## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 02 Nov 2012 18:13:10 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/02/t13518944273j5eqrexzs0i4wf.htm/, Retrieved Mon, 27 Jun 2022 05:15:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185676, Retrieved Mon, 27 Jun 2022 05:15:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS 7 ] [2012-11-02 22:13:10] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]
- RM      [Multiple Regression] [Paper 2012 multip...] [2012-12-14 14:54:35] [33fe548a21de6aef2b38519618b03303]
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Dataseries X:
14	501	11	20	91.81	77585	1303.2
14	485	11	19	91.98	77585	-58.7
15	464	11	18	91.72	77585	-378.9
13	460	11	13	90.27	78302	175.6
8	467	11	17	91.89	78302	233.7
7	460	9	17	92.07	78302	706.8
3	448	8	13	92.92	78224	-23.6
3	443	6	14	93.34	78224	420.9
4	436	7	13	93.6	78224	722.1
4	431	8	17	92.41	78178	1401.3
0	484	6	17	93.6	78178	-94.9
-4	510	5	15	93.77	78178	1043.6
-14	513	2	9	93.6	77988	1300.1
-18	503	3	10	93.6	77988	721.1
-8	471	3	9	93.51	77988	-45.6
-1	471	7	14	92.66	77876	787.5
1	476	8	18	94.2	77876	694.3
2	475	7	18	94.37	77876	1054.7
0	470	7	12	94.45	78432	821.9
1	461	6	16	94.62	78432	1100.7
0	455	6	12	94.37	78432	862.4
-1	456	7	19	93.43	79025	1656.1
-3	517	5	13	94.79	79025	-174
-3	525	5	12	94.88	79025	1337.6
-3	523	5	13	94.79	79407	1394.9
-4	519	4	11	94.62	79407	915.7
-8	509	4	10	94.71	79407	-481.1
-9	512	4	16	93.77	79644	167.9
-13	519	1	12	95.73	79644	208.2
-18	517	-1	6	95.99	79644	382.2
-11	510	3	8	95.82	79381	1004
-9	509	4	6	95.47	79381	864.7
-10	501	3	8	95.82	79381	1052.9
-13	507	2	8	94.71	79536	1417.6
-11	569	1	9	96.33	79536	-197.7
-5	580	4	13	96.5	79536	1262.1
-15	578	3	8	96.16	79813	1147.2
-6	565	5	11	96.33	79813	700.2
-6	547	6	8	96.33	79813	45.3
-3	555	6	10	95.05	80332	458.5
-1	562	6	15	96.84	80332	610.2
-3	561	6	12	96.92	80332	786.4
-4	555	6	13	97.44	81434	787.2
-6	544	5	12	97.78	81434	1040
0	537	6	15	97.69	81434	324.1
-4	543	5	13	96.67	82167	1343
-2	594	6	13	98.29	82167	-501.2
-2	611	5	16	98.2	82167	800.4
-6	613	7	14	98.71	82816	916.7
-7	611	4	12	98.54	82816	695.8
-6	594	5	15	98.2	82816	28
-6	595	6	14	96.92	83000	495.6
-3	591	6	19	99.06	83000	366.2
-2	589	5	16	99.65	83000	633
-5	584	3	16	99.82	83251	848.3
-11	573	2	11	99.99	83251	472.2
-11	567	3	13	100.33	83251	357.8
-11	569	3	12	99.31	83591	824.3
-10	621	2	11	101.1	83591	-880.1
-14	629	0	6	101.1	83591	1066.8
-8	628	4	9	100.93	83910	1052.8
-9	612	4	6	100.85	83910	-32.1
-5	595	5	15	100.93	83910	-1331.4
-1	597	6	17	99.6	84599	-767.1
-2	593	6	13	101.88	84599	-236.7
-5	590	5	12	101.81	84599	-184.9
-4	580	5	13	102.38	85275	-143.4
-6	574	3	10	102.74	85275	493.9
-2	573	5	14	102.82	85275	549.7
-2	573	5	13	101.72	85608	982.7
-2	620	5	10	103.47	85608	-856.3
-2	626	3	11	102.98	85608	967
2	620	6	12	102.68	86303	659.4
1	588	6	7	102.9	86303	577.2
-8	566	4	11	103.03	86303	-213.1
-1	557	6	9	101.29	87115	17.7
1	561	5	13	103.69	87115	390.1
-1	549	4	12	103.68	87115	509.3
2	532	5	5	104.2	87931	410
2	526	5	13	104.08	87931	212.5
1	511	4	11	104.16	87931	818
-1	499	3	8	103.05	88164	422.7
-2	555	2	8	104.66	88164	-158
-2	565	3	8	104.46	88164	427.2
-1	542	2	8	104.95	88792	243.4
-8	527	-1	0	105.85	88792	-419.3
-4	510	0	3	106.23	88792	-1459.8
-6	514	-2	0	104.86	89263	-1389.8
-3	517	1	-1	107.44	89263	-2.1
-3	508	-2	-1	108.23	89263	-938.6
-7	493	-2	-4	108.45	89881	-839.9
-9	490	-2	1	109.39	89881	-297.6
-11	469	-6	-1	110.15	89881	-376.3
-13	478	-4	0	109.13	90120	-79.4
-11	528	-2	-1	110.28	90120	-2091.3
-9	534	0	6	110.17	90120	-1023
-17	518	-5	0	109.99	89703	-765.6
-22	506	-4	-3	109.26	89703	-1592.3
-25	502	-5	-3	109.11	89703	-1588.8
-20	516	-1	4	107.06	87818	-1318
-24	528	-2	1	109.53	87818	-402.4
-24	533	-4	0	108.92	87818	-814.5
-22	536	-1	-4	109.24	86273	-98.4
-19	537	1	-2	109.12	86273	-305.9
-18	524	1	3	109	86273	-18.4
-17	536	-2	2	107.23	86316	610.3
-11	587	1	5	109.49	86316	-917.3
-11	597	1	6	109.04	86316	88.4
-12	581	3	6	109.02	87234	-740.2
-10	564	3	3	109.23	87234	29.3
-15	558	1	4	109.46	87234	-893.2
-15	575	1	7	107.9	87885	-1030.2
-15	580	0	5	110.42	87885	-403.4
-13	575	2	6	110.98	87885	-46.9
-8	563	2	1	111.48	88003	-321.2
-13	552	-1	3	111.88	88003	-239.9
-9	537	1	6	111.89	88003	640.9
-7	545	0	0	109.85	88910	511.6
-4	601	1	3	112.1	88910	-665.1
-4	604	1	4	112.24	88910	657.7
-2	586	3	7	112.39	89397	-207.7
0	564	2	6	112.52	89397	-885.2
-2	549	0	6	113.16	89397	-1595.8
-3	551	0	6	111.84	89813	-1374.9
1	556	3	6	114.33	89813	-316.6
-2	548	-2	2	114.82	89813	-283.4
-1	540	0	2	115.2	90539	-175.8
1	531	1	2	115.4	90539	-694.2
-3	521	-1	3	115.74	90539	-249.9
-4	519	-2	-1	114.19	90688	268.2
-9	572	-1	-4	115.94	90688	-2105.1
-9	581	-1	4	116.03	90688	-762.8
-7	563	1	5	116.24	90691	-117.1
-14	548	-2	3	116.66	90691	-1094.4
-12	539	-5	-1	116.79	90691	-2095.2
-16	541	-5	-4	115.48	90645	-1587.6
-20	562	-6	0	118.16	90645	-528
-12	559	-4	-1	118.38	90645	-324.2
-12	546	-3	-1	118.51	90861	-276.1
-10	536	-3	3	118.42	90861	-139.1
-10	528	-1	2	118.24	90861	268
-13	530	-2	-4	116.47	90401	570.5
-16	582	-3	-3	118.96	90401	-316.5

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

 Multiple Linear Regression - Estimated Regression Equation i[t] = -84.0731978333051 -0.0541036915164171w[t] + 2.04918398266427f[t] + 0.320926779095698s[t] + 0.0764278391768203c[t] + 0.00107723090722604b[t] + 7.41164214900091e-05h[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
i[t] =  -84.0731978333051 -0.0541036915164171w[t] +  2.04918398266427f[t] +  0.320926779095698s[t] +  0.0764278391768203c[t] +  0.00107723090722604b[t] +  7.41164214900091e-05h[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]i[t] =  -84.0731978333051 -0.0541036915164171w[t] +  2.04918398266427f[t] +  0.320926779095698s[t] +  0.0764278391768203c[t] +  0.00107723090722604b[t] +  7.41164214900091e-05h[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 i[t] = -84.0731978333051 -0.0541036915164171w[t] + 2.04918398266427f[t] + 0.320926779095698s[t] + 0.0764278391768203c[t] + 0.00107723090722604b[t] + 7.41164214900091e-05h[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -84.0731978333051 10.847935 -7.7502 0 0 w -0.0541036915164171 0.008085 -6.6916 0 0 f 2.04918398266427 0.188384 10.8777 0 0 s 0.320926779095698 0.122378 2.6224 0.009727 0.004863 c 0.0764278391768203 0.139559 0.5476 0.584837 0.292418 b 0.00107723090722604 0.00023 4.6906 7e-06 3e-06 h 7.41164214900091e-05 0.000509 0.1456 0.884442 0.442221

\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) & -84.0731978333051 & 10.847935 & -7.7502 & 0 & 0 \tabularnewline
w & -0.0541036915164171 & 0.008085 & -6.6916 & 0 & 0 \tabularnewline
f & 2.04918398266427 & 0.188384 & 10.8777 & 0 & 0 \tabularnewline
s & 0.320926779095698 & 0.122378 & 2.6224 & 0.009727 & 0.004863 \tabularnewline
c & 0.0764278391768203 & 0.139559 & 0.5476 & 0.584837 & 0.292418 \tabularnewline
b & 0.00107723090722604 & 0.00023 & 4.6906 & 7e-06 & 3e-06 \tabularnewline
h & 7.41164214900091e-05 & 0.000509 & 0.1456 & 0.884442 & 0.442221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&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]-84.0731978333051[/C][C]10.847935[/C][C]-7.7502[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]w[/C][C]-0.0541036915164171[/C][C]0.008085[/C][C]-6.6916[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]f[/C][C]2.04918398266427[/C][C]0.188384[/C][C]10.8777[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]s[/C][C]0.320926779095698[/C][C]0.122378[/C][C]2.6224[/C][C]0.009727[/C][C]0.004863[/C][/ROW]
[ROW][C]c[/C][C]0.0764278391768203[/C][C]0.139559[/C][C]0.5476[/C][C]0.584837[/C][C]0.292418[/C][/ROW]
[ROW][C]b[/C][C]0.00107723090722604[/C][C]0.00023[/C][C]4.6906[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]h[/C][C]7.41164214900091e-05[/C][C]0.000509[/C][C]0.1456[/C][C]0.884442[/C][C]0.442221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -84.0731978333051 10.847935 -7.7502 0 0 w -0.0541036915164171 0.008085 -6.6916 0 0 f 2.04918398266427 0.188384 10.8777 0 0 s 0.320926779095698 0.122378 2.6224 0.009727 0.004863 c 0.0764278391768203 0.139559 0.5476 0.584837 0.292418 b 0.00107723090722604 0.00023 4.6906 7e-06 3e-06 h 7.41164214900091e-05 0.000509 0.1456 0.884442 0.442221

 Multiple Linear Regression - Regression Statistics Multiple R 0.865040444862777 R-squared 0.748294971248391 Adjusted R-squared 0.737190337626996 F-TEST (value) 67.3858316051691 F-TEST (DF numerator) 6 F-TEST (DF denominator) 136 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.84278730448689 Sum Squared Residuals 2008.31394038349

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.865040444862777 \tabularnewline
R-squared & 0.748294971248391 \tabularnewline
F-TEST (value) & 67.3858316051691 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.84278730448689 \tabularnewline
Sum Squared Residuals & 2008.31394038349 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.865040444862777[/C][/ROW]
[ROW][C]R-squared[/C][C]0.748294971248391[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]67.3858316051691[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.84278730448689[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2008.31394038349[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 R 0.865040444862777 R-squared 0.748294971248391 Adjusted R-squared 0.737190337626996 F-TEST (value) 67.3858316051691 F-TEST (DF numerator) 6 F-TEST (DF denominator) 136 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.84278730448689 Sum Squared Residuals 2008.31394038349

