Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.073197833305110.847935-7.750200
w-0.05410369151641710.008085-6.691600
f2.049183982664270.18838410.877700
s0.3209267790956980.1223782.62240.0097270.004863
c0.07642783917682030.1395590.54760.5848370.292418
b0.001077230907226040.000234.69067e-063e-06
h7.41164214900091e-050.0005090.14560.8844420.442221

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.073197833305110.847935-7.750200
w-0.05410369151641710.008085-6.691600
f2.049183982664270.18838410.877700
s0.3209267790956980.1223782.62240.0097270.004863
c0.07642783917682030.1395590.54760.5848370.292418
b0.001077230907226040.000234.69067e-063e-06
h7.41164214900091e-050.0005090.14560.8844420.442221







Multiple Linear Regression - Regression Statistics
Multiple R0.865040444862777
R-squared0.748294971248391
Adjusted R-squared0.737190337626996
F-TEST (value)67.3858316051691
F-TEST (DF numerator)6
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.84278730448689
Sum Squared Residuals2008.31394038349

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.865040444862777 \tabularnewline
R-squared & 0.748294971248391 \tabularnewline
Adjusted R-squared & 0.737190337626996 \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]Adjusted R-squared[/C][C]0.737190337626996[/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 R0.865040444862777
R-squared0.748294971248391
Adjusted R-squared0.737190337626996
F-TEST (value)67.3858316051691
F-TEST (DF numerator)6
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.84278730448689
Sum Squared Residuals2008.31394038349







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1148.470800480632665.52919951936734
2148.927586344032415.07241365596759
3159.699233770434395.30076622956561
4139.013666390412463.98633360958754
5810.0467669297353-2.04676692973535
676.375946295080480.624053704919523
733.61910451251079-0.619104512510785
830.1772262256666022.8227737743334
942.326404374188861.67359562581114
1045.83965205394121-1.83965205394121
110-1.146155422970381.14615542297038
12-4-5.146514664726461.14651466472646
13-14-13.5805941047635-0.419405895236472
14-18-10.7123598358821-7.2876401641179
15-8-9.365672052334761.36567205233476
16-10.311830639634532-1.31183063963453
1713.48499450294894-2.48499450294894
1821.529618502766150.470381497233852
1900.462376594402993-0.462376594402993
2010.2174893427407430.782510657259257
210-0.778364527578820.77836452757882
22-14.08898518013437-5.08898518013437
23-3-5.26696724395822.2669672439582
24-3-5.901810666935022.90181066693502
25-3-5.063805932820682.06380593282068
26-4-7.586938029448753.58693802944875
27-8-7.46347520539161-0.536524794608393
28-9-5.46866249163329-3.53133750836671
29-13-13.12586194005120.125861940051202
30-18-19.00881570139591.00881570139586
31-11-10.0417192423105-0.958280757689514
32-9-8.61735928754664-0.38264071245336
33-10-9.55116172565187-0.44883827434813
34-13-11.8158017093635-1.18419829063653
35-11-16.89439494311635.89439494311627
36-5-9.937088600670084.93708860067008
37-15-13.2188075766277-1.78119242337228
38-6-7.474448592044631.47444859204463
39-6-5.46271734380575-0.537282656194248
40-3-4.761813205682041.76181320568204
41-1-3.387855857551922.38785585755192
42-3-4.277358962721921.27735896272192
43-4-2.40489980525548-1.59510019474452
44-6-4.13514786366207-1.86485213633793
450-0.8043961547663980.804396154766398
46-4-3.03288476382807-0.967115236171931
47-2-3.75586145354651.7558614535465
48-2-5.672436426017283.67243642601728
49-6-1.57740860532381-4.42259139467619
50-7-8.287971778642381.28797177864238
51-6-4.43172511450308-1.56827488549692
52-6-2.62253191097893-3.37746808902107
53-3-0.647518338537184-2.35248166146282
54-2-3.486408589087861.48640858908786
55-5-7.014923140913722.01492314091372
56-11-10.0884828658382-0.911517134161775
57-11-7.05531662918239-3.94468337081761
58-11-7.16157336818934-3.83842663181066
59-10-12.33459428546412.33459428546406
60-14-18.32612841740354.32612841740353
61-8-8.782902161098750.782902161098753
62-9-8.96654656693183-0.0334534330681684
63-5-3.19944405593501-1.80055594406499
64-10.065773067508206-1.06577306750821
65-2-0.787952459527467-1.21204754047253
66-5-2.99726286484738-2.00273713515262
67-4-1.36045137748009-2.63954862251991
68-6-6.022229113477980.0222291134779818
69-2-0.575800416796943-1.42419958320306
