Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 02 Nov 2012 17:53:51 -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/t1351893339pm5lmmdbo1809km.htm/, Retrieved Mon, 27 Jun 2022 06:19:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185673, Retrieved Mon, 27 Jun 2022 06:19:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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 21:53:51] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]
- RM      [Multiple Regression] [Paper 2012 multip...] [2012-12-14 14:36:09] [33fe548a21de6aef2b38519618b03303]
- RM      [Multiple Regression] [Paper 2012 multip...] [2012-12-14 14:50:28] [33fe548a21de6aef2b38519618b03303]
- RMPD    [Skewness and Kurtosis Test] [Normaliteitstest] [2012-12-15 14:33:48] [2c4ddb4bf62114b8025bb962e2c7a2b5]
Feedback Forum

Post a new message
Dataseries X:
14	501	11	20	91,81	77585	1303,2	13
14	485	11	19	91,98	77585	-58,7	15
15	464	11	18	91,72	77585	-378,9	3
13	460	11	13	90,27	78302	175,6	2
8	467	11	17	91,89	78302	233,7	-2
7	460	9	17	92,07	78302	706,8	1
3	448	8	13	92,92	78224	-23,6	1
3	443	6	14	93,34	78224	420,9	-1
4	436	7	13	93,6	78224	722,1	-6
4	431	8	17	92,41	78178	1401,3	-13
0	484	6	17	93,6	78178	-94,9	-25
-4	510	5	15	93,77	78178	1043,6	-26
-14	513	2	9	93,6	77988	1300,1	-9
-18	503	3	10	93,6	77988	721,1	1
-8	471	3	9	93,51	77988	-45,6	3
-1	471	7	14	92,66	77876	787,5	6
1	476	8	18	94,2	77876	694,3	2
2	475	7	18	94,37	77876	1054,7	5
0	470	7	12	94,45	78432	821,9	5
1	461	6	16	94,62	78432	1100,7	0
0	455	6	12	94,37	78432	862,4	-5
-1	456	7	19	93,43	79025	1656,1	-4
-3	517	5	13	94,79	79025	-174	-2
-3	525	5	12	94,88	79025	1337,6	-1
-3	523	5	13	94,79	79407	1394,9	-8
-4	519	4	11	94,62	79407	915,7	-16
-8	509	4	10	94,71	79407	-481,1	-19
-9	512	4	16	93,77	79644	167,9	-28
-13	519	1	12	95,73	79644	208,2	-11
-18	517	-1	6	95,99	79644	382,2	-4
-11	510	3	8	95,82	79381	1004	-9
-9	509	4	6	95,47	79381	864,7	-12
-10	501	3	8	95,82	79381	1052,9	-10
-13	507	2	8	94,71	79536	1417,6	-2
-11	569	1	9	96,33	79536	-197,7	-13
-5	580	4	13	96,5	79536	1262,1	0
-15	578	3	8	96,16	79813	1147,2	0
-6	565	5	11	96,33	79813	700,2	4
-6	547	6	8	96,33	79813	45,3	7
-3	555	6	10	95,05	80332	458,5	5
-1	562	6	15	96,84	80332	610,2	2
-3	561	6	12	96,92	80332	786,4	-2
-4	555	6	13	97,44	81434	787,2	6
-6	544	5	12	97,78	81434	1040	-3
0	537	6	15	97,69	81434	324,1	1
-4	543	5	13	96,67	82167	1343	0
-2	594	6	13	98,29	82167	-501,2	-7
-2	611	5	16	98,2	82167	800,4	-6
-6	613	7	14	98,71	82816	916,7	-4
-7	611	4	12	98,54	82816	695,8	-4
-6	594	5	15	98,2	82816	28	-2
-6	595	6	14	96,92	83000	495,6	2
-3	591	6	19	99,06	83000	366,2	-5
-2	589	5	16	99,65	83000	633	-15
-5	584	3	16	99,82	83251	848,3	-16
-11	573	2	11	99,99	83251	472,2	-18
-11	567	3	13	100,33	83251	357,8	-13
-11	569	3	12	99,31	83591	824,3	-23
-10	621	2	11	101,1	83591	-880,1	-10
-14	629	0	6	101,1	83591	1066,8	-10
-8	628	4	9	100,93	83910	1052,8	-6
-9	612	4	6	100,85	83910	-32,1	-3
-5	595	5	15	100,93	83910	-1331,4	-4
-1	597	6	17	99,6	84599	-767,1	-7
-2	593	6	13	101,88	84599	-236,7	-7
-5	590	5	12	101,81	84599	-184,9	-7
-4	580	5	13	102,38	85275	-143,4	-3
-6	574	3	10	102,74	85275	493,9	0
-2	573	5	14	102,82	85275	549,7	-5
-2	573	5	13	101,72	85608	982,7	-3
-2	620	5	10	103,47	85608	-856,3	3
-2	626	3	11	102,98	85608	967	2
2	620	6	12	102,68	86303	659,4	-7
1	588	6	7	102,9	86303	577,2	-1
-8	566	4	11	103,03	86303	-213,1	0
-1	557	6	9	101,29	87115	17,7	-3
1	561	5	13	103,69	87115	390,1	4
-1	549	4	12	103,68	87115	509,3	2
2	532	5	5	104,2	87931	410	3
2	526	5	13	104,08	87931	212,5	0
1	511	4	11	104,16	87931	818	-10
-1	499	3	8	103,05	88164	422,7	-10
-2	555	2	8	104,66	88164	-158	-9
-2	565	3	8	104,46	88164	427,2	-22
-1	542	2	8	104,95	88792	243,4	-16
-8	527	-1	0	105,85	88792	-419,3	-18
-4	510	0	3	106,23	88792	-1459,8	-14
-6	514	-2	0	104,86	89263	-1389,8	-12
-3	517	1	-1	107,44	89263	-2,1	-17
-3	508	-2	-1	108,23	89263	-938,6	-23
-7	493	-2	-4	108,45	89881	-839,9	-28
-9	490	-2	1	109,39	89881	-297,6	-31
-11	469	-6	-1	110,15	89881	-376,3	-21
-13	478	-4	0	109,13	90120	-79,4	-19
-11	528	-2	-1	110,28	90120	-2091,3	-22
-9	534	0	6	110,17	90120	-1023	-22
-17	518	-5	0	109,99	89703	-765,6	-25
-22	506	-4	-3	109,26	89703	-1592,3	-16
-25	502	-5	-3	109,11	89703	-1588,8	-22
-20	516	-1	4	107,06	87818	-1318	-21
-24	528	-2	1	109,53	87818	-402,4	-10
-24	533	-4	0	108,92	87818	-814,5	-7
-22	536	-1	-4	109,24	86273	-98,4	-5
-19	537	1	-2	109,12	86273	-305,9	-4
-18	524	1	3	109	86273	-18,4	7
-17	536	-2	2	107,23	86316	610,3	6
-11	587	1	5	109,49	86316	-917,3	3
-11	597	1	6	109,04	86316	88,4	10
-12	581	3	6	109,02	87234	-740,2	0
-10	564	3	3	109,23	87234	29,3	-2
-15	558	1	4	109,46	87234	-893,2	-1
-15	575	1	7	107,9	87885	-1030,2	2
-15	580	0	5	110,42	87885	-403,4	8
-13	575	2	6	110,98	87885	-46,9	-6
-8	563	2	1	111,48	88003	-321,2	-4
-13	552	-1	3	111,88	88003	-239,9	4
-9	537	1	6	111,89	88003	640,9	7
-7	545	0	0	109,85	88910	511,6	3
-4	601	1	3	112,1	88910	-665,1	3
-4	604	1	4	112,24	88910	657,7	8
-2	586	3	7	112,39	89397	-207,7	3
0	564	2	6	112,52	89397	-885,2	-3
-2	549	0	6	113,16	89397	-1595,8	4
-3	551	0	6	111,84	89813	-1374,9	-5
1	556	3	6	114,33	89813	-316,6	-1
-2	548	-2	2	114,82	89813	-283,4	5
-1	540	0	2	115,2	90539	-175,8	0
1	531	1	2	115,4	90539	-694,2	-6
-3	521	-1	3	115,74	90539	-249,9	-13
-4	519	-2	-1	114,19	90688	268,2	-15
-9	572	-1	-4	115,94	90688	-2105,1	-8
-9	581	-1	4	116,03	90688	-762,8	-20
-7	563	1	5	116,24	90691	-117,1	-10
-14	548	-2	3	116,66	90691	-1094,4	-22
-12	539	-5	-1	116,79	90691	-2095,2	-25
-16	541	-5	-4	115,48	90645	-1587,6	-10
-20	562	-6	0	118,16	90645	-528	-8
-12	559	-4	-1	118,38	90645	-324,2	-9
-12	546	-3	-1	118,51	90861	-276,1	-5
-10	536	-3	3	118,42	90861	-139,1	-7
-10	528	-1	2	118,24	90861	268	-11
-13	530	-2	-4	116,47	90401	570,5	-11
-16	582	-3	-3	118,96	90401	-316,5	-16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
i[t] = -84.1104264030526 -0.0539122069699521w[t] + 2.06108952316565f[t] + 0.318241526383104s[t] + 0.0831504465416917c[t] + 0.00106776969690798b[t] + 8.04318252046764e-05h[t] -0.005052724299794a[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
i[t] =  -84.1104264030526 -0.0539122069699521w[t] +  2.06108952316565f[t] +  0.318241526383104s[t] +  0.0831504465416917c[t] +  0.00106776969690798b[t] +  8.04318252046764e-05h[t] -0.005052724299794a[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]i[t] =  -84.1104264030526 -0.0539122069699521w[t] +  2.06108952316565f[t] +  0.318241526383104s[t] +  0.0831504465416917c[t] +  0.00106776969690798b[t] +  8.04318252046764e-05h[t] -0.005052724299794a[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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] = -84.1104264030526 -0.0539122069699521w[t] + 2.06108952316565f[t] + 0.318241526383104s[t] + 0.0831504465416917c[t] + 0.00106776969690798b[t] + 8.04318252046764e-05h[t] -0.005052724299794a[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.110426403052610.892347-7.72200
w-0.05391220696995210.008285-6.507400
f2.061089523165650.2156979.555500
s0.3182415263831040.1250372.54520.0120460.006023
c0.08315044654169170.1518390.54760.5848560.292428
b0.001067769696907980.0002454.36162.5e-051.3e-05
h8.04318252046764e-050.0005140.15650.8758420.437921
a-0.0050527242997940.044058-0.11470.9088670.454434

\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) & -84.1104264030526 & 10.892347 & -7.722 & 0 & 0 \tabularnewline
w & -0.0539122069699521 & 0.008285 & -6.5074 & 0 & 0 \tabularnewline
f & 2.06108952316565 & 0.215697 & 9.5555 & 0 & 0 \tabularnewline
s & 0.318241526383104 & 0.125037 & 2.5452 & 0.012046 & 0.006023 \tabularnewline
c & 0.0831504465416917 & 0.151839 & 0.5476 & 0.584856 & 0.292428 \tabularnewline
b & 0.00106776969690798 & 0.000245 & 4.3616 & 2.5e-05 & 1.3e-05 \tabularnewline
h & 8.04318252046764e-05 & 0.000514 & 0.1565 & 0.875842 & 0.437921 \tabularnewline
a & -0.005052724299794 & 0.044058 & -0.1147 & 0.908867 & 0.454434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&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]-84.1104264030526[/C][C]10.892347[/C][C]-7.722[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]w[/C][C]-0.0539122069699521[/C][C]0.008285[/C][C]-6.5074[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]f[/C][C]2.06108952316565[/C][C]0.215697[/C][C]9.5555[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]s[/C][C]0.318241526383104[/C][C]0.125037[/C][C]2.5452[/C][C]0.012046[/C][C]0.006023[/C][/ROW]
[ROW][C]c[/C][C]0.0831504465416917[/C][C]0.151839[/C][C]0.5476[/C][C]0.584856[/C][C]0.292428[/C][/ROW]
[ROW][C]b[/C][C]0.00106776969690798[/C][C]0.000245[/C][C]4.3616[/C][C]2.5e-05[/C][C]1.3e-05[/C][/ROW]
[ROW][C]h[/C][C]8.04318252046764e-05[/C][C]0.000514[/C][C]0.1565[/C][C]0.875842[/C][C]0.437921[/C][/ROW]
[ROW][C]a[/C][C]-0.005052724299794[/C][C]0.044058[/C][C]-0.1147[/C][C]0.908867[/C][C]0.454434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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)-84.110426403052610.892347-7.72200
w-0.05391220696995210.008285-6.507400
f2.061089523165650.2156979.555500
s0.3182415263831040.1250372.54520.0120460.006023
c0.08315044654169170.1518390.54760.5848560.292428
b0.001067769696907980.0002454.36162.5e-051.3e-05
h8.04318252046764e-050.0005140.15650.8758420.437921
a-0.0050527242997940.044058-0.11470.9088670.454434







Multiple Linear Regression - Regression Statistics
Multiple R0.865054617142639
R-squared0.748319490639798
Adjusted R-squared0.735269390154454
F-TEST (value)57.3420481689174
F-TEST (DF numerator)7
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.85680572504812
Sum Squared Residuals2008.11830410313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.865054617142639 \tabularnewline
R-squared & 0.748319490639798 \tabularnewline
Adjusted R-squared & 0.735269390154454 \tabularnewline
F-TEST (value) & 57.3420481689174 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.85680572504812 \tabularnewline
Sum Squared Residuals & 2008.11830410313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.865054617142639[/C][/ROW]
