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Description of Statistical Computation

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, 16 Nov 2012 15:32:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/16/t1353097989r5hxx4826ha2lw0.htm/, Retrieved Sat, 27 Apr 2024 07:36:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190016, Retrieved Sat, 27 Apr 2024 07:36:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [interactie-effect ] [2012-11-16 20:32:23] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
14	501	501	11	11	20	20	91,81	91,81	77585	77585	1303,2	1303,2	2000	1
14	485	485	11	11	19	19	91,98	91,98	77585	77585	-58,7	-58,7	2000	1
15	464	464	11	11	18	18	91,72	91,72	77585	77585	-378,9	-378,9	2000	1
13	460	0	11	0	13	0	90,27	0	78302	0	175,6	0	2001	0
8	467	0	11	0	17	0	91,89	0	78302	0	233,7	0	2001	0
7	460	0	9	0	17	0	92,07	0	78302	0	706,8	0	2001	0
3	448	0	8	0	13	0	92,92	0	78224	0	-23,6	0	2001	0
3	443	0	6	0	14	0	93,34	0	78224	0	420,9	0	2001	0
4	436	0	7	0	13	0	93,6	0	78224	0	722,1	0	2001	0
4	431	0	8	0	17	0	92,41	0	78178	0	1401,3	0	2001	0
0	484	0	6	0	17	0	93,6	0	78178	0	-94,9	0	2001	0
-4	510	0	5	0	15	0	93,77	0	78178	0	1043,6	0	2001	0
-14	513	0	2	0	9	0	93,6	0	77988	0	1300,1	0	2001	0
-18	503	0	3	0	10	0	93,6	0	77988	0	721,1	0	2001	0
-8	471	0	3	0	9	0	93,51	0	77988	0	-45,6	0	2001	0
-1	471	0	7	0	14	0	92,66	0	77876	0	787,5	0	2002	0
1	476	0	8	0	18	0	94,2	0	77876	0	694,3	0	2002	0
2	475	0	7	0	18	0	94,37	0	77876	0	1054,7	0	2002	0
0	470	0	7	0	12	0	94,45	0	78432	0	821,9	0	2002	0
1	461	0	6	0	16	0	94,62	0	78432	0	1100,7	0	2002	0
0	455	0	6	0	12	0	94,37	0	78432	0	862,4	0	2002	0
-1	456	0	7	0	19	0	93,43	0	79025	0	1656,1	0	2002	0
-3	517	0	5	0	13	0	94,79	0	79025	0	-174	0	2002	0
-3	525	0	5	0	12	0	94,88	0	79025	0	1337,6	0	2002	0
-3	523	0	5	0	13	0	94,79	0	79407	0	1394,9	0	2002	0
-4	519	0	4	0	11	0	94,62	0	79407	0	915,7	0	2002	0
-8	509	0	4	0	10	0	94,71	0	79407	0	-481,1	0	2002	0
-9	512	0	4	0	16	0	93,77	0	79644	0	167,9	0	2003	0
-13	519	0	1	0	12	0	95,73	0	79644	0	208,2	0	2003	0
-18	517	0	-1	0	6	0	95,99	0	79644	0	382,2	0	2003	0
-11	510	0	3	0	8	0	95,82	0	79381	0	1004	0	2003	0
-9	509	0	4	0	6	0	95,47	0	79381	0	864,7	0	2003	0
-10	501	0	3	0	8	0	95,82	0	79381	0	1052,9	0	2003	0
-13	507	0	2	0	8	0	94,71	0	79536	0	1417,6	0	2003	0
-11	569	0	1	0	9	0	96,33	0	79536	0	-197,7	0	2003	0
-5	580	0	4	0	13	0	96,5	0	79536	0	1262,1	0	2003	0
-15	578	0	3	0	8	0	96,16	0	79813	0	1147,2	0	2003	0
-6	565	0	5	0	11	0	96,33	0	79813	0	700,2	0	2003	0
-6	547	0	6	0	8	0	96,33	0	79813	0	45,3	0	2003	0
-3	555	0	6	0	10	0	95,05	0	80332	0	458,5	0	2004	0
-1	562	0	6	0	15	0	96,84	0	80332	0	610,2	0	2004	0
-3	561	0	6	0	12	0	96,92	0	80332	0	786,4	0	2004	0
-4	555	0	6	0	13	0	97,44	0	81434	0	787,2	0	2004	0
-6	544	0	5	0	12	0	97,78	0	81434	0	1040	0	2004	0
0	537	0	6	0	15	0	97,69	0	81434	0	324,1	0	2004	0
-4	543	0	5	0	13	0	96,67	0	82167	0	1343	0	2004	0
-2	594	0	6	0	13	0	98,29	0	82167	0	-501,2	0	2004	0
-2	611	0	5	0	16	0	98,2	0	82167	0	800,4	0	2004	0
-6	613	0	7	0	14	0	98,71	0	82816	0	916,7	0	2004	0
-7	611	0	4	0	12	0	98,54	0	82816	0	695,8	0	2004	0
-6	594	0	5	0	15	0	98,2	0	82816	0	28	0	2004	0
-6	595	595	6	6	14	14	96,92	96,92	83000	83000	495,6	495,6	2005	1
-3	591	591	6	6	19	19	99,06	99,06	83000	83000	366,2	366,2	2005	1
-2	589	589	5	5	16	16	99,65	99,65	83000	83000	633	633	2005	1
-5	584	584	3	3	16	16	99,82	99,82	83251	83251	848,3	848,3	2005	1
-11	573	573	2	2	11	11	99,99	99,99	83251	83251	472,2	472,2	2005	1
-11	567	567	3	3	13	13	100,33	100,33	83251	83251	357,8	357,8	2005	1
-11	569	569	3	3	12	12	99,31	99,31	83591	83591	824,3	824,3	2005	1
-10	621	621	2	2	11	11	101,1	101,1	83591	83591	-880,1	-880,1	2005	1
-14	629	629	0	0	6	6	101,1	101,1	83591	83591	1066,8	1066,8	2005	1
-8	628	628	4	4	9	9	100,93	100,93	83910	83910	1052,8	1052,8	2005	1
-9	612	612	4	4	6	6	100,85	100,85	83910	83910	-32,1	-32,1	2005	1
-5	595	595	5	5	15	15	100,93	100,93	83910	83910	-1331,4	-1331,4	2005	1
-1	597	597	6	6	17	17	99,6	99,6	84599	84599	-767,1	-767,1	2006	1
-2	593	593	6	6	13	13	101,88	101,88	84599	84599	-236,7	-236,7	2006	1
-5	590	590	5	5	12	12	101,81	101,81	84599	84599	-184,9	-184,9	2006	1
-4	580	580	5	5	13	13	102,38	102,38	85275	85275	-143,4	-143,4	2006	1
-6	574	574	3	3	10	10	102,74	102,74	85275	85275	493,9	493,9	2006	1
-2	573	573	5	5	14	14	102,82	102,82	85275	85275	549,7	549,7	2006	1
-2	573	573	5	5	13	13	101,72	101,72	85608	85608	982,7	982,7	2006	1
-2	620	620	5	5	10	10	103,47	103,47	85608	85608	-856,3	-856,3	2006	1
-2	626	626	3	3	11	11	102,98	102,98	85608	85608	967	967	2006	1
2	620	620	6	6	12	12	102,68	102,68	86303	86303	659,4	659,4	2006	1
1	588	588	6	6	7	7	102,9	102,9	86303	86303	577,2	577,2	2006	1
-8	566	566	4	4	11	11	103,03	103,03	86303	86303	-213,1	-213,1	2006	1
-1	557	557	6	6	9	9	101,29	101,29	87115	87115	17,7	17,7	2007	1
1	561	561	5	5	13	13	103,69	103,69	87115	87115	390,1	390,1	2007	1
-1	549	549	4	4	12	12	103,68	103,68	87115	87115	509,3	509,3	2007	1
2	532	532	5	5	5	5	104,2	104,2	87931	87931	410	410	2007	1
2	526	526	5	5	13	13	104,08	104,08	87931	87931	212,5	212,5	2007	1
1	511	511	4	4	11	11	104,16	104,16	87931	87931	818	818	2007	1
-1	499	499	3	3	8	8	103,05	103,05	88164	88164	422,7	422,7	2007	1
-2	555	555	2	2	8	8	104,66	104,66	88164	88164	-158	-158	2007	1
-2	565	565	3	3	8	8	104,46	104,46	88164	88164	427,2	427,2	2007	1
-1	542	542	2	2	8	8	104,95	104,95	88792	88792	243,4	243,4	2007	1
-8	527	527	-1	-1	0	0	105,85	105,85	88792	88792	-419,3	-419,3	2007	1
-4	510	510	0	0	3	3	106,23	106,23	88792	88792	-1459,8	-1459,8	2007	1
-6	514	514	-2	-2	0	0	104,86	104,86	89263	89263	-1389,8	-1389,8	2008	1
-3	517	517	1	1	-1	-1	107,44	107,44	89263	89263	-2,1	-2,1	2008	1
-3	508	508	-2	-2	-1	-1	108,23	108,23	89263	89263	-938,6	-938,6	2008	1
-7	493	493	-2	-2	-4	-4	108,45	108,45	89881	89881	-839,9	-839,9	2008	1
-9	490	490	-2	-2	1	1	109,39	109,39	89881	89881	-297,6	-297,6	2008	1
-11	469	469	-6	-6	-1	-1	110,15	110,15	89881	89881	-376,3	-376,3	2008	1
-13	478	478	-4	-4	0	0	109,13	109,13	90120	90120	-79,4	-79,4	2008	1
-11	528	528	-2	-2	-1	-1	110,28	110,28	90120	90120	-2091,3	-2091,3	2008	1
-9	534	534	0	0	6	6	110,17	110,17	90120	90120	-1023	-1023	2008	1
-17	518	518	-5	-5	0	0	109,99	109,99	89703	89703	-765,6	-765,6	2008	1
-22	506	506	-4	-4	-3	-3	109,26	109,26	89703	89703	-1592,3	-1592,3	2008	1
-25	502	502	-5	-5	-3	-3	109,11	109,11	89703	89703	-1588,8	-1588,8	2008	1
-20	516	0	-1	0	4	0	107,06	0	87818	0	-1318	0	2009	0
-24	528	0	-2	0	1	0	109,53	0	87818	0	-402,4	0	2009	0
-24	533	0	-4	0	0	0	108,92	0	87818	0	-814,5	0	2009	0
-22	536	0	-1	0	-4	0	109,24	0	86273	0	-98,4	0	2009	0
-19	537	0	1	0	-2	0	109,12	0	86273	0	-305,9	0	2009	0
-18	524	0	1	0	3	0	109	0	86273	0	-18,4	0	2009	0
-17	536	0	-2	0	2	0	107,23	0	86316	0	610,3	0	2009	0
-11	587	0	1	0	5	0	109,49	0	86316	0	-917,3	0	2009	0
-11	597	0	1	0	6	0	109,04	0	86316	0	88,4	0	2009	0
-12	581	0	3	0	6	0	109,02	0	87234	0	-740,2	0	2009	0
-10	564	0	3	0	3	0	109,23	0	87234	0	29,3	0	2009	0
-15	558	0	1	0	4	0	109,46	0	87234	0	-893,2	0	2009	0
-15	575	0	1	0	7	0	107,9	0	87885	0	-1030,2	0	2010	0
-15	580	0	0	0	5	0	110,42	0	87885	0	-403,4	0	2010	0
-13	575	0	2	0	6	0	110,98	0	87885	0	-46,9	0	2010	0
-8	563	0	2	0	1	0	111,48	0	88003	0	-321,2	0	2010	0
-13	552	0	-1	0	3	0	111,88	0	88003	0	-239,9	0	2010	0
-9	537	0	1	0	6	0	111,89	0	88003	0	640,9	0	2010	0
-7	545	0	0	0	0	0	109,85	0	88910	0	511,6	0	2010	0
-4	601	0	1	0	3	0	112,1	0	88910	0	-665,1	0	2010	0
-4	604	0	1	0	4	0	112,24	0	88910	0	657,7	0	2010	0
-2	586	0	3	0	7	0	112,39	0	89397	0	-207,7	0	2010	0
0	564	0	2	0	6	0	112,52	0	89397	0	-885,2	0	2010	0
-2	549	0	0	0	6	0	113,16	0	89397	0	-1595,8	0	2010	0
-3	551	0	0	0	6	0	111,84	0	89813	0	-1374,9	0	2011	0
1	556	0	3	0	6	0	114,33	0	89813	0	-316,6	0	2011	0
-2	548	0	-2	0	2	0	114,82	0	89813	0	-283,4	0	2011	0
-1	540	0	0	0	2	0	115,2	0	90539	0	-175,8	0	2011	0
1	531	0	1	0	2	0	115,4	0	90539	0	-694,2	0	2011	0
-3	521	0	-1	0	3	0	115,74	0	90539	0	-249,9	0	2011	0
-4	519	0	-2	0	-1	0	114,19	0	90688	0	268,2	0	2011	0
-9	572	0	-1	0	-4	0	115,94	0	90688	0	-2105,1	0	2011	0
-9	581	0	-1	0	4	0	116,03	0	90688	0	-762,8	0	2011	0
-7	563	0	1	0	5	0	116,24	0	90691	0	-117,1	0	2011	0
-14	548	0	-2	0	3	0	116,66	0	90691	0	-1094,4	0	2011	0
-12	539	0	-5	0	-1	0	116,79	0	90691	0	-2095,2	0	2011	0
-16	541	0	-5	0	-4	0	115,48	0	90645	0	-1587,6	0	2012	0
-20	562	0	-6	0	0	0	118,16	0	90645	0	-528	0	2012	0
-12	559	0	-4	0	-1	0	118,38	0	90645	0	-324,2	0	2012	0
-12	546	0	-3	0	-1	0	118,51	0	90861	0	-276,1	0	2012	0
-10	536	0	-3	0	3	0	118,42	0	90861	0	-139,1	0	2012	0
-10	528	0	-1	0	2	0	118,24	0	90861	0	268	0	2012	0
-13	530	0	-2	0	-4	0	116,47	0	90401	0	570,5	0	2012	0
-16	582	0	-3	0	-3	0	118,96	0	90401	0	-316,5	0	2012	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&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]11 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=190016&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=0

As an alternative you can also use a QR Code:  
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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
I[t] = + 5170.40781237318 -0.0418609505726249W[t] -0.0011753756969735W_c[t] + 1.97151735403983F[t] + 0.0355642411814071F_c[t] + 0.410444502909001S[t] -0.381510225668953S_c[t] + 0.885159096426792C[t] -0.887319104918851C_c[t] + 0.00177315046905713B[t] + 0.000410843463316142B_c[t] + 0.000148223968683879H[t] + 0.000627369505118702H_c[t] -2.69359233278076T[t] + 58.895824976102c[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I[t] =  +  5170.40781237318 -0.0418609505726249W[t] -0.0011753756969735W_c[t] +  1.97151735403983F[t] +  0.0355642411814071F_c[t] +  0.410444502909001S[t] -0.381510225668953S_c[t] +  0.885159096426792C[t] -0.887319104918851C_c[t] +  0.00177315046905713B[t] +  0.000410843463316142B_c[t] +  0.000148223968683879H[t] +  0.000627369505118702H_c[t] -2.69359233278076T[t] +  58.895824976102c[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I[t] =  +  5170.40781237318 -0.0418609505726249W[t] -0.0011753756969735W_c[t] +  1.97151735403983F[t] +  0.0355642411814071F_c[t] +  0.410444502909001S[t] -0.381510225668953S_c[t] +  0.885159096426792C[t] -0.887319104918851C_c[t] +  0.00177315046905713B[t] +  0.000410843463316142B_c[t] +  0.000148223968683879H[t] +  0.000627369505118702H_c[t] -2.69359233278076T[t] +  58.895824976102c[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
I[t] = + 5170.40781237318 -0.0418609505726249W[t] -0.0011753756969735W_c[t] + 1.97151735403983F[t] + 0.0355642411814071F_c[t] + 0.410444502909001S[t] -0.381510225668953S_c[t] + 0.885159096426792C[t] -0.887319104918851C_c[t] + 0.00177315046905713B[t] + 0.000410843463316142B_c[t] + 0.000148223968683879H[t] + 0.000627369505118702H_c[t] -2.69359233278076T[t] + 58.895824976102c[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5170.407812373181426.1766363.62540.0004150.000208
