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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 13:20:01 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291123257dptltrsn7mbojxw.htm/, Retrieved Thu, 31 Oct 2024 23:40:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103397, Retrieved Thu, 31 Oct 2024 23:40:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-30 13:20:01] [2980b4453f2452156691660add27a53b] [Current]
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Dataseries X:
13	13	14	13	3	2
12	12	8	13	5	1
15	10	12	16	6	0
12	9	7	12	6	3
10	10	10	11	5	3
12	12	7	12	3	1
15	13	16	18	8	3
9	12	11	11	4	1
12	12	14	14	4	4
11	6	6	9	4	0
11	5	16	14	6	3
11	12	11	12	6	2
15	11	16	11	5	4
7	14	12	12	4	3
11	14	7	13	6	1
11	12	13	11	4	1
10	12	11	12	6	2
14	11	15	16	6	3
10	11	7	9	4	1
6	7	9	11	4	1
11	9	7	13	2	2
15	11	14	15	7	3
11	11	15	10	5	4
12	12	7	11	4	2
14	12	15	13	6	1
15	11	17	16	6	2
9	11	15	15	7	2
13	8	14	14	5	4
13	9	14	14	6	2
16	12	8	14	4	3
13	10	8	8	4	3
12	10	14	13	7	3
14	12	14	15	7	4
11	8	8	13	4	2
9	12	11	11	4	2
16	11	16	15	6	4
12	12	10	15	6	3
10	7	8	9	5	4
13	11	14	13	6	2
16	11	16	16	7	5
14	12	13	13	6	3
15	9	5	11	3	1
5	15	8	12	3	1
8	11	10	12	4	1
11	11	8	12	6	2
16	11	13	14	7	3
17	11	15	14	5	9
9	15	6	8	4	0
9	11	12	13	5	0
13	12	16	16	6	2
10	12	5	13	6	2
6	9	15	11	6	3
12	12	12	14	5	1
8	12	8	13	4	2
14	13	13	13	5	0
12	11	14	13	5	5
11	9	12	12	4	2
16	9	16	16	6	4
8	11	10	15	2	3
15	11	15	15	8	0
7	12	8	12	3	0
16	12	16	14	6	4
14	9	19	12	6	1
16	11	14	15	6	1
9	9	6	12	5	4
14	12	13	13	5	2
11	12	15	12	6	4
13	12	7	12	5	1
15	12	13	13	6	4
5	14	4	5	2	2
15	11	14	13	5	5
13	12	13	13	5	4
11	11	11	14	5	4
11	6	14	17	6	4
12	10	12	13	6	4
12	12	15	13	6	3
12	13	14	12	5	3
12	8	13	13	5	3
14	12	8	14	4	2
6	12	6	11	2	1
7	12	7	12	4	1
14	6	13	12	6	5
14	11	13	16	6	4
10	10	11	12	5	2
13	12	5	12	3	3
12	13	12	12	6	2
9	11	8	10	4	2
12	7	11	15	5	2
16	11	14	15	8	2
10	11	9	12	4	3
14	11	10	16	6	2
10	11	13	15	6	3
16	12	16	16	7	4
15	10	16	13	6	3
12	11	11	12	5	3
10	12	8	11	4	0
8	7	4	13	6	1
8	13	7	10	3	2
11	8	14	15	5	2
13	12	11	13	6	3
16	11	17	16	7	4
16	12	15	15	7	4
14	14	17	18	6	1
11	10	5	13	3	2
4	10	4	10	2	2
14	13	10	16	8	3
9	10	11	13	3	3
14	11	15	15	8	3
8	10	10	14	3	1
8	7	9	15	4	1
11	10	12	14	5	1
12	8	15	13	7	1
11	12	7	13	6	0
14	12	13	15	6	1
15	12	12	16	7	3
16	11	14	14	6	3
16	12	14	14	6	0
11	12	8	16	6	2
14	12	15	14	6	5
14	11	12	12	4	2
12	12	12	13	4	3
14	11	16	12	5	3
8	11	9	12	4	5
13	13	15	14	6	4
16	12	15	14	6	4
12	12	6	14	5	0
16	12	14	16	8	3
12	12	15	13	6	0
11	8	10	14	5	2
4	8	6	4	4	0
16	12	14	16	8	6
15	11	12	13	6	3
10	12	8	16	4	1
13	13	11	15	6	6
15	12	13	14	6	2
12	12	9	13	4	1
14	11	15	14	6	3
7	12	13	12	3	1
19	12	15	15	6	2
12	10	14	14	5	4
12	11	16	13	4	1
13	12	14	14	6	2
15	12	14	16	4	0
8	10	10	6	4	5
12	12	10	13	4	2
10	13	4	13	6	1
8	12	8	14	5	1
10	15	15	15	6	4
15	11	16	14	6	3
16	12	12	15	8	0
13	11	12	13	7	3
16	12	15	16	7	3
9	11	9	12	4	0
14	10	12	15	6	2
14	11	14	12	6	5
12	11	11	14	2	2




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' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&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' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.0342768235376193 + 0.106305539542869FindingFriends[t] + 0.21144283590659KnowingPeople[t] + 0.357652188002145Liked[t] + 0.60600258577461Celebrity[t] + 0.212600241949771Sum[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.0342768235376193 +  0.106305539542869FindingFriends[t] +  0.21144283590659KnowingPeople[t] +  0.357652188002145Liked[t] +  0.60600258577461Celebrity[t] +  0.212600241949771Sum[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.0342768235376193 +  0.106305539542869FindingFriends[t] +  0.21144283590659KnowingPeople[t] +  0.357652188002145Liked[t] +  0.60600258577461Celebrity[t] +  0.212600241949771Sum[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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
Popularity[t] = + 0.0342768235376193 + 0.106305539542869FindingFriends[t] + 0.21144283590659KnowingPeople[t] + 0.357652188002145Liked[t] + 0.60600258577461Celebrity[t] + 0.212600241949771Sum[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.03427682353761931.4232830.02410.9808180.490409
FindingFriends0.1063055395428690.0955171.1130.2675090.133755
KnowingPeople0.211442835906590.0636263.32320.0011180.000559
Liked0.3576521880021450.0959313.72820.0002730.000136
Celebrity0.606002585774610.1553963.89970.0001457.2e-05
Sum0.2126002419497710.120031.77120.0785540.039277

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.0342768235376193 & 1.423283 & 0.0241 & 0.980818 & 0.490409 \tabularnewline
FindingFriends & 0.106305539542869 & 0.095517 & 1.113 & 0.267509 & 0.133755 \tabularnewline
KnowingPeople & 0.21144283590659 & 0.063626 & 3.3232 & 0.001118 & 0.000559 \tabularnewline
Liked & 0.357652188002145 & 0.095931 & 3.7282 & 0.000273 & 0.000136 \tabularnewline
Celebrity & 0.60600258577461 & 0.155396 & 3.8997 & 0.000145 & 7.2e-05 \tabularnewline
Sum & 0.212600241949771 & 0.12003 & 1.7712 & 0.078554 & 0.039277 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.0342768235376193[/C][C]1.423283[/C][C]0.0241[/C][C]0.980818[/C][C]0.490409[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.106305539542869[/C][C]0.095517[/C][C]1.113[/C][C]0.267509[/C][C]0.133755[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.21144283590659[/C][C]0.063626[/C][C]3.3232[/C][C]0.001118[/C][C]0.000559[/C][/ROW]
[ROW][C]Liked[/C][C]0.357652188002145[/C][C]0.095931[/C][C]3.7282[/C][C]0.000273[/C][C]0.000136[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.60600258577461[/C][C]0.155396[/C][C]3.8997[/C][C]0.000145[/C][C]7.2e-05[/C][/ROW]
[ROW][C]Sum[/C][C]0.212600241949771[/C][C]0.12003[/C][C]1.7712[/C][C]0.078554[/C][C]0.039277[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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)0.03427682353761931.4232830.02410.9808180.490409
FindingFriends0.1063055395428690.0955171.1130.2675090.133755
KnowingPeople0.211442835906590.0636263.32320.0011180.000559
Liked0.3576521880021450.0959313.72820.0002730.000136
Celebrity0.606002585774610.1553963.89970.0001457.2e-05
Sum0.2126002419497710.120031.77120.0785540.039277







Multiple Linear Regression - Regression Statistics
Multiple R0.713764528070818
R-squared0.509459801532158
Adjusted R-squared0.49310846158323
F-TEST (value)31.1570674405528
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09077045674136
Sum Squared Residuals655.69816541737

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.713764528070818 \tabularnewline
R-squared & 0.509459801532158 \tabularnewline
Adjusted R-squared & 0.49310846158323 \tabularnewline
F-TEST (value) & 31.1570674405528 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09077045674136 \tabularnewline
Sum Squared Residuals & 655.69816541737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.713764528070818[/C][/ROW]
[ROW][C]R-squared[/C][C]0.509459801532158[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.49310846158323[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.1570674405528[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.09077045674136[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]655.69816541737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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 R0.713764528070818
R-squared0.509459801532158
Adjusted R-squared0.49310846158323
F-TEST (value)31.1570674405528
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09077045674136
Sum Squared Residuals655.69816541737







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.26913522553841.73086477446163
21210.89357760015551.10642239984454
31512.99309677252732.00690322747266
41211.03676902729230.963230972707738
51010.8137483007782-0.813748300778157
6129.112477404697512.88752259530249
71516.7228950081851-1.72289500818514
8910.2065991460963-1.20659914609634
91212.5516849436719-0.551684943671851
10117.583647111352123.41635288864788
111113.2298367682844-2.2298367682844
121111.9888567475975-0.98885674759747
131512.40131109771032.59868890228966
14711.4135057329903-4.41350573299035
151111.5007484291092-0.500748429109222
161110.62948481790950.370515182090483
171011.9888567475975-1.98885674759747
181414.3715315456393-0.371531545639309
19108.539217886922821.46078211307718
2069.25218577656882-3.25218577656881
21118.757810630246212.24218936975379
221514.40843910750520.591560892494816
231111.8322160738016-0.832216073801606
24129.573428044419752.42657195558025
251412.97968003727621.02031996272380
261514.58181697550270.418183024497282
27914.407281701462-5.407281701462
281312.73246537127500.267534628725014
291313.0195730126929-0.0195730126929230
301611.07042768628254.92957231371746
31138.711903479183944.28809652081606
321213.5868291919580-1.58682919195803
331414.7273448889978-0.727344888997824
341110.07495309815920.925046901840848
35910.4191993880461-1.41919938804611
361614.43792243549351.56207756450647
371213.0629707176471-1.06297071764708
38109.569241876281860.430758123718142
391312.87453190377650.125468096223484
401615.61417745122000.385822548779951
411412.98199484936261.01800515063743
42158.013022926253586.98697707374642
4359.64283685923271-4.64283685923271
44810.2465029586490-2.24650295864902
451111.2482227003348-0.248222700334833
461613.83934408359652.16065591640355
471714.32582603555902.67417396444096
4898.182744779235790.81725522076421
49911.4204431622892-2.42044316228919
501314.476679679139-1.47667967913900
511011.0778519201601-1.07785192016008
52612.3706595265429-6.37065952654285
531212.0970011317840-0.0970011317839686
54810.5001752563306-2.50017525633063
551411.84449707728152.15550292271849
561212.9063300438512-0.906330043851219
571110.66937779332620.330622206673764
581614.58296354440991.41703645559007
59810.5326548350058-2.53265483500578
601514.58808380333710.411916196662928
6179.11131999865433-2.11131999865433
621614.18657578703431.81342421296575
631413.14888257427180.851117425728186
641613.37723603783102.62276396216897
65910.4319238475608-1.43192384756085
661412.16339202163821.83660797836181
671113.2598285751234-2.25982857512337
681310.32448257624672.67551742375327
691513.19459509131231.80540490868766
7055.79379231622363-0.793792316223632
711512.90633004385122.09366995614878
721312.58859250553770.411407494462273
731112.4170534821838-1.41705348218382
741114.1988134419703-3.19881344197029
751212.77054117632-0.770541176320009
761213.4048805211757-1.40488052117575
771212.3360884510353-0.33608845103527
781211.95077010541650.0492298945835183
791410.85782744433283.14217255566723
8067.93737979501417-1.93737979501417