[ROW][C]R-squared[/C][C]0.748319490639798[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.735269390154454[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]57.3420481689174[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/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]3.85680572504812[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2008.11830410313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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 R0.865054617142639
R-squared0.748319490639798
Adjusted R-squared0.735269390154454
F-TEST (value)57.3420481689174
F-TEST (DF numerator)7
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.85680572504812
Sum Squared Residuals2008.11830410313







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1148.432460957793675.56753904220633
2148.871304767496015.12869523250399
3159.698478892548045.30152110745196
4139.017594985085693.98240501491431
5810.0727633514695-2.07276335146955
676.365830957910370.634169042089626
733.60736625092386-0.607366250923857
830.1537703482760082.84622965172399
942.345112597199671.65488740280033
1045.89066118908269-1.89066118908269
110-1.049625200545161.04962520054516
12-4-5.038155224488361.03815522448836
13-14-13.5748869414499-0.425113058550112
14-18-10.7535310919931-7.24646890800694
15-8-9.425838064510471.42583806451047
16-10.271309155132142-1.27130915513214
1713.47657008774475-2.47657008774475
1821.497357804365530.502642195634472
1900.439077139213187-0.439077139213187
2010.197987174587650.80201282541235
210-0.7661965832077820.766196583207782
22-14.08248344355205-5.08248344355205
23-3-5.282008510854922.28200851085492
24-3-5.907536130129292.90753613012929
25-3-5.04108789209342.0410878920934
26-4-7.535268352297263.53526835229726
27-8-7.40409326933861-0.595906730661394
28-9-5.38380596027585-3.61619403972415
29-13-13.13710611941390.137106119413905
30-18-19.0606647265161.06066472651602
31-11-10.0221210080852-0.977878991914782
32-9-8.56875096735705-0.431249032642949
33-10-9.52792530480334-0.472074695196654
34-13-11.8503690701755-1.14963092982455
35-11-16.87541173565295.87541173565286
36-5-9.946346798844434.94634679884443
37-15-13.2325601024824-1.76743989751763
38-6-7.496826133545971.49682613354597
39-6-5.48787443929642-0.512125560703577
40-3-4.791609262393791.79160926239379
41-1-3.401588099175372.40158809917537
42-3-4.261365450831161.26136545083116
43-4-2.44008769337225-1.55991230662775
44-6-4.13230563031747-1.86769436968253
450-0.8243816602648010.824381660264801
46-4-3.0605610346545-0.939438965345497
47-2-3.727253645502781.72725364550278
48-2-5.657972308810443.65797230881044
49-6-1.545462695484-4.454537304516
50-7-8.289292869907041.28929286990704
51-6-4.44906022239834-1.55093977760166
52-6-2.65268835566152-3.34731164433848
53-3-0.642928748349896-2.3570712516501
54-2-3.429873219302811.42987321930281
55-5-6.977975764681981.97797576468198
56-11-10.0432480280415-0.956751971958517
57-11-7.02839608076813-3.97160391923188
58-11-7.08818509015902-3.91181490984098
59-10-12.32488502161182.32488502161183
60-14-18.31297663512734.31297663512727
61-8-8.754835741695910.754835741695906
62-9-8.95603570511326-0.043964294886742
63-5-3.20706523647579-1.79293476352421
64-10.0683320046476912-1.06833200464769
65-2-0.756741214801301-1.2432587851987
66-5-2.97598980615251-2.02401019384749
67-4-1.36629111688451-2.63370888311549
68-6-6.053686310486830.0536863104868315
69-2-0.568225198584444-1.43177480141556
70-2-0.597643375379014-1.40235662462099
71-2-4.118958873018282.11895887301828
72-2-8.135409282396136.13540928239613
732-0.5725374500426422.57253745004264
741-0.4571892025111141.45718920251111
75-8-2.17814202767967-5.82185797232033
76-12.54883288367834-3.54883288367834
7711.73920545147303-0.739205451473027
78-10.0256823032622722-1.02568230326227
7921.677087360571350.322912639428652
8024.53578764729235-2.53578764729235
8112.75277892479249-1.75277892479249
82-10.508609949331827-1.50860994933183
83-2-4.489450430415162.48945043041516
84-2-2.871358946250280.871358946250279
85-1-3.026264335914772.02626433591477
86-8-10.91514333200332.91514333200329
87-4-7.055124752837943.05512475283794
88-6-11.96314901155215.96314901155207
89-3-5.908451571935032.90845157193503
90-3-11.58582948443998.58582948443988
91-7-11.02049394546494.02049394546493
92-9-9.130611921182490.130611921182486
93-11-16.87295960851215.87295960851215
94-13-12.7335906361352-0.266409363864844
95-11-11.67630306739150.676303067391535
96-9-5.57312780845166-3.42687219154834
97-17-17.34979499034030.349794990340346
98-22-15.7691508972549-6.23084910274512
99-25-17.596466302335-7.40353369766501
100-20-10.0456645026864-9.95433549731361
101-24-13.4849800738231-10.5150199261769
102-24-18.2939875818439-5.70601241815605
103-22-15.1210259961893-6.87897400381071
104-19-10.4479964856765-8.55200351432349
105-18-8.198364034288-9.801635965712
106-17-15.3924625944132-1.60753740558675
107-11-10.9237814753336-0.0762185246663855
108-11-11.1365585030840.136558503084006
109-12-5.18935313976944-6.81064686023056
110-10-5.13811086856122-4.86188913143878
111-15-8.67870162703622-6.32129837296378
112-15-8.10125852324645-6.89874147675355
113-15-10.8387546865035-4.16124531349652
114-13-5.98279674299339-7.01720325700661
115-8-6.79165374201673-1.20834625798327
116-13-11.7460274904705-1.25397250952948
117-9-5.80392307723432-3.19607692276568
118-7-9.397108148107512.39710814810751
119-4-9.307933260109415.30793326010941
120-4-9.058655695238555.05865569523855
121-2-2.523198014956520.523198014956517
1220-3.729827168893313.72982716889331
123-2-7.082630749977635.08263074997763
124-3-6.792779650552993.79277965055299
1251-0.6071174006020691.60711740060207
126-2-11.74113575659969.74113575659964
127-1-6.34694299897675.3469429989767
1281-3.795393036160484.79539303616048
129-3-6.960832404547983.96083240454798
130-4-10.10507194936836.10507194936834
131-9-11.93679861416832.93679861416833
132-9-9.699996395074540.699996395074542
133-7-4.26708360749981-2.73291639250019
134-14-10.2612022688412-3.73879773115883
135-12-17.2867555208565.28675552085599
136-16-18.54231267499562.54231267499561
137-20-20.16462912887880.164629128878812
138-12-16.15921715950514.15921715950509
139-12-13.17216325955441.17216325955435
140-10-11.34643401585851.34643401585853
141-10-7.07321122728824-2.92678877271176
142-13-11.7655940465245-1.23440595347554
143-16-16.15091160131330.150911601313282

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 8.43246095779367 & 5.56753904220633 \tabularnewline
2 & 14 & 8.87130476749601 & 5.12869523250399 \tabularnewline
3 & 15 & 9.69847889254804 & 5.30152110745196 \tabularnewline
4 & 13 & 9.01759498508569 & 3.98240501491431 \tabularnewline
5 & 8 & 10.0727633514695 & -2.07276335146955 \tabularnewline
6 & 7 & 6.36583095791037 & 0.634169042089626 \tabularnewline
7 & 3 & 3.60736625092386 & -0.607366250923857 \tabularnewline
8 & 3 & 0.153770348276008 & 2.84622965172399 \tabularnewline
9 & 4 & 2.34511259719967 & 1.65488740280033 \tabularnewline
10 & 4 & 5.89066118908269 & -1.89066118908269 \tabularnewline
11 & 0 & -1.04962520054516 & 1.04962520054516 \tabularnewline
12 & -4 & -5.03815522448836 & 1.03815522448836 \tabularnewline
13 & -14 & -13.5748869414499 & -0.425113058550112 \tabularnewline
14 & -18 & -10.7535310919931 & -7.24646890800694 \tabularnewline
15 & -8 & -9.42583806451047 & 1.42583806451047 \tabularnewline
16 & -1 & 0.271309155132142 & -1.27130915513214 \tabularnewline
17 & 1 & 3.47657008774475 & -2.47657008774475 \tabularnewline
18 & 2 & 1.49735780436553 & 0.502642195634472 \tabularnewline
19 & 0 & 0.439077139213187 & -0.439077139213187 \tabularnewline
20 & 1 & 0.19798717458765 & 0.80201282541235 \tabularnewline
21 & 0 & -0.766196583207782 & 0.766196583207782 \tabularnewline
22 & -1 & 4.08248344355205 & -5.08248344355205 \tabularnewline
23 & -3 & -5.28200851085492 & 2.28200851085492 \tabularnewline
24 & -3 & -5.90753613012929 & 2.90753613012929 \tabularnewline
25 & -3 & -5.0410878920934 & 2.0410878920934 \tabularnewline
26 & -4 & -7.53526835229726 & 3.53526835229726 \tabularnewline
27 & -8 & -7.40409326933861 & -0.595906730661394 \tabularnewline
28 & -9 & -5.38380596027585 & -3.61619403972415 \tabularnewline
29 & -13 & -13.1371061194139 & 0.137106119413905 \tabularnewline
30 & -18 & -19.060664726516 & 1.06066472651602 \tabularnewline
31 & -11 & -10.0221210080852 & -0.977878991914782 \tabularnewline
32 & -9 & -8.56875096735705 & -0.431249032642949 \tabularnewline
33 & -10 & -9.52792530480334 & -0.472074695196654 \tabularnewline
34 & -13 & -11.8503690701755 & -1.14963092982455 \tabularnewline
35 & -11 & -16.8754117356529 & 5.87541173565286 \tabularnewline
36 & -5 & -9.94634679884443 & 4.94634679884443 \tabularnewline
37 & -15 & -13.2325601024824 & -1.76743989751763 \tabularnewline
38 & -6 & -7.49682613354597 & 1.49682613354597 \tabularnewline
39 & -6 & -5.48787443929642 & -0.512125560703577 \tabularnewline
40 & -3 & -4.79160926239379 & 1.79160926239379 \tabularnewline
41 & -1 & -3.40158809917537 & 2.40158809917537 \tabularnewline
42 & -3 & -4.26136545083116 & 1.26136545083116 \tabularnewline
43 & -4 & -2.44008769337225 & -1.55991230662775 \tabularnewline
44 & -6 & -4.13230563031747 & -1.86769436968253 \tabularnewline
45 & 0 & -0.824381660264801 & 0.824381660264801 \tabularnewline
46 & -4 & -3.0605610346545 & -0.939438965345497 \tabularnewline
47 & -2 & -3.72725364550278 & 1.72725364550278 \tabularnewline
48 & -2 & -5.65797230881044 & 3.65797230881044 \tabularnewline
49 & -6 & -1.545462695484 & -4.454537304516 \tabularnewline
50 & -7 & -8.28929286990704 & 1.28929286990704 \tabularnewline
51 & -6 & -4.44906022239834 & -1.55093977760166 \tabularnewline
52 & -6 & -2.65268835566152 & -3.34731164433848 \tabularnewline
53 & -3 & -0.642928748349896 & -2.3570712516501 \tabularnewline
54 & -2 & -3.42987321930281 & 1.42987321930281 \tabularnewline
55 & -5 & -6.97797576468198 & 1.97797576468198 \tabularnewline
56 & -11 & -10.0432480280415 & -0.956751971958517 \tabularnewline
57 & -11 & -7.02839608076813 & -3.97160391923188 \tabularnewline
58 & -11 & -7.08818509015902 & -3.91181490984098 \tabularnewline
59 & -10 & -12.3248850216118 & 2.32488502161183 \tabularnewline
60 & -14 & -18.3129766351273 & 4.31297663512727 \tabularnewline
61 & -8 & -8.75483574169591 & 0.754835741695906 \tabularnewline
62 & -9 & -8.95603570511326 & -0.043964294886742 \tabularnewline
