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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 17:48:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t129053442509lhzctc4rj9ax5.htm/, Retrieved Thu, 28 Mar 2024 19:09:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99493, Retrieved Thu, 28 Mar 2024 19:09:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Workshop 7 Determ...] [2010-11-23 17:48:05] [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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=99493&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=99493&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Concern_over_Mistakes[t] = -2.70676782746942 + 0.80482569659637Doubts_about_actions[t] + 0.246618734551964Parental_Expectations[t] + 0.190858166207394Parental_Criticism[t] + 0.568212974041801Personal_Standards[t] -0.100756792404193`Organization `[t] + 0.00564436253658337t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Concern_over_Mistakes[t] =  -2.70676782746942 +  0.80482569659637Doubts_about_actions[t] +  0.246618734551964Parental_Expectations[t] +  0.190858166207394Parental_Criticism[t] +  0.568212974041801Personal_Standards[t] -0.100756792404193`Organization
`[t] +  0.00564436253658337t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Concern_over_Mistakes[t] =  -2.70676782746942 +  0.80482569659637Doubts_about_actions[t] +  0.246618734551964Parental_Expectations[t] +  0.190858166207394Parental_Criticism[t] +  0.568212974041801Personal_Standards[t] -0.100756792404193`Organization
`[t] +  0.00564436253658337t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&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
Concern_over_Mistakes[t] = -2.70676782746942 + 0.80482569659637Doubts_about_actions[t] + 0.246618734551964Parental_Expectations[t] + 0.190858166207394Parental_Criticism[t] + 0.568212974041801Personal_Standards[t] -0.100756792404193`Organization `[t] + 0.00564436253658337t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2.706767827469423.230753-0.83780.4034510.201726
Doubts_about_actions0.804825696596370.1307656.154700
Parental_Expectations0.2466187345519640.1331411.85230.065920.03296
Parental_Criticism0.1908581662073940.1685681.13220.259320.12966
Personal_Standards0.5682129740418010.0960195.917700
`Organization `-0.1007567924041930.105352-0.95640.3403970.170199
t0.005644362536583370.0080040.70520.4817420.240871

\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) & -2.70676782746942 & 3.230753 & -0.8378 & 0.403451 & 0.201726 \tabularnewline
Doubts_about_actions & 0.80482569659637 & 0.130765 & 6.1547 & 0 & 0 \tabularnewline
Parental_Expectations & 0.246618734551964 & 0.133141 & 1.8523 & 0.06592 & 0.03296 \tabularnewline
Parental_Criticism & 0.190858166207394 & 0.168568 & 1.1322 & 0.25932 & 0.12966 \tabularnewline
Personal_Standards & 0.568212974041801 & 0.096019 & 5.9177 & 0 & 0 \tabularnewline
`Organization
` & -0.100756792404193 & 0.105352 & -0.9564 & 0.340397 & 0.170199 \tabularnewline
t & 0.00564436253658337 & 0.008004 & 0.7052 & 0.481742 & 0.240871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&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]-2.70676782746942[/C][C]3.230753[/C][C]-0.8378[/C][C]0.403451[/C][C]0.201726[/C][/ROW]
[ROW][C]Doubts_about_actions[/C][C]0.80482569659637[/C][C]0.130765[/C][C]6.1547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Parental_Expectations[/C][C]0.246618734551964[/C][C]0.133141[/C][C]1.8523[/C][C]0.06592[/C][C]0.03296[/C][/ROW]
[ROW][C]Parental_Criticism[/C][C]0.190858166207394[/C][C]0.168568[/C][C]1.1322[/C][C]0.25932[/C][C]0.12966[/C][/ROW]
[ROW][C]Personal_Standards[/C][C]0.568212974041801[/C][C]0.096019[/C][C]5.9177[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Organization
`[/C][C]-0.100756792404193[/C][C]0.105352[/C][C]-0.9564[/C][C]0.340397[/C][C]0.170199[/C][/ROW]
[ROW][C]t[/C][C]0.00564436253658337[/C][C]0.008004[/C][C]0.7052[/C][C]0.481742[/C][C]0.240871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&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)-2.706767827469423.230753-0.83780.4034510.201726
Doubts_about_actions0.804825696596370.1307656.154700
Parental_Expectations0.2466187345519640.1331411.85230.065920.03296
Parental_Criticism0.1908581662073940.1685681.13220.259320.12966
Personal_Standards0.5682129740418010.0960195.917700
`Organization `-0.1007567924041930.105352-0.95640.3403970.170199
t0.005644362536583370.0080040.70520.4817420.240871







Multiple Linear Regression - Regression Statistics
Multiple R0.639616097712746
R-squared0.409108752453280
Adjusted R-squared0.385784097944857
F-TEST (value)17.5397561539675
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.22044604925031e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.48508352276095
Sum Squared Residuals3057.62807933355

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.639616097712746 \tabularnewline
R-squared & 0.409108752453280 \tabularnewline
Adjusted R-squared & 0.385784097944857 \tabularnewline
F-TEST (value) & 17.5397561539675 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 2.22044604925031e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.48508352276095 \tabularnewline
Sum Squared Residuals & 3057.62807933355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.639616097712746[/C][/ROW]
[ROW][C]R-squared[/C][C]0.409108752453280[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.385784097944857[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.5397561539675[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]2.22044604925031e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.48508352276095[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3057.62807933355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&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 R0.639616097712746
R-squared0.409108752453280
Adjusted R-squared0.385784097944857
F-TEST (value)17.5397561539675
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.22044604925031e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.48508352276095
Sum Squared Residuals3057.62807933355







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.5869751364713-0.586975136471266
22521.29871815743543.70128184256465
31722.3859726679103-5.38597266791033
41819.4454109385100-1.44541093850996
51819.0235142417146-1.0235142417146
61619.2495257409872-3.24952574098717
72020.5436429620918-0.543642962091784
81621.9567214062097-5.95672140620966
91821.9596456847955-3.95964568479549
101720.1583618178568-3.15836181785681
112321.93889798349571.06110201650434
123021.85577854055028.14422145944983
132314.73314737815758.26685262184252
141818.6032699809839-0.603269980983944
151521.3229006529394-6.32290065293937
161217.5861185544217-5.58611855442169
172119.05545929268981.94454070731015
181514.81900780842570.180992191574261
192019.45012869555080.549871304449198
203126.01940004615384.98059995384625
212724.86768457315592.13231542684406
223426.7505329508887.24946704911197
232119.28045609096921.71954390903085
243120.599736608392610.4002633916074
251919.0701646017546-0.0701646017546488
261619.9463310706595-3.94633107065954
272021.3206525792539-1.32065257925392
282117.99635612705803.00364387294205
292221.68367835586360.316321644136378
301719.2404660680385-2.24046606803853
312420.16455935666393.83544064333613
322530.1993051078831-5.19930510788305
332626.390560835707-0.390560835707015
342523.88307340885351.11692659114646
351722.7338898244853-5.73388982448527
363227.59685762230484.40314237769521
373323.41318769743459.5868123025655
381321.7416158659416-8.74161586594162
393227.59401972731614.40598027268393
402525.6331170331415-0.633117033141505
412926.89076322585002.10923677414995
422221.83203057377710.167969426222923
431817.05245924122000.947540758780045
441721.5731241031246-4.5731241031246
452022.0446692276097-2.04466922760975
461520.2957922888318-5.29579228883185
472021.9969914249524-1.99699142495244
483327.96109108648485.03890891351517
492921.99227117281827.00772882718179
502326.4609674131011-3.46096741310109
512623.10556753028852.89443246971152
521818.8307698752602-0.830769875260195
532018.66999979073461.33000020926542
541111.5812129333856-0.581212933385631
552828.8095440113788-0.809544011378844
562623.27350507189642.72649492810355
572222.2026118775359-0.202611877535904
581720.0774839975063-3.07748399750625
591215.4292884866592-3.42928848665917
601420.780061210624-6.78006121062399
611720.6763094340180-3.67630943401805
622121.261580521485-0.261580521484977
631922.9113260083569-3.91132600835689
641823.0268271622096-5.02682716220961
651017.8541098959786-7.8541098959786
662924.28460062767994.71539937232013
673118.432203856202412.5677961437976
681922.8792466318609-3.87924663186087
69920.0338902827611-11.0338902827611
702022.4674961633225-2.46749616332248
712817.575629511418110.4243704885819
721918.11369954853280.886300451467232
733023.08778011783416.91221988216585
742927.0733696770651.92663032293499
752621.49082518860574.50917481139427
762319.50511767590533.49488232409466
771322.7601530313896-9.76015303138962
782122.6443090003536-1.64430900035360
791921.5370492704958-2.53704927049576
802822.93773130259005.06226869740997
812325.6175014577243-2.61750145772434
821813.86463944604104.13536055395897
832120.73192857496010.268071425039861
842021.8476069466243-1.84760694662434
852320.04341655982312.95658344017692
862120.85973339265540.140266607344566
872121.8550198976718-0.855019897671838
881522.9508811919658-7.95088119196579
892827.19650374341430.803496256585704
901917.68864166308471.31135833691528
912621.29541407010554.70458592989447
921013.3326243716318-3.3326243716318
931617.1873143264118-1.18731432641177
942221.20203881329270.797961186707256
951918.90630468498350.093695315016476
963128.91577792891492.08422207108510
973125.30188490848825.69811509151183
982924.85512878768244.14487121231755
991917.50282820335941.49717179664060
1002218.95228874806013.04771125193990
1012322.4724381975160.527561802483991
1021516.2874647056108-1.28746470561081
1032021.4645212040962-1.46452120409623
1041819.6425371514452-1.64253715144523
1052322.2171502344810.782849765518977
1062520.97445993751394.02554006248606
1072116.65294710756854.34705289243146
1082419.54557442782974.45442557217025
1092525.3595410763598-0.359541076359750
1101719.6935475859109-2.69354758591087
1111314.6943965458432-1.69439654584321
1122818.40987461177099.59012538822914