70-2-0.589987516375699-1.4100124836243
71-2-4.098192735495092.09819273549509
72-2-8.102569240720346.10256924072034
732-0.6065194470146082.60651944701461
741-0.4691134591953291.46911345919533
75-8-2.14213168359047-5.85786831640953
76-12.5601490737742-3.5601490737742
7711.7892852108143-0.7892852108143
78-10.0764891463011801-1.07648914630118
7921.710351567089090.289648432910911
8024.57857861500768-2.57857861500768
8112.75008816724462-1.75008816724462
82-10.524229823972655-1.52422982397266
83-2-4.474751468495542.47475146849554
84-2-2.938517038974860.938517038974855
85-1-3.04298806409682.0429880640968
86-8-10.92673076937122.92673076937123
87-4-7.043079251313943.04307925131394
88-6-11.91278455285975.91278455285972
89-3-5.948435275333982.94843527533398
90-3-11.61808603545478.61808603545474
91-7-11.07945288390994.07945288390992
92-9-9.200472409681940.20047240968194
93-11-16.85063218128255.85063218128254
94-13-12.716763704099-0.283236295900996
95-11-11.70572990702940.705729907029424
96-9-5.61472512636117-3.38527487363883
97-17-17.36443138246760.364431382467596
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
108-11-11.07585054774480.0758505477448361
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
118-7-9.349593653477472.34959365347747
119-4-9.282686213464915.28268621346491
120-4-9.015329409086735.01532940908673
121-2-2.508379382637430.508379382637432
1220-3.728487187502723.72848718750272
123-2-7.019053092122645.01905309212264
124-3-6.76364484795573.7636448479557
1251-0.6178686291318161.61786862913182
126-2-11.67475582031459.67475582031451
127-1-6.324471178369025.32447117836902
1281-3.811490357122054.81149035712205
129-3-6.988979236802593.98897923680259
130-4-10.13321898039026.13321898039024
131-9-11.9564627699462.95646276994597
132-9-9.769616782736190.769616782736188
133-7-4.30931707871153-2.69068292128847
134-14-10.3275014984174-3.67249850158259
135-12-17.33606743467955.33606743467947
136-16-18.51910675050512.5191067505051
137-20-20.13740076942660.13740076942665
138-12-16.1657294573264.16572945732599
139-12-13.16701499002081.16701499002082
140-10-11.33899551425561.33899551425563
141-10-7.1123090117547-2.8876909882453
142-13-11.8036443271922-1.19635567280779
143-16-16.22072943592580.220729435925833

\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 IndexActualsInterpolationForecastResidualsPrediction Error
1148.470800480632665.52919951936734
2148.927586344032415.07241365596759
3159.699233770434395.30076622956561
4139.013666390412463.98633360958754
5810.0467669297353-2.04676692973535
676.375946295080480.624053704919523
733.61910451251079-0.619104512510785
830.1772262256666022.8227737743334
942.326404374188861.67359562581114
1045.83965205394121-1.83965205394121
110-1.146155422970381.14615542297038
12-4-5.146514664726461.14651466472646
13-14-13.5805941047635-0.419405895236472
14-18-10.7123598358821-7.2876401641179
15-8-9.365672052334761.36567205233476
16-10.311830639634532-1.31183063963453
1713.48499450294894-2.48499450294894
1821.529618502766150.470381497233852
1900.462376594402993-0.462376594402993
2010.2174893427407430.782510657259257
210-0.778364527578820.77836452757882
22-14.08898518013437-5.08898518013437
23-3-5.26696724395822.2669672439582
24-3-5.901810666935022.90181066693502
25-3-5.063805932820682.06380593282068
26-4-7.586938029448753.58693802944875
27-8-7.46347520539161-0.536524794608393
28-9-5.46866249163329-3.53133750836671
29-13-13.12586194005120.125861940051202
30-18-19.00881570139591.00881570139586
31-11-10.0417192423105-0.958280757689514
32-9-8.61735928754664-0.38264071245336
33-10-9.55116172565187-0.44883827434813
34-13-11.8158017093635-1.18419829063653
35-11-16.89439494311635.89439494311627
36-5-9.937088600670084.93708860067008
37-15-13.2188075766277-1.78119242337228
38-6-7.474448592044631.47444859204463
39-6-5.46271734380575-0.537282656194248
40-3-4.761813205682041.76181320568204
41-1-3.387855857551922.38785585755192
42-3-4.277358962721921.27735896272192
43-4-2.40489980525548-1.59510019474452
44-6-4.13514786366207-1.86485213633793
450-0.8043961547663980.804396154766398
46-4-3.03288476382807-0.967115236171931
47-2-3.75586145354651.7558614535465
48-2-5.672436426017283.67243642601728
49-6-1.57740860532381-4.42259139467619
50-7-8.287971778642381.28797177864238
51-6-4.43172511450308-1.56827488549692
52-6-2.62253191097893-3.37746808902107
