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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 14:19:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t132216294055030wbiia7biqo.htm/, Retrieved Fri, 29 Mar 2024 06:16:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147166, Retrieved Fri, 29 Mar 2024 06:16:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
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]
-    D  [Multiple Regression] [Multiple regressi...] [2010-11-20 14:09:44] [95e8426e0df851c9330605aa1e892ab5]
- R         [Multiple Regression] [ws 7 a)] [2011-11-24 19:19:23] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
ParentalExpectations[t] = + 6.16887701869768 + 0.0910412769387536ConcernoverMistakes[t] -0.124974617942723Doubtsaboutactions[t] + 0.665424927458418ParentalCriticism[t] + 0.11660939935939PersonalStandards[t] -0.0883713236862746Organization[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ParentalExpectations[t] =  +  6.16887701869768 +  0.0910412769387536ConcernoverMistakes[t] -0.124974617942723Doubtsaboutactions[t] +  0.665424927458418ParentalCriticism[t] +  0.11660939935939PersonalStandards[t] -0.0883713236862746Organization[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ParentalExpectations[t] =  +  6.16887701869768 +  0.0910412769387536ConcernoverMistakes[t] -0.124974617942723Doubtsaboutactions[t] +  0.665424927458418ParentalCriticism[t] +  0.11660939935939PersonalStandards[t] -0.0883713236862746Organization[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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
ParentalExpectations[t] = + 6.16887701869768 + 0.0910412769387536ConcernoverMistakes[t] -0.124974617942723Doubtsaboutactions[t] + 0.665424927458418ParentalCriticism[t] + 0.11660939935939PersonalStandards[t] -0.0883713236862746Organization[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.168877018697681.7713413.48260.0006480.000324
ConcernoverMistakes0.09104127693875360.0481051.89250.0603070.030154
Doubtsaboutactions-0.1249746179427230.087221-1.43290.1539410.07697
ParentalCriticism0.6654249274584180.0863057.710200
PersonalStandards0.116609399359390.0632171.84460.0670320.033516
Organization-0.08837132368627460.06186-1.42860.1551640.077582

\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) & 6.16887701869768 & 1.771341 & 3.4826 & 0.000648 & 0.000324 \tabularnewline
ConcernoverMistakes & 0.0910412769387536 & 0.048105 & 1.8925 & 0.060307 & 0.030154 \tabularnewline
Doubtsaboutactions & -0.124974617942723 & 0.087221 & -1.4329 & 0.153941 & 0.07697 \tabularnewline
ParentalCriticism & 0.665424927458418 & 0.086305 & 7.7102 & 0 & 0 \tabularnewline
PersonalStandards & 0.11660939935939 & 0.063217 & 1.8446 & 0.067032 & 0.033516 \tabularnewline
Organization & -0.0883713236862746 & 0.06186 & -1.4286 & 0.155164 & 0.077582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&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]6.16887701869768[/C][C]1.771341[/C][C]3.4826[/C][C]0.000648[/C][C]0.000324[/C][/ROW]
[ROW][C]ConcernoverMistakes[/C][C]0.0910412769387536[/C][C]0.048105[/C][C]1.8925[/C][C]0.060307[/C][C]0.030154[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.124974617942723[/C][C]0.087221[/C][C]-1.4329[/C][C]0.153941[/C][C]0.07697[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.665424927458418[/C][C]0.086305[/C][C]7.7102[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.11660939935939[/C][C]0.063217[/C][C]1.8446[/C][C]0.067032[/C][C]0.033516[/C][/ROW]
[ROW][C]Organization[/C][C]-0.0883713236862746[/C][C]0.06186[/C][C]-1.4286[/C][C]0.155164[/C][C]0.077582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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)6.168877018697681.7713413.48260.0006480.000324
ConcernoverMistakes0.09104127693875360.0481051.89250.0603070.030154
Doubtsaboutactions-0.1249746179427230.087221-1.43290.1539410.07697
ParentalCriticism0.6654249274584180.0863057.710200
PersonalStandards0.116609399359390.0632171.84460.0670320.033516
Organization-0.08837132368627460.06186-1.42860.1551640.077582







Multiple Linear Regression - Regression Statistics
Multiple R0.638267527023895
R-squared0.407385436053198
Adjusted R-squared0.388018947035329
F-TEST (value)21.0355855249398
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.69537380350115
Sum Squared Residuals1111.55111091184

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.638267527023895 \tabularnewline
R-squared & 0.407385436053198 \tabularnewline
Adjusted R-squared & 0.388018947035329 \tabularnewline
F-TEST (value) & 21.0355855249398 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.55111512312578e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.69537380350115 \tabularnewline
Sum Squared Residuals & 1111.55111091184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.638267527023895[/C][/ROW]
[ROW][C]R-squared[/C][C]0.407385436053198[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.388018947035329[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.0355855249398[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]5.55111512312578e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.69537380350115[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1111.55111091184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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 R0.638267527023895
R-squared0.407385436053198
Adjusted R-squared0.388018947035329
F-TEST (value)21.0355855249398
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.69537380350115
Sum Squared Residuals1111.55111091184







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11115.0902933123129-4.0902933123129
2713.2762821036644-6.27628210366435
31713.57912932729233.42087067270766
41011.814362150994-1.81436215099401
51213.6829990430466-1.68299904304659
61211.03640416150410.963595838495945
71110.10760797970710.892392020292914
81114.3967092422446-3.39670924224459
91210.50419127673221.4958087232678
101311.23934671588581.76065328411421
111414.7179223038925-0.717922303892455
121614.67956394760731.32043605239274
131113.7222505886242-2.72225058862422
141012.171504542406-2.17150454240604
151112.2210218592443-1.22102185924431
161510.09723029951334.90276970048665
17913.7427769322339-4.74277693223386
181112.4301743557007-1.43017435570071
191712.09237512357594.90762487642411
201715.4757758661841.524224133816
211113.5674149618832-2.56741496188315
221815.2657316623962.73426833760396
231415.9662771203031-1.96627712030308
241012.631340115953-2.63134011595297
251111.7051308268035-0.705130826803506
261513.17566927009711.8243307299029
271511.65180377625293.34819622374708
281313.0394634394106-0.0394634394105843
291613.80192303683262.19807696316744
301311.11722307104481.88277692895524
31911.4491376476338-2.44913764763384
321818.0030465092912-0.00304650929119659
331812.27934281409685.72065718590321
341213.6253041781773-1.62530417817728
351713.64006869887073.35993130112926
36912.1591717239839-3.15917172398392
37913.111961986022-4.11196198602199
38128.718135037121593.28186496287841
391816.8884956558911.11150434410898
401212.9523201557008-0.952320155700772
411814.09438334606353.9056166539365
421413.9185324361920.0814675638080462
431512.01149233220022.98850766779985
441610.80162634166455.19837365833549
451013.0626597362727-3.0626597362727
461111.4811050721646-0.481105072164593
471412.87236460085721.12763539914275
48913.0267330822588-4.02673308225883
491213.7654218293621-1.76542182936209
501712.23059341587284.76940658412719
5159.75916045865077-4.75916045865077
521212.3341334798547-0.334133479854686
531211.9211467216850.078853278315018
5469.41738287861176-3.41738287861176
552422.75758988566561.24241011433443
561212.4705368032406-0.470536803240622
571212.8912569954051-0.891256995405105
581411.56378781139252.43621218860746
5979.13925944888823-2.13925944888823
601311.11082545841781.8891745415822
611213.1699740047662-1.16997400476625
621311.50126103588961.49873896411044
631411.38116887883992.61883112116007
64812.8494881336204-4.84948813362038
65119.339706388994181.66029361100583
66911.7252295593965-2.72522955939653
671113.7592540973606-2.7592540973606
681313.3899451240086-0.389945124008605
69109.362423538313120.637576461686878
701112.644333071598-1.64433307159803
711212.7642291877397-0.764229187739685
72911.8481211530213-2.8481211530213
731514.61496747936190.38503252063806
741814.90360472330493.09639527669505
751512.10968011419922.89031988580083
761212.8128698183052-0.812869818305165
77139.834832826844193.16516717315581
781413.01436778366060.985632216339413
791011.8416130832527-1.84161308325268
801312.30692740325010.693072596749949
811313.6966402561304-0.696640256130414
821112.0658770888727-1.06587708887274
831312.17585730549650.824142694503452
841614.43847810402931.56152189597069
8589.66985922268198-1.66985922268198
861611.57527374796774.42472625203231
871111.0690549071499-0.0690549071499214
88911.2274987396122-2.22749873961222
891617.7385710036935-1.73857100369345
901211.43345158501360.566548414986438
911411.97115300821352.02884699178646
92810.5416330824799-2.54163308247993
9399.52776063396519-0.527760633965194
941511.71256880885463.28743119114537
951113.4909106406515-2.49091064065145
962117.16035591753023.83964408246983
971413.21990135823160.780098641768432
981815.76250273530262.23749726469742
991211.67503560231570.324964397684325
1001312.66535239002020.33464760997982
1011514.49354787130020.506452128699829
1021211.01200420234710.987995797652922
1031914.43742707877984.56257292122016
1041513.96670060008681.03329939991321
1051112.9368506119609-1.93685061196091
1061110.6791788620760.320821137923996
1071012.335578068881-2.335578068881
1081314.7733541108617-1.77335411086173
1091514.72454748150580.275452518494183
110129.852826833187972.14717316681203
1111210.96406760856091.03593239143914
1121615.68139237894040.318607621059554
113915.4318202038122-6.43182020381219
1141817.4687772935220.531222706477954
115814.9072071421707-6.90720714217072
1161310.28075076154462.71924923845535
1171714.15024944492192.84975055507808
118910.9875403311856-1.98754033118563
1191513.12858365553021.87141634446984
12089.64377645368698-1.64377645368698
121711.1837282246314-4.18372822463136
1221211.29512720080840.704872799191556
1231414.9779944961403-0.977994496140275
124610.6501536890186-4.65015368901865
125810.06343890956-2.06343890956002
1261712.3546381215334.64536187846698
127109.640893956506870.359106043493133
