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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 16:16:23 +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/t1290529236g94n5vc7q2j8dh7.htm/, Retrieved Fri, 29 Mar 2024 12:14:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99380, Retrieved Fri, 29 Mar 2024 12:14:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-   PD    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:07:24] [87d60b8864dc39f7ed759c345edfb471]
- R PD      [Multiple Regression] [W7-model3] [2010-11-21 20:40:05] [48146708a479232c43a8f6e52fbf83b4]
-    D          [Multiple Regression] [workshop 7.3] [2010-11-23 16:16:23] [6e52d1bada9435d33ddf990b22ee4b00] [Current]
-                 [Multiple Regression] [Workshop 7: model...] [2010-11-24 02:15:45] [b20a509e36241371274681d9edf773da]
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Dataseries X:
9	4	2	5	4	3
9	4	2	4	3	2
9	5	4	4	2	2
9	3	2	4	2	2
9	4	3	2	2	2
9	3	4	5	2	2
10	4	3	5	3	2
10	3	3	4	2	1
10	2	3	3	1	2
10	4	2	4	2	2
10	2	4	4	2	2
10	2	3	3	2	2
10	1	3	3	2	2
10	4	4	4	2	2
10	4	4	5	1	1
10	2	3	4	2	2
10	2	3	2	2	1
10	3	3	4	3	2
10	3	4	4	4	2
10	3	2	4	4	2
10	4	5	4	4	4
10	3	4	4	4	2
10	2	2	4	4	4
10	2	3	5	2	2
10	4	4	4	2	2
10	4	4	4	4	2
10	3	3	4	2	2
10	4	4	4	3	2
10	2	4	4	2	2
10	4	1	4	4	2
10	4	4	4	3	3
10	5	5	2	4	2
10	5	2	4	2	2
10	4	4	4	2	2
10	4	3	5	4	3
10	4	2	5	5	4
10	2	4	4	2	1
10	4	5	3	4	2
10	4	4	4	4	3
10	4	4	5	5	3
10	3	4	4	3	2
10	2	3	4	2	2
10	3	4	5	3	2
10	4	2	4	2	2
10	3	2	5	1	2
10	2	4	4	2	2
10	4	2	4	4	4
10	4	4	4	4	4
10	3	4	3	4	2
10	4	1	4	4	3
10	3	4	4	2	2
10	4	2	4	2	2
10	2	1	2	1	1
10	4	4	3	4	3
10	4	3	5	2	4
10	4	2	4	4	2
10	4	4	4	2	2
10	3	3	5	2	1
10	1	2	3	1	2
10	3	2	5	2	2
10	3	3	4	2	2
10	4	2	5	2	2
10	2	1	4	2	2
10	3	3	4	1	1
10	5	2	5	5	2
10	4	3	4	3	3
10	4	3	4	2	2
10	3	3	5	1	1
10	4	2	4	2	2
10	2	3	3	4	4
10	3	2	4	2	2
10	4	4	5	5	3
10	3	4	5	4	4
10	4	4	5	3	2
10	4	2	4	2	4
10	3	3	4	2	1
10	3	4	5	2	2
10	2	3	5	2	2
10	4	4	4	4	4
10	3	2	5	2	3
10	2	3	3	2	2
10	2	3	4	4	2
10	3	4	4	4	2
10	2	2	4	2	3
10	2	4	4	2	2
10	4	2	4	3	2
10	4	2	5	2	1
10	4	4	4	4	2
10	2	3	4	2	2
10	2	4	4	4	4
10	4	2	5	1	1
10	2	2	3	2	2
10	3	3	3	3	2
10	3	3	5	2	2
10	5	5	5	4	4
10	3	2	4	2	4
10	4	3	4	3	3
10	3	4	4	2	2
10	2	3	4	2	3
10	4	4	4	2	2
10	3	3	4	2	1
10	3	3	4	2	2
10	3	2	4	2	2
10	4	3	5	3	2
10	1	2	2	2	4
10	3	3	4	2	2
10	2	2	2	4	3
10	3	4	4	3	3
10	2	2	5	2	2
10	2	4	3	1	1
10	2	4	4	2	4
10	4	1	3	2	2
10	5	5	4	5	2
10	5	2	4	1	1
10	3	3	4	2	2
10	4	4	2	2	2
10	4	1	1	2	2
10	3	5	4	2	3
10	2	3	3	2	1
10	4	3	4	5	3
10	2	3	3	2	2
10	3	3	3	2	3
10	2	2	5	2	1
10	2	2	4	2	2
10	2	4	3	2	3
10	4	4	4	2	1
10	4	3	4	3	2
10	4	3	4	2	2
10	4	3	4	4	3
10	3	4	3	4	2
10	2	3	4	2	2
10	4	4	4	4	2
10	3	4	4	4	2
10	2	2	4	2	2
10	4	4	4	4	2
10	3	2	3	3	3
10	3	4	4	2	2
10	3	3	4	2	2
10	3	3	2	3	3
10	3	2	2	4	2
10	4	2	4	4	2
10	5	5	2	5	1
10	2	2	4	2	1
10	4	3	4	3	4
10	3	3	3	5	3
10	3	3	2	2	3
10	1	3	2	2	2
10	2	4	4	2	2
10	4	4	3	2	2
10	4	4	4	2	4
10	5	4	4	5	3
10	2	4	2	3	3
10	4	5	5	2	2
10	3	3	4	2	2
10	2	3	4	3	2
10	4	4	4	3	3
10	2	4	3	2	5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 16 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
YT[t] = + 8.56713904540317 -0.735371589104438T1[t] + 0.0759378528567064X1[t] + 0.251758435553541X2[t] + 0.366360559218664X3[t] -0.117608217425018X4[t] + 0.000361144076985856t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
YT[t] =  +  8.56713904540317 -0.735371589104438T1[t] +  0.0759378528567064X1[t] +  0.251758435553541X2[t] +  0.366360559218664X3[t] -0.117608217425018X4[t] +  0.000361144076985856t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]YT[t] =  +  8.56713904540317 -0.735371589104438T1[t] +  0.0759378528567064X1[t] +  0.251758435553541X2[t] +  0.366360559218664X3[t] -0.117608217425018X4[t] +  0.000361144076985856t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
YT[t] = + 8.56713904540317 -0.735371589104438T1[t] + 0.0759378528567064X1[t] + 0.251758435553541X2[t] + 0.366360559218664X3[t] -0.117608217425018X4[t] + 0.000361144076985856t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.567139045403173.755632.28110.0239480.011974
T1-0.7353715891044380.380985-1.93020.0554710.027735
X10.07593785285670640.0733141.03580.3019650.150982
X20.2517584355535410.084422.98220.0033410.001671
X30.3663605592186640.0720635.08391e-061e-06
X4-0.1176082174250180.089717-1.31090.1919020.095951
t0.0003611440769858560.0016630.21720.8283350.414167

\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) & 8.56713904540317 & 3.75563 & 2.2811 & 0.023948 & 0.011974 \tabularnewline
T1 & -0.735371589104438 & 0.380985 & -1.9302 & 0.055471 & 0.027735 \tabularnewline
X1 & 0.0759378528567064 & 0.073314 & 1.0358 & 0.301965 & 0.150982 \tabularnewline
X2 & 0.251758435553541 & 0.08442 & 2.9822 & 0.003341 & 0.001671 \tabularnewline
X3 & 0.366360559218664 & 0.072063 & 5.0839 & 1e-06 & 1e-06 \tabularnewline
X4 & -0.117608217425018 & 0.089717 & -1.3109 & 0.191902 & 0.095951 \tabularnewline
t & 0.000361144076985856 & 0.001663 & 0.2172 & 0.828335 & 0.414167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&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]8.56713904540317[/C][C]3.75563[/C][C]2.2811[/C][C]0.023948[/C][C]0.011974[/C][/ROW]
[ROW][C]T1[/C][C]-0.735371589104438[/C][C]0.380985[/C][C]-1.9302[/C][C]0.055471[/C][C]0.027735[/C][/ROW]
[ROW][C]X1[/C][C]0.0759378528567064[/C][C]0.073314[/C][C]1.0358[/C][C]0.301965[/C][C]0.150982[/C][/ROW]
[ROW][C]X2[/C][C]0.251758435553541[/C][C]0.08442[/C][C]2.9822[/C][C]0.003341[/C][C]0.001671[/C][/ROW]
[ROW][C]X3[/C][C]0.366360559218664[/C][C]0.072063[/C][C]5.0839[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]X4[/C][C]-0.117608217425018[/C][C]0.089717[/C][C]-1.3109[/C][C]0.191902[/C][C]0.095951[/C][/ROW]
[ROW][C]t[/C][C]0.000361144076985856[/C][C]0.001663[/C][C]0.2172[/C][C]0.828335[/C][C]0.414167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.567139045403173.755632.28110.0239480.011974
T1-0.7353715891044380.380985-1.93020.0554710.027735
X10.07593785285670640.0733141.03580.3019650.150982
X20.2517584355535410.084422.98220.0033410.001671
X30.3663605592186640.0720635.08391e-061e-06
X4-0.1176082174250180.089717-1.31090.1919020.095951
t0.0003611440769858560.0016630.21720.8283350.414167







Multiple Linear Regression - Regression Statistics
Multiple R0.478480661179188
R-squared0.228943743122473
Adjusted R-squared0.198101492847372
F-TEST (value)7.42305574594532
F-TEST (DF numerator)6
F-TEST (DF denominator)150
p-value5.69652836457379e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.861155699499398
Sum Squared Residuals111.238370817045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.478480661179188 \tabularnewline
R-squared & 0.228943743122473 \tabularnewline
Adjusted R-squared & 0.198101492847372 \tabularnewline
F-TEST (value) & 7.42305574594532 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 5.69652836457379e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.861155699499398 \tabularnewline
Sum Squared Residuals & 111.238370817045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.478480661179188[/C][/ROW]
[ROW][C]R-squared[/C][C]0.228943743122473[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.198101492847372[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.42305574594532[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]5.69652836457379e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.861155699499398[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111.238370817045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.478480661179188
R-squared0.228943743122473
Adjusted R-squared0.198101492847372
F-TEST (value)7.42305574594532
F-TEST (DF numerator)6
F-TEST (DF denominator)150
p-value5.69652836457379e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.861155699499398
Sum Squared Residuals111.238370817045







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.47244135562101-0.472441355621013
243.972291722350760.0277082776492371
353.758168012922481.24183198707752
433.60665345128606-0.606653451286056
543.179435577112670.820564422887334
634.01100988070698-1.01100988070698
743.566422142041480.433577857958518
833.06627250877128-0.0662725087712795
922.33090644065104-0.330906440651041
1042.873448726643531.12655127335647
1123.02568557643392-1.02568557643392
1222.69835043210066-0.698350432100663
1312.69871157617765-1.69871157617765
1443.026769008664880.973230991335118
1543.030136246501760.969863753498236
1622.95155344396215-0.951553443962148
1722.56600593435707-0.566005934357069
1833.31863629133478-0.318636291334784
1933.76129584748714-0.76129584748714
2033.60978128585071-0.609781285850714
2143.602739553647780.397260446352219
2233.7623792797181-0.762379279718098
2323.37564828323163-1.37564828323163
2423.20620103213158-1.20620103213158
2543.030741593511730.969258406488273
2643.763823856026040.236176143973959
2732.955526028808990.0444739711910075
2843.398185584961350.601814415038651
2923.03218616981967-1.03218616981967
3043.537454873763870.462545126236133
3143.281660799767290.718339200232712
3253.338411702237581.66158829776242
3352.88175504041422.1182449595858
3443.03399189020460.9660081097954
3543.825286517990730.174713482009269
3643.998462151004660.00153784899534324
3723.15268353986058-1.15268353986058
3843.592337002253040.407662997746964
3943.650910511601840.349089488398161
4044.26939065045103-0.269390650451030
4133.40288045796216-0.402880457962165
4222.96094318996378-0.96094318996378
4333.65536118166968-0.655361181669678
4442.885727625261051.11427237473895
4532.771486645672910.228513354327091
4623.03832561912843-1.03832561912843
4743.384315741079300.615684258920705
4843.536552590869690.463447409130307
4933.52037173424317-0.520371734243174
5043.427069537878570.572930462121435
5133.04013133951336-0.040131339513359
5242.888616777876931.11138322212307
5322.06077085619648-0.0607708561964836
5443.404569237203090.595430762796915
5542.98218006366811.01781993633190
5643.622782472622200.377217527377796
5743.042298203975270.957701796024726
5833.33608814817411-0.336088148174114
5912.27302579164363-1.27302579164363
6033.14326436604636-0.143264366046361
6132.967804927426510.0321950725734884
6243.143986654200330.856013345799667
6322.81665150986707-0.816651509867071
6432.720136017863820.279863982136177
6554.244151764087280.755848235912717
6643.218362989605090.781637010394913
6742.969971791888431.03002820811157
6832.973339029725310.0266609702746920
6942.894756227185691.10524377281431
7023.21680147215313-1.21680147215313
7132.895478515339660.104521484660336
