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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 computationMon, 05 Nov 2012 15:53:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352148896vsrhd9t8hts0uyr.htm/, Retrieved Sun, 05 Feb 2023 23:19:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186292, Retrieved Sun, 05 Feb 2023 23:19:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact53
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2012-11-05 20:53:48] [70625068b3924f89f7a6efd1a4acaa7e] [Current]
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Dataseries X:
13	14	12
16	18	11
19	11	14
15	12	12
14	16	21
13	18	12
19	14	22
15	14	11
14	15	10
15	15	13
16	17	10
16	19	8
16	10	15
16	16	14
17	18	10
15	14	14
15	14	14
20	17	11
18	14	10
16	16	13
16	18	7
16	11	14
19	14	12
16	12	14
17	17	11
17	9	9
16	16	11
15	14	15
16	15	14
14	11	13
15	16	9
12	13	15
14	17	10
16	15	11
14	14	13
7	16	8
10	9	20
14	15	12
16	17	10
16	13	10
16	15	9
14	16	14
20	16	8
14	12	14
14	12	11
11	11	13
14	15	9
15	15	11
16	17	15
14	13	11
16	16	10
14	14	14
12	11	18
16	12	14
9	12	11
14	15	12
16	16	13
16	15	9
15	12	10
16	12	15
12	8	20
16	13	12
16	11	12
14	14	14
16	15	13
17	10	11
18	11	17
18	12	12
12	15	13
16	15	14
10	14	13
14	16	15
18	15	13
18	15	10
16	13	11
17	12	19
16	17	13
16	13	17
13	15	13
16	13	9
16	15	11
20	16	10
16	15	9
15	16	12
15	15	12
16	14	13
14	15	13
16	14	12
16	13	15
15	7	22
12	17	13
17	13	15
16	15	13
15	14	15
13	13	10
16	16	11
16	12	16
16	14	11
16	17	11
14	15	10
16	17	10
16	12	16
20	16	12
15	11	11
16	15	16
13	9	19
17	16	11
16	15	16
16	10	15
12	10	24
16	15	14
16	11	15
17	13	11
13	14	15
12	18	12
18	16	10
14	14	14
14	14	13
13	14	9
16	14	15
13	12	15
16	14	14
13	15	11
16	15	8
15	15	11
16	13	11
15	17	8
17	17	10
15	19	11
12	15	13
16	13	11
10	9	20
16	15	10
12	15	15
14	15	12
15	16	14
13	11	23
15	14	14
11	11	16
12	15	11
8	13	12
16	15	10
15	16	14
17	14	12
16	15	12
10	16	11
18	16	12
13	11	13
16	12	11
13	9	19
10	16	12
15	13	17
16	16	9
16	12	12
14	9	19
10	13	18
17	13	15
13	14	14
15	19	11
16	13	9
12	12	18
13	13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 15.6101778789857 + 0.0771210908317657Happiness[t] -0.134840982774543Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  15.6101778789857 +  0.0771210908317657Happiness[t] -0.134840982774543Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  15.6101778789857 +  0.0771210908317657Happiness[t] -0.134840982774543Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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
Learning[t] = + 15.6101778789857 + 0.0771210908317657Happiness[t] -0.134840982774543Depression[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.61017787898571.8518418.429500
Happiness0.07712109083176570.0885250.87120.3849730.192486
Depression-0.1348409827745430.06536-2.06310.0407320.020366

\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) & 15.6101778789857 & 1.851841 & 8.4295 & 0 & 0 \tabularnewline
Happiness & 0.0771210908317657 & 0.088525 & 0.8712 & 0.384973 & 0.192486 \tabularnewline
Depression & -0.134840982774543 & 0.06536 & -2.0631 & 0.040732 & 0.020366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&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]15.6101778789857[/C][C]1.851841[/C][C]8.4295[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0771210908317657[/C][C]0.088525[/C][C]0.8712[/C][C]0.384973[/C][C]0.192486[/C][/ROW]
[ROW][C]Depression[/C][C]-0.134840982774543[/C][C]0.06536[/C][C]-2.0631[/C][C]0.040732[/C][C]0.020366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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)15.61017787898571.8518418.429500
Happiness0.07712109083176570.0885250.87120.3849730.192486
Depression-0.1348409827745430.06536-2.06310.0407320.020366







Multiple Linear Regression - Regression Statistics
Multiple R0.242167285326478
R-squared0.0586449940823957
Adjusted R-squared0.0468040506117341
F-TEST (value)4.95272984181543
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.00819215566757125
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2028272449683
Sum Squared Residuals771.539211516765

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.242167285326478 \tabularnewline
R-squared & 0.0586449940823957 \tabularnewline
Adjusted R-squared & 0.0468040506117341 \tabularnewline
F-TEST (value) & 4.95272984181543 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0.00819215566757125 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.2028272449683 \tabularnewline
Sum Squared Residuals & 771.539211516765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.242167285326478[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0586449940823957[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0468040506117341[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.95272984181543[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.00819215566757125[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.2028272449683[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]771.539211516765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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 R0.242167285326478
R-squared0.0586449940823957
Adjusted R-squared0.0468040506117341
F-TEST (value)4.95272984181543
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.00819215566757125
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2028272449683
Sum Squared Residuals771.539211516765







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.071781357336-2.07178135733595
21615.51510670343750.484893296562493
31914.57073611929154.42926388070848
41514.91753917567240.0824608243276288
51414.0124546940286-0.0124546940285505
61315.380265720663-2.38026572066297
71913.72337152959055.27662847040952
81515.2066223401104-0.206622340110445
91415.4185844137168-1.41858441371675
101515.0140614653931-0.0140614653931256
111615.57282659538030.427173404619715
121615.99675074259290.00324925740709885
131614.35877404568521.64122595431479
141614.95634157345031.04365842654965
151715.6499476862121.35005231378795
161514.80209939178680.197900608213183
171514.80209939178680.197900608213183
182015.43798561260574.56201438739426
191815.3414633228852.65853667711501
201615.09118255622490.908817443775109
211616.0544706345357-0.0544706345356781
221614.57073611929151.42926388070848
231915.07178135733593.9282186426641
241614.64785721012331.35214278987671
251715.43798561260571.56201438739426
261715.09069885150071.9093011484993
271615.3608645217740.639135478226024
281514.66725840901230.332741590987725
291614.87922048261861.12077951738142
301414.7055771020661-0.705577102066063
311515.6305464873231-0.630546487323062
321214.5901373181805-2.59013731818051
331415.5728265953803-1.57282659538028
341615.28374343094220.716256569057789
351414.9369403745614-0.93694037456136
36715.7653874700976-8.7653874700976
371013.6074480409807-3.60744804098073
381415.1489024481677-1.14890244816767
391615.57282659538030.427173404619715
401615.26434223205320.735657767946778
411615.55342539649130.446574603508704
421414.9563415734503-0.956341573450349
432015.76538747009764.2346125299024
441414.6478572101233-0.647857210123286
451415.0523801584469-1.05238015844691
461114.7055771020661-3.70557710206606
471415.5534253964913-1.5534253964913
481515.2837434309422-0.283743430942211
491614.89862168150761.10137831849243
501415.1295012492787-1.12950124927868
511615.49570550454850.504294495451481
521414.8020993917868-0.802099391786817
531214.0313721881933-2.03137218819335
541614.64785721012331.35214278987671
55915.0523801584469-6.05238015844691
561415.1489024481677-1.14890244816767
571615.09118255622490.908817443775109
581615.55342539649130.446574603508704
591515.1872211412215-0.187221141221456
601614.51301622734871.48698377265126
611213.530326950149-1.53032695014897
621614.99466026650411.00533973349586
631614.84041808484061.15958191515939
641414.8020993917868-0.802099391786817
651615.01406146539310.985938534606874
661714.89813797678342.10186202321662
671814.16621317096793.83378682903211
681814.91753917567243.08246082432763
691215.0140614653931-3.01406146539313
701614.87922048261861.12077951738142
711014.9369403745614-4.93694037456136
721414.8215005906758-0.821500590675806
731815.01406146539312.98593853460687
741815.41858441371682.58141558628325
751615.12950124927870.87049875072132
761713.97365229625063.02634770374943
771615.16830364705670.831696352943343
781614.32045535263141.67954464736858
791315.0140614653931-2.01406146539313
801615.39918321482780.600816785172235
811615.28374343094220.716256569057789
822015.49570550454854.50429449545148
831615.55342539649130.446574603508704
841515.2260235389994-0.226023538999434
851515.1489024481677-0.148902448167668
861614.93694037456141.06305962543864
871415.0140614653931-1.01406146539313
881615.07178135733590.928218642664098
891614.59013731818051.40986268181949
901513.18352389376811.81647610623188
911215.1683036470567-3.16830364705666
921714.59013731818052.40986268181949
931615.01406146539310.985938534606874
941514.66725840901230.332741590987725
951315.2643422320532-2.26434223205322
961615.3608645217740.639135478226024
971614.37817524457421.6218247554258
981615.20662234011040.793377659889555
991615.43798561260570.562014387394258
1001415.4185844137168-1.41858441371675
1011615.57282659538030.427173404619715
1021614.37817524457421.6218247554258
1032015.22602353899944.77397646100057
1041514.97525906761510.024740932384852
1051614.60953851706951.3904614829305
1061313.7422890237553-0.742289023755276
1071715.3608645217741.63913547822602
1081614.60953851706951.3904614829305
1091614.35877404568521.64122595431479
1101213.1452052007143-1.14520520071433
1111614.87922048261861.12077951738142
1121614.4358951365171.56410486348302
1131715.12950124927871.87049875072132
1141314.6672584090123-1.66725840901227
1151215.380265720663-3.38026572066297
1161815.49570550454852.50429449545148
1171414.8020993917868-0.802099391786817
1181414.9369403745614-0.93694037456136
1191315.4763043056595-2.47630430565953
1201614.66725840901231.33274159098773
1211314.5130162273487-1.51301622734874
1221614.80209939178681.19790060821318
1231315.2837434309422-2.28374343094221
1241615.68826637926580.311733620734161
1251515.2837434309422-0.283743430942211
1261615.12950124927870.87049875072132
1271515.8425085609294-0.84250856092937
1281715.57282659538031.42717340461972
1291515.5922277942693-0.592227794269273
1301215.0140614653931-3.01406146539313
1311615.12950124927870.87049875072132
1321013.6074480409807-3.60744804098073
1331615.41858441371680.581415586283247
1341214.744379499844-2.74437949984404
1351415.1489024481677-1.14890244816767
1361514.95634157345030.0436584265496514
1371313.3571672743206-0.357167274320637
1381514.80209939178680.197900608213183
1391114.3010541537424-3.30105415374243
1401215.2837434309422-3.28374343094221
141814.9946602665041-6.99466026650414
