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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 13 Dec 2014 10:18:19 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/13/t14184660130w7gn35mwhkipxv.htm/, Retrieved Thu, 16 May 2024 17:21:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266942, Retrieved Thu, 16 May 2024 17:21:13 +0000

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Estimated Impact

129

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-       [Multiple Regression] [] [2014-12-13 10:18:19] [58179e1d3a5a39b9daf58e365d8a3352] [Current]

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12.9 26 50 4 13 12 13 13 21 2 149 96 86 7.5 1.8 2.1 1.5
12.8 37 54 5 14 11 11 11 22 0 148 88 71 6.5 2.2 2 2.1
7.4 67 71 4 16 13 14 10 18 0 158 114 108 1 2.3 2.1 1.9
6.7 43 54 4 14 11 15 9 23 4 128 69 64 1 2.1 2 1.6
12.6 52 65 9 13 10 14 8 12 0 224 176 119 5.5 2.7 2.3 2.1
14.8 52 73 8 15 7 11 26 20 -1 159 114 97 8.5 2.1 2.1 2.1
13.3 43 52 11 13 10 13 10 22 0 105 121 129 6.5 2.4 2.1 2.2
11.1 84 84 4 20 15 16 10 21 1 159 110 153 4.5 2.9 2.2 1.5
8.2 67 42 4 17 12 14 8 19 0 167 158 78 2 2.2 2.1 1.9
11.4 49 66 6 15 12 14 13 22 3 165 116 80 5 2.1 2.1 2.2
6.4 70 65 4 16 10 15 11 15 -1 159 181 99 0.5 2.2 2.1 1.6
12 58 73 4 17 14 13 12 19 4 176 141 147 5 2.7 2.3 1.9
6.3 68 75 4 11 6 14 24 18 1 54 35 40 2.5 1.9 1.8 0.1
11.3 62 72 11 16 12 11 21 15 0 91 80 57 5 2 2 2.2
11.9 43 66 4 16 14 12 5 20 -2 163 152 120 5.5 2.5 2.2 1.8
9.3 56 70 4 15 11 14 14 21 -4 124 97 71 3.5 2.2 2 1.6
10 74 81 6 14 12 12 9 15 2 121 84 68 4 1.9 2 2.1
13.8 63 69 8 16 13 15 17 23 2 148 101 137 6.5 3.5 2.2 1.6
10.8 58 71 5 17 11 14 18 21 -4 221 107 79 4.5 2.1 2.2 1.9
11.7 63 68 9 15 7 12 23 25 2 149 112 101 5.5 2.3 2.1 1.8
10.9 53 70 4 14 11 12 9 9 2 244 171 111 4 2.2 2.3 2.4
16.1 57 68 7 14 7 12 14 30 0 148 137 189 7.5 3.5 2.7 2.4
9.9 64 67 4 15 12 14 10 23 -3 150 66 81 4 1.9 2 1.9
11.5 53 76 4 17 13 16 8 16 2 153 93 63 5.5 1.9 2 2.1
8.3 29 70 7 14 9 12 10 16 0 94 105 69 2.5 1.9 1.9 1.9
11.7 54 60 12 16 11 12 19 19 4 156 131 71 5.5 2.1 2 2.1
9 58 72 7 15 12 14 11 25 2 132 102 64 3.5 2 2 1.5
10.8 51 71 8 16 12 15 12 23 2 105 120 85 4.5 2.3 2 2.1
10.4 54 70 4 8 5 14 11 10 -4 151 77 55 4.5 1.8 2 2.1
12.7 56 64 9 17 13 13 10 14 3 131 108 69 6 2.4 2.2 2.1
11.8 47 76 4 10 6 16 14 26 2 157 168 96 5 2.3 2.1 2.4
13 50 68 4 16 6 15 11 24 -1 162 75 100 6.5 2.3 2.1 2.1
10.8 35 76 4 16 12 13 13 24 -3 163 107 68 5 1.8 2 1.9
12.3 30 65 7 16 11 16 15 18 0 59 62 57 6 1.9 1.9 2.4
11.3 68 67 4 8 6 16 15 23 1 187 121 105 4.5 2.6 2.2 2.1
11.6 56 75 4 14 11 15 14 23 -3 116 97 69 5 2.1 2.2 2.4
10.9 43 60 4 16 12 13 12 19 3 148 126 49 5 1.8 2 2.1
12.1 67 73 4 19 13 12 13 21 0 155 104 50 6.5 1.9 2.2 1.5
13.3 62 63 4 19 14 14 7 18 0 125 148 93 7 2.4 2 1.9
10.1 57 70 4 14 12 14 8 27 0 116 146 58 4.5 1.9 1.9 1.8
14.3 54 66 12 13 14 10 20 13 3 138 97 74 8.5 2.1 2 1.6
9.3 61 64 4 15 11 16 16 28 0 164 118 107 3.5 2.1 2.1 1.5
12.5 56 70 5 11 10 14 11 23 2 162 58 65 6 2.4 2 2.1
7.6 41 75 15 9 7 14 26 21 -1 99 63 58 1.5 1.8 1.9 2.4
9.2 53 60 10 12 7 15 15 19 3 186 50 70 3.5 2.1 2.1 1.5
14.5 46 66 5 13 10 16 20 17 2 188 94 95 7.5 2.7 2.2 2.1
12.3 51 59 9 17 12 15 15 25 2 177 127 136 5 2.9 2.2 2.1
12.6 37 78 4 7 5 13 17 14 -2 139 128 82 6.5 2.1 2 1.9
13 42 67 7 15 10 12 19 16 0 162 146 102 6.5 2.3 2.1 2.1
