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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 02:44:33 -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/05/t13521015148pkqi36fevtl3ah.htm/, Retrieved Sun, 05 Feb 2023 23:42:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185963, Retrieved Sun, 05 Feb 2023 23:42:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 07:44:33] [a5d65c007476aeb95d503c7a121a195d] [Current]
- R PD    [Multiple Regression] [ws7du] [2012-11-05 22:53:07] [4a361cce6d88b3d167c89c496ff59bf0]
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Dataseries X:
2000	1	75,5	78,4	67,3	75,3	106,1	125,7	101,6
2000	2	83,2	79,3	75,2	83,6	112,7	153,8	113,4
2000	3	94,5	84,3	91,1	91,2	123,2	134,9	122,2
2000	4	83,3	81,2	83,7	85,2	101,7	95,3	102,2
2000	5	92,7	88,4	105,0	100,0	118,7	96,6	113,2
2000	6	89,8	83,1	106,2	89,8	107,1	100,5	115,3
2000	7	74,8	76,6	88,5	88,9	93,6	106,2	87,4
2000	8	81,5	82,6	100,1	85,6	77,5	153,4	98,7
2000	9	92,8	84,4	90,3	83,2	117,2	132,1	117,3
2000	10	92,8	94,6	85,3	97,1	124,5	110,9	121,2
2000	11	91,7	91,8	81,9	85,8	120,8	94,3	118,7
2000	12	83,5	89,3	77,2	80,9	97,0	91,7	112,1
2001	13	92,8	87,7	78,6	81,3	115,1	138,6	102,9
2001	14	91,3	83,1	75,1	83,2	112,9	154,3	108,8
2001	15	99,5	93,6	90,3	90,7	122,7	149,8	118,6
2001	16	87,6	85,1	88,5	88,4	106,9	99,2	99,2
2001	17	95,3	90,8	112,5	94,1	115,0	97,7	102,2
2001	18	98,5	90,5	101,1	92,0	114,9	107,7	108,8
2001	19	80,1	86,1	114,0	92,0	103,1	120,1	94,0
2001	20	84,2	93,3	107,7	89,3	80,8	164,5	96,2
2001	21	92,4	94,9	77,8	87,0	118,2	136,1	118,4
2001	22	98,0	102,6	101,4	97,7	129,6	117,5	120,0
2001	23	92,2	98,3	87,2	82,5	118,7	98,2	117,5
2001	24	80,0	93,4	75,9	96,5	88,4	91,9	102,6
2002	25	88,7	92,8	78,8	86,2	113,1	141,8	92,8
2002	26	87,4	86,5	82,3	84,9	109,8	154,2	100,3
2002	27	96,1	93,8	89,1	100,0	116,1	138,6	106,3
2002	28	94,1	90,4	100,1	92,7	113,6	97,9	103,9
2002	29	91,9	91,0	101,8	96,7	107,9	90,3	102,4
2002	30	93,6	89,1	98,5	105,8	107,4	90,9	114,5
2002	31	83,5	89,6	106,6	88,5	102,7	127,0	89,0
2002	32	80,8	89,3	101,8	78,7	78,3	156,8	94,3
2002	33	96,3	95,3	92,4	99,9	121,0	127,2	115,7
2002	34	101,5	104,1	94,4	107,8	132,2	111,3	120,2
2002	35	91,6	94,7	81,0	102,4	113,2	93,0	109,5
2002	36	84,0	97,6	94,6	106,0	89,2	89,5	99,4
2003	37	91,8	96,8	83,8	87,3	113,2	141,8	86,4
2003	38	90,4	92,8	79,4	93,3	107,6	152,0	95,1
2003	39	98,0	94,7	95,6	98,2	107,3	120,2	101,5
2003	40	95,5	95,8	106,0	102,0	110,9	88,8	92,9
2003	41	90,5	88,9	106,2	93,9	96,4	82,8	90,8
2003	42	97,1	91,2	115,0	106,6	101,2	82,8	100,4
2003	43	87,9	91,6	122,4	92,9	94,0	121,7	82,2
2003	44	79,8	87,3	113,7	78,0	70,5	147,1	75,3
2003	45	102,0	97,8	98,0	104,2	116,4	132,5	110,3
2003	46	104,3	105,1	105,8	115,9	121,9	107,5	113,5
2003	47	92,1	93,8	88,3	99,9	109,5	77,9	94,9
2003	48	95,9	99,0	95,7	103,9	91,1	85,5	95,7
2004	49	89,1	91,4	85,8	93,5	104,0	126,5	85,3
2004	50	92,2	89,0	83,9	101,7	101,2	135,4	92,5
2004	51	107,5	101,4	114,1	124,6	118,4	122,5	107,7
2004	52	99,7	95,4	102,0	124,2	106,9	79,2	97,9
2004	53	92,2	90,5	108,1	103,3	95,6	66,1	93,9
2004	54	108,9	98,7	125,4	120,5	114,2	77,9	111,5
2004	55	89,8	91,2	108,1	98,0	92,4	109,6	88,6
2004	56	89,4	91,7	110,4	100,4	75,3	142,9	82,5
2004	57	107,6	102,9	102,4	126,8	120,4	120,5	108,6
2004	58	105,6	105,5	89,6	120,2	115,9	96,3	113,8
2004	59	100,9	102,6	95,0	114,0	109,8	82,6	103,4
2004	60	102,9	107,2	93,7	109,1	94,9	78,4	99,0
2005	61	96,2	96,9	77,7	94,2	97,5	104,5	89,9
2005	62	94,7	88,9	80,1	86,0	101,3	137,9	97,9
2005	63	107,3	99,6	103,6	112,9	108,7	125,8	107,8
2005	64	103,0	96,7	103,1	99,7	105,1	78,0	103,7
2005	65	96,1	93,8	112,4	104,5	94,9	67,7	98,2
2005	66	109,8	101,9	119,2	111,6	108,9	78,4	111,7
2005	67	85,4	87,6	105,3	99,2	87,5	101,7	82,6
2005	68	89,9	100,0	107,2	90,9	73,0	154,1	86,1
2005	69	109,3	105,8	108,7	111,4	115,2	107,3	111,2
2005	70	101,2	105,5	93,7	98,2	107,5	86,5	105,3
2005	71	104,7	111,3	96,1	101,7	109,8	82,1	106,3
2005	72	102,4	112,1	92,9	89,7	90,7	76,1	99,4
2006	73	97,7	102,0	81,1	89,5	97,6	115,5	91,9
2006	74	98,9	93,2	83,2	85,1	98,7	129,6	96,2
2006	75	115,0	108,4	99,7	95,9	113,9	121,6	105,4
2006	76	97,5	97,9	96,8	88,9	96,6	64,0	95,0
2006	77	107,3	106,4	108,7	98,1	104,4	58,1	100,5
2006	78	112,3	102,8	120,9	109,7	115,1	79,7	111,6
2006	79	88,5	96,3	114,8	92,0	91,4	108,9	88,5
2006	80	92,9	105,7	108,7	74,3	76,2	138,5	83,7
2006	81	108,8	108,4	97,4	96,9	117,4	117,9	113,9
2006	82	112,3	115,8	98,6	100,3	122,0	96,7	115,2
2006	83	107,3	113,8	91,7	97,1	120,2	78,6	111,0
2006	84	101,8	106,4	91,2	86,0	93,6	64,1	96,9
2007	85	105,0	107,9	83,5	97,3	106,6	112,0	102,1
2007	86	103,4	98,2	82,4	86,4	108,4	139,4	101,5
2007	87	116,7	111,1	103,1	97,7	121,4	116,2	115,0
2007	88	103,6	99,8	110,3	90,6	104,8	63,4	105,0
2007	89	108,8	103,5	115,8	99,2	104,2	61,1	105,4
2007	90	117,0	105,4	120,1	107,4	115,0	65,5	119,7
2007	91	100,9	102,6	105,1	107,1	99,0	90,9	91,8
2007	92	100,8	107,4	108,6	78,9	82,8	115,3	89,1
2007	93	109,7	108,2	95,7	92,8	112,5	85,2	106,2
2007	94	121,0	121,7	103,2	106,2	127,9	87,0	119,9
2007	95	114,1	118,0	96,9	97,2	114,4	62,6	111,6
2007	96	105,5	109,6	95,7	80,0	83,7	62,7	95,1
2008	97	112,5	116,7	92,7	109,3	108,5	91,6	101,3
2008	98	113,8	110,6	81,3	111,3	109,7	104,3	118,3
2008	99	115,3	109,6	94,5	119,5	104,7	88,1	126,2
2008	100	120,4	117,4	105,6	119,8	112,2	62,3	113,2
2008	101	111,1	109,2	112,9	112,5	96,9	50,3	103,6
2008	102	120,1	110,8	102,6	125,6	103,8	64,1	116,2
2008	103	106,1	112,8	116,2	105,1	95,1	75,7	98,3
2008	104	95,9	106,5	104,9	91,9	66,7	85,5	84,2
2008	105	119,4	119,6	100,4	128,2	103,4	71,9	118,3
2008	106	117,4	127,2	97,1	122,6	105,4	66,9	117,4
2008	107	98,6	113,9	90,2	109,6	89,2	50,5	94,5
2008	108	99,7	120,0	100,5	120,4	72,5	57,9	93,3
2009	109	87,4	107,6	81,1	103,8	78,0	84,1	90,2
2009	110	90,8	105,2	87,2	96,6	77,3	87,0	88,5
2009	111	101,3	115,3	102,0	110,7	85,1	71,9	101,0
2009	112	93,2	113,9	107,0	111,7	80,9	45,0	87,0
2009	113	95,1	106,1	107,6	111,9	72,5	39,5	81,2
2009	114	101,9	114,3	123,5	131,5	82,1	53,8	98,1
2009	115	87,0	112,0	116,6	122,8	78,3	59,5	75,5
2009	116	86,2	109,0	103,2	98,3	57,8	68,4	70,7
2009	117	105,0	119,1	103,9	133,7	89,3	56,9	103,7
2009	118	104,1	124,4	95,4	120,0	91,4	61,9	100,4
2009	119	99,2	116,6	93,6	119,6	84,2	40,4	91,3
2009	120	95,2	118,5	102,1	108,7	72,5	49,4	97,2
2010	121	92,7	108,9	69,0	112,5	74,6	65,2	85,4
2010	122	99,3	107,5	88,9	102,7	80,3	82,1	86,5
2010	123	113,5	125,9	106,2	123,4	92,6	69,0	105,3
2010	124	104,7	117,7	103,0	116,5	86,3	45,9	97,7
2010	125	100,5	109,2	103,5	102,3	80,3	39,1	84,3
2010	126	116,2	118,8	124,5	148,4	93,6	56,9	109,8
2010	127	94,1	108,1	117,9	126,6	79,5	51,6	79,1
2010	128	94,8	112,1	104,2	106,6	61,8	62,9	83,4
2010	129	115,1	117,8	99,9	144,4	94,8	58,3	101,9
2010	130	110,0	121,8	89,4	132,4	91,6	56,9	113,0
2010	131	108,4	121,0	93,5	136,2	89,2	41,3	98,6
2010	132	103,9	121,7	89,6	121,6	74,1	46,9	94,7
2011	133	102,9	114,2	85,0	135,1	78,6	61,9	94,5
2011	134	107,7	109,8	90,0	124,7	78,2	74,8	90,7
2011	135	126,7	124,1	113,7	148,8	95,1	67,0	113,0
2011	136	108,8	112,9	112,1	145,6	78,7	53,3	89,9
2011	137	117,1	118,7	129,8	140,3	85,9	51,4	98,7
2011	138	112,2	113,3	119,1	138,5	81,2	50,3	102,2
2011	139	94,7	106,8	103,5	127,3	73,1	52,7	74,3
2011	140	102,7	119,3	105,5	117,9	58,7	70,3	84,5
2011	141	119,1	126,4	111,7	145,3	85,7	59,7	110,1
2011	142	110,6	126,6	98,6	120,7	81,8	52,0	100,4
2011	143	109,1	127,2	102,8	134,7	79,6	36,1	92,8
2011	144	105,3	123,8	101,1	124,4	70,7	39,7	92,2
2012	145	103,4	116,8	94,2	128,3	74,5	67,6	94,0
2012	146	103,7	113,8	92,6	128,4	84,8	72,8	100,7
2012	147	117,0	130,4	112,0	134,1	80,7	53,8	111,9
2012	148	101,2	112,8	108,6	133,3	69,9	39,6	95,9
2012	149	105,4	119,4	125,8	130,6	74,1	39,4	88,8
2012	150	110,3	117,5	138,7	165,7	76,1	41,2	102,0
2012	151	97,7	117,5	115,2	146,8	71,3	49,6	81,6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=185963&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=185963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -5680.43696651265 + 2.83310391579326jaar[t] -0.0664729163826431t + 0.276473061307123voeding[t] + 0.157370406217016dranken[t] -0.0188633638381299tabak[t] + 0.280629544445438textiel[t] + 0.0234089042541094kleding[t] + 0.306111987175081`apparatuur\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -5680.43696651265 +  2.83310391579326jaar[t] -0.0664729163826431t +  0.276473061307123voeding[t] +  0.157370406217016dranken[t] -0.0188633638381299tabak[t] +  0.280629544445438textiel[t] +  0.0234089042541094kleding[t] +  0.306111987175081`apparatuur\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -5680.43696651265 +  2.83310391579326jaar[t] -0.0664729163826431t +  0.276473061307123voeding[t] +  0.157370406217016dranken[t] -0.0188633638381299tabak[t] +  0.280629544445438textiel[t] +  0.0234089042541094kleding[t] +  0.306111987175081`apparatuur\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -5680.43696651265 + 2.83310391579326jaar[t] -0.0664729163826431t + 0.276473061307123voeding[t] + 0.157370406217016dranken[t] -0.0188633638381299tabak[t] + 0.280629544445438textiel[t] + 0.0234089042541094kleding[t] + 0.306111987175081`apparatuur\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-5680.436966512652685.382244-2.11530.0361490.018074
jaar2.833103915793261.3421712.11080.0365390.018269
t-0.06647291638264310.128591-0.51690.6060060.303003
voeding0.2764730613071230.0766593.60650.0004290.000214
dranken0.1573704062170160.0262176.002600
tabak-0.01886336383812990.027974-0.67430.5012060.250603
textiel0.2806295444454380.0362577.7400
kleding0.02340890425410940.0151871.54140.1254560.062728
`apparatuur\r`0.3061119871750810.0458636.674500

\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) & -5680.43696651265 & 2685.382244 & -2.1153 & 0.036149 & 0.018074 \tabularnewline
jaar & 2.83310391579326 & 1.342171 & 2.1108 & 0.036539 & 0.018269 \tabularnewline
t & -0.0664729163826431 & 0.128591 & -0.5169 & 0.606006 & 0.303003 \tabularnewline
voeding & 0.276473061307123 & 0.076659 & 3.6065 & 0.000429 & 0.000214 \tabularnewline
dranken & 0.157370406217016 & 0.026217 & 6.0026 & 0 & 0 \tabularnewline
tabak & -0.0188633638381299 & 0.027974 & -0.6743 & 0.501206 & 0.250603 \tabularnewline
textiel & 0.280629544445438 & 0.036257 & 7.74 & 0 & 0 \tabularnewline
kleding & 0.0234089042541094 & 0.015187 & 1.5414 & 0.125456 & 0.062728 \tabularnewline
`apparatuur\r` & 0.306111987175081 & 0.045863 & 6.6745 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&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]-5680.43696651265[/C][C]2685.382244[/C][C]-2.1153[/C][C]0.036149[/C][C]0.018074[/C][/ROW]
[ROW][C]jaar[/C][C]2.83310391579326[/C][C]1.342171[/C][C]2.1108[/C][C]0.036539[/C][C]0.018269[/C][/ROW]
[ROW][C]t[/C][C]-0.0664729163826431[/C][C]0.128591[/C][C]-0.5169[/C][C]0.606006[/C][C]0.303003[/C][/ROW]
