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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 18 Nov 2012 06:41:30 -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/18/t1353238927swejw955f4hkc2e.htm/, Retrieved Mon, 29 Apr 2024 23:28:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190160, Retrieved Mon, 29 Apr 2024 23:28:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
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-   P       [Multiple Regression] [WS7: Lineaire trend] [2012-11-18 11:41:30] [ed5db9d6207bcb51aca69986e23f030b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2001	100	95	102	103	91	99	101	91	114	101	103	85
2001	94	97	99	117	85	97	97	87	99	99	97	94
2001	105	97	108	115	110	113	108	103	98	104	110	107
2001	95	97	92	74	90	100	95	97	91	99	97	98
2001	103	103	99	74	103	105	99	96	111	101	103	111
2001	103	101	102	81	119	109	101	105	104	102	106	115
2001	100	96	87	86	76	91	92	74	100	93	89	76
2001	108	94	71	114	93	89	92	87	108	97	85	100
2001	108	97	105	102	105	105	100	105	113	91	100	103
2001	120	101	115	85	92	120	106	118	113	97	106	117
2001	112	77	103	63	75	107	99	102	114	94	95	101
2001	102	93	75	61	61	84	84	101	109	90	74	73
2002	105	45	97	87	80	101	106	86	116	105	94	84
2002	101	48	95	97	85	105	101	83	102	103	90	90
2002	108	52	99	88	94	119	113	92	107	112	99	105
2002	107	49	100	67	78	114	110	87	111	114	100	111
2002	109	53	92	59	92	114	103	94	122	111	96	110
2002	110	60	94	63	90	119	107	94	123	106	102	116
2002	111	51	89	86	72	99	98	75	108	112	88	85
2002	110	42	67	99	77	91	90	85	115	102	78	92
2002	117	56	109	85	76	121	105	104	120	103	99	117
2002	130	51	113	74	89	128	116	109	117	105	107	119
2002	114	53	106	55	55	112	102	121	115	101	93	100
2002	113	55	78	54	47	93	88	124	116	101	74	71
2003	110	44	102	81	91	108	114	88	118	117	96	82
2003	107	51	97	88	85	107	104	86	98	109	99	90
2003	110	52	96	75	89	115	111	98	121	120	103	109
2003	113	54	99	55	90	121	111	94	118	115	102	112
2003	106	50	86	47	72	112	102	102	120	107	96	103
2003	118	57	92	54	83	123	106	96	111	110	106	116
2003	118	49	86	71	72	101	104	79	117	110	95	89
2003	114	41	62	79	75	87	94	95	110	105	82	91
2003	121	58	105	77	85	124	116	106	107	116	109	121
2003	130	63	108	57	81	125	118	116	115	116	114	123
2003	115	54	96	40	69	111	101	101	106	111	95	98
2003	118	55	80	44	68	98	101	108	115	120	85	81
2004	111	56	95	67	94	102	109	92	112	111	98	84
2004	108	56	94	75	97	105	108	89	106	115	100	92
2004	124	70	108	75	102	128	124	109	106	125	119	116
2004	115	69	97	49	94	125	117	97	114	116	109	112
2004	113	57	89	37	89	116	104	99	109	113	99	106
2004	128	68	107	50	114	131	121	110	100	122	119	131
2004	117	53	87	63	82	98	101	76	105	123	94	83
2004	119	48	70	76	96	89	105	91	100	117	88	98
2004	130	61	111	69	104	133	121	105	104	136	116	120
2004	126	62	105	49	88	114	116	103	112	121	109	121
2004	125	58	99	40	85	113	106	108	97	120	103	107
2004	131	51	84	39	87	104	105	122	107	126	93	89
2005	116	51	87	54	86	108	107	92	104	116	100	81
2005	109	48	92	71	89	106	101	95	98	108	102	90
2005	124	59	98	68	105	117	113	106	100	117	113	103
2005	119	54	95	43	83	123	109	98	97	113	112	117
2005	119	56	85	42	87	114	103	110	81	113	104	110
2005	131	60	100	48	112	132	116	107	73	126	118	130
2005	111	51	79	58	97	92	98	69	89	114	94	79
2005	125	51	66	76	89	94	99	95	96	113	95	101
2005	132	56	105	57	109	121	117	114	97	112	121	123
2005	127	53	96	44	88	114	107	104	98	113	114	111
2005	132	53	103	40	91	116	107	110	89	116	114	109
2005	131	48	83	36	79	98	102	112	98	112	99	89
2006	122	50	91	60	115	112	103	92	91	119	112	87
2006	113	49	95	73	119	109	101	97	86	117	111	95
2006	134	55	109	71	125	133	117	114	97	125	126	119
2006	119	50	92	45	96	118	103	93	102	113	112	110
2006	129	57	99	45	117	132	106	115	80	120	124	124
2006	131	65	110	48	120	134	111	112	71	114	127	133
2006	117	53	88	60	104	97	94	76	91	114	101	84
2006	131	42	73	72	121	100	101	101	102	118	102	105
2006	132	56	111	63	127	128	111	119	91	117	126	128
2006	141	58	112	32	118	135	114	118	94	121	129	127
2006	138	54	111	34	108	131	110	120	53	115	122	120
2006	129	51	84	24	89	107	100	120	77	117	100	93
2007	127	59	102	65	137	122	104	99	70	119	122	98
2007	121	49	102	73	142	121	106	103	65	115	120	106
2007	139	61	114	62	137	141	116	118	89	126	137	122
2007	129	52	99	32	123	125	104	103	70	118	124	116
2007	131	58	100	31	126	130	107	114	78	118	130	122
2007	136	66	110	37	148	159	113	116	78	115	137	134
2007	129	62	93	48	116	111	104	84	73	122	114	88
2007	133	45	77	54	139	110	103	106	83	117	109	110
2007	136	52	108	44	151	133	109	117	74	106	126	122
2007	151	59	120	41	124	135	123	125	102	111	141	135
2007	145	58	106	32	109	119	110	123	54	114	130	116
2007	134	45	78	31	112	94	94	119	79	114	98	85
2008	136	65	100	49	136	118	114	100	86	125	130	106
2008	129	64	102	54	136	115	110	100	87	125	130	115
2008	129	69	97	44	139	114	110	103	79	120	125	111
2008	139	71	101	31	138	131	113	104	64	121	136	133
2008	133	63	89	24	142	117	105	99	70	111	124	124
2008	133	74	93	37	144	123	108	101	75	124	133	131
2008	137	63	89	38	147	106	101	73	72	120	121	97
2008	127	52	62	42	201	89	95	86	83	126	102	97
2008	144	73	96	36	196	116	112	110	74	116	131	131
2008	150	67	95	31	170	116	113	115	82	117	130	127
2008	132	63	80	24	177	97	96	101	78	106	106	101
2008	139	70	67	29	190	82	93	112	77	102	93	88
2009	123	66	71	38	138	92	91	89	77	106	100	76
2009	122	60	73	44	133	90	91	93	72	97	99	87
2009	136	66	81	33	131	99	101	103	76	108	112	110
2009	133	68	77	23	110	99	98	91	75	99	109	102
2009	127	68	68	19	124	89	94	88	69	101	102	99
2009	139	81	77	27	150	106	102	93	67	106	116	117
2009	131	75	73	29	163	84	96	65	68	105	103	83
2009	132	55	54	34	138	78	92	82	73	103	91	90
2009	136	79	85	26	133	101	106	102	69	102	119	116
2009	142	52	86	28	123	100	105	102	76	107	117	117
2009	133	56	79	18	107	96	97	122	67	100	106	96
2009	132	66	67	24	122	80	94	105	69	101	92	73
2010	121	66	72	29	141	87	95	83	68	105	102	66
2010	124	59	76	38	136	90	95	85	64	118	104	73
2010	145	78	90	33	140	113	114	102	69	129	124	114
2010	135	70	84	22	109	105	107	86	67	124	118	107
2010	128	65	75	20	109	100	100	84	71	128	109	102
2010	142	88	90	31	128	116	112	93	58	129	129	125
2010	130	75	77	27	162	89	101	64	57	128	105	80
2010	131	62	60	28	147	87	100	81	69	125	100	95
2010	141	85	92	28	148	111	111	100	76	125	125	120
2010	140	82	88	25	103	110	107	96	74	130	116	117
2010	142	83	83	21	102	104	105	93	77	125	112	99
2010	140	78	69	24	100	85	104	102	81	122	97	64
2011	132	81	73	28	117	96	106	78	77	129	107	82
2011	132	75	78	33	139	99	105	92	64	124	114	97
2011	151	91	92	31	122	117	114	99	67	144	130	121

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Textiel[t] = + 22933.3245377883 -11.4625923918182Jaar[t] -0.31542641429758Voedingsmiddelen[t] + 0.0405011063177686Tabaksproducten[t] + 0.0496012021407Kleding[t] -0.0397286124296505Leer[t] + 0.391928393429071Hout[t] + 0.607239921728974Papier[t] + 0.0309693470192784Uitgeverijen[t] -0.017570610781955Cokes[t] -0.225793819721557Chemische[t] + 0.795475450018092Rubber[t] -0.334148069816387Nietmetaalhoudende[t] + 0.841062777698478t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Textiel[t] =  +  22933.3245377883 -11.4625923918182Jaar[t] -0.31542641429758Voedingsmiddelen[t] +  0.0405011063177686Tabaksproducten[t] +  0.0496012021407Kleding[t] -0.0397286124296505Leer[t] +  0.391928393429071Hout[t] +  0.607239921728974Papier[t] +  0.0309693470192784Uitgeverijen[t] -0.017570610781955Cokes[t] -0.225793819721557Chemische[t] +  0.795475450018092Rubber[t] -0.334148069816387Nietmetaalhoudende[t] +  0.841062777698478t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Textiel[t] =  +  22933.3245377883 -11.4625923918182Jaar[t] -0.31542641429758Voedingsmiddelen[t] +  0.0405011063177686Tabaksproducten[t] +  0.0496012021407Kleding[t] -0.0397286124296505Leer[t] +  0.391928393429071Hout[t] +  0.607239921728974Papier[t] +  0.0309693470192784Uitgeverijen[t] -0.017570610781955Cokes[t] -0.225793819721557Chemische[t] +  0.795475450018092Rubber[t] -0.334148069816387Nietmetaalhoudende[t] +  0.841062777698478t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Textiel[t] = + 22933.3245377883 -11.4625923918182Jaar[t] -0.31542641429758Voedingsmiddelen[t] + 0.0405011063177686Tabaksproducten[t] + 0.0496012021407Kleding[t] -0.0397286124296505Leer[t] + 0.391928393429071Hout[t] + 0.607239921728974Papier[t] + 0.0309693470192784Uitgeverijen[t] -0.017570610781955Cokes[t] -0.225793819721557Chemische[t] + 0.795475450018092Rubber[t] -0.334148069816387Nietmetaalhoudende[t] + 0.841062777698478t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22933.32453778834875.703854.70368e-064e-06