1281112.4393523485524-1.43935234855237
1291412.69784114199781.30215885800217
1301113.5695552173331-2.56955521733305
1311315.3246802742953-2.32468027429533
1321211.83411790023910.165882099760948
1331110.30329357188690.69670642811309
13499.35386895969433-0.353868959694334
1351211.98760701961750.0123929803824522
1362014.65320472268025.34679527731984
1371210.62365164919331.37634835080672
1381313.596719114034-0.596719114033957
1391213.0334917483171-1.03349174831707
1401216.4374760656668-4.43747606566676
141914.5841126764461-5.58411267644606
1421515.4921726464561-0.492172646456069
1432421.47780571288252.52219428711755
14479.86113482063936-2.86113482063936
1451714.51359506736662.48640493263336
1461111.8295717418214-0.829571741821423
1471715.09138922338581.90861077661416
1481112.2042764010189-1.20427640101887
1491212.5068570210313-0.506857021031325
1501414.390998955855-0.390998955854967
1511114.1913798412456-3.19137984124556
1521612.71955212172133.28044787827867
1532113.73060343172967.26939656827036
1541412.19468581820941.80531418179062
1552016.29670492796213.70329507203789
1561310.94413752033912.05586247966086
1571112.9695013576068-1.96950135760678
1581513.82260201748761.17739798251241
1591917.85329487143431.14670512856569

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 15.0902933123129 & -4.0902933123129 \tabularnewline
2 & 7 & 13.2762821036644 & -6.27628210366435 \tabularnewline
3 & 17 & 13.5791293272923 & 3.42087067270766 \tabularnewline
4 & 10 & 11.814362150994 & -1.81436215099401 \tabularnewline
5 & 12 & 13.6829990430466 & -1.68299904304659 \tabularnewline
6 & 12 & 11.0364041615041 & 0.963595838495945 \tabularnewline
7 & 11 & 10.1076079797071 & 0.892392020292914 \tabularnewline
8 & 11 & 14.3967092422446 & -3.39670924224459 \tabularnewline
9 & 12 & 10.5041912767322 & 1.4958087232678 \tabularnewline
10 & 13 & 11.2393467158858 & 1.76065328411421 \tabularnewline
11 & 14 & 14.7179223038925 & -0.717922303892455 \tabularnewline
12 & 16 & 14.6795639476073 & 1.32043605239274 \tabularnewline
13 & 11 & 13.7222505886242 & -2.72225058862422 \tabularnewline
14 & 10 & 12.171504542406 & -2.17150454240604 \tabularnewline
15 & 11 & 12.2210218592443 & -1.22102185924431 \tabularnewline
16 & 15 & 10.0972302995133 & 4.90276970048665 \tabularnewline
17 & 9 & 13.7427769322339 & -4.74277693223386 \tabularnewline
18 & 11 & 12.4301743557007 & -1.43017435570071 \tabularnewline
19 & 17 & 12.0923751235759 & 4.90762487642411 \tabularnewline
20 & 17 & 15.475775866184 & 1.524224133816 \tabularnewline
21 & 11 & 13.5674149618832 & -2.56741496188315 \tabularnewline
22 & 18 & 15.265731662396 & 2.73426833760396 \tabularnewline
23 & 14 & 15.9662771203031 & -1.96627712030308 \tabularnewline
24 & 10 & 12.631340115953 & -2.63134011595297 \tabularnewline
25 & 11 & 11.7051308268035 & -0.705130826803506 \tabularnewline
26 & 15 & 13.1756692700971 & 1.8243307299029 \tabularnewline
27 & 15 & 11.6518037762529 & 3.34819622374708 \tabularnewline
28 & 13 & 13.0394634394106 & -0.0394634394105843 \tabularnewline
29 & 16 & 13.8019230368326 & 2.19807696316744 \tabularnewline
30 & 13 & 11.1172230710448 & 1.88277692895524 \tabularnewline
31 & 9 & 11.4491376476338 & -2.44913764763384 \tabularnewline
32 & 18 & 18.0030465092912 & -0.00304650929119659 \tabularnewline
33 & 18 & 12.2793428140968 & 5.72065718590321 \tabularnewline
34 & 12 & 13.6253041781773 & -1.62530417817728 \tabularnewline
35 & 17 & 13.6400686988707 & 3.35993130112926 \tabularnewline
36 & 9 & 12.1591717239839 & -3.15917172398392 \tabularnewline
37 & 9 & 13.111961986022 & -4.11196198602199 \tabularnewline
38 & 12 & 8.71813503712159 & 3.28186496287841 \tabularnewline
39 & 18 & 16.888495655891 & 1.11150434410898 \tabularnewline
40 & 12 & 12.9523201557008 & -0.952320155700772 \tabularnewline
41 & 18 & 14.0943833460635 & 3.9056166539365 \tabularnewline
42 & 14 & 13.918532436192 & 0.0814675638080462 \tabularnewline
43 & 15 & 12.0114923322002 & 2.98850766779985 \tabularnewline
44 & 16 & 10.8016263416645 & 5.19837365833549 \tabularnewline
45 & 10 & 13.0626597362727 & -3.0626597362727 \tabularnewline
46 & 11 & 11.4811050721646 & -0.481105072164593 \tabularnewline
47 & 14 & 12.8723646008572 & 1.12763539914275 \tabularnewline
48 & 9 & 13.0267330822588 & -4.02673308225883 \tabularnewline
49 & 12 & 13.7654218293621 & -1.76542182936209 \tabularnewline
50 & 17 & 12.2305934158728 & 4.76940658412719 \tabularnewline
51 & 5 & 9.75916045865077 & -4.75916045865077 \tabularnewline
52 & 12 & 12.3341334798547 & -0.334133479854686 \tabularnewline
53 & 12 & 11.921146721685 & 0.078853278315018 \tabularnewline
54 & 6 & 9.41738287861176 & -3.41738287861176 \tabularnewline
55 & 24 & 22.7575898856656 & 1.24241011433443 \tabularnewline
56 & 12 & 12.4705368032406 & -0.470536803240622 \tabularnewline
57 & 12 & 12.8912569954051 & -0.891256995405105 \tabularnewline
58 & 14 & 11.5637878113925 & 2.43621218860746 \tabularnewline
59 & 7 & 9.13925944888823 & -2.13925944888823 \tabularnewline
60 & 13 & 11.1108254584178 & 1.8891745415822 \tabularnewline
61 & 12 & 13.1699740047662 & -1.16997400476625 \tabularnewline
62 & 13 & 11.5012610358896 & 1.49873896411044 \tabularnewline
63 & 14 & 11.3811688788399 & 2.61883112116007 \tabularnewline
64 & 8 & 12.8494881336204 & -4.84948813362038 \tabularnewline
65 & 11 & 9.33970638899418 & 1.66029361100583 \tabularnewline
66 & 9 & 11.7252295593965 & -2.72522955939653 \tabularnewline
67 & 11 & 13.7592540973606 & -2.7592540973606 \tabularnewline
68 & 13 & 13.3899451240086 & -0.389945124008605 \tabularnewline
69 & 10 & 9.36242353831312 & 0.637576461686878 \tabularnewline
70 & 11 & 12.644333071598 & -1.64433307159803 \tabularnewline
71 & 12 & 12.7642291877397 & -0.764229187739685 \tabularnewline
72 & 9 & 11.8481211530213 & -2.8481211530213 \tabularnewline
73 & 15 & 14.6149674793619 & 0.38503252063806 \tabularnewline
74 & 18 & 14.9036047233049 & 3.09639527669505 \tabularnewline
75 & 15 & 12.1096801141992 & 2.89031988580083 \tabularnewline
76 & 12 & 12.8128698183052 & -0.812869818305165 \tabularnewline
77 & 13 & 9.83483282684419 & 3.16516717315581 \tabularnewline
78 & 14 & 13.0143677836606 & 0.985632216339413 \tabularnewline
79 & 10 & 11.8416130832527 & -1.84161308325268 \tabularnewline
80 & 13 & 12.3069274032501 & 0.693072596749949 \tabularnewline
81 & 13 & 13.6966402561304 & -0.696640256130414 \tabularnewline
82 & 11 & 12.0658770888727 & -1.06587708887274 \tabularnewline
83 & 13 & 12.1758573054965 & 0.824142694503452 \tabularnewline
84 & 16 & 14.4384781040293 & 1.56152189597069 \tabularnewline
85 & 8 & 9.66985922268198 & -1.66985922268198 \tabularnewline
86 & 16 & 11.5752737479677 & 4.42472625203231 \tabularnewline
87 & 11 & 11.0690549071499 & -0.0690549071499214 \tabularnewline
88 & 9 & 11.2274987396122 & -2.22749873961222 \tabularnewline
89 & 16 & 17.7385710036935 & -1.73857100369345 \tabularnewline
90 & 12 & 11.4334515850136 & 0.566548414986438 \tabularnewline
91 & 14 & 11.9711530082135 & 2.02884699178646 \tabularnewline
92 & 8 & 10.5416330824799 & -2.54163308247993 \tabularnewline
93 & 9 & 9.52776063396519 & -0.527760633965194 \tabularnewline
94 & 15 & 11.7125688088546 & 3.28743119114537 \tabularnewline
95 & 11 & 13.4909106406515 & -2.49091064065145 \tabularnewline
96 & 21 & 17.1603559175302 & 3.83964408246983 \tabularnewline
97 & 14 & 13.2199013582316 & 0.780098641768432 \tabularnewline
98 & 18 & 15.7625027353026 & 2.23749726469742 \tabularnewline
99 & 12 & 11.6750356023157 & 0.324964397684325 \tabularnewline
100 & 13 & 12.6653523900202 & 0.33464760997982 \tabularnewline
101 & 15 & 14.4935478713002 & 0.506452128699829 \tabularnewline
102 & 12 & 11.0120042023471 & 0.987995797652922 \tabularnewline
103 & 19 & 14.4374270787798 & 4.56257292122016 \tabularnewline
104 & 15 & 13.9667006000868 & 1.03329939991321 \tabularnewline
105 & 11 & 12.9368506119609 & -1.93685061196091 \tabularnewline
106 & 11 & 10.679178862076 & 0.320821137923996 \tabularnewline
107 & 10 & 12.335578068881 & -2.335578068881 \tabularnewline
108 & 13 & 14.7733541108617 & -1.77335411086173 \tabularnewline
109 & 15 & 14.7245474815058 & 0.275452518494183 \tabularnewline
110 & 12 & 9.85282683318797 & 2.14717316681203 \tabularnewline
111 & 12 & 10.9640676085609 & 1.03593239143914 \tabularnewline
112 & 16 & 15.6813923789404 & 0.318607621059554 \tabularnewline
113 & 9 & 15.4318202038122 & -6.43182020381219 \tabularnewline
114 & 18 & 17.468777293522 & 0.531222706477954 \tabularnewline
115 & 8 & 14.9072071421707 & -6.90720714217072 \tabularnewline
116 & 13 & 10.2807507615446 & 2.71924923845535 \tabularnewline
117 & 17 & 14.1502494449219 & 2.84975055507808 \tabularnewline
118 & 9 & 10.9875403311856 & -1.98754033118563 \tabularnewline
119 & 15 & 13.1285836555302 & 1.87141634446984 \tabularnewline
120 & 8 & 9.64377645368698 & -1.64377645368698 \tabularnewline
121 & 7 & 11.1837282246314 & -4.18372822463136 \tabularnewline
122 & 12 & 11.2951272008084 & 0.704872799191556 \tabularnewline
123 & 14 & 14.9779944961403 & -0.977994496140275 \tabularnewline
124 & 6 & 10.6501536890186 & -4.65015368901865 \tabularnewline
125 & 8 & 10.06343890956 & -2.06343890956002 \tabularnewline
126 & 17 & 12.354638121533 & 4.64536187846698 \tabularnewline
127 & 10 & 9.64089395650687 & 0.359106043493133 \tabularnewline
128 & 11 & 12.4393523485524 & -1.43935234855237 \tabularnewline
129 & 14 & 12.6978411419978 & 1.30215885800217 \tabularnewline
130 & 11 & 13.5695552173331 & -2.56955521733305 \tabularnewline
131 & 13 & 15.3246802742953 & -2.32468027429533 \tabularnewline
132 & 12 & 11.8341179002391 & 0.165882099760948 \tabularnewline
133 & 11 & 10.3032935718869 & 0.69670642811309 \tabularnewline
134 & 9 & 9.35386895969433 & -0.353868959694334 \tabularnewline
135 & 12 & 11.9876070196175 & 0.0123929803824522 \tabularnewline
136 & 20 & 14.6532047226802 & 5.34679527731984 \tabularnewline
137 & 12 & 10.6236516491933 & 1.37634835080672 \tabularnewline
138 & 13 & 13.596719114034 & -0.596719114033957 \tabularnewline
139 & 12 & 13.0334917483171 & -1.03349174831707 \tabularnewline
140 & 12 & 16.4374760656668 & -4.43747606566676 \tabularnewline
141 & 9 & 14.5841126764461 & -5.58411267644606 \tabularnewline
142 & 15 & 15.4921726464561 & -0.492172646456069 \tabularnewline
143 & 24 & 21.4778057128825 & 2.52219428711755 \tabularnewline
144 & 7 & 9.86113482063936 & -2.86113482063936 \tabularnewline
145 & 17 & 14.5135950673666 & 2.48640493263336 \tabularnewline
146 & 11 & 11.8295717418214 & -0.829571741821423 \tabularnewline