1132120.31063468010390.68936531989613
1142528.2836350486841-3.28363504868414
115921.0044477704288-12.0044477704288
1161618.0612276169349-2.06122761693494
1171921.2966898433180-2.29668984331795
1181719.6269476477759-2.62694764777592
1192524.73199799209820.268002007901782
1202015.63695124201494.36304875798511
1212921.87593440090527.12406559909482
1221419.1778709228727-5.17787092287272
1232227.0980959242537-5.09809592425374
1241515.9019397751253-0.901939775125261
1251925.6813751580878-6.6813751580878
1262022.1919545613241-2.19195456132412
1271517.7608142090159-2.76081420901587
1282022.1258994239983-2.12589942399832
1291820.5527935376764-2.55279353767642
1303325.78884797934847.21115202065161
1312224.0146988185677-2.01469881856772
1321616.7435243851904-0.743524385190442
1331719.4036823708245-2.40368237082452
1341615.3816109084570.618389091543015
1352117.36376958458823.63623041541182
1362627.9214437785666-1.92144377856657
1371821.4147283663736-3.41472836637364
1381823.2764637604010-5.27646376040095
1391718.7078618855540-1.70786188555397
1402224.9760915015815-2.97609150158152
1413024.9522424586755.04775754132501
1423027.62202950655532.37797049344472
1432430.0288532518421-6.02885325184213
1442122.3733372368221-1.37333723682208
1452125.7186060818219-4.71860608182192
1462927.77606260715771.22393739284233
1473123.5675720133917.432427986609
1482019.33817894762960.661821052370389
1491614.42711320950251.57288679049751
1502219.28885970434272.71114029565732
1512020.7667282598620-0.766728259862024
1522827.72009986542640.279900134573619
1533827.081132159721610.9188678402784
1542219.59067155144762.40932844855245
1552026.0566974089159-6.05669740891588
1561718.4485304103310-1.44853041033102
1572824.90557661215673.09442338784327
1582224.5396741854444-2.53967418544438
1593126.45186828782174.54813171217832

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.5869751364713 & -0.586975136471266 \tabularnewline
2 & 25 & 21.2987181574354 & 3.70128184256465 \tabularnewline
3 & 17 & 22.3859726679103 & -5.38597266791033 \tabularnewline
4 & 18 & 19.4454109385100 & -1.44541093850996 \tabularnewline
5 & 18 & 19.0235142417146 & -1.0235142417146 \tabularnewline
6 & 16 & 19.2495257409872 & -3.24952574098717 \tabularnewline
7 & 20 & 20.5436429620918 & -0.543642962091784 \tabularnewline
8 & 16 & 21.9567214062097 & -5.95672140620966 \tabularnewline
9 & 18 & 21.9596456847955 & -3.95964568479549 \tabularnewline
10 & 17 & 20.1583618178568 & -3.15836181785681 \tabularnewline
11 & 23 & 21.9388979834957 & 1.06110201650434 \tabularnewline
12 & 30 & 21.8557785405502 & 8.14422145944983 \tabularnewline
13 & 23 & 14.7331473781575 & 8.26685262184252 \tabularnewline
14 & 18 & 18.6032699809839 & -0.603269980983944 \tabularnewline
15 & 15 & 21.3229006529394 & -6.32290065293937 \tabularnewline
16 & 12 & 17.5861185544217 & -5.58611855442169 \tabularnewline
17 & 21 & 19.0554592926898 & 1.94454070731015 \tabularnewline
18 & 15 & 14.8190078084257 & 0.180992191574261 \tabularnewline
19 & 20 & 19.4501286955508 & 0.549871304449198 \tabularnewline
20 & 31 & 26.0194000461538 & 4.98059995384625 \tabularnewline
21 & 27 & 24.8676845731559 & 2.13231542684406 \tabularnewline
22 & 34 & 26.750532950888 & 7.24946704911197 \tabularnewline
23 & 21 & 19.2804560909692 & 1.71954390903085 \tabularnewline
24 & 31 & 20.5997366083926 & 10.4002633916074 \tabularnewline
25 & 19 & 19.0701646017546 & -0.0701646017546488 \tabularnewline
26 & 16 & 19.9463310706595 & -3.94633107065954 \tabularnewline
27 & 20 & 21.3206525792539 & -1.32065257925392 \tabularnewline
28 & 21 & 17.9963561270580 & 3.00364387294205 \tabularnewline
29 & 22 & 21.6836783558636 & 0.316321644136378 \tabularnewline
30 & 17 & 19.2404660680385 & -2.24046606803853 \tabularnewline
31 & 24 & 20.1645593566639 & 3.83544064333613 \tabularnewline
32 & 25 & 30.1993051078831 & -5.19930510788305 \tabularnewline
33 & 26 & 26.390560835707 & -0.390560835707015 \tabularnewline
34 & 25 & 23.8830734088535 & 1.11692659114646 \tabularnewline
35 & 17 & 22.7338898244853 & -5.73388982448527 \tabularnewline
36 & 32 & 27.5968576223048 & 4.40314237769521 \tabularnewline
37 & 33 & 23.4131876974345 & 9.5868123025655 \tabularnewline
38 & 13 & 21.7416158659416 & -8.74161586594162 \tabularnewline
39 & 32 & 27.5940197273161 & 4.40598027268393 \tabularnewline
40 & 25 & 25.6331170331415 & -0.633117033141505 \tabularnewline
41 & 29 & 26.8907632258500 & 2.10923677414995 \tabularnewline
42 & 22 & 21.8320305737771 & 0.167969426222923 \tabularnewline
43 & 18 & 17.0524592412200 & 0.947540758780045 \tabularnewline
44 & 17 & 21.5731241031246 & -4.5731241031246 \tabularnewline
45 & 20 & 22.0446692276097 & -2.04466922760975 \tabularnewline
46 & 15 & 20.2957922888318 & -5.29579228883185 \tabularnewline
47 & 20 & 21.9969914249524 & -1.99699142495244 \tabularnewline
48 & 33 & 27.9610910864848 & 5.03890891351517 \tabularnewline
49 & 29 & 21.9922711728182 & 7.00772882718179 \tabularnewline
50 & 23 & 26.4609674131011 & -3.46096741310109 \tabularnewline
51 & 26 & 23.1055675302885 & 2.89443246971152 \tabularnewline
52 & 18 & 18.8307698752602 & -0.830769875260195 \tabularnewline
53 & 20 & 18.6699997907346 & 1.33000020926542 \tabularnewline
54 & 11 & 11.5812129333856 & -0.581212933385631 \tabularnewline
55 & 28 & 28.8095440113788 & -0.809544011378844 \tabularnewline
56 & 26 & 23.2735050718964 & 2.72649492810355 \tabularnewline
57 & 22 & 22.2026118775359 & -0.202611877535904 \tabularnewline
58 & 17 & 20.0774839975063 & -3.07748399750625 \tabularnewline
59 & 12 & 15.4292884866592 & -3.42928848665917 \tabularnewline
60 & 14 & 20.780061210624 & -6.78006121062399 \tabularnewline
61 & 17 & 20.6763094340180 & -3.67630943401805 \tabularnewline
62 & 21 & 21.261580521485 & -0.261580521484977 \tabularnewline
63 & 19 & 22.9113260083569 & -3.91132600835689 \tabularnewline
64 & 18 & 23.0268271622096 & -5.02682716220961 \tabularnewline
65 & 10 & 17.8541098959786 & -7.8541098959786 \tabularnewline
66 & 29 & 24.2846006276799 & 4.71539937232013 \tabularnewline
67 & 31 & 18.4322038562024 & 12.5677961437976 \tabularnewline
68 & 19 & 22.8792466318609 & -3.87924663186087 \tabularnewline
69 & 9 & 20.0338902827611 & -11.0338902827611 \tabularnewline
70 & 20 & 22.4674961633225 & -2.46749616332248 \tabularnewline
71 & 28 & 17.5756295114181 & 10.4243704885819 \tabularnewline
72 & 19 & 18.1136995485328 & 0.886300451467232 \tabularnewline
73 & 30 & 23.0877801178341 & 6.91221988216585 \tabularnewline
74 & 29 & 27.073369677065 & 1.92663032293499 \tabularnewline
75 & 26 & 21.4908251886057 & 4.50917481139427 \tabularnewline
76 & 23 & 19.5051176759053 & 3.49488232409466 \tabularnewline
77 & 13 & 22.7601530313896 & -9.76015303138962 \tabularnewline
78 & 21 & 22.6443090003536 & -1.64430900035360 \tabularnewline
79 & 19 & 21.5370492704958 & -2.53704927049576 \tabularnewline
80 & 28 & 22.9377313025900 & 5.06226869740997 \tabularnewline
81 & 23 & 25.6175014577243 & -2.61750145772434 \tabularnewline
82 & 18 & 13.8646394460410 & 4.13536055395897 \tabularnewline
83 & 21 & 20.7319285749601 & 0.268071425039861 \tabularnewline
84 & 20 & 21.8476069466243 & -1.84760694662434 \tabularnewline
85 & 23 & 20.0434165598231 & 2.95658344017692 \tabularnewline
86 & 21 & 20.8597333926554 & 0.140266607344566 \tabularnewline
87 & 21 & 21.8550198976718 & -0.855019897671838 \tabularnewline
88 & 15 & 22.9508811919658 & -7.95088119196579 \tabularnewline
89 & 28 & 27.1965037434143 & 0.803496256585704 \tabularnewline
90 & 19 & 17.6886416630847 & 1.31135833691528 \tabularnewline
91 & 26 & 21.2954140701055 & 4.70458592989447 \tabularnewline
92 & 10 & 13.3326243716318 & -3.3326243716318 \tabularnewline
93 & 16 & 17.1873143264118 & -1.18731432641177 \tabularnewline
94 & 22 & 21.2020388132927 & 0.797961186707256 \tabularnewline
95 & 19 & 18.9063046849835 & 0.093695315016476 \tabularnewline
96 & 31 & 28.9157779289149 & 2.08422207108510 \tabularnewline
97 & 31 & 25.3018849084882 & 5.69811509151183 \tabularnewline
98 & 29 & 24.8551287876824 & 4.14487121231755 \tabularnewline
99 & 19 & 17.5028282033594 & 1.49717179664060 \tabularnewline
100 & 22 & 18.9522887480601 & 3.04771125193990 \tabularnewline
101 & 23 & 22.472438197516 & 0.527561802483991 \tabularnewline
102 & 15 & 16.2874647056108 & -1.28746470561081 \tabularnewline
103 & 20 & 21.4645212040962 & -1.46452120409623 \tabularnewline
104 & 18 & 19.6425371514452 & -1.64253715144523 \tabularnewline
105 & 23 & 22.217150234481 & 0.782849765518977 \tabularnewline
106 & 25 & 20.9744599375139 & 4.02554006248606 \tabularnewline
107 & 21 & 16.6529471075685 & 4.34705289243146 \tabularnewline
108 & 24 & 19.5455744278297 & 4.45442557217025 \tabularnewline
109 & 25 & 25.3595410763598 & -0.359541076359750 \tabularnewline
110 & 17 & 19.6935475859109 & -2.69354758591087 \tabularnewline
111 & 13 & 14.6943965458432 & -1.69439654584321 \tabularnewline
112 & 28 & 18.4098746117709 & 9.59012538822914 \tabularnewline
113 & 21 & 20.3106346801039 & 0.68936531989613 \tabularnewline
114 & 25 & 28.2836350486841 & -3.28363504868414 \tabularnewline
115 & 9 & 21.0044477704288 & -12.0044477704288 \tabularnewline
116 & 16 & 18.0612276169349 & -2.06122761693494 \tabularnewline
117 & 19 & 21.2966898433180 & -2.29668984331795 \tabularnewline
118 & 17 & 19.6269476477759 & -2.62694764777592 \tabularnewline
119 & 25 & 24.7319979920982 & 0.268002007901782 \tabularnewline
120 & 20 & 15.6369512420149 & 4.36304875798511 \tabularnewline
121 & 29 & 21.8759344009052 & 7.12406559909482 \tabularnewline
122 & 14 & 19.1778709228727 & -5.17787092287272 \tabularnewline
123 & 22 & 27.0980959242537 & -5.09809592425374 \tabularnewline
124 & 15 & 15.9019397751253 & -0.901939775125261 \tabularnewline
125 & 19 & 25.6813751580878 & -6.6813751580878 \tabularnewline
126 & 20 & 22.1919545613241 & -2.19195456132412 \tabularnewline
127 & 15 & 17.7608142090159 & -2.76081420901587 \tabularnewline
128 & 20 & 22.1258994239983 & -2.12589942399832 \tabularnewline
129 & 18 & 20.5527935376764 & -2.55279353767642 \tabularnewline
130 & 33 & 25.7888479793484 & 7.21115202065161 \tabularnewline
131 & 22 & 24.0146988185677 & -2.01469881856772 \tabularnewline
132 & 16 & 16.7435243851904 & -0.743524385190442 \tabularnewline
133 & 17 & 19.4036823708245 & -2.40368237082452 \tabularnewline
134 & 16 & 15.381610908457 & 0.618389091543015 \tabularnewline
135 & 21 & 17.3637695845882 & 3.63623041541182 \tabularnewline
136 & 26 & 27.9214437785666 & -1.92144377856657 \tabularnewline