7244.28094726091458-0.280947260914578
7333.79733962834788-0.797339628347881
7443.666556648056240.333443351943761
7542.661706656797571.33829334320243
7633.09083030600632-0.0908303060063177
7733.30127952106853-0.301279521068533
7823.22570281228881-1.22570281228881
7943.547748057256250.452251942743745
8033.03287903016106-0.0328790301610601
8122.72326937341269-0.723269373412687
8223.70811007148054-1.70811007148054
8333.78440906841423-0.784409068414235
8422.78256517091546-0.782565170915462
8523.05241023813088-1.05241023813088
8643.267256235713120.732743764286884
8743.270623473550.729376526450002
8843.786214788799160.213785211200836
8922.97791696158212-0.977916961582115
9023.5517206421031-1.5517206421031
9142.905707490639281.09429250936072
9222.65130410540283-0.651304105402826
9333.09396366155518-0.0939636615551816
9433.23148111752059-0.231481117520586
9553.881222650898281.11877734910172
9632.669290682414270.330709317585726
9743.229558455991650.770441544008352
9833.05710511113169-0.0571051111316942
9922.86392018492696-0.863920184926956
10043.057827399285670.942172600714334
10133.09985890793096-0.099858907930964
10232.982611834582930.0173881654170684
10332.907035125803210.0929648741967886
10443.601453117509110.398546882490891
10512.16902410800006-1.16902410800006
10632.984056410890870.0159435891091249
10723.02007573201638-1.02007573201638
10833.3094688936952-0.309468893695199
10923.16096042581867-1.16096042581867
11022.56092806270834-0.560928062708337
11122.82658354928247-0.826583549282474
11242.582589134085841.41741086591416
11354.237541802799180.76245819720082
11452.662255368856412.33774463114359
11532.987306707583750.0126932924162523
11642.560088833410361.43991116658964
11742.080877983363681.91912201663632
11833.0226576281031-0.0226576281030992
11922.85460106576317-0.854601065763168
12043.970585888199650.0294141118003487
12122.73771513649212-0.737715136492121
12232.620468063144090.379531936855911
12323.28362466032149-1.28362466032149
12422.91461915141991-0.914619151419914
12522.69748934823175-0.697489348231753
12643.184825362712320.815174637287684
12743.358000995726240.641999004273758
12842.992001580584561.00799841941544
12943.607475625673860.39252437432614
13033.54962440447903-0.549624404479028
13122.99308501281552-0.993085012815521
13243.802105128186540.197894871813458
13333.80246627226353-0.802466272263528
13422.91823059218977-0.918230592189773
13543.80318856041750.196811439582501
13632.915946786583850.0840532134161509
13733.07118973013414-0.0711897301341425
13832.995613021354420.0043869786455776
13932.741209636117970.258790363882029
14033.14960170398193-0.149601703981933
14143.6534797191660.346520280833998
14253.862106327349711.13789367265029
14323.03908910630766-1.03908910630766
14443.128924010184970.871075989815035
14533.72785605457076-0.727856054570756
14632.377377085438210.622622914561792
14712.49534644694021-1.49534644694021
14823.07516231498099-1.07516231498099
14942.823765023504431.17623497649557
15042.840668168284921.15933183171508
15154.057719207442920.942280792557081
15222.82184236197549-0.821842361975493
15343.404664323776160.595335676223836
15433.00139132658620-0.00139132658619608
15523.36811302988185-1.36811302988185
15643.326803809390520.67319619060948
15722.47382952384526-0.473829523845264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.47244135562101 & -0.472441355621013 \tabularnewline
2 & 4 & 3.97229172235076 & 0.0277082776492371 \tabularnewline
3 & 5 & 3.75816801292248 & 1.24183198707752 \tabularnewline
4 & 3 & 3.60665345128606 & -0.606653451286056 \tabularnewline
5 & 4 & 3.17943557711267 & 0.820564422887334 \tabularnewline
6 & 3 & 4.01100988070698 & -1.01100988070698 \tabularnewline
7 & 4 & 3.56642214204148 & 0.433577857958518 \tabularnewline
8 & 3 & 3.06627250877128 & -0.0662725087712795 \tabularnewline
9 & 2 & 2.33090644065104 & -0.330906440651041 \tabularnewline
10 & 4 & 2.87344872664353 & 1.12655127335647 \tabularnewline
11 & 2 & 3.02568557643392 & -1.02568557643392 \tabularnewline
12 & 2 & 2.69835043210066 & -0.698350432100663 \tabularnewline
13 & 1 & 2.69871157617765 & -1.69871157617765 \tabularnewline
14 & 4 & 3.02676900866488 & 0.973230991335118 \tabularnewline
15 & 4 & 3.03013624650176 & 0.969863753498236 \tabularnewline
16 & 2 & 2.95155344396215 & -0.951553443962148 \tabularnewline
17 & 2 & 2.56600593435707 & -0.566005934357069 \tabularnewline
18 & 3 & 3.31863629133478 & -0.318636291334784 \tabularnewline
19 & 3 & 3.76129584748714 & -0.76129584748714 \tabularnewline
20 & 3 & 3.60978128585071 & -0.609781285850714 \tabularnewline
21 & 4 & 3.60273955364778 & 0.397260446352219 \tabularnewline
22 & 3 & 3.7623792797181 & -0.762379279718098 \tabularnewline
23 & 2 & 3.37564828323163 & -1.37564828323163 \tabularnewline
24 & 2 & 3.20620103213158 & -1.20620103213158 \tabularnewline
25 & 4 & 3.03074159351173 & 0.969258406488273 \tabularnewline
26 & 4 & 3.76382385602604 & 0.236176143973959 \tabularnewline
27 & 3 & 2.95552602880899 & 0.0444739711910075 \tabularnewline
28 & 4 & 3.39818558496135 & 0.601814415038651 \tabularnewline
29 & 2 & 3.03218616981967 & -1.03218616981967 \tabularnewline
30 & 4 & 3.53745487376387 & 0.462545126236133 \tabularnewline
31 & 4 & 3.28166079976729 & 0.718339200232712 \tabularnewline
32 & 5 & 3.33841170223758 & 1.66158829776242 \tabularnewline
33 & 5 & 2.8817550404142 & 2.1182449595858 \tabularnewline
34 & 4 & 3.0339918902046 & 0.9660081097954 \tabularnewline
35 & 4 & 3.82528651799073 & 0.174713482009269 \tabularnewline
36 & 4 & 3.99846215100466 & 0.00153784899534324 \tabularnewline
37 & 2 & 3.15268353986058 & -1.15268353986058 \tabularnewline
38 & 4 & 3.59233700225304 & 0.407662997746964 \tabularnewline
39 & 4 & 3.65091051160184 & 0.349089488398161 \tabularnewline
40 & 4 & 4.26939065045103 & -0.269390650451030 \tabularnewline
41 & 3 & 3.40288045796216 & -0.402880457962165 \tabularnewline
42 & 2 & 2.96094318996378 & -0.96094318996378 \tabularnewline
43 & 3 & 3.65536118166968 & -0.655361181669678 \tabularnewline
44 & 4 & 2.88572762526105 & 1.11427237473895 \tabularnewline
45 & 3 & 2.77148664567291 & 0.228513354327091 \tabularnewline
46 & 2 & 3.03832561912843 & -1.03832561912843 \tabularnewline
47 & 4 & 3.38431574107930 & 0.615684258920705 \tabularnewline
48 & 4 & 3.53655259086969 & 0.463447409130307 \tabularnewline
49 & 3 & 3.52037173424317 & -0.520371734243174 \tabularnewline
50 & 4 & 3.42706953787857 & 0.572930462121435 \tabularnewline
51 & 3 & 3.04013133951336 & -0.040131339513359 \tabularnewline
52 & 4 & 2.88861677787693 & 1.11138322212307 \tabularnewline
53 & 2 & 2.06077085619648 & -0.0607708561964836 \tabularnewline
54 & 4 & 3.40456923720309 & 0.595430762796915 \tabularnewline
55 & 4 & 2.9821800636681 & 1.01781993633190 \tabularnewline
56 & 4 & 3.62278247262220 & 0.377217527377796 \tabularnewline
57 & 4 & 3.04229820397527 & 0.957701796024726 \tabularnewline
58 & 3 & 3.33608814817411 & -0.336088148174114 \tabularnewline
59 & 1 & 2.27302579164363 & -1.27302579164363 \tabularnewline
60 & 3 & 3.14326436604636 & -0.143264366046361 \tabularnewline
61 & 3 & 2.96780492742651 & 0.0321950725734884 \tabularnewline
62 & 4 & 3.14398665420033 & 0.856013345799667 \tabularnewline
63 & 2 & 2.81665150986707 & -0.816651509867071 \tabularnewline
64 & 3 & 2.72013601786382 & 0.279863982136177 \tabularnewline
65 & 5 & 4.24415176408728 & 0.755848235912717 \tabularnewline
66 & 4 & 3.21836298960509 & 0.781637010394913 \tabularnewline
67 & 4 & 2.96997179188843 & 1.03002820811157 \tabularnewline
68 & 3 & 2.97333902972531 & 0.0266609702746920 \tabularnewline
69 & 4 & 2.89475622718569 & 1.10524377281431 \tabularnewline
70 & 2 & 3.21680147215313 & -1.21680147215313 \tabularnewline
71 & 3 & 2.89547851533966 & 0.104521484660336 \tabularnewline
72 & 4 & 4.28094726091458 & -0.280947260914578 \tabularnewline
73 & 3 & 3.79733962834788 & -0.797339628347881 \tabularnewline
74 & 4 & 3.66655664805624 & 0.333443351943761 \tabularnewline
75 & 4 & 2.66170665679757 & 1.33829334320243 \tabularnewline
76 & 3 & 3.09083030600632 & -0.0908303060063177 \tabularnewline
77 & 3 & 3.30127952106853 & -0.301279521068533 \tabularnewline
78 & 2 & 3.22570281228881 & -1.22570281228881 \tabularnewline
79 & 4 & 3.54774805725625 & 0.452251942743745 \tabularnewline
80 & 3 & 3.03287903016106 & -0.0328790301610601 \tabularnewline
81 & 2 & 2.72326937341269 & -0.723269373412687 \tabularnewline
82 & 2 & 3.70811007148054 & -1.70811007148054 \tabularnewline
83 & 3 & 3.78440906841423 & -0.784409068414235 \tabularnewline
84 & 2 & 2.78256517091546 & -0.782565170915462 \tabularnewline
85 & 2 & 3.05241023813088 & -1.05241023813088 \tabularnewline
86 & 4 & 3.26725623571312 & 0.732743764286884 \tabularnewline
87 & 4 & 3.27062347355 & 0.729376526450002 \tabularnewline
88 & 4 & 3.78621478879916 & 0.213785211200836 \tabularnewline
89 & 2 & 2.97791696158212 & -0.977916961582115 \tabularnewline
90 & 2 & 3.5517206421031 & -1.5517206421031 \tabularnewline
91 & 4 & 2.90570749063928 & 1.09429250936072 \tabularnewline
92 & 2 & 2.65130410540283 & -0.651304105402826 \tabularnewline
93 & 3 & 3.09396366155518 & -0.0939636615551816 \tabularnewline
94 & 3 & 3.23148111752059 & -0.231481117520586 \tabularnewline
95 & 5 & 3.88122265089828 & 1.11877734910172 \tabularnewline
96 & 3 & 2.66929068241427 & 0.330709317585726 \tabularnewline
97 & 4 & 3.22955845599165 & 0.770441544008352 \tabularnewline
98 & 3 & 3.05710511113169 & -0.0571051111316942 \tabularnewline
99 & 2 & 2.86392018492696 & -0.863920184926956 \tabularnewline
100 & 4 & 3.05782739928567 & 0.942172600714334 \tabularnewline
101 & 3 & 3.09985890793096 & -0.099858907930964 \tabularnewline
102 & 3 & 2.98261183458293 & 0.0173881654170684 \tabularnewline
103 & 3 & 2.90703512580321 & 0.0929648741967886 \tabularnewline
104 & 4 & 3.60145311750911 & 0.398546882490891 \tabularnewline
105 & 1 & 2.16902410800006 & -1.16902410800006 \tabularnewline
106 & 3 & 2.98405641089087 & 0.0159435891091249 \tabularnewline
107 & 2 & 3.02007573201638 & -1.02007573201638 \tabularnewline
108 & 3 & 3.3094688936952 & -0.309468893695199 \tabularnewline
109 & 2 & 3.16096042581867 & -1.16096042581867 \tabularnewline
110 & 2 & 2.56092806270834 & -0.560928062708337 \tabularnewline
111 & 2 & 2.82658354928247 & -0.826583549282474 \tabularnewline
112 & 4 & 2.58258913408584 & 1.41741086591416 \tabularnewline
113 & 5 & 4.23754180279918 & 0.76245819720082 \tabularnewline
114 & 5 & 2.66225536885641 & 2.33774463114359 \tabularnewline
