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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 16 Nov 2012 04:50:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/16/t1353059474ilb1szyg65grajt.htm/, Retrieved Sat, 27 Apr 2024 05:47:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189840, Retrieved Sat, 27 Apr 2024 05:47:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100	95	102	103	91	99	101	91	114	101	103	85
94	97	99	117	85	97	97	87	99	99	97	94
105	97	108	115	110	113	108	103	98	104	110	107
95	97	92	74	90	100	95	97	91	99	97	98
103	103	99	74	103	105	99	96	111	101	103	111
103	101	102	81	119	109	101	105	104	102	106	115
100	96	87	86	76	91	92	74	100	93	89	76
108	94	71	114	93	89	92	87	108	97	85	100
108	97	105	102	105	105	100	105	113	91	100	103
120	101	115	85	92	120	106	118	113	97	106	117
112	77	103	63	75	107	99	102	114	94	95	101
102	93	75	61	61	84	84	101	109	90	74	73
105	45	97	87	80	101	106	86	116	105	94	84
101	48	95	97	85	105	101	83	102	103	90	90
108	52	99	88	94	119	113	92	107	112	99	105
107	49	100	67	78	114	110	87	111	114	100	111
109	53	92	59	92	114	103	94	122	111	96	110
110	60	94	63	90	119	107	94	123	106	102	116
111	51	89	86	72	99	98	75	108	112	88	85
110	42	67	99	77	91	90	85	115	102	78	92
117	56	109	85	76	121	105	104	120	103	99	117
130	51	113	74	89	128	116	109	117	105	107	119
114	53	106	55	55	112	102	121	115	101	93	100
113	55	78	54	47	93	88	124	116	101	74	71
110	44	102	81	91	108	114	88	118	117	96	82
107	51	97	88	85	107	104	86	98	109	99	90
110	52	96	75	89	115	111	98	121	120	103	109
113	54	99	55	90	121	111	94	118	115	102	112
106	50	86	47	72	112	102	102	120	107	96	103
118	57	92	54	83	123	106	96	111	110	106	116
118	49	86	71	72	101	104	79	117	110	95	89
114	41	62	79	75	87	94	95	110	105	82	91
121	58	105	77	85	124	116	106	107	116	109	121
130	63	108	57	81	125	118	116	115	116	114	123
115	54	96	40	69	111	101	101	106	111	95	98
118	55	80	44	68	98	101	108	115	120	85	81
111	56	95	67	94	102	109	92	112	111	98	84
108	56	94	75	97	105	108	89	106	115	100	92
124	70	108	75	102	128	124	109	106	125	119	116
115	69	97	49	94	125	117	97	114	116	109	112
113	57	89	37	89	116	104	99	109	113	99	106
128	68	107	50	114	131	121	110	100	122	119	131
117	53	87	63	82	98	101	76	105	123	94	83
119	48	70	76	96	89	105	91	100	117	88	98
130	61	111	69	104	133	121	105	104	136	116	120
126	62	105	49	88	114	116	103	112	121	109	121
125	58	99	40	85	113	106	108	97	120	103	107
131	51	84	39	87	104	105	122	107	126	93	89
116	51	87	54	86	108	107	92	104	116	100	81
109	48	92	71	89	106	101	95	98	108	102	90
124	59	98	68	105	117	113	106	100	117	113	103
119	54	95	43	83	123	109	98	97	113	112	117
119	56	85	42	87	114	103	110	81	113	104	110
131	60	100	48	112	132	116	107	73	126	118	130
111	51	79	58	97	92	98	69	89	114	94	79
125	51	66	76	89	94	99	95	96	113	95	101
132	56	105	57	109	121	117	114	97	112	121	123
127	53	96	44	88	114	107	104	98	113	114	111
132	53	103	40	91	116	107	110	89	116	114	109
131	48	83	36	79	98	102	112	98	112	99	89
122	50	91	60	115	112	103	92	91	119	112	87
113	49	95	73	119	109	101	97	86	117	111	95
134	55	109	71	125	133	117	114	97	125	126	119
119	50	92	45	96	118	103	93	102	113	112	110
129	57	99	45	117	132	106	115	80	120	124	124
131	65	110	48	120	134	111	112	71	114	127	133
117	53	88	60	104	97	94	76	91	114	101	84
131	42	73	72	121	100	101	101	102	118	102	105
132	56	111	63	127	128	111	119	91	117	126	128
141	58	112	32	118	135	114	118	94	121	129	127
138	54	111	34	108	131	110	120	53	115	122	120
129	51	84	24	89	107	100	120	77	117	100	93
127	59	102	65	137	122	104	99	70	119	122	98
121	49	102	73	142	121	106	103	65	115	120	106
139	61	114	62	137	141	116	118	89	126	137	122
129	52	99	32	123	125	104	103	70	118	124	116
131	58	100	31	126	130	107	114	78	118	130	122
136	66	110	37	148	159	113	116	78	115	137	134
129	62	93	48	116	111	104	84	73	122	114	88
133	45	77	54	139	110	103	106	83	117	109	110
136	52	108	44	151	133	109	117	74	106	126	122
151	59	120	41	124	135	123	125	102	111	141	135
145	58	106	32	109	119	110	123	54	114	130	116
134	45	78	31	112	94	94	119	79	114	98	85
136	65	100	49	136	118	114	100	86	125	130	106
129	64	102	54	136	115	110	100	87	125	130	115
129	69	97	44	139	114	110	103	79	120	125	111
139	71	101	31	138	131	113	104	64	121	136	133
133	63	89	24	142	117	105	99	70	111	124	124
133	74	93	37	144	123	108	101	75	124	133	131
137	63	89	38	147	106	101	73	72	120	121	97
127	52	62	42	201	89	95	86	83	126	102	97
144	73	96	36	196	116	112	110	74	116	131	131
150	67	95	31	170	116	113	115	82	117	130	127
132	63	80	24	177	97	96	101	78	106	106	101
139	70	67	29	190	82	93	112	77	102	93	88
123	66	71	38	138	92	91	89	77	106	100	76
122	60	73	44	133	90	91	93	72	97	99	87
136	66	81	33	131	99	101	103	76	108	112	110
133	68	77	23	110	99	98	91	75	99	109	102
127	68	68	19	124	89	94	88	69	101	102	99
139	81	77	27	150	106	102	93	67	106	116	117
131	75	73	29	163	84	96	65	68	105	103	83
132	55	54	34	138	78	92	82	73	103	91	90
136	79	85	26	133	101	106	102	69	102	119	116
142	52	86	28	123	100	105	102	76	107	117	117
133	56	79	18	107	96	97	122	67	100	106	96
132	66	67	24	122	80	94	105	69	101	92	73
121	66	72	29	141	87	95	83	68	105	102	66
124	59	76	38	136	90	95	85	64	118	104	73
145	78	90	33	140	113	114	102	69	129	124	114
135	70	84	22	109	105	107	86	67	124	118	107
128	65	75	20	109	100	100	84	71	128	109	102
142	88	90	31	128	116	112	93	58	129	129	125
130	75	77	27	162	89	101	64	57	128	105	80
131	62	60	28	147	87	100	81	69	125	100	95
141	85	92	28	148	111	111	100	76	125	125	120
140	82	88	25	103	110	107	96	74	130	116	117
142	83	83	21	102	104	105	93	77	125	112	99
140	78	69	24	100	85	104	102	81	122	97	64
132	81	73	28	117	96	106	78	77	129	107	82
132	75	78	33	139	99	105	92	64	124	114	97
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 time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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 time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Textiel[t] = -12.9038582451785 -0.164143076568454Voedingsmiddelen[t] + 0.0879466938746709Tabaksproducten[t] + 0.0924796096098854Kleding[t] -0.066575303047671Leer[t] + 0.503824865167603Hout[t] + 0.509904599253675Papier[t] + 0.122872141783344Uitgeverijen[t] + 0.0580122768636015Cokes[t] -0.286356682733116Chemische[t] + 0.48877492586319Rubber[t] -0.242068692402801Nietmetaalhoudende[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Textiel[t] =  -12.9038582451785 -0.164143076568454Voedingsmiddelen[t] +  0.0879466938746709Tabaksproducten[t] +  0.0924796096098854Kleding[t] -0.066575303047671Leer[t] +  0.503824865167603Hout[t] +  0.509904599253675Papier[t] +  0.122872141783344Uitgeverijen[t] +  0.0580122768636015Cokes[t] -0.286356682733116Chemische[t] +  0.48877492586319Rubber[t] -0.242068692402801Nietmetaalhoudende[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Textiel[t] =  -12.9038582451785 -0.164143076568454Voedingsmiddelen[t] +  0.0879466938746709Tabaksproducten[t] +  0.0924796096098854Kleding[t] -0.066575303047671Leer[t] +  0.503824865167603Hout[t] +  0.509904599253675Papier[t] +  0.122872141783344Uitgeverijen[t] +  0.0580122768636015Cokes[t] -0.286356682733116Chemische[t] +  0.48877492586319Rubber[t] -0.242068692402801Nietmetaalhoudende[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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] = -12.9038582451785 -0.164143076568454Voedingsmiddelen[t] + 0.0879466938746709Tabaksproducten[t] + 0.0924796096098854Kleding[t] -0.066575303047671Leer[t] + 0.503824865167603Hout[t] + 0.509904599253675Papier[t] + 0.122872141783344Uitgeverijen[t] + 0.0580122768636015Cokes[t] -0.286356682733116Chemische[t] + 0.48877492586319Rubber[t] -0.242068692402801Nietmetaalhoudende[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-12.903858245178510.867436-1.18740.237610.118805
Voedingsmiddelen-0.1641430765684540.088691-1.85070.0668680.033434
Tabaksproducten0.08794669387467090.0353922.48490.014450.007225
Kleding0.09247960960988540.0328272.81720.0057360.002868
Leer-0.0665753030476710.025898-2.57070.0114730.005737
Hout0.5038248651676030.0906415.558500
Papier0.5099045992536750.1347413.78430.000250.000125
Uitgeverijen0.1228721417833440.0505062.43280.0165790.00829
Cokes0.05801227686360150.0520721.11410.2676540.133827
Chemische-0.2863566827331160.071089-4.02810.0001035.2e-05
Rubber0.488774925863190.1094624.46531.9e-051e-05
Nietmetaalhoudende-0.2420686924028010.055-4.40122.5e-051.2e-05

\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) & -12.9038582451785 & 10.867436 & -1.1874 & 0.23761 & 0.118805 \tabularnewline