137 & 18 & 21.4147283663736 & -3.41472836637364 \tabularnewline
138 & 18 & 23.2764637604010 & -5.27646376040095 \tabularnewline
139 & 17 & 18.7078618855540 & -1.70786188555397 \tabularnewline
140 & 22 & 24.9760915015815 & -2.97609150158152 \tabularnewline
141 & 30 & 24.952242458675 & 5.04775754132501 \tabularnewline
142 & 30 & 27.6220295065553 & 2.37797049344472 \tabularnewline
143 & 24 & 30.0288532518421 & -6.02885325184213 \tabularnewline
144 & 21 & 22.3733372368221 & -1.37333723682208 \tabularnewline
145 & 21 & 25.7186060818219 & -4.71860608182192 \tabularnewline
146 & 29 & 27.7760626071577 & 1.22393739284233 \tabularnewline
147 & 31 & 23.567572013391 & 7.432427986609 \tabularnewline
148 & 20 & 19.3381789476296 & 0.661821052370389 \tabularnewline
149 & 16 & 14.4271132095025 & 1.57288679049751 \tabularnewline
150 & 22 & 19.2888597043427 & 2.71114029565732 \tabularnewline
151 & 20 & 20.7667282598620 & -0.766728259862024 \tabularnewline
152 & 28 & 27.7200998654264 & 0.279900134573619 \tabularnewline
153 & 38 & 27.0811321597216 & 10.9188678402784 \tabularnewline
154 & 22 & 19.5906715514476 & 2.40932844855245 \tabularnewline
155 & 20 & 26.0566974089159 & -6.05669740891588 \tabularnewline
156 & 17 & 18.4485304103310 & -1.44853041033102 \tabularnewline
157 & 28 & 24.9055766121567 & 3.09442338784327 \tabularnewline
158 & 22 & 24.5396741854444 & -2.53967418544438 \tabularnewline
159 & 31 & 26.4518682878217 & 4.54813171217832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&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]24[/C][C]24.5869751364713[/C][C]-0.586975136471266[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.2987181574354[/C][C]3.70128184256465[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]22.3859726679103[/C][C]-5.38597266791033[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]19.4454109385100[/C][C]-1.44541093850996[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]19.0235142417146[/C][C]-1.0235142417146[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.2495257409872[/C][C]-3.24952574098717[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.5436429620918[/C][C]-0.543642962091784[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]21.9567214062097[/C][C]-5.95672140620966[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]21.9596456847955[/C][C]-3.95964568479549[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.1583618178568[/C][C]-3.15836181785681[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]21.9388979834957[/C][C]1.06110201650434[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]21.8557785405502[/C][C]8.14422145944983[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]14.7331473781575[/C][C]8.26685262184252[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]18.6032699809839[/C][C]-0.603269980983944[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.3229006529394[/C][C]-6.32290065293937[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.5861185544217[/C][C]-5.58611855442169[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.0554592926898[/C][C]1.94454070731015[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]14.8190078084257[/C][C]0.180992191574261[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.4501286955508[/C][C]0.549871304449198[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.0194000461538[/C][C]4.98059995384625[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]24.8676845731559[/C][C]2.13231542684406[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]26.750532950888[/C][C]7.24946704911197[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.2804560909692[/C][C]1.71954390903085[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.5997366083926[/C][C]10.4002633916074[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.0701646017546[/C][C]-0.0701646017546488[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]19.9463310706595[/C][C]-3.94633107065954[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.3206525792539[/C][C]-1.32065257925392[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]17.9963561270580[/C][C]3.00364387294205[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.6836783558636[/C][C]0.316321644136378[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.2404660680385[/C][C]-2.24046606803853[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.1645593566639[/C][C]3.83544064333613[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.1993051078831[/C][C]-5.19930510788305[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.390560835707[/C][C]-0.390560835707015[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]23.8830734088535[/C][C]1.11692659114646[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]22.7338898244853[/C][C]-5.73388982448527[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.5968576223048[/C][C]4.40314237769521[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.4131876974345[/C][C]9.5868123025655[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]21.7416158659416[/C][C]-8.74161586594162[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.5940197273161[/C][C]4.40598027268393[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.6331170331415[/C][C]-0.633117033141505[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]26.8907632258500[/C][C]2.10923677414995[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.8320305737771[/C][C]0.167969426222923[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.0524592412200[/C][C]0.947540758780045[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.5731241031246[/C][C]-4.5731241031246[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.0446692276097[/C][C]-2.04466922760975[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.2957922888318[/C][C]-5.29579228883185[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]21.9969914249524[/C][C]-1.99699142495244[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]27.9610910864848[/C][C]5.03890891351517[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]21.9922711728182[/C][C]7.00772882718179[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.4609674131011[/C][C]-3.46096741310109[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.1055675302885[/C][C]2.89443246971152[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.8307698752602[/C][C]-0.830769875260195[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.6699997907346[/C][C]1.33000020926542[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.5812129333856[/C][C]-0.581212933385631[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.8095440113788[/C][C]-0.809544011378844[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.2735050718964[/C][C]2.72649492810355[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.2026118775359[/C][C]-0.202611877535904[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.0774839975063[/C][C]-3.07748399750625[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.4292884866592[/C][C]-3.42928848665917[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]20.780061210624[/C][C]-6.78006121062399[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.6763094340180[/C][C]-3.67630943401805[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.261580521485[/C][C]-0.261580521484977[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.9113260083569[/C][C]-3.91132600835689[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.0268271622096[/C][C]-5.02682716220961[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.8541098959786[/C][C]-7.8541098959786[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.2846006276799[/C][C]4.71539937232013[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.4322038562024[/C][C]12.5677961437976[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]22.8792466318609[/C][C]-3.87924663186087[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.0338902827611[/C][C]-11.0338902827611[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.4674961633225[/C][C]-2.46749616332248[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.5756295114181[/C][C]10.4243704885819[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.1136995485328[/C][C]0.886300451467232[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]23.0877801178341[/C][C]6.91221988216585[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.073369677065[/C][C]1.92663032293499[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.4908251886057[/C][C]4.50917481139427[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.5051176759053[/C][C]3.49488232409466[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7601530313896[/C][C]-9.76015303138962[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.6443090003536[/C][C]-1.64430900035360[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.5370492704958[/C][C]-2.53704927049576[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.9377313025900[/C][C]5.06226869740997[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6175014577243[/C][C]-2.61750145772434[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.8646394460410[/C][C]4.13536055395897[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7319285749601[/C][C]0.268071425039861[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.8476069466243[/C][C]-1.84760694662434[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.0434165598231[/C][C]2.95658344017692[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.8597333926554[/C][C]0.140266607344566[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.8550198976718[/C][C]-0.855019897671838[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.9508811919658[/C][C]-7.95088119196579[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.1965037434143[/C][C]0.803496256585704[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.6886416630847[/C][C]1.31135833691528[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.2954140701055[/C][C]4.70458592989447[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.3326243716318[/C][C]-3.3326243716318[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1873143264118[/C][C]-1.18731432641177[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.2020388132927[/C][C]0.797961186707256[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.9063046849835[/C][C]0.093695315016476[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.9157779289149[/C][C]2.08422207108510[