63 & -5 & -3.20706523647579 & -1.79293476352421 \tabularnewline
64 & -1 & 0.0683320046476912 & -1.06833200464769 \tabularnewline
65 & -2 & -0.756741214801301 & -1.2432587851987 \tabularnewline
66 & -5 & -2.97598980615251 & -2.02401019384749 \tabularnewline
67 & -4 & -1.36629111688451 & -2.63370888311549 \tabularnewline
68 & -6 & -6.05368631048683 & 0.0536863104868315 \tabularnewline
69 & -2 & -0.568225198584444 & -1.43177480141556 \tabularnewline
70 & -2 & -0.597643375379014 & -1.40235662462099 \tabularnewline
71 & -2 & -4.11895887301828 & 2.11895887301828 \tabularnewline
72 & -2 & -8.13540928239613 & 6.13540928239613 \tabularnewline
73 & 2 & -0.572537450042642 & 2.57253745004264 \tabularnewline
74 & 1 & -0.457189202511114 & 1.45718920251111 \tabularnewline
75 & -8 & -2.17814202767967 & -5.82185797232033 \tabularnewline
76 & -1 & 2.54883288367834 & -3.54883288367834 \tabularnewline
77 & 1 & 1.73920545147303 & -0.739205451473027 \tabularnewline
78 & -1 & 0.0256823032622722 & -1.02568230326227 \tabularnewline
79 & 2 & 1.67708736057135 & 0.322912639428652 \tabularnewline
80 & 2 & 4.53578764729235 & -2.53578764729235 \tabularnewline
81 & 1 & 2.75277892479249 & -1.75277892479249 \tabularnewline
82 & -1 & 0.508609949331827 & -1.50860994933183 \tabularnewline
83 & -2 & -4.48945043041516 & 2.48945043041516 \tabularnewline
84 & -2 & -2.87135894625028 & 0.871358946250279 \tabularnewline
85 & -1 & -3.02626433591477 & 2.02626433591477 \tabularnewline
86 & -8 & -10.9151433320033 & 2.91514333200329 \tabularnewline
87 & -4 & -7.05512475283794 & 3.05512475283794 \tabularnewline
88 & -6 & -11.9631490115521 & 5.96314901155207 \tabularnewline
89 & -3 & -5.90845157193503 & 2.90845157193503 \tabularnewline
90 & -3 & -11.5858294844399 & 8.58582948443988 \tabularnewline
91 & -7 & -11.0204939454649 & 4.02049394546493 \tabularnewline
92 & -9 & -9.13061192118249 & 0.130611921182486 \tabularnewline
93 & -11 & -16.8729596085121 & 5.87295960851215 \tabularnewline
94 & -13 & -12.7335906361352 & -0.266409363864844 \tabularnewline
95 & -11 & -11.6763030673915 & 0.676303067391535 \tabularnewline
96 & -9 & -5.57312780845166 & -3.42687219154834 \tabularnewline
97 & -17 & -17.3497949903403 & 0.349794990340346 \tabularnewline
98 & -22 & -15.7691508972549 & -6.23084910274512 \tabularnewline
99 & -25 & -17.596466302335 & -7.40353369766501 \tabularnewline
100 & -20 & -10.0456645026864 & -9.95433549731361 \tabularnewline
101 & -24 & -13.4849800738231 & -10.5150199261769 \tabularnewline
102 & -24 & -18.2939875818439 & -5.70601241815605 \tabularnewline
103 & -22 & -15.1210259961893 & -6.87897400381071 \tabularnewline
104 & -19 & -10.4479964856765 & -8.55200351432349 \tabularnewline
105 & -18 & -8.198364034288 & -9.801635965712 \tabularnewline
106 & -17 & -15.3924625944132 & -1.60753740558675 \tabularnewline
107 & -11 & -10.9237814753336 & -0.0762185246663855 \tabularnewline
108 & -11 & -11.136558503084 & 0.136558503084006 \tabularnewline
109 & -12 & -5.18935313976944 & -6.81064686023056 \tabularnewline
110 & -10 & -5.13811086856122 & -4.86188913143878 \tabularnewline
111 & -15 & -8.67870162703622 & -6.32129837296378 \tabularnewline
112 & -15 & -8.10125852324645 & -6.89874147675355 \tabularnewline
113 & -15 & -10.8387546865035 & -4.16124531349652 \tabularnewline
114 & -13 & -5.98279674299339 & -7.01720325700661 \tabularnewline
115 & -8 & -6.79165374201673 & -1.20834625798327 \tabularnewline
116 & -13 & -11.7460274904705 & -1.25397250952948 \tabularnewline
117 & -9 & -5.80392307723432 & -3.19607692276568 \tabularnewline
118 & -7 & -9.39710814810751 & 2.39710814810751 \tabularnewline
119 & -4 & -9.30793326010941 & 5.30793326010941 \tabularnewline
120 & -4 & -9.05865569523855 & 5.05865569523855 \tabularnewline
121 & -2 & -2.52319801495652 & 0.523198014956517 \tabularnewline
122 & 0 & -3.72982716889331 & 3.72982716889331 \tabularnewline
123 & -2 & -7.08263074997763 & 5.08263074997763 \tabularnewline
124 & -3 & -6.79277965055299 & 3.79277965055299 \tabularnewline
125 & 1 & -0.607117400602069 & 1.60711740060207 \tabularnewline
126 & -2 & -11.7411357565996 & 9.74113575659964 \tabularnewline
127 & -1 & -6.3469429989767 & 5.3469429989767 \tabularnewline
128 & 1 & -3.79539303616048 & 4.79539303616048 \tabularnewline
129 & -3 & -6.96083240454798 & 3.96083240454798 \tabularnewline
130 & -4 & -10.1050719493683 & 6.10507194936834 \tabularnewline
131 & -9 & -11.9367986141683 & 2.93679861416833 \tabularnewline
132 & -9 & -9.69999639507454 & 0.699996395074542 \tabularnewline
133 & -7 & -4.26708360749981 & -2.73291639250019 \tabularnewline
134 & -14 & -10.2612022688412 & -3.73879773115883 \tabularnewline
135 & -12 & -17.286755520856 & 5.28675552085599 \tabularnewline
136 & -16 & -18.5423126749956 & 2.54231267499561 \tabularnewline
137 & -20 & -20.1646291288788 & 0.164629128878812 \tabularnewline
138 & -12 & -16.1592171595051 & 4.15921715950509 \tabularnewline
139 & -12 & -13.1721632595544 & 1.17216325955435 \tabularnewline
140 & -10 & -11.3464340158585 & 1.34643401585853 \tabularnewline
141 & -10 & -7.07321122728824 & -2.92678877271176 \tabularnewline
142 & -13 & -11.7655940465245 & -1.23440595347554 \tabularnewline
143 & -16 & -16.1509116013133 & 0.150911601313282 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&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]8.43246095779367[/C][C]5.56753904220633[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]8.87130476749601[/C][C]5.12869523250399[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]9.69847889254804[/C][C]5.30152110745196[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]9.01759498508569[/C][C]3.98240501491431[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]10.0727633514695[/C][C]-2.07276335146955[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.36583095791037[/C][C]0.634169042089626[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.60736625092386[/C][C]-0.607366250923857[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.153770348276008[/C][C]2.84622965172399[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.34511259719967[/C][C]1.65488740280033[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.89066118908269[/C][C]-1.89066118908269[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-1.04962520054516[/C][C]1.04962520054516[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-5.03815522448836[/C][C]1.03815522448836[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-13.5748869414499[/C][C]-0.425113058550112[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-10.7535310919931[/C][C]-7.24646890800694[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-9.42583806451047[/C][C]1.42583806451047[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]0.271309155132142[/C][C]-1.27130915513214[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]3.47657008774475[/C][C]-2.47657008774475[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.49735780436553[/C][C]0.502642195634472[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.439077139213187[/C][C]-0.439077139213187[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.19798717458765[/C][C]0.80201282541235[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-0.766196583207782[/C][C]0.766196583207782[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]4.08248344355205[/C][C]-5.08248344355205[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-5.28200851085492[/C][C]2.28200851085492[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-5.90753613012929[/C][C]2.90753613012929[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-5.0410878920934[/C][C]2.0410878920934[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-7.53526835229726[/C][C]3.53526835229726[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-7.40409326933861[/C][C]-0.595906730661394[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-5.38380596027585[/C][C]-3.61619403972415[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-13.1371061194139[/C][C]0.137106119413905[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-19.060664726516[/C][C]1.06066472651602[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-10.0221210080852[/C][C]-0.977878991914782[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-8.56875096735705[/C][C]-0.431249032642949[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-9.52792530480334[/C][C]-0.472074695196654[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-11.8503690701755[/C][C]-1.14963092982455[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-16.8754117356529[/C][C]5.87541173565286[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-9.94634679884443[/C][C]4.94634679884443[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-13.2325601024824[/C][C]-1.76743989751763[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-7.49682613354597[/C][C]1.49682613354597[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-5.48787443929642[/C][C]-0.512125560703577[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-4.79160926239379[/C][C]1.79160926239379[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-3.40158809917537[/C][C]2.40158809917537[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-4.26136545083116[/C][C]1.26136545083116[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-2.44008769337225[/C][C]-1.55991230662775[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-4.13230563031747[/C][C]-1.86769436968253[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-0.824381660264801[/C][C]0.824381660264801[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-3.0605610346545[/C][C]-0.939438965345497[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-3.72725364550278[/C][C]1.72725364550278[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-5.65797230881044[/C][C]3.65797230881044[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]-1.545462695484[/C][C]-4.454537304516[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-8.28929286990704[/C][C]1.28929286990704[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-4.44906022239834[/C][C]-1.55093977760166[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-2.65268835