W-0.04186095057262490.010877-3.84870.0001879.3e-05
W_c-0.00117537569697350.017221-0.06830.9456910.472846
F1.971517354039830.2304018.556900
F_c0.03556424118140710.4120220.08630.931350.465675
S0.4104445029090010.1526982.6880.0081450.004073
S_c-0.3815102256689530.253893-1.50260.1353950.067697
C0.8851590964267920.323462.73650.0070920.003546
C_c-0.8873191049188510.646545-1.37240.1723390.086169
B0.001773150469057130.0006362.78750.0061220.003061
B_c0.0004108434633161420.000840.48910.6256230.312812
H0.0001482239686838790.0006410.23130.8174250.408712
H_c0.0006273695051187020.0010120.61970.5365790.268289
T-2.693592332780760.731443-3.68260.0003390.00017
c58.89582497610232.3076371.8230.0706420.035321

\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) & 5170.40781237318 & 1426.176636 & 3.6254 & 0.000415 & 0.000208 \tabularnewline
W & -0.0418609505726249 & 0.010877 & -3.8487 & 0.000187 & 9.3e-05 \tabularnewline
W_c & -0.0011753756969735 & 0.017221 & -0.0683 & 0.945691 & 0.472846 \tabularnewline
F & 1.97151735403983 & 0.230401 & 8.5569 & 0 & 0 \tabularnewline
F_c & 0.0355642411814071 & 0.412022 & 0.0863 & 0.93135 & 0.465675 \tabularnewline
S & 0.410444502909001 & 0.152698 & 2.688 & 0.008145 & 0.004073 \tabularnewline
S_c & -0.381510225668953 & 0.253893 & -1.5026 & 0.135395 & 0.067697 \tabularnewline
C & 0.885159096426792 & 0.32346 & 2.7365 & 0.007092 & 0.003546 \tabularnewline
C_c & -0.887319104918851 & 0.646545 & -1.3724 & 0.172339 & 0.086169 \tabularnewline
B & 0.00177315046905713 & 0.000636 & 2.7875 & 0.006122 & 0.003061 \tabularnewline
B_c & 0.000410843463316142 & 0.00084 & 0.4891 & 0.625623 & 0.312812 \tabularnewline
H & 0.000148223968683879 & 0.000641 & 0.2313 & 0.817425 & 0.408712 \tabularnewline
H_c & 0.000627369505118702 & 0.001012 & 0.6197 & 0.536579 & 0.268289 \tabularnewline
T & -2.69359233278076 & 0.731443 & -3.6826 & 0.000339 & 0.00017 \tabularnewline
c & 58.895824976102 & 32.307637 & 1.823 & 0.070642 & 0.035321 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&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]5170.40781237318[/C][C]1426.176636[/C][C]3.6254[/C][C]0.000415[/C][C]0.000208[/C][/ROW]
[ROW][C]W[/C][C]-0.0418609505726249[/C][C]0.010877[/C][C]-3.8487[/C][C]0.000187[/C][C]9.3e-05[/C][/ROW]
[ROW][C]W_c[/C][C]-0.0011753756969735[/C][C]0.017221[/C][C]-0.0683[/C][C]0.945691[/C][C]0.472846[/C][/ROW]
[ROW][C]F[/C][C]1.97151735403983[/C][C]0.230401[/C][C]8.5569[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]F_c[/C][C]0.0355642411814071[/C][C]0.412022[/C][C]0.0863[/C][C]0.93135[/C][C]0.465675[/C][/ROW]
[ROW][C]S[/C][C]0.410444502909001[/C][C]0.152698[/C][C]2.688[/C][C]0.008145[/C][C]0.004073[/C][/ROW]
[ROW][C]S_c[/C][C]-0.381510225668953[/C][C]0.253893[/C][C]-1.5026[/C][C]0.135395[/C][C]0.067697[/C][/ROW]
[ROW][C]C[/C][C]0.885159096426792[/C][C]0.32346[/C][C]2.7365[/C][C]0.007092[/C][C]0.003546[/C][/ROW]
[ROW][C]C_c[/C][C]-0.887319104918851[/C][C]0.646545[/C][C]-1.3724[/C][C]0.172339[/C][C]0.086169[/C][/ROW]
[ROW][C]B[/C][C]0.00177315046905713[/C][C]0.000636[/C][C]2.7875[/C][C]0.006122[/C][C]0.003061[/C][/ROW]
[ROW][C]B_c[/C][C]0.000410843463316142[/C][C]0.00084[/C][C]0.4891[/C][C]0.625623[/C][C]0.312812[/C][/ROW]
[ROW][C]H[/C][C]0.000148223968683879[/C][C]0.000641[/C][C]0.2313[/C][C]0.817425[/C][C]0.408712[/C][/ROW]
[ROW][C]H_c[/C][C]0.000627369505118702[/C][C]0.001012[/C][C]0.6197[/C][C]0.536579[/C][C]0.268289[/C][/ROW]
[ROW][C]T[/C][C]-2.69359233278076[/C][C]0.731443[/C][C]-3.6826[/C][C]0.000339[/C][C]0.00017[/C][/ROW]
[ROW][C]c[/C][C]58.895824976102[/C][C]32.307637[/C][C]1.823[/C][C]0.070642[/C][C]0.035321[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=2

As an alternative you can also use a QR Code:  
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)5170.407812373181426.1766363.62540.0004150.000208
W-0.04186095057262490.010877-3.84870.0001879.3e-05
W_c-0.00117537569697350.017221-0.06830.9456910.472846
F1.971517354039830.2304018.556900
F_c0.03556424118140710.4120220.08630.931350.465675
S0.4104445029090010.1526982.6880.0081450.004073
S_c-0.3815102256689530.253893-1.50260.1353950.067697
C0.8851590964267920.323462.73650.0070920.003546
C_c-0.8873191049188510.646545-1.37240.1723390.086169
B0.001773150469057130.0006362.78750.0061220.003061
B_c0.0004108434633161420.000840.48910.6256230.312812
H0.0001482239686838790.0006410.23130.8174250.408712
H_c0.0006273695051187020.0010120.61970.5365790.268289
T-2.693592332780760.731443-3.68260.0003390.00017
c58.89582497610232.3076371.8230.0706420.035321






Multiple Linear Regression - Regression Statistics
Multiple R0.889036959324517
R-squared0.790386715044982
Adjusted R-squared0.767460262003027
F-TEST (value)34.4748798952278
F-TEST (DF numerator)14
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.61471675782578
Sum Squared Residuals1672.47068663124

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.889036959324517 \tabularnewline
R-squared & 0.790386715044982 \tabularnewline
Adjusted R-squared & 0.767460262003027 \tabularnewline
F-TEST (value) & 34.4748798952278 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 128 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.61471675782578 \tabularnewline
Sum Squared Residuals & 1672.47068663124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.889036959324517[/C][/ROW]
[ROW][C]R-squared[/C][C]0.790386715044982[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.767460262003027[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.4748798952278[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]128[/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]3.61471675782578[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1672.47068663124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.889036959324517
R-squared0.790386715044982
Adjusted R-squared0.767460262003027
F-TEST (value)34.4748798952278
F-TEST (DF numerator)14
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.61471675782578
Sum Squared Residuals1672.47068663124






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.47196769749560.528032302504392
21413.07496668716170.925033312838294
31513.70201183347951.29798816652045
4137.066554439177675.93344556082233
589.85787534559723-1.85787534559723
676.437320688467110.562679311532895
733.83217343831239-0.83217343831239
830.946540360584092.05345963941591
943.075426290161840.924573709838158
1045.86379588190638-1.86379588190638
1100.53369741628066-0.53369741628066
12-4-3.02786362372626-0.972136376273741
13-14-11.9800217425636-2.01997825743637
14-18-9.26527205775652-8.73472794224348
15-8-8.529473777809840.529473777809844
16-1-2.112266876072931.11226687607293
1712.6410542713557-1.6410542713557
1820.9152948325947271.08470516740527
190-0.3158895833958540.315889583395854
201-0.07707848178443541.07707848178444
210-1.724302335828751.72430233582875
22-13.41553962945555-4.41553962945555
23-3-4.611128394956171.61112839495617
24-3-5.052740832705212.05274083270521
25-3-3.952402034743950.952402034743949
26-4-6.798870564497152.79887056449715
27-8-6.91808048245915-1.08191951754085
28-9-7.59020418330258-1.40979581669742
29-13-13.6986756561320.698675656131968
30-18-19.76472314489841.76472314489843
31-11-11.28938802493970.289388024939683
32-9-10.42735200873231.42735200873227
33-10-10.90539131771740.905391317717421
34-13-13.78170536814390.78170536814387
35-11-16.74362559518115.7436255951811
36-5-9.28091158184724.2809115818472
37-15-13.0477518961448-1.95224810385515
38-6-7.24497038950321.2449703895032
39-6-5.8483613109742-0.151638689025795
40-3-8.207444648643435.20744464864343
41-1-4.841328429453493.84132842945349
42-3-5.933871196611622.93387119661162
43-4-2.85784786404904-1.14215213595096
44-6-4.44091415263061-1.55908584736939
450-1.13081449371461.1308144937146
46-4-3.62650413985263-0.373495860147375
47-2-2.629292171852060.629292171852061
48-2-3.967848177938991.96784817793899
49-60.690019864331084-6.69001986433108
50-7-6.14491902353597-0.855080976464031
51-6-2.63037006010672-3.36962993989328
52-6-3.06150206432508-2.93849793567492
53-3-2.84966958672951-0.150330413270492
54-2-4.651827427331472.65182742733147
55-5-7.736008435934212.73600843593421
56-11-9.70642973533091-1.29357026466909
57-11-7.4727239243023-3.5272760756977
58-11-6.48115535288383-4.51884464711617
59-10-12.08084812331412.08084812331413
60-14-15.07397037596741.07397037596738
61-8-6.22960187985522-1.77039812014478
62-9-6.46909205031084-2.53090794968916
63-5-4.47788581453706-0.522114185462936
64-1-3.247288622189272.24728862218927
65-2-2.784430466928070.784430466928071
66-5-4.65101041804314-0.348989581956855
67-4-2.68437705550044-1.31562294449956
68-6-6.033617002248230.0336170022482292
69-2-1.81757506141715-0.182424938582853
70-2-0.781031375679113-1.21896862432089
71-2-4.320637955254432.32063795525443
72-2-7.148886841129095.14888684112909
7320.4394614123976241.56053858760238
7411.60772348140971-0.607723481409706
75-8-1.95713574559156-6.04286425440844
76-11.64906195563335-2.64906195563335
771-0.1307808464429361.13078084644294
78-1-1.557888461506860.557888461506859
7922.68227015206934-0.682270152069341
8023.01904181755034-1.01904181755034
8112.0690856096011-1.0690856096011
82-10.696313193369886-1.69631319336989
83-2-4.174667416858232.17466741685823
84-2-2.143639781765290.143639781765294
85-1-1.932950167901380.932950167901376
86-8-8.056054080173310.0560540801733087
87-4-6.038377919367332.03837791936733
88-6-11.9191696824415.91916968244099
89-3-4.985249911139791.98524991113979
90-3-11.3472174553028.34721745530196
91-7-9.362691268775382.36269126877538
92-9-8.67033697090574-0.329663029094258
93-11-15.91544986745144.91544986745145
94-13-11.5052278753242-1.49477212467579
95-11-11.2347157953110.234715795310984
96-9-6.44742651280835-2.55257348719165
97-17-16.6785578401555-0.321442159844465
98-22-14.8814494800127-7.11855051998734
99-25-16.7133471917234-8.28665280827662
100-20-12.6648724568671-7.33512754313286
101-24-14.0479978926043-9.95200210739566
102-24-19.2118120027711-4.78818799722891
103-22-17.4147441838676-4.58525581613235
104-19-12.8296569856157-6.17034301438428
105-18-10.2968468142012-7.70315318579881
106-17-18.52147250749561.5214725074956
107-11-11.73646279248990.736462792489922
108-11-11.99388054339380.993880543393837