Voedingsmiddelen & -0.164143076568454 & 0.088691 & -1.8507 & 0.066868 & 0.033434 \tabularnewline
Tabaksproducten & 0.0879466938746709 & 0.035392 & 2.4849 & 0.01445 & 0.007225 \tabularnewline
Kleding & 0.0924796096098854 & 0.032827 & 2.8172 & 0.005736 & 0.002868 \tabularnewline
Leer & -0.066575303047671 & 0.025898 & -2.5707 & 0.011473 & 0.005737 \tabularnewline
Hout & 0.503824865167603 & 0.090641 & 5.5585 & 0 & 0 \tabularnewline
Papier & 0.509904599253675 & 0.134741 & 3.7843 & 0.00025 & 0.000125 \tabularnewline
Uitgeverijen & 0.122872141783344 & 0.050506 & 2.4328 & 0.016579 & 0.00829 \tabularnewline
Cokes & 0.0580122768636015 & 0.052072 & 1.1141 & 0.267654 & 0.133827 \tabularnewline
Chemische & -0.286356682733116 & 0.071089 & -4.0281 & 0.000103 & 5.2e-05 \tabularnewline
Rubber & 0.48877492586319 & 0.109462 & 4.4653 & 1.9e-05 & 1e-05 \tabularnewline
Nietmetaalhoudende & -0.242068692402801 & 0.055 & -4.4012 & 2.5e-05 & 1.2e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&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]-12.9038582451785[/C][C]10.867436[/C][C]-1.1874[/C][C]0.23761[/C][C]0.118805[/C][/ROW]
[ROW][C]Voedingsmiddelen[/C][C]-0.164143076568454[/C][C]0.088691[/C][C]-1.8507[/C][C]0.066868[/C][C]0.033434[/C][/ROW]
[ROW][C]Tabaksproducten[/C][C]0.0879466938746709[/C][C]0.035392[/C][C]2.4849[/C][C]0.01445[/C][C]0.007225[/C][/ROW]
[ROW][C]Kleding[/C][C]0.0924796096098854[/C][C]0.032827[/C][C]2.8172[/C][C]0.005736[/C][C]0.002868[/C][/ROW]
[ROW][C]Leer[/C][C]-0.066575303047671[/C][C]0.025898[/C][C]-2.5707[/C][C]0.011473[/C][C]0.005737[/C][/ROW]
[ROW][C]Hout[/C][C]0.503824865167603[/C][C]0.090641[/C][C]5.5585[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Papier[/C][C]0.509904599253675[/C][C]0.134741[/C][C]3.7843[/C][C]0.00025[/C][C]0.000125[/C][/ROW]
[ROW][C]Uitgeverijen[/C][C]0.122872141783344[/C][C]0.050506[/C][C]2.4328[/C][C]0.016579[/C][C]0.00829[/C][/ROW]
[ROW][C]Cokes[/C][C]0.0580122768636015[/C][C]0.052072[/C][C]1.1141[/C][C]0.267654[/C][C]0.133827[/C][/ROW]
[ROW][C]Chemische[/C][C]-0.286356682733116[/C][C]0.071089[/C][C]-4.0281[/C][C]0.000103[/C][C]5.2e-05[/C][/ROW]
[ROW][C]Rubber[/C][C]0.48877492586319[/C][C]0.109462[/C][C]4.4653[/C][C]1.9e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]Nietmetaalhoudende[/C][C]-0.242068692402801[/C][C]0.055[/C][C]-4.4012[/C][C]2.5e-05[/C][C]1.2e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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)-12.903858245178510.867436-1.18740.237610.118805
Voedingsmiddelen-0.1641430765684540.088691-1.85070.0668680.033434
Tabaksproducten0.08794669387467090.0353922.48490.014450.007225
Kleding0.09247960960988540.0328272.81720.0057360.002868
Leer-0.0665753030476710.025898-2.57070.0114730.005737
Hout0.5038248651676030.0906415.558500
Papier0.5099045992536750.1347413.78430.000250.000125
Uitgeverijen0.1228721417833440.0505062.43280.0165790.00829
Cokes0.05801227686360150.0520721.11410.2676540.133827
Chemische-0.2863566827331160.071089-4.02810.0001035.2e-05
Rubber0.488774925863190.1094624.46531.9e-051e-05
Nietmetaalhoudende-0.2420686924028010.055-4.40122.5e-051.2e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.941018488927516
R-squared0.885515796503426
Adjusted R-squared0.874170515075837
F-TEST (value)78.0514614957112
F-TEST (DF numerator)11
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.95643510690689
Sum Squared Residuals2726.85363555668

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.941018488927516 \tabularnewline
R-squared & 0.885515796503426 \tabularnewline
Adjusted R-squared & 0.874170515075837 \tabularnewline
F-TEST (value) & 78.0514614957112 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 111 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.95643510690689 \tabularnewline
Sum Squared Residuals & 2726.85363555668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.941018488927516[/C][/ROW]
[ROW][C]R-squared[/C][C]0.885515796503426[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.874170515075837[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.0514614957112[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]111[/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]4.95643510690689[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2726.85363555668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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 R0.941018488927516
R-squared0.885515796503426
Adjusted R-squared0.874170515075837
F-TEST (value)78.0514614957112
F-TEST (DF numerator)11
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.95643510690689
Sum Squared Residuals2726.85363555668






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102102.523561423125-0.523561423124545
29996.43098435433362.56901564566639
3108110.129556764131-2.12955676413052
49292.2453553796106-0.245355379610605
59995.40357129750923.59642870249083
610298.75639565069423.24360434930583
78788.1438684297205-1.14386842972053
87180.2560906223946-9.25609062239456
9105101.5770242323953.4229757676047
10115109.2921058466855.707894153315
1110394.92040605515818.07959394484194
127576.7256812076075-1.72568120760754
139794.31467012643062.68532987356936
149591.27719176574723.72280823425279
1599101.807530557236-2.80753055723601
1610094.86346628005445.13653371994562
179290.29002266322641.70997733677363
189498.7733513024597-4.77335130245972
198982.21582131943556.78417868056454
206772.2641109232101-5.26411092321011
21109100.4323703423448.56762965765579
22113108.4054194151764.59458058482427
2310696.77451158899429.22548841100581
247879.0032312308418-1.00323123084184
2510298.11198878976113.88801121023889
269795.07862198383341.9213780161666
279697.8201535339983-1.82015353399829
289998.16167640067230.838323599327686
298692.9296683856453-6.92966838564526
309298.6947420382733-6.69474203827331
318687.6102717132191-1.61027171321906
326272.1042276544752-10.1042276544752
33105105.421524304635-0.421524304634902
34108107.9768700458130.0231299541873009
359688.9839044368257.01609556317502
368080.4986162471762-0.49861624717617
379594.29126087800450.708739121995458
389493.5042545667350.495745433265027
39108114.59373900963-6.59373900962952
4097106.677773958203-9.67777395820278
418991.3900532511901-2.3900532511901
42107107.634978379288-0.634978379288013
438779.85565986997647.14434013002357
447073.5704964378432-3.57049643784325
45111106.9267063915434.07329360845654
4610595.61485361269799.38514638730213
479989.67859385587329.32140614412677
488482.85982979969511.14017020030485
498794.1721938181004-7.17219381810044
509293.4730332469579-1.4730332469579
5198103.416624913352-5.41662491335243
5295100.044280194186-5.04428019418638
538590.5980920896013-5.59809208960128
54100101.0143879893-1.01438798929996
557976.40811785101492.59188214898511
566676.8752865061516-10.8752865061516
57105105.920530435157-0.920530435157494
589696.0737661308267-0.0737661308266892
5910395.5312458262717.46875417372902
608383.4893015286931-0.489301528693106
619194.4989209062629-3.49892090626291
629592.76460004521132.23539995478866
63109111.469218179329-2.46921817932946
649294.8036032991486-2.80360329914858
6599102.861737602515-3.8617376025147
66110106.9870303719063.01296962809401
678878.98480137130189.01519862869817
687378.748010865064-5.74801086506398
69111105.8124338999815.18756610001863
70112107.9139687099394.08603129006117
71111102.7088429504928.29115704950812
728483.67397006837730.326029931622705
73102100.8824612207721.11753877922809
74102100.3435519592151.65644804078456
75114117.457008935539-3.45700893553948
769996.72840856989872.27159143010127
77100103.980364360144-3.98036436014392
78110122.245206708267-12.2452067082669
799391.08424180581221.9157581941878
807783.8881993661682-6.88819936616817
81108106.3188527640411.68114723595922
82120119.4990103054740.500989694525774
83106101.2056548471964.79434515280382
847873.64377112439814.35622887560188
85100102.910080118945-2.91008011894507
8610298.76183406181993.23816593818012
879797.4339224251858-0.433922425185823
88101103.94480618871-2.94480618870972
898991.0902466121503-2.09024661215033
909397.1970698306748-4.19706983067482
918983.22748905136655.7725109486335
926260.28251715580751.71748284419254
939692.62313887662493.3768611233751
949594.16066676925030.839333230749726
958073.16987255728336.83012744271674
966762.3781571902824.62184280971798
977175.3200941359417-4.3200941359417
987374.463768276944-1.46376827694399
998180.5401311738460.459868826153944
1007781.6669821894091-4.66698218940912
1016870.2873790631904-2.28737906319038