Jaar-11.46259239181822.436011-4.70557e-064e-06
Voedingsmiddelen-0.315426414297580.115389-2.73360.0073120.003656
Tabaksproducten0.04050110631776860.0310551.30420.1949250.097463
Kleding0.04960120214070.0357641.38690.1683050.084152
Leer-0.03972861242965050.022547-1.76210.0808630.040432
Hout0.3919283934290710.0843084.64889e-065e-06
Papier0.6072399217289740.1180095.14571e-061e-06
Uitgeverijen0.03096934701927840.0453240.68330.4958750.247937
Cokes-0.0175706107819550.048461-0.36260.7176260.358813
Chemische-0.2257938197215570.062644-3.60440.0004730.000237
Rubber0.7954754500180920.1044117.618700
Nietmetaalhoudende-0.3341480698163870.048979-6.822300
t0.8410627776984780.2298563.65910.0003920.000196

\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) & 22933.3245377883 & 4875.70385 & 4.7036 & 8e-06 & 4e-06 \tabularnewline
Jaar & -11.4625923918182 & 2.436011 & -4.7055 & 7e-06 & 4e-06 \tabularnewline
Voedingsmiddelen & -0.31542641429758 & 0.115389 & -2.7336 & 0.007312 & 0.003656 \tabularnewline
Tabaksproducten & 0.0405011063177686 & 0.031055 & 1.3042 & 0.194925 & 0.097463 \tabularnewline
Kleding & 0.0496012021407 & 0.035764 & 1.3869 & 0.168305 & 0.084152 \tabularnewline
Leer & -0.0397286124296505 & 0.022547 & -1.7621 & 0.080863 & 0.040432 \tabularnewline
Hout & 0.391928393429071 & 0.084308 & 4.6488 & 9e-06 & 5e-06 \tabularnewline
Papier & 0.607239921728974 & 0.118009 & 5.1457 & 1e-06 & 1e-06 \tabularnewline
Uitgeverijen & 0.0309693470192784 & 0.045324 & 0.6833 & 0.495875 & 0.247937 \tabularnewline
Cokes & -0.017570610781955 & 0.048461 & -0.3626 & 0.717626 & 0.358813 \tabularnewline
Chemische & -0.225793819721557 & 0.062644 & -3.6044 & 0.000473 & 0.000237 \tabularnewline
Rubber & 0.795475450018092 & 0.104411 & 7.6187 & 0 & 0 \tabularnewline
Nietmetaalhoudende & -0.334148069816387 & 0.048979 & -6.8223 & 0 & 0 \tabularnewline
t & 0.841062777698478 & 0.229856 & 3.6591 & 0.000392 & 0.000196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&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]22933.3245377883[/C][C]4875.70385[/C][C]4.7036[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Jaar[/C][C]-11.4625923918182[/C][C]2.436011[/C][C]-4.7055[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Voedingsmiddelen[/C][C]-0.31542641429758[/C][C]0.115389[/C][C]-2.7336[/C][C]0.007312[/C][C]0.003656[/C][/ROW]
[ROW][C]Tabaksproducten[/C][C]0.0405011063177686[/C][C]0.031055[/C][C]1.3042[/C][C]0.194925[/C][C]0.097463[/C][/ROW]
[ROW][C]Kleding[/C][C]0.0496012021407[/C][C]0.035764[/C][C]1.3869[/C][C]0.168305[/C][C]0.084152[/C][/ROW]
[ROW][C]Leer[/C][C]-0.0397286124296505[/C][C]0.022547[/C][C]-1.7621[/C][C]0.080863[/C][C]0.040432[/C][/ROW]
[ROW][C]Hout[/C][C]0.391928393429071[/C][C]0.084308[/C][C]4.6488[/C][C]9e-06[/C][C]5e-06[/C][/ROW]
[ROW][C]Papier[/C][C]0.607239921728974[/C][C]0.118009[/C][C]5.1457[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Uitgeverijen[/C][C]0.0309693470192784[/C][C]0.045324[/C][C]0.6833[/C][C]0.495875[/C][C]0.247937[/C][/ROW]
[ROW][C]Cokes[/C][C]-0.017570610781955[/C][C]0.048461[/C][C]-0.3626[/C][C]0.717626[/C][C]0.358813[/C][/ROW]
[ROW][C]Chemische[/C][C]-0.225793819721557[/C][C]0.062644[/C][C]-3.6044[/C][C]0.000473[/C][C]0.000237[/C][/ROW]
[ROW][C]Rubber[/C][C]0.795475450018092[/C][C]0.104411[/C][C]7.6187[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nietmetaalhoudende[/C][C]-0.334148069816387[/C][C]0.048979[/C][C]-6.8223[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.841062777698478[/C][C]0.229856[/C][C]3.6591[/C][C]0.000392[/C][C]0.000196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22933.32453778834875.703854.70368e-064e-06
Jaar-11.46259239181822.436011-4.70557e-064e-06
Voedingsmiddelen-0.315426414297580.115389-2.73360.0073120.003656
Tabaksproducten0.04050110631776860.0310551.30420.1949250.097463
Kleding0.04960120214070.0357641.38690.1683050.084152
Leer-0.03972861242965050.022547-1.76210.0808630.040432
Hout0.3919283934290710.0843084.64889e-065e-06
Papier0.6072399217289740.1180095.14571e-061e-06
Uitgeverijen0.03096934701927840.0453240.68330.4958750.247937
Cokes-0.0175706107819550.048461-0.36260.7176260.358813
Chemische-0.2257938197215570.062644-3.60440.0004730.000237
Rubber0.7954754500180920.1044117.618700
Nietmetaalhoudende-0.3341480698163870.048979-6.822300
t0.8410627776984780.2298563.65910.0003920.000196






Multiple Linear Regression - Regression Statistics
Multiple R0.95818201544121
R-squared0.918112774714979
Adjusted R-squared0.908346408396582
F-TEST (value)94.0076119186235
F-TEST (DF numerator)13
F-TEST (DF denominator)109
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.23012264480904
Sum Squared Residuals1950.43919732376

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.95818201544121 \tabularnewline
R-squared & 0.918112774714979 \tabularnewline
Adjusted R-squared & 0.908346408396582 \tabularnewline
F-TEST (value) & 94.0076119186235 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.23012264480904 \tabularnewline
Sum Squared Residuals & 1950.43919732376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.95818201544121[/C][/ROW]
[ROW][C]R-squared[/C][C]0.918112774714979[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.908346408396582[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]94.0076119186235[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/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]4.23012264480904[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1950.43919732376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.95818201544121
R-squared0.918112774714979
Adjusted R-squared0.908346408396582
F-TEST (value)94.0076119186235
F-TEST (DF numerator)13
F-TEST (DF denominator)109
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.23012264480904
Sum Squared Residuals1950.43919732376






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102102.990321916938-0.990321916938441
29996.33600150846922.66399849153083
3108110.94681645486-2.94681645486023
49294.4461773093823-2.44617730938233
59996.47392369347662.5260763065234
6102100.9534869415391.04651305846138
78792.6258585339911-5.62585853399115
87178.9495041168939-7.94950411689395
9105102.7229317589872.27706824101275
10115108.279117262996.72088273701029
1110397.67041666390775.32958333609225
127578.2590280708524-3.25902807085243
139793.56345315220893.43654684779107
149590.03448576074324.96551423925684
1599101.105260995938-2.10526099593791
1610096.06678790402053.93321209597946
179289.08844907459352.91155092540646
189498.4434196387382-4.44341963873823
198984.69907406819524.30092593180476
206770.0947257507869-3.0947257507869
2110998.132634952822910.8673650471771
22113108.4831799094644.51682009053588
2310696.6103100023639.38968999763698
247876.81962862954051.1803713704595
2510297.01929576871674.98070423128332
269795.02046388327251.97953611672755
279695.85498961925390.145010380746104
289996.41047695543522.58952304456477
298690.8767548187331-4.87675481873311
309297.7723416103046-5.77234161030456
318689.3725915825054-3.37259158250541
326270.6075741579139-8.60757415791392
33105106.656348837884-1.65634883788423
34108109.11258775668-1.11258775667977
359692.20621611276853.79378388723146
368083.0368333016076-3.03683330160757
379592.12538966581942.87461033418057
389492.78600741105831.21399258894171
39108113.134740400749-5.13474040074865
4097105.277657918288-8.27765791828822
418989.3228214877206-0.322821487720601
42107107.754066300075-0.754066300075081
438783.0806152765673.919384723433
447074.2006213241236-4.20062132412365
45111109.3894195757791.61058042422111
4610597.97541128483487.02458871516522
479992.72776345273186.27223654726825
488484.0921186994648-0.0921186994648042
498791.391007689164-4.39100768916403
509291.20359086507390.796409134926056
5198101.252081841715-3.25208184171525
529598.2589651605365-3.25896516053648
538588.4297142530883-3.4297142530883
54100101.466918543533-1.46691854353287
557981.9381881675068-2.93818816750679
566675.3172488724027-9.31724887240274
57105108.055967372863-3.05596737286291
589699.61463695197-3.61463695197001
5910399.67970005880843.32029994119165
608386.378919691384-3.37891969138398
619193.4641938391286-2.46419383912858
629592.42485681345292.57514318654705
63109108.1089473375430.891052662457316
649292.8031073207143-0.803107320714256