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 14 8.47080048063266 5.52919951936734 2 14 8.92758634403241 5.07241365596759 3 15 9.69923377043439 5.30076622956561 4 13 9.01366639041246 3.98633360958754 5 8 10.0467669297353 -2.04676692973535 6 7 6.37594629508048 0.624053704919523 7 3 3.61910451251079 -0.619104512510785 8 3 0.177226225666602 2.8227737743334 9 4 2.32640437418886 1.67359562581114 10 4 5.83965205394121 -1.83965205394121 11 0 -1.14615542297038 1.14615542297038 12 -4 -5.14651466472646 1.14651466472646 13 -14 -13.5805941047635 -0.419405895236472 14 -18 -10.7123598358821 -7.2876401641179 15 -8 -9.36567205233476 1.36567205233476 16 -1 0.311830639634532 -1.31183063963453 17 1 3.48499450294894 -2.48499450294894 18 2 1.52961850276615 0.470381497233852 19 0 0.462376594402993 -0.462376594402993 20 1 0.217489342740743 0.782510657259257 21 0 -0.77836452757882 0.77836452757882 22 -1 4.08898518013437 -5.08898518013437 23 -3 -5.2669672439582 2.2669672439582 24 -3 -5.90181066693502 2.90181066693502 25 -3 -5.06380593282068 2.06380593282068 26 -4 -7.58693802944875 3.58693802944875 27 -8 -7.46347520539161 -0.536524794608393 28 -9 -5.46866249163329 -3.53133750836671 29 -13 -13.1258619400512 0.125861940051202 30 -18 -19.0088157013959 1.00881570139586 31 -11 -10.0417192423105 -0.958280757689514 32 -9 -8.61735928754664 -0.38264071245336 33 -10 -9.55116172565187 -0.44883827434813 34 -13 -11.8158017093635 -1.18419829063653 35 -11 -16.8943949431163 5.89439494311627 36 -5 -9.93708860067008 4.93708860067008 37 -15 -13.2188075766277 -1.78119242337228 38 -6 -7.47444859204463 1.47444859204463 39 -6 -5.46271734380575 -0.537282656194248 40 -3 -4.76181320568204 1.76181320568204 41 -1 -3.38785585755192 2.38785585755192 42 -3 -4.27735896272192 1.27735896272192 43 -4 -2.40489980525548 -1.59510019474452 44 -6 -4.13514786366207 -1.86485213633793 45 0 -0.804396154766398 0.804396154766398 46 -4 -3.03288476382807 -0.967115236171931 47 -2 -3.7558614535465 1.7558614535465 48 -2 -5.67243642601728 3.67243642601728 49 -6 -1.57740860532381 -4.42259139467619 50 -7 -8.28797177864238 1.28797177864238 51 -6 -4.43172511450308 -1.56827488549692 52 -6 -2.62253191097893 -3.37746808902107 53 -3 -0.647518338537184 -2.35248166146282 54 -2 -3.48640858908786 1.48640858908786 55 -5 -7.01492314091372 2.01492314091372 56 -11 -10.0884828658382 -0.911517134161775 57 -11 -7.05531662918239 -3.94468337081761 58 -11 -7.16157336818934 -3.83842663181066 59 -10 -12.3345942854641 2.33459428546406 60 -14 -18.3261284174035 4.32612841740353 61 -8 -8.78290216109875 0.782902161098753 62 -9 -8.96654656693183 -0.0334534330681684 63 -5 -3.19944405593501 -1.80055594406499 64 -1 0.065773067508206 -1.06577306750821 65 -2 -0.787952459527467 -1.21204754047253 66 -5 -2.99726286484738 -2.00273713515262 67 -4 -1.36045137748009 -2.63954862251991 68 -6 -6.02222911347798 0.0222291134779818 69 -2 -0.575800416796943 -1.42419958320306 70 -2 -0.589987516375699 -1.4100124836243 71 -2 -4.09819273549509 2.09819273549509 72 -2 -8.10256924072034 6.10256924072034 73 2 -0.606519447014608 2.60651944701461 74 1 -0.469113459195329 1.46911345919533 75 -8 -2.14213168359047 -5.85786831640953 76 -1 2.5601490737742 -3.5601490737742 77 1 1.7892852108143 -0.7892852108143 78 -1 0.0764891463011801 -1.07648914630118 79 2 1.71035156708909 0.289648432910911 80 2 4.57857861500768 -2.57857861500768 81 1 2.75008816724462 -1.75008816724462 82 -1 0.524229823972655 -1.52422982397266 83 -2 -4.47475146849554 2.47475146849554 84 -2 -2.93851703897486 0.938517038974855 85 -1 -3.0429880640968 2.0429880640968 86 -8 -10.9267307693712 2.92673076937123 87 -4 -7.04307925131394 3.04307925131394 88 -6 -11.9127845528597 5.91278455285972 89 -3 -5.94843527533398 2.94843527533398 90 -3 -11.6180860354547 8.61808603545474 91 -7 -11.0794528839099 4.07945288390992 92 -9 -9.20047240968194 0.20047240968194 93 -11 -16.8506321812825 5.85063218128254 94 -13 -12.716763704099 -0.283236295900996 95 -11 -11.7057299070294 0.705729907029424 96 -9 -5.61472512636117 -3.38527487363883 97 -17 -17.3644313824676 0.364431382467596 98 -22 -15.7458478071383 -6.25415219286171 99 -25 -17.5898217921382 -7.41017820786181 100 -20 -10.0712366925351 -9.92876330746487 101 -24 -13.4758075524005 -10.5241924475995 102 -24 -18.2427851136007 -5.75721488639928 103 -22 -15.1280414302386 -6.8719585697614 104 -19 -10.4664740963955 -8.53352590360452 105 -18 -8.1463550807264 -9.8536449192736 106 -17 -15.3064374581534 -1.6935625418466 107 -11 -10.8958867691393 -0.104113230860703 108 -11 -11.0758505477448 0.0758505477448361 109 -12 -5.18586696895028 -6.81413303104972 110 -10 -5.15580211789459 -4.84419788210541 111 -15 -8.65941515084279 -6.34058484915721 112 -15 -8.04450162759061 -6.95549837240939 113 -15 -10.7670032983128 -4.23299670168717 114 -13 -6.0079680021063 -6.9920319978937 115 -8 -6.81836056716142 -1.18163943283858 116 -13 -11.6923215495444 -1.30767845045562 117 -9 -5.75357185174232 -3.24642814825768 118 -7 -9.34959365347747 2.34959365347747 119 -4 -9.28268621346491 5.28268621346491 120 -4 -9.01532940908673 5.01532940908673 121 -2 -2.50837938263743 0.508379382637432 122 0 -3.72848718750272 3.72848718750272 123 -2 -7.01905309212264 5.01905309212264 124 -3 -6.7636448479557 3.7636448479557 125 1 -0.617868629131816 1.61786862913182 126 -2 -11.6747558203145 9.67475582031451 127 -1 -6.32447117836902 5.32447117836902 128 1 -3.81149035712205 4.81149035712205 129 -3 -6.98897923680259 3.98897923680259 130 -4 -10.1332189803902 6.13321898039024 131 -9 -11.956462769946 2.95646276994597 132 -9 -9.76961678273619 0.769616782736188 133 -7 -4.30931707871153 -2.69068292128847 134 -14 -10.3275014984174 -3.67249850158259 135 -12 -17.3360674346795 5.33606743467947 136 -16 -18.5191067505051 2.5191067505051 137 -20 -20.1374007694266 0.13740076942665 138 -12 -16.165729457326 4.16572945732599 139 -12 -13.1670149900208 1.16701499002082 140 -10 -11.3389955142556 1.33899551425563 141 -10 -7.1123090117547 -2.8876909882453 142 -13 -11.8036443271922 -1.19635567280779 143 -16 -16.2207294359258 0.220729435925833

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 8.47080048063266 & 5.52919951936734 \tabularnewline
2 & 14 & 8.92758634403241 & 5.07241365596759 \tabularnewline
3 & 15 & 9.69923377043439 & 5.30076622956561 \tabularnewline
4 & 13 & 9.01366639041246 & 3.98633360958754 \tabularnewline
5 & 8 & 10.0467669297353 & -2.04676692973535 \tabularnewline
6 & 7 & 6.37594629508048 & 0.624053704919523 \tabularnewline
7 & 3 & 3.61910451251079 & -0.619104512510785 \tabularnewline
8 & 3 & 0.177226225666602 & 2.8227737743334 \tabularnewline
9 & 4 & 2.32640437418886 & 1.67359562581114 \tabularnewline
10 & 4 & 5.83965205394121 & -1.83965205394121 \tabularnewline
11 & 0 & -1.14615542297038 & 1.14615542297038 \tabularnewline
12 & -4 & -5.14651466472646 & 1.14651466472646 \tabularnewline
13 & -14 & -13.5805941047635 & -0.419405895236472 \tabularnewline
14 & -18 & -10.7123598358821 & -7.2876401641179 \tabularnewline
15 & -8 & -9.36567205233476 & 1.36567205233476 \tabularnewline
16 & -1 & 0.311830639634532 & -1.31183063963453 \tabularnewline
17 & 1 & 3.48499450294894 & -2.48499450294894 \tabularnewline
18 & 2 & 1.52961850276615 & 0.470381497233852 \tabularnewline
19 & 0 & 0.462376594402993 & -0.462376594402993 \tabularnewline
20 & 1 & 0.217489342740743 & 0.782510657259257 \tabularnewline
21 & 0 & -0.77836452757882 & 0.77836452757882 \tabularnewline
22 & -1 & 4.08898518013437 & -5.08898518013437 \tabularnewline
23 & -3 & -5.2669672439582 & 2.2669672439582 \tabularnewline
24 & -3 & -5.90181066693502 & 2.90181066693502 \tabularnewline
25 & -3 & -5.06380593282068 & 2.06380593282068 \tabularnewline
26 & -4 & -7.58693802944875 & 3.58693802944875 \tabularnewline
27 & -8 & -7.46347520539161 & -0.536524794608393 \tabularnewline
28 & -9 & -5.46866249163329 & -3.53133750836671 \tabularnewline
29 & -13 & -13.1258619400512 & 0.125861940051202 \tabularnewline
30 & -18 & -19.0088157013959 & 1.00881570139586 \tabularnewline
31 & -11 & -10.0417192423105 & -0.958280757689514 \tabularnewline
32 & -9 & -8.61735928754664 & -0.38264071245336 \tabularnewline
33 & -10 & -9.55116172565187 & -0.44883827434813 \tabularnewline
34 & -13 & -11.8158017093635 & -1.18419829063653 \tabularnewline
35 & -11 & -16.8943949431163 & 5.89439494311627 \tabularnewline
36 & -5 & -9.93708860067008 & 4.93708860067008 \tabularnewline
37 & -15 & -13.2188075766277 & -1.78119242337228 \tabularnewline
38 & -6 & -7.47444859204463 & 1.47444859204463 \tabularnewline
39 & -6 & -5.46271734380575 & -0.537282656194248 \tabularnewline
40 & -3 & -4.76181320568204 & 1.76181320568204 \tabularnewline
41 & -1 & -3.38785585755192 & 2.38785585755192 \tabularnewline
42 & -3 & -4.27735896272192 & 1.27735896272192 \tabularnewline
43 & -4 & -2.40489980525548 & -1.59510019474452 \tabularnewline
44 & -6 & -4.13514786366207 & -1.86485213633793 \tabularnewline
45 & 0 & -0.804396154766398 & 0.804396154766398 \tabularnewline
46 & -4 & -3.03288476382807 & -0.967115236171931 \tabularnewline
47 & -2 & -3.7558614535465 & 1.7558614535465 \tabularnewline
48 & -2 & -5.67243642601728 & 3.67243642601728 \tabularnewline
49 & -6 & -1.57740860532381 & -4.42259139467619 \tabularnewline
50 & -7 & -8.28797177864238 & 1.28797177864238 \tabularnewline
51 & -6 & -4.43172511450308 & -1.56827488549692 \tabularnewline
52 & -6 & -2.62253191097893 & -3.37746808902107 \tabularnewline
53 & -3 & -0.647518338537184 & -2.35248166146282 \tabularnewline
54 & -2 & -3.48640858908786 & 1.48640858908786 \tabularnewline
55 & -5 & -7.01492314091372 & 2.01492314091372 \tabularnewline
56 & -11 & -10.0884828658382 & -0.911517134161775 \tabularnewline
57 & -11 & -7.05531662918239 & -3.94468337081761 \tabularnewline
58 & -11 & -7.16157336818934 & -3.83842663181066 \tabularnewline
59 & -10 & -12.3345942854641 & 2.33459428546406 \tabularnewline
60 & -14 & -18.3261284174035 & 4.32612841740353 \tabularnewline
61 & -8 & -8.78290216109875 & 0.782902161098753 \tabularnewline
62 & -9 & -8.96654656693183 & -0.0334534330681684 \tabularnewline
63 & -5 & -3.19944405593501 & -1.80055594406499 \tabularnewline
64 & -1 & 0.065773067508206 & -1.06577306750821 \tabularnewline
65 & -2 & -0.787952459527467 & -1.21204754047253 \tabularnewline
66 & -5 & -2.99726286484738 & -2.00273713515262 \tabularnewline
67 & -4 & -1.36045137748009 & -2.63954862251991 \tabularnewline
68 & -6 & -6.02222911347798 & 0.0222291134779818 \tabularnewline
69 & -2 & -0.575800416796943 & -1.42419958320306 \tabularnewline
70 & -2 & -0.589987516375699 & -1.4100124836243 \tabularnewline
71 & -2 & -4.09819273549509 & 2.09819273549509 \tabularnewline
72 & -2 & -8.10256924072034 & 6.10256924072034 \tabularnewline
73 & 2 & -0.606519447014608 & 2.60651944701461 \tabularnewline
74 & 1 & -0.469113459195329 & 1.46911345919533 \tabularnewline
75 & -8 & -2.14213168359047 & -5.85786831640953 \tabularnewline
76 & -1 & 2.5601490737742 & -3.5601490737742 \tabularnewline
77 & 1 & 1.7892852108143 & -0.7892852108143 \tabularnewline
78 & -1 & 0.0764891463011801 & -1.07648914630118 \tabularnewline
79 & 2 & 1.71035156708909 & 0.289648432910911 \tabularnewline
80 & 2 & 4.57857861500768 & -2.57857861500768 \tabularnewline
81 & 1 & 2.75008816724462 & -1.75008816724462 \tabularnewline