1027782.6662729046161-5.66627290461611
1037367.40785349944335.5921465005567
1045457.9408107845255-3.94081078452553
1058587.6182973906515-2.61829739065148
1068681.85054496215034.14945503784971
1077981.3722394037192-2.37223940371916
1086768.8467583143929-1.8467583143929
1097276.5620820806383-4.56208208063834
1107673.7048208965292.29517910347103
1119091.5559099099643-1.55590990996428
1128484.0520305803493-0.0520305803493067
1137574.14013012932220.859869870677827
1149092.0705505840995-2.07055058409951
1157766.878805020736210.1211949792638
1166062.7140485150546-2.71404851505456
1179289.63787622969112.36212377030886
1188884.00112098068233.99887901931765
1198383.0540205752193-0.0540205752192901
1206976.6083344343238-7.60833443432377
1217379.3303756364705-6.33037563647052
1227880.9902347448782-2.99023474487818
1239291.20123165551860.79876834448144

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102 & 102.523561423125 & -0.523561423124545 \tabularnewline
2 & 99 & 96.4309843543336 & 2.56901564566639 \tabularnewline
3 & 108 & 110.129556764131 & -2.12955676413052 \tabularnewline
4 & 92 & 92.2453553796106 & -0.245355379610605 \tabularnewline
5 & 99 & 95.4035712975092 & 3.59642870249083 \tabularnewline
6 & 102 & 98.7563956506942 & 3.24360434930583 \tabularnewline
7 & 87 & 88.1438684297205 & -1.14386842972053 \tabularnewline
8 & 71 & 80.2560906223946 & -9.25609062239456 \tabularnewline
9 & 105 & 101.577024232395 & 3.4229757676047 \tabularnewline
10 & 115 & 109.292105846685 & 5.707894153315 \tabularnewline
11 & 103 & 94.9204060551581 & 8.07959394484194 \tabularnewline
12 & 75 & 76.7256812076075 & -1.72568120760754 \tabularnewline
13 & 97 & 94.3146701264306 & 2.68532987356936 \tabularnewline
14 & 95 & 91.2771917657472 & 3.72280823425279 \tabularnewline
15 & 99 & 101.807530557236 & -2.80753055723601 \tabularnewline
16 & 100 & 94.8634662800544 & 5.13653371994562 \tabularnewline
17 & 92 & 90.2900226632264 & 1.70997733677363 \tabularnewline
18 & 94 & 98.7733513024597 & -4.77335130245972 \tabularnewline
19 & 89 & 82.2158213194355 & 6.78417868056454 \tabularnewline
20 & 67 & 72.2641109232101 & -5.26411092321011 \tabularnewline
21 & 109 & 100.432370342344 & 8.56762965765579 \tabularnewline
22 & 113 & 108.405419415176 & 4.59458058482427 \tabularnewline
23 & 106 & 96.7745115889942 & 9.22548841100581 \tabularnewline
24 & 78 & 79.0032312308418 & -1.00323123084184 \tabularnewline
25 & 102 & 98.1119887897611 & 3.88801121023889 \tabularnewline
26 & 97 & 95.0786219838334 & 1.9213780161666 \tabularnewline
27 & 96 & 97.8201535339983 & -1.82015353399829 \tabularnewline
28 & 99 & 98.1616764006723 & 0.838323599327686 \tabularnewline
29 & 86 & 92.9296683856453 & -6.92966838564526 \tabularnewline
30 & 92 & 98.6947420382733 & -6.69474203827331 \tabularnewline
31 & 86 & 87.6102717132191 & -1.61027171321906 \tabularnewline
32 & 62 & 72.1042276544752 & -10.1042276544752 \tabularnewline
33 & 105 & 105.421524304635 & -0.421524304634902 \tabularnewline
34 & 108 & 107.976870045813 & 0.0231299541873009 \tabularnewline
35 & 96 & 88.983904436825 & 7.01609556317502 \tabularnewline
36 & 80 & 80.4986162471762 & -0.49861624717617 \tabularnewline
37 & 95 & 94.2912608780045 & 0.708739121995458 \tabularnewline
38 & 94 & 93.504254566735 & 0.495745433265027 \tabularnewline
39 & 108 & 114.59373900963 & -6.59373900962952 \tabularnewline
40 & 97 & 106.677773958203 & -9.67777395820278 \tabularnewline
41 & 89 & 91.3900532511901 & -2.3900532511901 \tabularnewline
42 & 107 & 107.634978379288 & -0.634978379288013 \tabularnewline
43 & 87 & 79.8556598699764 & 7.14434013002357 \tabularnewline
44 & 70 & 73.5704964378432 & -3.57049643784325 \tabularnewline
45 & 111 & 106.926706391543 & 4.07329360845654 \tabularnewline
46 & 105 & 95.6148536126979 & 9.38514638730213 \tabularnewline
47 & 99 & 89.6785938558732 & 9.32140614412677 \tabularnewline
48 & 84 & 82.8598297996951 & 1.14017020030485 \tabularnewline
49 & 87 & 94.1721938181004 & -7.17219381810044 \tabularnewline
50 & 92 & 93.4730332469579 & -1.4730332469579 \tabularnewline
51 & 98 & 103.416624913352 & -5.41662491335243 \tabularnewline
52 & 95 & 100.044280194186 & -5.04428019418638 \tabularnewline
53 & 85 & 90.5980920896013 & -5.59809208960128 \tabularnewline
54 & 100 & 101.0143879893 & -1.01438798929996 \tabularnewline
55 & 79 & 76.4081178510149 & 2.59188214898511 \tabularnewline
56 & 66 & 76.8752865061516 & -10.8752865061516 \tabularnewline
57 & 105 & 105.920530435157 & -0.920530435157494 \tabularnewline
58 & 96 & 96.0737661308267 & -0.0737661308266892 \tabularnewline
59 & 103 & 95.531245826271 & 7.46875417372902 \tabularnewline
60 & 83 & 83.4893015286931 & -0.489301528693106 \tabularnewline
61 & 91 & 94.4989209062629 & -3.49892090626291 \tabularnewline
62 & 95 & 92.7646000452113 & 2.23539995478866 \tabularnewline
63 & 109 & 111.469218179329 & -2.46921817932946 \tabularnewline
64 & 92 & 94.8036032991486 & -2.80360329914858 \tabularnewline
65 & 99 & 102.861737602515 & -3.8617376025147 \tabularnewline
66 & 110 & 106.987030371906 & 3.01296962809401 \tabularnewline
67 & 88 & 78.9848013713018 & 9.01519862869817 \tabularnewline
68 & 73 & 78.748010865064 & -5.74801086506398 \tabularnewline
69 & 111 & 105.812433899981 & 5.18756610001863 \tabularnewline
70 & 112 & 107.913968709939 & 4.08603129006117 \tabularnewline
71 & 111 & 102.708842950492 & 8.29115704950812 \tabularnewline
72 & 84 & 83.6739700683773 & 0.326029931622705 \tabularnewline
73 & 102 & 100.882461220772 & 1.11753877922809 \tabularnewline
74 & 102 & 100.343551959215 & 1.65644804078456 \tabularnewline
75 & 114 & 117.457008935539 & -3.45700893553948 \tabularnewline
76 & 99 & 96.7284085698987 & 2.27159143010127 \tabularnewline
77 & 100 & 103.980364360144 & -3.98036436014392 \tabularnewline
78 & 110 & 122.245206708267 & -12.2452067082669 \tabularnewline
79 & 93 & 91.0842418058122 & 1.9157581941878 \tabularnewline
80 & 77 & 83.8881993661682 & -6.88819936616817 \tabularnewline
81 & 108 & 106.318852764041 & 1.68114723595922 \tabularnewline
82 & 120 & 119.499010305474 & 0.500989694525774 \tabularnewline
83 & 106 & 101.205654847196 & 4.79434515280382 \tabularnewline
84 & 78 & 73.6437711243981 & 4.35622887560188 \tabularnewline
85 & 100 & 102.910080118945 & -2.91008011894507 \tabularnewline
86 & 102 & 98.7618340618199 & 3.23816593818012 \tabularnewline
87 & 97 & 97.4339224251858 & -0.433922425185823 \tabularnewline
88 & 101 & 103.94480618871 & -2.94480618870972 \tabularnewline
89 & 89 & 91.0902466121503 & -2.09024661215033 \tabularnewline
90 & 93 & 97.1970698306748 & -4.19706983067482 \tabularnewline
91 & 89 & 83.2274890513665 & 5.7725109486335 \tabularnewline
92 & 62 & 60.2825171558075 & 1.71748284419254 \tabularnewline
93 & 96 & 92.6231388766249 & 3.3768611233751 \tabularnewline
94 & 95 & 94.1606667692503 & 0.839333230749726 \tabularnewline
95 & 80 & 73.1698725572833 & 6.83012744271674 \tabularnewline
96 & 67 & 62.378157190282 & 4.62184280971798 \tabularnewline
97 & 71 & 75.3200941359417 & -4.3200941359417 \tabularnewline
98 & 73 & 74.463768276944 & -1.46376827694399 \tabularnewline
99 & 81 & 80.540131173846 & 0.459868826153944 \tabularnewline
100 & 77 & 81.6669821894091 & -4.66698218940912 \tabularnewline
101 & 68 & 70.2873790631904 & -2.28737906319038 \tabularnewline
102 & 77 & 82.6662729046161 & -5.66627290461611 \tabularnewline
103 & 73 & 67.4078534994433 & 5.5921465005567 \tabularnewline
104 & 54 & 57.9408107845255 & -3.94081078452553 \tabularnewline
105 & 85 & 87.6182973906515 & -2.61829739065148 \tabularnewline
106 & 86 & 81.8505449621503 & 4.14945503784971 \tabularnewline
107 & 79 & 81.3722394037192 & -2.37223940371916 \tabularnewline
108 & 67 & 68.8467583143929 & -1.8467583143929 \tabularnewline
109 & 72 & 76.5620820806383 & -4.56208208063834 \tabularnewline
110 & 76 & 73.704820896529 & 2.29517910347103 \tabularnewline
111 & 90 & 91.5559099099643 & -1.55590990996428 \tabularnewline
112 & 84 & 84.0520305803493 & -0.0520305803493067 \tabularnewline
113 & 75 & 74.1401301293222 & 0.859869870677827 \tabularnewline
114 & 90 & 92.0705505840995 & -2.07055058409951 \tabularnewline
115 & 77 & 66.8788050207362 & 10.1211949792638 \tabularnewline
116 & 60 & 62.7140485150546 & -2.71404851505456 \tabularnewline
117 & 92 & 89.6378762296911 & 2.36212377030886 \tabularnewline
118 & 88 & 84.0011209806823 & 3.99887901931765 \tabularnewline
119 & 83 & 83.0540205752193 & -0.0540205752192901 \tabularnewline
120 & 69 & 76.6083344343238 & -7.60833443432377 \tabularnewline