12.6 38 59 5 12 12 12 13 24 -2 108 69 65 6.5 2.2 2.1 1.8
13.2 66 66 4 15 11 14 8 20 0 159 186 90 7 2 2.1 2.1
7.7 53 71 4 16 12 15 9 24 6 110 85 83 1.5 2.1 2 2.1
10.5 49 66 4 14 11 11 12 22 0 96 54 70 4 2.1 2.1 2.2
10.9 49 72 4 16 12 14 9 22 -2 87 106 77 4.5 2 2.1 2.2
4.3 59 71 6 13 10 16 14 20 1 97 34 37 0 1.7 1 1.6
10.3 40 59 10 16 9 13 14 10 0 127 60 81 3.5 2.2 2.2 2.4
11.4 63 78 4 10 7 11 13 22 2 74 62 71 4.5 2.4 2 2.4
5.6 34 65 11 12 9 12 16 20 2 114 64 40 0 1.8 2 1.8
8.8 32 65 14 14 10 12 14 22 -3 95 98 43 3 1.9 2 1.9
9 67 71 4 16 12 14 11 20 1 121 35 32 3.5 1.7 2 1.8
9.6 61 72 4 18 14 12 11 17 -4 130 55 76 3 2.1 2.2 2.2
6.4 60 66 5 12 9 13 14 18 1 52 54 30 1 1.7 1.8 1.9
11.6 63 69 4 15 12 14 15 19 0 118 51 51 5.5 1.9 2.1 2.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.237826 -0.000445445AMS.I[t] + 8.51892e-05AMS.E[t] -0.0021409AMS.A[t] + 0.000332719CONFSTATTOT[t] + 0.00477649CONFSOFTTOT[t] -0.00751386STRESSTOT[t] + 0.00228385CESDTOT[t] -0.00105714NUMERACYTOT[t] -0.00258652DECTESTTOT[t] -0.000127278LFM[t] -0.000392165BLOG[t] + 0.00139118HOURS[t] + 0.997725Ex[t] + 0.935277PR[t] + 0.962357PE[t] + 1.01288PA[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  0.237826 -0.000445445AMS.I[t] +  8.51892e-05AMS.E[t] -0.0021409AMS.A[t] +  0.000332719CONFSTATTOT[t] +  0.00477649CONFSOFTTOT[t] -0.00751386STRESSTOT[t] +  0.00228385CESDTOT[t] -0.00105714NUMERACYTOT[t] -0.00258652DECTESTTOT[t] -0.000127278LFM[t] -0.000392165BLOG[t] +  0.00139118HOURS[t] +  0.997725Ex[t] +  0.935277PR[t] +  0.962357PE[t] +  1.01288PA[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  0.237826 -0.000445445AMS.I[t] +  8.51892e-05AMS.E[t] -0.0021409AMS.A[t] +  0.000332719CONFSTATTOT[t] +  0.00477649CONFSOFTTOT[t] -0.00751386STRESSTOT[t] +  0.00228385CESDTOT[t] -0.00105714NUMERACYTOT[t] -0.00258652DECTESTTOT[t] -0.000127278LFM[t] -0.000392165BLOG[t] +  0.00139118HOURS[t] +  0.997725Ex[t] +  0.935277PR[t] +  0.962357PE[t] +  1.01288PA[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.237826 -0.000445445AMS.I[t] + 8.51892e-05AMS.E[t] -0.0021409AMS.A[t] + 0.000332719CONFSTATTOT[t] + 0.00477649CONFSOFTTOT[t] -0.00751386STRESSTOT[t] + 0.00228385CESDTOT[t] -0.00105714NUMERACYTOT[t] -0.00258652DECTESTTOT[t] -0.000127278LFM[t] -0.000392165BLOG[t] + 0.00139118HOURS[t] + 0.997725Ex[t] + 0.935277PR[t] + 0.962357PE[t] + 1.01288PA[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.237826	0.16453	1.445	0.155099	0.0775495
AMS.I	-0.000445445	0.000748322	-0.5953	0.554588	0.277294
AMS.E	8.51892e-05	0.00110515	0.07708	0.938891	0.469446
AMS.A	-0.0021409	0.00334276	-0.6405	0.525053	0.262526
CONFSTATTOT	0.000332719	0.00428016	0.07774	0.938376	0.469188
CONFSOFTTOT	0.00477649	0.00478522	0.9982	0.323416	0.161708
STRESSTOT	-0.00751386	0.00545599	-1.377	0.175123	0.0875614
CESDTOT	0.00228385	0.00227756	1.003	0.321223	0.160611
NUMERACYTOT	-0.00105714	0.00179385	-0.5893	0.558536	0.279268
DECTESTTOT	-0.00258652	0.00340244	-0.7602	0.451017	0.225509
LFM	-0.000127278	0.000255976	-0.4972	0.621398	0.310699
BLOG	-0.000392165	0.000258035	-1.52	0.135402	0.0677011
HOURS	0.00139118	0.000545116	2.552	0.0140942	0.00704709
Ex	0.997725	0.00434062	229.9	4.74237e-72	2.37119e-72
PR	0.935277	0.0417521	22.4	2.19621e-26	1.09811e-26
PE	0.962357	0.0571835	16.83	2.75771e-21	1.37885e-21
PA	1.01288	0.0227339	44.55	1.73323e-39	8.66614e-40

