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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 15:23:13 +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/t12905258211bk6hwg42fb95hb.htm/, Retrieved Fri, 26 Apr 2024 15:07:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99268, Retrieved Fri, 26 Apr 2024 15:07:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [ws 7 deterministi...] [2010-11-23 15:23:13] [de8ccb310fbbdc3d90ae577a3e011cf9] [Current]
-   PD      [Multiple Regression] [ws 7 trend] [2010-11-23 17:30:32] [04d4386fa51dbd2ef12d0f1f80644886]
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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'Gwilym Jenkins' @ 72.249.127.135
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 & 'Gwilym Jenkins' @ 72.249.127.135 \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=99268&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]'Gwilym Jenkins' @ 72.249.127.135[/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=99268&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&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'Gwilym Jenkins' @ 72.249.127.135
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] = + 0.417809867381582 + 0.154188016815281Popularity[t] -0.0205821634162743FindingFriends[t] + 0.103572657945756KnowingPeople[t] + 0.145905397542057Liked[t] + 0.000485950422142418t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Celebrity
[t] =  +  0.417809867381582 +  0.154188016815281Popularity[t] -0.0205821634162743FindingFriends[t] +  0.103572657945756KnowingPeople[t] +  0.145905397542057Liked[t] +  0.000485950422142418t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Celebrity
[t] =  +  0.417809867381582 +  0.154188016815281Popularity[t] -0.0205821634162743FindingFriends[t] +  0.103572657945756KnowingPeople[t] +  0.145905397542057Liked[t] +  0.000485950422142418t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&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] = + 0.417809867381582 + 0.154188016815281Popularity[t] -0.0205821634162743FindingFriends[t] + 0.103572657945756KnowingPeople[t] + 0.145905397542057Liked[t] + 0.000485950422142418t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4178098673815820.7082650.58990.5561410.278071
Popularity0.1541880168152810.0384634.00889.6e-054.8e-05
FindingFriends-0.02058216341627430.04805-0.42840.669010.334505
KnowingPeople0.1035726579457560.0309553.34590.0010370.000518
Liked0.1459053975420570.0490242.97620.0034030.001702
t0.0004859504221424180.0018950.25640.797980.39899

\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) & 0.417809867381582 & 0.708265 & 0.5899 & 0.556141 & 0.278071 \tabularnewline
Popularity & 0.154188016815281 & 0.038463 & 4.0088 & 9.6e-05 & 4.8e-05 \tabularnewline
FindingFriends & -0.0205821634162743 & 0.04805 & -0.4284 & 0.66901 & 0.334505 \tabularnewline
KnowingPeople & 0.103572657945756 & 0.030955 & 3.3459 & 0.001037 & 0.000518 \tabularnewline
Liked & 0.145905397542057 & 0.049024 & 2.9762 & 0.003403 & 0.001702 \tabularnewline
t & 0.000485950422142418 & 0.001895 & 0.2564 & 0.79798 & 0.39899 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&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]0.417809867381582[/C][C]0.708265[/C][C]0.5899[/C][C]0.556141[/C][C]0.278071[/C][/ROW]
[ROW][C]Popularity[/C][C]0.154188016815281[/C][C]0.038463[/C][C]4.0088[/C][C]9.6e-05[/C][C]4.8e-05[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0205821634162743[/C][C]0.04805[/C][C]-0.4284[/C][C]0.66901[/C][C]0.334505[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.103572657945756[/C][C]0.030955[/C][C]3.3459[/C][C]0.001037[/C][C]0.000518[/C][/ROW]
[ROW][C]Liked[/C][C]0.145905397542057[/C][C]0.049024[/C][C]2.9762[/C][C]0.003403[/C][C]0.001702[/C][/ROW]
[ROW][C]t[/C][C]0.000485950422142418[/C][C]0.001895[/C][C]0.2564[/C][C]0.79798[/C][C]0.39899[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&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)0.4178098673815820.7082650.58990.5561410.278071
Popularity0.1541880168152810.0384634.00889.6e-054.8e-05
FindingFriends-0.02058216341627430.04805-0.42840.669010.334505
KnowingPeople0.1035726579457560.0309553.34590.0010370.000518
Liked0.1459053975420570.0490242.97620.0034030.001702
t0.0004859504221424180.0018950.25640.797980.39899






Multiple Linear Regression - Regression Statistics
Multiple R0.677621027970981
R-squared0.459170257548449
Adjusted R-squared0.441142599466730
F-TEST (value)25.4703220721769
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04697227686073
Sum Squared Residuals164.422642277242

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.677621027970981 \tabularnewline
R-squared & 0.459170257548449 \tabularnewline
Adjusted R-squared & 0.441142599466730 \tabularnewline
F-TEST (value) & 25.4703220721769 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.04697227686073 \tabularnewline
Sum Squared Residuals & 164.422642277242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.677621027970981[/C][/ROW]
[ROW][C]R-squared[/C][C]0.459170257548449[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.441142599466730[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.4703220721769[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/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]1.04697227686073[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]164.422642277242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&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 R0.677621027970981
R-squared0.459170257548449
Adjusted R-squared0.441142599466730
F-TEST (value)25.4703220721769
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04697227686073
Sum Squared Residuals164.422642277242






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
135.50195929127812-2.50195929127812
254.747403440626730.252596559373271
366.10362459273646-0.103624592736458
464.560643776232031.43935622376797
554.396984105902540.603015894097457
634.49986918682749-1.49986918682749
786.749923331043341.25007666895666
844.30666227146690-0.306662271466895
945.51814643879832-1.51814643879832
1043.92982910162650.0701708983735044
1165.716150782632750.283849217367246
1264.762887504328081.23711249567192
1355.77266557761435-0.772665577614346
1444.20951566902445-0.209515669024452
1564.4547957945211.54520420547900
1644.82607122436611-0.826071224366108
1764.611129239623511.38887076037649
1866.2468616426743-0.246861642674306
1943.780676479474880.21932352052512
2043.745695127276620.254304872723378
2124.56062231413522-2.56062231413522
2276.153515405690340.846484594309657
2354.911294959086830.0887050409131663
2444.36271089688399-0.362710896883994
2565.791964939586860.208035060413142
2666.61208257875824-0.612082578758238
2775.334389714855131.66561028514487
2855.7638961672994-0.763896167299403
2965.743799954305270.256200045694729
3045.5236675172499-1.5236675172499
3144.22732135880640-0.227321358806405
3275.424582227798091.57541777220191
3375.984090680102361.01590931989764
3444.6910944909851-0.691094490985104
3544.31978293286474-0.319782932864741
3666.52165204430713-0.52165204430713
3765.263367816377340.73663218362266
3853.975810849106441.02418915089356
3965.561589734152090.438410265847911
4076.669501243537760.330498756462243
4165.592594830449620.407405169550375
4234.68862322928571-1.68862322928571
4333.48035940243672-0.480359402436723
4444.23288337286132-0.232883372861317
4564.488788057837791.51121194216221
4676.069888177149230.93011182285077
4756.43170746027817-1.43170746027816
4843.308774315748820.69122568425118
4954.742551855220880.257448144779122
5066.19121453389706-0.191214533897064
5164.152121003843881.84787899615612
5264.341517161627161.65848283837284
5355.33238294148107-0.332382941481073
5444.15592079531701-0.155920795317012
5555.57881597294334-0.578815972943344
5655.41566287451323-0.415662874513229
5744.95007442151907-0.950074421519071
5866.71941267796887-0.719412677968869
5924.67788882181962-2.67788882181962
6086.275554179677511.72444582032249
6133.85922903391467-0.859229033914671
6266.3677991943245-0.367799194324501
6366.14056278011806-0.140562780118058
6466.3281133402356-0.328113340235606
6554.024150043591110.975849956408886
6655.60474359100319-0.604743591003185
6765.203905409328940.79609459067106
6854.68418612981560.315813870184402
6965.760389459084890.239610540915107
7022.07843381267342-0.0784338126734208
7155.88551618129121-0.885516181291208
7255.45347127672076-0.453471276720759
7355.10492343857916-0.104923438579159
7465.956754372546110.0432456274538886
7565.23833278005870.761667219941302
7665.508372377485560.491627622514441
7755.23879810900361-0.238798109003615
7855.38452761610343-0.38452761610343
7945.23910305430431-1.23910305430432
8023.36122336168653-1.36122336168653
8143.765375384411760.234624615588236
8265.590106380713050.409893619286947
8366.07130310422205-0.0713031042220515
8454.684852244739610.315147755260394
8534.48530197110051-1.48530197110051
8665.036026346911380.963973653088615
8743.909011146853100.0909888531469036
8855.49463476293373-0.494634762933731
8986.340262100789171.65973789921083
9044.46004046796467-0.460040467964674
9165.764472733761920.235527266238076
9265.313019193218150.686980806781847
9376.674674452495030.325325547504970
9466.12442052030827-0.124420520308270
9554.977991569597460.0220084304025405
9644.19289595159344-0.192895951593442
9763.865436848767482.13456315123252
9833.61543159990308-0.615431599903077
9955.63592801118301-0.635928011183009
10065.259932572649240.740067427350765
10176.80271687723420.1972831227658
10276.42956995080650.570430049193501
10366.72537704928321-0.725377049283214
10434.37322871986526-1.37322871986526
10522.75310970200851-0.753109702008507
10685.730597663261512.26940233673849
10734.68774648517566-1.68774648517566
10886.144691783125071.85530821687493
10934.57686310880096-1.57686310880096
11044.68142828906823-0.681428289068229
11155.2475443759826-0.247544375982601
11275.608195246347781.39180475365222
11364.54358326272351.45641673727650
11465.919880006350130.080119993649865
11576.116886713183860.88311328681614
11666.20747736464496-0.207477364644955
11766.18738115165082-0.187381151650823
11865.087301865406140.91269813459386
11965.98354967681030.0164503231896983
12045.40208902172734-1.40208902172734
12145.2195221726447-1.2195221726447