8179.71847999047212-2.71847999047212
821412.41170990800281.58829009199725
831414.1612461157759-0.161246115775900
841011.1702430827371-1.17024308273712
85139.114792216783873.88520778321613
861212.3066051230469-0.306605123046929
8799.32091315278133-0.320913152781326
881211.92428302811500.0757169718850493
891614.80184145133001.19815854866998
901010.4602606066420-0.460260606641974
911413.10171712415660.898282875843412
921013.5909936858240-3.59099368582398
931615.50788274881310.492117251186852
941513.40371227799661.5962877220034
951211.48914886422980.510851135770237
96109.35967039642680.640329603573203
97810.1222811445894-2.12228114458937
9888.71607881018586-0.716078810185863
991112.6649170753776-1.66491707537759
1001312.55910917754940.440890822450614
1011615.61302004517690.386979954823131
1021614.93878772490441.06121227509559
1031415.4034377281858-1.40343772818584
104119.047233083750511.95276691624949
10547.15683109806288-3.15683109806288
1061414.7389336167413-0.738933616741316
107910.5284903411398-1.52849034113982
1081415.2258845291864-1.22588452918638
109810.2494992093358-2.24949920933583
110810.6827945285774-2.68279452857739
1111111.8843900526982-0.884390052698232
1121213.1604604648793-1.16046046487934
1131111.0755371080737-0.0755371080737139
1141413.27209874146730.727901258532688
1151514.44951116323700.550488836762983
1161613.44478433372842.55521566627157
1171612.9132891474223.08671085257801
1181112.7851369918863-1.78513699188628
1191414.1877331930774-0.187733193077431
1201410.88198887241203.11801112758803
1211211.55854684190680.441453158093244
1221412.54636304376271.45363695623729
123810.8854610905415-2.88546109054152
1241314.0814384906705-1.08143849067053
1251613.97513295112772.02486704887234
1261210.61574387439471.38425612560534
1271615.47839942082480.521600579175193
1281212.7670797953264-0.767079795326434
1291111.4614935437491-0.461493543749085
13046.00799725042713-2.00799725042713
1311616.1162001466741-0.116200146674119
1321512.66424647391312.33575352608689
1331011.3605315783873-1.36053157838729
1341314.0185198189459-1.01851981894585
1351513.12704679541491.87295320458506
1361210.49901785028741.50098214971256
1371413.65622716963500.343772830364979
138710.3811344201371-3.38113442013705
1391913.90758465523035.09241534476974
1401212.9450764503607-0.945076450360723
1411211.87281216209070.127187837909294
1421313.3384896313215-0.338489631321529
1431512.41658835187712.58341164812294
14488.84468525889237-0.844685258892373
1451210.92306092814381.07693907185619
1461010.7601143818466-0.760114381846583
147811.2512297881576-3.25122978815761
1481014.6517017577584-4.65170175775841
1491513.86767000554161.13232999445839
1501614.06006083516021.93993916483983
1511313.2702490596877-0.270249059687716
1521615.08383967095680.916160329043213
15399.82245988079266-0.822459880792662
1541413.06064506842480.939354931575244
1551413.15468044162370.845319558376316
1561210.17384524096051.82615475903955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2691352255384 & 1.73086477446163 \tabularnewline
2 & 12 & 10.8935776001555 & 1.10642239984454 \tabularnewline
3 & 15 & 12.9930967725273 & 2.00690322747266 \tabularnewline
4 & 12 & 11.0367690272923 & 0.963230972707738 \tabularnewline
5 & 10 & 10.8137483007782 & -0.813748300778157 \tabularnewline
6 & 12 & 9.11247740469751 & 2.88752259530249 \tabularnewline
7 & 15 & 16.7228950081851 & -1.72289500818514 \tabularnewline
8 & 9 & 10.2065991460963 & -1.20659914609634 \tabularnewline
9 & 12 & 12.5516849436719 & -0.551684943671851 \tabularnewline
10 & 11 & 7.58364711135212 & 3.41635288864788 \tabularnewline
11 & 11 & 13.2298367682844 & -2.2298367682844 \tabularnewline
12 & 11 & 11.9888567475975 & -0.98885674759747 \tabularnewline
13 & 15 & 12.4013110977103 & 2.59868890228966 \tabularnewline
14 & 7 & 11.4135057329903 & -4.41350573299035 \tabularnewline
15 & 11 & 11.5007484291092 & -0.500748429109222 \tabularnewline
16 & 11 & 10.6294848179095 & 0.370515182090483 \tabularnewline
17 & 10 & 11.9888567475975 & -1.98885674759747 \tabularnewline
18 & 14 & 14.3715315456393 & -0.371531545639309 \tabularnewline
19 & 10 & 8.53921788692282 & 1.46078211307718 \tabularnewline
20 & 6 & 9.25218577656882 & -3.25218577656881 \tabularnewline
21 & 11 & 8.75781063024621 & 2.24218936975379 \tabularnewline
22 & 15 & 14.4084391075052 & 0.591560892494816 \tabularnewline
23 & 11 & 11.8322160738016 & -0.832216073801606 \tabularnewline
24 & 12 & 9.57342804441975 & 2.42657195558025 \tabularnewline
25 & 14 & 12.9796800372762 & 1.02031996272380 \tabularnewline
26 & 15 & 14.5818169755027 & 0.418183024497282 \tabularnewline
27 & 9 & 14.407281701462 & -5.407281701462 \tabularnewline
28 & 13 & 12.7324653712750 & 0.267534628725014 \tabularnewline
29 & 13 & 13.0195730126929 & -0.0195730126929230 \tabularnewline
30 & 16 & 11.0704276862825 & 4.92957231371746 \tabularnewline
31 & 13 & 8.71190347918394 & 4.28809652081606 \tabularnewline
32 & 12 & 13.5868291919580 & -1.58682919195803 \tabularnewline
33 & 14 & 14.7273448889978 & -0.727344888997824 \tabularnewline
34 & 11 & 10.0749530981592 & 0.925046901840848 \tabularnewline
35 & 9 & 10.4191993880461 & -1.41919938804611 \tabularnewline
36 & 16 & 14.4379224354935 & 1.56207756450647 \tabularnewline
37 & 12 & 13.0629707176471 & -1.06297071764708 \tabularnewline
38 & 10 & 9.56924187628186 & 0.430758123718142 \tabularnewline
39 & 13 & 12.8745319037765 & 0.125468096223484 \tabularnewline
40 & 16 & 15.6141774512200 & 0.385822548779951 \tabularnewline
41 & 14 & 12.9819948493626 & 1.01800515063743 \tabularnewline
42 & 15 & 8.01302292625358 & 6.98697707374642 \tabularnewline
43 & 5 & 9.64283685923271 & -4.64283685923271 \tabularnewline
44 & 8 & 10.2465029586490 & -2.24650295864902 \tabularnewline
45 & 11 & 11.2482227003348 & -0.248222700334833 \tabularnewline
46 & 16 & 13.8393440835965 & 2.16065591640355 \tabularnewline
47 & 17 & 14.3258260355590 & 2.67417396444096 \tabularnewline
48 & 9 & 8.18274477923579 & 0.81725522076421 \tabularnewline
49 & 9 & 11.4204431622892 & -2.42044316228919 \tabularnewline
50 & 13 & 14.476679679139 & -1.47667967913900 \tabularnewline
51 & 10 & 11.0778519201601 & -1.07785192016008 \tabularnewline
52 & 6 & 12.3706595265429 & -6.37065952654285 \tabularnewline
53 & 12 & 12.0970011317840 & -0.0970011317839686 \tabularnewline
54 & 8 & 10.5001752563306 & -2.50017525633063 \tabularnewline
55 & 14 & 11.8444970772815 & 2.15550292271849 \tabularnewline
56 & 12 & 12.9063300438512 & -0.906330043851219 \tabularnewline
57 & 11 & 10.6693777933262 & 0.330622206673764 \tabularnewline
58 & 16 & 14.5829635444099 & 1.41703645559007 \tabularnewline
59 & 8 & 10.5326548350058 & -2.53265483500578 \tabularnewline
60 & 15 & 14.5880838033371 & 0.411916196662928 \tabularnewline
61 & 7 & 9.11131999865433 & -2.11131999865433 \tabularnewline
62 & 16 & 14.1865757870343 & 1.81342421296575 \tabularnewline
63 & 14 & 13.1488825742718 & 0.851117425728186 \tabularnewline
64 & 16 & 13.3772360378310 & 2.62276396216897 \tabularnewline
65 & 9 & 10.4319238475608 & -1.43192384756085 \tabularnewline
66 & 14 & 12.1633920216382 & 1.83660797836181 \tabularnewline
67 & 11 & 13.2598285751234 & -2.25982857512337 \tabularnewline
68 & 13 & 10.3244825762467 & 2.67551742375327 \tabularnewline
69 & 15 & 13.1945950913123 & 1.80540490868766 \tabularnewline
70 & 5 & 5.79379231622363 & -0.793792316223632 \tabularnewline
71 & 15 & 12.9063300438512 & 2.09366995614878 \tabularnewline
72 & 13 & 12.5885925055377 & 0.411407494462273 \tabularnewline
73 & 11 & 12.4170534821838 & -1.41705348218382 \tabularnewline
74 & 11 & 14.1988134419703 & -3.19881344197029 \tabularnewline
75 & 12 & 12.77054117632 & -0.770541176320009 \tabularnewline
76 & 12 & 13.4048805211757 & -1.40488052117575 \tabularnewline
77 & 12 & 12.3360884510353 & -0.33608845103527 \tabularnewline
78 & 12 & 11.9507701054165 & 0.0492298945835183 \tabularnewline
79 & 14 & 10.8578274443328 & 3.14217255566723 \tabularnewline
80 & 6 & 7.93737979501417 & -1.93737979501417 \tabularnewline
81 & 7 & 9.71847999047212 & -2.71847999047212 \tabularnewline
82 & 14 & 12.4117099080028 & 1.58829009199725 \tabularnewline
83 & 14 & 14.1612461157759 & -0.161246115775900 \tabularnewline
84 & 10 & 11.1702430827371 & -1.17024308273712 \tabularnewline
85 & 13 & 9.11479221678387 & 3.88520778321613 \tabularnewline
86 & 12 & 12.3066051230469 & -0.306605123046929 \tabularnewline
87 & 9 & 9.32091315278133 & -0.320913152781326 \tabularnewline
88 & 12 & 11.9242830281150 & 0.0757169718850493 \tabularnewline
89 & 16 & 14.8018414513300 & 1.19815854866998 \tabularnewline
90 & 10 & 10.4602606066420 & -0.460260606641974 \tabularnewline
91 & 14 & 13.1017171241566 & 0.898282875843412 \tabularnewline
92 & 10 & 13.5909936858240 & -3.59099368582398 \tabularnewline
93 & 16 & 15.5078827488131 & 0.492117251186852 \tabularnewline
94 & 15 & 13.4037122779966 & 1.5962877220034 \tabularnewline
95 & 12 & 11.4891488642298 & 0.510851135770237 \tabularnewline
96 & 10 & 9.3596703964268 & 0.640329603573203 \tabularnewline
97 & 8 & 10.1222811445894 & -2.12228114458937 \tabularnewline
98 & 8 & 8.71607881018586 & -0.716078810185863 \tabularnewline
99 & 11 & 12.6649170753776 & -1.66491707537759 \tabularnewline
100 & 13 & 12.5591091775494 & 0.440890822450614 \tabularnewline
101 & 16 & 15.6130200451769 & 0.386979954823131 \tabularnewline
102 & 16 & 14.9387877249044 & 1.06121227509559 \tabularnewline
103 & 14 & 15.4034377281858 & -1.40343772818584 \tabularnewline
104 & 11 & 9.04723308375051 & 1.95276691624949 \tabularnewline
105 & 4 & 7.15683109806288 & -3.15683109806288 \tabularnewline
106 & 14 & 14.7389336167413 & -0.738933616741316 \tabularnewline
107 & 9 & 10.5284903411398 & -1.52849034113982 \tabularnewline
108 & 14 & 15.2258845291864 & -1.22588452918638 \tabularnewline
109 & 8 & 10.2494992093358 & -2.24949920933583 \tabularnewline
110 & 8 & 10.6827945285774 & -2.68279452857739 \tabularnewline
111 & 11 & 11.8843900526982 & -0.884390052698232 \tabularnewline
112 & 12 & 13.1604604648793 & -1.16046046487934 \tabularnewline
113 & 11 & 11.0755371080737 & -0.0755371080737139 \tabularnewline
114 & 14 & 13.2720987414673 & 0.727901258532688 \tabularnewline
115 & 15 & 14.4495111632370 & 0.550488836762983 \tabularnewline
116 & 16 & 13.4447843337284 & 2.55521566627157 \tabularnewline
117 & 16 & 12.913289147422 & 3.08671085257801 \tabularnewline
118 & 11 & 12.7851369918863 & -1.78513699188628 \tabularnewline
119 & 14 & 14.1877331930774 & -0.187733193077431 \tabularnewline
120 & 14 & 10.8819888724120 & 3.11801112758803 \tabularnewline
