871249595508358 \tabularnewline
136 & 4 & 4.88948901148786 & -0.889489011487856 \tabularnewline
137 & 6 & 5.98493171871414 & 0.0150682812858566 \tabularnewline
138 & 3 & 4.38476698717679 & -1.38476698717679 \tabularnewline
139 & 6 & 6.88364208775963 & -0.883642087759634 \tabularnewline
140 & 5 & 5.59282482694425 & -0.592824826944255 \tabularnewline
141 & 4 & 5.63242228046199 & -1.63242228046199 \tabularnewline
142 & 6 & 5.70804347273942 & 0.291956527260583 \tabularnewline
143 & 4 & 6.31158729187113 & -2.31158729187113 \tabularnewline
144 & 4 & 3.38159966516262 & 0.618400334837378 \tabularnewline
145 & 4 & 4.992845521825 & -0.992845521824997 \tabularnewline
146 & 6 & 4.04508068514112 & 1.95491931485888 \tabularnewline
147 & 5 & 4.31743421559935 & 0.68256578440065 \tabularnewline
148 & 6 & 5.43848264670622 & 0.561517353293784 \tabularnewline
149 & 6 & 6.24238226807473 & -0.242382268074729 \tabularnewline
150 & 8 & 6.11129043967788 & 1.88870956032212 \tabularnewline
151 & 7 & 5.37309027813686 & 1.62690972186314 \tabularnewline
152 & 7 & 6.56903784123171 & 0.430962158768286 \tabularnewline
153 & 4 & 4.29896672048925 & -0.298966720489253 \tabularnewline
154 & 6 & 5.84197775485927 & 0.158022245140728 \tabularnewline
155 & 6 & 5.58621946729218 & 0.413780532707821 \tabularnewline
156 & 2 & 5.26331759931869 & -3.26331759931869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&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]3[/C][C]5.54092790558286[/C][C]-2.54092790558286[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.78613250115071[/C][C]0.213867498849285[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]6.14374966442513[/C][C]-0.143749664425127[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]4.59341121011358[/C][C]1.40658878988641[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]4.42817709592157[/C][C]0.571822904078432[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]4.53509812027116[/C][C]-1.53509812027116[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]6.7942183570161[/C][C]1.20578164298391[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.33856417400698[/C][C]-0.338564174006983[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.55394943371597[/C][C]-1.55394943371597[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.95124013896819[/C][C]0.048759861031813[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]5.7426322916658[/C][C]0.257367708334203[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]4.79443012259628[/C][C]1.20556987740372[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.79934865644749[/C][C]-0.799348656447494[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]4.24253508361137[/C][C]-0.242535083611366[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]4.48980655856185[/C][C]1.51019344143815[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.85346527272815[/C][C]-0.853465272728154[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]4.64033608357284[/C][C]1.35966391642716[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.28028745979897[/C][C]-0.280287459798966[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.80331412721118[/C][C]0.19668587278882[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.76675751933307[/C][C]0.233242480666927[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.58699504163255[/C][C]-2.58699504163255[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]6.18334711794286[/C][C]0.816652882057142[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.93193811947417[/C][C]0.0680618805258348[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.38742024972875[/C][C]-0.38742024972875[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]5.81781615155759[/C][C]0.182183848442409[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.6410945194967[/C][C]-0.641094519496693[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]5.36213939413934[/C][C]1.63786060586067[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]5.78579425919598[/C][C]-0.78579425919598[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.76635656258184[/C][C]0.23364343741816[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.5501865277869[/C][C]-1.5501865277869[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.24071258069038[/C][C]-0.240712580690383[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]5.44514695640184[/C][C]1.55485304359816[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]6.00981538230527[/C][C]0.990184617694727[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.70978924858383[/C][C]-0.709789248583834[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.33856417400698[/C][C]-0.338564174006983[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]6.54415417764058[/C][C]-0.544154177640584[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]5.28820126290982[/C][C]0.71179873709018[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]3.98442142400488[/C][C]1.01557857599512[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]5.57980329881115[/C][C]0.420196701188853[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.691832048183[/C][C]0.308167951817004[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]5.61110313088331[/C][C]0.388896869116691[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]4.70130243596722[/C][C]-1.70130243596722[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.50148326760177[/C][C]-0.501483267601773[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.24822919180295[/C][C]-0.24822919180295[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]4.503798288199[/C][C]1.496201711801[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.08640677608675[/C][C]0.91359322391325[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.44721383578448[/C][C]-1.44721383578448[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.32043492085162[/C][C]0.67956507914838[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]4.75671412204309[/C][C]0.243285877956912[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.21011223449852[/C][C]-0.210112234498523[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]4.1678748920924[/C][C]1.8321251079076[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]4.34802118812764[/C][C]1.65197881187236[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.34723641304169[/C][C]-0.347236413041691[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.16975634505694[/C][C]-0.169756345056939[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.59166543426917[/C][C]-0.591665434269168[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.4257092597877[/C][C]-0.425709259787703[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.95609972277585[/C][C]-0.956099722775846[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]6.73070744141128[/C][C]-0.730707441411277[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]4.69126280343018[/C][C]-2.69126280343019[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]6.28670362828[/C][C]1.71329637172[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.86798443549108[/C][C]-0.867984435491083[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.37703861048403[/C][C]-0.377038610484032[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]6.14187741220617[/C][C]-0.141877412206167[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]6.3374411569663[/C][C]-0.337441156966302[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.02777258270611[/C][C]0.972227417293889[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.61110313088331[/C][C]-0.611103130883309[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.20785616394485[/C][C]0.792143836055152[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]4.68919215929461