12255.81735155435465-0.817351554354645
12344.16770079826481-0.167700798264812
12465.811209248689460.188790751310541
12566.29484141297372-0.294841412973718
12654.746421374622940.253578625377065
12786.484051450956361.51594854904364
12865.533641799436960.466358200563035
12955.0903104945222-0.090310494522202
13042.138135720033791.86186427996621
13186.485995252644931.51400474735507
13265.708013841150380.291986158849616
13344.940403104923-0.940403104922996
13465.547683518669920.452316481330083
13565.938367584488350.0616324155116501
13644.91609345513957-0.91609345513957
13766.01287894782514-0.0128789478251396
13834.41451050614842-1.41451050614842
13966.91011416687161-0.910114166871612
14055.62297027093152-0.622970270931524
14145.66411397628685-1.66411397628685
14265.736965861758540.263034138241459
14346.33763864089536-2.33763864089536
14443.426628193239490.573371806760509
14545.02403966688461-1.02403966688461
14664.074131472585381.92586852741462
14754.347019582118310.652980417881686
14865.465049079084540.534950920915458
14966.27647102765189-0.276471027651885
15086.142177597232071.85782240276793
15175.408870865540531.59112913445947
15276.599772869455680.400227130544323
15344.33646732774437-0.336467327744365
15465.876909692122620.123090307877375
15565.626242602393830.373757397606167
15625.29944534043226-3.29944534043226

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 5.50195929127812 & -2.50195929127812 \tabularnewline
2 & 5 & 4.74740344062673 & 0.252596559373271 \tabularnewline
3 & 6 & 6.10362459273646 & -0.103624592736458 \tabularnewline
4 & 6 & 4.56064377623203 & 1.43935622376797 \tabularnewline
5 & 5 & 4.39698410590254 & 0.603015894097457 \tabularnewline
6 & 3 & 4.49986918682749 & -1.49986918682749 \tabularnewline
7 & 8 & 6.74992333104334 & 1.25007666895666 \tabularnewline
8 & 4 & 4.30666227146690 & -0.306662271466895 \tabularnewline
9 & 4 & 5.51814643879832 & -1.51814643879832 \tabularnewline
10 & 4 & 3.9298291016265 & 0.0701708983735044 \tabularnewline
11 & 6 & 5.71615078263275 & 0.283849217367246 \tabularnewline
12 & 6 & 4.76288750432808 & 1.23711249567192 \tabularnewline
13 & 5 & 5.77266557761435 & -0.772665577614346 \tabularnewline
14 & 4 & 4.20951566902445 & -0.209515669024452 \tabularnewline
15 & 6 & 4.454795794521 & 1.54520420547900 \tabularnewline
16 & 4 & 4.82607122436611 & -0.826071224366108 \tabularnewline
17 & 6 & 4.61112923962351 & 1.38887076037649 \tabularnewline
18 & 6 & 6.2468616426743 & -0.246861642674306 \tabularnewline
19 & 4 & 3.78067647947488 & 0.21932352052512 \tabularnewline
20 & 4 & 3.74569512727662 & 0.254304872723378 \tabularnewline
21 & 2 & 4.56062231413522 & -2.56062231413522 \tabularnewline
22 & 7 & 6.15351540569034 & 0.846484594309657 \tabularnewline
23 & 5 & 4.91129495908683 & 0.0887050409131663 \tabularnewline
24 & 4 & 4.36271089688399 & -0.362710896883994 \tabularnewline
25 & 6 & 5.79196493958686 & 0.208035060413142 \tabularnewline
26 & 6 & 6.61208257875824 & -0.612082578758238 \tabularnewline
27 & 7 & 5.33438971485513 & 1.66561028514487 \tabularnewline
28 & 5 & 5.7638961672994 & -0.763896167299403 \tabularnewline
29 & 6 & 5.74379995430527 & 0.256200045694729 \tabularnewline
30 & 4 & 5.5236675172499 & -1.5236675172499 \tabularnewline
31 & 4 & 4.22732135880640 & -0.227321358806405 \tabularnewline
32 & 7 & 5.42458222779809 & 1.57541777220191 \tabularnewline
33 & 7 & 5.98409068010236 & 1.01590931989764 \tabularnewline
34 & 4 & 4.6910944909851 & -0.691094490985104 \tabularnewline
35 & 4 & 4.31978293286474 & -0.319782932864741 \tabularnewline
36 & 6 & 6.52165204430713 & -0.52165204430713 \tabularnewline
37 & 6 & 5.26336781637734 & 0.73663218362266 \tabularnewline
38 & 5 & 3.97581084910644 & 1.02418915089356 \tabularnewline
39 & 6 & 5.56158973415209 & 0.438410265847911 \tabularnewline
40 & 7 & 6.66950124353776 & 0.330498756462243 \tabularnewline
41 & 6 & 5.59259483044962 & 0.407405169550375 \tabularnewline
42 & 3 & 4.68862322928571 & -1.68862322928571 \tabularnewline
43 & 3 & 3.48035940243672 & -0.480359402436723 \tabularnewline
44 & 4 & 4.23288337286132 & -0.232883372861317 \tabularnewline
45 & 6 & 4.48878805783779 & 1.51121194216221 \tabularnewline
46 & 7 & 6.06988817714923 & 0.93011182285077 \tabularnewline
47 & 5 & 6.43170746027817 & -1.43170746027816 \tabularnewline
48 & 4 & 3.30877431574882 & 0.69122568425118 \tabularnewline
49 & 5 & 4.74255185522088 & 0.257448144779122 \tabularnewline
50 & 6 & 6.19121453389706 & -0.191214533897064 \tabularnewline
51 & 6 & 4.15212100384388 & 1.84787899615612 \tabularnewline
52 & 6 & 4.34151716162716 & 1.65848283837284 \tabularnewline
53 & 5 & 5.33238294148107 & -0.332382941481073 \tabularnewline
54 & 4 & 4.15592079531701 & -0.155920795317012 \tabularnewline
55 & 5 & 5.57881597294334 & -0.578815972943344 \tabularnewline
56 & 5 & 5.41566287451323 & -0.415662874513229 \tabularnewline
57 & 4 & 4.95007442151907 & -0.950074421519071 \tabularnewline
58 & 6 & 6.71941267796887 & -0.719412677968869 \tabularnewline
59 & 2 & 4.67788882181962 & -2.67788882181962 \tabularnewline
60 & 8 & 6.27555417967751 & 1.72444582032249 \tabularnewline
61 & 3 & 3.85922903391467 & -0.859229033914671 \tabularnewline
62 & 6 & 6.3677991943245 & -0.367799194324501 \tabularnewline
63 & 6 & 6.14056278011806 & -0.140562780118058 \tabularnewline
64 & 6 & 6.3281133402356 & -0.328113340235606 \tabularnewline
65 & 5 & 4.02415004359111 & 0.975849956408886 \tabularnewline
66 & 5 & 5.60474359100319 & -0.604743591003185 \tabularnewline
67 & 6 & 5.20390540932894 & 0.79609459067106 \tabularnewline
68 & 5 & 4.6841861298156 & 0.315813870184402 \tabularnewline
69 & 6 & 5.76038945908489 & 0.239610540915107 \tabularnewline
70 & 2 & 2.07843381267342 & -0.0784338126734208 \tabularnewline
71 & 5 & 5.88551618129121 & -0.885516181291208 \tabularnewline
72 & 5 & 5.45347127672076 & -0.453471276720759 \tabularnewline
73 & 5 & 5.10492343857916 & -0.104923438579159 \tabularnewline
74 & 6 & 5.95675437254611 & 0.0432456274538886 \tabularnewline
75 & 6 & 5.2383327800587 & 0.761667219941302 \tabularnewline
76 & 6 & 5.50837237748556 & 0.491627622514441 \tabularnewline
77 & 5 & 5.23879810900361 & -0.238798109003615 \tabularnewline
78 & 5 & 5.38452761610343 & -0.38452761610343 \tabularnewline
79 & 4 & 5.23910305430431 & -1.23910305430432 \tabularnewline
80 & 2 & 3.36122336168653 & -1.36122336168653 \tabularnewline
81 & 4 & 3.76537538441176 & 0.234624615588236 \tabularnewline
82 & 6 & 5.59010638071305 & 0.409893619286947 \tabularnewline
83 & 6 & 6.07130310422205 & -0.0713031042220515 \tabularnewline
84 & 5 & 4.68485224473961 & 0.315147755260394 \tabularnewline
85 & 3 & 4.48530197110051 & -1.48530197110051 \tabularnewline
86 & 6 & 5.03602634691138 & 0.963973653088615 \tabularnewline
87 & 4 & 3.90901114685310 & 0.0909888531469036 \tabularnewline
88 & 5 & 5.49463476293373 & -0.494634762933731 \tabularnewline
89 & 8 & 6.34026210078917 & 1.65973789921083 \tabularnewline
90 & 4 & 4.46004046796467 & -0.460040467964674 \tabularnewline
91 & 6 & 5.76447273376192 & 0.235527266238076 \tabularnewline
92 & 6 & 5.31301919321815 & 0.686980806781847 \tabularnewline
93 & 7 & 6.67467445249503 & 0.325325547504970 \tabularnewline
94 & 6 & 6.12442052030827 & -0.124420520308270 \tabularnewline
95 & 5 & 4.97799156959746 & 0.0220084304025405 \tabularnewline
96 & 4 & 4.19289595159344 & -0.192895951593442 \tabularnewline
97 & 6 & 3.86543684876748 & 2.13456315123252 \tabularnewline
98 & 3 & 3.61543159990308 & -0.615431599903077 \tabularnewline
99 & 5 & 5.63592801118301 & -0.635928011183009 \tabularnewline
100 & 6 & 5.25993257264924 & 0.740067427350765 \tabularnewline
101 & 7 & 6.8027168772342 & 0.1972831227658 \tabularnewline
102 & 7 & 6.4295699508065 & 0.570430049193501 \tabularnewline
103 & 6 & 6.72537704928321 & -0.725377049283214 \tabularnewline
104 & 3 & 4.37322871986526 & -1.37322871986526 \tabularnewline
105 & 2 & 2.75310970200851 & -0.753109702008507 \tabularnewline
106 & 8 & 5.73059766326151 & 2.26940233673849 \tabularnewline
107 & 3 & 4.68774648517566 & -1.68774648517566 \tabularnewline
108 & 8 & 6.14469178312507 & 1.85530821687493 \tabularnewline
109 & 3 & 4.57686310880096 & -1.57686310880096 \tabularnewline
110 & 4 & 4.68142828906823 & -0.681428289068229 \tabularnewline
111 & 5 & 5.2475443759826 & -0.247544375982601 \tabularnewline
112 & 7 & 5.60819524634778 & 1.39180475365222 \tabularnewline
113 & 6 & 4.5435832627235 & 1.45641673727650 \tabularnewline
114 & 6 & 5.91988000635013 & 0.080119993649865 \tabularnewline
115 & 7 & 6.11688671318386 & 0.88311328681614 \tabularnewline
116 & 6 & 6.20747736464496 & -0.207477364644955 \tabularnewline
117 & 6 & 6.18738115165082 & -0.187381151650823 \tabularnewline
118 & 6 & 5.08730186540614 & 0.91269813459386 \tabularnewline
119 & 6 & 5.9835496768103 & 0.0164503231896983 \tabularnewline
120 & 4 & 5.40208902172734 & -1.40208902172734 \tabularnewline
121 & 4 & 5.2195221726447 & -1.2195221726447 \tabularnewline
122 & 5 & 5.81735155435465 & -0.817351554354645 \tabularnewline
123 & 4 & 4.16770079826481 & -0.167700798264812 \tabularnewline
124 & 6 & 5.81120924868946 & 0.188790751310541 \tabularnewline
125 & 6 & 6.29484141297372 & -0.294841412973718 \tabularnewline
126 & 5 & 4.74642137462294 & 0.253578625377065 \tabularnewline
127 & 8 & 6.48405145095636 & 1.51594854904364 \tabularnewline
128 & 6 & 5.53364179943696 & 0.466358200563035 \tabularnewline
129 & 5 & 5.0903104945222 & -0.090310494522202 \tabularnewline
130 & 4 & 2.13813572003379 & 1.86186427996621 \tabularnewline
131 & 8 & 6.48599525264493 & 1.51400474735507 \tabularnewline
132 & 6 & 5.70801384115038 & 0.291986158849616 \tabularnewline
133 & 4 & 4.940403104923 & -0.940403104922996 \tabularnewline
134 & 6 & 5.54768351866992 & 0.452316481330083 \tabularnewline
135 & 6 & 5.93836758448835 & 0.0616324155116501 \tabularnewline
136 & 4 & 4.91609345513957 & -0.91609345513957 \tabularnewline
137 & 6 & 6.01287894782514 & -0.0128789478251396 \tabularnewline
138 & 3 & 4.41451050614842 & -1.41451050614842 \tabularnewline
139 & 6 & 6.91011416687161 & -0.910114166871612 \tabularnewline
140 & 5 & 5.62297027093152 & -0.622970270931524 \tabularnewline
141 & 4 & 5.66411397628685 & -1.66411397628685 \tabularnewline
142 & 6 & 5.73696586175854 & 0.263034138241459 \tabularnewline