6599101.602784868196-2.60278486819648
66110106.7857619284943.214238071506
678882.18780924103955.81219075896054
687376.9698303577189-3.96983035771891
69111106.8066179283554.19338207164453
70112110.0087019163261.99129808367387
71111107.0416597726083.95834022739188
728486.0284636016348-2.02846360163483
7310299.64712704609172.3528729539083
7410299.847225513712.15277448628997
75114114.796450097167-0.796450097167489
769997.27705774116671.72294225883331
77100104.310911233246-4.3109112332459
78110120.629662934741-10.6296629347406
799395.6470659007711-2.64706590077111
807783.2285388648428-6.22853886484277
81108107.587703496250.412296503749738
82120120.404977842262-0.404977842261705
83106106.785295629908-0.785295629907511
847875.22693206603142.77306793396856
85100101.517947615428-1.51794761542784
8610298.14485209126263.85514790873743
879796.90295120352120.0970487964787866
88101104.017874592227-3.01787459222664
898991.035742284184-2.035742284184
909398.9199743053057-5.91997430530573
918988.97484684900550.0251531509944802
926264.012027442854-2.01202744285399
939696.0146372509316-0.0146372509316161
949596.441917597501-1.44191759750104
958076.12050717733443.87949282266556
966762.33213394592744.66786605407264
977169.77516001933241.22483998066756
987368.17380666303874.82619333696125
998174.38695929252686.61304070747319
1007776.73671363270380.263286367296238
1016867.36252993591650.637470064083492
1027780.0125677818665-3.01256778186653
1037370.81183650884342.18816349115664
1045455.9936136157726-1.99361361577258
1058588.3634686745196-3.36346867451956
1068682.53870096533863.46129903466138
1077980.7195774183249-1.71957741832491
1086769.6515170047565-2.65151700475645
1097274.07043968455-2.07043968455
1107671.95137042894074.04862957105928
1119087.24732259409542.75267740590456
1128482.45329238293311.54670761706694
1137572.46637558846272.53362441153732
1149091.6769485925032-1.67694859250317
1157772.25623627511474.74376372488526
1166063.5132066681557-3.51320666815565
1179290.1763305400091.823669459991
1188882.65487217978435.3451278202157
1198382.99696951686660.00303048313345005
1206977.0896348584839-8.08963485848395
1217373.8482810662349-0.848281066234868
1227876.73592400176011.26407599823991
1239285.68420772024976.31579227975034

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102 & 102.990321916938 & -0.990321916938441 \tabularnewline
2 & 99 & 96.3360015084692 & 2.66399849153083 \tabularnewline
3 & 108 & 110.94681645486 & -2.94681645486023 \tabularnewline
4 & 92 & 94.4461773093823 & -2.44617730938233 \tabularnewline
5 & 99 & 96.4739236934766 & 2.5260763065234 \tabularnewline
6 & 102 & 100.953486941539 & 1.04651305846138 \tabularnewline
7 & 87 & 92.6258585339911 & -5.62585853399115 \tabularnewline
8 & 71 & 78.9495041168939 & -7.94950411689395 \tabularnewline
9 & 105 & 102.722931758987 & 2.27706824101275 \tabularnewline
10 & 115 & 108.27911726299 & 6.72088273701029 \tabularnewline
11 & 103 & 97.6704166639077 & 5.32958333609225 \tabularnewline
12 & 75 & 78.2590280708524 & -3.25902807085243 \tabularnewline
13 & 97 & 93.5634531522089 & 3.43654684779107 \tabularnewline
14 & 95 & 90.0344857607432 & 4.96551423925684 \tabularnewline
15 & 99 & 101.105260995938 & -2.10526099593791 \tabularnewline
16 & 100 & 96.0667879040205 & 3.93321209597946 \tabularnewline
17 & 92 & 89.0884490745935 & 2.91155092540646 \tabularnewline
18 & 94 & 98.4434196387382 & -4.44341963873823 \tabularnewline
19 & 89 & 84.6990740681952 & 4.30092593180476 \tabularnewline
20 & 67 & 70.0947257507869 & -3.0947257507869 \tabularnewline
21 & 109 & 98.1326349528229 & 10.8673650471771 \tabularnewline
22 & 113 & 108.483179909464 & 4.51682009053588 \tabularnewline
23 & 106 & 96.610310002363 & 9.38968999763698 \tabularnewline
24 & 78 & 76.8196286295405 & 1.1803713704595 \tabularnewline
25 & 102 & 97.0192957687167 & 4.98070423128332 \tabularnewline
26 & 97 & 95.0204638832725 & 1.97953611672755 \tabularnewline
27 & 96 & 95.8549896192539 & 0.145010380746104 \tabularnewline
28 & 99 & 96.4104769554352 & 2.58952304456477 \tabularnewline
29 & 86 & 90.8767548187331 & -4.87675481873311 \tabularnewline
30 & 92 & 97.7723416103046 & -5.77234161030456 \tabularnewline
31 & 86 & 89.3725915825054 & -3.37259158250541 \tabularnewline
32 & 62 & 70.6075741579139 & -8.60757415791392 \tabularnewline
33 & 105 & 106.656348837884 & -1.65634883788423 \tabularnewline
34 & 108 & 109.11258775668 & -1.11258775667977 \tabularnewline
35 & 96 & 92.2062161127685 & 3.79378388723146 \tabularnewline
36 & 80 & 83.0368333016076 & -3.03683330160757 \tabularnewline
37 & 95 & 92.1253896658194 & 2.87461033418057 \tabularnewline
38 & 94 & 92.7860074110583 & 1.21399258894171 \tabularnewline
39 & 108 & 113.134740400749 & -5.13474040074865 \tabularnewline
40 & 97 & 105.277657918288 & -8.27765791828822 \tabularnewline
41 & 89 & 89.3228214877206 & -0.322821487720601 \tabularnewline
42 & 107 & 107.754066300075 & -0.754066300075081 \tabularnewline
43 & 87 & 83.080615276567 & 3.919384723433 \tabularnewline
44 & 70 & 74.2006213241236 & -4.20062132412365 \tabularnewline
45 & 111 & 109.389419575779 & 1.61058042422111 \tabularnewline
46 & 105 & 97.9754112848348 & 7.02458871516522 \tabularnewline
47 & 99 & 92.7277634527318 & 6.27223654726825 \tabularnewline
48 & 84 & 84.0921186994648 & -0.0921186994648042 \tabularnewline
49 & 87 & 91.391007689164 & -4.39100768916403 \tabularnewline
50 & 92 & 91.2035908650739 & 0.796409134926056 \tabularnewline
51 & 98 & 101.252081841715 & -3.25208184171525 \tabularnewline
52 & 95 & 98.2589651605365 & -3.25896516053648 \tabularnewline
53 & 85 & 88.4297142530883 & -3.4297142530883 \tabularnewline
54 & 100 & 101.466918543533 & -1.46691854353287 \tabularnewline
55 & 79 & 81.9381881675068 & -2.93818816750679 \tabularnewline
56 & 66 & 75.3172488724027 & -9.31724887240274 \tabularnewline
57 & 105 & 108.055967372863 & -3.05596737286291 \tabularnewline
58 & 96 & 99.61463695197 & -3.61463695197001 \tabularnewline
59 & 103 & 99.6797000588084 & 3.32029994119165 \tabularnewline
60 & 83 & 86.378919691384 & -3.37891969138398 \tabularnewline
61 & 91 & 93.4641938391286 & -2.46419383912858 \tabularnewline
62 & 95 & 92.4248568134529 & 2.57514318654705 \tabularnewline
63 & 109 & 108.108947337543 & 0.891052662457316 \tabularnewline
64 & 92 & 92.8031073207143 & -0.803107320714256 \tabularnewline
65 & 99 & 101.602784868196 & -2.60278486819648 \tabularnewline
66 & 110 & 106.785761928494 & 3.214238071506 \tabularnewline
67 & 88 & 82.1878092410395 & 5.81219075896054 \tabularnewline
68 & 73 & 76.9698303577189 & -3.96983035771891 \tabularnewline
69 & 111 & 106.806617928355 & 4.19338207164453 \tabularnewline
70 & 112 & 110.008701916326 & 1.99129808367387 \tabularnewline
71 & 111 & 107.041659772608 & 3.95834022739188 \tabularnewline
72 & 84 & 86.0284636016348 & -2.02846360163483 \tabularnewline
73 & 102 & 99.6471270460917 & 2.3528729539083 \tabularnewline
74 & 102 & 99.84722551371 & 2.15277448628997 \tabularnewline
75 & 114 & 114.796450097167 & -0.796450097167489 \tabularnewline
76 & 99 & 97.2770577411667 & 1.72294225883331 \tabularnewline
77 & 100 & 104.310911233246 & -4.3109112332459 \tabularnewline
78 & 110 & 120.629662934741 & -10.6296629347406 \tabularnewline
79 & 93 & 95.6470659007711 & -2.64706590077111 \tabularnewline
80 & 77 & 83.2285388648428 & -6.22853886484277 \tabularnewline
81 & 108 & 107.58770349625 & 0.412296503749738 \tabularnewline
82 & 120 & 120.404977842262 & -0.404977842261705 \tabularnewline
83 & 106 & 106.785295629908 & -0.785295629907511 \tabularnewline
84 & 78 & 75.2269320660314 & 2.77306793396856 \tabularnewline
85 & 100 & 101.517947615428 & -1.51794761542784 \tabularnewline
86 & 102 & 98.1448520912626 & 3.85514790873743 \tabularnewline
87 & 97 & 96.9029512035212 & 0.0970487964787866 \tabularnewline
88 & 101 & 104.017874592227 & -3.01787459222664 \tabularnewline
89 & 89 & 91.035742284184 & -2.035742284184 \tabularnewline
90 & 93 & 98.9199743053057 & -5.91997430530573 \tabularnewline
91 & 89 & 88.9748468490055 & 0.0251531509944802 \tabularnewline
92 & 62 & 64.012027442854 & -2.01202744285399 \tabularnewline
93 & 96 & 96.0146372509316 & -0.0146372509316161 \tabularnewline
94 & 95 & 96.441917597501 & -1.44191759750104 \tabularnewline
95 & 80 & 76.1205071773344 & 3.87949282266556 \tabularnewline
96 & 67 & 62.3321339459274 & 4.66786605407264 \tabularnewline
97 & 71 & 69.7751600193324 & 1.22483998066756 \tabularnewline