82 & -1 & 0.524229823972655 & -1.52422982397266 \tabularnewline
83 & -2 & -4.47475146849554 & 2.47475146849554 \tabularnewline
84 & -2 & -2.93851703897486 & 0.938517038974855 \tabularnewline
85 & -1 & -3.0429880640968 & 2.0429880640968 \tabularnewline
86 & -8 & -10.9267307693712 & 2.92673076937123 \tabularnewline
87 & -4 & -7.04307925131394 & 3.04307925131394 \tabularnewline
88 & -6 & -11.9127845528597 & 5.91278455285972 \tabularnewline
89 & -3 & -5.94843527533398 & 2.94843527533398 \tabularnewline
90 & -3 & -11.6180860354547 & 8.61808603545474 \tabularnewline
91 & -7 & -11.0794528839099 & 4.07945288390992 \tabularnewline
92 & -9 & -9.20047240968194 & 0.20047240968194 \tabularnewline
93 & -11 & -16.8506321812825 & 5.85063218128254 \tabularnewline
94 & -13 & -12.716763704099 & -0.283236295900996 \tabularnewline
95 & -11 & -11.7057299070294 & 0.705729907029424 \tabularnewline
96 & -9 & -5.61472512636117 & -3.38527487363883 \tabularnewline
97 & -17 & -17.3644313824676 & 0.364431382467596 \tabularnewline
98 & -22 & -15.7458478071383 & -6.25415219286171 \tabularnewline
99 & -25 & -17.5898217921382 & -7.41017820786181 \tabularnewline
100 & -20 & -10.0712366925351 & -9.92876330746487 \tabularnewline
101 & -24 & -13.4758075524005 & -10.5241924475995 \tabularnewline
102 & -24 & -18.2427851136007 & -5.75721488639928 \tabularnewline
103 & -22 & -15.1280414302386 & -6.8719585697614 \tabularnewline
104 & -19 & -10.4664740963955 & -8.53352590360452 \tabularnewline
105 & -18 & -8.1463550807264 & -9.8536449192736 \tabularnewline
106 & -17 & -15.3064374581534 & -1.6935625418466 \tabularnewline
107 & -11 & -10.8958867691393 & -0.104113230860703 \tabularnewline
108 & -11 & -11.0758505477448 & 0.0758505477448361 \tabularnewline
109 & -12 & -5.18586696895028 & -6.81413303104972 \tabularnewline
110 & -10 & -5.15580211789459 & -4.84419788210541 \tabularnewline
111 & -15 & -8.65941515084279 & -6.34058484915721 \tabularnewline
112 & -15 & -8.04450162759061 & -6.95549837240939 \tabularnewline
113 & -15 & -10.7670032983128 & -4.23299670168717 \tabularnewline
114 & -13 & -6.0079680021063 & -6.9920319978937 \tabularnewline
115 & -8 & -6.81836056716142 & -1.18163943283858 \tabularnewline
116 & -13 & -11.6923215495444 & -1.30767845045562 \tabularnewline
117 & -9 & -5.75357185174232 & -3.24642814825768 \tabularnewline
118 & -7 & -9.34959365347747 & 2.34959365347747 \tabularnewline
119 & -4 & -9.28268621346491 & 5.28268621346491 \tabularnewline
120 & -4 & -9.01532940908673 & 5.01532940908673 \tabularnewline
121 & -2 & -2.50837938263743 & 0.508379382637432 \tabularnewline
122 & 0 & -3.72848718750272 & 3.72848718750272 \tabularnewline
123 & -2 & -7.01905309212264 & 5.01905309212264 \tabularnewline
124 & -3 & -6.7636448479557 & 3.7636448479557 \tabularnewline
125 & 1 & -0.617868629131816 & 1.61786862913182 \tabularnewline
126 & -2 & -11.6747558203145 & 9.67475582031451 \tabularnewline
127 & -1 & -6.32447117836902 & 5.32447117836902 \tabularnewline
128 & 1 & -3.81149035712205 & 4.81149035712205 \tabularnewline
129 & -3 & -6.98897923680259 & 3.98897923680259 \tabularnewline
130 & -4 & -10.1332189803902 & 6.13321898039024 \tabularnewline
131 & -9 & -11.956462769946 & 2.95646276994597 \tabularnewline
132 & -9 & -9.76961678273619 & 0.769616782736188 \tabularnewline
133 & -7 & -4.30931707871153 & -2.69068292128847 \tabularnewline
134 & -14 & -10.3275014984174 & -3.67249850158259 \tabularnewline
135 & -12 & -17.3360674346795 & 5.33606743467947 \tabularnewline
136 & -16 & -18.5191067505051 & 2.5191067505051 \tabularnewline
137 & -20 & -20.1374007694266 & 0.13740076942665 \tabularnewline
138 & -12 & -16.165729457326 & 4.16572945732599 \tabularnewline
139 & -12 & -13.1670149900208 & 1.16701499002082 \tabularnewline
140 & -10 & -11.3389955142556 & 1.33899551425563 \tabularnewline
141 & -10 & -7.1123090117547 & -2.8876909882453 \tabularnewline
142 & -13 & -11.8036443271922 & -1.19635567280779 \tabularnewline
143 & -16 & -16.2207294359258 & 0.220729435925833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&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]14[/C][C]8.47080048063266[/C][C]5.52919951936734[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]8.92758634403241[/C][C]5.07241365596759[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]9.69923377043439[/C][C]5.30076622956561[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]9.01366639041246[/C][C]3.98633360958754[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]10.0467669297353[/C][C]-2.04676692973535[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.37594629508048[/C][C]0.624053704919523[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.61910451251079[/C][C]-0.619104512510785[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.177226225666602[/C][C]2.8227737743334[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.32640437418886[/C][C]1.67359562581114[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.83965205394121[/C][C]-1.83965205394121[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-1.14615542297038[/C][C]1.14615542297038[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-5.14651466472646[/C][C]1.14651466472646[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-13.5805941047635[/C][C]-0.419405895236472[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-10.7123598358821[/C][C]-7.2876401641179[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-9.36567205233476[/C][C]1.36567205233476[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]0.311830639634532[/C][C]-1.31183063963453[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]3.48499450294894[/C][C]-2.48499450294894[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.52961850276615[/C][C]0.470381497233852[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.462376594402993[/C][C]-0.462376594402993[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.217489342740743[/C][C]0.782510657259257[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-0.77836452757882[/C][C]0.77836452757882[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]4.08898518013437[/C][C]-5.08898518013437[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-5.2669672439582[/C][C]2.2669672439582[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-5.90181066693502[/C][C]2.90181066693502[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-5.06380593282068[/C][C]2.06380593282068[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-7.58693802944875[/C][C]3.58693802944875[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-7.46347520539161[/C][C]-0.536524794608393[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-5.46866249163329[/C][C]-3.53133750836671[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-13.1258619400512[/C][C]0.125861940051202[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-19.0088157013959[/C][C]1.00881570139586[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-10.0417192423105[/C][C]-0.958280757689514[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-8.61735928754664[/C][C]-0.38264071245336[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-9.55116172565187[/C][C]-0.44883827434813[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-11.8158017093635[/C][C]-1.18419829063653[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-16.8943949431163[/C][C]5.89439494311627[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-9.93708860067008[/C][C]4.93708860067008[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-13.2188075766277[/C][C]-1.78119242337228[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-7.47444859204463[/C][C]1.47444859204463[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-5.46271734380575[/C][C]-0.537282656194248[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-4.76181320568204[/C][C]1.76181320568204[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-3.38785585755192[/C][C]2.38785585755192[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-4.27735896272192[/C][C]1.27735896272192[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-2.40489980525548[/C][C]-1.59510019474452[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-4.13514786366207[/C][C]-1.86485213633793[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-0.804396154766398[/C][C]0.804396154766398[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-3.03288476382807[/C][C]-0.967115236171931[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-3.7558614535465[/C][C]1.7558614535465[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-5.67243642601728[/C][C]3.67243642601728[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]-1.57740860532381[/C][C]-4.42259139467619[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-8.28797177864238[/C][C]1.28797177864238[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-4.43172511450308[/C][C]-1.56827488549692[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-2.62253191097893[/C][C]-3.37746808902107[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-0.647518338537184[/C][C]-2.35248166146282[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-3.48640858908786[/C][C]1.48640858908786[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-7.01492314091372[/C][C]2.01492314091372[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-10.0884828658382[/C][C]-0.911517134161775[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-7.05531662918239[/C][C]-3.94468337081761[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-7.16157336818934[/C][C]-3.83842663181066[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-12.3345942854641[/C][C]2.33459428546406[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-18.3261284174035[/C][C]4.32612841740353[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-8.78290216109875[/C][C]0.782902161098753[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-8.96654656693183[/C][C]-0.0334534330681684[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-3.19944405593501[/C][C]-1.80055594406499[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]0.065773067508206[/C][C]-1.06577306750821[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-0.787952459527467[/C][C]-1.21204754047253[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-2.99726286484738[/C][C]-2.00273713515262[