566152[/C][C]-3.34731164433848[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-0.642928748349896[/C][C]-2.3570712516501[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-3.42987321930281[/C][C]1.42987321930281[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-6.97797576468198[/C][C]1.97797576468198[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-10.0432480280415[/C][C]-0.956751971958517[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-7.02839608076813[/C][C]-3.97160391923188[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-7.08818509015902[/C][C]-3.91181490984098[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-12.3248850216118[/C][C]2.32488502161183[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-18.3129766351273[/C][C]4.31297663512727[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-8.75483574169591[/C][C]0.754835741695906[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-8.95603570511326[/C][C]-0.043964294886742[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-3.20706523647579[/C][C]-1.79293476352421[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]0.0683320046476912[/C][C]-1.06833200464769[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-0.756741214801301[/C][C]-1.2432587851987[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-2.97598980615251[/C][C]-2.02401019384749[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-1.36629111688451[/C][C]-2.63370888311549[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-6.05368631048683[/C][C]0.0536863104868315[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-0.568225198584444[/C][C]-1.43177480141556[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-0.597643375379014[/C][C]-1.40235662462099[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-4.11895887301828[/C][C]2.11895887301828[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-8.13540928239613[/C][C]6.13540928239613[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]-0.572537450042642[/C][C]2.57253745004264[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]-0.457189202511114[/C][C]1.45718920251111[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-2.17814202767967[/C][C]-5.82185797232033[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]2.54883288367834[/C][C]-3.54883288367834[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.73920545147303[/C][C]-0.739205451473027[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]0.0256823032622722[/C][C]-1.02568230326227[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.67708736057135[/C][C]0.322912639428652[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]4.53578764729235[/C][C]-2.53578764729235[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.75277892479249[/C][C]-1.75277892479249[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]0.508609949331827[/C][C]-1.50860994933183[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-4.48945043041516[/C][C]2.48945043041516[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-2.87135894625028[/C][C]0.871358946250279[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-3.02626433591477[/C][C]2.02626433591477[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-10.9151433320033[/C][C]2.91514333200329[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-7.05512475283794[/C][C]3.05512475283794[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-11.9631490115521[/C][C]5.96314901155207[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-5.90845157193503[/C][C]2.90845157193503[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-11.5858294844399[/C][C]8.58582948443988[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-11.0204939454649[/C][C]4.02049394546493[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-9.13061192118249[/C][C]0.130611921182486[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-16.8729596085121[/C][C]5.87295960851215[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-12.7335906361352[/C][C]-0.266409363864844[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-11.6763030673915[/C][C]0.676303067391535[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-5.57312780845166[/C][C]-3.42687219154834[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-17.3497949903403[/C][C]0.349794990340346[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-15.7691508972549[/C][C]-6.23084910274512[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-17.596466302335[/C][C]-7.40353369766501[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-10.0456645026864[/C][C]-9.95433549731361[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-13.4849800738231[/C][C]-10.5150199261769[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-18.2939875818439[/C][C]-5.70601241815605[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-15.1210259961893[/C][C]-6.87897400381071[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-10.4479964856765[/C][C]-8.55200351432349[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-8.198364034288[/C][C]-9.801635965712[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-15.3924625944132[/C][C]-1.60753740558675[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-10.9237814753336[/C][C]-0.0762185246663855[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-11.136558503084[/C][C]0.136558503084006[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-5.18935313976944[/C][C]-6.81064686023056[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-5.13811086856122[/C][C]-4.86188913143878[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-8.67870162703622[/C][C]-6.32129837296378[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-8.10125852324645[/C][C]-6.89874147675355[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-10.8387546865035[/C][C]-4.16124531349652[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-5.98279674299339[/C][C]-7.01720325700661[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-6.79165374201673[/C][C]-1.20834625798327[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-11.7460274904705[/C][C]-1.25397250952948[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-5.80392307723432[/C][C]-3.19607692276568[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-9.39710814810751[/C][C]2.39710814810751[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-9.30793326010941[/C][C]5.30793326010941[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-9.05865569523855[/C][C]5.05865569523855[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-2.52319801495652[/C][C]0.523198014956517[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-3.72982716889331[/C][C]3.72982716889331[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-7.08263074997763[/C][C]5.08263074997763[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-6.79277965055299[/C][C]3.79277965055299[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]-0.607117400602069[/C][C]1.60711740060207[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-11.7411357565996[/C][C]9.74113575659964[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-6.3469429989767[/C][C]5.3469429989767[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.79539303616048[/C][C]4.79539303616048[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-6.96083240454798[/C][C]3.96083240454798[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-10.1050719493683[/C][C]6.10507194936834[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-11.9367986141683[/C][C]2.93679861416833[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-9.69999639507454[/C][C]0.699996395074542[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-4.26708360749981[/C][C]-2.73291639250019[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-10.2612022688412[/C][C]-3.73879773115883[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-17.286755520856[/C][C]5.28675552085599[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-18.5423126749956[/C][C]2.54231267499561[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-20.1646291288788[/C][C]0.164629128878812[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-16.1592171595051[/C][C]4.15921715950509[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-13.1721632595544[/C][C]1.17216325955435[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-11.3464340158585[/C][C]1.34643401585853[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-7.07321122728824[/C][C]-2.92678877271176[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-11.7655940465245[/C][C]-1.23440595347554[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-16.1509116013133[/C][C]0.150911601313282[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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
1148.432460957793675.56753904220633
2148.871304767496015.12869523250399
3159.698478892548045.30152110745196
4139.017594985085693.98240501491431
5810.0727633514695-2.07276335146955
676.365830957910370.634169042089626
733.60736625092386-0.607366250923857
830.1537703482760082.84622965172399
942.345112597199671.65488740280033
1045.89066118908269-1.89066118908269
110-1.049625200545161.04962520054516
12-4-5.038155224488361.03815522448836
13-14-13.5748869414499-0.425113058550112
14-18-10.7535310919931-7.24646890800694
15-8-9.425838064510471.42583806451047
16-10.271309155132142-1.27130915513214
1713.47657008774475-2.47657008774475
1821.497357804365530.502642195634472
1900.439077139213187-0.439077139213187
2010.197987174587650.80201282541235
210-0.7661965832077820.766196583207782
22-14.08248344355205-5.08248344355205
23-3-5.282008510854922.28200851085492
24-3-5.907536130129292.90753613012929
25-3-5.04108789209342.0410878920934
26-4-7.535268352297263.53526835229726
27-8-7.40409326933861-0.595906730661394
28-9-5.38380596027585-3.61619403972415
29-13-13.13710611941390.137106119413905
30-18-19.0606647265161.06066472651602
31-11-10.0221210080852-0.977878991914782
32-9-8.56875096735705-0.431249032642949
33-10-9.52792530480334-0.472074695196654
34-13-11.8503690701755-1.14963092982455
35-11-16.87541173565295.87541173565286
36-5-9.946346798844434.94634679884443