98 & 73 & 68.1738066630387 & 4.82619333696125 \tabularnewline
99 & 81 & 74.3869592925268 & 6.61304070747319 \tabularnewline
100 & 77 & 76.7367136327038 & 0.263286367296238 \tabularnewline
101 & 68 & 67.3625299359165 & 0.637470064083492 \tabularnewline
102 & 77 & 80.0125677818665 & -3.01256778186653 \tabularnewline
103 & 73 & 70.8118365088434 & 2.18816349115664 \tabularnewline
104 & 54 & 55.9936136157726 & -1.99361361577258 \tabularnewline
105 & 85 & 88.3634686745196 & -3.36346867451956 \tabularnewline
106 & 86 & 82.5387009653386 & 3.46129903466138 \tabularnewline
107 & 79 & 80.7195774183249 & -1.71957741832491 \tabularnewline
108 & 67 & 69.6515170047565 & -2.65151700475645 \tabularnewline
109 & 72 & 74.07043968455 & -2.07043968455 \tabularnewline
110 & 76 & 71.9513704289407 & 4.04862957105928 \tabularnewline
111 & 90 & 87.2473225940954 & 2.75267740590456 \tabularnewline
112 & 84 & 82.4532923829331 & 1.54670761706694 \tabularnewline
113 & 75 & 72.4663755884627 & 2.53362441153732 \tabularnewline
114 & 90 & 91.6769485925032 & -1.67694859250317 \tabularnewline
115 & 77 & 72.2562362751147 & 4.74376372488526 \tabularnewline
116 & 60 & 63.5132066681557 & -3.51320666815565 \tabularnewline
117 & 92 & 90.176330540009 & 1.823669459991 \tabularnewline
118 & 88 & 82.6548721797843 & 5.3451278202157 \tabularnewline
119 & 83 & 82.9969695168666 & 0.00303048313345005 \tabularnewline
120 & 69 & 77.0896348584839 & -8.08963485848395 \tabularnewline
121 & 73 & 73.8482810662349 & -0.848281066234868 \tabularnewline
122 & 78 & 76.7359240017601 & 1.26407599823991 \tabularnewline
123 & 92 & 85.6842077202497 & 6.31579227975034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&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]102[/C][C]102.990321916938[/C][C]-0.990321916938441[/C][/ROW]
[ROW][C]2[/C][C]99[/C][C]96.3360015084692[/C][C]2.66399849153083[/C][/ROW]
[ROW][C]3[/C][C]108[/C][C]110.94681645486[/C][C]-2.94681645486023[/C][/ROW]
[ROW][C]4[/C][C]92[/C][C]94.4461773093823[/C][C]-2.44617730938233[/C][/ROW]
[ROW][C]5[/C][C]99[/C][C]96.4739236934766[/C][C]2.5260763065234[/C][/ROW]
[ROW][C]6[/C][C]102[/C][C]100.953486941539[/C][C]1.04651305846138[/C][/ROW]
[ROW][C]7[/C][C]87[/C][C]92.6258585339911[/C][C]-5.62585853399115[/C][/ROW]
[ROW][C]8[/C][C]71[/C][C]78.9495041168939[/C][C]-7.94950411689395[/C][/ROW]
[ROW][C]9[/C][C]105[/C][C]102.722931758987[/C][C]2.27706824101275[/C][/ROW]
[ROW][C]10[/C][C]115[/C][C]108.27911726299[/C][C]6.72088273701029[/C][/ROW]
[ROW][C]11[/C][C]103[/C][C]97.6704166639077[/C][C]5.32958333609225[/C][/ROW]
[ROW][C]12[/C][C]75[/C][C]78.2590280708524[/C][C]-3.25902807085243[/C][/ROW]
[ROW][C]13[/C][C]97[/C][C]93.5634531522089[/C][C]3.43654684779107[/C][/ROW]
[ROW][C]14[/C][C]95[/C][C]90.0344857607432[/C][C]4.96551423925684[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]101.105260995938[/C][C]-2.10526099593791[/C][/ROW]
[ROW][C]16[/C][C]100[/C][C]96.0667879040205[/C][C]3.93321209597946[/C][/ROW]
[ROW][C]17[/C][C]92[/C][C]89.0884490745935[/C][C]2.91155092540646[/C][/ROW]
[ROW][C]18[/C][C]94[/C][C]98.4434196387382[/C][C]-4.44341963873823[/C][/ROW]
[ROW][C]19[/C][C]89[/C][C]84.6990740681952[/C][C]4.30092593180476[/C][/ROW]
[ROW][C]20[/C][C]67[/C][C]70.0947257507869[/C][C]-3.0947257507869[/C][/ROW]
[ROW][C]21[/C][C]109[/C][C]98.1326349528229[/C][C]10.8673650471771[/C][/ROW]
[ROW][C]22[/C][C]113[/C][C]108.483179909464[/C][C]4.51682009053588[/C][/ROW]
[ROW][C]23[/C][C]106[/C][C]96.610310002363[/C][C]9.38968999763698[/C][/ROW]
[ROW][C]24[/C][C]78[/C][C]76.8196286295405[/C][C]1.1803713704595[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]97.0192957687167[/C][C]4.98070423128332[/C][/ROW]
[ROW][C]26[/C][C]97[/C][C]95.0204638832725[/C][C]1.97953611672755[/C][/ROW]
[ROW][C]27[/C][C]96[/C][C]95.8549896192539[/C][C]0.145010380746104[/C][/ROW]
[ROW][C]28[/C][C]99[/C][C]96.4104769554352[/C][C]2.58952304456477[/C][/ROW]
[ROW][C]29[/C][C]86[/C][C]90.8767548187331[/C][C]-4.87675481873311[/C][/ROW]
[ROW][C]30[/C][C]92[/C][C]97.7723416103046[/C][C]-5.77234161030456[/C][/ROW]
[ROW][C]31[/C][C]86[/C][C]89.3725915825054[/C][C]-3.37259158250541[/C][/ROW]
[ROW][C]32[/C][C]62[/C][C]70.6075741579139[/C][C]-8.60757415791392[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]106.656348837884[/C][C]-1.65634883788423[/C][/ROW]
[ROW][C]34[/C][C]108[/C][C]109.11258775668[/C][C]-1.11258775667977[/C][/ROW]
[ROW][C]35[/C][C]96[/C][C]92.2062161127685[/C][C]3.79378388723146[/C][/ROW]
[ROW][C]36[/C][C]80[/C][C]83.0368333016076[/C][C]-3.03683330160757[/C][/ROW]
[ROW][C]37[/C][C]95[/C][C]92.1253896658194[/C][C]2.87461033418057[/C][/ROW]
[ROW][C]38[/C][C]94[/C][C]92.7860074110583[/C][C]1.21399258894171[/C][/ROW]
[ROW][C]39[/C][C]108[/C][C]113.134740400749[/C][C]-5.13474040074865[/C][/ROW]
[ROW][C]40[/C][C]97[/C][C]105.277657918288[/C][C]-8.27765791828822[/C][/ROW]
[ROW][C]41[/C][C]89[/C][C]89.3228214877206[/C][C]-0.322821487720601[/C][/ROW]
[ROW][C]42[/C][C]107[/C][C]107.754066300075[/C][C]-0.754066300075081[/C][/ROW]
[ROW][C]43[/C][C]87[/C][C]83.080615276567[/C][C]3.919384723433[/C][/ROW]
[ROW][C]44[/C][C]70[/C][C]74.2006213241236[/C][C]-4.20062132412365[/C][/ROW]
[ROW][C]45[/C][C]111[/C][C]109.389419575779[/C][C]1.61058042422111[/C][/ROW]
[ROW][C]46[/C][C]105[/C][C]97.9754112848348[/C][C]7.02458871516522[/C][/ROW]
[ROW][C]47[/C][C]99[/C][C]92.7277634527318[/C][C]6.27223654726825[/C][/ROW]
[ROW][C]48[/C][C]84[/C][C]84.0921186994648[/C][C]-0.0921186994648042[/C][/ROW]
[ROW][C]49[/C][C]87[/C][C]91.391007689164[/C][C]-4.39100768916403[/C][/ROW]
[ROW][C]50[/C][C]92[/C][C]91.2035908650739[/C][C]0.796409134926056[/C][/ROW]
[ROW][C]51[/C][C]98[/C][C]101.252081841715[/C][C]-3.25208184171525[/C][/ROW]
[ROW][C]52[/C][C]95[/C][C]98.2589651605365[/C][C]-3.25896516053648[/C][/ROW]
[ROW][C]53[/C][C]85[/C][C]88.4297142530883[/C][C]-3.4297142530883[/C][/ROW]
[ROW][C]54[/C][C]100[/C][C]101.466918543533[/C][C]-1.46691854353287[/C][/ROW]
[ROW][C]55[/C][C]79[/C][C]81.9381881675068[/C][C]-2.93818816750679[/C][/ROW]
[ROW][C]56[/C][C]66[/C][C]75.3172488724027[/C][C]-9.31724887240274[/C][/ROW]
[ROW][C]57[/C][C]105[/C][C]108.055967372863[/C][C]-3.05596737286291[/C][/ROW]
[ROW][C]58[/C][C]96[/C][C]99.61463695197[/C][C]-3.61463695197001[/C][/ROW]
[ROW][C]59[/C][C]103[/C][C]99.6797000588084[/C][C]3.32029994119165[/C][/ROW]
[ROW][C]60[/C][C]83[/C][C]86.378919691384[/C][C]-3.37891969138398[/C][/ROW]
[ROW][C]61[/C][C]91[/C][C]93.4641938391286[/C][C]-2.46419383912858[/C][/ROW]
[ROW][C]62[/C][C]95[/C][C]92.4248568134529[/C][C]2.57514318654705[/C][/ROW]
[ROW][C]63[/C][C]109[/C][C]108.108947337543[/C][C]0.891052662457316[/C][/ROW]
[ROW][C]64[/C][C]92[/C][C]92.8031073207143[/C][C]-0.803107320714256[/C][/ROW]
[ROW][C]65[/C][C]99[/C][C]101.602784868196[/C][C]-2.60278486819648[/C][/ROW]
[ROW][C]66[/C][C]110[/C][C]106.785761928494[/C][C]3.214238071506[/C][/ROW]
[ROW][C]67[/C][C]88[/C][C]82.1878092410395[/C][C]5.81219075896054[/C][/ROW]
[ROW][C]68[/C][C]73[/C][C]76.9698303577189[/C][C]-3.96983035771891[/C][/ROW]
[ROW][C]69[/C][C]111[/C][C]106.806617928355[/C][C]4.19338207164453[/C][/ROW]
[ROW][C]70[/C][C]112[/C][C]110.008701916326[/C][C]1.99129808367387[/C][/ROW]
[ROW][C]71[/C][C]111[/C][C]107.041659772608[/C][C]3.95834022739188[/C][/ROW]
[ROW][C]72[/C][C]84[/C][C]86.0284636016348[/C][C]-2.02846360163483[/C][/ROW]
[ROW][C]73[/C][C]102[/C][C]99.6471270460917[/C][C]2.3528729539083[/C][/ROW]
[ROW][C]74[/C][C]102[/C][C]99.84722551371[/C][C]2.15277448628997[/C][/ROW]
[ROW][C]75[/C][C]114[/C][C]114.796450097167[/C][C]-0.796450097167489[/C][/ROW]
[ROW][C]76[/C][C]99[/C][C]97.2770577411667[/C][C]1.72294225883331[/C][/ROW]
[ROW][C]77[/C][C]100[/C][C]104.310911233246[/C][C]-4.3109112332459[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]120.629662934741[/C][C]-10.6296629347406[/C][/ROW]
[ROW][C]79[/C][C]93[/C][C]95.6470659007711[/C][C]-2.64706590077111[/C][/ROW]
[ROW][C]80[/C][C]77[/C][C]83.2285388648428[/C][C]-6.22853886484277[/C][/ROW]
[ROW][C]81[/C][C]108[/C][C]107.58770349625[/C][C]0.412296503749738[/C][/ROW]
[ROW][C]82[/C][C]120[/C][C]120.404977842262[/C][C]-0.404977842261705[/C][/ROW]
[ROW][C]83[/C][C]106[/C][C]106.785295629908[/C][C]-0.785295629907511[/C][/ROW]
[ROW][C]84[/C][C]78[/C][C]75.2269320660314[/C][C]2.77306793396856[/C][/ROW]
[ROW][C]85[/C][C]100[/C][C]101.517947615428[/C][C]-1.51794761542784[/C][/ROW]
[ROW][C]86[/C][C]102[/C][C]98.1448520912626[/C][C]3.85514790873743[/C][/ROW]