\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) & 0.237826 & 0.16453 & 1.445 & 0.155099 & 0.0775495 \tabularnewline
AMS.I & -0.000445445 & 0.000748322 & -0.5953 & 0.554588 & 0.277294 \tabularnewline
AMS.E & 8.51892e-05 & 0.00110515 & 0.07708 & 0.938891 & 0.469446 \tabularnewline
AMS.A & -0.0021409 & 0.00334276 & -0.6405 & 0.525053 & 0.262526 \tabularnewline
CONFSTATTOT & 0.000332719 & 0.00428016 & 0.07774 & 0.938376 & 0.469188 \tabularnewline
CONFSOFTTOT & 0.00477649 & 0.00478522 & 0.9982 & 0.323416 & 0.161708 \tabularnewline
STRESSTOT & -0.00751386 & 0.00545599 & -1.377 & 0.175123 & 0.0875614 \tabularnewline
CESDTOT & 0.00228385 & 0.00227756 & 1.003 & 0.321223 & 0.160611 \tabularnewline
NUMERACYTOT & -0.00105714 & 0.00179385 & -0.5893 & 0.558536 & 0.279268 \tabularnewline
DECTESTTOT & -0.00258652 & 0.00340244 & -0.7602 & 0.451017 & 0.225509 \tabularnewline
LFM & -0.000127278 & 0.000255976 & -0.4972 & 0.621398 & 0.310699 \tabularnewline
BLOG & -0.000392165 & 0.000258035 & -1.52 & 0.135402 & 0.0677011 \tabularnewline
HOURS & 0.00139118 & 0.000545116 & 2.552 & 0.0140942 & 0.00704709 \tabularnewline
Ex & 0.997725 & 0.00434062 & 229.9 & 4.74237e-72 & 2.37119e-72 \tabularnewline
PR & 0.935277 & 0.0417521 & 22.4 & 2.19621e-26 & 1.09811e-26 \tabularnewline
PE & 0.962357 & 0.0571835 & 16.83 & 2.75771e-21 & 1.37885e-21 \tabularnewline
PA & 1.01288 & 0.0227339 & 44.55 & 1.73323e-39 & 8.66614e-40 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&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]0.237826[/C][C]0.16453[/C][C]1.445[/C][C]0.155099[/C][C]0.0775495[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.000445445[/C][C]0.000748322[/C][C]-0.5953[/C][C]0.554588[/C][C]0.277294[/C][/ROW]
[ROW][C]AMS.E[/C][C]8.51892e-05[/C][C]0.00110515[/C][C]0.07708[/C][C]0.938891[/C][C]0.469446[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0021409[/C][C]0.00334276[/C][C]-0.6405[/C][C]0.525053[/C][C]0.262526[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.000332719[/C][C]0.00428016[/C][C]0.07774[/C][C]0.938376[/C][C]0.469188[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.00477649[/C][C]0.00478522[/C][C]0.9982[/C][C]0.323416[/C][C]0.161708[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.00751386[/C][C]0.00545599[/C][C]-1.377[/C][C]0.175123[/C][C]0.0875614[/C][/ROW]
[ROW][C]CESDTOT[/C][C]0.00228385[/C][C]0.00227756[/C][C]1.003[/C][C]0.321223[/C][C]0.160611[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00105714[/C][C]0.00179385[/C][C]-0.5893[/C][C]0.558536[/C][C]0.279268[/C][/ROW]
[ROW][C]DECTESTTOT[/C][C]-0.00258652[/C][C]0.00340244[/C][C]-0.7602[/C][C]0.451017[/C][C]0.225509[/C][/ROW]
[ROW][C]LFM[/C][C]-0.000127278[/C][C]0.000255976[/C][C]-0.4972[/C][C]0.621398[/C][C]0.310699[/C][/ROW]
[ROW][C]BLOG[/C][C]-0.000392165[/C][C]0.000258035[/C][C]-1.52[/C][C]0.135402[/C][C]0.0677011[/C][/ROW]
[ROW][C]HOURS[/C][C]0.00139118[/C][C]0.000545116[/C][C]2.552[/C][C]0.0140942[/C][C]0.00704709[/C][/ROW]
[ROW][C]Ex[/C][C]0.997725[/C][C]0.00434062[/C][C]229.9[/C][C]4.74237e-72[/C][C]2.37119e-72[/C][/ROW]
[ROW][C]PR[/C][C]0.935277[/C][C]0.0417521[/C][C]22.4[/C][C]2.19621e-26[/C][C]1.09811e-26[/C][/ROW]
[ROW][C]PE[/C][C]0.962357[/C][C]0.0571835[/C][C]16.83[/C][C]2.75771e-21[/C][C]1.37885e-21[/C][/ROW]
[ROW][C]PA[/C][C]1.01288[/C][C]0.0227339[/C][C]44.55[/C][C]1.73323e-39[/C][C]8.66614e-40[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.237826	0.16453	1.445	0.155099	0.0775495
AMS.I	-0.000445445	0.000748322	-0.5953	0.554588	0.277294
AMS.E	8.51892e-05	0.00110515	0.07708	0.938891	0.469446
AMS.A	-0.0021409	0.00334276	-0.6405	0.525053	0.262526
CONFSTATTOT	0.000332719	0.00428016	0.07774	0.938376	0.469188
CONFSOFTTOT	0.00477649	0.00478522	0.9982	0.323416	0.161708
STRESSTOT	-0.00751386	0.00545599	-1.377	0.175123	0.0875614
CESDTOT	0.00228385	0.00227756	1.003	0.321223	0.160611
NUMERACYTOT	-0.00105714	0.00179385	-0.5893	0.558536	0.279268
DECTESTTOT	-0.00258652	0.00340244	-0.7602	0.451017	0.225509
LFM	-0.000127278	0.000255976	-0.4972	0.621398	0.310699
BLOG	-0.000392165	0.000258035	-1.52	0.135402	0.0677011
HOURS	0.00139118	0.000545116	2.552	0.0140942	0.00704709
Ex	0.997725	0.00434062	229.9	4.74237e-72	2.37119e-72
PR	0.935277	0.0417521	22.4	2.19621e-26	1.09811e-26
PE	0.962357	0.0571835	16.83	2.75771e-21	1.37885e-21
PA	1.01288	0.0227339	44.55	1.73323e-39	8.66614e-40