[ROW][C]voeding[/C][C]0.276473061307123[/C][C]0.076659[/C][C]3.6065[/C][C]0.000429[/C][C]0.000214[/C][/ROW]
[ROW][C]dranken[/C][C]0.157370406217016[/C][C]0.026217[/C][C]6.0026[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]tabak[/C][C]-0.0188633638381299[/C][C]0.027974[/C][C]-0.6743[/C][C]0.501206[/C][C]0.250603[/C][/ROW]
[ROW][C]textiel[/C][C]0.280629544445438[/C][C]0.036257[/C][C]7.74[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]kleding[/C][C]0.0234089042541094[/C][C]0.015187[/C][C]1.5414[/C][C]0.125456[/C][C]0.062728[/C][/ROW]
[ROW][C]`apparatuur\r`[/C][C]0.306111987175081[/C][C]0.045863[/C][C]6.6745[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-5680.436966512652685.382244-2.11530.0361490.018074
jaar2.833103915793261.3421712.11080.0365390.018269
t-0.06647291638264310.128591-0.51690.6060060.303003
voeding0.2764730613071230.0766593.60650.0004290.000214
dranken0.1573704062170160.0262176.002600
tabak-0.01886336383812990.027974-0.67430.5012060.250603
textiel0.2806295444454380.0362577.7400
kleding0.02340890425410940.0151871.54140.1254560.062728
`apparatuur\r`0.3061119871750810.0458636.674500







Multiple Linear Regression - Regression Statistics
Multiple R0.942192359713627
R-squared0.887726442702733
Adjusted R-squared0.881401171869085
F-TEST (value)140.345997199085
F-TEST (DF numerator)8
F-TEST (DF denominator)142
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.63232813215805
Sum Squared Residuals1873.52068767268

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.942192359713627 \tabularnewline
R-squared & 0.887726442702733 \tabularnewline
Adjusted R-squared & 0.881401171869085 \tabularnewline
F-TEST (value) & 140.345997199085 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 142 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.63232813215805 \tabularnewline
Sum Squared Residuals & 1873.52068767268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.942192359713627[/C][/ROW]
[ROW][C]R-squared[/C][C]0.887726442702733[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.881401171869085[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]140.345997199085[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]142[/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]3.63232813215805[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1873.52068767268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.942192359713627
R-squared0.887726442702733
Adjusted R-squared0.881401171869085
F-TEST (value)140.345997199085
F-TEST (DF numerator)8
F-TEST (DF denominator)142
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.63232813215805
Sum Squared Residuals1873.52068767268







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.580.368769032759-4.86876903275902
283.287.7598488123558-4.55984881235582
394.596.6325365096047-2.13253650960469
483.381.57486872265161.72513127734844
592.794.740179405327-2.04017940532701
689.891.0684772467176-1.26847724671757
774.874.2408577313020.559142268697994
881.577.76679906584613.73320093415386
992.893.0370859674289-0.237085967428943
1092.897.4877491421913-4.68774914219127
1191.794.1330511913776-2.43305119137765
1283.583.9679997710964-0.467999771096405
1392.889.86608917755362.93391082244637
1491.389.49739888944871.80260111055126
1599.5100.229175003241-0.729175003241153
1687.686.01579016269261.58420983730738
1795.394.35090418624380.949095813756247
1898.594.67344498810713.82655501189286
1980.187.8689532202763-7.76895322027626
2084.284.307346748035-0.107346748035005
2192.496.6476595174122-4.24765951741214
2298105.47568313378-7.47568313377964
2392.297.996505561337-5.79650556133702
248081.321322058157-1.321322058157
2588.789.672492642112-0.972492642112019
2687.490.0995910541377-2.69959105413774
2796.196.07611260030970.0238873996903083
2894.194.5494232664123-0.44942326641232
2991.992.6042463655975-0.704246365597498
3093.694.8991812964364-1.29918129643642
3183.588.29222830718-4.79222830718004
3280.883.0449144825089-2.24491448250895
3396.3100.598869309606-4.29886930960605
34101.5107.279432833275-5.77943283327532
3591.693.5714693069367-1.97146930693672
368486.4703263810307-2.47032638103066
3791.891.64886237392920.15113762607077
3890.491.0013069048551-0.601306904855095
399895.0476276021712.95237239782897
4095.594.49291017201711.00708982798285
4190.587.85062346789642.64937653210357
4297.193.85103033670453.24896966329545
4387.988.6369512243243-0.736951224324272
4479.878.18120489349961.61879510650038
45102101.3058092497390.694190750260944
46104.3106.202175739542-1.90217573954246
4792.190.69299606451.40700393549996
4895.988.41248426184317.48751573815694
4989.189.1124534888314-0.012453488831446
5092.289.55534470110162.64465529889837
51107.5106.4155084854331.0844915145672
5299.795.55281784187554.1471821581245
5392.290.7836122605661.41638773943395
54108.9106.2657821876052.63421781239457
5589.889.44205265985830.357947340141729
5689.483.94396431509375.45603568490633
57107.6105.3385894947552.26141050524512
58105.6103.8635574398811.7364425601189
59100.998.74595881905682.15404118094323
60102.994.2125205857378.68747941426303
6196.290.44960680869735.75039319130274
6294.793.00086352863171.69913647136831
63107.3103.9073519875443.39264801245572
6410399.82514726215793.17485273784206
6596.195.54275410275610.557245897243858
66109.8106.9637025866052.83629741339512
6785.486.6222183499002-1.22221834990016
6889.988.66847122896391.23152877103608
69109.3108.4853396534060.814660346593834
70101.2101.770551702991-0.570551702991123
71104.7104.4678505043590.232149495641317
72102.496.73268066724435.66731933275574
7397.795.41655040733212.2834495926679
7498.995.2856307991163.61436920088395
75115108.7089639112796.29103608872081
7697.596.02838454944571.47161545055432
77107.3103.7455113818833.55448861811659
78112.3111.2910508952191.00894910478124
7988.595.7628580394697-7.26285803946966
8092.992.62715091329570.272849086704345
81108.8111.426853465653-2.62685346565308
82112.3114.723562970943-2.42356297094342
83107.3110.864146200195-3.56414620019463
84101.896.76211675253215.03788324746788
85105104.8798021295340.120197870465513
86103.4103.1269137017290.273086298271018
87116.7116.908963999489-0.208963999488548
88103.6106.029781844187-2.4297818441866
89108.8107.5897981482441.21020185175569
90117116.0818368869380.91816311306183
91100.9100.4503313295340.449668670465624
92100.897.99214866783222.80785133216779
93109.7108.719179635620.980820364379551
94121121.870167254496-0.870167254496408
95114.1113.0586751192871.04132488071348
96105.597.14159994604118.35840005395893
97112.5110.38040625732.11959374269985
98113.8112.6336506277321.16634937226811
99115.3114.8492271561730.450772843826548
100120.4116.2017126381534.19828736184748
101111.1107.6414531825093.45854681749125
102120.1112.2657096876897.83429031231101
103106.1107.629781059392-1.52978105939208
10495.992.23558784945683.66441215054322
105119.4114.8171670468594.5828329531413
106117.4116.606915672550.793084327449667
10798.6100.082649811688-1.48264981168839
10899.798.23923153849421.46076846150585
10987.496.0455711624641-8.6455711624641
11090.895.7623933595302-4.96239335953018
111101.3106.21324277637-4.91324277636957
11293.2100.433784809846-7.23378480984639
11395.194.0399849138761.06001508612398
114101.9106.575142168604-4.67514216860382
1158797.0999442489081-10.0999442489081
11686.287.5435371676199-1.34353716761991
117105108.384162202808-3.38416220280804
118104.1108.399973150019-4.2999731500191
11999.2100.591845725022-1.39184572502176
12095.2101.327305936411-6.12730593641124
12192.793.5062156007897-0.80621560078974
12299.398.70113451903550.598865480964536
123113.5114.953794436651-1.45379443665056
124104.7107.611651407331-2.91165140733134
125100.599.59684399570140.903156004298552
126116.2116.574597035299-0.374597035298774
12794.199.4438572380664-5.34385723806641
12894.895.3182285047418-0.518228504741754
129115.1110.2540899078654.84591009213521
130110112.334536386952-2.33453638695176
131108.4107.1737204760741.22627952392603
132103.9101.6621862231112.23781377688928
133102.9102.929454098686-0.0294540986859869
134107.7101.6560282233886.04397177661183
135126.7120.2045388044926.4954611955081
136108.8104.8559242948733.94407570512658
137117.1113.948268441523.15173155847957
138112.2110.4656150040061.73438499599443
13994.795.6009161451453-0.900916145145288
140102.798.9756864716583.72431352834202
141119.1116.4963428265522.603657173448
142110.6110.643660889714-0.0436608897137277
143109.1107.8239027445421.27609725545791
144105.3104.1471862941241.15281370587562
145103.4106.089585217016-2.68958521701647
146103.7110.003175054364-6.30317505436385
147117119.304723605719-2.30472360571876
148101.2105.591358805043-4.39135880504301
149105.4109.10787735946-3.70787735945996
150110.3114.528153143334-4.22815314333428
15197.7103.724921701417-6.02492170141717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 75.5 & 80.368769032759 & -4.86876903275902 \tabularnewline
2 & 83.2 & 87.7598488123558 & -4.55984881235582 \tabularnewline
3 & 94.5 & 96.6325365096047 & -2.13253650960469 \tabularnewline
4 & 83.3 & 81.5748687226516 & 1.72513127734844 \tabularnewline
5 & 92.7 & 94.740179405327 & -2.04017940532701 \tabularnewline
6 & 89.8 & 91.0684772467176 & -1.26847724671757 \tabularnewline
7 & 74.8 & 74.240857731302 & 0.559142268697994 \tabularnewline
8 & 81.5 & 77.7667990658461 & 3.73320093415386 \tabularnewline
9 & 92.8 & 93.0370859674289 & -0.237085967428943 \tabularnewline
10 & 92.8 & 97.4877491421913 & -4.68774914219127 \tabularnewline
11 & 91.7 & 94.1330511913776 & -2.43305119137765 \tabularnewline
12 & 83.5 & 83.9679997710964 & -0.467999771096405 \tabularnewline
13 & 92.8 & 89.8660891775536 & 2.93391082244637 \tabularnewline
14 & 91.3 & 89.4973988894487 & 1.80260111055126 \tabularnewline
15 & 99.5 & 100.229175003241 & -0.729175003241153 \tabularnewline
16 & 87.6 & 86.0157901626926 & 1.58420983730738 \tabularnewline
17 & 95.3 & 94.3509041862438 & 0.949095813756247 \tabularnewline
18 & 98.5 & 94.6734449881071 & 3.82655501189286 \tabularnewline
19 & 80.1 & 87.8689532202763 & -7.76895322027626 \tabularnewline
20 & 84.2 & 84.307346748035 & -0.107346748035005 \tabularnewline
21 & 92.4 & 96.6476595174122 & -4.24765951741214 \tabularnewline
22 & 98 & 105.47568313378 & -7.47568313377964 \tabularnewline
23 & 92.2 & 97.996505561337 & -5.79650556133702 \tabularnewline
24 & 80 & 81.321322058157 & -1.321322058157 \tabularnewline
25 & 88.7 & 89.672492642112 & -0.972492642112019 \tabularnewline
26 & 87.4 & 90.0995910541377 & -2.69959105413774 \tabularnewline
27 & 96.1 & 96.0761126003097 & 0.0238873996903083 \tabularnewline
28 & 94.1 & 94.5494232664123 & -0.44942326641232 \tabularnewline
29 & 91.9 & 92.6042463655975 & -0.704246365597498 \tabularnewline
30 & 93.6 & 94.8991812964364 & -1.29918129643642 \tabularnewline