1421615.41858441371680.581415586283247
1431514.95634157345030.0436584265496514
1441715.07178135733591.9282186426641
1451615.14890244816770.851097551832332
1461015.360864521774-5.36086452177398
1471815.22602353899942.77397646100057
1481314.7055771020661-1.70557710206606
1491615.05238015844690.947619841553086
1501313.7422890237553-0.742289023755276
1511015.2260235389994-5.22602353899943
1521514.32045535263140.679544647368576
1531615.63054648732310.369453512676938
1541614.91753917567241.08246082432763
1551413.74228902375530.257710976244724
1561014.1856143698569-4.18561436985688
1571714.59013731818052.40986268181949
1581314.8020993917868-1.80209939178682
1591515.5922277942693-0.592227794269273
1601615.39918321482780.600816785172235
1611214.1084932790251-2.10849327902512
1621314.455296335406-1.45529633540597

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.071781357336 & -2.07178135733595 \tabularnewline
2 & 16 & 15.5151067034375 & 0.484893296562493 \tabularnewline
3 & 19 & 14.5707361192915 & 4.42926388070848 \tabularnewline
4 & 15 & 14.9175391756724 & 0.0824608243276288 \tabularnewline
5 & 14 & 14.0124546940286 & -0.0124546940285505 \tabularnewline
6 & 13 & 15.380265720663 & -2.38026572066297 \tabularnewline
7 & 19 & 13.7233715295905 & 5.27662847040952 \tabularnewline
8 & 15 & 15.2066223401104 & -0.206622340110445 \tabularnewline
9 & 14 & 15.4185844137168 & -1.41858441371675 \tabularnewline
10 & 15 & 15.0140614653931 & -0.0140614653931256 \tabularnewline
11 & 16 & 15.5728265953803 & 0.427173404619715 \tabularnewline
12 & 16 & 15.9967507425929 & 0.00324925740709885 \tabularnewline
13 & 16 & 14.3587740456852 & 1.64122595431479 \tabularnewline
14 & 16 & 14.9563415734503 & 1.04365842654965 \tabularnewline
15 & 17 & 15.649947686212 & 1.35005231378795 \tabularnewline
16 & 15 & 14.8020993917868 & 0.197900608213183 \tabularnewline
17 & 15 & 14.8020993917868 & 0.197900608213183 \tabularnewline
18 & 20 & 15.4379856126057 & 4.56201438739426 \tabularnewline
19 & 18 & 15.341463322885 & 2.65853667711501 \tabularnewline
20 & 16 & 15.0911825562249 & 0.908817443775109 \tabularnewline
21 & 16 & 16.0544706345357 & -0.0544706345356781 \tabularnewline
22 & 16 & 14.5707361192915 & 1.42926388070848 \tabularnewline
23 & 19 & 15.0717813573359 & 3.9282186426641 \tabularnewline
24 & 16 & 14.6478572101233 & 1.35214278987671 \tabularnewline
25 & 17 & 15.4379856126057 & 1.56201438739426 \tabularnewline
26 & 17 & 15.0906988515007 & 1.9093011484993 \tabularnewline
27 & 16 & 15.360864521774 & 0.639135478226024 \tabularnewline
28 & 15 & 14.6672584090123 & 0.332741590987725 \tabularnewline
29 & 16 & 14.8792204826186 & 1.12077951738142 \tabularnewline
30 & 14 & 14.7055771020661 & -0.705577102066063 \tabularnewline
31 & 15 & 15.6305464873231 & -0.630546487323062 \tabularnewline
32 & 12 & 14.5901373181805 & -2.59013731818051 \tabularnewline
33 & 14 & 15.5728265953803 & -1.57282659538028 \tabularnewline
34 & 16 & 15.2837434309422 & 0.716256569057789 \tabularnewline
35 & 14 & 14.9369403745614 & -0.93694037456136 \tabularnewline
36 & 7 & 15.7653874700976 & -8.7653874700976 \tabularnewline
37 & 10 & 13.6074480409807 & -3.60744804098073 \tabularnewline
38 & 14 & 15.1489024481677 & -1.14890244816767 \tabularnewline
39 & 16 & 15.5728265953803 & 0.427173404619715 \tabularnewline
40 & 16 & 15.2643422320532 & 0.735657767946778 \tabularnewline
41 & 16 & 15.5534253964913 & 0.446574603508704 \tabularnewline
42 & 14 & 14.9563415734503 & -0.956341573450349 \tabularnewline
43 & 20 & 15.7653874700976 & 4.2346125299024 \tabularnewline
44 & 14 & 14.6478572101233 & -0.647857210123286 \tabularnewline
45 & 14 & 15.0523801584469 & -1.05238015844691 \tabularnewline
46 & 11 & 14.7055771020661 & -3.70557710206606 \tabularnewline
47 & 14 & 15.5534253964913 & -1.5534253964913 \tabularnewline
48 & 15 & 15.2837434309422 & -0.283743430942211 \tabularnewline
49 & 16 & 14.8986216815076 & 1.10137831849243 \tabularnewline
50 & 14 & 15.1295012492787 & -1.12950124927868 \tabularnewline
51 & 16 & 15.4957055045485 & 0.504294495451481 \tabularnewline
52 & 14 & 14.8020993917868 & -0.802099391786817 \tabularnewline
53 & 12 & 14.0313721881933 & -2.03137218819335 \tabularnewline
54 & 16 & 14.6478572101233 & 1.35214278987671 \tabularnewline
55 & 9 & 15.0523801584469 & -6.05238015844691 \tabularnewline
56 & 14 & 15.1489024481677 & -1.14890244816767 \tabularnewline
57 & 16 & 15.0911825562249 & 0.908817443775109 \tabularnewline
58 & 16 & 15.5534253964913 & 0.446574603508704 \tabularnewline
59 & 15 & 15.1872211412215 & -0.187221141221456 \tabularnewline
60 & 16 & 14.5130162273487 & 1.48698377265126 \tabularnewline
61 & 12 & 13.530326950149 & -1.53032695014897 \tabularnewline
62 & 16 & 14.9946602665041 & 1.00533973349586 \tabularnewline
63 & 16 & 14.8404180848406 & 1.15958191515939 \tabularnewline
64 & 14 & 14.8020993917868 & -0.802099391786817 \tabularnewline
65 & 16 & 15.0140614653931 & 0.985938534606874 \tabularnewline
66 & 17 & 14.8981379767834 & 2.10186202321662 \tabularnewline
67 & 18 & 14.1662131709679 & 3.83378682903211 \tabularnewline
68 & 18 & 14.9175391756724 & 3.08246082432763 \tabularnewline
69 & 12 & 15.0140614653931 & -3.01406146539313 \tabularnewline
70 & 16 & 14.8792204826186 & 1.12077951738142 \tabularnewline
71 & 10 & 14.9369403745614 & -4.93694037456136 \tabularnewline
72 & 14 & 14.8215005906758 & -0.821500590675806 \tabularnewline
73 & 18 & 15.0140614653931 & 2.98593853460687 \tabularnewline
74 & 18 & 15.4185844137168 & 2.58141558628325 \tabularnewline
75 & 16 & 15.1295012492787 & 0.87049875072132 \tabularnewline
76 & 17 & 13.9736522962506 & 3.02634770374943 \tabularnewline
77 & 16 & 15.1683036470567 & 0.831696352943343 \tabularnewline
78 & 16 & 14.3204553526314 & 1.67954464736858 \tabularnewline
79 & 13 & 15.0140614653931 & -2.01406146539313 \tabularnewline
80 & 16 & 15.3991832148278 & 0.600816785172235 \tabularnewline
81 & 16 & 15.2837434309422 & 0.716256569057789 \tabularnewline
82 & 20 & 15.4957055045485 & 4.50429449545148 \tabularnewline
83 & 16 & 15.5534253964913 & 0.446574603508704 \tabularnewline
84 & 15 & 15.2260235389994 & -0.226023538999434 \tabularnewline
85 & 15 & 15.1489024481677 & -0.148902448167668 \tabularnewline
86 & 16 & 14.9369403745614 & 1.06305962543864 \tabularnewline
87 & 14 & 15.0140614653931 & -1.01406146539313 \tabularnewline
88 & 16 & 15.0717813573359 & 0.928218642664098 \tabularnewline
89 & 16 & 14.5901373181805 & 1.40986268181949 \tabularnewline
90 & 15 & 13.1835238937681 & 1.81647610623188 \tabularnewline
91 & 12 & 15.1683036470567 & -3.16830364705666 \tabularnewline
92 & 17 & 14.5901373181805 & 2.40986268181949 \tabularnewline
93 & 16 & 15.0140614653931 & 0.985938534606874 \tabularnewline
94 & 15 & 14.6672584090123 & 0.332741590987725 \tabularnewline
95 & 13 & 15.2643422320532 & -2.26434223205322 \tabularnewline
96 & 16 & 15.360864521774 & 0.639135478226024 \tabularnewline
97 & 16 & 14.3781752445742 & 1.6218247554258 \tabularnewline
98 & 16 & 15.2066223401104 & 0.793377659889555 \tabularnewline
99 & 16 & 15.4379856126057 & 0.562014387394258 \tabularnewline
100 & 14 & 15.4185844137168 & -1.41858441371675 \tabularnewline
101 & 16 & 15.5728265953803 & 0.427173404619715 \tabularnewline
102 & 16 & 14.3781752445742 & 1.6218247554258 \tabularnewline
103 & 20 & 15.2260235389994 & 4.77397646100057 \tabularnewline
104 & 15 & 14.9752590676151 & 0.024740932384852 \tabularnewline
105 & 16 & 14.6095385170695 & 1.3904614829305 \tabularnewline
106 & 13 & 13.7422890237553 & -0.742289023755276 \tabularnewline
107 & 17 & 15.360864521774 & 1.63913547822602 \tabularnewline
108 & 16 & 14.6095385170695 & 1.3904614829305 \tabularnewline
109 & 16 & 14.3587740456852 & 1.64122595431479 \tabularnewline
110 & 12 & 13.1452052007143 & -1.14520520071433 \tabularnewline
111 & 16 & 14.8792204826186 & 1.12077951738142 \tabularnewline
112 & 16 & 14.435895136517 & 1.56410486348302 \tabularnewline
113 & 17 & 15.1295012492787 & 1.87049875072132 \tabularnewline
114 & 13 & 14.6672584090123 & -1.66725840901227 \tabularnewline
115 & 12 & 15.380265720663 & -3.38026572066297 \tabularnewline
116 & 18 & 15.4957055045485 & 2.50429449545148 \tabularnewline
117 & 14 & 14.8020993917868 & -0.802099391786817 \tabularnewline
118 & 14 & 14.9369403745614 & -0.93694037456136 \tabularnewline
119 & 13 & 15.4763043056595 & -2.47630430565953 \tabularnewline
120 & 16 & 14.6672584090123 & 1.33274159098773 \tabularnewline
121 & 13 & 14.5130162273487 & -1.51301622734874 \tabularnewline
122 & 16 & 14.8020993917868 & 1.19790060821318 \tabularnewline
123 & 13 & 15.2837434309422 & -2.28374343094221 \tabularnewline
124 & 16 & 15.6882663792658 & 0.311733620734161 \tabularnewline
125 & 15 & 15.2837434309422 & -0.283743430942211 \tabularnewline
126 & 16 & 15.1295012492787 & 0.87049875072132 \tabularnewline
127 & 15 & 15.8425085609294 & -0.84250856092937 \tabularnewline
128 & 17 & 15.5728265953803 & 1.42717340461972 \tabularnewline
129 & 15 & 15.5922277942693 & -0.592227794269273 \tabularnewline
130 & 12 & 15.0140614653931 & -3.01406146539313 \tabularnewline
131 & 16 & 15.1295012492787 & 0.87049875072132 \tabularnewline
132 & 10 & 13.6074480409807 & -3.60744804098073 \tabularnewline
133 & 16 & 15.4185844137168 & 0.581415586283247 \tabularnewline
134 & 12 & 14.744379499844 & -2.74437949984404 \tabularnewline
135 & 14 & 15.1489024481677 & -1.14890244816767 \tabularnewline
136 & 15 & 14.9563415734503 & 0.0436584265496514 \tabularnewline
137 & 13 & 13.3571672743206 & -0.357167274320637 \tabularnewline
138 & 15 & 14.8020993917868 & 0.197900608213183 \tabularnewline
139 & 11 & 14.3010541537424 & -3.30105415374243 \tabularnewline
140 & 12 & 15.2837434309422 & -3.28374343094221 \tabularnewline
141 & 8 & 14.9946602665041 & -6.99466026650414 \tabularnewline
142 & 16 & 15.4185844137168 & 0.581415586283247 \tabularnewline
143 & 15 & 14.9563415734503 & 0.0436584265496514 \tabularnewline
144 & 17 & 15.0717813573359 & 1.9282186426641 \tabularnewline
145 & 16 & 15.1489024481677 & 0.851097551832332 \tabularnewline
146 & 10 & 15.360864521774 & -5.36086452177398 \tabularnewline
147 & 18 & 15.2260235389994 & 2.77397646100057 \tabularnewline
148 & 13 & 14.7055771020661 & -1.70557710206606 \tabularnewline
149 & 16 & 15.0523801584469 & 0.947619841553086 \tabularnewline
150 & 13 & 13.7422890237553 & -0.742289023755276 \tabularnewline
151 & 10 & 15.2260235389994 & -5.22602353899943 \tabularnewline
152 & 15 & 14.3204553526314 & 0.679544647368576 \tabularnewline
153 & 16 & 15.6305464873231 & 0.369453512676938 \tabularnewline
154 & 16 & 14.9175391756724 & 1.08246082432763 \tabularnewline
155 & 14 & 13.7422890237553 & 0.257710976244724 \tabularnewline
156 & 10 & 14.1856143698569 & -4.18561436985688 \tabularnewline
157 & 17 & 14.5901373181805 & 2.40986268181949 \tabularnewline