Multiple Linear Regression - Regression Statistics
Multiple R	0.999806
R-squared	0.999613
Adjusted R-squared	0.999478
F-TEST (value)	7423.84
F-TEST (DF numerator)	16
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0548182
Sum Squared Residuals	0.138231

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999806 \tabularnewline
R-squared & 0.999613 \tabularnewline
Adjusted R-squared & 0.999478 \tabularnewline
F-TEST (value) & 7423.84 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0548182 \tabularnewline
Sum Squared Residuals & 0.138231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999806[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999613[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999478[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7423.84[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/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]0.0548182[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.138231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.999806
R-squared	0.999613
Adjusted R-squared	0.999478
F-TEST (value)	7423.84
F-TEST (DF numerator)	16
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0548182
Sum Squared Residuals	0.138231

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	12.958	-0.057957
2	12.8	12.8317	-0.0316641
3	7.4	7.35122	0.0487848
4	6.7	6.69791	0.0020917
5	12.6	12.5746	0.0253614
6	14.8	14.8631	-0.0630962
7	13.3	13.2514	0.0486458
8	11.1	11.1432	-0.0431809
9	8.2	8.18274	0.0172628
10	11.4	11.4114	-0.0113604
11	6.4	6.40039	-0.000388825
12	12	11.96	0.0399583
13	6.3	6.3056	-0.00560185
14	11.3	11.2535	0.0465433
15	11.9	12.0458	-0.145845
16	9.3	9.32266	-0.0226601
17	10	10.0362	-0.0361992
18	13.8	13.7912	0.0088148
19	10.8	10.7249	0.075091
20	11.7	11.7259	-0.0258567
21	10.9	10.9337	-0.0336662
22	16.1	16.1275	-0.0274836
23	9.9	9.85468	0.0453155
24	11.5	11.5038	-0.00381708
25	8.3	8.24643	0.0535706
26	11.7	11.7047	-0.00472982
27	9	8.99263	0.00736659
28	10.8	10.9024	-0.102371
29	10.4	10.4095	-0.00954217
30	12.7	12.6816	0.0183718
31	11.8	11.7652	0.0348074
32	13	13.0098	-0.00983064
33	10.8	10.7504	0.0496113
34	12.3	12.2381	0.0618724
35	11.3	11.3637	-0.0636742
36	11.6	11.7146	-0.11461
37	10.9	10.9035	-0.00354639
38	12.1	12.0992	0.000832623
39	13.3	13.3053	-0.00532568
40	10.1	10.0835	0.0165345
41	14.3	14.2513	0.0486533
42	9.3	9.22989	0.0701083
43	12.5	12.4798	0.0201539
44	7.6	7.6477	-0.0476996
45	9.2	9.17954	0.020457
46	14.5	14.4904	0.0095545
47	12.3	12.2158	0.0841805
48	12.6	12.5218	0.0781748
49	13	13.0473	-0.0472811
50	12.6	12.6324	-0.032435
51	13.2	13.1896	0.0103938
52	7.7	7.72187	-0.0218653
53	10.5	10.46	0.0399903
54	10.9	10.8376	0.0624312
55	4.3	4.34359	-0.0435914
56	10.3	10.3464	-0.0463963
57	11.4	11.3183	0.0816902
58	5.6	5.61914	-0.0191446
59	8.8	8.80655	-0.00655474
60	9	9.0096	-0.00960492
61	9.6	9.57881	0.0211921
62	6.4	6.42403	-0.024028
63	11.6	11.6268	-0.0268078