[/C][C]0.310807840705394[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]5.76519716990675[/C][C]0.234802830093247[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]2.07374982907047[/C][C]-0.0737498290704678[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.88799137685803[/C][C]-0.887991376858035[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]5.45700909185986[/C][C]-0.457009091859865[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.10922356029525[/C][C]-0.109223560295247[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]5.95951518600461[/C][C]0.0404848139953915[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]5.23843393572756[/C][C]0.761566064272439[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]5.5096280735107[/C][C]0.490371926489297[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.23915599601701[/C][C]-0.23915599601701[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.38066583929298[/C][C]-0.380665839292984[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]5.24199844974001[/C][C]-1.24199844974001[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.35949950525094[/C][C]-1.35949950525094[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.76462792515394[/C][C]0.235372074846058[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]5.58005144002574[/C][C]0.419948559974258[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]6.07357443912468[/C][C]-0.0735744391246839[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.67921147680112[/C][C]0.32078852319888[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]4.48247913862032[/C][C]-1.48247913862032[/C][/ROW]
[ROW][C]86[/C][C]6[/C][C]5.03244297534273[/C][C]0.967557024657273[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.90025446906729[/C][C]0.099745530932711[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]5.48874625631767[/C][C]-0.488746256317665[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]6.3374411569663[/C][C]1.6625588430337[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.4530607595127[/C][C]-0.453060759512697[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]5.76350490811326[/C][C]0.23649509188674[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.3095204124885[/C][C]0.690479587511504[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]6.67239435156885[/C][C]0.327605648431145[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]6.11414209414646[/C][C]-0.114142094146458[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]4.96796185823387[/C][C]0.0320381417661324[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.182588682019[/C][C]-0.182588682019004[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]3.85351878677908[/C][C]2.14648121322092[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]3.60392852647842[/C][C]-0.603928526478422[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]5.6252840516915[/C][C]-0.625284051691504[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]5.25029607118558[/C][C]0.749703928814418[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]6.79518855852014[/C][C]0.204811441479863[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]6.4213599706893[/C][C]0.578640029310698[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]6.72404313171565[/C][C]-0.72404313171565[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.36084432434413[/C][C]-1.36084432434413[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.73579592921564[/C][C]-0.735795929215645[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]5.72462951488498[/C][C]2.27537048511502[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]4.67279530832009[/C][C]-1.67279530832009[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]6.13260958925656[/C][C]1.86739041074345[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.56302262950191[/C][C]-1.56302262950191[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.66565707954961[/C][C]-0.665657079549607[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]5.23201776724653[/C][C]-0.232017767246529[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]5.58737885996727[/C][C]1.41262114003273[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]4.52868195179013[/C][C]1.47131804820987[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.90645887196813[/C][C]0.0935411280318684[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]6.10487427119685[/C][C]0.895125728803154[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]6.18976328642389[/C][C]-0.18976328642389[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]6.17032558980975[/C][C]-0.170325589809749[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]5.0750720737545[/C][C]0.924927926245495[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.9654940221[/C][C]0.0345059778999974[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]5.3795064466179[/C][C]-1.3795064466179[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.19955854249928[/C][C]-1.19955854249928[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.79293248796646[/C][C]-0.792932487966462[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]4.14487268146581[/C][C]-0.144872681465809[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]5.79196228646242[/C][C]0.208037713537582[/C][/ROW]
[ROW][C]125[/C][C]6[/C][C]6.27368210014689[/C][C]-0.273682100146891[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.72709735101884[/C][C]0.272902648981156[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]6.46568133089457[/C][C]1.53431866910543[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.5096280735107[/C][C]0.490371926489297[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.06418013980053[/C][C]-0.0641801398005278[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]2.09531711986374[/C][C]1.90468288013626[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]6.46568133089457[/C][C]1.53431866910543[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]5.68127835618375[/C][C]0.318721643816248[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]4.92097803473106[/C][C]-0.920978034731061[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.52621411565626[/C][C]0.473785884343736[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]5.91287504044916[/C][C]0.0871249595508358[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.88948901148786[/C][C]-0.889489011487856[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]5.98493171871414[/C][C]0.0150682812858566[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]4.38476698717679[/C][C]-1.38476698717679[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.88364208775963[/C][C]-0.883642087759634[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]5.59282482694425[/C][C]-0.592824826944255[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.63242228046199[/C][C]-1.63242228046199[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.70804347273942[/C][C]0.291956527260583[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]6.31158729187113[/C][C]-2.31158729187113[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.38159966516262[/C][C]0.618400334837378[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]4.992845521825[/C][C]-0.992845521824997[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]4.04508068514112[/C][C]1.95491931485888[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.31743421559935[/C][C]0.68256578440065[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]5.43848264670622[/C][C]0.561517353293784[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.24238226807473[/C][C]-0.242382268074729[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]6.11129043967788[/C][C]1.88870956032212[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]5.37309027813686[/C][C]1.62690972186314[/C][/ROW]