158 & 13 & 14.8020993917868 & -1.80209939178682 \tabularnewline
159 & 15 & 15.5922277942693 & -0.592227794269273 \tabularnewline
160 & 16 & 15.3991832148278 & 0.600816785172235 \tabularnewline
161 & 12 & 14.1084932790251 & -2.10849327902512 \tabularnewline
162 & 13 & 14.455296335406 & -1.45529633540597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&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]13[/C][C]15.071781357336[/C][C]-2.07178135733595[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.5151067034375[/C][C]0.484893296562493[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.5707361192915[/C][C]4.42926388070848[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.9175391756724[/C][C]0.0824608243276288[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]14.0124546940286[/C][C]-0.0124546940285505[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.380265720663[/C][C]-2.38026572066297[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]13.7233715295905[/C][C]5.27662847040952[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.2066223401104[/C][C]-0.206622340110445[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.4185844137168[/C][C]-1.41858441371675[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.0140614653931[/C][C]-0.0140614653931256[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5728265953803[/C][C]0.427173404619715[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.9967507425929[/C][C]0.00324925740709885[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.3587740456852[/C][C]1.64122595431479[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.9563415734503[/C][C]1.04365842654965[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]15.649947686212[/C][C]1.35005231378795[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.8020993917868[/C][C]0.197900608213183[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8020993917868[/C][C]0.197900608213183[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.4379856126057[/C][C]4.56201438739426[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.341463322885[/C][C]2.65853667711501[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.0911825562249[/C][C]0.908817443775109[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]16.0544706345357[/C][C]-0.0544706345356781[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.5707361192915[/C][C]1.42926388070848[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.0717813573359[/C][C]3.9282186426641[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6478572101233[/C][C]1.35214278987671[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4379856126057[/C][C]1.56201438739426[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.0906988515007[/C][C]1.9093011484993[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.360864521774[/C][C]0.639135478226024[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.6672584090123[/C][C]0.332741590987725[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.8792204826186[/C][C]1.12077951738142[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.7055771020661[/C][C]-0.705577102066063[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6305464873231[/C][C]-0.630546487323062[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]14.5901373181805[/C][C]-2.59013731818051[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.5728265953803[/C][C]-1.57282659538028[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.2837434309422[/C][C]0.716256569057789[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.9369403745614[/C][C]-0.93694037456136[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]15.7653874700976[/C][C]-8.7653874700976[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.6074480409807[/C][C]-3.60744804098073[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.1489024481677[/C][C]-1.14890244816767[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.5728265953803[/C][C]0.427173404619715[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2643422320532[/C][C]0.735657767946778[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.5534253964913[/C][C]0.446574603508704[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.9563415734503[/C][C]-0.956341573450349[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]15.7653874700976[/C][C]4.2346125299024[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.6478572101233[/C][C]-0.647857210123286[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0523801584469[/C][C]-1.05238015844691[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.7055771020661[/C][C]-3.70557710206606[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.5534253964913[/C][C]-1.5534253964913[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.2837434309422[/C][C]-0.283743430942211[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.8986216815076[/C][C]1.10137831849243[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.1295012492787[/C][C]-1.12950124927868[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.4957055045485[/C][C]0.504294495451481[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.8020993917868[/C][C]-0.802099391786817[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.0313721881933[/C][C]-2.03137218819335[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.6478572101233[/C][C]1.35214278987671[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]15.0523801584469[/C][C]-6.05238015844691[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.1489024481677[/C][C]-1.14890244816767[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.0911825562249[/C][C]0.908817443775109[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.5534253964913[/C][C]0.446574603508704[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1872211412215[/C][C]-0.187221141221456[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.5130162273487[/C][C]1.48698377265126[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.530326950149[/C][C]-1.53032695014897[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.9946602665041[/C][C]1.00533973349586[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.8404180848406[/C][C]1.15958191515939[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.8020993917868[/C][C]-0.802099391786817[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.0140614653931[/C][C]0.985938534606874[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]14.8981379767834[/C][C]2.10186202321662[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]14.1662131709679[/C][C]3.83378682903211[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.9175391756724[/C][C]3.08246082432763[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.0140614653931[/C][C]-3.01406146539313[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]14.8792204826186[/C][C]1.12077951738142[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]14.9369403745614[/C][C]-4.93694037456136[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.8215005906758[/C][C]-0.821500590675806[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]15.0140614653931[/C][C]2.98593853460687[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]15.4185844137168[/C][C]2.58141558628325[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.1295012492787[/C][C]0.87049875072132[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9736522962506[/C][C]3.02634770374943[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.1683036470567[/C][C]0.831696352943343[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.3204553526314[/C][C]1.67954464736858[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0140614653931[/C][C]-2.01406146539313[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.3991832148278[/C][C]0.600816785172235[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.2837434309422[/C][C]0.716256569057789[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.4957055045485[/C][C]4.50429449545148[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5534253964913[/C][C]0.446574603508704[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.2260235389994[/C][C]-0.226023538999434[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.1489024481677[/C][C]-0.148902448167668[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.9369403745614[/C][C]1.06305962543864[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.0140614653931[/C][C]-1.01406146539313[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.0717813573359[/C][C]0.928218642664098[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5901373181805[/C][C]1.40986268181949[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.1835238937681[/C][C]1.81647610623188[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]15.1683036470567[/C][C]-3.16830364705666[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]14.5901373181805[/C][C]2.40986268181949[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0140614653931[/C][C]0.985938534606874[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.6672584090123[/C][C]0.332741590987725[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2643422320532[/C][C]-2.26434223205322[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.360864521774[/C][C]0.639135478226024[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.3781752445742[/C][C]1.6218247554258[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.2066223401104[/C][C]0.793377659889555[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.4379856126057[/C][C]0.562014387394258[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]15.4185844137168[/C][C]-1.41858441371675[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5728265953803[/C][C]0.427173404619715[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.3781752445742[/C][C]1.6218247554258[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]15.2260235389994[/C][C]4.77397646100057[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.9752590676151[/C][C]0.024740932384852[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.6095385170695[/C][C]1.3904614829305[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]13.7422890237553[/C][C]-0.742289023755276[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.360864521774[/C][C]1.63913547822602[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.6095385170695[/C][C]1.3904614829305[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.3587740456852[/C][C]1.64122595431479[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]13.1452052007143[/C][C]-1.14520520071433[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8792204826186[/C][C]1.12077951738142[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]14.435895136517[/C][C]1.56410486348302[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]15.1295012492787[/C][C]1.87049875072132[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