31 & 83.5 & 88.29222830718 & -4.79222830718004 \tabularnewline
32 & 80.8 & 83.0449144825089 & -2.24491448250895 \tabularnewline
33 & 96.3 & 100.598869309606 & -4.29886930960605 \tabularnewline
34 & 101.5 & 107.279432833275 & -5.77943283327532 \tabularnewline
35 & 91.6 & 93.5714693069367 & -1.97146930693672 \tabularnewline
36 & 84 & 86.4703263810307 & -2.47032638103066 \tabularnewline
37 & 91.8 & 91.6488623739292 & 0.15113762607077 \tabularnewline
38 & 90.4 & 91.0013069048551 & -0.601306904855095 \tabularnewline
39 & 98 & 95.047627602171 & 2.95237239782897 \tabularnewline
40 & 95.5 & 94.4929101720171 & 1.00708982798285 \tabularnewline
41 & 90.5 & 87.8506234678964 & 2.64937653210357 \tabularnewline
42 & 97.1 & 93.8510303367045 & 3.24896966329545 \tabularnewline
43 & 87.9 & 88.6369512243243 & -0.736951224324272 \tabularnewline
44 & 79.8 & 78.1812048934996 & 1.61879510650038 \tabularnewline
45 & 102 & 101.305809249739 & 0.694190750260944 \tabularnewline
46 & 104.3 & 106.202175739542 & -1.90217573954246 \tabularnewline
47 & 92.1 & 90.6929960645 & 1.40700393549996 \tabularnewline
48 & 95.9 & 88.4124842618431 & 7.48751573815694 \tabularnewline
49 & 89.1 & 89.1124534888314 & -0.012453488831446 \tabularnewline
50 & 92.2 & 89.5553447011016 & 2.64465529889837 \tabularnewline
51 & 107.5 & 106.415508485433 & 1.0844915145672 \tabularnewline
52 & 99.7 & 95.5528178418755 & 4.1471821581245 \tabularnewline
53 & 92.2 & 90.783612260566 & 1.41638773943395 \tabularnewline
54 & 108.9 & 106.265782187605 & 2.63421781239457 \tabularnewline
55 & 89.8 & 89.4420526598583 & 0.357947340141729 \tabularnewline
56 & 89.4 & 83.9439643150937 & 5.45603568490633 \tabularnewline
57 & 107.6 & 105.338589494755 & 2.26141050524512 \tabularnewline
58 & 105.6 & 103.863557439881 & 1.7364425601189 \tabularnewline
59 & 100.9 & 98.7459588190568 & 2.15404118094323 \tabularnewline
60 & 102.9 & 94.212520585737 & 8.68747941426303 \tabularnewline
61 & 96.2 & 90.4496068086973 & 5.75039319130274 \tabularnewline
62 & 94.7 & 93.0008635286317 & 1.69913647136831 \tabularnewline
63 & 107.3 & 103.907351987544 & 3.39264801245572 \tabularnewline
64 & 103 & 99.8251472621579 & 3.17485273784206 \tabularnewline
65 & 96.1 & 95.5427541027561 & 0.557245897243858 \tabularnewline
66 & 109.8 & 106.963702586605 & 2.83629741339512 \tabularnewline
67 & 85.4 & 86.6222183499002 & -1.22221834990016 \tabularnewline
68 & 89.9 & 88.6684712289639 & 1.23152877103608 \tabularnewline
69 & 109.3 & 108.485339653406 & 0.814660346593834 \tabularnewline
70 & 101.2 & 101.770551702991 & -0.570551702991123 \tabularnewline
71 & 104.7 & 104.467850504359 & 0.232149495641317 \tabularnewline
72 & 102.4 & 96.7326806672443 & 5.66731933275574 \tabularnewline
73 & 97.7 & 95.4165504073321 & 2.2834495926679 \tabularnewline
74 & 98.9 & 95.285630799116 & 3.61436920088395 \tabularnewline
75 & 115 & 108.708963911279 & 6.29103608872081 \tabularnewline
76 & 97.5 & 96.0283845494457 & 1.47161545055432 \tabularnewline
77 & 107.3 & 103.745511381883 & 3.55448861811659 \tabularnewline
78 & 112.3 & 111.291050895219 & 1.00894910478124 \tabularnewline
79 & 88.5 & 95.7628580394697 & -7.26285803946966 \tabularnewline
80 & 92.9 & 92.6271509132957 & 0.272849086704345 \tabularnewline
81 & 108.8 & 111.426853465653 & -2.62685346565308 \tabularnewline
82 & 112.3 & 114.723562970943 & -2.42356297094342 \tabularnewline
83 & 107.3 & 110.864146200195 & -3.56414620019463 \tabularnewline
84 & 101.8 & 96.7621167525321 & 5.03788324746788 \tabularnewline
85 & 105 & 104.879802129534 & 0.120197870465513 \tabularnewline
86 & 103.4 & 103.126913701729 & 0.273086298271018 \tabularnewline
87 & 116.7 & 116.908963999489 & -0.208963999488548 \tabularnewline
88 & 103.6 & 106.029781844187 & -2.4297818441866 \tabularnewline
89 & 108.8 & 107.589798148244 & 1.21020185175569 \tabularnewline
90 & 117 & 116.081836886938 & 0.91816311306183 \tabularnewline
91 & 100.9 & 100.450331329534 & 0.449668670465624 \tabularnewline
92 & 100.8 & 97.9921486678322 & 2.80785133216779 \tabularnewline
93 & 109.7 & 108.71917963562 & 0.980820364379551 \tabularnewline
94 & 121 & 121.870167254496 & -0.870167254496408 \tabularnewline
95 & 114.1 & 113.058675119287 & 1.04132488071348 \tabularnewline
96 & 105.5 & 97.1415999460411 & 8.35840005395893 \tabularnewline
97 & 112.5 & 110.3804062573 & 2.11959374269985 \tabularnewline
98 & 113.8 & 112.633650627732 & 1.16634937226811 \tabularnewline
99 & 115.3 & 114.849227156173 & 0.450772843826548 \tabularnewline
100 & 120.4 & 116.201712638153 & 4.19828736184748 \tabularnewline
101 & 111.1 & 107.641453182509 & 3.45854681749125 \tabularnewline
102 & 120.1 & 112.265709687689 & 7.83429031231101 \tabularnewline
103 & 106.1 & 107.629781059392 & -1.52978105939208 \tabularnewline
104 & 95.9 & 92.2355878494568 & 3.66441215054322 \tabularnewline
105 & 119.4 & 114.817167046859 & 4.5828329531413 \tabularnewline
106 & 117.4 & 116.60691567255 & 0.793084327449667 \tabularnewline
107 & 98.6 & 100.082649811688 & -1.48264981168839 \tabularnewline
108 & 99.7 & 98.2392315384942 & 1.46076846150585 \tabularnewline
109 & 87.4 & 96.0455711624641 & -8.6455711624641 \tabularnewline
110 & 90.8 & 95.7623933595302 & -4.96239335953018 \tabularnewline
111 & 101.3 & 106.21324277637 & -4.91324277636957 \tabularnewline
112 & 93.2 & 100.433784809846 & -7.23378480984639 \tabularnewline
113 & 95.1 & 94.039984913876 & 1.06001508612398 \tabularnewline
114 & 101.9 & 106.575142168604 & -4.67514216860382 \tabularnewline
115 & 87 & 97.0999442489081 & -10.0999442489081 \tabularnewline
116 & 86.2 & 87.5435371676199 & -1.34353716761991 \tabularnewline
117 & 105 & 108.384162202808 & -3.38416220280804 \tabularnewline
118 & 104.1 & 108.399973150019 & -4.2999731500191 \tabularnewline
119 & 99.2 & 100.591845725022 & -1.39184572502176 \tabularnewline
120 & 95.2 & 101.327305936411 & -6.12730593641124 \tabularnewline
121 & 92.7 & 93.5062156007897 & -0.80621560078974 \tabularnewline
122 & 99.3 & 98.7011345190355 & 0.598865480964536 \tabularnewline
123 & 113.5 & 114.953794436651 & -1.45379443665056 \tabularnewline
124 & 104.7 & 107.611651407331 & -2.91165140733134 \tabularnewline
125 & 100.5 & 99.5968439957014 & 0.903156004298552 \tabularnewline
126 & 116.2 & 116.574597035299 & -0.374597035298774 \tabularnewline
127 & 94.1 & 99.4438572380664 & -5.34385723806641 \tabularnewline
128 & 94.8 & 95.3182285047418 & -0.518228504741754 \tabularnewline
129 & 115.1 & 110.254089907865 & 4.84591009213521 \tabularnewline
130 & 110 & 112.334536386952 & -2.33453638695176 \tabularnewline
131 & 108.4 & 107.173720476074 & 1.22627952392603 \tabularnewline
132 & 103.9 & 101.662186223111 & 2.23781377688928 \tabularnewline
133 & 102.9 & 102.929454098686 & -0.0294540986859869 \tabularnewline
134 & 107.7 & 101.656028223388 & 6.04397177661183 \tabularnewline
135 & 126.7 & 120.204538804492 & 6.4954611955081 \tabularnewline
136 & 108.8 & 104.855924294873 & 3.94407570512658 \tabularnewline
137 & 117.1 & 113.94826844152 & 3.15173155847957 \tabularnewline
138 & 112.2 & 110.465615004006 & 1.73438499599443 \tabularnewline
139 & 94.7 & 95.6009161451453 & -0.900916145145288 \tabularnewline
140 & 102.7 & 98.975686471658 & 3.72431352834202 \tabularnewline
141 & 119.1 & 116.496342826552 & 2.603657173448 \tabularnewline
142 & 110.6 & 110.643660889714 & -0.0436608897137277 \tabularnewline
143 & 109.1 & 107.823902744542 & 1.27609725545791 \tabularnewline
144 & 105.3 & 104.147186294124 & 1.15281370587562 \tabularnewline
145 & 103.4 & 106.089585217016 & -2.68958521701647 \tabularnewline
146 & 103.7 & 110.003175054364 & -6.30317505436385 \tabularnewline
147 & 117 & 119.304723605719 & -2.30472360571876 \tabularnewline
148 & 101.2 & 105.591358805043 & -4.39135880504301 \tabularnewline
149 & 105.4 & 109.10787735946 & -3.70787735945996 \tabularnewline
150 & 110.3 & 114.528153143334 & -4.22815314333428 \tabularnewline
151 & 97.7 & 103.724921701417 & -6.02492170141717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&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]75.5[/C][C]80.368769032759[/C][C]-4.86876903275902[/C][/ROW]
[ROW][C]2[/C][C]83.2[/C][C]87.7598488123558[/C][C]-4.55984881235582[/C][/ROW]
[ROW][C]3[/C][C]94.5[/C][C]96.6325365096047[/C][C]-2.13253650960469[/C][/ROW]
[ROW][C]4[/C][C]83.3[/C][C]81.5748687226516[/C][C]1.72513127734844[/C][/ROW]
[ROW][C]5[/C][C]92.7[/C][C]94.740179405327[/C][C]-2.04017940532701[/C][/ROW]
[ROW][C]6[/C][C]89.8[/C][C]91.0684772467176[/C][C]-1.26847724671757[/C][/ROW]
[ROW][C]7[/C][C]74.8[/C][C]74.240857731302[/C][C]0.559142268697994[/C][/ROW]
[ROW][C]8[/C][C]81.5[/C][C]77.7667990658461[/C][C]3.73320093415386[/C][/ROW]
[ROW][C]9[/C][C]92.8[/C][C]93.0370859674289[/C][C]-0.237085967428943[/C][/ROW]
[ROW][C]10[/C][C]92.8[/C][C]97.4877491421913[/C][C]-4.68774914219127[/C][/ROW]
[ROW][C]11[/C][C]91.7[/C][C]94.1330511913776[/C][C]-2.43305119137765[/C][/ROW]
[ROW][C]12[/C][C]83.5[/C][C]83.9679997710964[/C][C]-0.467999771096405[/C][/ROW]
[ROW][C]13[/C][C]92.8[/C][C]89.8660891775536[/C][C]2.93391082244637[/C][/ROW]
[ROW][C]14[/C][C]91.3[/C][C]89.4973988894487[/C][C]1.80260111055126[/C][/ROW]
[ROW][C]15[/C][C]99.5[/C][C]100.229175003241[/C][C]-0.729175003241153[/C][/ROW]
[ROW][C]16[/C][C]87.6[/C][C]86.0157901626926[/C][C]1.58420983730738[/C][/ROW]
[ROW][C]17[/C][C]95.3[/C][C]94.3509041862438[/C][C]0.949095813756247[/C][/ROW]
[ROW][C]18[/C][C]98.5[/C][C]94.6734449881071[/C][C]3.82655501189286[/C][/ROW]
[ROW][C]19[/C][C]80.1[/C][C]87.8689532202763[/C][C]-7.76895322027626[/C][/ROW]
[ROW][C]20[/C][C]84.2[/C][C]84.307346748035[/C][C]-0.107346748035005[/C][/ROW]
[ROW][C]21[/C][C]92.4[/C][C]96.6476595174122[/C][C]-4.24765951741214[/C][/ROW]
[ROW][C]22[/C][C]98[/C][C]105.47568313378[/C][C]-7.47568313377964[/C][/ROW]
[ROW][C]23[/C][C]92.2[/C][C]97.996505561337[/C][C]-5.79650556133702[/C][/ROW]
[ROW][C]24[/C][C]80[/C][C]81.321322058157[/C][C]-1.321322058157[/C][/ROW]
[ROW][C]25[/C][C]88.7[/C][C]89.672492642112[/C][C]-0.972492642112019[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]90.0995910541377[/C][C]-2.69959105413774[/C][/ROW]
[ROW][C]27[/C][C]96.1[/C][C]96.0761126003097[/C][C]0.0238873996903083[/C][/ROW]
[ROW][C]28[/C][C]94.1[/C][C]94.5494232664123[/C][C]-0.44942326641232[/C][/ROW]
[ROW][C]29[/C][C]91.9[/C][C]92.6042463655975[/C][C]-0.704246365597498[/C][/ROW]
[ROW][C]30[/C][C]93.6[/C][C]94.8991812964364[/C][C]-1.29918129643642[/C][/ROW]
[ROW][C]31[/C][C]83.5[/C][C]88.29222830718[/C][C]-4.79222830718004[/C][/ROW]
[ROW][C]32[/C][C]80.8[/C][C]83.0449144825089[/C][C]-2.24491448250895[/C][/ROW]
[ROW][C]33[/C][C]96.3[/C][C]100.598869309606[/C][C]-4.29886930960605[/C][/ROW]
[ROW][C]34[/C][C]101.5[/C][C]107.279432833275[/C][C]-5.77943283327532[/C][/ROW]
[ROW][C]35[/C][C]91.6[/C][C]93.5714693069367[/C][C]-1.97146930693672[/C][/ROW]