[ROW][C]152[/C][C]7[/C][C]6.56903784123171[/C][C]0.430962158768286[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.29896672048925[/C][C]-0.298966720489253[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]5.84197775485927[/C][C]0.158022245140728[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.58621946729218[/C][C]0.413780532707821[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]5.26331759931869[/C][C]-3.26331759931869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&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
135.54092790558286-2.54092790558286
254.786132501150710.213867498849285
366.14374966442513-0.143749664425127
464.593411210113581.40658878988641
554.428177095921570.571822904078432
634.53509812027116-1.53509812027116
786.79421835701611.20578164298391
844.33856417400698-0.338564174006983
945.55394943371597-1.55394943371597
1043.951240138968190.048759861031813
1165.74263229166580.257367708334203
1264.794430122596281.20556987740372
1355.79934865644749-0.799348656447494
1444.24253508361137-0.242535083611366
1564.489806558561851.51019344143815
1644.85346527272815-0.853465272728154
1764.640336083572841.35966391642716
1866.28028745979897-0.280287459798966
1943.803314127211180.19668587278882
2043.766757519333070.233242480666927
2124.58699504163255-2.58699504163255
2276.183347117942860.816652882057142
2354.931938119474170.0680618805258348
2444.38742024972875-0.38742024972875
2565.817816151557590.182183848442409
2666.6410945194967-0.641094519496693
2775.362139394139341.63786060586067
2855.78579425919598-0.78579425919598
2965.766356562581840.23364343741816
3045.5501865277869-1.5501865277869
3144.24071258069038-0.240712580690383
3275.445146956401841.55485304359816
3376.009815382305270.990184617694727
3444.70978924858383-0.709789248583834
3544.33856417400698-0.338564174006983
3666.54415417764058-0.544154177640584
3765.288201262909820.71179873709018
3853.984421424004881.01557857599512
3965.579803298811150.420196701188853
4076.6918320481830.308167951817004
4165.611103130883310.388896869116691
4234.70130243596722-1.70130243596722
4333.50148326760177-0.501483267601773
4444.24822919180295-0.24822919180295
4564.5037982881991.496201711801
4676.086406776086750.91359322391325
4756.44721383578448-1.44721383578448
4843.320434920851620.67956507914838
4954.756714122043090.243285877956912
5066.21011223449852-0.210112234498523
5164.16787489209241.8321251079076
5264.348021188127641.65197881187236
5355.34723641304169-0.347236413041691
5444.16975634505694-0.169756345056939
5555.59166543426917-0.591665434269168
5655.4257092597877-0.425709259787703
5744.95609972277585-0.956099722775846
5866.73070744141128-0.730707441411277
5924.69126280343018-2.69126280343019
6086.286703628281.71329637172
6133.86798443549108-0.867984435491083
6266.37703861048403-0.377038610484032
6366.14187741220617-0.141877412206167
6466.3374411569663-0.337441156966302
6554.027772582706110.972227417293889
6655.61110313088331-0.611103130883309
6765.207856163944850.792143836055152
6854.689192159294610.310807840705394
6965.765197169906750.234802830093247
7022.07374982907047-0.0737498290704678
7155.88799137685803-0.887991376858035
7255.45700909185986-0.457009091859865
7355.10922356029525-0.109223560295247
7465.959515186004610.0404848139953915
7565.238433935727560.761566064272439
7665.50962807351070.490371926489297
7755.23915599601701-0.23915599601701
7855.38066583929298-0.380665839292984
7945.24199844974001-1.24199844974001
8023.35949950525094-1.35949950525094
8143.764627925153940.235372074846058
8265.580051440025740.419948559974258
8366.07357443912468-0.0735744391246839
8454.679211476801120.32078852319888
8534.48247913862032-1.48247913862032
8665.032442975342730.967557024657273
8743.900254469067290.099745530932711
8855.48874625631767-0.488746256317665
8986.33744115696631.6625588430337
9044.4530607595127-0.453060759512697
9165.763504908113260.23649509188674
9265.30952041248850.690479587511504
9376.672394351568850.327605648431145
9466.11414209414646-0.114142094146458
9554.967961858233870.0320381417661324
9644.182588682019-0.182588682019004
9763.853518786779082.14648121322092
9833.60392852647842-0.603928526478422
9955.6252840516915-0.625284051691504
10065.250296071185580.749703928814418
10176.795188558520140.204811441479863
10276.42135997068930.578640029310698
10366.72404313171565-0.72404313171565
10434.36084432434413-1.36084432434413
10522.73579592921564-0.735795929215645
10685.724629514884982.27537048511502
10734.67279530832009-1.67279530832009
10886.132609589256561.86739041074345
10934.56302262950191-1.56302262950191
11044.66565707954961-0.665657079549607
11155.23201776724653-0.232017767246529
11275.587378859967271.41262114003273
11364.528681951790131.47131804820987
11465.906458871968130.0935411280318684
11576.104874271196850.895125728803154
11666.18976328642389-0.18976328642389
11766.17032558980975-0.170325589809749
11865.07507207375450.924927926245495
11965.96549402210.0345059778999974
12045.3795064466179-1.3795064466179
12145.19955854249928-1.19955854249928
12255.79293248796646-0.792932487966462
12344.14487268146581-0.144872681465809
12465.791962286462420.208037713537582
12566.27368210014689-0.273682100146891
12654.727097351018840.272902648981156
12786.465681330894571.53431866910543
12865.50962807351070.490371926489297
12955.06418013980053-0.0641801398005278
13042.095317119863741.90468288013626
13186.465681330894571.53431866910543
13265.681278356183750.318721643816248
13344.92097803473106-0.920978034731061
13465.526214115656260.473785884343736
13565.912875040449160.0871249595508358
13644.88948901148786-0.889489011487856
13765.984931718714140.0150682812858566
13834.38476698717679-1.38476698717679
13966.88364208775963-0.883642087759634
14055.59282482694425-0.592824826944255
14145.63242228046199-1.63242228046199
14265.708043472739420.291956527260583
14346.31158729187113-2.31158729187113
14443.381599665162620.618400334837378
14544.992845521825-0.992845521824997
14664.045080685141121.95491931485888
14754.317434215599350.68256578440065
14865.438482646706220.561517353293784
14966.24238226807473-0.242382268074729
15086.111290439677881.88870956032212
15175.373090278136861.62690972186314
15276.569037841231710.430962158768286
15344.29896672048925-0.298966720489253
15465.841977754859270.158022245140728
15565.586219467292180.413780532707821
15625.26331759931869-3.26331759931869






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4569425415262590.9138850830525180.543057458473741
90.581608144669770.836783710660460.41839185533023
100.4356314601843830.8712629203687670.564368539815617
110.5707932408418730.8584135183162530.429206759158127
120.769143892972110.4617122140557790.23085610702789
130.8502638431764490.2994723136471020.149736156823551
140.787057473314890.4258850533702190.21294252668511
150.8099990831076890.3800018337846220.190000916892311
160.7484016101014160.5031967797971670.251598389898584
170.7862863479790210.4274273040419580.213713652020979
180.7258965763396360.5482068473207280.274103423660364
190.6598457406054570.6803085187890860.340154259394543
200.6176058744728070.7647882510543860.382394125527193
210.9324951936242320.1350096127515370.0675048063757683
220.9277161053178180.1445677893643630.0722838946821815
230.9073711623420880.1852576753158240.0926288376579122
240.8780129298005990.2439741403988030.121987070199401
250.8474142922593580.3051714154812850.152585707740642
260.8150414142021830.3699171715956330.184958585797817
270.8356400833139220.3287198333721560.164359916686078
280.8116769774629410.3766460450741180.188323022537059
290.7705752250551020.4588495498897960.229424774944898
300.7795274586272720.4409450827454570.220472541372728
310.7446011236254450.510797752749110.255398876374555