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-1.36045137748009[/C][C]-2.63954862251991[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-6.02222911347798[/C][C]0.0222291134779818[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-0.575800416796943[/C][C]-1.42419958320306[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-0.589987516375699[/C][C]-1.4100124836243[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-4.09819273549509[/C][C]2.09819273549509[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-8.10256924072034[/C][C]6.10256924072034[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]-0.606519447014608[/C][C]2.60651944701461[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]-0.469113459195329[/C][C]1.46911345919533[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-2.14213168359047[/C][C]-5.85786831640953[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]2.5601490737742[/C][C]-3.5601490737742[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.7892852108143[/C][C]-0.7892852108143[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]0.0764891463011801[/C][C]-1.07648914630118[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.71035156708909[/C][C]0.289648432910911[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]4.57857861500768[/C][C]-2.57857861500768[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.75008816724462[/C][C]-1.75008816724462[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]0.524229823972655[/C][C]-1.52422982397266[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-4.47475146849554[/C][C]2.47475146849554[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-2.93851703897486[/C][C]0.938517038974855[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-3.0429880640968[/C][C]2.0429880640968[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-10.9267307693712[/C][C]2.92673076937123[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-7.04307925131394[/C][C]3.04307925131394[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-11.9127845528597[/C][C]5.91278455285972[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-5.94843527533398[/C][C]2.94843527533398[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-11.6180860354547[/C][C]8.61808603545474[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-11.0794528839099[/C][C]4.07945288390992[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-9.20047240968194[/C][C]0.20047240968194[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-16.8506321812825[/C][C]5.85063218128254[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-12.716763704099[/C][C]-0.283236295900996[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-11.7057299070294[/C][C]0.705729907029424[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-5.61472512636117[/C][C]-3.38527487363883[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-17.3644313824676[/C][C]0.364431382467596[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-15.7458478071383[/C][C]-6.25415219286171[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-17.5898217921382[/C][C]-7.41017820786181[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-10.0712366925351[/C][C]-9.92876330746487[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-13.4758075524005[/C][C]-10.5241924475995[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-18.2427851136007[/C][C]-5.75721488639928[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-15.1280414302386[/C][C]-6.8719585697614[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-10.4664740963955[/C][C]-8.53352590360452[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-8.1463550807264[/C][C]-9.8536449192736[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-15.3064374581534[/C][C]-1.6935625418466[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-10.8958867691393[/C][C]-0.104113230860703[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-11.0758505477448[/C][C]0.0758505477448361[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-5.18586696895028[/C][C]-6.81413303104972[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-5.15580211789459[/C][C]-4.84419788210541[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-8.65941515084279[/C][C]-6.34058484915721[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-8.04450162759061[/C][C]-6.95549837240939[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-10.7670032983128[/C][C]-4.23299670168717[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-6.0079680021063[/C][C]-6.9920319978937[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-6.81836056716142[/C][C]-1.18163943283858[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-11.6923215495444[/C][C]-1.30767845045562[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-5.75357185174232[/C][C]-3.24642814825768[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-9.34959365347747[/C][C]2.34959365347747[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-9.28268621346491[/C][C]5.28268621346491[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-9.01532940908673[/C][C]5.01532940908673[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-2.50837938263743[/C][C]0.508379382637432[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-3.72848718750272[/C][C]3.72848718750272[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-7.01905309212264[/C][C]5.01905309212264[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-6.7636448479557[/C][C]3.7636448479557[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]-0.617868629131816[/C][C]1.61786862913182[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-11.6747558203145[/C][C]9.67475582031451[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-6.32447117836902[/C][C]5.32447117836902[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.81149035712205[/C][C]4.81149035712205[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-6.98897923680259[/C][C]3.98897923680259[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-10.1332189803902[/C][C]6.13321898039024[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-11.956462769946[/C][C]2.95646276994597[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-9.76961678273619[/C][C]0.769616782736188[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-4.30931707871153[/C][C]-2.69068292128847[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-10.3275014984174[/C][C]-3.67249850158259[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-17.3360674346795[/C][C]5.33606743467947[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-18.5191067505051[/C][C]2.5191067505051[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-20.1374007694266[/C][C]0.13740076942665[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-16.165729457326[/C][C]4.16572945732599[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-13.1670149900208[/C][C]1.16701499002082[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-11.3389955142556[/C][C]1.33899551425563[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-7.1123090117547[/C][C]-2.8876909882453[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-11.8036443271922[/C][C]-1.19635567280779[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-16.2207294359258[/C][C]0.220729435925833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 14 8.47080048063266 5.52919951936734 2 14 8.92758634403241 5.07241365596759 3 15 9.69923377043439 5.30076622956561 4 13 9.01366639041246 3.98633360958754 5 8 10.0467669297353 -2.04676692973535 6 7 6.37594629508048 0.624053704919523 7 3 3.61910451251079 -0.619104512510785 8 3 0.177226225666602 2.8227737743334 9 4 2.32640437418886 1.67359562581114 10 4 5.83965205394121 -1.83965205394121 11 0 -1.14615542297038 1.14615542297038 12 -4 -5.14651466472646 1.14651466472646 13 -14 -13.5805941047635 -0.419405895236472 14 -18 -10.7123598358821 -7.2876401641179 15 -8 -9.36567205233476 1.36567205233476 16 -1 0.311830639634532 -1.31183063963453 17 1 3.48499450294894 -2.48499450294894 18 2 1.52961850276615 0.470381497233852 19 0 0.462376594402993 -0.462376594402993 20 1 0.217489342740743 0.782510657259257 21 0 -0.77836452757882 0.77836452757882 22 -1 4.08898518013437 -5.08898518013437 23 -3 -5.2669672439582 2.2669672439582 24 -3 -5.90181066693502 2.90181066693502 25 -3 -5.06380593282068 2.06380593282068 26 -4 -7.58693802944875 3.58693802944875 27 -8 -7.46347520539161 -0.536524794608393 28 -9 -5.46866249163329 -3.53133750836671 29 -13 -13.1258619400512 0.125861940051202 30 -18 -19.0088157013959 1.00881570139586 31 -11 -10.0417192423105 -0.958280757689514 32 -9 -8.61735928754664 -0.38264071245336 33 -10 -9.55116172565187 -0.44883827434813 34 -13 -11.8158017093635 -1.18419829063653 35 -11 -16.8943949431163 5.89439494311627 36 -5 -9.93708860067008 4.93708860067008 37 -15 -13.2188075766277 -1.78119242337228 38 -6 -7.47444859204463 1.47444859204463 39 -6 -5.46271734380575 -0.537282656194248 40 -3 -4.76181320568204 1.76181320568204 41 -1 -3.38785585755192 2.38785585755192 42 -3 -4.27735896272192 1.27735896272192 43 -4 -2.40489980525548 -1.59510019474452 44 -6 -4.13514786366207 -1.86485213633793 45 0 -0.804396154766398 0.804396154766398 46 -4 -3.03288476382807 -0.967115236171931 47 -2 -3.7558614535465 1.7558614535465 48 -2 -5.67243642601728 3.67243642601728 49 -6 -1.57740860532381 -4.42259139467619 50 -7 -8.28797177864238 1.28797177864238 51 -6 -4.43172511450308 -1.56827488549692 52 -6 -2.62253191097893 -3.37746808902107 53 -3 -0.647518338537184 -2.35248166146282 54 -2 -3.48640858908786 1.48640858908786 55 -5 -7.01492314091372 2.01492314091372 56 -11 -10.0884828658382 -0.911517134161775 57 -11 -7.05531662918239 -3.94468337081761 58 -11 -7.16157336818934 -3.83842663181066 59 -10 -12.3345942854641 2.33459428546406 60 -14 -18.3261284174035 4.32612841740353 61 -8 -8.78290216109875 0.782902161098753 62 -9 -8.96654656693183 -0.0334534330681684 63 -5 -3.19944405593501 -1.80055594406499 64 -1 0.065773067508206 -1.06577306750821 65 -2 -0.787952459527467 -1.21204754047253 66 -5 -2.99726286484738 -2.00273713515262 67 -4 -1.36045137748009 -2.63954862251991 68 -6 -6.02222911347798 0.0222291134779818 69 -2 -0.575800416796943 -1.42419958320306 70 -2 -0.589987516375699 -1.4100124836243 71 -2 -4.09819273549509 2.09819273549509 72 -2 -8.10256924072034 6.10256924072034 73 2 -0.606519447014608 