121 & 73 & 79.3303756364705 & -6.33037563647052 \tabularnewline
122 & 78 & 80.9902347448782 & -2.99023474487818 \tabularnewline
123 & 92 & 91.2012316555186 & 0.79876834448144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&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]102.523561423125[/C][C]-0.523561423124545[/C][/ROW]
[ROW][C]2[/C][C]99[/C][C]96.4309843543336[/C][C]2.56901564566639[/C][/ROW]
[ROW][C]3[/C][C]108[/C][C]110.129556764131[/C][C]-2.12955676413052[/C][/ROW]
[ROW][C]4[/C][C]92[/C][C]92.2453553796106[/C][C]-0.245355379610605[/C][/ROW]
[ROW][C]5[/C][C]99[/C][C]95.4035712975092[/C][C]3.59642870249083[/C][/ROW]
[ROW][C]6[/C][C]102[/C][C]98.7563956506942[/C][C]3.24360434930583[/C][/ROW]
[ROW][C]7[/C][C]87[/C][C]88.1438684297205[/C][C]-1.14386842972053[/C][/ROW]
[ROW][C]8[/C][C]71[/C][C]80.2560906223946[/C][C]-9.25609062239456[/C][/ROW]
[ROW][C]9[/C][C]105[/C][C]101.577024232395[/C][C]3.4229757676047[/C][/ROW]
[ROW][C]10[/C][C]115[/C][C]109.292105846685[/C][C]5.707894153315[/C][/ROW]
[ROW][C]11[/C][C]103[/C][C]94.9204060551581[/C][C]8.07959394484194[/C][/ROW]
[ROW][C]12[/C][C]75[/C][C]76.7256812076075[/C][C]-1.72568120760754[/C][/ROW]
[ROW][C]13[/C][C]97[/C][C]94.3146701264306[/C][C]2.68532987356936[/C][/ROW]
[ROW][C]14[/C][C]95[/C][C]91.2771917657472[/C][C]3.72280823425279[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]101.807530557236[/C][C]-2.80753055723601[/C][/ROW]
[ROW][C]16[/C][C]100[/C][C]94.8634662800544[/C][C]5.13653371994562[/C][/ROW]
[ROW][C]17[/C][C]92[/C][C]90.2900226632264[/C][C]1.70997733677363[/C][/ROW]
[ROW][C]18[/C][C]94[/C][C]98.7733513024597[/C][C]-4.77335130245972[/C][/ROW]
[ROW][C]19[/C][C]89[/C][C]82.2158213194355[/C][C]6.78417868056454[/C][/ROW]
[ROW][C]20[/C][C]67[/C][C]72.2641109232101[/C][C]-5.26411092321011[/C][/ROW]
[ROW][C]21[/C][C]109[/C][C]100.432370342344[/C][C]8.56762965765579[/C][/ROW]
[ROW][C]22[/C][C]113[/C][C]108.405419415176[/C][C]4.59458058482427[/C][/ROW]
[ROW][C]23[/C][C]106[/C][C]96.7745115889942[/C][C]9.22548841100581[/C][/ROW]
[ROW][C]24[/C][C]78[/C][C]79.0032312308418[/C][C]-1.00323123084184[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]98.1119887897611[/C][C]3.88801121023889[/C][/ROW]
[ROW][C]26[/C][C]97[/C][C]95.0786219838334[/C][C]1.9213780161666[/C][/ROW]
[ROW][C]27[/C][C]96[/C][C]97.8201535339983[/C][C]-1.82015353399829[/C][/ROW]
[ROW][C]28[/C][C]99[/C][C]98.1616764006723[/C][C]0.838323599327686[/C][/ROW]
[ROW][C]29[/C][C]86[/C][C]92.9296683856453[/C][C]-6.92966838564526[/C][/ROW]
[ROW][C]30[/C][C]92[/C][C]98.6947420382733[/C][C]-6.69474203827331[/C][/ROW]
[ROW][C]31[/C][C]86[/C][C]87.6102717132191[/C][C]-1.61027171321906[/C][/ROW]
[ROW][C]32[/C][C]62[/C][C]72.1042276544752[/C][C]-10.1042276544752[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]105.421524304635[/C][C]-0.421524304634902[/C][/ROW]
[ROW][C]34[/C][C]108[/C][C]107.976870045813[/C][C]0.0231299541873009[/C][/ROW]
[ROW][C]35[/C][C]96[/C][C]88.983904436825[/C][C]7.01609556317502[/C][/ROW]
[ROW][C]36[/C][C]80[/C][C]80.4986162471762[/C][C]-0.49861624717617[/C][/ROW]
[ROW][C]37[/C][C]95[/C][C]94.2912608780045[/C][C]0.708739121995458[/C][/ROW]
[ROW][C]38[/C][C]94[/C][C]93.504254566735[/C][C]0.495745433265027[/C][/ROW]
[ROW][C]39[/C][C]108[/C][C]114.59373900963[/C][C]-6.59373900962952[/C][/ROW]
[ROW][C]40[/C][C]97[/C][C]106.677773958203[/C][C]-9.67777395820278[/C][/ROW]
[ROW][C]41[/C][C]89[/C][C]91.3900532511901[/C][C]-2.3900532511901[/C][/ROW]
[ROW][C]42[/C][C]107[/C][C]107.634978379288[/C][C]-0.634978379288013[/C][/ROW]
[ROW][C]43[/C][C]87[/C][C]79.8556598699764[/C][C]7.14434013002357[/C][/ROW]
[ROW][C]44[/C][C]70[/C][C]73.5704964378432[/C][C]-3.57049643784325[/C][/ROW]
[ROW][C]45[/C][C]111[/C][C]106.926706391543[/C][C]4.07329360845654[/C][/ROW]
[ROW][C]46[/C][C]105[/C][C]95.6148536126979[/C][C]9.38514638730213[/C][/ROW]
[ROW][C]47[/C][C]99[/C][C]89.6785938558732[/C][C]9.32140614412677[/C][/ROW]
[ROW][C]48[/C][C]84[/C][C]82.8598297996951[/C][C]1.14017020030485[/C][/ROW]
[ROW][C]49[/C][C]87[/C][C]94.1721938181004[/C][C]-7.17219381810044[/C][/ROW]
[ROW][C]50[/C][C]92[/C][C]93.4730332469579[/C][C]-1.4730332469579[/C][/ROW]
[ROW][C]51[/C][C]98[/C][C]103.416624913352[/C][C]-5.41662491335243[/C][/ROW]
[ROW][C]52[/C][C]95[/C][C]100.044280194186[/C][C]-5.04428019418638[/C][/ROW]
[ROW][C]53[/C][C]85[/C][C]90.5980920896013[/C][C]-5.59809208960128[/C][/ROW]
[ROW][C]54[/C][C]100[/C][C]101.0143879893[/C][C]-1.01438798929996[/C][/ROW]
[ROW][C]55[/C][C]79[/C][C]76.4081178510149[/C][C]2.59188214898511[/C][/ROW]
[ROW][C]56[/C][C]66[/C][C]76.8752865061516[/C][C]-10.8752865061516[/C][/ROW]
[ROW][C]57[/C][C]105[/C][C]105.920530435157[/C][C]-0.920530435157494[/C][/ROW]
[ROW][C]58[/C][C]96[/C][C]96.0737661308267[/C][C]-0.0737661308266892[/C][/ROW]
[ROW][C]59[/C][C]103[/C][C]95.531245826271[/C][C]7.46875417372902[/C][/ROW]
[ROW][C]60[/C][C]83[/C][C]83.4893015286931[/C][C]-0.489301528693106[/C][/ROW]
[ROW][C]61[/C][C]91[/C][C]94.4989209062629[/C][C]-3.49892090626291[/C][/ROW]
[ROW][C]62[/C][C]95[/C][C]92.7646000452113[/C][C]2.23539995478866[/C][/ROW]
[ROW][C]63[/C][C]109[/C][C]111.469218179329[/C][C]-2.46921817932946[/C][/ROW]
[ROW][C]64[/C][C]92[/C][C]94.8036032991486[/C][C]-2.80360329914858[/C][/ROW]
[ROW][C]65[/C][C]99[/C][C]102.861737602515[/C][C]-3.8617376025147[/C][/ROW]
[ROW][C]66[/C][C]110[/C][C]106.987030371906[/C][C]3.01296962809401[/C][/ROW]
[ROW][C]67[/C][C]88[/C][C]78.9848013713018[/C][C]9.01519862869817[/C][/ROW]
[ROW][C]68[/C][C]73[/C][C]78.748010865064[/C][C]-5.74801086506398[/C][/ROW]
[ROW][C]69[/C][C]111[/C][C]105.812433899981[/C][C]5.18756610001863[/C][/ROW]
[ROW][C]70[/C][C]112[/C][C]107.913968709939[/C][C]4.08603129006117[/C][/ROW]
[ROW][C]71[/C][C]111[/C][C]102.708842950492[/C][C]8.29115704950812[/C][/ROW]
[ROW][C]72[/C][C]84[/C][C]83.6739700683773[/C][C]0.326029931622705[/C][/ROW]
[ROW][C]73[/C][C]102[/C][C]100.882461220772[/C][C]1.11753877922809[/C][/ROW]
[ROW][C]74[/C][C]102[/C][C]100.343551959215[/C][C]1.65644804078456[/C][/ROW]
[ROW][C]75[/C][C]114[/C][C]117.457008935539[/C][C]-3.45700893553948[/C][/ROW]
[ROW][C]76[/C][C]99[/C][C]96.7284085698987[/C][C]2.27159143010127[/C][/ROW]
[ROW][C]77[/C][C]100[/C][C]103.980364360144[/C][C]-3.98036436014392[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]122.245206708267[/C][C]-12.2452067082669[/C][/ROW]
[ROW][C]79[/C][C]93[/C][C]91.0842418058122[/C][C]1.9157581941878[/C][/ROW]
[ROW][C]80[/C][C]77[/C][C]83.8881993661682[/C][C]-6.88819936616817[/C][/ROW]
[ROW][C]81[/C][C]108[/C][C]106.318852764041[/C][C]1.68114723595922[/C][/ROW]
[ROW][C]82[/C][C]120[/C][C]119.499010305474[/C][C]0.500989694525774[/C][/ROW]
[ROW][C]83[/C][C]106[/C][C]101.205654847196[/C][C]4.79434515280382[/C][/ROW]
[ROW][C]84[/C][C]78[/C][C]73.6437711243981[/C][C]4.35622887560188[/C][/ROW]
[ROW][C]85[/C][C]100[/C][C]102.910080118945[/C][C]-2.91008011894507[/C][/ROW]
[ROW][C]86[/C][C]102[/C][C]98.7618340618199[/C][C]3.23816593818012[/C][/ROW]
[ROW][C]87[/C][C]97[/C][C]97.4339224251858[/C][C]-0.433922425185823[/C][/ROW]
[ROW][C]88[/C][C]101[/C][C]103.94480618871[/C][C]-2.94480618870972[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]91.0902466121503[/C][C]-2.09024661215033[/C][/ROW]
[ROW][C]90[/C][C]93[/C][C]97.1970698306748[/C][C]-4.19706983067482[/C][/ROW]
[ROW][C]91[/C][C]89[/C][C]83.2274890513665[/C][C]5.7725109486335[/C][/ROW]
[ROW][C]92[/C][C]62[/C][C]60.2825171558075[/C][C]1.71748284419254[/C][/ROW]
[ROW][C]93[/C][C]96[/C][C]92.6231388766249[/C][C]3.3768611233751[/C][/ROW]
[ROW][C]94[/C][C]95[/C][C]94.1606667692503[/C][C]0.839333230749726[/C][/ROW]
[ROW][C]95[/C][C]80[/C][C]73.1698725572833[/C][C]6.83012744271674[/C][/ROW]
[ROW][C]96[/C][C]67[/C][C]62.378157190282[/C][C]4.62184280971798[/C][/ROW]
[ROW][C]97[/C][C]71[/C][C]75.3200941359417[/C][C]-4.3200941359417[/C][/ROW]
[ROW][C]98[/C][C]73[/C][C]74.463768276944[/C][C]-1.46376827694399[/C][/ROW]
[ROW][C]99[/C][C]81[/C][C]80.540131173846[/C][C]0.459868826153944[/C][/ROW]
[ROW][C]100[/C][C]77[/C][C]81.6669821894091[/C][C]-4.66698218940912[/C][/ROW]
[ROW][C]101[/C][C]68[/C][C]70.2873790631904[/C][C]-2.28737906319038[/C][/ROW]
[ROW][C]102[/C][C]77[/C][C]82.6662729046161[/C][C]-5.66627290461611[/C][/ROW]
[ROW][C]103[/C][C]73[/C][C]67.4078534994433[/C][C]5.5921465005567[/C][/ROW]
[ROW][C]104[/C][C]54[/C][C]57.9408107845255[/C][C]-3.94081078452553[/C][/ROW]
[ROW][C]105[/C][C]85[/C][C]87.6182973906515[/C][C]-2.61829739065148[/C][/ROW]