115 & 3 & 2.98730670758375 & 0.0126932924162523 \tabularnewline
116 & 4 & 2.56008883341036 & 1.43991116658964 \tabularnewline
117 & 4 & 2.08087798336368 & 1.91912201663632 \tabularnewline
118 & 3 & 3.0226576281031 & -0.0226576281030992 \tabularnewline
119 & 2 & 2.85460106576317 & -0.854601065763168 \tabularnewline
120 & 4 & 3.97058588819965 & 0.0294141118003487 \tabularnewline
121 & 2 & 2.73771513649212 & -0.737715136492121 \tabularnewline
122 & 3 & 2.62046806314409 & 0.379531936855911 \tabularnewline
123 & 2 & 3.28362466032149 & -1.28362466032149 \tabularnewline
124 & 2 & 2.91461915141991 & -0.914619151419914 \tabularnewline
125 & 2 & 2.69748934823175 & -0.697489348231753 \tabularnewline
126 & 4 & 3.18482536271232 & 0.815174637287684 \tabularnewline
127 & 4 & 3.35800099572624 & 0.641999004273758 \tabularnewline
128 & 4 & 2.99200158058456 & 1.00799841941544 \tabularnewline
129 & 4 & 3.60747562567386 & 0.39252437432614 \tabularnewline
130 & 3 & 3.54962440447903 & -0.549624404479028 \tabularnewline
131 & 2 & 2.99308501281552 & -0.993085012815521 \tabularnewline
132 & 4 & 3.80210512818654 & 0.197894871813458 \tabularnewline
133 & 3 & 3.80246627226353 & -0.802466272263528 \tabularnewline
134 & 2 & 2.91823059218977 & -0.918230592189773 \tabularnewline
135 & 4 & 3.8031885604175 & 0.196811439582501 \tabularnewline
136 & 3 & 2.91594678658385 & 0.0840532134161509 \tabularnewline
137 & 3 & 3.07118973013414 & -0.0711897301341425 \tabularnewline
138 & 3 & 2.99561302135442 & 0.0043869786455776 \tabularnewline
139 & 3 & 2.74120963611797 & 0.258790363882029 \tabularnewline
140 & 3 & 3.14960170398193 & -0.149601703981933 \tabularnewline
141 & 4 & 3.653479719166 & 0.346520280833998 \tabularnewline
142 & 5 & 3.86210632734971 & 1.13789367265029 \tabularnewline
143 & 2 & 3.03908910630766 & -1.03908910630766 \tabularnewline
144 & 4 & 3.12892401018497 & 0.871075989815035 \tabularnewline
145 & 3 & 3.72785605457076 & -0.727856054570756 \tabularnewline
146 & 3 & 2.37737708543821 & 0.622622914561792 \tabularnewline
147 & 1 & 2.49534644694021 & -1.49534644694021 \tabularnewline
148 & 2 & 3.07516231498099 & -1.07516231498099 \tabularnewline
149 & 4 & 2.82376502350443 & 1.17623497649557 \tabularnewline
150 & 4 & 2.84066816828492 & 1.15933183171508 \tabularnewline
151 & 5 & 4.05771920744292 & 0.942280792557081 \tabularnewline
152 & 2 & 2.82184236197549 & -0.821842361975493 \tabularnewline
153 & 4 & 3.40466432377616 & 0.595335676223836 \tabularnewline
154 & 3 & 3.00139132658620 & -0.00139132658619608 \tabularnewline
155 & 2 & 3.36811302988185 & -1.36811302988185 \tabularnewline
156 & 4 & 3.32680380939052 & 0.67319619060948 \tabularnewline
157 & 2 & 2.47382952384526 & -0.473829523845264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&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]4[/C][C]4.47244135562101[/C][C]-0.472441355621013[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]3.97229172235076[/C][C]0.0277082776492371[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]3.75816801292248[/C][C]1.24183198707752[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]3.60665345128606[/C][C]-0.606653451286056[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]3.17943557711267[/C][C]0.820564422887334[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]4.01100988070698[/C][C]-1.01100988070698[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.56642214204148[/C][C]0.433577857958518[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]3.06627250877128[/C][C]-0.0662725087712795[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]2.33090644065104[/C][C]-0.330906440651041[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]2.87344872664353[/C][C]1.12655127335647[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]3.02568557643392[/C][C]-1.02568557643392[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]2.69835043210066[/C][C]-0.698350432100663[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]2.69871157617765[/C][C]-1.69871157617765[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.02676900866488[/C][C]0.973230991335118[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.03013624650176[/C][C]0.969863753498236[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]2.95155344396215[/C][C]-0.951553443962148[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.56600593435707[/C][C]-0.566005934357069[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]3.31863629133478[/C][C]-0.318636291334784[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]3.76129584748714[/C][C]-0.76129584748714[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]3.60978128585071[/C][C]-0.609781285850714[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.60273955364778[/C][C]0.397260446352219[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]3.7623792797181[/C][C]-0.762379279718098[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]3.37564828323163[/C][C]-1.37564828323163[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]3.20620103213158[/C][C]-1.20620103213158[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]3.03074159351173[/C][C]0.969258406488273[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.76382385602604[/C][C]0.236176143973959[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]2.95552602880899[/C][C]0.0444739711910075[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.39818558496135[/C][C]0.601814415038651[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]3.03218616981967[/C][C]-1.03218616981967[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.53745487376387[/C][C]0.462545126236133[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.28166079976729[/C][C]0.718339200232712[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]3.33841170223758[/C][C]1.66158829776242[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]2.8817550404142[/C][C]2.1182449595858[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.0339918902046[/C][C]0.9660081097954[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.82528651799073[/C][C]0.174713482009269[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.99846215100466[/C][C]0.00153784899534324[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]3.15268353986058[/C][C]-1.15268353986058[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]3.59233700225304[/C][C]0.407662997746964[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.65091051160184[/C][C]0.349089488398161[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.26939065045103[/C][C]-0.269390650451030[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]3.40288045796216[/C][C]-0.402880457962165[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]2.96094318996378[/C][C]-0.96094318996378[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.65536118166968[/C][C]-0.655361181669678[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]2.88572762526105[/C][C]1.11427237473895[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]2.77148664567291[/C][C]0.228513354327091[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]3.03832561912843[/C][C]-1.03832561912843[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.38431574107930[/C][C]0.615684258920705[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.53655259086969[/C][C]0.463447409130307[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]3.52037173424317[/C][C]-0.520371734243174[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]3.42706953787857[/C][C]0.572930462121435[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]3.04013133951336[/C][C]-0.040131339513359[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]2.88861677787693[/C][C]1.11138322212307[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]2.06077085619648[/C][C]-0.0607708561964836[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.40456923720309[/C][C]0.595430762796915[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]2.9821800636681[/C][C]1.01781993633190[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.62278247262220[/C][C]0.377217527377796[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.04229820397527[/C][C]0.957701796024726[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]3.33608814817411[/C][C]-0.336088148174114[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]2.27302579164363[/C][C]-1.27302579164363[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]3.14326436604636[/C][C]-0.143264366046361[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]2.96780492742651[/C][C]0.0321950725734884[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.14398665420033[/C][C]0.856013345799667[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]2.81665150986707[/C][C]-0.816651509867071[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]2.72013601786382[/C][C]0.279863982136177[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.24415176408728[/C][C]0.755848235912717[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.21836298960509[/C][C]0.781637010394913[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]2.96997179188843[/C][C]1.03002820811157[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]2.97333902972531[/C][C]0.0266609702746920[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]2.89475622718569[/C][C]1.10524377281431[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]3.21680147215313[/C][C]-1.21680147215313[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.89547851533966[/C][C]0.104521484660336[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.28094726091458[/C][C]-0.280947260914578[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]3.79733962834788[/C][C]-0.797339628347881[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]3.66655664805624[/C][C]0.333443351943761[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]2.66170665679757[/C][C]1.33829334320243[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]3.09083030600632[/C][C]-0.0908303060063177[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.30127952106853[/C][C]-0.301279521068533[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]3.22570281228881[/C][C]-1.22570281228881[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.54774805725625[/C][C]0.452251942743745[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]3.03287903016106[/C][C]-0.0328790301610601[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]2.72326937341269[/C][C]-0.723269373412687[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]3.70811007148054[/C][C]-1.70811007148054[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]3.78440906841423[/C][C]-0.784409068414235[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]2.78256517091546[/C][C]-0.782565170915462[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]3.05241023813088[/C][C]-1.05241023813088[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]3.26725623571312[/C][C]0.732743764286884[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.27062347355[/C][C]0.729376526450002[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]3.78621478879916[/C][C]0.213785211200836[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.97791696158212[/C][C]-0.977916961582115[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]3.5517206421031[/C][C]-1.5517206421031[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]2.90570749063928[/C][C]1.09429250936072[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]2.65130410540283[/C][C]-0.651304105402826[/C][/ROW]