2.60651944701461 74 1 -0.469113459195329 1.46911345919533 75 -8 -2.14213168359047 -5.85786831640953 76 -1 2.5601490737742 -3.5601490737742 77 1 1.7892852108143 -0.7892852108143 78 -1 0.0764891463011801 -1.07648914630118 79 2 1.71035156708909 0.289648432910911 80 2 4.57857861500768 -2.57857861500768 81 1 2.75008816724462 -1.75008816724462 82 -1 0.524229823972655 -1.52422982397266 83 -2 -4.47475146849554 2.47475146849554 84 -2 -2.93851703897486 0.938517038974855 85 -1 -3.0429880640968 2.0429880640968 86 -8 -10.9267307693712 2.92673076937123 87 -4 -7.04307925131394 3.04307925131394 88 -6 -11.9127845528597 5.91278455285972 89 -3 -5.94843527533398 2.94843527533398 90 -3 -11.6180860354547 8.61808603545474 91 -7 -11.0794528839099 4.07945288390992 92 -9 -9.20047240968194 0.20047240968194 93 -11 -16.8506321812825 5.85063218128254 94 -13 -12.716763704099 -0.283236295900996 95 -11 -11.7057299070294 0.705729907029424 96 -9 -5.61472512636117 -3.38527487363883 97 -17 -17.3644313824676 0.364431382467596 98 -22 -15.7458478071383 -6.25415219286171 99 -25 -17.5898217921382 -7.41017820786181 100 -20 -10.0712366925351 -9.92876330746487 101 -24 -13.4758075524005 -10.5241924475995 102 -24 -18.2427851136007 -5.75721488639928 103 -22 -15.1280414302386 -6.8719585697614 104 -19 -10.4664740963955 -8.53352590360452 105 -18 -8.1463550807264 -9.8536449192736 106 -17 -15.3064374581534 -1.6935625418466 107 -11 -10.8958867691393 -0.104113230860703 108 -11 -11.0758505477448 0.0758505477448361 109 -12 -5.18586696895028 -6.81413303104972 110 -10 -5.15580211789459 -4.84419788210541 111 -15 -8.65941515084279 -6.34058484915721 112 -15 -8.04450162759061 -6.95549837240939 113 -15 -10.7670032983128 -4.23299670168717 114 -13 -6.0079680021063 -6.9920319978937 115 -8 -6.81836056716142 -1.18163943283858 116 -13 -11.6923215495444 -1.30767845045562 117 -9 -5.75357185174232 -3.24642814825768 118 -7 -9.34959365347747 2.34959365347747 119 -4 -9.28268621346491 5.28268621346491 120 -4 -9.01532940908673 5.01532940908673 121 -2 -2.50837938263743 0.508379382637432 122 0 -3.72848718750272 3.72848718750272 123 -2 -7.01905309212264 5.01905309212264 124 -3 -6.7636448479557 3.7636448479557 125 1 -0.617868629131816 1.61786862913182 126 -2 -11.6747558203145 9.67475582031451 127 -1 -6.32447117836902 5.32447117836902 128 1 -3.81149035712205 4.81149035712205 129 -3 -6.98897923680259 3.98897923680259 130 -4 -10.1332189803902 6.13321898039024 131 -9 -11.956462769946 2.95646276994597 132 -9 -9.76961678273619 0.769616782736188 133 -7 -4.30931707871153 -2.69068292128847 134 -14 -10.3275014984174 -3.67249850158259 135 -12 -17.3360674346795 5.33606743467947 136 -16 -18.5191067505051 2.5191067505051 137 -20 -20.1374007694266 0.13740076942665 138 -12 -16.165729457326 4.16572945732599 139 -12 -13.1670149900208 1.16701499002082 140 -10 -11.3389955142556 1.33899551425563 141 -10 -7.1123090117547 -2.8876909882453 142 -13 -11.8036443271922 -1.19635567280779 143 -16 -16.2207294359258 0.220729435925833

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.110655678946208 0.221311357892415 0.889344321053792 11 0.0535156150978646 0.107031230195729 0.946484384902135 12 0.0430459103255239 0.0860918206510477 0.956954089674476 13 0.112647052078738 0.225294104157477 0.887352947921262 14 0.446999820729367 0.893999641458733 0.553000179270633 15 0.365919671113597 0.731839342227194 0.634080328886403 16 0.317334479081425 0.63466895816285 0.682665520918575 17 0.279549589438369 0.559099178876738 0.720450410561631 18 0.210675955235049 0.421351910470098 0.789324044764951 19 0.176275575869785 0.35255115173957 0.823724424130215 20 0.150005087385452 0.300010174770904 0.849994912614548 21 0.121011700821828 0.242023401643656 0.878988299178172 22 0.0956656616199818 0.191331323239964 0.904334338380018 23 0.09346979174588 0.18693958349176 0.90653020825412 24 0.0953616835325865 0.190723367065173 0.904638316467414 25 0.0757631445894694 0.151526289178939 0.924236855410531 26 0.0698272795511077 0.139654559102215 0.930172720448892 27 0.0594096195303283 0.118819239060657 0.940590380469672 28 0.0464088959385104 0.0928177918770208 0.95359110406149 29 0.0359397422426379 0.0718794844852758 0.964060257757362 30 0.0290105617205811 0.0580211234411623 0.970989438279419 31 0.022229804546305 0.04445960909261 0.977770195453695 32 0.0171894540017453 0.0343789080034907 0.982810545998255 33 0.0113454851296203 0.0226909702592407 0.98865451487038 34 0.00720629583239052 0.014412591664781 0.992793704167609 35 0.0088574471972527 0.0177148943945054 0.991142552802747 36 0.00726436647294272 0.0145287329458854 0.992735633527057 37 0.0110465855319583 0.0220931710639166 0.988953414468042 38 0.00884831558214533 0.0176966311642907 0.991151684417855 39 0.0104703610303207 0.0209407220606413 0.989529638969679 40 0.00828134820392309 0.0165626964078462 0.991718651796077 41 0.00717245426434951 0.014344908528699 0.99282754573565 42 0.00630620660287696 0.0126124132057539 0.993693793397123 43 0.0050011441138625 0.010002288227725 0.994998855886137 44 0.00362802461298067 0.00725604922596135 0.996371975387019 45 0.00421426718582344 0.00842853437164688 0.995785732814177 46 0.00333342385718418 0.00666684771436836 0.996666576142816 47 0.00322786357621244 0.00645572715242488 0.996772136423788 48 0.00339438276051062 0.00678876552102123 0.996605617239489 49 0.00656782753846692 0.0131356550769338 0.993432172461533 50 0.00491100243392733 0.00982200486785465 0.995088997566073 51 0.00373172168685447 0.00746344337370895 0.996268278313146 52 0.00315858347001369 0.00631716694002738 0.996841416529986 53 0.00227013674523304 0.00454027349046608 0.997729863254767 54 0.00248863502625361 0.00497727005250723 0.997511364973746 55 0.00347367305202194 0.00694734610404388 0.996526326947978 56 0.0026574948232219 0.00531498964644381 0.997342505176778 57 0.00220137661185477 0.00440275322370955 0.997798623388145 58 0.00147428106452197 0.00294856212904395 0.998525718935478 59 0.00130695922482382 0.00261391844964763 0.998693040775176 60 0.00262506753929826 0.00525013507859653 0.997374932460702 61 0.00182526873694721 0.00365053747389442 0.998174731263053 62 0.00129999957255799 0.00259999914511599 0.998700000427442 63 0.00125285804593617 0.00250571609187233 0.998747141954064 64 0.000872626694062139 0.00174525338812428 0.999127373305938 65 0.000656026327459295 0.00131205265491859 0.999343973672541 66 0.000469959484867828 0.000939918969735657 0.999530040515132 67 0.000305819996095217 0.000611639992190433 0.999694180003905 68 0.000344056562742105 0.00068811312548421 0.999655943437258 69 0.000268011358804318 0.000536022717608637 0.999731988641196 70 0.000196902907461637 0.000393805814923274 0.999803097092538 71 0.000197047889167346 0.000394095778334691 0.999802952110833 72 0.00126460060712006 0.00252920121424012 0.99873539939288 73 0.00128257731302715 0.0025651546260543 0.998717422686973 74 0.0010914697573763 0.00218293951475261 0.998908530242624 75 0.00105560123978471 0.00211120247956942 0.998944398760215 76 0.00079505885978197 0.00159011771956394 0.999204941140218 77 0.000613562805435247 0.00122712561087049 0.999386437194565 78 0.000471155147793443 0.000942310295586885 0.999528844852207 79 0.00039397557476814 0.00078795114953628 0.999606024425232 80 0.000260586196129024 0.000521172392258049 0.999739413803871 81 0.000183477577934631 0.000366955155869263 0.999816522422065 82 0.000142629663837628 0.000285259327675256 0.999857370336162 83 0.000180873526786191 0.000361747053572382 0.999819126473214 84 0.000138564636037073 0.000277129272074145 0.999861435363963 85 0.000141261915115715 0.00028252383023143 0.999858738084884 86 0.000132648645648313 0.000265297291296626 0.999867351354352 87 0.000144682198135589 0.000289364396271177 0.999855317801864 88 0.000294008087829093 0.000588016175658185 0.999705991912171 89 0.000200784302140246 0.000401568604280491 0.99979921569786 90 0.00148656611488788 0.00297313222977576 0.998513433885112 91 0.00152198979835233 0.00304397959670466 0.998478010201648 92 0.0011188880410006 0.00223777608200121 0.998881111958999 93 0.00354615205315481 0.00709230410630962 0.996453847946845 94 0.00298947347333092 0.00597894694666184 0.997010526526669 95 0.00273136124395477 0.00546272248790954 0.997268638756045 96 0.00329465467324138 0.00658930934648277 0.996705345326759 97 0.00257063610793965 0.00514127221587931 0.99742936389206 98 0.00677821599495226 0.0135564319899045 0.993221784005048 99 0.0201352659561793 0.0402705319123585 0.979864734043821 100 0.062820958785435 0.12564191757087 0.937179041214565 101 0.192007350122476 0.384014700244952 0.807992649877524 102 0.230043530497202 0.460087060994404 0.769956469502798 103 0.214721850407298 0.429443700814597 0.785278149592702 104 0.207961095253373 0.415922190506745 0.792038904746627 105 0.219563006035974 0.439126012071949 0.780436993964026 106 0.183258111273959 0.366516222547917 0.816741888726041 107 0.229326653667353 0.458653307334707 0.770673346332647 108 0.316403079782238 0.632806159564476 0.683596920217762 109 0.297144269303175 0.594288538606349 0.702855730696825 110 0.251924206643084 0.503848413286168 0.748075793356916 111 0.231502070150351 0.463004140300703 0.768497929849649 112 0.436502502624296 0.873005005248593 0.563497497375704 113 0.460581288485142 0.921162576970285 0.539418711514858 114 0.608687554711052 0.782624890577897 0.391312445288948 115 0.570509777089325 0.858980445821349 0.429490222910675 116 0.560063257907362 0.879873484185275 0.439936742092638 117 0.713644488654047 0.572711022691906 0.286355511345953 118 0.826358906827389 0.347282186345221 0.173641093172611 119 0.816301822969827 0.367396354060345 0.183698177030173 120 0.799490559221305 0.40101888155739 0.200509440778695 121 0.768520395059016 0.462959209881967 0.231479604940984 122 0.734075087011038 0.531849825977924 0.265924912988962 123 0.708848706226928 0.582302587546144 0.291151293773072 124 0.725735882362358 0.548528235275284 0.274264117637642 125 0.728333928787653 0.543332142424695 0.271666071212347 126 0.737079280217398 0.525841439565203 0.262920719782602 127 0.734742096415945 0.530515807168111 0.265257903584055 128 0.746176930829117 0.507646138341765 0.253823069170883 129 0.824481304305479 0.351037391389042 0.175518695694521 130 0.884626241023391 0.230747517953217 0.115373758976609 131 0.80215137463198 0.39569725073604 0.19784862536802 132 0.716792454314253 0.566415091371494 0.283207545685747 133 0.793472279624471 0.413055440751058 0.206527720375529