109-12-5.89384005793775-6.10615994206225
110-10-6.11359565277825-3.88640434722175
111-15-9.32817017344589-5.67182982655411
112-15-11.7488990760135-3.25110092398645
113-15-12.4271024821679-2.57289751783207
114-13-7.31578757948134-5.68421242051866
115-8-8.25452521820270.254525218202699
116-13-12.5216035709806-0.478396429019395
117-9-6.57991383300353-2.42008616699647
118-7-11.5656282495054.56562824950504
119-4-8.889797795795244.88979779579524
120-4-8.284943205329334.28494320532933
121-2-1.4890527578196-0.510947242180402
1220-2.935424758418532.93542475841853
123-2-5.789371338342443.78937133834244
124-3-8.964722309741775.96472230974177
1251-0.8985634243245481.89856342432455
126-2-11.62439160856939.62439160856927
127-1-5.706852699700574.70685269970057
1281-3.258394276587474.25839427658747
129-3-6.005564973960543.00556497396054
130-4-10.5661407798886.56614077988801
131-9-10.84733884105491.84733884105486
132-9-7.6619060210937-1.3380939789063
133-7-2.26801862156182-4.73198137843818
134-14-8.14863789500544-5.85136210499456
135-12-15.36149127893063.36149127893064
136-16-20.53602387297544.53602387297544
137-20-19.2155586817632-0.784441318236836
138-12-15.3324425788433.33244257884296
139-12-12.31153211061350.311532110613483
140-10-10.310502228220.310502228219952
141-10-6.54201107817392-3.45798892182608
142-13-13.39746041672780.39746041672783
143-16-15.062731207755-0.937268792244962

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.4719676974956 & 0.528032302504392 \tabularnewline
2 & 14 & 13.0749666871617 & 0.925033312838294 \tabularnewline
3 & 15 & 13.7020118334795 & 1.29798816652045 \tabularnewline
4 & 13 & 7.06655443917767 & 5.93344556082233 \tabularnewline
5 & 8 & 9.85787534559723 & -1.85787534559723 \tabularnewline
6 & 7 & 6.43732068846711 & 0.562679311532895 \tabularnewline
7 & 3 & 3.83217343831239 & -0.83217343831239 \tabularnewline
8 & 3 & 0.94654036058409 & 2.05345963941591 \tabularnewline
9 & 4 & 3.07542629016184 & 0.924573709838158 \tabularnewline
10 & 4 & 5.86379588190638 & -1.86379588190638 \tabularnewline
11 & 0 & 0.53369741628066 & -0.53369741628066 \tabularnewline
12 & -4 & -3.02786362372626 & -0.972136376273741 \tabularnewline
13 & -14 & -11.9800217425636 & -2.01997825743637 \tabularnewline
14 & -18 & -9.26527205775652 & -8.73472794224348 \tabularnewline
15 & -8 & -8.52947377780984 & 0.529473777809844 \tabularnewline
16 & -1 & -2.11226687607293 & 1.11226687607293 \tabularnewline
17 & 1 & 2.6410542713557 & -1.6410542713557 \tabularnewline
18 & 2 & 0.915294832594727 & 1.08470516740527 \tabularnewline
19 & 0 & -0.315889583395854 & 0.315889583395854 \tabularnewline
20 & 1 & -0.0770784817844354 & 1.07707848178444 \tabularnewline
21 & 0 & -1.72430233582875 & 1.72430233582875 \tabularnewline
22 & -1 & 3.41553962945555 & -4.41553962945555 \tabularnewline
23 & -3 & -4.61112839495617 & 1.61112839495617 \tabularnewline
24 & -3 & -5.05274083270521 & 2.05274083270521 \tabularnewline
25 & -3 & -3.95240203474395 & 0.952402034743949 \tabularnewline
26 & -4 & -6.79887056449715 & 2.79887056449715 \tabularnewline
27 & -8 & -6.91808048245915 & -1.08191951754085 \tabularnewline
28 & -9 & -7.59020418330258 & -1.40979581669742 \tabularnewline
29 & -13 & -13.698675656132 & 0.698675656131968 \tabularnewline
30 & -18 & -19.7647231448984 & 1.76472314489843 \tabularnewline
31 & -11 & -11.2893880249397 & 0.289388024939683 \tabularnewline
32 & -9 & -10.4273520087323 & 1.42735200873227 \tabularnewline
33 & -10 & -10.9053913177174 & 0.905391317717421 \tabularnewline
34 & -13 & -13.7817053681439 & 0.78170536814387 \tabularnewline
35 & -11 & -16.7436255951811 & 5.7436255951811 \tabularnewline
36 & -5 & -9.2809115818472 & 4.2809115818472 \tabularnewline
37 & -15 & -13.0477518961448 & -1.95224810385515 \tabularnewline
38 & -6 & -7.2449703895032 & 1.2449703895032 \tabularnewline
39 & -6 & -5.8483613109742 & -0.151638689025795 \tabularnewline
40 & -3 & -8.20744464864343 & 5.20744464864343 \tabularnewline
41 & -1 & -4.84132842945349 & 3.84132842945349 \tabularnewline
42 & -3 & -5.93387119661162 & 2.93387119661162 \tabularnewline
43 & -4 & -2.85784786404904 & -1.14215213595096 \tabularnewline
44 & -6 & -4.44091415263061 & -1.55908584736939 \tabularnewline
45 & 0 & -1.1308144937146 & 1.1308144937146 \tabularnewline
46 & -4 & -3.62650413985263 & -0.373495860147375 \tabularnewline
47 & -2 & -2.62929217185206 & 0.629292171852061 \tabularnewline
48 & -2 & -3.96784817793899 & 1.96784817793899 \tabularnewline
49 & -6 & 0.690019864331084 & -6.69001986433108 \tabularnewline
50 & -7 & -6.14491902353597 & -0.855080976464031 \tabularnewline
51 & -6 & -2.63037006010672 & -3.36962993989328 \tabularnewline
52 & -6 & -3.06150206432508 & -2.93849793567492 \tabularnewline
53 & -3 & -2.84966958672951 & -0.150330413270492 \tabularnewline
54 & -2 & -4.65182742733147 & 2.65182742733147 \tabularnewline
55 & -5 & -7.73600843593421 & 2.73600843593421 \tabularnewline
56 & -11 & -9.70642973533091 & -1.29357026466909 \tabularnewline
57 & -11 & -7.4727239243023 & -3.5272760756977 \tabularnewline
58 & -11 & -6.48115535288383 & -4.51884464711617 \tabularnewline
59 & -10 & -12.0808481233141 & 2.08084812331413 \tabularnewline
60 & -14 & -15.0739703759674 & 1.07397037596738 \tabularnewline
61 & -8 & -6.22960187985522 & -1.77039812014478 \tabularnewline
62 & -9 & -6.46909205031084 & -2.53090794968916 \tabularnewline
63 & -5 & -4.47788581453706 & -0.522114185462936 \tabularnewline
64 & -1 & -3.24728862218927 & 2.24728862218927 \tabularnewline
65 & -2 & -2.78443046692807 & 0.784430466928071 \tabularnewline
66 & -5 & -4.65101041804314 & -0.348989581956855 \tabularnewline
67 & -4 & -2.68437705550044 & -1.31562294449956 \tabularnewline
68 & -6 & -6.03361700224823 & 0.0336170022482292 \tabularnewline
69 & -2 & -1.81757506141715 & -0.182424938582853 \tabularnewline
70 & -2 & -0.781031375679113 & -1.21896862432089 \tabularnewline
71 & -2 & -4.32063795525443 & 2.32063795525443 \tabularnewline
72 & -2 & -7.14888684112909 & 5.14888684112909 \tabularnewline
73 & 2 & 0.439461412397624 & 1.56053858760238 \tabularnewline
74 & 1 & 1.60772348140971 & -0.607723481409706 \tabularnewline
75 & -8 & -1.95713574559156 & -6.04286425440844 \tabularnewline
76 & -1 & 1.64906195563335 & -2.64906195563335 \tabularnewline
77 & 1 & -0.130780846442936 & 1.13078084644294 \tabularnewline
78 & -1 & -1.55788846150686 & 0.557888461506859 \tabularnewline
79 & 2 & 2.68227015206934 & -0.682270152069341 \tabularnewline
80 & 2 & 3.01904181755034 & -1.01904181755034 \tabularnewline
81 & 1 & 2.0690856096011 & -1.0690856096011 \tabularnewline
82 & -1 & 0.696313193369886 & -1.69631319336989 \tabularnewline
83 & -2 & -4.17466741685823 & 2.17466741685823 \tabularnewline
84 & -2 & -2.14363978176529 & 0.143639781765294 \tabularnewline
85 & -1 & -1.93295016790138 & 0.932950167901376 \tabularnewline
86 & -8 & -8.05605408017331 & 0.0560540801733087 \tabularnewline
87 & -4 & -6.03837791936733 & 2.03837791936733 \tabularnewline
88 & -6 & -11.919169682441 & 5.91916968244099 \tabularnewline
89 & -3 & -4.98524991113979 & 1.98524991113979 \tabularnewline
90 & -3 & -11.347217455302 & 8.34721745530196 \tabularnewline
91 & -7 & -9.36269126877538 & 2.36269126877538 \tabularnewline
92 & -9 & -8.67033697090574 & -0.329663029094258 \tabularnewline
93 & -11 & -15.9154498674514 & 4.91544986745145 \tabularnewline
94 & -13 & -11.5052278753242 & -1.49477212467579 \tabularnewline
95 & -11 & -11.234715795311 & 0.234715795310984 \tabularnewline
96 & -9 & -6.44742651280835 & -2.55257348719165 \tabularnewline
97 & -17 & -16.6785578401555 & -0.321442159844465 \tabularnewline
98 & -22 & -14.8814494800127 & -7.11855051998734 \tabularnewline
99 & -25 & -16.7133471917234 & -8.28665280827662 \tabularnewline
100 & -20 & -12.6648724568671 & -7.33512754313286 \tabularnewline
101 & -24 & -14.0479978926043 & -9.95200210739566 \tabularnewline
102 & -24 & -19.2118120027711 & -4.78818799722891 \tabularnewline
103 & -22 & -17.4147441838676 & -4.58525581613235 \tabularnewline
104 & -19 & -12.8296569856157 & -6.17034301438428 \tabularnewline
105 & -18 & -10.2968468142012 & -7.70315318579881 \tabularnewline
106 & -17 & -18.5214725074956 & 1.5214725074956 \tabularnewline
107 & -11 & -11.7364627924899 & 0.736462792489922 \tabularnewline
108 & -11 & -11.9938805433938 & 0.993880543393837 \tabularnewline
109 & -12 & -5.89384005793775 & -6.10615994206225 \tabularnewline
110 & -10 & -6.11359565277825 & -3.88640434722175 \tabularnewline
111 & -15 & -9.32817017344589 & -5.67182982655411 \tabularnewline
112 & -15 & -11.7488990760135 & -3.25110092398645 \tabularnewline
113 & -15 & -12.4271024821679 & -2.57289751783207 \tabularnewline
114 & -13 & -7.31578757948134 & -5.68421242051866 \tabularnewline
115 & -8 & -8.2545252182027 & 0.254525218202699 \tabularnewline
116 & -13 & -12.5216035709806 & -0.478396429019395 \tabularnewline
117 & -9 & -6.57991383300353 & -2.42008616699647 \tabularnewline
118 & -7 & -11.565628249505 & 4.56562824950504 \tabularnewline
119 & -4 & -8.88979779579524 & 4.88979779579524 \tabularnewline
120 & -4 & -8.28494320532933 & 4.28494320532933 \tabularnewline
121 & -2 & -1.4890527578196 & -0.510947242180402 \tabularnewline
122 & 0 & -2.93542475841853 & 2.93542475841853 \tabularnewline
123 & -2 & -5.78937133834244 & 3.78937133834244 \tabularnewline
124 & -3 & -8.96472230974177 & 5.96472230974177 \tabularnewline
125 & 1 & -0.898563424324548 & 1.89856342432455 \tabularnewline
126 & -2 & -11.6243916085693 & 9.62439160856927 \tabularnewline
127 & -1 & -5.70685269970057 & 4.70685269970057 \tabularnewline
128 & 1 & -3.25839427658747 & 4.25839427658747 \tabularnewline
129 & -3 & -6.00556497396054 & 3.00556497396054 \tabularnewline
130 & -4 & -10.566140779888 & 6.56614077988801 \tabularnewline
131 & -9 & -10.8473388410549 & 1.84733884105486 \tabularnewline
132 & -9 & -7.6619060210937 & -1.3380939789063 \tabularnewline
133 & -7 & -2.26801862156182 & -4.73198137843818 \tabularnewline
134 & -14 & -8.14863789500544 & -5.85136210499456 \tabularnewline
135 & -12 & -15.3614912789306 & 3.36149127893064 \tabularnewline
136 & -16 & -20.5360238729754 & 4.53602387297544 \tabularnewline
137 & -20 & -19.2155586817632 & -0.784441318236836 \tabularnewline
138 & -12 & -15.332442578843 & 3.33244257884296 \tabularnewline
139 & -12 & -12.3115321106135 & 0.311532110613483 \tabularnewline
140 & -10 & -10.31050222822 & 0.310502228219952 \tabularnewline
141 & -10 & -6.54201107817392 & -3.45798892182608 \tabularnewline