[ROW][C]87[/C][C]97[/C][C]96.9029512035212[/C][C]0.0970487964787866[/C][/ROW]
[ROW][C]88[/C][C]101[/C][C]104.017874592227[/C][C]-3.01787459222664[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]91.035742284184[/C][C]-2.035742284184[/C][/ROW]
[ROW][C]90[/C][C]93[/C][C]98.9199743053057[/C][C]-5.91997430530573[/C][/ROW]
[ROW][C]91[/C][C]89[/C][C]88.9748468490055[/C][C]0.0251531509944802[/C][/ROW]
[ROW][C]92[/C][C]62[/C][C]64.012027442854[/C][C]-2.01202744285399[/C][/ROW]
[ROW][C]93[/C][C]96[/C][C]96.0146372509316[/C][C]-0.0146372509316161[/C][/ROW]
[ROW][C]94[/C][C]95[/C][C]96.441917597501[/C][C]-1.44191759750104[/C][/ROW]
[ROW][C]95[/C][C]80[/C][C]76.1205071773344[/C][C]3.87949282266556[/C][/ROW]
[ROW][C]96[/C][C]67[/C][C]62.3321339459274[/C][C]4.66786605407264[/C][/ROW]
[ROW][C]97[/C][C]71[/C][C]69.7751600193324[/C][C]1.22483998066756[/C][/ROW]
[ROW][C]98[/C][C]73[/C][C]68.1738066630387[/C][C]4.82619333696125[/C][/ROW]
[ROW][C]99[/C][C]81[/C][C]74.3869592925268[/C][C]6.61304070747319[/C][/ROW]
[ROW][C]100[/C][C]77[/C][C]76.7367136327038[/C][C]0.263286367296238[/C][/ROW]
[ROW][C]101[/C][C]68[/C][C]67.3625299359165[/C][C]0.637470064083492[/C][/ROW]
[ROW][C]102[/C][C]77[/C][C]80.0125677818665[/C][C]-3.01256778186653[/C][/ROW]
[ROW][C]103[/C][C]73[/C][C]70.8118365088434[/C][C]2.18816349115664[/C][/ROW]
[ROW][C]104[/C][C]54[/C][C]55.9936136157726[/C][C]-1.99361361577258[/C][/ROW]
[ROW][C]105[/C][C]85[/C][C]88.3634686745196[/C][C]-3.36346867451956[/C][/ROW]
[ROW][C]106[/C][C]86[/C][C]82.5387009653386[/C][C]3.46129903466138[/C][/ROW]
[ROW][C]107[/C][C]79[/C][C]80.7195774183249[/C][C]-1.71957741832491[/C][/ROW]
[ROW][C]108[/C][C]67[/C][C]69.6515170047565[/C][C]-2.65151700475645[/C][/ROW]
[ROW][C]109[/C][C]72[/C][C]74.07043968455[/C][C]-2.07043968455[/C][/ROW]
[ROW][C]110[/C][C]76[/C][C]71.9513704289407[/C][C]4.04862957105928[/C][/ROW]
[ROW][C]111[/C][C]90[/C][C]87.2473225940954[/C][C]2.75267740590456[/C][/ROW]
[ROW][C]112[/C][C]84[/C][C]82.4532923829331[/C][C]1.54670761706694[/C][/ROW]
[ROW][C]113[/C][C]75[/C][C]72.4663755884627[/C][C]2.53362441153732[/C][/ROW]
[ROW][C]114[/C][C]90[/C][C]91.6769485925032[/C][C]-1.67694859250317[/C][/ROW]
[ROW][C]115[/C][C]77[/C][C]72.2562362751147[/C][C]4.74376372488526[/C][/ROW]
[ROW][C]116[/C][C]60[/C][C]63.5132066681557[/C][C]-3.51320666815565[/C][/ROW]
[ROW][C]117[/C][C]92[/C][C]90.176330540009[/C][C]1.823669459991[/C][/ROW]
[ROW][C]118[/C][C]88[/C][C]82.6548721797843[/C][C]5.3451278202157[/C][/ROW]
[ROW][C]119[/C][C]83[/C][C]82.9969695168666[/C][C]0.00303048313345005[/C][/ROW]
[ROW][C]120[/C][C]69[/C][C]77.0896348584839[/C][C]-8.08963485848395[/C][/ROW]
[ROW][C]121[/C][C]73[/C][C]73.8482810662349[/C][C]-0.848281066234868[/C][/ROW]
[ROW][C]122[/C][C]78[/C][C]76.7359240017601[/C][C]1.26407599823991[/C][/ROW]
[ROW][C]123[/C][C]92[/C][C]85.6842077202497[/C][C]6.31579227975034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102102.990321916938-0.990321916938441
29996.33600150846922.66399849153083
3108110.94681645486-2.94681645486023
49294.4461773093823-2.44617730938233
59996.47392369347662.5260763065234
6102100.9534869415391.04651305846138
78792.6258585339911-5.62585853399115
87178.9495041168939-7.94950411689395
9105102.7229317589872.27706824101275
10115108.279117262996.72088273701029
1110397.67041666390775.32958333609225
127578.2590280708524-3.25902807085243
139793.56345315220893.43654684779107
149590.03448576074324.96551423925684
1599101.105260995938-2.10526099593791
1610096.06678790402053.93321209597946
179289.08844907459352.91155092540646
189498.4434196387382-4.44341963873823
198984.69907406819524.30092593180476
206770.0947257507869-3.0947257507869
2110998.132634952822910.8673650471771
22113108.4831799094644.51682009053588
2310696.6103100023639.38968999763698
247876.81962862954051.1803713704595
2510297.01929576871674.98070423128332
269795.02046388327251.97953611672755
279695.85498961925390.145010380746104
289996.41047695543522.58952304456477
298690.8767548187331-4.87675481873311
309297.7723416103046-5.77234161030456
318689.3725915825054-3.37259158250541
326270.6075741579139-8.60757415791392
33105106.656348837884-1.65634883788423
34108109.11258775668-1.11258775667977
359692.20621611276853.79378388723146
368083.0368333016076-3.03683330160757
379592.12538966581942.87461033418057
389492.78600741105831.21399258894171
39108113.134740400749-5.13474040074865
4097105.277657918288-8.27765791828822
418989.3228214877206-0.322821487720601
42107107.754066300075-0.754066300075081
438783.0806152765673.919384723433
447074.2006213241236-4.20062132412365
45111109.3894195757791.61058042422111
4610597.97541128483487.02458871516522
479992.72776345273186.27223654726825
488484.0921186994648-0.0921186994648042
498791.391007689164-4.39100768916403
509291.20359086507390.796409134926056
5198101.252081841715-3.25208184171525
529598.2589651605365-3.25896516053648
538588.4297142530883-3.4297142530883
54100101.466918543533-1.46691854353287
557981.9381881675068-2.93818816750679
566675.3172488724027-9.31724887240274
57105108.055967372863-3.05596737286291
589699.61463695197-3.61463695197001
5910399.67970005880843.32029994119165
608386.378919691384-3.37891969138398
619193.4641938391286-2.46419383912858
629592.42485681345292.57514318654705
63109108.1089473375430.891052662457316
649292.8031073207143-0.803107320714256
6599101.602784868196-2.60278486819648
66110106.7857619284943.214238071506
678882.18780924103955.81219075896054
687376.9698303577189-3.96983035771891
69111106.8066179283554.19338207164453
70112110.0087019163261.99129808367387
71111107.0416597726083.95834022739188
728486.0284636016348-2.02846360163483
7310299.64712704609172.3528729539083
7410299.847225513712.15277448628997
75114114.796450097167-0.796450097167489
769997.27705774116671.72294225883331
77100104.310911233246-4.3109112332459
78110120.629662934741-10.6296629347406
799395.6470659007711-2.64706590077111
807783.2285388648428-6.22853886484277
81108107.587703496250.412296503749738
82120120.404977842262-0.404977842261705
83106106.785295629908-0.785295629907511
847875.22693206603142.77306793396856
85100101.517947615428-1.51794761542784
8610298.14485209126263.85514790873743
879796.90295120352120.0970487964787866
88101104.017874592227-3.01787459222664
898991.035742284184-2.035742284184
909398.9199743053057-5.91997430530573
918988.97484684900550.0251531509944802
926264.012027442854-2.01202744285399
939696.0146372509316-0.0146372509316161
949596.441917597501-1.44191759750104
958076.12050717733443.87949282266556
966762.33213394592744.66786605407264
977169.77516001933241.22483998066756
987368.17380666303874.82619333696125
998174.38695929252686.61304070747319
1007776.73671363270380.263286367296238
1016867.36252993591650.637470064083492
1027780.0125677818665-3.01256778186653
1037370.81183650884342.18816349115664
1045455.9936136157726-1.99361361577258
1058588.3634686745196-3.36346867451956
1068682.53870096533863.46129903466138
1077980.7195774183249-1.71957741832491
1086769.6515170047565-2.65151700475645
1097274.07043968455-2.07043968455
1107671.95137042894074.04862957105928
1119087.24732259409542.75267740590456
1128482.45329238293311.54670761706694
1137572.46637558846272.53362441153732
1149091.6769485925032-1.67694859250317
1157772.25623627511474.74376372488526
1166063.5132066681557-3.51320666815565
1179290.1763305400091.823669459991
1188882.65487217978435.3451278202157
1198382.99696951686660.00303048313345005
1206977.0896348584839-8.08963485848395
1217373.8482810662349-0.848281066234868
1227876.73592400176011.26407599823991
1239285.68420772024976.31579227975034






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2921234706577140.5842469413154270.707876529342287
180.8793700084146870.2412599831706250.120629991585313
190.8360713128471010.3278573743057980.163928687152899
200.8743622855968220.2512754288063560.125637714403178
210.8640908971458070.2718182057083860.135909102854193
220.8376799476255020.3246401047489960.162320052374498
230.8285251988732870.3429496022534260.171474801126713
240.7920656298507690.4158687402984630.207934370149231
250.8473979392078430.3052041215843140.152602060792157
260.8272840978167060.3454318043665880.172715902183294
270.8126457880309550.374708423938090.187354211969045
280.7717260215615660.4565479568768680.228273978438434
290.8580267678225430.2839464643549150.141973232177457