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.110655678946208 & 0.221311357892415 & 0.889344321053792 \tabularnewline
11 & 0.0535156150978646 & 0.107031230195729 & 0.946484384902135 \tabularnewline
12 & 0.0430459103255239 & 0.0860918206510477 & 0.956954089674476 \tabularnewline
13 & 0.112647052078738 & 0.225294104157477 & 0.887352947921262 \tabularnewline
14 & 0.446999820729367 & 0.893999641458733 & 0.553000179270633 \tabularnewline
15 & 0.365919671113597 & 0.731839342227194 & 0.634080328886403 \tabularnewline
16 & 0.317334479081425 & 0.63466895816285 & 0.682665520918575 \tabularnewline
17 & 0.279549589438369 & 0.559099178876738 & 0.720450410561631 \tabularnewline
18 & 0.210675955235049 & 0.421351910470098 & 0.789324044764951 \tabularnewline
19 & 0.176275575869785 & 0.35255115173957 & 0.823724424130215 \tabularnewline
20 & 0.150005087385452 & 0.300010174770904 & 0.849994912614548 \tabularnewline
21 & 0.121011700821828 & 0.242023401643656 & 0.878988299178172 \tabularnewline
22 & 0.0956656616199818 & 0.191331323239964 & 0.904334338380018 \tabularnewline
23 & 0.09346979174588 & 0.18693958349176 & 0.90653020825412 \tabularnewline
24 & 0.0953616835325865 & 0.190723367065173 & 0.904638316467414 \tabularnewline
25 & 0.0757631445894694 & 0.151526289178939 & 0.924236855410531 \tabularnewline
26 & 0.0698272795511077 & 0.139654559102215 & 0.930172720448892 \tabularnewline
27 & 0.0594096195303283 & 0.118819239060657 & 0.940590380469672 \tabularnewline
28 & 0.0464088959385104 & 0.0928177918770208 & 0.95359110406149 \tabularnewline
29 & 0.0359397422426379 & 0.0718794844852758 & 0.964060257757362 \tabularnewline
30 & 0.0290105617205811 & 0.0580211234411623 & 0.970989438279419 \tabularnewline
31 & 0.022229804546305 & 0.04445960909261 & 0.977770195453695 \tabularnewline
32 & 0.0171894540017453 & 0.0343789080034907 & 0.982810545998255 \tabularnewline
33 & 0.0113454851296203 & 0.0226909702592407 & 0.98865451487038 \tabularnewline
34 & 0.00720629583239052 & 0.014412591664781 & 0.992793704167609 \tabularnewline
35 & 0.0088574471972527 & 0.0177148943945054 & 0.991142552802747 \tabularnewline
36 & 0.00726436647294272 & 0.0145287329458854 & 0.992735633527057 \tabularnewline
37 & 0.0110465855319583 & 0.0220931710639166 & 0.988953414468042 \tabularnewline
38 & 0.00884831558214533 & 0.0176966311642907 & 0.991151684417855 \tabularnewline
39 & 0.0104703610303207 & 0.0209407220606413 & 0.989529638969679 \tabularnewline
40 & 0.00828134820392309 & 0.0165626964078462 & 0.991718651796077 \tabularnewline
41 & 0.00717245426434951 & 0.014344908528699 & 0.99282754573565 \tabularnewline
42 & 0.00630620660287696 & 0.0126124132057539 & 0.993693793397123 \tabularnewline
43 & 0.0050011441138625 & 0.010002288227725 & 0.994998855886137 \tabularnewline
44 & 0.00362802461298067 & 0.00725604922596135 & 0.996371975387019 \tabularnewline
45 & 0.00421426718582344 & 0.00842853437164688 & 0.995785732814177 \tabularnewline
46 & 0.00333342385718418 & 0.00666684771436836 & 0.996666576142816 \tabularnewline
47 & 0.00322786357621244 & 0.00645572715242488 & 0.996772136423788 \tabularnewline
48 & 0.00339438276051062 & 0.00678876552102123 & 0.996605617239489 \tabularnewline
49 & 0.00656782753846692 & 0.0131356550769338 & 0.993432172461533 \tabularnewline
50 & 0.00491100243392733 & 0.00982200486785465 & 0.995088997566073 \tabularnewline
51 & 0.00373172168685447 & 0.00746344337370895 & 0.996268278313146 \tabularnewline
52 & 0.00315858347001369 & 0.00631716694002738 & 0.996841416529986 \tabularnewline
53 & 0.00227013674523304 & 0.00454027349046608 & 0.997729863254767 \tabularnewline
54 & 0.00248863502625361 & 0.00497727005250723 & 0.997511364973746 \tabularnewline
55 & 0.00347367305202194 & 0.00694734610404388 & 0.996526326947978 \tabularnewline
56 & 0.0026574948232219 & 0.00531498964644381 & 0.997342505176778 \tabularnewline
57 & 0.00220137661185477 & 0.00440275322370955 & 0.997798623388145 \tabularnewline
58 & 0.00147428106452197 & 0.00294856212904395 & 0.998525718935478 \tabularnewline
59 & 0.00130695922482382 & 0.00261391844964763 & 0.998693040775176 \tabularnewline
60 & 0.00262506753929826 & 0.00525013507859653 & 0.997374932460702 \tabularnewline
61 & 0.00182526873694721 & 0.00365053747389442 & 0.998174731263053 \tabularnewline
62 & 0.00129999957255799 & 0.00259999914511599 & 0.998700000427442 \tabularnewline
63 & 0.00125285804593617 & 0.00250571609187233 & 0.998747141954064 \tabularnewline
64 & 0.000872626694062139 & 0.00174525338812428 & 0.999127373305938 \tabularnewline
65 & 0.000656026327459295 & 0.00131205265491859 & 0.999343973672541 \tabularnewline
66 & 0.000469959484867828 & 0.000939918969735657 & 0.999530040515132 \tabularnewline
67 & 0.000305819996095217 & 0.000611639992190433 & 0.999694180003905 \tabularnewline
68 & 0.000344056562742105 & 0.00068811312548421 & 0.999655943437258 \tabularnewline
69 & 0.000268011358804318 & 0.000536022717608637 & 0.999731988641196 \tabularnewline
70 & 0.000196902907461637 & 0.000393805814923274 & 0.999803097092538 \tabularnewline
71 & 0.000197047889167346 & 0.000394095778334691 & 0.999802952110833 \tabularnewline
72 & 0.00126460060712006 & 0.00252920121424012 & 0.99873539939288 \tabularnewline
73 & 0.00128257731302715 & 0.0025651546260543 & 0.998717422686973 \tabularnewline
74 & 0.0010914697573763 & 0.00218293951475261 & 0.998908530242624 \tabularnewline
75 & 0.00105560123978471 & 0.00211120247956942 & 0.998944398760215 \tabularnewline
76 & 0.00079505885978197 & 0.00159011771956394 & 0.999204941140218 \tabularnewline
77 & 0.000613562805435247 & 0.00122712561087049 & 0.999386437194565 \tabularnewline
78 & 0.000471155147793443 & 0.000942310295586885 & 0.999528844852207 \tabularnewline
79 & 0.00039397557476814 & 0.00078795114953628 & 0.999606024425232 \tabularnewline
80 & 0.000260586196129024 & 0.000521172392258049 & 0.999739413803871 \tabularnewline
81 & 0.000183477577934631 & 0.000366955155869263 & 0.999816522422065 \tabularnewline
82 & 0.000142629663837628 & 0.000285259327675256 & 0.999857370336162 \tabularnewline
83 & 0.000180873526786191 & 0.000361747053572382 & 0.999819126473214 \tabularnewline
84 & 0.000138564636037073 & 0.000277129272074145 & 0.999861435363963 \tabularnewline
85 & 0.000141261915115715 & 0.00028252383023143 & 0.999858738084884 \tabularnewline
86 & 0.000132648645648313 & 0.000265297291296626 & 0.999867351354352 \tabularnewline
87 & 0.000144682198135589 & 0.000289364396271177 & 0.999855317801864 \tabularnewline
88 & 0.000294008087829093 & 0.000588016175658185 & 0.999705991912171 \tabularnewline
89 & 0.000200784302140246 & 0.000401568604280491 & 0.99979921569786 \tabularnewline
90 & 0.00148656611488788 & 0.00297313222977576 & 0.998513433885112 \tabularnewline
91 & 0.00152198979835233 & 0.00304397959670466 & 0.998478010201648 \tabularnewline
92 & 0.0011188880410006 & 0.00223777608200121 & 0.998881111958999 \tabularnewline
93 & 0.00354615205315481 & 0.00709230410630962 & 0.996453847946845 \tabularnewline
94 & 0.00298947347333092 & 0.00597894694666184 & 0.997010526526669 \tabularnewline
95 & 0.00273136124395477 & 0.00546272248790954 & 0.997268638756045 \tabularnewline
96 & 0.00329465467324138 & 0.00658930934648277 & 0.996705345326759 \tabularnewline
97 & 0.00257063610793965 & 0.00514127221587931 & 0.99742936389206 \tabularnewline
98 & 0.00677821599495226 & 0.0135564319899045 & 0.993221784005048 \tabularnewline
99 & 0.0201352659561793 & 0.0402705319123585 & 0.979864734043821 \tabularnewline
100 & 0.062820958785435 & 0.12564191757087 & 0.937179041214565 \tabularnewline
101 & 0.192007350122476 & 0.384014700244952 & 0.807992649877524 \tabularnewline
102 & 0.230043530497202 & 0.460087060994404 & 0.769956469502798 \tabularnewline
103 & 0.214721850407298 & 0.429443700814597 & 0.785278149592702 \tabularnewline
104 & 0.207961095253373 & 0.415922190506745 & 0.792038904746627 \tabularnewline
105 & 0.219563006035974 & 0.439126012071949 & 0.780436993964026 \tabularnewline
106 & 0.183258111273959 & 0.366516222547917 & 0.816741888726041 \tabularnewline
107 & 0.229326653667353 & 0.458653307334707 & 0.770673346332647 \tabularnewline
108 & 0.316403079782238 & 0.632806159564476 & 0.683596920217762 \tabularnewline
109 & 0.297144269303175 & 0.594288538606349 & 0.702855730696825 \tabularnewline
110 & 0.251924206643084 & 0.503848413286168 & 0.748075793356916 \tabularnewline
111 & 0.231502070150351 & 0.463004140300703 & 0.768497929849649 \tabularnewline
112 & 0.436502502624296 & 0.873005005248593 & 0.563497497375704 \tabularnewline
113 & 0.460581288485142 & 0.921162576970285 & 0.539418711514858 \tabularnewline
114 & 0.608687554711052 & 0.782624890577897 & 0.391312445288948 \tabularnewline
115 & 0.570509777089325 & 0.858980445821349 & 0.429490222910675 \tabularnewline
116 & 0.560063257907362 & 0.879873484185275 & 0.439936742092638 \tabularnewline
117 & 0.713644488654047 & 0.572711022691906 & 0.286355511345953 \tabularnewline
118 & 0.826358906827389 & 0.347282186345221 & 0.173641093172611 \tabularnewline
119 & 0.816301822969827 & 0.367396354060345 & 0.183698177030173 \tabularnewline
120 & 0.799490559221305 & 0.40101888155739 & 0.200509440778695 \tabularnewline
121 & 0.768520395059016 & 0.462959209881967 & 0.231479604940984 \tabularnewline
122 & 0.734075087011038 & 0.531849825977924 & 0.265924912988962 \tabularnewline
123 & 0.708848706226928 & 0.582302587546144 & 0.291151293773072 \tabularnewline
124 & 0.725735882362358 & 0.548528235275284 & 0.274264117637642 \tabularnewline
125 & 0.728333928787653 & 0.543332142424695 & 0.271666071212347 \tabularnewline
126 & 0.737079280217398 & 0.525841439565203 & 0.262920719782602 \tabularnewline
127 & 0.734742096415945 & 0.530515807168111 & 0.265257903584055 \tabularnewline
128 & 0.746176930829117 & 0.507646138341765 & 0.253823069170883 \tabularnewline
129 & 0.824481304305479 & 0.351037391389042 & 0.175518695694521 \tabularnewline
130 & 0.884626241023391 & 0.230747517953217 & 0.115373758976609 \tabularnewline
131 & 0.80215137463198 & 0.39569725073604 & 0.19784862536802 \tabularnewline
132 & 0.716792454314253 & 0.566415091371494 & 0.283207545685747 \tabularnewline
133 & 0.793472279624471 & 0.413055440751058 & 0.206527720375529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&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]0.110655678946208[/C][C]0.221311357892415[/C][C]0.889344321053792[/C][/ROW]