142 & -13 & -13.3974604167278 & 0.39746041672783 \tabularnewline
143 & -16 & -15.062731207755 & -0.937268792244962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&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]13.4719676974956[/C][C]0.528032302504392[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]13.0749666871617[/C][C]0.925033312838294[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.7020118334795[/C][C]1.29798816652045[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]7.06655443917767[/C][C]5.93344556082233[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]9.85787534559723[/C][C]-1.85787534559723[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.43732068846711[/C][C]0.562679311532895[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.83217343831239[/C][C]-0.83217343831239[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.94654036058409[/C][C]2.05345963941591[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.07542629016184[/C][C]0.924573709838158[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.86379588190638[/C][C]-1.86379588190638[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.53369741628066[/C][C]-0.53369741628066[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-3.02786362372626[/C][C]-0.972136376273741[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-11.9800217425636[/C][C]-2.01997825743637[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-9.26527205775652[/C][C]-8.73472794224348[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-8.52947377780984[/C][C]0.529473777809844[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]-2.11226687607293[/C][C]1.11226687607293[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]2.6410542713557[/C][C]-1.6410542713557[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]0.915294832594727[/C][C]1.08470516740527[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.315889583395854[/C][C]0.315889583395854[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]-0.0770784817844354[/C][C]1.07707848178444[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-1.72430233582875[/C][C]1.72430233582875[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]3.41553962945555[/C][C]-4.41553962945555[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-4.61112839495617[/C][C]1.61112839495617[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-5.05274083270521[/C][C]2.05274083270521[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-3.95240203474395[/C][C]0.952402034743949[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-6.79887056449715[/C][C]2.79887056449715[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-6.91808048245915[/C][C]-1.08191951754085[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-7.59020418330258[/C][C]-1.40979581669742[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-13.698675656132[/C][C]0.698675656131968[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-19.7647231448984[/C][C]1.76472314489843[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-11.2893880249397[/C][C]0.289388024939683[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-10.4273520087323[/C][C]1.42735200873227[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-10.9053913177174[/C][C]0.905391317717421[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-13.7817053681439[/C][C]0.78170536814387[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-16.7436255951811[/C][C]5.7436255951811[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-9.2809115818472[/C][C]4.2809115818472[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-13.0477518961448[/C][C]-1.95224810385515[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-7.2449703895032[/C][C]1.2449703895032[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-5.8483613109742[/C][C]-0.151638689025795[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-8.20744464864343[/C][C]5.20744464864343[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-4.84132842945349[/C][C]3.84132842945349[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-5.93387119661162[/C][C]2.93387119661162[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-2.85784786404904[/C][C]-1.14215213595096[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-4.44091415263061[/C][C]-1.55908584736939[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-1.1308144937146[/C][C]1.1308144937146[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-3.62650413985263[/C][C]-0.373495860147375[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-2.62929217185206[/C][C]0.629292171852061[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-3.96784817793899[/C][C]1.96784817793899[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]0.690019864331084[/C][C]-6.69001986433108[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-6.14491902353597[/C][C]-0.855080976464031[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-2.63037006010672[/C][C]-3.36962993989328[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-3.06150206432508[/C][C]-2.93849793567492[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-2.84966958672951[/C][C]-0.150330413270492[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-4.65182742733147[/C][C]2.65182742733147[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-7.73600843593421[/C][C]2.73600843593421[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-9.70642973533091[/C][C]-1.29357026466909[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-7.4727239243023[/C][C]-3.5272760756977[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-6.48115535288383[/C][C]-4.51884464711617[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-12.0808481233141[/C][C]2.08084812331413[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-15.0739703759674[/C][C]1.07397037596738[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-6.22960187985522[/C][C]-1.77039812014478[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-6.46909205031084[/C][C]-2.53090794968916[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-4.47788581453706[/C][C]-0.522114185462936[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-3.24728862218927[/C][C]2.24728862218927[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-2.78443046692807[/C][C]0.784430466928071[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-4.65101041804314[/C][C]-0.348989581956855[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-2.68437705550044[/C][C]-1.31562294449956[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-6.03361700224823[/C][C]0.0336170022482292[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-1.81757506141715[/C][C]-0.182424938582853[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-0.781031375679113[/C][C]-1.21896862432089[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-4.32063795525443[/C][C]2.32063795525443[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-7.14888684112909[/C][C]5.14888684112909[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]0.439461412397624[/C][C]1.56053858760238[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.60772348140971[/C][C]-0.607723481409706[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-1.95713574559156[/C][C]-6.04286425440844[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]1.64906195563335[/C][C]-2.64906195563335[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]-0.130780846442936[/C][C]1.13078084644294[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]-1.55788846150686[/C][C]0.557888461506859[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]2.68227015206934[/C][C]-0.682270152069341[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.01904181755034[/C][C]-1.01904181755034[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.0690856096011[/C][C]-1.0690856096011[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]0.696313193369886[/C][C]-1.69631319336989[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-4.17466741685823[/C][C]2.17466741685823[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-2.14363978176529[/C][C]0.143639781765294[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.93295016790138[/C][C]0.932950167901376[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-8.05605408017331[/C][C]0.0560540801733087[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-6.03837791936733[/C][C]2.03837791936733[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-11.919169682441[/C][C]5.91916968244099[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-4.98524991113979[/C][C]1.98524991113979[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-11.347217455302[/C][C]8.34721745530196[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-9.36269126877538[/C][C]2.36269126877538[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-8.67033697090574[/C][C]-0.329663029094258[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-15.9154498674514[/C][C]4.91544986745145[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-11.5052278753242[/C][C]-1.49477212467579[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-11.234715795311[/C][C]0.234715795310984[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-6.44742651280835[/C][C]-2.55257348719165[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-16.6785578401555[/C][C]-0.321442159844465[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-14.8814494800127[/C][C]-7.11855051998734[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-16.7133471917234[/C][C]-8.28665280827662[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-12.6648724568671[/C][C]-7.33512754313286[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-14.0479978926043[/C][C]-9.95200210739566[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-19.2118120027711[/C][C]-4.78818799722891[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-17.4147441838676[/C][C]-4.58525581613235[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-12.8296569856157[/C][C]-6.17034301438428[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-10.2968468142012[/C][C]-7.70315318579881[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-18.5214725074956[/C][C]1.5214725074956[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-11.7364627924899[/C][C]0.736462792489922[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-11.9938805433938[/C][C]0.993880543393837[