143 & 4 & 6.33763864089536 & -2.33763864089536 \tabularnewline
144 & 4 & 3.42662819323949 & 0.573371806760509 \tabularnewline
145 & 4 & 5.02403966688461 & -1.02403966688461 \tabularnewline
146 & 6 & 4.07413147258538 & 1.92586852741462 \tabularnewline
147 & 5 & 4.34701958211831 & 0.652980417881686 \tabularnewline
148 & 6 & 5.46504907908454 & 0.534950920915458 \tabularnewline
149 & 6 & 6.27647102765189 & -0.276471027651885 \tabularnewline
150 & 8 & 6.14217759723207 & 1.85782240276793 \tabularnewline
151 & 7 & 5.40887086554053 & 1.59112913445947 \tabularnewline
152 & 7 & 6.59977286945568 & 0.400227130544323 \tabularnewline
153 & 4 & 4.33646732774437 & -0.336467327744365 \tabularnewline
154 & 6 & 5.87690969212262 & 0.123090307877375 \tabularnewline
155 & 6 & 5.62624260239383 & 0.373757397606167 \tabularnewline
156 & 2 & 5.29944534043226 & -3.29944534043226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&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]5.50195929127812[/C][C]-2.50195929127812[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.74740344062673[/C][C]0.252596559373271[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]6.10362459273646[/C][C]-0.103624592736458[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]4.56064377623203[/C][C]1.43935622376797[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]4.39698410590254[/C][C]0.603015894097457[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]4.49986918682749[/C][C]-1.49986918682749[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]6.74992333104334[/C][C]1.25007666895666[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.30666227146690[/C][C]-0.306662271466895[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.51814643879832[/C][C]-1.51814643879832[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.9298291016265[/C][C]0.0701708983735044[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]5.71615078263275[/C][C]0.283849217367246[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]4.76288750432808[/C][C]1.23711249567192[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.77266557761435[/C][C]-0.772665577614346[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]4.20951566902445[/C][C]-0.209515669024452[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]4.454795794521[/C][C]1.54520420547900[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.82607122436611[/C][C]-0.826071224366108[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]4.61112923962351[/C][C]1.38887076037649[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.2468616426743[/C][C]-0.246861642674306[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.78067647947488[/C][C]0.21932352052512[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.74569512727662[/C][C]0.254304872723378[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.56062231413522[/C][C]-2.56062231413522[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]6.15351540569034[/C][C]0.846484594309657[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.91129495908683[/C][C]0.0887050409131663[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.36271089688399[/C][C]-0.362710896883994[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]5.79196493958686[/C][C]0.208035060413142[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.61208257875824[/C][C]-0.612082578758238[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]5.33438971485513[/C][C]1.66561028514487[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]5.7638961672994[/C][C]-0.763896167299403[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.74379995430527[/C][C]0.256200045694729[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.5236675172499[/C][C]-1.5236675172499[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.22732135880640[/C][C]-0.227321358806405[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]5.42458222779809[/C][C]1.57541777220191[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]5.98409068010236[/C][C]1.01590931989764[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.6910944909851[/C][C]-0.691094490985104[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.31978293286474[/C][C]-0.319782932864741[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]6.52165204430713[/C][C]-0.52165204430713[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]5.26336781637734[/C][C]0.73663218362266[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]3.97581084910644[/C][C]1.02418915089356[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]5.56158973415209[/C][C]0.438410265847911[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.66950124353776[/C][C]0.330498756462243[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]5.59259483044962[/C][C]0.407405169550375[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]4.68862322928571[/C][C]-1.68862322928571[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.48035940243672[/C][C]-0.480359402436723[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.23288337286132[/C][C]-0.232883372861317[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]4.48878805783779[/C][C]1.51121194216221[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.06988817714923[/C][C]0.93011182285077[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.43170746027817[/C][C]-1.43170746027816[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.30877431574882[/C][C]0.69122568425118[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]4.74255185522088[/C][C]0.257448144779122[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.19121453389706[/C][C]-0.191214533897064[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]4.15212100384388[/C][C]1.84787899615612[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]4.34151716162716[/C][C]1.65848283837284[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.33238294148107[/C][C]-0.332382941481073[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.15592079531701[/C][C]-0.155920795317012[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.57881597294334[/C][C]-0.578815972943344[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.41566287451323[/C][C]-0.415662874513229[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.95007442151907[/C][C]-0.950074421519071[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]6.71941267796887[/C][C]-0.719412677968869[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]4.67788882181962[/C][C]-2.67788882181962[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]6.27555417967751[/C][C]1.72444582032249[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.85922903391467[/C][C]-0.859229033914671[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.3677991943245[/C][C]-0.367799194324501[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]6.14056278011806[/C][C]-0.140562780118058[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]6.3281133402356[/C][C]-0.328113340235606[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.02415004359111[/C][C]0.975849956408886[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.60474359100319[/C][C]-0.604743591003185[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.20390540932894[/C][C]0.79609459067106[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]4.6841861298156[/C][C]0.315813870184402[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]5.76038945908489[/C][C]0.239610540915107[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]2.07843381267342[/C][C]-0.0784338126734208[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.88551618129121[/C][C]-0.885516181291208[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]5.45347127672076[/C][C]-0.453471276720759[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.10492343857916[/C][C]-0.104923438579159[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]5.95675437254611[/C][C]0.0432456274538886[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]5.2383327800587[/C][C]0.761667219941302[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]5.50837237748556[/C][C]0.491627622514441[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.23879810900361[/C][C]-0.238798109003615[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.38452761610343[/C][C]-0.38452761610343[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]5.23910305430431[/C][C]-1.23910305430432[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.36122336168653[/C][C]-1.36122336168653[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.76537538441176[/C][C]0.234624615588236[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]5.59010638071305[/C][C]0.409893619286947[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]6.07130310422205[/C][C]-0.0713031042220515[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.68485224473961[/C][C]0.315147755260394[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]4.48530197110051[/C][C]-1.48530197110051[/C][/ROW]