53-3-0.647518338537184-2.35248166146282
54-2-3.486408589087861.48640858908786
55-5-7.014923140913722.01492314091372
56-11-10.0884828658382-0.911517134161775
57-11-7.05531662918239-3.94468337081761
58-11-7.16157336818934-3.83842663181066
59-10-12.33459428546412.33459428546406
60-14-18.32612841740354.32612841740353
61-8-8.782902161098750.782902161098753
62-9-8.96654656693183-0.0334534330681684
63-5-3.19944405593501-1.80055594406499
64-10.065773067508206-1.06577306750821
65-2-0.787952459527467-1.21204754047253
66-5-2.99726286484738-2.00273713515262
67-4-1.36045137748009-2.63954862251991
68-6-6.022229113477980.0222291134779818
69-2-0.575800416796943-1.42419958320306
70-2-0.589987516375699-1.4100124836243
71-2-4.098192735495092.09819273549509
72-2-8.102569240720346.10256924072034
732-0.6065194470146082.60651944701461
741-0.4691134591953291.46911345919533
75-8-2.14213168359047-5.85786831640953
76-12.5601490737742-3.5601490737742
7711.7892852108143-0.7892852108143
78-10.0764891463011801-1.07648914630118
7921.710351567089090.289648432910911
8024.57857861500768-2.57857861500768
8112.75008816724462-1.75008816724462
82-10.524229823972655-1.52422982397266
83-2-4.474751468495542.47475146849554
84-2-2.938517038974860.938517038974855
85-1-3.04298806409682.0429880640968
86-8-10.92673076937122.92673076937123
87-4-7.043079251313943.04307925131394
88-6-11.91278455285975.91278455285972
89-3-5.948435275333982.94843527533398
90-3-11.61808603545478.61808603545474
91-7-11.07945288390994.07945288390992
92-9-9.200472409681940.20047240968194
93-11-16.85063218128255.85063218128254
94-13-12.716763704099-0.283236295900996
95-11-11.70572990702940.705729907029424
96-9-5.61472512636117-3.38527487363883
97-17-17.36443138246760.364431382467596
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
108-11-11.07585054774480.0758505477448361
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
118-7-9.349593653477472.34959365347747
119-4-9.282686213464915.28268621346491
120-4-9.015329409086735.01532940908673
121-2-2.508379382637430.508379382637432
1220-3.728487187502723.72848718750272
123-2-7.019053092122645.01905309212264
124-3-6.76364484795573.7636448479557
1251-0.6178686291318161.61786862913182
126-2-11.67475582031459.67475582031451
127-1-6.324471178369025.32447117836902
1281-3.811490357122054.81149035712205
129-3-6.988979236802593.98897923680259
130-4-10.13321898039026.13321898039024
131-9-11.9564627699462.95646276994597
132-9-9.769616782736190.769616782736188
133-7-4.30931707871153-2.69068292128847
134-14-10.3275014984174-3.67249850158259
135-12-17.33606743467955.33606743467947
136-16-18.51910675050512.5191067505051
137-20-20.13740076942660.13740076942665
138-12-16.1657294573264.16572945732599
139-12-13.16701499002081.16701499002082
140-10-11.33899551425561.33899551425563
141-10-7.1123090117547-2.8876909882453
142-13-11.8036443271922-1.19635567280779
143-16-16.22072943592580.220729435925833







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1106556789462080.2213113578924150.889344321053792
110.05351561509786460.1070312301957290.946484384902135
120.04304591032552390.08609182065104770.956954089674476
130.1126470520787380.2252941041574770.887352947921262
140.4469998207293670.8939996414587330.553000179270633
150.3659196711135970.7318393422271940.634080328886403
160.3173344790814250.634668958162850.682665520918575
170.2795495894383690.5590991788767380.720450410561631
180.2106759552350490.4213519104700980.789324044764951
190.1762755758697850.352551151739570.823724424130215
200.1500050873854520.3000101747709040.849994912614548
210.1210117008218280.2420234016436560.878988299178172
220.09566566161998180.1913313232399640.904334338380018
230.093469791745880.186939583491760.90653020825412
240.09536168353258650.1907233670651730.904638316467414
250.07576314458946940.1515262891789390.924236855410531
260.06982727955110770.1396545591022150.930172720448892
270.05940961953032830.1188192390606570.940590380469672
280.04640889593851040.09281779187702080.95359110406149
290.03593974224263790.07187948448527580.964060257757362
300.02901056172058110.05802112344116230.970989438279419
310.0222298045463050.044459609092610.977770195453695
320.01718945400174530.03437890800349070.982810545998255
330.01134548512962030.02269097025924070.98865451487038