[ROW][C]106[/C][C]86[/C][C]81.8505449621503[/C][C]4.14945503784971[/C][/ROW]
[ROW][C]107[/C][C]79[/C][C]81.3722394037192[/C][C]-2.37223940371916[/C][/ROW]
[ROW][C]108[/C][C]67[/C][C]68.8467583143929[/C][C]-1.8467583143929[/C][/ROW]
[ROW][C]109[/C][C]72[/C][C]76.5620820806383[/C][C]-4.56208208063834[/C][/ROW]
[ROW][C]110[/C][C]76[/C][C]73.704820896529[/C][C]2.29517910347103[/C][/ROW]
[ROW][C]111[/C][C]90[/C][C]91.5559099099643[/C][C]-1.55590990996428[/C][/ROW]
[ROW][C]112[/C][C]84[/C][C]84.0520305803493[/C][C]-0.0520305803493067[/C][/ROW]
[ROW][C]113[/C][C]75[/C][C]74.1401301293222[/C][C]0.859869870677827[/C][/ROW]
[ROW][C]114[/C][C]90[/C][C]92.0705505840995[/C][C]-2.07055058409951[/C][/ROW]
[ROW][C]115[/C][C]77[/C][C]66.8788050207362[/C][C]10.1211949792638[/C][/ROW]
[ROW][C]116[/C][C]60[/C][C]62.7140485150546[/C][C]-2.71404851505456[/C][/ROW]
[ROW][C]117[/C][C]92[/C][C]89.6378762296911[/C][C]2.36212377030886[/C][/ROW]
[ROW][C]118[/C][C]88[/C][C]84.0011209806823[/C][C]3.99887901931765[/C][/ROW]
[ROW][C]119[/C][C]83[/C][C]83.0540205752193[/C][C]-0.0540205752192901[/C][/ROW]
[ROW][C]120[/C][C]69[/C][C]76.6083344343238[/C][C]-7.60833443432377[/C][/ROW]
[ROW][C]121[/C][C]73[/C][C]79.3303756364705[/C][C]-6.33037563647052[/C][/ROW]
[ROW][C]122[/C][C]78[/C][C]80.9902347448782[/C][C]-2.99023474487818[/C][/ROW]
[ROW][C]123[/C][C]92[/C][C]91.2012316555186[/C][C]0.79876834448144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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
1102102.523561423125-0.523561423124545
29996.43098435433362.56901564566639
3108110.129556764131-2.12955676413052
49292.2453553796106-0.245355379610605
59995.40357129750923.59642870249083
610298.75639565069423.24360434930583
78788.1438684297205-1.14386842972053
87180.2560906223946-9.25609062239456
9105101.5770242323953.4229757676047
10115109.2921058466855.707894153315
1110394.92040605515818.07959394484194
127576.7256812076075-1.72568120760754
139794.31467012643062.68532987356936
149591.27719176574723.72280823425279
1599101.807530557236-2.80753055723601
1610094.86346628005445.13653371994562
179290.29002266322641.70997733677363
189498.7733513024597-4.77335130245972
198982.21582131943556.78417868056454
206772.2641109232101-5.26411092321011
21109100.4323703423448.56762965765579
22113108.4054194151764.59458058482427
2310696.77451158899429.22548841100581
247879.0032312308418-1.00323123084184
2510298.11198878976113.88801121023889
269795.07862198383341.9213780161666
279697.8201535339983-1.82015353399829
289998.16167640067230.838323599327686
298692.9296683856453-6.92966838564526
309298.6947420382733-6.69474203827331
318687.6102717132191-1.61027171321906
326272.1042276544752-10.1042276544752
33105105.421524304635-0.421524304634902
34108107.9768700458130.0231299541873009
359688.9839044368257.01609556317502
368080.4986162471762-0.49861624717617
379594.29126087800450.708739121995458
389493.5042545667350.495745433265027
39108114.59373900963-6.59373900962952
4097106.677773958203-9.67777395820278
418991.3900532511901-2.3900532511901
42107107.634978379288-0.634978379288013
438779.85565986997647.14434013002357
447073.5704964378432-3.57049643784325
45111106.9267063915434.07329360845654
4610595.61485361269799.38514638730213
479989.67859385587329.32140614412677
488482.85982979969511.14017020030485
498794.1721938181004-7.17219381810044
509293.4730332469579-1.4730332469579
5198103.416624913352-5.41662491335243
5295100.044280194186-5.04428019418638
538590.5980920896013-5.59809208960128
54100101.0143879893-1.01438798929996
557976.40811785101492.59188214898511
566676.8752865061516-10.8752865061516
57105105.920530435157-0.920530435157494
589696.0737661308267-0.0737661308266892
5910395.5312458262717.46875417372902
608383.4893015286931-0.489301528693106
619194.4989209062629-3.49892090626291
629592.76460004521132.23539995478866
63109111.469218179329-2.46921817932946
649294.8036032991486-2.80360329914858
6599102.861737602515-3.8617376025147
66110106.9870303719063.01296962809401
678878.98480137130189.01519862869817
687378.748010865064-5.74801086506398
69111105.8124338999815.18756610001863
70112107.9139687099394.08603129006117
71111102.7088429504928.29115704950812
728483.67397006837730.326029931622705
73102100.8824612207721.11753877922809
74102100.3435519592151.65644804078456
75114117.457008935539-3.45700893553948
769996.72840856989872.27159143010127
77100103.980364360144-3.98036436014392
78110122.245206708267-12.2452067082669
799391.08424180581221.9157581941878
807783.8881993661682-6.88819936616817
81108106.3188527640411.68114723595922
82120119.4990103054740.500989694525774
83106101.2056548471964.79434515280382
847873.64377112439814.35622887560188
85100102.910080118945-2.91008011894507
8610298.76183406181993.23816593818012
879797.4339224251858-0.433922425185823
88101103.94480618871-2.94480618870972
898991.0902466121503-2.09024661215033
909397.1970698306748-4.19706983067482
918983.22748905136655.7725109486335
926260.28251715580751.71748284419254
939692.62313887662493.3768611233751
949594.16066676925030.839333230749726
958073.16987255728336.83012744271674
966762.3781571902824.62184280971798
977175.3200941359417-4.3200941359417
987374.463768276944-1.46376827694399
998180.5401311738460.459868826153944
1007781.6669821894091-4.66698218940912
1016870.2873790631904-2.28737906319038
1027782.6662729046161-5.66627290461611
1037367.40785349944335.5921465005567
1045457.9408107845255-3.94081078452553
1058587.6182973906515-2.61829739065148
1068681.85054496215034.14945503784971
1077981.3722394037192-2.37223940371916
1086768.8467583143929-1.8467583143929
1097276.5620820806383-4.56208208063834
1107673.7048208965292.29517910347103
1119091.5559099099643-1.55590990996428
1128484.0520305803493-0.0520305803493067
1137574.14013012932220.859869870677827
1149092.0705505840995-2.07055058409951
1157766.878805020736210.1211949792638
1166062.7140485150546-2.71404851505456
1179289.63787622969112.36212377030886
1188884.00112098068233.99887901931765
1198383.0540205752193-0.0540205752192901
1206976.6083344343238-7.60833443432377
1217379.3303756364705-6.33037563647052
1227880.9902347448782-2.99023474487818
1239291.20123165551860.79876834448144






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.1478021249777070.2956042499554150.852197875022293
160.06551570349411020.131031406988220.93448429650589
170.05711174583728870.1142234916745770.942888254162711
180.5257402432619850.9485195134760290.474259756738015
190.473904962637990.947809925275980.52609503736201
200.5391599351948870.9216801296102260.460840064805113
210.5018300546825420.9963398906349160.498169945317458
220.4698525726160050.9397051452320090.530147427383995
230.448850457717760.897700915435520.55114954228224
240.4242540800942960.8485081601885930.575745919905703
250.4749896111650910.9499792223301810.525010388834909
260.4439194474128130.8878388948256270.556080552587187
270.4060000151930030.8120000303860070.593999984806997
280.3425357985683280.6850715971366560.657464201431672
290.5864023209942960.8271953580114080.413597679005704
300.7553782943534390.4892434112931210.244621705646561
310.706347009223430.587305981553140.29365299077657
320.7322503687279790.5354992625440430.267749631272021
330.6850407740430080.6299184519139830.314959225956992
340.6269138948640.7461722102720.373086105136
350.7020416842922170.5959166314155670.297958315707783
360.6502091528684490.6995816942631020.349790847131551
370.5984813147949080.8030373704101850.401518685205092
380.5479687362956570.9040625274086860.452031263704343
390.6019423204366030.7961153591267940.398057679563397
400.7523120912025690.4953758175948620.247687908797431
410.7032926701488380.5934146597023240.296707329851162
420.6607829552042910.6784340895914180.339217044795709
430.7750288250563910.4499423498872170.224971174943609
440.757126770431390.4857464591372190.24287322956861
450.7382553235056260.5234893529887490.261744676494375
460.8939296755380530.2121406489238940.106070324461947
470.9518801982190990.09623960356180230.0481198017809011
480.9429274923544210.1141450152911590.0570725076455794
490.9593797413536640.08124051729267120.0406202586463356
500.9494069867902290.1011860264195430.0505930132097715
510.9448770077095380.1102459845809230.0551229922904615
520.9448385496046490.1103229007907020.0551614503953511
530.9367812902872980.1264374194254050.0632187097127023
540.9171115827350710.1657768345298590.0828884172649293
550.916376360972530.167247278054940.0836236390274698
560.9532825831879390.09343483362412250.0467174168120613