[ROW][C]86[/C][C]6[/C][C]5.03602634691138[/C][C]0.963973653088615[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.90901114685310[/C][C]0.0909888531469036[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]5.49463476293373[/C][C]-0.494634762933731[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]6.34026210078917[/C][C]1.65973789921083[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.46004046796467[/C][C]-0.460040467964674[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]5.76447273376192[/C][C]0.235527266238076[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.31301919321815[/C][C]0.686980806781847[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]6.67467445249503[/C][C]0.325325547504970[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]6.12442052030827[/C][C]-0.124420520308270[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]4.97799156959746[/C][C]0.0220084304025405[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.19289595159344[/C][C]-0.192895951593442[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]3.86543684876748[/C][C]2.13456315123252[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]3.61543159990308[/C][C]-0.615431599903077[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]5.63592801118301[/C][C]-0.635928011183009[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]5.25993257264924[/C][C]0.740067427350765[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]6.8027168772342[/C][C]0.1972831227658[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]6.4295699508065[/C][C]0.570430049193501[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]6.72537704928321[/C][C]-0.725377049283214[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.37322871986526[/C][C]-1.37322871986526[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.75310970200851[/C][C]-0.753109702008507[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]5.73059766326151[/C][C]2.26940233673849[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]4.68774648517566[/C][C]-1.68774648517566[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]6.14469178312507[/C][C]1.85530821687493[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.57686310880096[/C][C]-1.57686310880096[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.68142828906823[/C][C]-0.681428289068229[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]5.2475443759826[/C][C]-0.247544375982601[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]5.60819524634778[/C][C]1.39180475365222[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]4.5435832627235[/C][C]1.45641673727650[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.91988000635013[/C][C]0.080119993649865[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]6.11688671318386[/C][C]0.88311328681614[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]6.20747736464496[/C][C]-0.207477364644955[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]6.18738115165082[/C][C]-0.187381151650823[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]5.08730186540614[/C][C]0.91269813459386[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.9835496768103[/C][C]0.0164503231896983[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]5.40208902172734[/C][C]-1.40208902172734[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.2195221726447[/C][C]-1.2195221726447[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.81735155435465[/C][C]-0.817351554354645[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]4.16770079826481[/C][C]-0.167700798264812[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]5.81120924868946[/C][C]0.188790751310541[/C][/ROW]
[ROW][C]125[/C][C]6[/C][C]6.29484141297372[/C][C]-0.294841412973718[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.74642137462294[/C][C]0.253578625377065[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]6.48405145095636[/C][C]1.51594854904364[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.53364179943696[/C][C]0.466358200563035[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.0903104945222[/C][C]-0.090310494522202[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]2.13813572003379[/C][C]1.86186427996621[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]6.48599525264493[/C][C]1.51400474735507[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]5.70801384115038[/C][C]0.291986158849616[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]4.940403104923[/C][C]-0.940403104922996[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.54768351866992[/C][C]0.452316481330083[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]5.93836758448835[/C][C]0.0616324155116501[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.91609345513957[/C][C]-0.91609345513957[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]6.01287894782514[/C][C]-0.0128789478251396[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]4.41451050614842[/C][C]-1.41451050614842[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.91011416687161[/C][C]-0.910114166871612[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]5.62297027093152[/C][C]-0.622970270931524[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.66411397628685[/C][C]-1.66411397628685[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.73696586175854[/C][C]0.263034138241459[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]6.33763864089536[/C][C]-2.33763864089536[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.42662819323949[/C][C]0.573371806760509[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]5.02403966688461[/C][C]-1.02403966688461[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]4.07413147258538[/C][C]1.92586852741462[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.34701958211831[/C][C]0.652980417881686[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]5.46504907908454[/C][C]0.534950920915458[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.27647102765189[/C][C]-0.276471027651885[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]6.14217759723207[/C][C]1.85782240276793[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]5.40887086554053[/C][C]1.59112913445947[/C][/ROW]
[ROW][C]152[/C][C]7[/C][C]6.59977286945568[/C][C]0.400227130544323[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.33646732774437[/C][C]-0.336467327744365[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]5.87690969212262[/C][C]0.123090307877375[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.62624260239383[/C][C]0.373757397606167[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]5.29944534043226[/C][C]-3.29944534043226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&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
135.50195929127812-2.50195929127812
254.747403440626730.252596559373271
366.10362459273646-0.103624592736458
464.560643776232031.43935622376797
554.396984105902540.603015894097457
634.49986918682749-1.49986918682749
786.749923331043341.25007666895666
844.30666227146690-0.306662271466895
945.51814643879832-1.51814643879832
1043.92982910162650.0701708983735044
1165.716150782632750.283849217367246
1264.762887504328081.23711249567192
1355.77266557761435-0.772665577614346
1444.20951566902445-0.209515669024452
1564.4547957945211.54520420547900
1644.82607122436611-0.826071224366108
1764.611129239623511.38887076037649
1866.2468616426743-0.246861642674306
1943.780676479474880.21932352052512
2043.745695127276620.254304872723378
2124.56062231413522-2.56062231413522
2276.153515405690340.846484594309657
2354.911294959086830.0887050409131663
2444.36271089688399-0.362710896883994
2565.791964939586860.208035060413142
2666.61208257875824-0.612082578758238
2775.334389714855131.66561028514487
2855.7638961672994-0.763896167299403
2965.743799954305270.256200045694729
3045.5236675172499-1.5236675172499
3144.22732135880640-0.227321358806405
3275.424582227798091.57541777220191
3375.984090680102361.01590931989764
3444.6910944909851-0.691094490985104
3544.31978293286474-0.319782932864741
3666.52165204430713-0.52165204430713
3765.263367816377340.73663218362266
3853.975810849106441.02418915089356
3965.561589734152090.438410265847911
4076.669501243537760.330498756462243
4165.592594830449620.407405169550375
4234.68862322928571-1.68862322928571
4333.48035940243672-0.480359402436723
4444.23288337286132-0.232883372861317
4564.488788057837791.51121194216221
4676.069888177149230.93011182285077
4756.43170746027817-1.43170746027816
4843.308774315748820.69122568425118
4954.742551855220880.257448144779122
5066.19121453389706-0.191214533897064
5164.152121003843881.84787899615612
5264.341517161627161.65848283837284
5355.33238294148107-0.332382941481073
5444.15592079531701-0.155920795317012
5555.57881597294334-0.578815972943344
5655.41566287451323-0.415662874513229
5744.95007442151907-0.950074421519071
5866.71941267796887-0.719412677968869
5924.67788882181962-2.67788882181962
6086.275554179677511.72444582032249
6133.85922903391467-0.859229033914671
6266.3677991943245-0.367799194324501
6366.14056278011806-0.140562780118058
6466.3281133402356-0.328113340235606
6554.024150043591110.975849956408886
6655.60474359100319-0.604743591003185
6765.203905409328940.79609459067106
6854.68418612981560.315813870184402
6965.760389459084890.239610540915107
7022.07843381267342-0.0784338126734208
7155.88551618129121-0.885516181291208
7255.45347127672076-0.453471276720759
7355.10492343857916-0.104923438579159
7465.956754372546110.0432456274538886
7565.23833278005870.761667219941302
7665.508372377485560.491627622514441
7755.23879810900361-0.238798109003615
7855.38452761610343-0.38452761610343
7945.23910305430431-1.23910305430432
8023.36122336168653-1.36122336168653
8143.765375384411760.234624615588236
8265.590106380713050.409893619286947
8366.07130310422205-0.0713031042220515
8454.684852244739610.315147755260394
8534.48530197110051-1.48530197110051
8665.036026346911380.963973653088615
8743.909011146853100.0909888531469036
8855.49463476293373-0.494634762933731
8986.340262100789171.65973789921083
9044.46004046796467-0.460040467964674
9165.764472733761920.235527266238076
9265.313019193218150.686980806781847
9376.674674452495030.325325547504970
9466.12442052030827-0.124420520308270
9554.977991569597460.0220084304025405
9644.19289595159344-0.192895951593442
9763.865436848767482.13456315123252
9833.61543159990308-0.615431599903077
9955.63592801118301-0.635928011183009
10065.259932572649240.740067427350765
10176.80271687723420.1972831227658
10276.42956995080650.570430049193501
10366.72537704928321-0.725377049283214
10434.37322871986526-1.37322871986526
10522.75310970200851-0.753109702008507
10685.730597663261512.26940233673849
10734.68774648517566-1.68774648517566
10886.144691783125071.85530821687493
10934.57686310880096-1.57686310880096
11044.68142828906823-0.681428289068229
11155.2475443759826-0.247544375982601
11275.608195246347781.39180475365222
11364.54358326272351.45641673727650
11465.919880006350130.080119993649865
11576.116886713183860.88311328681614
11666.20747736464496-0.207477364644955
11766.18738115165082-0.187381151650823
11865.087301865406140.91269813459386
11965.98354967681030.0164503231896983
12045.40208902172734-1.40208902172734
12145.2195221726447-1.2195221726447
12255.81735155435465-0.817351554354645
12344.16770079826481-0.167700798264812
12465.811209248689460.188790751310541
12566.29484141297372-0.294841412973718
12654.746421374622940.253578625377065
12786.484051450956361.51594854904364
12865.533641799436960.466358200563035
12955.0903104945222-0.090310494522202
13042.138135720033791.86186427996621
13186.485995252644931.51400474735507
13265.708013841150380.291986158849616