.6672584090123[/C][C]-1.66725840901227[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.380265720663[/C][C]-3.38026572066297[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]15.4957055045485[/C][C]2.50429449545148[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.8020993917868[/C][C]-0.802099391786817[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.9369403745614[/C][C]-0.93694037456136[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.4763043056595[/C][C]-2.47630430565953[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]14.6672584090123[/C][C]1.33274159098773[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.5130162273487[/C][C]-1.51301622734874[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.8020993917868[/C][C]1.19790060821318[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.2837434309422[/C][C]-2.28374343094221[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.6882663792658[/C][C]0.311733620734161[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.2837434309422[/C][C]-0.283743430942211[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]15.1295012492787[/C][C]0.87049875072132[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.8425085609294[/C][C]-0.84250856092937[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.5728265953803[/C][C]1.42717340461972[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]15.5922277942693[/C][C]-0.592227794269273[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0140614653931[/C][C]-3.01406146539313[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.1295012492787[/C][C]0.87049875072132[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.6074480409807[/C][C]-3.60744804098073[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]15.4185844137168[/C][C]0.581415586283247[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.744379499844[/C][C]-2.74437949984404[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.1489024481677[/C][C]-1.14890244816767[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9563415734503[/C][C]0.0436584265496514[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.3571672743206[/C][C]-0.357167274320637[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.8020993917868[/C][C]0.197900608213183[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]14.3010541537424[/C][C]-3.30105415374243[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]15.2837434309422[/C][C]-3.28374343094221[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]14.9946602665041[/C][C]-6.99466026650414[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]15.4185844137168[/C][C]0.581415586283247[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]14.9563415734503[/C][C]0.0436584265496514[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]15.0717813573359[/C][C]1.9282186426641[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]15.1489024481677[/C][C]0.851097551832332[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]15.360864521774[/C][C]-5.36086452177398[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.2260235389994[/C][C]2.77397646100057[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.7055771020661[/C][C]-1.70557710206606[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0523801584469[/C][C]0.947619841553086[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.7422890237553[/C][C]-0.742289023755276[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]15.2260235389994[/C][C]-5.22602353899943[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]14.3204553526314[/C][C]0.679544647368576[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.6305464873231[/C][C]0.369453512676938[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]14.9175391756724[/C][C]1.08246082432763[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.7422890237553[/C][C]0.257710976244724[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]14.1856143698569[/C][C]-4.18561436985688[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]14.5901373181805[/C][C]2.40986268181949[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]14.8020993917868[/C][C]-1.80209939178682[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]15.5922277942693[/C][C]-0.592227794269273[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.3991832148278[/C][C]0.600816785172235[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.1084932790251[/C][C]-2.10849327902512[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]14.455296335406[/C][C]-1.45529633540597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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
11315.071781357336-2.07178135733595
21615.51510670343750.484893296562493
31914.57073611929154.42926388070848
41514.91753917567240.0824608243276288
51414.0124546940286-0.0124546940285505
61315.380265720663-2.38026572066297
71913.72337152959055.27662847040952
81515.2066223401104-0.206622340110445
91415.4185844137168-1.41858441371675
101515.0140614653931-0.0140614653931256
111615.57282659538030.427173404619715
121615.99675074259290.00324925740709885
131614.35877404568521.64122595431479
141614.95634157345031.04365842654965
151715.6499476862121.35005231378795
161514.80209939178680.197900608213183
171514.80209939178680.197900608213183
182015.43798561260574.56201438739426
191815.3414633228852.65853667711501
201615.09118255622490.908817443775109
211616.0544706345357-0.0544706345356781
221614.57073611929151.42926388070848
231915.07178135733593.9282186426641
241614.64785721012331.35214278987671
251715.43798561260571.56201438739426
261715.09069885150071.9093011484993
271615.3608645217740.639135478226024
281514.66725840901230.332741590987725
291614.87922048261861.12077951738142
301414.7055771020661-0.705577102066063
311515.6305464873231-0.630546487323062
321214.5901373181805-2.59013731818051
331415.5728265953803-1.57282659538028
341615.28374343094220.716256569057789
351414.9369403745614-0.93694037456136
36715.7653874700976-8.7653874700976
371013.6074480409807-3.60744804098073
381415.1489024481677-1.14890244816767
391615.57282659538030.427173404619715
401615.26434223205320.735657767946778
411615.55342539649130.446574603508704
421414.9563415734503-0.956341573450349
432015.76538747009764.2346125299024
441414.6478572101233-0.647857210123286
451415.0523801584469-1.05238015844691
461114.7055771020661-3.70557710206606
471415.5534253964913-1.5534253964913
481515.2837434309422-0.283743430942211
491614.89862168150761.10137831849243
501415.1295012492787-1.12950124927868
511615.49570550454850.504294495451481
521414.8020993917868-0.802099391786817
531214.0313721881933-2.03137218819335
541614.64785721012331.35214278987671
55915.0523801584469-6.05238015844691
561415.1489024481677-1.14890244816767
571615.09118255622490.908817443775109
581615.55342539649130.446574603508704
591515.1872211412215-0.187221141221456
601614.51301622734871.48698377265126
611213.530326950149-1.53032695014897
621614.99466026650411.00533973349586
631614.84041808484061.15958191515939
641414.8020993917868-0.802099391786817
651615.01406146539310.985938534606874
661714.89813797678342.10186202321662
671814.16621317096793.83378682903211
681814.91753917567243.08246082432763
691215.0140614653931-3.01406146539313
701614.87922048261861.12077951738142
711014.9369403745614-4.93694037456136
721414.8215005906758-0.821500590675806
731815.01406146539312.98593853460687
741815.41858441371682.58141558628325
751615.12950124927870.87049875072132
761713.97365229625063.02634770374943
771615.16830364705670.831696352943343
781614.32045535263141.67954464736858
791315.0140614653931-2.01406146539313
801615.39918321482780.600816785172235
811615.28374343094220.716256569057789
822015.49570550454854.50429449545148
831615.55342539649130.446574603508704
841515.2260235389994-0.226023538999434
851515.1489024481677-0.148902448167668
861614.93694037456141.06305962543864
871415.0140614653931-1.01406146539313
881615.07178135733590.928218642664098
891614.59013731818051.40986268181949
901513.18352389376811.81647610623188
911215.1683036470567-3.16830364705666
921714.59013731818052.40986268181949
931615.01406146539310.985938534606874
941514.66725840901230.332741590987725
951315.2643422320532-2.26434223205322
961615.3608645217740.639135478226024
971614.37817524457421.6218247554258
981615.20662234011040.793377659889555
991615.43798561260570.562014387394258
1001415.4185844137168-1.41858441371675
1011615.57282659538030.427173404619715
1021614.37817524457421.6218247554258
1032015.22602353899944.77397646100057
1041514.97525906761510.024740932384852
1051614.60953851706951.3904614829305
1061313.7422890237553-0.742289023755276
1071715.3608645217741.63913547822602
1081614.60953851706951.3904614829305
1091614.35877404568521.64122595431479
1101213.1452052007143-1.14520520071433
1111614.87922048261861.12077951738142
1121614.4358951365171.56410486348302
1131715.12950124927871.87049875072132
1141314.6672584090123-1.66725840901227
1151215.380265720663-3.38026572066297
1161815.49570550454852.50429449545148
1171414.8020993917868-0.802099391786817
1181414.9369403745614-0.93694037456136
1191315.4763043056595-2.47630430565953
1201614.66725840901231.33274159098773
1211314.5130162273487-1.51301622734874
1221614.80209939178681.19790060821318
1231315.2837434309422-2.28374343094221
1241615.68826637926580.311733620734161
1251515.2837434309422-0.283743430942211
1261615.12950124927870.87049875072132
1271515.8425085609294-0.84250856092937
1281715.57282659538031.42717340461972
1291515.5922277942693-0.592227794269273
1301215.0140614653931-3.01406146539313
1311615.12950124927870.87049875072132
1321013.6074480409807-3.60744804098073
1331615.41858441371680.581415586283247
1341214.744379499844-2.74437949984404
1351415.1489024481677-1.14890244816767
1361514.95634157345030.0436584265496514
1371313.3571672743206-0.357167274320637
1381514.80209939178680.197900608213183
1391114.3010541537424-3.30105415374243
1401215.2837434309422-3.28374343094221
141814.9946602665041-6.99466026650414
1421615.41858441371680.581415586283247
1431514.95634157345030.0436584265496514
1441715.07178135733591.9282186426641
1451615.14890244816770.851097551832332
1461015.360864521774-5.36086452177398
1471815.22602353899942.77397646100057
1481314.7055771020661-1.70557710206606
1491615.05238015844690.947619841553086
1501313.7422890237553-0.742289023755276
1511015.2260235389994-5.22602353899943
1521514.32045535263140.679544647368576
1531615.63054648732310.369453512676938
1541614.91753917567241.08246082432763
1551413.74228902375530.257710976244724
1561014.1856143698569-4.18561436985688
1571714.59013731818052.40986268181949
1581314.8020993917868-1.80209939178682
1591515.5922277942693-0.592227794269273
1601615.39918321482780.600816785172235
1611214.1084932790251-2.10849327902512
1621314.455296335406-1.45529633540597