570.9391269423845420.1217461152309160.0608730576154582
580.9215419423240120.1569161153519770.0784580576759883
590.9530582777128120.09388344457437510.0469417222871875
600.9385480195807310.1229039608385370.0614519804192685
610.9242853522935710.1514292954128590.0757146477064295
620.916918328912330.1661633421753390.0830816710876695
630.8950092669032440.2099814661935120.104990733096756
640.8707236102447060.2585527795105890.129276389755294
650.8476670701843010.3046658596313970.152332929815699
660.8483631658347060.3032736683305880.151636834165294
670.9521394265594090.09572114688118240.0478605734405912
680.9491400826934450.101719834613110.0508599173065549
690.9702318607729410.05953627845411770.0297681392270588
700.9784880471288890.04302390574222210.021511952871111
710.9891868899440920.02162622011181590.010813110055908
720.9903156929308320.0193686141383360.00968430706916801
730.986106950432870.02778609913425910.0138930495671296
740.9865447488776390.02691050224472110.0134552511223606
750.982423428309610.03515314338078050.0175765716903902
760.9786830215934010.0426339568131980.021316978406599
770.9733001516163540.05339969676729280.0266998483836464
780.988080049769760.02383990046047980.0119199502302399
790.9866011452806640.02679770943867210.013398854719336
800.9877464341794090.02450713164118190.0122535658205909
810.9856782343683230.02864353126335360.0143217656316768
820.9837777264625890.03244454707482270.0162222735374113
830.9794631786087420.04107364278251680.0205368213912584
840.9813118750506790.03737624989864260.0186881249493213
850.9726575106795640.05468497864087120.0273424893204356
860.9772790285360510.04544194292789880.0227209714639494
870.9858232762476360.02835344750472810.0141767237523641
880.9786421491822040.04271570163559220.0213578508177961
890.9693213735414870.06135725291702540.0306786264585127
900.96260363129760.07479273740480040.0373963687024002
910.9505853268094530.09882934638109360.0494146731905468
920.9594977318778730.08100453624425350.0405022681221267
930.9413958740358220.1172082519283560.0586041259641782
940.9306190404872720.1387619190254570.0693809595127283
950.9098474925668760.1803050148662480.0901525074331238
960.8831596627460320.2336806745079360.116840337253968
970.8585283141682220.2829433716635560.141471685831778
980.8718412141825070.2563175716349870.128158785817493
990.8465571033902050.306885793219590.153442896609795
1000.7994175023108280.4011649953783450.200582497689172
1010.7366637123175250.526672575364950.263336287682475
1020.8449483661913030.3101032676173930.155051633808697
1030.7732599591335150.453480081732970.226740040866485
1040.8024299000134130.3951401999731740.197570099986587
1050.7062951711376980.5874096577246040.293704828862302
1060.6494842198358590.7010315603282830.350515780164141
1070.5183419570139870.9633160859720260.481658042986013
1080.3616404134993870.7232808269987750.638359586500613

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.147802124977707 & 0.295604249955415 & 0.852197875022293 \tabularnewline
16 & 0.0655157034941102 & 0.13103140698822 & 0.93448429650589 \tabularnewline
17 & 0.0571117458372887 & 0.114223491674577 & 0.942888254162711 \tabularnewline
18 & 0.525740243261985 & 0.948519513476029 & 0.474259756738015 \tabularnewline
19 & 0.47390496263799 & 0.94780992527598 & 0.52609503736201 \tabularnewline
20 & 0.539159935194887 & 0.921680129610226 & 0.460840064805113 \tabularnewline
21 & 0.501830054682542 & 0.996339890634916 & 0.498169945317458 \tabularnewline
22 & 0.469852572616005 & 0.939705145232009 & 0.530147427383995 \tabularnewline
23 & 0.44885045771776 & 0.89770091543552 & 0.55114954228224 \tabularnewline
24 & 0.424254080094296 & 0.848508160188593 & 0.575745919905703 \tabularnewline
25 & 0.474989611165091 & 0.949979222330181 & 0.525010388834909 \tabularnewline
26 & 0.443919447412813 & 0.887838894825627 & 0.556080552587187 \tabularnewline
27 & 0.406000015193003 & 0.812000030386007 & 0.593999984806997 \tabularnewline
28 & 0.342535798568328 & 0.685071597136656 & 0.657464201431672 \tabularnewline
29 & 0.586402320994296 & 0.827195358011408 & 0.413597679005704 \tabularnewline
30 & 0.755378294353439 & 0.489243411293121 & 0.244621705646561 \tabularnewline
31 & 0.70634700922343 & 0.58730598155314 & 0.29365299077657 \tabularnewline
32 & 0.732250368727979 & 0.535499262544043 & 0.267749631272021 \tabularnewline
33 & 0.685040774043008 & 0.629918451913983 & 0.314959225956992 \tabularnewline
34 & 0.626913894864 & 0.746172210272 & 0.373086105136 \tabularnewline
35 & 0.702041684292217 & 0.595916631415567 & 0.297958315707783 \tabularnewline
36 & 0.650209152868449 & 0.699581694263102 & 0.349790847131551 \tabularnewline
37 & 0.598481314794908 & 0.803037370410185 & 0.401518685205092 \tabularnewline
38 & 0.547968736295657 & 0.904062527408686 & 0.452031263704343 \tabularnewline
39 & 0.601942320436603 & 0.796115359126794 & 0.398057679563397 \tabularnewline
40 & 0.752312091202569 & 0.495375817594862 & 0.247687908797431 \tabularnewline
41 & 0.703292670148838 & 0.593414659702324 & 0.296707329851162 \tabularnewline
42 & 0.660782955204291 & 0.678434089591418 & 0.339217044795709 \tabularnewline
43 & 0.775028825056391 & 0.449942349887217 & 0.224971174943609 \tabularnewline
44 & 0.75712677043139 & 0.485746459137219 & 0.24287322956861 \tabularnewline
45 & 0.738255323505626 & 0.523489352988749 & 0.261744676494375 \tabularnewline
46 & 0.893929675538053 & 0.212140648923894 & 0.106070324461947 \tabularnewline
47 & 0.951880198219099 & 0.0962396035618023 & 0.0481198017809011 \tabularnewline
48 & 0.942927492354421 & 0.114145015291159 & 0.0570725076455794 \tabularnewline
49 & 0.959379741353664 & 0.0812405172926712 & 0.0406202586463356 \tabularnewline
50 & 0.949406986790229 & 0.101186026419543 & 0.0505930132097715 \tabularnewline
51 & 0.944877007709538 & 0.110245984580923 & 0.0551229922904615 \tabularnewline
52 & 0.944838549604649 & 0.110322900790702 & 0.0551614503953511 \tabularnewline
53 & 0.936781290287298 & 0.126437419425405 & 0.0632187097127023 \tabularnewline
54 & 0.917111582735071 & 0.165776834529859 & 0.0828884172649293 \tabularnewline
55 & 0.91637636097253 & 0.16724727805494 & 0.0836236390274698 \tabularnewline
56 & 0.953282583187939 & 0.0934348336241225 & 0.0467174168120613 \tabularnewline
57 & 0.939126942384542 & 0.121746115230916 & 0.0608730576154582 \tabularnewline
58 & 0.921541942324012 & 0.156916115351977 & 0.0784580576759883 \tabularnewline
59 & 0.953058277712812 & 0.0938834445743751 & 0.0469417222871875 \tabularnewline
60 & 0.938548019580731 & 0.122903960838537 & 0.0614519804192685 \tabularnewline
61 & 0.924285352293571 & 0.151429295412859 & 0.0757146477064295 \tabularnewline
62 & 0.91691832891233 & 0.166163342175339 & 0.0830816710876695 \tabularnewline
63 & 0.895009266903244 & 0.209981466193512 & 0.104990733096756 \tabularnewline
64 & 0.870723610244706 & 0.258552779510589 & 0.129276389755294 \tabularnewline
65 & 0.847667070184301 & 0.304665859631397 & 0.152332929815699 \tabularnewline
66 & 0.848363165834706 & 0.303273668330588 & 0.151636834165294 \tabularnewline
67 & 0.952139426559409 & 0.0957211468811824 & 0.0478605734405912 \tabularnewline
68 & 0.949140082693445 & 0.10171983461311 & 0.0508599173065549 \tabularnewline
69 & 0.970231860772941 & 0.0595362784541177 & 0.0297681392270588 \tabularnewline
70 & 0.978488047128889 & 0.0430239057422221 & 0.021511952871111 \tabularnewline
71 & 0.989186889944092 & 0.0216262201118159 & 0.010813110055908 \tabularnewline
72 & 0.990315692930832 & 0.019368614138336 & 0.00968430706916801 \tabularnewline
73 & 0.98610695043287 & 0.0277860991342591 & 0.0138930495671296 \tabularnewline
74 & 0.986544748877639 & 0.0269105022447211 & 0.0134552511223606 \tabularnewline
75 & 0.98242342830961 & 0.0351531433807805 & 0.0175765716903902 \tabularnewline
76 & 0.978683021593401 & 0.042633956813198 & 0.021316978406599 \tabularnewline
77 & 0.973300151616354 & 0.0533996967672928 & 0.0266998483836464 \tabularnewline
78 & 0.98808004976976 & 0.0238399004604798 & 0.0119199502302399 \tabularnewline
79 & 0.986601145280664 & 0.0267977094386721 & 0.013398854719336 \tabularnewline
80 & 0.987746434179409 & 0.0245071316411819 & 0.0122535658205909 \tabularnewline
81 & 0.985678234368323 & 0.0286435312633536 & 0.0143217656316768 \tabularnewline
82 & 0.983777726462589 & 0.0324445470748227 & 0.0162222735374113 \tabularnewline
83 & 0.979463178608742 & 0.0410736427825168 & 0.0205368213912584 \tabularnewline
84 & 0.981311875050679 & 0.0373762498986426 & 0.0186881249493213 \tabularnewline