340.007206295832390520.0144125916647810.992793704167609
350.00885744719725270.01771489439450540.991142552802747
360.007264366472942720.01452873294588540.992735633527057
370.01104658553195830.02209317106391660.988953414468042
380.008848315582145330.01769663116429070.991151684417855
390.01047036103032070.02094072206064130.989529638969679
400.008281348203923090.01656269640784620.991718651796077
410.007172454264349510.0143449085286990.99282754573565
420.006306206602876960.01261241320575390.993693793397123
430.00500114411386250.0100022882277250.994998855886137
440.003628024612980670.007256049225961350.996371975387019
450.004214267185823440.008428534371646880.995785732814177
460.003333423857184180.006666847714368360.996666576142816
470.003227863576212440.006455727152424880.996772136423788
480.003394382760510620.006788765521021230.996605617239489
490.006567827538466920.01313565507693380.993432172461533
500.004911002433927330.009822004867854650.995088997566073
510.003731721686854470.007463443373708950.996268278313146
520.003158583470013690.006317166940027380.996841416529986
530.002270136745233040.004540273490466080.997729863254767
540.002488635026253610.004977270052507230.997511364973746
550.003473673052021940.006947346104043880.996526326947978
560.00265749482322190.005314989646443810.997342505176778
570.002201376611854770.004402753223709550.997798623388145
580.001474281064521970.002948562129043950.998525718935478
590.001306959224823820.002613918449647630.998693040775176
600.002625067539298260.005250135078596530.997374932460702
610.001825268736947210.003650537473894420.998174731263053
620.001299999572557990.002599999145115990.998700000427442
630.001252858045936170.002505716091872330.998747141954064
640.0008726266940621390.001745253388124280.999127373305938
650.0006560263274592950.001312052654918590.999343973672541
660.0004699594848678280.0009399189697356570.999530040515132
670.0003058199960952170.0006116399921904330.999694180003905
680.0003440565627421050.000688113125484210.999655943437258
690.0002680113588043180.0005360227176086370.999731988641196
700.0001969029074616370.0003938058149232740.999803097092538
710.0001970478891673460.0003940957783346910.999802952110833
720.001264600607120060.002529201214240120.99873539939288
730.001282577313027150.00256515462605430.998717422686973
740.00109146975737630.002182939514752610.998908530242624
750.001055601239784710.002111202479569420.998944398760215
760.000795058859781970.001590117719563940.999204941140218
770.0006135628054352470.001227125610870490.999386437194565
780.0004711551477934430.0009423102955868850.999528844852207
790.000393975574768140.000787951149536280.999606024425232
800.0002605861961290240.0005211723922580490.999739413803871
810.0001834775779346310.0003669551558692630.999816522422065
820.0001426296638376280.0002852593276752560.999857370336162
830.0001808735267861910.0003617470535723820.999819126473214
840.0001385646360370730.0002771292720741450.999861435363963
850.0001412619151157150.000282523830231430.999858738084884
860.0001326486456483130.0002652972912966260.999867351354352
870.0001446821981355890.0002893643962711770.999855317801864
880.0002940080878290930.0005880161756581850.999705991912171
890.0002007843021402460.0004015686042804910.99979921569786
900.001486566114887880.002973132229775760.998513433885112
910.001521989798352330.003043979596704660.998478010201648
920.00111888804100060.002237776082001210.998881111958999
930.003546152053154810.007092304106309620.996453847946845
940.002989473473330920.005978946946661840.997010526526669
950.002731361243954770.005462722487909540.997268638756045
960.003294654673241380.006589309346482770.996705345326759
970.002570636107939650.005141272215879310.99742936389206
980.006778215994952260.01355643198990450.993221784005048
990.02013526595617930.04027053191235850.979864734043821
1000.0628209587854350.125641917570870.937179041214565
1010.1920073501224760.3840147002449520.807992649877524
1020.2300435304972020.4600870609944040.769956469502798
1030.2147218504072980.4294437008145970.785278149592702
1040.2079610952533730.4159221905067450.792038904746627
1050.2195630060359740.4391260120719490.780436993964026
1060.1832581112739590.3665162225479170.816741888726041
1070.2293266536673530.4586533073347070.770673346332647
1080.3164030797822380.6328061595644760.683596920217762