320.7952865095481750.4094269809036490.204713490451825
330.792953561618860.4140928767622810.207046438381141
340.770534108073440.4589317838531210.229465891926561
350.7332804789099240.5334390421801520.266719521090076
360.6900869781233180.6198260437533640.309913021876682
370.6561258956241620.6877482087516760.343874104375838
380.6603923930544060.6792152138911880.339607606945594
390.6192231820812480.7615536358375050.380776817918752
400.5746023469772330.8507953060455350.425397653022767
410.5345291235434260.9309417529131470.465470876456574
420.5715137879932170.8569724240135670.428486212006783
430.5577464606888350.884507078622330.442253539311165
440.5135295314933480.9729409370133030.486470468506652
450.5761286446431430.8477427107137140.423871355356857
460.5762541304800480.8474917390399050.423745869519952
470.6035494414739030.7929011170521940.396450558526097
480.5839149096481020.8321701807037970.416085090351898
490.5361285059318020.9277429881363970.463871494068198
500.4899279419712710.9798558839425420.510072058028729
510.5729243301683760.8541513396632470.427075669831624
520.6213087216797270.7573825566405460.378691278320273
530.5803458030421450.839308393915710.419654196957855
540.5434046011169880.9131907977660230.456595398883012
550.5050766587352050.989846682529590.494923341264795
560.4643073160879530.9286146321759070.535692683912047
570.4617893652553410.9235787305106830.538210634744659
580.4325376901342260.8650753802684530.567462309865774
590.7246774426695750.5506451146608490.275322557330425
600.7933903702370090.4132192595259820.206609629762991
610.7833851823285570.4332296353428850.216614817671443
620.7505992577007460.4988014845985090.249400742299254
630.7121615106453780.5756769787092430.287838489354622
640.6744290555151280.6511418889697440.325570944484872
650.6653338715133280.6693322569733440.334666128486672
660.6347315475496650.730536904900670.365268452450335
670.6167390012163840.7665219975672310.383260998783616
680.5772804705411020.8454390589177950.422719529458898
690.5345231435683290.9309537128633430.465476856431671
700.4889531970258970.9779063940517940.511046802974103
710.4751262062375420.9502524124750850.524873793762458
720.4363712467742930.8727424935485860.563628753225707
730.3908473614340770.7816947228681540.609152638565923
740.3476299047423890.6952598094847770.652370095257611
750.3271872499029870.6543744998059730.672812750097013
760.2962822661031920.5925645322063840.703717733896808
770.2584583662931510.5169167325863020.741541633706849
780.2267687116495480.4535374232990960.773231288350452
790.2463016887388490.4926033774776980.753698311261151
800.2732238152404070.5464476304808130.726776184759593
810.2379084078413910.4758168156827810.76209159215861
820.207212544111930.414425088223860.79278745588807
830.1754490533732280.3508981067464560.824550946626772
840.1494671392772270.2989342785544530.850532860722773
850.2032271935677740.4064543871355480.796772806432226
860.198117752100360.396235504200720.80188224789964
870.1670584008590310.3341168017180630.832941599140969
880.144915474182260.2898309483645190.85508452581774
890.1882847430758590.3765694861517180.81171525692414
900.1643946535766990.3287893071533970.835605346423301
910.139542431935230.2790848638704590.86045756806477
920.133134560809570.266269121619140.86686543919043
930.1120760191079420.2241520382158830.887923980892058
940.09118448115813880.1823689623162780.908815518841861
950.07328805722522120.1465761144504420.926711942774779
960.05959723263858060.1191944652771610.94040276736142
970.1140648631956030.2281297263912060.885935136804397
980.10464965856490.20929931712980.8953503414351
990.09036414368495480.180728287369910.909635856315045
1000.07878486359201810.1575697271840360.921215136407982
1010.06422268151710380.1284453630342080.935777318482896
1020.05398301611882930.1079660322376590.94601698388117
1030.04487922341693430.08975844683386860.955120776583066
1040.06243903969415220.1248780793883040.937560960305848
1050.05706803648283060.1141360729656610.94293196351717
1060.1134633654613950.226926730922790.886536634538605
1070.1415979998467670.2831959996935340.858402000153233
1080.2344029793143410.4688059586286820.765597020685659
1090.2620995856002180.5241991712004370.737900414399782
1100.2301401809829320.4602803619658630.769859819017068
1110.193406395319840.386812790639680.80659360468016
1120.2772159982986280.5544319965972560.722784001701372
1130.2796754459816550.5593508919633090.720324554018345
1140.2375449083144470.4750898166288930.762455091685553
1150.2249067365638620.4498134731277240.775093263436138
1160.1872728955439510.3745457910879020.812727104456049
1170.1546699165509360.3093398331018720.845330083449064
1180.1457173751519580.2914347503039170.854282624848042
1190.1176438571801250.235287714360250.882356142819875
1200.156235625930720.3124712518614410.84376437406928
1210.1712141703957150.342428340791430.828785829604285
1220.1538781877537630.3077563755075250.846121812246237
1230.1223338654066330.2446677308132670.877666134593367
1240.09664516949326810.1932903389865360.903354830506732
1250.07712922581684140.1542584516336830.922870774183159
1260.0606389121879260.1212778243758520.939361087812074
1270.08456153404926950.1691230680985390.91543846595073
1280.07208643603214780.1441728720642960.927913563967852
1290.06089306605561760.1217861321112350.939106933944382
1300.08385564923991910.1677112984798380.91614435076008
1310.1184285906448090.2368571812896180.881571409355191
1320.08933067925493150.1786613585098630.910669320745069
1330.07169915036971180.1433983007394240.928300849630288
1340.05202553599359790.1040510719871960.947974464006402
1350.0359390360193620.07187807203872410.964060963980638
1360.04176298597506090.08352597195012170.95823701402494
1370.03137876819164330.06275753638328670.968621231808357
1380.02640894674742540.05281789349485090.973591053252575
1390.03063694858893580.06127389717787160.969363051411064
1400.02537973168652510.05075946337305020.974620268313475
1410.01932017786915430.03864035573830860.980679822130846
1420.01186890310771210.02373780621542410.988131096892288
1430.04267307013607820.08534614027215630.957326929863922
1440.02796650822450410.05593301644900830.972033491775496
1450.04814843274662460.09629686549324910.951851567253375
1460.03343124461142630.06686248922285250.966568755388574
1470.03346119857295510.06692239714591020.966538801427045
1480.02891844151925070.05783688303850140.97108155848075

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.456942541526259 & 0.913885083052518 & 0.543057458473741 \tabularnewline
9 & 0.58160814466977 & 0.83678371066046 & 0.41839185533023 \tabularnewline
10 & 0.435631460184383 & 0.871262920368767 & 0.564368539815617 \tabularnewline
11 & 0.570793240841873 & 0.858413518316253 & 0.429206759158127 \tabularnewline
12 & 0.76914389297211 & 0.461712214055779 & 0.23085610702789 \tabularnewline
13 & 0.850263843176449 & 0.299472313647102 & 0.149736156823551 \tabularnewline
14 & 0.78705747331489 & 0.425885053370219 & 0.21294252668511 \tabularnewline
15 & 0.809999083107689 & 0.380001833784622 & 0.190000916892311 \tabularnewline
16 & 0.748401610101416 & 0.503196779797167 & 0.251598389898584 \tabularnewline
17 & 0.786286347979021 & 0.427427304041958 & 0.213713652020979 \tabularnewline
18 & 0.725896576339636 & 0.548206847320728 & 0.274103423660364 \tabularnewline
19 & 0.659845740605457 & 0.680308518789086 & 0.340154259394543 \tabularnewline
20 & 0.617605874472807 & 0.764788251054386 & 0.382394125527193 \tabularnewline
21 & 0.932495193624232 & 0.135009612751537 & 0.0675048063757683 \tabularnewline
22 & 0.927716105317818 & 0.144567789364363 & 0.0722838946821815 \tabularnewline
23 & 0.907371162342088 & 0.185257675315824 & 0.0926288376579122 \tabularnewline
24 & 0.878012929800599 & 0.243974140398803 & 0.121987070199401 \tabularnewline