37-15-13.2325601024824-1.76743989751763
38-6-7.496826133545971.49682613354597
39-6-5.48787443929642-0.512125560703577
40-3-4.791609262393791.79160926239379
41-1-3.401588099175372.40158809917537
42-3-4.261365450831161.26136545083116
43-4-2.44008769337225-1.55991230662775
44-6-4.13230563031747-1.86769436968253
450-0.8243816602648010.824381660264801
46-4-3.0605610346545-0.939438965345497
47-2-3.727253645502781.72725364550278
48-2-5.657972308810443.65797230881044
49-6-1.545462695484-4.454537304516
50-7-8.289292869907041.28929286990704
51-6-4.44906022239834-1.55093977760166
52-6-2.65268835566152-3.34731164433848
53-3-0.642928748349896-2.3570712516501
54-2-3.429873219302811.42987321930281
55-5-6.977975764681981.97797576468198
56-11-10.0432480280415-0.956751971958517
57-11-7.02839608076813-3.97160391923188
58-11-7.08818509015902-3.91181490984098
59-10-12.32488502161182.32488502161183
60-14-18.31297663512734.31297663512727
61-8-8.754835741695910.754835741695906
62-9-8.95603570511326-0.043964294886742
63-5-3.20706523647579-1.79293476352421
64-10.0683320046476912-1.06833200464769
65-2-0.756741214801301-1.2432587851987
66-5-2.97598980615251-2.02401019384749
67-4-1.36629111688451-2.63370888311549
68-6-6.053686310486830.0536863104868315
69-2-0.568225198584444-1.43177480141556
70-2-0.597643375379014-1.40235662462099
71-2-4.118958873018282.11895887301828
72-2-8.135409282396136.13540928239613
732-0.5725374500426422.57253745004264
741-0.4571892025111141.45718920251111
75-8-2.17814202767967-5.82185797232033
76-12.54883288367834-3.54883288367834
7711.73920545147303-0.739205451473027
78-10.0256823032622722-1.02568230326227
7921.677087360571350.322912639428652
8024.53578764729235-2.53578764729235
8112.75277892479249-1.75277892479249
82-10.508609949331827-1.50860994933183
83-2-4.489450430415162.48945043041516
84-2-2.871358946250280.871358946250279
85-1-3.026264335914772.02626433591477
86-8-10.91514333200332.91514333200329
87-4-7.055124752837943.05512475283794
88-6-11.96314901155215.96314901155207
89-3-5.908451571935032.90845157193503
90-3-11.58582948443998.58582948443988
91-7-11.02049394546494.02049394546493
92-9-9.130611921182490.130611921182486
93-11-16.87295960851215.87295960851215
94-13-12.7335906361352-0.266409363864844
95-11-11.67630306739150.676303067391535
96-9-5.57312780845166-3.42687219154834
97-17-17.34979499034030.349794990340346
98-22-15.7691508972549-6.23084910274512
99-25-17.596466302335-7.40353369766501
100-20-10.0456645026864-9.95433549731361
101-24-13.4849800738231-10.5150199261769
102-24-18.2939875818439-5.70601241815605
103-22-15.1210259961893-6.87897400381071
104-19-10.4479964856765-8.55200351432349
105-18-8.198364034288-9.801635965712
106-17-15.3924625944132-1.60753740558675
107-11-10.9237814753336-0.0762185246663855
108-11-11.1365585030840.136558503084006
109-12-5.18935313976944-6.81064686023056
110-10-5.13811086856122-4.86188913143878
111-15-8.67870162703622-6.32129837296378
112-15-8.10125852324645-6.89874147675355
113-15-10.8387546865035-4.16124531349652
114-13-5.98279674299339-7.01720325700661
115-8-6.79165374201673-1.20834625798327
116-13-11.7460274904705-1.25397250952948
117-9-5.80392307723432-3.19607692276568
118-7-9.397108148107512.39710814810751
119-4-9.307933260109415.30793326010941
120-4-9.058655695238555.05865569523855
121-2-2.523198014956520.523198014956517
1220-3.729827168893313.72982716889331
123-2-7.082630749977635.08263074997763
124-3-6.792779650552993.79277965055299
1251-0.6071174006020691.60711740060207
126-2-11.74113575659969.74113575659964
127-1-6.34694299897675.3469429989767
1281-3.795393036160484.79539303616048
129-3-6.960832404547983.96083240454798
130-4-10.10507194936836.10507194936834
131-9-11.93679861416832.93679861416833
132-9-9.699996395074540.699996395074542
133-7-4.26708360749981-2.73291639250019
134-14-10.2612022688412-3.73879773115883
135-12-17.2867555208565.28675552085599
136-16-18.54231267499562.54231267499561
137-20-20.16462912887880.164629128878812
138-12-16.15921715950514.15921715950509
139-12-13.17216325955441.17216325955435
140-10-11.34643401585851.34643401585853
141-10-7.07321122728824-2.92678877271176
142-13-11.7655940465245-1.23440595347554
143-16-16.15091160131330.150911601313282







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1128309879862920.2256619759725850.887169012013708
120.05984775119421350.1196955023884270.940152248805787
130.188922211420360.377844422840720.81107778857964
140.5187608043095630.9624783913808750.481239195690437
150.4433671681327080.8867343362654150.556632831867292
160.3795404399299840.7590808798599690.620459560070016
170.3204893335096650.640978667019330.679510666490335
180.2541986534505270.5083973069010530.745801346549473
190.2190393037306280.4380786074612560.780960696269372
200.199341271005260.3986825420105190.80065872899474
210.1603114251705970.3206228503411950.839688574829403
220.1188977104946810.2377954209893610.881102289505319
230.1234209925140890.2468419850281790.876579007485911
240.1246956565853010.2493913131706020.875304343414699
250.09941642489899210.1988328497979840.900583575101008
260.08881999852526650.1776399970505330.911180001474733
270.07697461425368510.153949228507370.923025385746315
280.06037861447435890.1207572289487180.939621385525641
290.04819147909014130.09638295818028270.951808520909859
300.03869435530712660.07738871061425320.961305644692873
310.02943766152621790.05887532305243590.970562338473782
320.02204090204884530.04408180409769060.977959097951155
330.01461362946099930.02922725892199850.985386370539001
340.009330827853700020.01866165570740.9906691721463
350.01119169615951010.02238339231902020.98880830384049
360.009006568412954350.01801313682590870.990993431587046
370.01401430246902930.02802860493805870.985985697530971
380.01139901763016130.02279803526032260.988600982369839
390.01345522525840950.0269104505168190.986544774741591
400.0106791951589570.0213583903179140.989320804841043
410.009224710045440560.01844942009088110.990775289954559
420.008040613183799870.01608122636759970.9919593868162
430.006445339882228490.0128906797644570.993554660117771
440.004686319378611430.009372638757222860.995313680621389
450.005384020385151810.01076804077030360.994615979614848
460.004263330120490140.008526660240980280.99573666987951
470.004053001830344650.008106003660689310.995946998169655
480.00421346027206610.008426920544132190.995786539727934
490.007679288696998180.01535857739399640.992320711303002
500.005711179294405020.011422358588810.994288820705595
510.004423355683064290.008846711366128570.995576644316936
520.003806874210873470.007613748421746950.996193125789127
530.00274143470403350.005482869408066990.997258565295966
540.002992047142871720.005984094285743440.997007952857128
550.004139246646656880.008278493293313760.995860753353343
560.003216409602416480.006432819204832960.996783590397584
570.00269820966776150.0053964193355230.997301790332239
580.001829616260175680.003659232520351360.998170383739824
590.001669476062995030.003338952125990060.998330523937005
600.003395677491824880.006791354983649760.996604322508175
610.002422913422226220.004845826844452440.997577086577774
620.001781910555343180.003563821110686360.998218089444657
630.001775994636055630.003551989272111270.998224005363944
640.001295947719032660.002591895438065310.998704052280967
650.001100030719660480.002200061439320960.99889996928034
660.000900162375414580.001800324750829160.999099837624585
670.0006255360251523510.00125107205030470.999374463974848
680.0007335361424883230.001467072284976650.999266463857512
690.0007009830603577230.001401966120715450.999299016939642
700.0005547449255075610.001109489851015120.999445255074492
710.0005962930408959170.001192586081791830.999403706959104
720.003661826329634290.007323652659268580.996338173670366
730.00485190015374020.009703800307480410.99514809984626
740.00474627923455210.009492558469104190.995253720765448
750.004631095188186120.009262190376372240.995368904811814
760.003674564613969380.007349129227938760.996325435386031
770.002858817538331310.005717635076662620.997141182461669
780.002197453401420640.004394906802841270.997802546598579
790.00198853719122030.003977074382440590.99801146280878
800.001474081629405750.00294816325881150.998525918370594
810.001089712408748550.002179424817497090.998910287591251
820.0009116591160031760.001823318232006350.999088340883997
830.001125053407798980.002250106815597970.998874946592201
840.0009878047961105530.001975609592221110.999012195203889
850.001029477694792770.002058955389585550.998970522305207
860.001033131170702480.002066262341404950.998966868829298
870.001057802042814220.002115604085628440.998942197957186
880.001519734092659640.003039468185319280.99848026590734
890.001124176499535830.002248352999071650.998875823500464
900.0105280347161080.02105606943221590.989471965283892
910.01446341623861920.02892683247723840.985536583761381
920.01707923580479920.03415847160959830.982920764195201
930.05055756099192320.1011151219838460.949442439008077
940.04581176524152980.09162353048305960.95418823475847
950.04555517675487790.09111035350975580.954444823245122
960.04512919356691790.09025838713383590.954870806433082
970.06381781028856410.1276356205771280.936182189711436
980.1271541406948180.2543082813896360.872845859305182
990.2218274806918470.4436549613836950.778172519308153
1000.3100871836619340.6201743673238670.689912816338066
1010.4894760411349290.9789520822698580.510523958865071
1020.5088961929518970.9822076140962070.491103807048103
1030.4835207633611080.9670415267222150.516479236638892
1040.4595522057740560.9191044115481120.540447794225944
1050.5108035466092110.9783929067815790.489196453390789
1060.4652691398909280.9305382797818560.534730860109072
1070.5640921260304480.8718157479391040.435907873969552
1080.6788041133021380.6423917733957240.321195886697862
1090.6409914733252590.7180170533494830.359008526674741
1100.5838116982096510.8323766035806990.416188301790349
1110.5337846585803730.9324306828392540.466215341419627
1120.7338010919972130.5323978160055740.266198908002787
1130.8046462222111260.3907075555777470.195353777788874
1140.8076820613062710.3846358773874590.192317938693729
1150.7702079090937020.4595841818125960.229792090906298