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.3018849084882[/C][C]5.69811509151183[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.8551287876824[/C][C]4.14487121231755[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.5028282033594[/C][C]1.49717179664060[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.9522887480601[/C][C]3.04771125193990[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.472438197516[/C][C]0.527561802483991[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.2874647056108[/C][C]-1.28746470561081[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4645212040962[/C][C]-1.46452120409623[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.6425371514452[/C][C]-1.64253715144523[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]22.217150234481[/C][C]0.782849765518977[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.9744599375139[/C][C]4.02554006248606[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.6529471075685[/C][C]4.34705289243146[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.5455744278297[/C][C]4.45442557217025[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.3595410763598[/C][C]-0.359541076359750[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.6935475859109[/C][C]-2.69354758591087[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.6943965458432[/C][C]-1.69439654584321[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.4098746117709[/C][C]9.59012538822914[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.3106346801039[/C][C]0.68936531989613[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.2836350486841[/C][C]-3.28363504868414[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]21.0044477704288[/C][C]-12.0044477704288[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]18.0612276169349[/C][C]-2.06122761693494[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2966898433180[/C][C]-2.29668984331795[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.6269476477759[/C][C]-2.62694764777592[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.7319979920982[/C][C]0.268002007901782[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.6369512420149[/C][C]4.36304875798511[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.8759344009052[/C][C]7.12406559909482[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]19.1778709228727[/C][C]-5.17787092287272[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]27.0980959242537[/C][C]-5.09809592425374[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9019397751253[/C][C]-0.901939775125261[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.6813751580878[/C][C]-6.6813751580878[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]22.1919545613241[/C][C]-2.19195456132412[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.7608142090159[/C][C]-2.76081420901587[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]22.1258994239983[/C][C]-2.12589942399832[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.5527935376764[/C][C]-2.55279353767642[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.7888479793484[/C][C]7.21115202065161[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]24.0146988185677[/C][C]-2.01469881856772[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.7435243851904[/C][C]-0.743524385190442[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.4036823708245[/C][C]-2.40368237082452[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.381610908457[/C][C]0.618389091543015[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.3637695845882[/C][C]3.63623041541182[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.9214437785666[/C][C]-1.92144377856657[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.4147283663736[/C][C]-3.41472836637364[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.2764637604010[/C][C]-5.27646376040095[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.7078618855540[/C][C]-1.70786188555397[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.9760915015815[/C][C]-2.97609150158152[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.952242458675[/C][C]5.04775754132501[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.6220295065553[/C][C]2.37797049344472[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]30.0288532518421[/C][C]-6.02885325184213[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]22.3733372368221[/C][C]-1.37333723682208[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.7186060818219[/C][C]-4.71860608182192[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.7760626071577[/C][C]1.22393739284233[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.567572013391[/C][C]7.432427986609[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.3381789476296[/C][C]0.661821052370389[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.4271132095025[/C][C]1.57288679049751[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]19.2888597043427[/C][C]2.71114029565732[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.7667282598620[/C][C]-0.766728259862024[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.7200998654264[/C][C]0.279900134573619[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]27.0811321597216[/C][C]10.9188678402784[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.5906715514476[/C][C]2.40932844855245[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]26.0566974089159[/C][C]-6.05669740891588[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.4485304103310[/C][C]-1.44853041033102[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.9055766121567[/C][C]3.09442338784327[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.5396741854444[/C][C]-2.53967418544438[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.4518682878217[/C][C]4.54813171217832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&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
12424.5869751364713-0.586975136471266
22521.29871815743543.70128184256465
31722.3859726679103-5.38597266791033
41819.4454109385100-1.44541093850996
51819.0235142417146-1.0235142417146
61619.2495257409872-3.24952574098717
72020.5436429620918-0.543642962091784
81621.9567214062097-5.95672140620966
91821.9596456847955-3.95964568479549
101720.1583618178568-3.15836181785681
112321.93889798349571.06110201650434
123021.85577854055028.14422145944983
132314.73314737815758.26685262184252
141818.6032699809839-0.603269980983944
151521.3229006529394-6.32290065293937
161217.5861185544217-5.58611855442169
172119.05545929268981.94454070731015
181514.81900780842570.180992191574261
192019.45012869555080.549871304449198
203126.01940004615384.98059995384625
212724.86768457315592.13231542684406
223426.7505329508887.24946704911197
232119.28045609096921.71954390903085
243120.599736608392610.4002633916074
251919.0701646017546-0.0701646017546488
261619.9463310706595-3.94633107065954
272021.3206525792539-1.32065257925392
282117.99635612705803.00364387294205
292221.68367835586360.316321644136378
301719.2404660680385-2.24046606803853
312420.16455935666393.83544064333613
322530.1993051078831-5.19930510788305
332626.390560835707-0.390560835707015
342523.88307340885351.11692659114646
351722.7338898244853-5.73388982448527
363227.59685762230484.40314237769521
373323.41318769743459.5868123025655
381321.7416158659416-8.74161586594162
393227.59401972731614.40598027268393
402525.6331170331415-0.633117033141505
412926.89076322585002.10923677414995
422221.83203057377710.167969426222923
431817.05245924122000.947540758780045
441721.5731241031246-4.5731241031246
452022.0446692276097-2.04466922760975
461520.2957922888318-5.29579228883185
472021.9969914249524-1.99699142495244
483327.96109108648485.03890891351517
492921.99227117281827.00772882718179
502326.4609674131011-3.46096741310109
512623.10556753028852.89443246971152
521818.8307698752602-0.830769875260195
532018.66999979073461.33000020926542
541111.5812129333856-0.581212933385631
552828.8095440113788-0.809544011378844
562623.27350507189642.72649492810355
572222.2026118775359-0.202611877535904
581720.0774839975063-3.07748399750625
591215.4292884866592-3.42928848665917
601420.780061210624-6.78006121062399
611720.6763094340180-3.67630943401805
622121.261580521485-0.261580521484977
631922.9113260083569-3.91132600835689
641823.0268271622096-5.02682716220961
651017.8541098959786-7.8541098959786
662924.28460062767994.71539937232013
673118.432203856202412.5677961437976
681922.8792466318609-3.87924663186087
69920.0338902827611-11.0338902827611
702022.4674961633225-2.46749616332248
712817.575629511418110.4243704885819
721918.11369954853280.886300451467232
733023.08778011783416.91221988216585
742927.0733696770651.92663032293499
752621.49082518860574.50917481139427
762319.50511767590533.49488232409466
771322.7601530313896-9.76015303138962
782122.6443090003536-1.64430900035360
791921.5370492704958-2.53704927049576
802822.93773130259005.06226869740997
812325.6175014577243-2.61750145772434
821813.86463944604104.13536055395897
832120.73192857496010.268071425039861
842021.8476069466243-1.84760694662434
852320.04341655982312.95658344017692
862120.85973339265540.140266607344566
872121.8550198976718-0.855019897671838
881522.9508811919658-7.95088119196579
892827.19650374341430.803496256585704
901917.68864166308471.31135833691528
912621.29541407010554.70458592989447
921013.3326243716318-3.3326243716318
931617.1873143264118-1.18731432641177
942221.20203881329270.797961186707256
951918.90630468498350.093695315016476
963128.91577792891492.08422207108510
973125.30188490848825.69811509151183
982924.85512878768244.14487121231755
991917.50282820335941.49717179664060
1002218.95228874806013.04771125193990
1012322.4724381975160.527561802483991
1021516.2874647056108-1.28746470561081
1032021.4645212040962-1.46452120409623
1041819.6425371514452-1.64253715144523
1052322.2171502344810.782849765518977
1062520.97445993751394.02554006248606
1072116.65294710756854.34705289243146
1082419.54557442782974.45442557217025
1092525.3595410763598-0.359541076359750
1101719.6935475859109-2.69354758591087
1111314.6943965458432-1.69439654584321
1122818.40987461177099.59012538822914
1132120.31063468010390.68936531989613
1142528.2836350486841-3.28363504868414
115921.0044477704288-12.0044477704288
1161618.0612276169349-2.06122761693494
1171921.2966898433180-2.29668984331795
1181719.6269476477759-2.62694764777592
1192524.73199799209820.268002007901782