121 & 12 & 11.5585468419068 & 0.441453158093244 \tabularnewline
122 & 14 & 12.5463630437627 & 1.45363695623729 \tabularnewline
123 & 8 & 10.8854610905415 & -2.88546109054152 \tabularnewline
124 & 13 & 14.0814384906705 & -1.08143849067053 \tabularnewline
125 & 16 & 13.9751329511277 & 2.02486704887234 \tabularnewline
126 & 12 & 10.6157438743947 & 1.38425612560534 \tabularnewline
127 & 16 & 15.4783994208248 & 0.521600579175193 \tabularnewline
128 & 12 & 12.7670797953264 & -0.767079795326434 \tabularnewline
129 & 11 & 11.4614935437491 & -0.461493543749085 \tabularnewline
130 & 4 & 6.00799725042713 & -2.00799725042713 \tabularnewline
131 & 16 & 16.1162001466741 & -0.116200146674119 \tabularnewline
132 & 15 & 12.6642464739131 & 2.33575352608689 \tabularnewline
133 & 10 & 11.3605315783873 & -1.36053157838729 \tabularnewline
134 & 13 & 14.0185198189459 & -1.01851981894585 \tabularnewline
135 & 15 & 13.1270467954149 & 1.87295320458506 \tabularnewline
136 & 12 & 10.4990178502874 & 1.50098214971256 \tabularnewline
137 & 14 & 13.6562271696350 & 0.343772830364979 \tabularnewline
138 & 7 & 10.3811344201371 & -3.38113442013705 \tabularnewline
139 & 19 & 13.9075846552303 & 5.09241534476974 \tabularnewline
140 & 12 & 12.9450764503607 & -0.945076450360723 \tabularnewline
141 & 12 & 11.8728121620907 & 0.127187837909294 \tabularnewline
142 & 13 & 13.3384896313215 & -0.338489631321529 \tabularnewline
143 & 15 & 12.4165883518771 & 2.58341164812294 \tabularnewline
144 & 8 & 8.84468525889237 & -0.844685258892373 \tabularnewline
145 & 12 & 10.9230609281438 & 1.07693907185619 \tabularnewline
146 & 10 & 10.7601143818466 & -0.760114381846583 \tabularnewline
147 & 8 & 11.2512297881576 & -3.25122978815761 \tabularnewline
148 & 10 & 14.6517017577584 & -4.65170175775841 \tabularnewline
149 & 15 & 13.8676700055416 & 1.13232999445839 \tabularnewline
150 & 16 & 14.0600608351602 & 1.93993916483983 \tabularnewline
151 & 13 & 13.2702490596877 & -0.270249059687716 \tabularnewline
152 & 16 & 15.0838396709568 & 0.916160329043213 \tabularnewline
153 & 9 & 9.82245988079266 & -0.822459880792662 \tabularnewline
154 & 14 & 13.0606450684248 & 0.939354931575244 \tabularnewline
155 & 14 & 13.1546804416237 & 0.845319558376316 \tabularnewline
156 & 12 & 10.1738452409605 & 1.82615475903955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&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]11.2691352255384[/C][C]1.73086477446163[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.8935776001555[/C][C]1.10642239984454[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]12.9930967725273[/C][C]2.00690322747266[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.0367690272923[/C][C]0.963230972707738[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.8137483007782[/C][C]-0.813748300778157[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.11247740469751[/C][C]2.88752259530249[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7228950081851[/C][C]-1.72289500818514[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2065991460963[/C][C]-1.20659914609634[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.5516849436719[/C][C]-0.551684943671851[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.58364711135212[/C][C]3.41635288864788[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.2298367682844[/C][C]-2.2298367682844[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.9888567475975[/C][C]-0.98885674759747[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.4013110977103[/C][C]2.59868890228966[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4135057329903[/C][C]-4.41350573299035[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.5007484291092[/C][C]-0.500748429109222[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.6294848179095[/C][C]0.370515182090483[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.9888567475975[/C][C]-1.98885674759747[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3715315456393[/C][C]-0.371531545639309[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.53921788692282[/C][C]1.46078211307718[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.25218577656882[/C][C]-3.25218577656881[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.75781063024621[/C][C]2.24218936975379[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4084391075052[/C][C]0.591560892494816[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.8322160738016[/C][C]-0.832216073801606[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.57342804441975[/C][C]2.42657195558025[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.9796800372762[/C][C]1.02031996272380[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5818169755027[/C][C]0.418183024497282[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.407281701462[/C][C]-5.407281701462[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.7324653712750[/C][C]0.267534628725014[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.0195730126929[/C][C]-0.0195730126929230[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.0704276862825[/C][C]4.92957231371746[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.71190347918394[/C][C]4.28809652081606[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5868291919580[/C][C]-1.58682919195803[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7273448889978[/C][C]-0.727344888997824[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0749530981592[/C][C]0.925046901840848[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4191993880461[/C][C]-1.41919938804611[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.4379224354935[/C][C]1.56207756450647[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.0629707176471[/C][C]-1.06297071764708[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.56924187628186[/C][C]0.430758123718142[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8745319037765[/C][C]0.125468096223484[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.6141774512200[/C][C]0.385822548779951[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9819948493626[/C][C]1.01800515063743[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.01302292625358[/C][C]6.98697707374642[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.64283685923271[/C][C]-4.64283685923271[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.2465029586490[/C][C]-2.24650295864902[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2482227003348[/C][C]-0.248222700334833[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8393440835965[/C][C]2.16065591640355[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.3258260355590[/C][C]2.67417396444096[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.18274477923579[/C][C]0.81725522076421[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4204431622892[/C][C]-2.42044316228919[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.476679679139[/C][C]-1.47667967913900[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.0778519201601[/C][C]-1.07785192016008[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.3706595265429[/C][C]-6.37065952654285[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.0970011317840[/C][C]-0.0970011317839686[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.5001752563306[/C][C]-2.50017525633063[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8444970772815[/C][C]2.15550292271849[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9063300438512[/C][C]-0.906330043851219[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.6693777933262[/C][C]0.330622206673764[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.5829635444099[/C][C]1.41703645559007[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.5326548350058[/C][C]-2.53265483500578[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5880838033371[/C][C]0.411916196662928[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.11131999865433[/C][C]-2.11131999865433[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.1865757870343[/C][C]1.81342421296575[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.1488825742718[/C][C]0.851117425728186[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.3772360378310[/C][C]2.62276396216897[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.4319238475608[/C][C]-1.43192384756085[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1633920216382[/C][C]1.83660797836181[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2598285751234[/C][C]-2.25982857512337[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3244825762467[/C][C]2.67551742375327[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.1945950913123[/C][C]1.80540490868766[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.79379231622363[/C][C]-0.793792316223632[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.9063300438512[/C][C]2.09366995614878[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.5885925055377[/C][C]0.411407494462273[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.4170534821838[/C][C]-1.41705348218382[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1988134419703[/C][C]-3.19881344197029[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.77054117632[/C][C]-0.770541176320009[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4048805211757[/C][C]-1.40488052117575[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.3360884510353[/C][C]-0.33608845103527[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9507701054165[/C][C]0.0492298945835183[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.8578274443328[/C][C]3.14217255566723[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.93737979501417[/C][C]-1.93737979501417[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.71847999047212[/C][C]-2.71847999047212[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.4117099080028[/C][C]1.58829009199725[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.1612461157759[/C][C]-0.161246115775900[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1702430827371[/C][C]-1.17024308273712[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.11479221678387[/C][C]3.88520778321613[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.3066051230469[/C][C]-0.306605123046929[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.32091315278133[/C][C]-0.320913152781326[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9242830281150[/C][C]0.0757169718850493[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8018414513300[/C][C]1.19815854866998[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