[ROW][C]36[/C][C]84[/C][C]86.4703263810307[/C][C]-2.47032638103066[/C][/ROW]
[ROW][C]37[/C][C]91.8[/C][C]91.6488623739292[/C][C]0.15113762607077[/C][/ROW]
[ROW][C]38[/C][C]90.4[/C][C]91.0013069048551[/C][C]-0.601306904855095[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]95.047627602171[/C][C]2.95237239782897[/C][/ROW]
[ROW][C]40[/C][C]95.5[/C][C]94.4929101720171[/C][C]1.00708982798285[/C][/ROW]
[ROW][C]41[/C][C]90.5[/C][C]87.8506234678964[/C][C]2.64937653210357[/C][/ROW]
[ROW][C]42[/C][C]97.1[/C][C]93.8510303367045[/C][C]3.24896966329545[/C][/ROW]
[ROW][C]43[/C][C]87.9[/C][C]88.6369512243243[/C][C]-0.736951224324272[/C][/ROW]
[ROW][C]44[/C][C]79.8[/C][C]78.1812048934996[/C][C]1.61879510650038[/C][/ROW]
[ROW][C]45[/C][C]102[/C][C]101.305809249739[/C][C]0.694190750260944[/C][/ROW]
[ROW][C]46[/C][C]104.3[/C][C]106.202175739542[/C][C]-1.90217573954246[/C][/ROW]
[ROW][C]47[/C][C]92.1[/C][C]90.6929960645[/C][C]1.40700393549996[/C][/ROW]
[ROW][C]48[/C][C]95.9[/C][C]88.4124842618431[/C][C]7.48751573815694[/C][/ROW]
[ROW][C]49[/C][C]89.1[/C][C]89.1124534888314[/C][C]-0.012453488831446[/C][/ROW]
[ROW][C]50[/C][C]92.2[/C][C]89.5553447011016[/C][C]2.64465529889837[/C][/ROW]
[ROW][C]51[/C][C]107.5[/C][C]106.415508485433[/C][C]1.0844915145672[/C][/ROW]
[ROW][C]52[/C][C]99.7[/C][C]95.5528178418755[/C][C]4.1471821581245[/C][/ROW]
[ROW][C]53[/C][C]92.2[/C][C]90.783612260566[/C][C]1.41638773943395[/C][/ROW]
[ROW][C]54[/C][C]108.9[/C][C]106.265782187605[/C][C]2.63421781239457[/C][/ROW]
[ROW][C]55[/C][C]89.8[/C][C]89.4420526598583[/C][C]0.357947340141729[/C][/ROW]
[ROW][C]56[/C][C]89.4[/C][C]83.9439643150937[/C][C]5.45603568490633[/C][/ROW]
[ROW][C]57[/C][C]107.6[/C][C]105.338589494755[/C][C]2.26141050524512[/C][/ROW]
[ROW][C]58[/C][C]105.6[/C][C]103.863557439881[/C][C]1.7364425601189[/C][/ROW]
[ROW][C]59[/C][C]100.9[/C][C]98.7459588190568[/C][C]2.15404118094323[/C][/ROW]
[ROW][C]60[/C][C]102.9[/C][C]94.212520585737[/C][C]8.68747941426303[/C][/ROW]
[ROW][C]61[/C][C]96.2[/C][C]90.4496068086973[/C][C]5.75039319130274[/C][/ROW]
[ROW][C]62[/C][C]94.7[/C][C]93.0008635286317[/C][C]1.69913647136831[/C][/ROW]
[ROW][C]63[/C][C]107.3[/C][C]103.907351987544[/C][C]3.39264801245572[/C][/ROW]
[ROW][C]64[/C][C]103[/C][C]99.8251472621579[/C][C]3.17485273784206[/C][/ROW]
[ROW][C]65[/C][C]96.1[/C][C]95.5427541027561[/C][C]0.557245897243858[/C][/ROW]
[ROW][C]66[/C][C]109.8[/C][C]106.963702586605[/C][C]2.83629741339512[/C][/ROW]
[ROW][C]67[/C][C]85.4[/C][C]86.6222183499002[/C][C]-1.22221834990016[/C][/ROW]
[ROW][C]68[/C][C]89.9[/C][C]88.6684712289639[/C][C]1.23152877103608[/C][/ROW]
[ROW][C]69[/C][C]109.3[/C][C]108.485339653406[/C][C]0.814660346593834[/C][/ROW]
[ROW][C]70[/C][C]101.2[/C][C]101.770551702991[/C][C]-0.570551702991123[/C][/ROW]
[ROW][C]71[/C][C]104.7[/C][C]104.467850504359[/C][C]0.232149495641317[/C][/ROW]
[ROW][C]72[/C][C]102.4[/C][C]96.7326806672443[/C][C]5.66731933275574[/C][/ROW]
[ROW][C]73[/C][C]97.7[/C][C]95.4165504073321[/C][C]2.2834495926679[/C][/ROW]
[ROW][C]74[/C][C]98.9[/C][C]95.285630799116[/C][C]3.61436920088395[/C][/ROW]
[ROW][C]75[/C][C]115[/C][C]108.708963911279[/C][C]6.29103608872081[/C][/ROW]
[ROW][C]76[/C][C]97.5[/C][C]96.0283845494457[/C][C]1.47161545055432[/C][/ROW]
[ROW][C]77[/C][C]107.3[/C][C]103.745511381883[/C][C]3.55448861811659[/C][/ROW]
[ROW][C]78[/C][C]112.3[/C][C]111.291050895219[/C][C]1.00894910478124[/C][/ROW]
[ROW][C]79[/C][C]88.5[/C][C]95.7628580394697[/C][C]-7.26285803946966[/C][/ROW]
[ROW][C]80[/C][C]92.9[/C][C]92.6271509132957[/C][C]0.272849086704345[/C][/ROW]
[ROW][C]81[/C][C]108.8[/C][C]111.426853465653[/C][C]-2.62685346565308[/C][/ROW]
[ROW][C]82[/C][C]112.3[/C][C]114.723562970943[/C][C]-2.42356297094342[/C][/ROW]
[ROW][C]83[/C][C]107.3[/C][C]110.864146200195[/C][C]-3.56414620019463[/C][/ROW]
[ROW][C]84[/C][C]101.8[/C][C]96.7621167525321[/C][C]5.03788324746788[/C][/ROW]
[ROW][C]85[/C][C]105[/C][C]104.879802129534[/C][C]0.120197870465513[/C][/ROW]
[ROW][C]86[/C][C]103.4[/C][C]103.126913701729[/C][C]0.273086298271018[/C][/ROW]
[ROW][C]87[/C][C]116.7[/C][C]116.908963999489[/C][C]-0.208963999488548[/C][/ROW]
[ROW][C]88[/C][C]103.6[/C][C]106.029781844187[/C][C]-2.4297818441866[/C][/ROW]
[ROW][C]89[/C][C]108.8[/C][C]107.589798148244[/C][C]1.21020185175569[/C][/ROW]
[ROW][C]90[/C][C]117[/C][C]116.081836886938[/C][C]0.91816311306183[/C][/ROW]
[ROW][C]91[/C][C]100.9[/C][C]100.450331329534[/C][C]0.449668670465624[/C][/ROW]
[ROW][C]92[/C][C]100.8[/C][C]97.9921486678322[/C][C]2.80785133216779[/C][/ROW]
[ROW][C]93[/C][C]109.7[/C][C]108.71917963562[/C][C]0.980820364379551[/C][/ROW]
[ROW][C]94[/C][C]121[/C][C]121.870167254496[/C][C]-0.870167254496408[/C][/ROW]
[ROW][C]95[/C][C]114.1[/C][C]113.058675119287[/C][C]1.04132488071348[/C][/ROW]
[ROW][C]96[/C][C]105.5[/C][C]97.1415999460411[/C][C]8.35840005395893[/C][/ROW]
[ROW][C]97[/C][C]112.5[/C][C]110.3804062573[/C][C]2.11959374269985[/C][/ROW]
[ROW][C]98[/C][C]113.8[/C][C]112.633650627732[/C][C]1.16634937226811[/C][/ROW]
[ROW][C]99[/C][C]115.3[/C][C]114.849227156173[/C][C]0.450772843826548[/C][/ROW]
[ROW][C]100[/C][C]120.4[/C][C]116.201712638153[/C][C]4.19828736184748[/C][/ROW]
[ROW][C]101[/C][C]111.1[/C][C]107.641453182509[/C][C]3.45854681749125[/C][/ROW]
[ROW][C]102[/C][C]120.1[/C][C]112.265709687689[/C][C]7.83429031231101[/C][/ROW]
[ROW][C]103[/C][C]106.1[/C][C]107.629781059392[/C][C]-1.52978105939208[/C][/ROW]
[ROW][C]104[/C][C]95.9[/C][C]92.2355878494568[/C][C]3.66441215054322[/C][/ROW]
[ROW][C]105[/C][C]119.4[/C][C]114.817167046859[/C][C]4.5828329531413[/C][/ROW]
[ROW][C]106[/C][C]117.4[/C][C]116.60691567255[/C][C]0.793084327449667[/C][/ROW]
[ROW][C]107[/C][C]98.6[/C][C]100.082649811688[/C][C]-1.48264981168839[/C][/ROW]
[ROW][C]108[/C][C]99.7[/C][C]98.2392315384942[/C][C]1.46076846150585[/C][/ROW]
[ROW][C]109[/C][C]87.4[/C][C]96.0455711624641[/C][C]-8.6455711624641[/C][/ROW]
[ROW][C]110[/C][C]90.8[/C][C]95.7623933595302[/C][C]-4.96239335953018[/C][/ROW]
[ROW][C]111[/C][C]101.3[/C][C]106.21324277637[/C][C]-4.91324277636957[/C][/ROW]
[ROW][C]112[/C][C]93.2[/C][C]100.433784809846[/C][C]-7.23378480984639[/C][/ROW]
[ROW][C]113[/C][C]95.1[/C][C]94.039984913876[/C][C]1.06001508612398[/C][/ROW]
[ROW][C]114[/C][C]101.9[/C][C]106.575142168604[/C][C]-4.67514216860382[/C][/ROW]
[ROW][C]115[/C][C]87[/C][C]97.0999442489081[/C][C]-10.0999442489081[/C][/ROW]
[ROW][C]116[/C][C]86.2[/C][C]87.5435371676199[/C][C]-1.34353716761991[/C][/ROW]
[ROW][C]117[/C][C]105[/C][C]108.384162202808[/C][C]-3.38416220280804[/C][/ROW]
[ROW][C]118[/C][C]104.1[/C][C]108.399973150019[/C][C]-4.2999731500191[/C][/ROW]
[ROW][C]119[/C][C]99.2[/C][C]100.591845725022[/C][C]-1.39184572502176[/C][/ROW]
[ROW][C]120[/C][C]95.2[/C][C]101.327305936411[/C][C]-6.12730593641124[/C][/ROW]
[ROW][C]121[/C][C]92.7[/C][C]93.5062156007897[/C][C]-0.80621560078974[/C][/ROW]
[ROW][C]122[/C][C]99.3[/C][C]98.7011345190355[/C][C]0.598865480964536[/C][/ROW]
[ROW][C]123[/C][C]113.5[/C][C]114.953794436651[/C][C]-1.45379443665056[/C][/ROW]
[ROW][C]124[/C][C]104.7[/C][C]107.611651407331[/C][C]-2.91165140733134[/C][/ROW]
[ROW][C]125[/C][C]100.5[/C][C]99.5968439957014[/C][C]0.903156004298552[/C][/ROW]
[ROW][C]126[/C][C]116.2[/C][C]116.574597035299[/C][C]-0.374597035298774[/C][/ROW]
[ROW][C]127[/C][C]94.1[/C][C]99.4438572380664[/C][C]-5.34385723806641[/C][/ROW]
[ROW][C]128[/C][C]94.8[/C][C]95.3182285047418[/C][C]-0.518228504741754[/C][/ROW]
[ROW][C]129[/C][C]115.1[/C][C]110.254089907865[/C][C]4.84591009213521[/C][/ROW]
[ROW][C]130[/C][C]110[/C][C]112.334536386952[/C][C]-2.33453638695176[/C][/ROW]
[ROW][C]131[/C][C]108.4[/C][C]107.173720476074[/C][C]1.22627952392603[/C][/ROW]
[ROW][C]132[/C][C]103.9[/C][C]101.662186223111[/C][C]2.23781377688928[/C][/ROW]
[ROW][C]133[/C][C]102.9[/C][C]102.929454098686[/C][C]-0.0294540986859869[/C][/ROW]
[ROW][C]134[/C][C]107.7[/C][C]101.656028223388[/C][C]6.04397177661183[/C][/ROW]
[ROW][C]135[/C][C]126.7[/C][C]120.204538804492[/C][C]6.4954611955081[/C][/ROW]
[ROW][C]136[/C][C]108.8[/C][C]104.855924294873[/C][C]3.94407570512658[/C][/ROW]
[ROW][C]137[/C][C]117.1[/C][C]113.94826844152[/C][C]3.15173155847957[/C][/ROW]
[ROW][C]138[/C][C]112.2[/C][C]110.465615004006[/C][C]1.73438499599443[/C][/ROW]
[ROW][C]139[/C][C]94.7[/C][C]95.6009161451453[/C][C]-0.900916145145288[/C][/ROW]
[ROW][C]140[/C][C]102.7[/C][C]98.975686471658[/C][C]3.72431352834202[/C][/ROW]
[ROW][C]141[/C][C]119.1[/C][C]116.496342826552[/C][C]2.603657173448[/C][/ROW]
[ROW][C]142[/C][C]110.6[/C][C]110.643660889714[/C][C]-0.0436608897137277[/C][/ROW]
[ROW][C]143[/C][C]109.1[/C][C]107.823902744542[/C][C]1.27609725545791[/C][/ROW]
[ROW][C]144[/C][C]105.3[/C][C]104.147186294124[/C][C]1.15281370587562[/C][/ROW]
[ROW][C]145[/C][C]103.4[/C][C]106.089585217016[/C][C]-2.68958521701647[/C][/ROW]
[ROW][C]146[/C][C]103.7[/C][C]110.003175054364[/C][C]-6.30317505436385[/C][/ROW]
[ROW][C]147[/C][C]117[/C][C]119.304723605719[/C][C]-2.30472360571876[/C][/ROW]
[ROW][C]148[/C][C]101.2[/C][C]105.591358805043[/C][C]-4.39135880504301[/C][/ROW]
[ROW][C]149[/C][C]105.4[/C][C]109.10787735946[/C][C]-3.70787735945996[/C][/ROW]
[ROW][C]150[/C][C]110.3[/C][C]114.528153143334[/C][C]-4.22815314333428[/C][/ROW]
[ROW][C]151[/C][C]97.7[/C][C]103.724921701417[/C][C]-6.02492170141717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.580.368769032759-4.86876903275902
283.287.7598488123558-4.55984881235582
394.596.6325365096047-2.13253650960469
483.381.57486872265161.72513127734844
592.794.740179405327-2.04017940532701
689.891.0684772467176-1.26847724671757
774.874.2408577313020.559142268697994
881.577.76679906584613.73320093415386
992.893.0370859674289-0.237085967428943
1092.897.4877491421913-4.68774914219127
1191.794.1330511913776-2.43305119137765
1283.583.9679997710964-0.467999771096405
1392.889.86608917755362.93391082244637
1491.389.49739888944871.80260111055126
1599.5100.229175003241-0.729175003241153
1687.686.01579016269261.58420983730738
1795.394.35090418624380.949095813756247
1898.594.67344498810713.82655501189286
1980.187.8689532202763-7.76895322027626
2084.284.307346748035-0.107346748035005
2192.496.6476595174122-4.24765951741214
2298105.47568313378-7.47568313377964
2392.297.996505561337-5.79650556133702
248081.321322058157-1.321322058157
2588.789.672492642112-0.972492642112019
2687.490.0995910541377-2.69959105413774
2796.196.07611260030970.0238873996903083
2894.194.5494232664123-0.44942326641232
2991.992.6042463655975-0.704246365597498
3093.694.8991812964364-1.29918129643642
3183.588.29222830718-4.79222830718004
3280.883.0449144825089-2.24491448250895
3396.3100.598869309606-4.29886930960605
34101.5107.279432833275-5.77943283327532
3591.693.5714693069367-1.97146930693672
368486.4703263810307-2.47032638103066
3791.891.64886237392920.15113762607077