25 & 0.847414292259358 & 0.305171415481285 & 0.152585707740642 \tabularnewline
26 & 0.815041414202183 & 0.369917171595633 & 0.184958585797817 \tabularnewline
27 & 0.835640083313922 & 0.328719833372156 & 0.164359916686078 \tabularnewline
28 & 0.811676977462941 & 0.376646045074118 & 0.188323022537059 \tabularnewline
29 & 0.770575225055102 & 0.458849549889796 & 0.229424774944898 \tabularnewline
30 & 0.779527458627272 & 0.440945082745457 & 0.220472541372728 \tabularnewline
31 & 0.744601123625445 & 0.51079775274911 & 0.255398876374555 \tabularnewline
32 & 0.795286509548175 & 0.409426980903649 & 0.204713490451825 \tabularnewline
33 & 0.79295356161886 & 0.414092876762281 & 0.207046438381141 \tabularnewline
34 & 0.77053410807344 & 0.458931783853121 & 0.229465891926561 \tabularnewline
35 & 0.733280478909924 & 0.533439042180152 & 0.266719521090076 \tabularnewline
36 & 0.690086978123318 & 0.619826043753364 & 0.309913021876682 \tabularnewline
37 & 0.656125895624162 & 0.687748208751676 & 0.343874104375838 \tabularnewline
38 & 0.660392393054406 & 0.679215213891188 & 0.339607606945594 \tabularnewline
39 & 0.619223182081248 & 0.761553635837505 & 0.380776817918752 \tabularnewline
40 & 0.574602346977233 & 0.850795306045535 & 0.425397653022767 \tabularnewline
41 & 0.534529123543426 & 0.930941752913147 & 0.465470876456574 \tabularnewline
42 & 0.571513787993217 & 0.856972424013567 & 0.428486212006783 \tabularnewline
43 & 0.557746460688835 & 0.88450707862233 & 0.442253539311165 \tabularnewline
44 & 0.513529531493348 & 0.972940937013303 & 0.486470468506652 \tabularnewline
45 & 0.576128644643143 & 0.847742710713714 & 0.423871355356857 \tabularnewline
46 & 0.576254130480048 & 0.847491739039905 & 0.423745869519952 \tabularnewline
47 & 0.603549441473903 & 0.792901117052194 & 0.396450558526097 \tabularnewline
48 & 0.583914909648102 & 0.832170180703797 & 0.416085090351898 \tabularnewline
49 & 0.536128505931802 & 0.927742988136397 & 0.463871494068198 \tabularnewline
50 & 0.489927941971271 & 0.979855883942542 & 0.510072058028729 \tabularnewline
51 & 0.572924330168376 & 0.854151339663247 & 0.427075669831624 \tabularnewline
52 & 0.621308721679727 & 0.757382556640546 & 0.378691278320273 \tabularnewline
53 & 0.580345803042145 & 0.83930839391571 & 0.419654196957855 \tabularnewline
54 & 0.543404601116988 & 0.913190797766023 & 0.456595398883012 \tabularnewline
55 & 0.505076658735205 & 0.98984668252959 & 0.494923341264795 \tabularnewline
56 & 0.464307316087953 & 0.928614632175907 & 0.535692683912047 \tabularnewline
57 & 0.461789365255341 & 0.923578730510683 & 0.538210634744659 \tabularnewline
58 & 0.432537690134226 & 0.865075380268453 & 0.567462309865774 \tabularnewline
59 & 0.724677442669575 & 0.550645114660849 & 0.275322557330425 \tabularnewline
60 & 0.793390370237009 & 0.413219259525982 & 0.206609629762991 \tabularnewline
61 & 0.783385182328557 & 0.433229635342885 & 0.216614817671443 \tabularnewline
62 & 0.750599257700746 & 0.498801484598509 & 0.249400742299254 \tabularnewline
63 & 0.712161510645378 & 0.575676978709243 & 0.287838489354622 \tabularnewline
64 & 0.674429055515128 & 0.651141888969744 & 0.325570944484872 \tabularnewline
65 & 0.665333871513328 & 0.669332256973344 & 0.334666128486672 \tabularnewline
66 & 0.634731547549665 & 0.73053690490067 & 0.365268452450335 \tabularnewline
67 & 0.616739001216384 & 0.766521997567231 & 0.383260998783616 \tabularnewline
68 & 0.577280470541102 & 0.845439058917795 & 0.422719529458898 \tabularnewline
69 & 0.534523143568329 & 0.930953712863343 & 0.465476856431671 \tabularnewline
70 & 0.488953197025897 & 0.977906394051794 & 0.511046802974103 \tabularnewline
71 & 0.475126206237542 & 0.950252412475085 & 0.524873793762458 \tabularnewline
72 & 0.436371246774293 & 0.872742493548586 & 0.563628753225707 \tabularnewline
73 & 0.390847361434077 & 0.781694722868154 & 0.609152638565923 \tabularnewline
74 & 0.347629904742389 & 0.695259809484777 & 0.652370095257611 \tabularnewline
75 & 0.327187249902987 & 0.654374499805973 & 0.672812750097013 \tabularnewline
76 & 0.296282266103192 & 0.592564532206384 & 0.703717733896808 \tabularnewline
77 & 0.258458366293151 & 0.516916732586302 & 0.741541633706849 \tabularnewline
78 & 0.226768711649548 & 0.453537423299096 & 0.773231288350452 \tabularnewline
79 & 0.246301688738849 & 0.492603377477698 & 0.753698311261151 \tabularnewline
80 & 0.273223815240407 & 0.546447630480813 & 0.726776184759593 \tabularnewline
81 & 0.237908407841391 & 0.475816815682781 & 0.76209159215861 \tabularnewline
82 & 0.20721254411193 & 0.41442508822386 & 0.79278745588807 \tabularnewline
83 & 0.175449053373228 & 0.350898106746456 & 0.824550946626772 \tabularnewline
84 & 0.149467139277227 & 0.298934278554453 & 0.850532860722773 \tabularnewline
85 & 0.203227193567774 & 0.406454387135548 & 0.796772806432226 \tabularnewline
86 & 0.19811775210036 & 0.39623550420072 & 0.80188224789964 \tabularnewline
87 & 0.167058400859031 & 0.334116801718063 & 0.832941599140969 \tabularnewline
88 & 0.14491547418226 & 0.289830948364519 & 0.85508452581774 \tabularnewline
89 & 0.188284743075859 & 0.376569486151718 & 0.81171525692414 \tabularnewline
90 & 0.164394653576699 & 0.328789307153397 & 0.835605346423301 \tabularnewline
91 & 0.13954243193523 & 0.279084863870459 & 0.86045756806477 \tabularnewline
92 & 0.13313456080957 & 0.26626912161914 & 0.86686543919043 \tabularnewline
93 & 0.112076019107942 & 0.224152038215883 & 0.887923980892058 \tabularnewline
94 & 0.0911844811581388 & 0.182368962316278 & 0.908815518841861 \tabularnewline
95 & 0.0732880572252212 & 0.146576114450442 & 0.926711942774779 \tabularnewline
96 & 0.0595972326385806 & 0.119194465277161 & 0.94040276736142 \tabularnewline
97 & 0.114064863195603 & 0.228129726391206 & 0.885935136804397 \tabularnewline
98 & 0.1046496585649 & 0.2092993171298 & 0.8953503414351 \tabularnewline
99 & 0.0903641436849548 & 0.18072828736991 & 0.909635856315045 \tabularnewline
100 & 0.0787848635920181 & 0.157569727184036 & 0.921215136407982 \tabularnewline
101 & 0.0642226815171038 & 0.128445363034208 & 0.935777318482896 \tabularnewline
102 & 0.0539830161188293 & 0.107966032237659 & 0.94601698388117 \tabularnewline
103 & 0.0448792234169343 & 0.0897584468338686 & 0.955120776583066 \tabularnewline
104 & 0.0624390396941522 & 0.124878079388304 & 0.937560960305848 \tabularnewline
105 & 0.0570680364828306 & 0.114136072965661 & 0.94293196351717 \tabularnewline
106 & 0.113463365461395 & 0.22692673092279 & 0.886536634538605 \tabularnewline
107 & 0.141597999846767 & 0.283195999693534 & 0.858402000153233 \tabularnewline
108 & 0.234402979314341 & 0.468805958628682 & 0.765597020685659 \tabularnewline
109 & 0.262099585600218 & 0.524199171200437 & 0.737900414399782 \tabularnewline
110 & 0.230140180982932 & 0.460280361965863 & 0.769859819017068 \tabularnewline
111 & 0.19340639531984 & 0.38681279063968 & 0.80659360468016 \tabularnewline
112 & 0.277215998298628 & 0.554431996597256 & 0.722784001701372 \tabularnewline
113 & 0.279675445981655 & 0.559350891963309 & 0.720324554018345 \tabularnewline
114 & 0.237544908314447 & 0.475089816628893 & 0.762455091685553 \tabularnewline
115 & 0.224906736563862 & 0.449813473127724 & 0.775093263436138 \tabularnewline
116 & 0.187272895543951 & 0.374545791087902 & 0.812727104456049 \tabularnewline
117 & 0.154669916550936 & 0.309339833101872 & 0.845330083449064 \tabularnewline
118 & 0.145717375151958 & 0.291434750303917 & 0.854282624848042 \tabularnewline
119 & 0.117643857180125 & 0.23528771436025 & 0.882356142819875 \tabularnewline
120 & 0.15623562593072 & 0.312471251861441 & 0.84376437406928 \tabularnewline
121 & 0.171214170395715 & 0.34242834079143 & 0.828785829604285 \tabularnewline
122 & 0.153878187753763 & 0.307756375507525 & 0.846121812246237 \tabularnewline