147 & 17 & 15.0913892233858 & 1.90861077661416 \tabularnewline
148 & 11 & 12.2042764010189 & -1.20427640101887 \tabularnewline
149 & 12 & 12.5068570210313 & -0.506857021031325 \tabularnewline
150 & 14 & 14.390998955855 & -0.390998955854967 \tabularnewline
151 & 11 & 14.1913798412456 & -3.19137984124556 \tabularnewline
152 & 16 & 12.7195521217213 & 3.28044787827867 \tabularnewline
153 & 21 & 13.7306034317296 & 7.26939656827036 \tabularnewline
154 & 14 & 12.1946858182094 & 1.80531418179062 \tabularnewline
155 & 20 & 16.2967049279621 & 3.70329507203789 \tabularnewline
156 & 13 & 10.9441375203391 & 2.05586247966086 \tabularnewline
157 & 11 & 12.9695013576068 & -1.96950135760678 \tabularnewline
158 & 15 & 13.8226020174876 & 1.17739798251241 \tabularnewline
159 & 19 & 17.8532948714343 & 1.14670512856569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&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]11[/C][C]15.0902933123129[/C][C]-4.0902933123129[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]13.2762821036644[/C][C]-6.27628210366435[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]13.5791293272923[/C][C]3.42087067270766[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]11.814362150994[/C][C]-1.81436215099401[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]13.6829990430466[/C][C]-1.68299904304659[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]11.0364041615041[/C][C]0.963595838495945[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]10.1076079797071[/C][C]0.892392020292914[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]14.3967092422446[/C][C]-3.39670924224459[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.5041912767322[/C][C]1.4958087232678[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.2393467158858[/C][C]1.76065328411421[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.7179223038925[/C][C]-0.717922303892455[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]14.6795639476073[/C][C]1.32043605239274[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.7222505886242[/C][C]-2.72225058862422[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]12.171504542406[/C][C]-2.17150454240604[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]12.2210218592443[/C][C]-1.22102185924431[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]10.0972302995133[/C][C]4.90276970048665[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]13.7427769322339[/C][C]-4.74277693223386[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.4301743557007[/C][C]-1.43017435570071[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]12.0923751235759[/C][C]4.90762487642411[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]15.475775866184[/C][C]1.524224133816[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]13.5674149618832[/C][C]-2.56741496188315[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]15.265731662396[/C][C]2.73426833760396[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]15.9662771203031[/C][C]-1.96627712030308[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]12.631340115953[/C][C]-2.63134011595297[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.7051308268035[/C][C]-0.705130826803506[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.1756692700971[/C][C]1.8243307299029[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]11.6518037762529[/C][C]3.34819622374708[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.0394634394106[/C][C]-0.0394634394105843[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]13.8019230368326[/C][C]2.19807696316744[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]11.1172230710448[/C][C]1.88277692895524[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.4491376476338[/C][C]-2.44913764763384[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.0030465092912[/C][C]-0.00304650929119659[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]12.2793428140968[/C][C]5.72065718590321[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]13.6253041781773[/C][C]-1.62530417817728[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]13.6400686988707[/C][C]3.35993130112926[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]12.1591717239839[/C][C]-3.15917172398392[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]13.111961986022[/C][C]-4.11196198602199[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]8.71813503712159[/C][C]3.28186496287841[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]16.888495655891[/C][C]1.11150434410898[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]12.9523201557008[/C][C]-0.952320155700772[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]14.0943833460635[/C][C]3.9056166539365[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.918532436192[/C][C]0.0814675638080462[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]12.0114923322002[/C][C]2.98850766779985[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]10.8016263416645[/C][C]5.19837365833549[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]13.0626597362727[/C][C]-3.0626597362727[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.4811050721646[/C][C]-0.481105072164593[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]12.8723646008572[/C][C]1.12763539914275[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]13.0267330822588[/C][C]-4.02673308225883[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]13.7654218293621[/C][C]-1.76542182936209[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]12.2305934158728[/C][C]4.76940658412719[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]9.75916045865077[/C][C]-4.75916045865077[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.3341334798547[/C][C]-0.334133479854686[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.921146721685[/C][C]0.078853278315018[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]9.41738287861176[/C][C]-3.41738287861176[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]22.7575898856656[/C][C]1.24241011433443[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4705368032406[/C][C]-0.470536803240622[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.8912569954051[/C][C]-0.891256995405105[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]11.5637878113925[/C][C]2.43621218860746[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.13925944888823[/C][C]-2.13925944888823[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]11.1108254584178[/C][C]1.8891745415822[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.1699740047662[/C][C]-1.16997400476625[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]11.5012610358896[/C][C]1.49873896411044[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.3811688788399[/C][C]2.61883112116007[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]12.8494881336204[/C][C]-4.84948813362038[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]9.33970638899418[/C][C]1.66029361100583[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]11.7252295593965[/C][C]-2.72522955939653[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.7592540973606[/C][C]-2.7592540973606[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.3899451240086[/C][C]-0.389945124008605[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.36242353831312[/C][C]0.637576461686878[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]12.644333071598[/C][C]-1.64433307159803[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]12.7642291877397[/C][C]-0.764229187739685[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]11.8481211530213[/C][C]-2.8481211530213[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.6149674793619[/C][C]0.38503252063806[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]14.9036047233049[/C][C]3.09639527669505[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]12.1096801141992[/C][C]2.89031988580083[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.8128698183052[/C][C]-0.812869818305165[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]9.83483282684419[/C][C]3.16516717315581[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]13.0143677836606[/C][C]0.985632216339413[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]11.8416130832527[/C][C]-1.84161308325268[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.3069274032501[/C][C]0.693072596749949[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]13.6966402561304[/C][C]-0.696640256130414[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]12.0658770888727[/C][C]-1.06587708887274[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]12.1758573054965[/C][C]0.824142694503452[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.4384781040293[/C][C]1.56152189597069[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]9.66985922268198[/C][C]-1.66985922268198[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]11.5752737479677[/C][C]4.42472625203231[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]11.0690549071499[/C][C]-0.0690549071499214[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]11.2274987396122[/C][C]-2.22749873961222[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]17.7385710036935[/C][C]-1.73857100369345[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.4334515850136[/C][C]0.566548414986438[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]11.9711530082135[/C][C]2.02884699178646[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]10.5416330824799[/C][C]-2.54163308247993[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.52776063396519[/C][C]-0.527760633965194[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]11.7125688088546[/C][C]3.28743119114537[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]13.4909106406515[/C][C]-2.49091064065145[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]17.1603559175302[/C][C]3.83964408246983[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.2199013582316[/C][C]0.780098641768432[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]15.7625027353026[/C][C]2.23749726469742[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.6750356023157[/C][C]0.324964397684325[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.6653523900202[/C][C]0.33464760997982[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]14.4935478713002[/C][C]0.506452128699829[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.0120042023471[/C][C]0