[ROW][C]93[/C][C]3[/C][C]3.09396366155518[/C][C]-0.0939636615551816[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]3.23148111752059[/C][C]-0.231481117520586[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]3.88122265089828[/C][C]1.11877734910172[/C][/ROW]
[ROW][C]96[/C][C]3[/C][C]2.66929068241427[/C][C]0.330709317585726[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.22955845599165[/C][C]0.770441544008352[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]3.05710511113169[/C][C]-0.0571051111316942[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]2.86392018492696[/C][C]-0.863920184926956[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.05782739928567[/C][C]0.942172600714334[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]3.09985890793096[/C][C]-0.099858907930964[/C][/ROW]
[ROW][C]102[/C][C]3[/C][C]2.98261183458293[/C][C]0.0173881654170684[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]2.90703512580321[/C][C]0.0929648741967886[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]3.60145311750911[/C][C]0.398546882490891[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]2.16902410800006[/C][C]-1.16902410800006[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.98405641089087[/C][C]0.0159435891091249[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]3.02007573201638[/C][C]-1.02007573201638[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]3.3094688936952[/C][C]-0.309468893695199[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]3.16096042581867[/C][C]-1.16096042581867[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]2.56092806270834[/C][C]-0.560928062708337[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]2.82658354928247[/C][C]-0.826583549282474[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]2.58258913408584[/C][C]1.41741086591416[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]4.23754180279918[/C][C]0.76245819720082[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]2.66225536885641[/C][C]2.33774463114359[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.98730670758375[/C][C]0.0126932924162523[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]2.56008883341036[/C][C]1.43991116658964[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]2.08087798336368[/C][C]1.91912201663632[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]3.0226576281031[/C][C]-0.0226576281030992[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]2.85460106576317[/C][C]-0.854601065763168[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]3.97058588819965[/C][C]0.0294141118003487[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]2.73771513649212[/C][C]-0.737715136492121[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]2.62046806314409[/C][C]0.379531936855911[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]3.28362466032149[/C][C]-1.28362466032149[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]2.91461915141991[/C][C]-0.914619151419914[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.69748934823175[/C][C]-0.697489348231753[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.18482536271232[/C][C]0.815174637287684[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]3.35800099572624[/C][C]0.641999004273758[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]2.99200158058456[/C][C]1.00799841941544[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]3.60747562567386[/C][C]0.39252437432614[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]3.54962440447903[/C][C]-0.549624404479028[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]2.99308501281552[/C][C]-0.993085012815521[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]3.80210512818654[/C][C]0.197894871813458[/C][/ROW]
[ROW][C]133[/C][C]3[/C][C]3.80246627226353[/C][C]-0.802466272263528[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]2.91823059218977[/C][C]-0.918230592189773[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]3.8031885604175[/C][C]0.196811439582501[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]2.91594678658385[/C][C]0.0840532134161509[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]3.07118973013414[/C][C]-0.0711897301341425[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]2.99561302135442[/C][C]0.0043869786455776[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]2.74120963611797[/C][C]0.258790363882029[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]3.14960170398193[/C][C]-0.149601703981933[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]3.653479719166[/C][C]0.346520280833998[/C][/ROW]
[ROW][C]142[/C][C]5[/C][C]3.86210632734971[/C][C]1.13789367265029[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]3.03908910630766[/C][C]-1.03908910630766[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.12892401018497[/C][C]0.871075989815035[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]3.72785605457076[/C][C]-0.727856054570756[/C][/ROW]
[ROW][C]146[/C][C]3[/C][C]2.37737708543821[/C][C]0.622622914561792[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]2.49534644694021[/C][C]-1.49534644694021[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]3.07516231498099[/C][C]-1.07516231498099[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]2.82376502350443[/C][C]1.17623497649557[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]2.84066816828492[/C][C]1.15933183171508[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]4.05771920744292[/C][C]0.942280792557081[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.82184236197549[/C][C]-0.821842361975493[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]3.40466432377616[/C][C]0.595335676223836[/C][/ROW]
[ROW][C]154[/C][C]3[/C][C]3.00139132658620[/C][C]-0.00139132658619608[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]3.36811302988185[/C][C]-1.36811302988185[/C][/ROW]
[ROW][C]156[/C][C]4[/C][C]3.32680380939052[/C][C]0.67319619060948[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]2.47382952384526[/C][C]-0.473829523845264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.47244135562101-0.472441355621013
243.972291722350760.0277082776492371
353.758168012922481.24183198707752
433.60665345128606-0.606653451286056
543.179435577112670.820564422887334
634.01100988070698-1.01100988070698
743.566422142041480.433577857958518
833.06627250877128-0.0662725087712795
922.33090644065104-0.330906440651041
1042.873448726643531.12655127335647
1123.02568557643392-1.02568557643392
1222.69835043210066-0.698350432100663
1312.69871157617765-1.69871157617765
1443.026769008664880.973230991335118
1543.030136246501760.969863753498236
1622.95155344396215-0.951553443962148
1722.56600593435707-0.566005934357069
1833.31863629133478-0.318636291334784
1933.76129584748714-0.76129584748714
2033.60978128585071-0.609781285850714
2143.602739553647780.397260446352219
2233.7623792797181-0.762379279718098
2323.37564828323163-1.37564828323163
2423.20620103213158-1.20620103213158
2543.030741593511730.969258406488273
2643.763823856026040.236176143973959
2732.955526028808990.0444739711910075
2843.398185584961350.601814415038651
2923.03218616981967-1.03218616981967
3043.537454873763870.462545126236133
3143.281660799767290.718339200232712
3253.338411702237581.66158829776242
3352.88175504041422.1182449595858
3443.03399189020460.9660081097954
3543.825286517990730.174713482009269
3643.998462151004660.00153784899534324
3723.15268353986058-1.15268353986058
3843.592337002253040.407662997746964
3943.650910511601840.349089488398161
4044.26939065045103-0.269390650451030
4133.40288045796216-0.402880457962165
4222.96094318996378-0.96094318996378
4333.65536118166968-0.655361181669678
4442.885727625261051.11427237473895
4532.771486645672910.228513354327091
4623.03832561912843-1.03832561912843
4743.384315741079300.615684258920705
4843.536552590869690.463447409130307
4933.52037173424317-0.520371734243174
5043.427069537878570.572930462121435
5133.04013133951336-0.040131339513359
5242.888616777876931.11138322212307
5322.06077085619648-0.0607708561964836
5443.404569237203090.595430762796915
5542.98218006366811.01781993633190
5643.622782472622200.377217527377796
5743.042298203975270.957701796024726
5833.33608814817411-0.336088148174114
5912.27302579164363-1.27302579164363
6033.14326436604636-0.143264366046361
6132.967804927426510.0321950725734884
6243.143986654200330.856013345799667
6322.81665150986707-0.816651509867071
6432.720136017863820.279863982136177
6554.244151764087280.755848235912717
6643.218362989605090.781637010394913
6742.969971791888431.03002820811157
6832.973339029725310.0266609702746920
6942.894756227185691.10524377281431
7023.21680147215313-1.21680147215313
7132.895478515339660.104521484660336
7244.28094726091458-0.280947260914578
7333.79733962834788-0.797339628347881
7443.666556648056240.333443351943761
7542.661706656797571.33829334320243
7633.09083030600632-0.0908303060063177
7733.30127952106853-0.301279521068533
7823.22570281228881-1.22570281228881
7943.547748057256250.452251942743745
8033.03287903016106-0.0328790301610601
8122.72326937341269-0.723269373412687
8223.70811007148054-1.70811007148054
8333.78440906841423-0.784409068414235
8422.78256517091546-0.782565170915462
8523.05241023813088-1.05241023813088
8643.267256235713120.732743764286884
8743.270623473550.729376526450002
8843.786214788799160.213785211200836
8922.97791696158212-0.977916961582115