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.958 & -0.057957 \tabularnewline
2 & 12.8 & 12.8317 & -0.0316641 \tabularnewline
3 & 7.4 & 7.35122 & 0.0487848 \tabularnewline
4 & 6.7 & 6.69791 & 0.0020917 \tabularnewline
5 & 12.6 & 12.5746 & 0.0253614 \tabularnewline
6 & 14.8 & 14.8631 & -0.0630962 \tabularnewline
7 & 13.3 & 13.2514 & 0.0486458 \tabularnewline
8 & 11.1 & 11.1432 & -0.0431809 \tabularnewline
9 & 8.2 & 8.18274 & 0.0172628 \tabularnewline
10 & 11.4 & 11.4114 & -0.0113604 \tabularnewline
11 & 6.4 & 6.40039 & -0.000388825 \tabularnewline
12 & 12 & 11.96 & 0.0399583 \tabularnewline
13 & 6.3 & 6.3056 & -0.00560185 \tabularnewline
14 & 11.3 & 11.2535 & 0.0465433 \tabularnewline
15 & 11.9 & 12.0458 & -0.145845 \tabularnewline
16 & 9.3 & 9.32266 & -0.0226601 \tabularnewline
17 & 10 & 10.0362 & -0.0361992 \tabularnewline
18 & 13.8 & 13.7912 & 0.0088148 \tabularnewline
19 & 10.8 & 10.7249 & 0.075091 \tabularnewline
20 & 11.7 & 11.7259 & -0.0258567 \tabularnewline
21 & 10.9 & 10.9337 & -0.0336662 \tabularnewline
22 & 16.1 & 16.1275 & -0.0274836 \tabularnewline
23 & 9.9 & 9.85468 & 0.0453155 \tabularnewline
24 & 11.5 & 11.5038 & -0.00381708 \tabularnewline
25 & 8.3 & 8.24643 & 0.0535706 \tabularnewline
26 & 11.7 & 11.7047 & -0.00472982 \tabularnewline
27 & 9 & 8.99263 & 0.00736659 \tabularnewline
28 & 10.8 & 10.9024 & -0.102371 \tabularnewline
29 & 10.4 & 10.4095 & -0.00954217 \tabularnewline
30 & 12.7 & 12.6816 & 0.0183718 \tabularnewline
31 & 11.8 & 11.7652 & 0.0348074 \tabularnewline
32 & 13 & 13.0098 & -0.00983064 \tabularnewline
33 & 10.8 & 10.7504 & 0.0496113 \tabularnewline
34 & 12.3 & 12.2381 & 0.0618724 \tabularnewline
35 & 11.3 & 11.3637 & -0.0636742 \tabularnewline
36 & 11.6 & 11.7146 & -0.11461 \tabularnewline
37 & 10.9 & 10.9035 & -0.00354639 \tabularnewline
38 & 12.1 & 12.0992 & 0.000832623 \tabularnewline
39 & 13.3 & 13.3053 & -0.00532568 \tabularnewline
40 & 10.1 & 10.0835 & 0.0165345 \tabularnewline
41 & 14.3 & 14.2513 & 0.0486533 \tabularnewline
42 & 9.3 & 9.22989 & 0.0701083 \tabularnewline
43 & 12.5 & 12.4798 & 0.0201539 \tabularnewline
44 & 7.6 & 7.6477 & -0.0476996 \tabularnewline
45 & 9.2 & 9.17954 & 0.020457 \tabularnewline
46 & 14.5 & 14.4904 & 0.0095545 \tabularnewline
47 & 12.3 & 12.2158 & 0.0841805 \tabularnewline
48 & 12.6 & 12.5218 & 0.0781748 \tabularnewline
49 & 13 & 13.0473 & -0.0472811 \tabularnewline
50 & 12.6 & 12.6324 & -0.032435 \tabularnewline
51 & 13.2 & 13.1896 & 0.0103938 \tabularnewline
52 & 7.7 & 7.72187 & -0.0218653 \tabularnewline
53 & 10.5 & 10.46 & 0.0399903 \tabularnewline
54 & 10.9 & 10.8376 & 0.0624312 \tabularnewline
55 & 4.3 & 4.34359 & -0.0435914 \tabularnewline
56 & 10.3 & 10.3464 & -0.0463963 \tabularnewline
57 & 11.4 & 11.3183 & 0.0816902 \tabularnewline
58 & 5.6 & 5.61914 & -0.0191446 \tabularnewline
59 & 8.8 & 8.80655 & -0.00655474 \tabularnewline
60 & 9 & 9.0096 & -0.00960492 \tabularnewline
61 & 9.6 & 9.57881 & 0.0211921 \tabularnewline
62 & 6.4 & 6.42403 & -0.024028 \tabularnewline
63 & 11.6 & 11.6268 & -0.0268078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&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]12.9[/C][C]12.958[/C][C]-0.057957[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]12.8317[/C][C]-0.0316641[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]7.35122[/C][C]0.0487848[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]6.69791[/C][C]0.0020917[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]12.5746[/C][C]0.0253614[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]14.8631[/C][C]-0.0630962[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]13.2514[/C][C]0.0486458[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]11.1432[/C][C]-0.0431809[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]8.18274[/C][C]0.0172628[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]11.4114[/C][C]-0.0113604[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]6.40039[/C][C]-0.000388825[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.96[/C][C]0.0399583[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]6.3056[/C][C]-0.00560185[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]11.2535[/C][C]0.0465433[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]12.0458[/C][C]-0.145845[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]9.32266[/C][C]-0.0226601[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.0362[/C][C]-0.0361992[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]13.7912[/C][C]0.0088148[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]10.7249[/C][C]0.075091[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]11.7259[/C][C]-0.0258567[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]10.9337[/C][C]-0.0336662[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]16.1275[/C][C]-0.0274836[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