85 & 0.972657510679564 & 0.0546849786408712 & 0.0273424893204356 \tabularnewline
86 & 0.977279028536051 & 0.0454419429278988 & 0.0227209714639494 \tabularnewline
87 & 0.985823276247636 & 0.0283534475047281 & 0.0141767237523641 \tabularnewline
88 & 0.978642149182204 & 0.0427157016355922 & 0.0213578508177961 \tabularnewline
89 & 0.969321373541487 & 0.0613572529170254 & 0.0306786264585127 \tabularnewline
90 & 0.9626036312976 & 0.0747927374048004 & 0.0373963687024002 \tabularnewline
91 & 0.950585326809453 & 0.0988293463810936 & 0.0494146731905468 \tabularnewline
92 & 0.959497731877873 & 0.0810045362442535 & 0.0405022681221267 \tabularnewline
93 & 0.941395874035822 & 0.117208251928356 & 0.0586041259641782 \tabularnewline
94 & 0.930619040487272 & 0.138761919025457 & 0.0693809595127283 \tabularnewline
95 & 0.909847492566876 & 0.180305014866248 & 0.0901525074331238 \tabularnewline
96 & 0.883159662746032 & 0.233680674507936 & 0.116840337253968 \tabularnewline
97 & 0.858528314168222 & 0.282943371663556 & 0.141471685831778 \tabularnewline
98 & 0.871841214182507 & 0.256317571634987 & 0.128158785817493 \tabularnewline
99 & 0.846557103390205 & 0.30688579321959 & 0.153442896609795 \tabularnewline
100 & 0.799417502310828 & 0.401164995378345 & 0.200582497689172 \tabularnewline
101 & 0.736663712317525 & 0.52667257536495 & 0.263336287682475 \tabularnewline
102 & 0.844948366191303 & 0.310103267617393 & 0.155051633808697 \tabularnewline
103 & 0.773259959133515 & 0.45348008173297 & 0.226740040866485 \tabularnewline
104 & 0.802429900013413 & 0.395140199973174 & 0.197570099986587 \tabularnewline
105 & 0.706295171137698 & 0.587409657724604 & 0.293704828862302 \tabularnewline
106 & 0.649484219835859 & 0.701031560328283 & 0.350515780164141 \tabularnewline
107 & 0.518341957013987 & 0.963316085972026 & 0.481658042986013 \tabularnewline
108 & 0.361640413499387 & 0.723280826998775 & 0.638359586500613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&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]15[/C][C]0.147802124977707[/C][C]0.295604249955415[/C][C]0.852197875022293[/C][/ROW]
[ROW][C]16[/C][C]0.0655157034941102[/C][C]0.13103140698822[/C][C]0.93448429650589[/C][/ROW]
[ROW][C]17[/C][C]0.0571117458372887[/C][C]0.114223491674577[/C][C]0.942888254162711[/C][/ROW]
[ROW][C]18[/C][C]0.525740243261985[/C][C]0.948519513476029[/C][C]0.474259756738015[/C][/ROW]
[ROW][C]19[/C][C]0.47390496263799[/C][C]0.94780992527598[/C][C]0.52609503736201[/C][/ROW]
[ROW][C]20[/C][C]0.539159935194887[/C][C]0.921680129610226[/C][C]0.460840064805113[/C][/ROW]
[ROW][C]21[/C][C]0.501830054682542[/C][C]0.996339890634916[/C][C]0.498169945317458[/C][/ROW]
[ROW][C]22[/C][C]0.469852572616005[/C][C]0.939705145232009[/C][C]0.530147427383995[/C][/ROW]
[ROW][C]23[/C][C]0.44885045771776[/C][C]0.89770091543552[/C][C]0.55114954228224[/C][/ROW]
[ROW][C]24[/C][C]0.424254080094296[/C][C]0.848508160188593[/C][C]0.575745919905703[/C][/ROW]
[ROW][C]25[/C][C]0.474989611165091[/C][C]0.949979222330181[/C][C]0.525010388834909[/C][/ROW]
[ROW][C]26[/C][C]0.443919447412813[/C][C]0.887838894825627[/C][C]0.556080552587187[/C][/ROW]
[ROW][C]27[/C][C]0.406000015193003[/C][C]0.812000030386007[/C][C]0.593999984806997[/C][/ROW]
[ROW][C]28[/C][C]0.342535798568328[/C][C]0.685071597136656[/C][C]0.657464201431672[/C][/ROW]
[ROW][C]29[/C][C]0.586402320994296[/C][C]0.827195358011408[/C][C]0.413597679005704[/C][/ROW]
[ROW][C]30[/C][C]0.755378294353439[/C][C]0.489243411293121[/C][C]0.244621705646561[/C][/ROW]
[ROW][C]31[/C][C]0.70634700922343[/C][C]0.58730598155314[/C][C]0.29365299077657[/C][/ROW]
[ROW][C]32[/C][C]0.732250368727979[/C][C]0.535499262544043[/C][C]0.267749631272021[/C][/ROW]
[ROW][C]33[/C][C]0.685040774043008[/C][C]0.629918451913983[/C][C]0.314959225956992[/C][/ROW]
[ROW][C]34[/C][C]0.626913894864[/C][C]0.746172210272[/C][C]0.373086105136[/C][/ROW]
[ROW][C]35[/C][C]0.702041684292217[/C][C]0.595916631415567[/C][C]0.297958315707783[/C][/ROW]
[ROW][C]36[/C][C]0.650209152868449[/C][C]0.699581694263102[/C][C]0.349790847131551[/C][/ROW]
[ROW][C]37[/C][C]0.598481314794908[/C][C]0.803037370410185[/C][C]0.401518685205092[/C][/ROW]
[ROW][C]38[/C][C]0.547968736295657[/C][C]0.904062527408686[/C][C]0.452031263704343[/C][/ROW]
[ROW][C]39[/C][C]0.601942320436603[/C][C]0.796115359126794[/C][C]0.398057679563397[/C][/ROW]
[ROW][C]40[/C][C]0.752312091202569[/C][C]0.495375817594862[/C][C]0.247687908797431[/C][/ROW]
[ROW][C]41[/C][C]0.703292670148838[/C][C]0.593414659702324[/C][C]0.296707329851162[/C][/ROW]
[ROW][C]42[/C][C]0.660782955204291[/C][C]0.678434089591418[/C][C]0.339217044795709[/C][/ROW]
[ROW][C]43[/C][C]0.775028825056391[/C][C]0.449942349887217[/C][C]0.224971174943609[/C][/ROW]
[ROW][C]44[/C][C]0.75712677043139[/C][C]0.485746459137219[/C][C]0.24287322956861[/C][/ROW]
[ROW][C]45[/C][C]0.738255323505626[/C][C]0.523489352988749[/C][C]0.261744676494375[/C][/ROW]
[ROW][C]46[/C][C]0.893929675538053[/C][C]0.212140648923894[/C][C]0.106070324461947[/C][/ROW]
[ROW][C]47[/C][C]0.951880198219099[/C][C]0.0962396035618023[/C][C]0.0481198017809011[/C][/ROW]
[ROW][C]48[/C][C]0.942927492354421[/C][C]0.114145015291159[/C][C]0.0570725076455794[/C][/ROW]
[ROW][C]49[/C][C]0.959379741353664[/C][C]0.0812405172926712[/C][C]0.0406202586463356[/C][/ROW]
[ROW][C]50[/C][C]0.949406986790229[/C][C]0.101186026419543[/C][C]0.0505930132097715[/C][/ROW]
[ROW][C]51[/C][C]0.944877007709538[/C][C]0.110245984580923[/C][C]0.0551229922904615[/C][/ROW]
[ROW][C]52[/C][C]0.944838549604649[/C][C]0.110322900790702[/C][C]0.0551614503953511[/C][/ROW]
[ROW][C]53[/C][C]0.936781290287298[/C][C]0.126437419425405[/C][C]0.0632187097127023[/C][/ROW]
[ROW][C]54[/C][C]0.917111582735071[/C][C]0.165776834529859[/C][C]0.0828884172649293[/C][/ROW]
[ROW][C]55[/C][C]0.91637636097253[/C][C]0.16724727805494[/C][C]0.0836236390274698[/C][/ROW]
[ROW][C]56[/C][C]0.953282583187939[/C][C]0.0934348336241225[/C][C]0.0467174168120613[/C][/ROW]
[ROW][C]57[/C][C]0.939126942384542[/C][C]0.121746115230916[/C][C]0.0608730576154582[/C][/ROW]
[ROW][C]58[/C][C]0.921541942324012[/C][C]0.156916115351977[/C][C]0.0784580576759883[/C][/ROW]
[ROW][C]59[/C][C]0.953058277712812[/C][C]0.0938834445743751[/C][C]0.0469417222871875[/C][/ROW]
[ROW][C]60[/C][C]0.938548019580731[/C][C]0.122903960838537[/C][C]0.0614519804192685[/C][/ROW]
[ROW][C]61[/C][C]0.924285352293571[/C][C]0.151429295412859[/C][C]0.0757146477064295[/C][/ROW]
[ROW][C]62[/C][C]0.91691832891233[/C][C]0.166163342175339[/C][C]0.0830816710876695[/C][/ROW]
[ROW][C]63[/C][C]0.895009266903244[/C][C]0.209981466193512[/C][C]0.104990733096756[/C][/ROW]
[ROW][C]64[/C][C]0.870723610244706[/C][C]0.258552779510589[/C][C]0.129276389755294[/C][/ROW]
[ROW][C]65[/C][C]0.847667070184301[/C][C]0.304665859631397[/C][C]0.152332929815699[/C][/ROW]
[ROW][C]66[/C][C]0.848363165834706[/C][C]0.303273668330588[/C][C]0.151636834165294[/C][/ROW]
[ROW][C]67[/C][C]0.952139426559409[/C][C]0.0957211468811824[/C][C]0.0478605734405912[/C][/ROW]
[ROW][C]68[/C][C]0.949140082693445[/C][C]0.10171983461311[/C][C]0.0508599173065549[/C][/ROW]
[ROW][C]69[/C][C]0.970231860772941[/C][C]0.0595362784541177[/C][C]0.0297681392270588[/C][/ROW]
[ROW][C]70[/C][C]0.978488047128889[/C][C]0.0430239057422221[/C][C]0.021511952871111[/C][/ROW]
[ROW][C]71[/C][C]0.989186889944092[/C][C]0.0216262201118159[/C][C]0.010813110055908[/C][/ROW]
[ROW][C]72[/C][C]0.990315692930832[/C][C]0.019368614138336[/C][C]0.00968430706916801[/C][/ROW]
[ROW][C]73[/C][C]0.98610695043287[/C][C]0.0277860991342591[/C][C]0.0138930495671296[/C][/ROW]
[ROW][C]74[/C][C]0.986544748877639[/C][C]0.0269105022447211[/C][C]0.0134552511223606[/C][/ROW]
[ROW][C]75[/C][C]0.98242342830961[/C][C]0.0351531433807805[/C][C]0.0175765716903902[/C][/ROW]
[ROW][C]76[/C][C]0.978683021593401[/C][C]0.042633956813198[/C][C]0.021316978406599[/C][/ROW]
[ROW][C]77[/C][C]0.973300151616354[/C][C]0.0533996967672928[/C][C]0.0266998483836464[/C][/ROW]
[ROW][C]78[/C][C]0.98808004976976[/C][C]0.0238399004604798[/C][C]0.0119199502302399[/C][/ROW]
[ROW][C]79[/C][C]0.986601145280664[/C][C]0.0267977094386721[/C][C]0.013398854719336[/C][/ROW]
[ROW][C]80[/C][C]0.987746434179409[/C][C]0.0245071316411819[/C][C]0.0122535658205909[/C][/ROW]
[ROW][C]81[/C][C]0.985678234368323[/C][C]0.0286435312633536[/C][C]0.0143217656316768[/C][/ROW]
[ROW][C]82[/C][C]0.983777726462589[/C][C]0.0324445470748227[/C][C]0.0162222735374113[/C][/ROW]
[ROW][C]83[/C][C]0.979463178608742[/C][C]0.0410736427825168[/C][C]0.0205368213912584[/C][/ROW]