123 & 0.122333865406633 & 0.244667730813267 & 0.877666134593367 \tabularnewline
124 & 0.0966451694932681 & 0.193290338986536 & 0.903354830506732 \tabularnewline
125 & 0.0771292258168414 & 0.154258451633683 & 0.922870774183159 \tabularnewline
126 & 0.060638912187926 & 0.121277824375852 & 0.939361087812074 \tabularnewline
127 & 0.0845615340492695 & 0.169123068098539 & 0.91543846595073 \tabularnewline
128 & 0.0720864360321478 & 0.144172872064296 & 0.927913563967852 \tabularnewline
129 & 0.0608930660556176 & 0.121786132111235 & 0.939106933944382 \tabularnewline
130 & 0.0838556492399191 & 0.167711298479838 & 0.91614435076008 \tabularnewline
131 & 0.118428590644809 & 0.236857181289618 & 0.881571409355191 \tabularnewline
132 & 0.0893306792549315 & 0.178661358509863 & 0.910669320745069 \tabularnewline
133 & 0.0716991503697118 & 0.143398300739424 & 0.928300849630288 \tabularnewline
134 & 0.0520255359935979 & 0.104051071987196 & 0.947974464006402 \tabularnewline
135 & 0.035939036019362 & 0.0718780720387241 & 0.964060963980638 \tabularnewline
136 & 0.0417629859750609 & 0.0835259719501217 & 0.95823701402494 \tabularnewline
137 & 0.0313787681916433 & 0.0627575363832867 & 0.968621231808357 \tabularnewline
138 & 0.0264089467474254 & 0.0528178934948509 & 0.973591053252575 \tabularnewline
139 & 0.0306369485889358 & 0.0612738971778716 & 0.969363051411064 \tabularnewline
140 & 0.0253797316865251 & 0.0507594633730502 & 0.974620268313475 \tabularnewline
141 & 0.0193201778691543 & 0.0386403557383086 & 0.980679822130846 \tabularnewline
142 & 0.0118689031077121 & 0.0237378062154241 & 0.988131096892288 \tabularnewline
143 & 0.0426730701360782 & 0.0853461402721563 & 0.957326929863922 \tabularnewline
144 & 0.0279665082245041 & 0.0559330164490083 & 0.972033491775496 \tabularnewline
145 & 0.0481484327466246 & 0.0962968654932491 & 0.951851567253375 \tabularnewline
146 & 0.0334312446114263 & 0.0668624892228525 & 0.966568755388574 \tabularnewline
147 & 0.0334611985729551 & 0.0669223971459102 & 0.966538801427045 \tabularnewline
148 & 0.0289184415192507 & 0.0578368830385014 & 0.97108155848075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&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]8[/C][C]0.456942541526259[/C][C]0.913885083052518[/C][C]0.543057458473741[/C][/ROW]
[ROW][C]9[/C][C]0.58160814466977[/C][C]0.83678371066046[/C][C]0.41839185533023[/C][/ROW]
[ROW][C]10[/C][C]0.435631460184383[/C][C]0.871262920368767[/C][C]0.564368539815617[/C][/ROW]
[ROW][C]11[/C][C]0.570793240841873[/C][C]0.858413518316253[/C][C]0.429206759158127[/C][/ROW]
[ROW][C]12[/C][C]0.76914389297211[/C][C]0.461712214055779[/C][C]0.23085610702789[/C][/ROW]
[ROW][C]13[/C][C]0.850263843176449[/C][C]0.299472313647102[/C][C]0.149736156823551[/C][/ROW]
[ROW][C]14[/C][C]0.78705747331489[/C][C]0.425885053370219[/C][C]0.21294252668511[/C][/ROW]
[ROW][C]15[/C][C]0.809999083107689[/C][C]0.380001833784622[/C][C]0.190000916892311[/C][/ROW]
[ROW][C]16[/C][C]0.748401610101416[/C][C]0.503196779797167[/C][C]0.251598389898584[/C][/ROW]
[ROW][C]17[/C][C]0.786286347979021[/C][C]0.427427304041958[/C][C]0.213713652020979[/C][/ROW]
[ROW][C]18[/C][C]0.725896576339636[/C][C]0.548206847320728[/C][C]0.274103423660364[/C][/ROW]
[ROW][C]19[/C][C]0.659845740605457[/C][C]0.680308518789086[/C][C]0.340154259394543[/C][/ROW]
[ROW][C]20[/C][C]0.617605874472807[/C][C]0.764788251054386[/C][C]0.382394125527193[/C][/ROW]
[ROW][C]21[/C][C]0.932495193624232[/C][C]0.135009612751537[/C][C]0.0675048063757683[/C][/ROW]
[ROW][C]22[/C][C]0.927716105317818[/C][C]0.144567789364363[/C][C]0.0722838946821815[/C][/ROW]
[ROW][C]23[/C][C]0.907371162342088[/C][C]0.185257675315824[/C][C]0.0926288376579122[/C][/ROW]
[ROW][C]24[/C][C]0.878012929800599[/C][C]0.243974140398803[/C][C]0.121987070199401[/C][/ROW]
[ROW][C]25[/C][C]0.847414292259358[/C][C]0.305171415481285[/C][C]0.152585707740642[/C][/ROW]
[ROW][C]26[/C][C]0.815041414202183[/C][C]0.369917171595633[/C][C]0.184958585797817[/C][/ROW]
[ROW][C]27[/C][C]0.835640083313922[/C][C]0.328719833372156[/C][C]0.164359916686078[/C][/ROW]
[ROW][C]28[/C][C]0.811676977462941[/C][C]0.376646045074118[/C][C]0.188323022537059[/C][/ROW]
[ROW][C]29[/C][C]0.770575225055102[/C][C]0.458849549889796[/C][C]0.229424774944898[/C][/ROW]
[ROW][C]30[/C][C]0.779527458627272[/C][C]0.440945082745457[/C][C]0.220472541372728[/C][/ROW]
[ROW][C]31[/C][C]0.744601123625445[/C][C]0.51079775274911[/C][C]0.255398876374555[/C][/ROW]
[ROW][C]32[/C][C]0.795286509548175[/C][C]0.409426980903649[/C][C]0.204713490451825[/C][/ROW]
[ROW][C]33[/C][C]0.79295356161886[/C][C]0.414092876762281[/C][C]0.207046438381141[/C][/ROW]
[ROW][C]34[/C][C]0.77053410807344[/C][C]0.458931783853121[/C][C]0.229465891926561[/C][/ROW]
[ROW][C]35[/C][C]0.733280478909924[/C][C]0.533439042180152[/C][C]0.266719521090076[/C][/ROW]
[ROW][C]36[/C][C]0.690086978123318[/C][C]0.619826043753364[/C][C]0.309913021876682[/C][/ROW]
[ROW][C]37[/C][C]0.656125895624162[/C][C]0.687748208751676[/C][C]0.343874104375838[/C][/ROW]
[ROW][C]38[/C][C]0.660392393054406[/C][C]0.679215213891188[/C][C]0.339607606945594[/C][/ROW]
[ROW][C]39[/C][C]0.619223182081248[/C][C]0.761553635837505[/C][C]0.380776817918752[/C][/ROW]
[ROW][C]40[/C][C]0.574602346977233[/C][C]0.850795306045535[/C][C]0.425397653022767[/C][/ROW]
[ROW][C]41[/C][C]0.534529123543426[/C][C]0.930941752913147[/C][C]0.465470876456574[/C][/ROW]
[ROW][C]42[/C][C]0.571513787993217[/C][C]0.856972424013567[/C][C]0.428486212006783[/C][/ROW]
[ROW][C]43[/C][C]0.557746460688835[/C][C]0.88450707862233[/C][C]0.442253539311165[/C][/ROW]
[ROW][C]44[/C][C]0.513529531493348[/C][C]0.972940937013303[/C][C]0.486470468506652[/C][/ROW]
[ROW][C]45[/C][C]0.576128644643143[/C][C]0.847742710713714[/C][C]0.423871355356857[/C][/ROW]
[ROW][C]46[/C][C]0.576254130480048[/C][C]0.847491739039905[/C][C]0.423745869519952[/C][/ROW]
[ROW][C]47[/C][C]0.603549441473903[/C][C]0.792901117052194[/C][C]0.396450558526097[/C][/ROW]
[ROW][C]48[/C][C]0.583914909648102[/C][C]0.832170180703797[/C][C]0.416085090351898[/C][/ROW]
[ROW][C]49[/C][C]0.536128505931802[/C][C]0.927742988136397[/C][C]0.463871494068198[/C][/ROW]
[ROW][C]50[/C][C]0.489927941971271[/C][C]0.979855883942542[/C][C]0.510072058028729[/C][/ROW]
[ROW][C]51[/C][C]0.572924330168376[/C][C]0.854151339663247[/C][C]0.427075669831624[/C][/ROW]
[ROW][C]52[/C][C]0.621308721679727[/C][C]0.757382556640546[/C][C]0.378691278320273[/C][/ROW]
[ROW][C]53[/C][C]0.580345803042145[/C][C]0.83930839391571[/C][C]0.419654196957855[/C][/ROW]
[ROW][C]54[/C][C]0.543404601116988[/C][C]0.913190797766023[/C][C]0.456595398883012[/C][/ROW]
[ROW][C]55[/C][C]0.505076658735205[/C][C]0.98984668252959[/C][C]0.494923341264795[/C][/ROW]
[ROW][C]56[/C][C]0.464307316087953[/C][C]0.928614632175907[/C][C]0.535692683912047[/C][/ROW]
[ROW][C]57[/C][C]0.461789365255341[/C][C]0.923578730510683[/C][C]0.538210634744659[/C][/ROW]
[ROW][C]58[/C][C]0.432537690134226[/C][C]0.865075380268453[/C][C]0.567462309865774[/C][/ROW]
[ROW][C]59[/C][C]0.724677442669575[/C][C]0.550645114660849[/C][C]0.275322557330425[/C][/ROW]
[ROW][C]60[/C][C]0.793390370237009[/C][C]0.413219259525982[/C][C]0.206609629762991[/C][/ROW]
[ROW][C]61[/C][C]0.783385182328557[/C][C]0.433229635342885[/C][C]0.216614817671443[/C][/ROW]
[ROW][C]62[/C][C]0.750599257700746[/C][C]0.498801484598509[/C][C]0.249400742299254[/C][/ROW]
[ROW][C]63[/C][C]0.712161510645378[/C][C]0.575676978709243[/C][C]0.287838489354622[/C][/ROW]
[ROW][C]64[/C][C]0.674429055515128[/C][C]0.651141888969744[/C][C]0.325570944484872[/C][/ROW]
[ROW][C]65[/C][C]0.665333871513328[/C][C]0.669332256973344[/C][C]0.334666128486672[/C][/ROW]
[ROW][C]66[/C][C]0.634731547549665[/C][C]0.73053690490067[/C][C]0.365268452450335[/C][/ROW]
[ROW][C]67[/C][C]0.616739001216384[/C][C]0.766521997567231[/C][C]0.383260998783616[/C][/ROW]