4602606066420[/C][C]-0.460260606641974[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1017171241566[/C][C]0.898282875843412[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5909936858240[/C][C]-3.59099368582398[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5078827488131[/C][C]0.492117251186852[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4037122779966[/C][C]1.5962877220034[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4891488642298[/C][C]0.510851135770237[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.3596703964268[/C][C]0.640329603573203[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.1222811445894[/C][C]-2.12228114458937[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.71607881018586[/C][C]-0.716078810185863[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.6649170753776[/C][C]-1.66491707537759[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.5591091775494[/C][C]0.440890822450614[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.6130200451769[/C][C]0.386979954823131[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9387877249044[/C][C]1.06121227509559[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.4034377281858[/C][C]-1.40343772818584[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.04723308375051[/C][C]1.95276691624949[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.15683109806288[/C][C]-3.15683109806288[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.7389336167413[/C][C]-0.738933616741316[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5284903411398[/C][C]-1.52849034113982[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2258845291864[/C][C]-1.22588452918638[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.2494992093358[/C][C]-2.24949920933583[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.6827945285774[/C][C]-2.68279452857739[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.8843900526982[/C][C]-0.884390052698232[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.1604604648793[/C][C]-1.16046046487934[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0755371080737[/C][C]-0.0755371080737139[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.2720987414673[/C][C]0.727901258532688[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4495111632370[/C][C]0.550488836762983[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4447843337284[/C][C]2.55521566627157[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.913289147422[/C][C]3.08671085257801[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7851369918863[/C][C]-1.78513699188628[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.1877331930774[/C][C]-0.187733193077431[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8819888724120[/C][C]3.11801112758803[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.5585468419068[/C][C]0.441453158093244[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5463630437627[/C][C]1.45363695623729[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8854610905415[/C][C]-2.88546109054152[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.0814384906705[/C][C]-1.08143849067053[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.9751329511277[/C][C]2.02486704887234[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.6157438743947[/C][C]1.38425612560534[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.4783994208248[/C][C]0.521600579175193[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.7670797953264[/C][C]-0.767079795326434[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4614935437491[/C][C]-0.461493543749085[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.00799725042713[/C][C]-2.00799725042713[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.1162001466741[/C][C]-0.116200146674119[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.6642464739131[/C][C]2.33575352608689[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.3605315783873[/C][C]-1.36053157838729[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]14.0185198189459[/C][C]-1.01851981894585[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.1270467954149[/C][C]1.87295320458506[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.4990178502874[/C][C]1.50098214971256[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6562271696350[/C][C]0.343772830364979[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.3811344201371[/C][C]-3.38113442013705[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.9075846552303[/C][C]5.09241534476974[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.9450764503607[/C][C]-0.945076450360723[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.8728121620907[/C][C]0.127187837909294[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.3384896313215[/C][C]-0.338489631321529[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4165883518771[/C][C]2.58341164812294[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.84468525889237[/C][C]-0.844685258892373[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9230609281438[/C][C]1.07693907185619[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.7601143818466[/C][C]-0.760114381846583[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.2512297881576[/C][C]-3.25122978815761[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.6517017577584[/C][C]-4.65170175775841[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8676700055416[/C][C]1.13232999445839[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.0600608351602[/C][C]1.93993916483983[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.2702490596877[/C][C]-0.270249059687716[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0838396709568[/C][C]0.916160329043213[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.82245988079266[/C][C]-0.822459880792662[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.0606450684248[/C][C]0.939354931575244[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.1546804416237[/C][C]0.845319558376316[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1738452409605[/C][C]1.82615475903955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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
11311.26913522553841.73086477446163
21210.89357760015551.10642239984454
31512.99309677252732.00690322747266
41211.03676902729230.963230972707738
51010.8137483007782-0.813748300778157
6129.112477404697512.88752259530249
71516.7228950081851-1.72289500818514
8910.2065991460963-1.20659914609634
91212.5516849436719-0.551684943671851
10117.583647111352123.41635288864788
111113.2298367682844-2.2298367682844
121111.9888567475975-0.98885674759747
131512.40131109771032.59868890228966
14711.4135057329903-4.41350573299035
151111.5007484291092-0.500748429109222
161110.62948481790950.370515182090483
171011.9888567475975-1.98885674759747
181414.3715315456393-0.371531545639309
19108.539217886922821.46078211307718
2069.25218577656882-3.25218577656881
21118.757810630246212.24218936975379
221514.40843910750520.591560892494816
231111.8322160738016-0.832216073801606
24129.573428044419752.42657195558025
251412.97968003727621.02031996272380
261514.58181697550270.418183024497282
27914.407281701462-5.407281701462
281312.73246537127500.267534628725014
291313.0195730126929-0.0195730126929230
301611.07042768628254.92957231371746
31138.711903479183944.28809652081606
321213.5868291919580-1.58682919195803
331414.7273448889978-0.727344888997824
341110.07495309815920.925046901840848
35910.4191993880461-1.41919938804611
361614.43792243549351.56207756450647
371213.0629707176471-1.06297071764708
38109.569241876281860.430758123718142
391312.87453190377650.125468096223484
401615.61417745122000.385822548779951
411412.98199484936261.01800515063743
42158.013022926253586.98697707374642
4359.64283685923271-4.64283685923271
44810.2465029586490-2.24650295864902
451111.2482227003348-0.248222700334833
461613.83934408359652.16065591640355
471714.32582603555902.67417396444096
4898.182744779235790.81725522076421
49911.4204431622892-2.42044316228919
501314.476679679139-1.47667967913900
511011.0778519201601-1.07785192016008
52612.3706595265429-6.37065952654285
531212.0970011317840-0.0970011317839686
54810.5001752563306-2.50017525633063
551411.84449707728152.15550292271849
561212.9063300438512-0.906330043851219
571110.66937779332620.330622206673764
581614.58296354440991.41703645559007
59810.5326548350058-2.53265483500578
601514.58808380333710.411916196662928
6179.11131999865433-2.11131999865433
621614.18657578703431.81342421296575
631413.14888257427180.851117425728186
641613.37723603783102.62276396216897
65910.4319238475608-1.43192384756085
661412.16339202163821.83660797836181
671113.2598285751234-2.25982857512337
681310.32448257624672.67551742375327
691513.19459509131231.80540490868766
7055.79379231622363-0.793792316223632
711512.90633004385122.09366995614878
721312.58859250553770.411407494462273
731112.4170534821838-1.41705348218382
741114.1988134419703-3.19881344197029
751212.77054117632-0.770541176320009
761213.4048805211757-1.40488052117575
771212.3360884510353-0.33608845103527
781211.95077010541650.0492298945835183
791410.85782744433283.14217255566723
8067.93737979501417-1.93737979501417
8179.71847999047212-2.71847999047212
821412.41170990800281.58829009199725
831414.1612461157759-0.161246115775900
841011.1702430827371-1.17024308273712
85139.114792216783873.88520778321613
861212.3066051230469-0.306605123046929
8799.32091315278133-0.320913152781326
881211.92428302811500.0757169718850493
891614.80184145133001.19815854866998
901010.4602606066420-0.460260606641974
911413.10171712415660.898282875843412
921013.5909936858240-3.59099368582398
931615.50788274881310.492117251186852
941513.40371227799661.5962877220034
951211.48914886422980.510851135770237
96109.35967039642680.640329603573203
97810.1222811445894-2.12228114458937
9888.71607881018586-0.716078810185863
991112.6649170753776-1.66491707537759
1001312.55910917754940.440890822450614
1011615.61302004517690.386979954823131
1021614.93878772490441.06121227509559
1031415.4034377281858-1.40343772818584
104119.047233083750511.95276691624949
10547.15683109806288-3.15683109806288
1061414.7389336167413-0.738933616741316
107910.5284903411398-1.52849034113982
1081415.2258845291864-1.22588452918638
109810.2494992093358-2.24949920933583
110810.6827945285774-2.68279452857739
1111111.8843900526982-0.884390052698232
1121213.1604604648793-1.16046046487934
1131111.0755371080737-0.0755371080737139
1141413.27209874146730.727901258532688