13344.940403104923-0.940403104922996
13465.547683518669920.452316481330083
13565.938367584488350.0616324155116501
13644.91609345513957-0.91609345513957
13766.01287894782514-0.0128789478251396
13834.41451050614842-1.41451050614842
13966.91011416687161-0.910114166871612
14055.62297027093152-0.622970270931524
14145.66411397628685-1.66411397628685
14265.736965861758540.263034138241459
14346.33763864089536-2.33763864089536
14443.426628193239490.573371806760509
14545.02403966688461-1.02403966688461
14664.074131472585381.92586852741462
14754.347019582118310.652980417881686
14865.465049079084540.534950920915458
14966.27647102765189-0.276471027651885
15086.142177597232071.85782240276793
15175.408870865540531.59112913445947
15276.599772869455680.400227130544323
15344.33646732774437-0.336467327744365
15465.876909692122620.123090307877375
15565.626242602393830.373757397606167
15625.29944534043226-3.29944534043226






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5869903871889910.8260192256220180.413009612811009
100.4944740233872470.9889480467744950.505525976612752
110.6720414613632180.6559170772735640.327958538636782
120.7928979641694860.4142040716610270.207102035830514
130.805276099632270.3894478007354590.194723900367729
140.7434304218892130.5131391562215740.256569578110787
150.6686436899615520.6627126200768950.331356310038448
160.6122245853885140.7755508292229720.387775414611486
170.5598199365934630.8803601268130730.440180063406537
180.6109074690410910.7781850619178180.389092530958909
190.5403620836565040.9192758326869910.459637916343496
200.5453959226733520.9092081546532960.454604077326648
210.948037101511310.1039257969773800.0519628984886898
220.941501563816050.1169968723679010.0584984361839504
230.9211772456461370.1576455087077260.078822754353863
240.897175504873330.2056489902533410.102824495126671
250.865798461971260.2684030760574810.134201538028740
260.8403762920708650.3192474158582700.159623707929135
270.8488024248445860.3023951503108280.151197575155414
280.8329457772625070.3341084454749850.167054222737493
290.7914405096101620.4171189807796760.208559490389838
300.8074434802975840.3851130394048320.192556519702416
310.7728873005012540.4542253989974920.227112699498746
320.8077881846341450.384423630731710.192211815365855
330.791988194212940.4160236115741180.208011805787059
340.7824202255885140.4351595488229730.217579774411487
350.7525453991537680.4949092016924630.247454600846232
360.7126837476000310.5746325047999380.287316252399969
370.6734563353776020.6530873292447970.326543664622399
380.6629936597473890.6740126805052220.337006340252611
390.614792825111880.7704143497762410.385207174888120
400.5636456713236230.8727086573527530.436354328676377
410.5136150075991070.9727699848017850.486384992400893
420.5680998028005350.863800394398930.431900197199465
430.5759107704878650.848178459024270.424089229512135
440.5354541242104630.9290917515790740.464545875789537
450.5829159712370580.8341680575258830.417084028762942
460.5689299841308810.8621400317382380.431070015869119
470.6098039088584680.7803921822830650.390196091141532
480.5801862068723870.8396275862552250.419813793127613
490.5351002552431770.9297994895136460.464899744756823
500.4943694843566150.988738968713230.505630515643385
510.5592030202303760.8815939595392480.440796979769624
520.5963996919802890.8072006160394220.403600308019711
530.5646001659555230.8707996680889530.435399834044477
540.538507348558890.922985302882220.46149265144111
550.5036672476465170.9926655047069660.496332752353483
560.4683614999539930.9367229999079870.531638500046007
570.4730989075796540.9461978151593080.526901092420346
580.4447629997545460.8895259995090910.555237000245454
590.7323971779364080.5352056441271840.267602822063592
600.800486849163230.3990263016735380.199513150836769
610.790014209102160.4199715817956790.209985790897839
620.7571989367451850.4856021265096290.242801063254815
630.7186773065180010.5626453869639970.281322693481999
640.681061776374250.63787644725150.31893822362575
650.6720756763762870.6558486472474260.327924323623713
660.641545557995210.7169088840095820.358454442004791
670.6228265951627090.7543468096745810.377173404837291
680.5824590163291340.8350819673417310.417540983670866
690.5386145939222470.9227708121555060.461385406077753
700.4933730303621310.9867460607242610.506626969637869
710.4801619332481310.9603238664962620.519838066751869
720.4418811363802820.8837622727605650.558118863619718
730.3960089327184990.7920178654369970.603991067281501
740.3519960661046350.7039921322092690.648003933895365
750.3301033463379680.6602066926759360.669896653662032
760.2981141447072760.5962282894145530.701885855292724
770.2601428289105230.5202856578210470.739857171089477
780.2283725779197280.4567451558394570.771627422080272
790.2490890146713520.4981780293427030.750910985328648
800.2765914578320710.5531829156641410.723408542167929
810.2402671102145520.4805342204291050.759732889785448
820.2098867488951030.4197734977902070.790113251104897
830.1783459368614810.3566918737229630.821654063138519
840.1513167312614530.3026334625229070.848683268738547
850.2135882083252760.4271764166505510.786411791674724
860.2046197538192740.4092395076385470.795380246180726
870.1736319865090110.3472639730180210.82636801349099
880.1516539960353810.3033079920707630.848346003964619
890.1903465891223990.3806931782447980.8096534108776
900.1687538921796270.3375077843592530.831246107820373
910.1438483509584720.2876967019169440.856151649041528
920.1333581924274580.2667163848549150.866641807572542
930.1110082706512690.2220165413025370.888991729348731
940.0905577578748560.1811155157497120.909442242125144
950.07301961633659460.1460392326731890.926980383663405
960.06058977377604980.1211795475521000.93941022622395
970.1098147185133140.2196294370266270.890185281486686
980.1058996688378670.2117993376757330.894100331162133
990.09179186047794020.1835837209558800.90820813952206
1000.07798752551368370.1559750510273670.922012474486316
1010.06242806812060130.1248561362412030.937571931879399
1020.05099490998230530.1019898199646110.949005090017695
1030.04341542490378560.08683084980757130.956584575096214
1040.06563246124544170.1312649224908830.934367538754558
1050.06390732245746330.1278146449149270.936092677542537
1060.1106645261825510.2213290523651010.88933547381745
1070.1481099916351730.2962199832703450.851890008364827
1080.2237831751669530.4475663503339060.776216824833047
1090.2608725711955250.521745142391050.739127428804475
1100.2314332104598400.4628664209196790.76856678954016
1110.1962560434920410.3925120869840820.803743956507959
1120.2605281640860710.5210563281721430.739471835913929
1130.2570282466768570.5140564933537150.742971753323143
1140.2160600530453680.4321201060907350.783939946954632
1150.2018492226651840.4036984453303690.798150777334816
1160.1668200701245870.3336401402491740.833179929875413
1170.1366691831410360.2733383662820720.863330816858964
1180.1285577929407330.2571155858814660.871442207059267
1190.1036552304272750.2073104608545510.896344769572725
1200.1377788879189120.2755577758378240.862221112081088
1210.1565218245769530.3130436491539060.843478175423047
1220.1462193121610220.2924386243220450.853780687838978
1230.1171255083137350.2342510166274700.882874491686265
1240.09063462724207050.1812692544841410.90936537275793
1250.07615382265682650.1523076453136530.923846177343174
1260.0639845151543380.1279690303086760.936015484845662
1270.07539989712706020.1507997942541200.92460010287294
1280.06061757966933810.1212351593386760.939382420330662
1290.04947050090765440.09894100181530880.950529499092346
1300.06874269062316960.1374853812463390.93125730937683
1310.1126731969133820.2253463938267630.887326803086618
1320.08837882551750560.1767576510350110.911621174482494
1330.0672607093192960.1345214186385920.932739290680704
1340.0512586457372930.1025172914745860.948741354262707
1350.03609028629297180.07218057258594350.963909713707028
1360.03296868244050010.06593736488100030.9670313175595
1370.02783362055196660.05566724110393310.972166379448034
1380.02133214609331890.04266429218663770.97866785390668
1390.02114948940501970.04229897881003940.97885051059498
1400.01914071492332760.03828142984665510.980859285076672
1410.01319935245840730.02639870491681470.986800647541593
1420.008607958094315590.01721591618863120.991392041905684
1430.03846742100843730.07693484201687460.961532578991563
1440.02177519306124940.04355038612249880.97822480693875
1450.1704763802338950.3409527604677910.829523619766105
1460.1647050022184420.3294100044368840.835294997781558
1470.09133935429342820.1826787085868560.908660645706572

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.586990387188991 & 0.826019225622018 & 0.413009612811009 \tabularnewline
10 & 0.494474023387247 & 0.988948046774495 & 0.505525976612752 \tabularnewline
11 & 0.672041461363218 & 0.655917077273564 & 0.327958538636782 \tabularnewline
12 & 0.792897964169486 & 0.414204071661027 & 0.207102035830514 \tabularnewline
13 & 0.80527609963227 & 0.389447800735459 & 0.194723900367729 \tabularnewline
14 & 0.743430421889213 & 0.513139156221574 & 0.256569578110787 \tabularnewline
15 & 0.668643689961552 & 0.662712620076895 & 0.331356310038448 \tabularnewline
16 & 0.612224585388514 & 0.775550829222972 & 0.387775414611486 \tabularnewline
17 & 0.559819936593463 & 0.880360126813073 & 0.440180063406537 \tabularnewline
18 & 0.610907469041091 & 0.778185061917818 & 0.389092530958909 \tabularnewline
19 & 0.540362083656504 & 0.919275832686991 & 0.459637916343496 \tabularnewline
20 & 0.545395922673352 & 0.909208154653296 & 0.454604077326648 \tabularnewline
21 & 0.94803710151131 & 0.103925796977380 & 0.0519628984886898 \tabularnewline
22 & 0.94150156381605 & 0.116996872367901 & 0.0584984361839504 \tabularnewline
23 & 0.921177245646137 & 0.157645508707726 & 0.078822754353863 \tabularnewline
24 & 0.89717550487333 & 0.205648990253341 & 0.102824495126671 \tabularnewline
25 & 0.86579846197126 & 0.268403076057481 & 0.134201538028740 \tabularnewline
26 & 0.840376292070865 & 0.319247415858270 & 0.159623707929135 \tabularnewline
27 & 0.848802424844586 & 0.302395150310828 & 0.151197575155414 \tabularnewline
28 & 0.832945777262507 & 0.334108445474985 & 0.167054222737493 \tabularnewline
29 & 0.791440509610162 & 0.417118980779676 & 0.208559490389838 \tabularnewline