]9.85468[/C][C]0.0453155[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]11.5038[/C][C]-0.00381708[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]8.24643[/C][C]0.0535706[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.7047[/C][C]-0.00472982[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.99263[/C][C]0.00736659[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]10.9024[/C][C]-0.102371[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]10.4095[/C][C]-0.00954217[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]12.6816[/C][C]0.0183718[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]11.7652[/C][C]0.0348074[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]13.0098[/C][C]-0.00983064[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]10.7504[/C][C]0.0496113[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]12.2381[/C][C]0.0618724[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]11.3637[/C][C]-0.0636742[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]11.7146[/C][C]-0.11461[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]10.9035[/C][C]-0.00354639[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]12.0992[/C][C]0.000832623[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]13.3053[/C][C]-0.00532568[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]10.0835[/C][C]0.0165345[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]14.2513[/C][C]0.0486533[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]9.22989[/C][C]0.0701083[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]12.4798[/C][C]0.0201539[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]7.6477[/C][C]-0.0476996[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]9.17954[/C][C]0.020457[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]14.4904[/C][C]0.0095545[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]12.2158[/C][C]0.0841805[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]12.5218[/C][C]0.0781748[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.0473[/C][C]-0.0472811[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]12.6324[/C][C]-0.032435[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]13.1896[/C][C]0.0103938[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]7.72187[/C][C]-0.0218653[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]10.46[/C][C]0.0399903[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.8376[/C][C]0.0624312[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]4.34359[/C][C]-0.0435914[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]10.3464[/C][C]-0.0463963[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]11.3183[/C][C]0.0816902[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]5.61914[/C][C]-0.0191446[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]8.80655[/C][C]-0.00655474[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]9.0096[/C][C]-0.00960492[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]9.57881[/C][C]0.0211921[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]6.42403[/C][C]-0.024028[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]11.6268[/C][C]-0.0268078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	12.958	-0.057957
2	12.8	12.8317	-0.0316641
3	7.4	7.35122	0.0487848
4	6.7	6.69791	0.0020917
5	12.6	12.5746	0.0253614
6	14.8	14.8631	-0.0630962
7	13.3	13.2514	0.0486458
8	11.1	11.1432	-0.0431809
9	8.2	8.18274	0.0172628
10	11.4	11.4114	-0.0113604
11	6.4	6.40039	-0.000388825
12	12	11.96	0.0399583
13	6.3	6.3056	-0.00560185
14	11.3	11.2535	0.0465433
15	11.9	12.0458	-0.145845
16	9.3	9.32266	-0.0226601
17	10	10.0362	-0.0361992
18	13.8	13.7912	0.0088148
19	10.8	10.7249	0.075091
20	11.7	11.7259	-0.0258567
21	10.9	10.9337	-0.0336662
22	16.1	16.1275	-0.0274836
23	9.9	9.85468	0.0453155
24	11.5	11.5038	-0.00381708
25	8.3	8.24643	0.0535706
26	11.7	11.7047	-0.00472982
27	9	8.99263	0.00736659
28	10.8	10.9024	-0.102371
29	10.4	10.4095	-0.00954217
30	12.7	12.6816	0.0183718
31	11.8	11.7652	0.0348074
32	13	13.0098	-0.00983064
33	10.8	10.7504	0.0496113
34	12.3	12.2381	0.0618724
35	11.3	11.3637	-0.0636742
36	11.6	11.7146	-0.11461
37	10.9	10.9035	-0.00354639
38	12.1	12.0992	0.000832623
39	13.3	13.3053	-0.00532568
40	10.1	10.0835	0.0165345
41	14.3	14.2513	0.0486533
42	9.3	9.22989	0.0701083
43	12.5	12.4798	0.0201539
44	7.6	7.6477	-0.0476996
45	9.2	9.17954	0.020457
46	14.5	14.4904	0.0095545
47	12.3	12.2158	0.0841805
48	12.6	12.5218	0.0781748
49	13	13.0473	-0.0472811
50	12.6	12.6324	-0.032435
51	13.2	13.1896	0.0103938
52	7.7	7.72187	-0.0218653
53	10.5	10.46	0.0399903
54	10.9	10.8376	0.0624312
55	4.3	4.34359	-0.0435914
56	10.3	10.3464	-0.0463963
57	11.4	11.3183	0.0816902
58	5.6	5.61914	-0.0191446
59	8.8	8.80655	-0.00655474
60	9	9.0096	-0.00960492
61	9.6	9.57881	0.0211921
62	6.4	6.42403	-0.024028
63	11.6	11.6268	-0.0268078