1151514.44951116323700.550488836762983
1161613.44478433372842.55521566627157
1171612.9132891474223.08671085257801
1181112.7851369918863-1.78513699188628
1191414.1877331930774-0.187733193077431
1201410.88198887241203.11801112758803
1211211.55854684190680.441453158093244
1221412.54636304376271.45363695623729
123810.8854610905415-2.88546109054152
1241314.0814384906705-1.08143849067053
1251613.97513295112772.02486704887234
1261210.61574387439471.38425612560534
1271615.47839942082480.521600579175193
1281212.7670797953264-0.767079795326434
1291111.4614935437491-0.461493543749085
13046.00799725042713-2.00799725042713
1311616.1162001466741-0.116200146674119
1321512.66424647391312.33575352608689
1331011.3605315783873-1.36053157838729
1341314.0185198189459-1.01851981894585
1351513.12704679541491.87295320458506
1361210.49901785028741.50098214971256
1371413.65622716963500.343772830364979
138710.3811344201371-3.38113442013705
1391913.90758465523035.09241534476974
1401212.9450764503607-0.945076450360723
1411211.87281216209070.127187837909294
1421313.3384896313215-0.338489631321529
1431512.41658835187712.58341164812294
14488.84468525889237-0.844685258892373
1451210.92306092814381.07693907185619
1461010.7601143818466-0.760114381846583
147811.2512297881576-3.25122978815761
1481014.6517017577584-4.65170175775841
1491513.86767000554161.13232999445839
1501614.06006083516021.93993916483983
1511313.2702490596877-0.270249059687716
1521615.08383967095680.916160329043213
15399.82245988079266-0.822459880792662
1541413.06064506842480.939354931575244
1551413.15468044162370.845319558376316
1561210.17384524096051.82615475903955







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1130433994221450.2260867988442890.886956600577856
100.05013382791177690.1002676558235540.949866172088223
110.07267912027590160.1453582405518030.927320879724098
120.03629398921487670.07258797842975350.963706010785123
130.4557232195718910.9114464391437830.544276780428109
140.769116895654180.4617662086916400.230883104345820
150.6908433223386940.6183133553226120.309156677661306
160.6024926471737240.7950147056525520.397507352826276
170.5467760851519180.9064478296961640.453223914848082
180.4604837494362260.9209674988724520.539516250563774
190.3839618847202820.7679237694405640.616038115279718
200.6816489995600280.6367020008799430.318351000439972
210.6224804753700670.7550390492598670.377519524629933
220.5913843595604920.8172312808790170.408615640439508
230.52172208386810.9565558322638010.478277916131900
240.5054717242320790.9890565515358410.494528275767921
250.4666003742831140.9332007485662290.533399625716886
260.4057023431920110.8114046863840220.594297656807989
270.6656525385961270.6686949228077450.334347461403873
280.6092406590240310.7815186819519370.390759340975969
290.548217805706860.903564388586280.45178219429314
300.6967292435969250.606541512806150.303270756403075
310.7967796950251990.4064406099496030.203220304974801
320.7590158307003850.4819683385992300.240984169299615
330.7126324869308850.574735026138230.287367513069115
340.6708621790828510.6582756418342980.329137820917149
350.6604854681737820.6790290636524370.339514531826218
360.6620864751166070.6758270497667860.337913524883393
370.6315670397406530.7368659205186950.368432960259347
380.5814582939711880.8370834120576240.418541706028812
390.5342609186702430.9314781626595130.465739081329757
400.4937529209909910.9875058419819830.506247079009009
410.4610262260658950.922052452131790.538973773934105
420.7869745799540760.4260508400918480.213025420045924
430.9441507641276810.1116984717446380.0558492358723188
440.9501602579188950.09967948416221070.0498397420811053
450.9360855693976350.1278288612047300.0639144306023652
460.942926136853230.1141477262935400.0570738631467698
470.9410155042204510.1179689915590970.0589844957795486
480.9286144569959340.1427710860081320.0713855430040658
490.9282167447691530.1435665104616950.0717832552308476
500.9172115817422640.1655768365154710.0827884182577357
510.9110089431719230.1779821136561550.0889910568280774
520.9881368005063830.02372639898723420.0118631994936171
530.9839156407870940.03216871842581130.0160843592129057
540.9891141881810530.02177162363789300.0108858118189465
550.9916049927321370.01679001453572570.00839500726786285
560.989274817612040.02145036477592180.0107251823879609
570.985510046847790.02897990630442120.0144899531522106
580.9830998248337850.03380035033242950.0169001751662147
590.9897317601582780.02053647968344500.0102682398417225
600.9879167923304770.02416641533904560.0120832076695228
610.9883954728405830.02320905431883310.0116045271594166
620.9881815610111650.0236368779776690.0118184389888345
630.9861950841846160.02760983163076880.0138049158153844
640.9888387174343390.02232256513132230.0111612825656612
650.9879279038055050.02414419238898960.0120720961944948
660.9870471864368760.02590562712624850.0129528135631242
670.9876899085261730.02462018294765380.0123100914738269
680.9900355028826320.01992899423473560.00996449711736778
690.9893371327934220.02132573441315510.0106628672065776
700.9862189658359140.02756206832817190.0137810341640860
710.9864901484033160.02701970319336890.0135098515966845
720.982042957120470.03591408575905970.0179570428795298
730.9790989610863860.04180207782722850.0209010389136143
740.986222139464540.02755572107091920.0137778605354596
750.9820889389724890.03582212205502310.0179110610275116
760.9793754198191980.04124916036160450.0206245801808022
770.9730638967713460.05387220645730770.0269361032286539
780.964812546686530.07037490662694180.0351874533134709
790.9767971775259170.04640564494816590.0232028224740829
800.9758336570371320.04833268592573540.0241663429628677
810.9796966465752120.04060670684957580.0203033534247879
820.9789508435858160.0420983128283690.0210491564141845
830.9721156473077480.05576870538450460.0278843526922523
840.9661591855420550.06768162891589010.0338408144579451
850.9887493606561960.02250127868760740.0112506393438037
860.9850009684572570.02999806308548620.0149990315427431
870.9800846270988810.03983074580223710.0199153729011185
880.9741862017701840.05162759645963250.0258137982298163
890.9685426880007010.06291462399859730.0314573119992987
900.9597027456007290.08059450879854170.0402972543992709
910.9518277578170310.0963444843659380.048172242182969
920.9728773979058950.05424520418821060.0271226020941053
930.9648325104257250.0703349791485490.0351674895742745
940.9600134843719680.07997303125606360.0399865156280318
950.949843462806240.1003130743875210.0501565371937606
960.9377078963427230.1245842073145530.0622921036572765
970.9326663289990020.1346673420019950.0673336710009975
980.916412566796770.1671748664064610.0835874332032303
990.9111653818034430.1776692363931140.0888346181965571
1000.8911113366458350.2177773267083310.108888663354165
1010.8669461625978430.2661076748043150.133053837402158
1020.8438511403902870.3122977192194250.156148859609713
1030.8564073990979180.2871852018041640.143592600902082
1040.8970960783838270.2058078432323460.102903921616173
1050.899191487777780.2016170244444390.100808512222220
1060.8769145014821860.2461709970356290.123085498517814
1070.8562531297270540.2874937405458920.143746870272946
1080.851737827603690.2965243447926210.148262172396310
1090.8492406760226680.3015186479546640.150759323977332
1100.8738840206992920.2522319586014170.126115979300708
1110.8620991218746370.2758017562507260.137900878125363
1120.9024246962304320.1951506075391360.097575303769568
1130.8765287222660060.2469425554679880.123471277733994
1140.8486910473382820.3026179053234360.151308952661718
1150.8139301874578690.3721396250842630.186069812542131
1160.8128092568573170.3743814862853660.187190743142683
1170.8253832241903190.3492335516193620.174616775809681
1180.8149325031828430.3701349936343140.185067496817157
1190.773355081836850.4532898363262990.226644918163149
1200.8345619402205520.3308761195588960.165438059779448
1210.8046057280695740.3907885438608520.195394271930426
1220.7774115026583020.4451769946833960.222588497341698
1230.7757768662253660.4484462675492670.224223133774634
1240.7342428456112490.5315143087775030.265757154388751
1250.7303588052703450.5392823894593110.269641194729655
1260.7137838632860440.5724322734279120.286216136713956
1270.6581854586856680.6836290826286650.341814541314332
1280.6270213796300450.7459572407399110.372978620369955
1290.644154788415310.711690423169380.35584521158469
1300.6387987303957710.7224025392084570.361201269604229
1310.5730707520884020.8538584958231960.426929247911598
1320.5600256200099990.8799487599800030.439974379990001
1330.5277134776274870.9445730447450260.472286522372513
1340.4601172862362330.9202345724724660.539882713763767
1350.433758222992540.867516445985080.56624177700746
1360.4283149547483770.8566299094967530.571685045251623
1370.3559294526942620.7118589053885230.644070547305738
1380.4400521787412330.8801043574824670.559947821258767
1390.7891160615449870.4217678769100260.210883938455013
1400.8113383097895780.3773233804208430.188661690210422
1410.7683123913540730.4633752172918540.231687608645927
1420.684830836244760.6303383275104790.315169163755239
1430.6422633627484810.7154732745030380.357736637251519
1440.5279450142397360.9441099715205270.472054985760264
1450.5080457681899910.9839084636200180.491954231810009
1460.6727966931690960.6544066136618080.327203306830904
1470.5714480724847210.8571038550305590.428551927515279

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.113043399422145 & 0.226086798844289 & 0.886956600577856 \tabularnewline
10 & 0.0501338279117769 & 0.100267655823554 & 0.949866172088223 \tabularnewline
11 & 0.0726791202759016 & 0.145358240551803 & 0.927320879724098 \tabularnewline
12 & 0.0362939892148767 & 0.0725879784297535 & 0.963706010785123 \tabularnewline
13 & 0.455723219571891 & 0.911446439143783 & 0.544276780428109 \tabularnewline
14 & 0.76911689565418 & 0.461766208691640 & 0.230883104345820 \tabularnewline
15 & 0.690843322338694 & 0.618313355322612 & 0.309156677661306 \tabularnewline
16 & 0.602492647173724 & 0.795014705652552 & 0.397507352826276 \tabularnewline
17 & 0.546776085151918 & 0.906447829696164 & 0.453223914848082 \tabularnewline
18 & 0.460483749436226 & 0.920967498872452 & 0.539516250563774 \tabularnewline
19 & 0.383961884720282 & 0.767923769440564 & 0.616038115279718 \tabularnewline
20 & 0.681648999560028 & 0.636702000879943 & 0.318351000439972 \tabularnewline
21 & 0.622480475370067 & 0.755039049259867 & 0.377519524629933 \tabularnewline
22 & 0.591384359560492 & 0.817231280879017 & 0.408615640439508 \tabularnewline
23 & 0.5217220838681 & 0.956555832263801 & 0.478277916131900 \tabularnewline