3890.491.0013069048551-0.601306904855095
399895.0476276021712.95237239782897
4095.594.49291017201711.00708982798285
4190.587.85062346789642.64937653210357
4297.193.85103033670453.24896966329545
4387.988.6369512243243-0.736951224324272
4479.878.18120489349961.61879510650038
45102101.3058092497390.694190750260944
46104.3106.202175739542-1.90217573954246
4792.190.69299606451.40700393549996
4895.988.41248426184317.48751573815694
4989.189.1124534888314-0.012453488831446
5092.289.55534470110162.64465529889837
51107.5106.4155084854331.0844915145672
5299.795.55281784187554.1471821581245
5392.290.7836122605661.41638773943395
54108.9106.2657821876052.63421781239457
5589.889.44205265985830.357947340141729
5689.483.94396431509375.45603568490633
57107.6105.3385894947552.26141050524512
58105.6103.8635574398811.7364425601189
59100.998.74595881905682.15404118094323
60102.994.2125205857378.68747941426303
6196.290.44960680869735.75039319130274
6294.793.00086352863171.69913647136831
63107.3103.9073519875443.39264801245572
6410399.82514726215793.17485273784206
6596.195.54275410275610.557245897243858
66109.8106.9637025866052.83629741339512
6785.486.6222183499002-1.22221834990016
6889.988.66847122896391.23152877103608
69109.3108.4853396534060.814660346593834
70101.2101.770551702991-0.570551702991123
71104.7104.4678505043590.232149495641317
72102.496.73268066724435.66731933275574
7397.795.41655040733212.2834495926679
7498.995.2856307991163.61436920088395
75115108.7089639112796.29103608872081
7697.596.02838454944571.47161545055432
77107.3103.7455113818833.55448861811659
78112.3111.2910508952191.00894910478124
7988.595.7628580394697-7.26285803946966
8092.992.62715091329570.272849086704345
81108.8111.426853465653-2.62685346565308
82112.3114.723562970943-2.42356297094342
83107.3110.864146200195-3.56414620019463
84101.896.76211675253215.03788324746788
85105104.8798021295340.120197870465513
86103.4103.1269137017290.273086298271018
87116.7116.908963999489-0.208963999488548
88103.6106.029781844187-2.4297818441866
89108.8107.5897981482441.21020185175569
90117116.0818368869380.91816311306183
91100.9100.4503313295340.449668670465624
92100.897.99214866783222.80785133216779
93109.7108.719179635620.980820364379551
94121121.870167254496-0.870167254496408
95114.1113.0586751192871.04132488071348
96105.597.14159994604118.35840005395893
97112.5110.38040625732.11959374269985
98113.8112.6336506277321.16634937226811
99115.3114.8492271561730.450772843826548
100120.4116.2017126381534.19828736184748
101111.1107.6414531825093.45854681749125
102120.1112.2657096876897.83429031231101
103106.1107.629781059392-1.52978105939208
10495.992.23558784945683.66441215054322
105119.4114.8171670468594.5828329531413
106117.4116.606915672550.793084327449667
10798.6100.082649811688-1.48264981168839
10899.798.23923153849421.46076846150585
10987.496.0455711624641-8.6455711624641
11090.895.7623933595302-4.96239335953018
111101.3106.21324277637-4.91324277636957
11293.2100.433784809846-7.23378480984639
11395.194.0399849138761.06001508612398
114101.9106.575142168604-4.67514216860382
1158797.0999442489081-10.0999442489081
11686.287.5435371676199-1.34353716761991
117105108.384162202808-3.38416220280804
118104.1108.399973150019-4.2999731500191
11999.2100.591845725022-1.39184572502176
12095.2101.327305936411-6.12730593641124
12192.793.5062156007897-0.80621560078974
12299.398.70113451903550.598865480964536
123113.5114.953794436651-1.45379443665056
124104.7107.611651407331-2.91165140733134
125100.599.59684399570140.903156004298552
126116.2116.574597035299-0.374597035298774
12794.199.4438572380664-5.34385723806641
12894.895.3182285047418-0.518228504741754
129115.1110.2540899078654.84591009213521
130110112.334536386952-2.33453638695176
131108.4107.1737204760741.22627952392603
132103.9101.6621862231112.23781377688928
133102.9102.929454098686-0.0294540986859869
134107.7101.6560282233886.04397177661183
135126.7120.2045388044926.4954611955081
136108.8104.8559242948733.94407570512658
137117.1113.948268441523.15173155847957
138112.2110.4656150040061.73438499599443
13994.795.6009161451453-0.900916145145288
140102.798.9756864716583.72431352834202
141119.1116.4963428265522.603657173448
142110.6110.643660889714-0.0436608897137277
143109.1107.8239027445421.27609725545791
144105.3104.1471862941241.15281370587562
145103.4106.089585217016-2.68958521701647
146103.7110.003175054364-6.30317505436385
147117119.304723605719-2.30472360571876
148101.2105.591358805043-4.39135880504301
149105.4109.10787735946-3.70787735945996
150110.3114.528153143334-4.22815314333428
15197.7103.724921701417-6.02492170141717







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3030683196391890.6061366392783770.696931680360811
130.1619568693550830.3239137387101660.838043130644917
140.08111220088095260.1622244017619050.918887799119047
150.06072064092871940.1214412818574390.939279359071281
160.03952935700675210.07905871401350430.960470642993248
170.03369084490977480.06738168981954970.966309155090225
180.02102111030331090.04204222060662180.978978889696689
190.5001032021661750.999793595667650.499896797833825
200.4392692014100240.8785384028200480.560730798589976
210.4514195767880410.9028391535760820.548580423211959
220.4695169929768640.9390339859537280.530483007023136
230.4879792917939320.9759585835878640.512020708206068
240.4608329864298390.9216659728596790.539167013570161
250.3854342896161020.7708685792322040.614565710383898
260.3593676273339010.7187352546678030.640632372666099
270.2987081077419840.5974162154839680.701291892258016
280.2431076156253880.4862152312507750.756892384374612
290.1994651125273820.3989302250547630.800534887472618
300.1707000875784680.3414001751569360.829299912421532
310.1608979372200820.3217958744401650.839102062779918
320.1452099329570080.2904198659140160.854790067042992
330.1309774551123160.2619549102246320.869022544887684
340.1306951720290960.2613903440581930.869304827970904
350.1232202013134320.2464404026268640.876779798686568
360.1260873758543750.252174751708750.873912624145625
370.1030141695947650.2060283391895310.896985830405235
380.08148030944500220.1629606188900040.918519690554998
390.0761298309486450.152259661897290.923870169051355
400.05875967310662610.1175193462132520.941240326893374
410.04566954513363320.09133909026726650.954330454866367
420.03775217351338850.07550434702677690.962247826486612
430.02741643676103290.05483287352206590.972583563238967
440.02032365620421470.04064731240842930.979676343795785
450.02438450212907350.04876900425814690.975615497870926
460.02146483781781580.04292967563563160.978535162182184
470.0211295832002220.04225916640044390.978870416799778
480.05595924230179570.1119184846035910.944040757698204
490.04768732373398170.09537464746796340.952312676266018
500.03540355009742570.07080710019485150.964596449902574
510.02618535924323450.05237071848646890.973814640756765
520.0191906249155460.0383812498310920.980809375084454
530.01737785060600220.03475570121200440.982622149393998
540.01250544567586110.02501089135172230.987494554324139
550.009429134745459380.01885826949091880.990570865254541
560.009123929212304020.0182478584246080.990876070787696
570.007618146875336290.01523629375067260.992381853124664
580.005675638930995510.0113512778619910.994324361069004
590.004128086782143830.008256173564287660.995871913217856
600.009984089904579860.01996817980915970.99001591009542
610.008453295983901170.01690659196780230.991546704016099
620.006032303567816780.01206460713563360.993967696432183
630.004257653741101960.008515307482203930.995742346258898
640.003009797934691250.006019595869382490.996990202065309
650.003359072305148950.006718144610297910.996640927694851
660.002295866227397360.004591732454794720.997704133772603
670.002362942387370320.004725884774740630.99763705761263
680.00176844904386120.00353689808772240.998231550956139
690.001192575499177950.00238515099835590.998807424500822
700.0009094872588531230.001818974517706250.999090512741147
710.0006223926908021410.001244785381604280.999377607309198
720.0006035370883709450.001207074176741890.999396462911629
730.0004795487824839060.0009590975649678130.999520451217516
740.0003724415735567360.0007448831471134720.999627558426443
750.0008412457214991390.001682491442998280.999158754278501
760.000684243558504250.00136848711700850.999315756441496
770.0005729043949704610.001145808789940920.99942709560503
780.0003737474464103680.0007474948928207360.99962625255359
790.002906461981039470.005812923962078940.997093538018961
800.002046849036592980.004093698073185950.997953150963407
810.00197103728253730.003942074565074610.998028962717463
820.001810595791439590.003621191582879180.99818940420856
830.002246550723599840.004493101447199680.9977534492764
840.002736174514785360.005472349029570720.997263825485215
850.002840712509319720.005681425018639450.99715928749068
860.0020102749369780.004020549873955990.997989725063022
870.001412593879440270.002825187758880530.99858740612056
880.001449303894319360.002898607788638720.998550696105681
890.0009698149722462620.001939629944492520.999030185027754
900.0006760928542067650.001352185708413530.999323907145793
910.0004667925883664620.0009335851767329240.999533207411634
920.0003587505188971580.0007175010377943160.999641249481103
930.0002644748661129540.0005289497322259080.999735525133887
940.0002362659563528250.0004725319127056490.999763734043647
950.0001667674674570030.0003335349349140070.999833232532543
960.0006764036121854860.001352807224370970.999323596387815
970.000737238027341050.00147447605468210.999262761972659
980.0006428030040818170.001285606008163630.999357196995918
990.0007896237319136050.001579247463827210.999210376268086
1000.0007204211418442150.001440842283688430.999279578858156
1010.0007213983774048720.001442796754809740.999278601622595
1020.001535717172247370.003071434344494740.998464282827753
1030.00160218485516410.00320436971032820.998397815144836
1040.002251408103264810.004502816206529610.997748591896735
1050.002274043949469630.004548087898939250.99772595605053
1060.002063163395619840.004126326791239680.99793683660438
1070.002670006269847720.005340012539695440.997329993730152
1080.003495829503176850.00699165900635370.996504170496823
1090.04880319266719480.09760638533438970.951196807332805
1100.07227797807963960.1445559561592790.92772202192036
1110.09529237540923210.1905847508184640.904707624590768
1120.1568329615982520.3136659231965040.843167038401748
1130.1714174813472880.3428349626945750.828582518652712
1140.179209518538260.3584190370765190.82079048146174
1150.4536214804985240.9072429609970470.546378519501476
1160.4000910242839870.8001820485679730.599908975716013
1170.4140103591273860.8280207182547720.585989640872614
1180.5136358735132270.9727282529735460.486364126486773
1190.461997167943580.923994335887160.53800283205642