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7191905804715180.5616188390569640.280809419528482
70.7549911309954680.4900177380090650.245008869004532
80.6340472231713680.7319055536572640.365952776828632
90.514835451533570.9703290969328590.485164548466429
100.3949275324069630.7898550648139250.605072467593037
110.396846401291840.7936928025836790.60315359870816
120.4140748645293360.8281497290586730.585925135470664
130.3289328846443650.6578657692887290.671067115355635
140.2538504844947630.5077009689895260.746149515505237
150.259563174878160.5191263497563210.74043682512184
160.2009842409129560.4019684818259130.799015759087044
170.1516165128237310.3032330256474630.848383487176269
180.4144440458885490.8288880917770980.585555954111451
190.4247844531203570.8495689062407140.575215546879643
200.3538120079398110.7076240158796220.646187992060189
210.288941398514150.57788279702830.71105860148585
220.232516918971560.465033837943120.76748308102844
230.3028347561694170.6056695123388350.697165243830583
240.2486791401545490.4973582803090990.751320859845451
250.213096271331760.426192542663520.78690372866824
260.1734504566924780.3469009133849560.826549543307522
270.1345823224165550.2691646448331090.865417677583445
280.1106526427586530.2213052855173060.889347357241347
290.08419390286115410.1683878057223080.915806097138846
300.08723561355171050.1744712271034210.912764386448289
310.06906815918134350.1381363183626870.930931840818656
320.1340901610105010.2681803220210020.865909838989499
330.1257062765980770.2514125531961540.874293723401923
340.09817324576647720.1963464915329540.901826754233523
350.08985140357214760.1797028071442950.910148596427852
360.7678800977319870.4642398045360270.232119902268013
370.9006965349380960.1986069301238070.0993034650619036
380.8837303577557370.2325392844885260.116269642244263
390.8570769651534660.2858460696930680.142923034846534
400.8286597781096520.3426804437806970.171340221890348
410.7953495369688330.4093009260623330.204650463031167
420.7687138031662230.4625723936675530.231286196833777
430.8579271927086940.2841456145826120.142072807291306
440.8337580131278320.3324839737443360.166241986872168
450.8102556157270420.3794887685459160.189744384272958
460.8676813411303440.2646373177393110.132318658869656
470.8521435404233990.2957129191532020.147856459576601
480.8222671936898040.3554656126203910.177732806310196
490.7942563643816820.4114872712366350.205743635618318
500.7669671586792540.4660656826414910.233032841320746
510.7298457988916430.5403084022167140.270154201108357
520.6962935331994240.6074129336011520.303706466800576
530.6966889839565390.6066220320869210.303311016043461
540.6681843296919550.6636313406160910.331815670308045
550.8769341396206630.2461317207586750.123065860379337
560.85835031075030.28329937849940.1416496892497
570.8344461511611090.3311076976777830.165553848838891
580.8056093580751080.3887812838497840.194390641924892
590.7736435479280140.4527129041439710.226356452071986
600.7525163875244020.4949672249511950.247483612475598
610.7340201667584990.5319596664830030.265979833241501
620.7033717647903080.5932564704193850.296628235209692
630.6761443788691810.6477112422616380.323855621130819
640.6399366955462450.720126608907510.360063304453755
650.6037434987742160.7925130024515680.396256501225784
660.6030813714100740.7938372571798520.396918628589926
670.6801592384997630.6396815230004730.319840761500237
680.7144467314812490.5711065370375010.285553268518751
690.748898530788480.502202938423040.25110146921152
700.7201715718604540.5596568562790920.279828428139546
710.8508706748579590.2982586502840820.149129325142041
720.8275063489842260.3449873020315480.172493651015774
730.8492303799547450.301539240090510.150769620045255
740.858104858097010.2837902838059790.14189514190299
750.8349937532468290.3300124935063420.165006246753171
760.857063069651950.28587386069610.14293693034805
770.8349005108008690.3301989783982620.165099489199131
780.8235369787041790.3529260425916420.176463021295821
790.818679477759920.362641044480160.18132052224008
800.7893930438709740.4212139122580530.210606956129026
810.7588407110338620.4823185779322750.241159288966138
820.8580946093518080.2838107812963830.141905390648192
830.8321565428925020.3356869142149960.167843457107498
840.8027693756057650.394461248788470.197230624394235
850.7700787425066260.4598425149867490.229921257493374
860.7436086763364290.5127826473271410.256391323663571
870.7136374890335350.572725021932930.286362510966465
880.6815743313734150.636851337253170.318425668626585
890.6588745207926330.6822509584147340.341125479207367
900.6478738657475980.7042522685048050.352126134252402
910.6888369109301290.6223261781397420.311163089069871
920.702000518030150.5959989639396990.29799948196985
930.6718294319200870.6563411361598250.328170568079912
940.6327679384656970.7344641230686060.367232061534303
950.6349150935757210.7301698128485590.365084906424279
960.5957303824486860.8085392351026280.404269617551314
970.581933841733490.8361323165330190.41806615826651
980.5432649730421730.9134700539156550.456735026957827
990.5019632575926280.9960734848147430.498036742407372
1000.4729533919357480.9459067838714970.527046608064252
1010.4294880754654210.8589761509308430.570511924534579
1020.4170001640314430.8340003280628870.582999835968557
1030.6162482279958580.7675035440082840.383751772004142
1040.5699991948480710.8600016103038570.430000805151929
1050.5620823453190920.8758353093618150.437917654680908
1060.5219519340083080.9560961319833840.478048065991692
1070.5129578489026560.9740843021946880.487042151097344
1080.5121886253127540.9756227493744910.487811374687246
1090.4977511247609120.9955022495218240.502248875239088
1100.4694139243117810.9388278486235620.530586075688219
1110.4539327359881880.9078654719763750.546067264011812
1120.4461147585440620.8922295170881240.553885241455938
1130.4433111749523640.8866223499047270.556688825047637
1140.411474202977550.82294840595510.58852579702245
1150.4525728171588520.9051456343177040.547427182841148
1160.4883091079710430.9766182159420850.511690892028957
1170.4417630783100230.8835261566200470.558236921689977
1180.3962574715112550.7925149430225110.603742528488745
1190.4007448274665380.8014896549330750.599255172533462
1200.3977454093975260.7954908187950510.602254590602474
1210.3600944991462990.7201889982925980.639905500853701
1220.3489847590063460.6979695180126910.651015240993654
1230.3373474087060420.6746948174120840.662652591293958
1240.2910239895274540.5820479790549070.708976010472546
1250.2473687746811360.4947375493622720.752631225318864
1260.2183521574567620.4367043149135230.781647842543238
1270.1826519870852610.3653039741705220.817348012914739
1280.1736448824959070.3472897649918140.826355117504093
1290.1422887741990850.284577548398170.857711225800915
1300.1458378283218970.2916756566437930.854162171678103
1310.125213994975510.250427989951020.87478600502449
1320.1433122478649820.2866244957299630.856687752135018
1330.1205720132030860.2411440264061720.879427986796914
1340.1134305936854910.2268611873709830.886569406314509
1350.08883967039391530.1776793407878310.911160329606085
1360.07074375168203750.1414875033640750.929256248317962
1370.05604530932965390.1120906186593080.943954690670346
1380.04381373940588140.08762747881176270.956186260594119
1390.04768477793326370.09536955586652750.952315222066736
1400.05241438922806340.1048287784561270.947585610771937
1410.3711267178467480.7422534356934950.628873282153252
1420.3098682405267830.6197364810535650.690131759473217
1430.2706100630259750.5412201260519490.729389936974026
1440.2687990941604690.5375981883209390.731200905839531
1450.2385815912230310.4771631824460630.761418408776969
1460.477452749843110.954905499686220.52254725015689
1470.6385178675195850.7229642649608310.361482132480415
1480.6478441813257680.7043116373484630.352155818674232
1490.5574143407534140.8851713184931730.442585659246586
1500.4651798886449210.9303597772898420.534820111355079
1510.7767292845816180.4465414308367650.223270715418382
1520.781890851102320.436218297795360.21810914889768
1530.6782905281264450.6434189437471090.321709471873555
1540.5515316827593270.8969366344813460.448468317240673
1550.4743148577903060.9486297155806110.525685142209694
1560.4893709375997420.9787418751994840.510629062400258

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.719190580471518 & 0.561618839056964 & 0.280809419528482 \tabularnewline
7 & 0.754991130995468 & 0.490017738009065 & 0.245008869004532 \tabularnewline
8 & 0.634047223171368 & 0.731905553657264 & 0.365952776828632 \tabularnewline
9 & 0.51483545153357 & 0.970329096932859 & 0.485164548466429 \tabularnewline
10 & 0.394927532406963 & 0.789855064813925 & 0.605072467593037 \tabularnewline
11 & 0.39684640129184 & 0.793692802583679 & 0.60315359870816 \tabularnewline
12 & 0.414074864529336 & 0.828149729058673 & 0.585925135470664 \tabularnewline
13 & 0.328932884644365 & 0.657865769288729 & 0.671067115355635 \tabularnewline
14 & 0.253850484494763 & 0.507700968989526 & 0.746149515505237 \tabularnewline
15 & 0.25956317487816 & 0.519126349756321 & 0.74043682512184 \tabularnewline
16 & 0.200984240912956 & 0.401968481825913 & 0.799015759087044 \tabularnewline
17 & 0.151616512823731 & 0.303233025647463 & 0.848383487176269 \tabularnewline
18 & 0.414444045888549 & 0.828888091777098 & 0.585555954111451 \tabularnewline
19 & 0.424784453120357 & 0.849568906240714 & 0.575215546879643 \tabularnewline
20 & 0.353812007939811 & 0.707624015879622 & 0.646187992060189 \tabularnewline
21 & 0.28894139851415 & 0.5778827970283 & 0.71105860148585 \tabularnewline
22 & 0.23251691897156 & 0.46503383794312 & 0.76748308102844 \tabularnewline
23 & 0.302834756169417 & 0.605669512338835 & 0.697165243830583 \tabularnewline
24 & 0.248679140154549 & 0.497358280309099 & 0.751320859845451 \tabularnewline
25 & 0.21309627133176 & 0.42619254266352 & 0.78690372866824 \tabularnewline
26 & 0.173450456692478 & 0.346900913384956 & 0.826549543307522 \tabularnewline
27 & 0.134582322416555 & 0.269164644833109 & 0.865417677583445 \tabularnewline
28 & 0.110652642758653 & 0.221305285517306 & 0.889347357241347 \tabularnewline
29 & 0.0841939028611541 & 0.168387805722308 & 0.915806097138846 \tabularnewline
30 & 0.0872356135517105 & 0.174471227103421 & 0.912764386448289 \tabularnewline
31 & 0.0690681591813435 & 0.138136318362687 & 0.930931840818656 \tabularnewline
32 & 0.134090161010501 & 0.268180322021002 & 0.865909838989499 \tabularnewline
33 & 0.125706276598077 & 0.251412553196154 & 0.874293723401923 \tabularnewline
34 & 0.0981732457664772 & 0.196346491532954 & 0.901826754233523 \tabularnewline
35 & 0.0898514035721476 & 0.179702807144295 & 0.910148596427852 \tabularnewline
36 & 0.767880097731987 & 0.464239804536027 & 0.232119902268013 \tabularnewline
37 & 0.900696534938096 & 0.198606930123807 & 0.0993034650619036 \tabularnewline