1202015.63695124201494.36304875798511
1212921.87593440090527.12406559909482
1221419.1778709228727-5.17787092287272
1232227.0980959242537-5.09809592425374
1241515.9019397751253-0.901939775125261
1251925.6813751580878-6.6813751580878
1262022.1919545613241-2.19195456132412
1271517.7608142090159-2.76081420901587
1282022.1258994239983-2.12589942399832
1291820.5527935376764-2.55279353767642
1303325.78884797934847.21115202065161
1312224.0146988185677-2.01469881856772
1321616.7435243851904-0.743524385190442
1331719.4036823708245-2.40368237082452
1341615.3816109084570.618389091543015
1352117.36376958458823.63623041541182
1362627.9214437785666-1.92144377856657
1371821.4147283663736-3.41472836637364
1381823.2764637604010-5.27646376040095
1391718.7078618855540-1.70786188555397
1402224.9760915015815-2.97609150158152
1413024.9522424586755.04775754132501
1423027.62202950655532.37797049344472
1432430.0288532518421-6.02885325184213
1442122.3733372368221-1.37333723682208
1452125.7186060818219-4.71860608182192
1462927.77606260715771.22393739284233
1473123.5675720133917.432427986609
1482019.33817894762960.661821052370389
1491614.42711320950251.57288679049751
1502219.28885970434272.71114029565732
1512020.7667282598620-0.766728259862024
1522827.72009986542640.279900134573619
1533827.081132159721610.9188678402784
1542219.59067155144762.40932844855245
1552026.0566974089159-6.05669740891588
1561718.4485304103310-1.44853041033102
1572824.90557661215673.09442338784327
1582224.5396741854444-2.53967418544438
1593126.45186828782174.54813171217832







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09861376610248360.1972275322049670.901386233897516
110.3549755718872730.7099511437745470.645024428112727
120.7492056729243740.5015886541512530.250794327075626
130.7439942746184630.5120114507630750.256005725381537
140.727142351575680.5457152968486410.272857648424320
150.6898060323229990.6203879353540020.310193967677001
160.6075205230409110.7849589539181780.392479476959089
170.5529639770054770.8940720459890470.447036022994523
180.5244892972136250.951021405572750.475510702786375
190.4697051197851490.9394102395702980.530294880214851
200.4986893216137590.9973786432275170.501310678386241
210.4311761806996560.8623523613993120.568823819300344
220.4194510925287390.8389021850574790.58054890747126
230.3769374835137510.7538749670275010.62306251648625
240.5309343490541260.9381313018917470.469065650945874
250.4887782716235480.9775565432470950.511221728376452
260.505724304437270.988551391125460.49427569556273
270.4383934621962860.8767869243925720.561606537803714
280.3793593171190680.7587186342381360.620640682880932
290.3178232475813530.6356464951627060.682176752418647
300.2780751083641660.5561502167283310.721924891635834
310.2696041099587250.5392082199174490.730395890041275
320.3990065914215260.7980131828430520.600993408578474
330.3432220705289270.6864441410578540.656777929471073
340.3136417154916670.6272834309833340.686358284508333
350.3567734065587750.713546813117550.643226593441225
360.3239982756858640.6479965513717290.676001724314136
370.4385551926157250.877110385231450.561444807384275
380.7456244379030740.5087511241938520.254375562096926
390.72998173480890.5400365303821990.270018265191100
400.6939340840255190.6121318319489610.306065915974481
410.6567229917388230.6865540165223550.343277008261177
420.6096354860561080.7807290278877840.390364513943892
430.5594323516939850.881135296612030.440567648306015
440.5527989117313380.8944021765373240.447201088268662
450.5401932485095580.9196135029808840.459806751490442
460.5841750100225660.8316499799548680.415824989977434
470.5471175912121250.905764817575750.452882408787875
480.5370116247742380.9259767504515230.462988375225762
490.5896696370225960.8206607259548070.410330362977404
500.568422033221750.86315593355650.43157796677825
510.5381782265344350.923643546931130.461821773465565
520.4910579843459150.982115968691830.508942015654085
530.4455369726952120.8910739453904240.554463027304788
540.4049352117232150.809870423446430.595064788276785
550.3665446293371980.7330892586743960.633455370662802
560.3357626061080490.6715252122160980.664237393891951
570.2927338204310590.5854676408621190.70726617956894
580.2673715418270750.534743083654150.732628458172925
590.2495105008442370.4990210016884750.750489499155763
600.3086708782982850.6173417565965710.691329121701715
610.2940977137318810.5881954274637620.70590228626812
620.2550995796658190.5101991593316380.744900420334181
630.2394300655797740.4788601311595480.760569934420226
640.2656442274263840.5312884548527680.734355772573616
650.3303643276374650.660728655274930.669635672362535
660.3389668445756520.6779336891513050.661033155424348
670.6838004982063630.6323990035872740.316199501793637
680.6733154810450920.6533690379098170.326684518954908
690.8411901611838290.3176196776323420.158809838816171
700.8191383275213730.3617233449572540.180861672478627
710.9308517380612750.138296523877450.069148261938725
720.9146569017311740.1706861965376520.0853430982688258
730.9382829337850670.1234341324298660.061717066214933
740.9263861039557440.1472277920885120.0736138960442558
750.9276281033889790.1447437932220420.0723718966110212
760.9215329032608190.1569341934783630.0784670967391814
770.9693287817645060.06134243647098850.0306712182354943
780.9616673404392940.07666531912141290.0383326595607064
790.954107486897140.09178502620571850.0458925131028593
800.9566019515869030.08679609682619440.0433980484130972
810.948666401436410.1026671971271790.0513335985635893
820.9476745572405540.1046508855188910.0523254427594454
830.933876863951650.1322462720966990.0661231360483494
840.9207784649871150.1584430700257710.0792215350128854
850.9112558434902310.1774883130195370.0887441565097685
860.8936859551560790.2126280896878430.106314044843921
870.8716472922200250.2567054155599490.128352707779975
880.9193797757638680.1612404484722640.0806202242361318
890.9026115625421930.1947768749156140.097388437457807
900.881765243633470.2364695127330590.118234756366529
910.881397413989920.2372051720201590.118602586010079
920.8713980396681860.2572039206636290.128601960331814
930.8491324834995320.3017350330009370.150867516500468
940.8204223519995750.3591552960008510.179577648000425
950.7870177432280470.4259645135439060.212982256771953
960.7557157175194140.4885685649611710.244284282480585
970.7732214987018420.4535570025963170.226778501298158
980.7699916228250970.4600167543498060.230008377174903
990.7343600261062780.5312799477874450.265639973893722
1000.7122853425320170.5754293149359650.287714657467983
1010.6738041808869680.6523916382260630.326195819113032
1020.6328806957375120.7342386085249760.367119304262488
1030.5917954923026490.8164090153947020.408204507697351
1040.5486312193378190.9027375613243630.451368780662181
1050.5063404886872430.9873190226255150.493659511312757
1060.4926916343885430.9853832687770850.507308365611457
1070.5000020890271860.9999958219456280.499997910972814
1080.5393036213969160.9213927572061670.460696378603084
1090.5018653437622560.9962693124754890.498134656237744
1100.460704289802820.921408579605640.53929571019718
1110.4137439132316120.8274878264632240.586256086768388
1120.74061884066950.5187623186610.2593811593305
1130.7755791622930020.4488416754139970.224420837706998
1140.7511924937027930.4976150125944140.248807506297207
1150.8754598640234440.2490802719531120.124540135976556
1160.8470931873958190.3058136252083630.152906812604181
1170.8176305257582430.3647389484835130.182369474241757
1180.784169778505270.4316604429894610.215830221494730
1190.756239619419450.4875207611611010.243760380580550
1200.8049705765881380.3900588468237250.195029423411862
1210.8840595190699330.2318809618601330.115940480930067
1220.8652497380235410.2695005239529180.134750261976459
1230.8449103247330520.3101793505338970.155089675266948
1240.8069001298555650.3861997402888710.193099870144435
1250.8115641803558520.3768716392882950.188435819644148
1260.7682122314611830.4635755370776330.231787768538817
1270.7368674094556410.5262651810887170.263132590544359
1280.6903307560793210.6193384878413580.309669243920679
1290.6450159225602050.709968154879590.354984077439795
1300.7658939350196670.4682121299606670.234106064980333
1310.7135802827241450.572839434551710.286419717275855
1320.6534074306808310.6931851386383370.346592569319169
1330.6153801898032810.7692396203934380.384619810196719
1340.5456903414100290.9086193171799420.454309658589971
1350.5710007073765480.8579985852469040.428999292623452
1360.5005357463897360.9989285072205280.499464253610264
1370.4534084939071960.9068169878143920.546591506092804
1380.4701866307889560.9403732615779110.529813369211044
1390.3962812666779530.7925625333559060.603718733322047
1400.3228377971210870.6456755942421730.677162202878913
1410.4259405330405810.8518810660811620.574059466959419
1420.3759469279344000.7518938558688010.6240530720656
1430.3033504922152390.6067009844304790.696649507784761
1440.2271858956990660.4543717913981320.772814104300934
1450.3335255447779450.667051089555890.666474455222055
1460.2541505742180360.5083011484360710.745849425781964
1470.2528706232855880.5057412465711750.747129376714412
1480.1596123012853570.3192246025707130.840387698714643
1490.08634341410370050.1726868282074010.9136565858963

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0986137661024836 & 0.197227532204967 & 0.901386233897516 \tabularnewline
11 & 0.354975571887273 & 0.709951143774547 & 0.645024428112727 \tabularnewline
12 & 0.749205672924374 & 0.501588654151253 & 0.250794327075626 \tabularnewline
13 & 0.743994274618463 & 0.512011450763075 & 0.256005725381537 \tabularnewline
14 & 0.72714235157568 & 0.545715296848641 & 0.272857648424320 \tabularnewline
15 & 0.689806032322999 & 0.620387935354002 & 0.310193967677001 \tabularnewline
16 & 0.607520523040911 & 0.784958953918178 & 0.392479476959089 \tabularnewline
17 & 0.552963977005477 & 0.894072045989047 & 0.447036022994523 \tabularnewline
18 & 0.524489297213625 & 0.95102140557275 & 0.475510702786375 \tabularnewline
19 & 0.469705119785149 & 0.939410239570298 & 0.530294880214851 \tabularnewline
20 & 0.498689321613759 & 0.997378643227517 & 0.501310678386241 \tabularnewline
21 & 0.431176180699656 & 0.862352361399312 & 0.568823819300344 \tabularnewline
22 & 0.419451092528739 & 0.838902185057479 & 0.58054890747126 \tabularnewline
23 & 0.376937483513751 & 0.753874967027501 & 0.62306251648625 \tabularnewline
24 & 0.530934349054126 & 0.938131301891747 & 0.469065650945874 \tabularnewline