1200.6253998093006880.7492003813986230.374600190699312
1210.575432181203250.8491356375934990.424567818796749
1220.507307571607170.9853848567856610.492692428392831
1230.5463422460503630.9073155078992730.453657753949637
1240.5718574393572540.8562851212854920.428142560642746
1250.5319355467408660.9361289065182680.468064453259134
1260.5507120431869350.898575913626130.449287956813065
1270.8399665933526790.3200668132946430.160033406647321
1280.9510139298889020.09797214022219670.0489860701110984
1290.9302306962378070.1395386075243870.0697693037621933
1300.9791818360738030.04163632785239450.0208181639261972
1310.9770667863872720.0458664272254560.022933213612728
1320.9896559078906780.02068818421864490.0103440921093224
1330.999786585482730.0004268290345403190.00021341451727016
1340.9999292664108370.0001414671783252377.07335891626186e-05
1350.9999514692278179.70615443658444e-054.85307721829222e-05
1360.9997626842283410.0004746315433184990.000237315771659249
1370.9987674498078580.002465100384284060.00123255019214203
1380.9938802180272550.01223956394549080.00611978197274542
1390.9889625550456270.02207488990874660.0110374449543733

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.303068319639189 & 0.606136639278377 & 0.696931680360811 \tabularnewline
13 & 0.161956869355083 & 0.323913738710166 & 0.838043130644917 \tabularnewline
14 & 0.0811122008809526 & 0.162224401761905 & 0.918887799119047 \tabularnewline
15 & 0.0607206409287194 & 0.121441281857439 & 0.939279359071281 \tabularnewline
16 & 0.0395293570067521 & 0.0790587140135043 & 0.960470642993248 \tabularnewline
17 & 0.0336908449097748 & 0.0673816898195497 & 0.966309155090225 \tabularnewline
18 & 0.0210211103033109 & 0.0420422206066218 & 0.978978889696689 \tabularnewline
19 & 0.500103202166175 & 0.99979359566765 & 0.499896797833825 \tabularnewline
20 & 0.439269201410024 & 0.878538402820048 & 0.560730798589976 \tabularnewline
21 & 0.451419576788041 & 0.902839153576082 & 0.548580423211959 \tabularnewline
22 & 0.469516992976864 & 0.939033985953728 & 0.530483007023136 \tabularnewline
23 & 0.487979291793932 & 0.975958583587864 & 0.512020708206068 \tabularnewline
24 & 0.460832986429839 & 0.921665972859679 & 0.539167013570161 \tabularnewline
25 & 0.385434289616102 & 0.770868579232204 & 0.614565710383898 \tabularnewline
26 & 0.359367627333901 & 0.718735254667803 & 0.640632372666099 \tabularnewline
27 & 0.298708107741984 & 0.597416215483968 & 0.701291892258016 \tabularnewline
28 & 0.243107615625388 & 0.486215231250775 & 0.756892384374612 \tabularnewline
29 & 0.199465112527382 & 0.398930225054763 & 0.800534887472618 \tabularnewline
30 & 0.170700087578468 & 0.341400175156936 & 0.829299912421532 \tabularnewline
31 & 0.160897937220082 & 0.321795874440165 & 0.839102062779918 \tabularnewline
32 & 0.145209932957008 & 0.290419865914016 & 0.854790067042992 \tabularnewline
33 & 0.130977455112316 & 0.261954910224632 & 0.869022544887684 \tabularnewline
34 & 0.130695172029096 & 0.261390344058193 & 0.869304827970904 \tabularnewline
35 & 0.123220201313432 & 0.246440402626864 & 0.876779798686568 \tabularnewline
36 & 0.126087375854375 & 0.25217475170875 & 0.873912624145625 \tabularnewline
37 & 0.103014169594765 & 0.206028339189531 & 0.896985830405235 \tabularnewline
38 & 0.0814803094450022 & 0.162960618890004 & 0.918519690554998 \tabularnewline
39 & 0.076129830948645 & 0.15225966189729 & 0.923870169051355 \tabularnewline
40 & 0.0587596731066261 & 0.117519346213252 & 0.941240326893374 \tabularnewline
41 & 0.0456695451336332 & 0.0913390902672665 & 0.954330454866367 \tabularnewline
42 & 0.0377521735133885 & 0.0755043470267769 & 0.962247826486612 \tabularnewline
43 & 0.0274164367610329 & 0.0548328735220659 & 0.972583563238967 \tabularnewline
44 & 0.0203236562042147 & 0.0406473124084293 & 0.979676343795785 \tabularnewline
45 & 0.0243845021290735 & 0.0487690042581469 & 0.975615497870926 \tabularnewline
46 & 0.0214648378178158 & 0.0429296756356316 & 0.978535162182184 \tabularnewline
47 & 0.021129583200222 & 0.0422591664004439 & 0.978870416799778 \tabularnewline
48 & 0.0559592423017957 & 0.111918484603591 & 0.944040757698204 \tabularnewline
49 & 0.0476873237339817 & 0.0953746474679634 & 0.952312676266018 \tabularnewline
50 & 0.0354035500974257 & 0.0708071001948515 & 0.964596449902574 \tabularnewline
51 & 0.0261853592432345 & 0.0523707184864689 & 0.973814640756765 \tabularnewline
52 & 0.019190624915546 & 0.038381249831092 & 0.980809375084454 \tabularnewline
53 & 0.0173778506060022 & 0.0347557012120044 & 0.982622149393998 \tabularnewline
54 & 0.0125054456758611 & 0.0250108913517223 & 0.987494554324139 \tabularnewline
55 & 0.00942913474545938 & 0.0188582694909188 & 0.990570865254541 \tabularnewline
56 & 0.00912392921230402 & 0.018247858424608 & 0.990876070787696 \tabularnewline
57 & 0.00761814687533629 & 0.0152362937506726 & 0.992381853124664 \tabularnewline
58 & 0.00567563893099551 & 0.011351277861991 & 0.994324361069004 \tabularnewline
59 & 0.00412808678214383 & 0.00825617356428766 & 0.995871913217856 \tabularnewline
60 & 0.00998408990457986 & 0.0199681798091597 & 0.99001591009542 \tabularnewline
61 & 0.00845329598390117 & 0.0169065919678023 & 0.991546704016099 \tabularnewline
62 & 0.00603230356781678 & 0.0120646071356336 & 0.993967696432183 \tabularnewline
63 & 0.00425765374110196 & 0.00851530748220393 & 0.995742346258898 \tabularnewline
64 & 0.00300979793469125 & 0.00601959586938249 & 0.996990202065309 \tabularnewline
65 & 0.00335907230514895 & 0.00671814461029791 & 0.996640927694851 \tabularnewline
66 & 0.00229586622739736 & 0.00459173245479472 & 0.997704133772603 \tabularnewline
67 & 0.00236294238737032 & 0.00472588477474063 & 0.99763705761263 \tabularnewline
68 & 0.0017684490438612 & 0.0035368980877224 & 0.998231550956139 \tabularnewline
69 & 0.00119257549917795 & 0.0023851509983559 & 0.998807424500822 \tabularnewline
70 & 0.000909487258853123 & 0.00181897451770625 & 0.999090512741147 \tabularnewline
71 & 0.000622392690802141 & 0.00124478538160428 & 0.999377607309198 \tabularnewline
72 & 0.000603537088370945 & 0.00120707417674189 & 0.999396462911629 \tabularnewline
73 & 0.000479548782483906 & 0.000959097564967813 & 0.999520451217516 \tabularnewline
74 & 0.000372441573556736 & 0.000744883147113472 & 0.999627558426443 \tabularnewline
75 & 0.000841245721499139 & 0.00168249144299828 & 0.999158754278501 \tabularnewline
76 & 0.00068424355850425 & 0.0013684871170085 & 0.999315756441496 \tabularnewline
77 & 0.000572904394970461 & 0.00114580878994092 & 0.99942709560503 \tabularnewline
78 & 0.000373747446410368 & 0.000747494892820736 & 0.99962625255359 \tabularnewline
79 & 0.00290646198103947 & 0.00581292396207894 & 0.997093538018961 \tabularnewline
80 & 0.00204684903659298 & 0.00409369807318595 & 0.997953150963407 \tabularnewline
81 & 0.0019710372825373 & 0.00394207456507461 & 0.998028962717463 \tabularnewline
82 & 0.00181059579143959 & 0.00362119158287918 & 0.99818940420856 \tabularnewline
83 & 0.00224655072359984 & 0.00449310144719968 & 0.9977534492764 \tabularnewline
84 & 0.00273617451478536 & 0.00547234902957072 & 0.997263825485215 \tabularnewline
85 & 0.00284071250931972 & 0.00568142501863945 & 0.99715928749068 \tabularnewline
86 & 0.002010274936978 & 0.00402054987395599 & 0.997989725063022 \tabularnewline
87 & 0.00141259387944027 & 0.00282518775888053 & 0.99858740612056 \tabularnewline
88 & 0.00144930389431936 & 0.00289860778863872 & 0.998550696105681 \tabularnewline
89 & 0.000969814972246262 & 0.00193962994449252 & 0.999030185027754 \tabularnewline
90 & 0.000676092854206765 & 0.00135218570841353 & 0.999323907145793 \tabularnewline
91 & 0.000466792588366462 & 0.000933585176732924 & 0.999533207411634 \tabularnewline
92 & 0.000358750518897158 & 0.000717501037794316 & 0.999641249481103 \tabularnewline
93 & 0.000264474866112954 & 0.000528949732225908 & 0.999735525133887 \tabularnewline
94 & 0.000236265956352825 & 0.000472531912705649 & 0.999763734043647 \tabularnewline
95 & 0.000166767467457003 & 0.000333534934914007 & 0.999833232532543 \tabularnewline
96 & 0.000676403612185486 & 0.00135280722437097 & 0.999323596387815 \tabularnewline
97 & 0.00073723802734105 & 0.0014744760546821 & 0.999262761972659 \tabularnewline
98 & 0.000642803004081817 & 0.00128560600816363 & 0.999357196995918 \tabularnewline
99 & 0.000789623731913605 & 0.00157924746382721 & 0.999210376268086 \tabularnewline
100 & 0.000720421141844215 & 0.00144084228368843 & 0.999279578858156 \tabularnewline
101 & 0.000721398377404872 & 0.00144279675480974 & 0.999278601622595 \tabularnewline
102 & 0.00153571717224737 & 0.00307143434449474 & 0.998464282827753 \tabularnewline
103 & 0.0016021848551641 & 0.0032043697103282 & 0.998397815144836 \tabularnewline
104 & 0.00225140810326481 & 0.00450281620652961 & 0.997748591896735 \tabularnewline
105 & 0.00227404394946963 & 0.00454808789893925 & 0.99772595605053 \tabularnewline
106 & 0.00206316339561984 & 0.00412632679123968 & 0.99793683660438 \tabularnewline
107 & 0.00267000626984772 & 0.00534001253969544 & 0.997329993730152 \tabularnewline
108 & 0.00349582950317685 & 0.0069916590063537 & 0.996504170496823 \tabularnewline
109 & 0.0488031926671948 & 0.0976063853343897 & 0.951196807332805 \tabularnewline
110 & 0.0722779780796396 & 0.144555956159279 & 0.92772202192036 \tabularnewline
111 & 0.0952923754092321 & 0.190584750818464 & 0.904707624590768 \tabularnewline
112 & 0.156832961598252 & 0.313665923196504 & 0.843167038401748 \tabularnewline
113 & 0.171417481347288 & 0.342834962694575 & 0.828582518652712 \tabularnewline
114 & 0.17920951853826 & 0.358419037076519 & 0.82079048146174 \tabularnewline
115 & 0.453621480498524 & 0.907242960997047 & 0.546378519501476 \tabularnewline
116 & 0.400091024283987 & 0.800182048567973 & 0.599908975716013 \tabularnewline
117 & 0.414010359127386 & 0.828020718254772 & 0.585989640872614 \tabularnewline
118 & 0.513635873513227 & 0.972728252973546 & 0.486364126486773 \tabularnewline
119 & 0.46199716794358 & 0.92399433588716 & 0.53800283205642 \tabularnewline
120 & 0.625399809300688 & 0.749200381398623 & 0.374600190699312 \tabularnewline
121 & 0.57543218120325 & 0.849135637593499 & 0.424567818796749 \tabularnewline
122 & 0.50730757160717 & 0.985384856785661 & 0.492692428392831 \tabularnewline
123 & 0.546342246050363 & 0.907315507899273 & 0.453657753949637 \tabularnewline
124 & 0.571857439357254 & 0.856285121285492 & 0.428142560642746 \tabularnewline
125 & 0.531935546740866 & 0.936128906518268 & 0.468064453259134 \tabularnewline
126 & 0.550712043186935 & 0.89857591362613 & 0.449287956813065 \tabularnewline
127 & 0.839966593352679 & 0.320066813294643 & 0.160033406647321 \tabularnewline
128 & 0.951013929888902 & 0.0979721402221967 & 0.0489860701110984 \tabularnewline