[ROW][C]11[/C][C]0.0535156150978646[/C][C]0.107031230195729[/C][C]0.946484384902135[/C][/ROW]
[ROW][C]12[/C][C]0.0430459103255239[/C][C]0.0860918206510477[/C][C]0.956954089674476[/C][/ROW]
[ROW][C]13[/C][C]0.112647052078738[/C][C]0.225294104157477[/C][C]0.887352947921262[/C][/ROW]
[ROW][C]14[/C][C]0.446999820729367[/C][C]0.893999641458733[/C][C]0.553000179270633[/C][/ROW]
[ROW][C]15[/C][C]0.365919671113597[/C][C]0.731839342227194[/C][C]0.634080328886403[/C][/ROW]
[ROW][C]16[/C][C]0.317334479081425[/C][C]0.63466895816285[/C][C]0.682665520918575[/C][/ROW]
[ROW][C]17[/C][C]0.279549589438369[/C][C]0.559099178876738[/C][C]0.720450410561631[/C][/ROW]
[ROW][C]18[/C][C]0.210675955235049[/C][C]0.421351910470098[/C][C]0.789324044764951[/C][/ROW]
[ROW][C]19[/C][C]0.176275575869785[/C][C]0.35255115173957[/C][C]0.823724424130215[/C][/ROW]
[ROW][C]20[/C][C]0.150005087385452[/C][C]0.300010174770904[/C][C]0.849994912614548[/C][/ROW]
[ROW][C]21[/C][C]0.121011700821828[/C][C]0.242023401643656[/C][C]0.878988299178172[/C][/ROW]
[ROW][C]22[/C][C]0.0956656616199818[/C][C]0.191331323239964[/C][C]0.904334338380018[/C][/ROW]
[ROW][C]23[/C][C]0.09346979174588[/C][C]0.18693958349176[/C][C]0.90653020825412[/C][/ROW]
[ROW][C]24[/C][C]0.0953616835325865[/C][C]0.190723367065173[/C][C]0.904638316467414[/C][/ROW]
[ROW][C]25[/C][C]0.0757631445894694[/C][C]0.151526289178939[/C][C]0.924236855410531[/C][/ROW]
[ROW][C]26[/C][C]0.0698272795511077[/C][C]0.139654559102215[/C][C]0.930172720448892[/C][/ROW]
[ROW][C]27[/C][C]0.0594096195303283[/C][C]0.118819239060657[/C][C]0.940590380469672[/C][/ROW]
[ROW][C]28[/C][C]0.0464088959385104[/C][C]0.0928177918770208[/C][C]0.95359110406149[/C][/ROW]
[ROW][C]29[/C][C]0.0359397422426379[/C][C]0.0718794844852758[/C][C]0.964060257757362[/C][/ROW]
[ROW][C]30[/C][C]0.0290105617205811[/C][C]0.0580211234411623[/C][C]0.970989438279419[/C][/ROW]
[ROW][C]31[/C][C]0.022229804546305[/C][C]0.04445960909261[/C][C]0.977770195453695[/C][/ROW]
[ROW][C]32[/C][C]0.0171894540017453[/C][C]0.0343789080034907[/C][C]0.982810545998255[/C][/ROW]
[ROW][C]33[/C][C]0.0113454851296203[/C][C]0.0226909702592407[/C][C]0.98865451487038[/C][/ROW]
[ROW][C]34[/C][C]0.00720629583239052[/C][C]0.014412591664781[/C][C]0.992793704167609[/C][/ROW]
[ROW][C]35[/C][C]0.0088574471972527[/C][C]0.0177148943945054[/C][C]0.991142552802747[/C][/ROW]
[ROW][C]36[/C][C]0.00726436647294272[/C][C]0.0145287329458854[/C][C]0.992735633527057[/C][/ROW]
[ROW][C]37[/C][C]0.0110465855319583[/C][C]0.0220931710639166[/C][C]0.988953414468042[/C][/ROW]
[ROW][C]38[/C][C]0.00884831558214533[/C][C]0.0176966311642907[/C][C]0.991151684417855[/C][/ROW]
[ROW][C]39[/C][C]0.0104703610303207[/C][C]0.0209407220606413[/C][C]0.989529638969679[/C][/ROW]
[ROW][C]40[/C][C]0.00828134820392309[/C][C]0.0165626964078462[/C][C]0.991718651796077[/C][/ROW]
[ROW][C]41[/C][C]0.00717245426434951[/C][C]0.014344908528699[/C][C]0.99282754573565[/C][/ROW]
[ROW][C]42[/C][C]0.00630620660287696[/C][C]0.0126124132057539[/C][C]0.993693793397123[/C][/ROW]
[ROW][C]43[/C][C]0.0050011441138625[/C][C]0.010002288227725[/C][C]0.994998855886137[/C][/ROW]
[ROW][C]44[/C][C]0.00362802461298067[/C][C]0.00725604922596135[/C][C]0.996371975387019[/C][/ROW]
[ROW][C]45[/C][C]0.00421426718582344[/C][C]0.00842853437164688[/C][C]0.995785732814177[/C][/ROW]
[ROW][C]46[/C][C]0.00333342385718418[/C][C]0.00666684771436836[/C][C]0.996666576142816[/C][/ROW]
[ROW][C]47[/C][C]0.00322786357621244[/C][C]0.00645572715242488[/C][C]0.996772136423788[/C][/ROW]
[ROW][C]48[/C][C]0.00339438276051062[/C][C]0.00678876552102123[/C][C]0.996605617239489[/C][/ROW]
[ROW][C]49[/C][C]0.00656782753846692[/C][C]0.0131356550769338[/C][C]0.993432172461533[/C][/ROW]
[ROW][C]50[/C][C]0.00491100243392733[/C][C]0.00982200486785465[/C][C]0.995088997566073[/C][/ROW]
[ROW][C]51[/C][C]0.00373172168685447[/C][C]0.00746344337370895[/C][C]0.996268278313146[/C][/ROW]
[ROW][C]52[/C][C]0.00315858347001369[/C][C]0.00631716694002738[/C][C]0.996841416529986[/C][/ROW]
[ROW][C]53[/C][C]0.00227013674523304[/C][C]0.00454027349046608[/C][C]0.997729863254767[/C][/ROW]
[ROW][C]54[/C][C]0.00248863502625361[/C][C]0.00497727005250723[/C][C]0.997511364973746[/C][/ROW]
[ROW][C]55[/C][C]0.00347367305202194[/C][C]0.00694734610404388[/C][C]0.996526326947978[/C][/ROW]
[ROW][C]56[/C][C]0.0026574948232219[/C][C]0.00531498964644381[/C][C]0.997342505176778[/C][/ROW]
[ROW][C]57[/C][C]0.00220137661185477[/C][C]0.00440275322370955[/C][C]0.997798623388145[/C][/ROW]
[ROW][C]58[/C][C]0.00147428106452197[/C][C]0.00294856212904395[/C][C]0.998525718935478[/C][/ROW]
[ROW][C]59[/C][C]0.00130695922482382[/C][C]0.00261391844964763[/C][C]0.998693040775176[/C][/ROW]
[ROW][C]60[/C][C]0.00262506753929826[/C][C]0.00525013507859653[/C][C]0.997374932460702[/C][/ROW]
[ROW][C]61[/C][C]0.00182526873694721[/C][C]0.00365053747389442[/C][C]0.998174731263053[/C][/ROW]
[ROW][C]62[/C][C]0.00129999957255799[/C][C]0.00259999914511599[/C][C]0.998700000427442[/C][/ROW]
[ROW][C]63[/C][C]0.00125285804593617[/C][C]0.00250571609187233[/C][C]0.998747141954064[/C][/ROW]
[ROW][C]64[/C][C]0.000872626694062139[/C][C]0.00174525338812428[/C][C]0.999127373305938[/C][/ROW]
[ROW][C]65[/C][C]0.000656026327459295[/C][C]0.00131205265491859[/C][C]0.999343973672541[/C][/ROW]
[ROW][C]66[/C][C]0.000469959484867828[/C][C]0.000939918969735657[/C][C]0.999530040515132[/C][/ROW]
[ROW][C]67[/C][C]0.000305819996095217[/C][C]0.000611639992190433[/C][C]0.999694180003905[/C][/ROW]
[ROW][C]68[/C][C]0.000344056562742105[/C][C]0.00068811312548421[/C][C]0.999655943437258[/C][/ROW]
[ROW][C]69[/C][C]0.000268011358804318[/C][C]0.000536022717608637[/C][C]0.999731988641196[/C][/ROW]
[ROW][C]70[/C][C]0.000196902907461637[/C][C]0.000393805814923274[/C][C]0.999803097092538[/C][/ROW]
[ROW][C]71[/C][C]0.000197047889167346[/C][C]0.000394095778334691[/C][C]0.999802952110833[/C][/ROW]
[ROW][C]72[/C][C]0.00126460060712006[/C][C]0.00252920121424012[/C][C]0.99873539939288[/C][/ROW]
[ROW][C]73[/C][C]0.00128257731302715[/C][C]0.0025651546260543[/C][C]0.998717422686973[/C][/ROW]
[ROW][C]74[/C][C]0.0010914697573763[/C][C]0.00218293951475261[/C][C]0.998908530242624[/C][/ROW]
[ROW][C]75[/C][C]0.00105560123978471[/C][C]0.00211120247956942[/C][C]0.998944398760215[/C][/ROW]
[ROW][C]76[/C][C]0.00079505885978197[/C][C]0.00159011771956394[/C][C]0.999204941140218[/C][/ROW]
[ROW][C]77[/C][C]0.000613562805435247[/C][C]0.00122712561087049[/C][C]0.999386437194565[/C][/ROW]
[ROW][C]78[/C][C]0.000471155147793443[/C][C]0.000942310295586885[/C][C]0.999528844852207[/C][/ROW]
[ROW][C]79[/C][C]0.00039397557476814[/C][C]0.00078795114953628[/C][C]0.999606024425232[/C][/ROW]
[ROW][C]80[/C][C]0.000260586196129024[/C][C]0.000521172392258049[/C][C]0.999739413803871[/C][/ROW]
[ROW][C]81[/C][C]0.000183477577934631[/C][C]0.000366955155869263[/C][C]0.999816522422065[/C][/ROW]
[ROW][C]82[/C][C]0.000142629663837628[/C][C]0.000285259327675256[/C][C]0.999857370336162[/C][/ROW]
[ROW][C]83[/C][C]0.000180873526786191[/C][C]0.000361747053572382[/C][C]0.999819126473214[/C][/ROW]
[ROW][C]84[/C][C]0.000138564636037073[/C][C]0.000277129272074145[/C][C]0.999861435363963[/C][/ROW]
[ROW][C]85[/C][C]0.000141261915115715[/C][C]0.00028252383023143[/C][C]0.999858738084884[/C][/ROW]
[ROW][C]86[/C][C]0.000132648645648313[/C][C]0.000265297291296626[/C][C]0.999867351354352[/C][/ROW]
[ROW][C]87[/C][C]0.000144682198135589[/C][C]0.000289364396271177[/C][C]0.999855317801864[/C][/ROW]
[ROW][C]88[/C][C]0.000294008087829093[/C][C]0.000588016175658185[/C][C]0.999705991912171[/C][/ROW]
[ROW][C]89[/C][C]0.000200784302140246[/C][C]0.000401568604280491[/C][C]0.99979921569786[/C][/ROW]
[ROW][C]90[/C][C]0.00148656611488788[/C][C]0.00297313222977576[/C][C]0.998513433885112[/C][/ROW]
[ROW][C]91[/C][C]0.00152198979835233[/C][C]0.00304397959670466[/C][C]0.998478010201648[/C][/ROW]
[ROW][C]92[/C][C]0.0011188880410006[/C][C]0.00223777608200121[/C][C]0.998881111958999[/C][/ROW]
[ROW][C]93[/C][C]0.00354615205315481[/C][C]0.00709230410630962[/C][C]0.996453847946845[/C][/ROW]
[ROW][C]94[/C][C]0.00298947347333092[/C][C]0.00597894694666184[/C][C]0.997010526526669[/C][/ROW]
[ROW][C]95[/C][C]0.00273136124395477[/C][C]0.00546272248790954[/C][C]0.997268638756045[/C][/ROW]
[ROW][C]96[/C][C]0.00329465467324138[/C][C]0.00658930934648277[/C][C]0.996705345326759[/C][/ROW]
[ROW][C]97[/C][C]0.00257063610793965[/C][C]0.00514127221587931[/C][C]0.99742936389206[/C][/ROW]
[ROW][C]98[/C][C]0.00677821599495226[/C][C]0.0135564319899045[/C][C]0.993221784005048[/C][/ROW]
[ROW][C]99[/C][C]0.0201352659561793[/C][C]0.0402705319123585[/C][C]0.979864734043821[/C][/ROW]
[ROW][C]100[/C][C]0.062820958785435[/C][C]0.12564191757087[/C][C]0.937179041214565[/C][/ROW]
[ROW][C]101[/C][C]0.192007350122476[/C][C]0.384014700244952[/C][C]0.807992649877524[/C][/ROW]
[ROW][C]102[/C][C]0.230043530497202[/C][C]0.460087060994404[/C][C]0.769956469502798[/C][/ROW]
[ROW][C]103[/C][C]0.214721850407298[/C][C]0.429443700814597[/C][C]0.785278149592702[/C][/ROW]
[ROW][C]104[/C][C]0.207961095253373[/C][C]0.415922190506745[/C][C]0.792038904746627[/C][/ROW]
[ROW][C]105[/C][C]0.219563006035974[/C][C]0.439126012071949[/C][C]0.780436993964026[/C][/ROW]
[ROW][C]106[/C][C]0.183258111273959[/C][C]0.366516222547917[/C][C]0.816741888726041[/C][/ROW]
[ROW][C]107[/C][C]0.229326653667353[/C][C]0.458653307334707[/C][C]0.770673346332647[/C][/ROW]
[ROW][C]108[/C][C]0.316403079782238[/C][C]0.632806159564476[/C][C]0.683596920217762[/C][/ROW]
[ROW][C]109[/C][C]0.297144269303175[/C][C]0.594288538606349[/C][C]0.702855730696825[/C][/ROW]
[ROW][C]110[/C][C]0.251924206643084[/C][C]0.503848413286168[/C][C]0.748075793356916[/C][/ROW]
[ROW][C]111[/C][C]0.231502070150351[/C][C]0.463004140300703[/C][C]0.768497929849649[/C][/ROW]
[ROW][C]112[/C][C]0.436502502624296[/C][C]0.873005005248593[/C][C]0.563497497375704[/C][/ROW]
[ROW][C]113[/C][C]0.460581288485142[/C][C]0.921162576970285[/C][C]0.539418711514858[/C][/ROW]
[ROW][C]114[/C][C]0.608687554711052[/C][C]0.782624890577897[/C][C]0.391312445288948[/C][/ROW]
[ROW][C]115[/C][C]0.570509777089325[/C][C]0.858980445821349[/C][C]0.429490222910675[/C][/ROW]
[ROW][C]116[/C][C]0.560063257907362[/C][C]0.879873484185275[/C][C]0.439936742092638[/C][/ROW]
[ROW][C]117[/C][C]0.713644488654047[/C][C]0.572711022691906[/C][C]0.286355511345953[/C][/ROW]