1160.7416532322688740.5166935354622510.258346767731126
1170.7995638122169250.400872375566150.200436187783075
1180.8700736133895470.2598527732209050.129926386610453
1190.8597163936590990.2805672126818020.140283606340901
1200.8426583326220260.3146833347559480.157341667377974
1210.8188809175713710.3622381648572570.181119082428629
1220.7787147612443510.4425704775112980.221285238755649
1230.7599964384665580.4800071230668830.240003561533442
1240.7790336958076630.4419326083846750.220966304192337
1250.7741020289588620.4517959420822770.225897971041138
1260.7409987183220880.5180025633558250.259001281677912
1270.6807822112071050.638435577585790.319217788792895
1280.6549865629618520.6900268740762970.345013437038148
1290.8023558906199870.3952882187600260.197644109380013
1300.8046389566120630.3907220867758730.195361043387937
1310.6810597403235710.6378805193528590.318940259676429
1320.7223266535807570.5553466928384860.277673346419243

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.112830987986292 & 0.225661975972585 & 0.887169012013708 \tabularnewline
12 & 0.0598477511942135 & 0.119695502388427 & 0.940152248805787 \tabularnewline
13 & 0.18892221142036 & 0.37784442284072 & 0.81107778857964 \tabularnewline
14 & 0.518760804309563 & 0.962478391380875 & 0.481239195690437 \tabularnewline
15 & 0.443367168132708 & 0.886734336265415 & 0.556632831867292 \tabularnewline
16 & 0.379540439929984 & 0.759080879859969 & 0.620459560070016 \tabularnewline
17 & 0.320489333509665 & 0.64097866701933 & 0.679510666490335 \tabularnewline
18 & 0.254198653450527 & 0.508397306901053 & 0.745801346549473 \tabularnewline
19 & 0.219039303730628 & 0.438078607461256 & 0.780960696269372 \tabularnewline
20 & 0.19934127100526 & 0.398682542010519 & 0.80065872899474 \tabularnewline
21 & 0.160311425170597 & 0.320622850341195 & 0.839688574829403 \tabularnewline
22 & 0.118897710494681 & 0.237795420989361 & 0.881102289505319 \tabularnewline
23 & 0.123420992514089 & 0.246841985028179 & 0.876579007485911 \tabularnewline
24 & 0.124695656585301 & 0.249391313170602 & 0.875304343414699 \tabularnewline
25 & 0.0994164248989921 & 0.198832849797984 & 0.900583575101008 \tabularnewline
26 & 0.0888199985252665 & 0.177639997050533 & 0.911180001474733 \tabularnewline
27 & 0.0769746142536851 & 0.15394922850737 & 0.923025385746315 \tabularnewline
28 & 0.0603786144743589 & 0.120757228948718 & 0.939621385525641 \tabularnewline
29 & 0.0481914790901413 & 0.0963829581802827 & 0.951808520909859 \tabularnewline
30 & 0.0386943553071266 & 0.0773887106142532 & 0.961305644692873 \tabularnewline
31 & 0.0294376615262179 & 0.0588753230524359 & 0.970562338473782 \tabularnewline
32 & 0.0220409020488453 & 0.0440818040976906 & 0.977959097951155 \tabularnewline
33 & 0.0146136294609993 & 0.0292272589219985 & 0.985386370539001 \tabularnewline
34 & 0.00933082785370002 & 0.0186616557074 & 0.9906691721463 \tabularnewline
35 & 0.0111916961595101 & 0.0223833923190202 & 0.98880830384049 \tabularnewline
36 & 0.00900656841295435 & 0.0180131368259087 & 0.990993431587046 \tabularnewline
37 & 0.0140143024690293 & 0.0280286049380587 & 0.985985697530971 \tabularnewline
38 & 0.0113990176301613 & 0.0227980352603226 & 0.988600982369839 \tabularnewline
39 & 0.0134552252584095 & 0.026910450516819 & 0.986544774741591 \tabularnewline
40 & 0.010679195158957 & 0.021358390317914 & 0.989320804841043 \tabularnewline
41 & 0.00922471004544056 & 0.0184494200908811 & 0.990775289954559 \tabularnewline
42 & 0.00804061318379987 & 0.0160812263675997 & 0.9919593868162 \tabularnewline
43 & 0.00644533988222849 & 0.012890679764457 & 0.993554660117771 \tabularnewline
44 & 0.00468631937861143 & 0.00937263875722286 & 0.995313680621389 \tabularnewline
45 & 0.00538402038515181 & 0.0107680407703036 & 0.994615979614848 \tabularnewline
46 & 0.00426333012049014 & 0.00852666024098028 & 0.99573666987951 \tabularnewline
47 & 0.00405300183034465 & 0.00810600366068931 & 0.995946998169655 \tabularnewline
48 & 0.0042134602720661 & 0.00842692054413219 & 0.995786539727934 \tabularnewline
49 & 0.00767928869699818 & 0.0153585773939964 & 0.992320711303002 \tabularnewline
50 & 0.00571117929440502 & 0.01142235858881 & 0.994288820705595 \tabularnewline
51 & 0.00442335568306429 & 0.00884671136612857 & 0.995576644316936 \tabularnewline
52 & 0.00380687421087347 & 0.00761374842174695 & 0.996193125789127 \tabularnewline
53 & 0.0027414347040335 & 0.00548286940806699 & 0.997258565295966 \tabularnewline
54 & 0.00299204714287172 & 0.00598409428574344 & 0.997007952857128 \tabularnewline
55 & 0.00413924664665688 & 0.00827849329331376 & 0.995860753353343 \tabularnewline
56 & 0.00321640960241648 & 0.00643281920483296 & 0.996783590397584 \tabularnewline
57 & 0.0026982096677615 & 0.005396419335523 & 0.997301790332239 \tabularnewline
58 & 0.00182961626017568 & 0.00365923252035136 & 0.998170383739824 \tabularnewline
59 & 0.00166947606299503 & 0.00333895212599006 & 0.998330523937005 \tabularnewline
60 & 0.00339567749182488 & 0.00679135498364976 & 0.996604322508175 \tabularnewline
61 & 0.00242291342222622 & 0.00484582684445244 & 0.997577086577774 \tabularnewline
62 & 0.00178191055534318 & 0.00356382111068636 & 0.998218089444657 \tabularnewline
63 & 0.00177599463605563 & 0.00355198927211127 & 0.998224005363944 \tabularnewline
64 & 0.00129594771903266 & 0.00259189543806531 & 0.998704052280967 \tabularnewline
65 & 0.00110003071966048 & 0.00220006143932096 & 0.99889996928034 \tabularnewline
66 & 0.00090016237541458 & 0.00180032475082916 & 0.999099837624585 \tabularnewline
67 & 0.000625536025152351 & 0.0012510720503047 & 0.999374463974848 \tabularnewline
68 & 0.000733536142488323 & 0.00146707228497665 & 0.999266463857512 \tabularnewline
69 & 0.000700983060357723 & 0.00140196612071545 & 0.999299016939642 \tabularnewline
70 & 0.000554744925507561 & 0.00110948985101512 & 0.999445255074492 \tabularnewline
71 & 0.000596293040895917 & 0.00119258608179183 & 0.999403706959104 \tabularnewline
72 & 0.00366182632963429 & 0.00732365265926858 & 0.996338173670366 \tabularnewline
73 & 0.0048519001537402 & 0.00970380030748041 & 0.99514809984626 \tabularnewline
74 & 0.0047462792345521 & 0.00949255846910419 & 0.995253720765448 \tabularnewline
75 & 0.00463109518818612 & 0.00926219037637224 & 0.995368904811814 \tabularnewline
76 & 0.00367456461396938 & 0.00734912922793876 & 0.996325435386031 \tabularnewline
77 & 0.00285881753833131 & 0.00571763507666262 & 0.997141182461669 \tabularnewline
78 & 0.00219745340142064 & 0.00439490680284127 & 0.997802546598579 \tabularnewline
79 & 0.0019885371912203 & 0.00397707438244059 & 0.99801146280878 \tabularnewline
80 & 0.00147408162940575 & 0.0029481632588115 & 0.998525918370594 \tabularnewline
81 & 0.00108971240874855 & 0.00217942481749709 & 0.998910287591251 \tabularnewline
82 & 0.000911659116003176 & 0.00182331823200635 & 0.999088340883997 \tabularnewline
83 & 0.00112505340779898 & 0.00225010681559797 & 0.998874946592201 \tabularnewline
84 & 0.000987804796110553 & 0.00197560959222111 & 0.999012195203889 \tabularnewline
85 & 0.00102947769479277 & 0.00205895538958555 & 0.998970522305207 \tabularnewline
86 & 0.00103313117070248 & 0.00206626234140495 & 0.998966868829298 \tabularnewline
87 & 0.00105780204281422 & 0.00211560408562844 & 0.998942197957186 \tabularnewline
88 & 0.00151973409265964 & 0.00303946818531928 & 0.99848026590734 \tabularnewline
89 & 0.00112417649953583 & 0.00224835299907165 & 0.998875823500464 \tabularnewline
90 & 0.010528034716108 & 0.0210560694322159 & 0.989471965283892 \tabularnewline
91 & 0.0144634162386192 & 0.0289268324772384 & 0.985536583761381 \tabularnewline
92 & 0.0170792358047992 & 0.0341584716095983 & 0.982920764195201 \tabularnewline
93 & 0.0505575609919232 & 0.101115121983846 & 0.949442439008077 \tabularnewline
94 & 0.0458117652415298 & 0.0916235304830596 & 0.95418823475847 \tabularnewline
95 & 0.0455551767548779 & 0.0911103535097558 & 0.954444823245122 \tabularnewline
96 & 0.0451291935669179 & 0.0902583871338359 & 0.954870806433082 \tabularnewline
97 & 0.0638178102885641 & 0.127635620577128 & 0.936182189711436 \tabularnewline
98 & 0.127154140694818 & 0.254308281389636 & 0.872845859305182 \tabularnewline
99 & 0.221827480691847 & 0.443654961383695 & 0.778172519308153 \tabularnewline
100 & 0.310087183661934 & 0.620174367323867 & 0.689912816338066 \tabularnewline
101 & 0.489476041134929 & 0.978952082269858 & 0.510523958865071 \tabularnewline
102 & 0.508896192951897 & 0.982207614096207 & 0.491103807048103 \tabularnewline
103 & 0.483520763361108 & 0.967041526722215 & 0.516479236638892 \tabularnewline
104 & 0.459552205774056 & 0.919104411548112 & 0.540447794225944 \tabularnewline
105 & 0.510803546609211 & 0.978392906781579 & 0.489196453390789 \tabularnewline
106 & 0.465269139890928 & 0.930538279781856 & 0.534730860109072 \tabularnewline
107 & 0.564092126030448 & 0.871815747939104 & 0.435907873969552 \tabularnewline
108 & 0.678804113302138 & 0.642391773395724 & 0.321195886697862 \tabularnewline
109 & 0.640991473325259 & 0.718017053349483 & 0.359008526674741 \tabularnewline
110 & 0.583811698209651 & 0.832376603580699 & 0.416188301790349 \tabularnewline
111 & 0.533784658580373 & 0.932430682839254 & 0.466215341419627 \tabularnewline
112 & 0.733801091997213 & 0.532397816005574 & 0.266198908002787 \tabularnewline
113 & 0.804646222211126 & 0.390707555577747 & 0.195353777788874 \tabularnewline
114 & 0.807682061306271 & 0.384635877387459 & 0.192317938693729 \tabularnewline
115 & 0.770207909093702 & 0.459584181812596 & 0.229792090906298 \tabularnewline
116 & 0.741653232268874 & 0.516693535462251 & 0.258346767731126 \tabularnewline
117 & 0.799563812216925 & 0.40087237556615 & 0.200436187783075 \tabularnewline
118 & 0.870073613389547 & 0.259852773220905 & 0.129926386610453 \tabularnewline
119 & 0.859716393659099 & 0.280567212681802 & 0.140283606340901 \tabularnewline
120 & 0.842658332622026 & 0.314683334755948 & 0.157341667377974 \tabularnewline
121 & 0.818880917571371 & 0.362238164857257 & 0.181119082428629 \tabularnewline
122 & 0.778714761244351 & 0.442570477511298 & 0.221285238755649 \tabularnewline
123 & 0.759996438466558 & 0.480007123066883 & 0.240003561533442 \tabularnewline