30 & 0.807443480297584 & 0.385113039404832 & 0.192556519702416 \tabularnewline
31 & 0.772887300501254 & 0.454225398997492 & 0.227112699498746 \tabularnewline
32 & 0.807788184634145 & 0.38442363073171 & 0.192211815365855 \tabularnewline
33 & 0.79198819421294 & 0.416023611574118 & 0.208011805787059 \tabularnewline
34 & 0.782420225588514 & 0.435159548822973 & 0.217579774411487 \tabularnewline
35 & 0.752545399153768 & 0.494909201692463 & 0.247454600846232 \tabularnewline
36 & 0.712683747600031 & 0.574632504799938 & 0.287316252399969 \tabularnewline
37 & 0.673456335377602 & 0.653087329244797 & 0.326543664622399 \tabularnewline
38 & 0.662993659747389 & 0.674012680505222 & 0.337006340252611 \tabularnewline
39 & 0.61479282511188 & 0.770414349776241 & 0.385207174888120 \tabularnewline
40 & 0.563645671323623 & 0.872708657352753 & 0.436354328676377 \tabularnewline
41 & 0.513615007599107 & 0.972769984801785 & 0.486384992400893 \tabularnewline
42 & 0.568099802800535 & 0.86380039439893 & 0.431900197199465 \tabularnewline
43 & 0.575910770487865 & 0.84817845902427 & 0.424089229512135 \tabularnewline
44 & 0.535454124210463 & 0.929091751579074 & 0.464545875789537 \tabularnewline
45 & 0.582915971237058 & 0.834168057525883 & 0.417084028762942 \tabularnewline
46 & 0.568929984130881 & 0.862140031738238 & 0.431070015869119 \tabularnewline
47 & 0.609803908858468 & 0.780392182283065 & 0.390196091141532 \tabularnewline
48 & 0.580186206872387 & 0.839627586255225 & 0.419813793127613 \tabularnewline
49 & 0.535100255243177 & 0.929799489513646 & 0.464899744756823 \tabularnewline
50 & 0.494369484356615 & 0.98873896871323 & 0.505630515643385 \tabularnewline
51 & 0.559203020230376 & 0.881593959539248 & 0.440796979769624 \tabularnewline
52 & 0.596399691980289 & 0.807200616039422 & 0.403600308019711 \tabularnewline
53 & 0.564600165955523 & 0.870799668088953 & 0.435399834044477 \tabularnewline
54 & 0.53850734855889 & 0.92298530288222 & 0.46149265144111 \tabularnewline
55 & 0.503667247646517 & 0.992665504706966 & 0.496332752353483 \tabularnewline
56 & 0.468361499953993 & 0.936722999907987 & 0.531638500046007 \tabularnewline
57 & 0.473098907579654 & 0.946197815159308 & 0.526901092420346 \tabularnewline
58 & 0.444762999754546 & 0.889525999509091 & 0.555237000245454 \tabularnewline
59 & 0.732397177936408 & 0.535205644127184 & 0.267602822063592 \tabularnewline
60 & 0.80048684916323 & 0.399026301673538 & 0.199513150836769 \tabularnewline
61 & 0.79001420910216 & 0.419971581795679 & 0.209985790897839 \tabularnewline
62 & 0.757198936745185 & 0.485602126509629 & 0.242801063254815 \tabularnewline
63 & 0.718677306518001 & 0.562645386963997 & 0.281322693481999 \tabularnewline
64 & 0.68106177637425 & 0.6378764472515 & 0.31893822362575 \tabularnewline
65 & 0.672075676376287 & 0.655848647247426 & 0.327924323623713 \tabularnewline
66 & 0.64154555799521 & 0.716908884009582 & 0.358454442004791 \tabularnewline
67 & 0.622826595162709 & 0.754346809674581 & 0.377173404837291 \tabularnewline
68 & 0.582459016329134 & 0.835081967341731 & 0.417540983670866 \tabularnewline
69 & 0.538614593922247 & 0.922770812155506 & 0.461385406077753 \tabularnewline
70 & 0.493373030362131 & 0.986746060724261 & 0.506626969637869 \tabularnewline
71 & 0.480161933248131 & 0.960323866496262 & 0.519838066751869 \tabularnewline
72 & 0.441881136380282 & 0.883762272760565 & 0.558118863619718 \tabularnewline
73 & 0.396008932718499 & 0.792017865436997 & 0.603991067281501 \tabularnewline
74 & 0.351996066104635 & 0.703992132209269 & 0.648003933895365 \tabularnewline
75 & 0.330103346337968 & 0.660206692675936 & 0.669896653662032 \tabularnewline
76 & 0.298114144707276 & 0.596228289414553 & 0.701885855292724 \tabularnewline
77 & 0.260142828910523 & 0.520285657821047 & 0.739857171089477 \tabularnewline
78 & 0.228372577919728 & 0.456745155839457 & 0.771627422080272 \tabularnewline
79 & 0.249089014671352 & 0.498178029342703 & 0.750910985328648 \tabularnewline
80 & 0.276591457832071 & 0.553182915664141 & 0.723408542167929 \tabularnewline
81 & 0.240267110214552 & 0.480534220429105 & 0.759732889785448 \tabularnewline
82 & 0.209886748895103 & 0.419773497790207 & 0.790113251104897 \tabularnewline
83 & 0.178345936861481 & 0.356691873722963 & 0.821654063138519 \tabularnewline
84 & 0.151316731261453 & 0.302633462522907 & 0.848683268738547 \tabularnewline
85 & 0.213588208325276 & 0.427176416650551 & 0.786411791674724 \tabularnewline
86 & 0.204619753819274 & 0.409239507638547 & 0.795380246180726 \tabularnewline
87 & 0.173631986509011 & 0.347263973018021 & 0.82636801349099 \tabularnewline
88 & 0.151653996035381 & 0.303307992070763 & 0.848346003964619 \tabularnewline
89 & 0.190346589122399 & 0.380693178244798 & 0.8096534108776 \tabularnewline
90 & 0.168753892179627 & 0.337507784359253 & 0.831246107820373 \tabularnewline
91 & 0.143848350958472 & 0.287696701916944 & 0.856151649041528 \tabularnewline
92 & 0.133358192427458 & 0.266716384854915 & 0.866641807572542 \tabularnewline
93 & 0.111008270651269 & 0.222016541302537 & 0.888991729348731 \tabularnewline
94 & 0.090557757874856 & 0.181115515749712 & 0.909442242125144 \tabularnewline
95 & 0.0730196163365946 & 0.146039232673189 & 0.926980383663405 \tabularnewline
96 & 0.0605897737760498 & 0.121179547552100 & 0.93941022622395 \tabularnewline
97 & 0.109814718513314 & 0.219629437026627 & 0.890185281486686 \tabularnewline
98 & 0.105899668837867 & 0.211799337675733 & 0.894100331162133 \tabularnewline
99 & 0.0917918604779402 & 0.183583720955880 & 0.90820813952206 \tabularnewline
100 & 0.0779875255136837 & 0.155975051027367 & 0.922012474486316 \tabularnewline
101 & 0.0624280681206013 & 0.124856136241203 & 0.937571931879399 \tabularnewline
102 & 0.0509949099823053 & 0.101989819964611 & 0.949005090017695 \tabularnewline
103 & 0.0434154249037856 & 0.0868308498075713 & 0.956584575096214 \tabularnewline
104 & 0.0656324612454417 & 0.131264922490883 & 0.934367538754558 \tabularnewline
105 & 0.0639073224574633 & 0.127814644914927 & 0.936092677542537 \tabularnewline
106 & 0.110664526182551 & 0.221329052365101 & 0.88933547381745 \tabularnewline
107 & 0.148109991635173 & 0.296219983270345 & 0.851890008364827 \tabularnewline
108 & 0.223783175166953 & 0.447566350333906 & 0.776216824833047 \tabularnewline
109 & 0.260872571195525 & 0.52174514239105 & 0.739127428804475 \tabularnewline
110 & 0.231433210459840 & 0.462866420919679 & 0.76856678954016 \tabularnewline
111 & 0.196256043492041 & 0.392512086984082 & 0.803743956507959 \tabularnewline
112 & 0.260528164086071 & 0.521056328172143 & 0.739471835913929 \tabularnewline
113 & 0.257028246676857 & 0.514056493353715 & 0.742971753323143 \tabularnewline
114 & 0.216060053045368 & 0.432120106090735 & 0.783939946954632 \tabularnewline
115 & 0.201849222665184 & 0.403698445330369 & 0.798150777334816 \tabularnewline
116 & 0.166820070124587 & 0.333640140249174 & 0.833179929875413 \tabularnewline
117 & 0.136669183141036 & 0.273338366282072 & 0.863330816858964 \tabularnewline
118 & 0.128557792940733 & 0.257115585881466 & 0.871442207059267 \tabularnewline
119 & 0.103655230427275 & 0.207310460854551 & 0.896344769572725 \tabularnewline
120 & 0.137778887918912 & 0.275557775837824 & 0.862221112081088 \tabularnewline
121 & 0.156521824576953 & 0.313043649153906 & 0.843478175423047 \tabularnewline
122 & 0.146219312161022 & 0.292438624322045 & 0.853780687838978 \tabularnewline
123 & 0.117125508313735 & 0.234251016627470 & 0.882874491686265 \tabularnewline
124 & 0.0906346272420705 & 0.181269254484141 & 0.90936537275793 \tabularnewline
125 & 0.0761538226568265 & 0.152307645313653 & 0.923846177343174 \tabularnewline
126 & 0.063984515154338 & 0.127969030308676 & 0.936015484845662 \tabularnewline
127 & 0.0753998971270602 & 0.150799794254120 & 0.92460010287294 \tabularnewline
128 & 0.0606175796693381 & 0.121235159338676 & 0.939382420330662 \tabularnewline
129 & 0.0494705009076544 & 0.0989410018153088 & 0.950529499092346 \tabularnewline
130 & 0.0687426906231696 & 0.137485381246339 & 0.93125730937683 \tabularnewline
131 & 0.112673196913382 & 0.225346393826763 & 0.887326803086618 \tabularnewline
132 & 0.0883788255175056 & 0.176757651035011 & 0.911621174482494 \tabularnewline
133 & 0.067260709319296 & 0.134521418638592 & 0.932739290680704 \tabularnewline
134 & 0.051258645737293 & 0.102517291474586 & 0.948741354262707 \tabularnewline
135 & 0.0360902862929718 & 0.0721805725859435 & 0.963909713707028 \tabularnewline
136 & 0.0329686824405001 & 0.0659373648810003 & 0.9670313175595 \tabularnewline
137 & 0.0278336205519666 & 0.0556672411039331 & 0.972166379448034 \tabularnewline
138 & 0.0213321460933189 & 0.0426642921866377 & 0.97866785390668 \tabularnewline
139 & 0.0211494894050197 & 0.0422989788100394 & 0.97885051059498 \tabularnewline
140 & 0.0191407149233276 & 0.0382814298466551 & 0.980859285076672 \tabularnewline
141 & 0.0131993524584073 & 0.0263987049168147 & 0.986800647541593 \tabularnewline
142 & 0.00860795809431559 & 0.0172159161886312 & 0.991392041905684 \tabularnewline
143 & 0.0384674210084373 & 0.0769348420168746 & 0.961532578991563 \tabularnewline
144 & 0.0217751930612494 & 0.0435503861224988 & 0.97822480693875 \tabularnewline
145 & 0.170476380233895 & 0.340952760467791 & 0.829523619766105 \tabularnewline
146 & 0.164705002218442 & 0.329410004436884 & 0.835294997781558 \tabularnewline
147 & 0.0913393542934282 & 0.182678708586856 & 0.908660645706572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.586990387188991[/C][C]0.826019225622018[/C][C]0.413009612811009[/C][/ROW]
[ROW][C]10[/C][C]0.494474023387247[/C][C]0.988948046774495[/C][C]0.505525976612752[/C][/ROW]
[ROW][C]11[/C][C]0.672041461363218[/C][C]0.655917077273564[/C][C]0.327958538636782[/C][/ROW]
[ROW][C]12[/C][C]0.792897964169486[/C][C]0.414204071661027[/C][C]0.207102035830514[/C][/ROW]
[ROW][C]13[/C][C]0.80527609963227[/C][C]0.389447800735459[/C][C]0.194723900367729[/C][/ROW]
[ROW][C]14[/C][C]0.743430421889213[/C][C]0.513139156221574[/C][C]0.256569578110787[/C][/ROW]
[ROW][C]15[/C][C]0.668643689961552[/C][C]0.662712620076895[/C][C]0.331356310038448[/C][/ROW]
[ROW][C]16[/C][C]0.612224585388514[/C][C]0.775550829222972[/C][C]0.387775414611486[/C][/ROW]
[ROW][C]17[/C][C]0.559819936593463[/C][C]0.880360126813073[/C][C]0.440180063406537[/C][/ROW]
[ROW][C]18[/C][C]0.610907469041091[/C][C]0.778185061917818[/C][C]0.389092530958909[/C][/ROW]