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.651264	0.697473	0.348736
21	0.636621	0.726757	0.363379
22	0.565047	0.869906	0.434953
23	0.431342	0.862685	0.568658
24	0.398071	0.796142	0.601929
25	0.436556	0.873111	0.563444
26	0.325743	0.651486	0.674257
27	0.297009	0.594018	0.702991
28	0.54825	0.903501	0.45175
29	0.46786	0.93572	0.53214
30	0.365998	0.731997	0.634002
31	0.526966	0.946067	0.473034
32	0.535394	0.929213	0.464606
33	0.500851	0.998299	0.499149
34	0.452154	0.904307	0.547846
35	0.538655	0.92269	0.461345
36	0.803093	0.393815	0.196907
37	0.93029	0.13942	0.0697099
38	0.899958	0.200083	0.100042
39	0.907718	0.184564	0.092282
40	0.850305	0.299389	0.149695
41	0.923844	0.152311	0.0761555
42	0.963381	0.0732382	0.0366191
43	0.898541	0.202918	0.101459

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.651264 & 0.697473 & 0.348736 \tabularnewline
21 & 0.636621 & 0.726757 & 0.363379 \tabularnewline
22 & 0.565047 & 0.869906 & 0.434953 \tabularnewline
23 & 0.431342 & 0.862685 & 0.568658 \tabularnewline
24 & 0.398071 & 0.796142 & 0.601929 \tabularnewline
25 & 0.436556 & 0.873111 & 0.563444 \tabularnewline
26 & 0.325743 & 0.651486 & 0.674257 \tabularnewline
27 & 0.297009 & 0.594018 & 0.702991 \tabularnewline
28 & 0.54825 & 0.903501 & 0.45175 \tabularnewline
29 & 0.46786 & 0.93572 & 0.53214 \tabularnewline
30 & 0.365998 & 0.731997 & 0.634002 \tabularnewline
31 & 0.526966 & 0.946067 & 0.473034 \tabularnewline
32 & 0.535394 & 0.929213 & 0.464606 \tabularnewline
33 & 0.500851 & 0.998299 & 0.499149 \tabularnewline
34 & 0.452154 & 0.904307 & 0.547846 \tabularnewline
35 & 0.538655 & 0.92269 & 0.461345 \tabularnewline
36 & 0.803093 & 0.393815 & 0.196907 \tabularnewline
37 & 0.93029 & 0.13942 & 0.0697099 \tabularnewline
38 & 0.899958 & 0.200083 & 0.100042 \tabularnewline
39 & 0.907718 & 0.184564 & 0.092282 \tabularnewline
40 & 0.850305 & 0.299389 & 0.149695 \tabularnewline
41 & 0.923844 & 0.152311 & 0.0761555 \tabularnewline
42 & 0.963381 & 0.0732382 & 0.0366191 \tabularnewline
43 & 0.898541 & 0.202918 & 0.101459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&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]20[/C][C]0.651264[/C][C]0.697473[/C][C]0.348736[/C][/ROW]
[ROW][C]21[/C][C]0.636621[/C][C]0.726757[/C][C]0.363379[/C][/ROW]
[ROW][C]22[/C][C]0.565047[/C][C]0.869906[/C][C]0.434953[/C][/ROW]
[ROW][C]23[/C][C]0.431342[/C][C]0.862685[/C][C]0.568658[/C][/ROW]
[ROW][C]24[/C][C]0.398071[/C][C]0.796142[/C][C]0.601929[/C][/ROW]
[ROW][C]25[/C][C]0.436556[/C][C]0.873111[/C][C]0.563444[/C][/ROW]
[ROW][C]26[/C][C]0.325743[/C][C]0.651486[/C][C]0.674257[/C][/ROW]