[ROW][C]68[/C][C]0.577280470541102[/C][C]0.845439058917795[/C][C]0.422719529458898[/C][/ROW]
[ROW][C]69[/C][C]0.534523143568329[/C][C]0.930953712863343[/C][C]0.465476856431671[/C][/ROW]
[ROW][C]70[/C][C]0.488953197025897[/C][C]0.977906394051794[/C][C]0.511046802974103[/C][/ROW]
[ROW][C]71[/C][C]0.475126206237542[/C][C]0.950252412475085[/C][C]0.524873793762458[/C][/ROW]
[ROW][C]72[/C][C]0.436371246774293[/C][C]0.872742493548586[/C][C]0.563628753225707[/C][/ROW]
[ROW][C]73[/C][C]0.390847361434077[/C][C]0.781694722868154[/C][C]0.609152638565923[/C][/ROW]
[ROW][C]74[/C][C]0.347629904742389[/C][C]0.695259809484777[/C][C]0.652370095257611[/C][/ROW]
[ROW][C]75[/C][C]0.327187249902987[/C][C]0.654374499805973[/C][C]0.672812750097013[/C][/ROW]
[ROW][C]76[/C][C]0.296282266103192[/C][C]0.592564532206384[/C][C]0.703717733896808[/C][/ROW]
[ROW][C]77[/C][C]0.258458366293151[/C][C]0.516916732586302[/C][C]0.741541633706849[/C][/ROW]
[ROW][C]78[/C][C]0.226768711649548[/C][C]0.453537423299096[/C][C]0.773231288350452[/C][/ROW]
[ROW][C]79[/C][C]0.246301688738849[/C][C]0.492603377477698[/C][C]0.753698311261151[/C][/ROW]
[ROW][C]80[/C][C]0.273223815240407[/C][C]0.546447630480813[/C][C]0.726776184759593[/C][/ROW]
[ROW][C]81[/C][C]0.237908407841391[/C][C]0.475816815682781[/C][C]0.76209159215861[/C][/ROW]
[ROW][C]82[/C][C]0.20721254411193[/C][C]0.41442508822386[/C][C]0.79278745588807[/C][/ROW]
[ROW][C]83[/C][C]0.175449053373228[/C][C]0.350898106746456[/C][C]0.824550946626772[/C][/ROW]
[ROW][C]84[/C][C]0.149467139277227[/C][C]0.298934278554453[/C][C]0.850532860722773[/C][/ROW]
[ROW][C]85[/C][C]0.203227193567774[/C][C]0.406454387135548[/C][C]0.796772806432226[/C][/ROW]
[ROW][C]86[/C][C]0.19811775210036[/C][C]0.39623550420072[/C][C]0.80188224789964[/C][/ROW]
[ROW][C]87[/C][C]0.167058400859031[/C][C]0.334116801718063[/C][C]0.832941599140969[/C][/ROW]
[ROW][C]88[/C][C]0.14491547418226[/C][C]0.289830948364519[/C][C]0.85508452581774[/C][/ROW]
[ROW][C]89[/C][C]0.188284743075859[/C][C]0.376569486151718[/C][C]0.81171525692414[/C][/ROW]
[ROW][C]90[/C][C]0.164394653576699[/C][C]0.328789307153397[/C][C]0.835605346423301[/C][/ROW]
[ROW][C]91[/C][C]0.13954243193523[/C][C]0.279084863870459[/C][C]0.86045756806477[/C][/ROW]
[ROW][C]92[/C][C]0.13313456080957[/C][C]0.26626912161914[/C][C]0.86686543919043[/C][/ROW]
[ROW][C]93[/C][C]0.112076019107942[/C][C]0.224152038215883[/C][C]0.887923980892058[/C][/ROW]
[ROW][C]94[/C][C]0.0911844811581388[/C][C]0.182368962316278[/C][C]0.908815518841861[/C][/ROW]
[ROW][C]95[/C][C]0.0732880572252212[/C][C]0.146576114450442[/C][C]0.926711942774779[/C][/ROW]
[ROW][C]96[/C][C]0.0595972326385806[/C][C]0.119194465277161[/C][C]0.94040276736142[/C][/ROW]
[ROW][C]97[/C][C]0.114064863195603[/C][C]0.228129726391206[/C][C]0.885935136804397[/C][/ROW]
[ROW][C]98[/C][C]0.1046496585649[/C][C]0.2092993171298[/C][C]0.8953503414351[/C][/ROW]
[ROW][C]99[/C][C]0.0903641436849548[/C][C]0.18072828736991[/C][C]0.909635856315045[/C][/ROW]
[ROW][C]100[/C][C]0.0787848635920181[/C][C]0.157569727184036[/C][C]0.921215136407982[/C][/ROW]
[ROW][C]101[/C][C]0.0642226815171038[/C][C]0.128445363034208[/C][C]0.935777318482896[/C][/ROW]
[ROW][C]102[/C][C]0.0539830161188293[/C][C]0.107966032237659[/C][C]0.94601698388117[/C][/ROW]
[ROW][C]103[/C][C]0.0448792234169343[/C][C]0.0897584468338686[/C][C]0.955120776583066[/C][/ROW]
[ROW][C]104[/C][C]0.0624390396941522[/C][C]0.124878079388304[/C][C]0.937560960305848[/C][/ROW]
[ROW][C]105[/C][C]0.0570680364828306[/C][C]0.114136072965661[/C][C]0.94293196351717[/C][/ROW]
[ROW][C]106[/C][C]0.113463365461395[/C][C]0.22692673092279[/C][C]0.886536634538605[/C][/ROW]
[ROW][C]107[/C][C]0.141597999846767[/C][C]0.283195999693534[/C][C]0.858402000153233[/C][/ROW]
[ROW][C]108[/C][C]0.234402979314341[/C][C]0.468805958628682[/C][C]0.765597020685659[/C][/ROW]
[ROW][C]109[/C][C]0.262099585600218[/C][C]0.524199171200437[/C][C]0.737900414399782[/C][/ROW]
[ROW][C]110[/C][C]0.230140180982932[/C][C]0.460280361965863[/C][C]0.769859819017068[/C][/ROW]
[ROW][C]111[/C][C]0.19340639531984[/C][C]0.38681279063968[/C][C]0.80659360468016[/C][/ROW]
[ROW][C]112[/C][C]0.277215998298628[/C][C]0.554431996597256[/C][C]0.722784001701372[/C][/ROW]
[ROW][C]113[/C][C]0.279675445981655[/C][C]0.559350891963309[/C][C]0.720324554018345[/C][/ROW]
[ROW][C]114[/C][C]0.237544908314447[/C][C]0.475089816628893[/C][C]0.762455091685553[/C][/ROW]
[ROW][C]115[/C][C]0.224906736563862[/C][C]0.449813473127724[/C][C]0.775093263436138[/C][/ROW]
[ROW][C]116[/C][C]0.187272895543951[/C][C]0.374545791087902[/C][C]0.812727104456049[/C][/ROW]
[ROW][C]117[/C][C]0.154669916550936[/C][C]0.309339833101872[/C][C]0.845330083449064[/C][/ROW]
[ROW][C]118[/C][C]0.145717375151958[/C][C]0.291434750303917[/C][C]0.854282624848042[/C][/ROW]
[ROW][C]119[/C][C]0.117643857180125[/C][C]0.23528771436025[/C][C]0.882356142819875[/C][/ROW]
[ROW][C]120[/C][C]0.15623562593072[/C][C]0.312471251861441[/C][C]0.84376437406928[/C][/ROW]
[ROW][C]121[/C][C]0.171214170395715[/C][C]0.34242834079143[/C][C]0.828785829604285[/C][/ROW]
[ROW][C]122[/C][C]0.153878187753763[/C][C]0.307756375507525[/C][C]0.846121812246237[/C][/ROW]
[ROW][C]123[/C][C]0.122333865406633[/C][C]0.244667730813267[/C][C]0.877666134593367[/C][/ROW]
[ROW][C]124[/C][C]0.0966451694932681[/C][C]0.193290338986536[/C][C]0.903354830506732[/C][/ROW]
[ROW][C]125[/C][C]0.0771292258168414[/C][C]0.154258451633683[/C][C]0.922870774183159[/C][/ROW]
[ROW][C]126[/C][C]0.060638912187926[/C][C]0.121277824375852[/C][C]0.939361087812074[/C][/ROW]
[ROW][C]127[/C][C]0.0845615340492695[/C][C]0.169123068098539[/C][C]0.91543846595073[/C][/ROW]
[ROW][C]128[/C][C]0.0720864360321478[/C][C]0.144172872064296[/C][C]0.927913563967852[/C][/ROW]
[ROW][C]129[/C][C]0.0608930660556176[/C][C]0.121786132111235[/C][C]0.939106933944382[/C][/ROW]
[ROW][C]130[/C][C]0.0838556492399191[/C][C]0.167711298479838[/C][C]0.91614435076008[/C][/ROW]
[ROW][C]131[/C][C]0.118428590644809[/C][C]0.236857181289618[/C][C]0.881571409355191[/C][/ROW]
[ROW][C]132[/C][C]0.0893306792549315[/C][C]0.178661358509863[/C][C]0.910669320745069[/C][/ROW]
[ROW][C]133[/C][C]0.0716991503697118[/C][C]0.143398300739424[/C][C]0.928300849630288[/C][/ROW]
[ROW][C]134[/C][C]0.0520255359935979[/C][C]0.104051071987196[/C][C]0.947974464006402[/C][/ROW]
[ROW][C]135[/C][C]0.035939036019362[/C][C]0.0718780720387241[/C][C]0.964060963980638[/C][/ROW]
[ROW][C]136[/C][C]0.0417629859750609[/C][C]0.0835259719501217[/C][C]0.95823701402494[/C][/ROW]
[ROW][C]137[/C][C]0.0313787681916433[/C][C]0.0627575363832867[/C][C]0.968621231808357[/C][/ROW]
[ROW][C]138[/C][C]0.0264089467474254[/C][C]0.0528178934948509[/C][C]0.973591053252575[/C][/ROW]
[ROW][C]139[/C][C]0.0306369485889358[/C][C]0.0612738971778716[/C][C]0.969363051411064[/C][/ROW]
[ROW][C]140[/C][C]0.0253797316865251[/C][C]0.0507594633730502[/C][C]0.974620268313475[/C][/ROW]
[ROW][C]141[/C][C]0.0193201778691543[/C][C]0.0386403557383086[/C][C]0.980679822130846[/C][/ROW]
[ROW][C]142[/C][C]0.0118689031077121[/C][C]0.0237378062154241[/C][C]0.988131096892288[/C][/ROW]
[ROW][C]143[/C][C]0.0426730701360782[/C][C]0.0853461402721563[/C][C]0.957326929863922[/C][/ROW]
[ROW][C]144[/C][C]0.0279665082245041[/C][C]0.0559330164490083[/C][C]0.972033491775496[/C][/ROW]
[ROW][C]145[/C][C]0.0481484327466246[/C][C]0.0962968654932491[/C][C]0.951851567253375[/C][/ROW]
[ROW][C]146[/C][C]0.0334312446114263[/C][C]0.0668624892228525[/C][C]0.966568755388574[/C][/ROW]
[ROW][C]147[/C][C]0.0334611985729551[/C][C]0.0669223971459102[/C][C]0.966538801427045[/C][/ROW]
[ROW][C]148[/C][C]0.0289184415192507[/C][C]0.0578368830385014[/C][C]0.97108155848075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4569425415262590.9138850830525180.543057458473741
90.581608144669770.836783710660460.41839185533023