[ROW][C]19[/C][C]0.540362083656504[/C][C]0.919275832686991[/C][C]0.459637916343496[/C][/ROW]
[ROW][C]20[/C][C]0.545395922673352[/C][C]0.909208154653296[/C][C]0.454604077326648[/C][/ROW]
[ROW][C]21[/C][C]0.94803710151131[/C][C]0.103925796977380[/C][C]0.0519628984886898[/C][/ROW]
[ROW][C]22[/C][C]0.94150156381605[/C][C]0.116996872367901[/C][C]0.0584984361839504[/C][/ROW]
[ROW][C]23[/C][C]0.921177245646137[/C][C]0.157645508707726[/C][C]0.078822754353863[/C][/ROW]
[ROW][C]24[/C][C]0.89717550487333[/C][C]0.205648990253341[/C][C]0.102824495126671[/C][/ROW]
[ROW][C]25[/C][C]0.86579846197126[/C][C]0.268403076057481[/C][C]0.134201538028740[/C][/ROW]
[ROW][C]26[/C][C]0.840376292070865[/C][C]0.319247415858270[/C][C]0.159623707929135[/C][/ROW]
[ROW][C]27[/C][C]0.848802424844586[/C][C]0.302395150310828[/C][C]0.151197575155414[/C][/ROW]
[ROW][C]28[/C][C]0.832945777262507[/C][C]0.334108445474985[/C][C]0.167054222737493[/C][/ROW]
[ROW][C]29[/C][C]0.791440509610162[/C][C]0.417118980779676[/C][C]0.208559490389838[/C][/ROW]
[ROW][C]30[/C][C]0.807443480297584[/C][C]0.385113039404832[/C][C]0.192556519702416[/C][/ROW]
[ROW][C]31[/C][C]0.772887300501254[/C][C]0.454225398997492[/C][C]0.227112699498746[/C][/ROW]
[ROW][C]32[/C][C]0.807788184634145[/C][C]0.38442363073171[/C][C]0.192211815365855[/C][/ROW]
[ROW][C]33[/C][C]0.79198819421294[/C][C]0.416023611574118[/C][C]0.208011805787059[/C][/ROW]
[ROW][C]34[/C][C]0.782420225588514[/C][C]0.435159548822973[/C][C]0.217579774411487[/C][/ROW]
[ROW][C]35[/C][C]0.752545399153768[/C][C]0.494909201692463[/C][C]0.247454600846232[/C][/ROW]
[ROW][C]36[/C][C]0.712683747600031[/C][C]0.574632504799938[/C][C]0.287316252399969[/C][/ROW]
[ROW][C]37[/C][C]0.673456335377602[/C][C]0.653087329244797[/C][C]0.326543664622399[/C][/ROW]
[ROW][C]38[/C][C]0.662993659747389[/C][C]0.674012680505222[/C][C]0.337006340252611[/C][/ROW]
[ROW][C]39[/C][C]0.61479282511188[/C][C]0.770414349776241[/C][C]0.385207174888120[/C][/ROW]
[ROW][C]40[/C][C]0.563645671323623[/C][C]0.872708657352753[/C][C]0.436354328676377[/C][/ROW]
[ROW][C]41[/C][C]0.513615007599107[/C][C]0.972769984801785[/C][C]0.486384992400893[/C][/ROW]
[ROW][C]42[/C][C]0.568099802800535[/C][C]0.86380039439893[/C][C]0.431900197199465[/C][/ROW]
[ROW][C]43[/C][C]0.575910770487865[/C][C]0.84817845902427[/C][C]0.424089229512135[/C][/ROW]
[ROW][C]44[/C][C]0.535454124210463[/C][C]0.929091751579074[/C][C]0.464545875789537[/C][/ROW]
[ROW][C]45[/C][C]0.582915971237058[/C][C]0.834168057525883[/C][C]0.417084028762942[/C][/ROW]
[ROW][C]46[/C][C]0.568929984130881[/C][C]0.862140031738238[/C][C]0.431070015869119[/C][/ROW]
[ROW][C]47[/C][C]0.609803908858468[/C][C]0.780392182283065[/C][C]0.390196091141532[/C][/ROW]
[ROW][C]48[/C][C]0.580186206872387[/C][C]0.839627586255225[/C][C]0.419813793127613[/C][/ROW]
[ROW][C]49[/C][C]0.535100255243177[/C][C]0.929799489513646[/C][C]0.464899744756823[/C][/ROW]
[ROW][C]50[/C][C]0.494369484356615[/C][C]0.98873896871323[/C][C]0.505630515643385[/C][/ROW]
[ROW][C]51[/C][C]0.559203020230376[/C][C]0.881593959539248[/C][C]0.440796979769624[/C][/ROW]
[ROW][C]52[/C][C]0.596399691980289[/C][C]0.807200616039422[/C][C]0.403600308019711[/C][/ROW]
[ROW][C]53[/C][C]0.564600165955523[/C][C]0.870799668088953[/C][C]0.435399834044477[/C][/ROW]
[ROW][C]54[/C][C]0.53850734855889[/C][C]0.92298530288222[/C][C]0.46149265144111[/C][/ROW]
[ROW][C]55[/C][C]0.503667247646517[/C][C]0.992665504706966[/C][C]0.496332752353483[/C][/ROW]
[ROW][C]56[/C][C]0.468361499953993[/C][C]0.936722999907987[/C][C]0.531638500046007[/C][/ROW]
[ROW][C]57[/C][C]0.473098907579654[/C][C]0.946197815159308[/C][C]0.526901092420346[/C][/ROW]
[ROW][C]58[/C][C]0.444762999754546[/C][C]0.889525999509091[/C][C]0.555237000245454[/C][/ROW]
[ROW][C]59[/C][C]0.732397177936408[/C][C]0.535205644127184[/C][C]0.267602822063592[/C][/ROW]
[ROW][C]60[/C][C]0.80048684916323[/C][C]0.399026301673538[/C][C]0.199513150836769[/C][/ROW]
[ROW][C]61[/C][C]0.79001420910216[/C][C]0.419971581795679[/C][C]0.209985790897839[/C][/ROW]
[ROW][C]62[/C][C]0.757198936745185[/C][C]0.485602126509629[/C][C]0.242801063254815[/C][/ROW]
[ROW][C]63[/C][C]0.718677306518001[/C][C]0.562645386963997[/C][C]0.281322693481999[/C][/ROW]
[ROW][C]64[/C][C]0.68106177637425[/C][C]0.6378764472515[/C][C]0.31893822362575[/C][/ROW]
[ROW][C]65[/C][C]0.672075676376287[/C][C]0.655848647247426[/C][C]0.327924323623713[/C][/ROW]
[ROW][C]66[/C][C]0.64154555799521[/C][C]0.716908884009582[/C][C]0.358454442004791[/C][/ROW]
[ROW][C]67[/C][C]0.622826595162709[/C][C]0.754346809674581[/C][C]0.377173404837291[/C][/ROW]
[ROW][C]68[/C][C]0.582459016329134[/C][C]0.835081967341731[/C][C]0.417540983670866[/C][/ROW]
[ROW][C]69[/C][C]0.538614593922247[/C][C]0.922770812155506[/C][C]0.461385406077753[/C][/ROW]
[ROW][C]70[/C][C]0.493373030362131[/C][C]0.986746060724261[/C][C]0.506626969637869[/C][/ROW]
[ROW][C]71[/C][C]0.480161933248131[/C][C]0.960323866496262[/C][C]0.519838066751869[/C][/ROW]
[ROW][C]72[/C][C]0.441881136380282[/C][C]0.883762272760565[/C][C]0.558118863619718[/C][/ROW]
[ROW][C]73[/C][C]0.396008932718499[/C][C]0.792017865436997[/C][C]0.603991067281501[/C][/ROW]
[ROW][C]74[/C][C]0.351996066104635[/C][C]0.703992132209269[/C][C]0.648003933895365[/C][/ROW]
[ROW][C]75[/C][C]0.330103346337968[/C][C]0.660206692675936[/C][C]0.669896653662032[/C][/ROW]
[ROW][C]76[/C][C]0.298114144707276[/C][C]0.596228289414553[/C][C]0.701885855292724[/C][/ROW]
[ROW][C]77[/C][C]0.260142828910523[/C][C]0.520285657821047[/C][C]0.739857171089477[/C][/ROW]
[ROW][C]78[/C][C]0.228372577919728[/C][C]0.456745155839457[/C][C]0.771627422080272[/C][/ROW]
[ROW][C]79[/C][C]0.249089014671352[/C][C]0.498178029342703[/C][C]0.750910985328648[/C][/ROW]
[ROW][C]80[/C][C]0.276591457832071[/C][C]0.553182915664141[/C][C]0.723408542167929[/C][/ROW]
[ROW][C]81[/C][C]0.240267110214552[/C][C]0.480534220429105[/C][C]0.759732889785448[/C][/ROW]
[ROW][C]82[/C][C]0.209886748895103[/C][C]0.419773497790207[/C][C]0.790113251104897[/C][/ROW]
[ROW][C]83[/C][C]0.178345936861481[/C][C]0.356691873722963[/C][C]0.821654063138519[/C][/ROW]
[ROW][C]84[/C][C]0.151316731261453[/C][C]0.302633462522907[/C][C]0.848683268738547[/C][/ROW]
[ROW][C]85[/C][C]0.213588208325276[/C][C]0.427176416650551[/C][C]0.786411791674724[/C][/ROW]
[ROW][C]86[/C][C]0.204619753819274[/C][C]0.409239507638547[/C][C]0.795380246180726[/C][/ROW]
[ROW][C]87[/C][C]0.173631986509011[/C][C]0.347263973018021[/C][C]0.82636801349099[/C][/ROW]
[ROW][C]88[/C][C]0.151653996035381[/C][C]0.303307992070763[/C][C]0.848346003964619[/C][/ROW]
[ROW][C]89[/C][C]0.190346589122399[/C][C]0.380693178244798[/C][C]0.8096534108776[/C][/ROW]
[ROW][C]90[/C][C]0.168753892179627[/C][C]0.337507784359253[/C][C]0.831246107820373[/C][/ROW]
[ROW][C]91[/C][C]0.143848350958472[/C][C]0.287696701916944[/C][C]0.856151649041528[/C][/ROW]
[ROW][C]92[/C][C]0.133358192427458[/C][C]0.266716384854915[/C][C]0.866641807572542[/C][/ROW]
[ROW][C]93[/C][C]0.111008270651269[/C][C]0.222016541302537[/C][C]0.888991729348731[/C][/ROW]
[ROW][C]94[/C][C]0.090557757874856[/C][C]0.181115515749712[/C][C]0.909442242125144[/C][/ROW]
[ROW][C]95[/C][C]0.0730196163365946[/C][C]0.146039232673189[/C][C]0.926980383663405[/C][/ROW]
[ROW][C]96[/C][C]0.0605897737760498[/C][C]0.121179547552100[/C][C]0.93941022622395[/C][/ROW]
[ROW][C]97[/C][C]0.109814718513314[/C][C]0.219629437026627[/C][C]0.890185281486686[/C][/ROW]
[ROW][C]98[/C][C]0.105899668837867[/C][C]0.211799337675733[/C][C]0.894100331162133[/C][/ROW]
[ROW][C]99[/C][C]0.0917918604779402[/C][C]0.183583720955880[/C][C]0.90820813952206[/C][/ROW]
[ROW][C]100[/C][C]0.0779875255136837[/C][C]0.155975051027367[/C][C]0.922012474486316[/C][/ROW]
[ROW][C]101[/C][C]0.0624280681206013[/C][C]0.124856136241203[/C][C]0.937571931879399[/C][/ROW]
[ROW][C]102[/C][C]0.0509949099823053[/C][C]0.101989819964611[/C][C]0.949005090017695[/C][/ROW]
[ROW][C]103[/C][C]0.0434154249037856[/C][C]0.0868308498075713[/C][C]0.956584575096214[/C][/ROW]
[ROW][C]104[/C][C]0.0656324612454417[/C][C]0.131264922490883[/C][C]0.934367538754558[/C][/ROW]
[ROW][C]105[/C][C]0.0639073224574633[/C][C]0.127814644914927[/C][C]0.936092677542537[/C][/ROW]
[ROW][C]106[/C][C]0.110664526182551[/C][C]0.221329052365101[/C][C]0.88933547381745[/C][/ROW]
[ROW][C]107[/C][C]0.148109991635173[/C][C]0.296219983270345[/C][C]0.851890008364827[/C][/ROW]
[ROW][C]108[/C][C]0.223783175166953[/C][C]0.447566350333906[/C][C]0.776216824833047[/C][/ROW]
[ROW][C]109[/C][C]0.260872571195525[/C][C]0.52174514239105[/C][C]0.739127428804475[/C][/ROW]
[ROW][C]110[/C][C]0.231433210459840[/C][C]0.462866420919679[/C][C]0.76856678954016[/C][/ROW]
[ROW][C]111[/C][C]0.196256043492041[/C][C]0.392512086984082[/C][C]0.803743956507959[/C][/ROW]
[ROW][C]112[/C][C]0.260528164086071[/C][C]0.521056328172143[/C][C]0.739471835913929[/C][/ROW]
[ROW][C]113[/C][C]0.257028246676857[/C][C]0.514056493353715[/C][C]0.742971753323143[/C][/ROW]
[ROW][C]114[/C][C]0.216060053045368[/C][C]0.432120106090735[/C][C]0.783939946954632[/C][/ROW]
[ROW][C]115[/C][C]0.201849222665184[/C][C]0.403698445330369[/C][C]0.798150777334816[/C][/ROW]
[ROW][C]116[/C][C]0.166820070124587[/C][C]0.333640140249174[/C][C]0.833179929875413[/C][/ROW]
[ROW][C]117[/C][C]0.136669183141036[/C][C]0.273338366282072[/C][C]0.863330816858964[/C][/ROW]
[ROW][C]118[/C][C]0.128557792940733[/C][C]0.257115585881466[/C][C]0.871442207059267[/C][/ROW]
[ROW][C]119[/C][C]0.103655230427275[/C][C]0.207310460854551[/C][C]0.896344769572725[/C][/ROW]
[ROW][C]120[/C][C]0.137778887918912[/C][C]0.275557775837824[/C][C]0.862221112081088[/C][/ROW]
[ROW][C]121[/C][C]0.156521824576953[/C][C]0.313043649153906[/C][C]0.843478175423047[/C][/ROW]
[ROW][C]122[/C][C]0.146219312161022[/C][C]0.292438624322045[/C][C]0.853780687838978[/C][/ROW]
[ROW][C]123[/C][C]0.117125508313735[/C][C]0.234251016627470[/C][C]0.882874491686265[/C][/ROW]
[ROW][C]124[/C][C]0.0906346272420705[/C][C]0.181269254484141[/C][C]0.90936537275793[/C][/ROW]
[ROW][C]125[/C][C]0.0761538226568265[/C][C]0.152307645313653[/C][C]0.923846177343174[/C][/ROW]
[ROW][C]126[/C][C]0.063984515154338[/C][C]0.127969030308676[/C][C]0.936015484845662[/C][/ROW]
[ROW][C]127[/C][C]0.0753998971270602[/C][C]0.150799794254120[/C][C]0.92460010287294[/C][/ROW]
[ROW][C]128[/C][C]0.0606175796693381[/C][C]0.121235159338676[/C][C]0.939382420330662[/C][/ROW]