300.8857830440892970.2284339118214070.114216955910703
310.849730222190380.3005395556192390.15026977780962
320.849978668042650.3000426639147010.15002133195735
330.8242967748342020.3514064503315960.175703225165798
340.776687152774980.446625694450040.22331284722502
350.7868241486437090.4263517027125820.213175851356291
360.7456152320838880.5087695358322240.254384767916112
370.8588299475701960.2823401048596080.141170052429804
380.8496136042992290.3007727914015430.150386395700771
390.8380552516636330.3238894966727340.161944748336367
400.8644865825802570.2710268348394860.135513417419743
410.8456832783991910.3086334432016170.154316721600809
420.821825805386750.3563483892264990.17817419461325
430.8740583343946580.2518833312106840.125941665605342
440.869083973313120.261832053373760.13091602668688
450.8417504258369950.316499148326010.158249574163005
460.9470742017981470.1058515964037060.0529257982018531
470.9699943479930380.06001130401392440.0300056520069622
480.9626391406067370.07472171878652560.0373608593932628
490.9496838965568350.1006322068863310.0503161034431653
500.9362732165084740.1274535669830530.0637267834915264
510.9185071964124680.1629856071750650.0814928035875324
520.8945714673356770.2108570653286470.105428532664323
530.8679636311558260.2640727376883490.132036368844174
540.835762195007380.3284756099852390.16423780499262
550.8037862352029220.3924275295941560.196213764797078
560.8970653627742750.2058692744514490.102934637225725
570.8853192082789290.2293615834421430.114680791721071
580.8751211995724540.2497576008550910.124878800427545
590.8684386244744710.2631227510510580.131561375525529
600.8452782675282650.3094434649434710.154721732471735
610.8192507313274640.3614985373450720.180749268672536
620.7915830641528330.4168338716943350.208416935847167
630.7534342606270950.493131478745810.246565739372905
640.7125322171157930.5749355657684140.287467782884207
650.6863556062122610.6272887875754790.313644393787739
660.6899038883613960.6201922232772070.310096111638604
670.7651732113600230.4696535772799530.234826788639977
680.7846571697105620.4306856605788770.215342830289438
690.7854584763583640.4290830472832720.214541523641636
700.8044764117786580.3910471764426840.195523588221342
710.8421008607283630.3157982785432730.157899139271637
720.8271251725222310.3457496549555380.172874827477769
730.7949419235853020.4101161528293970.205058076414698
740.784541136899480.430917726201040.21545886310052
750.7448598225821250.5102803548357510.255140177417875
760.740999465311780.5180010693764390.25900053468822
770.7218329517495940.5563340965008120.278167048250406
780.8724951963649290.2550096072701410.127504803635071
790.8406537718686860.3186924562626290.159346228131314
800.8752374678563930.2495250642872140.124762532143607
810.8425328582689830.3149342834620340.157467141731017
820.8267479684955010.3465040630089970.173252031504499
830.7844951628670680.4310096742658640.215504837132932
840.784425080084070.4311498398318590.21557491991593
850.7451740080695950.5096519838608090.254825991930405
860.7916560520933810.4166878958132380.208343947906619
870.8728212382906950.2543575234186090.127178761709305
880.8333239572677820.3333520854644370.166676042732218
890.7913436983272780.4173126033454450.208656301672722
900.790142291791140.419715416417720.20985770820886
910.7515991657774620.4968016684450760.248400834222538
920.8327566126194170.3344867747611660.167243387380583
930.7937589276564670.4124821446870660.206241072343533
940.8510741545637580.2978516908724850.148925845436242
950.8163910981999840.3672178036000320.183608901800016
960.8202565102531860.3594869794936290.179743489746814
970.8526199794060350.2947600411879310.147380020593966
980.8347255786913890.3305488426172230.165274421308611
990.8608062719315810.2783874561368380.139193728068419
1000.7947833195950880.4104333608098240.205216680404912
1010.7507974733406140.4984050533187720.249202526659386
1020.7717111658270290.4565776683459410.22828883417297
1030.6682246410033540.6635507179932920.331775358996646
1040.5735990328592010.8528019342815970.426400967140799
1050.4390621156425970.8781242312851940.560937884357403
1060.3570045068651630.7140090137303250.642995493134837

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.292123470657714 & 0.584246941315427 & 0.707876529342287 \tabularnewline
18 & 0.879370008414687 & 0.241259983170625 & 0.120629991585313 \tabularnewline
19 & 0.836071312847101 & 0.327857374305798 & 0.163928687152899 \tabularnewline
20 & 0.874362285596822 & 0.251275428806356 & 0.125637714403178 \tabularnewline
21 & 0.864090897145807 & 0.271818205708386 & 0.135909102854193 \tabularnewline
22 & 0.837679947625502 & 0.324640104748996 & 0.162320052374498 \tabularnewline
23 & 0.828525198873287 & 0.342949602253426 & 0.171474801126713 \tabularnewline
24 & 0.792065629850769 & 0.415868740298463 & 0.207934370149231 \tabularnewline
25 & 0.847397939207843 & 0.305204121584314 & 0.152602060792157 \tabularnewline
26 & 0.827284097816706 & 0.345431804366588 & 0.172715902183294 \tabularnewline
27 & 0.812645788030955 & 0.37470842393809 & 0.187354211969045 \tabularnewline
28 & 0.771726021561566 & 0.456547956876868 & 0.228273978438434 \tabularnewline
29 & 0.858026767822543 & 0.283946464354915 & 0.141973232177457 \tabularnewline
30 & 0.885783044089297 & 0.228433911821407 & 0.114216955910703 \tabularnewline
31 & 0.84973022219038 & 0.300539555619239 & 0.15026977780962 \tabularnewline
32 & 0.84997866804265 & 0.300042663914701 & 0.15002133195735 \tabularnewline
33 & 0.824296774834202 & 0.351406450331596 & 0.175703225165798 \tabularnewline
34 & 0.77668715277498 & 0.44662569445004 & 0.22331284722502 \tabularnewline
35 & 0.786824148643709 & 0.426351702712582 & 0.213175851356291 \tabularnewline
36 & 0.745615232083888 & 0.508769535832224 & 0.254384767916112 \tabularnewline
37 & 0.858829947570196 & 0.282340104859608 & 0.141170052429804 \tabularnewline
38 & 0.849613604299229 & 0.300772791401543 & 0.150386395700771 \tabularnewline
39 & 0.838055251663633 & 0.323889496672734 & 0.161944748336367 \tabularnewline
40 & 0.864486582580257 & 0.271026834839486 & 0.135513417419743 \tabularnewline
41 & 0.845683278399191 & 0.308633443201617 & 0.154316721600809 \tabularnewline
42 & 0.82182580538675 & 0.356348389226499 & 0.17817419461325 \tabularnewline
43 & 0.874058334394658 & 0.251883331210684 & 0.125941665605342 \tabularnewline
44 & 0.86908397331312 & 0.26183205337376 & 0.13091602668688 \tabularnewline
45 & 0.841750425836995 & 0.31649914832601 & 0.158249574163005 \tabularnewline
46 & 0.947074201798147 & 0.105851596403706 & 0.0529257982018531 \tabularnewline
47 & 0.969994347993038 & 0.0600113040139244 & 0.0300056520069622 \tabularnewline
48 & 0.962639140606737 & 0.0747217187865256 & 0.0373608593932628 \tabularnewline
49 & 0.949683896556835 & 0.100632206886331 & 0.0503161034431653 \tabularnewline
50 & 0.936273216508474 & 0.127453566983053 & 0.0637267834915264 \tabularnewline
51 & 0.918507196412468 & 0.162985607175065 & 0.0814928035875324 \tabularnewline
52 & 0.894571467335677 & 0.210857065328647 & 0.105428532664323 \tabularnewline
53 & 0.867963631155826 & 0.264072737688349 & 0.132036368844174 \tabularnewline
54 & 0.83576219500738 & 0.328475609985239 & 0.16423780499262 \tabularnewline
55 & 0.803786235202922 & 0.392427529594156 & 0.196213764797078 \tabularnewline
56 & 0.897065362774275 & 0.205869274451449 & 0.102934637225725 \tabularnewline
57 & 0.885319208278929 & 0.229361583442143 & 0.114680791721071 \tabularnewline
58 & 0.875121199572454 & 0.249757600855091 & 0.124878800427545 \tabularnewline
59 & 0.868438624474471 & 0.263122751051058 & 0.131561375525529 \tabularnewline
60 & 0.845278267528265 & 0.309443464943471 & 0.154721732471735 \tabularnewline
61 & 0.819250731327464 & 0.361498537345072 & 0.180749268672536 \tabularnewline
62 & 0.791583064152833 & 0.416833871694335 & 0.208416935847167 \tabularnewline
63 & 0.753434260627095 & 0.49313147874581 & 0.246565739372905 \tabularnewline
64 & 0.712532217115793 & 0.574935565768414 & 0.287467782884207 \tabularnewline
65 & 0.686355606212261 & 0.627288787575479 & 0.313644393787739 \tabularnewline
66 & 0.689903888361396 & 0.620192223277207 & 0.310096111638604 \tabularnewline
67 & 0.765173211360023 & 0.469653577279953 & 0.234826788639977 \tabularnewline
68 & 0.784657169710562 & 0.430685660578877 & 0.215342830289438 \tabularnewline
69 & 0.785458476358364 & 0.429083047283272 & 0.214541523641636 \tabularnewline