[ROW][C]27[/C][C]0.297009[/C][C]0.594018[/C][C]0.702991[/C][/ROW]
[ROW][C]28[/C][C]0.54825[/C][C]0.903501[/C][C]0.45175[/C][/ROW]
[ROW][C]29[/C][C]0.46786[/C][C]0.93572[/C][C]0.53214[/C][/ROW]
[ROW][C]30[/C][C]0.365998[/C][C]0.731997[/C][C]0.634002[/C][/ROW]
[ROW][C]31[/C][C]0.526966[/C][C]0.946067[/C][C]0.473034[/C][/ROW]
[ROW][C]32[/C][C]0.535394[/C][C]0.929213[/C][C]0.464606[/C][/ROW]
[ROW][C]33[/C][C]0.500851[/C][C]0.998299[/C][C]0.499149[/C][/ROW]
[ROW][C]34[/C][C]0.452154[/C][C]0.904307[/C][C]0.547846[/C][/ROW]
[ROW][C]35[/C][C]0.538655[/C][C]0.92269[/C][C]0.461345[/C][/ROW]
[ROW][C]36[/C][C]0.803093[/C][C]0.393815[/C][C]0.196907[/C][/ROW]
[ROW][C]37[/C][C]0.93029[/C][C]0.13942[/C][C]0.0697099[/C][/ROW]
[ROW][C]38[/C][C]0.899958[/C][C]0.200083[/C][C]0.100042[/C][/ROW]
[ROW][C]39[/C][C]0.907718[/C][C]0.184564[/C][C]0.092282[/C][/ROW]
[ROW][C]40[/C][C]0.850305[/C][C]0.299389[/C][C]0.149695[/C][/ROW]
[ROW][C]41[/C][C]0.923844[/C][C]0.152311[/C][C]0.0761555[/C][/ROW]
[ROW][C]42[/C][C]0.963381[/C][C]0.0732382[/C][C]0.0366191[/C][/ROW]
[ROW][C]43[/C][C]0.898541[/C][C]0.202918[/C][C]0.101459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.651264	0.697473	0.348736
21	0.636621	0.726757	0.363379
22	0.565047	0.869906	0.434953
23	0.431342	0.862685	0.568658
24	0.398071	0.796142	0.601929
25	0.436556	0.873111	0.563444
26	0.325743	0.651486	0.674257
27	0.297009	0.594018	0.702991
28	0.54825	0.903501	0.45175
29	0.46786	0.93572	0.53214
30	0.365998	0.731997	0.634002
31	0.526966	0.946067	0.473034
32	0.535394	0.929213	0.464606
33	0.500851	0.998299	0.499149
34	0.452154	0.904307	0.547846
35	0.538655	0.92269	0.461345
36	0.803093	0.393815	0.196907
37	0.93029	0.13942	0.0697099
38	0.899958	0.200083	0.100042
39	0.907718	0.184564	0.092282
40	0.850305	0.299389	0.149695
41	0.923844	0.152311	0.0761555
42	0.963381	0.0732382	0.0366191
43	0.898541	0.202918	0.101459

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0416667	OK

\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 & 1 & 0.0416667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266942&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]1[/C][C]0.0416667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266942&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266942&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0416667	OK

Figure 1

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Postscript link

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

PNG link

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Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code