38 & 0.883730357755737 & 0.232539284488526 & 0.116269642244263 \tabularnewline
39 & 0.857076965153466 & 0.285846069693068 & 0.142923034846534 \tabularnewline
40 & 0.828659778109652 & 0.342680443780697 & 0.171340221890348 \tabularnewline
41 & 0.795349536968833 & 0.409300926062333 & 0.204650463031167 \tabularnewline
42 & 0.768713803166223 & 0.462572393667553 & 0.231286196833777 \tabularnewline
43 & 0.857927192708694 & 0.284145614582612 & 0.142072807291306 \tabularnewline
44 & 0.833758013127832 & 0.332483973744336 & 0.166241986872168 \tabularnewline
45 & 0.810255615727042 & 0.379488768545916 & 0.189744384272958 \tabularnewline
46 & 0.867681341130344 & 0.264637317739311 & 0.132318658869656 \tabularnewline
47 & 0.852143540423399 & 0.295712919153202 & 0.147856459576601 \tabularnewline
48 & 0.822267193689804 & 0.355465612620391 & 0.177732806310196 \tabularnewline
49 & 0.794256364381682 & 0.411487271236635 & 0.205743635618318 \tabularnewline
50 & 0.766967158679254 & 0.466065682641491 & 0.233032841320746 \tabularnewline
51 & 0.729845798891643 & 0.540308402216714 & 0.270154201108357 \tabularnewline
52 & 0.696293533199424 & 0.607412933601152 & 0.303706466800576 \tabularnewline
53 & 0.696688983956539 & 0.606622032086921 & 0.303311016043461 \tabularnewline
54 & 0.668184329691955 & 0.663631340616091 & 0.331815670308045 \tabularnewline
55 & 0.876934139620663 & 0.246131720758675 & 0.123065860379337 \tabularnewline
56 & 0.8583503107503 & 0.2832993784994 & 0.1416496892497 \tabularnewline
57 & 0.834446151161109 & 0.331107697677783 & 0.165553848838891 \tabularnewline
58 & 0.805609358075108 & 0.388781283849784 & 0.194390641924892 \tabularnewline
59 & 0.773643547928014 & 0.452712904143971 & 0.226356452071986 \tabularnewline
60 & 0.752516387524402 & 0.494967224951195 & 0.247483612475598 \tabularnewline
61 & 0.734020166758499 & 0.531959666483003 & 0.265979833241501 \tabularnewline
62 & 0.703371764790308 & 0.593256470419385 & 0.296628235209692 \tabularnewline
63 & 0.676144378869181 & 0.647711242261638 & 0.323855621130819 \tabularnewline
64 & 0.639936695546245 & 0.72012660890751 & 0.360063304453755 \tabularnewline
65 & 0.603743498774216 & 0.792513002451568 & 0.396256501225784 \tabularnewline
66 & 0.603081371410074 & 0.793837257179852 & 0.396918628589926 \tabularnewline
67 & 0.680159238499763 & 0.639681523000473 & 0.319840761500237 \tabularnewline
68 & 0.714446731481249 & 0.571106537037501 & 0.285553268518751 \tabularnewline
69 & 0.74889853078848 & 0.50220293842304 & 0.25110146921152 \tabularnewline
70 & 0.720171571860454 & 0.559656856279092 & 0.279828428139546 \tabularnewline
71 & 0.850870674857959 & 0.298258650284082 & 0.149129325142041 \tabularnewline
72 & 0.827506348984226 & 0.344987302031548 & 0.172493651015774 \tabularnewline
73 & 0.849230379954745 & 0.30153924009051 & 0.150769620045255 \tabularnewline
74 & 0.85810485809701 & 0.283790283805979 & 0.14189514190299 \tabularnewline
75 & 0.834993753246829 & 0.330012493506342 & 0.165006246753171 \tabularnewline
76 & 0.85706306965195 & 0.2858738606961 & 0.14293693034805 \tabularnewline
77 & 0.834900510800869 & 0.330198978398262 & 0.165099489199131 \tabularnewline
78 & 0.823536978704179 & 0.352926042591642 & 0.176463021295821 \tabularnewline
79 & 0.81867947775992 & 0.36264104448016 & 0.18132052224008 \tabularnewline
80 & 0.789393043870974 & 0.421213912258053 & 0.210606956129026 \tabularnewline
81 & 0.758840711033862 & 0.482318577932275 & 0.241159288966138 \tabularnewline
82 & 0.858094609351808 & 0.283810781296383 & 0.141905390648192 \tabularnewline
83 & 0.832156542892502 & 0.335686914214996 & 0.167843457107498 \tabularnewline
84 & 0.802769375605765 & 0.39446124878847 & 0.197230624394235 \tabularnewline
85 & 0.770078742506626 & 0.459842514986749 & 0.229921257493374 \tabularnewline
86 & 0.743608676336429 & 0.512782647327141 & 0.256391323663571 \tabularnewline
87 & 0.713637489033535 & 0.57272502193293 & 0.286362510966465 \tabularnewline
88 & 0.681574331373415 & 0.63685133725317 & 0.318425668626585 \tabularnewline
89 & 0.658874520792633 & 0.682250958414734 & 0.341125479207367 \tabularnewline
90 & 0.647873865747598 & 0.704252268504805 & 0.352126134252402 \tabularnewline
91 & 0.688836910930129 & 0.622326178139742 & 0.311163089069871 \tabularnewline
92 & 0.70200051803015 & 0.595998963939699 & 0.29799948196985 \tabularnewline
93 & 0.671829431920087 & 0.656341136159825 & 0.328170568079912 \tabularnewline
94 & 0.632767938465697 & 0.734464123068606 & 0.367232061534303 \tabularnewline
95 & 0.634915093575721 & 0.730169812848559 & 0.365084906424279 \tabularnewline
96 & 0.595730382448686 & 0.808539235102628 & 0.404269617551314 \tabularnewline
97 & 0.58193384173349 & 0.836132316533019 & 0.41806615826651 \tabularnewline
98 & 0.543264973042173 & 0.913470053915655 & 0.456735026957827 \tabularnewline
99 & 0.501963257592628 & 0.996073484814743 & 0.498036742407372 \tabularnewline
100 & 0.472953391935748 & 0.945906783871497 & 0.527046608064252 \tabularnewline
101 & 0.429488075465421 & 0.858976150930843 & 0.570511924534579 \tabularnewline
102 & 0.417000164031443 & 0.834000328062887 & 0.582999835968557 \tabularnewline
103 & 0.616248227995858 & 0.767503544008284 & 0.383751772004142 \tabularnewline
104 & 0.569999194848071 & 0.860001610303857 & 0.430000805151929 \tabularnewline
105 & 0.562082345319092 & 0.875835309361815 & 0.437917654680908 \tabularnewline
106 & 0.521951934008308 & 0.956096131983384 & 0.478048065991692 \tabularnewline
107 & 0.512957848902656 & 0.974084302194688 & 0.487042151097344 \tabularnewline
108 & 0.512188625312754 & 0.975622749374491 & 0.487811374687246 \tabularnewline
109 & 0.497751124760912 & 0.995502249521824 & 0.502248875239088 \tabularnewline
110 & 0.469413924311781 & 0.938827848623562 & 0.530586075688219 \tabularnewline
111 & 0.453932735988188 & 0.907865471976375 & 0.546067264011812 \tabularnewline
112 & 0.446114758544062 & 0.892229517088124 & 0.553885241455938 \tabularnewline
113 & 0.443311174952364 & 0.886622349904727 & 0.556688825047637 \tabularnewline
114 & 0.41147420297755 & 0.8229484059551 & 0.58852579702245 \tabularnewline
115 & 0.452572817158852 & 0.905145634317704 & 0.547427182841148 \tabularnewline
116 & 0.488309107971043 & 0.976618215942085 & 0.511690892028957 \tabularnewline
117 & 0.441763078310023 & 0.883526156620047 & 0.558236921689977 \tabularnewline
118 & 0.396257471511255 & 0.792514943022511 & 0.603742528488745 \tabularnewline
119 & 0.400744827466538 & 0.801489654933075 & 0.599255172533462 \tabularnewline
120 & 0.397745409397526 & 0.795490818795051 & 0.602254590602474 \tabularnewline
121 & 0.360094499146299 & 0.720188998292598 & 0.639905500853701 \tabularnewline
122 & 0.348984759006346 & 0.697969518012691 & 0.651015240993654 \tabularnewline
123 & 0.337347408706042 & 0.674694817412084 & 0.662652591293958 \tabularnewline
124 & 0.291023989527454 & 0.582047979054907 & 0.708976010472546 \tabularnewline
125 & 0.247368774681136 & 0.494737549362272 & 0.752631225318864 \tabularnewline
126 & 0.218352157456762 & 0.436704314913523 & 0.781647842543238 \tabularnewline
127 & 0.182651987085261 & 0.365303974170522 & 0.817348012914739 \tabularnewline
128 & 0.173644882495907 & 0.347289764991814 & 0.826355117504093 \tabularnewline
129 & 0.142288774199085 & 0.28457754839817 & 0.857711225800915 \tabularnewline
130 & 0.145837828321897 & 0.291675656643793 & 0.854162171678103 \tabularnewline
131 & 0.12521399497551 & 0.25042798995102 & 0.87478600502449 \tabularnewline
132 & 0.143312247864982 & 0.286624495729963 & 0.856687752135018 \tabularnewline
133 & 0.120572013203086 & 0.241144026406172 & 0.879427986796914 \tabularnewline
134 & 0.113430593685491 & 0.226861187370983 & 0.886569406314509 \tabularnewline
135 & 0.0888396703939153 & 0.177679340787831 & 0.911160329606085 \tabularnewline
136 & 0.0707437516820375 & 0.141487503364075 & 0.929256248317962 \tabularnewline
137 & 0.0560453093296539 & 0.112090618659308 & 0.943954690670346 \tabularnewline
138 & 0.0438137394058814 & 0.0876274788117627 & 0.956186260594119 \tabularnewline
139 & 0.0476847779332637 & 0.0953695558665275 & 0.952315222066736 \tabularnewline
140 & 0.0524143892280634 & 0.104828778456127 & 0.947585610771937 \tabularnewline
141 & 0.371126717846748 & 0.742253435693495 & 0.628873282153252 \tabularnewline
142 & 0.309868240526783 & 0.619736481053565 & 0.690131759473217 \tabularnewline
143 & 0.270610063025975 & 0.541220126051949 & 0.729389936974026 \tabularnewline
144 & 0.268799094160469 & 0.537598188320939 & 0.731200905839531 \tabularnewline
145 & 0.238581591223031 & 0.477163182446063 & 0.761418408776969 \tabularnewline
146 & 0.47745274984311 & 0.95490549968622 & 0.52254725015689 \tabularnewline
147 & 0.638517867519585 & 0.722964264960831 & 0.361482132480415 \tabularnewline
148 & 0.647844181325768 & 0.704311637348463 & 0.352155818674232 \tabularnewline
149 & 0.557414340753414 & 0.885171318493173 & 0.442585659246586 \tabularnewline
150 & 0.465179888644921 & 0.930359777289842 & 0.534820111355079 \tabularnewline
151 & 0.776729284581618 & 0.446541430836765 & 0.223270715418382 \tabularnewline
152 & 0.78189085110232 & 0.43621829779536 & 0.21810914889768 \tabularnewline
153 & 0.678290528126445 & 0.643418943747109 & 0.321709471873555 \tabularnewline
154 & 0.551531682759327 & 0.896936634481346 & 0.448468317240673 \tabularnewline
155 & 0.474314857790306 & 0.948629715580611 & 0.525685142209694 \tabularnewline
156 & 0.489370937599742 & 0.978741875199484 & 0.510629062400258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&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]6[/C][C]0.719190580471518[/C][C]0.561618839056964[/C][C]0.280809419528482[/C][/ROW]
[ROW][C]7[/C][C]0.754991130995468[/C][C]0.490017738009065[/C][C]0.245008869004532[/C][/ROW]
[ROW][C]8[/C][C]0.634047223171368[/C][C]0.731905553657264[/C][C]0.365952776828632[/C][/ROW]
[ROW][C]9[/C][C]0.51483545153357[/C][C]0.970329096932859[/C][C]0.485164548466429[/C][/ROW]
[ROW][C]10[/C][C]0.394927532406963[/C][C]0.789855064813925[/C][C]0.605072467593037[/C][/ROW]
[ROW][C]11[/C][C]0.39684640129184[/C][C]0.793692802583679[/C][C]0.60315359870816[/C][/ROW]
[ROW][C]12[/C][C]0.414074864529336[/C][C]0.828149729058673[/C][C]0.585925135470664[/C][/ROW]
[ROW][C]13[/C][C]0.328932884644365[/C][C]0.657865769288729[/C][C]0.671067115355635[/C][/ROW]
[ROW][C]14[/C][C]0.253850484494763[/C][C]0.507700968989526[/C][C]0.746149515505237[/C][/ROW]
[ROW][C]15[/C][C]0.25956317487816[/C][C]0.519126349756321[/C][C]0.74043682512184[/C][/ROW]
[ROW][C]16[/C][C]0.200984240912956[/C][C]0.401968481825913[/C][C]0.799015759087044[/C][/ROW]
[ROW][C]17[/C][C]0.151616512823731[/C][C]0.303233025647463[/C][C]0.848383487176269[/C][/ROW]
[ROW][C]18[/C][C]0.414444045888549[/C][C]0.828888091777098[/C][C]0.585555954111451[/C][/ROW]
[ROW][C]19[/C][C]0.424784453120357[/C][C]0.849568906240714[/C][C]0.575215546879643[/C][/ROW]