124 & 0.779033695807663 & 0.441932608384675 & 0.220966304192337 \tabularnewline
125 & 0.774102028958862 & 0.451795942082277 & 0.225897971041138 \tabularnewline
126 & 0.740998718322088 & 0.518002563355825 & 0.259001281677912 \tabularnewline
127 & 0.680782211207105 & 0.63843557758579 & 0.319217788792895 \tabularnewline
128 & 0.654986562961852 & 0.690026874076297 & 0.345013437038148 \tabularnewline
129 & 0.802355890619987 & 0.395288218760026 & 0.197644109380013 \tabularnewline
130 & 0.804638956612063 & 0.390722086775873 & 0.195361043387937 \tabularnewline
131 & 0.681059740323571 & 0.637880519352859 & 0.318940259676429 \tabularnewline
132 & 0.722326653580757 & 0.555346692838486 & 0.277673346419243 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&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]11[/C][C]0.112830987986292[/C][C]0.225661975972585[/C][C]0.887169012013708[/C][/ROW]
[ROW][C]12[/C][C]0.0598477511942135[/C][C]0.119695502388427[/C][C]0.940152248805787[/C][/ROW]
[ROW][C]13[/C][C]0.18892221142036[/C][C]0.37784442284072[/C][C]0.81107778857964[/C][/ROW]
[ROW][C]14[/C][C]0.518760804309563[/C][C]0.962478391380875[/C][C]0.481239195690437[/C][/ROW]
[ROW][C]15[/C][C]0.443367168132708[/C][C]0.886734336265415[/C][C]0.556632831867292[/C][/ROW]
[ROW][C]16[/C][C]0.379540439929984[/C][C]0.759080879859969[/C][C]0.620459560070016[/C][/ROW]
[ROW][C]17[/C][C]0.320489333509665[/C][C]0.64097866701933[/C][C]0.679510666490335[/C][/ROW]
[ROW][C]18[/C][C]0.254198653450527[/C][C]0.508397306901053[/C][C]0.745801346549473[/C][/ROW]
[ROW][C]19[/C][C]0.219039303730628[/C][C]0.438078607461256[/C][C]0.780960696269372[/C][/ROW]
[ROW][C]20[/C][C]0.19934127100526[/C][C]0.398682542010519[/C][C]0.80065872899474[/C][/ROW]
[ROW][C]21[/C][C]0.160311425170597[/C][C]0.320622850341195[/C][C]0.839688574829403[/C][/ROW]
[ROW][C]22[/C][C]0.118897710494681[/C][C]0.237795420989361[/C][C]0.881102289505319[/C][/ROW]
[ROW][C]23[/C][C]0.123420992514089[/C][C]0.246841985028179[/C][C]0.876579007485911[/C][/ROW]
[ROW][C]24[/C][C]0.124695656585301[/C][C]0.249391313170602[/C][C]0.875304343414699[/C][/ROW]
[ROW][C]25[/C][C]0.0994164248989921[/C][C]0.198832849797984[/C][C]0.900583575101008[/C][/ROW]
[ROW][C]26[/C][C]0.0888199985252665[/C][C]0.177639997050533[/C][C]0.911180001474733[/C][/ROW]
[ROW][C]27[/C][C]0.0769746142536851[/C][C]0.15394922850737[/C][C]0.923025385746315[/C][/ROW]
[ROW][C]28[/C][C]0.0603786144743589[/C][C]0.120757228948718[/C][C]0.939621385525641[/C][/ROW]
[ROW][C]29[/C][C]0.0481914790901413[/C][C]0.0963829581802827[/C][C]0.951808520909859[/C][/ROW]
[ROW][C]30[/C][C]0.0386943553071266[/C][C]0.0773887106142532[/C][C]0.961305644692873[/C][/ROW]
[ROW][C]31[/C][C]0.0294376615262179[/C][C]0.0588753230524359[/C][C]0.970562338473782[/C][/ROW]
[ROW][C]32[/C][C]0.0220409020488453[/C][C]0.0440818040976906[/C][C]0.977959097951155[/C][/ROW]
[ROW][C]33[/C][C]0.0146136294609993[/C][C]0.0292272589219985[/C][C]0.985386370539001[/C][/ROW]
[ROW][C]34[/C][C]0.00933082785370002[/C][C]0.0186616557074[/C][C]0.9906691721463[/C][/ROW]
[ROW][C]35[/C][C]0.0111916961595101[/C][C]0.0223833923190202[/C][C]0.98880830384049[/C][/ROW]
[ROW][C]36[/C][C]0.00900656841295435[/C][C]0.0180131368259087[/C][C]0.990993431587046[/C][/ROW]
[ROW][C]37[/C][C]0.0140143024690293[/C][C]0.0280286049380587[/C][C]0.985985697530971[/C][/ROW]
[ROW][C]38[/C][C]0.0113990176301613[/C][C]0.0227980352603226[/C][C]0.988600982369839[/C][/ROW]
[ROW][C]39[/C][C]0.0134552252584095[/C][C]0.026910450516819[/C][C]0.986544774741591[/C][/ROW]
[ROW][C]40[/C][C]0.010679195158957[/C][C]0.021358390317914[/C][C]0.989320804841043[/C][/ROW]
[ROW][C]41[/C][C]0.00922471004544056[/C][C]0.0184494200908811[/C][C]0.990775289954559[/C][/ROW]
[ROW][C]42[/C][C]0.00804061318379987[/C][C]0.0160812263675997[/C][C]0.9919593868162[/C][/ROW]
[ROW][C]43[/C][C]0.00644533988222849[/C][C]0.012890679764457[/C][C]0.993554660117771[/C][/ROW]
[ROW][C]44[/C][C]0.00468631937861143[/C][C]0.00937263875722286[/C][C]0.995313680621389[/C][/ROW]
[ROW][C]45[/C][C]0.00538402038515181[/C][C]0.0107680407703036[/C][C]0.994615979614848[/C][/ROW]
[ROW][C]46[/C][C]0.00426333012049014[/C][C]0.00852666024098028[/C][C]0.99573666987951[/C][/ROW]
[ROW][C]47[/C][C]0.00405300183034465[/C][C]0.00810600366068931[/C][C]0.995946998169655[/C][/ROW]
[ROW][C]48[/C][C]0.0042134602720661[/C][C]0.00842692054413219[/C][C]0.995786539727934[/C][/ROW]
[ROW][C]49[/C][C]0.00767928869699818[/C][C]0.0153585773939964[/C][C]0.992320711303002[/C][/ROW]
[ROW][C]50[/C][C]0.00571117929440502[/C][C]0.01142235858881[/C][C]0.994288820705595[/C][/ROW]
[ROW][C]51[/C][C]0.00442335568306429[/C][C]0.00884671136612857[/C][C]0.995576644316936[/C][/ROW]
[ROW][C]52[/C][C]0.00380687421087347[/C][C]0.00761374842174695[/C][C]0.996193125789127[/C][/ROW]
[ROW][C]53[/C][C]0.0027414347040335[/C][C]0.00548286940806699[/C][C]0.997258565295966[/C][/ROW]
[ROW][C]54[/C][C]0.00299204714287172[/C][C]0.00598409428574344[/C][C]0.997007952857128[/C][/ROW]
[ROW][C]55[/C][C]0.00413924664665688[/C][C]0.00827849329331376[/C][C]0.995860753353343[/C][/ROW]
[ROW][C]56[/C][C]0.00321640960241648[/C][C]0.00643281920483296[/C][C]0.996783590397584[/C][/ROW]
[ROW][C]57[/C][C]0.0026982096677615[/C][C]0.005396419335523[/C][C]0.997301790332239[/C][/ROW]
[ROW][C]58[/C][C]0.00182961626017568[/C][C]0.00365923252035136[/C][C]0.998170383739824[/C][/ROW]
[ROW][C]59[/C][C]0.00166947606299503[/C][C]0.00333895212599006[/C][C]0.998330523937005[/C][/ROW]
[ROW][C]60[/C][C]0.00339567749182488[/C][C]0.00679135498364976[/C][C]0.996604322508175[/C][/ROW]
[ROW][C]61[/C][C]0.00242291342222622[/C][C]0.00484582684445244[/C][C]0.997577086577774[/C][/ROW]
[ROW][C]62[/C][C]0.00178191055534318[/C][C]0.00356382111068636[/C][C]0.998218089444657[/C][/ROW]
[ROW][C]63[/C][C]0.00177599463605563[/C][C]0.00355198927211127[/C][C]0.998224005363944[/C][/ROW]
[ROW][C]64[/C][C]0.00129594771903266[/C][C]0.00259189543806531[/C][C]0.998704052280967[/C][/ROW]
[ROW][C]65[/C][C]0.00110003071966048[/C][C]0.00220006143932096[/C][C]0.99889996928034[/C][/ROW]
[ROW][C]66[/C][C]0.00090016237541458[/C][C]0.00180032475082916[/C][C]0.999099837624585[/C][/ROW]
[ROW][C]67[/C][C]0.000625536025152351[/C][C]0.0012510720503047[/C][C]0.999374463974848[/C][/ROW]
[ROW][C]68[/C][C]0.000733536142488323[/C][C]0.00146707228497665[/C][C]0.999266463857512[/C][/ROW]
[ROW][C]69[/C][C]0.000700983060357723[/C][C]0.00140196612071545[/C][C]0.999299016939642[/C][/ROW]
[ROW][C]70[/C][C]0.000554744925507561[/C][C]0.00110948985101512[/C][C]0.999445255074492[/C][/ROW]
[ROW][C]71[/C][C]0.000596293040895917[/C][C]0.00119258608179183[/C][C]0.999403706959104[/C][/ROW]
[ROW][C]72[/C][C]0.00366182632963429[/C][C]0.00732365265926858[/C][C]0.996338173670366[/C][/ROW]
[ROW][C]73[/C][C]0.0048519001537402[/C][C]0.00970380030748041[/C][C]0.99514809984626[/C][/ROW]
[ROW][C]74[/C][C]0.0047462792345521[/C][C]0.00949255846910419[/C][C]0.995253720765448[/C][/ROW]
[ROW][C]75[/C][C]0.00463109518818612[/C][C]0.00926219037637224[/C][C]0.995368904811814[/C][/ROW]
[ROW][C]76[/C][C]0.00367456461396938[/C][C]0.00734912922793876[/C][C]0.996325435386031[/C][/ROW]
[ROW][C]77[/C][C]0.00285881753833131[/C][C]0.00571763507666262[/C][C]0.997141182461669[/C][/ROW]
[ROW][C]78[/C][C]0.00219745340142064[/C][C]0.00439490680284127[/C][C]0.997802546598579[/C][/ROW]
[ROW][C]79[/C][C]0.0019885371912203[/C][C]0.00397707438244059[/C][C]0.99801146280878[/C][/ROW]
[ROW][C]80[/C][C]0.00147408162940575[/C][C]0.0029481632588115[/C][C]0.998525918370594[/C][/ROW]
[ROW][C]81[/C][C]0.00108971240874855[/C][C]0.00217942481749709[/C][C]0.998910287591251[/C][/ROW]
[ROW][C]82[/C][C]0.000911659116003176[/C][C]0.00182331823200635[/C][C]0.999088340883997[/C][/ROW]
[ROW][C]83[/C][C]0.00112505340779898[/C][C]0.00225010681559797[/C][C]0.998874946592201[/C][/ROW]
[ROW][C]84[/C][C]0.000987804796110553[/C][C]0.00197560959222111[/C][C]0.999012195203889[/C][/ROW]
[ROW][C]85[/C][C]0.00102947769479277[/C][C]0.00205895538958555[/C][C]0.998970522305207[/C][/ROW]
[ROW][C]86[/C][C]0.00103313117070248[/C][C]0.00206626234140495[/C][C]0.998966868829298[/C][/ROW]
[ROW][C]87[/C][C]0.00105780204281422[/C][C]0.00211560408562844[/C][C]0.998942197957186[/C][/ROW]
[ROW][C]88[/C][C]0.00151973409265964[/C][C]0.00303946818531928[/C][C]0.99848026590734[/C][/ROW]
[ROW][C]89[/C][C]0.00112417649953583[/C][C]0.00224835299907165[/C][C]0.998875823500464[/C][/ROW]
[ROW][C]90[/C][C]0.010528034716108[/C][C]0.0210560694322159[/C][C]0.989471965283892[/C][/ROW]
[ROW][C]91[/C][C]0.0144634162386192[/C][C]0.0289268324772384[/C][C]0.985536583761381[/C][/ROW]
[ROW][C]92[/C][C]0.0170792358047992[/C][C]0.0341584716095983[/C][C]0.982920764195201[/C][/ROW]
[ROW][C]93[/C][C]0.0505575609919232[/C][C]0.101115121983846[/C][C]0.949442439008077[/C][/ROW]
[ROW][C]94[/C][C]0.0458117652415298[/C][C]0.0916235304830596[/C][C]0.95418823475847[/C][/ROW]
[ROW][C]95[/C][C]0.0455551767548779[/C][C]0.0911103535097558[/C][C]0.954444823245122[/C][/ROW]
[ROW][C]96[/C][C]0.0451291935669179[/C][C]0.0902583871338359[/C][C]0.954870806433082[/C][/ROW]
[ROW][C]97[/C][C]0.0638178102885641[/C][C]0.127635620577128[/C][C]0.936182189711436[/C][/ROW]
[ROW][C]98[/C][C]0.127154140694818[/C][C]0.254308281389636[/C][C]0.872845859305182[/C][/ROW]
[ROW][C]99[/C][C]0.221827480691847[/C][C]0.443654961383695[/C][C]0.778172519308153[/C][/ROW]
[ROW][C]100[/C][C]0.310087183661934[/C][C]0.620174367323867[/C][C]0.689912816338066[/C][/ROW]
[ROW][C]101[/C][C]0.489476041134929[/C][C]0.978952082269858[/C][C]0.510523958865071[/C][/ROW]
[ROW][C]102[/C][C]0.508896192951897[/C][C]0.982207614096207[/C][C]0.491103807048103[/C][/ROW]
[ROW][C]103[/C][C]0.483520763361108[/C][C]0.967041526722215[/C][C]0.516479236638892[/C][/ROW]
[ROW][C]104[/C][C]0.459552205774056[/C][C]0.919104411548112[/C][C]0.540447794225944[/C][/ROW]
[ROW][C]105[/C][C]0.510803546609211[/C][C]0.978392906781579[/C][C]0.489196453390789[/C][/ROW]
[ROW][C]106[/C][C]0.465269139890928[/C][C]0.930538279781856[/C][C]0.534730860109072[/C][/ROW]
[ROW][C]107[/C][C]0.564092126030448[/C][C]0.871815747939104[/C][C]0.435907873969552[/C][/ROW]
[ROW][C]108[/C][C]0.678804113302138[/C][C]0.642391773395724[/C][C]0.321195886697862[/C][/ROW]