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-5.89384005793775[/C][C]-6.10615994206225[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-6.11359565277825[/C][C]-3.88640434722175[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-9.32817017344589[/C][C]-5.67182982655411[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-11.7488990760135[/C][C]-3.25110092398645[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-12.4271024821679[/C][C]-2.57289751783207[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-7.31578757948134[/C][C]-5.68421242051866[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-8.2545252182027[/C][C]0.254525218202699[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-12.5216035709806[/C][C]-0.478396429019395[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-6.57991383300353[/C][C]-2.42008616699647[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-11.565628249505[/C][C]4.56562824950504[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-8.88979779579524[/C][C]4.88979779579524[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-8.28494320532933[/C][C]4.28494320532933[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-1.4890527578196[/C][C]-0.510947242180402[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-2.93542475841853[/C][C]2.93542475841853[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-5.78937133834244[/C][C]3.78937133834244[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-8.96472230974177[/C][C]5.96472230974177[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]-0.898563424324548[/C][C]1.89856342432455[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-11.6243916085693[/C][C]9.62439160856927[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-5.70685269970057[/C][C]4.70685269970057[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.25839427658747[/C][C]4.25839427658747[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-6.00556497396054[/C][C]3.00556497396054[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-10.566140779888[/C][C]6.56614077988801[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-10.8473388410549[/C][C]1.84733884105486[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-7.6619060210937[/C][C]-1.3380939789063[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-2.26801862156182[/C][C]-4.73198137843818[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-8.14863789500544[/C][C]-5.85136210499456[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-15.3614912789306[/C][C]3.36149127893064[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-20.5360238729754[/C][C]4.53602387297544[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-19.2155586817632[/C][C]-0.784441318236836[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-15.332442578843[/C][C]3.33244257884296[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-12.3115321106135[/C][C]0.311532110613483[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-10.31050222822[/C][C]0.310502228219952[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-6.54201107817392[/C][C]-3.45798892182608[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-13.3974604167278[/C][C]0.39746041672783[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-15.062731207755[/C][C]-0.937268792244962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.47196769749560.528032302504392
21413.07496668716170.925033312838294
31513.70201183347951.29798816652045
4137.066554439177675.93344556082233
589.85787534559723-1.85787534559723
676.437320688467110.562679311532895
733.83217343831239-0.83217343831239
830.946540360584092.05345963941591
943.075426290161840.924573709838158
1045.86379588190638-1.86379588190638
1100.53369741628066-0.53369741628066
12-4-3.02786362372626-0.972136376273741
13-14-11.9800217425636-2.01997825743637
14-18-9.26527205775652-8.73472794224348
15-8-8.529473777809840.529473777809844
16-1-2.112266876072931.11226687607293
1712.6410542713557-1.6410542713557
1820.9152948325947271.08470516740527
190-0.3158895833958540.315889583395854
201-0.07707848178443541.07707848178444
210-1.724302335828751.72430233582875
22-13.41553962945555-4.41553962945555
23-3-4.611128394956171.61112839495617
24-3-5.052740832705212.05274083270521
25-3-3.952402034743950.952402034743949
26-4-6.798870564497152.79887056449715
27-8-6.91808048245915-1.08191951754085
28-9-7.59020418330258-1.40979581669742
29-13-13.6986756561320.698675656131968
30-18-19.76472314489841.76472314489843
31-11-11.28938802493970.289388024939683
32-9-10.42735200873231.42735200873227
33-10-10.90539131771740.905391317717421
34-13-13.78170536814390.78170536814387
35-11-16.74362559518115.7436255951811
36-5-9.28091158184724.2809115818472
37-15-13.0477518961448-1.95224810385515
38-6-7.24497038950321.2449703895032
39-6-5.8483613109742-0.151638689025795
40-3-8.207444648643435.20744464864343
41-1-4.841328429453493.84132842945349
42-3-5.933871196611622.93387119661162
43-4-2.85784786404904-1.14215213595096
44-6-4.44091415263061-1.55908584736939
450-1.13081449371461.1308144937146
46-4-3.62650413985263-0.373495860147375
47-2-2.629292171852060.629292171852061
48-2-3.967848177938991.96784817793899
49-60.690019864331084-6.69001986433108
50-7-6.14491902353597-0.855080976464031
51-6-2.63037006010672-3.36962993989328
52-6-3.06150206432508-2.93849793567492
53-3-2.84966958672951-0.150330413270492
54-2-4.651827427331472.65182742733147
55-5-7.736008435934212.73600843593421
56-11-9.70642973533091-1.29357026466909
57-11-7.4727239243023-3.5272760756977
58-11-6.48115535288383-4.51884464711617
59-10-12.08084812331412.08084812331413
60-14-15.07397037596741.07397037596738
61-8-6.22960187985522-1.77039812014478
62-9-6.46909205031084-2.53090794968916
63-5-4.47788581453706-0.522114185462936
64-1-3.247288622189272.24728862218927
65-2-2.784430466928070.784430466928071
66-5-4.65101041804314-0.348989581956855
67-4-2.68437705550044-1.31562294449956
68-6-6.033617002248230.0336170022482292
69-2-1.81757506141715-0.182424938582853
70-2-0.781031375679113-1.21896862432089
71-2-4.320637955254432.32063795525443
72-2-7.148886841129095.14888684112909
7320.4394614123976241.56053858760238
7411.60772348140971-0.607723481409706
75-8-1.95713574559156-6.04286425440844
76-11.64906195563335-2.64906195563335
771-0.1307808464429361.13078084644294
78-1-1.557888461506860.557888461506859
7922.68227015206934-0.682270152069341
8023.01904181755034-1.01904181755034
8112.0690856096011-1.0690856096011
82-10.696313193369886-1.69631319336989
83-2-4.174667416858232.17466741685823
84-2-2.143639781765290.143639781765294
85-1-1.932950167901380.932950167901376
86-8-8.056054080173310.0560540801733087
87-4-6.038377919367332.03837791936733
88-6-11.9191696824415.91916968244099
89-3-4.985249911139791.98524991113979
90-3-11.3472174553028.34721745530196
91-7-9.362691268775382.36269126877538
92-9-8.67033697090574-0.329663029094258
93-11-15.91544986745144.91544986745145
94-13-11.5052278753242-1.49477212467579
95-11-11.2347157953110.234715795310984
96-9-6.44742651280835-2.55257348719165
97-17-16.6785578401555-0.321442159844465
98-22-14.8814494800127-7.11855051998734
99-25-16.7133471917234-8.28665280827662
100-20-12.6648724568671-7.33512754313286
101-24-14.0479978926043-9.95200210739566
102-24-19.2118120027711-4.78818799722891
103-22-17.4147441838676-4.58525581613235
104-19-12.8296569856157-6.17034301438428
105-18-10.2968468142012-7.70315318579881
106-17-18.52147250749561.5214725074956
107-11-11.73646279248990.736462792489922
108-11-11.99388054339380.993880543393837
109-12-5.89384005793775-6.10615994206225
110-10-6.11359565277825-3.88640434722175
111-15-9.32817017344589-5.67182982655411
112-15-11.7488990760135-3.25110092398645
113-15-12.4271024821679-2.57289751783207
114-13-7.31578757948134-5.68421242051866
115-8-8.25452521820270.254525218202699
116-13-12.5216035709806-0.478396429019395
117-9-6.57991383300353-2.42008616699647
118-7-11.5656282495054.56562824950504
119-4-8.889797795795244.88979779579524
120-4-8.284943205329334.28494320532933
121-2-1.4890527578196-0.510947242180402
1220-2.935424758418532.93542475841853
123-2-5.789371338342443.78937133834244
124-3-8.964722309741775.96472230974177
1251-0.8985634243245481.89856342432455
126-2-11.62439160856939.62439160856927
127-1-5.706852699700574.70685269970057
1281-3.258394276587474.25839427658747
129-3-6.005564973960543.00556497396054
130-4-10.5661407798886.56614077988801
131-9-10.84733884105491.84733884105486
132-9-7.6619060210937-1.3380939789063
133-7-2.26801862156182-4.73198137843818
134-14-8.14863789500544-5.85136210499456
135-12-15.36149127893063.36149127893064
136-16-20.53602387297544.53602387297544
137-20-19.2155586817632-0.784441318236836
138-12-15.3324425788433.33244257884296
139-12-12.31153211061350.311532110613483
140-10-10.310502228220.310502228219952
141-10-6.54201107817392-3.45798892182608
142-13-13.39746041672780.39746041672783
143-16-15.062731207755-0.937268792244962






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.703576219109470.5928475617810610.29642378089053
190.7501408521624570.4997182956750860.249859147837543
200.6366954187423920.7266091625152160.363304581257608
210.5127086313319310.9745827373361370.487291368668068
220.5814987257306810.8370025485386380.418501274269319
230.4981401747181510.9962803494363020.501859825281849
240.4608989725244890.9217979450489780.539101027475511
250.3683512398901610.7367024797803220.631648760109839
260.2984008546711130.5968017093422260.701599145328887
270.2909525469223610.5819050938447210.709047453077639
280.2237741623528130.4475483247056250.776225837647187
290.1715273058499350.3430546116998690.828472694150065
300.1248171885798890.2496343771597790.87518281142011
310.1010673419559850.202134683911970.898932658044015
320.07772942909267630.1554588581853530.922270570907324
330.05349352980230830.1069870596046170.946506470197692
340.03532124596386250.07064249192772510.964678754036137
350.05075042877582820.1015008575516560.949249571224172
360.04280589759120480.08561179518240960.957194102408795
370.05649786629915050.1129957325983010.943502133700849
380.04319283990689640.08638567981379270.956807160093104
390.04639670157670320.09279340315340640.953603298423297
400.04268595136815060.08537190273630120.957314048631849
410.03610625212913350.07221250425826710.963893747870866
420.03247693192874570.06495386385749150.967523068071254
430.03280377408739890.06560754817479770.967196225912601
440.02681843956119580.05363687912239160.973181560438804