129 & 0.930230696237807 & 0.139538607524387 & 0.0697693037621933 \tabularnewline
130 & 0.979181836073803 & 0.0416363278523945 & 0.0208181639261972 \tabularnewline
131 & 0.977066786387272 & 0.045866427225456 & 0.022933213612728 \tabularnewline
132 & 0.989655907890678 & 0.0206881842186449 & 0.0103440921093224 \tabularnewline
133 & 0.99978658548273 & 0.000426829034540319 & 0.00021341451727016 \tabularnewline
134 & 0.999929266410837 & 0.000141467178325237 & 7.07335891626186e-05 \tabularnewline
135 & 0.999951469227817 & 9.70615443658444e-05 & 4.85307721829222e-05 \tabularnewline
136 & 0.999762684228341 & 0.000474631543318499 & 0.000237315771659249 \tabularnewline
137 & 0.998767449807858 & 0.00246510038428406 & 0.00123255019214203 \tabularnewline
138 & 0.993880218027255 & 0.0122395639454908 & 0.00611978197274542 \tabularnewline
139 & 0.988962555045627 & 0.0220748899087466 & 0.0110374449543733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&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]12[/C][C]0.303068319639189[/C][C]0.606136639278377[/C][C]0.696931680360811[/C][/ROW]
[ROW][C]13[/C][C]0.161956869355083[/C][C]0.323913738710166[/C][C]0.838043130644917[/C][/ROW]
[ROW][C]14[/C][C]0.0811122008809526[/C][C]0.162224401761905[/C][C]0.918887799119047[/C][/ROW]
[ROW][C]15[/C][C]0.0607206409287194[/C][C]0.121441281857439[/C][C]0.939279359071281[/C][/ROW]
[ROW][C]16[/C][C]0.0395293570067521[/C][C]0.0790587140135043[/C][C]0.960470642993248[/C][/ROW]
[ROW][C]17[/C][C]0.0336908449097748[/C][C]0.0673816898195497[/C][C]0.966309155090225[/C][/ROW]
[ROW][C]18[/C][C]0.0210211103033109[/C][C]0.0420422206066218[/C][C]0.978978889696689[/C][/ROW]
[ROW][C]19[/C][C]0.500103202166175[/C][C]0.99979359566765[/C][C]0.499896797833825[/C][/ROW]
[ROW][C]20[/C][C]0.439269201410024[/C][C]0.878538402820048[/C][C]0.560730798589976[/C][/ROW]
[ROW][C]21[/C][C]0.451419576788041[/C][C]0.902839153576082[/C][C]0.548580423211959[/C][/ROW]
[ROW][C]22[/C][C]0.469516992976864[/C][C]0.939033985953728[/C][C]0.530483007023136[/C][/ROW]
[ROW][C]23[/C][C]0.487979291793932[/C][C]0.975958583587864[/C][C]0.512020708206068[/C][/ROW]
[ROW][C]24[/C][C]0.460832986429839[/C][C]0.921665972859679[/C][C]0.539167013570161[/C][/ROW]
[ROW][C]25[/C][C]0.385434289616102[/C][C]0.770868579232204[/C][C]0.614565710383898[/C][/ROW]
[ROW][C]26[/C][C]0.359367627333901[/C][C]0.718735254667803[/C][C]0.640632372666099[/C][/ROW]
[ROW][C]27[/C][C]0.298708107741984[/C][C]0.597416215483968[/C][C]0.701291892258016[/C][/ROW]
[ROW][C]28[/C][C]0.243107615625388[/C][C]0.486215231250775[/C][C]0.756892384374612[/C][/ROW]
[ROW][C]29[/C][C]0.199465112527382[/C][C]0.398930225054763[/C][C]0.800534887472618[/C][/ROW]
[ROW][C]30[/C][C]0.170700087578468[/C][C]0.341400175156936[/C][C]0.829299912421532[/C][/ROW]
[ROW][C]31[/C][C]0.160897937220082[/C][C]0.321795874440165[/C][C]0.839102062779918[/C][/ROW]
[ROW][C]32[/C][C]0.145209932957008[/C][C]0.290419865914016[/C][C]0.854790067042992[/C][/ROW]
[ROW][C]33[/C][C]0.130977455112316[/C][C]0.261954910224632[/C][C]0.869022544887684[/C][/ROW]
[ROW][C]34[/C][C]0.130695172029096[/C][C]0.261390344058193[/C][C]0.869304827970904[/C][/ROW]
[ROW][C]35[/C][C]0.123220201313432[/C][C]0.246440402626864[/C][C]0.876779798686568[/C][/ROW]
[ROW][C]36[/C][C]0.126087375854375[/C][C]0.25217475170875[/C][C]0.873912624145625[/C][/ROW]
[ROW][C]37[/C][C]0.103014169594765[/C][C]0.206028339189531[/C][C]0.896985830405235[/C][/ROW]
[ROW][C]38[/C][C]0.0814803094450022[/C][C]0.162960618890004[/C][C]0.918519690554998[/C][/ROW]
[ROW][C]39[/C][C]0.076129830948645[/C][C]0.15225966189729[/C][C]0.923870169051355[/C][/ROW]
[ROW][C]40[/C][C]0.0587596731066261[/C][C]0.117519346213252[/C][C]0.941240326893374[/C][/ROW]
[ROW][C]41[/C][C]0.0456695451336332[/C][C]0.0913390902672665[/C][C]0.954330454866367[/C][/ROW]
[ROW][C]42[/C][C]0.0377521735133885[/C][C]0.0755043470267769[/C][C]0.962247826486612[/C][/ROW]
[ROW][C]43[/C][C]0.0274164367610329[/C][C]0.0548328735220659[/C][C]0.972583563238967[/C][/ROW]
[ROW][C]44[/C][C]0.0203236562042147[/C][C]0.0406473124084293[/C][C]0.979676343795785[/C][/ROW]
[ROW][C]45[/C][C]0.0243845021290735[/C][C]0.0487690042581469[/C][C]0.975615497870926[/C][/ROW]
[ROW][C]46[/C][C]0.0214648378178158[/C][C]0.0429296756356316[/C][C]0.978535162182184[/C][/ROW]
[ROW][C]47[/C][C]0.021129583200222[/C][C]0.0422591664004439[/C][C]0.978870416799778[/C][/ROW]
[ROW][C]48[/C][C]0.0559592423017957[/C][C]0.111918484603591[/C][C]0.944040757698204[/C][/ROW]
[ROW][C]49[/C][C]0.0476873237339817[/C][C]0.0953746474679634[/C][C]0.952312676266018[/C][/ROW]
[ROW][C]50[/C][C]0.0354035500974257[/C][C]0.0708071001948515[/C][C]0.964596449902574[/C][/ROW]
[ROW][C]51[/C][C]0.0261853592432345[/C][C]0.0523707184864689[/C][C]0.973814640756765[/C][/ROW]
[ROW][C]52[/C][C]0.019190624915546[/C][C]0.038381249831092[/C][C]0.980809375084454[/C][/ROW]
[ROW][C]53[/C][C]0.0173778506060022[/C][C]0.0347557012120044[/C][C]0.982622149393998[/C][/ROW]
[ROW][C]54[/C][C]0.0125054456758611[/C][C]0.0250108913517223[/C][C]0.987494554324139[/C][/ROW]
[ROW][C]55[/C][C]0.00942913474545938[/C][C]0.0188582694909188[/C][C]0.990570865254541[/C][/ROW]
[ROW][C]56[/C][C]0.00912392921230402[/C][C]0.018247858424608[/C][C]0.990876070787696[/C][/ROW]
[ROW][C]57[/C][C]0.00761814687533629[/C][C]0.0152362937506726[/C][C]0.992381853124664[/C][/ROW]
[ROW][C]58[/C][C]0.00567563893099551[/C][C]0.011351277861991[/C][C]0.994324361069004[/C][/ROW]
[ROW][C]59[/C][C]0.00412808678214383[/C][C]0.00825617356428766[/C][C]0.995871913217856[/C][/ROW]
[ROW][C]60[/C][C]0.00998408990457986[/C][C]0.0199681798091597[/C][C]0.99001591009542[/C][/ROW]
[ROW][C]61[/C][C]0.00845329598390117[/C][C]0.0169065919678023[/C][C]0.991546704016099[/C][/ROW]
[ROW][C]62[/C][C]0.00603230356781678[/C][C]0.0120646071356336[/C][C]0.993967696432183[/C][/ROW]
[ROW][C]63[/C][C]0.00425765374110196[/C][C]0.00851530748220393[/C][C]0.995742346258898[/C][/ROW]
[ROW][C]64[/C][C]0.00300979793469125[/C][C]0.00601959586938249[/C][C]0.996990202065309[/C][/ROW]
[ROW][C]65[/C][C]0.00335907230514895[/C][C]0.00671814461029791[/C][C]0.996640927694851[/C][/ROW]
[ROW][C]66[/C][C]0.00229586622739736[/C][C]0.00459173245479472[/C][C]0.997704133772603[/C][/ROW]
[ROW][C]67[/C][C]0.00236294238737032[/C][C]0.00472588477474063[/C][C]0.99763705761263[/C][/ROW]
[ROW][C]68[/C][C]0.0017684490438612[/C][C]0.0035368980877224[/C][C]0.998231550956139[/C][/ROW]
[ROW][C]69[/C][C]0.00119257549917795[/C][C]0.0023851509983559[/C][C]0.998807424500822[/C][/ROW]
[ROW][C]70[/C][C]0.000909487258853123[/C][C]0.00181897451770625[/C][C]0.999090512741147[/C][/ROW]
[ROW][C]71[/C][C]0.000622392690802141[/C][C]0.00124478538160428[/C][C]0.999377607309198[/C][/ROW]
[ROW][C]72[/C][C]0.000603537088370945[/C][C]0.00120707417674189[/C][C]0.999396462911629[/C][/ROW]
[ROW][C]73[/C][C]0.000479548782483906[/C][C]0.000959097564967813[/C][C]0.999520451217516[/C][/ROW]
[ROW][C]74[/C][C]0.000372441573556736[/C][C]0.000744883147113472[/C][C]0.999627558426443[/C][/ROW]
[ROW][C]75[/C][C]0.000841245721499139[/C][C]0.00168249144299828[/C][C]0.999158754278501[/C][/ROW]
[ROW][C]76[/C][C]0.00068424355850425[/C][C]0.0013684871170085[/C][C]0.999315756441496[/C][/ROW]
[ROW][C]77[/C][C]0.000572904394970461[/C][C]0.00114580878994092[/C][C]0.99942709560503[/C][/ROW]
[ROW][C]78[/C][C]0.000373747446410368[/C][C]0.000747494892820736[/C][C]0.99962625255359[/C][/ROW]
[ROW][C]79[/C][C]0.00290646198103947[/C][C]0.00581292396207894[/C][C]0.997093538018961[/C][/ROW]
[ROW][C]80[/C][C]0.00204684903659298[/C][C]0.00409369807318595[/C][C]0.997953150963407[/C][/ROW]
[ROW][C]81[/C][C]0.0019710372825373[/C][C]0.00394207456507461[/C][C]0.998028962717463[/C][/ROW]
[ROW][C]82[/C][C]0.00181059579143959[/C][C]0.00362119158287918[/C][C]0.99818940420856[/C][/ROW]
[ROW][C]83[/C][C]0.00224655072359984[/C][C]0.00449310144719968[/C][C]0.9977534492764[/C][/ROW]
[ROW][C]84[/C][C]0.00273617451478536[/C][C]0.00547234902957072[/C][C]0.997263825485215[/C][/ROW]
[ROW][C]85[/C][C]0.00284071250931972[/C][C]0.00568142501863945[/C][C]0.99715928749068[/C][/ROW]
[ROW][C]86[/C][C]0.002010274936978[/C][C]0.00402054987395599[/C][C]0.997989725063022[/C][/ROW]
[ROW][C]87[/C][C]0.00141259387944027[/C][C]0.00282518775888053[/C][C]0.99858740612056[/C][/ROW]
[ROW][C]88[/C][C]0.00144930389431936[/C][C]0.00289860778863872[/C][C]0.998550696105681[/C][/ROW]
[ROW][C]89[/C][C]0.000969814972246262[/C][C]0.00193962994449252[/C][C]0.999030185027754[/C][/ROW]
[ROW][C]90[/C][C]0.000676092854206765[/C][C]0.00135218570841353[/C][C]0.999323907145793[/C][/ROW]
[ROW][C]91[/C][C]0.000466792588366462[/C][C]0.000933585176732924[/C][C]0.999533207411634[/C][/ROW]
[ROW][C]92[/C][C]0.000358750518897158[/C][C]0.000717501037794316[/C][C]0.999641249481103[/C][/ROW]
[ROW][C]93[/C][C]0.000264474866112954[/C][C]0.000528949732225908[/C][C]0.999735525133887[/C][/ROW]
[ROW][C]94[/C][C]0.000236265956352825[/C][C]0.000472531912705649[/C][C]0.999763734043647[/C][/ROW]
[ROW][C]95[/C][C]0.000166767467457003[/C][C]0.000333534934914007[/C][C]0.999833232532543[/C][/ROW]
[ROW][C]96[/C][C]0.000676403612185486[/C][C]0.00135280722437097[/C][C]0.999323596387815[/C][/ROW]
[ROW][C]97[/C][C]0.00073723802734105[/C][C]0.0014744760546821[/C][C]0.999262761972659[/C][/ROW]
[ROW][C]98[/C][C]0.000642803004081817[/C][C]0.00128560600816363[/C][C]0.999357196995918[/C][/ROW]
[ROW][C]99[/C][C]0.000789623731913605[/C][C]0.00157924746382721[/C][C]0.999210376268086[/C][/ROW]
[ROW][C]100[/C][C]0.000720421141844215[/C][C]0.00144084228368843[/C][C]0.999279578858156[/C][/ROW]
[ROW][C]101[/C][C]0.000721398377404872[/C][C]0.00144279675480974[/C][C]0.999278601622595[/C][/ROW]
[ROW][C]102[/C][C]0.00153571717224737[/C][C]0.00307143434449474[/C][C]0.998464282827753[/C][/ROW]
[ROW][C]103[/C][C]0.0016021848551641[/C][C]0.0032043697103282[/C][C]0.998397815144836[/C][/ROW]
[ROW][C]104[/C][C]0.00225140810326481[/C][C]0.00450281620652961[/C][C]0.997748591896735[/C][/ROW]
[ROW][C]105[/C][C]0.00227404394946963[/C][C]0.00454808789893925[/C][C]0.99772595605053[/C][/ROW]
[ROW][C]106[/C][C]0.00206316339561984[/C][C]0.00412632679123968[/C][C]0.99793683660438[/C][/ROW]
[ROW][C]107[/C][C]0.00267000626984772[/C][C]0.00534001253969544[/C][C]0.997329993730152[/C][/ROW]
[ROW][C]108[/C][C]0.00349582950317685[/C][C]0.0069916590063537[/C][C]0.996504170496823[/C][/ROW]
[ROW][C]109[/C][C]0.0488031926671948[/C][C]0.0976063853343897[/C][C]0.951196807332805[/C][/ROW]
[ROW][C]110[/C][C]0.0722779780796396[/C][C]0.144555956159279[/C][C]0.92772202192036[/C][/ROW]
[ROW][C]111[/C][C]0.0952923754092321[/C][C]0.190584750818464[/C][C]0.904707624590768[/C][/ROW]
[ROW][C]112[/C][C]0.156832961598252[/C][C]0.313665923196504[/C][C]0.843167038401748[/C][/ROW]
[ROW][C]113[/C][C]0.171417481347288[/C][C]0.342834962694575[/C][C]0.828582518652712[/C][/ROW]