9023.5517206421031-1.5517206421031
9142.905707490639281.09429250936072
9222.65130410540283-0.651304105402826
9333.09396366155518-0.0939636615551816
9433.23148111752059-0.231481117520586
9553.881222650898281.11877734910172
9632.669290682414270.330709317585726
9743.229558455991650.770441544008352
9833.05710511113169-0.0571051111316942
9922.86392018492696-0.863920184926956
10043.057827399285670.942172600714334
10133.09985890793096-0.099858907930964
10232.982611834582930.0173881654170684
10332.907035125803210.0929648741967886
10443.601453117509110.398546882490891
10512.16902410800006-1.16902410800006
10632.984056410890870.0159435891091249
10723.02007573201638-1.02007573201638
10833.3094688936952-0.309468893695199
10923.16096042581867-1.16096042581867
11022.56092806270834-0.560928062708337
11122.82658354928247-0.826583549282474
11242.582589134085841.41741086591416
11354.237541802799180.76245819720082
11452.662255368856412.33774463114359
11532.987306707583750.0126932924162523
11642.560088833410361.43991116658964
11742.080877983363681.91912201663632
11833.0226576281031-0.0226576281030992
11922.85460106576317-0.854601065763168
12043.970585888199650.0294141118003487
12122.73771513649212-0.737715136492121
12232.620468063144090.379531936855911
12323.28362466032149-1.28362466032149
12422.91461915141991-0.914619151419914
12522.69748934823175-0.697489348231753
12643.184825362712320.815174637287684
12743.358000995726240.641999004273758
12842.992001580584561.00799841941544
12943.607475625673860.39252437432614
13033.54962440447903-0.549624404479028
13122.99308501281552-0.993085012815521
13243.802105128186540.197894871813458
13333.80246627226353-0.802466272263528
13422.91823059218977-0.918230592189773
13543.80318856041750.196811439582501
13632.915946786583850.0840532134161509
13733.07118973013414-0.0711897301341425
13832.995613021354420.0043869786455776
13932.741209636117970.258790363882029
14033.14960170398193-0.149601703981933
14143.6534797191660.346520280833998
14253.862106327349711.13789367265029
14323.03908910630766-1.03908910630766
14443.128924010184970.871075989815035
14533.72785605457076-0.727856054570756
14632.377377085438210.622622914561792
14712.49534644694021-1.49534644694021
14823.07516231498099-1.07516231498099
14942.823765023504431.17623497649557
15042.840668168284921.15933183171508
15154.057719207442920.942280792557081
15222.82184236197549-0.821842361975493
15343.404664323776160.595335676223836
15433.00139132658620-0.00139132658619608
15523.36811302988185-1.36811302988185
15643.326803809390520.67319619060948
15722.47382952384526-0.473829523845264







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8149006286561090.3701987426877820.185099371343891
110.8327424513566170.3345150972867660.167257548643383
120.7535535132377820.4928929735244370.246446486762218
130.7317873541446620.5364252917106750.268212645855338
140.9213849593841120.1572300812317760.0786150406158878
150.9181432590849630.1637134818300740.0818567409150371
160.8862712013359230.2274575973281530.113728798664077
170.8396108627536650.3207782744926690.160389137246335
180.7995668784925770.4008662430148470.200433121507423
190.7400151476328630.5199697047342740.259984852367137
200.68161900395140.6367619920971990.318380996048600
210.6990695174049970.6018609651900050.300930482595003
220.6418027593556970.7163944812886060.358197240644303
230.602429266403450.79514146719310.39757073359655
240.5603936276907970.8792127446184060.439606372309203
250.6662591280425660.6674817439148680.333740871957434
260.6314044939702820.7371910120594360.368595506029718
270.5841685859164010.8316628281671970.415831414083599
280.5586152202643880.8827695594712240.441384779735612
290.5767518498510590.8464963002978820.423248150148941
300.623919658930790.752160682138420.37608034106921
310.619969599354790.760060801290420.38003040064521
320.6770857442536640.6458285114926720.322914255746336
330.855053469007730.2898930619845390.144946530992269
340.8313337672316540.3373324655366930.168666232768346
350.791761673755630.4164766524887390.208238326244370
360.748281876488570.503436247022860.25171812351143
370.8603121127342530.2793757745314940.139687887265747
380.8297501139997460.3404997720005090.170249886000254
390.7934523612699350.4130952774601310.206547638730065
400.7606999591565690.4786000816868630.239300040843432
410.7451072465608740.5097855068782530.254892753439126
420.7722557680686970.4554884638626060.227744231931303
430.7559484803106720.4881030393786570.244051519689328
440.7685043550055260.4629912899889480.231495644994474
450.7264018666329190.5471962667341620.273598133367081
460.7646005507246950.470798898550610.235399449275305
470.733664099383860.5326718012322810.266335900616140
480.6932008991844370.6135982016311270.306799100815563
490.677406912956420.6451861740871610.322593087043580
500.6414428029383960.7171143941232080.358557197061604
510.5949581679236720.8100836641526560.405041832076328
520.5987863860370360.8024272279259290.401213613962964
530.5632795667820590.8734408664358820.436720433217941
540.5228839423360940.9542321153278110.477116057663906
550.5097302330955370.9805395338089260.490269766904463
560.4652253065388930.9304506130777870.534774693461107
570.4521152476848870.9042304953697750.547884752315113
580.4144119208658860.8288238417317720.585588079134114
590.521495145987710.957009708024580.47850485401229
600.4754005689395240.9508011378790480.524599431060476
610.4279091845541660.8558183691083320.572090815445834
620.4176843139874740.8353686279749480.582315686012526
630.4221432139336840.8442864278673690.577856786066316
640.37752807520330.75505615040660.6224719247967
650.3629324745313700.7258649490627390.63706752546863
660.3421473916189360.6842947832378730.657852608381064
670.3443062721121530.6886125442243070.655693727887847
680.3020593151145740.6041186302291480.697940684885426
690.3155236371899890.6310472743799780.684476362810011
700.4001643104302210.8003286208604410.59983568956978
710.3574939390455120.7149878780910240.642506060954488
720.3269586034417120.6539172068834230.673041396558288
730.3313252884068380.6626505768136760.668674711593162
740.2945334415514530.5890668831029060.705466558448547
750.3445992806042970.6891985612085930.655400719395703
760.3052358458113620.6104716916227250.694764154188638
770.2738669070822560.5477338141645120.726133092917744
780.3182437551624150.6364875103248310.681756244837584
790.2904381173014760.5808762346029520.709561882698524
800.2545228224025850.509045644805170.745477177597415
810.247114366232420.494228732464840.75288563376758
820.3522912163916470.7045824327832930.647708783608353
830.3402364366609590.6804728733219180.659763563339041
840.3326164751101680.6652329502203360.667383524889832
850.3527527882149580.7055055764299160.647247211785042
860.3426133237671720.6852266475343450.657386676232828
870.3328126887984890.6656253775969780.66718731120151
880.2939704873670530.5879409747341060.706029512632947
890.3038405832222970.6076811664445930.696159416777703
900.4034848173079440.8069696346158890.596515182692056
910.4344221126693420.8688442253386840.565577887330658
920.4123185453153960.8246370906307910.587681454684604
930.3697369852852030.7394739705704060.630263014714797
940.328309195412670.656618390825340.67169080458733
950.3455316794027320.6910633588054640.654468320597268
960.3112056984743190.6224113969486370.688794301525681
970.3035951000139910.6071902000279820.696404899986009
980.2627921539221910.5255843078443810.73720784607781
990.2580932734912880.5161865469825770.741906726508712
1000.2597455933822930.5194911867645850.740254406617707
1010.2219319277103610.4438638554207230.778068072289639
1020.1867245307263870.3734490614527750.813275469273613
1030.1561575343187040.3123150686374090.843842465681296
1040.1358506666875280.2717013333750570.864149333312472
1050.1545218078269800.3090436156539590.84547819217302
1060.1263895780565950.252779156113190.873610421943405
1070.1471161713180160.2942323426360320.852883828681984
1080.1254660065429880.2509320130859760.874533993457012
1090.1423880974809750.2847761949619500.857611902519025
1100.1342492747705450.2684985495410890.865750725229455
1110.1565676200391720.3131352400783430.843432379960828
1120.1969276784060440.3938553568120880.803072321593956
1130.1747790159099730.3495580318199470.825220984090027
1140.4946030954116470.9892061908232940.505396904588353
1150.4399370682659670.8798741365319330.560062931734034
1160.4895328709616810.9790657419233620.510467129038319
1170.8124842605040270.3750314789919450.187515739495973
1180.783303973962030.4333920520759390.216696026037970
1190.7583890214975870.4832219570048260.241610978502413
1200.7148363690872960.5703272618254080.285163630912704
1210.6859252205416330.6281495589167340.314074779458367
1220.6456339698328810.7087320603342380.354366030167119
1230.6532553838374430.6934892323251140.346744616162557
1240.6282174518679370.7435650962641260.371782548132063
1250.6538231628527430.6923536742945130.346176837147257
1260.6355667671381240.7288664657237520.364433232861876
1270.6062399504230340.7875200991539310.393760049576966
1280.6730624080816640.6538751838366710.326937591918336
1290.6172737659194480.7654524681611040.382726234080552
1300.5929810999613490.8140378000773030.407018900038651
1310.5799068612674790.8401862774650420.420093138732521
1320.5117010666093480.9765978667813030.488298933390652
1330.6148941345112240.7702117309775530.385105865488776
1340.5860843250505440.8278313498989120.413915674949456
1350.5629468293782290.8741063412435420.437053170621771
1360.4831440396325160.9662880792650320.516855960367484
1370.4874873251353990.9749746502707980.512512674864601
1380.4369389334938370.8738778669876750.563061066506163
1390.3555419908373270.7110839816746530.644458009162673
1400.2887960390995820.5775920781991640.711203960900418
1410.2404078817040330.4808157634080660.759592118295967
1420.1957946588509560.3915893177019120.804205341149044
1430.1441316299856420.2882632599712830.855868370014358
1440.09471648933896750.1894329786779350.905283510661033
1450.0847266641485860.1694533282971720.915273335851414
1460.08570808306785130.1714161661357030.914291916932149
1470.05281654414789150.1056330882957830.947183455852109

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.814900628656109 & 0.370198742687782 & 0.185099371343891 \tabularnewline
11 & 0.832742451356617 & 0.334515097286766 & 0.167257548643383 \tabularnewline
12 & 0.753553513237782 & 0.492892973524437 & 0.246446486762218 \tabularnewline