450.02585802832524680.05171605665049370.974141971674753
460.0197635740496030.03952714809920610.980236425950397
470.01863322657682280.03726645315364560.981366773423177
480.0206876476317760.04137529526355190.979312352368224
490.03846451726888470.07692903453776940.961535482731115
500.03104342353015630.06208684706031260.968956576469844
510.03188667048799190.06377334097598380.968113329512008
520.02404496685882150.0480899337176430.975955033141178
530.01681078219249790.03362156438499580.983189217807502
540.01284594367083380.02569188734166760.987154056329166
550.009818752919606550.01963750583921310.990181247080393
560.008742485889139770.01748497177827950.99125751411086
570.008401553972412240.01680310794482450.991598446027588
580.006367262247672020.0127345244953440.993632737752328
590.004448923564451980.008897847128903960.995551076435548
600.003035142655994660.006070285311989320.996964857344005
610.00223538064064950.004470761281298990.99776461935935
620.001594026172314070.003188052344628140.998405973827686
630.001045158490362520.002090316980725040.998954841509638
640.001451244239516490.002902488479032980.998548755760484
650.001051181318314780.002102362636629550.998948818681685
660.0006600521086367620.001320104217273520.999339947891363
670.0004505316179433380.0009010632358866750.999549468382057
680.0004177903189070730.0008355806378141450.999582209681093
690.000265535632705120.0005310712654102410.999734464367295
700.0002046451994119540.0004092903988239080.999795354800588
710.0001913469911020230.0003826939822040460.999808653008898
720.0006029315470491860.001205863094098370.999397068452951
730.0005235683259913530.001047136651982710.999476431674009
740.0003546867106785640.0007093734213571290.999645313289321
750.0002936689139472280.0005873378278944550.999706331086053
760.0002891695584201590.0005783391168403170.99971083044158
770.0002307814360311270.0004615628720622540.999769218563969
780.000173548737092540.000347097474185080.999826451262907
790.0001317465255785890.0002634930511571790.999868253474421
808.1852872821614e-050.0001637057456432280.999918147127178
815.57371434587005e-050.0001114742869174010.999944262856541
820.0001019652502337030.0002039305004674070.999898034749766
830.0001330326320938050.000266065264187610.999866967367906
849.5079600192768e-050.0001901592003855360.999904920399807
858.5916246553692e-050.0001718324931073840.999914083753446
866.36258685536094e-050.0001272517371072190.999936374131446
875.70866246547629e-050.0001141732493095260.999942913375345
880.0001881110607833480.0003762221215666960.999811888939217
890.0002178007633875640.0004356015267751290.999782199236612
900.001189741995797760.002379483991595520.998810258004202
910.001056174563678750.00211234912735750.998943825436321
920.0008808442451493990.00176168849029880.999119155754851
930.0005609500399463720.001121900079892740.999439049960054
940.000482928379263040.000965856758526080.999517071620737
950.0003550788722921050.0007101577445842110.999644921127708
960.0002763095489223380.0005526190978446750.999723690451078
970.0001811964006462060.0003623928012924120.999818803599354
980.0006653273766769330.001330654753353870.999334672623323
990.001702582543307250.00340516508661450.998297417456693
1000.002367886519086580.004735773038173150.997632113480913
1010.007012234182118880.01402446836423780.992987765817881
1020.01436448101600860.02872896203201720.985635518983991
1030.01026184428682830.02052368857365650.989738155713172
1040.007927309498361020.0158546189967220.992072690501639
1050.007809772631414330.01561954526282870.992190227368586
1060.00818421034725910.01636842069451820.991815789652741
1070.01251204669490140.02502409338980270.987487953305099
1080.02163130340363280.04326260680726570.978368696596367
1090.01888031625020680.03776063250041360.981119683749793
1100.01446818785140360.02893637570280720.985531812148596
1110.01698314914245280.03396629828490560.983016850857547
1120.02500747324661440.05001494649322890.974992526753386
1130.02433847614977240.04867695229954490.975661523850228
1140.04338879954146630.08677759908293260.956611200458534
1150.03624719120599890.07249438241199790.963752808794001
1160.03531316452334650.07062632904669310.964686835476653
1170.0819268250061710.1638536500123420.918073174993829
1180.2122873986886950.424574797377390.787712601311305
1190.1935115578864020.3870231157728050.806488442113598
1200.1553538438018310.3107076876036630.844646156198169
1210.1287845291439960.2575690582879910.871215470856004
1220.1047559893098390.2095119786196770.895244010690161
1230.1071874349570250.214374869914050.892812565042975
1240.07238062019356030.1447612403871210.92761937980644
1250.03968351949658680.07936703899317360.960316480503413

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.70357621910947 & 0.592847561781061 & 0.29642378089053 \tabularnewline
19 & 0.750140852162457 & 0.499718295675086 & 0.249859147837543 \tabularnewline
20 & 0.636695418742392 & 0.726609162515216 & 0.363304581257608 \tabularnewline
21 & 0.512708631331931 & 0.974582737336137 & 0.487291368668068 \tabularnewline
22 & 0.581498725730681 & 0.837002548538638 & 0.418501274269319 \tabularnewline
23 & 0.498140174718151 & 0.996280349436302 & 0.501859825281849 \tabularnewline
24 & 0.460898972524489 & 0.921797945048978 & 0.539101027475511 \tabularnewline
25 & 0.368351239890161 & 0.736702479780322 & 0.631648760109839 \tabularnewline
26 & 0.298400854671113 & 0.596801709342226 & 0.701599145328887 \tabularnewline
27 & 0.290952546922361 & 0.581905093844721 & 0.709047453077639 \tabularnewline
28 & 0.223774162352813 & 0.447548324705625 & 0.776225837647187 \tabularnewline
29 & 0.171527305849935 & 0.343054611699869 & 0.828472694150065 \tabularnewline
30 & 0.124817188579889 & 0.249634377159779 & 0.87518281142011 \tabularnewline
31 & 0.101067341955985 & 0.20213468391197 & 0.898932658044015 \tabularnewline
32 & 0.0777294290926763 & 0.155458858185353 & 0.922270570907324 \tabularnewline
33 & 0.0534935298023083 & 0.106987059604617 & 0.946506470197692 \tabularnewline
34 & 0.0353212459638625 & 0.0706424919277251 & 0.964678754036137 \tabularnewline
35 & 0.0507504287758282 & 0.101500857551656 & 0.949249571224172 \tabularnewline
36 & 0.0428058975912048 & 0.0856117951824096 & 0.957194102408795 \tabularnewline
37 & 0.0564978662991505 & 0.112995732598301 & 0.943502133700849 \tabularnewline
38 & 0.0431928399068964 & 0.0863856798137927 & 0.956807160093104 \tabularnewline
39 & 0.0463967015767032 & 0.0927934031534064 & 0.953603298423297 \tabularnewline
40 & 0.0426859513681506 & 0.0853719027363012 & 0.957314048631849 \tabularnewline
41 & 0.0361062521291335 & 0.0722125042582671 & 0.963893747870866 \tabularnewline
42 & 0.0324769319287457 & 0.0649538638574915 & 0.967523068071254 \tabularnewline
43 & 0.0328037740873989 & 0.0656075481747977 & 0.967196225912601 \tabularnewline
44 & 0.0268184395611958 & 0.0536368791223916 & 0.973181560438804 \tabularnewline
45 & 0.0258580283252468 & 0.0517160566504937 & 0.974141971674753 \tabularnewline
46 & 0.019763574049603 & 0.0395271480992061 & 0.980236425950397 \tabularnewline
47 & 0.0186332265768228 & 0.0372664531536456 & 0.981366773423177 \tabularnewline
48 & 0.020687647631776 & 0.0413752952635519 & 0.979312352368224 \tabularnewline
49 & 0.0384645172688847 & 0.0769290345377694 & 0.961535482731115 \tabularnewline
50 & 0.0310434235301563 & 0.0620868470603126 & 0.968956576469844 \tabularnewline
51 & 0.0318866704879919 & 0.0637733409759838 & 0.968113329512008 \tabularnewline
52 & 0.0240449668588215 & 0.048089933717643 & 0.975955033141178 \tabularnewline
53 & 0.0168107821924979 & 0.0336215643849958 & 0.983189217807502 \tabularnewline
54 & 0.0128459436708338 & 0.0256918873416676 & 0.987154056329166 \tabularnewline
55 & 0.00981875291960655 & 0.0196375058392131 & 0.990181247080393 \tabularnewline
56 & 0.00874248588913977 & 0.0174849717782795 & 0.99125751411086 \tabularnewline
57 & 0.00840155397241224 & 0.0168031079448245 & 0.991598446027588 \tabularnewline
58 & 0.00636726224767202 & 0.012734524495344 & 0.993632737752328 \tabularnewline
59 & 0.00444892356445198 & 0.00889784712890396 & 0.995551076435548 \tabularnewline
60 & 0.00303514265599466 & 0.00607028531198932 & 0.996964857344005 \tabularnewline
61 & 0.0022353806406495 & 0.00447076128129899 & 0.99776461935935 \tabularnewline
62 & 0.00159402617231407 & 0.00318805234462814 & 0.998405973827686 \tabularnewline
63 & 0.00104515849036252 & 0.00209031698072504 & 0.998954841509638 \tabularnewline
64 & 0.00145124423951649 & 0.00290248847903298 & 0.998548755760484 \tabularnewline
65 & 0.00105118131831478 & 0.00210236263662955 & 0.998948818681685 \tabularnewline
66 & 0.000660052108636762 & 0.00132010421727352 & 0.999339947891363 \tabularnewline
67 & 0.000450531617943338 & 0.000901063235886675 & 0.999549468382057 \tabularnewline
68 & 0.000417790318907073 & 0.000835580637814145 & 0.999582209681093 \tabularnewline
69 & 0.00026553563270512 & 0.000531071265410241 & 0.999734464367295 \tabularnewline
70 & 0.000204645199411954 & 0.000409290398823908 & 0.999795354800588 \tabularnewline
71 & 0.000191346991102023 & 0.000382693982204046 & 0.999808653008898 \tabularnewline
72 & 0.000602931547049186 & 0.00120586309409837 & 0.999397068452951 \tabularnewline
73 & 0.000523568325991353 & 0.00104713665198271 & 0.999476431674009 \tabularnewline
74 & 0.000354686710678564 & 0.000709373421357129 & 0.999645313289321 \tabularnewline
75 & 0.000293668913947228 & 0.000587337827894455 & 0.999706331086053 \tabularnewline
76 & 0.000289169558420159 & 0.000578339116840317 & 0.99971083044158 \tabularnewline
77 & 0.000230781436031127 & 0.000461562872062254 & 0.999769218563969 \tabularnewline
78 & 0.00017354873709254 & 0.00034709747418508 & 0.999826451262907 \tabularnewline
79 & 0.000131746525578589 & 0.000263493051157179 & 0.999868253474421 \tabularnewline
80 & 8.1852872821614e-05 & 0.000163705745643228 & 0.999918147127178 \tabularnewline
81 & 5.57371434587005e-05 & 0.000111474286917401 & 0.999944262856541 \tabularnewline
82 & 0.000101965250233703 & 0.000203930500467407 & 0.999898034749766 \tabularnewline
83 & 0.000133032632093805 & 0.00026606526418761 & 0.999866967367906 \tabularnewline
84 & 9.5079600192768e-05 & 0.000190159200385536 & 0.999904920399807 \tabularnewline
85 & 8.5916246553692e-05 & 0.000171832493107384 & 0.999914083753446 \tabularnewline
86 & 6.36258685536094e-05 & 0.000127251737107219 & 0.999936374131446 \tabularnewline
87 & 5.70866246547629e-05 & 0.000114173249309526 & 0.999942913375345 \tabularnewline