.987995797652922[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]14.4374270787798[/C][C]4.56257292122016[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.9667006000868[/C][C]1.03329939991321[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]12.9368506119609[/C][C]-1.93685061196091[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]10.679178862076[/C][C]0.320821137923996[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]12.335578068881[/C][C]-2.335578068881[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]14.7733541108617[/C][C]-1.77335411086173[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.7245474815058[/C][C]0.275452518494183[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]9.85282683318797[/C][C]2.14717316681203[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]10.9640676085609[/C][C]1.03593239143914[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.6813923789404[/C][C]0.318607621059554[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]15.4318202038122[/C][C]-6.43182020381219[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]17.468777293522[/C][C]0.531222706477954[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]14.9072071421707[/C][C]-6.90720714217072[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]10.2807507615446[/C][C]2.71924923845535[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]14.1502494449219[/C][C]2.84975055507808[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]10.9875403311856[/C][C]-1.98754033118563[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]13.1285836555302[/C][C]1.87141634446984[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]9.64377645368698[/C][C]-1.64377645368698[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]11.1837282246314[/C][C]-4.18372822463136[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]11.2951272008084[/C][C]0.704872799191556[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]14.9779944961403[/C][C]-0.977994496140275[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]10.6501536890186[/C][C]-4.65015368901865[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]10.06343890956[/C][C]-2.06343890956002[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]12.354638121533[/C][C]4.64536187846698[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]9.64089395650687[/C][C]0.359106043493133[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.4393523485524[/C][C]-1.43935234855237[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]12.6978411419978[/C][C]1.30215885800217[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]13.5695552173331[/C][C]-2.56955521733305[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]15.3246802742953[/C][C]-2.32468027429533[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.8341179002391[/C][C]0.165882099760948[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]10.3032935718869[/C][C]0.69670642811309[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]9.35386895969433[/C][C]-0.353868959694334[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.9876070196175[/C][C]0.0123929803824522[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]14.6532047226802[/C][C]5.34679527731984[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]10.6236516491933[/C][C]1.37634835080672[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]13.596719114034[/C][C]-0.596719114033957[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0334917483171[/C][C]-1.03349174831707[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]16.4374760656668[/C][C]-4.43747606566676[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]14.5841126764461[/C][C]-5.58411267644606[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.4921726464561[/C][C]-0.492172646456069[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]21.4778057128825[/C][C]2.52219428711755[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]9.86113482063936[/C][C]-2.86113482063936[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]14.5135950673666[/C][C]2.48640493263336[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.8295717418214[/C][C]-0.829571741821423[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]15.0913892233858[/C][C]1.90861077661416[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.2042764010189[/C][C]-1.20427640101887[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]12.5068570210313[/C][C]-0.506857021031325[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]14.390998955855[/C][C]-0.390998955854967[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]14.1913798412456[/C][C]-3.19137984124556[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.7195521217213[/C][C]3.28044787827867[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]13.7306034317296[/C][C]7.26939656827036[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.1946858182094[/C][C]1.80531418179062[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]16.2967049279621[/C][C]3.70329507203789[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]10.9441375203391[/C][C]2.05586247966086[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]12.9695013576068[/C][C]-1.96950135760678[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.8226020174876[/C][C]1.17739798251241[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]17.8532948714343[/C][C]1.14670512856569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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
11115.0902933123129-4.0902933123129
2713.2762821036644-6.27628210366435
31713.57912932729233.42087067270766
41011.814362150994-1.81436215099401
51213.6829990430466-1.68299904304659
61211.03640416150410.963595838495945
71110.10760797970710.892392020292914
81114.3967092422446-3.39670924224459
91210.50419127673221.4958087232678
101311.23934671588581.76065328411421
111414.7179223038925-0.717922303892455
121614.67956394760731.32043605239274
131113.7222505886242-2.72225058862422
141012.171504542406-2.17150454240604
151112.2210218592443-1.22102185924431
161510.09723029951334.90276970048665
17913.7427769322339-4.74277693223386
181112.4301743557007-1.43017435570071
191712.09237512357594.90762487642411
201715.4757758661841.524224133816
211113.5674149618832-2.56741496188315
221815.2657316623962.73426833760396
231415.9662771203031-1.96627712030308
241012.631340115953-2.63134011595297
251111.7051308268035-0.705130826803506
261513.17566927009711.8243307299029
271511.65180377625293.34819622374708
281313.0394634394106-0.0394634394105843
291613.80192303683262.19807696316744
301311.11722307104481.88277692895524
31911.4491376476338-2.44913764763384
321818.0030465092912-0.00304650929119659
331812.27934281409685.72065718590321
341213.6253041781773-1.62530417817728
351713.64006869887073.35993130112926
36912.1591717239839-3.15917172398392
37913.111961986022-4.11196198602199
38128.718135037121593.28186496287841
391816.8884956558911.11150434410898
401212.9523201557008-0.952320155700772
411814.09438334606353.9056166539365
421413.9185324361920.0814675638080462
431512.01149233220022.98850766779985
441610.80162634166455.19837365833549
451013.0626597362727-3.0626597362727
461111.4811050721646-0.481105072164593
471412.87236460085721.12763539914275
48913.0267330822588-4.02673308225883
491213.7654218293621-1.76542182936209
501712.23059341587284.76940658412719
5159.75916045865077-4.75916045865077
521212.3341334798547-0.334133479854686
531211.9211467216850.078853278315018
5469.41738287861176-3.41738287861176
552422.75758988566561.24241011433443
561212.4705368032406-0.470536803240622
571212.8912569954051-0.891256995405105
581411.56378781139252.43621218860746
5979.13925944888823-2.13925944888823
601311.11082545841781.8891745415822
611213.1699740047662-1.16997400476625
621311.50126103588961.49873896411044
631411.38116887883992.61883112116007
64812.8494881336204-4.84948813362038
65119.339706388994181.66029361100583
66911.7252295593965-2.72522955939653
671113.7592540973606-2.7592540973606
681313.3899451240086-0.389945124008605
69109.362423538313120.637576461686878
701112.644333071598-1.64433307159803
711212.7642291877397-0.764229187739685
72911.8481211530213-2.8481211530213
731514.61496747936190.38503252063806
741814.90360472330493.09639527669505
751512.10968011419922.89031988580083
761212.8128698183052-0.812869818305165
77139.834832826844193.16516717315581
781413.01436778366060.985632216339413
791011.8416130832527-1.84161308325268
801312.30692740325010.693072596749949
811313.6966402561304-0.696640256130414
821112.0658770888727-1.06587708887274
831312.17585730549650.824142694503452
841614.43847810402931.56152189597069
8589.66985922268198-1.66985922268198
861611.57527374796774.42472625203231
871111.0690549071499-0.0690549071499214
88911.2274987396122-2.22749873961222
891617.7385710036935-1.73857100369345
901211.43345158501360.566548414986438
911411.97115300821352.02884699178646
92810.5416330824799-2.54163308247993
9399.52776063396519-0.527760633965194
941511.71256880885463.28743119114537
951113.4909106406515-2.49091064065145
962117.16035591753023.83964408246983
971413.21990135823160.780098641768432
981815.76250273530262.23749726469742
991211.67503560231570.324964397684325
1001312.66535239002020.33464760997982
1011514.49354787130020.506452128699829
1021211.01200420234710.987995797652922
1031914.43742707877984.56257292122016
1041513.96670060008681.03329939991321
1051112.9368506119609-1.93685061196091
1061110.6791788620760.320821137923996
1071012.335578068881-2.335578068881
1081314.7733541108617-1.77335411086173
1091514.72454748150580.275452518494183
110129.852826833187972.14717316681203
1111210.96406760856091.03593239143914
1121615.68139237894040.318607621059554
113915.4318202038122-6.43182020381219
1141817.4687772935220.531222706477954
115814.9072071421707-6.90720714217072
1161310.28075076154462.71924923845535
1171714.15024944492192.84975055507808
118910.9875403311856-1.98754033118563
1191513.12858365553021.87141634446984
12089.64377645368698-1.64377645368698
121711.1837282246314-4.18372822463136
1221211.29512720080840.704872799191556
1231414.9779944961403-0.977994496140275
124610.6501536890186-4.65015368901865
125810.06343890956-2.06343890956002
1261712.3546381215334.64536187846698
127109.640893956506870.359106043493133
1281112.4393523485524-1.43935234855237
1291412.69784114199781.30215885800217
1301113.5695552173331-2.56955521733305
1311315.3246802742953-2.32468027429533