13 & 0.731787354144662 & 0.536425291710675 & 0.268212645855338 \tabularnewline
14 & 0.921384959384112 & 0.157230081231776 & 0.0786150406158878 \tabularnewline
15 & 0.918143259084963 & 0.163713481830074 & 0.0818567409150371 \tabularnewline
16 & 0.886271201335923 & 0.227457597328153 & 0.113728798664077 \tabularnewline
17 & 0.839610862753665 & 0.320778274492669 & 0.160389137246335 \tabularnewline
18 & 0.799566878492577 & 0.400866243014847 & 0.200433121507423 \tabularnewline
19 & 0.740015147632863 & 0.519969704734274 & 0.259984852367137 \tabularnewline
20 & 0.6816190039514 & 0.636761992097199 & 0.318380996048600 \tabularnewline
21 & 0.699069517404997 & 0.601860965190005 & 0.300930482595003 \tabularnewline
22 & 0.641802759355697 & 0.716394481288606 & 0.358197240644303 \tabularnewline
23 & 0.60242926640345 & 0.7951414671931 & 0.39757073359655 \tabularnewline
24 & 0.560393627690797 & 0.879212744618406 & 0.439606372309203 \tabularnewline
25 & 0.666259128042566 & 0.667481743914868 & 0.333740871957434 \tabularnewline
26 & 0.631404493970282 & 0.737191012059436 & 0.368595506029718 \tabularnewline
27 & 0.584168585916401 & 0.831662828167197 & 0.415831414083599 \tabularnewline
28 & 0.558615220264388 & 0.882769559471224 & 0.441384779735612 \tabularnewline
29 & 0.576751849851059 & 0.846496300297882 & 0.423248150148941 \tabularnewline
30 & 0.62391965893079 & 0.75216068213842 & 0.37608034106921 \tabularnewline
31 & 0.61996959935479 & 0.76006080129042 & 0.38003040064521 \tabularnewline
32 & 0.677085744253664 & 0.645828511492672 & 0.322914255746336 \tabularnewline
33 & 0.85505346900773 & 0.289893061984539 & 0.144946530992269 \tabularnewline
34 & 0.831333767231654 & 0.337332465536693 & 0.168666232768346 \tabularnewline
35 & 0.79176167375563 & 0.416476652488739 & 0.208238326244370 \tabularnewline
36 & 0.74828187648857 & 0.50343624702286 & 0.25171812351143 \tabularnewline
37 & 0.860312112734253 & 0.279375774531494 & 0.139687887265747 \tabularnewline
38 & 0.829750113999746 & 0.340499772000509 & 0.170249886000254 \tabularnewline
39 & 0.793452361269935 & 0.413095277460131 & 0.206547638730065 \tabularnewline
40 & 0.760699959156569 & 0.478600081686863 & 0.239300040843432 \tabularnewline
41 & 0.745107246560874 & 0.509785506878253 & 0.254892753439126 \tabularnewline
42 & 0.772255768068697 & 0.455488463862606 & 0.227744231931303 \tabularnewline
43 & 0.755948480310672 & 0.488103039378657 & 0.244051519689328 \tabularnewline
44 & 0.768504355005526 & 0.462991289988948 & 0.231495644994474 \tabularnewline
45 & 0.726401866632919 & 0.547196266734162 & 0.273598133367081 \tabularnewline
46 & 0.764600550724695 & 0.47079889855061 & 0.235399449275305 \tabularnewline
47 & 0.73366409938386 & 0.532671801232281 & 0.266335900616140 \tabularnewline
48 & 0.693200899184437 & 0.613598201631127 & 0.306799100815563 \tabularnewline
49 & 0.67740691295642 & 0.645186174087161 & 0.322593087043580 \tabularnewline
50 & 0.641442802938396 & 0.717114394123208 & 0.358557197061604 \tabularnewline
51 & 0.594958167923672 & 0.810083664152656 & 0.405041832076328 \tabularnewline
52 & 0.598786386037036 & 0.802427227925929 & 0.401213613962964 \tabularnewline
53 & 0.563279566782059 & 0.873440866435882 & 0.436720433217941 \tabularnewline
54 & 0.522883942336094 & 0.954232115327811 & 0.477116057663906 \tabularnewline
55 & 0.509730233095537 & 0.980539533808926 & 0.490269766904463 \tabularnewline
56 & 0.465225306538893 & 0.930450613077787 & 0.534774693461107 \tabularnewline
57 & 0.452115247684887 & 0.904230495369775 & 0.547884752315113 \tabularnewline
58 & 0.414411920865886 & 0.828823841731772 & 0.585588079134114 \tabularnewline
59 & 0.52149514598771 & 0.95700970802458 & 0.47850485401229 \tabularnewline
60 & 0.475400568939524 & 0.950801137879048 & 0.524599431060476 \tabularnewline
61 & 0.427909184554166 & 0.855818369108332 & 0.572090815445834 \tabularnewline
62 & 0.417684313987474 & 0.835368627974948 & 0.582315686012526 \tabularnewline
63 & 0.422143213933684 & 0.844286427867369 & 0.577856786066316 \tabularnewline
64 & 0.3775280752033 & 0.7550561504066 & 0.6224719247967 \tabularnewline
65 & 0.362932474531370 & 0.725864949062739 & 0.63706752546863 \tabularnewline
66 & 0.342147391618936 & 0.684294783237873 & 0.657852608381064 \tabularnewline
67 & 0.344306272112153 & 0.688612544224307 & 0.655693727887847 \tabularnewline
68 & 0.302059315114574 & 0.604118630229148 & 0.697940684885426 \tabularnewline
69 & 0.315523637189989 & 0.631047274379978 & 0.684476362810011 \tabularnewline
70 & 0.400164310430221 & 0.800328620860441 & 0.59983568956978 \tabularnewline
71 & 0.357493939045512 & 0.714987878091024 & 0.642506060954488 \tabularnewline
72 & 0.326958603441712 & 0.653917206883423 & 0.673041396558288 \tabularnewline
73 & 0.331325288406838 & 0.662650576813676 & 0.668674711593162 \tabularnewline
74 & 0.294533441551453 & 0.589066883102906 & 0.705466558448547 \tabularnewline
75 & 0.344599280604297 & 0.689198561208593 & 0.655400719395703 \tabularnewline
76 & 0.305235845811362 & 0.610471691622725 & 0.694764154188638 \tabularnewline
77 & 0.273866907082256 & 0.547733814164512 & 0.726133092917744 \tabularnewline
78 & 0.318243755162415 & 0.636487510324831 & 0.681756244837584 \tabularnewline
79 & 0.290438117301476 & 0.580876234602952 & 0.709561882698524 \tabularnewline
80 & 0.254522822402585 & 0.50904564480517 & 0.745477177597415 \tabularnewline
81 & 0.24711436623242 & 0.49422873246484 & 0.75288563376758 \tabularnewline
82 & 0.352291216391647 & 0.704582432783293 & 0.647708783608353 \tabularnewline
83 & 0.340236436660959 & 0.680472873321918 & 0.659763563339041 \tabularnewline
84 & 0.332616475110168 & 0.665232950220336 & 0.667383524889832 \tabularnewline
85 & 0.352752788214958 & 0.705505576429916 & 0.647247211785042 \tabularnewline
86 & 0.342613323767172 & 0.685226647534345 & 0.657386676232828 \tabularnewline
87 & 0.332812688798489 & 0.665625377596978 & 0.66718731120151 \tabularnewline
88 & 0.293970487367053 & 0.587940974734106 & 0.706029512632947 \tabularnewline
89 & 0.303840583222297 & 0.607681166444593 & 0.696159416777703 \tabularnewline
90 & 0.403484817307944 & 0.806969634615889 & 0.596515182692056 \tabularnewline
91 & 0.434422112669342 & 0.868844225338684 & 0.565577887330658 \tabularnewline
92 & 0.412318545315396 & 0.824637090630791 & 0.587681454684604 \tabularnewline
93 & 0.369736985285203 & 0.739473970570406 & 0.630263014714797 \tabularnewline
94 & 0.32830919541267 & 0.65661839082534 & 0.67169080458733 \tabularnewline
95 & 0.345531679402732 & 0.691063358805464 & 0.654468320597268 \tabularnewline
96 & 0.311205698474319 & 0.622411396948637 & 0.688794301525681 \tabularnewline
97 & 0.303595100013991 & 0.607190200027982 & 0.696404899986009 \tabularnewline
98 & 0.262792153922191 & 0.525584307844381 & 0.73720784607781 \tabularnewline
99 & 0.258093273491288 & 0.516186546982577 & 0.741906726508712 \tabularnewline
100 & 0.259745593382293 & 0.519491186764585 & 0.740254406617707 \tabularnewline
101 & 0.221931927710361 & 0.443863855420723 & 0.778068072289639 \tabularnewline
102 & 0.186724530726387 & 0.373449061452775 & 0.813275469273613 \tabularnewline
103 & 0.156157534318704 & 0.312315068637409 & 0.843842465681296 \tabularnewline
104 & 0.135850666687528 & 0.271701333375057 & 0.864149333312472 \tabularnewline
105 & 0.154521807826980 & 0.309043615653959 & 0.84547819217302 \tabularnewline
106 & 0.126389578056595 & 0.25277915611319 & 0.873610421943405 \tabularnewline
107 & 0.147116171318016 & 0.294232342636032 & 0.852883828681984 \tabularnewline
108 & 0.125466006542988 & 0.250932013085976 & 0.874533993457012 \tabularnewline
109 & 0.142388097480975 & 0.284776194961950 & 0.857611902519025 \tabularnewline
110 & 0.134249274770545 & 0.268498549541089 & 0.865750725229455 \tabularnewline
111 & 0.156567620039172 & 0.313135240078343 & 0.843432379960828 \tabularnewline
112 & 0.196927678406044 & 0.393855356812088 & 0.803072321593956 \tabularnewline
113 & 0.174779015909973 & 0.349558031819947 & 0.825220984090027 \tabularnewline
114 & 0.494603095411647 & 0.989206190823294 & 0.505396904588353 \tabularnewline
115 & 0.439937068265967 & 0.879874136531933 & 0.560062931734034 \tabularnewline
116 & 0.489532870961681 & 0.979065741923362 & 0.510467129038319 \tabularnewline
117 & 0.812484260504027 & 0.375031478991945 & 0.187515739495973 \tabularnewline
118 & 0.78330397396203 & 0.433392052075939 & 0.216696026037970 \tabularnewline
119 & 0.758389021497587 & 0.483221957004826 & 0.241610978502413 \tabularnewline
120 & 0.714836369087296 & 0.570327261825408 & 0.285163630912704 \tabularnewline
121 & 0.685925220541633 & 0.628149558916734 & 0.314074779458367 \tabularnewline
122 & 0.645633969832881 & 0.708732060334238 & 0.354366030167119 \tabularnewline
123 & 0.653255383837443 & 0.693489232325114 & 0.346744616162557 \tabularnewline
124 & 0.628217451867937 & 0.743565096264126 & 0.371782548132063 \tabularnewline
125 & 0.653823162852743 & 0.692353674294513 & 0.346176837147257 \tabularnewline
126 & 0.635566767138124 & 0.728866465723752 & 0.364433232861876 \tabularnewline
127 & 0.606239950423034 & 0.787520099153931 & 0.393760049576966 \tabularnewline
128 & 0.673062408081664 & 0.653875183836671 & 0.326937591918336 \tabularnewline
129 & 0.617273765919448 & 0.765452468161104 & 0.382726234080552 \tabularnewline
130 & 0.592981099961349 & 0.814037800077303 & 0.407018900038651 \tabularnewline
131 & 0.579906861267479 & 0.840186277465042 & 0.420093138732521 \tabularnewline
132 & 0.511701066609348 & 0.976597866781303 & 0.488298933390652 \tabularnewline
133 & 0.614894134511224 & 0.770211730977553 & 0.385105865488776 \tabularnewline
134 & 0.586084325050544 & 0.827831349898912 & 0.413915674949456 \tabularnewline
135 & 0.562946829378229 & 0.874106341243542 & 0.437053170621771 \tabularnewline
136 & 0.483144039632516 & 0.966288079265032 & 0.516855960367484 \tabularnewline
137 & 0.487487325135399 & 0.974974650270798 & 0.512512674864601 \tabularnewline
138 & 0.436938933493837 & 0.873877866987675 & 0.563061066506163 \tabularnewline
139 & 0.355541990837327 & 0.711083981674653 & 0.644458009162673 \tabularnewline
140 & 0.288796039099582 & 0.577592078199164 & 0.711203960900418 \tabularnewline
141 & 0.240407881704033 & 0.480815763408066 & 0.759592118295967 \tabularnewline
142 & 0.195794658850956 & 0.391589317701912 & 0.804205341149044 \tabularnewline
143 & 0.144131629985642 & 0.288263259971283 & 0.855868370014358 \tabularnewline
144 & 0.0947164893389675 & 0.189432978677935 & 0.905283510661033 \tabularnewline