[ROW][C]118[/C][C]0.826358906827389[/C][C]0.347282186345221[/C][C]0.173641093172611[/C][/ROW]
[ROW][C]119[/C][C]0.816301822969827[/C][C]0.367396354060345[/C][C]0.183698177030173[/C][/ROW]
[ROW][C]120[/C][C]0.799490559221305[/C][C]0.40101888155739[/C][C]0.200509440778695[/C][/ROW]
[ROW][C]121[/C][C]0.768520395059016[/C][C]0.462959209881967[/C][C]0.231479604940984[/C][/ROW]
[ROW][C]122[/C][C]0.734075087011038[/C][C]0.531849825977924[/C][C]0.265924912988962[/C][/ROW]
[ROW][C]123[/C][C]0.708848706226928[/C][C]0.582302587546144[/C][C]0.291151293773072[/C][/ROW]
[ROW][C]124[/C][C]0.725735882362358[/C][C]0.548528235275284[/C][C]0.274264117637642[/C][/ROW]
[ROW][C]125[/C][C]0.728333928787653[/C][C]0.543332142424695[/C][C]0.271666071212347[/C][/ROW]
[ROW][C]126[/C][C]0.737079280217398[/C][C]0.525841439565203[/C][C]0.262920719782602[/C][/ROW]
[ROW][C]127[/C][C]0.734742096415945[/C][C]0.530515807168111[/C][C]0.265257903584055[/C][/ROW]
[ROW][C]128[/C][C]0.746176930829117[/C][C]0.507646138341765[/C][C]0.253823069170883[/C][/ROW]
[ROW][C]129[/C][C]0.824481304305479[/C][C]0.351037391389042[/C][C]0.175518695694521[/C][/ROW]
[ROW][C]130[/C][C]0.884626241023391[/C][C]0.230747517953217[/C][C]0.115373758976609[/C][/ROW]
[ROW][C]131[/C][C]0.80215137463198[/C][C]0.39569725073604[/C][C]0.19784862536802[/C][/ROW]
[ROW][C]132[/C][C]0.716792454314253[/C][C]0.566415091371494[/C][C]0.283207545685747[/C][/ROW]
[ROW][C]133[/C][C]0.793472279624471[/C][C]0.413055440751058[/C][C]0.206527720375529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.110655678946208 0.221311357892415 0.889344321053792 11 0.0535156150978646 0.107031230195729 0.946484384902135 12 0.0430459103255239 0.0860918206510477 0.956954089674476 13 0.112647052078738 0.225294104157477 0.887352947921262 14 0.446999820729367 0.893999641458733 0.553000179270633 15 0.365919671113597 0.731839342227194 0.634080328886403 16 0.317334479081425 0.63466895816285 0.682665520918575 17 0.279549589438369 0.559099178876738 0.720450410561631 18 0.210675955235049 0.421351910470098 0.789324044764951 19 0.176275575869785 0.35255115173957 0.823724424130215 20 0.150005087385452 0.300010174770904 0.849994912614548 21 0.121011700821828 0.242023401643656 0.878988299178172 22 0.0956656616199818 0.191331323239964 0.904334338380018 23 0.09346979174588 0.18693958349176 0.90653020825412 24 0.0953616835325865 0.190723367065173 0.904638316467414 25 0.0757631445894694 0.151526289178939 0.924236855410531 26 0.0698272795511077 0.139654559102215 0.930172720448892 27 0.0594096195303283 0.118819239060657 0.940590380469672 28 0.0464088959385104 0.0928177918770208 0.95359110406149 29 0.0359397422426379 0.0718794844852758 0.964060257757362 30 0.0290105617205811 0.0580211234411623 0.970989438279419 31 0.022229804546305 0.04445960909261 0.977770195453695 32 0.0171894540017453 0.0343789080034907 0.982810545998255 33 0.0113454851296203 0.0226909702592407 0.98865451487038 34 0.00720629583239052 0.014412591664781 0.992793704167609 35 0.0088574471972527 0.0177148943945054 0.991142552802747 36 0.00726436647294272 0.0145287329458854 0.992735633527057 37 0.0110465855319583 0.0220931710639166 0.988953414468042 38 0.00884831558214533 0.0176966311642907 0.991151684417855 39 0.0104703610303207 0.0209407220606413 0.989529638969679 40 0.00828134820392309 0.0165626964078462 0.991718651796077 41 0.00717245426434951 0.014344908528699 0.99282754573565 42 0.00630620660287696 0.0126124132057539 0.993693793397123 43 0.0050011441138625 0.010002288227725 0.994998855886137 44 0.00362802461298067 0.00725604922596135 0.996371975387019 45 0.00421426718582344 0.00842853437164688 0.995785732814177 46 0.00333342385718418 0.00666684771436836 0.996666576142816 47 0.00322786357621244 0.00645572715242488 0.996772136423788 48 0.00339438276051062 0.00678876552102123 0.996605617239489 49 0.00656782753846692 0.0131356550769338 0.993432172461533 50 0.00491100243392733 0.00982200486785465 0.995088997566073 51 0.00373172168685447 0.00746344337370895 0.996268278313146 52 0.00315858347001369 0.00631716694002738 0.996841416529986 53 0.00227013674523304 0.00454027349046608 0.997729863254767 54 0.00248863502625361 0.00497727005250723 0.997511364973746 55 0.00347367305202194 0.00694734610404388 0.996526326947978 56 0.0026574948232219 0.00531498964644381 0.997342505176778 57 0.00220137661185477 0.00440275322370955 0.997798623388145 58 0.00147428106452197 0.00294856212904395 0.998525718935478 59 0.00130695922482382 0.00261391844964763 0.998693040775176 60 0.00262506753929826 0.00525013507859653 0.997374932460702 61 0.00182526873694721 0.00365053747389442 0.998174731263053 62 0.00129999957255799 0.00259999914511599 0.998700000427442 63 0.00125285804593617 0.00250571609187233 0.998747141954064 64 0.000872626694062139 0.00174525338812428 0.999127373305938 65 0.000656026327459295 0.00131205265491859 0.999343973672541 66 0.000469959484867828 0.000939918969735657 0.999530040515132 67 0.000305819996095217 0.000611639992190433 0.999694180003905 68 0.000344056562742105 0.00068811312548421 0.999655943437258 69 0.000268011358804318 0.000536022717608637 0.999731988641196 70 0.000196902907461637 0.000393805814923274 0.999803097092538 71 0.000197047889167346 0.000394095778334691 0.999802952110833 72 0.00126460060712006 0.00252920121424012 0.99873539939288 73 0.00128257731302715 0.0025651546260543 0.998717422686973 74 0.0010914697573763 0.00218293951475261 0.998908530242624 75 0.00105560123978471 0.00211120247956942 0.998944398760215 76 0.00079505885978197 0.00159011771956394 0.999204941140218 77 0.000613562805435247 0.00122712561087049 0.999386437194565 78 0.000471155147793443 0.000942310295586885 0.999528844852207 79 0.00039397557476814 0.00078795114953628 0.999606024425232 80 0.000260586196129024 0.000521172392258049 0.999739413803871 81 0.000183477577934631 0.000366955155869263 0.999816522422065 82 0.000142629663837628 0.000285259327675256 0.999857370336162 83 0.000180873526786191 0.000361747053572382 0.999819126473214 84 0.000138564636037073 0.000277129272074145 0.999861435363963 85 0.000141261915115715 0.00028252383023143 0.999858738084884 86 0.000132648645648313 0.000265297291296626 0.999867351354352 87 0.000144682198135589 0.000289364396271177 0.999855317801864 88 0.000294008087829093 0.000588016175658185 0.999705991912171 89 0.000200784302140246 0.000401568604280491 0.99979921569786 90 0.00148656611488788 0.00297313222977576 0.998513433885112 91 0.00152198979835233 0.00304397959670466 0.998478010201648 92 0.0011188880410006 0.00223777608200121 0.998881111958999 93 0.00354615205315481 0.00709230410630962 0.996453847946845 94 0.00298947347333092 0.00597894694666184 0.997010526526669 95 0.00273136124395477 0.00546272248790954 0.997268638756045 96 0.00329465467324138 0.00658930934648277 0.996705345326759 97 0.00257063610793965 0.00514127221587931 0.99742936389206 98 0.00677821599495226 0.0135564319899045 0.993221784005048 99 0.0201352659561793 0.0402705319123585 0.979864734043821 100 0.062820958785435 0.12564191757087 0.937179041214565 101 0.192007350122476 0.384014700244952 0.807992649877524 102 0.230043530497202 0.460087060994404 0.769956469502798 103 0.214721850407298 0.429443700814597 0.785278149592702 104 0.207961095253373 0.415922190506745 0.792038904746627 105 0.219563006035974 0.439126012071949 0.780436993964026 106 0.183258111273959 0.366516222547917 0.816741888726041 107 0.229326653667353 0.458653307334707 0.770673346332647 108 0.316403079782238 0.632806159564476 0.683596920217762 109 0.297144269303175 0.594288538606349 0.702855730696825 110 0.251924206643084 0.503848413286168 0.748075793356916 111 0.231502070150351 0.463004140300703 0.768497929849649 112 0.436502502624296 0.873005005248593 0.563497497375704 113 0.460581288485142 0.921162576970285 0.539418711514858 114 0.608687554711052 0.782624890577897 0.391312445288948 115 0.570509777089325 0.858980445821349 0.429490222910675 116 0.560063257907362 0.879873484185275 0.439936742092638 117 0.713644488654047 0.572711022691906 0.286355511345953 118 0.826358906827389 0.347282186345221 0.173641093172611 119 0.816301822969827 0.367396354060345 0.183698177030173 120 0.799490559221305 0.40101888155739 0.200509440778695 121 0.768520395059016 0.462959209881967 0.231479604940984 122 0.734075087011038 0.531849825977924 0.265924912988962 123 0.708848706226928 0.582302587546144 0.291151293773072 124 0.725735882362358 0.548528235275284 0.274264117637642 125 0.728333928787653 0.543332142424695 0.271666071212347 126 0.737079280217398 0.525841439565203 0.262920719782602 127 0.734742096415945 0.530515807168111 0.265257903584055 128 0.746176930829117 0.507646138341765 0.253823069170883 129 0.824481304305479 0.351037391389042 0.175518695694521 130 0.884626241023391 0.230747517953217 0.115373758976609 131 0.80215137463198 0.39569725073604 0.19784862536802 132 0.716792454314253 0.566415091371494 0.283207545685747 133 0.793472279624471 0.413055440751058 0.206527720375529

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 53 0.42741935483871 NOK 5% type I error level 69 0.556451612903226 NOK 10% type I error level 73 0.588709677419355 NOK

\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 & 53 & 0.42741935483871 & NOK \tabularnewline
5% type I error level & 69 & 0.556451612903226 & NOK \tabularnewline
10% type I error level & 73 & 0.588709677419355 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185676&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]53[/C][C]0.42741935483871[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]69[/C][C]0.556451612903226[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.588709677419355[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185676&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185676&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 tests OK/NOK 1% type I error level 53 0.42741935483871 NOK 5% type I error level 69 0.556451612903226 NOK 10% type I error level 73 0.588709677419355 NOK

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
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.row.end(a)
a<-table.row.start(a)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
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.row.end(a)
a<-table.row.start(a)
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,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)