24 & 0.505471724232079 & 0.989056551535841 & 0.494528275767921 \tabularnewline
25 & 0.466600374283114 & 0.933200748566229 & 0.533399625716886 \tabularnewline
26 & 0.405702343192011 & 0.811404686384022 & 0.594297656807989 \tabularnewline
27 & 0.665652538596127 & 0.668694922807745 & 0.334347461403873 \tabularnewline
28 & 0.609240659024031 & 0.781518681951937 & 0.390759340975969 \tabularnewline
29 & 0.54821780570686 & 0.90356438858628 & 0.45178219429314 \tabularnewline
30 & 0.696729243596925 & 0.60654151280615 & 0.303270756403075 \tabularnewline
31 & 0.796779695025199 & 0.406440609949603 & 0.203220304974801 \tabularnewline
32 & 0.759015830700385 & 0.481968338599230 & 0.240984169299615 \tabularnewline
33 & 0.712632486930885 & 0.57473502613823 & 0.287367513069115 \tabularnewline
34 & 0.670862179082851 & 0.658275641834298 & 0.329137820917149 \tabularnewline
35 & 0.660485468173782 & 0.679029063652437 & 0.339514531826218 \tabularnewline
36 & 0.662086475116607 & 0.675827049766786 & 0.337913524883393 \tabularnewline
37 & 0.631567039740653 & 0.736865920518695 & 0.368432960259347 \tabularnewline
38 & 0.581458293971188 & 0.837083412057624 & 0.418541706028812 \tabularnewline
39 & 0.534260918670243 & 0.931478162659513 & 0.465739081329757 \tabularnewline
40 & 0.493752920990991 & 0.987505841981983 & 0.506247079009009 \tabularnewline
41 & 0.461026226065895 & 0.92205245213179 & 0.538973773934105 \tabularnewline
42 & 0.786974579954076 & 0.426050840091848 & 0.213025420045924 \tabularnewline
43 & 0.944150764127681 & 0.111698471744638 & 0.0558492358723188 \tabularnewline
44 & 0.950160257918895 & 0.0996794841622107 & 0.0498397420811053 \tabularnewline
45 & 0.936085569397635 & 0.127828861204730 & 0.0639144306023652 \tabularnewline
46 & 0.94292613685323 & 0.114147726293540 & 0.0570738631467698 \tabularnewline
47 & 0.941015504220451 & 0.117968991559097 & 0.0589844957795486 \tabularnewline
48 & 0.928614456995934 & 0.142771086008132 & 0.0713855430040658 \tabularnewline
49 & 0.928216744769153 & 0.143566510461695 & 0.0717832552308476 \tabularnewline
50 & 0.917211581742264 & 0.165576836515471 & 0.0827884182577357 \tabularnewline
51 & 0.911008943171923 & 0.177982113656155 & 0.0889910568280774 \tabularnewline
52 & 0.988136800506383 & 0.0237263989872342 & 0.0118631994936171 \tabularnewline
53 & 0.983915640787094 & 0.0321687184258113 & 0.0160843592129057 \tabularnewline
54 & 0.989114188181053 & 0.0217716236378930 & 0.0108858118189465 \tabularnewline
55 & 0.991604992732137 & 0.0167900145357257 & 0.00839500726786285 \tabularnewline
56 & 0.98927481761204 & 0.0214503647759218 & 0.0107251823879609 \tabularnewline
57 & 0.98551004684779 & 0.0289799063044212 & 0.0144899531522106 \tabularnewline
58 & 0.983099824833785 & 0.0338003503324295 & 0.0169001751662147 \tabularnewline
59 & 0.989731760158278 & 0.0205364796834450 & 0.0102682398417225 \tabularnewline
60 & 0.987916792330477 & 0.0241664153390456 & 0.0120832076695228 \tabularnewline
61 & 0.988395472840583 & 0.0232090543188331 & 0.0116045271594166 \tabularnewline
62 & 0.988181561011165 & 0.023636877977669 & 0.0118184389888345 \tabularnewline
63 & 0.986195084184616 & 0.0276098316307688 & 0.0138049158153844 \tabularnewline
64 & 0.988838717434339 & 0.0223225651313223 & 0.0111612825656612 \tabularnewline
65 & 0.987927903805505 & 0.0241441923889896 & 0.0120720961944948 \tabularnewline
66 & 0.987047186436876 & 0.0259056271262485 & 0.0129528135631242 \tabularnewline
67 & 0.987689908526173 & 0.0246201829476538 & 0.0123100914738269 \tabularnewline
68 & 0.990035502882632 & 0.0199289942347356 & 0.00996449711736778 \tabularnewline
69 & 0.989337132793422 & 0.0213257344131551 & 0.0106628672065776 \tabularnewline
70 & 0.986218965835914 & 0.0275620683281719 & 0.0137810341640860 \tabularnewline
71 & 0.986490148403316 & 0.0270197031933689 & 0.0135098515966845 \tabularnewline
72 & 0.98204295712047 & 0.0359140857590597 & 0.0179570428795298 \tabularnewline
73 & 0.979098961086386 & 0.0418020778272285 & 0.0209010389136143 \tabularnewline
74 & 0.98622213946454 & 0.0275557210709192 & 0.0137778605354596 \tabularnewline
75 & 0.982088938972489 & 0.0358221220550231 & 0.0179110610275116 \tabularnewline
76 & 0.979375419819198 & 0.0412491603616045 & 0.0206245801808022 \tabularnewline
77 & 0.973063896771346 & 0.0538722064573077 & 0.0269361032286539 \tabularnewline
78 & 0.96481254668653 & 0.0703749066269418 & 0.0351874533134709 \tabularnewline
79 & 0.976797177525917 & 0.0464056449481659 & 0.0232028224740829 \tabularnewline
80 & 0.975833657037132 & 0.0483326859257354 & 0.0241663429628677 \tabularnewline
81 & 0.979696646575212 & 0.0406067068495758 & 0.0203033534247879 \tabularnewline
82 & 0.978950843585816 & 0.042098312828369 & 0.0210491564141845 \tabularnewline
83 & 0.972115647307748 & 0.0557687053845046 & 0.0278843526922523 \tabularnewline
84 & 0.966159185542055 & 0.0676816289158901 & 0.0338408144579451 \tabularnewline
85 & 0.988749360656196 & 0.0225012786876074 & 0.0112506393438037 \tabularnewline
86 & 0.985000968457257 & 0.0299980630854862 & 0.0149990315427431 \tabularnewline
87 & 0.980084627098881 & 0.0398307458022371 & 0.0199153729011185 \tabularnewline
88 & 0.974186201770184 & 0.0516275964596325 & 0.0258137982298163 \tabularnewline
89 & 0.968542688000701 & 0.0629146239985973 & 0.0314573119992987 \tabularnewline
90 & 0.959702745600729 & 0.0805945087985417 & 0.0402972543992709 \tabularnewline
91 & 0.951827757817031 & 0.096344484365938 & 0.048172242182969 \tabularnewline
92 & 0.972877397905895 & 0.0542452041882106 & 0.0271226020941053 \tabularnewline
93 & 0.964832510425725 & 0.070334979148549 & 0.0351674895742745 \tabularnewline
94 & 0.960013484371968 & 0.0799730312560636 & 0.0399865156280318 \tabularnewline
95 & 0.94984346280624 & 0.100313074387521 & 0.0501565371937606 \tabularnewline
96 & 0.937707896342723 & 0.124584207314553 & 0.0622921036572765 \tabularnewline
97 & 0.932666328999002 & 0.134667342001995 & 0.0673336710009975 \tabularnewline
98 & 0.91641256679677 & 0.167174866406461 & 0.0835874332032303 \tabularnewline
99 & 0.911165381803443 & 0.177669236393114 & 0.0888346181965571 \tabularnewline
100 & 0.891111336645835 & 0.217777326708331 & 0.108888663354165 \tabularnewline
101 & 0.866946162597843 & 0.266107674804315 & 0.133053837402158 \tabularnewline
102 & 0.843851140390287 & 0.312297719219425 & 0.156148859609713 \tabularnewline
103 & 0.856407399097918 & 0.287185201804164 & 0.143592600902082 \tabularnewline
104 & 0.897096078383827 & 0.205807843232346 & 0.102903921616173 \tabularnewline
105 & 0.89919148777778 & 0.201617024444439 & 0.100808512222220 \tabularnewline
106 & 0.876914501482186 & 0.246170997035629 & 0.123085498517814 \tabularnewline
107 & 0.856253129727054 & 0.287493740545892 & 0.143746870272946 \tabularnewline
108 & 0.85173782760369 & 0.296524344792621 & 0.148262172396310 \tabularnewline
109 & 0.849240676022668 & 0.301518647954664 & 0.150759323977332 \tabularnewline
110 & 0.873884020699292 & 0.252231958601417 & 0.126115979300708 \tabularnewline
111 & 0.862099121874637 & 0.275801756250726 & 0.137900878125363 \tabularnewline
112 & 0.902424696230432 & 0.195150607539136 & 0.097575303769568 \tabularnewline
113 & 0.876528722266006 & 0.246942555467988 & 0.123471277733994 \tabularnewline
114 & 0.848691047338282 & 0.302617905323436 & 0.151308952661718 \tabularnewline
115 & 0.813930187457869 & 0.372139625084263 & 0.186069812542131 \tabularnewline
116 & 0.812809256857317 & 0.374381486285366 & 0.187190743142683 \tabularnewline
117 & 0.825383224190319 & 0.349233551619362 & 0.174616775809681 \tabularnewline
118 & 0.814932503182843 & 0.370134993634314 & 0.185067496817157 \tabularnewline
119 & 0.77335508183685 & 0.453289836326299 & 0.226644918163149 \tabularnewline
120 & 0.834561940220552 & 0.330876119558896 & 0.165438059779448 \tabularnewline
121 & 0.804605728069574 & 0.390788543860852 & 0.195394271930426 \tabularnewline
122 & 0.777411502658302 & 0.445176994683396 & 0.222588497341698 \tabularnewline
123 & 0.775776866225366 & 0.448446267549267 & 0.224223133774634 \tabularnewline
124 & 0.734242845611249 & 0.531514308777503 & 0.265757154388751 \tabularnewline
125 & 0.730358805270345 & 0.539282389459311 & 0.269641194729655 \tabularnewline
126 & 0.713783863286044 & 0.572432273427912 & 0.286216136713956 \tabularnewline
127 & 0.658185458685668 & 0.683629082628665 & 0.341814541314332 \tabularnewline
128 & 0.627021379630045 & 0.745957240739911 & 0.372978620369955 \tabularnewline
129 & 0.64415478841531 & 0.71169042316938 & 0.35584521158469 \tabularnewline
130 & 0.638798730395771 & 0.722402539208457 & 0.361201269604229 \tabularnewline
131 & 0.573070752088402 & 0.853858495823196 & 0.426929247911598 \tabularnewline
132 & 0.560025620009999 & 0.879948759980003 & 0.439974379990001 \tabularnewline
133 & 0.527713477627487 & 0.944573044745026 & 0.472286522372513 \tabularnewline
134 & 0.460117286236233 & 0.920234572472466 & 0.539882713763767 \tabularnewline
135 & 0.43375822299254 & 0.86751644598508 & 0.56624177700746 \tabularnewline
136 & 0.428314954748377 & 0.856629909496753 & 0.571685045251623 \tabularnewline
137 & 0.355929452694262 & 0.711858905388523 & 0.644070547305738 \tabularnewline
138 & 0.440052178741233 & 0.880104357482467 & 0.559947821258767 \tabularnewline
139 & 0.789116061544987 & 0.421767876910026 & 0.210883938455013 \tabularnewline
140 & 0.811338309789578 & 0.377323380420843 & 0.188661690210422 \tabularnewline
141 & 0.768312391354073 & 0.463375217291854 & 0.231687608645927 \tabularnewline
142 & 0.68483083624476 & 0.630338327510479 & 0.315169163755239 \tabularnewline
143 & 0.642263362748481 & 0.715473274503038 & 0.357736637251519 \tabularnewline
144 & 0.527945014239736 & 0.944109971520527 & 0.472054985760264 \tabularnewline
145 & 0.508045768189991 & 0.983908463620018 & 0.491954231810009 \tabularnewline
146 & 0.672796693169096 & 0.654406613661808 & 0.327203306830904 \tabularnewline
147 & 0.571448072484721 & 0.857103855030559 & 0.428551927515279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.113043399422145[/C][C]0.226086798844289[/C][C]0.886956600577856[/C][/ROW]
[ROW][C]10[/C][C]0.0501338279117769[/C][C]0.100267655823554[/C][C]0.949866172088223[/C][/ROW]
[ROW][C]11[/C][C]0.0726791202759016[/C][C]0.145358240551803[/C][C]0.927320879724098[/C][/ROW]
[ROW][C]12[/C][C]0.0362939892148767[/C][C]0.0725879784297535[/C][C]0.963706010785123[/C][/ROW]
[ROW][C]13[/C][C]0.455723219571891[/C][C]0.911446439143783[/C][C]0.544276780428109[/C][/ROW]
[ROW][C]14[/C][C]0.76911689565418[/C][C]0.461766208691640[/C][C]0.230883104345820[/C][/ROW]