1321211.83411790023910.165882099760948
1331110.30329357188690.69670642811309
13499.35386895969433-0.353868959694334
1351211.98760701961750.0123929803824522
1362014.65320472268025.34679527731984
1371210.62365164919331.37634835080672
1381313.596719114034-0.596719114033957
1391213.0334917483171-1.03349174831707
1401216.4374760656668-4.43747606566676
141914.5841126764461-5.58411267644606
1421515.4921726464561-0.492172646456069
1432421.47780571288252.52219428711755
14479.86113482063936-2.86113482063936
1451714.51359506736662.48640493263336
1461111.8295717418214-0.829571741821423
1471715.09138922338581.90861077661416
1481112.2042764010189-1.20427640101887
1491212.5068570210313-0.506857021031325
1501414.390998955855-0.390998955854967
1511114.1913798412456-3.19137984124556
1521612.71955212172133.28044787827867
1532113.73060343172967.26939656827036
1541412.19468581820941.80531418179062
1552016.29670492796213.70329507203789
1561310.94413752033912.05586247966086
1571112.9695013576068-1.96950135760678
1581513.82260201748761.17739798251241
1591917.85329487143431.14670512856569







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5581471178983810.8837057642032370.441852882101619
100.4176223819275730.8352447638551460.582377618072427
110.615513367973810.768973264052380.38448663202619
120.7923856346093060.4152287307813890.207614365390694
130.803498339255660.393003321488680.19650166074434
140.743119346740850.5137613065182990.25688065325915
150.6613252658852450.677349468229510.338674734114755
160.6235050260728720.7529899478542570.376494973927128
170.6692033218133340.6615933563733320.330796678186666
180.5895478708673680.8209042582652640.410452129132632
190.6969339357995870.6061321284008260.303066064200413
200.7816884057803880.4366231884392250.218311594219612
210.739238624604440.5215227507911190.26076137539556
220.7988613407850290.4022773184299410.201138659214971
230.7540373439285040.4919253121429920.245962656071496
240.7803268067357070.4393463865285870.219673193264293
250.7312663267206980.5374673465586030.268733673279302
260.7078011610683370.5843976778633260.292198838931663
270.6888593794940230.6222812410119530.311140620505977
280.6285093538923620.7429812922152770.371490646107638
290.6007103981804020.7985792036391970.399289601819598
300.5492493657657750.9015012684684490.450750634234225
310.6290924349644970.7418151300710060.370907565035503
320.615865464746040.7682690705079190.38413453525396
330.681275673295820.637448653408360.31872432670418
340.7262608300304340.5474783399391310.273739169969566
350.7401198721668760.5197602556662480.259880127833124
360.7775055174800910.4449889650398180.222494482519909
370.8304965746308190.3390068507383610.169503425369181
380.8272742585872680.3454514828254630.172725741412732
390.8151570057223230.3696859885553530.184842994277677
400.7863290135645210.4273419728709590.213670986435479
410.8424379812178260.3151240375643490.157562018782174
420.8079055335837130.3841889328325740.192094466416287
430.8203358460147590.3593283079704830.179664153985241
440.8720794533449140.2558410933101710.127920546655086
450.8862167978328330.2275664043343340.113783202167167
460.8626566936755150.274686612648970.137343306324485
470.8396132533105130.3207734933789750.160386746689487
480.8669832423605750.266033515278850.133016757639425
490.8466502959590980.3066994080818040.153349704040902
500.8954406232394290.2091187535211410.104559376760571
510.9363844114129250.1272311771741510.0636155885870754
520.9203981984319240.1592036031361520.0796018015680761
530.9004019686949350.199196062610130.0995980313050651
540.9208374069455030.1583251861089950.0791625930544975
550.9060081300930830.1879837398138340.093991869906917
560.8844949839615590.2310100320768820.115505016038441
570.8628591473554580.2742817052890850.137140852644542
580.8552978921352440.2894042157295120.144702107864756
590.8525686470229410.2948627059541180.147431352977059
600.8347805031455840.3304389937088320.165219496854416
610.8127191047439930.3745617905120140.187280895256007
620.7901604858767190.4196790282465610.209839514123281
630.7862877229003890.4274245541992220.213712277099611
640.8618296459686630.2763407080626740.138170354031337
650.8457573026515460.3084853946969080.154242697348454
660.8420311355307180.3159377289385630.157968864469282
670.8417061245389910.3165877509220190.158293875461009
680.8144096335780830.3711807328438330.185590366421917
690.7896277960663120.4207444078673770.210372203933688
700.767400803556090.4651983928878210.23259919644391
710.739420006055760.5211599878884790.26057999394424
720.7421771381084450.5156457237831090.257822861891555
730.7097707865575270.5804584268849460.290229213442473
740.7281455466929550.543708906614090.271854453307045
750.7357158881842990.5285682236314030.264284111815701
760.700497195062970.599005609874060.29950280493703
770.7311506579475270.5376986841049470.268849342052473
780.6976995716739960.6046008566520070.302300428326004
790.672804031872050.65439193625590.32719596812795
800.6339852672169930.7320294655660130.366014732783007
810.5912018250243370.8175963499513260.408798174975663
820.5554180112876050.8891639774247890.444581988712395
830.5140592735917940.9718814528164110.485940726408206
840.4826892679748380.9653785359496750.517310732025162
850.4538429369413550.9076858738827110.546157063058645
860.5367236301989140.9265527396021720.463276369801086
870.490720896113040.981441792226080.50927910388696
880.4690317939804740.9380635879609490.530968206019526
890.4467662603421720.8935325206843450.553233739657828
900.4025001964243040.8050003928486070.597499803575696
910.3840327480685170.7680654961370330.615967251931483
920.3754161115664660.7508322231329320.624583888433534
930.3324113454573410.6648226909146820.667588654542659
940.3528485874239110.7056971748478230.647151412576089
950.3491333329137210.6982666658274410.650866667086279
960.3831885452147080.7663770904294160.616811454785292
970.3447328462183590.6894656924367170.655267153781641
980.3232778804218020.6465557608436040.676722119578198
990.2815594739651380.5631189479302760.718440526034862
1000.2424519311738330.4849038623476650.757548068826168
1010.206834051015140.413668102030280.79316594898486
1020.1795647555890870.3591295111781750.820435244410913
1030.239907732942750.4798154658855010.76009226705725
1040.2091692250695690.4183384501391370.790830774930432
1050.1999080516510710.3998161033021410.800091948348929
1060.1676150114000690.3352300228001370.832384988599931
1070.1601046850519050.320209370103810.839895314948095
1080.1465836975283130.2931673950566260.853416302471687
1090.1196715331626360.2393430663252720.880328466837364
1100.1117409170863310.2234818341726620.888259082913669
1110.09381494408625410.1876298881725080.906185055913746
1120.08206789210684270.1641357842136850.917932107893157
1130.2222175395925510.4444350791851030.777782460407449
1140.1922811164624680.3845622329249360.807718883537532
1150.4034142809617980.8068285619235950.596585719038202
1160.4019705463769880.8039410927539760.598029453623012
1170.4287901565080280.8575803130160560.571209843491972
1180.3899990751857910.7799981503715810.610000924814209
1190.3536689006059380.7073378012118760.646331099394062
1200.3125419931893610.6250839863787220.687458006810639
1210.3635507810305530.7271015620611070.636449218969447
1220.3386833476355450.677366695271090.661316652364455
1230.2910493319081940.5820986638163870.708950668091806
1240.4072656006978370.8145312013956740.592734399302163
1250.3651729999244230.7303459998488460.634827000075577
1260.4516890549088430.9033781098176870.548310945091157
1270.3960138899124530.7920277798249060.603986110087547
1280.3691453453963040.7382906907926080.630854654603696
1290.3147978309705830.6295956619411660.685202169029417
1300.3653007481637740.7306014963275480.634699251836226
1310.3507259327869630.7014518655739260.649274067213037
1320.2918166545305540.5836333090611080.708183345469446
1330.238339507669760.476679015339520.76166049233024
1340.1894153795253510.3788307590507010.81058462047465
1350.1523600459186780.3047200918373560.847639954081322
1360.2423718239173160.4847436478346320.757628176082684
1370.2303411887183840.4606823774367690.769658811281616
1380.2041528739041720.4083057478083430.795847126095828
1390.184590200825020.369180401650040.81540979917498
1400.2263827296307680.4527654592615360.773617270369232
1410.4303350846003320.8606701692006640.569664915399668
1420.4183420156665560.8366840313331130.581657984333444
1430.3391105212426470.6782210424852940.660889478757353
1440.3144321448091260.6288642896182520.685567855190874
1450.2813344650239080.5626689300478160.718665534976092
1460.2714190593980610.5428381187961230.728580940601939
1470.1951632767171070.3903265534342150.804836723282893
1480.1426781546923990.2853563093847990.857321845307601
1490.08214864036797910.1642972807359580.917851359632021
1500.04394661974549270.08789323949098540.956053380254507

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.558147117898381 & 0.883705764203237 & 0.441852882101619 \tabularnewline
10 & 0.417622381927573 & 0.835244763855146 & 0.582377618072427 \tabularnewline
11 & 0.61551336797381 & 0.76897326405238 & 0.38448663202619 \tabularnewline
12 & 0.792385634609306 & 0.415228730781389 & 0.207614365390694 \tabularnewline
13 & 0.80349833925566 & 0.39300332148868 & 0.19650166074434 \tabularnewline
14 & 0.74311934674085 & 0.513761306518299 & 0.25688065325915 \tabularnewline
15 & 0.661325265885245 & 0.67734946822951 & 0.338674734114755 \tabularnewline
16 & 0.623505026072872 & 0.752989947854257 & 0.376494973927128 \tabularnewline
17 & 0.669203321813334 & 0.661593356373332 & 0.330796678186666 \tabularnewline
18 & 0.589547870867368 & 0.820904258265264 & 0.410452129132632 \tabularnewline
19 & 0.696933935799587 & 0.606132128400826 & 0.303066064200413 \tabularnewline
20 & 0.781688405780388 & 0.436623188439225 & 0.218311594219612 \tabularnewline
21 & 0.73923862460444 & 0.521522750791119 & 0.26076137539556 \tabularnewline
22 & 0.798861340785029 & 0.402277318429941 & 0.201138659214971 \tabularnewline
23 & 0.754037343928504 & 0.491925312142992 & 0.245962656071496 \tabularnewline
24 & 0.780326806735707 & 0.439346386528587 & 0.219673193264293 \tabularnewline
25 & 0.731266326720698 & 0.537467346558603 & 0.268733673279302 \tabularnewline
26 & 0.707801161068337 & 0.584397677863326 & 0.292198838931663 \tabularnewline
27 & 0.688859379494023 & 0.622281241011953 & 0.311140620505977 \tabularnewline
28 & 0.628509353892362 & 0.742981292215277 & 0.371490646107638 \tabularnewline