25 & 0.488778271623548 & 0.977556543247095 & 0.511221728376452 \tabularnewline
26 & 0.50572430443727 & 0.98855139112546 & 0.49427569556273 \tabularnewline
27 & 0.438393462196286 & 0.876786924392572 & 0.561606537803714 \tabularnewline
28 & 0.379359317119068 & 0.758718634238136 & 0.620640682880932 \tabularnewline
29 & 0.317823247581353 & 0.635646495162706 & 0.682176752418647 \tabularnewline
30 & 0.278075108364166 & 0.556150216728331 & 0.721924891635834 \tabularnewline
31 & 0.269604109958725 & 0.539208219917449 & 0.730395890041275 \tabularnewline
32 & 0.399006591421526 & 0.798013182843052 & 0.600993408578474 \tabularnewline
33 & 0.343222070528927 & 0.686444141057854 & 0.656777929471073 \tabularnewline
34 & 0.313641715491667 & 0.627283430983334 & 0.686358284508333 \tabularnewline
35 & 0.356773406558775 & 0.71354681311755 & 0.643226593441225 \tabularnewline
36 & 0.323998275685864 & 0.647996551371729 & 0.676001724314136 \tabularnewline
37 & 0.438555192615725 & 0.87711038523145 & 0.561444807384275 \tabularnewline
38 & 0.745624437903074 & 0.508751124193852 & 0.254375562096926 \tabularnewline
39 & 0.7299817348089 & 0.540036530382199 & 0.270018265191100 \tabularnewline
40 & 0.693934084025519 & 0.612131831948961 & 0.306065915974481 \tabularnewline
41 & 0.656722991738823 & 0.686554016522355 & 0.343277008261177 \tabularnewline
42 & 0.609635486056108 & 0.780729027887784 & 0.390364513943892 \tabularnewline
43 & 0.559432351693985 & 0.88113529661203 & 0.440567648306015 \tabularnewline
44 & 0.552798911731338 & 0.894402176537324 & 0.447201088268662 \tabularnewline
45 & 0.540193248509558 & 0.919613502980884 & 0.459806751490442 \tabularnewline
46 & 0.584175010022566 & 0.831649979954868 & 0.415824989977434 \tabularnewline
47 & 0.547117591212125 & 0.90576481757575 & 0.452882408787875 \tabularnewline
48 & 0.537011624774238 & 0.925976750451523 & 0.462988375225762 \tabularnewline
49 & 0.589669637022596 & 0.820660725954807 & 0.410330362977404 \tabularnewline
50 & 0.56842203322175 & 0.8631559335565 & 0.43157796677825 \tabularnewline
51 & 0.538178226534435 & 0.92364354693113 & 0.461821773465565 \tabularnewline
52 & 0.491057984345915 & 0.98211596869183 & 0.508942015654085 \tabularnewline
53 & 0.445536972695212 & 0.891073945390424 & 0.554463027304788 \tabularnewline
54 & 0.404935211723215 & 0.80987042344643 & 0.595064788276785 \tabularnewline
55 & 0.366544629337198 & 0.733089258674396 & 0.633455370662802 \tabularnewline
56 & 0.335762606108049 & 0.671525212216098 & 0.664237393891951 \tabularnewline
57 & 0.292733820431059 & 0.585467640862119 & 0.70726617956894 \tabularnewline
58 & 0.267371541827075 & 0.53474308365415 & 0.732628458172925 \tabularnewline
59 & 0.249510500844237 & 0.499021001688475 & 0.750489499155763 \tabularnewline
60 & 0.308670878298285 & 0.617341756596571 & 0.691329121701715 \tabularnewline
61 & 0.294097713731881 & 0.588195427463762 & 0.70590228626812 \tabularnewline
62 & 0.255099579665819 & 0.510199159331638 & 0.744900420334181 \tabularnewline
63 & 0.239430065579774 & 0.478860131159548 & 0.760569934420226 \tabularnewline
64 & 0.265644227426384 & 0.531288454852768 & 0.734355772573616 \tabularnewline
65 & 0.330364327637465 & 0.66072865527493 & 0.669635672362535 \tabularnewline
66 & 0.338966844575652 & 0.677933689151305 & 0.661033155424348 \tabularnewline
67 & 0.683800498206363 & 0.632399003587274 & 0.316199501793637 \tabularnewline
68 & 0.673315481045092 & 0.653369037909817 & 0.326684518954908 \tabularnewline
69 & 0.841190161183829 & 0.317619677632342 & 0.158809838816171 \tabularnewline
70 & 0.819138327521373 & 0.361723344957254 & 0.180861672478627 \tabularnewline
71 & 0.930851738061275 & 0.13829652387745 & 0.069148261938725 \tabularnewline
72 & 0.914656901731174 & 0.170686196537652 & 0.0853430982688258 \tabularnewline
73 & 0.938282933785067 & 0.123434132429866 & 0.061717066214933 \tabularnewline
74 & 0.926386103955744 & 0.147227792088512 & 0.0736138960442558 \tabularnewline
75 & 0.927628103388979 & 0.144743793222042 & 0.0723718966110212 \tabularnewline
76 & 0.921532903260819 & 0.156934193478363 & 0.0784670967391814 \tabularnewline
77 & 0.969328781764506 & 0.0613424364709885 & 0.0306712182354943 \tabularnewline
78 & 0.961667340439294 & 0.0766653191214129 & 0.0383326595607064 \tabularnewline
79 & 0.95410748689714 & 0.0917850262057185 & 0.0458925131028593 \tabularnewline
80 & 0.956601951586903 & 0.0867960968261944 & 0.0433980484130972 \tabularnewline
81 & 0.94866640143641 & 0.102667197127179 & 0.0513335985635893 \tabularnewline
82 & 0.947674557240554 & 0.104650885518891 & 0.0523254427594454 \tabularnewline
83 & 0.93387686395165 & 0.132246272096699 & 0.0661231360483494 \tabularnewline
84 & 0.920778464987115 & 0.158443070025771 & 0.0792215350128854 \tabularnewline
85 & 0.911255843490231 & 0.177488313019537 & 0.0887441565097685 \tabularnewline
86 & 0.893685955156079 & 0.212628089687843 & 0.106314044843921 \tabularnewline
87 & 0.871647292220025 & 0.256705415559949 & 0.128352707779975 \tabularnewline
88 & 0.919379775763868 & 0.161240448472264 & 0.0806202242361318 \tabularnewline
89 & 0.902611562542193 & 0.194776874915614 & 0.097388437457807 \tabularnewline
90 & 0.88176524363347 & 0.236469512733059 & 0.118234756366529 \tabularnewline
91 & 0.88139741398992 & 0.237205172020159 & 0.118602586010079 \tabularnewline
92 & 0.871398039668186 & 0.257203920663629 & 0.128601960331814 \tabularnewline
93 & 0.849132483499532 & 0.301735033000937 & 0.150867516500468 \tabularnewline
94 & 0.820422351999575 & 0.359155296000851 & 0.179577648000425 \tabularnewline
95 & 0.787017743228047 & 0.425964513543906 & 0.212982256771953 \tabularnewline
96 & 0.755715717519414 & 0.488568564961171 & 0.244284282480585 \tabularnewline
97 & 0.773221498701842 & 0.453557002596317 & 0.226778501298158 \tabularnewline
98 & 0.769991622825097 & 0.460016754349806 & 0.230008377174903 \tabularnewline
99 & 0.734360026106278 & 0.531279947787445 & 0.265639973893722 \tabularnewline
100 & 0.712285342532017 & 0.575429314935965 & 0.287714657467983 \tabularnewline
101 & 0.673804180886968 & 0.652391638226063 & 0.326195819113032 \tabularnewline
102 & 0.632880695737512 & 0.734238608524976 & 0.367119304262488 \tabularnewline
103 & 0.591795492302649 & 0.816409015394702 & 0.408204507697351 \tabularnewline
104 & 0.548631219337819 & 0.902737561324363 & 0.451368780662181 \tabularnewline
105 & 0.506340488687243 & 0.987319022625515 & 0.493659511312757 \tabularnewline
106 & 0.492691634388543 & 0.985383268777085 & 0.507308365611457 \tabularnewline
107 & 0.500002089027186 & 0.999995821945628 & 0.499997910972814 \tabularnewline
108 & 0.539303621396916 & 0.921392757206167 & 0.460696378603084 \tabularnewline
109 & 0.501865343762256 & 0.996269312475489 & 0.498134656237744 \tabularnewline
110 & 0.46070428980282 & 0.92140857960564 & 0.53929571019718 \tabularnewline
111 & 0.413743913231612 & 0.827487826463224 & 0.586256086768388 \tabularnewline
112 & 0.7406188406695 & 0.518762318661 & 0.2593811593305 \tabularnewline
113 & 0.775579162293002 & 0.448841675413997 & 0.224420837706998 \tabularnewline
114 & 0.751192493702793 & 0.497615012594414 & 0.248807506297207 \tabularnewline
115 & 0.875459864023444 & 0.249080271953112 & 0.124540135976556 \tabularnewline
116 & 0.847093187395819 & 0.305813625208363 & 0.152906812604181 \tabularnewline
117 & 0.817630525758243 & 0.364738948483513 & 0.182369474241757 \tabularnewline
118 & 0.78416977850527 & 0.431660442989461 & 0.215830221494730 \tabularnewline
119 & 0.75623961941945 & 0.487520761161101 & 0.243760380580550 \tabularnewline
120 & 0.804970576588138 & 0.390058846823725 & 0.195029423411862 \tabularnewline
121 & 0.884059519069933 & 0.231880961860133 & 0.115940480930067 \tabularnewline
122 & 0.865249738023541 & 0.269500523952918 & 0.134750261976459 \tabularnewline
123 & 0.844910324733052 & 0.310179350533897 & 0.155089675266948 \tabularnewline
124 & 0.806900129855565 & 0.386199740288871 & 0.193099870144435 \tabularnewline
125 & 0.811564180355852 & 0.376871639288295 & 0.188435819644148 \tabularnewline
126 & 0.768212231461183 & 0.463575537077633 & 0.231787768538817 \tabularnewline
127 & 0.736867409455641 & 0.526265181088717 & 0.263132590544359 \tabularnewline
128 & 0.690330756079321 & 0.619338487841358 & 0.309669243920679 \tabularnewline
129 & 0.645015922560205 & 0.70996815487959 & 0.354984077439795 \tabularnewline
130 & 0.765893935019667 & 0.468212129960667 & 0.234106064980333 \tabularnewline
131 & 0.713580282724145 & 0.57283943455171 & 0.286419717275855 \tabularnewline
132 & 0.653407430680831 & 0.693185138638337 & 0.346592569319169 \tabularnewline
133 & 0.615380189803281 & 0.769239620393438 & 0.384619810196719 \tabularnewline
134 & 0.545690341410029 & 0.908619317179942 & 0.454309658589971 \tabularnewline
135 & 0.571000707376548 & 0.857998585246904 & 0.428999292623452 \tabularnewline
136 & 0.500535746389736 & 0.998928507220528 & 0.499464253610264 \tabularnewline
137 & 0.453408493907196 & 0.906816987814392 & 0.546591506092804 \tabularnewline
138 & 0.470186630788956 & 0.940373261577911 & 0.529813369211044 \tabularnewline
139 & 0.396281266677953 & 0.792562533355906 & 0.603718733322047 \tabularnewline
140 & 0.322837797121087 & 0.645675594242173 & 0.677162202878913 \tabularnewline
141 & 0.425940533040581 & 0.851881066081162 & 0.574059466959419 \tabularnewline
142 & 0.375946927934400 & 0.751893855868801 & 0.6240530720656 \tabularnewline
143 & 0.303350492215239 & 0.606700984430479 & 0.696649507784761 \tabularnewline
144 & 0.227185895699066 & 0.454371791398132 & 0.772814104300934 \tabularnewline
145 & 0.333525544777945 & 0.66705108955589 & 0.666474455222055 \tabularnewline
146 & 0.254150574218036 & 0.508301148436071 & 0.745849425781964 \tabularnewline
147 & 0.252870623285588 & 0.505741246571175 & 0.747129376714412 \tabularnewline
148 & 0.159612301285357 & 0.319224602570713 & 0.840387698714643 \tabularnewline
149 & 0.0863434141037005 & 0.172686828207401 & 0.9136565858963 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0986137661024836[/C][C]0.197227532204967[/C][C]0.901386233897516[/C][/ROW]
[ROW][C]11[/C][C]0.354975571887273[/C][C]0.709951143774547[/C][C]0.645024428112727[/C][/ROW]
[ROW][C]12[/C][C]0.749205672924374[/C][C]0.501588654151253[/C][C]0.250794327075626[/C][/ROW]
[ROW][C]13[/C][C]0.743994274618463[/C][C]0.512011450763075[/C][C]0.256005725381537[/C][/ROW]
[ROW][C]14[/C][C]0.72714235157568[/C][C]0.545715296848641[/C][C]0.272857648424320[/C][/ROW]