1090.2971442693031750.5942885386063490.702855730696825
1100.2519242066430840.5038484132861680.748075793356916
1110.2315020701503510.4630041403007030.768497929849649
1120.4365025026242960.8730050052485930.563497497375704
1130.4605812884851420.9211625769702850.539418711514858
1140.6086875547110520.7826248905778970.391312445288948
1150.5705097770893250.8589804458213490.429490222910675
1160.5600632579073620.8798734841852750.439936742092638
1170.7136444886540470.5727110226919060.286355511345953
1180.8263589068273890.3472821863452210.173641093172611
1190.8163018229698270.3673963540603450.183698177030173
1200.7994905592213050.401018881557390.200509440778695
1210.7685203950590160.4629592098819670.231479604940984
1220.7340750870110380.5318498259779240.265924912988962
1230.7088487062269280.5823025875461440.291151293773072
1240.7257358823623580.5485282352752840.274264117637642
1250.7283339287876530.5433321424246950.271666071212347
1260.7370792802173980.5258414395652030.262920719782602
1270.7347420964159450.5305158071681110.265257903584055
1280.7461769308291170.5076461383417650.253823069170883
1290.8244813043054790.3510373913890420.175518695694521
1300.8846262410233910.2307475179532170.115373758976609
1310.802151374631980.395697250736040.19784862536802
1320.7167924543142530.5664150913714940.283207545685747
1330.7934722796244710.4130554407510580.206527720375529

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1106556789462080.2213113578924150.889344321053792
110.05351561509786460.1070312301957290.946484384902135
120.04304591032552390.08609182065104770.956954089674476
130.1126470520787380.2252941041574770.887352947921262
140.4469998207293670.8939996414587330.553000179270633
150.3659196711135970.7318393422271940.634080328886403
160.3173344790814250.634668958162850.682665520918575
170.2795495894383690.5590991788767380.720450410561631
180.2106759552350490.4213519104700980.789324044764951
190.1762755758697850.352551151739570.823724424130215
200.1500050873854520.3000101747709040.849994912614548
210.1210117008218280.2420234016436560.878988299178172
220.09566566161998180.1913313232399640.904334338380018
230.093469791745880.186939583491760.90653020825412
240.09536168353258650.1907233670651730.904638316467414
250.07576314458946940.1515262891789390.924236855410531
260.06982727955110770.1396545591022150.930172720448892
270.05940961953032830.1188192390606570.940590380469672
280.04640889593851040.09281779187702080.95359110406149
290.03593974224263790.07187948448527580.964060257757362
300.02901056172058110.05802112344116230.970989438279419
310.0222298045463050.044459609092610.977770195453695
320.01718945400174530.03437890800349070.982810545998255
330.01134548512962030.02269097025924070.98865451487038
340.007206295832390520.0144125916647810.992793704167609
350.00885744719725270.01771489439450540.991142552802747
360.007264366472942720.01452873294588540.992735633527057
370.01104658553195830.02209317106391660.988953414468042
380.008848315582145330.01769663116429070.991151684417855
390.01047036103032070.02094072206064130.989529638969679
400.008281348203923090.01656269640784620.991718651796077
410.007172454264349510.0143449085286990.99282754573565
420.006306206602876960.01261241320575390.993693793397123
430.00500114411386250.0100022882277250.994998855886137
440.003628024612980670.007256049225961350.996371975387019
450.004214267185823440.008428534371646880.995785732814177
460.003333423857184180.006666847714368360.996666576142816
470.003227863576212440.006455727152424880.996772136423788
480.003394382760510620.006788765521021230.996605617239489
490.006567827538466920.01313565507693380.993432172461533
500.004911002433927330.009822004867854650.995088997566073
510.003731721686854470.007463443373708950.996268278313146
520.003158583470013690.006317166940027380.996841416529986
530.002270136745233040.004540273490466080.997729863254767
540.002488635026253610.004977270052507230.997511364973746
550.003473673052021940.006947346104043880.996526326947978
560.00265749482322190.005314989646443810.997342505176778
570.002201376611854770.004402753223709550.997798623388145
580.001474281064521970.002948562129043950.998525718935478
590.001306959224823820.002613918449647630.998693040775176
600.002625067539298260.005250135078596530.997374932460702
610.001825268736947210.003650537473894420.998174731263053