29 & 0.600710398180402 & 0.798579203639197 & 0.399289601819598 \tabularnewline
30 & 0.549249365765775 & 0.901501268468449 & 0.450750634234225 \tabularnewline
31 & 0.629092434964497 & 0.741815130071006 & 0.370907565035503 \tabularnewline
32 & 0.61586546474604 & 0.768269070507919 & 0.38413453525396 \tabularnewline
33 & 0.68127567329582 & 0.63744865340836 & 0.31872432670418 \tabularnewline
34 & 0.726260830030434 & 0.547478339939131 & 0.273739169969566 \tabularnewline
35 & 0.740119872166876 & 0.519760255666248 & 0.259880127833124 \tabularnewline
36 & 0.777505517480091 & 0.444988965039818 & 0.222494482519909 \tabularnewline
37 & 0.830496574630819 & 0.339006850738361 & 0.169503425369181 \tabularnewline
38 & 0.827274258587268 & 0.345451482825463 & 0.172725741412732 \tabularnewline
39 & 0.815157005722323 & 0.369685988555353 & 0.184842994277677 \tabularnewline
40 & 0.786329013564521 & 0.427341972870959 & 0.213670986435479 \tabularnewline
41 & 0.842437981217826 & 0.315124037564349 & 0.157562018782174 \tabularnewline
42 & 0.807905533583713 & 0.384188932832574 & 0.192094466416287 \tabularnewline
43 & 0.820335846014759 & 0.359328307970483 & 0.179664153985241 \tabularnewline
44 & 0.872079453344914 & 0.255841093310171 & 0.127920546655086 \tabularnewline
45 & 0.886216797832833 & 0.227566404334334 & 0.113783202167167 \tabularnewline
46 & 0.862656693675515 & 0.27468661264897 & 0.137343306324485 \tabularnewline
47 & 0.839613253310513 & 0.320773493378975 & 0.160386746689487 \tabularnewline
48 & 0.866983242360575 & 0.26603351527885 & 0.133016757639425 \tabularnewline
49 & 0.846650295959098 & 0.306699408081804 & 0.153349704040902 \tabularnewline
50 & 0.895440623239429 & 0.209118753521141 & 0.104559376760571 \tabularnewline
51 & 0.936384411412925 & 0.127231177174151 & 0.0636155885870754 \tabularnewline
52 & 0.920398198431924 & 0.159203603136152 & 0.0796018015680761 \tabularnewline
53 & 0.900401968694935 & 0.19919606261013 & 0.0995980313050651 \tabularnewline
54 & 0.920837406945503 & 0.158325186108995 & 0.0791625930544975 \tabularnewline
55 & 0.906008130093083 & 0.187983739813834 & 0.093991869906917 \tabularnewline
56 & 0.884494983961559 & 0.231010032076882 & 0.115505016038441 \tabularnewline
57 & 0.862859147355458 & 0.274281705289085 & 0.137140852644542 \tabularnewline
58 & 0.855297892135244 & 0.289404215729512 & 0.144702107864756 \tabularnewline
59 & 0.852568647022941 & 0.294862705954118 & 0.147431352977059 \tabularnewline
60 & 0.834780503145584 & 0.330438993708832 & 0.165219496854416 \tabularnewline
61 & 0.812719104743993 & 0.374561790512014 & 0.187280895256007 \tabularnewline
62 & 0.790160485876719 & 0.419679028246561 & 0.209839514123281 \tabularnewline
63 & 0.786287722900389 & 0.427424554199222 & 0.213712277099611 \tabularnewline
64 & 0.861829645968663 & 0.276340708062674 & 0.138170354031337 \tabularnewline
65 & 0.845757302651546 & 0.308485394696908 & 0.154242697348454 \tabularnewline
66 & 0.842031135530718 & 0.315937728938563 & 0.157968864469282 \tabularnewline
67 & 0.841706124538991 & 0.316587750922019 & 0.158293875461009 \tabularnewline
68 & 0.814409633578083 & 0.371180732843833 & 0.185590366421917 \tabularnewline
69 & 0.789627796066312 & 0.420744407867377 & 0.210372203933688 \tabularnewline
70 & 0.76740080355609 & 0.465198392887821 & 0.23259919644391 \tabularnewline
71 & 0.73942000605576 & 0.521159987888479 & 0.26057999394424 \tabularnewline
72 & 0.742177138108445 & 0.515645723783109 & 0.257822861891555 \tabularnewline
73 & 0.709770786557527 & 0.580458426884946 & 0.290229213442473 \tabularnewline
74 & 0.728145546692955 & 0.54370890661409 & 0.271854453307045 \tabularnewline
75 & 0.735715888184299 & 0.528568223631403 & 0.264284111815701 \tabularnewline
76 & 0.70049719506297 & 0.59900560987406 & 0.29950280493703 \tabularnewline
77 & 0.731150657947527 & 0.537698684104947 & 0.268849342052473 \tabularnewline
78 & 0.697699571673996 & 0.604600856652007 & 0.302300428326004 \tabularnewline
79 & 0.67280403187205 & 0.6543919362559 & 0.32719596812795 \tabularnewline
80 & 0.633985267216993 & 0.732029465566013 & 0.366014732783007 \tabularnewline
81 & 0.591201825024337 & 0.817596349951326 & 0.408798174975663 \tabularnewline
82 & 0.555418011287605 & 0.889163977424789 & 0.444581988712395 \tabularnewline
83 & 0.514059273591794 & 0.971881452816411 & 0.485940726408206 \tabularnewline
84 & 0.482689267974838 & 0.965378535949675 & 0.517310732025162 \tabularnewline
85 & 0.453842936941355 & 0.907685873882711 & 0.546157063058645 \tabularnewline
86 & 0.536723630198914 & 0.926552739602172 & 0.463276369801086 \tabularnewline
87 & 0.49072089611304 & 0.98144179222608 & 0.50927910388696 \tabularnewline
88 & 0.469031793980474 & 0.938063587960949 & 0.530968206019526 \tabularnewline
89 & 0.446766260342172 & 0.893532520684345 & 0.553233739657828 \tabularnewline
90 & 0.402500196424304 & 0.805000392848607 & 0.597499803575696 \tabularnewline
91 & 0.384032748068517 & 0.768065496137033 & 0.615967251931483 \tabularnewline
92 & 0.375416111566466 & 0.750832223132932 & 0.624583888433534 \tabularnewline
93 & 0.332411345457341 & 0.664822690914682 & 0.667588654542659 \tabularnewline
94 & 0.352848587423911 & 0.705697174847823 & 0.647151412576089 \tabularnewline
95 & 0.349133332913721 & 0.698266665827441 & 0.650866667086279 \tabularnewline
96 & 0.383188545214708 & 0.766377090429416 & 0.616811454785292 \tabularnewline
97 & 0.344732846218359 & 0.689465692436717 & 0.655267153781641 \tabularnewline
98 & 0.323277880421802 & 0.646555760843604 & 0.676722119578198 \tabularnewline
99 & 0.281559473965138 & 0.563118947930276 & 0.718440526034862 \tabularnewline
100 & 0.242451931173833 & 0.484903862347665 & 0.757548068826168 \tabularnewline
101 & 0.20683405101514 & 0.41366810203028 & 0.79316594898486 \tabularnewline
102 & 0.179564755589087 & 0.359129511178175 & 0.820435244410913 \tabularnewline
103 & 0.23990773294275 & 0.479815465885501 & 0.76009226705725 \tabularnewline
104 & 0.209169225069569 & 0.418338450139137 & 0.790830774930432 \tabularnewline
105 & 0.199908051651071 & 0.399816103302141 & 0.800091948348929 \tabularnewline
106 & 0.167615011400069 & 0.335230022800137 & 0.832384988599931 \tabularnewline
107 & 0.160104685051905 & 0.32020937010381 & 0.839895314948095 \tabularnewline
108 & 0.146583697528313 & 0.293167395056626 & 0.853416302471687 \tabularnewline
109 & 0.119671533162636 & 0.239343066325272 & 0.880328466837364 \tabularnewline
110 & 0.111740917086331 & 0.223481834172662 & 0.888259082913669 \tabularnewline
111 & 0.0938149440862541 & 0.187629888172508 & 0.906185055913746 \tabularnewline
112 & 0.0820678921068427 & 0.164135784213685 & 0.917932107893157 \tabularnewline
113 & 0.222217539592551 & 0.444435079185103 & 0.777782460407449 \tabularnewline
114 & 0.192281116462468 & 0.384562232924936 & 0.807718883537532 \tabularnewline
115 & 0.403414280961798 & 0.806828561923595 & 0.596585719038202 \tabularnewline
116 & 0.401970546376988 & 0.803941092753976 & 0.598029453623012 \tabularnewline
117 & 0.428790156508028 & 0.857580313016056 & 0.571209843491972 \tabularnewline
118 & 0.389999075185791 & 0.779998150371581 & 0.610000924814209 \tabularnewline
119 & 0.353668900605938 & 0.707337801211876 & 0.646331099394062 \tabularnewline
120 & 0.312541993189361 & 0.625083986378722 & 0.687458006810639 \tabularnewline
121 & 0.363550781030553 & 0.727101562061107 & 0.636449218969447 \tabularnewline
122 & 0.338683347635545 & 0.67736669527109 & 0.661316652364455 \tabularnewline
123 & 0.291049331908194 & 0.582098663816387 & 0.708950668091806 \tabularnewline
124 & 0.407265600697837 & 0.814531201395674 & 0.592734399302163 \tabularnewline
125 & 0.365172999924423 & 0.730345999848846 & 0.634827000075577 \tabularnewline
126 & 0.451689054908843 & 0.903378109817687 & 0.548310945091157 \tabularnewline
127 & 0.396013889912453 & 0.792027779824906 & 0.603986110087547 \tabularnewline
128 & 0.369145345396304 & 0.738290690792608 & 0.630854654603696 \tabularnewline
129 & 0.314797830970583 & 0.629595661941166 & 0.685202169029417 \tabularnewline
130 & 0.365300748163774 & 0.730601496327548 & 0.634699251836226 \tabularnewline
131 & 0.350725932786963 & 0.701451865573926 & 0.649274067213037 \tabularnewline
132 & 0.291816654530554 & 0.583633309061108 & 0.708183345469446 \tabularnewline
133 & 0.23833950766976 & 0.47667901533952 & 0.76166049233024 \tabularnewline
134 & 0.189415379525351 & 0.378830759050701 & 0.81058462047465 \tabularnewline
135 & 0.152360045918678 & 0.304720091837356 & 0.847639954081322 \tabularnewline
136 & 0.242371823917316 & 0.484743647834632 & 0.757628176082684 \tabularnewline
137 & 0.230341188718384 & 0.460682377436769 & 0.769658811281616 \tabularnewline
138 & 0.204152873904172 & 0.408305747808343 & 0.795847126095828 \tabularnewline
139 & 0.18459020082502 & 0.36918040165004 & 0.81540979917498 \tabularnewline
140 & 0.226382729630768 & 0.452765459261536 & 0.773617270369232 \tabularnewline
141 & 0.430335084600332 & 0.860670169200664 & 0.569664915399668 \tabularnewline
142 & 0.418342015666556 & 0.836684031333113 & 0.581657984333444 \tabularnewline
143 & 0.339110521242647 & 0.678221042485294 & 0.660889478757353 \tabularnewline
144 & 0.314432144809126 & 0.628864289618252 & 0.685567855190874 \tabularnewline
145 & 0.281334465023908 & 0.562668930047816 & 0.718665534976092 \tabularnewline
146 & 0.271419059398061 & 0.542838118796123 & 0.728580940601939 \tabularnewline
147 & 0.195163276717107 & 0.390326553434215 & 0.804836723282893 \tabularnewline
148 & 0.142678154692399 & 0.285356309384799 & 0.857321845307601 \tabularnewline
149 & 0.0821486403679791 & 0.164297280735958 & 0.917851359632021 \tabularnewline
150 & 0.0439466197454927 & 0.0878932394909854 & 0.956053380254507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&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]0.558147117898381[/C][C]0.883705764203237[/C][C]0.441852882101619[/C][/ROW]
[ROW][C]10[/C][C]0.417622381927573[/C][C]0.835244763855146[/C][C]0.582377618072427[/C][/ROW]
[ROW][C]11[/C][C]0.61551336797381[/C][C]0.76897326405238[/C][C]0.38448663202619[/C][/ROW]
[ROW][C]12[/C][C]0.792385634609306[/C][C]0.415228730781389[/C][C]0.207614365390694[/C][/ROW]
[ROW][C]13[/C][C]0.80349833925566[/C][C]0.39300332148868[/C][C]0.19650166074434[/C][/ROW]
[ROW][C]14[/C][C]0.74311934674085[/C][C]0.513761306518299[/C][C]0.25688065325915[/C][/ROW]
[ROW][C]15[/C][C]0.661325265885245[/C][C]0.67734946822951[/C][C]0.338674734114755[/C][/ROW]