[ROW][C]84[/C][C]0.981311875050679[/C][C]0.0373762498986426[/C][C]0.0186881249493213[/C][/ROW]
[ROW][C]85[/C][C]0.972657510679564[/C][C]0.0546849786408712[/C][C]0.0273424893204356[/C][/ROW]
[ROW][C]86[/C][C]0.977279028536051[/C][C]0.0454419429278988[/C][C]0.0227209714639494[/C][/ROW]
[ROW][C]87[/C][C]0.985823276247636[/C][C]0.0283534475047281[/C][C]0.0141767237523641[/C][/ROW]
[ROW][C]88[/C][C]0.978642149182204[/C][C]0.0427157016355922[/C][C]0.0213578508177961[/C][/ROW]
[ROW][C]89[/C][C]0.969321373541487[/C][C]0.0613572529170254[/C][C]0.0306786264585127[/C][/ROW]
[ROW][C]90[/C][C]0.9626036312976[/C][C]0.0747927374048004[/C][C]0.0373963687024002[/C][/ROW]
[ROW][C]91[/C][C]0.950585326809453[/C][C]0.0988293463810936[/C][C]0.0494146731905468[/C][/ROW]
[ROW][C]92[/C][C]0.959497731877873[/C][C]0.0810045362442535[/C][C]0.0405022681221267[/C][/ROW]
[ROW][C]93[/C][C]0.941395874035822[/C][C]0.117208251928356[/C][C]0.0586041259641782[/C][/ROW]
[ROW][C]94[/C][C]0.930619040487272[/C][C]0.138761919025457[/C][C]0.0693809595127283[/C][/ROW]
[ROW][C]95[/C][C]0.909847492566876[/C][C]0.180305014866248[/C][C]0.0901525074331238[/C][/ROW]
[ROW][C]96[/C][C]0.883159662746032[/C][C]0.233680674507936[/C][C]0.116840337253968[/C][/ROW]
[ROW][C]97[/C][C]0.858528314168222[/C][C]0.282943371663556[/C][C]0.141471685831778[/C][/ROW]
[ROW][C]98[/C][C]0.871841214182507[/C][C]0.256317571634987[/C][C]0.128158785817493[/C][/ROW]
[ROW][C]99[/C][C]0.846557103390205[/C][C]0.30688579321959[/C][C]0.153442896609795[/C][/ROW]
[ROW][C]100[/C][C]0.799417502310828[/C][C]0.401164995378345[/C][C]0.200582497689172[/C][/ROW]
[ROW][C]101[/C][C]0.736663712317525[/C][C]0.52667257536495[/C][C]0.263336287682475[/C][/ROW]
[ROW][C]102[/C][C]0.844948366191303[/C][C]0.310103267617393[/C][C]0.155051633808697[/C][/ROW]
[ROW][C]103[/C][C]0.773259959133515[/C][C]0.45348008173297[/C][C]0.226740040866485[/C][/ROW]
[ROW][C]104[/C][C]0.802429900013413[/C][C]0.395140199973174[/C][C]0.197570099986587[/C][/ROW]
[ROW][C]105[/C][C]0.706295171137698[/C][C]0.587409657724604[/C][C]0.293704828862302[/C][/ROW]
[ROW][C]106[/C][C]0.649484219835859[/C][C]0.701031560328283[/C][C]0.350515780164141[/C][/ROW]
[ROW][C]107[/C][C]0.518341957013987[/C][C]0.963316085972026[/C][C]0.481658042986013[/C][/ROW]
[ROW][C]108[/C][C]0.361640413499387[/C][C]0.723280826998775[/C][C]0.638359586500613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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
150.1478021249777070.2956042499554150.852197875022293
160.06551570349411020.131031406988220.93448429650589
170.05711174583728870.1142234916745770.942888254162711
180.5257402432619850.9485195134760290.474259756738015
190.473904962637990.947809925275980.52609503736201
200.5391599351948870.9216801296102260.460840064805113
210.5018300546825420.9963398906349160.498169945317458
220.4698525726160050.9397051452320090.530147427383995
230.448850457717760.897700915435520.55114954228224
240.4242540800942960.8485081601885930.575745919905703
250.4749896111650910.9499792223301810.525010388834909
260.4439194474128130.8878388948256270.556080552587187
270.4060000151930030.8120000303860070.593999984806997
280.3425357985683280.6850715971366560.657464201431672
290.5864023209942960.8271953580114080.413597679005704
300.7553782943534390.4892434112931210.244621705646561
310.706347009223430.587305981553140.29365299077657
320.7322503687279790.5354992625440430.267749631272021
330.6850407740430080.6299184519139830.314959225956992
340.6269138948640.7461722102720.373086105136
350.7020416842922170.5959166314155670.297958315707783
360.6502091528684490.6995816942631020.349790847131551
370.5984813147949080.8030373704101850.401518685205092
380.5479687362956570.9040625274086860.452031263704343
390.6019423204366030.7961153591267940.398057679563397
400.7523120912025690.4953758175948620.247687908797431
410.7032926701488380.5934146597023240.296707329851162
420.6607829552042910.6784340895914180.339217044795709
430.7750288250563910.4499423498872170.224971174943609
440.757126770431390.4857464591372190.24287322956861
450.7382553235056260.5234893529887490.261744676494375
460.8939296755380530.2121406489238940.106070324461947
470.9518801982190990.09623960356180230.0481198017809011
480.9429274923544210.1141450152911590.0570725076455794
490.9593797413536640.08124051729267120.0406202586463356
500.9494069867902290.1011860264195430.0505930132097715
510.9448770077095380.1102459845809230.0551229922904615
520.9448385496046490.1103229007907020.0551614503953511
530.9367812902872980.1264374194254050.0632187097127023
540.9171115827350710.1657768345298590.0828884172649293
550.916376360972530.167247278054940.0836236390274698
560.9532825831879390.09343483362412250.0467174168120613
570.9391269423845420.1217461152309160.0608730576154582
580.9215419423240120.1569161153519770.0784580576759883
590.9530582777128120.09388344457437510.0469417222871875
600.9385480195807310.1229039608385370.0614519804192685
610.9242853522935710.1514292954128590.0757146477064295
620.916918328912330.1661633421753390.0830816710876695
630.8950092669032440.2099814661935120.104990733096756
640.8707236102447060.2585527795105890.129276389755294
650.8476670701843010.3046658596313970.152332929815699
660.8483631658347060.3032736683305880.151636834165294
670.9521394265594090.09572114688118240.0478605734405912
680.9491400826934450.101719834613110.0508599173065549
690.9702318607729410.05953627845411770.0297681392270588
700.9784880471288890.04302390574222210.021511952871111
710.9891868899440920.02162622011181590.010813110055908
720.9903156929308320.0193686141383360.00968430706916801
730.986106950432870.02778609913425910.0138930495671296
740.9865447488776390.02691050224472110.0134552511223606
750.982423428309610.03515314338078050.0175765716903902
760.9786830215934010.0426339568131980.021316978406599
770.9733001516163540.05339969676729280.0266998483836464
780.988080049769760.02383990046047980.0119199502302399
790.9866011452806640.02679770943867210.013398854719336
800.9877464341794090.02450713164118190.0122535658205909
810.9856782343683230.02864353126335360.0143217656316768
820.9837777264625890.03244454707482270.0162222735374113
830.9794631786087420.04107364278251680.0205368213912584
840.9813118750506790.03737624989864260.0186881249493213
850.9726575106795640.05468497864087120.0273424893204356
860.9772790285360510.04544194292789880.0227209714639494
870.9858232762476360.02835344750472810.0141767237523641
880.9786421491822040.04271570163559220.0213578508177961
890.9693213735414870.06135725291702540.0306786264585127
900.96260363129760.07479273740480040.0373963687024002
910.9505853268094530.09882934638109360.0494146731905468
920.9594977318778730.08100453624425350.0405022681221267
930.9413958740358220.1172082519283560.0586041259641782
940.9306190404872720.1387619190254570.0693809595127283
950.9098474925668760.1803050148662480.0901525074331238
960.8831596627460320.2336806745079360.116840337253968
970.8585283141682220.2829433716635560.141471685831778
980.8718412141825070.2563175716349870.128158785817493
990.8465571033902050.306885793219590.153442896609795
1000.7994175023108280.4011649953783450.200582497689172
1010.7366637123175250.526672575364950.263336287682475
1020.8449483661913030.3101032676173930.155051633808697
1030.7732599591335150.453480081732970.226740040866485
1040.8024299000134130.3951401999731740.197570099986587
1050.7062951711376980.5874096577246040.293704828862302
1060.6494842198358590.7010315603282830.350515780164141
1070.5183419570139870.9633160859720260.481658042986013
1080.3616404134993870.7232808269987750.638359586500613






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level170.180851063829787NOK
10% type I error level290.308510638297872NOK

\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 & 17 & 0.180851063829787 & NOK \tabularnewline
10% type I error level & 29 & 0.308510638297872 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189840&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]17[/C][C]0.180851063829787[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.308510638297872[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189840&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189840&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 level170.180851063829787NOK
10% type I error level290.308510638297872NOK


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 = 6 ; 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')
}