[ROW][C]15[/C][C]0.689806032322999[/C][C]0.620387935354002[/C][C]0.310193967677001[/C][/ROW]
[ROW][C]16[/C][C]0.607520523040911[/C][C]0.784958953918178[/C][C]0.392479476959089[/C][/ROW]
[ROW][C]17[/C][C]0.552963977005477[/C][C]0.894072045989047[/C][C]0.447036022994523[/C][/ROW]
[ROW][C]18[/C][C]0.524489297213625[/C][C]0.95102140557275[/C][C]0.475510702786375[/C][/ROW]
[ROW][C]19[/C][C]0.469705119785149[/C][C]0.939410239570298[/C][C]0.530294880214851[/C][/ROW]
[ROW][C]20[/C][C]0.498689321613759[/C][C]0.997378643227517[/C][C]0.501310678386241[/C][/ROW]
[ROW][C]21[/C][C]0.431176180699656[/C][C]0.862352361399312[/C][C]0.568823819300344[/C][/ROW]
[ROW][C]22[/C][C]0.419451092528739[/C][C]0.838902185057479[/C][C]0.58054890747126[/C][/ROW]
[ROW][C]23[/C][C]0.376937483513751[/C][C]0.753874967027501[/C][C]0.62306251648625[/C][/ROW]
[ROW][C]24[/C][C]0.530934349054126[/C][C]0.938131301891747[/C][C]0.469065650945874[/C][/ROW]
[ROW][C]25[/C][C]0.488778271623548[/C][C]0.977556543247095[/C][C]0.511221728376452[/C][/ROW]
[ROW][C]26[/C][C]0.50572430443727[/C][C]0.98855139112546[/C][C]0.49427569556273[/C][/ROW]
[ROW][C]27[/C][C]0.438393462196286[/C][C]0.876786924392572[/C][C]0.561606537803714[/C][/ROW]
[ROW][C]28[/C][C]0.379359317119068[/C][C]0.758718634238136[/C][C]0.620640682880932[/C][/ROW]
[ROW][C]29[/C][C]0.317823247581353[/C][C]0.635646495162706[/C][C]0.682176752418647[/C][/ROW]
[ROW][C]30[/C][C]0.278075108364166[/C][C]0.556150216728331[/C][C]0.721924891635834[/C][/ROW]
[ROW][C]31[/C][C]0.269604109958725[/C][C]0.539208219917449[/C][C]0.730395890041275[/C][/ROW]
[ROW][C]32[/C][C]0.399006591421526[/C][C]0.798013182843052[/C][C]0.600993408578474[/C][/ROW]
[ROW][C]33[/C][C]0.343222070528927[/C][C]0.686444141057854[/C][C]0.656777929471073[/C][/ROW]
[ROW][C]34[/C][C]0.313641715491667[/C][C]0.627283430983334[/C][C]0.686358284508333[/C][/ROW]
[ROW][C]35[/C][C]0.356773406558775[/C][C]0.71354681311755[/C][C]0.643226593441225[/C][/ROW]
[ROW][C]36[/C][C]0.323998275685864[/C][C]0.647996551371729[/C][C]0.676001724314136[/C][/ROW]
[ROW][C]37[/C][C]0.438555192615725[/C][C]0.87711038523145[/C][C]0.561444807384275[/C][/ROW]
[ROW][C]38[/C][C]0.745624437903074[/C][C]0.508751124193852[/C][C]0.254375562096926[/C][/ROW]
[ROW][C]39[/C][C]0.7299817348089[/C][C]0.540036530382199[/C][C]0.270018265191100[/C][/ROW]
[ROW][C]40[/C][C]0.693934084025519[/C][C]0.612131831948961[/C][C]0.306065915974481[/C][/ROW]
[ROW][C]41[/C][C]0.656722991738823[/C][C]0.686554016522355[/C][C]0.343277008261177[/C][/ROW]
[ROW][C]42[/C][C]0.609635486056108[/C][C]0.780729027887784[/C][C]0.390364513943892[/C][/ROW]
[ROW][C]43[/C][C]0.559432351693985[/C][C]0.88113529661203[/C][C]0.440567648306015[/C][/ROW]
[ROW][C]44[/C][C]0.552798911731338[/C][C]0.894402176537324[/C][C]0.447201088268662[/C][/ROW]
[ROW][C]45[/C][C]0.540193248509558[/C][C]0.919613502980884[/C][C]0.459806751490442[/C][/ROW]
[ROW][C]46[/C][C]0.584175010022566[/C][C]0.831649979954868[/C][C]0.415824989977434[/C][/ROW]
[ROW][C]47[/C][C]0.547117591212125[/C][C]0.90576481757575[/C][C]0.452882408787875[/C][/ROW]
[ROW][C]48[/C][C]0.537011624774238[/C][C]0.925976750451523[/C][C]0.462988375225762[/C][/ROW]
[ROW][C]49[/C][C]0.589669637022596[/C][C]0.820660725954807[/C][C]0.410330362977404[/C][/ROW]
[ROW][C]50[/C][C]0.56842203322175[/C][C]0.8631559335565[/C][C]0.43157796677825[/C][/ROW]
[ROW][C]51[/C][C]0.538178226534435[/C][C]0.92364354693113[/C][C]0.461821773465565[/C][/ROW]
[ROW][C]52[/C][C]0.491057984345915[/C][C]0.98211596869183[/C][C]0.508942015654085[/C][/ROW]
[ROW][C]53[/C][C]0.445536972695212[/C][C]0.891073945390424[/C][C]0.554463027304788[/C][/ROW]
[ROW][C]54[/C][C]0.404935211723215[/C][C]0.80987042344643[/C][C]0.595064788276785[/C][/ROW]
[ROW][C]55[/C][C]0.366544629337198[/C][C]0.733089258674396[/C][C]0.633455370662802[/C][/ROW]
[ROW][C]56[/C][C]0.335762606108049[/C][C]0.671525212216098[/C][C]0.664237393891951[/C][/ROW]
[ROW][C]57[/C][C]0.292733820431059[/C][C]0.585467640862119[/C][C]0.70726617956894[/C][/ROW]
[ROW][C]58[/C][C]0.267371541827075[/C][C]0.53474308365415[/C][C]0.732628458172925[/C][/ROW]
[ROW][C]59[/C][C]0.249510500844237[/C][C]0.499021001688475[/C][C]0.750489499155763[/C][/ROW]
[ROW][C]60[/C][C]0.308670878298285[/C][C]0.617341756596571[/C][C]0.691329121701715[/C][/ROW]
[ROW][C]61[/C][C]0.294097713731881[/C][C]0.588195427463762[/C][C]0.70590228626812[/C][/ROW]
[ROW][C]62[/C][C]0.255099579665819[/C][C]0.510199159331638[/C][C]0.744900420334181[/C][/ROW]
[ROW][C]63[/C][C]0.239430065579774[/C][C]0.478860131159548[/C][C]0.760569934420226[/C][/ROW]
[ROW][C]64[/C][C]0.265644227426384[/C][C]0.531288454852768[/C][C]0.734355772573616[/C][/ROW]
[ROW][C]65[/C][C]0.330364327637465[/C][C]0.66072865527493[/C][C]0.669635672362535[/C][/ROW]
[ROW][C]66[/C][C]0.338966844575652[/C][C]0.677933689151305[/C][C]0.661033155424348[/C][/ROW]
[ROW][C]67[/C][C]0.683800498206363[/C][C]0.632399003587274[/C][C]0.316199501793637[/C][/ROW]
[ROW][C]68[/C][C]0.673315481045092[/C][C]0.653369037909817[/C][C]0.326684518954908[/C][/ROW]
[ROW][C]69[/C][C]0.841190161183829[/C][C]0.317619677632342[/C][C]0.158809838816171[/C][/ROW]
[ROW][C]70[/C][C]0.819138327521373[/C][C]0.361723344957254[/C][C]0.180861672478627[/C][/ROW]
[ROW][C]71[/C][C]0.930851738061275[/C][C]0.13829652387745[/C][C]0.069148261938725[/C][/ROW]
[ROW][C]72[/C][C]0.914656901731174[/C][C]0.170686196537652[/C][C]0.0853430982688258[/C][/ROW]
[ROW][C]73[/C][C]0.938282933785067[/C][C]0.123434132429866[/C][C]0.061717066214933[/C][/ROW]
[ROW][C]74[/C][C]0.926386103955744[/C][C]0.147227792088512[/C][C]0.0736138960442558[/C][/ROW]
[ROW][C]75[/C][C]0.927628103388979[/C][C]0.144743793222042[/C][C]0.0723718966110212[/C][/ROW]
[ROW][C]76[/C][C]0.921532903260819[/C][C]0.156934193478363[/C][C]0.0784670967391814[/C][/ROW]
[ROW][C]77[/C][C]0.969328781764506[/C][C]0.0613424364709885[/C][C]0.0306712182354943[/C][/ROW]
[ROW][C]78[/C][C]0.961667340439294[/C][C]0.0766653191214129[/C][C]0.0383326595607064[/C][/ROW]
[ROW][C]79[/C][C]0.95410748689714[/C][C]0.0917850262057185[/C][C]0.0458925131028593[/C][/ROW]
[ROW][C]80[/C][C]0.956601951586903[/C][C]0.0867960968261944[/C][C]0.0433980484130972[/C][/ROW]
[ROW][C]81[/C][C]0.94866640143641[/C][C]0.102667197127179[/C][C]0.0513335985635893[/C][/ROW]
[ROW][C]82[/C][C]0.947674557240554[/C][C]0.104650885518891[/C][C]0.0523254427594454[/C][/ROW]
[ROW][C]83[/C][C]0.93387686395165[/C][C]0.132246272096699[/C][C]0.0661231360483494[/C][/ROW]
[ROW][C]84[/C][C]0.920778464987115[/C][C]0.158443070025771[/C][C]0.0792215350128854[/C][/ROW]
[ROW][C]85[/C][C]0.911255843490231[/C][C]0.177488313019537[/C][C]0.0887441565097685[/C][/ROW]
[ROW][C]86[/C][C]0.893685955156079[/C][C]0.212628089687843[/C][C]0.106314044843921[/C][/ROW]
[ROW][C]87[/C][C]0.871647292220025[/C][C]0.256705415559949[/C][C]0.128352707779975[/C][/ROW]
[ROW][C]88[/C][C]0.919379775763868[/C][C]0.161240448472264[/C][C]0.0806202242361318[/C][/ROW]
[ROW][C]89[/C][C]0.902611562542193[/C][C]0.194776874915614[/C][C]0.097388437457807[/C][/ROW]
[ROW][C]90[/C][C]0.88176524363347[/C][C]0.236469512733059[/C][C]0.118234756366529[/C][/ROW]
[ROW][C]91[/C][C]0.88139741398992[/C][C]0.237205172020159[/C][C]0.118602586010079[/C][/ROW]
[ROW][C]92[/C][C]0.871398039668186[/C][C]0.257203920663629[/C][C]0.128601960331814[/C][/ROW]
[ROW][C]93[/C][C]0.849132483499532[/C][C]0.301735033000937[/C][C]0.150867516500468[/C][/ROW]
[ROW][C]94[/C][C]0.820422351999575[/C][C]0.359155296000851[/C][C]0.179577648000425[/C][/ROW]
[ROW][C]95[/C][C]0.787017743228047[/C][C]0.425964513543906[/C][C]0.212982256771953[/C][/ROW]
[ROW][C]96[/C][C]0.755715717519414[/C][C]0.488568564961171[/C][C]0.244284282480585[/C][/ROW]
[ROW][C]97[/C][C]0.773221498701842[/C][C]0.453557002596317[/C][C]0.226778501298158[/C][/ROW]
[ROW][C]98[/C][C]0.769991622825097[/C][C]0.460016754349806[/C][C]0.230008377174903[/C][/ROW]
[ROW][C]99[/C][C]0.734360026106278[/C][C]0.531279947787445[/C][C]0.265639973893722[/C][/ROW]
[ROW][C]100[/C][C]0.712285342532017[/C][C]0.575429314935965[/C][C]0.287714657467983[/C][/ROW]
[ROW][C]101[/C][C]0.673804180886968[/C][C]0.652391638226063[/C][C]0.326195819113032[/C][/ROW]
[ROW][C]102[/C][C]0.632880695737512[/C][C]0.734238608524976[/C][C]0.367119304262488[/C][/ROW]
[ROW][C]103[/C][C]0.591795492302649[/C][C]0.816409015394702[/C][C]0.408204507697351[/C][/ROW]
[ROW][C]104[/C][C]0.548631219337819[/C][C]0.902737561324363[/C][C]0.451368780662181[/C][/ROW]
[ROW][C]105[/C][C]0.506340488687243[/C][C]0.987319022625515[/C][C]0.493659511312757[/C][/ROW]
[ROW][C]106[/C][C]0.492691634388543[/C][C]0.985383268777085[/C][C]0.507308365611457[/C][/ROW]
[ROW][C]107[/C][C]0.500002089027186[/C][C]0.999995821945628[/C][C]0.499997910972814[/C][/ROW]
[ROW][C]108[/C][C]0.539303621396916[/C][C]0.921392757206167[/C][C]0.460696378603084[/C][/ROW]
[ROW][C]109[/C][C]0.501865343762256[/C][C]0.996269312475489[/C][C]0.498134656237744[/C][/ROW]
[ROW][C]110[/C][C]0.46070428980282[/C][C]0.92140857960564[/C][C]0.53929571019718[/C][/ROW]
[ROW][C]111[/C][C]0.413743913231612[/C][C]0.827487826463224[/C][C]0.586256086768388[/C][/ROW]
[ROW][C]112[/C][C]0.7406188406695[/C][C]0.518762318661[/C][C]0.2593811593305[/C][/ROW]
[ROW][C]113[/C][C]0.775579162293002[/C][C]0.448841675413997[/C][C]0.224420837706998[/C][/ROW]
[ROW][C]114[/C][C]0.751192493702793[/C][C]0.497615012594414[/C][C]0.248807506297207[/C][/ROW]
[ROW][C]115[/C][C]0.875459864023444[/C][C]0.249080271953112[/C][C]0.124540135976556[/C][/ROW]
[ROW][C]116[/C][C]0.847093187395819[/C][C]0.305813625208363[/C][C]0.152906812604181[/C][/ROW]
[ROW][C]117[/C][C]0.817630525758243[/C][C]0.364738948483513[/C][C]0.182369474241757[/C][/ROW]
[ROW][C]118[/C][C]0.78416977850527[/C][C]0.431660442989461[/C][C]0.215830221494730[/C][/ROW]
[ROW][C]119[/C][C]0.75623961941945[/C][C]0.487520761161101[/C][C]0.243760380580550[/C][/ROW]
[ROW][C]120[/C][C]0.804970576588138[/C][C]0.390058846823725[/C][C]0.195029423411862[/C][/ROW]
[ROW][C]121[/C][C]0.884059519069933[/C][C]0.231880961860133[/C][C]0.115940480930067[/C][/ROW]
[ROW][C]122[/C][C]0.865249738023541[/C][C]0.269500523952918[/C][C]0.134750261976459[/C][/ROW]
[ROW][C]123[/C][C]0.844910324733052[/C][C]0.310179350533897[/C][C]0.155089675266948[/C][/ROW]
[ROW][C]124[/C][C]0.806900129855565[/C][C]0.386199740288871[/C][C]0.193099870144435[/C][/ROW]
[ROW][C]125[/C][C]0.811564180355852[/C][C]0.376871639288295[/C][C]0.188435819644148[/C][/ROW]