[ROW][C]20[/C][C]0.353812007939811[/C][C]0.707624015879622[/C][C]0.646187992060189[/C][/ROW]
[ROW][C]21[/C][C]0.28894139851415[/C][C]0.5778827970283[/C][C]0.71105860148585[/C][/ROW]
[ROW][C]22[/C][C]0.23251691897156[/C][C]0.46503383794312[/C][C]0.76748308102844[/C][/ROW]
[ROW][C]23[/C][C]0.302834756169417[/C][C]0.605669512338835[/C][C]0.697165243830583[/C][/ROW]
[ROW][C]24[/C][C]0.248679140154549[/C][C]0.497358280309099[/C][C]0.751320859845451[/C][/ROW]
[ROW][C]25[/C][C]0.21309627133176[/C][C]0.42619254266352[/C][C]0.78690372866824[/C][/ROW]
[ROW][C]26[/C][C]0.173450456692478[/C][C]0.346900913384956[/C][C]0.826549543307522[/C][/ROW]
[ROW][C]27[/C][C]0.134582322416555[/C][C]0.269164644833109[/C][C]0.865417677583445[/C][/ROW]
[ROW][C]28[/C][C]0.110652642758653[/C][C]0.221305285517306[/C][C]0.889347357241347[/C][/ROW]
[ROW][C]29[/C][C]0.0841939028611541[/C][C]0.168387805722308[/C][C]0.915806097138846[/C][/ROW]
[ROW][C]30[/C][C]0.0872356135517105[/C][C]0.174471227103421[/C][C]0.912764386448289[/C][/ROW]
[ROW][C]31[/C][C]0.0690681591813435[/C][C]0.138136318362687[/C][C]0.930931840818656[/C][/ROW]
[ROW][C]32[/C][C]0.134090161010501[/C][C]0.268180322021002[/C][C]0.865909838989499[/C][/ROW]
[ROW][C]33[/C][C]0.125706276598077[/C][C]0.251412553196154[/C][C]0.874293723401923[/C][/ROW]
[ROW][C]34[/C][C]0.0981732457664772[/C][C]0.196346491532954[/C][C]0.901826754233523[/C][/ROW]
[ROW][C]35[/C][C]0.0898514035721476[/C][C]0.179702807144295[/C][C]0.910148596427852[/C][/ROW]
[ROW][C]36[/C][C]0.767880097731987[/C][C]0.464239804536027[/C][C]0.232119902268013[/C][/ROW]
[ROW][C]37[/C][C]0.900696534938096[/C][C]0.198606930123807[/C][C]0.0993034650619036[/C][/ROW]
[ROW][C]38[/C][C]0.883730357755737[/C][C]0.232539284488526[/C][C]0.116269642244263[/C][/ROW]
[ROW][C]39[/C][C]0.857076965153466[/C][C]0.285846069693068[/C][C]0.142923034846534[/C][/ROW]
[ROW][C]40[/C][C]0.828659778109652[/C][C]0.342680443780697[/C][C]0.171340221890348[/C][/ROW]
[ROW][C]41[/C][C]0.795349536968833[/C][C]0.409300926062333[/C][C]0.204650463031167[/C][/ROW]
[ROW][C]42[/C][C]0.768713803166223[/C][C]0.462572393667553[/C][C]0.231286196833777[/C][/ROW]
[ROW][C]43[/C][C]0.857927192708694[/C][C]0.284145614582612[/C][C]0.142072807291306[/C][/ROW]
[ROW][C]44[/C][C]0.833758013127832[/C][C]0.332483973744336[/C][C]0.166241986872168[/C][/ROW]
[ROW][C]45[/C][C]0.810255615727042[/C][C]0.379488768545916[/C][C]0.189744384272958[/C][/ROW]
[ROW][C]46[/C][C]0.867681341130344[/C][C]0.264637317739311[/C][C]0.132318658869656[/C][/ROW]
[ROW][C]47[/C][C]0.852143540423399[/C][C]0.295712919153202[/C][C]0.147856459576601[/C][/ROW]
[ROW][C]48[/C][C]0.822267193689804[/C][C]0.355465612620391[/C][C]0.177732806310196[/C][/ROW]
[ROW][C]49[/C][C]0.794256364381682[/C][C]0.411487271236635[/C][C]0.205743635618318[/C][/ROW]
[ROW][C]50[/C][C]0.766967158679254[/C][C]0.466065682641491[/C][C]0.233032841320746[/C][/ROW]
[ROW][C]51[/C][C]0.729845798891643[/C][C]0.540308402216714[/C][C]0.270154201108357[/C][/ROW]
[ROW][C]52[/C][C]0.696293533199424[/C][C]0.607412933601152[/C][C]0.303706466800576[/C][/ROW]
[ROW][C]53[/C][C]0.696688983956539[/C][C]0.606622032086921[/C][C]0.303311016043461[/C][/ROW]
[ROW][C]54[/C][C]0.668184329691955[/C][C]0.663631340616091[/C][C]0.331815670308045[/C][/ROW]
[ROW][C]55[/C][C]0.876934139620663[/C][C]0.246131720758675[/C][C]0.123065860379337[/C][/ROW]
[ROW][C]56[/C][C]0.8583503107503[/C][C]0.2832993784994[/C][C]0.1416496892497[/C][/ROW]
[ROW][C]57[/C][C]0.834446151161109[/C][C]0.331107697677783[/C][C]0.165553848838891[/C][/ROW]
[ROW][C]58[/C][C]0.805609358075108[/C][C]0.388781283849784[/C][C]0.194390641924892[/C][/ROW]
[ROW][C]59[/C][C]0.773643547928014[/C][C]0.452712904143971[/C][C]0.226356452071986[/C][/ROW]
[ROW][C]60[/C][C]0.752516387524402[/C][C]0.494967224951195[/C][C]0.247483612475598[/C][/ROW]
[ROW][C]61[/C][C]0.734020166758499[/C][C]0.531959666483003[/C][C]0.265979833241501[/C][/ROW]
[ROW][C]62[/C][C]0.703371764790308[/C][C]0.593256470419385[/C][C]0.296628235209692[/C][/ROW]
[ROW][C]63[/C][C]0.676144378869181[/C][C]0.647711242261638[/C][C]0.323855621130819[/C][/ROW]
[ROW][C]64[/C][C]0.639936695546245[/C][C]0.72012660890751[/C][C]0.360063304453755[/C][/ROW]
[ROW][C]65[/C][C]0.603743498774216[/C][C]0.792513002451568[/C][C]0.396256501225784[/C][/ROW]
[ROW][C]66[/C][C]0.603081371410074[/C][C]0.793837257179852[/C][C]0.396918628589926[/C][/ROW]
[ROW][C]67[/C][C]0.680159238499763[/C][C]0.639681523000473[/C][C]0.319840761500237[/C][/ROW]
[ROW][C]68[/C][C]0.714446731481249[/C][C]0.571106537037501[/C][C]0.285553268518751[/C][/ROW]
[ROW][C]69[/C][C]0.74889853078848[/C][C]0.50220293842304[/C][C]0.25110146921152[/C][/ROW]
[ROW][C]70[/C][C]0.720171571860454[/C][C]0.559656856279092[/C][C]0.279828428139546[/C][/ROW]
[ROW][C]71[/C][C]0.850870674857959[/C][C]0.298258650284082[/C][C]0.149129325142041[/C][/ROW]
[ROW][C]72[/C][C]0.827506348984226[/C][C]0.344987302031548[/C][C]0.172493651015774[/C][/ROW]
[ROW][C]73[/C][C]0.849230379954745[/C][C]0.30153924009051[/C][C]0.150769620045255[/C][/ROW]
[ROW][C]74[/C][C]0.85810485809701[/C][C]0.283790283805979[/C][C]0.14189514190299[/C][/ROW]
[ROW][C]75[/C][C]0.834993753246829[/C][C]0.330012493506342[/C][C]0.165006246753171[/C][/ROW]
[ROW][C]76[/C][C]0.85706306965195[/C][C]0.2858738606961[/C][C]0.14293693034805[/C][/ROW]
[ROW][C]77[/C][C]0.834900510800869[/C][C]0.330198978398262[/C][C]0.165099489199131[/C][/ROW]
[ROW][C]78[/C][C]0.823536978704179[/C][C]0.352926042591642[/C][C]0.176463021295821[/C][/ROW]
[ROW][C]79[/C][C]0.81867947775992[/C][C]0.36264104448016[/C][C]0.18132052224008[/C][/ROW]
[ROW][C]80[/C][C]0.789393043870974[/C][C]0.421213912258053[/C][C]0.210606956129026[/C][/ROW]
[ROW][C]81[/C][C]0.758840711033862[/C][C]0.482318577932275[/C][C]0.241159288966138[/C][/ROW]
[ROW][C]82[/C][C]0.858094609351808[/C][C]0.283810781296383[/C][C]0.141905390648192[/C][/ROW]
[ROW][C]83[/C][C]0.832156542892502[/C][C]0.335686914214996[/C][C]0.167843457107498[/C][/ROW]
[ROW][C]84[/C][C]0.802769375605765[/C][C]0.39446124878847[/C][C]0.197230624394235[/C][/ROW]
[ROW][C]85[/C][C]0.770078742506626[/C][C]0.459842514986749[/C][C]0.229921257493374[/C][/ROW]
[ROW][C]86[/C][C]0.743608676336429[/C][C]0.512782647327141[/C][C]0.256391323663571[/C][/ROW]
[ROW][C]87[/C][C]0.713637489033535[/C][C]0.57272502193293[/C][C]0.286362510966465[/C][/ROW]
[ROW][C]88[/C][C]0.681574331373415[/C][C]0.63685133725317[/C][C]0.318425668626585[/C][/ROW]
[ROW][C]89[/C][C]0.658874520792633[/C][C]0.682250958414734[/C][C]0.341125479207367[/C][/ROW]
[ROW][C]90[/C][C]0.647873865747598[/C][C]0.704252268504805[/C][C]0.352126134252402[/C][/ROW]
[ROW][C]91[/C][C]0.688836910930129[/C][C]0.622326178139742[/C][C]0.311163089069871[/C][/ROW]
[ROW][C]92[/C][C]0.70200051803015[/C][C]0.595998963939699[/C][C]0.29799948196985[/C][/ROW]
[ROW][C]93[/C][C]0.671829431920087[/C][C]0.656341136159825[/C][C]0.328170568079912[/C][/ROW]
[ROW][C]94[/C][C]0.632767938465697[/C][C]0.734464123068606[/C][C]0.367232061534303[/C][/ROW]
[ROW][C]95[/C][C]0.634915093575721[/C][C]0.730169812848559[/C][C]0.365084906424279[/C][/ROW]
[ROW][C]96[/C][C]0.595730382448686[/C][C]0.808539235102628[/C][C]0.404269617551314[/C][/ROW]
[ROW][C]97[/C][C]0.58193384173349[/C][C]0.836132316533019[/C][C]0.41806615826651[/C][/ROW]
[ROW][C]98[/C][C]0.543264973042173[/C][C]0.913470053915655[/C][C]0.456735026957827[/C][/ROW]
[ROW][C]99[/C][C]0.501963257592628[/C][C]0.996073484814743[/C][C]0.498036742407372[/C][/ROW]
[ROW][C]100[/C][C]0.472953391935748[/C][C]0.945906783871497[/C][C]0.527046608064252[/C][/ROW]
[ROW][C]101[/C][C]0.429488075465421[/C][C]0.858976150930843[/C][C]0.570511924534579[/C][/ROW]
[ROW][C]102[/C][C]0.417000164031443[/C][C]0.834000328062887[/C][C]0.582999835968557[/C][/ROW]
[ROW][C]103[/C][C]0.616248227995858[/C][C]0.767503544008284[/C][C]0.383751772004142[/C][/ROW]
[ROW][C]104[/C][C]0.569999194848071[/C][C]0.860001610303857[/C][C]0.430000805151929[/C][/ROW]
[ROW][C]105[/C][C]0.562082345319092[/C][C]0.875835309361815[/C][C]0.437917654680908[/C][/ROW]
[ROW][C]106[/C][C]0.521951934008308[/C][C]0.956096131983384[/C][C]0.478048065991692[/C][/ROW]
[ROW][C]107[/C][C]0.512957848902656[/C][C]0.974084302194688[/C][C]0.487042151097344[/C][/ROW]
[ROW][C]108[/C][C]0.512188625312754[/C][C]0.975622749374491[/C][C]0.487811374687246[/C][/ROW]
[ROW][C]109[/C][C]0.497751124760912[/C][C]0.995502249521824[/C][C]0.502248875239088[/C][/ROW]
[ROW][C]110[/C][C]0.469413924311781[/C][C]0.938827848623562[/C][C]0.530586075688219[/C][/ROW]
[ROW][C]111[/C][C]0.453932735988188[/C][C]0.907865471976375[/C][C]0.546067264011812[/C][/ROW]
[ROW][C]112[/C][C]0.446114758544062[/C][C]0.892229517088124[/C][C]0.553885241455938[/C][/ROW]
[ROW][C]113[/C][C]0.443311174952364[/C][C]0.886622349904727[/C][C]0.556688825047637[/C][/ROW]
[ROW][C]114[/C][C]0.41147420297755[/C][C]0.8229484059551[/C][C]0.58852579702245[/C][/ROW]
[ROW][C]115[/C][C]0.452572817158852[/C][C]0.905145634317704[/C][C]0.547427182841148[/C][/ROW]
[ROW][C]116[/C][C]0.488309107971043[/C][C]0.976618215942085[/C][C]0.511690892028957[/C][/ROW]
[ROW][C]117[/C][C]0.441763078310023[/C][C]0.883526156620047[/C][C]0.558236921689977[/C][/ROW]
[ROW][C]118[/C][C]0.396257471511255[/C][C]0.792514943022511[/C][C]0.603742528488745[/C][/ROW]
[ROW][C]119[/C][C]0.400744827466538[/C][C]0.801489654933075[/C][C]0.599255172533462[/C][/ROW]
[ROW][C]120[/C][C]0.397745409397526[/C][C]0.795490818795051[/C][C]0.602254590602474[/C][/ROW]
[ROW][C]121[/C][C]0.360094499146299[/C][C]0.720188998292598[/C][C]0.639905500853701[/C][/ROW]
[ROW][C]122[/C][C]0.348984759006346[/C][C]0.697969518012691[/C][C]0.651015240993654[/C][/ROW]
[ROW][C]123[/C][C]0.337347408706042[/C][C]0.674694817412084[/C][C]0.662652591293958[/C][/ROW]
[ROW][C]124[/C][C]0.291023989527454[/C][C]0.582047979054907[/C][C]0.708976010472546[/C][/ROW]
[ROW][C]125[/C][C]0.247368774681136[/C][C]0.494737549362272[/C][C]0.752631225318864[/C][/ROW]
[ROW][C]126[/C][C]0.218352157456762[/C][C]0.436704314913523[/C][C]0.781647842543238[/C][/ROW]
[ROW][C]127[/C][C]0.182651987085261[/C][C]0.365303974170522[/C][C]0.817348012914739[/C][/ROW]
[ROW][C]128[/C][C]0.173644882495907[/C][C]0.347289764991814[/C][C]0.826355117504093[/C][/ROW]
[ROW][C]129[/C][C]0.142288774199085[/C][C]0.28457754839817[/C][C]0.857711225800915[/C][/ROW]
[ROW][C]130[/C][C]0.145837828321897[/C][C]0.291675656643793[/C][C]0.854162171678103[/C][/ROW]
[ROW][C]131[/C][C]0.12521399497551[/C][C]0.25042798995102[/C][C]0.87478600502449[/C][/ROW]
[ROW][C]132[/C][C]0.143312247864982[/C][C]0.286624495729963[/C][C]0.856687752135018[/C][/ROW]
[ROW][C]133[/C][C]0.120572013203086[/C][C]0.241144026406172[/C][C]0.879427986796914[/C][/ROW]
[ROW][C]134[/C][C]0.113430593685491[/C][C]0.226861187370983[/C][C]0.886569406314509[/C][/ROW]
[ROW][C]135[/C][C]0.0888396703939153[/C][C]0.177679340787831[/C][C]0.911160329606085[/C][/ROW]