[ROW][C]16[/C][C]0.623505026072872[/C][C]0.752989947854257[/C][C]0.376494973927128[/C][/ROW]
[ROW][C]17[/C][C]0.669203321813334[/C][C]0.661593356373332[/C][C]0.330796678186666[/C][/ROW]
[ROW][C]18[/C][C]0.589547870867368[/C][C]0.820904258265264[/C][C]0.410452129132632[/C][/ROW]
[ROW][C]19[/C][C]0.696933935799587[/C][C]0.606132128400826[/C][C]0.303066064200413[/C][/ROW]
[ROW][C]20[/C][C]0.781688405780388[/C][C]0.436623188439225[/C][C]0.218311594219612[/C][/ROW]
[ROW][C]21[/C][C]0.73923862460444[/C][C]0.521522750791119[/C][C]0.26076137539556[/C][/ROW]
[ROW][C]22[/C][C]0.798861340785029[/C][C]0.402277318429941[/C][C]0.201138659214971[/C][/ROW]
[ROW][C]23[/C][C]0.754037343928504[/C][C]0.491925312142992[/C][C]0.245962656071496[/C][/ROW]
[ROW][C]24[/C][C]0.780326806735707[/C][C]0.439346386528587[/C][C]0.219673193264293[/C][/ROW]
[ROW][C]25[/C][C]0.731266326720698[/C][C]0.537467346558603[/C][C]0.268733673279302[/C][/ROW]
[ROW][C]26[/C][C]0.707801161068337[/C][C]0.584397677863326[/C][C]0.292198838931663[/C][/ROW]
[ROW][C]27[/C][C]0.688859379494023[/C][C]0.622281241011953[/C][C]0.311140620505977[/C][/ROW]
[ROW][C]28[/C][C]0.628509353892362[/C][C]0.742981292215277[/C][C]0.371490646107638[/C][/ROW]
[ROW][C]29[/C][C]0.600710398180402[/C][C]0.798579203639197[/C][C]0.399289601819598[/C][/ROW]
[ROW][C]30[/C][C]0.549249365765775[/C][C]0.901501268468449[/C][C]0.450750634234225[/C][/ROW]
[ROW][C]31[/C][C]0.629092434964497[/C][C]0.741815130071006[/C][C]0.370907565035503[/C][/ROW]
[ROW][C]32[/C][C]0.61586546474604[/C][C]0.768269070507919[/C][C]0.38413453525396[/C][/ROW]
[ROW][C]33[/C][C]0.68127567329582[/C][C]0.63744865340836[/C][C]0.31872432670418[/C][/ROW]
[ROW][C]34[/C][C]0.726260830030434[/C][C]0.547478339939131[/C][C]0.273739169969566[/C][/ROW]
[ROW][C]35[/C][C]0.740119872166876[/C][C]0.519760255666248[/C][C]0.259880127833124[/C][/ROW]
[ROW][C]36[/C][C]0.777505517480091[/C][C]0.444988965039818[/C][C]0.222494482519909[/C][/ROW]
[ROW][C]37[/C][C]0.830496574630819[/C][C]0.339006850738361[/C][C]0.169503425369181[/C][/ROW]
[ROW][C]38[/C][C]0.827274258587268[/C][C]0.345451482825463[/C][C]0.172725741412732[/C][/ROW]
[ROW][C]39[/C][C]0.815157005722323[/C][C]0.369685988555353[/C][C]0.184842994277677[/C][/ROW]
[ROW][C]40[/C][C]0.786329013564521[/C][C]0.427341972870959[/C][C]0.213670986435479[/C][/ROW]
[ROW][C]41[/C][C]0.842437981217826[/C][C]0.315124037564349[/C][C]0.157562018782174[/C][/ROW]
[ROW][C]42[/C][C]0.807905533583713[/C][C]0.384188932832574[/C][C]0.192094466416287[/C][/ROW]
[ROW][C]43[/C][C]0.820335846014759[/C][C]0.359328307970483[/C][C]0.179664153985241[/C][/ROW]
[ROW][C]44[/C][C]0.872079453344914[/C][C]0.255841093310171[/C][C]0.127920546655086[/C][/ROW]
[ROW][C]45[/C][C]0.886216797832833[/C][C]0.227566404334334[/C][C]0.113783202167167[/C][/ROW]
[ROW][C]46[/C][C]0.862656693675515[/C][C]0.27468661264897[/C][C]0.137343306324485[/C][/ROW]
[ROW][C]47[/C][C]0.839613253310513[/C][C]0.320773493378975[/C][C]0.160386746689487[/C][/ROW]
[ROW][C]48[/C][C]0.866983242360575[/C][C]0.26603351527885[/C][C]0.133016757639425[/C][/ROW]
[ROW][C]49[/C][C]0.846650295959098[/C][C]0.306699408081804[/C][C]0.153349704040902[/C][/ROW]
[ROW][C]50[/C][C]0.895440623239429[/C][C]0.209118753521141[/C][C]0.104559376760571[/C][/ROW]
[ROW][C]51[/C][C]0.936384411412925[/C][C]0.127231177174151[/C][C]0.0636155885870754[/C][/ROW]
[ROW][C]52[/C][C]0.920398198431924[/C][C]0.159203603136152[/C][C]0.0796018015680761[/C][/ROW]
[ROW][C]53[/C][C]0.900401968694935[/C][C]0.19919606261013[/C][C]0.0995980313050651[/C][/ROW]
[ROW][C]54[/C][C]0.920837406945503[/C][C]0.158325186108995[/C][C]0.0791625930544975[/C][/ROW]
[ROW][C]55[/C][C]0.906008130093083[/C][C]0.187983739813834[/C][C]0.093991869906917[/C][/ROW]
[ROW][C]56[/C][C]0.884494983961559[/C][C]0.231010032076882[/C][C]0.115505016038441[/C][/ROW]
[ROW][C]57[/C][C]0.862859147355458[/C][C]0.274281705289085[/C][C]0.137140852644542[/C][/ROW]
[ROW][C]58[/C][C]0.855297892135244[/C][C]0.289404215729512[/C][C]0.144702107864756[/C][/ROW]
[ROW][C]59[/C][C]0.852568647022941[/C][C]0.294862705954118[/C][C]0.147431352977059[/C][/ROW]
[ROW][C]60[/C][C]0.834780503145584[/C][C]0.330438993708832[/C][C]0.165219496854416[/C][/ROW]
[ROW][C]61[/C][C]0.812719104743993[/C][C]0.374561790512014[/C][C]0.187280895256007[/C][/ROW]
[ROW][C]62[/C][C]0.790160485876719[/C][C]0.419679028246561[/C][C]0.209839514123281[/C][/ROW]
[ROW][C]63[/C][C]0.786287722900389[/C][C]0.427424554199222[/C][C]0.213712277099611[/C][/ROW]
[ROW][C]64[/C][C]0.861829645968663[/C][C]0.276340708062674[/C][C]0.138170354031337[/C][/ROW]
[ROW][C]65[/C][C]0.845757302651546[/C][C]0.308485394696908[/C][C]0.154242697348454[/C][/ROW]
[ROW][C]66[/C][C]0.842031135530718[/C][C]0.315937728938563[/C][C]0.157968864469282[/C][/ROW]
[ROW][C]67[/C][C]0.841706124538991[/C][C]0.316587750922019[/C][C]0.158293875461009[/C][/ROW]
[ROW][C]68[/C][C]0.814409633578083[/C][C]0.371180732843833[/C][C]0.185590366421917[/C][/ROW]
[ROW][C]69[/C][C]0.789627796066312[/C][C]0.420744407867377[/C][C]0.210372203933688[/C][/ROW]
[ROW][C]70[/C][C]0.76740080355609[/C][C]0.465198392887821[/C][C]0.23259919644391[/C][/ROW]
[ROW][C]71[/C][C]0.73942000605576[/C][C]0.521159987888479[/C][C]0.26057999394424[/C][/ROW]
[ROW][C]72[/C][C]0.742177138108445[/C][C]0.515645723783109[/C][C]0.257822861891555[/C][/ROW]
[ROW][C]73[/C][C]0.709770786557527[/C][C]0.580458426884946[/C][C]0.290229213442473[/C][/ROW]
[ROW][C]74[/C][C]0.728145546692955[/C][C]0.54370890661409[/C][C]0.271854453307045[/C][/ROW]
[ROW][C]75[/C][C]0.735715888184299[/C][C]0.528568223631403[/C][C]0.264284111815701[/C][/ROW]
[ROW][C]76[/C][C]0.70049719506297[/C][C]0.59900560987406[/C][C]0.29950280493703[/C][/ROW]
[ROW][C]77[/C][C]0.731150657947527[/C][C]0.537698684104947[/C][C]0.268849342052473[/C][/ROW]
[ROW][C]78[/C][C]0.697699571673996[/C][C]0.604600856652007[/C][C]0.302300428326004[/C][/ROW]
[ROW][C]79[/C][C]0.67280403187205[/C][C]0.6543919362559[/C][C]0.32719596812795[/C][/ROW]
[ROW][C]80[/C][C]0.633985267216993[/C][C]0.732029465566013[/C][C]0.366014732783007[/C][/ROW]
[ROW][C]81[/C][C]0.591201825024337[/C][C]0.817596349951326[/C][C]0.408798174975663[/C][/ROW]
[ROW][C]82[/C][C]0.555418011287605[/C][C]0.889163977424789[/C][C]0.444581988712395[/C][/ROW]
[ROW][C]83[/C][C]0.514059273591794[/C][C]0.971881452816411[/C][C]0.485940726408206[/C][/ROW]
[ROW][C]84[/C][C]0.482689267974838[/C][C]0.965378535949675[/C][C]0.517310732025162[/C][/ROW]
[ROW][C]85[/C][C]0.453842936941355[/C][C]0.907685873882711[/C][C]0.546157063058645[/C][/ROW]
[ROW][C]86[/C][C]0.536723630198914[/C][C]0.926552739602172[/C][C]0.463276369801086[/C][/ROW]
[ROW][C]87[/C][C]0.49072089611304[/C][C]0.98144179222608[/C][C]0.50927910388696[/C][/ROW]
[ROW][C]88[/C][C]0.469031793980474[/C][C]0.938063587960949[/C][C]0.530968206019526[/C][/ROW]
[ROW][C]89[/C][C]0.446766260342172[/C][C]0.893532520684345[/C][C]0.553233739657828[/C][/ROW]
[ROW][C]90[/C][C]0.402500196424304[/C][C]0.805000392848607[/C][C]0.597499803575696[/C][/ROW]
[ROW][C]91[/C][C]0.384032748068517[/C][C]0.768065496137033[/C][C]0.615967251931483[/C][/ROW]
[ROW][C]92[/C][C]0.375416111566466[/C][C]0.750832223132932[/C][C]0.624583888433534[/C][/ROW]
[ROW][C]93[/C][C]0.332411345457341[/C][C]0.664822690914682[/C][C]0.667588654542659[/C][/ROW]
[ROW][C]94[/C][C]0.352848587423911[/C][C]0.705697174847823[/C][C]0.647151412576089[/C][/ROW]
[ROW][C]95[/C][C]0.349133332913721[/C][C]0.698266665827441[/C][C]0.650866667086279[/C][/ROW]
[ROW][C]96[/C][C]0.383188545214708[/C][C]0.766377090429416[/C][C]0.616811454785292[/C][/ROW]
[ROW][C]97[/C][C]0.344732846218359[/C][C]0.689465692436717[/C][C]0.655267153781641[/C][/ROW]
[ROW][C]98[/C][C]0.323277880421802[/C][C]0.646555760843604[/C][C]0.676722119578198[/C][/ROW]
[ROW][C]99[/C][C]0.281559473965138[/C][C]0.563118947930276[/C][C]0.718440526034862[/C][/ROW]
[ROW][C]100[/C][C]0.242451931173833[/C][C]0.484903862347665[/C][C]0.757548068826168[/C][/ROW]
[ROW][C]101[/C][C]0.20683405101514[/C][C]0.41366810203028[/C][C]0.79316594898486[/C][/ROW]
[ROW][C]102[/C][C]0.179564755589087[/C][C]0.359129511178175[/C][C]0.820435244410913[/C][/ROW]
[ROW][C]103[/C][C]0.23990773294275[/C][C]0.479815465885501[/C][C]0.76009226705725[/C][/ROW]
[ROW][C]104[/C][C]0.209169225069569[/C][C]0.418338450139137[/C][C]0.790830774930432[/C][/ROW]
[ROW][C]105[/C][C]0.199908051651071[/C][C]0.399816103302141[/C][C]0.800091948348929[/C][/ROW]
[ROW][C]106[/C][C]0.167615011400069[/C][C]0.335230022800137[/C][C]0.832384988599931[/C][/ROW]
[ROW][C]107[/C][C]0.160104685051905[/C][C]0.32020937010381[/C][C]0.839895314948095[/C][/ROW]
[ROW][C]108[/C][C]0.146583697528313[/C][C]0.293167395056626[/C][C]0.853416302471687[/C][/ROW]
[ROW][C]109[/C][C]0.119671533162636[/C][C]0.239343066325272[/C][C]0.880328466837364[/C][/ROW]
[ROW][C]110[/C][C]0.111740917086331[/C][C]0.223481834172662[/C][C]0.888259082913669[/C][/ROW]
[ROW][C]111[/C][C]0.0938149440862541[/C][C]0.187629888172508[/C][C]0.906185055913746[/C][/ROW]
[ROW][C]112[/C][C]0.0820678921068427[/C][C]0.164135784213685[/C][C]0.917932107893157[/C][/ROW]
[ROW][C]113[/C][C]0.222217539592551[/C][C]0.444435079185103[/C][C]0.777782460407449[/C][/ROW]
[ROW][C]114[/C][C]0.192281116462468[/C][C]0.384562232924936[/C][C]0.807718883537532[/C][/ROW]
[ROW][C]115[/C][C]0.403414280961798[/C][C]0.806828561923595[/C][C]0.596585719038202[/C][/ROW]
[ROW][C]116[/C][C]0.401970546376988[/C][C]0.803941092753976[/C][C]0.598029453623012[/C][/ROW]
[ROW][C]117[/C][C]0.428790156508028[/C][C]0.857580313016056[/C][C]0.571209843491972[/C][/ROW]
[ROW][C]118[/C][C]0.389999075185791[/C][C]0.779998150371581[/C][C]0.610000924814209[/C][/ROW]
[ROW][C]119[/C][C]0.353668900605938[/C][C]0.707337801211876[/C][C]0.646331099394062[/C][/ROW]
[ROW][C]120[/C][C]0.312541993189361[/C][C]0.625083986378722[/C][C]0.687458006810639[/C][/ROW]
[ROW][C]121[/C][C]0.363550781030553[/C][C]0.727101562061107[/C][C]0.636449218969447[/C][/ROW]
[ROW][C]122[/C][C]0.338683347635545[/C][C]0.67736669527109[/C][C]0.661316652364455[/C][/ROW]
[ROW][C]123[/C][C]0.291049331908194[/C][C]0.582098663816387[/C][C]0.708950668091806[/C][/ROW]
[ROW][C]124[/C][C]0.407265600697837[/C][C]0.814531201395674[/C][C]0.592734399302163[/C][/ROW]
[ROW][C]125[/C][C]0.365172999924423[/C][C]0.730345999848846[/C][C]0.634827000075577[/C][/ROW]
[ROW][C]126[/C][C]0.451689054908843[/C][C]0.903378109817687[/C][C]0.548310945091157[/C][/ROW]
[ROW][C]127[/C][C]0.396013889912453[/C][C]0.792027779824906[/C][C]0.603986110087547[/C][/ROW]