70 & 0.804476411778658 & 0.391047176442684 & 0.195523588221342 \tabularnewline
71 & 0.842100860728363 & 0.315798278543273 & 0.157899139271637 \tabularnewline
72 & 0.827125172522231 & 0.345749654955538 & 0.172874827477769 \tabularnewline
73 & 0.794941923585302 & 0.410116152829397 & 0.205058076414698 \tabularnewline
74 & 0.78454113689948 & 0.43091772620104 & 0.21545886310052 \tabularnewline
75 & 0.744859822582125 & 0.510280354835751 & 0.255140177417875 \tabularnewline
76 & 0.74099946531178 & 0.518001069376439 & 0.25900053468822 \tabularnewline
77 & 0.721832951749594 & 0.556334096500812 & 0.278167048250406 \tabularnewline
78 & 0.872495196364929 & 0.255009607270141 & 0.127504803635071 \tabularnewline
79 & 0.840653771868686 & 0.318692456262629 & 0.159346228131314 \tabularnewline
80 & 0.875237467856393 & 0.249525064287214 & 0.124762532143607 \tabularnewline
81 & 0.842532858268983 & 0.314934283462034 & 0.157467141731017 \tabularnewline
82 & 0.826747968495501 & 0.346504063008997 & 0.173252031504499 \tabularnewline
83 & 0.784495162867068 & 0.431009674265864 & 0.215504837132932 \tabularnewline
84 & 0.78442508008407 & 0.431149839831859 & 0.21557491991593 \tabularnewline
85 & 0.745174008069595 & 0.509651983860809 & 0.254825991930405 \tabularnewline
86 & 0.791656052093381 & 0.416687895813238 & 0.208343947906619 \tabularnewline
87 & 0.872821238290695 & 0.254357523418609 & 0.127178761709305 \tabularnewline
88 & 0.833323957267782 & 0.333352085464437 & 0.166676042732218 \tabularnewline
89 & 0.791343698327278 & 0.417312603345445 & 0.208656301672722 \tabularnewline
90 & 0.79014229179114 & 0.41971541641772 & 0.20985770820886 \tabularnewline
91 & 0.751599165777462 & 0.496801668445076 & 0.248400834222538 \tabularnewline
92 & 0.832756612619417 & 0.334486774761166 & 0.167243387380583 \tabularnewline
93 & 0.793758927656467 & 0.412482144687066 & 0.206241072343533 \tabularnewline
94 & 0.851074154563758 & 0.297851690872485 & 0.148925845436242 \tabularnewline
95 & 0.816391098199984 & 0.367217803600032 & 0.183608901800016 \tabularnewline
96 & 0.820256510253186 & 0.359486979493629 & 0.179743489746814 \tabularnewline
97 & 0.852619979406035 & 0.294760041187931 & 0.147380020593966 \tabularnewline
98 & 0.834725578691389 & 0.330548842617223 & 0.165274421308611 \tabularnewline
99 & 0.860806271931581 & 0.278387456136838 & 0.139193728068419 \tabularnewline
100 & 0.794783319595088 & 0.410433360809824 & 0.205216680404912 \tabularnewline
101 & 0.750797473340614 & 0.498405053318772 & 0.249202526659386 \tabularnewline
102 & 0.771711165827029 & 0.456577668345941 & 0.22828883417297 \tabularnewline
103 & 0.668224641003354 & 0.663550717993292 & 0.331775358996646 \tabularnewline
104 & 0.573599032859201 & 0.852801934281597 & 0.426400967140799 \tabularnewline
105 & 0.439062115642597 & 0.878124231285194 & 0.560937884357403 \tabularnewline
106 & 0.357004506865163 & 0.714009013730325 & 0.642995493134837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&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]17[/C][C]0.292123470657714[/C][C]0.584246941315427[/C][C]0.707876529342287[/C][/ROW]
[ROW][C]18[/C][C]0.879370008414687[/C][C]0.241259983170625[/C][C]0.120629991585313[/C][/ROW]
[ROW][C]19[/C][C]0.836071312847101[/C][C]0.327857374305798[/C][C]0.163928687152899[/C][/ROW]
[ROW][C]20[/C][C]0.874362285596822[/C][C]0.251275428806356[/C][C]0.125637714403178[/C][/ROW]
[ROW][C]21[/C][C]0.864090897145807[/C][C]0.271818205708386[/C][C]0.135909102854193[/C][/ROW]
[ROW][C]22[/C][C]0.837679947625502[/C][C]0.324640104748996[/C][C]0.162320052374498[/C][/ROW]
[ROW][C]23[/C][C]0.828525198873287[/C][C]0.342949602253426[/C][C]0.171474801126713[/C][/ROW]
[ROW][C]24[/C][C]0.792065629850769[/C][C]0.415868740298463[/C][C]0.207934370149231[/C][/ROW]
[ROW][C]25[/C][C]0.847397939207843[/C][C]0.305204121584314[/C][C]0.152602060792157[/C][/ROW]
[ROW][C]26[/C][C]0.827284097816706[/C][C]0.345431804366588[/C][C]0.172715902183294[/C][/ROW]
[ROW][C]27[/C][C]0.812645788030955[/C][C]0.37470842393809[/C][C]0.187354211969045[/C][/ROW]
[ROW][C]28[/C][C]0.771726021561566[/C][C]0.456547956876868[/C][C]0.228273978438434[/C][/ROW]
[ROW][C]29[/C][C]0.858026767822543[/C][C]0.283946464354915[/C][C]0.141973232177457[/C][/ROW]
[ROW][C]30[/C][C]0.885783044089297[/C][C]0.228433911821407[/C][C]0.114216955910703[/C][/ROW]
[ROW][C]31[/C][C]0.84973022219038[/C][C]0.300539555619239[/C][C]0.15026977780962[/C][/ROW]
[ROW][C]32[/C][C]0.84997866804265[/C][C]0.300042663914701[/C][C]0.15002133195735[/C][/ROW]
[ROW][C]33[/C][C]0.824296774834202[/C][C]0.351406450331596[/C][C]0.175703225165798[/C][/ROW]
[ROW][C]34[/C][C]0.77668715277498[/C][C]0.44662569445004[/C][C]0.22331284722502[/C][/ROW]
[ROW][C]35[/C][C]0.786824148643709[/C][C]0.426351702712582[/C][C]0.213175851356291[/C][/ROW]
[ROW][C]36[/C][C]0.745615232083888[/C][C]0.508769535832224[/C][C]0.254384767916112[/C][/ROW]
[ROW][C]37[/C][C]0.858829947570196[/C][C]0.282340104859608[/C][C]0.141170052429804[/C][/ROW]
[ROW][C]38[/C][C]0.849613604299229[/C][C]0.300772791401543[/C][C]0.150386395700771[/C][/ROW]
[ROW][C]39[/C][C]0.838055251663633[/C][C]0.323889496672734[/C][C]0.161944748336367[/C][/ROW]
[ROW][C]40[/C][C]0.864486582580257[/C][C]0.271026834839486[/C][C]0.135513417419743[/C][/ROW]
[ROW][C]41[/C][C]0.845683278399191[/C][C]0.308633443201617[/C][C]0.154316721600809[/C][/ROW]
[ROW][C]42[/C][C]0.82182580538675[/C][C]0.356348389226499[/C][C]0.17817419461325[/C][/ROW]
[ROW][C]43[/C][C]0.874058334394658[/C][C]0.251883331210684[/C][C]0.125941665605342[/C][/ROW]
[ROW][C]44[/C][C]0.86908397331312[/C][C]0.26183205337376[/C][C]0.13091602668688[/C][/ROW]
[ROW][C]45[/C][C]0.841750425836995[/C][C]0.31649914832601[/C][C]0.158249574163005[/C][/ROW]
[ROW][C]46[/C][C]0.947074201798147[/C][C]0.105851596403706[/C][C]0.0529257982018531[/C][/ROW]
[ROW][C]47[/C][C]0.969994347993038[/C][C]0.0600113040139244[/C][C]0.0300056520069622[/C][/ROW]
[ROW][C]48[/C][C]0.962639140606737[/C][C]0.0747217187865256[/C][C]0.0373608593932628[/C][/ROW]
[ROW][C]49[/C][C]0.949683896556835[/C][C]0.100632206886331[/C][C]0.0503161034431653[/C][/ROW]
[ROW][C]50[/C][C]0.936273216508474[/C][C]0.127453566983053[/C][C]0.0637267834915264[/C][/ROW]
[ROW][C]51[/C][C]0.918507196412468[/C][C]0.162985607175065[/C][C]0.0814928035875324[/C][/ROW]
[ROW][C]52[/C][C]0.894571467335677[/C][C]0.210857065328647[/C][C]0.105428532664323[/C][/ROW]
[ROW][C]53[/C][C]0.867963631155826[/C][C]0.264072737688349[/C][C]0.132036368844174[/C][/ROW]
[ROW][C]54[/C][C]0.83576219500738[/C][C]0.328475609985239[/C][C]0.16423780499262[/C][/ROW]
[ROW][C]55[/C][C]0.803786235202922[/C][C]0.392427529594156[/C][C]0.196213764797078[/C][/ROW]
[ROW][C]56[/C][C]0.897065362774275[/C][C]0.205869274451449[/C][C]0.102934637225725[/C][/ROW]
[ROW][C]57[/C][C]0.885319208278929[/C][C]0.229361583442143[/C][C]0.114680791721071[/C][/ROW]
[ROW][C]58[/C][C]0.875121199572454[/C][C]0.249757600855091[/C][C]0.124878800427545[/C][/ROW]
[ROW][C]59[/C][C]0.868438624474471[/C][C]0.263122751051058[/C][C]0.131561375525529[/C][/ROW]
[ROW][C]60[/C][C]0.845278267528265[/C][C]0.309443464943471[/C][C]0.154721732471735[/C][/ROW]
[ROW][C]61[/C][C]0.819250731327464[/C][C]0.361498537345072[/C][C]0.180749268672536[/C][/ROW]
[ROW][C]62[/C][C]0.791583064152833[/C][C]0.416833871694335[/C][C]0.208416935847167[/C][/ROW]
[ROW][C]63[/C][C]0.753434260627095[/C][C]0.49313147874581[/C][C]0.246565739372905[/C][/ROW]
[ROW][C]64[/C][C]0.712532217115793[/C][C]0.574935565768414[/C][C]0.287467782884207[/C][/ROW]
[ROW][C]65[/C][C]0.686355606212261[/C][C]0.627288787575479[/C][C]0.313644393787739[/C][/ROW]
[ROW][C]66[/C][C]0.689903888361396[/C][C]0.620192223277207[/C][C]0.310096111638604[/C][/ROW]
[ROW][C]67[/C][C]0.765173211360023[/C][C]0.469653577279953[/C][C]0.234826788639977[/C][/ROW]
[ROW][C]68[/C][C]0.784657169710562[/C][C]0.430685660578877[/C][C]0.215342830289438[/C][/ROW]
[ROW][C]69[/C][C]0.785458476358364[/C][C]0.429083047283272[/C][C]0.214541523641636[/C][/ROW]
[ROW][C]70[/C][C]0.804476411778658[/C][C]0.391047176442684[/C][C]0.195523588221342[/C][/ROW]
[ROW][C]71[/C][C]0.842100860728363[/C][C]0.315798278543273[/C][C]0.157899139271637[/C][/ROW]
[ROW][C]72[/C][C]0.827125172522231[/C][C]0.345749654955538[/C][C]0.172874827477769[/C][/ROW]
[ROW][C]73[/C][C]0.794941923585302[/C][C]0.410116152829397[/C][C]0.205058076414698[/C][/ROW]
[ROW][C]74[/C][C]0.78454113689948[/C][C]0.43091772620104[/C][C]0.21545886310052[/C][/ROW]