[ROW][C]136[/C][C]0.0707437516820375[/C][C]0.141487503364075[/C][C]0.929256248317962[/C][/ROW]
[ROW][C]137[/C][C]0.0560453093296539[/C][C]0.112090618659308[/C][C]0.943954690670346[/C][/ROW]
[ROW][C]138[/C][C]0.0438137394058814[/C][C]0.0876274788117627[/C][C]0.956186260594119[/C][/ROW]
[ROW][C]139[/C][C]0.0476847779332637[/C][C]0.0953695558665275[/C][C]0.952315222066736[/C][/ROW]
[ROW][C]140[/C][C]0.0524143892280634[/C][C]0.104828778456127[/C][C]0.947585610771937[/C][/ROW]
[ROW][C]141[/C][C]0.371126717846748[/C][C]0.742253435693495[/C][C]0.628873282153252[/C][/ROW]
[ROW][C]142[/C][C]0.309868240526783[/C][C]0.619736481053565[/C][C]0.690131759473217[/C][/ROW]
[ROW][C]143[/C][C]0.270610063025975[/C][C]0.541220126051949[/C][C]0.729389936974026[/C][/ROW]
[ROW][C]144[/C][C]0.268799094160469[/C][C]0.537598188320939[/C][C]0.731200905839531[/C][/ROW]
[ROW][C]145[/C][C]0.238581591223031[/C][C]0.477163182446063[/C][C]0.761418408776969[/C][/ROW]
[ROW][C]146[/C][C]0.47745274984311[/C][C]0.95490549968622[/C][C]0.52254725015689[/C][/ROW]
[ROW][C]147[/C][C]0.638517867519585[/C][C]0.722964264960831[/C][C]0.361482132480415[/C][/ROW]
[ROW][C]148[/C][C]0.647844181325768[/C][C]0.704311637348463[/C][C]0.352155818674232[/C][/ROW]
[ROW][C]149[/C][C]0.557414340753414[/C][C]0.885171318493173[/C][C]0.442585659246586[/C][/ROW]
[ROW][C]150[/C][C]0.465179888644921[/C][C]0.930359777289842[/C][C]0.534820111355079[/C][/ROW]
[ROW][C]151[/C][C]0.776729284581618[/C][C]0.446541430836765[/C][C]0.223270715418382[/C][/ROW]
[ROW][C]152[/C][C]0.78189085110232[/C][C]0.43621829779536[/C][C]0.21810914889768[/C][/ROW]
[ROW][C]153[/C][C]0.678290528126445[/C][C]0.643418943747109[/C][C]0.321709471873555[/C][/ROW]
[ROW][C]154[/C][C]0.551531682759327[/C][C]0.896936634481346[/C][C]0.448468317240673[/C][/ROW]
[ROW][C]155[/C][C]0.474314857790306[/C][C]0.948629715580611[/C][C]0.525685142209694[/C][/ROW]
[ROW][C]156[/C][C]0.489370937599742[/C][C]0.978741875199484[/C][C]0.510629062400258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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
60.7191905804715180.5616188390569640.280809419528482
70.7549911309954680.4900177380090650.245008869004532
80.6340472231713680.7319055536572640.365952776828632
90.514835451533570.9703290969328590.485164548466429
100.3949275324069630.7898550648139250.605072467593037
110.396846401291840.7936928025836790.60315359870816
120.4140748645293360.8281497290586730.585925135470664
130.3289328846443650.6578657692887290.671067115355635
140.2538504844947630.5077009689895260.746149515505237
150.259563174878160.5191263497563210.74043682512184
160.2009842409129560.4019684818259130.799015759087044
170.1516165128237310.3032330256474630.848383487176269
180.4144440458885490.8288880917770980.585555954111451
190.4247844531203570.8495689062407140.575215546879643
200.3538120079398110.7076240158796220.646187992060189
210.288941398514150.57788279702830.71105860148585
220.232516918971560.465033837943120.76748308102844
230.3028347561694170.6056695123388350.697165243830583
240.2486791401545490.4973582803090990.751320859845451
250.213096271331760.426192542663520.78690372866824
260.1734504566924780.3469009133849560.826549543307522
270.1345823224165550.2691646448331090.865417677583445
280.1106526427586530.2213052855173060.889347357241347
290.08419390286115410.1683878057223080.915806097138846
300.08723561355171050.1744712271034210.912764386448289
310.06906815918134350.1381363183626870.930931840818656
320.1340901610105010.2681803220210020.865909838989499
330.1257062765980770.2514125531961540.874293723401923
340.09817324576647720.1963464915329540.901826754233523
350.08985140357214760.1797028071442950.910148596427852
360.7678800977319870.4642398045360270.232119902268013
370.9006965349380960.1986069301238070.0993034650619036
380.8837303577557370.2325392844885260.116269642244263
390.8570769651534660.2858460696930680.142923034846534
400.8286597781096520.3426804437806970.171340221890348
410.7953495369688330.4093009260623330.204650463031167
420.7687138031662230.4625723936675530.231286196833777
430.8579271927086940.2841456145826120.142072807291306
440.8337580131278320.3324839737443360.166241986872168
450.8102556157270420.3794887685459160.189744384272958
460.8676813411303440.2646373177393110.132318658869656
470.8521435404233990.2957129191532020.147856459576601
480.8222671936898040.3554656126203910.177732806310196
490.7942563643816820.4114872712366350.205743635618318
500.7669671586792540.4660656826414910.233032841320746
510.7298457988916430.5403084022167140.270154201108357
520.6962935331994240.6074129336011520.303706466800576
530.6966889839565390.6066220320869210.303311016043461
540.6681843296919550.6636313406160910.331815670308045
550.8769341396206630.2461317207586750.123065860379337
560.85835031075030.28329937849940.1416496892497
570.8344461511611090.3311076976777830.165553848838891
580.8056093580751080.3887812838497840.194390641924892
590.7736435479280140.4527129041439710.226356452071986
600.7525163875244020.4949672249511950.247483612475598
610.7340201667584990.5319596664830030.265979833241501
620.7033717647903080.5932564704193850.296628235209692
630.6761443788691810.6477112422616380.323855621130819
640.6399366955462450.720126608907510.360063304453755
650.6037434987742160.7925130024515680.396256501225784
660.6030813714100740.7938372571798520.396918628589926
670.6801592384997630.6396815230004730.319840761500237
680.7144467314812490.5711065370375010.285553268518751
690.748898530788480.502202938423040.25110146921152
700.7201715718604540.5596568562790920.279828428139546
710.8508706748579590.2982586502840820.149129325142041
720.8275063489842260.3449873020315480.172493651015774
730.8492303799547450.301539240090510.150769620045255
740.858104858097010.2837902838059790.14189514190299
750.8349937532468290.3300124935063420.165006246753171
760.857063069651950.28587386069610.14293693034805
770.8349005108008690.3301989783982620.165099489199131
780.8235369787041790.3529260425916420.176463021295821
790.818679477759920.362641044480160.18132052224008
800.7893930438709740.4212139122580530.210606956129026
810.7588407110338620.4823185779322750.241159288966138
820.8580946093518080.2838107812963830.141905390648192
830.8321565428925020.3356869142149960.167843457107498
840.8027693756057650.394461248788470.197230624394235
850.7700787425066260.4598425149867490.229921257493374
860.7436086763364290.5127826473271410.256391323663571
870.7136374890335350.572725021932930.286362510966465
880.6815743313734150.636851337253170.318425668626585
890.6588745207926330.6822509584147340.341125479207367
900.6478738657475980.7042522685048050.352126134252402
910.6888369109301290.6223261781397420.311163089069871
920.702000518030150.5959989639396990.29799948196985
930.6718294319200870.6563411361598250.328170568079912
940.6327679384656970.7344641230686060.367232061534303
950.6349150935757210.7301698128485590.365084906424279
960.5957303824486860.8085392351026280.404269617551314
970.581933841733490.8361323165330190.41806615826651
980.5432649730421730.9134700539156550.456735026957827
990.5019632575926280.9960734848147430.498036742407372
1000.4729533919357480.9459067838714970.527046608064252
1010.4294880754654210.8589761509308430.570511924534579
1020.4170001640314430.8340003280628870.582999835968557
1030.6162482279958580.7675035440082840.383751772004142
1040.5699991948480710.8600016103038570.430000805151929
1050.5620823453190920.8758353093618150.437917654680908
1060.5219519340083080.9560961319833840.478048065991692
1070.5129578489026560.9740843021946880.487042151097344
1080.5121886253127540.9756227493744910.487811374687246
1090.4977511247609120.9955022495218240.502248875239088
1100.4694139243117810.9388278486235620.530586075688219
1110.4539327359881880.9078654719763750.546067264011812
1120.4461147585440620.8922295170881240.553885241455938
1130.4433111749523640.8866223499047270.556688825047637
1140.411474202977550.82294840595510.58852579702245
1150.4525728171588520.9051456343177040.547427182841148
1160.4883091079710430.9766182159420850.511690892028957
1170.4417630783100230.8835261566200470.558236921689977
1180.3962574715112550.7925149430225110.603742528488745
1190.4007448274665380.8014896549330750.599255172533462
1200.3977454093975260.7954908187950510.602254590602474
1210.3600944991462990.7201889982925980.639905500853701
1220.3489847590063460.6979695180126910.651015240993654
1230.3373474087060420.6746948174120840.662652591293958
1240.2910239895274540.5820479790549070.708976010472546
1250.2473687746811360.4947375493622720.752631225318864
1260.2183521574567620.4367043149135230.781647842543238
1270.1826519870852610.3653039741705220.817348012914739
1280.1736448824959070.3472897649918140.826355117504093
1290.1422887741990850.284577548398170.857711225800915
1300.1458378283218970.2916756566437930.854162171678103
1310.125213994975510.250427989951020.87478600502449
1320.1433122478649820.2866244957299630.856687752135018
1330.1205720132030860.2411440264061720.879427986796914
1340.1134305936854910.2268611873709830.886569406314509
1350.08883967039391530.1776793407878310.911160329606085
1360.07074375168203750.1414875033640750.929256248317962
1370.05604530932965390.1120906186593080.943954690670346
1380.04381373940588140.08762747881176270.956186260594119
1390.04768477793326370.09536955586652750.952315222066736
1400.05241438922806340.1048287784561270.947585610771937
1410.3711267178467480.7422534356934950.628873282153252
1420.3098682405267830.6197364810535650.690131759473217
1430.2706100630259750.5412201260519490.729389936974026
1440.2687990941604690.5375981883209390.731200905839531
1450.2385815912230310.4771631824460630.761418408776969
1460.477452749843110.954905499686220.52254725015689
1470.6385178675195850.7229642649608310.361482132480415
1480.6478441813257680.7043116373484630.352155818674232
1490.5574143407534140.8851713184931730.442585659246586
1500.4651798886449210.9303597772898420.534820111355079
1510.7767292845816180.4465414308367650.223270715418382
1520.781890851102320.436218297795360.21810914889768
1530.6782905281264450.6434189437471090.321709471873555
1540.5515316827593270.8969366344813460.448468317240673
1550.4743148577903060.9486297155806110.525685142209694
1560.4893709375997420.9787418751994840.510629062400258







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0132450331125828OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0132450331125828 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186292&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0132450331125828[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186292&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186292&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 level20.0132450331125828OK



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