[ROW][C]126[/C][C]0.768212231461183[/C][C]0.463575537077633[/C][C]0.231787768538817[/C][/ROW]
[ROW][C]127[/C][C]0.736867409455641[/C][C]0.526265181088717[/C][C]0.263132590544359[/C][/ROW]
[ROW][C]128[/C][C]0.690330756079321[/C][C]0.619338487841358[/C][C]0.309669243920679[/C][/ROW]
[ROW][C]129[/C][C]0.645015922560205[/C][C]0.70996815487959[/C][C]0.354984077439795[/C][/ROW]
[ROW][C]130[/C][C]0.765893935019667[/C][C]0.468212129960667[/C][C]0.234106064980333[/C][/ROW]
[ROW][C]131[/C][C]0.713580282724145[/C][C]0.57283943455171[/C][C]0.286419717275855[/C][/ROW]
[ROW][C]132[/C][C]0.653407430680831[/C][C]0.693185138638337[/C][C]0.346592569319169[/C][/ROW]
[ROW][C]133[/C][C]0.615380189803281[/C][C]0.769239620393438[/C][C]0.384619810196719[/C][/ROW]
[ROW][C]134[/C][C]0.545690341410029[/C][C]0.908619317179942[/C][C]0.454309658589971[/C][/ROW]
[ROW][C]135[/C][C]0.571000707376548[/C][C]0.857998585246904[/C][C]0.428999292623452[/C][/ROW]
[ROW][C]136[/C][C]0.500535746389736[/C][C]0.998928507220528[/C][C]0.499464253610264[/C][/ROW]
[ROW][C]137[/C][C]0.453408493907196[/C][C]0.906816987814392[/C][C]0.546591506092804[/C][/ROW]
[ROW][C]138[/C][C]0.470186630788956[/C][C]0.940373261577911[/C][C]0.529813369211044[/C][/ROW]
[ROW][C]139[/C][C]0.396281266677953[/C][C]0.792562533355906[/C][C]0.603718733322047[/C][/ROW]
[ROW][C]140[/C][C]0.322837797121087[/C][C]0.645675594242173[/C][C]0.677162202878913[/C][/ROW]
[ROW][C]141[/C][C]0.425940533040581[/C][C]0.851881066081162[/C][C]0.574059466959419[/C][/ROW]
[ROW][C]142[/C][C]0.375946927934400[/C][C]0.751893855868801[/C][C]0.6240530720656[/C][/ROW]
[ROW][C]143[/C][C]0.303350492215239[/C][C]0.606700984430479[/C][C]0.696649507784761[/C][/ROW]
[ROW][C]144[/C][C]0.227185895699066[/C][C]0.454371791398132[/C][C]0.772814104300934[/C][/ROW]
[ROW][C]145[/C][C]0.333525544777945[/C][C]0.66705108955589[/C][C]0.666474455222055[/C][/ROW]
[ROW][C]146[/C][C]0.254150574218036[/C][C]0.508301148436071[/C][C]0.745849425781964[/C][/ROW]
[ROW][C]147[/C][C]0.252870623285588[/C][C]0.505741246571175[/C][C]0.747129376714412[/C][/ROW]
[ROW][C]148[/C][C]0.159612301285357[/C][C]0.319224602570713[/C][C]0.840387698714643[/C][/ROW]
[ROW][C]149[/C][C]0.0863434141037005[/C][C]0.172686828207401[/C][C]0.9136565858963[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09861376610248360.1972275322049670.901386233897516
110.3549755718872730.7099511437745470.645024428112727
120.7492056729243740.5015886541512530.250794327075626
130.7439942746184630.5120114507630750.256005725381537
140.727142351575680.5457152968486410.272857648424320
150.6898060323229990.6203879353540020.310193967677001
160.6075205230409110.7849589539181780.392479476959089
170.5529639770054770.8940720459890470.447036022994523
180.5244892972136250.951021405572750.475510702786375
190.4697051197851490.9394102395702980.530294880214851
200.4986893216137590.9973786432275170.501310678386241
210.4311761806996560.8623523613993120.568823819300344
220.4194510925287390.8389021850574790.58054890747126
230.3769374835137510.7538749670275010.62306251648625
240.5309343490541260.9381313018917470.469065650945874
250.4887782716235480.9775565432470950.511221728376452
260.505724304437270.988551391125460.49427569556273
270.4383934621962860.8767869243925720.561606537803714
280.3793593171190680.7587186342381360.620640682880932
290.3178232475813530.6356464951627060.682176752418647
300.2780751083641660.5561502167283310.721924891635834
310.2696041099587250.5392082199174490.730395890041275
320.3990065914215260.7980131828430520.600993408578474
330.3432220705289270.6864441410578540.656777929471073
340.3136417154916670.6272834309833340.686358284508333
350.3567734065587750.713546813117550.643226593441225
360.3239982756858640.6479965513717290.676001724314136
370.4385551926157250.877110385231450.561444807384275
380.7456244379030740.5087511241938520.254375562096926
390.72998173480890.5400365303821990.270018265191100
400.6939340840255190.6121318319489610.306065915974481
410.6567229917388230.6865540165223550.343277008261177
420.6096354860561080.7807290278877840.390364513943892
430.5594323516939850.881135296612030.440567648306015
440.5527989117313380.8944021765373240.447201088268662
450.5401932485095580.9196135029808840.459806751490442
460.5841750100225660.8316499799548680.415824989977434
470.5471175912121250.905764817575750.452882408787875
480.5370116247742380.9259767504515230.462988375225762
490.5896696370225960.8206607259548070.410330362977404
500.568422033221750.86315593355650.43157796677825
510.5381782265344350.923643546931130.461821773465565
520.4910579843459150.982115968691830.508942015654085
530.4455369726952120.8910739453904240.554463027304788
540.4049352117232150.809870423446430.595064788276785
550.3665446293371980.7330892586743960.633455370662802
560.3357626061080490.6715252122160980.664237393891951
570.2927338204310590.5854676408621190.70726617956894
580.2673715418270750.534743083654150.732628458172925
590.2495105008442370.4990210016884750.750489499155763
600.3086708782982850.6173417565965710.691329121701715
610.2940977137318810.5881954274637620.70590228626812
620.2550995796658190.5101991593316380.744900420334181
630.2394300655797740.4788601311595480.760569934420226
640.2656442274263840.5312884548527680.734355772573616
650.3303643276374650.660728655274930.669635672362535
660.3389668445756520.6779336891513050.661033155424348
670.6838004982063630.6323990035872740.316199501793637
680.6733154810450920.6533690379098170.326684518954908
690.8411901611838290.3176196776323420.158809838816171
700.8191383275213730.3617233449572540.180861672478627
710.9308517380612750.138296523877450.069148261938725
720.9146569017311740.1706861965376520.0853430982688258
730.9382829337850670.1234341324298660.061717066214933
740.9263861039557440.1472277920885120.0736138960442558
750.9276281033889790.1447437932220420.0723718966110212
760.9215329032608190.1569341934783630.0784670967391814
770.9693287817645060.06134243647098850.0306712182354943
780.9616673404392940.07666531912141290.0383326595607064
790.954107486897140.09178502620571850.0458925131028593
800.9566019515869030.08679609682619440.0433980484130972
810.948666401436410.1026671971271790.0513335985635893
820.9476745572405540.1046508855188910.0523254427594454
830.933876863951650.1322462720966990.0661231360483494
840.9207784649871150.1584430700257710.0792215350128854
850.9112558434902310.1774883130195370.0887441565097685
860.8936859551560790.2126280896878430.106314044843921
870.8716472922200250.2567054155599490.128352707779975
880.9193797757638680.1612404484722640.0806202242361318
890.9026115625421930.1947768749156140.097388437457807
900.881765243633470.2364695127330590.118234756366529
910.881397413989920.2372051720201590.118602586010079
920.8713980396681860.2572039206636290.128601960331814
930.8491324834995320.3017350330009370.150867516500468
940.8204223519995750.3591552960008510.179577648000425
950.7870177432280470.4259645135439060.212982256771953
960.7557157175194140.4885685649611710.244284282480585
970.7732214987018420.4535570025963170.226778501298158
980.7699916228250970.4600167543498060.230008377174903
990.7343600261062780.5312799477874450.265639973893722
1000.7122853425320170.5754293149359650.287714657467983
1010.6738041808869680.6523916382260630.326195819113032
1020.6328806957375120.7342386085249760.367119304262488
1030.5917954923026490.8164090153947020.408204507697351
1040.5486312193378190.9027375613243630.451368780662181
1050.5063404886872430.9873190226255150.493659511312757
1060.4926916343885430.9853832687770850.507308365611457
1070.5000020890271860.9999958219456280.499997910972814
1080.5393036213969160.9213927572061670.460696378603084
1090.5018653437622560.9962693124754890.498134656237744
1100.460704289802820.921408579605640.53929571019718
1110.4137439132316120.8274878264632240.586256086768388
1120.74061884066950.5187623186610.2593811593305
1130.7755791622930020.4488416754139970.224420837706998
1140.7511924937027930.4976150125944140.248807506297207
1150.8754598640234440.2490802719531120.124540135976556
1160.8470931873958190.3058136252083630.152906812604181
1170.8176305257582430.3647389484835130.182369474241757
1180.784169778505270.4316604429894610.215830221494730
1190.756239619419450.4875207611611010.243760380580550
1200.8049705765881380.3900588468237250.195029423411862
1210.8840595190699330.2318809618601330.115940480930067
1220.8652497380235410.2695005239529180.134750261976459
1230.8449103247330520.3101793505338970.155089675266948
1240.8069001298555650.3861997402888710.193099870144435
1250.8115641803558520.3768716392882950.188435819644148
1260.7682122314611830.4635755370776330.231787768538817
1270.7368674094556410.5262651810887170.263132590544359
1280.6903307560793210.6193384878413580.309669243920679
1290.6450159225602050.709968154879590.354984077439795
1300.7658939350196670.4682121299606670.234106064980333
1310.7135802827241450.572839434551710.286419717275855
1320.6534074306808310.6931851386383370.346592569319169
1330.6153801898032810.7692396203934380.384619810196719
1340.5456903414100290.9086193171799420.454309658589971
1350.5710007073765480.8579985852469040.428999292623452
1360.5005357463897360.9989285072205280.499464253610264
1370.4534084939071960.9068169878143920.546591506092804
1380.4701866307889560.9403732615779110.529813369211044
1390.3962812666779530.7925625333559060.603718733322047
1400.3228377971210870.6456755942421730.677162202878913
1410.4259405330405810.8518810660811620.574059466959419
1420.3759469279344000.7518938558688010.6240530720656
1430.3033504922152390.6067009844304790.696649507784761
1440.2271858956990660.4543717913981320.772814104300934
1450.3335255447779450.667051089555890.666474455222055
1460.2541505742180360.5083011484360710.745849425781964
1470.2528706232855880.5057412465711750.747129376714412
1480.1596123012853570.3192246025707130.840387698714643
1490.08634341410370050.1726868282074010.9136565858963







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 level40.0285714285714286OK

\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 & 4 & 0.0285714285714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99493&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]4[/C][C]0.0285714285714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99493&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99493&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 level40.0285714285714286OK



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