[ROW][C]109[/C][C]0.640991473325259[/C][C]0.718017053349483[/C][C]0.359008526674741[/C][/ROW]
[ROW][C]110[/C][C]0.583811698209651[/C][C]0.832376603580699[/C][C]0.416188301790349[/C][/ROW]
[ROW][C]111[/C][C]0.533784658580373[/C][C]0.932430682839254[/C][C]0.466215341419627[/C][/ROW]
[ROW][C]112[/C][C]0.733801091997213[/C][C]0.532397816005574[/C][C]0.266198908002787[/C][/ROW]
[ROW][C]113[/C][C]0.804646222211126[/C][C]0.390707555577747[/C][C]0.195353777788874[/C][/ROW]
[ROW][C]114[/C][C]0.807682061306271[/C][C]0.384635877387459[/C][C]0.192317938693729[/C][/ROW]
[ROW][C]115[/C][C]0.770207909093702[/C][C]0.459584181812596[/C][C]0.229792090906298[/C][/ROW]
[ROW][C]116[/C][C]0.741653232268874[/C][C]0.516693535462251[/C][C]0.258346767731126[/C][/ROW]
[ROW][C]117[/C][C]0.799563812216925[/C][C]0.40087237556615[/C][C]0.200436187783075[/C][/ROW]
[ROW][C]118[/C][C]0.870073613389547[/C][C]0.259852773220905[/C][C]0.129926386610453[/C][/ROW]
[ROW][C]119[/C][C]0.859716393659099[/C][C]0.280567212681802[/C][C]0.140283606340901[/C][/ROW]
[ROW][C]120[/C][C]0.842658332622026[/C][C]0.314683334755948[/C][C]0.157341667377974[/C][/ROW]
[ROW][C]121[/C][C]0.818880917571371[/C][C]0.362238164857257[/C][C]0.181119082428629[/C][/ROW]
[ROW][C]122[/C][C]0.778714761244351[/C][C]0.442570477511298[/C][C]0.221285238755649[/C][/ROW]
[ROW][C]123[/C][C]0.759996438466558[/C][C]0.480007123066883[/C][C]0.240003561533442[/C][/ROW]
[ROW][C]124[/C][C]0.779033695807663[/C][C]0.441932608384675[/C][C]0.220966304192337[/C][/ROW]
[ROW][C]125[/C][C]0.774102028958862[/C][C]0.451795942082277[/C][C]0.225897971041138[/C][/ROW]
[ROW][C]126[/C][C]0.740998718322088[/C][C]0.518002563355825[/C][C]0.259001281677912[/C][/ROW]
[ROW][C]127[/C][C]0.680782211207105[/C][C]0.63843557758579[/C][C]0.319217788792895[/C][/ROW]
[ROW][C]128[/C][C]0.654986562961852[/C][C]0.690026874076297[/C][C]0.345013437038148[/C][/ROW]
[ROW][C]129[/C][C]0.802355890619987[/C][C]0.395288218760026[/C][C]0.197644109380013[/C][/ROW]
[ROW][C]130[/C][C]0.804638956612063[/C][C]0.390722086775873[/C][C]0.195361043387937[/C][/ROW]
[ROW][C]131[/C][C]0.681059740323571[/C][C]0.637880519352859[/C][C]0.318940259676429[/C][/ROW]
[ROW][C]132[/C][C]0.722326653580757[/C][C]0.555346692838486[/C][C]0.277673346419243[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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
110.1128309879862920.2256619759725850.887169012013708
120.05984775119421350.1196955023884270.940152248805787
130.188922211420360.377844422840720.81107778857964
140.5187608043095630.9624783913808750.481239195690437
150.4433671681327080.8867343362654150.556632831867292
160.3795404399299840.7590808798599690.620459560070016
170.3204893335096650.640978667019330.679510666490335
180.2541986534505270.5083973069010530.745801346549473
190.2190393037306280.4380786074612560.780960696269372
200.199341271005260.3986825420105190.80065872899474
210.1603114251705970.3206228503411950.839688574829403
220.1188977104946810.2377954209893610.881102289505319
230.1234209925140890.2468419850281790.876579007485911
240.1246956565853010.2493913131706020.875304343414699
250.09941642489899210.1988328497979840.900583575101008
260.08881999852526650.1776399970505330.911180001474733
270.07697461425368510.153949228507370.923025385746315
280.06037861447435890.1207572289487180.939621385525641
290.04819147909014130.09638295818028270.951808520909859
300.03869435530712660.07738871061425320.961305644692873
310.02943766152621790.05887532305243590.970562338473782
320.02204090204884530.04408180409769060.977959097951155
330.01461362946099930.02922725892199850.985386370539001
340.009330827853700020.01866165570740.9906691721463
350.01119169615951010.02238339231902020.98880830384049
360.009006568412954350.01801313682590870.990993431587046
370.01401430246902930.02802860493805870.985985697530971
380.01139901763016130.02279803526032260.988600982369839
390.01345522525840950.0269104505168190.986544774741591
400.0106791951589570.0213583903179140.989320804841043
410.009224710045440560.01844942009088110.990775289954559
420.008040613183799870.01608122636759970.9919593868162
430.006445339882228490.0128906797644570.993554660117771
440.004686319378611430.009372638757222860.995313680621389
450.005384020385151810.01076804077030360.994615979614848
460.004263330120490140.008526660240980280.99573666987951
470.004053001830344650.008106003660689310.995946998169655
480.00421346027206610.008426920544132190.995786539727934
490.007679288696998180.01535857739399640.992320711303002
500.005711179294405020.011422358588810.994288820705595
510.004423355683064290.008846711366128570.995576644316936
520.003806874210873470.007613748421746950.996193125789127
530.00274143470403350.005482869408066990.997258565295966
540.002992047142871720.005984094285743440.997007952857128
550.004139246646656880.008278493293313760.995860753353343
560.003216409602416480.006432819204832960.996783590397584
570.00269820966776150.0053964193355230.997301790332239
580.001829616260175680.003659232520351360.998170383739824
590.001669476062995030.003338952125990060.998330523937005
600.003395677491824880.006791354983649760.996604322508175
610.002422913422226220.004845826844452440.997577086577774
620.001781910555343180.003563821110686360.998218089444657
630.001775994636055630.003551989272111270.998224005363944
640.001295947719032660.002591895438065310.998704052280967
650.001100030719660480.002200061439320960.99889996928034
660.000900162375414580.001800324750829160.999099837624585
670.0006255360251523510.00125107205030470.999374463974848
680.0007335361424883230.001467072284976650.999266463857512
690.0007009830603577230.001401966120715450.999299016939642
700.0005547449255075610.001109489851015120.999445255074492
710.0005962930408959170.001192586081791830.999403706959104
720.003661826329634290.007323652659268580.996338173670366
730.00485190015374020.009703800307480410.99514809984626
740.00474627923455210.009492558469104190.995253720765448
750.004631095188186120.009262190376372240.995368904811814
760.003674564613969380.007349129227938760.996325435386031
770.002858817538331310.005717635076662620.997141182461669
780.002197453401420640.004394906802841270.997802546598579
790.00198853719122030.003977074382440590.99801146280878
800.001474081629405750.00294816325881150.998525918370594
810.001089712408748550.002179424817497090.998910287591251
820.0009116591160031760.001823318232006350.999088340883997
830.001125053407798980.002250106815597970.998874946592201
840.0009878047961105530.001975609592221110.999012195203889
850.001029477694792770.002058955389585550.998970522305207
860.001033131170702480.002066262341404950.998966868829298
870.001057802042814220.002115604085628440.998942197957186
880.001519734092659640.003039468185319280.99848026590734
890.001124176499535830.002248352999071650.998875823500464
900.0105280347161080.02105606943221590.989471965283892
910.01446341623861920.02892683247723840.985536583761381
920.01707923580479920.03415847160959830.982920764195201
930.05055756099192320.1011151219838460.949442439008077
940.04581176524152980.09162353048305960.95418823475847
950.04555517675487790.09111035350975580.954444823245122
960.04512919356691790.09025838713383590.954870806433082
970.06381781028856410.1276356205771280.936182189711436
980.1271541406948180.2543082813896360.872845859305182
990.2218274806918470.4436549613836950.778172519308153
1000.3100871836619340.6201743673238670.689912816338066
1010.4894760411349290.9789520822698580.510523958865071
1020.5088961929518970.9822076140962070.491103807048103
1030.4835207633611080.9670415267222150.516479236638892
1040.4595522057740560.9191044115481120.540447794225944
1050.5108035466092110.9783929067815790.489196453390789
1060.4652691398909280.9305382797818560.534730860109072
1070.5640921260304480.8718157479391040.435907873969552
1080.6788041133021380.6423917733957240.321195886697862
1090.6409914733252590.7180170533494830.359008526674741
1100.5838116982096510.8323766035806990.416188301790349
1110.5337846585803730.9324306828392540.466215341419627
1120.7338010919972130.5323978160055740.266198908002787
1130.8046462222111260.3907075555777470.195353777788874
1140.8076820613062710.3846358773874590.192317938693729
1150.7702079090937020.4595841818125960.229792090906298
1160.7416532322688740.5166935354622510.258346767731126
1170.7995638122169250.400872375566150.200436187783075
1180.8700736133895470.2598527732209050.129926386610453
1190.8597163936590990.2805672126818020.140283606340901
1200.8426583326220260.3146833347559480.157341667377974
1210.8188809175713710.3622381648572570.181119082428629
1220.7787147612443510.4425704775112980.221285238755649
1230.7599964384665580.4800071230668830.240003561533442
1240.7790336958076630.4419326083846750.220966304192337
1250.7741020289588620.4517959420822770.225897971041138
1260.7409987183220880.5180025633558250.259001281677912
1270.6807822112071050.638435577585790.319217788792895
1280.6549865629618520.6900268740762970.345013437038148
1290.8023558906199870.3952882187600260.197644109380013
1300.8046389566120630.3907220867758730.195361043387937
1310.6810597403235710.6378805193528590.318940259676429
1320.7223266535807570.5553466928384860.277673346419243







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level430.352459016393443NOK
5% type I error level610.5NOK
10% type I error level670.549180327868853NOK

\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 & 43 & 0.352459016393443 & NOK \tabularnewline
5% type I error level & 61 & 0.5 & NOK \tabularnewline
10% type I error level & 67 & 0.549180327868853 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185673&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]43[/C][C]0.352459016393443[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]67[/C][C]0.549180327868853[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185673&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185673&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 level430.352459016393443NOK
5% type I error level610.5NOK
10% type I error level670.549180327868853NOK



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')
}