145 & 0.084726664148586 & 0.169453328297172 & 0.915273335851414 \tabularnewline
146 & 0.0857080830678513 & 0.171416166135703 & 0.914291916932149 \tabularnewline
147 & 0.0528165441478915 & 0.105633088295783 & 0.947183455852109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.814900628656109[/C][C]0.370198742687782[/C][C]0.185099371343891[/C][/ROW]
[ROW][C]11[/C][C]0.832742451356617[/C][C]0.334515097286766[/C][C]0.167257548643383[/C][/ROW]
[ROW][C]12[/C][C]0.753553513237782[/C][C]0.492892973524437[/C][C]0.246446486762218[/C][/ROW]
[ROW][C]13[/C][C]0.731787354144662[/C][C]0.536425291710675[/C][C]0.268212645855338[/C][/ROW]
[ROW][C]14[/C][C]0.921384959384112[/C][C]0.157230081231776[/C][C]0.0786150406158878[/C][/ROW]
[ROW][C]15[/C][C]0.918143259084963[/C][C]0.163713481830074[/C][C]0.0818567409150371[/C][/ROW]
[ROW][C]16[/C][C]0.886271201335923[/C][C]0.227457597328153[/C][C]0.113728798664077[/C][/ROW]
[ROW][C]17[/C][C]0.839610862753665[/C][C]0.320778274492669[/C][C]0.160389137246335[/C][/ROW]
[ROW][C]18[/C][C]0.799566878492577[/C][C]0.400866243014847[/C][C]0.200433121507423[/C][/ROW]
[ROW][C]19[/C][C]0.740015147632863[/C][C]0.519969704734274[/C][C]0.259984852367137[/C][/ROW]
[ROW][C]20[/C][C]0.6816190039514[/C][C]0.636761992097199[/C][C]0.318380996048600[/C][/ROW]
[ROW][C]21[/C][C]0.699069517404997[/C][C]0.601860965190005[/C][C]0.300930482595003[/C][/ROW]
[ROW][C]22[/C][C]0.641802759355697[/C][C]0.716394481288606[/C][C]0.358197240644303[/C][/ROW]
[ROW][C]23[/C][C]0.60242926640345[/C][C]0.7951414671931[/C][C]0.39757073359655[/C][/ROW]
[ROW][C]24[/C][C]0.560393627690797[/C][C]0.879212744618406[/C][C]0.439606372309203[/C][/ROW]
[ROW][C]25[/C][C]0.666259128042566[/C][C]0.667481743914868[/C][C]0.333740871957434[/C][/ROW]
[ROW][C]26[/C][C]0.631404493970282[/C][C]0.737191012059436[/C][C]0.368595506029718[/C][/ROW]
[ROW][C]27[/C][C]0.584168585916401[/C][C]0.831662828167197[/C][C]0.415831414083599[/C][/ROW]
[ROW][C]28[/C][C]0.558615220264388[/C][C]0.882769559471224[/C][C]0.441384779735612[/C][/ROW]
[ROW][C]29[/C][C]0.576751849851059[/C][C]0.846496300297882[/C][C]0.423248150148941[/C][/ROW]
[ROW][C]30[/C][C]0.62391965893079[/C][C]0.75216068213842[/C][C]0.37608034106921[/C][/ROW]
[ROW][C]31[/C][C]0.61996959935479[/C][C]0.76006080129042[/C][C]0.38003040064521[/C][/ROW]
[ROW][C]32[/C][C]0.677085744253664[/C][C]0.645828511492672[/C][C]0.322914255746336[/C][/ROW]
[ROW][C]33[/C][C]0.85505346900773[/C][C]0.289893061984539[/C][C]0.144946530992269[/C][/ROW]
[ROW][C]34[/C][C]0.831333767231654[/C][C]0.337332465536693[/C][C]0.168666232768346[/C][/ROW]
[ROW][C]35[/C][C]0.79176167375563[/C][C]0.416476652488739[/C][C]0.208238326244370[/C][/ROW]
[ROW][C]36[/C][C]0.74828187648857[/C][C]0.50343624702286[/C][C]0.25171812351143[/C][/ROW]
[ROW][C]37[/C][C]0.860312112734253[/C][C]0.279375774531494[/C][C]0.139687887265747[/C][/ROW]
[ROW][C]38[/C][C]0.829750113999746[/C][C]0.340499772000509[/C][C]0.170249886000254[/C][/ROW]
[ROW][C]39[/C][C]0.793452361269935[/C][C]0.413095277460131[/C][C]0.206547638730065[/C][/ROW]
[ROW][C]40[/C][C]0.760699959156569[/C][C]0.478600081686863[/C][C]0.239300040843432[/C][/ROW]
[ROW][C]41[/C][C]0.745107246560874[/C][C]0.509785506878253[/C][C]0.254892753439126[/C][/ROW]
[ROW][C]42[/C][C]0.772255768068697[/C][C]0.455488463862606[/C][C]0.227744231931303[/C][/ROW]
[ROW][C]43[/C][C]0.755948480310672[/C][C]0.488103039378657[/C][C]0.244051519689328[/C][/ROW]
[ROW][C]44[/C][C]0.768504355005526[/C][C]0.462991289988948[/C][C]0.231495644994474[/C][/ROW]
[ROW][C]45[/C][C]0.726401866632919[/C][C]0.547196266734162[/C][C]0.273598133367081[/C][/ROW]
[ROW][C]46[/C][C]0.764600550724695[/C][C]0.47079889855061[/C][C]0.235399449275305[/C][/ROW]
[ROW][C]47[/C][C]0.73366409938386[/C][C]0.532671801232281[/C][C]0.266335900616140[/C][/ROW]
[ROW][C]48[/C][C]0.693200899184437[/C][C]0.613598201631127[/C][C]0.306799100815563[/C][/ROW]
[ROW][C]49[/C][C]0.67740691295642[/C][C]0.645186174087161[/C][C]0.322593087043580[/C][/ROW]
[ROW][C]50[/C][C]0.641442802938396[/C][C]0.717114394123208[/C][C]0.358557197061604[/C][/ROW]
[ROW][C]51[/C][C]0.594958167923672[/C][C]0.810083664152656[/C][C]0.405041832076328[/C][/ROW]
[ROW][C]52[/C][C]0.598786386037036[/C][C]0.802427227925929[/C][C]0.401213613962964[/C][/ROW]
[ROW][C]53[/C][C]0.563279566782059[/C][C]0.873440866435882[/C][C]0.436720433217941[/C][/ROW]
[ROW][C]54[/C][C]0.522883942336094[/C][C]0.954232115327811[/C][C]0.477116057663906[/C][/ROW]
[ROW][C]55[/C][C]0.509730233095537[/C][C]0.980539533808926[/C][C]0.490269766904463[/C][/ROW]
[ROW][C]56[/C][C]0.465225306538893[/C][C]0.930450613077787[/C][C]0.534774693461107[/C][/ROW]
[ROW][C]57[/C][C]0.452115247684887[/C][C]0.904230495369775[/C][C]0.547884752315113[/C][/ROW]
[ROW][C]58[/C][C]0.414411920865886[/C][C]0.828823841731772[/C][C]0.585588079134114[/C][/ROW]
[ROW][C]59[/C][C]0.52149514598771[/C][C]0.95700970802458[/C][C]0.47850485401229[/C][/ROW]
[ROW][C]60[/C][C]0.475400568939524[/C][C]0.950801137879048[/C][C]0.524599431060476[/C][/ROW]
[ROW][C]61[/C][C]0.427909184554166[/C][C]0.855818369108332[/C][C]0.572090815445834[/C][/ROW]
[ROW][C]62[/C][C]0.417684313987474[/C][C]0.835368627974948[/C][C]0.582315686012526[/C][/ROW]
[ROW][C]63[/C][C]0.422143213933684[/C][C]0.844286427867369[/C][C]0.577856786066316[/C][/ROW]
[ROW][C]64[/C][C]0.3775280752033[/C][C]0.7550561504066[/C][C]0.6224719247967[/C][/ROW]
[ROW][C]65[/C][C]0.362932474531370[/C][C]0.725864949062739[/C][C]0.63706752546863[/C][/ROW]
[ROW][C]66[/C][C]0.342147391618936[/C][C]0.684294783237873[/C][C]0.657852608381064[/C][/ROW]
[ROW][C]67[/C][C]0.344306272112153[/C][C]0.688612544224307[/C][C]0.655693727887847[/C][/ROW]
[ROW][C]68[/C][C]0.302059315114574[/C][C]0.604118630229148[/C][C]0.697940684885426[/C][/ROW]
[ROW][C]69[/C][C]0.315523637189989[/C][C]0.631047274379978[/C][C]0.684476362810011[/C][/ROW]
[ROW][C]70[/C][C]0.400164310430221[/C][C]0.800328620860441[/C][C]0.59983568956978[/C][/ROW]
[ROW][C]71[/C][C]0.357493939045512[/C][C]0.714987878091024[/C][C]0.642506060954488[/C][/ROW]
[ROW][C]72[/C][C]0.326958603441712[/C][C]0.653917206883423[/C][C]0.673041396558288[/C][/ROW]
[ROW][C]73[/C][C]0.331325288406838[/C][C]0.662650576813676[/C][C]0.668674711593162[/C][/ROW]
[ROW][C]74[/C][C]0.294533441551453[/C][C]0.589066883102906[/C][C]0.705466558448547[/C][/ROW]
[ROW][C]75[/C][C]0.344599280604297[/C][C]0.689198561208593[/C][C]0.655400719395703[/C][/ROW]
[ROW][C]76[/C][C]0.305235845811362[/C][C]0.610471691622725[/C][C]0.694764154188638[/C][/ROW]
[ROW][C]77[/C][C]0.273866907082256[/C][C]0.547733814164512[/C][C]0.726133092917744[/C][/ROW]
[ROW][C]78[/C][C]0.318243755162415[/C][C]0.636487510324831[/C][C]0.681756244837584[/C][/ROW]
[ROW][C]79[/C][C]0.290438117301476[/C][C]0.580876234602952[/C][C]0.709561882698524[/C][/ROW]
[ROW][C]80[/C][C]0.254522822402585[/C][C]0.50904564480517[/C][C]0.745477177597415[/C][/ROW]
[ROW][C]81[/C][C]0.24711436623242[/C][C]0.49422873246484[/C][C]0.75288563376758[/C][/ROW]
[ROW][C]82[/C][C]0.352291216391647[/C][C]0.704582432783293[/C][C]0.647708783608353[/C][/ROW]
[ROW][C]83[/C][C]0.340236436660959[/C][C]0.680472873321918[/C][C]0.659763563339041[/C][/ROW]
[ROW][C]84[/C][C]0.332616475110168[/C][C]0.665232950220336[/C][C]0.667383524889832[/C][/ROW]
[ROW][C]85[/C][C]0.352752788214958[/C][C]0.705505576429916[/C][C]0.647247211785042[/C][/ROW]
[ROW][C]86[/C][C]0.342613323767172[/C][C]0.685226647534345[/C][C]0.657386676232828[/C][/ROW]
[ROW][C]87[/C][C]0.332812688798489[/C][C]0.665625377596978[/C][C]0.66718731120151[/C][/ROW]
[ROW][C]88[/C][C]0.293970487367053[/C][C]0.587940974734106[/C][C]0.706029512632947[/C][/ROW]
[ROW][C]89[/C][C]0.303840583222297[/C][C]0.607681166444593[/C][C]0.696159416777703[/C][/ROW]
[ROW][C]90[/C][C]0.403484817307944[/C][C]0.806969634615889[/C][C]0.596515182692056[/C][/ROW]
[ROW][C]91[/C][C]0.434422112669342[/C][C]0.868844225338684[/C][C]0.565577887330658[/C][/ROW]
[ROW][C]92[/C][C]0.412318545315396[/C][C]0.824637090630791[/C][C]0.587681454684604[/C][/ROW]
[ROW][C]93[/C][C]0.369736985285203[/C][C]0.739473970570406[/C][C]0.630263014714797[/C][/ROW]
[ROW][C]94[/C][C]0.32830919541267[/C][C]0.65661839082534[/C][C]0.67169080458733[/C][/ROW]
[ROW][C]95[/C][C]0.345531679402732[/C][C]0.691063358805464[/C][C]0.654468320597268[/C][/ROW]
[ROW][C]96[/C][C]0.311205698474319[/C][C]0.622411396948637[/C][C]0.688794301525681[/C][/ROW]
[ROW][C]97[/C][C]0.303595100013991[/C][C]0.607190200027982[/C][C]0.696404899986009[/C][/ROW]
[ROW][C]98[/C][C]0.262792153922191[/C][C]0.525584307844381[/C][C]0.73720784607781[/C][/ROW]
[ROW][C]99[/C][C]0.258093273491288[/C][C]0.516186546982577[/C][C]0.741906726508712[/C][/ROW]
[ROW][C]100[/C][C]0.259745593382293[/C][C]0.519491186764585[/C][C]0.740254406617707[/C][/ROW]
[ROW][C]101[/C][C]0.221931927710361[/C][C]0.443863855420723[/C][C]0.778068072289639[/C][/ROW]
[ROW][C]102[/C][C]0.186724530726387[/C][C]0.373449061452775[/C][C]0.813275469273613[/C][/ROW]
[ROW][C]103[/C][C]0.156157534318704[/C][C]0.312315068637409[/C][C]0.843842465681296[/C][/ROW]
[ROW][C]104[/C][C]0.135850666687528[/C][C]0.271701333375057[/C][C]0.864149333312472[/C][/ROW]
[ROW][C]105[/C][C]0.154521807826980[/C][C]0.309043615653959[/C][C]0.84547819217302[/C][/ROW]
[ROW][C]106[/C][C]0.126389578056595[/C][C]0.25277915611319[/C][C]0.873610421943405[/C][/ROW]
[ROW][C]107[/C][C]0.147116171318016[/C][C]0.294232342636032[/C][C]0.852883828681984[/C][/ROW]
[ROW][C]108[/C][C]0.125466006542988[/C][C]0.250932013085976[/C][C]0.874533993457012[/C][/ROW]
[ROW][C]109[/C][C]0.142388097480975[/C][C]0.284776194961950[/C][C]0.857611902519025[/C][/ROW]
[ROW][C]110[/C][C]0.134249274770545[/C][C]0.268498549541089[/C][C]0.865750725229455[/C][/ROW]
[ROW][C]111[/C][C]0.156567620039172[/C][C]0.313135240078343[/C][C]0.843432379960828[/C][/ROW]
[ROW][C]112[/C][C]0.196927678406044[/C][C]0.393855356812088[/C][C]0.803072321593956[/C][/ROW]
[ROW][C]113[/C][C]0.174779015909973[/C][C]0.349558031819947[/C][C]0.825220984090027[/C][/ROW]
[ROW][C]114[/C][C]0.494603095411647[/C][C]0.989206190823294[/C][C]0.505396904588353[/C][/ROW]
[ROW][C]115[/C][C]0.439937068265967[/C][C]0.879874136531933[/C][C]0.560062931734034[/C][/ROW]
[ROW][C]116[/C][C]0.489532870961681[/C][C]0.979065741923362[/C][C]0.510467129038319[/C][/ROW]
[ROW][C]117[/C][C]0.812484260504027[/C][C]0.375031478991945[/C][C]0.187515739495973[/C][/ROW]
[ROW][C]118[/C][C]0.78330397396203[/C][C]0.433392052075939[/C][C]0.216696026037970[/C][/ROW]
[ROW][C]119[/C][C]0.758389021497587[/C][C]0.483221957004826[/C][C]0.241610978502413[/C][/ROW]