88 & 0.000188111060783348 & 0.000376222121566696 & 0.999811888939217 \tabularnewline
89 & 0.000217800763387564 & 0.000435601526775129 & 0.999782199236612 \tabularnewline
90 & 0.00118974199579776 & 0.00237948399159552 & 0.998810258004202 \tabularnewline
91 & 0.00105617456367875 & 0.0021123491273575 & 0.998943825436321 \tabularnewline
92 & 0.000880844245149399 & 0.0017616884902988 & 0.999119155754851 \tabularnewline
93 & 0.000560950039946372 & 0.00112190007989274 & 0.999439049960054 \tabularnewline
94 & 0.00048292837926304 & 0.00096585675852608 & 0.999517071620737 \tabularnewline
95 & 0.000355078872292105 & 0.000710157744584211 & 0.999644921127708 \tabularnewline
96 & 0.000276309548922338 & 0.000552619097844675 & 0.999723690451078 \tabularnewline
97 & 0.000181196400646206 & 0.000362392801292412 & 0.999818803599354 \tabularnewline
98 & 0.000665327376676933 & 0.00133065475335387 & 0.999334672623323 \tabularnewline
99 & 0.00170258254330725 & 0.0034051650866145 & 0.998297417456693 \tabularnewline
100 & 0.00236788651908658 & 0.00473577303817315 & 0.997632113480913 \tabularnewline
101 & 0.00701223418211888 & 0.0140244683642378 & 0.992987765817881 \tabularnewline
102 & 0.0143644810160086 & 0.0287289620320172 & 0.985635518983991 \tabularnewline
103 & 0.0102618442868283 & 0.0205236885736565 & 0.989738155713172 \tabularnewline
104 & 0.00792730949836102 & 0.015854618996722 & 0.992072690501639 \tabularnewline
105 & 0.00780977263141433 & 0.0156195452628287 & 0.992190227368586 \tabularnewline
106 & 0.0081842103472591 & 0.0163684206945182 & 0.991815789652741 \tabularnewline
107 & 0.0125120466949014 & 0.0250240933898027 & 0.987487953305099 \tabularnewline
108 & 0.0216313034036328 & 0.0432626068072657 & 0.978368696596367 \tabularnewline
109 & 0.0188803162502068 & 0.0377606325004136 & 0.981119683749793 \tabularnewline
110 & 0.0144681878514036 & 0.0289363757028072 & 0.985531812148596 \tabularnewline
111 & 0.0169831491424528 & 0.0339662982849056 & 0.983016850857547 \tabularnewline
112 & 0.0250074732466144 & 0.0500149464932289 & 0.974992526753386 \tabularnewline
113 & 0.0243384761497724 & 0.0486769522995449 & 0.975661523850228 \tabularnewline
114 & 0.0433887995414663 & 0.0867775990829326 & 0.956611200458534 \tabularnewline
115 & 0.0362471912059989 & 0.0724943824119979 & 0.963752808794001 \tabularnewline
116 & 0.0353131645233465 & 0.0706263290466931 & 0.964686835476653 \tabularnewline
117 & 0.081926825006171 & 0.163853650012342 & 0.918073174993829 \tabularnewline
118 & 0.212287398688695 & 0.42457479737739 & 0.787712601311305 \tabularnewline
119 & 0.193511557886402 & 0.387023115772805 & 0.806488442113598 \tabularnewline
120 & 0.155353843801831 & 0.310707687603663 & 0.844646156198169 \tabularnewline
121 & 0.128784529143996 & 0.257569058287991 & 0.871215470856004 \tabularnewline
122 & 0.104755989309839 & 0.209511978619677 & 0.895244010690161 \tabularnewline
123 & 0.107187434957025 & 0.21437486991405 & 0.892812565042975 \tabularnewline
124 & 0.0723806201935603 & 0.144761240387121 & 0.92761937980644 \tabularnewline
125 & 0.0396835194965868 & 0.0793670389931736 & 0.960316480503413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&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]18[/C][C]0.70357621910947[/C][C]0.592847561781061[/C][C]0.29642378089053[/C][/ROW]
[ROW][C]19[/C][C]0.750140852162457[/C][C]0.499718295675086[/C][C]0.249859147837543[/C][/ROW]
[ROW][C]20[/C][C]0.636695418742392[/C][C]0.726609162515216[/C][C]0.363304581257608[/C][/ROW]
[ROW][C]21[/C][C]0.512708631331931[/C][C]0.974582737336137[/C][C]0.487291368668068[/C][/ROW]
[ROW][C]22[/C][C]0.581498725730681[/C][C]0.837002548538638[/C][C]0.418501274269319[/C][/ROW]
[ROW][C]23[/C][C]0.498140174718151[/C][C]0.996280349436302[/C][C]0.501859825281849[/C][/ROW]
[ROW][C]24[/C][C]0.460898972524489[/C][C]0.921797945048978[/C][C]0.539101027475511[/C][/ROW]
[ROW][C]25[/C][C]0.368351239890161[/C][C]0.736702479780322[/C][C]0.631648760109839[/C][/ROW]
[ROW][C]26[/C][C]0.298400854671113[/C][C]0.596801709342226[/C][C]0.701599145328887[/C][/ROW]
[ROW][C]27[/C][C]0.290952546922361[/C][C]0.581905093844721[/C][C]0.709047453077639[/C][/ROW]
[ROW][C]28[/C][C]0.223774162352813[/C][C]0.447548324705625[/C][C]0.776225837647187[/C][/ROW]
[ROW][C]29[/C][C]0.171527305849935[/C][C]0.343054611699869[/C][C]0.828472694150065[/C][/ROW]
[ROW][C]30[/C][C]0.124817188579889[/C][C]0.249634377159779[/C][C]0.87518281142011[/C][/ROW]
[ROW][C]31[/C][C]0.101067341955985[/C][C]0.20213468391197[/C][C]0.898932658044015[/C][/ROW]
[ROW][C]32[/C][C]0.0777294290926763[/C][C]0.155458858185353[/C][C]0.922270570907324[/C][/ROW]
[ROW][C]33[/C][C]0.0534935298023083[/C][C]0.106987059604617[/C][C]0.946506470197692[/C][/ROW]
[ROW][C]34[/C][C]0.0353212459638625[/C][C]0.0706424919277251[/C][C]0.964678754036137[/C][/ROW]
[ROW][C]35[/C][C]0.0507504287758282[/C][C]0.101500857551656[/C][C]0.949249571224172[/C][/ROW]
[ROW][C]36[/C][C]0.0428058975912048[/C][C]0.0856117951824096[/C][C]0.957194102408795[/C][/ROW]
[ROW][C]37[/C][C]0.0564978662991505[/C][C]0.112995732598301[/C][C]0.943502133700849[/C][/ROW]
[ROW][C]38[/C][C]0.0431928399068964[/C][C]0.0863856798137927[/C][C]0.956807160093104[/C][/ROW]
[ROW][C]39[/C][C]0.0463967015767032[/C][C]0.0927934031534064[/C][C]0.953603298423297[/C][/ROW]
[ROW][C]40[/C][C]0.0426859513681506[/C][C]0.0853719027363012[/C][C]0.957314048631849[/C][/ROW]
[ROW][C]41[/C][C]0.0361062521291335[/C][C]0.0722125042582671[/C][C]0.963893747870866[/C][/ROW]
[ROW][C]42[/C][C]0.0324769319287457[/C][C]0.0649538638574915[/C][C]0.967523068071254[/C][/ROW]
[ROW][C]43[/C][C]0.0328037740873989[/C][C]0.0656075481747977[/C][C]0.967196225912601[/C][/ROW]
[ROW][C]44[/C][C]0.0268184395611958[/C][C]0.0536368791223916[/C][C]0.973181560438804[/C][/ROW]
[ROW][C]45[/C][C]0.0258580283252468[/C][C]0.0517160566504937[/C][C]0.974141971674753[/C][/ROW]
[ROW][C]46[/C][C]0.019763574049603[/C][C]0.0395271480992061[/C][C]0.980236425950397[/C][/ROW]
[ROW][C]47[/C][C]0.0186332265768228[/C][C]0.0372664531536456[/C][C]0.981366773423177[/C][/ROW]
[ROW][C]48[/C][C]0.020687647631776[/C][C]0.0413752952635519[/C][C]0.979312352368224[/C][/ROW]
[ROW][C]49[/C][C]0.0384645172688847[/C][C]0.0769290345377694[/C][C]0.961535482731115[/C][/ROW]
[ROW][C]50[/C][C]0.0310434235301563[/C][C]0.0620868470603126[/C][C]0.968956576469844[/C][/ROW]
[ROW][C]51[/C][C]0.0318866704879919[/C][C]0.0637733409759838[/C][C]0.968113329512008[/C][/ROW]
[ROW][C]52[/C][C]0.0240449668588215[/C][C]0.048089933717643[/C][C]0.975955033141178[/C][/ROW]
[ROW][C]53[/C][C]0.0168107821924979[/C][C]0.0336215643849958[/C][C]0.983189217807502[/C][/ROW]
[ROW][C]54[/C][C]0.0128459436708338[/C][C]0.0256918873416676[/C][C]0.987154056329166[/C][/ROW]
[ROW][C]55[/C][C]0.00981875291960655[/C][C]0.0196375058392131[/C][C]0.990181247080393[/C][/ROW]
[ROW][C]56[/C][C]0.00874248588913977[/C][C]0.0174849717782795[/C][C]0.99125751411086[/C][/ROW]
[ROW][C]57[/C][C]0.00840155397241224[/C][C]0.0168031079448245[/C][C]0.991598446027588[/C][/ROW]
[ROW][C]58[/C][C]0.00636726224767202[/C][C]0.012734524495344[/C][C]0.993632737752328[/C][/ROW]
[ROW][C]59[/C][C]0.00444892356445198[/C][C]0.00889784712890396[/C][C]0.995551076435548[/C][/ROW]
[ROW][C]60[/C][C]0.00303514265599466[/C][C]0.00607028531198932[/C][C]0.996964857344005[/C][/ROW]
[ROW][C]61[/C][C]0.0022353806406495[/C][C]0.00447076128129899[/C][C]0.99776461935935[/C][/ROW]
[ROW][C]62[/C][C]0.00159402617231407[/C][C]0.00318805234462814[/C][C]0.998405973827686[/C][/ROW]
[ROW][C]63[/C][C]0.00104515849036252[/C][C]0.00209031698072504[/C][C]0.998954841509638[/C][/ROW]
[ROW][C]64[/C][C]0.00145124423951649[/C][C]0.00290248847903298[/C][C]0.998548755760484[/C][/ROW]
[ROW][C]65[/C][C]0.00105118131831478[/C][C]0.00210236263662955[/C][C]0.998948818681685[/C][/ROW]
[ROW][C]66[/C][C]0.000660052108636762[/C][C]0.00132010421727352[/C][C]0.999339947891363[/C][/ROW]
[ROW][C]67[/C][C]0.000450531617943338[/C][C]0.000901063235886675[/C][C]0.999549468382057[/C][/ROW]
[ROW][C]68[/C][C]0.000417790318907073[/C][C]0.000835580637814145[/C][C]0.999582209681093[/C][/ROW]
[ROW][C]69[/C][C]0.00026553563270512[/C][C]0.000531071265410241[/C][C]0.999734464367295[/C][/ROW]
[ROW][C]70[/C][C]0.000204645199411954[/C][C]0.000409290398823908[/C][C]0.999795354800588[/C][/ROW]
[ROW][C]71[/C][C]0.000191346991102023[/C][C]0.000382693982204046[/C][C]0.999808653008898[/C][/ROW]
[ROW][C]72[/C][C]0.000602931547049186[/C][C]0.00120586309409837[/C][C]0.999397068452951[/C][/ROW]
[ROW][C]73[/C][C]0.000523568325991353[/C][C]0.00104713665198271[/C][C]0.999476431674009[/C][/ROW]
[ROW][C]74[/C][C]0.000354686710678564[/C][C]0.000709373421357129[/C][C]0.999645313289321[/C][/ROW]
[ROW][C]75[/C][C]0.000293668913947228[/C][C]0.000587337827894455[/C][C]0.999706331086053[/C][/ROW]
[ROW][C]76[/C][C]0.000289169558420159[/C][C]0.000578339116840317[/C][C]0.99971083044158[/C][/ROW]
[ROW][C]77[/C][C]0.000230781436031127[/C][C]0.000461562872062254[/C][C]0.999769218563969[/C][/ROW]
[ROW][C]78[/C][C]0.00017354873709254[/C][C]0.00034709747418508[/C][C]0.999826451262907[/C][/ROW]
[ROW][C]79[/C][C]0.000131746525578589[/C][C]0.000263493051157179[/C][C]0.999868253474421[/C][/ROW]
[ROW][C]80[/C][C]8.1852872821614e-05[/C][C]0.000163705745643228[/C][C]0.999918147127178[/C][/ROW]
[ROW][C]81[/C][C]5.57371434587005e-05[/C][C]0.000111474286917401[/C][C]0.999944262856541[/C][/ROW]
[ROW][C]82[/C][C]0.000101965250233703[/C][C]0.000203930500467407[/C][C]0.999898034749766[/C][/ROW]
[ROW][C]83[/C][C]0.000133032632093805[/C][C]0.00026606526418761[/C][C]0.999866967367906[/C][/ROW]
[ROW][C]84[/C][C]9.5079600192768e-05[/C][C]0.000190159200385536[/C][C]0.999904920399807[/C][/ROW]
[ROW][C]85[/C][C]8.5916246553692e-05[/C][C]0.000171832493107384[/C][C]0.999914083753446[/C][/ROW]
[ROW][C]86[/C][C]6.36258685536094e-05[/C][C]0.000127251737107219[/C][C]0.999936374131446[/C][/ROW]
[ROW][C]87[/C][C]5.70866246547629e-05[/C][C]0.000114173249309526[/C][C]0.999942913375345[/C][/ROW]
[ROW][C]88[/C][C]0.000188111060783348[/C][C]0.000376222121566696[/C][C]0.999811888939217[/C][/ROW]
[ROW][C]89[/C][C]0.000217800763387564[/C][C]0.000435601526775129[/C][C]0.999782199236612[/C][/ROW]
[ROW][C]90[/C][C]0.00118974199579776[/C][C]0.00237948399159552[/C][C]0.998810258004202[/C][/ROW]
[ROW][C]91[/C][C]0.00105617456367875[/C][C]0.0021123491273575[/C][C]0.998943825436321[/C][/ROW]
[ROW][C]92[/C][C]0.000880844245149399[/C][C]0.0017616884902988[/C][C]0.999119155754851[/C][/ROW]