[ROW][C]114[/C][C]0.17920951853826[/C][C]0.358419037076519[/C][C]0.82079048146174[/C][/ROW]
[ROW][C]115[/C][C]0.453621480498524[/C][C]0.907242960997047[/C][C]0.546378519501476[/C][/ROW]
[ROW][C]116[/C][C]0.400091024283987[/C][C]0.800182048567973[/C][C]0.599908975716013[/C][/ROW]
[ROW][C]117[/C][C]0.414010359127386[/C][C]0.828020718254772[/C][C]0.585989640872614[/C][/ROW]
[ROW][C]118[/C][C]0.513635873513227[/C][C]0.972728252973546[/C][C]0.486364126486773[/C][/ROW]
[ROW][C]119[/C][C]0.46199716794358[/C][C]0.92399433588716[/C][C]0.53800283205642[/C][/ROW]
[ROW][C]120[/C][C]0.625399809300688[/C][C]0.749200381398623[/C][C]0.374600190699312[/C][/ROW]
[ROW][C]121[/C][C]0.57543218120325[/C][C]0.849135637593499[/C][C]0.424567818796749[/C][/ROW]
[ROW][C]122[/C][C]0.50730757160717[/C][C]0.985384856785661[/C][C]0.492692428392831[/C][/ROW]
[ROW][C]123[/C][C]0.546342246050363[/C][C]0.907315507899273[/C][C]0.453657753949637[/C][/ROW]
[ROW][C]124[/C][C]0.571857439357254[/C][C]0.856285121285492[/C][C]0.428142560642746[/C][/ROW]
[ROW][C]125[/C][C]0.531935546740866[/C][C]0.936128906518268[/C][C]0.468064453259134[/C][/ROW]
[ROW][C]126[/C][C]0.550712043186935[/C][C]0.89857591362613[/C][C]0.449287956813065[/C][/ROW]
[ROW][C]127[/C][C]0.839966593352679[/C][C]0.320066813294643[/C][C]0.160033406647321[/C][/ROW]
[ROW][C]128[/C][C]0.951013929888902[/C][C]0.0979721402221967[/C][C]0.0489860701110984[/C][/ROW]
[ROW][C]129[/C][C]0.930230696237807[/C][C]0.139538607524387[/C][C]0.0697693037621933[/C][/ROW]
[ROW][C]130[/C][C]0.979181836073803[/C][C]0.0416363278523945[/C][C]0.0208181639261972[/C][/ROW]
[ROW][C]131[/C][C]0.977066786387272[/C][C]0.045866427225456[/C][C]0.022933213612728[/C][/ROW]
[ROW][C]132[/C][C]0.989655907890678[/C][C]0.0206881842186449[/C][C]0.0103440921093224[/C][/ROW]
[ROW][C]133[/C][C]0.99978658548273[/C][C]0.000426829034540319[/C][C]0.00021341451727016[/C][/ROW]
[ROW][C]134[/C][C]0.999929266410837[/C][C]0.000141467178325237[/C][C]7.07335891626186e-05[/C][/ROW]
[ROW][C]135[/C][C]0.999951469227817[/C][C]9.70615443658444e-05[/C][C]4.85307721829222e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999762684228341[/C][C]0.000474631543318499[/C][C]0.000237315771659249[/C][/ROW]
[ROW][C]137[/C][C]0.998767449807858[/C][C]0.00246510038428406[/C][C]0.00123255019214203[/C][/ROW]
[ROW][C]138[/C][C]0.993880218027255[/C][C]0.0122395639454908[/C][C]0.00611978197274542[/C][/ROW]
[ROW][C]139[/C][C]0.988962555045627[/C][C]0.0220748899087466[/C][C]0.0110374449543733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3030683196391890.6061366392783770.696931680360811
130.1619568693550830.3239137387101660.838043130644917
140.08111220088095260.1622244017619050.918887799119047
150.06072064092871940.1214412818574390.939279359071281
160.03952935700675210.07905871401350430.960470642993248
170.03369084490977480.06738168981954970.966309155090225
180.02102111030331090.04204222060662180.978978889696689
190.5001032021661750.999793595667650.499896797833825
200.4392692014100240.8785384028200480.560730798589976
210.4514195767880410.9028391535760820.548580423211959
220.4695169929768640.9390339859537280.530483007023136
230.4879792917939320.9759585835878640.512020708206068
240.4608329864298390.9216659728596790.539167013570161
250.3854342896161020.7708685792322040.614565710383898
260.3593676273339010.7187352546678030.640632372666099
270.2987081077419840.5974162154839680.701291892258016
280.2431076156253880.4862152312507750.756892384374612
290.1994651125273820.3989302250547630.800534887472618
300.1707000875784680.3414001751569360.829299912421532
310.1608979372200820.3217958744401650.839102062779918
320.1452099329570080.2904198659140160.854790067042992
330.1309774551123160.2619549102246320.869022544887684
340.1306951720290960.2613903440581930.869304827970904
350.1232202013134320.2464404026268640.876779798686568
360.1260873758543750.252174751708750.873912624145625
370.1030141695947650.2060283391895310.896985830405235
380.08148030944500220.1629606188900040.918519690554998
390.0761298309486450.152259661897290.923870169051355
400.05875967310662610.1175193462132520.941240326893374
410.04566954513363320.09133909026726650.954330454866367
420.03775217351338850.07550434702677690.962247826486612
430.02741643676103290.05483287352206590.972583563238967
440.02032365620421470.04064731240842930.979676343795785
450.02438450212907350.04876900425814690.975615497870926
460.02146483781781580.04292967563563160.978535162182184
470.0211295832002220.04225916640044390.978870416799778
480.05595924230179570.1119184846035910.944040757698204
490.04768732373398170.09537464746796340.952312676266018
500.03540355009742570.07080710019485150.964596449902574
510.02618535924323450.05237071848646890.973814640756765
520.0191906249155460.0383812498310920.980809375084454
530.01737785060600220.03475570121200440.982622149393998
540.01250544567586110.02501089135172230.987494554324139
550.009429134745459380.01885826949091880.990570865254541
560.009123929212304020.0182478584246080.990876070787696
570.007618146875336290.01523629375067260.992381853124664
580.005675638930995510.0113512778619910.994324361069004
590.004128086782143830.008256173564287660.995871913217856
600.009984089904579860.01996817980915970.99001591009542
610.008453295983901170.01690659196780230.991546704016099
620.006032303567816780.01206460713563360.993967696432183
630.004257653741101960.008515307482203930.995742346258898
640.003009797934691250.006019595869382490.996990202065309
650.003359072305148950.006718144610297910.996640927694851
660.002295866227397360.004591732454794720.997704133772603
670.002362942387370320.004725884774740630.99763705761263
680.00176844904386120.00353689808772240.998231550956139
690.001192575499177950.00238515099835590.998807424500822
700.0009094872588531230.001818974517706250.999090512741147
710.0006223926908021410.001244785381604280.999377607309198
720.0006035370883709450.001207074176741890.999396462911629
730.0004795487824839060.0009590975649678130.999520451217516
740.0003724415735567360.0007448831471134720.999627558426443
750.0008412457214991390.001682491442998280.999158754278501
760.000684243558504250.00136848711700850.999315756441496
770.0005729043949704610.001145808789940920.99942709560503
780.0003737474464103680.0007474948928207360.99962625255359
790.002906461981039470.005812923962078940.997093538018961
800.002046849036592980.004093698073185950.997953150963407
810.00197103728253730.003942074565074610.998028962717463
820.001810595791439590.003621191582879180.99818940420856
830.002246550723599840.004493101447199680.9977534492764
840.002736174514785360.005472349029570720.997263825485215
850.002840712509319720.005681425018639450.99715928749068
860.0020102749369780.004020549873955990.997989725063022
870.001412593879440270.002825187758880530.99858740612056
880.001449303894319360.002898607788638720.998550696105681
890.0009698149722462620.001939629944492520.999030185027754
900.0006760928542067650.001352185708413530.999323907145793
910.0004667925883664620.0009335851767329240.999533207411634
920.0003587505188971580.0007175010377943160.999641249481103
930.0002644748661129540.0005289497322259080.999735525133887
940.0002362659563528250.0004725319127056490.999763734043647
950.0001667674674570030.0003335349349140070.999833232532543
960.0006764036121854860.001352807224370970.999323596387815
970.000737238027341050.00147447605468210.999262761972659
980.0006428030040818170.001285606008163630.999357196995918
990.0007896237319136050.001579247463827210.999210376268086
1000.0007204211418442150.001440842283688430.999279578858156
1010.0007213983774048720.001442796754809740.999278601622595
1020.001535717172247370.003071434344494740.998464282827753
1030.00160218485516410.00320436971032820.998397815144836
1040.002251408103264810.004502816206529610.997748591896735
1050.002274043949469630.004548087898939250.99772595605053
1060.002063163395619840.004126326791239680.99793683660438
1070.002670006269847720.005340012539695440.997329993730152
1080.003495829503176850.00699165900635370.996504170496823
1090.04880319266719480.09760638533438970.951196807332805
1100.07227797807963960.1445559561592790.92772202192036
1110.09529237540923210.1905847508184640.904707624590768
1120.1568329615982520.3136659231965040.843167038401748
1130.1714174813472880.3428349626945750.828582518652712
1140.179209518538260.3584190370765190.82079048146174
1150.4536214804985240.9072429609970470.546378519501476
1160.4000910242839870.8001820485679730.599908975716013
1170.4140103591273860.8280207182547720.585989640872614
1180.5136358735132270.9727282529735460.486364126486773
1190.461997167943580.923994335887160.53800283205642
1200.6253998093006880.7492003813986230.374600190699312
1210.575432181203250.8491356375934990.424567818796749
1220.507307571607170.9853848567856610.492692428392831
1230.5463422460503630.9073155078992730.453657753949637
1240.5718574393572540.8562851212854920.428142560642746
1250.5319355467408660.9361289065182680.468064453259134
1260.5507120431869350.898575913626130.449287956813065
1270.8399665933526790.3200668132946430.160033406647321
1280.9510139298889020.09797214022219670.0489860701110984
1290.9302306962378070.1395386075243870.0697693037621933
1300.9791818360738030.04163632785239450.0208181639261972
1310.9770667863872720.0458664272254560.022933213612728
1320.9896559078906780.02068818421864490.0103440921093224
1330.999786585482730.0004268290345403190.00021341451727016
1340.9999292664108370.0001414671783252377.07335891626186e-05
1350.9999514692278179.70615443658444e-054.85307721829222e-05
1360.9997626842283410.0004746315433184990.000237315771659249
1370.9987674498078580.002465100384284060.00123255019214203
1380.9938802180272550.01223956394549080.00611978197274542
1390.9889625550456270.02207488990874660.0110374449543733







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level520.40625NOK
5% type I error level720.5625NOK
10% type I error level820.640625NOK

\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 & 52 & 0.40625 & NOK \tabularnewline
5% type I error level & 72 & 0.5625 & NOK \tabularnewline
10% type I error level & 82 & 0.640625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185963&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]52[/C][C]0.40625[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]72[/C][C]0.5625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.640625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185963&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185963&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level520.40625NOK
5% type I error level720.5625NOK
10% type I error level820.640625NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}