[ROW][C]120[/C][C]0.714836369087296[/C][C]0.570327261825408[/C][C]0.285163630912704[/C][/ROW]
[ROW][C]121[/C][C]0.685925220541633[/C][C]0.628149558916734[/C][C]0.314074779458367[/C][/ROW]
[ROW][C]122[/C][C]0.645633969832881[/C][C]0.708732060334238[/C][C]0.354366030167119[/C][/ROW]
[ROW][C]123[/C][C]0.653255383837443[/C][C]0.693489232325114[/C][C]0.346744616162557[/C][/ROW]
[ROW][C]124[/C][C]0.628217451867937[/C][C]0.743565096264126[/C][C]0.371782548132063[/C][/ROW]
[ROW][C]125[/C][C]0.653823162852743[/C][C]0.692353674294513[/C][C]0.346176837147257[/C][/ROW]
[ROW][C]126[/C][C]0.635566767138124[/C][C]0.728866465723752[/C][C]0.364433232861876[/C][/ROW]
[ROW][C]127[/C][C]0.606239950423034[/C][C]0.787520099153931[/C][C]0.393760049576966[/C][/ROW]
[ROW][C]128[/C][C]0.673062408081664[/C][C]0.653875183836671[/C][C]0.326937591918336[/C][/ROW]
[ROW][C]129[/C][C]0.617273765919448[/C][C]0.765452468161104[/C][C]0.382726234080552[/C][/ROW]
[ROW][C]130[/C][C]0.592981099961349[/C][C]0.814037800077303[/C][C]0.407018900038651[/C][/ROW]
[ROW][C]131[/C][C]0.579906861267479[/C][C]0.840186277465042[/C][C]0.420093138732521[/C][/ROW]
[ROW][C]132[/C][C]0.511701066609348[/C][C]0.976597866781303[/C][C]0.488298933390652[/C][/ROW]
[ROW][C]133[/C][C]0.614894134511224[/C][C]0.770211730977553[/C][C]0.385105865488776[/C][/ROW]
[ROW][C]134[/C][C]0.586084325050544[/C][C]0.827831349898912[/C][C]0.413915674949456[/C][/ROW]
[ROW][C]135[/C][C]0.562946829378229[/C][C]0.874106341243542[/C][C]0.437053170621771[/C][/ROW]
[ROW][C]136[/C][C]0.483144039632516[/C][C]0.966288079265032[/C][C]0.516855960367484[/C][/ROW]
[ROW][C]137[/C][C]0.487487325135399[/C][C]0.974974650270798[/C][C]0.512512674864601[/C][/ROW]
[ROW][C]138[/C][C]0.436938933493837[/C][C]0.873877866987675[/C][C]0.563061066506163[/C][/ROW]
[ROW][C]139[/C][C]0.355541990837327[/C][C]0.711083981674653[/C][C]0.644458009162673[/C][/ROW]
[ROW][C]140[/C][C]0.288796039099582[/C][C]0.577592078199164[/C][C]0.711203960900418[/C][/ROW]
[ROW][C]141[/C][C]0.240407881704033[/C][C]0.480815763408066[/C][C]0.759592118295967[/C][/ROW]
[ROW][C]142[/C][C]0.195794658850956[/C][C]0.391589317701912[/C][C]0.804205341149044[/C][/ROW]
[ROW][C]143[/C][C]0.144131629985642[/C][C]0.288263259971283[/C][C]0.855868370014358[/C][/ROW]
[ROW][C]144[/C][C]0.0947164893389675[/C][C]0.189432978677935[/C][C]0.905283510661033[/C][/ROW]
[ROW][C]145[/C][C]0.084726664148586[/C][C]0.169453328297172[/C][C]0.915273335851414[/C][/ROW]
[ROW][C]146[/C][C]0.0857080830678513[/C][C]0.171416166135703[/C][C]0.914291916932149[/C][/ROW]
[ROW][C]147[/C][C]0.0528165441478915[/C][C]0.105633088295783[/C][C]0.947183455852109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8149006286561090.3701987426877820.185099371343891
110.8327424513566170.3345150972867660.167257548643383
120.7535535132377820.4928929735244370.246446486762218
130.7317873541446620.5364252917106750.268212645855338
140.9213849593841120.1572300812317760.0786150406158878
150.9181432590849630.1637134818300740.0818567409150371
160.8862712013359230.2274575973281530.113728798664077
170.8396108627536650.3207782744926690.160389137246335
180.7995668784925770.4008662430148470.200433121507423
190.7400151476328630.5199697047342740.259984852367137
200.68161900395140.6367619920971990.318380996048600
210.6990695174049970.6018609651900050.300930482595003
220.6418027593556970.7163944812886060.358197240644303
230.602429266403450.79514146719310.39757073359655
240.5603936276907970.8792127446184060.439606372309203
250.6662591280425660.6674817439148680.333740871957434
260.6314044939702820.7371910120594360.368595506029718
270.5841685859164010.8316628281671970.415831414083599
280.5586152202643880.8827695594712240.441384779735612
290.5767518498510590.8464963002978820.423248150148941
300.623919658930790.752160682138420.37608034106921
310.619969599354790.760060801290420.38003040064521
320.6770857442536640.6458285114926720.322914255746336
330.855053469007730.2898930619845390.144946530992269
340.8313337672316540.3373324655366930.168666232768346
350.791761673755630.4164766524887390.208238326244370
360.748281876488570.503436247022860.25171812351143
370.8603121127342530.2793757745314940.139687887265747
380.8297501139997460.3404997720005090.170249886000254
390.7934523612699350.4130952774601310.206547638730065
400.7606999591565690.4786000816868630.239300040843432
410.7451072465608740.5097855068782530.254892753439126
420.7722557680686970.4554884638626060.227744231931303
430.7559484803106720.4881030393786570.244051519689328
440.7685043550055260.4629912899889480.231495644994474
450.7264018666329190.5471962667341620.273598133367081
460.7646005507246950.470798898550610.235399449275305
470.733664099383860.5326718012322810.266335900616140
480.6932008991844370.6135982016311270.306799100815563
490.677406912956420.6451861740871610.322593087043580
500.6414428029383960.7171143941232080.358557197061604
510.5949581679236720.8100836641526560.405041832076328
520.5987863860370360.8024272279259290.401213613962964
530.5632795667820590.8734408664358820.436720433217941
540.5228839423360940.9542321153278110.477116057663906
550.5097302330955370.9805395338089260.490269766904463
560.4652253065388930.9304506130777870.534774693461107
570.4521152476848870.9042304953697750.547884752315113
580.4144119208658860.8288238417317720.585588079134114
590.521495145987710.957009708024580.47850485401229
600.4754005689395240.9508011378790480.524599431060476
610.4279091845541660.8558183691083320.572090815445834
620.4176843139874740.8353686279749480.582315686012526
630.4221432139336840.8442864278673690.577856786066316
640.37752807520330.75505615040660.6224719247967
650.3629324745313700.7258649490627390.63706752546863
660.3421473916189360.6842947832378730.657852608381064
670.3443062721121530.6886125442243070.655693727887847
680.3020593151145740.6041186302291480.697940684885426
690.3155236371899890.6310472743799780.684476362810011
700.4001643104302210.8003286208604410.59983568956978
710.3574939390455120.7149878780910240.642506060954488
720.3269586034417120.6539172068834230.673041396558288
730.3313252884068380.6626505768136760.668674711593162
740.2945334415514530.5890668831029060.705466558448547
750.3445992806042970.6891985612085930.655400719395703
760.3052358458113620.6104716916227250.694764154188638
770.2738669070822560.5477338141645120.726133092917744
780.3182437551624150.6364875103248310.681756244837584
790.2904381173014760.5808762346029520.709561882698524
800.2545228224025850.509045644805170.745477177597415
810.247114366232420.494228732464840.75288563376758
820.3522912163916470.7045824327832930.647708783608353
830.3402364366609590.6804728733219180.659763563339041
840.3326164751101680.6652329502203360.667383524889832
850.3527527882149580.7055055764299160.647247211785042
860.3426133237671720.6852266475343450.657386676232828
870.3328126887984890.6656253775969780.66718731120151
880.2939704873670530.5879409747341060.706029512632947
890.3038405832222970.6076811664445930.696159416777703
900.4034848173079440.8069696346158890.596515182692056
910.4344221126693420.8688442253386840.565577887330658
920.4123185453153960.8246370906307910.587681454684604
930.3697369852852030.7394739705704060.630263014714797
940.328309195412670.656618390825340.67169080458733
950.3455316794027320.6910633588054640.654468320597268
960.3112056984743190.6224113969486370.688794301525681
970.3035951000139910.6071902000279820.696404899986009
980.2627921539221910.5255843078443810.73720784607781
990.2580932734912880.5161865469825770.741906726508712
1000.2597455933822930.5194911867645850.740254406617707
1010.2219319277103610.4438638554207230.778068072289639
1020.1867245307263870.3734490614527750.813275469273613
1030.1561575343187040.3123150686374090.843842465681296
1040.1358506666875280.2717013333750570.864149333312472
1050.1545218078269800.3090436156539590.84547819217302
1060.1263895780565950.252779156113190.873610421943405
1070.1471161713180160.2942323426360320.852883828681984
1080.1254660065429880.2509320130859760.874533993457012
1090.1423880974809750.2847761949619500.857611902519025
1100.1342492747705450.2684985495410890.865750725229455
1110.1565676200391720.3131352400783430.843432379960828
1120.1969276784060440.3938553568120880.803072321593956
1130.1747790159099730.3495580318199470.825220984090027
1140.4946030954116470.9892061908232940.505396904588353
1150.4399370682659670.8798741365319330.560062931734034
1160.4895328709616810.9790657419233620.510467129038319
1170.8124842605040270.3750314789919450.187515739495973
1180.783303973962030.4333920520759390.216696026037970
1190.7583890214975870.4832219570048260.241610978502413
1200.7148363690872960.5703272618254080.285163630912704
1210.6859252205416330.6281495589167340.314074779458367
1220.6456339698328810.7087320603342380.354366030167119
1230.6532553838374430.6934892323251140.346744616162557
1240.6282174518679370.7435650962641260.371782548132063
1250.6538231628527430.6923536742945130.346176837147257
1260.6355667671381240.7288664657237520.364433232861876
1270.6062399504230340.7875200991539310.393760049576966
1280.6730624080816640.6538751838366710.326937591918336
1290.6172737659194480.7654524681611040.382726234080552
1300.5929810999613490.8140378000773030.407018900038651
1310.5799068612674790.8401862774650420.420093138732521
1320.5117010666093480.9765978667813030.488298933390652
1330.6148941345112240.7702117309775530.385105865488776
1340.5860843250505440.8278313498989120.413915674949456
1350.5629468293782290.8741063412435420.437053170621771
1360.4831440396325160.9662880792650320.516855960367484
1370.4874873251353990.9749746502707980.512512674864601
1380.4369389334938370.8738778669876750.563061066506163
1390.3555419908373270.7110839816746530.644458009162673
1400.2887960390995820.5775920781991640.711203960900418
1410.2404078817040330.4808157634080660.759592118295967
1420.1957946588509560.3915893177019120.804205341149044
1430.1441316299856420.2882632599712830.855868370014358
1440.09471648933896750.1894329786779350.905283510661033
1450.0847266641485860.1694533282971720.915273335851414
1460.08570808306785130.1714161661357030.914291916932149
1470.05281654414789150.1056330882957830.947183455852109







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99380&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99380&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99380&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}