[ROW][C]15[/C][C]0.690843322338694[/C][C]0.618313355322612[/C][C]0.309156677661306[/C][/ROW]
[ROW][C]16[/C][C]0.602492647173724[/C][C]0.795014705652552[/C][C]0.397507352826276[/C][/ROW]
[ROW][C]17[/C][C]0.546776085151918[/C][C]0.906447829696164[/C][C]0.453223914848082[/C][/ROW]
[ROW][C]18[/C][C]0.460483749436226[/C][C]0.920967498872452[/C][C]0.539516250563774[/C][/ROW]
[ROW][C]19[/C][C]0.383961884720282[/C][C]0.767923769440564[/C][C]0.616038115279718[/C][/ROW]
[ROW][C]20[/C][C]0.681648999560028[/C][C]0.636702000879943[/C][C]0.318351000439972[/C][/ROW]
[ROW][C]21[/C][C]0.622480475370067[/C][C]0.755039049259867[/C][C]0.377519524629933[/C][/ROW]
[ROW][C]22[/C][C]0.591384359560492[/C][C]0.817231280879017[/C][C]0.408615640439508[/C][/ROW]
[ROW][C]23[/C][C]0.5217220838681[/C][C]0.956555832263801[/C][C]0.478277916131900[/C][/ROW]
[ROW][C]24[/C][C]0.505471724232079[/C][C]0.989056551535841[/C][C]0.494528275767921[/C][/ROW]
[ROW][C]25[/C][C]0.466600374283114[/C][C]0.933200748566229[/C][C]0.533399625716886[/C][/ROW]
[ROW][C]26[/C][C]0.405702343192011[/C][C]0.811404686384022[/C][C]0.594297656807989[/C][/ROW]
[ROW][C]27[/C][C]0.665652538596127[/C][C]0.668694922807745[/C][C]0.334347461403873[/C][/ROW]
[ROW][C]28[/C][C]0.609240659024031[/C][C]0.781518681951937[/C][C]0.390759340975969[/C][/ROW]
[ROW][C]29[/C][C]0.54821780570686[/C][C]0.90356438858628[/C][C]0.45178219429314[/C][/ROW]
[ROW][C]30[/C][C]0.696729243596925[/C][C]0.60654151280615[/C][C]0.303270756403075[/C][/ROW]
[ROW][C]31[/C][C]0.796779695025199[/C][C]0.406440609949603[/C][C]0.203220304974801[/C][/ROW]
[ROW][C]32[/C][C]0.759015830700385[/C][C]0.481968338599230[/C][C]0.240984169299615[/C][/ROW]
[ROW][C]33[/C][C]0.712632486930885[/C][C]0.57473502613823[/C][C]0.287367513069115[/C][/ROW]
[ROW][C]34[/C][C]0.670862179082851[/C][C]0.658275641834298[/C][C]0.329137820917149[/C][/ROW]
[ROW][C]35[/C][C]0.660485468173782[/C][C]0.679029063652437[/C][C]0.339514531826218[/C][/ROW]
[ROW][C]36[/C][C]0.662086475116607[/C][C]0.675827049766786[/C][C]0.337913524883393[/C][/ROW]
[ROW][C]37[/C][C]0.631567039740653[/C][C]0.736865920518695[/C][C]0.368432960259347[/C][/ROW]
[ROW][C]38[/C][C]0.581458293971188[/C][C]0.837083412057624[/C][C]0.418541706028812[/C][/ROW]
[ROW][C]39[/C][C]0.534260918670243[/C][C]0.931478162659513[/C][C]0.465739081329757[/C][/ROW]
[ROW][C]40[/C][C]0.493752920990991[/C][C]0.987505841981983[/C][C]0.506247079009009[/C][/ROW]
[ROW][C]41[/C][C]0.461026226065895[/C][C]0.92205245213179[/C][C]0.538973773934105[/C][/ROW]
[ROW][C]42[/C][C]0.786974579954076[/C][C]0.426050840091848[/C][C]0.213025420045924[/C][/ROW]
[ROW][C]43[/C][C]0.944150764127681[/C][C]0.111698471744638[/C][C]0.0558492358723188[/C][/ROW]
[ROW][C]44[/C][C]0.950160257918895[/C][C]0.0996794841622107[/C][C]0.0498397420811053[/C][/ROW]
[ROW][C]45[/C][C]0.936085569397635[/C][C]0.127828861204730[/C][C]0.0639144306023652[/C][/ROW]
[ROW][C]46[/C][C]0.94292613685323[/C][C]0.114147726293540[/C][C]0.0570738631467698[/C][/ROW]
[ROW][C]47[/C][C]0.941015504220451[/C][C]0.117968991559097[/C][C]0.0589844957795486[/C][/ROW]
[ROW][C]48[/C][C]0.928614456995934[/C][C]0.142771086008132[/C][C]0.0713855430040658[/C][/ROW]
[ROW][C]49[/C][C]0.928216744769153[/C][C]0.143566510461695[/C][C]0.0717832552308476[/C][/ROW]
[ROW][C]50[/C][C]0.917211581742264[/C][C]0.165576836515471[/C][C]0.0827884182577357[/C][/ROW]
[ROW][C]51[/C][C]0.911008943171923[/C][C]0.177982113656155[/C][C]0.0889910568280774[/C][/ROW]
[ROW][C]52[/C][C]0.988136800506383[/C][C]0.0237263989872342[/C][C]0.0118631994936171[/C][/ROW]
[ROW][C]53[/C][C]0.983915640787094[/C][C]0.0321687184258113[/C][C]0.0160843592129057[/C][/ROW]
[ROW][C]54[/C][C]0.989114188181053[/C][C]0.0217716236378930[/C][C]0.0108858118189465[/C][/ROW]
[ROW][C]55[/C][C]0.991604992732137[/C][C]0.0167900145357257[/C][C]0.00839500726786285[/C][/ROW]
[ROW][C]56[/C][C]0.98927481761204[/C][C]0.0214503647759218[/C][C]0.0107251823879609[/C][/ROW]
[ROW][C]57[/C][C]0.98551004684779[/C][C]0.0289799063044212[/C][C]0.0144899531522106[/C][/ROW]
[ROW][C]58[/C][C]0.983099824833785[/C][C]0.0338003503324295[/C][C]0.0169001751662147[/C][/ROW]
[ROW][C]59[/C][C]0.989731760158278[/C][C]0.0205364796834450[/C][C]0.0102682398417225[/C][/ROW]
[ROW][C]60[/C][C]0.987916792330477[/C][C]0.0241664153390456[/C][C]0.0120832076695228[/C][/ROW]
[ROW][C]61[/C][C]0.988395472840583[/C][C]0.0232090543188331[/C][C]0.0116045271594166[/C][/ROW]
[ROW][C]62[/C][C]0.988181561011165[/C][C]0.023636877977669[/C][C]0.0118184389888345[/C][/ROW]
[ROW][C]63[/C][C]0.986195084184616[/C][C]0.0276098316307688[/C][C]0.0138049158153844[/C][/ROW]
[ROW][C]64[/C][C]0.988838717434339[/C][C]0.0223225651313223[/C][C]0.0111612825656612[/C][/ROW]
[ROW][C]65[/C][C]0.987927903805505[/C][C]0.0241441923889896[/C][C]0.0120720961944948[/C][/ROW]
[ROW][C]66[/C][C]0.987047186436876[/C][C]0.0259056271262485[/C][C]0.0129528135631242[/C][/ROW]
[ROW][C]67[/C][C]0.987689908526173[/C][C]0.0246201829476538[/C][C]0.0123100914738269[/C][/ROW]
[ROW][C]68[/C][C]0.990035502882632[/C][C]0.0199289942347356[/C][C]0.00996449711736778[/C][/ROW]
[ROW][C]69[/C][C]0.989337132793422[/C][C]0.0213257344131551[/C][C]0.0106628672065776[/C][/ROW]
[ROW][C]70[/C][C]0.986218965835914[/C][C]0.0275620683281719[/C][C]0.0137810341640860[/C][/ROW]
[ROW][C]71[/C][C]0.986490148403316[/C][C]0.0270197031933689[/C][C]0.0135098515966845[/C][/ROW]
[ROW][C]72[/C][C]0.98204295712047[/C][C]0.0359140857590597[/C][C]0.0179570428795298[/C][/ROW]
[ROW][C]73[/C][C]0.979098961086386[/C][C]0.0418020778272285[/C][C]0.0209010389136143[/C][/ROW]
[ROW][C]74[/C][C]0.98622213946454[/C][C]0.0275557210709192[/C][C]0.0137778605354596[/C][/ROW]
[ROW][C]75[/C][C]0.982088938972489[/C][C]0.0358221220550231[/C][C]0.0179110610275116[/C][/ROW]
[ROW][C]76[/C][C]0.979375419819198[/C][C]0.0412491603616045[/C][C]0.0206245801808022[/C][/ROW]
[ROW][C]77[/C][C]0.973063896771346[/C][C]0.0538722064573077[/C][C]0.0269361032286539[/C][/ROW]
[ROW][C]78[/C][C]0.96481254668653[/C][C]0.0703749066269418[/C][C]0.0351874533134709[/C][/ROW]
[ROW][C]79[/C][C]0.976797177525917[/C][C]0.0464056449481659[/C][C]0.0232028224740829[/C][/ROW]
[ROW][C]80[/C][C]0.975833657037132[/C][C]0.0483326859257354[/C][C]0.0241663429628677[/C][/ROW]
[ROW][C]81[/C][C]0.979696646575212[/C][C]0.0406067068495758[/C][C]0.0203033534247879[/C][/ROW]
[ROW][C]82[/C][C]0.978950843585816[/C][C]0.042098312828369[/C][C]0.0210491564141845[/C][/ROW]
[ROW][C]83[/C][C]0.972115647307748[/C][C]0.0557687053845046[/C][C]0.0278843526922523[/C][/ROW]
[ROW][C]84[/C][C]0.966159185542055[/C][C]0.0676816289158901[/C][C]0.0338408144579451[/C][/ROW]
[ROW][C]85[/C][C]0.988749360656196[/C][C]0.0225012786876074[/C][C]0.0112506393438037[/C][/ROW]
[ROW][C]86[/C][C]0.985000968457257[/C][C]0.0299980630854862[/C][C]0.0149990315427431[/C][/ROW]
[ROW][C]87[/C][C]0.980084627098881[/C][C]0.0398307458022371[/C][C]0.0199153729011185[/C][/ROW]
[ROW][C]88[/C][C]0.974186201770184[/C][C]0.0516275964596325[/C][C]0.0258137982298163[/C][/ROW]
[ROW][C]89[/C][C]0.968542688000701[/C][C]0.0629146239985973[/C][C]0.0314573119992987[/C][/ROW]
[ROW][C]90[/C][C]0.959702745600729[/C][C]0.0805945087985417[/C][C]0.0402972543992709[/C][/ROW]
[ROW][C]91[/C][C]0.951827757817031[/C][C]0.096344484365938[/C][C]0.048172242182969[/C][/ROW]
[ROW][C]92[/C][C]0.972877397905895[/C][C]0.0542452041882106[/C][C]0.0271226020941053[/C][/ROW]
[ROW][C]93[/C][C]0.964832510425725[/C][C]0.070334979148549[/C][C]0.0351674895742745[/C][/ROW]
[ROW][C]94[/C][C]0.960013484371968[/C][C]0.0799730312560636[/C][C]0.0399865156280318[/C][/ROW]
[ROW][C]95[/C][C]0.94984346280624[/C][C]0.100313074387521[/C][C]0.0501565371937606[/C][/ROW]
[ROW][C]96[/C][C]0.937707896342723[/C][C]0.124584207314553[/C][C]0.0622921036572765[/C][/ROW]
[ROW][C]97[/C][C]0.932666328999002[/C][C]0.134667342001995[/C][C]0.0673336710009975[/C][/ROW]
[ROW][C]98[/C][C]0.91641256679677[/C][C]0.167174866406461[/C][C]0.0835874332032303[/C][/ROW]
[ROW][C]99[/C][C]0.911165381803443[/C][C]0.177669236393114[/C][C]0.0888346181965571[/C][/ROW]
[ROW][C]100[/C][C]0.891111336645835[/C][C]0.217777326708331[/C][C]0.108888663354165[/C][/ROW]
[ROW][C]101[/C][C]0.866946162597843[/C][C]0.266107674804315[/C][C]0.133053837402158[/C][/ROW]
[ROW][C]102[/C][C]0.843851140390287[/C][C]0.312297719219425[/C][C]0.156148859609713[/C][/ROW]
[ROW][C]103[/C][C]0.856407399097918[/C][C]0.287185201804164[/C][C]0.143592600902082[/C][/ROW]
[ROW][C]104[/C][C]0.897096078383827[/C][C]0.205807843232346[/C][C]0.102903921616173[/C][/ROW]
[ROW][C]105[/C][C]0.89919148777778[/C][C]0.201617024444439[/C][C]0.100808512222220[/C][/ROW]
[ROW][C]106[/C][C]0.876914501482186[/C][C]0.246170997035629[/C][C]0.123085498517814[/C][/ROW]
[ROW][C]107[/C][C]0.856253129727054[/C][C]0.287493740545892[/C][C]0.143746870272946[/C][/ROW]
[ROW][C]108[/C][C]0.85173782760369[/C][C]0.296524344792621[/C][C]0.148262172396310[/C][/ROW]
[ROW][C]109[/C][C]0.849240676022668[/C][C]0.301518647954664[/C][C]0.150759323977332[/C][/ROW]
[ROW][C]110[/C][C]0.873884020699292[/C][C]0.252231958601417[/C][C]0.126115979300708[/C][/ROW]
[ROW][C]111[/C][C]0.862099121874637[/C][C]0.275801756250726[/C][C]0.137900878125363[/C][/ROW]
[ROW][C]112[/C][C]0.902424696230432[/C][C]0.195150607539136[/C][C]0.097575303769568[/C][/ROW]
[ROW][C]113[/C][C]0.876528722266006[/C][C]0.246942555467988[/C][C]0.123471277733994[/C][/ROW]
[ROW][C]114[/C][C]0.848691047338282[/C][C]0.302617905323436[/C][C]0.151308952661718[/C][/ROW]
[ROW][C]115[/C][C]0.813930187457869[/C][C]0.372139625084263[/C][C]0.186069812542131[/C][/ROW]
[ROW][C]116[/C][C]0.812809256857317[/C][C]0.374381486285366[/C][C]0.187190743142683[/C][/ROW]
[ROW][C]117[/C][C]0.825383224190319[/C][C]0.349233551619362[/C][C]0.174616775809681[/C][/ROW]
[ROW][C]118[/C][C]0.814932503182843[/C][C]0.370134993634314[/C][C]0.185067496817157[/C][/ROW]
[ROW][C]119[/C][C]0.77335508183685[/C][C]0.453289836326299[/C][C]0.226644918163149[/C][/ROW]
[ROW][C]120[/C][C]0.834561940220552[/C][C]0.330876119558896[/C][C]0.165438059779448[/C][/ROW]
[ROW][C]121[/C][C]0.804605728069574[/C][C]0.390788543860852[/C][C]0.195394271930426[/C][/ROW]
[ROW][C]122[/C][C]0.777411502658302[/C][C]0.445176994683396[/C][C]0.222588497341698[/C][/ROW]
[ROW][C]123[/C][C]0.775776866225366[/C][C]0.448446267549267[/C][C]0.224223133774634[/C][/ROW]
[ROW][C]124[/C][C]0.734242845611249[/C][C]0.531514308777503[/C][C]0.265757154388751[/C][/ROW]
[ROW][C]125[/C][C]0.730358805270345[/C][C]0.539282389459311[/C][C]0.269641194729655[/C][/ROW]
[ROW][C]126[/C][C]0.713783863286044[/C][C]0.572432273427912[/C][C]0.286216136713956[/C][/ROW]