100.4356314601843830.8712629203687670.564368539815617
110.5707932408418730.8584135183162530.429206759158127
120.769143892972110.4617122140557790.23085610702789
130.8502638431764490.2994723136471020.149736156823551
140.787057473314890.4258850533702190.21294252668511
150.8099990831076890.3800018337846220.190000916892311
160.7484016101014160.5031967797971670.251598389898584
170.7862863479790210.4274273040419580.213713652020979
180.7258965763396360.5482068473207280.274103423660364
190.6598457406054570.6803085187890860.340154259394543
200.6176058744728070.7647882510543860.382394125527193
210.9324951936242320.1350096127515370.0675048063757683
220.9277161053178180.1445677893643630.0722838946821815
230.9073711623420880.1852576753158240.0926288376579122
240.8780129298005990.2439741403988030.121987070199401
250.8474142922593580.3051714154812850.152585707740642
260.8150414142021830.3699171715956330.184958585797817
270.8356400833139220.3287198333721560.164359916686078
280.8116769774629410.3766460450741180.188323022537059
290.7705752250551020.4588495498897960.229424774944898
300.7795274586272720.4409450827454570.220472541372728
310.7446011236254450.510797752749110.255398876374555
320.7952865095481750.4094269809036490.204713490451825
330.792953561618860.4140928767622810.207046438381141
340.770534108073440.4589317838531210.229465891926561
350.7332804789099240.5334390421801520.266719521090076
360.6900869781233180.6198260437533640.309913021876682
370.6561258956241620.6877482087516760.343874104375838
380.6603923930544060.6792152138911880.339607606945594
390.6192231820812480.7615536358375050.380776817918752
400.5746023469772330.8507953060455350.425397653022767
410.5345291235434260.9309417529131470.465470876456574
420.5715137879932170.8569724240135670.428486212006783
430.5577464606888350.884507078622330.442253539311165
440.5135295314933480.9729409370133030.486470468506652
450.5761286446431430.8477427107137140.423871355356857
460.5762541304800480.8474917390399050.423745869519952
470.6035494414739030.7929011170521940.396450558526097
480.5839149096481020.8321701807037970.416085090351898
490.5361285059318020.9277429881363970.463871494068198
500.4899279419712710.9798558839425420.510072058028729
510.5729243301683760.8541513396632470.427075669831624
520.6213087216797270.7573825566405460.378691278320273
530.5803458030421450.839308393915710.419654196957855
540.5434046011169880.9131907977660230.456595398883012
550.5050766587352050.989846682529590.494923341264795
560.4643073160879530.9286146321759070.535692683912047
570.4617893652553410.9235787305106830.538210634744659
580.4325376901342260.8650753802684530.567462309865774
590.7246774426695750.5506451146608490.275322557330425
600.7933903702370090.4132192595259820.206609629762991
610.7833851823285570.4332296353428850.216614817671443
620.7505992577007460.4988014845985090.249400742299254
630.7121615106453780.5756769787092430.287838489354622
640.6744290555151280.6511418889697440.325570944484872
650.6653338715133280.6693322569733440.334666128486672
660.6347315475496650.730536904900670.365268452450335
670.6167390012163840.7665219975672310.383260998783616
680.5772804705411020.8454390589177950.422719529458898
690.5345231435683290.9309537128633430.465476856431671
700.4889531970258970.9779063940517940.511046802974103
710.4751262062375420.9502524124750850.524873793762458
720.4363712467742930.8727424935485860.563628753225707
730.3908473614340770.7816947228681540.609152638565923
740.3476299047423890.6952598094847770.652370095257611
750.3271872499029870.6543744998059730.672812750097013
760.2962822661031920.5925645322063840.703717733896808
770.2584583662931510.5169167325863020.741541633706849
780.2267687116495480.4535374232990960.773231288350452
790.2463016887388490.4926033774776980.753698311261151
800.2732238152404070.5464476304808130.726776184759593
810.2379084078413910.4758168156827810.76209159215861
820.207212544111930.414425088223860.79278745588807
830.1754490533732280.3508981067464560.824550946626772
840.1494671392772270.2989342785544530.850532860722773
850.2032271935677740.4064543871355480.796772806432226
860.198117752100360.396235504200720.80188224789964
870.1670584008590310.3341168017180630.832941599140969
880.144915474182260.2898309483645190.85508452581774
890.1882847430758590.3765694861517180.81171525692414
900.1643946535766990.3287893071533970.835605346423301
910.139542431935230.2790848638704590.86045756806477
920.133134560809570.266269121619140.86686543919043
930.1120760191079420.2241520382158830.887923980892058
940.09118448115813880.1823689623162780.908815518841861
950.07328805722522120.1465761144504420.926711942774779
960.05959723263858060.1191944652771610.94040276736142
970.1140648631956030.2281297263912060.885935136804397
980.10464965856490.20929931712980.8953503414351
990.09036414368495480.180728287369910.909635856315045
1000.07878486359201810.1575697271840360.921215136407982
1010.06422268151710380.1284453630342080.935777318482896
1020.05398301611882930.1079660322376590.94601698388117
1030.04487922341693430.08975844683386860.955120776583066
1040.06243903969415220.1248780793883040.937560960305848
1050.05706803648283060.1141360729656610.94293196351717
1060.1134633654613950.226926730922790.886536634538605
1070.1415979998467670.2831959996935340.858402000153233
1080.2344029793143410.4688059586286820.765597020685659
1090.2620995856002180.5241991712004370.737900414399782
1100.2301401809829320.4602803619658630.769859819017068
1110.193406395319840.386812790639680.80659360468016
1120.2772159982986280.5544319965972560.722784001701372
1130.2796754459816550.5593508919633090.720324554018345
1140.2375449083144470.4750898166288930.762455091685553
1150.2249067365638620.4498134731277240.775093263436138
1160.1872728955439510.3745457910879020.812727104456049
1170.1546699165509360.3093398331018720.845330083449064
1180.1457173751519580.2914347503039170.854282624848042
1190.1176438571801250.235287714360250.882356142819875
1200.156235625930720.3124712518614410.84376437406928
1210.1712141703957150.342428340791430.828785829604285
1220.1538781877537630.3077563755075250.846121812246237
1230.1223338654066330.2446677308132670.877666134593367
1240.09664516949326810.1932903389865360.903354830506732
1250.07712922581684140.1542584516336830.922870774183159
1260.0606389121879260.1212778243758520.939361087812074
1270.08456153404926950.1691230680985390.91543846595073
1280.07208643603214780.1441728720642960.927913563967852
1290.06089306605561760.1217861321112350.939106933944382
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1310.1184285906448090.2368571812896180.881571409355191
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1360.04176298597506090.08352597195012170.95823701402494
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1470.03346119857295510.06692239714591020.966538801427045
1480.02891844151925070.05783688303850140.97108155848075






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0141843971631206OK
10% type I error level150.106382978723404NOK

\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 & 2 & 0.0141843971631206 & OK \tabularnewline
10% type I error level & 15 & 0.106382978723404 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99093&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]2[/C][C]0.0141843971631206[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.106382978723404[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99093&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99093&T=6

As an alternative you can also use a QR Code:  
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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 level20.0141843971631206OK
10% type I error level150.106382978723404NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}