[ROW][C]93[/C][C]0.000560950039946372[/C][C]0.00112190007989274[/C][C]0.999439049960054[/C][/ROW]
[ROW][C]94[/C][C]0.00048292837926304[/C][C]0.00096585675852608[/C][C]0.999517071620737[/C][/ROW]
[ROW][C]95[/C][C]0.000355078872292105[/C][C]0.000710157744584211[/C][C]0.999644921127708[/C][/ROW]
[ROW][C]96[/C][C]0.000276309548922338[/C][C]0.000552619097844675[/C][C]0.999723690451078[/C][/ROW]
[ROW][C]97[/C][C]0.000181196400646206[/C][C]0.000362392801292412[/C][C]0.999818803599354[/C][/ROW]
[ROW][C]98[/C][C]0.000665327376676933[/C][C]0.00133065475335387[/C][C]0.999334672623323[/C][/ROW]
[ROW][C]99[/C][C]0.00170258254330725[/C][C]0.0034051650866145[/C][C]0.998297417456693[/C][/ROW]
[ROW][C]100[/C][C]0.00236788651908658[/C][C]0.00473577303817315[/C][C]0.997632113480913[/C][/ROW]
[ROW][C]101[/C][C]0.00701223418211888[/C][C]0.0140244683642378[/C][C]0.992987765817881[/C][/ROW]
[ROW][C]102[/C][C]0.0143644810160086[/C][C]0.0287289620320172[/C][C]0.985635518983991[/C][/ROW]
[ROW][C]103[/C][C]0.0102618442868283[/C][C]0.0205236885736565[/C][C]0.989738155713172[/C][/ROW]
[ROW][C]104[/C][C]0.00792730949836102[/C][C]0.015854618996722[/C][C]0.992072690501639[/C][/ROW]
[ROW][C]105[/C][C]0.00780977263141433[/C][C]0.0156195452628287[/C][C]0.992190227368586[/C][/ROW]
[ROW][C]106[/C][C]0.0081842103472591[/C][C]0.0163684206945182[/C][C]0.991815789652741[/C][/ROW]
[ROW][C]107[/C][C]0.0125120466949014[/C][C]0.0250240933898027[/C][C]0.987487953305099[/C][/ROW]
[ROW][C]108[/C][C]0.0216313034036328[/C][C]0.0432626068072657[/C][C]0.978368696596367[/C][/ROW]
[ROW][C]109[/C][C]0.0188803162502068[/C][C]0.0377606325004136[/C][C]0.981119683749793[/C][/ROW]
[ROW][C]110[/C][C]0.0144681878514036[/C][C]0.0289363757028072[/C][C]0.985531812148596[/C][/ROW]
[ROW][C]111[/C][C]0.0169831491424528[/C][C]0.0339662982849056[/C][C]0.983016850857547[/C][/ROW]
[ROW][C]112[/C][C]0.0250074732466144[/C][C]0.0500149464932289[/C][C]0.974992526753386[/C][/ROW]
[ROW][C]113[/C][C]0.0243384761497724[/C][C]0.0486769522995449[/C][C]0.975661523850228[/C][/ROW]
[ROW][C]114[/C][C]0.0433887995414663[/C][C]0.0867775990829326[/C][C]0.956611200458534[/C][/ROW]
[ROW][C]115[/C][C]0.0362471912059989[/C][C]0.0724943824119979[/C][C]0.963752808794001[/C][/ROW]
[ROW][C]116[/C][C]0.0353131645233465[/C][C]0.0706263290466931[/C][C]0.964686835476653[/C][/ROW]
[ROW][C]117[/C][C]0.081926825006171[/C][C]0.163853650012342[/C][C]0.918073174993829[/C][/ROW]
[ROW][C]118[/C][C]0.212287398688695[/C][C]0.42457479737739[/C][C]0.787712601311305[/C][/ROW]
[ROW][C]119[/C][C]0.193511557886402[/C][C]0.387023115772805[/C][C]0.806488442113598[/C][/ROW]
[ROW][C]120[/C][C]0.155353843801831[/C][C]0.310707687603663[/C][C]0.844646156198169[/C][/ROW]
[ROW][C]121[/C][C]0.128784529143996[/C][C]0.257569058287991[/C][C]0.871215470856004[/C][/ROW]
[ROW][C]122[/C][C]0.104755989309839[/C][C]0.209511978619677[/C][C]0.895244010690161[/C][/ROW]
[ROW][C]123[/C][C]0.107187434957025[/C][C]0.21437486991405[/C][C]0.892812565042975[/C][/ROW]
[ROW][C]124[/C][C]0.0723806201935603[/C][C]0.144761240387121[/C][C]0.92761937980644[/C][/ROW]
[ROW][C]125[/C][C]0.0396835194965868[/C][C]0.0793670389931736[/C][C]0.960316480503413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.703576219109470.5928475617810610.29642378089053
190.7501408521624570.4997182956750860.249859147837543
200.6366954187423920.7266091625152160.363304581257608
210.5127086313319310.9745827373361370.487291368668068
220.5814987257306810.8370025485386380.418501274269319
230.4981401747181510.9962803494363020.501859825281849
240.4608989725244890.9217979450489780.539101027475511
250.3683512398901610.7367024797803220.631648760109839
260.2984008546711130.5968017093422260.701599145328887
270.2909525469223610.5819050938447210.709047453077639
280.2237741623528130.4475483247056250.776225837647187
290.1715273058499350.3430546116998690.828472694150065
300.1248171885798890.2496343771597790.87518281142011
310.1010673419559850.202134683911970.898932658044015
320.07772942909267630.1554588581853530.922270570907324
330.05349352980230830.1069870596046170.946506470197692
340.03532124596386250.07064249192772510.964678754036137
350.05075042877582820.1015008575516560.949249571224172
360.04280589759120480.08561179518240960.957194102408795
370.05649786629915050.1129957325983010.943502133700849
380.04319283990689640.08638567981379270.956807160093104
390.04639670157670320.09279340315340640.953603298423297
400.04268595136815060.08537190273630120.957314048631849
410.03610625212913350.07221250425826710.963893747870866
420.03247693192874570.06495386385749150.967523068071254
430.03280377408739890.06560754817479770.967196225912601
440.02681843956119580.05363687912239160.973181560438804
450.02585802832524680.05171605665049370.974141971674753
460.0197635740496030.03952714809920610.980236425950397
470.01863322657682280.03726645315364560.981366773423177
480.0206876476317760.04137529526355190.979312352368224
490.03846451726888470.07692903453776940.961535482731115
500.03104342353015630.06208684706031260.968956576469844
510.03188667048799190.06377334097598380.968113329512008
520.02404496685882150.0480899337176430.975955033141178
530.01681078219249790.03362156438499580.983189217807502
540.01284594367083380.02569188734166760.987154056329166
550.009818752919606550.01963750583921310.990181247080393
560.008742485889139770.01748497177827950.99125751411086
570.008401553972412240.01680310794482450.991598446027588
580.006367262247672020.0127345244953440.993632737752328
590.004448923564451980.008897847128903960.995551076435548
600.003035142655994660.006070285311989320.996964857344005
610.00223538064064950.004470761281298990.99776461935935
620.001594026172314070.003188052344628140.998405973827686
630.001045158490362520.002090316980725040.998954841509638
640.001451244239516490.002902488479032980.998548755760484
650.001051181318314780.002102362636629550.998948818681685
660.0006600521086367620.001320104217273520.999339947891363
670.0004505316179433380.0009010632358866750.999549468382057
680.0004177903189070730.0008355806378141450.999582209681093
690.000265535632705120.0005310712654102410.999734464367295
700.0002046451994119540.0004092903988239080.999795354800588
710.0001913469911020230.0003826939822040460.999808653008898
720.0006029315470491860.001205863094098370.999397068452951
730.0005235683259913530.001047136651982710.999476431674009
740.0003546867106785640.0007093734213571290.999645313289321
750.0002936689139472280.0005873378278944550.999706331086053
760.0002891695584201590.0005783391168403170.99971083044158
770.0002307814360311270.0004615628720622540.999769218563969
780.000173548737092540.000347097474185080.999826451262907
790.0001317465255785890.0002634930511571790.999868253474421
808.1852872821614e-050.0001637057456432280.999918147127178
815.57371434587005e-050.0001114742869174010.999944262856541
820.0001019652502337030.0002039305004674070.999898034749766
830.0001330326320938050.000266065264187610.999866967367906
849.5079600192768e-050.0001901592003855360.999904920399807
858.5916246553692e-050.0001718324931073840.999914083753446
866.36258685536094e-050.0001272517371072190.999936374131446
875.70866246547629e-050.0001141732493095260.999942913375345
880.0001881110607833480.0003762221215666960.999811888939217
890.0002178007633875640.0004356015267751290.999782199236612
900.001189741995797760.002379483991595520.998810258004202
910.001056174563678750.00211234912735750.998943825436321
920.0008808442451493990.00176168849029880.999119155754851
930.0005609500399463720.001121900079892740.999439049960054
940.000482928379263040.000965856758526080.999517071620737
950.0003550788722921050.0007101577445842110.999644921127708
960.0002763095489223380.0005526190978446750.999723690451078
970.0001811964006462060.0003623928012924120.999818803599354
980.0006653273766769330.001330654753353870.999334672623323
990.001702582543307250.00340516508661450.998297417456693
1000.002367886519086580.004735773038173150.997632113480913
1010.007012234182118880.01402446836423780.992987765817881
1020.01436448101600860.02872896203201720.985635518983991
1030.01026184428682830.02052368857365650.989738155713172
1040.007927309498361020.0158546189967220.992072690501639
1050.007809772631414330.01561954526282870.992190227368586
1060.00818421034725910.01636842069451820.991815789652741
1070.01251204669490140.02502409338980270.987487953305099
1080.02163130340363280.04326260680726570.978368696596367
1090.01888031625020680.03776063250041360.981119683749793
1100.01446818785140360.02893637570280720.985531812148596
1110.01698314914245280.03396629828490560.983016850857547
1120.02500747324661440.05001494649322890.974992526753386
1130.02433847614977240.04867695229954490.975661523850228
1140.04338879954146630.08677759908293260.956611200458534
1150.03624719120599890.07249438241199790.963752808794001
1160.03531316452334650.07062632904669310.964686835476653
1170.0819268250061710.1638536500123420.918073174993829
1180.2122873986886950.424574797377390.787712601311305
1190.1935115578864020.3870231157728050.806488442113598
1200.1553538438018310.3107076876036630.844646156198169
1210.1287845291439960.2575690582879910.871215470856004
1220.1047559893098390.2095119786196770.895244010690161
1230.1071874349570250.214374869914050.892812565042975
1240.07238062019356030.1447612403871210.92761937980644
1250.03968351949658680.07936703899317360.960316480503413






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level420.388888888888889NOK
5% type I error level640.592592592592593NOK
10% type I error level820.759259259259259NOK

\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 & 42 & 0.388888888888889 & NOK \tabularnewline
5% type I error level & 64 & 0.592592592592593 & NOK \tabularnewline
10% type I error level & 82 & 0.759259259259259 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190016&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]42[/C][C]0.388888888888889[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]64[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.759259259259259[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190016&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190016&T=6

As an alternative you can also use a QR Code:  
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 level420.388888888888889NOK
5% type I error level640.592592592592593NOK
10% type I error level820.759259259259259NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}