[ROW][C]127[/C][C]0.658185458685668[/C][C]0.683629082628665[/C][C]0.341814541314332[/C][/ROW]
[ROW][C]128[/C][C]0.627021379630045[/C][C]0.745957240739911[/C][C]0.372978620369955[/C][/ROW]
[ROW][C]129[/C][C]0.64415478841531[/C][C]0.71169042316938[/C][C]0.35584521158469[/C][/ROW]
[ROW][C]130[/C][C]0.638798730395771[/C][C]0.722402539208457[/C][C]0.361201269604229[/C][/ROW]
[ROW][C]131[/C][C]0.573070752088402[/C][C]0.853858495823196[/C][C]0.426929247911598[/C][/ROW]
[ROW][C]132[/C][C]0.560025620009999[/C][C]0.879948759980003[/C][C]0.439974379990001[/C][/ROW]
[ROW][C]133[/C][C]0.527713477627487[/C][C]0.944573044745026[/C][C]0.472286522372513[/C][/ROW]
[ROW][C]134[/C][C]0.460117286236233[/C][C]0.920234572472466[/C][C]0.539882713763767[/C][/ROW]
[ROW][C]135[/C][C]0.43375822299254[/C][C]0.86751644598508[/C][C]0.56624177700746[/C][/ROW]
[ROW][C]136[/C][C]0.428314954748377[/C][C]0.856629909496753[/C][C]0.571685045251623[/C][/ROW]
[ROW][C]137[/C][C]0.355929452694262[/C][C]0.711858905388523[/C][C]0.644070547305738[/C][/ROW]
[ROW][C]138[/C][C]0.440052178741233[/C][C]0.880104357482467[/C][C]0.559947821258767[/C][/ROW]
[ROW][C]139[/C][C]0.789116061544987[/C][C]0.421767876910026[/C][C]0.210883938455013[/C][/ROW]
[ROW][C]140[/C][C]0.811338309789578[/C][C]0.377323380420843[/C][C]0.188661690210422[/C][/ROW]
[ROW][C]141[/C][C]0.768312391354073[/C][C]0.463375217291854[/C][C]0.231687608645927[/C][/ROW]
[ROW][C]142[/C][C]0.68483083624476[/C][C]0.630338327510479[/C][C]0.315169163755239[/C][/ROW]
[ROW][C]143[/C][C]0.642263362748481[/C][C]0.715473274503038[/C][C]0.357736637251519[/C][/ROW]
[ROW][C]144[/C][C]0.527945014239736[/C][C]0.944109971520527[/C][C]0.472054985760264[/C][/ROW]
[ROW][C]145[/C][C]0.508045768189991[/C][C]0.983908463620018[/C][C]0.491954231810009[/C][/ROW]
[ROW][C]146[/C][C]0.672796693169096[/C][C]0.654406613661808[/C][C]0.327203306830904[/C][/ROW]
[ROW][C]147[/C][C]0.571448072484721[/C][C]0.857103855030559[/C][C]0.428551927515279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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
90.1130433994221450.2260867988442890.886956600577856
100.05013382791177690.1002676558235540.949866172088223
110.07267912027590160.1453582405518030.927320879724098
120.03629398921487670.07258797842975350.963706010785123
130.4557232195718910.9114464391437830.544276780428109
140.769116895654180.4617662086916400.230883104345820
150.6908433223386940.6183133553226120.309156677661306
160.6024926471737240.7950147056525520.397507352826276
170.5467760851519180.9064478296961640.453223914848082
180.4604837494362260.9209674988724520.539516250563774
190.3839618847202820.7679237694405640.616038115279718
200.6816489995600280.6367020008799430.318351000439972
210.6224804753700670.7550390492598670.377519524629933
220.5913843595604920.8172312808790170.408615640439508
230.52172208386810.9565558322638010.478277916131900
240.5054717242320790.9890565515358410.494528275767921
250.4666003742831140.9332007485662290.533399625716886
260.4057023431920110.8114046863840220.594297656807989
270.6656525385961270.6686949228077450.334347461403873
280.6092406590240310.7815186819519370.390759340975969
290.548217805706860.903564388586280.45178219429314
300.6967292435969250.606541512806150.303270756403075
310.7967796950251990.4064406099496030.203220304974801
320.7590158307003850.4819683385992300.240984169299615
330.7126324869308850.574735026138230.287367513069115
340.6708621790828510.6582756418342980.329137820917149
350.6604854681737820.6790290636524370.339514531826218
360.6620864751166070.6758270497667860.337913524883393
370.6315670397406530.7368659205186950.368432960259347
380.5814582939711880.8370834120576240.418541706028812
390.5342609186702430.9314781626595130.465739081329757
400.4937529209909910.9875058419819830.506247079009009
410.4610262260658950.922052452131790.538973773934105
420.7869745799540760.4260508400918480.213025420045924
430.9441507641276810.1116984717446380.0558492358723188
440.9501602579188950.09967948416221070.0498397420811053
450.9360855693976350.1278288612047300.0639144306023652
460.942926136853230.1141477262935400.0570738631467698
470.9410155042204510.1179689915590970.0589844957795486
480.9286144569959340.1427710860081320.0713855430040658
490.9282167447691530.1435665104616950.0717832552308476
500.9172115817422640.1655768365154710.0827884182577357
510.9110089431719230.1779821136561550.0889910568280774
520.9881368005063830.02372639898723420.0118631994936171
530.9839156407870940.03216871842581130.0160843592129057
540.9891141881810530.02177162363789300.0108858118189465
550.9916049927321370.01679001453572570.00839500726786285
560.989274817612040.02145036477592180.0107251823879609
570.985510046847790.02897990630442120.0144899531522106
580.9830998248337850.03380035033242950.0169001751662147
590.9897317601582780.02053647968344500.0102682398417225
600.9879167923304770.02416641533904560.0120832076695228
610.9883954728405830.02320905431883310.0116045271594166
620.9881815610111650.0236368779776690.0118184389888345
630.9861950841846160.02760983163076880.0138049158153844
640.9888387174343390.02232256513132230.0111612825656612
650.9879279038055050.02414419238898960.0120720961944948
660.9870471864368760.02590562712624850.0129528135631242
670.9876899085261730.02462018294765380.0123100914738269
680.9900355028826320.01992899423473560.00996449711736778
690.9893371327934220.02132573441315510.0106628672065776
700.9862189658359140.02756206832817190.0137810341640860
710.9864901484033160.02701970319336890.0135098515966845
720.982042957120470.03591408575905970.0179570428795298
730.9790989610863860.04180207782722850.0209010389136143
740.986222139464540.02755572107091920.0137778605354596
750.9820889389724890.03582212205502310.0179110610275116
760.9793754198191980.04124916036160450.0206245801808022
770.9730638967713460.05387220645730770.0269361032286539
780.964812546686530.07037490662694180.0351874533134709
790.9767971775259170.04640564494816590.0232028224740829
800.9758336570371320.04833268592573540.0241663429628677
810.9796966465752120.04060670684957580.0203033534247879
820.9789508435858160.0420983128283690.0210491564141845
830.9721156473077480.05576870538450460.0278843526922523
840.9661591855420550.06768162891589010.0338408144579451
850.9887493606561960.02250127868760740.0112506393438037
860.9850009684572570.02999806308548620.0149990315427431
870.9800846270988810.03983074580223710.0199153729011185
880.9741862017701840.05162759645963250.0258137982298163
890.9685426880007010.06291462399859730.0314573119992987
900.9597027456007290.08059450879854170.0402972543992709
910.9518277578170310.0963444843659380.048172242182969
920.9728773979058950.05424520418821060.0271226020941053
930.9648325104257250.0703349791485490.0351674895742745
940.9600134843719680.07997303125606360.0399865156280318
950.949843462806240.1003130743875210.0501565371937606
960.9377078963427230.1245842073145530.0622921036572765
970.9326663289990020.1346673420019950.0673336710009975
980.916412566796770.1671748664064610.0835874332032303
990.9111653818034430.1776692363931140.0888346181965571
1000.8911113366458350.2177773267083310.108888663354165
1010.8669461625978430.2661076748043150.133053837402158
1020.8438511403902870.3122977192194250.156148859609713
1030.8564073990979180.2871852018041640.143592600902082
1040.8970960783838270.2058078432323460.102903921616173
1050.899191487777780.2016170244444390.100808512222220
1060.8769145014821860.2461709970356290.123085498517814
1070.8562531297270540.2874937405458920.143746870272946
1080.851737827603690.2965243447926210.148262172396310
1090.8492406760226680.3015186479546640.150759323977332
1100.8738840206992920.2522319586014170.126115979300708
1110.8620991218746370.2758017562507260.137900878125363
1120.9024246962304320.1951506075391360.097575303769568
1130.8765287222660060.2469425554679880.123471277733994
1140.8486910473382820.3026179053234360.151308952661718
1150.8139301874578690.3721396250842630.186069812542131
1160.8128092568573170.3743814862853660.187190743142683
1170.8253832241903190.3492335516193620.174616775809681
1180.8149325031828430.3701349936343140.185067496817157
1190.773355081836850.4532898363262990.226644918163149
1200.8345619402205520.3308761195588960.165438059779448
1210.8046057280695740.3907885438608520.195394271930426
1220.7774115026583020.4451769946833960.222588497341698
1230.7757768662253660.4484462675492670.224223133774634
1240.7342428456112490.5315143087775030.265757154388751
1250.7303588052703450.5392823894593110.269641194729655
1260.7137838632860440.5724322734279120.286216136713956
1270.6581854586856680.6836290826286650.341814541314332
1280.6270213796300450.7459572407399110.372978620369955
1290.644154788415310.711690423169380.35584521158469
1300.6387987303957710.7224025392084570.361201269604229
1310.5730707520884020.8538584958231960.426929247911598
1320.5600256200099990.8799487599800030.439974379990001
1330.5277134776274870.9445730447450260.472286522372513
1340.4601172862362330.9202345724724660.539882713763767
1350.433758222992540.867516445985080.56624177700746
1360.4283149547483770.8566299094967530.571685045251623
1370.3559294526942620.7118589053885230.644070547305738
1380.4400521787412330.8801043574824670.559947821258767
1390.7891160615449870.4217678769100260.210883938455013
1400.8113383097895780.3773233804208430.188661690210422
1410.7683123913540730.4633752172918540.231687608645927
1420.684830836244760.6303383275104790.315169163755239
1430.6422633627484810.7154732745030380.357736637251519
1440.5279450142397360.9441099715205270.472054985760264
1450.5080457681899910.9839084636200180.491954231810009
1460.6727966931690960.6544066136618080.327203306830904
1470.5714480724847210.8571038550305590.428551927515279







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level320.230215827338130NOK
10% type I error level450.323741007194245NOK

\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 & 32 & 0.230215827338130 & NOK \tabularnewline
10% type I error level & 45 & 0.323741007194245 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103397&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]32[/C][C]0.230215827338130[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.323741007194245[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103397&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103397&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 level320.230215827338130NOK
10% type I error level450.323741007194245NOK



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')
}