[ROW][C]128[/C][C]0.369145345396304[/C][C]0.738290690792608[/C][C]0.630854654603696[/C][/ROW]
[ROW][C]129[/C][C]0.314797830970583[/C][C]0.629595661941166[/C][C]0.685202169029417[/C][/ROW]
[ROW][C]130[/C][C]0.365300748163774[/C][C]0.730601496327548[/C][C]0.634699251836226[/C][/ROW]
[ROW][C]131[/C][C]0.350725932786963[/C][C]0.701451865573926[/C][C]0.649274067213037[/C][/ROW]
[ROW][C]132[/C][C]0.291816654530554[/C][C]0.583633309061108[/C][C]0.708183345469446[/C][/ROW]
[ROW][C]133[/C][C]0.23833950766976[/C][C]0.47667901533952[/C][C]0.76166049233024[/C][/ROW]
[ROW][C]134[/C][C]0.189415379525351[/C][C]0.378830759050701[/C][C]0.81058462047465[/C][/ROW]
[ROW][C]135[/C][C]0.152360045918678[/C][C]0.304720091837356[/C][C]0.847639954081322[/C][/ROW]
[ROW][C]136[/C][C]0.242371823917316[/C][C]0.484743647834632[/C][C]0.757628176082684[/C][/ROW]
[ROW][C]137[/C][C]0.230341188718384[/C][C]0.460682377436769[/C][C]0.769658811281616[/C][/ROW]
[ROW][C]138[/C][C]0.204152873904172[/C][C]0.408305747808343[/C][C]0.795847126095828[/C][/ROW]
[ROW][C]139[/C][C]0.18459020082502[/C][C]0.36918040165004[/C][C]0.81540979917498[/C][/ROW]
[ROW][C]140[/C][C]0.226382729630768[/C][C]0.452765459261536[/C][C]0.773617270369232[/C][/ROW]
[ROW][C]141[/C][C]0.430335084600332[/C][C]0.860670169200664[/C][C]0.569664915399668[/C][/ROW]
[ROW][C]142[/C][C]0.418342015666556[/C][C]0.836684031333113[/C][C]0.581657984333444[/C][/ROW]
[ROW][C]143[/C][C]0.339110521242647[/C][C]0.678221042485294[/C][C]0.660889478757353[/C][/ROW]
[ROW][C]144[/C][C]0.314432144809126[/C][C]0.628864289618252[/C][C]0.685567855190874[/C][/ROW]
[ROW][C]145[/C][C]0.281334465023908[/C][C]0.562668930047816[/C][C]0.718665534976092[/C][/ROW]
[ROW][C]146[/C][C]0.271419059398061[/C][C]0.542838118796123[/C][C]0.728580940601939[/C][/ROW]
[ROW][C]147[/C][C]0.195163276717107[/C][C]0.390326553434215[/C][C]0.804836723282893[/C][/ROW]
[ROW][C]148[/C][C]0.142678154692399[/C][C]0.285356309384799[/C][C]0.857321845307601[/C][/ROW]
[ROW][C]149[/C][C]0.0821486403679791[/C][C]0.164297280735958[/C][C]0.917851359632021[/C][/ROW]
[ROW][C]150[/C][C]0.0439466197454927[/C][C]0.0878932394909854[/C][C]0.956053380254507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&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
90.5581471178983810.8837057642032370.441852882101619
100.4176223819275730.8352447638551460.582377618072427
110.615513367973810.768973264052380.38448663202619
120.7923856346093060.4152287307813890.207614365390694
130.803498339255660.393003321488680.19650166074434
140.743119346740850.5137613065182990.25688065325915
150.6613252658852450.677349468229510.338674734114755
160.6235050260728720.7529899478542570.376494973927128
170.6692033218133340.6615933563733320.330796678186666
180.5895478708673680.8209042582652640.410452129132632
190.6969339357995870.6061321284008260.303066064200413
200.7816884057803880.4366231884392250.218311594219612
210.739238624604440.5215227507911190.26076137539556
220.7988613407850290.4022773184299410.201138659214971
230.7540373439285040.4919253121429920.245962656071496
240.7803268067357070.4393463865285870.219673193264293
250.7312663267206980.5374673465586030.268733673279302
260.7078011610683370.5843976778633260.292198838931663
270.6888593794940230.6222812410119530.311140620505977
280.6285093538923620.7429812922152770.371490646107638
290.6007103981804020.7985792036391970.399289601819598
300.5492493657657750.9015012684684490.450750634234225
310.6290924349644970.7418151300710060.370907565035503
320.615865464746040.7682690705079190.38413453525396
330.681275673295820.637448653408360.31872432670418
340.7262608300304340.5474783399391310.273739169969566
350.7401198721668760.5197602556662480.259880127833124
360.7775055174800910.4449889650398180.222494482519909
370.8304965746308190.3390068507383610.169503425369181
380.8272742585872680.3454514828254630.172725741412732
390.8151570057223230.3696859885553530.184842994277677
400.7863290135645210.4273419728709590.213670986435479
410.8424379812178260.3151240375643490.157562018782174
420.8079055335837130.3841889328325740.192094466416287
430.8203358460147590.3593283079704830.179664153985241
440.8720794533449140.2558410933101710.127920546655086
450.8862167978328330.2275664043343340.113783202167167
460.8626566936755150.274686612648970.137343306324485
470.8396132533105130.3207734933789750.160386746689487
480.8669832423605750.266033515278850.133016757639425
490.8466502959590980.3066994080818040.153349704040902
500.8954406232394290.2091187535211410.104559376760571
510.9363844114129250.1272311771741510.0636155885870754
520.9203981984319240.1592036031361520.0796018015680761
530.9004019686949350.199196062610130.0995980313050651
540.9208374069455030.1583251861089950.0791625930544975
550.9060081300930830.1879837398138340.093991869906917
560.8844949839615590.2310100320768820.115505016038441
570.8628591473554580.2742817052890850.137140852644542
580.8552978921352440.2894042157295120.144702107864756
590.8525686470229410.2948627059541180.147431352977059
600.8347805031455840.3304389937088320.165219496854416
610.8127191047439930.3745617905120140.187280895256007
620.7901604858767190.4196790282465610.209839514123281
630.7862877229003890.4274245541992220.213712277099611
640.8618296459686630.2763407080626740.138170354031337
650.8457573026515460.3084853946969080.154242697348454
660.8420311355307180.3159377289385630.157968864469282
670.8417061245389910.3165877509220190.158293875461009
680.8144096335780830.3711807328438330.185590366421917
690.7896277960663120.4207444078673770.210372203933688
700.767400803556090.4651983928878210.23259919644391
710.739420006055760.5211599878884790.26057999394424
720.7421771381084450.5156457237831090.257822861891555
730.7097707865575270.5804584268849460.290229213442473
740.7281455466929550.543708906614090.271854453307045
750.7357158881842990.5285682236314030.264284111815701
760.700497195062970.599005609874060.29950280493703
770.7311506579475270.5376986841049470.268849342052473
780.6976995716739960.6046008566520070.302300428326004
790.672804031872050.65439193625590.32719596812795
800.6339852672169930.7320294655660130.366014732783007
810.5912018250243370.8175963499513260.408798174975663
820.5554180112876050.8891639774247890.444581988712395
830.5140592735917940.9718814528164110.485940726408206
840.4826892679748380.9653785359496750.517310732025162
850.4538429369413550.9076858738827110.546157063058645
860.5367236301989140.9265527396021720.463276369801086
870.490720896113040.981441792226080.50927910388696
880.4690317939804740.9380635879609490.530968206019526
890.4467662603421720.8935325206843450.553233739657828
900.4025001964243040.8050003928486070.597499803575696
910.3840327480685170.7680654961370330.615967251931483
920.3754161115664660.7508322231329320.624583888433534
930.3324113454573410.6648226909146820.667588654542659
940.3528485874239110.7056971748478230.647151412576089
950.3491333329137210.6982666658274410.650866667086279
960.3831885452147080.7663770904294160.616811454785292
970.3447328462183590.6894656924367170.655267153781641
980.3232778804218020.6465557608436040.676722119578198
990.2815594739651380.5631189479302760.718440526034862
1000.2424519311738330.4849038623476650.757548068826168
1010.206834051015140.413668102030280.79316594898486
1020.1795647555890870.3591295111781750.820435244410913
1030.239907732942750.4798154658855010.76009226705725
1040.2091692250695690.4183384501391370.790830774930432
1050.1999080516510710.3998161033021410.800091948348929
1060.1676150114000690.3352300228001370.832384988599931
1070.1601046850519050.320209370103810.839895314948095
1080.1465836975283130.2931673950566260.853416302471687
1090.1196715331626360.2393430663252720.880328466837364
1100.1117409170863310.2234818341726620.888259082913669
1110.09381494408625410.1876298881725080.906185055913746
1120.08206789210684270.1641357842136850.917932107893157
1130.2222175395925510.4444350791851030.777782460407449
1140.1922811164624680.3845622329249360.807718883537532
1150.4034142809617980.8068285619235950.596585719038202
1160.4019705463769880.8039410927539760.598029453623012
1170.4287901565080280.8575803130160560.571209843491972
1180.3899990751857910.7799981503715810.610000924814209
1190.3536689006059380.7073378012118760.646331099394062
1200.3125419931893610.6250839863787220.687458006810639
1210.3635507810305530.7271015620611070.636449218969447
1220.3386833476355450.677366695271090.661316652364455
1230.2910493319081940.5820986638163870.708950668091806
1240.4072656006978370.8145312013956740.592734399302163
1250.3651729999244230.7303459998488460.634827000075577
1260.4516890549088430.9033781098176870.548310945091157
1270.3960138899124530.7920277798249060.603986110087547
1280.3691453453963040.7382906907926080.630854654603696
1290.3147978309705830.6295956619411660.685202169029417
1300.3653007481637740.7306014963275480.634699251836226
1310.3507259327869630.7014518655739260.649274067213037
1320.2918166545305540.5836333090611080.708183345469446
1330.238339507669760.476679015339520.76166049233024
1340.1894153795253510.3788307590507010.81058462047465
1350.1523600459186780.3047200918373560.847639954081322
1360.2423718239173160.4847436478346320.757628176082684
1370.2303411887183840.4606823774367690.769658811281616
1380.2041528739041720.4083057478083430.795847126095828
1390.184590200825020.369180401650040.81540979917498
1400.2263827296307680.4527654592615360.773617270369232
1410.4303350846003320.8606701692006640.569664915399668
1420.4183420156665560.8366840313331130.581657984333444
1430.3391105212426470.6782210424852940.660889478757353
1440.3144321448091260.6288642896182520.685567855190874
1450.2813344650239080.5626689300478160.718665534976092
1460.2714190593980610.5428381187961230.728580940601939
1470.1951632767171070.3903265534342150.804836723282893
1480.1426781546923990.2853563093847990.857321845307601
1490.08214864036797910.1642972807359580.917851359632021
1500.04394661974549270.08789323949098540.956053380254507







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00704225352112676OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00704225352112676 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147166&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00704225352112676[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147166&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147166&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00704225352112676OK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}