[ROW][C]75[/C][C]0.744859822582125[/C][C]0.510280354835751[/C][C]0.255140177417875[/C][/ROW]
[ROW][C]76[/C][C]0.74099946531178[/C][C]0.518001069376439[/C][C]0.25900053468822[/C][/ROW]
[ROW][C]77[/C][C]0.721832951749594[/C][C]0.556334096500812[/C][C]0.278167048250406[/C][/ROW]
[ROW][C]78[/C][C]0.872495196364929[/C][C]0.255009607270141[/C][C]0.127504803635071[/C][/ROW]
[ROW][C]79[/C][C]0.840653771868686[/C][C]0.318692456262629[/C][C]0.159346228131314[/C][/ROW]
[ROW][C]80[/C][C]0.875237467856393[/C][C]0.249525064287214[/C][C]0.124762532143607[/C][/ROW]
[ROW][C]81[/C][C]0.842532858268983[/C][C]0.314934283462034[/C][C]0.157467141731017[/C][/ROW]
[ROW][C]82[/C][C]0.826747968495501[/C][C]0.346504063008997[/C][C]0.173252031504499[/C][/ROW]
[ROW][C]83[/C][C]0.784495162867068[/C][C]0.431009674265864[/C][C]0.215504837132932[/C][/ROW]
[ROW][C]84[/C][C]0.78442508008407[/C][C]0.431149839831859[/C][C]0.21557491991593[/C][/ROW]
[ROW][C]85[/C][C]0.745174008069595[/C][C]0.509651983860809[/C][C]0.254825991930405[/C][/ROW]
[ROW][C]86[/C][C]0.791656052093381[/C][C]0.416687895813238[/C][C]0.208343947906619[/C][/ROW]
[ROW][C]87[/C][C]0.872821238290695[/C][C]0.254357523418609[/C][C]0.127178761709305[/C][/ROW]
[ROW][C]88[/C][C]0.833323957267782[/C][C]0.333352085464437[/C][C]0.166676042732218[/C][/ROW]
[ROW][C]89[/C][C]0.791343698327278[/C][C]0.417312603345445[/C][C]0.208656301672722[/C][/ROW]
[ROW][C]90[/C][C]0.79014229179114[/C][C]0.41971541641772[/C][C]0.20985770820886[/C][/ROW]
[ROW][C]91[/C][C]0.751599165777462[/C][C]0.496801668445076[/C][C]0.248400834222538[/C][/ROW]
[ROW][C]92[/C][C]0.832756612619417[/C][C]0.334486774761166[/C][C]0.167243387380583[/C][/ROW]
[ROW][C]93[/C][C]0.793758927656467[/C][C]0.412482144687066[/C][C]0.206241072343533[/C][/ROW]
[ROW][C]94[/C][C]0.851074154563758[/C][C]0.297851690872485[/C][C]0.148925845436242[/C][/ROW]
[ROW][C]95[/C][C]0.816391098199984[/C][C]0.367217803600032[/C][C]0.183608901800016[/C][/ROW]
[ROW][C]96[/C][C]0.820256510253186[/C][C]0.359486979493629[/C][C]0.179743489746814[/C][/ROW]
[ROW][C]97[/C][C]0.852619979406035[/C][C]0.294760041187931[/C][C]0.147380020593966[/C][/ROW]
[ROW][C]98[/C][C]0.834725578691389[/C][C]0.330548842617223[/C][C]0.165274421308611[/C][/ROW]
[ROW][C]99[/C][C]0.860806271931581[/C][C]0.278387456136838[/C][C]0.139193728068419[/C][/ROW]
[ROW][C]100[/C][C]0.794783319595088[/C][C]0.410433360809824[/C][C]0.205216680404912[/C][/ROW]
[ROW][C]101[/C][C]0.750797473340614[/C][C]0.498405053318772[/C][C]0.249202526659386[/C][/ROW]
[ROW][C]102[/C][C]0.771711165827029[/C][C]0.456577668345941[/C][C]0.22828883417297[/C][/ROW]
[ROW][C]103[/C][C]0.668224641003354[/C][C]0.663550717993292[/C][C]0.331775358996646[/C][/ROW]
[ROW][C]104[/C][C]0.573599032859201[/C][C]0.852801934281597[/C][C]0.426400967140799[/C][/ROW]
[ROW][C]105[/C][C]0.439062115642597[/C][C]0.878124231285194[/C][C]0.560937884357403[/C][/ROW]
[ROW][C]106[/C][C]0.357004506865163[/C][C]0.714009013730325[/C][C]0.642995493134837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2921234706577140.5842469413154270.707876529342287
180.8793700084146870.2412599831706250.120629991585313
190.8360713128471010.3278573743057980.163928687152899
200.8743622855968220.2512754288063560.125637714403178
210.8640908971458070.2718182057083860.135909102854193
220.8376799476255020.3246401047489960.162320052374498
230.8285251988732870.3429496022534260.171474801126713
240.7920656298507690.4158687402984630.207934370149231
250.8473979392078430.3052041215843140.152602060792157
260.8272840978167060.3454318043665880.172715902183294
270.8126457880309550.374708423938090.187354211969045
280.7717260215615660.4565479568768680.228273978438434
290.8580267678225430.2839464643549150.141973232177457
300.8857830440892970.2284339118214070.114216955910703
310.849730222190380.3005395556192390.15026977780962
320.849978668042650.3000426639147010.15002133195735
330.8242967748342020.3514064503315960.175703225165798
340.776687152774980.446625694450040.22331284722502
350.7868241486437090.4263517027125820.213175851356291
360.7456152320838880.5087695358322240.254384767916112
370.8588299475701960.2823401048596080.141170052429804
380.8496136042992290.3007727914015430.150386395700771
390.8380552516636330.3238894966727340.161944748336367
400.8644865825802570.2710268348394860.135513417419743
410.8456832783991910.3086334432016170.154316721600809
420.821825805386750.3563483892264990.17817419461325
430.8740583343946580.2518833312106840.125941665605342
440.869083973313120.261832053373760.13091602668688
450.8417504258369950.316499148326010.158249574163005
460.9470742017981470.1058515964037060.0529257982018531
470.9699943479930380.06001130401392440.0300056520069622
480.9626391406067370.07472171878652560.0373608593932628
490.9496838965568350.1006322068863310.0503161034431653
500.9362732165084740.1274535669830530.0637267834915264
510.9185071964124680.1629856071750650.0814928035875324
520.8945714673356770.2108570653286470.105428532664323
530.8679636311558260.2640727376883490.132036368844174
540.835762195007380.3284756099852390.16423780499262
550.8037862352029220.3924275295941560.196213764797078
560.8970653627742750.2058692744514490.102934637225725
570.8853192082789290.2293615834421430.114680791721071
580.8751211995724540.2497576008550910.124878800427545
590.8684386244744710.2631227510510580.131561375525529
600.8452782675282650.3094434649434710.154721732471735
610.8192507313274640.3614985373450720.180749268672536
620.7915830641528330.4168338716943350.208416935847167
630.7534342606270950.493131478745810.246565739372905
640.7125322171157930.5749355657684140.287467782884207
650.6863556062122610.6272887875754790.313644393787739
660.6899038883613960.6201922232772070.310096111638604
670.7651732113600230.4696535772799530.234826788639977
680.7846571697105620.4306856605788770.215342830289438
690.7854584763583640.4290830472832720.214541523641636
700.8044764117786580.3910471764426840.195523588221342
710.8421008607283630.3157982785432730.157899139271637
720.8271251725222310.3457496549555380.172874827477769
730.7949419235853020.4101161528293970.205058076414698
740.784541136899480.430917726201040.21545886310052
750.7448598225821250.5102803548357510.255140177417875
760.740999465311780.5180010693764390.25900053468822
770.7218329517495940.5563340965008120.278167048250406
780.8724951963649290.2550096072701410.127504803635071
790.8406537718686860.3186924562626290.159346228131314
800.8752374678563930.2495250642872140.124762532143607
810.8425328582689830.3149342834620340.157467141731017
820.8267479684955010.3465040630089970.173252031504499
830.7844951628670680.4310096742658640.215504837132932
840.784425080084070.4311498398318590.21557491991593
850.7451740080695950.5096519838608090.254825991930405
860.7916560520933810.4166878958132380.208343947906619
870.8728212382906950.2543575234186090.127178761709305
880.8333239572677820.3333520854644370.166676042732218
890.7913436983272780.4173126033454450.208656301672722
900.790142291791140.419715416417720.20985770820886
910.7515991657774620.4968016684450760.248400834222538
920.8327566126194170.3344867747611660.167243387380583
930.7937589276564670.4124821446870660.206241072343533
940.8510741545637580.2978516908724850.148925845436242
950.8163910981999840.3672178036000320.183608901800016
960.8202565102531860.3594869794936290.179743489746814
970.8526199794060350.2947600411879310.147380020593966
980.8347255786913890.3305488426172230.165274421308611
990.8608062719315810.2783874561368380.139193728068419
1000.7947833195950880.4104333608098240.205216680404912
1010.7507974733406140.4984050533187720.249202526659386
1020.7717111658270290.4565776683459410.22828883417297
1030.6682246410033540.6635507179932920.331775358996646
1040.5735990328592010.8528019342815970.426400967140799
1050.4390621156425970.8781242312851940.560937884357403
1060.3570045068651630.7140090137303250.642995493134837






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0222222222222222OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0222222222222222 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190160&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0222222222222222[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190160&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190160&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0222222222222222OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}