[ROW][C]129[/C][C]0.0494705009076544[/C][C]0.0989410018153088[/C][C]0.950529499092346[/C][/ROW]
[ROW][C]130[/C][C]0.0687426906231696[/C][C]0.137485381246339[/C][C]0.93125730937683[/C][/ROW]
[ROW][C]131[/C][C]0.112673196913382[/C][C]0.225346393826763[/C][C]0.887326803086618[/C][/ROW]
[ROW][C]132[/C][C]0.0883788255175056[/C][C]0.176757651035011[/C][C]0.911621174482494[/C][/ROW]
[ROW][C]133[/C][C]0.067260709319296[/C][C]0.134521418638592[/C][C]0.932739290680704[/C][/ROW]
[ROW][C]134[/C][C]0.051258645737293[/C][C]0.102517291474586[/C][C]0.948741354262707[/C][/ROW]
[ROW][C]135[/C][C]0.0360902862929718[/C][C]0.0721805725859435[/C][C]0.963909713707028[/C][/ROW]
[ROW][C]136[/C][C]0.0329686824405001[/C][C]0.0659373648810003[/C][C]0.9670313175595[/C][/ROW]
[ROW][C]137[/C][C]0.0278336205519666[/C][C]0.0556672411039331[/C][C]0.972166379448034[/C][/ROW]
[ROW][C]138[/C][C]0.0213321460933189[/C][C]0.0426642921866377[/C][C]0.97866785390668[/C][/ROW]
[ROW][C]139[/C][C]0.0211494894050197[/C][C]0.0422989788100394[/C][C]0.97885051059498[/C][/ROW]
[ROW][C]140[/C][C]0.0191407149233276[/C][C]0.0382814298466551[/C][C]0.980859285076672[/C][/ROW]
[ROW][C]141[/C][C]0.0131993524584073[/C][C]0.0263987049168147[/C][C]0.986800647541593[/C][/ROW]
[ROW][C]142[/C][C]0.00860795809431559[/C][C]0.0172159161886312[/C][C]0.991392041905684[/C][/ROW]
[ROW][C]143[/C][C]0.0384674210084373[/C][C]0.0769348420168746[/C][C]0.961532578991563[/C][/ROW]
[ROW][C]144[/C][C]0.0217751930612494[/C][C]0.0435503861224988[/C][C]0.97822480693875[/C][/ROW]
[ROW][C]145[/C][C]0.170476380233895[/C][C]0.340952760467791[/C][C]0.829523619766105[/C][/ROW]
[ROW][C]146[/C][C]0.164705002218442[/C][C]0.329410004436884[/C][C]0.835294997781558[/C][/ROW]
[ROW][C]147[/C][C]0.0913393542934282[/C][C]0.182678708586856[/C][C]0.908660645706572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5869903871889910.8260192256220180.413009612811009
100.4944740233872470.9889480467744950.505525976612752
110.6720414613632180.6559170772735640.327958538636782
120.7928979641694860.4142040716610270.207102035830514
130.805276099632270.3894478007354590.194723900367729
140.7434304218892130.5131391562215740.256569578110787
150.6686436899615520.6627126200768950.331356310038448
160.6122245853885140.7755508292229720.387775414611486
170.5598199365934630.8803601268130730.440180063406537
180.6109074690410910.7781850619178180.389092530958909
190.5403620836565040.9192758326869910.459637916343496
200.5453959226733520.9092081546532960.454604077326648
210.948037101511310.1039257969773800.0519628984886898
220.941501563816050.1169968723679010.0584984361839504
230.9211772456461370.1576455087077260.078822754353863
240.897175504873330.2056489902533410.102824495126671
250.865798461971260.2684030760574810.134201538028740
260.8403762920708650.3192474158582700.159623707929135
270.8488024248445860.3023951503108280.151197575155414
280.8329457772625070.3341084454749850.167054222737493
290.7914405096101620.4171189807796760.208559490389838
300.8074434802975840.3851130394048320.192556519702416
310.7728873005012540.4542253989974920.227112699498746
320.8077881846341450.384423630731710.192211815365855
330.791988194212940.4160236115741180.208011805787059
340.7824202255885140.4351595488229730.217579774411487
350.7525453991537680.4949092016924630.247454600846232
360.7126837476000310.5746325047999380.287316252399969
370.6734563353776020.6530873292447970.326543664622399
380.6629936597473890.6740126805052220.337006340252611
390.614792825111880.7704143497762410.385207174888120
400.5636456713236230.8727086573527530.436354328676377
410.5136150075991070.9727699848017850.486384992400893
420.5680998028005350.863800394398930.431900197199465
430.5759107704878650.848178459024270.424089229512135
440.5354541242104630.9290917515790740.464545875789537
450.5829159712370580.8341680575258830.417084028762942
460.5689299841308810.8621400317382380.431070015869119
470.6098039088584680.7803921822830650.390196091141532
480.5801862068723870.8396275862552250.419813793127613
490.5351002552431770.9297994895136460.464899744756823
500.4943694843566150.988738968713230.505630515643385
510.5592030202303760.8815939595392480.440796979769624
520.5963996919802890.8072006160394220.403600308019711
530.5646001659555230.8707996680889530.435399834044477
540.538507348558890.922985302882220.46149265144111
550.5036672476465170.9926655047069660.496332752353483
560.4683614999539930.9367229999079870.531638500046007
570.4730989075796540.9461978151593080.526901092420346
580.4447629997545460.8895259995090910.555237000245454
590.7323971779364080.5352056441271840.267602822063592
600.800486849163230.3990263016735380.199513150836769
610.790014209102160.4199715817956790.209985790897839
620.7571989367451850.4856021265096290.242801063254815
630.7186773065180010.5626453869639970.281322693481999
640.681061776374250.63787644725150.31893822362575
650.6720756763762870.6558486472474260.327924323623713
660.641545557995210.7169088840095820.358454442004791
670.6228265951627090.7543468096745810.377173404837291
680.5824590163291340.8350819673417310.417540983670866
690.5386145939222470.9227708121555060.461385406077753
700.4933730303621310.9867460607242610.506626969637869
710.4801619332481310.9603238664962620.519838066751869
720.4418811363802820.8837622727605650.558118863619718
730.3960089327184990.7920178654369970.603991067281501
740.3519960661046350.7039921322092690.648003933895365
750.3301033463379680.6602066926759360.669896653662032
760.2981141447072760.5962282894145530.701885855292724
770.2601428289105230.5202856578210470.739857171089477
780.2283725779197280.4567451558394570.771627422080272
790.2490890146713520.4981780293427030.750910985328648
800.2765914578320710.5531829156641410.723408542167929
810.2402671102145520.4805342204291050.759732889785448
820.2098867488951030.4197734977902070.790113251104897
830.1783459368614810.3566918737229630.821654063138519
840.1513167312614530.3026334625229070.848683268738547
850.2135882083252760.4271764166505510.786411791674724
860.2046197538192740.4092395076385470.795380246180726
870.1736319865090110.3472639730180210.82636801349099
880.1516539960353810.3033079920707630.848346003964619
890.1903465891223990.3806931782447980.8096534108776
900.1687538921796270.3375077843592530.831246107820373
910.1438483509584720.2876967019169440.856151649041528
920.1333581924274580.2667163848549150.866641807572542
930.1110082706512690.2220165413025370.888991729348731
940.0905577578748560.1811155157497120.909442242125144
950.07301961633659460.1460392326731890.926980383663405
960.06058977377604980.1211795475521000.93941022622395
970.1098147185133140.2196294370266270.890185281486686
980.1058996688378670.2117993376757330.894100331162133
990.09179186047794020.1835837209558800.90820813952206
1000.07798752551368370.1559750510273670.922012474486316
1010.06242806812060130.1248561362412030.937571931879399
1020.05099490998230530.1019898199646110.949005090017695
1030.04341542490378560.08683084980757130.956584575096214
1040.06563246124544170.1312649224908830.934367538754558
1050.06390732245746330.1278146449149270.936092677542537
1060.1106645261825510.2213290523651010.88933547381745
1070.1481099916351730.2962199832703450.851890008364827
1080.2237831751669530.4475663503339060.776216824833047
1090.2608725711955250.521745142391050.739127428804475
1100.2314332104598400.4628664209196790.76856678954016
1110.1962560434920410.3925120869840820.803743956507959
1120.2605281640860710.5210563281721430.739471835913929
1130.2570282466768570.5140564933537150.742971753323143
1140.2160600530453680.4321201060907350.783939946954632
1150.2018492226651840.4036984453303690.798150777334816
1160.1668200701245870.3336401402491740.833179929875413
1170.1366691831410360.2733383662820720.863330816858964
1180.1285577929407330.2571155858814660.871442207059267
1190.1036552304272750.2073104608545510.896344769572725
1200.1377788879189120.2755577758378240.862221112081088
1210.1565218245769530.3130436491539060.843478175423047
1220.1462193121610220.2924386243220450.853780687838978
1230.1171255083137350.2342510166274700.882874491686265
1240.09063462724207050.1812692544841410.90936537275793
1250.07615382265682650.1523076453136530.923846177343174
1260.0639845151543380.1279690303086760.936015484845662
1270.07539989712706020.1507997942541200.92460010287294
1280.06061757966933810.1212351593386760.939382420330662
1290.04947050090765440.09894100181530880.950529499092346
1300.06874269062316960.1374853812463390.93125730937683
1310.1126731969133820.2253463938267630.887326803086618
1320.08837882551750560.1767576510350110.911621174482494
1330.0672607093192960.1345214186385920.932739290680704
1340.0512586457372930.1025172914745860.948741354262707
1350.03609028629297180.07218057258594350.963909713707028
1360.03296868244050010.06593736488100030.9670313175595
1370.02783362055196660.05566724110393310.972166379448034
1380.02133214609331890.04266429218663770.97866785390668
1390.02114948940501970.04229897881003940.97885051059498
1400.01914071492332760.03828142984665510.980859285076672
1410.01319935245840730.02639870491681470.986800647541593
1420.008607958094315590.01721591618863120.991392041905684
1430.03846742100843730.07693484201687460.961532578991563
1440.02177519306124940.04355038612249880.97822480693875
1450.1704763802338950.3409527604677910.829523619766105
1460.1647050022184420.3294100044368840.835294997781558
1470.09133935429342820.1826787085868560.908660645706572






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.0431654676258993OK
10% type I error level120.0863309352517986OK

\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 & 6 & 0.0431654676258993 & OK \tabularnewline
10% type I error level & 12 & 0.0863309352517986 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99268&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]6[/C][C]0.0431654676258993[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.0863309352517986[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99268&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99268&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.0431654676258993OK
10% type I error level120.0863309352517986OK


Figures (Output of Computation)

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

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