620.001299999572557990.002599999145115990.998700000427442
630.001252858045936170.002505716091872330.998747141954064
640.0008726266940621390.001745253388124280.999127373305938
650.0006560263274592950.001312052654918590.999343973672541
660.0004699594848678280.0009399189697356570.999530040515132
670.0003058199960952170.0006116399921904330.999694180003905
680.0003440565627421050.000688113125484210.999655943437258
690.0002680113588043180.0005360227176086370.999731988641196
700.0001969029074616370.0003938058149232740.999803097092538
710.0001970478891673460.0003940957783346910.999802952110833
720.001264600607120060.002529201214240120.99873539939288
730.001282577313027150.00256515462605430.998717422686973
740.00109146975737630.002182939514752610.998908530242624
750.001055601239784710.002111202479569420.998944398760215
760.000795058859781970.001590117719563940.999204941140218
770.0006135628054352470.001227125610870490.999386437194565
780.0004711551477934430.0009423102955868850.999528844852207
790.000393975574768140.000787951149536280.999606024425232
800.0002605861961290240.0005211723922580490.999739413803871
810.0001834775779346310.0003669551558692630.999816522422065
820.0001426296638376280.0002852593276752560.999857370336162
830.0001808735267861910.0003617470535723820.999819126473214
840.0001385646360370730.0002771292720741450.999861435363963
850.0001412619151157150.000282523830231430.999858738084884
860.0001326486456483130.0002652972912966260.999867351354352
870.0001446821981355890.0002893643962711770.999855317801864
880.0002940080878290930.0005880161756581850.999705991912171
890.0002007843021402460.0004015686042804910.99979921569786
900.001486566114887880.002973132229775760.998513433885112
910.001521989798352330.003043979596704660.998478010201648
920.00111888804100060.002237776082001210.998881111958999
930.003546152053154810.007092304106309620.996453847946845
940.002989473473330920.005978946946661840.997010526526669
950.002731361243954770.005462722487909540.997268638756045
960.003294654673241380.006589309346482770.996705345326759
970.002570636107939650.005141272215879310.99742936389206
980.006778215994952260.01355643198990450.993221784005048
990.02013526595617930.04027053191235850.979864734043821
1000.0628209587854350.125641917570870.937179041214565
1010.1920073501224760.3840147002449520.807992649877524
1020.2300435304972020.4600870609944040.769956469502798
1030.2147218504072980.4294437008145970.785278149592702
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1080.3164030797822380.6328061595644760.683596920217762
1090.2971442693031750.5942885386063490.702855730696825
1100.2519242066430840.5038484132861680.748075793356916
1110.2315020701503510.4630041403007030.768497929849649
1120.4365025026242960.8730050052485930.563497497375704
1130.4605812884851420.9211625769702850.539418711514858
1140.6086875547110520.7826248905778970.391312445288948
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1160.5600632579073620.8798734841852750.439936742092638
1170.7136444886540470.5727110226919060.286355511345953
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1190.8163018229698270.3673963540603450.183698177030173
1200.7994905592213050.401018881557390.200509440778695
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1220.7340750870110380.5318498259779240.265924912988962
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1250.7283339287876530.5433321424246950.271666071212347
1260.7370792802173980.5258414395652030.262920719782602
1270.7347420964159450.5305158071681110.265257903584055
1280.7461769308291170.5076461383417650.253823069170883
1290.8244813043054790.3510373913890420.175518695694521
1300.8846262410233910.2307475179532170.115373758976609
1310.802151374631980.395697250736040.19784862536802
1320.7167924543142530.5664150913714940.283207545685747
1330.7934722796244710.4130554407510580.206527720375529







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.42741935483871NOK
5% type I error level690.556451612903226NOK
10% type I error level730.588709677419355NOK

\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 testsOK/NOK
1% type I error level530.42741935483871NOK
5% type I error level690.556451612903226NOK
10% type I error level730.588709677419355NOK



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